Execution Details COMPLETED
Documentation Search with AI Assistant
Execution Information
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Status: COMPLETED
Started: 2025-08-08 11:26:21
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Ended: 2025-08-08 11:26:51
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Ended: 2025-08-08 11:26:51
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fetch_documentation
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Thread: N/A
Started: 11:26:23
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"ai_response": "Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\n\n## Defining Model Clients\n\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\n\n### Basic Structure\n\nHere\u0027s the fundamental structure for defining model clients:\n\n```yaml\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\n config: # REQUIRED: Provider-specific config\n model: gpt-4 # REQUIRED: Model name\n api_key: your_api_key # REQUIRED: API key for the provider\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\n max_tokens: 1000 # Optional: Maximum tokens to generate\n timeout: 30 # Optional: Request timeout in seconds\n```\n\n### Complete Example with Multiple Model Clients\n\nYou can define multiple model clients for different use cases:\n\n```yaml\nmodel_clients:\n # Fast, cost-effective model for simple tasks\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n # High-quality model for complex analysis\n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n # Creative model for content generation\n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n## Using Model Clients in Tasks\n\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\n\n```yaml\n# AI configuration\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References \"smart_model\" from above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References \"creative_model\"\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n## Security Best Practice\n\nFor production deployments, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n- id: secure_example\n provider: openai\n config:\n model: gpt-4\n api_key: \"${OPENAI_API_KEY}\" # Secure: Reads from environment variable\n```\n\n### Configuration Summary\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\n- **Referenced by**: `model_client_id` field in task configurations\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\n\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.",
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"output": "Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\n\n## Defining Model Clients\n\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\n\n### Basic Structure\n\nHere\u0027s the fundamental structure for defining model clients:\n\n```yaml\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\n config: # REQUIRED: Provider-specific config\n model: gpt-4 # REQUIRED: Model name\n api_key: your_api_key # REQUIRED: API key for the provider\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\n max_tokens: 1000 # Optional: Maximum tokens to generate\n timeout: 30 # Optional: Request timeout in seconds\n```\n\n### Complete Example with Multiple Model Clients\n\nYou can define multiple model clients for different use cases:\n\n```yaml\nmodel_clients:\n # Fast, cost-effective model for simple tasks\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n # High-quality model for complex analysis\n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n # Creative model for content generation\n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n## Using Model Clients in Tasks\n\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\n\n```yaml\n# AI configuration\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References \"smart_model\" from above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References \"creative_model\"\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n## Security Best Practice\n\nFor production deployments, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n- id: secure_example\n provider: openai\n config:\n model: gpt-4\n api_key: \"${OPENAI_API_KEY}\" # Secure: Reads from environment variable\n```\n\n### Configuration Summary\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\n- **Referenced by**: `model_client_id` field in task configurations\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\n\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\n__OUTPUTS__ {\"ai_response\": \"Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\\n\\n## Defining Model Clients\\n\\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\\n\\n### Basic Structure\\n\\nHere\u0027s the fundamental structure for defining model clients:\\n\\n```yaml\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\\n config: # REQUIRED: Provider-specific config\\n model: gpt-4 # REQUIRED: Model name\\n api_key: your_api_key # REQUIRED: API key for the provider\\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\\n max_tokens: 1000 # Optional: Maximum tokens to generate\\n timeout: 30 # Optional: Request timeout in seconds\\n```\\n\\n### Complete Example with Multiple Model Clients\\n\\nYou can define multiple model clients for different use cases:\\n\\n```yaml\\nmodel_clients:\\n # Fast, cost-effective model for simple tasks\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n # High-quality model for complex analysis\\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n # Creative model for content generation\\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n## Using Model Clients in Tasks\\n\\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\\n\\n```yaml\\n# AI configuration\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References \\\"smart_model\\\" from above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References \\\"creative_model\\\"\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n## Security Best Practice\\n\\nFor production deployments, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n- id: secure_example\\n provider: openai\\n config:\\n model: gpt-4\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure: Reads from environment variable\\n```\\n\\n### Configuration Summary\\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\\n- **Referenced by**: `model_client_id` field in task configurations\\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\\n\\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\", \"model_client_id\": \"openrouter_kimi\", \"agent_type\": \"assistant\", \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\", \"status\": \"completed\", \"token_usage\": {\"prompt_tokens\": 1867, \"completion_tokens\": 745, \"total_tokens\": 2612, \"estimated_cost\": {\"amount\": 0.10071, \"currency\": \"USD\"}, \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\"}}",
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"output": "Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\n\n## Defining Model Clients\n\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\n\n### Basic Structure\n\nHere\u0027s the fundamental structure for defining model clients:\n\n```yaml\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\n config: # REQUIRED: Provider-specific config\n model: gpt-4 # REQUIRED: Model name\n api_key: your_api_key # REQUIRED: API key for the provider\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\n max_tokens: 1000 # Optional: Maximum tokens to generate\n timeout: 30 # Optional: Request timeout in seconds\n```\n\n### Complete Example with Multiple Model Clients\n\nYou can define multiple model clients for different use cases:\n\n```yaml\nmodel_clients:\n # Fast, cost-effective model for simple tasks\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n # High-quality model for complex analysis\n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n # Creative model for content generation\n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n## Using Model Clients in Tasks\n\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\n\n```yaml\n# AI configuration\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References \"smart_model\" from above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References \"creative_model\"\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n## Security Best Practice\n\nFor production deployments, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n- id: secure_example\n provider: openai\n config:\n model: gpt-4\n api_key: \"${OPENAI_API_KEY}\" # Secure: Reads from environment variable\n```\n\n### Configuration Summary\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\n- **Referenced by**: `model_client_id` field in task configurations\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\n\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\n__OUTPUTS__ {\"ai_response\": \"Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\\n\\n## Defining Model Clients\\n\\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\\n\\n### Basic Structure\\n\\nHere\u0027s the fundamental structure for defining model clients:\\n\\n```yaml\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\\n config: # REQUIRED: Provider-specific config\\n model: gpt-4 # REQUIRED: Model name\\n api_key: your_api_key # REQUIRED: API key for the provider\\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\\n max_tokens: 1000 # Optional: Maximum tokens to generate\\n timeout: 30 # Optional: Request timeout in seconds\\n```\\n\\n### Complete Example with Multiple Model Clients\\n\\nYou can define multiple model clients for different use cases:\\n\\n```yaml\\nmodel_clients:\\n # Fast, cost-effective model for simple tasks\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n # High-quality model for complex analysis\\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n # Creative model for content generation\\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n## Using Model Clients in Tasks\\n\\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\\n\\n```yaml\\n# AI configuration\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References \\\"smart_model\\\" from above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References \\\"creative_model\\\"\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n## Security Best Practice\\n\\nFor production deployments, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n- id: secure_example\\n provider: openai\\n config:\\n model: gpt-4\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure: Reads from environment variable\\n```\\n\\n### Configuration Summary\\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\\n- **Referenced by**: `model_client_id` field in task configurations\\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\\n\\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\", \"model_client_id\": \"openrouter_kimi\", \"agent_type\": \"assistant\", \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\", \"status\": \"completed\", \"token_usage\": {\"prompt_tokens\": 1867, \"completion_tokens\": 745, \"total_tokens\": 2612, \"estimated_cost\": {\"amount\": 0.10071, \"currency\": \"USD\"}, \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\"}}",
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"content": "# YAML Workflow Structure Reference\n\nComplete reference for the YAML workflow definition structure supported by the Assistants Workflow Engine.\n\n## Table of Contents\n\n1. [Complete Schema Overview](#complete-schema-overview)\n2. [Required vs Optional Fields](#required-vs-optional-fields)\n3. [Data Types Reference](#data-types-reference)\n4. [Field Validation Rules](#field-validation-rules)\n5. [Parser Behavior](#parser-behavior)\n6. [Migration Guide](#migration-guide)\n\n## Complete Schema Overview\n\n```yaml\n# === WORKFLOW METADATA (Required) ===\nid: string # REQUIRED: Unique workflow identifier\nname: string # REQUIRED: Human-readable workflow name\n\n# === WORKFLOW METADATA (Optional) ===\nversion: string | integer # Default: 1\nnamespace: string # Default: \"default\"\ndescription: string # Default: \"\"\ntenant_id: string # Default: null\n\n# === EXECUTION CONFIGURATION ===\ntimeout_seconds: integer|string # Default: 3600 (1 hour) - Workflow-level timeout (supports duration strings like \"1h\", \"30m\")\nlabels: object # Default: {}\nmetadata: object # Default: {}\n\n# === DATA FLOW DEFINITIONS ===\ninputs: # Default: []\n - name: string # REQUIRED\n type: string # REQUIRED: string|integer|boolean|object|array\n description: string # Optional\n required: boolean # Default: false\n default: any # Optional default value\n\noutputs: object # Default: {}\n output_name:\n type: string # REQUIRED\n source: string # REQUIRED: task_id or task_id.field\n description: string # Optional\n\nvariables: object # Default: {}\n\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n - id: string # REQUIRED: Unique identifier to reference in tasks\n provider: string # REQUIRED: openai|anthropic|openrouter|groq\n config: object # REQUIRED: Provider-specific config\n model: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\n api_key: string # REQUIRED: API key for the provider\n temperature: number # Optional: 0.0-2.0, controls randomness\n max_tokens: integer # Optional: Maximum tokens to generate\n timeout: integer # Optional: Request timeout in seconds\n base_url: string # Optional: Custom API base URL\n\n# Model Client Examples:\n# OpenAI Configuration:\n# model_clients:\n# - id: gpt4_model\n# provider: openai\n# config:\n# model: gpt-4-turbo-preview\n# api_key: \"sk-your-openai-api-key\"\n# temperature: 0.7\n# max_tokens: 2000\n#\n# Anthropic Configuration:\n# model_clients:\n# - id: claude_model\n# provider: anthropic\n# config:\n# model: claude-3-sonnet-20240229\n# api_key: \"sk-ant-your-anthropic-api-key\"\n# temperature: 0.7\n# max_tokens: 1500\n#\n# Multiple Models:\n# model_clients:\n# - id: gpt4_model\n# provider: openai\n# config:\n# model: gpt-4-turbo-preview\n# api_key: \"sk-your-openai-api-key\"\n# - id: claude_model\n# provider: anthropic\n# config:\n# model: claude-3-sonnet-20240229\n# api_key: \"sk-ant-your-anthropic-api-key\"\n\n# === TASK DEFINITIONS (Required) ===\ntasks: # REQUIRED: List of tasks\n - id: string # REQUIRED: Unique task identifier\n name: string # Optional: Human-readable name\n type: string # REQUIRED: Task type\n description: string # Optional\n \n # DEPENDENCY CONFIGURATION\n depends_on: array # Optional: Execution dependencies and template resolution\n previous_node: string # REQUIRED: Task connectivity and UI hierarchy (essential for proper workflow execution)\n \n timeout_seconds: integer|string # Optional: Task-specific timeout (supports duration strings like \"5m\", \"30s\")\n worker_group: string # Optional: Target worker group\n conditions: array # Optional: Execution conditions\n execute_on_routes: array # Optional: Routes for conditional execution\n \n # Input/Output mapping\n input_mapping: object # Optional: Map workflow context to task inputs\n output_mapping: object # Optional: Map task outputs to workflow context\n \n # Retry configuration\n retry: object # Optional: Task-specific retry policy\n maxAttempts: integer # Default: 1\n initialDelay: string|integer # Default: \"1s\"\n maxDelay: string|integer # Default: \"300s\"\n backoffMultiplier: number # Default: 2.0\n retryOn: array # Default: [\"TEMPORARY_FAILURE\"]\n \n # === TASK TYPE SPECIFIC CONFIGURATIONS ===\n # See detailed sections below for each task type\n\n# === WORKFLOW-LEVEL RETRY POLICY ===\nretry: object # Optional: Global retry policy\n retryOn: array # Default: [\"TEMPORARY_FAILURE\"]\n maxAttempts: integer # Default: 1\n initialDelay: string|integer # Default: \"1s\"\n maxDelay: string|integer # Default: \"300s\"\n backoffMultiplier: number # Default: 2.0\n\n# === TRIGGERS ===\ntriggers: # Optional: List of triggers\n - name: string # REQUIRED\n type: string # REQUIRED: webhook|schedule|event\n description: string # Optional\n config: object # REQUIRED: Trigger-specific config\n input_mapping: array # Optional: Map trigger data to workflow inputs\n - webhook_field: string # Source field\n workflow_input: string # Target workflow input\n```\n\n## Required vs Optional Fields\n\n### Required Fields (Workflow Level)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `id` | string | Unique identifier for the workflow |\n| `tasks` | array | List of task definitions (can be empty) |\n\n### Required Fields (Task Level)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `id` | string | Unique identifier for the task |\n| `type` | string | Task type (script, http, ai_agent, etc.) |\n| `previous_node` | string | Reference to previous task for UI connectivity (required for proper task linking, except for the first task) |\n\n### Required Fields (Input Definition)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `name` | string | Input parameter name |\n| `type` | string | Data type (string, integer, boolean, object, array) |\n\n### Required Fields (Output Definition)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `type` | string | Output data type |\n| `source` | string | Source task ID or task_id.field_name |\n\n## Data Types Reference\n\n### Primitive Types\n\n```yaml\n# String type\ninputs:\n - name: customer_name\n type: string\n default: \"John Doe\"\n description: \"Customer full name\"\n\n# Integer type \ninputs:\n - name: batch_size\n type: integer\n default: 100\n description: \"Number of records per batch\"\n\n# Boolean type\ninputs:\n - name: include_archived\n type: boolean\n default: false\n description: \"Include archived records\"\n```\n\n### Complex Types\n\n```yaml\n# Object type\ninputs:\n - name: config_params\n type: object\n default:\n timeout: 30\n retries: 3\n debug: false\n description: \"Configuration parameters\"\n\n# Array type\ninputs:\n - name: customer_ids\n type: array\n default: [\"cust_001\", \"cust_002\"]\n description: \"List of customer identifiers\"\n```\n\n### Duration Types\n\nDuration values can be specified as strings or integers:\n\n```yaml\n# String format (recommended)\ntimeout_seconds: \"1h\" # 1 hour\ntimeout_seconds: \"30m\" # 30 minutes \ntimeout_seconds: \"45s\" # 45 seconds\ntimeout_seconds: \"2d\" # 2 days\n\n# Integer format (seconds)\ntimeout_seconds: 3600 # 1 hour in seconds\n\n# Supported units\n# s, sec, second, seconds\n# m, min, minute, minutes \n# h, hour, hours\n# d, day, days\n```\n\n### Timeout Configuration and Execution Behavior\n\nThe workflow engine supports configurable timeouts at both workflow and task levels with intelligent timeout-free communication for long-running tasks.\n\n#### Timeout Hierarchy\n\nTimeout values are resolved in this order of precedence:\n1. **Task-level `timeout_seconds`**: Specific timeout for individual tasks\n2. **Workflow-level `timeout_seconds`**: Default for all tasks in the workflow\n3. **System default**: 300 seconds (5 minutes) for tasks, 3600 seconds (1 hour) for workflows\n\n```yaml\n# Workflow-level timeout (applies to all tasks by default)\ntimeout_seconds: \"2h\" # 2 hour workflow timeout\n\ntasks:\n - id: quick_task\n type: script\n # Uses workflow-level timeout (2h)\n script: \"print(\u0027Quick task\u0027)\"\n \n - id: custom_timeout_task\n type: script\n timeout_seconds: \"30m\" # Override with 30 minute timeout\n script: \"print(\u0027Custom timeout task\u0027)\"\n \n - id: long_running_task\n type: script\n timeout_seconds: 600 # 10 minutes (integer format)\n script: |\n import time\n time.sleep(300) # 5 minute processing\n print(\"Long task completed\")\n```\n\n#### Timeout-Free Communication\n\nThe workflow engine automatically uses **timeout-free communication** for long-running tasks to prevent timeout-related failures:\n\n- **Loop tasks**: Always use timeout-free communication (event-driven execution)\n- **Script tasks**: Use timeout-free communication when `timeout_seconds \u003e 180` (host timeout threshold)\n- **Other task types**: Use standard timeout-based communication\n\n```yaml\ntasks:\n - id: short_script\n type: script\n timeout_seconds: 60 # Uses standard timeout-based communication\n script: \"print(\u0027Short script\u0027)\"\n \n - id: long_script\n type: script\n timeout_seconds: 600 # Uses timeout-free communication (600 \u003e 180)\n script: |\n import time\n print(\"\ud83d\ude80 This uses timeout-free communication\")\n time.sleep(300) # Won\u0027t cause timeout issues\n print(\"Long script completed\")\n \n - id: data_processing_loop\n type: loop\n timeout_seconds: \"1h\" # Always uses timeout-free communication\n loop_type: for\n max_iterations: 10\n # Loop tasks automatically use timeout-free communication\n```\n\n#### Loop Task Timeout Inheritance\n\nLoop tasks can pass timeout configuration to their inner script tasks:\n\n```yaml\n- id: batch_processing_loop\n type: loop\n timeout_seconds: \"30m\" # Loop-level timeout\n loop_type: for\n max_iterations: 5\n loop_tasks:\n - id: process_batch\n type: script\n # Inherits 30-minute timeout from parent loop\n script: |\n print(\"Processing batch with inherited timeout\")\n # This script will use the 30-minute timeout from the loop\n```\n\n#### Best Practices for Timeout Configuration\n\n1. **Use duration strings** for readability: `\"5m\"` vs `300`\n2. **Set appropriate timeouts** based on expected execution time plus buffer\n3. **Long-running tasks**: Set `timeout_seconds \u003e 180` to enable timeout-free communication\n4. **Loop tasks**: Always get timeout-free communication automatically\n5. **Critical tasks**: Use shorter timeouts for fail-fast behavior\n6. **External API calls**: Use longer timeouts to account for network latency\n\n```yaml\n# Example: Well-configured timeout workflow\ntimeout_seconds: \"1h\" # Reasonable workflow timeout\n\ntasks:\n - id: api_call\n type: http\n timeout_seconds: \"30s\" # Quick timeout for API calls\n url: \"https://api.example.com/data\"\n \n - id: data_processing\n type: script\n timeout_seconds: \"10m\" # Longer timeout for processing (uses timeout-free)\n script: |\n # Process large dataset\n process_data()\n \n - id: batch_processing\n type: loop\n timeout_seconds: \"45m\" # Generous timeout for loop (timeout-free)\n loop_type: for\n max_iterations: 100\n # Inner tasks inherit the 45-minute timeout\n```\n\n## Field Validation Rules\n\n### ID Fields\n\n```yaml\n# Valid IDs\nid: \"customer_processor\" # \u2705 Valid\nid: \"process-data\" # \u2705 Valid\nid: \"task_001\" # \u2705 Valid\n\n# Invalid IDs\nid: \"process data\" # \u274c Spaces not allowed\nid: \"123_start\" # \u274c Cannot start with number\nid: \"\" # \u274c Cannot be empty\n```\n\n### Timeout Values\n\n```yaml\n# Valid timeouts\ntimeout_seconds: 300 # \u2705 Positive integer\ntimeout_seconds: \"5m\" # \u2705 Duration string\ntimeout_seconds: \"1h30m\" # \u2705 Complex duration\n\n# Invalid timeouts\ntimeout_seconds: 0 # \u274c Must be positive\ntimeout_seconds: -60 # \u274c Cannot be negative\ntimeout_seconds: \"invalid\" # \u274c Invalid duration format\n```\n\n### Dependency Types and Validation\n\n#### `depends_on` vs `previous_node`\n\n**`depends_on` - Execution Dependencies:**\n- Controls actual task execution order\n- Enables template resolution and data access\n- Required for accessing outputs from other tasks\n- Validates that referenced tasks exist\n- Multiple tasks can be specified\n\n**`previous_node` - Task Connectivity and UI Hierarchy:**\n- **REQUIRED for proper task connectivity in UI**\n- Essential for workflow visualization and task linking\n- Critical for execution flow representation\n- Single task reference only\n- **Without this field, tasks won\u0027t be properly connected in the UI**\n\n```yaml\ntasks:\n - id: fetch_data\n type: http\n url: \"https://api.example.com/data\"\n \n - id: validate_data\n type: script\n depends_on: [fetch_data] # \u2705 EXECUTION: wait for fetch_data to complete\n previous_node: fetch_data # \u2705 UI: show as child of fetch_data in hierarchy\n script: |\n data = ${fetch_data} # \u2705 Can access because of depends_on\n if not validate(data):\n raise ValueError(\"Invalid data\")\n \n # Required: Use outputs dict and __OUTPUTS__ marker for proper parsing\n outputs[\u0027status\u0027] = \u0027valid\u0027\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n - id: process_data\n type: script\n depends_on: [validate_data] # \u2705 EXECUTION: wait for validation\n previous_node: validate_data # \u2705 UI: show as child of validate_data\n script: |\n validation_result = ${validate_data} # \u2705 Accessible via depends_on\n # Process data...\n\n - id: separate_task\n type: script\n previous_node: process_data # \u2705 UI: appears under process_data in hierarchy\n # NO depends_on means this can run immediately (parallel to other tasks)\n script: |\n # This task runs independently but appears under process_data in UI\n # Cannot access ${process_data} because no depends_on relationship\n print(\"Independent task\")\n\n### Complete Example with Proper Hierarchy\n\n```yaml\ntasks:\n - id: fetch_customer_data\n name: \"Fetch Customer Data\"\n type: http\n url: \"https://api.example.com/customers/${customer_id}\"\n \n - id: validate_customer\n name: \"Validate Customer Data\"\n type: script\n depends_on: [fetch_customer_data] # \u2705 EXECUTION: Wait for data fetch\n previous_node: fetch_customer_data # \u2705 UI: Show under fetch_customer_data\n script: |\n customer = ${fetch_customer_data} # \u2705 Can access because of depends_on\n if not customer.get(\u0027email\u0027):\n raise ValueError(\"Invalid customer data\")\n print(\"__OUTPUTS__ {\u0027status\u0027: \u0027valid\u0027}\")\n \n - id: process_customer\n name: \"Process Customer\"\n type: ai_agent\n depends_on: [validate_customer] # \u2705 EXECUTION: Wait for validation\n previous_node: validate_customer # \u2705 UI: Show under validate_customer\n config:\n model_client_id: gpt4_model\n user_message: \"Process customer: ${fetch_customer_data}\"\n \n - id: send_notification\n name: \"Send Notification\"\n type: script\n depends_on: [process_customer] # \u2705 EXECUTION: Wait for processing\n previous_node: process_customer # \u2705 UI: Show under process_customer\n script: |\n result = ${process_customer}\n print(f\"Notification sent for: ${fetch_customer_data.name}\")\n \n - id: parallel_audit_log\n name: \"Audit Log (Parallel)\"\n type: script\n depends_on: [fetch_customer_data] # \u2705 EXECUTION: Only needs initial data\n previous_node: fetch_customer_data # \u2705 UI: Show under fetch_customer_data\n script: |\n # This runs in parallel with validation/processing\n customer = ${fetch_customer_data}\n print(f\"Audit: Customer {customer.id} accessed at ${current_timestamp}\")\n```\n\n # Invalid examples\n - id: invalid_task\n type: script\n depends_on: [nonexistent_task] # \u274c VALIDATION ERROR: task doesn\u0027t exist\n previous_node: another_missing # \u26a0\ufe0f UI WARNING: task doesn\u0027t exist (but not fatal)\n script: \"print(\u0027Invalid\u0027)\"\n```\n\n#### Key Differences:\n\n| Aspect | `depends_on` | `previous_node` |\n|--------|--------------|-----------------|\n| **Purpose** | Execution control | Task connectivity \u0026 UI organization |\n| **Impact** | Blocks task until dependencies complete | **REQUIRED for UI task linking** |\n| **Template Access** | \u2705 Enables `${task_id}` resolution | \u274c No template access |\n| **Validation** | \u274c Parser error if task missing | \u26a0\ufe0f Warning if task missing |\n| **Multiple References** | \u2705 Array of task IDs | \u274c Single task ID only |\n| **Execution Order** | \u2705 Enforced | \u274c No execution impact |\n| **UI Connectivity** | \u274c No UI impact | \u2705 **ESSENTIAL for proper task connection** |\n\n### Circular Dependency Detection\n\n```yaml\n# This will be rejected by the parser\ntasks:\n - id: task_a\n depends_on: [task_b] # \u274c Circular dependency\n \n - id: task_b \n depends_on: [task_a] # \u274c Circular dependency\n```\n\n## Parser Behavior\n\n### Field Name Compatibility\n\nThe parser supports both camelCase and snake_case for field names:\n\n```yaml\ntasks:\n - id: flexible_task\n # Both formats are accepted\n dependsOn: [other_task] # camelCase\n depends_on: [other_task] # snake_case\n \n inputMapping: # camelCase\n source_field: \"target_field\"\n input_mapping: # snake_case \n source_field: \"target_field\"\n \n outputMapping: {} # camelCase\n output_mapping: {} # snake_case\n \n workerGroup: \"gpu-workers\" # camelCase\n worker_group: \"gpu-workers\" # snake_case\n```\n\n### Default Value Assignment\n\n```yaml\n# Fields with defaults are automatically populated\nid: my_workflow\n# name defaults to id value: \"my_workflow\"\n# version defaults to: 1\n# namespace defaults to: \"default\" \n# timeout_seconds defaults to: 3600\n# description defaults to: \"\"\n\ntasks:\n - id: simple_task\n type: script\n # name defaults to id: \"simple_task\"\n # timeout_seconds defaults to: 300\n # retry.maxAttempts defaults to: 1\n```\n\n### Template Resolution Context\n\nThe parser provides these built-in variables for template resolution:\n\n```yaml\ntasks:\n - id: context_aware_task\n type: script\n script: |\n # Workflow context\n workflow_id = \"${workflow_id}\" # Current workflow ID\n execution_id = \"${execution_id}\" # Unique execution ID\n namespace = \"${namespace}\" # Workflow namespace\n \n # Timing context\n current_timestamp = \"${current_timestamp}\" # ISO timestamp\n started_at = \"${started_at}\" # Workflow start time\n \n # Input context\n user_input = \"${input_parameter}\" # Workflow inputs\n \n # Task context\n previous_result = ${previous_task} # Previous task output\n specific_field = ${task_id.field_name} # Specific output field\n \n # Route context (for conditional workflows)\n route_info = ${_route_context} # Current route information\n```\n\n### Error Handling During Parsing\n\n```yaml\n# The parser will validate and report these errors:\n\n# 1. Missing required fields\n# ERROR: Missing required field: \u0027id\u0027\n\n# 2. Invalid YAML syntax \n# ERROR: Invalid YAML: mapping values are not allowed here\n\n# 3. Circular dependencies\n# ERROR: Circular dependency detected involving task \u0027task_a\u0027\n\n# 4. Invalid task references\n# ERROR: Task \u0027task_b\u0027 depends on non-existent task \u0027invalid_task\u0027\n\n# 5. Invalid duration formats\n# ERROR: Invalid duration: \u0027not_a_duration\u0027\n\n# 6. Invalid field values\n# ERROR: Timeout must be positive, got -10\n```\n\n## Migration Guide\n\n### From Version 1.0 to 2.0\n\n```yaml\n# OLD FORMAT (v1.0)\nworkflow:\n id: my_workflow\n tasks:\n - task_id: process_data\n task_type: python_script\n \n# NEW FORMAT (v2.0) \nid: my_workflow\ntasks:\n - id: process_data\n type: script\n language: python\n```\n\n### Deprecated Fields\n\n| Old Field | New Field | Status |\n|-----------|-----------|--------|\n| `task_type` | `type` | Deprecated in v2.0 |\n| `task_id` | `id` | Deprecated in v2.0 |\n| `python_script` | `script` (type) | Deprecated in v2.0 |\n| `plugin` | `mcp` (type) | Deprecated in v2.1 |\n\n### Backward Compatibility\n\nThe parser maintains backward compatibility for deprecated fields but will show warnings:\n\n```yaml\n# This will work but show a warning\ntasks:\n - task_id: old_style_task # \u26a0\ufe0f Warning: Use \u0027id\u0027 instead\n task_type: python_script # \u26a0\ufe0f Warning: Use \u0027type: script\u0027 instead\n```\n\n## Advanced Schema Features\n\n### Conditional Field Requirements\n\nSome fields become required based on task type:\n\n```yaml\n# HTTP tasks require URL\n- id: api_call\n type: http\n url: \"https://api.example.com\" # \u2705 Required for HTTP tasks\n \n# AI agent tasks require model_client_id\n- id: ai_task\n type: ai_agent\n config:\n model_client_id: gpt4 # \u2705 Required for AI agent tasks\n```\n\n### Dynamic Field Validation\n\nField validation can depend on other field values:\n\n```yaml\n# Loop tasks require loop-specific fields\n- id: data_loop\n type: loop\n loop_type: \"for\" # Determines required fields\n max_iterations: 10 # \u2705 Required for \u0027for\u0027 loops\n # exit_condition: not needed for \u0027for\u0027 loops\n \n- id: condition_loop \n type: loop\n loop_type: \"while\" # Determines required fields\n exit_condition: \"${count} \u003e= 10\" # \u2705 Required for \u0027while\u0027 loops\n # max_iterations: optional for \u0027while\u0027 loops\n```\n\n## Template Resolution and Data Flow\n\nTemplate resolution is a core feature that allows tasks to access data from previous tasks, workflow inputs, and built-in variables using the `${variable_name}` syntax. This is essential for creating dynamic workflows where tasks pass data to each other.\n\n### Basic Template Syntax\n\n```yaml\n# Access workflow inputs\nscript: |\n customer_name = \"${customer_name}\" # From workflow inputs\n \n# Access previous task outputs\nscript: |\n previous_result = ${data_fetch_task} # Entire output from data_fetch_task\n specific_field = ${data_fetch_task.customer_id} # Specific field\n \n# Access built-in variables\nscript: |\n workflow_id = \"${workflow_id}\"\n execution_id = \"${execution_id}\"\n timestamp = \"${current_timestamp}\"\n```\n\n### Available Built-in Variables\n\n| Variable | Description | Example Value |\n|----------|-------------|---------------|\n| `${workflow_id}` | Current workflow identifier | `customer_processor` |\n| `${execution_id}` | Unique execution identifier | `exec_12345_abcde` |\n| `${namespace}` | Workflow namespace | `production` |\n| `${current_timestamp}` | ISO timestamp | `2024-01-15T10:30:00Z` |\n| `${started_at}` | Workflow start time | `2024-01-15T10:29:45Z` |\n\n### Task Output Resolution\n\nTasks can access outputs from other tasks they depend on:\n\n```yaml\ntasks:\n - id: fetch_customer\n type: http\n url: \"https://api.example.com/customers/${customer_id}\"\n \n - id: process_customer\n type: script\n depends_on: [fetch_customer] # Required for template access\n previous_node: fetch_customer\n script: |\n # Access entire response\n customer_data = ${fetch_customer}\n \n # Access specific fields\n customer_name = \"${fetch_customer.name}\"\n customer_email = \"${fetch_customer.email}\"\n \n # Process the data\n result = {\n \"processed_name\": customer_name.upper(),\n \"domain\": customer_email.split(\"@\")[1]\n }\n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n```\n\n### AI Agent Output Resolution\n\nAI agent tasks return structured output that requires specific access patterns:\n\n```yaml\n# AI Agent task\n- id: ai_analysis_task\n type: ai_agent\n config:\n model_client_id: gpt4_model\n system_message: \"Analyze the customer data and provide insights.\"\n user_message: \"Customer data: ${customer_data}\"\n\n# Accessing AI agent output in subsequent tasks\n- id: process_ai_response\n type: script\n depends_on: [ai_analysis_task]\n previous_node: ai_analysis_task\n script: |\n # CORRECT: AI agents return structured output\n agent_output = ${ai_analysis_task}\n ai_response = agent_output[\u0027ai_response\u0027].strip()\n \n # Additional metadata available\n token_usage = agent_output.get(\u0027token_usage\u0027, {})\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\n \n # Process the AI response\n processed_analysis = {\n \"analysis\": ai_response,\n \"tokens_used\": token_usage.get(\u0027total_tokens\u0027, 0),\n \"model\": model_used,\n \"processed_at\": \"${current_timestamp}\"\n }\n \n print(f\"__OUTPUTS__ {json.dumps(processed_analysis)}\")\n\n# INCORRECT: Direct access won\u0027t work\n- id: incorrect_ai_access\n type: script\n depends_on: [ai_analysis_task]\n script: |\n # \u274c This will not work correctly\n ai_response = ${ai_analysis_task} # Returns full agent object\n # ai_response is now a dict, not the actual response text\n```\n\n### AI Agent Response Structure\n\nAI agent tasks return a structured object with the following format:\n\n```json\n{\n \"ai_response\": \"The actual AI-generated text response\",\n \"model\": \"gpt-4-turbo-preview\",\n \"token_usage\": {\n \"prompt_tokens\": 150,\n \"completion_tokens\": 75,\n \"total_tokens\": 225\n },\n \"estimated_cost\": {\n \"amount\": 0.00337,\n \"currency\": \"USD\"\n },\n \"provider\": \"openai\",\n \"timestamp\": \"2024-01-15T10:30:00Z\"\n}\n```\n\n### Loop Task Data Access and Dependencies\n\nLoop tasks have a special execution context that differs from regular workflow tasks. Understanding how to access data within loops is crucial for building functional workflows.\n\n#### Loop Task Internal Data Access\n\nWithin loop tasks, data access works through injected context files rather than template resolution:\n\n```yaml\n# Loop task with state variables and internal task dependencies\n- id: data_processing_loop\n type: loop\n loop_type: for\n max_iterations: 3\n state_variables:\n processed_items: []\n total_processed: 0\n error_count: 0\n loop_tasks:\n # First task in loop\n - id: fetch_data\n type: script\n script: |\n # Access loop state variables through injected context\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\n processed_items = loop_state.get(\u0027processed_items\u0027, [])\n \n # Access workflow inputs and external task outputs\n external_data = inputs.get(\u0027previous_workflow_task\u0027, {})\n iteration_num = inputs.get(\u0027iteration\u0027, 0) # Loop iteration variable\n \n # Process data\n batch_data = fetch_batch_data(iteration_num)\n \n outputs = {\"batch_data\": batch_data, \"batch_size\": len(batch_data)}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n # Second task depends on first task in same loop\n - id: process_data\n type: script\n depends_on: [fetch_data] # \u2705 CORRECT: Dependencies within loop tasks work normally\n script: |\n # \u2705 CORRECT: Access previous loop task through inputs context\n fetch_result = inputs.get(\u0027fetch_data\u0027, {})\n batch_data = fetch_result.get(\u0027batch_data\u0027, [])\n \n # \u274c INCORRECT: This won\u0027t work in loop context\n # batch_data = ${fetch_data.batch_data} # Template resolution fails in loops\n \n # Access and update state variables\n current_processed = loop_state.get(\u0027processed_items\u0027, [])\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\n \n # Process the batch\n processed_batch = process_batch(batch_data)\n current_processed.extend(processed_batch)\n \n # Update state variables using state_updates\n state_updates[\u0027processed_items\u0027] = current_processed\n state_updates[\u0027total_processed\u0027] = current_total + len(processed_batch)\n \n outputs = {\"processed_count\": len(processed_batch)}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n print(f\"__STATE_UPDATES__ {json.dumps(state_updates)}\") # \u2705 REQUIRED for state updates\n```\n\n#### Key Differences in Loop Task Data Access\n\n| Access Pattern | Regular Tasks | Loop Tasks |\n|---------------|---------------|------------|\n| **Previous Task Output** | `${previous_task}` | `inputs.get(\u0027field_name\u0027, default)` (flattened) |\n| **State Variables** | N/A | `loop_state.get(\u0027variable_name\u0027, default)` |\n| **State Updates** | N/A | `state_updates[\u0027variable_name\u0027] = value` |\n| **Workflow Inputs** | `\"${input_name}\"` | `inputs.get(\u0027input_name\u0027, \u0027default\u0027)` |\n| **Dependencies** | `depends_on: [task_id]` | Same: `depends_on: [task_id]` |\n\n#### Critical: Loop Task Output Flattening\n\n**Important:** The loop executor flattens task outputs into individual fields in the `inputs` dictionary, rather than storing them as nested objects under the task name.\n\n```yaml\n# First loop task outputs:\noutputs = {\"company_name\": \"ACME Corp\", \"company_id\": \"123\", \"status\": \"success\"}\nprint(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n\n# Second loop task accessing the data:\nscript: |\n # \u274c INCORRECT: This won\u0027t work - no nested object\n # company_data = inputs.get(\u0027first_task\u0027, {})\n # company_name = company_data.get(\u0027company_name\u0027)\n \n # \u2705 CORRECT: Access flattened fields directly\n company_name = inputs.get(\u0027company_name\u0027, \u0027\u0027)\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\n \n print(f\"Company: {company_name}, ID: {company_id}, Status: {task_status}\")\n```\n\n#### Complete Loop Task Example\n\n```yaml\n- id: financial_processing_loop\n type: loop\n loop_type: for\n max_iterations: 3\n iteration_variable: company_index\n depends_on: [list_companies] # External dependency\n \n state_variables:\n processed_companies: []\n total_extractions: 0\n successful_extractions: 0\n \n loop_tasks:\n - id: process_current_company\n type: script\n script: |\n # Access external workflow task data\n companies_data = inputs.get(\u0027list_companies\u0027, {})\n company_idx = inputs.get(\u0027company_index\u0027, 0)\n \n # Access state variables\n processed = loop_state.get(\u0027processed_companies\u0027, [])\n \n # Process company\n company = companies_data[\u0027companies\u0027][company_idx]\n result = process_company(company)\n \n outputs = {\"company_name\": company[\u0027name\u0027], \"status\": \"success\"}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n - id: extract_financial_data\n type: script\n depends_on: [process_current_company] # Depend on previous loop task\n requirements:\n - requests\u003e=2.28.0\n - some-ai-library\u003e=1.0.0\n script: |\n # \u2705 CORRECT: Access flattened outputs from previous loop task\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\n \n # \u274c INCORRECT: Loop executor flattens outputs, no nested access\n # company_data = inputs.get(\u0027process_current_company\u0027, {})\n # company_name = company_data.get(\u0027company_name\u0027, \u0027Unknown\u0027)\n \n # \u274c INCORRECT: Template resolution doesn\u0027t work in loops\n # company_data = ${process_current_company}\n \n # Access external data\n companies_list = inputs.get(\u0027list_companies\u0027, {})\n \n # Perform AI extraction\n extraction_result = extract_with_ai({\"company_name\": company_name, \"company_id\": company_id})\n \n # Update state variables\n current_processed = loop_state.get(\u0027processed_companies\u0027, [])\n current_total = loop_state.get(\u0027total_extractions\u0027, 0)\n current_successful = loop_state.get(\u0027successful_extractions\u0027, 0)\n \n current_processed.append({\n \"company_name\": company_name,\n \"extraction_result\": extraction_result\n })\n \n # State updates are required for persistence\n state_updates[\u0027processed_companies\u0027] = current_processed\n state_updates[\u0027total_extractions\u0027] = current_total + 1\n state_updates[\u0027successful_extractions\u0027] = current_successful + (1 if extraction_result else 0)\n \n outputs = {\"extracted\": bool(extraction_result)}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n print(f\"__STATE_UPDATES__ {json.dumps(state_updates)}\") # \u2705 REQUIRED\n\n# Accessing loop task outputs in subsequent workflow tasks\n- id: process_loop_results\n type: script\n depends_on: [financial_processing_loop]\n previous_node: financial_processing_loop\n script: |\n # \u2705 CORRECT: Access final state of loop\n loop_result = ${financial_processing_loop}\n loop_final_state = loop_result.get(\u0027final_state\u0027, {})\n \n # Access specific state variables from final state\n processed_companies = loop_final_state.get(\u0027processed_companies\u0027, [])\n total_processed = loop_final_state.get(\u0027total_extractions\u0027, 0)\n successful_count = loop_final_state.get(\u0027successful_extractions\u0027, 0)\n \n # Access loop execution metadata\n iterations_completed = loop_result.get(\u0027iterations_completed\u0027, 0)\n \n print(f\"Loop completed {iterations_completed} iterations\")\n print(f\"Processed {len(processed_companies)} companies\")\n print(f\"Success rate: {successful_count}/{total_processed}\")\n \n outputs = {\n \"companies_processed\": len(processed_companies),\n \"success_rate\": f\"{successful_count}/{total_processed}\",\n \"iterations\": iterations_completed\n }\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n#### Loop Task Context Injection and Output Flattening\n\nThe loop executor injects context through temporary files and flattens task outputs for easier access:\n\n```python\n# Auto-injected at the start of each loop task script:\nimport json\nimport os\n\n# Load context from temporary files (auto-generated)\nwith open(\u0027/tmp/loop_context_task_iter_N/inputs.json\u0027, \u0027r\u0027) as f:\n inputs = json.load(f) # Contains all workflow data + previous loop task outputs (FLATTENED)\n\nwith open(\u0027/tmp/loop_context_task_iter_N/loop_state.json\u0027, \u0027r\u0027) as f:\n loop_state = json.load(f) # Contains current state variables\n\noutputs = {}\nstate_updates = {}\n\n# Your script code follows...\n```\n\n#### Debugging Loop Task Data Access\n\nWhen debugging loop tasks, always print available keys to understand what data is accessible:\n\n```yaml\n- id: debug_loop_task\n type: script\n depends_on: [previous_loop_task]\n script: |\n # Debug: Print all available data\n print(f\"\ud83d\udd0d Available inputs: {list(inputs.keys())}\")\n print(f\"\ud83d\udd0d Loop state keys: {list(loop_state.keys())}\")\n \n # Debug: Print specific values\n for key in [\u0027company_name\u0027, \u0027company_id\u0027, \u0027status\u0027, \u0027fy_folders\u0027]:\n value = inputs.get(key, \u0027NOT_FOUND\u0027)\n print(f\"\ud83d\udd0d {key}: {value}\")\n \n # Now access the flattened data\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\n \n # Process your data...\n outputs = {\"processed\": f\"Company {company_name}\"}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n#### Real Example: Task Output Flattening\n\n```yaml\n# Task 1 outputs this structure:\n__OUTPUTS__ {\"company_name\": \"LTTS\", \"company_id\": \"123\", \"fy_folders\": [{\"id\": \"456\", \"name\": \"FY24\"}], \"status\": \"success\"}\n\n# Task 2 receives flattened inputs:\ninputs = {\n \"folder_id\": \"...\", # Workflow input\n \"gemini_api_key\": \"...\", # Workflow input \n \"list_company_folders\": {...}, # External task output\n \"company_index\": 0, # Loop iteration variable\n \"company_name\": \"LTTS\", # \u2705 Flattened from Task 1\n \"company_id\": \"123\", # \u2705 Flattened from Task 1\n \"fy_folders\": [{\"id\": \"456\", \"name\": \"FY24\"}], # \u2705 Flattened from Task 1\n \"status\": \"success\", # \u2705 Flattened from Task 1\n \"total_extractions\": 0, # Loop state variable\n \"processed_companies\": [] # Loop state variable\n}\n```\n\n#### Loop Task Special Output Fields\n\n| Field | Description | Example Access |\n|-------|-------------|----------------|\n| `final_state` | Final values of all state variables | `${loop_task.final_state}` |\n| `final_state[\"variable_name\"]` | Specific state variable from final state | `${loop_task.final_state[\"total_processed\"]}` |\n| `iterations_completed` | Number of iterations executed | `${loop_task.iterations_completed}` |\n\n#### Safe Template Resolution Pattern for Loop States\n\nWhen accessing loop final state fields, use this safe parsing pattern:\n\n```yaml\nscript: |\n try:\n # Template resolution with safety checks\n final_state_str = \u0027\u0027\u0027${loop_task.final_state}\u0027\u0027\u0027\n processed_count_str = \u0027\u0027\u0027${loop_task.final_state[\"processed_count\"]}\u0027\u0027\u0027\n items_str = \u0027\u0027\u0027${loop_task.final_state[\"processed_items\"]}\u0027\u0027\u0027\n iterations_str = \u0027\u0027\u0027${loop_task.iterations_completed}\u0027\u0027\u0027\n \n # Safe parsing with fallbacks\n final_state = json.loads(final_state_str) if not final_state_str.startswith(\u0027UNRESOLVED_\u0027) else {}\n processed_count = int(processed_count_str) if processed_count_str.isdigit() else 0\n processed_items = json.loads(items_str) if not items_str.startswith(\u0027UNRESOLVED_\u0027) else []\n iterations = int(iterations_str) if iterations_str.isdigit() else 0\n \n print(f\"Successfully processed {processed_count} items in {iterations} iterations\")\n \n except Exception as e:\n print(f\"Error accessing loop results: {str(e)}\")\n # Handle error case\n```\n```\n\n### Real-World AI Agent Example\n\nBased on actual WhatsApp workflow patterns:\n\n```yaml\n# AI greeting agent\n- id: greeting_agent\n type: ai_agent\n config:\n model_client_id: assistant_model\n system_message: \"You are a helpful assistant. Generate a friendly greeting.\"\n user_message: \"User said: ${user_message}\"\n\n# Send the AI response via API\n- id: send_greeting\n type: script\n depends_on: [greeting_agent]\n previous_node: greeting_agent\n script: |\n import requests\n import json\n \n # CORRECT: Access AI response from structured output\n agent_output = ${greeting_agent}\n ai_greeting = agent_output[\u0027ai_response\u0027].strip()\n \n phone_number = \"${phone_number}\"\n api_token = \"${API_TOKEN}\"\n \n print(f\"\ud83e\udd16 AI generated greeting: {ai_greeting}\")\n \n # Send via API\n url = f\"https://api.example.com/messages/{phone_number}\"\n headers = {\"Authorization\": f\"Bearer {api_token}\"}\n \n response = requests.post(url, headers=headers, json={\n \"message\": ai_greeting\n })\n \n result = {\n \"status\": \"sent\",\n \"ai_response\": ai_greeting,\n \"api_response\": response.json(),\n \"tokens_used\": agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\n }\n \n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n```\n\n### Complex Template Patterns\n\n```yaml\n# Conditional templates with router outputs\n- id: router_task\n type: conditional_router\n conditions:\n - name: \"premium_customer\"\n condition: \"${customer_data.tier} == \u0027premium\u0027\"\n route: \"premium_processing\"\n\n- id: premium_handler\n type: script\n depends_on: [router_task]\n previous_node: router_task\n execute_on_routes: [\"premium_processing\"]\n script: |\n # Access router decision\n selected_route = \"${router_task.selected_route}\"\n route_data = ${router_task.route_data}\n \n # Process premium customer\n customer = ${customer_data}\n premium_benefits = calculate_benefits(customer)\n\n# Loop through arrays from previous tasks\n- id: process_items\n type: script\n depends_on: [fetch_items]\n previous_node: fetch_items\n script: |\n items = ${fetch_items.data}\n processed_items = []\n \n for item in items:\n processed_item = {\n \"id\": item[\"id\"],\n \"processed_at\": \"${current_timestamp}\",\n \"status\": \"processed\"\n }\n processed_items.append(processed_item)\n \n print(f\"__OUTPUTS__ {json.dumps({\u0027processed_items\u0027: processed_items})}\")\n\n# Nested object access\n- id: user_preferences\n type: script\n depends_on: [fetch_user]\n previous_node: fetch_user\n script: |\n # Access nested user preferences\n email_notifications = ${fetch_user.preferences.notifications.email}\n theme_setting = \"${fetch_user.preferences.ui.theme}\"\n language = \"${fetch_user.profile.language}\"\n \n settings = {\n \"notifications\": email_notifications,\n \"theme\": theme_setting,\n \"locale\": language\n }\n print(f\"__OUTPUTS__ {json.dumps(settings)}\")\n```\n\n### Template Resolution Rules\n\n1. **Dependency Requirement**: Tasks must have `depends_on` relationship to access other task outputs\n2. **String vs Object**: Use quotes for string values, no quotes for objects/arrays\n3. **Field Navigation**: Use dot notation for nested field access\n4. **Error Handling**: Unresolved templates become `UNRESOLVED_variable_name`\n\n```yaml\n# \u2705 Correct template usage\n- id: correct_task\n depends_on: [previous_task]\n previous_node: previous_task\n script: |\n string_value = \"${previous_task.name}\" # String with quotes\n object_value = ${previous_task.data} # Object without quotes\n nested_value = \"${previous_task.user.email}\" # Nested field\n\n# \u274c Incorrect template usage \n- id: incorrect_task\n # Missing depends_on - template won\u0027t resolve\n script: |\n value = ${previous_task.name} # Will become UNRESOLVED_previous_task.name\n```\n\n## Script Task Output Handling\n\nScript tasks must use the `__OUTPUTS__` marker pattern for proper output parsing by the workflow engine.\n\n### Required Output Pattern\n\n```yaml\ntasks:\n - id: example_script\n type: script\n script: |\n # Your script logic here\n data = process_data()\n \n # Required: Store outputs in outputs dict\n outputs = {}\n outputs[\u0027result\u0027] = data\n outputs[\u0027status\u0027] = \u0027success\u0027\n outputs[\u0027count\u0027] = len(data)\n \n # Required: Print with __OUTPUTS__ marker for engine parsing\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n### Output Access in Subsequent Tasks\n\n```yaml\ntasks:\n - id: data_processor\n type: script\n script: |\n # Process and store outputs\n results = {\"processed_count\": 100, \"errors\": []}\n \n outputs = {}\n outputs[\u0027results\u0027] = results\n outputs[\u0027status\u0027] = \u0027completed\u0027\n \n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n - id: result_handler\n type: script\n depends_on: [data_processor]\n script: |\n # Access previous task outputs\n processor_output = ${data_processor}\n processed_count = processor_output[\u0027results\u0027][\u0027processed_count\u0027]\n status = processor_output[\u0027status\u0027]\n \n print(f\"Previous task processed {processed_count} items with status: {status}\")\n \n # Store new outputs\n outputs = {\u0027message\u0027: f\u0027Handled {processed_count} results\u0027}\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n### Error Handling in Scripts\n\n```yaml\ntasks:\n - id: safe_script\n type: script\n script: |\n try:\n result = risky_operation()\n outputs = {\u0027result\u0027: result, \u0027status\u0027: \u0027success\u0027}\n except Exception as e:\n outputs = {\u0027error\u0027: str(e), \u0027status\u0027: \u0027failed\u0027}\n \n # Always print outputs, even for errors\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n## Triggers and Workflow Activation\n\nTriggers define how workflows are started automatically based on external events. They enable event-driven workflow execution.\n\n### Webhook Triggers\n\nWebhook triggers allow external systems to start workflows by sending HTTP requests.\n\n```yaml\ntriggers:\n - name: customer_webhook\n type: webhook\n description: \"Triggered when customer data is updated\"\n config:\n path: \"/webhooks/customer-updated\" # Webhook endpoint path\n method: POST # HTTP method (GET, POST, PUT, etc.)\n authentication:\n type: token # none|token|signature|basic\n token: \"${WEBHOOK_SECRET}\" # Required if type is token\n header_name: \"X-Webhook-Token\" # Optional: custom header name\n validation:\n required_fields: [\"customer.id\", \"customer.email\"] # Required payload fields\n content_type: \"application/json\" # Expected content type\n input_mapping:\n - webhook_field: customer.id # Field from webhook payload\n workflow_input: customer_id # Map to workflow input\n - webhook_field: customer.email\n workflow_input: customer_email\n - webhook_field: customer.updated_at\n workflow_input: update_timestamp\n```\n\n### Scheduled Triggers\n\nSchedule workflows to run at specific times or intervals.\n\n```yaml\ntriggers:\n - name: daily_report\n type: schedule\n description: \"Generate daily customer report\"\n config:\n cron: \"0 9 * * *\" # Daily at 9 AM (cron format)\n timezone: \"UTC\" # Optional: timezone\n enabled: true # Optional: enable/disable\n max_instances: 1 # Optional: max concurrent instances\n input_mapping:\n - workflow_input: report_date\n default_value: \"${current_date}\" # Use built-in variable\n - workflow_input: report_type\n default_value: \"daily\"\n\n - name: weekly_cleanup\n type: schedule\n description: \"Weekly database cleanup\"\n config:\n cron: \"0 2 * * 0\" # Weekly on Sunday at 2 AM\n timezone: \"America/New_York\"\n input_mapping:\n - workflow_input: cleanup_before_date\n default_value: \"${date_minus_7_days}\"\n```\n\n### Event Triggers\n\nEvent triggers respond to system events from various sources.\n\n```yaml\ntriggers:\n - name: file_uploaded\n type: event\n description: \"Process uploaded files\"\n config:\n event_type: \"file.uploaded\" # Event type to listen for\n source: \"storage_service\" # Event source system\n filter: # Optional: event filtering\n file_type: [\"csv\", \"json\", \"xlsx\"]\n file_size_max: \"100MB\"\n path_pattern: \"/uploads/customers/*\"\n retry: # Optional: retry configuration\n max_attempts: 3\n initial_delay: \"5s\"\n input_mapping:\n - webhook_field: file.path\n workflow_input: file_path\n - webhook_field: file.size\n workflow_input: file_size\n - webhook_field: file.type\n workflow_input: file_type\n\n - name: order_status_changed\n type: event\n description: \"Handle order status changes\"\n config:\n event_type: \"order.status_changed\"\n source: \"order_management\"\n filter:\n status: [\"completed\", \"cancelled\", \"refunded\"]\n priority: [\"high\", \"urgent\"]\n input_mapping:\n - webhook_field: order.id\n workflow_input: order_id\n - webhook_field: order.status\n workflow_input: new_status\n - webhook_field: order.customer_id\n workflow_input: customer_id\n```\n\n### Dependency Triggers\n\nDependency triggers start workflows when other workflows complete.\n\n```yaml\ntriggers:\n - name: post_processing\n type: dependency\n description: \"Run after main processing completes\"\n config:\n depends_on_workflow: \"main_data_processing\" # Workflow to depend on\n depends_on_status: \"SUCCESS\" # SUCCESS|FAILED|COMPLETED\n namespace: \"production\" # Optional: workflow namespace\n timeout_minutes: 60 # Optional: wait timeout\n input_mapping:\n - webhook_field: execution_id # From completed workflow\n workflow_input: source_execution_id\n - webhook_field: workflow_outputs.processed_count\n workflow_input: items_processed\n```\n\n## Model Client Configuration\n\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\n\n### Basic Structure\n\n```yaml\nmodel_clients:\n - id: unique_identifier # Used to reference this model in tasks\n provider: provider_name # AI provider (openai, anthropic, etc.)\n config:\n model: model_name # Specific model to use\n api_key: your_api_key # Authentication key\n # Additional provider-specific options...\n```\n\n### Supported Providers\n\n#### OpenAI Provider\n\n```yaml\nmodel_clients:\n - id: gpt4_model\n provider: openai\n config:\n model: gpt-4-turbo-preview # Available: gpt-4, gpt-4-turbo, gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.7 # Optional: 0.0-2.0\n max_tokens: 2000 # Optional: Maximum response tokens\n timeout: 30 # Optional: Request timeout (seconds)\n```\n\n#### Anthropic Provider\n\n```yaml\nmodel_clients:\n - id: claude_model\n provider: anthropic\n config:\n model: claude-3-sonnet-20240229 # Available: claude-3-opus, claude-3-sonnet, claude-3-haiku\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7 # Optional: 0.0-1.0\n max_tokens: 1500 # Optional: Maximum response tokens\n timeout: 30 # Optional: Request timeout (seconds)\n```\n\n#### OpenRouter Provider\n\n```yaml\nmodel_clients:\n - id: openrouter_model\n provider: openrouter\n config:\n model: anthropic/claude-3-sonnet # Available: Various models via OpenRouter\n api_key: \"sk-or-your-openrouter-api-key\"\n base_url: \"https://openrouter.ai/api/v1\" # Optional: Custom endpoint\n temperature: 0.7\n max_tokens: 2000\n```\n\n#### Multiple Model Clients\n\nYou can define multiple model clients and reference them by their `id` in tasks:\n\n```yaml\nmodel_clients:\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n### Using Model Clients in Tasks\n\nReference model clients in AI tasks using the `model_client_id` field:\n\n```yaml\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References the claude model above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References the creative GPT-4 model\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n### Environment Variables for API Keys\n\nFor security, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n - id: secure_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"${OPENAI_API_KEY}\" # References environment variable\n temperature: 0.7\n```\n\n### Common Configuration Options\n\n| Field | Description | Required | Default |\n|-------|-------------|----------|---------|\n| `id` | Unique identifier for referencing in tasks | Yes | - |\n| `provider` | AI provider name | Yes | - |\n| `config.model` | Specific model name | Yes | - |\n| `config.api_key` | Authentication API key | Yes | - |\n| `config.temperature` | Response randomness (0.0-2.0) | No | 0.7 |\n| `config.max_tokens` | Maximum response tokens | No | Provider default |\n| `config.timeout` | Request timeout in seconds | No | 30 |\n| `config.base_url` | Custom API endpoint | No | Provider default |\n\n## Secrets Management and Environment Variables\n\nProper secrets management is crucial for security. Never hardcode sensitive information like API keys, passwords, or tokens directly in workflow YAML files.\n\n### Using Environment Variables\n\nThe recommended approach is to use environment variables for all sensitive data:\n\n```yaml\n# \u2705 RECOMMENDED: Use environment variables\nmodel_clients:\n - id: secure_openai\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"${OPENAI_API_KEY}\" # References environment variable\n\n# HTTP tasks with environment variables\ntasks:\n - id: secure_api_call\n type: http\n url: \"${API_BASE_URL}/customers\" # Environment variable for URL\n headers:\n Authorization: \"Bearer ${API_TOKEN}\" # Environment variable for token\n X-API-Key: \"${EXTERNAL_API_KEY}\" # Environment variable for API key\n```\n\n### Environment Variable Patterns\n\n```yaml\n# Database connections\n- id: database_task\n type: storage\n config:\n storage_type: database\n connection_string: \"${DATABASE_URL}\" # Full connection string\n # or individual components:\n host: \"${DB_HOST}\"\n port: \"${DB_PORT}\"\n database: \"${DB_NAME}\"\n username: \"${DB_USER}\"\n password: \"${DB_PASSWORD}\"\n\n# Third-party service credentials\n- id: external_service\n type: http\n url: \"${SERVICE_BASE_URL}/api/v1/data\"\n headers:\n Authorization: \"Bearer ${SERVICE_TOKEN}\"\n X-Client-ID: \"${SERVICE_CLIENT_ID}\"\n X-Client-Secret: \"${SERVICE_CLIENT_SECRET}\"\n\n# MCP server credentials\n- id: mcp_task\n type: mcp\n mcp_server: \"@agentic/mcp-notion-server\"\n environment_vars:\n NOTION_TOKEN: \"${NOTION_API_TOKEN}\"\n WORKSPACE_ID: \"${NOTION_WORKSPACE_ID}\"\n```\n\n### Security Best Practices\n\n#### \u274c What NOT to Do\n\n```yaml\n# DON\u0027T: Hardcode secrets\nmodel_clients:\n - id: insecure_example\n provider: openai\n config:\n api_key: \"sk-actual-api-key-here\" # SECURITY RISK!\n\n# DON\u0027T: Put secrets in comments\ntasks:\n - id: bad_example\n type: http\n url: \"https://api.example.com\"\n # API key is: sk-12345... DON\u0027T DO THIS!\n headers:\n Authorization: \"Bearer sk-real-token\" # SECURITY RISK!\n```\n\n#### \u2705 What TO Do\n\n```yaml\n# DO: Use environment variables\nmodel_clients:\n - id: secure_example\n provider: openai\n config:\n api_key: \"${OPENAI_API_KEY}\" # Secure approach\n\n# DO: Use different variables for different environments\n- id: environment_aware_task\n type: http\n url: \"${API_BASE_URL}\" # Different URLs per environment\n headers:\n Authorization: \"Bearer ${API_TOKEN}\" # Different tokens per environment\n X-Environment: \"${ENVIRONMENT_NAME}\" # Track environment\n\n# DO: Use descriptive variable names\nenvironment_vars:\n CUSTOMER_API_TOKEN: \"${CUSTOMER_SERVICE_TOKEN}\"\n BILLING_API_KEY: \"${BILLING_SERVICE_API_KEY}\"\n NOTIFICATION_WEBHOOK_SECRET: \"${NOTIFICATION_SECRET}\"\n```\n\n### Environment Variable Naming Conventions\n\n| Pattern | Example | Use Case |\n|---------|---------|----------|\n| `SERVICE_API_KEY` | `OPENAI_API_KEY` | Service API keys |\n| `SERVICE_BASE_URL` | `CUSTOMER_API_BASE_URL` | Service endpoints |\n| `DB_*` | `DB_HOST`, `DB_PASSWORD` | Database credentials |\n| `WEBHOOK_*` | `WEBHOOK_SECRET_TOKEN` | Webhook authentication |\n| `*_TIMEOUT` | `HTTP_REQUEST_TIMEOUT` | Timeout configurations |\n\n### Dynamic Environment Variables\n\nUse template resolution to create dynamic environment variable names:\n\n```yaml\n# Different API keys per customer\n- id: customer_specific_api\n type: http\n url: \"https://api.partner.com/data\"\n headers:\n Authorization: \"Bearer ${PARTNER_API_KEY_${customer_tier}}\" # Resolves to PARTNER_API_KEY_PREMIUM\n\n# Environment-specific configurations\n- id: env_specific_task\n type: storage\n config:\n connection_string: \"${DATABASE_URL_${environment}}\" # DATABASE_URL_PRODUCTION\n```\n\n### Secrets in Different Task Types\n\n```yaml\n# AI Agent with multiple secure credentials\n- id: secure_ai_task\n type: ai_agent\n config:\n model_client_id: secure_model\n system_message: \"You are a secure assistant with access to customer data.\"\n user_message: \"Process customer ${customer_id}\"\n environment_vars:\n CUSTOMER_DB_TOKEN: \"${CUSTOMER_DATABASE_TOKEN}\"\n AUDIT_LOG_KEY: \"${AUDIT_LOGGING_API_KEY}\"\n\n# Script task with secure file access\n- id: secure_script_task\n type: script\n environment_vars:\n AWS_ACCESS_KEY_ID: \"${AWS_ACCESS_KEY}\"\n AWS_SECRET_ACCESS_KEY: \"${AWS_SECRET_KEY}\"\n S3_BUCKET_NAME: \"${SECURE_BUCKET_NAME}\"\n script: |\n import boto3\n import os\n \n # Credentials automatically available from environment\n s3_client = boto3.client(\u0027s3\u0027)\n bucket = os.environ[\u0027S3_BUCKET_NAME\u0027]\n \n # Process secure files...\n\n# Storage task with encrypted connections\n- id: secure_storage_task\n type: storage\n config:\n storage_type: database\n connection_string: \"${ENCRYPTED_DB_CONNECTION_STRING}\"\n ssl_mode: \"require\"\n ssl_cert: \"${SSL_CERT_PATH}\"\n ssl_key: \"${SSL_KEY_PATH}\"\n```\n\n### Environment Setup Examples\n\n#### Development Environment (.env file)\n```bash\n# Development API keys (non-production)\nOPENAI_API_KEY=sk-dev-api-key-here\nCUSTOMER_API_BASE_URL=https://api-dev.example.com\nDATABASE_URL=postgresql://dev:pass@localhost:5432/workflow_dev\nENVIRONMENT_NAME=development\n```\n\n#### Production Environment\n```bash\n# Production API keys (secure)\nOPENAI_API_KEY=sk-prod-api-key-here\nCUSTOMER_API_BASE_URL=https://api.example.com\nDATABASE_URL=postgresql://prod_user:secure_pass@prod-db:5432/workflow_prod\nENVIRONMENT_NAME=production\n```\n\n## Workflow Execution and Orchestration\n\nUnderstanding how workflows execute and how tasks are orchestrated is essential for building reliable, efficient workflows.\n\n### Execution Flow\n\nWorkflows follow a directed acyclic graph (DAG) execution model:\n\n1. **Initialization**: Workflow starts with inputs and model clients\n2. **Task Scheduling**: Tasks are scheduled based on dependencies\n3. **Parallel Execution**: Independent tasks run simultaneously\n4. **Data Flow**: Outputs flow between tasks via template resolution\n5. **Completion**: Workflow completes when all tasks finish\n\n```yaml\n# Example execution flow\ntasks:\n # Stage 1: Independent tasks (run in parallel)\n - id: fetch_customer # Runs immediately\n type: http\n url: \"https://api.example.com/customers/${customer_id}\"\n \n - id: fetch_products # Runs in parallel with fetch_customer\n type: http\n url: \"https://api.example.com/products\"\n \n # Stage 2: Dependent tasks (wait for Stage 1)\n - id: validate_customer # Waits for fetch_customer\n type: script\n depends_on: [fetch_customer]\n previous_node: fetch_customer\n script: # validation logic...\n \n - id: filter_products # Waits for fetch_products\n type: script\n depends_on: [fetch_products]\n previous_node: fetch_products\n script: # filtering logic...\n \n # Stage 3: Final processing (waits for Stage 2)\n - id: generate_recommendations # Waits for both validation and filtering\n type: ai_agent\n depends_on: [validate_customer, filter_products]\n previous_node: validate_customer # UI hierarchy under customer validation\n config:\n model_client_id: gpt4_model\n system_message: \"Generate product recommendations for customer.\"\n user_message: |\n Customer: ${validate_customer}\n Available products: ${filter_products}\n```\n\n### Task States and Lifecycle\n\nEach task goes through these states during execution:\n\n| State | Description | Next States |\n|-------|-------------|-------------|\n| `PENDING` | Task created, waiting for dependencies | `RUNNING`, `SKIPPED` |\n| `RUNNING` | Task is actively executing | `COMPLETED`, `FAILED` |\n| `COMPLETED` | Task finished successfully | - |\n| `FAILED` | Task encountered an error | `RUNNING` (if retries) |\n| `SKIPPED` | Task skipped due to conditions | - |\n| `TIMEOUT` | Task exceeded timeout limit | `RUNNING` (if retries) |\n\n### Conditional Execution\n\nTasks can be executed conditionally based on routes or conditions:\n\n```yaml\n# Router-based conditional execution\n- id: data_router\n type: conditional_router\n depends_on: [fetch_data]\n previous_node: fetch_data\n conditions:\n - name: \"large_dataset\"\n condition: \"${fetch_data.record_count} \u003e 10000\"\n route: \"batch_processing\"\n - name: \"small_dataset\"\n condition: \"${fetch_data.record_count} \u003c= 1000\"\n route: \"direct_processing\"\n default_route: \"standard_processing\"\n\n# Tasks that execute on specific routes\n- id: batch_processor\n type: script\n depends_on: [data_router]\n previous_node: data_router\n execute_on_routes: [\"batch_processing\"] # Only runs for large datasets\n script: |\n # Handle large dataset in batches\n data = ${fetch_data}\n process_in_batches(data, batch_size=1000)\n\n- id: direct_processor\n type: script\n depends_on: [data_router]\n previous_node: data_router\n execute_on_routes: [\"direct_processing\"] # Only runs for small datasets\n script: |\n # Process small dataset directly\n data = ${fetch_data}\n process_directly(data)\n```\n\n### Error Handling and Retries\n\nConfigure retry policies for resilient workflow execution:\n\n```yaml\n# Workflow-level retry policy\nretry:\n retryOn: [\"TEMPORARY_FAILURE\", \"TIMEOUT\", \"NETWORK_ERROR\"]\n maxAttempts: 3\n initialDelay: \"1s\"\n maxDelay: \"60s\"\n backoffMultiplier: 2.0\n\ntasks:\n # Task-specific retry policy (overrides workflow-level)\n - id: external_api_call\n type: http\n url: \"https://unreliable-api.example.com/data\"\n retry:\n maxAttempts: 5 # More retries for unreliable API\n initialDelay: \"2s\"\n maxDelay: \"120s\"\n backoffMultiplier: 2.0\n retryOn: [\"TIMEOUT\", \"HTTP_5XX\", \"NETWORK_ERROR\"]\n \n # Task with no retries\n - id: critical_operation\n type: script\n retry:\n maxAttempts: 1 # No retries for critical operations\n script: |\n # This operation should not be retried if it fails\n perform_critical_database_operation()\n```\n\n### Parallel Execution Patterns\n\n```yaml\n# Fan-out pattern: One task triggers multiple parallel tasks\n- id: data_source\n type: http\n url: \"https://api.example.com/data\"\n\n- id: process_customers # Parallel processing\n type: script\n depends_on: [data_source]\n previous_node: data_source\n script: # process customer data from data_source\n\n- id: process_orders # Parallel processing\n type: script\n depends_on: [data_source]\n previous_node: data_source\n script: # process order data from data_source\n\n- id: process_products # Parallel processing\n type: script\n depends_on: [data_source]\n previous_node: data_source\n script: # process product data from data_source\n\n# Fan-in pattern: Multiple tasks feed into one task\n- id: generate_report # Waits for all parallel tasks\n type: ai_agent\n depends_on: [process_customers, process_orders, process_products]\n previous_node: process_customers # UI hierarchy\n config:\n model_client_id: gpt4_model\n system_message: \"Generate comprehensive report from processed data.\"\n user_message: |\n Customer data: ${process_customers}\n Order data: ${process_orders}\n Product data: ${process_products}\n```\n\n### Resource Management\n\nControl resource usage and task distribution:\n\n```yaml\n# Worker group targeting\n- id: cpu_intensive_task\n type: script\n worker_group: \"high-cpu-workers\" # Target specific worker group\n script: |\n # CPU-intensive processing\n perform_heavy_computation()\n\n- id: gpu_ml_task\n type: ai_agent\n worker_group: \"gpu-workers\" # Target GPU workers\n config:\n model_client_id: large_model\n # Large model processing...\n\n# Timeout configuration\n- id: long_running_task\n type: script\n timeout_seconds: 3600 # 1 hour timeout\n script: |\n # Long-running data processing\n process_large_dataset()\n\n# Workflow-level timeout\ntimeout_seconds: 7200 # 2 hour workflow timeout\n```\n\n## Task Type Configurations\n\nThis section provides the complete YAML configuration for each supported task type.\n\n**Supported Task Types:**\n- `script` - Execute Python code with full workflow context\n- `http` - Make HTTP requests to external APIs\n- `ai_agent` - Integrate with AI models for intelligent processing \n- `loop` - Execute tasks repeatedly (for/while loops)\n- `conditional_router` - Route execution based on conditions\n- `mcp` - Execute tools via Model Context Protocol servers\n- `storage` - Data persistence and retrieval operations\n- `continuous_conversation` - Interactive AI conversations\n- `human_input` - Tasks requiring human input or approval\n- `chatbot` - Session-based chatbot conversations\n\n### Script Tasks\n\nExecute Python code with full workflow context access.\n\n```yaml\n- id: script_task\n name: \"Python Script Task\"\n type: script\n description: \"Execute Python code with dependencies and environment\"\n \n # Script configuration - script field is directly under task, NOT under config\n script: | # REQUIRED: Python code to execute\n import json\n import requests\n \n # Access workflow inputs\n input_value = \"${workflow_input}\"\n \n # Access previous task outputs\n previous_result = ${previous_task}\n \n # Your processing logic\n result = {\"processed\": True, \"value\": input_value}\n \n # Output results (must be JSON serializable)\n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n \n # Alternative: use \u0027code\u0027 field instead of \u0027script\u0027\n code: | # Alternative to \u0027script\u0027 field\n print(\"Hello World\")\n \n # Package management\n requirements: # Optional: Python packages\n - requests==2.31.0\n - pandas==1.5.3\n - numpy==1.24.3\n \n # Environment configuration\n environment: # Optional: Environment variables\n API_KEY: \"${secrets.api_key}\"\n DEBUG: \"true\"\n CUSTOM_VAR: \"value\"\n \n environment_vars: # Alternative to \u0027environment\u0027\n DATABASE_URL: \"${secrets.db_url}\"\n \n # Secrets configuration\n secrets_config: # Optional: Secrets access\n api_key: \"secret_name\"\n db_password: \"db_secret\"\n```\n\n### HTTP Tasks\n\nMake HTTP requests to external APIs and services.\n\n```yaml\n- id: http_task\n name: \"HTTP API Call\"\n type: http\n description: \"Call external REST API\"\n \n # HTTP configuration\n method: POST # Default: GET\n url: \"https://api.example.com/data\" # REQUIRED: Target URL\n \n # Headers configuration\n headers: # Optional: HTTP headers\n Content-Type: \"application/json\"\n Authorization: \"Bearer ${secrets.api_token}\"\n X-Custom-Header: \"workflow-engine\"\n User-Agent: \"WorkflowEngine/1.0\"\n \n # Request body\n body: | # Optional: Request body (for POST/PUT)\n {\n \"customer_id\": \"${customer_id}\",\n \"timestamp\": \"${current_timestamp}\",\n \"data\": ${previous_task}\n }\n \n # Response validation\n expected_status: [200, 201, 202] # Default: [200]\n expectedStatus: [200, 201] # Alternative field name\n \n # Additional HTTP options\n timeout_seconds: 30 # HTTP request timeout\n follow_redirects: true # Follow HTTP redirects\n verify_ssl: true # Verify SSL certificates\n```\n\n### AI Agent Tasks\n\nIntegrate with AI models for intelligent processing.\n\n```yaml\n- id: ai_agent_task\n name: \"AI Processing Agent\"\n type: ai_agent\n description: \"Process data using AI model\"\n \n # AI configuration\n config:\n model_client_id: gpt4_model # REQUIRED: Reference to model client\n \n system_message: | # Optional: System prompt\n You are a data analyst. Analyze the provided data and extract insights.\n Return your analysis in JSON format with the following structure:\n {\n \"insights\": [\"insight1\", \"insight2\"],\n \"recommendations\": [\"rec1\", \"rec2\"],\n \"confidence_score\": 0.95\n }\n \n user_message: | # REQUIRED: User prompt with template resolution\n Please analyze this customer data:\n ${previous_task}\n \n Customer ID: ${customer_id}\n Date Range: ${date_range}\n \n # Model parameters\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\n max_tokens: 1500 # Optional: Maximum response tokens\n top_p: 1.0 # Optional: Nucleus sampling\n frequency_penalty: 0.0 # Optional: Frequency penalty\n presence_penalty: 0.0 # Optional: Presence penalty\n \n # Tools configuration for function calling\n tools: # Optional: List of MCP tool names\n - current_time # Time-related tools\n - relative_time\n - days_in_month\n - weather_api # Weather tools\n - database_query # Database tools\n - notification_service # Communication tools\n \n # Required for tool resolution\n tenant_id: \"your-tenant-uuid\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\n \n # Input/output format\n input_format: \"text\" # Optional: text|json|structured\n output_format: \"structured\" # Optional: text|json|structured\n\n### Important: AI Agent Output Access\n\nAI agent tasks return structured objects, not direct text responses. Always access the AI response using this pattern:\n\n```yaml\n# In subsequent script tasks\nscript: |\n # CORRECT: Access AI response from structured output\n agent_output = ${ai_agent_task}\n ai_response = agent_output[\u0027ai_response\u0027].strip()\n \n # Additional metadata available\n tokens_used = agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\n cost_estimate = agent_output.get(\u0027estimated_cost\u0027, {}).get(\u0027amount\u0027, 0.0)\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\n \n print(f\"AI Response: {ai_response}\")\n print(f\"Tokens used: {tokens_used}\")\n print(f\"Estimated cost: ${cost_estimate:.6f}\")\n```\n\n```yaml\n# In workflow outputs - specify the ai_response field\noutputs:\n ai_analysis:\n type: string\n source: ai_agent_task.ai_response # Access the ai_response field\n description: \"AI-generated analysis text\"\n \n total_tokens:\n type: integer \n source: ai_agent_task.token_usage.total_tokens\n description: \"Total tokens consumed\"\n```\n```\n\n### Loop Tasks\n\nExecute tasks repeatedly with for-loop and while-loop patterns.\n\n```yaml\n# For-Loop (Fixed Iterations)\n- id: for_loop_task\n name: \"For Loop Processor\"\n type: loop\n description: \"Process data in fixed batches\"\n \n # Loop configuration\n loop_type: for # REQUIRED: for|while\n max_iterations: 10 # REQUIRED for \u0027for\u0027 loops\n iteration_variable: batch_index # Default: iteration\n \n # State variables (persistent across iterations)\n state_variables:\n total_processed: 0\n error_count: 0\n batch_results: []\n \n # Tasks to execute in each iteration\n loop_tasks: # REQUIRED: Tasks to run in loop\n - id: process_batch\n name: \"Process Current Batch\"\n type: script\n script: |\n batch_idx = ${batch_index} # Access iteration variable\n total = ${total_processed} # Access state variable\n \n # Process current batch\n result = process_batch_data(batch_idx)\n \n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n \n - id: update_state\n name: \"Update Loop State\"\n type: script\n depends_on: [process_batch]\n script: |\n # Update state variables\n new_total = ${total_processed} + ${process_batch.items_processed}\n \n result = {\"total_processed\": new_total}\n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n\n# While-Loop (Conditional)\n- id: while_loop_task\n name: \"While Loop Quality Processor\"\n type: loop\n description: \"Process until quality threshold met\"\n \n # Loop configuration\n loop_type: while # REQUIRED: for|while\n exit_condition: \"${average_quality} \u003e= 85 || ${iteration_count} \u003e= 20\" # REQUIRED for \u0027while\u0027\n max_iterations: 50 # Optional: Safety limit\n iteration_variable: iteration_count # Default: iteration\n \n # State variables\n state_variables:\n average_quality: 0\n items_processed: 0\n quality_scores: []\n \n # Loop tasks\n loop_tasks:\n - id: assess_quality\n type: script\n script: |\n current_quality = calculate_quality()\n print(f\"__OUTPUTS__ {json.dumps({\u0027quality\u0027: current_quality})}\")\n```\n\n\n### Conditional Router Tasks\n\nRoute execution to different paths based on conditions.\n\n```yaml\n- id: conditional_router_task\n name: \"Data Type Router\"\n type: conditional_router\n description: \"Route processing based on data characteristics\"\n \n # Router configuration\n router_type: condition # Default: condition\n \n # Routing conditions\n conditions: # REQUIRED: List of conditions\n - name: \"large_dataset\"\n condition: \"${data_size} \u003e 10000\"\n route: \"batch_processing\"\n - name: \"small_dataset\" \n condition: \"${data_size} \u003c= 1000\"\n route: \"direct_processing\"\n - name: \"medium_dataset\"\n condition: \"${data_size} \u003e 1000 \u0026\u0026 ${data_size} \u003c= 10000\"\n route: \"standard_processing\"\n \n default_route: \"standard_processing\" # REQUIRED: Default route if no conditions match\n \n # Alternative: evaluation script for complex routing\n evaluation_script: | # Optional: Custom routing logic\n if data_type == \"json\":\n return \"json_processing\"\n elif data_type == \"xml\":\n return \"xml_processing\"\n else:\n return \"default_processing\"\n \n # Route mapping for complex scenarios\n route_mapping: # Optional: Route name mappings\n json_processing: \"specialized_json\"\n xml_processing: \"specialized_xml\"\n\n# Tasks that execute on specific routes\n- id: batch_handler\n name: \"Handle Large Dataset\"\n type: script\n execute_on_routes: [\"batch_processing\"] # REQUIRED: Routes this task runs on\n depends_on: [conditional_router_task]\n script: |\n print(\"Processing large dataset in batches...\")\n\n- id: direct_handler\n name: \"Handle Small Dataset\" \n type: script\n execute_on_routes: [\"direct_processing\"]\n depends_on: [conditional_router_task]\n script: |\n print(\"Processing small dataset directly...\")\n```\n\n### MCP Tasks\n\nExecute tools via Model Context Protocol servers.\n\n```yaml\n- id: mcp_task\n name: \"MCP Tool Execution\"\n type: mcp\n description: \"Execute external tool via MCP server\"\n \n # MCP server configuration\n mcp_server: \"@agentic/mcp-notion-server\" # REQUIRED: MCP server package\n tool_name: \"update_database\" # REQUIRED: Tool to execute\n runtime: \"npx\" # REQUIRED: npx|uvx\n \n # Server startup\n server_args: [] # Optional: Server startup arguments\n server_startup_timeout: 30 # Optional: Server startup timeout (seconds)\n \n # Tool execution\n tool_arguments: # REQUIRED: Arguments for the tool\n database_id: \"${secrets.notion_database_id}\"\n properties:\n title: \"Workflow Results\"\n status: \"Completed\"\n processed_count: \"${previous_task.count}\"\n timestamp: \"${current_timestamp}\"\n \n # Environment configuration\n environment_vars: # Optional: Environment variables\n NOTION_TOKEN: \"${secrets.notion_token}\"\n DEBUG: \"true\"\n \n # Timeout configuration\n timeout_seconds: 120 # Optional: Tool execution timeout\n```\n\n### Storage Tasks\n\nManage data persistence and retrieval operations.\n\n```yaml\n- id: storage_task\n name: \"Data Storage Operation\"\n type: storage\n description: \"Save/load data to/from storage\"\n \n # Storage operation\n operation: save # REQUIRED: save|load|delete|list\n \n # Storage configuration\n storage_type: \"file\" # REQUIRED: file|database|cache\n path: \"/data/results/${execution_id}.json\" # REQUIRED: Storage path\n \n # Data configuration\n data: ${previous_task} # REQUIRED for \u0027save\u0027: Data to store\n format: \"json\" # Optional: json|yaml|csv|parquet\n compression: \"gzip\" # Optional: gzip|bz2|none\n \n # Metadata\n metadata: # Optional: Additional metadata\n workflow_id: \"${workflow_id}\"\n created_at: \"${current_timestamp}\"\n source_task: \"previous_task\"\n \n # File options\n create_directories: true # Optional: Create parent directories\n overwrite: true # Optional: Overwrite existing files\n backup: false # Optional: Create backup before overwrite\n \n # Database options (when storage_type: \"database\")\n table_name: \"workflow_results\" # Required for database storage\n connection_string: \"${secrets.db_url}\" # Required for database storage\n \n # Cache options (when storage_type: \"cache\")\n cache_key: \"result_${execution_id}\" # Required for cache storage\n ttl: 3600 # Optional: Time to live (seconds)\n```\n\n\n### Continuous Conversation Tasks\n\nCreate interactive AI-powered conversations.\n\n```yaml\n- id: conversation_task\n name: \"AI Conversation Interface\"\n type: continuous_conversation\n description: \"Interactive conversation with AI assistant\"\n \n # Conversation configuration\n config:\n # AI configuration\n ai_config:\n model_client_id: support_assistant # REQUIRED: AI model to use\n system_prompt: | # REQUIRED: System instructions\n You are a helpful customer support representative.\n Be friendly, professional, and solution-oriented.\n \n Guidelines:\n - Always be polite and respectful\n - Ask clarifying questions when needed\n - Provide clear and actionable solutions\n \n temperature: 0.7 # Optional: AI creativity\n max_tokens: 1000 # Optional: Response length limit\n \n # UI configuration\n ui_config:\n title: \"Customer Support Chat\" # Optional: Interface title\n placeholder: \"How can I help you today?\" # Optional: Input placeholder\n theme: \"light\" # Optional: light|dark\n show_timestamp: true # Optional: Show message timestamps\n \n # Termination conditions\n termination:\n keywords: [\"goodbye\", \"bye\", \"thanks\", \"done\"] # Optional: End conversation keywords\n max_turns: 50 # Optional: Maximum conversation turns\n timeout_minutes: 30 # Optional: Conversation timeout\n inactivity_timeout: 10 # Optional: Inactivity timeout (minutes)\n \n # Context configuration\n context:\n system_context: \"Customer support conversation\" # Optional: Context description\n user_id: \"${customer_id}\" # Optional: User identifier\n session_data: ${previous_task} # Optional: Additional context data\n```\n\n### Human Input Tasks\n\nTasks requiring human input or approval.\n\n```yaml\n- id: human_input_task\n name: \"Human Input Required\"\n type: human_input\n description: \"Wait for human input or approval\"\n \n # Human input configuration\n config:\n prompt: \"Please review and approve the processed data\" # REQUIRED: Prompt for human\n input_type: \"approval\" # REQUIRED: approval|text|selection\n \n # Input options (for selection type)\n options: [\"approve\", \"reject\", \"request_changes\"] # Optional: For selection input\n \n # Timeout configuration\n timeout_minutes: 60 # Optional: How long to wait for input (minutes)\n \n # Conversation settings\n conversation_mode: false # Optional: Add to conversation history\n next_task: \"process_approval\" # Optional: Task to trigger after input\n end_condition: \"bye|exit|quit\" # Optional: Keywords to end conversation\n \n # Input validation\n required: true # Optional: Whether input is required\n \n # Additional context\n context_data: ${previous_task} # Optional: Data to show to human\n```\n\n### ChatBot Tasks\n\nSession-based chatbot conversations with streaming support.\n\n```yaml\n- id: chatbot_task\n name: \"AI Chatbot\"\n type: chatbot\n description: \"Interactive chatbot with session management\"\n \n # Chatbot configuration\n config:\n # AI model configuration\n model_client_id: gpt4_model # REQUIRED: Reference to model client\n \n system_message: | # REQUIRED: System prompt for chatbot\n You are a helpful AI assistant. Be friendly, helpful, and concise.\n Answer questions clearly and ask for clarification when needed.\n \n # Session management\n session_id: \"${session_id}\" # Optional: Session identifier (auto-generated if not provided)\n max_history_messages: 20 # Optional: Maximum conversation history to maintain\n \n # Model parameters\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\n max_tokens: 1000 # Optional: Maximum response tokens\n stream: true # Optional: Enable streaming responses\n \n # Tools configuration for function calling\n tools: # Optional: List of MCP tool names\n - current_time # Time-related tools\n - relative_time\n - days_in_month\n - weather_forecast # Weather tools\n - send_notification # Communication tools\n \n # Required for tool resolution\n tenant_id: \"your-tenant-uuid\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\n \n # UI configuration\n ui_config:\n title: \"AI Assistant\" # Optional: Chat interface title\n placeholder: \"Type your message...\" # Optional: Input placeholder\n theme: \"light\" # Optional: light|dark\n show_typing_indicator: true # Optional: Show typing animation\n \n # Response configuration\n response_config:\n max_retries: 3 # Optional: Retry failed requests\n timeout_seconds: 30 # Optional: Response timeout\n \n # Input message (for single-turn mode)\n user_message: \"${user_input}\" # Optional: Direct message input\n \n # Context data\n context_data: ${previous_task} # Optional: Additional context\n```\n\n---\n\n## Common Errors and Corrections\n\nThis section documents frequent mistakes encountered when creating workflows and their proper corrections.\n\n### 1. Missing Required `id` Field\n\n**\u274c Error:**\n```yaml\nname: \"My Workflow\"\ndescription: \"A workflow without id\"\ntasks:\n - name: \"Task 1\"\n type: script\n```\n\n**\u2705 Correct:**\n```yaml\nid: \"my_workflow\" # REQUIRED: Unique workflow identifier\nname: \"My Workflow\"\ndescription: \"A workflow with proper id\"\ntasks:\n - id: task_1 # REQUIRED: Unique task identifier\n name: \"Task 1\"\n type: script\n```\n\n### 2. Incorrect Task Structure (Object vs Array)\n\n**\u274c Error:**\n```yaml\ntasks:\n task_1: # Wrong: tasks as object mapping\n type: script\n script: \"print(\u0027hello\u0027)\"\n task_2:\n type: ai_agent\n```\n\n**\u2705 Correct:**\n```yaml\ntasks:\n - id: task_1 # Correct: tasks as array with id field\n type: script\n script: \"print(\u0027hello\u0027)\"\n - id: task_2\n type: ai_agent\n```\n\n### 3. Incorrect Inputs Format (Object vs Array)\n\n**\u274c Error:**\n```yaml\ninputs:\n user_name: # Wrong: inputs as object mapping\n type: string\n required: true\n age:\n type: integer\n```\n\n**\u2705 Correct:**\n```yaml\ninputs:\n - name: user_name # Correct: inputs as array with name field\n type: string\n required: true\n - name: age\n type: integer\n```\n\n### 4. Script Configuration Location Error\n\n**\u274c Error:**\n```yaml\n- id: script_task\n type: script\n config: # Wrong: script under config\n script: |\n print(\"Hello\")\n requirements:\n - requests\n```\n\n**\u2705 Correct:**\n```yaml\n- id: script_task\n type: script\n script: | # Correct: script directly under task\n print(\"Hello\")\n # requirements can be at task level if needed\n```\n\n### 5. Individual Task Inputs (Not Supported)\n\n**\u274c Error:**\n```yaml\n- id: task_1\n type: script\n script: \"print(\u0027hello\u0027)\"\n inputs: # Wrong: tasks don\u0027t have individual inputs\n search_query: ${search_query}\n prev_result: ${previous_task}\n```\n\n**\u2705 Correct:**\n```yaml\n- id: task_1\n type: script\n depends_on: [previous_task] # Use depends_on for data access\n script: |\n # Access workflow inputs directly\n search_query = \"${search_query}\"\n # Access previous task outputs via template resolution\n prev_result = ${previous_task}\n```\n\n### 6. Incorrect Script Output Handling\n\n**\u274c Error:**\n```yaml\nscript: |\n result = process_data()\n outputs[\u0027result\u0027] = result # Wrong: missing __OUTPUTS__ pattern\n```\n\n**\u2705 Correct:**\n```yaml\nscript: |\n result = process_data()\n \n # Required: Use outputs dict and __OUTPUTS__ marker\n outputs = {}\n outputs[\u0027result\u0027] = result\n outputs[\u0027status\u0027] = \u0027success\u0027\n \n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n### 7. AI Agent Configuration Errors\n\n**\u274c Error:**\n```yaml\n- id: ai_task\n type: ai_agent\n config:\n model_client_id: my_model\n prompt: \"Process this data: ${data}\" # Wrong: use user_message\n```\n\n**\u2705 Correct:**\n```yaml\n- id: ai_task\n type: ai_agent\n config:\n model_client_id: my_model\n system_message: \"You are a helpful assistant.\"\n user_message: \"Process this data: ${data}\" # Correct field name\n```\n\n### 8. Missing Dependencies for Template Resolution\n\n**\u274c Error:**\n```yaml\n- id: task_2\n type: script\n # Missing depends_on\n script: |\n data = ${task_1} # Will become UNRESOLVED_task_1\n```\n\n**\u2705 Correct:**\n```yaml\n- id: task_2\n type: script\n depends_on: [task_1] # Required for template access\n script: |\n data = ${task_1} # Now resolves correctly\n```\n\n### 9. Incorrect Model Client Configuration\n\n**\u274c Error:**\n```yaml\nmodel_clients:\n my_model: # Wrong: object mapping format\n type: openai\n api_key: \"sk-...\"\n```\n\n**\u2705 Correct:**\n```yaml\nmodel_clients:\n - id: my_model # Correct: array format with id field\n provider: openai\n config:\n model: \"gpt-4\"\n api_key: \"sk-...\"\n```\n\n### 10. Empty String Literals Causing Python Syntax Errors\n\n**\u274c Error:**\n```yaml\nscript: |\n def process_data(path=\"\"): # Wrong: empty string can cause syntax issues\n current_path = f\"{path}.{key}\" if path else key\n \n # This can lead to: SyntaxError: unterminated string literal\n```\n\n**\u2705 Correct:**\n```yaml\nscript: |\n def process_data(path=None): # Correct: use None instead of empty string\n current_path = f\"{path}.{key}\" if path else key\n # Or use path = path or \"default_value\"\n```\n\n### 11. YAML Indentation and Structure Issues\n\n**\u274c Error:**\n```yaml\ntasks:\n- id: task_1\n type: script\nscript: | # Wrong: inconsistent indentation\n print(\"hello\")\n```\n\n**\u2705 Correct:**\n```yaml\ntasks:\n- id: task_1\n type: script\n script: | # Correct: consistent indentation\n print(\"hello\")\n```\n\n### Best Practices Summary\n\n1. **Always include required `id` fields** for workflows and tasks\n2. **Use array format** for `tasks`, `inputs`, and `model_clients`\n3. **Script field goes directly under task**, not under `config`\n4. **No individual task inputs** - use template resolution instead\n5. **Use `depends_on`** to access previous task outputs\n6. **Print `__OUTPUTS__`** in scripts for proper output parsing\n7. **Use `user_message`** (not `prompt`) for AI agent tasks\n8. **Avoid empty string literals** - use `None` or meaningful defaults\n9. **Follow consistent YAML indentation** throughout the file\n\n## MCP Tools and Function Calling\n\nAI agents and chatbots can use MCP (Model Context Protocol) tools for function calling, enabling them to execute external tools and services.\n\n### Tools Configuration\n\nBoth `ai_agent` and `chatbot` tasks support tools configuration:\n\n```yaml\n# AI Agent with MCP tools\n- id: ai_with_tools\n type: ai_agent\n config:\n model_client_id: gpt4_model\n system_message: |\n You are a helpful assistant with access to various tools.\n Use the available tools when needed to help users.\n user_message: \"What time is it and what\u0027s the weather like?\"\n \n # Tools configuration\n tools: # List of MCP tool names\n - current_time # Time tools\n - relative_time\n - days_in_month\n - weather_forecast # Weather tools\n - database_query # Database tools\n - send_notification # Communication tools\n \n tenant_id: \"550e8400-e29b-41d4-a716-446655440000\" # Required for tool resolution\n\n# Chatbot with MCP tools\n- id: chatbot_with_tools\n type: chatbot\n config:\n model_client_id: gpt4_model\n system_message: |\n You are a helpful chatbot with access to tools.\n Use tools when users ask for specific information or actions.\n \n # Tools configuration (same format as AI agent)\n tools:\n - current_time\n - weather_api\n - user_preferences\n \n tenant_id: \"550e8400-e29b-41d4-a716-446655440000\" # Required for tool resolution\n```\n\n### How Tools Work\n\n1. **Tool Resolution**: The workflow engine resolves tool names to actual MCP server deployments using the `tenant_id`\n2. **Function Calling**: When the AI model decides to use a tool, it calls the `tool_executor` function\n3. **Tool Execution**: The engine executes the specified tool via the appropriate MCP server\n4. **Response Integration**: Tool results are fed back to the AI model for response generation\n\n### Tool Configuration Requirements\n\n| Field | Required | Description |\n|-------|----------|-------------|\n| `tools` | No | List of MCP tool names available to the AI |\n| `tenant_id` | **Yes** (when using tools) | Tenant UUID for tool resolution |\n\n### Example: Complete AI Agent with Tools\n\n```yaml\nid: intelligent_assistant\nname: \"Intelligent Assistant with Tools\"\n\nmodel_clients:\n - id: gpt4_model\n provider: openai\n config:\n model: gpt-4o-mini\n api_key: \"${OPENAI_API_KEY}\"\n temperature: 0.3\n\n# Tenant ID can be set at workflow level for all tasks\ntenant_id: \"0572fa8d-d7c3-47db-9219-ef40b80d42b7\"\n\ntasks:\n - id: assistant_with_tools\n type: ai_agent\n config:\n model_client_id: gpt4_model\n system_message: |\n You are a helpful assistant with access to various tools:\n - Time-related tools: current_time, relative_time, days_in_month\n - Weather tools: weather_forecast\n - Communication: send_notification\n \n Use these tools when users ask for:\n - Current time or date information\n - Weather forecasts\n - Sending notifications\n \n Always explain what tool you\u0027re using and why.\n \n user_message: \"${user_query}\"\n \n # MCP tools configuration\n tools:\n - current_time\n - relative_time\n - days_in_month\n - weather_forecast\n - send_notification\n \n # Tool resolution requires tenant_id\n tenant_id: \"0572fa8d-d7c3-47db-9219-ef40b80d42b7\"\n \n temperature: 0.7\n max_tokens: 1000\n\ninputs:\n - name: user_query\n type: string\n required: true\n default: \"What time is it and can you send a notification to user123?\"\n\noutputs:\n ai_response:\n type: string\n source: assistant_with_tools.ai_response\n description: \"AI response using tools when needed\"\n```\n\n### Tool Execution Flow\n\n1. **User Input**: User provides a query that might require tool usage\n2. **AI Analysis**: AI model analyzes the query and decides if tools are needed\n3. **Tool Calling**: AI calls `tool_executor(deployment_id, tool_name, arguments)`\n4. **Tool Resolution**: Engine resolves tool name to MCP server using tenant_id\n5. **Tool Execution**: MCP server executes the requested tool\n6. **Result Processing**: Tool results are returned to the AI model\n7. **Response Generation**: AI generates final response incorporating tool results\n\n### Best Practices for Tools\n\n1. **Clear System Messages**: Explain available tools and when to use them\n2. **Tool Categories**: Group related tools (time, weather, database, etc.)\n3. **Error Handling**: AI models handle tool execution failures gracefully\n4. **Tool Documentation**: Include tool descriptions in system messages\n5. **Tenant Configuration**: Ensure tenant_id matches your MCP server setup\n\n---\n\n*This comprehensive reference covers all supported task types and their YAML configurations. Use this as a quick reference when building workflows with different task types.*",
"format": "markdown"
},
"execution_details": {
"actual_result": {
"output": "Trying to fetch documentation from: http://host.docker.internal:5000/api/docs/yaml-reference\n\u274c Connection failed: http://host.docker.internal:5000/api/docs/yaml-reference - HTTPConnectionPool(host=\u0027host.docker.internal\u0027, port=5000): Max retries exceeded with url: /api/docs/yaml-reference (Caused by NameResolutionError(\"\u003curllib3.connection.HTTPConnection object at 0x75595e911cd0\u003e: Failed to resolve \u0027host.docker.internal\u0027 ([Errno -2] Name or service not known)\"))\nTrying to fetch documentation from: http://172.17.0.1:5000/api/docs/yaml-reference\n\u2705 Successfully connected to: http://172.17.0.1:5000/api/docs/yaml-reference\nSuccessfully fetched documentation from http://172.17.0.1:5000/api/docs/yaml-reference: 97047 characters\n__OUTPUTS__ {\"documentation\": {\"content\": \"# YAML Workflow Structure Reference\\n\\nComplete reference for the YAML workflow definition structure supported by the Assistants Workflow Engine.\\n\\n## Table of Contents\\n\\n1. [Complete Schema Overview](#complete-schema-overview)\\n2. [Required vs Optional Fields](#required-vs-optional-fields)\\n3. [Data Types Reference](#data-types-reference)\\n4. [Field Validation Rules](#field-validation-rules)\\n5. [Parser Behavior](#parser-behavior)\\n6. [Migration Guide](#migration-guide)\\n\\n## Complete Schema Overview\\n\\n```yaml\\n# === WORKFLOW METADATA (Required) ===\\nid: string # REQUIRED: Unique workflow identifier\\nname: string # REQUIRED: Human-readable workflow name\\n\\n# === WORKFLOW METADATA (Optional) ===\\nversion: string | integer # Default: 1\\nnamespace: string # Default: \\\"default\\\"\\ndescription: string # Default: \\\"\\\"\\ntenant_id: string # Default: null\\n\\n# === EXECUTION CONFIGURATION ===\\ntimeout_seconds: integer|string # Default: 3600 (1 hour) - Workflow-level timeout (supports duration strings like \\\"1h\\\", \\\"30m\\\")\\nlabels: object # Default: {}\\nmetadata: object # Default: {}\\n\\n# === DATA FLOW DEFINITIONS ===\\ninputs: # Default: []\\n - name: string # REQUIRED\\n type: string # REQUIRED: string|integer|boolean|object|array\\n description: string # Optional\\n required: boolean # Default: false\\n default: any # Optional default value\\n\\noutputs: object # Default: {}\\n output_name:\\n type: string # REQUIRED\\n source: string # REQUIRED: task_id or task_id.field\\n description: string # Optional\\n\\nvariables: object # Default: {}\\n\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n - id: string # REQUIRED: Unique identifier to reference in tasks\\n provider: string # REQUIRED: openai|anthropic|openrouter|groq\\n config: object # REQUIRED: Provider-specific config\\n model: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\n api_key: string # REQUIRED: API key for the provider\\n temperature: number # Optional: 0.0-2.0, controls randomness\\n max_tokens: integer # Optional: Maximum tokens to generate\\n timeout: integer # Optional: Request timeout in seconds\\n base_url: string # Optional: Custom API base URL\\n\\n# Model Client Examples:\\n# OpenAI Configuration:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 2000\\n#\\n# Anthropic Configuration:\\n# model_clients:\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 1500\\n#\\n# Multiple Models:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n\\n# === TASK DEFINITIONS (Required) ===\\ntasks: # REQUIRED: List of tasks\\n - id: string # REQUIRED: Unique task identifier\\n name: string # Optional: Human-readable name\\n type: string # REQUIRED: Task type\\n description: string # Optional\\n \\n # DEPENDENCY CONFIGURATION\\n depends_on: array # Optional: Execution dependencies and template resolution\\n previous_node: string # REQUIRED: Task connectivity and UI hierarchy (essential for proper workflow execution)\\n \\n timeout_seconds: integer|string # Optional: Task-specific timeout (supports duration strings like \\\"5m\\\", \\\"30s\\\")\\n worker_group: string # Optional: Target worker group\\n conditions: array # Optional: Execution conditions\\n execute_on_routes: array # Optional: Routes for conditional execution\\n \\n # Input/Output mapping\\n input_mapping: object # Optional: Map workflow context to task inputs\\n output_mapping: object # Optional: Map task outputs to workflow context\\n \\n # Retry configuration\\n retry: object # Optional: Task-specific retry policy\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n \\n # === TASK TYPE SPECIFIC CONFIGURATIONS ===\\n # See detailed sections below for each task type\\n\\n# === WORKFLOW-LEVEL RETRY POLICY ===\\nretry: object # Optional: Global retry policy\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n\\n# === TRIGGERS ===\\ntriggers: # Optional: List of triggers\\n - name: string # REQUIRED\\n type: string # REQUIRED: webhook|schedule|event\\n description: string # Optional\\n config: object # REQUIRED: Trigger-specific config\\n input_mapping: array # Optional: Map trigger data to workflow inputs\\n - webhook_field: string # Source field\\n workflow_input: string # Target workflow input\\n```\\n\\n## Required vs Optional Fields\\n\\n### Required Fields (Workflow Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the workflow |\\n| `tasks` | array | List of task definitions (can be empty) |\\n\\n### Required Fields (Task Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the task |\\n| `type` | string | Task type (script, http, ai_agent, etc.) |\\n| `previous_node` | string | Reference to previous task for UI connectivity (required for proper task linking, except for the first task) |\\n\\n### Required Fields (Input Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `name` | string | Input parameter name |\\n| `type` | string | Data type (string, integer, boolean, object, array) |\\n\\n### Required Fields (Output Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `type` | string | Output data type |\\n| `source` | string | Source task ID or task_id.field_name |\\n\\n## Data Types Reference\\n\\n### Primitive Types\\n\\n```yaml\\n# String type\\ninputs:\\n - name: customer_name\\n type: string\\n default: \\\"John Doe\\\"\\n description: \\\"Customer full name\\\"\\n\\n# Integer type \\ninputs:\\n - name: batch_size\\n type: integer\\n default: 100\\n description: \\\"Number of records per batch\\\"\\n\\n# Boolean type\\ninputs:\\n - name: include_archived\\n type: boolean\\n default: false\\n description: \\\"Include archived records\\\"\\n```\\n\\n### Complex Types\\n\\n```yaml\\n# Object type\\ninputs:\\n - name: config_params\\n type: object\\n default:\\n timeout: 30\\n retries: 3\\n debug: false\\n description: \\\"Configuration parameters\\\"\\n\\n# Array type\\ninputs:\\n - name: customer_ids\\n type: array\\n default: [\\\"cust_001\\\", \\\"cust_002\\\"]\\n description: \\\"List of customer identifiers\\\"\\n```\\n\\n### Duration Types\\n\\nDuration values can be specified as strings or integers:\\n\\n```yaml\\n# String format (recommended)\\ntimeout_seconds: \\\"1h\\\" # 1 hour\\ntimeout_seconds: \\\"30m\\\" # 30 minutes \\ntimeout_seconds: \\\"45s\\\" # 45 seconds\\ntimeout_seconds: \\\"2d\\\" # 2 days\\n\\n# Integer format (seconds)\\ntimeout_seconds: 3600 # 1 hour in seconds\\n\\n# Supported units\\n# s, sec, second, seconds\\n# m, min, minute, minutes \\n# h, hour, hours\\n# d, day, days\\n```\\n\\n### Timeout Configuration and Execution Behavior\\n\\nThe workflow engine supports configurable timeouts at both workflow and task levels with intelligent timeout-free communication for long-running tasks.\\n\\n#### Timeout Hierarchy\\n\\nTimeout values are resolved in this order of precedence:\\n1. **Task-level `timeout_seconds`**: Specific timeout for individual tasks\\n2. **Workflow-level `timeout_seconds`**: Default for all tasks in the workflow\\n3. **System default**: 300 seconds (5 minutes) for tasks, 3600 seconds (1 hour) for workflows\\n\\n```yaml\\n# Workflow-level timeout (applies to all tasks by default)\\ntimeout_seconds: \\\"2h\\\" # 2 hour workflow timeout\\n\\ntasks:\\n - id: quick_task\\n type: script\\n # Uses workflow-level timeout (2h)\\n script: \\\"print(\u0027Quick task\u0027)\\\"\\n \\n - id: custom_timeout_task\\n type: script\\n timeout_seconds: \\\"30m\\\" # Override with 30 minute timeout\\n script: \\\"print(\u0027Custom timeout task\u0027)\\\"\\n \\n - id: long_running_task\\n type: script\\n timeout_seconds: 600 # 10 minutes (integer format)\\n script: |\\n import time\\n time.sleep(300) # 5 minute processing\\n print(\\\"Long task completed\\\")\\n```\\n\\n#### Timeout-Free Communication\\n\\nThe workflow engine automatically uses **timeout-free communication** for long-running tasks to prevent timeout-related failures:\\n\\n- **Loop tasks**: Always use timeout-free communication (event-driven execution)\\n- **Script tasks**: Use timeout-free communication when `timeout_seconds \u003e 180` (host timeout threshold)\\n- **Other task types**: Use standard timeout-based communication\\n\\n```yaml\\ntasks:\\n - id: short_script\\n type: script\\n timeout_seconds: 60 # Uses standard timeout-based communication\\n script: \\\"print(\u0027Short script\u0027)\\\"\\n \\n - id: long_script\\n type: script\\n timeout_seconds: 600 # Uses timeout-free communication (600 \u003e 180)\\n script: |\\n import time\\n print(\\\"\\ud83d\\ude80 This uses timeout-free communication\\\")\\n time.sleep(300) # Won\u0027t cause timeout issues\\n print(\\\"Long script completed\\\")\\n \\n - id: data_processing_loop\\n type: loop\\n timeout_seconds: \\\"1h\\\" # Always uses timeout-free communication\\n loop_type: for\\n max_iterations: 10\\n # Loop tasks automatically use timeout-free communication\\n```\\n\\n#### Loop Task Timeout Inheritance\\n\\nLoop tasks can pass timeout configuration to their inner script tasks:\\n\\n```yaml\\n- id: batch_processing_loop\\n type: loop\\n timeout_seconds: \\\"30m\\\" # Loop-level timeout\\n loop_type: for\\n max_iterations: 5\\n loop_tasks:\\n - id: process_batch\\n type: script\\n # Inherits 30-minute timeout from parent loop\\n script: |\\n print(\\\"Processing batch with inherited timeout\\\")\\n # This script will use the 30-minute timeout from the loop\\n```\\n\\n#### Best Practices for Timeout Configuration\\n\\n1. **Use duration strings** for readability: `\\\"5m\\\"` vs `300`\\n2. **Set appropriate timeouts** based on expected execution time plus buffer\\n3. **Long-running tasks**: Set `timeout_seconds \u003e 180` to enable timeout-free communication\\n4. **Loop tasks**: Always get timeout-free communication automatically\\n5. **Critical tasks**: Use shorter timeouts for fail-fast behavior\\n6. **External API calls**: Use longer timeouts to account for network latency\\n\\n```yaml\\n# Example: Well-configured timeout workflow\\ntimeout_seconds: \\\"1h\\\" # Reasonable workflow timeout\\n\\ntasks:\\n - id: api_call\\n type: http\\n timeout_seconds: \\\"30s\\\" # Quick timeout for API calls\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: data_processing\\n type: script\\n timeout_seconds: \\\"10m\\\" # Longer timeout for processing (uses timeout-free)\\n script: |\\n # Process large dataset\\n process_data()\\n \\n - id: batch_processing\\n type: loop\\n timeout_seconds: \\\"45m\\\" # Generous timeout for loop (timeout-free)\\n loop_type: for\\n max_iterations: 100\\n # Inner tasks inherit the 45-minute timeout\\n```\\n\\n## Field Validation Rules\\n\\n### ID Fields\\n\\n```yaml\\n# Valid IDs\\nid: \\\"customer_processor\\\" # \\u2705 Valid\\nid: \\\"process-data\\\" # \\u2705 Valid\\nid: \\\"task_001\\\" # \\u2705 Valid\\n\\n# Invalid IDs\\nid: \\\"process data\\\" # \\u274c Spaces not allowed\\nid: \\\"123_start\\\" # \\u274c Cannot start with number\\nid: \\\"\\\" # \\u274c Cannot be empty\\n```\\n\\n### Timeout Values\\n\\n```yaml\\n# Valid timeouts\\ntimeout_seconds: 300 # \\u2705 Positive integer\\ntimeout_seconds: \\\"5m\\\" # \\u2705 Duration string\\ntimeout_seconds: \\\"1h30m\\\" # \\u2705 Complex duration\\n\\n# Invalid timeouts\\ntimeout_seconds: 0 # \\u274c Must be positive\\ntimeout_seconds: -60 # \\u274c Cannot be negative\\ntimeout_seconds: \\\"invalid\\\" # \\u274c Invalid duration format\\n```\\n\\n### Dependency Types and Validation\\n\\n#### `depends_on` vs `previous_node`\\n\\n**`depends_on` - Execution Dependencies:**\\n- Controls actual task execution order\\n- Enables template resolution and data access\\n- Required for accessing outputs from other tasks\\n- Validates that referenced tasks exist\\n- Multiple tasks can be specified\\n\\n**`previous_node` - Task Connectivity and UI Hierarchy:**\\n- **REQUIRED for proper task connectivity in UI**\\n- Essential for workflow visualization and task linking\\n- Critical for execution flow representation\\n- Single task reference only\\n- **Without this field, tasks won\u0027t be properly connected in the UI**\\n\\n```yaml\\ntasks:\\n - id: fetch_data\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: validate_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 EXECUTION: wait for fetch_data to complete\\n previous_node: fetch_data # \\u2705 UI: show as child of fetch_data in hierarchy\\n script: |\\n data = ${fetch_data} # \\u2705 Can access because of depends_on\\n if not validate(data):\\n raise ValueError(\\\"Invalid data\\\")\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker for proper parsing\\n outputs[\u0027status\u0027] = \u0027valid\u0027\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: process_data\\n type: script\\n depends_on: [validate_data] # \\u2705 EXECUTION: wait for validation\\n previous_node: validate_data # \\u2705 UI: show as child of validate_data\\n script: |\\n validation_result = ${validate_data} # \\u2705 Accessible via depends_on\\n # Process data...\\n\\n - id: separate_task\\n type: script\\n previous_node: process_data # \\u2705 UI: appears under process_data in hierarchy\\n # NO depends_on means this can run immediately (parallel to other tasks)\\n script: |\\n # This task runs independently but appears under process_data in UI\\n # Cannot access ${process_data} because no depends_on relationship\\n print(\\\"Independent task\\\")\\n\\n### Complete Example with Proper Hierarchy\\n\\n```yaml\\ntasks:\\n - id: fetch_customer_data\\n name: \\\"Fetch Customer Data\\\"\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: validate_customer\\n name: \\\"Validate Customer Data\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Wait for data fetch\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n customer = ${fetch_customer_data} # \\u2705 Can access because of depends_on\\n if not customer.get(\u0027email\u0027):\\n raise ValueError(\\\"Invalid customer data\\\")\\n print(\\\"__OUTPUTS__ {\u0027status\u0027: \u0027valid\u0027}\\\")\\n \\n - id: process_customer\\n name: \\\"Process Customer\\\"\\n type: ai_agent\\n depends_on: [validate_customer] # \\u2705 EXECUTION: Wait for validation\\n previous_node: validate_customer # \\u2705 UI: Show under validate_customer\\n config:\\n model_client_id: gpt4_model\\n user_message: \\\"Process customer: ${fetch_customer_data}\\\"\\n \\n - id: send_notification\\n name: \\\"Send Notification\\\"\\n type: script\\n depends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\n previous_node: process_customer # \\u2705 UI: Show under process_customer\\n script: |\\n result = ${process_customer}\\n print(f\\\"Notification sent for: ${fetch_customer_data.name}\\\")\\n \\n - id: parallel_audit_log\\n name: \\\"Audit Log (Parallel)\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n # This runs in parallel with validation/processing\\n customer = ${fetch_customer_data}\\n print(f\\\"Audit: Customer {customer.id} accessed at ${current_timestamp}\\\")\\n```\\n\\n # Invalid examples\\n - id: invalid_task\\n type: script\\n depends_on: [nonexistent_task] # \\u274c VALIDATION ERROR: task doesn\u0027t exist\\n previous_node: another_missing # \\u26a0\\ufe0f UI WARNING: task doesn\u0027t exist (but not fatal)\\n script: \\\"print(\u0027Invalid\u0027)\\\"\\n```\\n\\n#### Key Differences:\\n\\n| Aspect | `depends_on` | `previous_node` |\\n|--------|--------------|-----------------|\\n| **Purpose** | Execution control | Task connectivity \u0026 UI organization |\\n| **Impact** | Blocks task until dependencies complete | **REQUIRED for UI task linking** |\\n| **Template Access** | \\u2705 Enables `${task_id}` resolution | \\u274c No template access |\\n| **Validation** | \\u274c Parser error if task missing | \\u26a0\\ufe0f Warning if task missing |\\n| **Multiple References** | \\u2705 Array of task IDs | \\u274c Single task ID only |\\n| **Execution Order** | \\u2705 Enforced | \\u274c No execution impact |\\n| **UI Connectivity** | \\u274c No UI impact | \\u2705 **ESSENTIAL for proper task connection** |\\n\\n### Circular Dependency Detection\\n\\n```yaml\\n# This will be rejected by the parser\\ntasks:\\n - id: task_a\\n depends_on: [task_b] # \\u274c Circular dependency\\n \\n - id: task_b \\n depends_on: [task_a] # \\u274c Circular dependency\\n```\\n\\n## Parser Behavior\\n\\n### Field Name Compatibility\\n\\nThe parser supports both camelCase and snake_case for field names:\\n\\n```yaml\\ntasks:\\n - id: flexible_task\\n # Both formats are accepted\\n dependsOn: [other_task] # camelCase\\n depends_on: [other_task] # snake_case\\n \\n inputMapping: # camelCase\\n source_field: \\\"target_field\\\"\\n input_mapping: # snake_case \\n source_field: \\\"target_field\\\"\\n \\n outputMapping: {} # camelCase\\n output_mapping: {} # snake_case\\n \\n workerGroup: \\\"gpu-workers\\\" # camelCase\\n worker_group: \\\"gpu-workers\\\" # snake_case\\n```\\n\\n### Default Value Assignment\\n\\n```yaml\\n# Fields with defaults are automatically populated\\nid: my_workflow\\n# name defaults to id value: \\\"my_workflow\\\"\\n# version defaults to: 1\\n# namespace defaults to: \\\"default\\\" \\n# timeout_seconds defaults to: 3600\\n# description defaults to: \\\"\\\"\\n\\ntasks:\\n - id: simple_task\\n type: script\\n # name defaults to id: \\\"simple_task\\\"\\n # timeout_seconds defaults to: 300\\n # retry.maxAttempts defaults to: 1\\n```\\n\\n### Template Resolution Context\\n\\nThe parser provides these built-in variables for template resolution:\\n\\n```yaml\\ntasks:\\n - id: context_aware_task\\n type: script\\n script: |\\n # Workflow context\\n workflow_id = \\\"${workflow_id}\\\" # Current workflow ID\\n execution_id = \\\"${execution_id}\\\" # Unique execution ID\\n namespace = \\\"${namespace}\\\" # Workflow namespace\\n \\n # Timing context\\n current_timestamp = \\\"${current_timestamp}\\\" # ISO timestamp\\n started_at = \\\"${started_at}\\\" # Workflow start time\\n \\n # Input context\\n user_input = \\\"${input_parameter}\\\" # Workflow inputs\\n \\n # Task context\\n previous_result = ${previous_task} # Previous task output\\n specific_field = ${task_id.field_name} # Specific output field\\n \\n # Route context (for conditional workflows)\\n route_info = ${_route_context} # Current route information\\n```\\n\\n### Error Handling During Parsing\\n\\n```yaml\\n# The parser will validate and report these errors:\\n\\n# 1. Missing required fields\\n# ERROR: Missing required field: \u0027id\u0027\\n\\n# 2. Invalid YAML syntax \\n# ERROR: Invalid YAML: mapping values are not allowed here\\n\\n# 3. Circular dependencies\\n# ERROR: Circular dependency detected involving task \u0027task_a\u0027\\n\\n# 4. Invalid task references\\n# ERROR: Task \u0027task_b\u0027 depends on non-existent task \u0027invalid_task\u0027\\n\\n# 5. Invalid duration formats\\n# ERROR: Invalid duration: \u0027not_a_duration\u0027\\n\\n# 6. Invalid field values\\n# ERROR: Timeout must be positive, got -10\\n```\\n\\n## Migration Guide\\n\\n### From Version 1.0 to 2.0\\n\\n```yaml\\n# OLD FORMAT (v1.0)\\nworkflow:\\n id: my_workflow\\n tasks:\\n - task_id: process_data\\n task_type: python_script\\n \\n# NEW FORMAT (v2.0) \\nid: my_workflow\\ntasks:\\n - id: process_data\\n type: script\\n language: python\\n```\\n\\n### Deprecated Fields\\n\\n| Old Field | New Field | Status |\\n|-----------|-----------|--------|\\n| `task_type` | `type` | Deprecated in v2.0 |\\n| `task_id` | `id` | Deprecated in v2.0 |\\n| `python_script` | `script` (type) | Deprecated in v2.0 |\\n| `plugin` | `mcp` (type) | Deprecated in v2.1 |\\n\\n### Backward Compatibility\\n\\nThe parser maintains backward compatibility for deprecated fields but will show warnings:\\n\\n```yaml\\n# This will work but show a warning\\ntasks:\\n - task_id: old_style_task # \\u26a0\\ufe0f Warning: Use \u0027id\u0027 instead\\n task_type: python_script # \\u26a0\\ufe0f Warning: Use \u0027type: script\u0027 instead\\n```\\n\\n## Advanced Schema Features\\n\\n### Conditional Field Requirements\\n\\nSome fields become required based on task type:\\n\\n```yaml\\n# HTTP tasks require URL\\n- id: api_call\\n type: http\\n url: \\\"https://api.example.com\\\" # \\u2705 Required for HTTP tasks\\n \\n# AI agent tasks require model_client_id\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4 # \\u2705 Required for AI agent tasks\\n```\\n\\n### Dynamic Field Validation\\n\\nField validation can depend on other field values:\\n\\n```yaml\\n# Loop tasks require loop-specific fields\\n- id: data_loop\\n type: loop\\n loop_type: \\\"for\\\" # Determines required fields\\n max_iterations: 10 # \\u2705 Required for \u0027for\u0027 loops\\n # exit_condition: not needed for \u0027for\u0027 loops\\n \\n- id: condition_loop \\n type: loop\\n loop_type: \\\"while\\\" # Determines required fields\\n exit_condition: \\\"${count} \u003e= 10\\\" # \\u2705 Required for \u0027while\u0027 loops\\n # max_iterations: optional for \u0027while\u0027 loops\\n```\\n\\n## Template Resolution and Data Flow\\n\\nTemplate resolution is a core feature that allows tasks to access data from previous tasks, workflow inputs, and built-in variables using the `${variable_name}` syntax. This is essential for creating dynamic workflows where tasks pass data to each other.\\n\\n### Basic Template Syntax\\n\\n```yaml\\n# Access workflow inputs\\nscript: |\\n customer_name = \\\"${customer_name}\\\" # From workflow inputs\\n \\n# Access previous task outputs\\nscript: |\\n previous_result = ${data_fetch_task} # Entire output from data_fetch_task\\n specific_field = ${data_fetch_task.customer_id} # Specific field\\n \\n# Access built-in variables\\nscript: |\\n workflow_id = \\\"${workflow_id}\\\"\\n execution_id = \\\"${execution_id}\\\"\\n timestamp = \\\"${current_timestamp}\\\"\\n```\\n\\n### Available Built-in Variables\\n\\n| Variable | Description | Example Value |\\n|----------|-------------|---------------|\\n| `${workflow_id}` | Current workflow identifier | `customer_processor` |\\n| `${execution_id}` | Unique execution identifier | `exec_12345_abcde` |\\n| `${namespace}` | Workflow namespace | `production` |\\n| `${current_timestamp}` | ISO timestamp | `2024-01-15T10:30:00Z` |\\n| `${started_at}` | Workflow start time | `2024-01-15T10:29:45Z` |\\n\\n### Task Output Resolution\\n\\nTasks can access outputs from other tasks they depend on:\\n\\n```yaml\\ntasks:\\n - id: fetch_customer\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: process_customer\\n type: script\\n depends_on: [fetch_customer] # Required for template access\\n previous_node: fetch_customer\\n script: |\\n # Access entire response\\n customer_data = ${fetch_customer}\\n \\n # Access specific fields\\n customer_name = \\\"${fetch_customer.name}\\\"\\n customer_email = \\\"${fetch_customer.email}\\\"\\n \\n # Process the data\\n result = {\\n \\\"processed_name\\\": customer_name.upper(),\\n \\\"domain\\\": customer_email.split(\\\"@\\\")[1]\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### AI Agent Output Resolution\\n\\nAI agent tasks return structured output that requires specific access patterns:\\n\\n```yaml\\n# AI Agent task\\n- id: ai_analysis_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Analyze the customer data and provide insights.\\\"\\n user_message: \\\"Customer data: ${customer_data}\\\"\\n\\n# Accessing AI agent output in subsequent tasks\\n- id: process_ai_response\\n type: script\\n depends_on: [ai_analysis_task]\\n previous_node: ai_analysis_task\\n script: |\\n # CORRECT: AI agents return structured output\\n agent_output = ${ai_analysis_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n token_usage = agent_output.get(\u0027token_usage\u0027, {})\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n # Process the AI response\\n processed_analysis = {\\n \\\"analysis\\\": ai_response,\\n \\\"tokens_used\\\": token_usage.get(\u0027total_tokens\u0027, 0),\\n \\\"model\\\": model_used,\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\"\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(processed_analysis)}\\\")\\n\\n# INCORRECT: Direct access won\u0027t work\\n- id: incorrect_ai_access\\n type: script\\n depends_on: [ai_analysis_task]\\n script: |\\n # \\u274c This will not work correctly\\n ai_response = ${ai_analysis_task} # Returns full agent object\\n # ai_response is now a dict, not the actual response text\\n```\\n\\n### AI Agent Response Structure\\n\\nAI agent tasks return a structured object with the following format:\\n\\n```json\\n{\\n \\\"ai_response\\\": \\\"The actual AI-generated text response\\\",\\n \\\"model\\\": \\\"gpt-4-turbo-preview\\\",\\n \\\"token_usage\\\": {\\n \\\"prompt_tokens\\\": 150,\\n \\\"completion_tokens\\\": 75,\\n \\\"total_tokens\\\": 225\\n },\\n \\\"estimated_cost\\\": {\\n \\\"amount\\\": 0.00337,\\n \\\"currency\\\": \\\"USD\\\"\\n },\\n \\\"provider\\\": \\\"openai\\\",\\n \\\"timestamp\\\": \\\"2024-01-15T10:30:00Z\\\"\\n}\\n```\\n\\n### Loop Task Data Access and Dependencies\\n\\nLoop tasks have a special execution context that differs from regular workflow tasks. Understanding how to access data within loops is crucial for building functional workflows.\\n\\n#### Loop Task Internal Data Access\\n\\nWithin loop tasks, data access works through injected context files rather than template resolution:\\n\\n```yaml\\n# Loop task with state variables and internal task dependencies\\n- id: data_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n state_variables:\\n processed_items: []\\n total_processed: 0\\n error_count: 0\\n loop_tasks:\\n # First task in loop\\n - id: fetch_data\\n type: script\\n script: |\\n # Access loop state variables through injected context\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n processed_items = loop_state.get(\u0027processed_items\u0027, [])\\n \\n # Access workflow inputs and external task outputs\\n external_data = inputs.get(\u0027previous_workflow_task\u0027, {})\\n iteration_num = inputs.get(\u0027iteration\u0027, 0) # Loop iteration variable\\n \\n # Process data\\n batch_data = fetch_batch_data(iteration_num)\\n \\n outputs = {\\\"batch_data\\\": batch_data, \\\"batch_size\\\": len(batch_data)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n # Second task depends on first task in same loop\\n - id: process_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 CORRECT: Dependencies within loop tasks work normally\\n script: |\\n # \\u2705 CORRECT: Access previous loop task through inputs context\\n fetch_result = inputs.get(\u0027fetch_data\u0027, {})\\n batch_data = fetch_result.get(\u0027batch_data\u0027, [])\\n \\n # \\u274c INCORRECT: This won\u0027t work in loop context\\n # batch_data = ${fetch_data.batch_data} # Template resolution fails in loops\\n \\n # Access and update state variables\\n current_processed = loop_state.get(\u0027processed_items\u0027, [])\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n \\n # Process the batch\\n processed_batch = process_batch(batch_data)\\n current_processed.extend(processed_batch)\\n \\n # Update state variables using state_updates\\n state_updates[\u0027processed_items\u0027] = current_processed\\n state_updates[\u0027total_processed\u0027] = current_total + len(processed_batch)\\n \\n outputs = {\\\"processed_count\\\": len(processed_batch)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED for state updates\\n```\\n\\n#### Key Differences in Loop Task Data Access\\n\\n| Access Pattern | Regular Tasks | Loop Tasks |\\n|---------------|---------------|------------|\\n| **Previous Task Output** | `${previous_task}` | `inputs.get(\u0027field_name\u0027, default)` (flattened) |\\n| **State Variables** | N/A | `loop_state.get(\u0027variable_name\u0027, default)` |\\n| **State Updates** | N/A | `state_updates[\u0027variable_name\u0027] = value` |\\n| **Workflow Inputs** | `\\\"${input_name}\\\"` | `inputs.get(\u0027input_name\u0027, \u0027default\u0027)` |\\n| **Dependencies** | `depends_on: [task_id]` | Same: `depends_on: [task_id]` |\\n\\n#### Critical: Loop Task Output Flattening\\n\\n**Important:** The loop executor flattens task outputs into individual fields in the `inputs` dictionary, rather than storing them as nested objects under the task name.\\n\\n```yaml\\n# First loop task outputs:\\noutputs = {\\\"company_name\\\": \\\"ACME Corp\\\", \\\"company_id\\\": \\\"123\\\", \\\"status\\\": \\\"success\\\"}\\nprint(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n\\n# Second loop task accessing the data:\\nscript: |\\n # \\u274c INCORRECT: This won\u0027t work - no nested object\\n # company_data = inputs.get(\u0027first_task\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027)\\n \\n # \\u2705 CORRECT: Access flattened fields directly\\n company_name = inputs.get(\u0027company_name\u0027, \u0027\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n print(f\\\"Company: {company_name}, ID: {company_id}, Status: {task_status}\\\")\\n```\\n\\n#### Complete Loop Task Example\\n\\n```yaml\\n- id: financial_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n iteration_variable: company_index\\n depends_on: [list_companies] # External dependency\\n \\n state_variables:\\n processed_companies: []\\n total_extractions: 0\\n successful_extractions: 0\\n \\n loop_tasks:\\n - id: process_current_company\\n type: script\\n script: |\\n # Access external workflow task data\\n companies_data = inputs.get(\u0027list_companies\u0027, {})\\n company_idx = inputs.get(\u0027company_index\u0027, 0)\\n \\n # Access state variables\\n processed = loop_state.get(\u0027processed_companies\u0027, [])\\n \\n # Process company\\n company = companies_data[\u0027companies\u0027][company_idx]\\n result = process_company(company)\\n \\n outputs = {\\\"company_name\\\": company[\u0027name\u0027], \\\"status\\\": \\\"success\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: extract_financial_data\\n type: script\\n depends_on: [process_current_company] # Depend on previous loop task\\n requirements:\\n - requests\u003e=2.28.0\\n - some-ai-library\u003e=1.0.0\\n script: |\\n # \\u2705 CORRECT: Access flattened outputs from previous loop task\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n # \\u274c INCORRECT: Loop executor flattens outputs, no nested access\\n # company_data = inputs.get(\u0027process_current_company\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n \\n # \\u274c INCORRECT: Template resolution doesn\u0027t work in loops\\n # company_data = ${process_current_company}\\n \\n # Access external data\\n companies_list = inputs.get(\u0027list_companies\u0027, {})\\n \\n # Perform AI extraction\\n extraction_result = extract_with_ai({\\\"company_name\\\": company_name, \\\"company_id\\\": company_id})\\n \\n # Update state variables\\n current_processed = loop_state.get(\u0027processed_companies\u0027, [])\\n current_total = loop_state.get(\u0027total_extractions\u0027, 0)\\n current_successful = loop_state.get(\u0027successful_extractions\u0027, 0)\\n \\n current_processed.append({\\n \\\"company_name\\\": company_name,\\n \\\"extraction_result\\\": extraction_result\\n })\\n \\n # State updates are required for persistence\\n state_updates[\u0027processed_companies\u0027] = current_processed\\n state_updates[\u0027total_extractions\u0027] = current_total + 1\\n state_updates[\u0027successful_extractions\u0027] = current_successful + (1 if extraction_result else 0)\\n \\n outputs = {\\\"extracted\\\": bool(extraction_result)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED\\n\\n# Accessing loop task outputs in subsequent workflow tasks\\n- id: process_loop_results\\n type: script\\n depends_on: [financial_processing_loop]\\n previous_node: financial_processing_loop\\n script: |\\n # \\u2705 CORRECT: Access final state of loop\\n loop_result = ${financial_processing_loop}\\n loop_final_state = loop_result.get(\u0027final_state\u0027, {})\\n \\n # Access specific state variables from final state\\n processed_companies = loop_final_state.get(\u0027processed_companies\u0027, [])\\n total_processed = loop_final_state.get(\u0027total_extractions\u0027, 0)\\n successful_count = loop_final_state.get(\u0027successful_extractions\u0027, 0)\\n \\n # Access loop execution metadata\\n iterations_completed = loop_result.get(\u0027iterations_completed\u0027, 0)\\n \\n print(f\\\"Loop completed {iterations_completed} iterations\\\")\\n print(f\\\"Processed {len(processed_companies)} companies\\\")\\n print(f\\\"Success rate: {successful_count}/{total_processed}\\\")\\n \\n outputs = {\\n \\\"companies_processed\\\": len(processed_companies),\\n \\\"success_rate\\\": f\\\"{successful_count}/{total_processed}\\\",\\n \\\"iterations\\\": iterations_completed\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Loop Task Context Injection and Output Flattening\\n\\nThe loop executor injects context through temporary files and flattens task outputs for easier access:\\n\\n```python\\n# Auto-injected at the start of each loop task script:\\nimport json\\nimport os\\n\\n# Load context from temporary files (auto-generated)\\nwith open(\u0027/tmp/loop_context_task_iter_N/inputs.json\u0027, \u0027r\u0027) as f:\\n inputs = json.load(f) # Contains all workflow data + previous loop task outputs (FLATTENED)\\n\\nwith open(\u0027/tmp/loop_context_task_iter_N/loop_state.json\u0027, \u0027r\u0027) as f:\\n loop_state = json.load(f) # Contains current state variables\\n\\noutputs = {}\\nstate_updates = {}\\n\\n# Your script code follows...\\n```\\n\\n#### Debugging Loop Task Data Access\\n\\nWhen debugging loop tasks, always print available keys to understand what data is accessible:\\n\\n```yaml\\n- id: debug_loop_task\\n type: script\\n depends_on: [previous_loop_task]\\n script: |\\n # Debug: Print all available data\\n print(f\\\"\\ud83d\\udd0d Available inputs: {list(inputs.keys())}\\\")\\n print(f\\\"\\ud83d\\udd0d Loop state keys: {list(loop_state.keys())}\\\")\\n \\n # Debug: Print specific values\\n for key in [\u0027company_name\u0027, \u0027company_id\u0027, \u0027status\u0027, \u0027fy_folders\u0027]:\\n value = inputs.get(key, \u0027NOT_FOUND\u0027)\\n print(f\\\"\\ud83d\\udd0d {key}: {value}\\\")\\n \\n # Now access the flattened data\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n \\n # Process your data...\\n outputs = {\\\"processed\\\": f\\\"Company {company_name}\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Real Example: Task Output Flattening\\n\\n```yaml\\n# Task 1 outputs this structure:\\n__OUTPUTS__ {\\\"company_name\\\": \\\"LTTS\\\", \\\"company_id\\\": \\\"123\\\", \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], \\\"status\\\": \\\"success\\\"}\\n\\n# Task 2 receives flattened inputs:\\ninputs = {\\n \\\"folder_id\\\": \\\"...\\\", # Workflow input\\n \\\"gemini_api_key\\\": \\\"...\\\", # Workflow input \\n \\\"list_company_folders\\\": {...}, # External task output\\n \\\"company_index\\\": 0, # Loop iteration variable\\n \\\"company_name\\\": \\\"LTTS\\\", # \\u2705 Flattened from Task 1\\n \\\"company_id\\\": \\\"123\\\", # \\u2705 Flattened from Task 1\\n \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], # \\u2705 Flattened from Task 1\\n \\\"status\\\": \\\"success\\\", # \\u2705 Flattened from Task 1\\n \\\"total_extractions\\\": 0, # Loop state variable\\n \\\"processed_companies\\\": [] # Loop state variable\\n}\\n```\\n\\n#### Loop Task Special Output Fields\\n\\n| Field | Description | Example Access |\\n|-------|-------------|----------------|\\n| `final_state` | Final values of all state variables | `${loop_task.final_state}` |\\n| `final_state[\\\"variable_name\\\"]` | Specific state variable from final state | `${loop_task.final_state[\\\"total_processed\\\"]}` |\\n| `iterations_completed` | Number of iterations executed | `${loop_task.iterations_completed}` |\\n\\n#### Safe Template Resolution Pattern for Loop States\\n\\nWhen accessing loop final state fields, use this safe parsing pattern:\\n\\n```yaml\\nscript: |\\n try:\\n # Template resolution with safety checks\\n final_state_str = \u0027\u0027\u0027${loop_task.final_state}\u0027\u0027\u0027\\n processed_count_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_count\\\"]}\u0027\u0027\u0027\\n items_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_items\\\"]}\u0027\u0027\u0027\\n iterations_str = \u0027\u0027\u0027${loop_task.iterations_completed}\u0027\u0027\u0027\\n \\n # Safe parsing with fallbacks\\n final_state = json.loads(final_state_str) if not final_state_str.startswith(\u0027UNRESOLVED_\u0027) else {}\\n processed_count = int(processed_count_str) if processed_count_str.isdigit() else 0\\n processed_items = json.loads(items_str) if not items_str.startswith(\u0027UNRESOLVED_\u0027) else []\\n iterations = int(iterations_str) if iterations_str.isdigit() else 0\\n \\n print(f\\\"Successfully processed {processed_count} items in {iterations} iterations\\\")\\n \\n except Exception as e:\\n print(f\\\"Error accessing loop results: {str(e)}\\\")\\n # Handle error case\\n```\\n```\\n\\n### Real-World AI Agent Example\\n\\nBased on actual WhatsApp workflow patterns:\\n\\n```yaml\\n# AI greeting agent\\n- id: greeting_agent\\n type: ai_agent\\n config:\\n model_client_id: assistant_model\\n system_message: \\\"You are a helpful assistant. Generate a friendly greeting.\\\"\\n user_message: \\\"User said: ${user_message}\\\"\\n\\n# Send the AI response via API\\n- id: send_greeting\\n type: script\\n depends_on: [greeting_agent]\\n previous_node: greeting_agent\\n script: |\\n import requests\\n import json\\n \\n # CORRECT: Access AI response from structured output\\n agent_output = ${greeting_agent}\\n ai_greeting = agent_output[\u0027ai_response\u0027].strip()\\n \\n phone_number = \\\"${phone_number}\\\"\\n api_token = \\\"${API_TOKEN}\\\"\\n \\n print(f\\\"\\ud83e\\udd16 AI generated greeting: {ai_greeting}\\\")\\n \\n # Send via API\\n url = f\\\"https://api.example.com/messages/{phone_number}\\\"\\n headers = {\\\"Authorization\\\": f\\\"Bearer {api_token}\\\"}\\n \\n response = requests.post(url, headers=headers, json={\\n \\\"message\\\": ai_greeting\\n })\\n \\n result = {\\n \\\"status\\\": \\\"sent\\\",\\n \\\"ai_response\\\": ai_greeting,\\n \\\"api_response\\\": response.json(),\\n \\\"tokens_used\\\": agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### Complex Template Patterns\\n\\n```yaml\\n# Conditional templates with router outputs\\n- id: router_task\\n type: conditional_router\\n conditions:\\n - name: \\\"premium_customer\\\"\\n condition: \\\"${customer_data.tier} == \u0027premium\u0027\\\"\\n route: \\\"premium_processing\\\"\\n\\n- id: premium_handler\\n type: script\\n depends_on: [router_task]\\n previous_node: router_task\\n execute_on_routes: [\\\"premium_processing\\\"]\\n script: |\\n # Access router decision\\n selected_route = \\\"${router_task.selected_route}\\\"\\n route_data = ${router_task.route_data}\\n \\n # Process premium customer\\n customer = ${customer_data}\\n premium_benefits = calculate_benefits(customer)\\n\\n# Loop through arrays from previous tasks\\n- id: process_items\\n type: script\\n depends_on: [fetch_items]\\n previous_node: fetch_items\\n script: |\\n items = ${fetch_items.data}\\n processed_items = []\\n \\n for item in items:\\n processed_item = {\\n \\\"id\\\": item[\\\"id\\\"],\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\",\\n \\\"status\\\": \\\"processed\\\"\\n }\\n processed_items.append(processed_item)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027processed_items\u0027: processed_items})}\\\")\\n\\n# Nested object access\\n- id: user_preferences\\n type: script\\n depends_on: [fetch_user]\\n previous_node: fetch_user\\n script: |\\n # Access nested user preferences\\n email_notifications = ${fetch_user.preferences.notifications.email}\\n theme_setting = \\\"${fetch_user.preferences.ui.theme}\\\"\\n language = \\\"${fetch_user.profile.language}\\\"\\n \\n settings = {\\n \\\"notifications\\\": email_notifications,\\n \\\"theme\\\": theme_setting,\\n \\\"locale\\\": language\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(settings)}\\\")\\n```\\n\\n### Template Resolution Rules\\n\\n1. **Dependency Requirement**: Tasks must have `depends_on` relationship to access other task outputs\\n2. **String vs Object**: Use quotes for string values, no quotes for objects/arrays\\n3. **Field Navigation**: Use dot notation for nested field access\\n4. **Error Handling**: Unresolved templates become `UNRESOLVED_variable_name`\\n\\n```yaml\\n# \\u2705 Correct template usage\\n- id: correct_task\\n depends_on: [previous_task]\\n previous_node: previous_task\\n script: |\\n string_value = \\\"${previous_task.name}\\\" # String with quotes\\n object_value = ${previous_task.data} # Object without quotes\\n nested_value = \\\"${previous_task.user.email}\\\" # Nested field\\n\\n# \\u274c Incorrect template usage \\n- id: incorrect_task\\n # Missing depends_on - template won\u0027t resolve\\n script: |\\n value = ${previous_task.name} # Will become UNRESOLVED_previous_task.name\\n```\\n\\n## Script Task Output Handling\\n\\nScript tasks must use the `__OUTPUTS__` marker pattern for proper output parsing by the workflow engine.\\n\\n### Required Output Pattern\\n\\n```yaml\\ntasks:\\n - id: example_script\\n type: script\\n script: |\\n # Your script logic here\\n data = process_data()\\n \\n # Required: Store outputs in outputs dict\\n outputs = {}\\n outputs[\u0027result\u0027] = data\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n outputs[\u0027count\u0027] = len(data)\\n \\n # Required: Print with __OUTPUTS__ marker for engine parsing\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Output Access in Subsequent Tasks\\n\\n```yaml\\ntasks:\\n - id: data_processor\\n type: script\\n script: |\\n # Process and store outputs\\n results = {\\\"processed_count\\\": 100, \\\"errors\\\": []}\\n \\n outputs = {}\\n outputs[\u0027results\u0027] = results\\n outputs[\u0027status\u0027] = \u0027completed\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: result_handler\\n type: script\\n depends_on: [data_processor]\\n script: |\\n # Access previous task outputs\\n processor_output = ${data_processor}\\n processed_count = processor_output[\u0027results\u0027][\u0027processed_count\u0027]\\n status = processor_output[\u0027status\u0027]\\n \\n print(f\\\"Previous task processed {processed_count} items with status: {status}\\\")\\n \\n # Store new outputs\\n outputs = {\u0027message\u0027: f\u0027Handled {processed_count} results\u0027}\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Error Handling in Scripts\\n\\n```yaml\\ntasks:\\n - id: safe_script\\n type: script\\n script: |\\n try:\\n result = risky_operation()\\n outputs = {\u0027result\u0027: result, \u0027status\u0027: \u0027success\u0027}\\n except Exception as e:\\n outputs = {\u0027error\u0027: str(e), \u0027status\u0027: \u0027failed\u0027}\\n \\n # Always print outputs, even for errors\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n## Triggers and Workflow Activation\\n\\nTriggers define how workflows are started automatically based on external events. They enable event-driven workflow execution.\\n\\n### Webhook Triggers\\n\\nWebhook triggers allow external systems to start workflows by sending HTTP requests.\\n\\n```yaml\\ntriggers:\\n - name: customer_webhook\\n type: webhook\\n description: \\\"Triggered when customer data is updated\\\"\\n config:\\n path: \\\"/webhooks/customer-updated\\\" # Webhook endpoint path\\n method: POST # HTTP method (GET, POST, PUT, etc.)\\n authentication:\\n type: token # none|token|signature|basic\\n token: \\\"${WEBHOOK_SECRET}\\\" # Required if type is token\\n header_name: \\\"X-Webhook-Token\\\" # Optional: custom header name\\n validation:\\n required_fields: [\\\"customer.id\\\", \\\"customer.email\\\"] # Required payload fields\\n content_type: \\\"application/json\\\" # Expected content type\\n input_mapping:\\n - webhook_field: customer.id # Field from webhook payload\\n workflow_input: customer_id # Map to workflow input\\n - webhook_field: customer.email\\n workflow_input: customer_email\\n - webhook_field: customer.updated_at\\n workflow_input: update_timestamp\\n```\\n\\n### Scheduled Triggers\\n\\nSchedule workflows to run at specific times or intervals.\\n\\n```yaml\\ntriggers:\\n - name: daily_report\\n type: schedule\\n description: \\\"Generate daily customer report\\\"\\n config:\\n cron: \\\"0 9 * * *\\\" # Daily at 9 AM (cron format)\\n timezone: \\\"UTC\\\" # Optional: timezone\\n enabled: true # Optional: enable/disable\\n max_instances: 1 # Optional: max concurrent instances\\n input_mapping:\\n - workflow_input: report_date\\n default_value: \\\"${current_date}\\\" # Use built-in variable\\n - workflow_input: report_type\\n default_value: \\\"daily\\\"\\n\\n - name: weekly_cleanup\\n type: schedule\\n description: \\\"Weekly database cleanup\\\"\\n config:\\n cron: \\\"0 2 * * 0\\\" # Weekly on Sunday at 2 AM\\n timezone: \\\"America/New_York\\\"\\n input_mapping:\\n - workflow_input: cleanup_before_date\\n default_value: \\\"${date_minus_7_days}\\\"\\n```\\n\\n### Event Triggers\\n\\nEvent triggers respond to system events from various sources.\\n\\n```yaml\\ntriggers:\\n - name: file_uploaded\\n type: event\\n description: \\\"Process uploaded files\\\"\\n config:\\n event_type: \\\"file.uploaded\\\" # Event type to listen for\\n source: \\\"storage_service\\\" # Event source system\\n filter: # Optional: event filtering\\n file_type: [\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"]\\n file_size_max: \\\"100MB\\\"\\n path_pattern: \\\"/uploads/customers/*\\\"\\n retry: # Optional: retry configuration\\n max_attempts: 3\\n initial_delay: \\\"5s\\\"\\n input_mapping:\\n - webhook_field: file.path\\n workflow_input: file_path\\n - webhook_field: file.size\\n workflow_input: file_size\\n - webhook_field: file.type\\n workflow_input: file_type\\n\\n - name: order_status_changed\\n type: event\\n description: \\\"Handle order status changes\\\"\\n config:\\n event_type: \\\"order.status_changed\\\"\\n source: \\\"order_management\\\"\\n filter:\\n status: [\\\"completed\\\", \\\"cancelled\\\", \\\"refunded\\\"]\\n priority: [\\\"high\\\", \\\"urgent\\\"]\\n input_mapping:\\n - webhook_field: order.id\\n workflow_input: order_id\\n - webhook_field: order.status\\n workflow_input: new_status\\n - webhook_field: order.customer_id\\n workflow_input: customer_id\\n```\\n\\n### Dependency Triggers\\n\\nDependency triggers start workflows when other workflows complete.\\n\\n```yaml\\ntriggers:\\n - name: post_processing\\n type: dependency\\n description: \\\"Run after main processing completes\\\"\\n config:\\n depends_on_workflow: \\\"main_data_processing\\\" # Workflow to depend on\\n depends_on_status: \\\"SUCCESS\\\" # SUCCESS|FAILED|COMPLETED\\n namespace: \\\"production\\\" # Optional: workflow namespace\\n timeout_minutes: 60 # Optional: wait timeout\\n input_mapping:\\n - webhook_field: execution_id # From completed workflow\\n workflow_input: source_execution_id\\n - webhook_field: workflow_outputs.processed_count\\n workflow_input: items_processed\\n```\\n\\n## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n - id: unique_identifier # Used to reference this model in tasks\\n provider: provider_name # AI provider (openai, anthropic, etc.)\\n config:\\n model: model_name # Specific model to use\\n api_key: your_api_key # Authentication key\\n # Additional provider-specific options...\\n```\\n\\n### Supported Providers\\n\\n#### OpenAI Provider\\n\\n```yaml\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview # Available: gpt-4, gpt-4-turbo, gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-2.0\\n max_tokens: 2000 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### Anthropic Provider\\n\\n```yaml\\nmodel_clients:\\n - id: claude_model\\n provider: anthropic\\n config:\\n model: claude-3-sonnet-20240229 # Available: claude-3-opus, claude-3-sonnet, claude-3-haiku\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-1.0\\n max_tokens: 1500 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### OpenRouter Provider\\n\\n```yaml\\nmodel_clients:\\n - id: openrouter_model\\n provider: openrouter\\n config:\\n model: anthropic/claude-3-sonnet # Available: Various models via OpenRouter\\n api_key: \\\"sk-or-your-openrouter-api-key\\\"\\n base_url: \\\"https://openrouter.ai/api/v1\\\" # Optional: Custom endpoint\\n temperature: 0.7\\n max_tokens: 2000\\n```\\n\\n#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References the claude model above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References the creative GPT-4 model\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n - id: secure_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n temperature: 0.7\\n```\\n\\n### Common Configuration Options\\n\\n| Field | Description | Required | Default |\\n|-------|-------------|----------|---------|\\n| `id` | Unique identifier for referencing in tasks | Yes | - |\\n| `provider` | AI provider name | Yes | - |\\n| `config.model` | Specific model name | Yes | - |\\n| `config.api_key` | Authentication API key | Yes | - |\\n| `config.temperature` | Response randomness (0.0-2.0) | No | 0.7 |\\n| `config.max_tokens` | Maximum response tokens | No | Provider default |\\n| `config.timeout` | Request timeout in seconds | No | 30 |\\n| `config.base_url` | Custom API endpoint | No | Provider default |\\n\\n## Secrets Management and Environment Variables\\n\\nProper secrets management is crucial for security. Never hardcode sensitive information like API keys, passwords, or tokens directly in workflow YAML files.\\n\\n### Using Environment Variables\\n\\nThe recommended approach is to use environment variables for all sensitive data:\\n\\n```yaml\\n# \\u2705 RECOMMENDED: Use environment variables\\nmodel_clients:\\n - id: secure_openai\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n\\n# HTTP tasks with environment variables\\ntasks:\\n - id: secure_api_call\\n type: http\\n url: \\\"${API_BASE_URL}/customers\\\" # Environment variable for URL\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Environment variable for token\\n X-API-Key: \\\"${EXTERNAL_API_KEY}\\\" # Environment variable for API key\\n```\\n\\n### Environment Variable Patterns\\n\\n```yaml\\n# Database connections\\n- id: database_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${DATABASE_URL}\\\" # Full connection string\\n # or individual components:\\n host: \\\"${DB_HOST}\\\"\\n port: \\\"${DB_PORT}\\\"\\n database: \\\"${DB_NAME}\\\"\\n username: \\\"${DB_USER}\\\"\\n password: \\\"${DB_PASSWORD}\\\"\\n\\n# Third-party service credentials\\n- id: external_service\\n type: http\\n url: \\\"${SERVICE_BASE_URL}/api/v1/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${SERVICE_TOKEN}\\\"\\n X-Client-ID: \\\"${SERVICE_CLIENT_ID}\\\"\\n X-Client-Secret: \\\"${SERVICE_CLIENT_SECRET}\\\"\\n\\n# MCP server credentials\\n- id: mcp_task\\n type: mcp\\n mcp_server: \\\"@agentic/mcp-notion-server\\\"\\n environment_vars:\\n NOTION_TOKEN: \\\"${NOTION_API_TOKEN}\\\"\\n WORKSPACE_ID: \\\"${NOTION_WORKSPACE_ID}\\\"\\n```\\n\\n### Security Best Practices\\n\\n#### \\u274c What NOT to Do\\n\\n```yaml\\n# DON\u0027T: Hardcode secrets\\nmodel_clients:\\n - id: insecure_example\\n provider: openai\\n config:\\n api_key: \\\"sk-actual-api-key-here\\\" # SECURITY RISK!\\n\\n# DON\u0027T: Put secrets in comments\\ntasks:\\n - id: bad_example\\n type: http\\n url: \\\"https://api.example.com\\\"\\n # API key is: sk-12345... DON\u0027T DO THIS!\\n headers:\\n Authorization: \\\"Bearer sk-real-token\\\" # SECURITY RISK!\\n```\\n\\n#### \\u2705 What TO Do\\n\\n```yaml\\n# DO: Use environment variables\\nmodel_clients:\\n - id: secure_example\\n provider: openai\\n config:\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\\n\\n# DO: Use different variables for different environments\\n- id: environment_aware_task\\n type: http\\n url: \\\"${API_BASE_URL}\\\" # Different URLs per environment\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Different tokens per environment\\n X-Environment: \\\"${ENVIRONMENT_NAME}\\\" # Track environment\\n\\n# DO: Use descriptive variable names\\nenvironment_vars:\\n CUSTOMER_API_TOKEN: \\\"${CUSTOMER_SERVICE_TOKEN}\\\"\\n BILLING_API_KEY: \\\"${BILLING_SERVICE_API_KEY}\\\"\\n NOTIFICATION_WEBHOOK_SECRET: \\\"${NOTIFICATION_SECRET}\\\"\\n```\\n\\n### Environment Variable Naming Conventions\\n\\n| Pattern | Example | Use Case |\\n|---------|---------|----------|\\n| `SERVICE_API_KEY` | `OPENAI_API_KEY` | Service API keys |\\n| `SERVICE_BASE_URL` | `CUSTOMER_API_BASE_URL` | Service endpoints |\\n| `DB_*` | `DB_HOST`, `DB_PASSWORD` | Database credentials |\\n| `WEBHOOK_*` | `WEBHOOK_SECRET_TOKEN` | Webhook authentication |\\n| `*_TIMEOUT` | `HTTP_REQUEST_TIMEOUT` | Timeout configurations |\\n\\n### Dynamic Environment Variables\\n\\nUse template resolution to create dynamic environment variable names:\\n\\n```yaml\\n# Different API keys per customer\\n- id: customer_specific_api\\n type: http\\n url: \\\"https://api.partner.com/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${PARTNER_API_KEY_${customer_tier}}\\\" # Resolves to PARTNER_API_KEY_PREMIUM\\n\\n# Environment-specific configurations\\n- id: env_specific_task\\n type: storage\\n config:\\n connection_string: \\\"${DATABASE_URL_${environment}}\\\" # DATABASE_URL_PRODUCTION\\n```\\n\\n### Secrets in Different Task Types\\n\\n```yaml\\n# AI Agent with multiple secure credentials\\n- id: secure_ai_task\\n type: ai_agent\\n config:\\n model_client_id: secure_model\\n system_message: \\\"You are a secure assistant with access to customer data.\\\"\\n user_message: \\\"Process customer ${customer_id}\\\"\\n environment_vars:\\n CUSTOMER_DB_TOKEN: \\\"${CUSTOMER_DATABASE_TOKEN}\\\"\\n AUDIT_LOG_KEY: \\\"${AUDIT_LOGGING_API_KEY}\\\"\\n\\n# Script task with secure file access\\n- id: secure_script_task\\n type: script\\n environment_vars:\\n AWS_ACCESS_KEY_ID: \\\"${AWS_ACCESS_KEY}\\\"\\n AWS_SECRET_ACCESS_KEY: \\\"${AWS_SECRET_KEY}\\\"\\n S3_BUCKET_NAME: \\\"${SECURE_BUCKET_NAME}\\\"\\n script: |\\n import boto3\\n import os\\n \\n # Credentials automatically available from environment\\n s3_client = boto3.client(\u0027s3\u0027)\\n bucket = os.environ[\u0027S3_BUCKET_NAME\u0027]\\n \\n # Process secure files...\\n\\n# Storage task with encrypted connections\\n- id: secure_storage_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${ENCRYPTED_DB_CONNECTION_STRING}\\\"\\n ssl_mode: \\\"require\\\"\\n ssl_cert: \\\"${SSL_CERT_PATH}\\\"\\n ssl_key: \\\"${SSL_KEY_PATH}\\\"\\n```\\n\\n### Environment Setup Examples\\n\\n#### Development Environment (.env file)\\n```bash\\n# Development API keys (non-production)\\nOPENAI_API_KEY=sk-dev-api-key-here\\nCUSTOMER_API_BASE_URL=https://api-dev.example.com\\nDATABASE_URL=postgresql://dev:pass@localhost:5432/workflow_dev\\nENVIRONMENT_NAME=development\\n```\\n\\n#### Production Environment\\n```bash\\n# Production API keys (secure)\\nOPENAI_API_KEY=sk-prod-api-key-here\\nCUSTOMER_API_BASE_URL=https://api.example.com\\nDATABASE_URL=postgresql://prod_user:secure_pass@prod-db:5432/workflow_prod\\nENVIRONMENT_NAME=production\\n```\\n\\n## Workflow Execution and Orchestration\\n\\nUnderstanding how workflows execute and how tasks are orchestrated is essential for building reliable, efficient workflows.\\n\\n### Execution Flow\\n\\nWorkflows follow a directed acyclic graph (DAG) execution model:\\n\\n1. **Initialization**: Workflow starts with inputs and model clients\\n2. **Task Scheduling**: Tasks are scheduled based on dependencies\\n3. **Parallel Execution**: Independent tasks run simultaneously\\n4. **Data Flow**: Outputs flow between tasks via template resolution\\n5. **Completion**: Workflow completes when all tasks finish\\n\\n```yaml\\n# Example execution flow\\ntasks:\\n # Stage 1: Independent tasks (run in parallel)\\n - id: fetch_customer # Runs immediately\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: fetch_products # Runs in parallel with fetch_customer\\n type: http\\n url: \\\"https://api.example.com/products\\\"\\n \\n # Stage 2: Dependent tasks (wait for Stage 1)\\n - id: validate_customer # Waits for fetch_customer\\n type: script\\n depends_on: [fetch_customer]\\n previous_node: fetch_customer\\n script: # validation logic...\\n \\n - id: filter_products # Waits for fetch_products\\n type: script\\n depends_on: [fetch_products]\\n previous_node: fetch_products\\n script: # filtering logic...\\n \\n # Stage 3: Final processing (waits for Stage 2)\\n - id: generate_recommendations # Waits for both validation and filtering\\n type: ai_agent\\n depends_on: [validate_customer, filter_products]\\n previous_node: validate_customer # UI hierarchy under customer validation\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate product recommendations for customer.\\\"\\n user_message: |\\n Customer: ${validate_customer}\\n Available products: ${filter_products}\\n```\\n\\n### Task States and Lifecycle\\n\\nEach task goes through these states during execution:\\n\\n| State | Description | Next States |\\n|-------|-------------|-------------|\\n| `PENDING` | Task created, waiting for dependencies | `RUNNING`, `SKIPPED` |\\n| `RUNNING` | Task is actively executing | `COMPLETED`, `FAILED` |\\n| `COMPLETED` | Task finished successfully | - |\\n| `FAILED` | Task encountered an error | `RUNNING` (if retries) |\\n| `SKIPPED` | Task skipped due to conditions | - |\\n| `TIMEOUT` | Task exceeded timeout limit | `RUNNING` (if retries) |\\n\\n### Conditional Execution\\n\\nTasks can be executed conditionally based on routes or conditions:\\n\\n```yaml\\n# Router-based conditional execution\\n- id: data_router\\n type: conditional_router\\n depends_on: [fetch_data]\\n previous_node: fetch_data\\n conditions:\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n default_route: \\\"standard_processing\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"batch_processing\\\"] # Only runs for large datasets\\n script: |\\n # Handle large dataset in batches\\n data = ${fetch_data}\\n process_in_batches(data, batch_size=1000)\\n\\n- id: direct_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"direct_processing\\\"] # Only runs for small datasets\\n script: |\\n # Process small dataset directly\\n data = ${fetch_data}\\n process_directly(data)\\n```\\n\\n### Error Handling and Retries\\n\\nConfigure retry policies for resilient workflow execution:\\n\\n```yaml\\n# Workflow-level retry policy\\nretry:\\n retryOn: [\\\"TEMPORARY_FAILURE\\\", \\\"TIMEOUT\\\", \\\"NETWORK_ERROR\\\"]\\n maxAttempts: 3\\n initialDelay: \\\"1s\\\"\\n maxDelay: \\\"60s\\\"\\n backoffMultiplier: 2.0\\n\\ntasks:\\n # Task-specific retry policy (overrides workflow-level)\\n - id: external_api_call\\n type: http\\n url: \\\"https://unreliable-api.example.com/data\\\"\\n retry:\\n maxAttempts: 5 # More retries for unreliable API\\n initialDelay: \\\"2s\\\"\\n maxDelay: \\\"120s\\\"\\n backoffMultiplier: 2.0\\n retryOn: [\\\"TIMEOUT\\\", \\\"HTTP_5XX\\\", \\\"NETWORK_ERROR\\\"]\\n \\n # Task with no retries\\n - id: critical_operation\\n type: script\\n retry:\\n maxAttempts: 1 # No retries for critical operations\\n script: |\\n # This operation should not be retried if it fails\\n perform_critical_database_operation()\\n```\\n\\n### Parallel Execution Patterns\\n\\n```yaml\\n# Fan-out pattern: One task triggers multiple parallel tasks\\n- id: data_source\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n\\n- id: process_customers # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process customer data from data_source\\n\\n- id: process_orders # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process order data from data_source\\n\\n- id: process_products # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process product data from data_source\\n\\n# Fan-in pattern: Multiple tasks feed into one task\\n- id: generate_report # Waits for all parallel tasks\\n type: ai_agent\\n depends_on: [process_customers, process_orders, process_products]\\n previous_node: process_customers # UI hierarchy\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate comprehensive report from processed data.\\\"\\n user_message: |\\n Customer data: ${process_customers}\\n Order data: ${process_orders}\\n Product data: ${process_products}\\n```\\n\\n### Resource Management\\n\\nControl resource usage and task distribution:\\n\\n```yaml\\n# Worker group targeting\\n- id: cpu_intensive_task\\n type: script\\n worker_group: \\\"high-cpu-workers\\\" # Target specific worker group\\n script: |\\n # CPU-intensive processing\\n perform_heavy_computation()\\n\\n- id: gpu_ml_task\\n type: ai_agent\\n worker_group: \\\"gpu-workers\\\" # Target GPU workers\\n config:\\n model_client_id: large_model\\n # Large model processing...\\n\\n# Timeout configuration\\n- id: long_running_task\\n type: script\\n timeout_seconds: 3600 # 1 hour timeout\\n script: |\\n # Long-running data processing\\n process_large_dataset()\\n\\n# Workflow-level timeout\\ntimeout_seconds: 7200 # 2 hour workflow timeout\\n```\\n\\n## Task Type Configurations\\n\\nThis section provides the complete YAML configuration for each supported task type.\\n\\n**Supported Task Types:**\\n- `script` - Execute Python code with full workflow context\\n- `http` - Make HTTP requests to external APIs\\n- `ai_agent` - Integrate with AI models for intelligent processing \\n- `loop` - Execute tasks repeatedly (for/while loops)\\n- `conditional_router` - Route execution based on conditions\\n- `mcp` - Execute tools via Model Context Protocol servers\\n- `storage` - Data persistence and retrieval operations\\n- `continuous_conversation` - Interactive AI conversations\\n- `human_input` - Tasks requiring human input or approval\\n- `chatbot` - Session-based chatbot conversations\\n\\n### Script Tasks\\n\\nExecute Python code with full workflow context access.\\n\\n```yaml\\n- id: script_task\\n name: \\\"Python Script Task\\\"\\n type: script\\n description: \\\"Execute Python code with dependencies and environment\\\"\\n \\n # Script configuration - script field is directly under task, NOT under config\\n script: | # REQUIRED: Python code to execute\\n import json\\n import requests\\n \\n # Access workflow inputs\\n input_value = \\\"${workflow_input}\\\"\\n \\n # Access previous task outputs\\n previous_result = ${previous_task}\\n \\n # Your processing logic\\n result = {\\\"processed\\\": True, \\\"value\\\": input_value}\\n \\n # Output results (must be JSON serializable)\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n # Alternative: use \u0027code\u0027 field instead of \u0027script\u0027\\n code: | # Alternative to \u0027script\u0027 field\\n print(\\\"Hello World\\\")\\n \\n # Package management\\n requirements: # Optional: Python packages\\n - requests==2.31.0\\n - pandas==1.5.3\\n - numpy==1.24.3\\n \\n # Environment configuration\\n environment: # Optional: Environment variables\\n API_KEY: \\\"${secrets.api_key}\\\"\\n DEBUG: \\\"true\\\"\\n CUSTOM_VAR: \\\"value\\\"\\n \\n environment_vars: # Alternative to \u0027environment\u0027\\n DATABASE_URL: \\\"${secrets.db_url}\\\"\\n \\n # Secrets configuration\\n secrets_config: # Optional: Secrets access\\n api_key: \\\"secret_name\\\"\\n db_password: \\\"db_secret\\\"\\n```\\n\\n### HTTP Tasks\\n\\nMake HTTP requests to external APIs and services.\\n\\n```yaml\\n- id: http_task\\n name: \\\"HTTP API Call\\\"\\n type: http\\n description: \\\"Call external REST API\\\"\\n \\n # HTTP configuration\\n method: POST # Default: GET\\n url: \\\"https://api.example.com/data\\\" # REQUIRED: Target URL\\n \\n # Headers configuration\\n headers: # Optional: HTTP headers\\n Content-Type: \\\"application/json\\\"\\n Authorization: \\\"Bearer ${secrets.api_token}\\\"\\n X-Custom-Header: \\\"workflow-engine\\\"\\n User-Agent: \\\"WorkflowEngine/1.0\\\"\\n \\n # Request body\\n body: | # Optional: Request body (for POST/PUT)\\n {\\n \\\"customer_id\\\": \\\"${customer_id}\\\",\\n \\\"timestamp\\\": \\\"${current_timestamp}\\\",\\n \\\"data\\\": ${previous_task}\\n }\\n \\n # Response validation\\n expected_status: [200, 201, 202] # Default: [200]\\n expectedStatus: [200, 201] # Alternative field name\\n \\n # Additional HTTP options\\n timeout_seconds: 30 # HTTP request timeout\\n follow_redirects: true # Follow HTTP redirects\\n verify_ssl: true # Verify SSL certificates\\n```\\n\\n### AI Agent Tasks\\n\\nIntegrate with AI models for intelligent processing.\\n\\n```yaml\\n- id: ai_agent_task\\n name: \\\"AI Processing Agent\\\"\\n type: ai_agent\\n description: \\\"Process data using AI model\\\"\\n \\n # AI configuration\\n config:\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # Optional: System prompt\\n You are a data analyst. Analyze the provided data and extract insights.\\n Return your analysis in JSON format with the following structure:\\n {\\n \\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n \\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n \\\"confidence_score\\\": 0.95\\n }\\n \\n user_message: | # REQUIRED: User prompt with template resolution\\n Please analyze this customer data:\\n ${previous_task}\\n \\n Customer ID: ${customer_id}\\n Date Range: ${date_range}\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1500 # Optional: Maximum response tokens\\n top_p: 1.0 # Optional: Nucleus sampling\\n frequency_penalty: 0.0 # Optional: Frequency penalty\\n presence_penalty: 0.0 # Optional: Presence penalty\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_api # Weather tools\\n - database_query # Database tools\\n - notification_service # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # Input/output format\\n input_format: \\\"text\\\" # Optional: text|json|structured\\n output_format: \\\"structured\\\" # Optional: text|json|structured\\n\\n### Important: AI Agent Output Access\\n\\nAI agent tasks return structured objects, not direct text responses. Always access the AI response using this pattern:\\n\\n```yaml\\n# In subsequent script tasks\\nscript: |\\n # CORRECT: Access AI response from structured output\\n agent_output = ${ai_agent_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n tokens_used = agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n cost_estimate = agent_output.get(\u0027estimated_cost\u0027, {}).get(\u0027amount\u0027, 0.0)\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n print(f\\\"AI Response: {ai_response}\\\")\\n print(f\\\"Tokens used: {tokens_used}\\\")\\n print(f\\\"Estimated cost: ${cost_estimate:.6f}\\\")\\n```\\n\\n```yaml\\n# In workflow outputs - specify the ai_response field\\noutputs:\\n ai_analysis:\\n type: string\\n source: ai_agent_task.ai_response # Access the ai_response field\\n description: \\\"AI-generated analysis text\\\"\\n \\n total_tokens:\\n type: integer \\n source: ai_agent_task.token_usage.total_tokens\\n description: \\\"Total tokens consumed\\\"\\n```\\n```\\n\\n### Loop Tasks\\n\\nExecute tasks repeatedly with for-loop and while-loop patterns.\\n\\n```yaml\\n# For-Loop (Fixed Iterations)\\n- id: for_loop_task\\n name: \\\"For Loop Processor\\\"\\n type: loop\\n description: \\\"Process data in fixed batches\\\"\\n \\n # Loop configuration\\n loop_type: for # REQUIRED: for|while\\n max_iterations: 10 # REQUIRED for \u0027for\u0027 loops\\n iteration_variable: batch_index # Default: iteration\\n \\n # State variables (persistent across iterations)\\n state_variables:\\n total_processed: 0\\n error_count: 0\\n batch_results: []\\n \\n # Tasks to execute in each iteration\\n loop_tasks: # REQUIRED: Tasks to run in loop\\n - id: process_batch\\n name: \\\"Process Current Batch\\\"\\n type: script\\n script: |\\n batch_idx = ${batch_index} # Access iteration variable\\n total = ${total_processed} # Access state variable\\n \\n # Process current batch\\n result = process_batch_data(batch_idx)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n - id: update_state\\n name: \\\"Update Loop State\\\"\\n type: script\\n depends_on: [process_batch]\\n script: |\\n # Update state variables\\n new_total = ${total_processed} + ${process_batch.items_processed}\\n \\n result = {\\\"total_processed\\\": new_total}\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n\\n# While-Loop (Conditional)\\n- id: while_loop_task\\n name: \\\"While Loop Quality Processor\\\"\\n type: loop\\n description: \\\"Process until quality threshold met\\\"\\n \\n # Loop configuration\\n loop_type: while # REQUIRED: for|while\\n exit_condition: \\\"${average_quality} \u003e= 85 || ${iteration_count} \u003e= 20\\\" # REQUIRED for \u0027while\u0027\\n max_iterations: 50 # Optional: Safety limit\\n iteration_variable: iteration_count # Default: iteration\\n \\n # State variables\\n state_variables:\\n average_quality: 0\\n items_processed: 0\\n quality_scores: []\\n \\n # Loop tasks\\n loop_tasks:\\n - id: assess_quality\\n type: script\\n script: |\\n current_quality = calculate_quality()\\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027quality\u0027: current_quality})}\\\")\\n```\\n\\n\\n### Conditional Router Tasks\\n\\nRoute execution to different paths based on conditions.\\n\\n```yaml\\n- id: conditional_router_task\\n name: \\\"Data Type Router\\\"\\n type: conditional_router\\n description: \\\"Route processing based on data characteristics\\\"\\n \\n # Router configuration\\n router_type: condition # Default: condition\\n \\n # Routing conditions\\n conditions: # REQUIRED: List of conditions\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${data_size} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\" \\n condition: \\\"${data_size} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n - name: \\\"medium_dataset\\\"\\n condition: \\\"${data_size} \u003e 1000 \u0026\u0026 ${data_size} \u003c= 10000\\\"\\n route: \\\"standard_processing\\\"\\n \\n default_route: \\\"standard_processing\\\" # REQUIRED: Default route if no conditions match\\n \\n # Alternative: evaluation script for complex routing\\n evaluation_script: | # Optional: Custom routing logic\\n if data_type == \\\"json\\\":\\n return \\\"json_processing\\\"\\n elif data_type == \\\"xml\\\":\\n return \\\"xml_processing\\\"\\n else:\\n return \\\"default_processing\\\"\\n \\n # Route mapping for complex scenarios\\n route_mapping: # Optional: Route name mappings\\n json_processing: \\\"specialized_json\\\"\\n xml_processing: \\\"specialized_xml\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_handler\\n name: \\\"Handle Large Dataset\\\"\\n type: script\\n execute_on_routes: [\\\"batch_processing\\\"] # REQUIRED: Routes this task runs on\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing large dataset in batches...\\\")\\n\\n- id: direct_handler\\n name: \\\"Handle Small Dataset\\\" \\n type: script\\n execute_on_routes: [\\\"direct_processing\\\"]\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing small dataset directly...\\\")\\n```\\n\\n### MCP Tasks\\n\\nExecute tools via Model Context Protocol servers.\\n\\n```yaml\\n- id: mcp_task\\n name: \\\"MCP Tool Execution\\\"\\n type: mcp\\n description: \\\"Execute external tool via MCP server\\\"\\n \\n # MCP server configuration\\n mcp_server: \\\"@agentic/mcp-notion-server\\\" # REQUIRED: MCP server package\\n tool_name: \\\"update_database\\\" # REQUIRED: Tool to execute\\n runtime: \\\"npx\\\" # REQUIRED: npx|uvx\\n \\n # Server startup\\n server_args: [] # Optional: Server startup arguments\\n server_startup_timeout: 30 # Optional: Server startup timeout (seconds)\\n \\n # Tool execution\\n tool_arguments: # REQUIRED: Arguments for the tool\\n database_id: \\\"${secrets.notion_database_id}\\\"\\n properties:\\n title: \\\"Workflow Results\\\"\\n status: \\\"Completed\\\"\\n processed_count: \\\"${previous_task.count}\\\"\\n timestamp: \\\"${current_timestamp}\\\"\\n \\n # Environment configuration\\n environment_vars: # Optional: Environment variables\\n NOTION_TOKEN: \\\"${secrets.notion_token}\\\"\\n DEBUG: \\\"true\\\"\\n \\n # Timeout configuration\\n timeout_seconds: 120 # Optional: Tool execution timeout\\n```\\n\\n### Storage Tasks\\n\\nManage data persistence and retrieval operations.\\n\\n```yaml\\n- id: storage_task\\n name: \\\"Data Storage Operation\\\"\\n type: storage\\n description: \\\"Save/load data to/from storage\\\"\\n \\n # Storage operation\\n operation: save # REQUIRED: save|load|delete|list\\n \\n # Storage configuration\\n storage_type: \\\"file\\\" # REQUIRED: file|database|cache\\n path: \\\"/data/results/${execution_id}.json\\\" # REQUIRED: Storage path\\n \\n # Data configuration\\n data: ${previous_task} # REQUIRED for \u0027save\u0027: Data to store\\n format: \\\"json\\\" # Optional: json|yaml|csv|parquet\\n compression: \\\"gzip\\\" # Optional: gzip|bz2|none\\n \\n # Metadata\\n metadata: # Optional: Additional metadata\\n workflow_id: \\\"${workflow_id}\\\"\\n created_at: \\\"${current_timestamp}\\\"\\n source_task: \\\"previous_task\\\"\\n \\n # File options\\n create_directories: true # Optional: Create parent directories\\n overwrite: true # Optional: Overwrite existing files\\n backup: false # Optional: Create backup before overwrite\\n \\n # Database options (when storage_type: \\\"database\\\")\\n table_name: \\\"workflow_results\\\" # Required for database storage\\n connection_string: \\\"${secrets.db_url}\\\" # Required for database storage\\n \\n # Cache options (when storage_type: \\\"cache\\\")\\n cache_key: \\\"result_${execution_id}\\\" # Required for cache storage\\n ttl: 3600 # Optional: Time to live (seconds)\\n```\\n\\n\\n### Continuous Conversation Tasks\\n\\nCreate interactive AI-powered conversations.\\n\\n```yaml\\n- id: conversation_task\\n name: \\\"AI Conversation Interface\\\"\\n type: continuous_conversation\\n description: \\\"Interactive conversation with AI assistant\\\"\\n \\n # Conversation configuration\\n config:\\n # AI configuration\\n ai_config:\\n model_client_id: support_assistant # REQUIRED: AI model to use\\n system_prompt: | # REQUIRED: System instructions\\n You are a helpful customer support representative.\\n Be friendly, professional, and solution-oriented.\\n \\n Guidelines:\\n - Always be polite and respectful\\n - Ask clarifying questions when needed\\n - Provide clear and actionable solutions\\n \\n temperature: 0.7 # Optional: AI creativity\\n max_tokens: 1000 # Optional: Response length limit\\n \\n # UI configuration\\n ui_config:\\n title: \\\"Customer Support Chat\\\" # Optional: Interface title\\n placeholder: \\\"How can I help you today?\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_timestamp: true # Optional: Show message timestamps\\n \\n # Termination conditions\\n termination:\\n keywords: [\\\"goodbye\\\", \\\"bye\\\", \\\"thanks\\\", \\\"done\\\"] # Optional: End conversation keywords\\n max_turns: 50 # Optional: Maximum conversation turns\\n timeout_minutes: 30 # Optional: Conversation timeout\\n inactivity_timeout: 10 # Optional: Inactivity timeout (minutes)\\n \\n # Context configuration\\n context:\\n system_context: \\\"Customer support conversation\\\" # Optional: Context description\\n user_id: \\\"${customer_id}\\\" # Optional: User identifier\\n session_data: ${previous_task} # Optional: Additional context data\\n```\\n\\n### Human Input Tasks\\n\\nTasks requiring human input or approval.\\n\\n```yaml\\n- id: human_input_task\\n name: \\\"Human Input Required\\\"\\n type: human_input\\n description: \\\"Wait for human input or approval\\\"\\n \\n # Human input configuration\\n config:\\n prompt: \\\"Please review and approve the processed data\\\" # REQUIRED: Prompt for human\\n input_type: \\\"approval\\\" # REQUIRED: approval|text|selection\\n \\n # Input options (for selection type)\\n options: [\\\"approve\\\", \\\"reject\\\", \\\"request_changes\\\"] # Optional: For selection input\\n \\n # Timeout configuration\\n timeout_minutes: 60 # Optional: How long to wait for input (minutes)\\n \\n # Conversation settings\\n conversation_mode: false # Optional: Add to conversation history\\n next_task: \\\"process_approval\\\" # Optional: Task to trigger after input\\n end_condition: \\\"bye|exit|quit\\\" # Optional: Keywords to end conversation\\n \\n # Input validation\\n required: true # Optional: Whether input is required\\n \\n # Additional context\\n context_data: ${previous_task} # Optional: Data to show to human\\n```\\n\\n### ChatBot Tasks\\n\\nSession-based chatbot conversations with streaming support.\\n\\n```yaml\\n- id: chatbot_task\\n name: \\\"AI Chatbot\\\"\\n type: chatbot\\n description: \\\"Interactive chatbot with session management\\\"\\n \\n # Chatbot configuration\\n config:\\n # AI model configuration\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # REQUIRED: System prompt for chatbot\\n You are a helpful AI assistant. Be friendly, helpful, and concise.\\n Answer questions clearly and ask for clarification when needed.\\n \\n # Session management\\n session_id: \\\"${session_id}\\\" # Optional: Session identifier (auto-generated if not provided)\\n max_history_messages: 20 # Optional: Maximum conversation history to maintain\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1000 # Optional: Maximum response tokens\\n stream: true # Optional: Enable streaming responses\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - send_notification # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # UI configuration\\n ui_config:\\n title: \\\"AI Assistant\\\" # Optional: Chat interface title\\n placeholder: \\\"Type your message...\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_typing_indicator: true # Optional: Show typing animation\\n \\n # Response configuration\\n response_config:\\n max_retries: 3 # Optional: Retry failed requests\\n timeout_seconds: 30 # Optional: Response timeout\\n \\n # Input message (for single-turn mode)\\n user_message: \\\"${user_input}\\\" # Optional: Direct message input\\n \\n # Context data\\n context_data: ${previous_task} # Optional: Additional context\\n```\\n\\n---\\n\\n## Common Errors and Corrections\\n\\nThis section documents frequent mistakes encountered when creating workflows and their proper corrections.\\n\\n### 1. Missing Required `id` Field\\n\\n**\\u274c Error:**\\n```yaml\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow without id\\\"\\ntasks:\\n - name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nid: \\\"my_workflow\\\" # REQUIRED: Unique workflow identifier\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow with proper id\\\"\\ntasks:\\n - id: task_1 # REQUIRED: Unique task identifier\\n name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n### 2. Incorrect Task Structure (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n task_1: # Wrong: tasks as object mapping\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n task_2:\\n type: ai_agent\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n - id: task_1 # Correct: tasks as array with id field\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n - id: task_2\\n type: ai_agent\\n```\\n\\n### 3. Incorrect Inputs Format (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ninputs:\\n user_name: # Wrong: inputs as object mapping\\n type: string\\n required: true\\n age:\\n type: integer\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ninputs:\\n - name: user_name # Correct: inputs as array with name field\\n type: string\\n required: true\\n - name: age\\n type: integer\\n```\\n\\n### 4. Script Configuration Location Error\\n\\n**\\u274c Error:**\\n```yaml\\n- id: script_task\\n type: script\\n config: # Wrong: script under config\\n script: |\\n print(\\\"Hello\\\")\\n requirements:\\n - requests\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: script_task\\n type: script\\n script: | # Correct: script directly under task\\n print(\\\"Hello\\\")\\n # requirements can be at task level if needed\\n```\\n\\n### 5. Individual Task Inputs (Not Supported)\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_1\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n inputs: # Wrong: tasks don\u0027t have individual inputs\\n search_query: ${search_query}\\n prev_result: ${previous_task}\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_1\\n type: script\\n depends_on: [previous_task] # Use depends_on for data access\\n script: |\\n # Access workflow inputs directly\\n search_query = \\\"${search_query}\\\"\\n # Access previous task outputs via template resolution\\n prev_result = ${previous_task}\\n```\\n\\n### 6. Incorrect Script Output Handling\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n result = process_data()\\n outputs[\u0027result\u0027] = result # Wrong: missing __OUTPUTS__ pattern\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n result = process_data()\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker\\n outputs = {}\\n outputs[\u0027result\u0027] = result\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### 7. AI Agent Configuration Errors\\n\\n**\\u274c Error:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n prompt: \\\"Process this data: ${data}\\\" # Wrong: use user_message\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n system_message: \\\"You are a helpful assistant.\\\"\\n user_message: \\\"Process this data: ${data}\\\" # Correct field name\\n```\\n\\n### 8. Missing Dependencies for Template Resolution\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_2\\n type: script\\n # Missing depends_on\\n script: |\\n data = ${task_1} # Will become UNRESOLVED_task_1\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_2\\n type: script\\n depends_on: [task_1] # Required for template access\\n script: |\\n data = ${task_1} # Now resolves correctly\\n```\\n\\n### 9. Incorrect Model Client Configuration\\n\\n**\\u274c Error:**\\n```yaml\\nmodel_clients:\\n my_model: # Wrong: object mapping format\\n type: openai\\n api_key: \\\"sk-...\\\"\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nmodel_clients:\\n - id: my_model # Correct: array format with id field\\n provider: openai\\n config:\\n model: \\\"gpt-4\\\"\\n api_key: \\\"sk-...\\\"\\n```\\n\\n### 10. Empty String Literals Causing Python Syntax Errors\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n def process_data(path=\\\"\\\"): # Wrong: empty string can cause syntax issues\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n \\n # This can lead to: SyntaxError: unterminated string literal\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n def process_data(path=None): # Correct: use None instead of empty string\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n # Or use path = path or \\\"default_value\\\"\\n```\\n\\n### 11. YAML Indentation and Structure Issues\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\nscript: | # Wrong: inconsistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\n script: | # Correct: consistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n### Best Practices Summary\\n\\n1. **Always include required `id` fields** for workflows and tasks\\n2. **Use array format** for `tasks`, `inputs`, and `model_clients`\\n3. **Script field goes directly under task**, not under `config`\\n4. **No individual task inputs** - use template resolution instead\\n5. **Use `depends_on`** to access previous task outputs\\n6. **Print `__OUTPUTS__`** in scripts for proper output parsing\\n7. **Use `user_message`** (not `prompt`) for AI agent tasks\\n8. **Avoid empty string literals** - use `None` or meaningful defaults\\n9. **Follow consistent YAML indentation** throughout the file\\n\\n## MCP Tools and Function Calling\\n\\nAI agents and chatbots can use MCP (Model Context Protocol) tools for function calling, enabling them to execute external tools and services.\\n\\n### Tools Configuration\\n\\nBoth `ai_agent` and `chatbot` tasks support tools configuration:\\n\\n```yaml\\n# AI Agent with MCP tools\\n- id: ai_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools.\\n Use the available tools when needed to help users.\\n user_message: \\\"What time is it and what\u0027s the weather like?\\\"\\n \\n # Tools configuration\\n tools: # List of MCP tool names\\n - current_time # Time tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - database_query # Database tools\\n - send_notification # Communication tools\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n\\n# Chatbot with MCP tools\\n- id: chatbot_with_tools\\n type: chatbot\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful chatbot with access to tools.\\n Use tools when users ask for specific information or actions.\\n \\n # Tools configuration (same format as AI agent)\\n tools:\\n - current_time\\n - weather_api\\n - user_preferences\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n```\\n\\n### How Tools Work\\n\\n1. **Tool Resolution**: The workflow engine resolves tool names to actual MCP server deployments using the `tenant_id`\\n2. **Function Calling**: When the AI model decides to use a tool, it calls the `tool_executor` function\\n3. **Tool Execution**: The engine executes the specified tool via the appropriate MCP server\\n4. **Response Integration**: Tool results are fed back to the AI model for response generation\\n\\n### Tool Configuration Requirements\\n\\n| Field | Required | Description |\\n|-------|----------|-------------|\\n| `tools` | No | List of MCP tool names available to the AI |\\n| `tenant_id` | **Yes** (when using tools) | Tenant UUID for tool resolution |\\n\\n### Example: Complete AI Agent with Tools\\n\\n```yaml\\nid: intelligent_assistant\\nname: \\\"Intelligent Assistant with Tools\\\"\\n\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4o-mini\\n api_key: \\\"${OPENAI_API_KEY}\\\"\\n temperature: 0.3\\n\\n# Tenant ID can be set at workflow level for all tasks\\ntenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n\\ntasks:\\n - id: assistant_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools:\\n - Time-related tools: current_time, relative_time, days_in_month\\n - Weather tools: weather_forecast\\n - Communication: send_notification\\n \\n Use these tools when users ask for:\\n - Current time or date information\\n - Weather forecasts\\n - Sending notifications\\n \\n Always explain what tool you\u0027re using and why.\\n \\n user_message: \\\"${user_query}\\\"\\n \\n # MCP tools configuration\\n tools:\\n - current_time\\n - relative_time\\n - days_in_month\\n - weather_forecast\\n - send_notification\\n \\n # Tool resolution requires tenant_id\\n tenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n \\n temperature: 0.7\\n max_tokens: 1000\\n\\ninputs:\\n - name: user_query\\n type: string\\n required: true\\n default: \\\"What time is it and can you send a notification to user123?\\\"\\n\\noutputs:\\n ai_response:\\n type: string\\n source: assistant_with_tools.ai_response\\n description: \\\"AI response using tools when needed\\\"\\n```\\n\\n### Tool Execution Flow\\n\\n1. **User Input**: User provides a query that might require tool usage\\n2. **AI Analysis**: AI model analyzes the query and decides if tools are needed\\n3. **Tool Calling**: AI calls `tool_executor(deployment_id, tool_name, arguments)`\\n4. **Tool Resolution**: Engine resolves tool name to MCP server using tenant_id\\n5. **Tool Execution**: MCP server executes the requested tool\\n6. **Result Processing**: Tool results are returned to the AI model\\n7. **Response Generation**: AI generates final response incorporating tool results\\n\\n### Best Practices for Tools\\n\\n1. **Clear System Messages**: Explain available tools and when to use them\\n2. **Tool Categories**: Group related tools (time, weather, database, etc.)\\n3. **Error Handling**: AI models handle tool execution failures gracefully\\n4. **Tool Documentation**: Include tool descriptions in system messages\\n5. **Tenant Configuration**: Ensure tenant_id matches your MCP server setup\\n\\n---\\n\\n*This comprehensive reference covers all supported task types and their YAML configurations. Use this as a quick reference when building workflows with different task types.*\", \"format\": \"markdown\"}, \"status\": \"success\", \"successful_url\": \"http://172.17.0.1:5000/api/docs/yaml-reference\"}\n",
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"output": "Trying to fetch documentation from: http://host.docker.internal:5000/api/docs/yaml-reference\n\u274c Connection failed: http://host.docker.internal:5000/api/docs/yaml-reference - HTTPConnectionPool(host=\u0027host.docker.internal\u0027, port=5000): Max retries exceeded with url: /api/docs/yaml-reference (Caused by NameResolutionError(\"\u003curllib3.connection.HTTPConnection object at 0x75595e911cd0\u003e: Failed to resolve \u0027host.docker.internal\u0027 ([Errno -2] Name or service not known)\"))\nTrying to fetch documentation from: http://172.17.0.1:5000/api/docs/yaml-reference\n\u2705 Successfully connected to: http://172.17.0.1:5000/api/docs/yaml-reference\nSuccessfully fetched documentation from http://172.17.0.1:5000/api/docs/yaml-reference: 97047 characters\n__OUTPUTS__ {\"documentation\": {\"content\": \"# YAML Workflow Structure Reference\\n\\nComplete reference for the YAML workflow definition structure supported by the Assistants Workflow Engine.\\n\\n## Table of Contents\\n\\n1. [Complete Schema Overview](#complete-schema-overview)\\n2. [Required vs Optional Fields](#required-vs-optional-fields)\\n3. [Data Types Reference](#data-types-reference)\\n4. [Field Validation Rules](#field-validation-rules)\\n5. [Parser Behavior](#parser-behavior)\\n6. [Migration Guide](#migration-guide)\\n\\n## Complete Schema Overview\\n\\n```yaml\\n# === WORKFLOW METADATA (Required) ===\\nid: string # REQUIRED: Unique workflow identifier\\nname: string # REQUIRED: Human-readable workflow name\\n\\n# === WORKFLOW METADATA (Optional) ===\\nversion: string | integer # Default: 1\\nnamespace: string # Default: \\\"default\\\"\\ndescription: string # Default: \\\"\\\"\\ntenant_id: string # Default: null\\n\\n# === EXECUTION CONFIGURATION ===\\ntimeout_seconds: integer|string # Default: 3600 (1 hour) - Workflow-level timeout (supports duration strings like \\\"1h\\\", \\\"30m\\\")\\nlabels: object # Default: {}\\nmetadata: object # Default: {}\\n\\n# === DATA FLOW DEFINITIONS ===\\ninputs: # Default: []\\n - name: string # REQUIRED\\n type: string # REQUIRED: string|integer|boolean|object|array\\n description: string # Optional\\n required: boolean # Default: false\\n default: any # Optional default value\\n\\noutputs: object # Default: {}\\n output_name:\\n type: string # REQUIRED\\n source: string # REQUIRED: task_id or task_id.field\\n description: string # Optional\\n\\nvariables: object # Default: {}\\n\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n - id: string # REQUIRED: Unique identifier to reference in tasks\\n provider: string # REQUIRED: openai|anthropic|openrouter|groq\\n config: object # REQUIRED: Provider-specific config\\n model: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\n api_key: string # REQUIRED: API key for the provider\\n temperature: number # Optional: 0.0-2.0, controls randomness\\n max_tokens: integer # Optional: Maximum tokens to generate\\n timeout: integer # Optional: Request timeout in seconds\\n base_url: string # Optional: Custom API base URL\\n\\n# Model Client Examples:\\n# OpenAI Configuration:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 2000\\n#\\n# Anthropic Configuration:\\n# model_clients:\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 1500\\n#\\n# Multiple Models:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n\\n# === TASK DEFINITIONS (Required) ===\\ntasks: # REQUIRED: List of tasks\\n - id: string # REQUIRED: Unique task identifier\\n name: string # Optional: Human-readable name\\n type: string # REQUIRED: Task type\\n description: string # Optional\\n \\n # DEPENDENCY CONFIGURATION\\n depends_on: array # Optional: Execution dependencies and template resolution\\n previous_node: string # REQUIRED: Task connectivity and UI hierarchy (essential for proper workflow execution)\\n \\n timeout_seconds: integer|string # Optional: Task-specific timeout (supports duration strings like \\\"5m\\\", \\\"30s\\\")\\n worker_group: string # Optional: Target worker group\\n conditions: array # Optional: Execution conditions\\n execute_on_routes: array # Optional: Routes for conditional execution\\n \\n # Input/Output mapping\\n input_mapping: object # Optional: Map workflow context to task inputs\\n output_mapping: object # Optional: Map task outputs to workflow context\\n \\n # Retry configuration\\n retry: object # Optional: Task-specific retry policy\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n \\n # === TASK TYPE SPECIFIC CONFIGURATIONS ===\\n # See detailed sections below for each task type\\n\\n# === WORKFLOW-LEVEL RETRY POLICY ===\\nretry: object # Optional: Global retry policy\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n\\n# === TRIGGERS ===\\ntriggers: # Optional: List of triggers\\n - name: string # REQUIRED\\n type: string # REQUIRED: webhook|schedule|event\\n description: string # Optional\\n config: object # REQUIRED: Trigger-specific config\\n input_mapping: array # Optional: Map trigger data to workflow inputs\\n - webhook_field: string # Source field\\n workflow_input: string # Target workflow input\\n```\\n\\n## Required vs Optional Fields\\n\\n### Required Fields (Workflow Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the workflow |\\n| `tasks` | array | List of task definitions (can be empty) |\\n\\n### Required Fields (Task Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the task |\\n| `type` | string | Task type (script, http, ai_agent, etc.) |\\n| `previous_node` | string | Reference to previous task for UI connectivity (required for proper task linking, except for the first task) |\\n\\n### Required Fields (Input Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `name` | string | Input parameter name |\\n| `type` | string | Data type (string, integer, boolean, object, array) |\\n\\n### Required Fields (Output Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `type` | string | Output data type |\\n| `source` | string | Source task ID or task_id.field_name |\\n\\n## Data Types Reference\\n\\n### Primitive Types\\n\\n```yaml\\n# String type\\ninputs:\\n - name: customer_name\\n type: string\\n default: \\\"John Doe\\\"\\n description: \\\"Customer full name\\\"\\n\\n# Integer type \\ninputs:\\n - name: batch_size\\n type: integer\\n default: 100\\n description: \\\"Number of records per batch\\\"\\n\\n# Boolean type\\ninputs:\\n - name: include_archived\\n type: boolean\\n default: false\\n description: \\\"Include archived records\\\"\\n```\\n\\n### Complex Types\\n\\n```yaml\\n# Object type\\ninputs:\\n - name: config_params\\n type: object\\n default:\\n timeout: 30\\n retries: 3\\n debug: false\\n description: \\\"Configuration parameters\\\"\\n\\n# Array type\\ninputs:\\n - name: customer_ids\\n type: array\\n default: [\\\"cust_001\\\", \\\"cust_002\\\"]\\n description: \\\"List of customer identifiers\\\"\\n```\\n\\n### Duration Types\\n\\nDuration values can be specified as strings or integers:\\n\\n```yaml\\n# String format (recommended)\\ntimeout_seconds: \\\"1h\\\" # 1 hour\\ntimeout_seconds: \\\"30m\\\" # 30 minutes \\ntimeout_seconds: \\\"45s\\\" # 45 seconds\\ntimeout_seconds: \\\"2d\\\" # 2 days\\n\\n# Integer format (seconds)\\ntimeout_seconds: 3600 # 1 hour in seconds\\n\\n# Supported units\\n# s, sec, second, seconds\\n# m, min, minute, minutes \\n# h, hour, hours\\n# d, day, days\\n```\\n\\n### Timeout Configuration and Execution Behavior\\n\\nThe workflow engine supports configurable timeouts at both workflow and task levels with intelligent timeout-free communication for long-running tasks.\\n\\n#### Timeout Hierarchy\\n\\nTimeout values are resolved in this order of precedence:\\n1. **Task-level `timeout_seconds`**: Specific timeout for individual tasks\\n2. **Workflow-level `timeout_seconds`**: Default for all tasks in the workflow\\n3. **System default**: 300 seconds (5 minutes) for tasks, 3600 seconds (1 hour) for workflows\\n\\n```yaml\\n# Workflow-level timeout (applies to all tasks by default)\\ntimeout_seconds: \\\"2h\\\" # 2 hour workflow timeout\\n\\ntasks:\\n - id: quick_task\\n type: script\\n # Uses workflow-level timeout (2h)\\n script: \\\"print(\u0027Quick task\u0027)\\\"\\n \\n - id: custom_timeout_task\\n type: script\\n timeout_seconds: \\\"30m\\\" # Override with 30 minute timeout\\n script: \\\"print(\u0027Custom timeout task\u0027)\\\"\\n \\n - id: long_running_task\\n type: script\\n timeout_seconds: 600 # 10 minutes (integer format)\\n script: |\\n import time\\n time.sleep(300) # 5 minute processing\\n print(\\\"Long task completed\\\")\\n```\\n\\n#### Timeout-Free Communication\\n\\nThe workflow engine automatically uses **timeout-free communication** for long-running tasks to prevent timeout-related failures:\\n\\n- **Loop tasks**: Always use timeout-free communication (event-driven execution)\\n- **Script tasks**: Use timeout-free communication when `timeout_seconds \u003e 180` (host timeout threshold)\\n- **Other task types**: Use standard timeout-based communication\\n\\n```yaml\\ntasks:\\n - id: short_script\\n type: script\\n timeout_seconds: 60 # Uses standard timeout-based communication\\n script: \\\"print(\u0027Short script\u0027)\\\"\\n \\n - id: long_script\\n type: script\\n timeout_seconds: 600 # Uses timeout-free communication (600 \u003e 180)\\n script: |\\n import time\\n print(\\\"\\ud83d\\ude80 This uses timeout-free communication\\\")\\n time.sleep(300) # Won\u0027t cause timeout issues\\n print(\\\"Long script completed\\\")\\n \\n - id: data_processing_loop\\n type: loop\\n timeout_seconds: \\\"1h\\\" # Always uses timeout-free communication\\n loop_type: for\\n max_iterations: 10\\n # Loop tasks automatically use timeout-free communication\\n```\\n\\n#### Loop Task Timeout Inheritance\\n\\nLoop tasks can pass timeout configuration to their inner script tasks:\\n\\n```yaml\\n- id: batch_processing_loop\\n type: loop\\n timeout_seconds: \\\"30m\\\" # Loop-level timeout\\n loop_type: for\\n max_iterations: 5\\n loop_tasks:\\n - id: process_batch\\n type: script\\n # Inherits 30-minute timeout from parent loop\\n script: |\\n print(\\\"Processing batch with inherited timeout\\\")\\n # This script will use the 30-minute timeout from the loop\\n```\\n\\n#### Best Practices for Timeout Configuration\\n\\n1. **Use duration strings** for readability: `\\\"5m\\\"` vs `300`\\n2. **Set appropriate timeouts** based on expected execution time plus buffer\\n3. **Long-running tasks**: Set `timeout_seconds \u003e 180` to enable timeout-free communication\\n4. **Loop tasks**: Always get timeout-free communication automatically\\n5. **Critical tasks**: Use shorter timeouts for fail-fast behavior\\n6. **External API calls**: Use longer timeouts to account for network latency\\n\\n```yaml\\n# Example: Well-configured timeout workflow\\ntimeout_seconds: \\\"1h\\\" # Reasonable workflow timeout\\n\\ntasks:\\n - id: api_call\\n type: http\\n timeout_seconds: \\\"30s\\\" # Quick timeout for API calls\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: data_processing\\n type: script\\n timeout_seconds: \\\"10m\\\" # Longer timeout for processing (uses timeout-free)\\n script: |\\n # Process large dataset\\n process_data()\\n \\n - id: batch_processing\\n type: loop\\n timeout_seconds: \\\"45m\\\" # Generous timeout for loop (timeout-free)\\n loop_type: for\\n max_iterations: 100\\n # Inner tasks inherit the 45-minute timeout\\n```\\n\\n## Field Validation Rules\\n\\n### ID Fields\\n\\n```yaml\\n# Valid IDs\\nid: \\\"customer_processor\\\" # \\u2705 Valid\\nid: \\\"process-data\\\" # \\u2705 Valid\\nid: \\\"task_001\\\" # \\u2705 Valid\\n\\n# Invalid IDs\\nid: \\\"process data\\\" # \\u274c Spaces not allowed\\nid: \\\"123_start\\\" # \\u274c Cannot start with number\\nid: \\\"\\\" # \\u274c Cannot be empty\\n```\\n\\n### Timeout Values\\n\\n```yaml\\n# Valid timeouts\\ntimeout_seconds: 300 # \\u2705 Positive integer\\ntimeout_seconds: \\\"5m\\\" # \\u2705 Duration string\\ntimeout_seconds: \\\"1h30m\\\" # \\u2705 Complex duration\\n\\n# Invalid timeouts\\ntimeout_seconds: 0 # \\u274c Must be positive\\ntimeout_seconds: -60 # \\u274c Cannot be negative\\ntimeout_seconds: \\\"invalid\\\" # \\u274c Invalid duration format\\n```\\n\\n### Dependency Types and Validation\\n\\n#### `depends_on` vs `previous_node`\\n\\n**`depends_on` - Execution Dependencies:**\\n- Controls actual task execution order\\n- Enables template resolution and data access\\n- Required for accessing outputs from other tasks\\n- Validates that referenced tasks exist\\n- Multiple tasks can be specified\\n\\n**`previous_node` - Task Connectivity and UI Hierarchy:**\\n- **REQUIRED for proper task connectivity in UI**\\n- Essential for workflow visualization and task linking\\n- Critical for execution flow representation\\n- Single task reference only\\n- **Without this field, tasks won\u0027t be properly connected in the UI**\\n\\n```yaml\\ntasks:\\n - id: fetch_data\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: validate_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 EXECUTION: wait for fetch_data to complete\\n previous_node: fetch_data # \\u2705 UI: show as child of fetch_data in hierarchy\\n script: |\\n data = ${fetch_data} # \\u2705 Can access because of depends_on\\n if not validate(data):\\n raise ValueError(\\\"Invalid data\\\")\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker for proper parsing\\n outputs[\u0027status\u0027] = \u0027valid\u0027\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: process_data\\n type: script\\n depends_on: [validate_data] # \\u2705 EXECUTION: wait for validation\\n previous_node: validate_data # \\u2705 UI: show as child of validate_data\\n script: |\\n validation_result = ${validate_data} # \\u2705 Accessible via depends_on\\n # Process data...\\n\\n - id: separate_task\\n type: script\\n previous_node: process_data # \\u2705 UI: appears under process_data in hierarchy\\n # NO depends_on means this can run immediately (parallel to other tasks)\\n script: |\\n # This task runs independently but appears under process_data in UI\\n # Cannot access ${process_data} because no depends_on relationship\\n print(\\\"Independent task\\\")\\n\\n### Complete Example with Proper Hierarchy\\n\\n```yaml\\ntasks:\\n - id: fetch_customer_data\\n name: \\\"Fetch Customer Data\\\"\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: validate_customer\\n name: \\\"Validate Customer Data\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Wait for data fetch\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n customer = ${fetch_customer_data} # \\u2705 Can access because of depends_on\\n if not customer.get(\u0027email\u0027):\\n raise ValueError(\\\"Invalid customer data\\\")\\n print(\\\"__OUTPUTS__ {\u0027status\u0027: \u0027valid\u0027}\\\")\\n \\n - id: process_customer\\n name: \\\"Process Customer\\\"\\n type: ai_agent\\n depends_on: [validate_customer] # \\u2705 EXECUTION: Wait for validation\\n previous_node: validate_customer # \\u2705 UI: Show under validate_customer\\n config:\\n model_client_id: gpt4_model\\n user_message: \\\"Process customer: ${fetch_customer_data}\\\"\\n \\n - id: send_notification\\n name: \\\"Send Notification\\\"\\n type: script\\n depends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\n previous_node: process_customer # \\u2705 UI: Show under process_customer\\n script: |\\n result = ${process_customer}\\n print(f\\\"Notification sent for: ${fetch_customer_data.name}\\\")\\n \\n - id: parallel_audit_log\\n name: \\\"Audit Log (Parallel)\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n # This runs in parallel with validation/processing\\n customer = ${fetch_customer_data}\\n print(f\\\"Audit: Customer {customer.id} accessed at ${current_timestamp}\\\")\\n```\\n\\n # Invalid examples\\n - id: invalid_task\\n type: script\\n depends_on: [nonexistent_task] # \\u274c VALIDATION ERROR: task doesn\u0027t exist\\n previous_node: another_missing # \\u26a0\\ufe0f UI WARNING: task doesn\u0027t exist (but not fatal)\\n script: \\\"print(\u0027Invalid\u0027)\\\"\\n```\\n\\n#### Key Differences:\\n\\n| Aspect | `depends_on` | `previous_node` |\\n|--------|--------------|-----------------|\\n| **Purpose** | Execution control | Task connectivity \u0026 UI organization |\\n| **Impact** | Blocks task until dependencies complete | **REQUIRED for UI task linking** |\\n| **Template Access** | \\u2705 Enables `${task_id}` resolution | \\u274c No template access |\\n| **Validation** | \\u274c Parser error if task missing | \\u26a0\\ufe0f Warning if task missing |\\n| **Multiple References** | \\u2705 Array of task IDs | \\u274c Single task ID only |\\n| **Execution Order** | \\u2705 Enforced | \\u274c No execution impact |\\n| **UI Connectivity** | \\u274c No UI impact | \\u2705 **ESSENTIAL for proper task connection** |\\n\\n### Circular Dependency Detection\\n\\n```yaml\\n# This will be rejected by the parser\\ntasks:\\n - id: task_a\\n depends_on: [task_b] # \\u274c Circular dependency\\n \\n - id: task_b \\n depends_on: [task_a] # \\u274c Circular dependency\\n```\\n\\n## Parser Behavior\\n\\n### Field Name Compatibility\\n\\nThe parser supports both camelCase and snake_case for field names:\\n\\n```yaml\\ntasks:\\n - id: flexible_task\\n # Both formats are accepted\\n dependsOn: [other_task] # camelCase\\n depends_on: [other_task] # snake_case\\n \\n inputMapping: # camelCase\\n source_field: \\\"target_field\\\"\\n input_mapping: # snake_case \\n source_field: \\\"target_field\\\"\\n \\n outputMapping: {} # camelCase\\n output_mapping: {} # snake_case\\n \\n workerGroup: \\\"gpu-workers\\\" # camelCase\\n worker_group: \\\"gpu-workers\\\" # snake_case\\n```\\n\\n### Default Value Assignment\\n\\n```yaml\\n# Fields with defaults are automatically populated\\nid: my_workflow\\n# name defaults to id value: \\\"my_workflow\\\"\\n# version defaults to: 1\\n# namespace defaults to: \\\"default\\\" \\n# timeout_seconds defaults to: 3600\\n# description defaults to: \\\"\\\"\\n\\ntasks:\\n - id: simple_task\\n type: script\\n # name defaults to id: \\\"simple_task\\\"\\n # timeout_seconds defaults to: 300\\n # retry.maxAttempts defaults to: 1\\n```\\n\\n### Template Resolution Context\\n\\nThe parser provides these built-in variables for template resolution:\\n\\n```yaml\\ntasks:\\n - id: context_aware_task\\n type: script\\n script: |\\n # Workflow context\\n workflow_id = \\\"${workflow_id}\\\" # Current workflow ID\\n execution_id = \\\"${execution_id}\\\" # Unique execution ID\\n namespace = \\\"${namespace}\\\" # Workflow namespace\\n \\n # Timing context\\n current_timestamp = \\\"${current_timestamp}\\\" # ISO timestamp\\n started_at = \\\"${started_at}\\\" # Workflow start time\\n \\n # Input context\\n user_input = \\\"${input_parameter}\\\" # Workflow inputs\\n \\n # Task context\\n previous_result = ${previous_task} # Previous task output\\n specific_field = ${task_id.field_name} # Specific output field\\n \\n # Route context (for conditional workflows)\\n route_info = ${_route_context} # Current route information\\n```\\n\\n### Error Handling During Parsing\\n\\n```yaml\\n# The parser will validate and report these errors:\\n\\n# 1. Missing required fields\\n# ERROR: Missing required field: \u0027id\u0027\\n\\n# 2. Invalid YAML syntax \\n# ERROR: Invalid YAML: mapping values are not allowed here\\n\\n# 3. Circular dependencies\\n# ERROR: Circular dependency detected involving task \u0027task_a\u0027\\n\\n# 4. Invalid task references\\n# ERROR: Task \u0027task_b\u0027 depends on non-existent task \u0027invalid_task\u0027\\n\\n# 5. Invalid duration formats\\n# ERROR: Invalid duration: \u0027not_a_duration\u0027\\n\\n# 6. Invalid field values\\n# ERROR: Timeout must be positive, got -10\\n```\\n\\n## Migration Guide\\n\\n### From Version 1.0 to 2.0\\n\\n```yaml\\n# OLD FORMAT (v1.0)\\nworkflow:\\n id: my_workflow\\n tasks:\\n - task_id: process_data\\n task_type: python_script\\n \\n# NEW FORMAT (v2.0) \\nid: my_workflow\\ntasks:\\n - id: process_data\\n type: script\\n language: python\\n```\\n\\n### Deprecated Fields\\n\\n| Old Field | New Field | Status |\\n|-----------|-----------|--------|\\n| `task_type` | `type` | Deprecated in v2.0 |\\n| `task_id` | `id` | Deprecated in v2.0 |\\n| `python_script` | `script` (type) | Deprecated in v2.0 |\\n| `plugin` | `mcp` (type) | Deprecated in v2.1 |\\n\\n### Backward Compatibility\\n\\nThe parser maintains backward compatibility for deprecated fields but will show warnings:\\n\\n```yaml\\n# This will work but show a warning\\ntasks:\\n - task_id: old_style_task # \\u26a0\\ufe0f Warning: Use \u0027id\u0027 instead\\n task_type: python_script # \\u26a0\\ufe0f Warning: Use \u0027type: script\u0027 instead\\n```\\n\\n## Advanced Schema Features\\n\\n### Conditional Field Requirements\\n\\nSome fields become required based on task type:\\n\\n```yaml\\n# HTTP tasks require URL\\n- id: api_call\\n type: http\\n url: \\\"https://api.example.com\\\" # \\u2705 Required for HTTP tasks\\n \\n# AI agent tasks require model_client_id\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4 # \\u2705 Required for AI agent tasks\\n```\\n\\n### Dynamic Field Validation\\n\\nField validation can depend on other field values:\\n\\n```yaml\\n# Loop tasks require loop-specific fields\\n- id: data_loop\\n type: loop\\n loop_type: \\\"for\\\" # Determines required fields\\n max_iterations: 10 # \\u2705 Required for \u0027for\u0027 loops\\n # exit_condition: not needed for \u0027for\u0027 loops\\n \\n- id: condition_loop \\n type: loop\\n loop_type: \\\"while\\\" # Determines required fields\\n exit_condition: \\\"${count} \u003e= 10\\\" # \\u2705 Required for \u0027while\u0027 loops\\n # max_iterations: optional for \u0027while\u0027 loops\\n```\\n\\n## Template Resolution and Data Flow\\n\\nTemplate resolution is a core feature that allows tasks to access data from previous tasks, workflow inputs, and built-in variables using the `${variable_name}` syntax. This is essential for creating dynamic workflows where tasks pass data to each other.\\n\\n### Basic Template Syntax\\n\\n```yaml\\n# Access workflow inputs\\nscript: |\\n customer_name = \\\"${customer_name}\\\" # From workflow inputs\\n \\n# Access previous task outputs\\nscript: |\\n previous_result = ${data_fetch_task} # Entire output from data_fetch_task\\n specific_field = ${data_fetch_task.customer_id} # Specific field\\n \\n# Access built-in variables\\nscript: |\\n workflow_id = \\\"${workflow_id}\\\"\\n execution_id = \\\"${execution_id}\\\"\\n timestamp = \\\"${current_timestamp}\\\"\\n```\\n\\n### Available Built-in Variables\\n\\n| Variable | Description | Example Value |\\n|----------|-------------|---------------|\\n| `${workflow_id}` | Current workflow identifier | `customer_processor` |\\n| `${execution_id}` | Unique execution identifier | `exec_12345_abcde` |\\n| `${namespace}` | Workflow namespace | `production` |\\n| `${current_timestamp}` | ISO timestamp | `2024-01-15T10:30:00Z` |\\n| `${started_at}` | Workflow start time | `2024-01-15T10:29:45Z` |\\n\\n### Task Output Resolution\\n\\nTasks can access outputs from other tasks they depend on:\\n\\n```yaml\\ntasks:\\n - id: fetch_customer\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: process_customer\\n type: script\\n depends_on: [fetch_customer] # Required for template access\\n previous_node: fetch_customer\\n script: |\\n # Access entire response\\n customer_data = ${fetch_customer}\\n \\n # Access specific fields\\n customer_name = \\\"${fetch_customer.name}\\\"\\n customer_email = \\\"${fetch_customer.email}\\\"\\n \\n # Process the data\\n result = {\\n \\\"processed_name\\\": customer_name.upper(),\\n \\\"domain\\\": customer_email.split(\\\"@\\\")[1]\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### AI Agent Output Resolution\\n\\nAI agent tasks return structured output that requires specific access patterns:\\n\\n```yaml\\n# AI Agent task\\n- id: ai_analysis_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Analyze the customer data and provide insights.\\\"\\n user_message: \\\"Customer data: ${customer_data}\\\"\\n\\n# Accessing AI agent output in subsequent tasks\\n- id: process_ai_response\\n type: script\\n depends_on: [ai_analysis_task]\\n previous_node: ai_analysis_task\\n script: |\\n # CORRECT: AI agents return structured output\\n agent_output = ${ai_analysis_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n token_usage = agent_output.get(\u0027token_usage\u0027, {})\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n # Process the AI response\\n processed_analysis = {\\n \\\"analysis\\\": ai_response,\\n \\\"tokens_used\\\": token_usage.get(\u0027total_tokens\u0027, 0),\\n \\\"model\\\": model_used,\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\"\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(processed_analysis)}\\\")\\n\\n# INCORRECT: Direct access won\u0027t work\\n- id: incorrect_ai_access\\n type: script\\n depends_on: [ai_analysis_task]\\n script: |\\n # \\u274c This will not work correctly\\n ai_response = ${ai_analysis_task} # Returns full agent object\\n # ai_response is now a dict, not the actual response text\\n```\\n\\n### AI Agent Response Structure\\n\\nAI agent tasks return a structured object with the following format:\\n\\n```json\\n{\\n \\\"ai_response\\\": \\\"The actual AI-generated text response\\\",\\n \\\"model\\\": \\\"gpt-4-turbo-preview\\\",\\n \\\"token_usage\\\": {\\n \\\"prompt_tokens\\\": 150,\\n \\\"completion_tokens\\\": 75,\\n \\\"total_tokens\\\": 225\\n },\\n \\\"estimated_cost\\\": {\\n \\\"amount\\\": 0.00337,\\n \\\"currency\\\": \\\"USD\\\"\\n },\\n \\\"provider\\\": \\\"openai\\\",\\n \\\"timestamp\\\": \\\"2024-01-15T10:30:00Z\\\"\\n}\\n```\\n\\n### Loop Task Data Access and Dependencies\\n\\nLoop tasks have a special execution context that differs from regular workflow tasks. Understanding how to access data within loops is crucial for building functional workflows.\\n\\n#### Loop Task Internal Data Access\\n\\nWithin loop tasks, data access works through injected context files rather than template resolution:\\n\\n```yaml\\n# Loop task with state variables and internal task dependencies\\n- id: data_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n state_variables:\\n processed_items: []\\n total_processed: 0\\n error_count: 0\\n loop_tasks:\\n # First task in loop\\n - id: fetch_data\\n type: script\\n script: |\\n # Access loop state variables through injected context\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n processed_items = loop_state.get(\u0027processed_items\u0027, [])\\n \\n # Access workflow inputs and external task outputs\\n external_data = inputs.get(\u0027previous_workflow_task\u0027, {})\\n iteration_num = inputs.get(\u0027iteration\u0027, 0) # Loop iteration variable\\n \\n # Process data\\n batch_data = fetch_batch_data(iteration_num)\\n \\n outputs = {\\\"batch_data\\\": batch_data, \\\"batch_size\\\": len(batch_data)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n # Second task depends on first task in same loop\\n - id: process_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 CORRECT: Dependencies within loop tasks work normally\\n script: |\\n # \\u2705 CORRECT: Access previous loop task through inputs context\\n fetch_result = inputs.get(\u0027fetch_data\u0027, {})\\n batch_data = fetch_result.get(\u0027batch_data\u0027, [])\\n \\n # \\u274c INCORRECT: This won\u0027t work in loop context\\n # batch_data = ${fetch_data.batch_data} # Template resolution fails in loops\\n \\n # Access and update state variables\\n current_processed = loop_state.get(\u0027processed_items\u0027, [])\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n \\n # Process the batch\\n processed_batch = process_batch(batch_data)\\n current_processed.extend(processed_batch)\\n \\n # Update state variables using state_updates\\n state_updates[\u0027processed_items\u0027] = current_processed\\n state_updates[\u0027total_processed\u0027] = current_total + len(processed_batch)\\n \\n outputs = {\\\"processed_count\\\": len(processed_batch)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED for state updates\\n```\\n\\n#### Key Differences in Loop Task Data Access\\n\\n| Access Pattern | Regular Tasks | Loop Tasks |\\n|---------------|---------------|------------|\\n| **Previous Task Output** | `${previous_task}` | `inputs.get(\u0027field_name\u0027, default)` (flattened) |\\n| **State Variables** | N/A | `loop_state.get(\u0027variable_name\u0027, default)` |\\n| **State Updates** | N/A | `state_updates[\u0027variable_name\u0027] = value` |\\n| **Workflow Inputs** | `\\\"${input_name}\\\"` | `inputs.get(\u0027input_name\u0027, \u0027default\u0027)` |\\n| **Dependencies** | `depends_on: [task_id]` | Same: `depends_on: [task_id]` |\\n\\n#### Critical: Loop Task Output Flattening\\n\\n**Important:** The loop executor flattens task outputs into individual fields in the `inputs` dictionary, rather than storing them as nested objects under the task name.\\n\\n```yaml\\n# First loop task outputs:\\noutputs = {\\\"company_name\\\": \\\"ACME Corp\\\", \\\"company_id\\\": \\\"123\\\", \\\"status\\\": \\\"success\\\"}\\nprint(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n\\n# Second loop task accessing the data:\\nscript: |\\n # \\u274c INCORRECT: This won\u0027t work - no nested object\\n # company_data = inputs.get(\u0027first_task\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027)\\n \\n # \\u2705 CORRECT: Access flattened fields directly\\n company_name = inputs.get(\u0027company_name\u0027, \u0027\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n print(f\\\"Company: {company_name}, ID: {company_id}, Status: {task_status}\\\")\\n```\\n\\n#### Complete Loop Task Example\\n\\n```yaml\\n- id: financial_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n iteration_variable: company_index\\n depends_on: [list_companies] # External dependency\\n \\n state_variables:\\n processed_companies: []\\n total_extractions: 0\\n successful_extractions: 0\\n \\n loop_tasks:\\n - id: process_current_company\\n type: script\\n script: |\\n # Access external workflow task data\\n companies_data = inputs.get(\u0027list_companies\u0027, {})\\n company_idx = inputs.get(\u0027company_index\u0027, 0)\\n \\n # Access state variables\\n processed = loop_state.get(\u0027processed_companies\u0027, [])\\n \\n # Process company\\n company = companies_data[\u0027companies\u0027][company_idx]\\n result = process_company(company)\\n \\n outputs = {\\\"company_name\\\": company[\u0027name\u0027], \\\"status\\\": \\\"success\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: extract_financial_data\\n type: script\\n depends_on: [process_current_company] # Depend on previous loop task\\n requirements:\\n - requests\u003e=2.28.0\\n - some-ai-library\u003e=1.0.0\\n script: |\\n # \\u2705 CORRECT: Access flattened outputs from previous loop task\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n # \\u274c INCORRECT: Loop executor flattens outputs, no nested access\\n # company_data = inputs.get(\u0027process_current_company\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n \\n # \\u274c INCORRECT: Template resolution doesn\u0027t work in loops\\n # company_data = ${process_current_company}\\n \\n # Access external data\\n companies_list = inputs.get(\u0027list_companies\u0027, {})\\n \\n # Perform AI extraction\\n extraction_result = extract_with_ai({\\\"company_name\\\": company_name, \\\"company_id\\\": company_id})\\n \\n # Update state variables\\n current_processed = loop_state.get(\u0027processed_companies\u0027, [])\\n current_total = loop_state.get(\u0027total_extractions\u0027, 0)\\n current_successful = loop_state.get(\u0027successful_extractions\u0027, 0)\\n \\n current_processed.append({\\n \\\"company_name\\\": company_name,\\n \\\"extraction_result\\\": extraction_result\\n })\\n \\n # State updates are required for persistence\\n state_updates[\u0027processed_companies\u0027] = current_processed\\n state_updates[\u0027total_extractions\u0027] = current_total + 1\\n state_updates[\u0027successful_extractions\u0027] = current_successful + (1 if extraction_result else 0)\\n \\n outputs = {\\\"extracted\\\": bool(extraction_result)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED\\n\\n# Accessing loop task outputs in subsequent workflow tasks\\n- id: process_loop_results\\n type: script\\n depends_on: [financial_processing_loop]\\n previous_node: financial_processing_loop\\n script: |\\n # \\u2705 CORRECT: Access final state of loop\\n loop_result = ${financial_processing_loop}\\n loop_final_state = loop_result.get(\u0027final_state\u0027, {})\\n \\n # Access specific state variables from final state\\n processed_companies = loop_final_state.get(\u0027processed_companies\u0027, [])\\n total_processed = loop_final_state.get(\u0027total_extractions\u0027, 0)\\n successful_count = loop_final_state.get(\u0027successful_extractions\u0027, 0)\\n \\n # Access loop execution metadata\\n iterations_completed = loop_result.get(\u0027iterations_completed\u0027, 0)\\n \\n print(f\\\"Loop completed {iterations_completed} iterations\\\")\\n print(f\\\"Processed {len(processed_companies)} companies\\\")\\n print(f\\\"Success rate: {successful_count}/{total_processed}\\\")\\n \\n outputs = {\\n \\\"companies_processed\\\": len(processed_companies),\\n \\\"success_rate\\\": f\\\"{successful_count}/{total_processed}\\\",\\n \\\"iterations\\\": iterations_completed\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Loop Task Context Injection and Output Flattening\\n\\nThe loop executor injects context through temporary files and flattens task outputs for easier access:\\n\\n```python\\n# Auto-injected at the start of each loop task script:\\nimport json\\nimport os\\n\\n# Load context from temporary files (auto-generated)\\nwith open(\u0027/tmp/loop_context_task_iter_N/inputs.json\u0027, \u0027r\u0027) as f:\\n inputs = json.load(f) # Contains all workflow data + previous loop task outputs (FLATTENED)\\n\\nwith open(\u0027/tmp/loop_context_task_iter_N/loop_state.json\u0027, \u0027r\u0027) as f:\\n loop_state = json.load(f) # Contains current state variables\\n\\noutputs = {}\\nstate_updates = {}\\n\\n# Your script code follows...\\n```\\n\\n#### Debugging Loop Task Data Access\\n\\nWhen debugging loop tasks, always print available keys to understand what data is accessible:\\n\\n```yaml\\n- id: debug_loop_task\\n type: script\\n depends_on: [previous_loop_task]\\n script: |\\n # Debug: Print all available data\\n print(f\\\"\\ud83d\\udd0d Available inputs: {list(inputs.keys())}\\\")\\n print(f\\\"\\ud83d\\udd0d Loop state keys: {list(loop_state.keys())}\\\")\\n \\n # Debug: Print specific values\\n for key in [\u0027company_name\u0027, \u0027company_id\u0027, \u0027status\u0027, \u0027fy_folders\u0027]:\\n value = inputs.get(key, \u0027NOT_FOUND\u0027)\\n print(f\\\"\\ud83d\\udd0d {key}: {value}\\\")\\n \\n # Now access the flattened data\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n \\n # Process your data...\\n outputs = {\\\"processed\\\": f\\\"Company {company_name}\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Real Example: Task Output Flattening\\n\\n```yaml\\n# Task 1 outputs this structure:\\n__OUTPUTS__ {\\\"company_name\\\": \\\"LTTS\\\", \\\"company_id\\\": \\\"123\\\", \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], \\\"status\\\": \\\"success\\\"}\\n\\n# Task 2 receives flattened inputs:\\ninputs = {\\n \\\"folder_id\\\": \\\"...\\\", # Workflow input\\n \\\"gemini_api_key\\\": \\\"...\\\", # Workflow input \\n \\\"list_company_folders\\\": {...}, # External task output\\n \\\"company_index\\\": 0, # Loop iteration variable\\n \\\"company_name\\\": \\\"LTTS\\\", # \\u2705 Flattened from Task 1\\n \\\"company_id\\\": \\\"123\\\", # \\u2705 Flattened from Task 1\\n \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], # \\u2705 Flattened from Task 1\\n \\\"status\\\": \\\"success\\\", # \\u2705 Flattened from Task 1\\n \\\"total_extractions\\\": 0, # Loop state variable\\n \\\"processed_companies\\\": [] # Loop state variable\\n}\\n```\\n\\n#### Loop Task Special Output Fields\\n\\n| Field | Description | Example Access |\\n|-------|-------------|----------------|\\n| `final_state` | Final values of all state variables | `${loop_task.final_state}` |\\n| `final_state[\\\"variable_name\\\"]` | Specific state variable from final state | `${loop_task.final_state[\\\"total_processed\\\"]}` |\\n| `iterations_completed` | Number of iterations executed | `${loop_task.iterations_completed}` |\\n\\n#### Safe Template Resolution Pattern for Loop States\\n\\nWhen accessing loop final state fields, use this safe parsing pattern:\\n\\n```yaml\\nscript: |\\n try:\\n # Template resolution with safety checks\\n final_state_str = \u0027\u0027\u0027${loop_task.final_state}\u0027\u0027\u0027\\n processed_count_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_count\\\"]}\u0027\u0027\u0027\\n items_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_items\\\"]}\u0027\u0027\u0027\\n iterations_str = \u0027\u0027\u0027${loop_task.iterations_completed}\u0027\u0027\u0027\\n \\n # Safe parsing with fallbacks\\n final_state = json.loads(final_state_str) if not final_state_str.startswith(\u0027UNRESOLVED_\u0027) else {}\\n processed_count = int(processed_count_str) if processed_count_str.isdigit() else 0\\n processed_items = json.loads(items_str) if not items_str.startswith(\u0027UNRESOLVED_\u0027) else []\\n iterations = int(iterations_str) if iterations_str.isdigit() else 0\\n \\n print(f\\\"Successfully processed {processed_count} items in {iterations} iterations\\\")\\n \\n except Exception as e:\\n print(f\\\"Error accessing loop results: {str(e)}\\\")\\n # Handle error case\\n```\\n```\\n\\n### Real-World AI Agent Example\\n\\nBased on actual WhatsApp workflow patterns:\\n\\n```yaml\\n# AI greeting agent\\n- id: greeting_agent\\n type: ai_agent\\n config:\\n model_client_id: assistant_model\\n system_message: \\\"You are a helpful assistant. Generate a friendly greeting.\\\"\\n user_message: \\\"User said: ${user_message}\\\"\\n\\n# Send the AI response via API\\n- id: send_greeting\\n type: script\\n depends_on: [greeting_agent]\\n previous_node: greeting_agent\\n script: |\\n import requests\\n import json\\n \\n # CORRECT: Access AI response from structured output\\n agent_output = ${greeting_agent}\\n ai_greeting = agent_output[\u0027ai_response\u0027].strip()\\n \\n phone_number = \\\"${phone_number}\\\"\\n api_token = \\\"${API_TOKEN}\\\"\\n \\n print(f\\\"\\ud83e\\udd16 AI generated greeting: {ai_greeting}\\\")\\n \\n # Send via API\\n url = f\\\"https://api.example.com/messages/{phone_number}\\\"\\n headers = {\\\"Authorization\\\": f\\\"Bearer {api_token}\\\"}\\n \\n response = requests.post(url, headers=headers, json={\\n \\\"message\\\": ai_greeting\\n })\\n \\n result = {\\n \\\"status\\\": \\\"sent\\\",\\n \\\"ai_response\\\": ai_greeting,\\n \\\"api_response\\\": response.json(),\\n \\\"tokens_used\\\": agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### Complex Template Patterns\\n\\n```yaml\\n# Conditional templates with router outputs\\n- id: router_task\\n type: conditional_router\\n conditions:\\n - name: \\\"premium_customer\\\"\\n condition: \\\"${customer_data.tier} == \u0027premium\u0027\\\"\\n route: \\\"premium_processing\\\"\\n\\n- id: premium_handler\\n type: script\\n depends_on: [router_task]\\n previous_node: router_task\\n execute_on_routes: [\\\"premium_processing\\\"]\\n script: |\\n # Access router decision\\n selected_route = \\\"${router_task.selected_route}\\\"\\n route_data = ${router_task.route_data}\\n \\n # Process premium customer\\n customer = ${customer_data}\\n premium_benefits = calculate_benefits(customer)\\n\\n# Loop through arrays from previous tasks\\n- id: process_items\\n type: script\\n depends_on: [fetch_items]\\n previous_node: fetch_items\\n script: |\\n items = ${fetch_items.data}\\n processed_items = []\\n \\n for item in items:\\n processed_item = {\\n \\\"id\\\": item[\\\"id\\\"],\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\",\\n \\\"status\\\": \\\"processed\\\"\\n }\\n processed_items.append(processed_item)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027processed_items\u0027: processed_items})}\\\")\\n\\n# Nested object access\\n- id: user_preferences\\n type: script\\n depends_on: [fetch_user]\\n previous_node: fetch_user\\n script: |\\n # Access nested user preferences\\n email_notifications = ${fetch_user.preferences.notifications.email}\\n theme_setting = \\\"${fetch_user.preferences.ui.theme}\\\"\\n language = \\\"${fetch_user.profile.language}\\\"\\n \\n settings = {\\n \\\"notifications\\\": email_notifications,\\n \\\"theme\\\": theme_setting,\\n \\\"locale\\\": language\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(settings)}\\\")\\n```\\n\\n### Template Resolution Rules\\n\\n1. **Dependency Requirement**: Tasks must have `depends_on` relationship to access other task outputs\\n2. **String vs Object**: Use quotes for string values, no quotes for objects/arrays\\n3. **Field Navigation**: Use dot notation for nested field access\\n4. **Error Handling**: Unresolved templates become `UNRESOLVED_variable_name`\\n\\n```yaml\\n# \\u2705 Correct template usage\\n- id: correct_task\\n depends_on: [previous_task]\\n previous_node: previous_task\\n script: |\\n string_value = \\\"${previous_task.name}\\\" # String with quotes\\n object_value = ${previous_task.data} # Object without quotes\\n nested_value = \\\"${previous_task.user.email}\\\" # Nested field\\n\\n# \\u274c Incorrect template usage \\n- id: incorrect_task\\n # Missing depends_on - template won\u0027t resolve\\n script: |\\n value = ${previous_task.name} # Will become UNRESOLVED_previous_task.name\\n```\\n\\n## Script Task Output Handling\\n\\nScript tasks must use the `__OUTPUTS__` marker pattern for proper output parsing by the workflow engine.\\n\\n### Required Output Pattern\\n\\n```yaml\\ntasks:\\n - id: example_script\\n type: script\\n script: |\\n # Your script logic here\\n data = process_data()\\n \\n # Required: Store outputs in outputs dict\\n outputs = {}\\n outputs[\u0027result\u0027] = data\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n outputs[\u0027count\u0027] = len(data)\\n \\n # Required: Print with __OUTPUTS__ marker for engine parsing\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Output Access in Subsequent Tasks\\n\\n```yaml\\ntasks:\\n - id: data_processor\\n type: script\\n script: |\\n # Process and store outputs\\n results = {\\\"processed_count\\\": 100, \\\"errors\\\": []}\\n \\n outputs = {}\\n outputs[\u0027results\u0027] = results\\n outputs[\u0027status\u0027] = \u0027completed\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: result_handler\\n type: script\\n depends_on: [data_processor]\\n script: |\\n # Access previous task outputs\\n processor_output = ${data_processor}\\n processed_count = processor_output[\u0027results\u0027][\u0027processed_count\u0027]\\n status = processor_output[\u0027status\u0027]\\n \\n print(f\\\"Previous task processed {processed_count} items with status: {status}\\\")\\n \\n # Store new outputs\\n outputs = {\u0027message\u0027: f\u0027Handled {processed_count} results\u0027}\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Error Handling in Scripts\\n\\n```yaml\\ntasks:\\n - id: safe_script\\n type: script\\n script: |\\n try:\\n result = risky_operation()\\n outputs = {\u0027result\u0027: result, \u0027status\u0027: \u0027success\u0027}\\n except Exception as e:\\n outputs = {\u0027error\u0027: str(e), \u0027status\u0027: \u0027failed\u0027}\\n \\n # Always print outputs, even for errors\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n## Triggers and Workflow Activation\\n\\nTriggers define how workflows are started automatically based on external events. They enable event-driven workflow execution.\\n\\n### Webhook Triggers\\n\\nWebhook triggers allow external systems to start workflows by sending HTTP requests.\\n\\n```yaml\\ntriggers:\\n - name: customer_webhook\\n type: webhook\\n description: \\\"Triggered when customer data is updated\\\"\\n config:\\n path: \\\"/webhooks/customer-updated\\\" # Webhook endpoint path\\n method: POST # HTTP method (GET, POST, PUT, etc.)\\n authentication:\\n type: token # none|token|signature|basic\\n token: \\\"${WEBHOOK_SECRET}\\\" # Required if type is token\\n header_name: \\\"X-Webhook-Token\\\" # Optional: custom header name\\n validation:\\n required_fields: [\\\"customer.id\\\", \\\"customer.email\\\"] # Required payload fields\\n content_type: \\\"application/json\\\" # Expected content type\\n input_mapping:\\n - webhook_field: customer.id # Field from webhook payload\\n workflow_input: customer_id # Map to workflow input\\n - webhook_field: customer.email\\n workflow_input: customer_email\\n - webhook_field: customer.updated_at\\n workflow_input: update_timestamp\\n```\\n\\n### Scheduled Triggers\\n\\nSchedule workflows to run at specific times or intervals.\\n\\n```yaml\\ntriggers:\\n - name: daily_report\\n type: schedule\\n description: \\\"Generate daily customer report\\\"\\n config:\\n cron: \\\"0 9 * * *\\\" # Daily at 9 AM (cron format)\\n timezone: \\\"UTC\\\" # Optional: timezone\\n enabled: true # Optional: enable/disable\\n max_instances: 1 # Optional: max concurrent instances\\n input_mapping:\\n - workflow_input: report_date\\n default_value: \\\"${current_date}\\\" # Use built-in variable\\n - workflow_input: report_type\\n default_value: \\\"daily\\\"\\n\\n - name: weekly_cleanup\\n type: schedule\\n description: \\\"Weekly database cleanup\\\"\\n config:\\n cron: \\\"0 2 * * 0\\\" # Weekly on Sunday at 2 AM\\n timezone: \\\"America/New_York\\\"\\n input_mapping:\\n - workflow_input: cleanup_before_date\\n default_value: \\\"${date_minus_7_days}\\\"\\n```\\n\\n### Event Triggers\\n\\nEvent triggers respond to system events from various sources.\\n\\n```yaml\\ntriggers:\\n - name: file_uploaded\\n type: event\\n description: \\\"Process uploaded files\\\"\\n config:\\n event_type: \\\"file.uploaded\\\" # Event type to listen for\\n source: \\\"storage_service\\\" # Event source system\\n filter: # Optional: event filtering\\n file_type: [\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"]\\n file_size_max: \\\"100MB\\\"\\n path_pattern: \\\"/uploads/customers/*\\\"\\n retry: # Optional: retry configuration\\n max_attempts: 3\\n initial_delay: \\\"5s\\\"\\n input_mapping:\\n - webhook_field: file.path\\n workflow_input: file_path\\n - webhook_field: file.size\\n workflow_input: file_size\\n - webhook_field: file.type\\n workflow_input: file_type\\n\\n - name: order_status_changed\\n type: event\\n description: \\\"Handle order status changes\\\"\\n config:\\n event_type: \\\"order.status_changed\\\"\\n source: \\\"order_management\\\"\\n filter:\\n status: [\\\"completed\\\", \\\"cancelled\\\", \\\"refunded\\\"]\\n priority: [\\\"high\\\", \\\"urgent\\\"]\\n input_mapping:\\n - webhook_field: order.id\\n workflow_input: order_id\\n - webhook_field: order.status\\n workflow_input: new_status\\n - webhook_field: order.customer_id\\n workflow_input: customer_id\\n```\\n\\n### Dependency Triggers\\n\\nDependency triggers start workflows when other workflows complete.\\n\\n```yaml\\ntriggers:\\n - name: post_processing\\n type: dependency\\n description: \\\"Run after main processing completes\\\"\\n config:\\n depends_on_workflow: \\\"main_data_processing\\\" # Workflow to depend on\\n depends_on_status: \\\"SUCCESS\\\" # SUCCESS|FAILED|COMPLETED\\n namespace: \\\"production\\\" # Optional: workflow namespace\\n timeout_minutes: 60 # Optional: wait timeout\\n input_mapping:\\n - webhook_field: execution_id # From completed workflow\\n workflow_input: source_execution_id\\n - webhook_field: workflow_outputs.processed_count\\n workflow_input: items_processed\\n```\\n\\n## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n - id: unique_identifier # Used to reference this model in tasks\\n provider: provider_name # AI provider (openai, anthropic, etc.)\\n config:\\n model: model_name # Specific model to use\\n api_key: your_api_key # Authentication key\\n # Additional provider-specific options...\\n```\\n\\n### Supported Providers\\n\\n#### OpenAI Provider\\n\\n```yaml\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview # Available: gpt-4, gpt-4-turbo, gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-2.0\\n max_tokens: 2000 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### Anthropic Provider\\n\\n```yaml\\nmodel_clients:\\n - id: claude_model\\n provider: anthropic\\n config:\\n model: claude-3-sonnet-20240229 # Available: claude-3-opus, claude-3-sonnet, claude-3-haiku\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-1.0\\n max_tokens: 1500 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### OpenRouter Provider\\n\\n```yaml\\nmodel_clients:\\n - id: openrouter_model\\n provider: openrouter\\n config:\\n model: anthropic/claude-3-sonnet # Available: Various models via OpenRouter\\n api_key: \\\"sk-or-your-openrouter-api-key\\\"\\n base_url: \\\"https://openrouter.ai/api/v1\\\" # Optional: Custom endpoint\\n temperature: 0.7\\n max_tokens: 2000\\n```\\n\\n#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References the claude model above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References the creative GPT-4 model\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n - id: secure_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n temperature: 0.7\\n```\\n\\n### Common Configuration Options\\n\\n| Field | Description | Required | Default |\\n|-------|-------------|----------|---------|\\n| `id` | Unique identifier for referencing in tasks | Yes | - |\\n| `provider` | AI provider name | Yes | - |\\n| `config.model` | Specific model name | Yes | - |\\n| `config.api_key` | Authentication API key | Yes | - |\\n| `config.temperature` | Response randomness (0.0-2.0) | No | 0.7 |\\n| `config.max_tokens` | Maximum response tokens | No | Provider default |\\n| `config.timeout` | Request timeout in seconds | No | 30 |\\n| `config.base_url` | Custom API endpoint | No | Provider default |\\n\\n## Secrets Management and Environment Variables\\n\\nProper secrets management is crucial for security. Never hardcode sensitive information like API keys, passwords, or tokens directly in workflow YAML files.\\n\\n### Using Environment Variables\\n\\nThe recommended approach is to use environment variables for all sensitive data:\\n\\n```yaml\\n# \\u2705 RECOMMENDED: Use environment variables\\nmodel_clients:\\n - id: secure_openai\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n\\n# HTTP tasks with environment variables\\ntasks:\\n - id: secure_api_call\\n type: http\\n url: \\\"${API_BASE_URL}/customers\\\" # Environment variable for URL\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Environment variable for token\\n X-API-Key: \\\"${EXTERNAL_API_KEY}\\\" # Environment variable for API key\\n```\\n\\n### Environment Variable Patterns\\n\\n```yaml\\n# Database connections\\n- id: database_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${DATABASE_URL}\\\" # Full connection string\\n # or individual components:\\n host: \\\"${DB_HOST}\\\"\\n port: \\\"${DB_PORT}\\\"\\n database: \\\"${DB_NAME}\\\"\\n username: \\\"${DB_USER}\\\"\\n password: \\\"${DB_PASSWORD}\\\"\\n\\n# Third-party service credentials\\n- id: external_service\\n type: http\\n url: \\\"${SERVICE_BASE_URL}/api/v1/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${SERVICE_TOKEN}\\\"\\n X-Client-ID: \\\"${SERVICE_CLIENT_ID}\\\"\\n X-Client-Secret: \\\"${SERVICE_CLIENT_SECRET}\\\"\\n\\n# MCP server credentials\\n- id: mcp_task\\n type: mcp\\n mcp_server: \\\"@agentic/mcp-notion-server\\\"\\n environment_vars:\\n NOTION_TOKEN: \\\"${NOTION_API_TOKEN}\\\"\\n WORKSPACE_ID: \\\"${NOTION_WORKSPACE_ID}\\\"\\n```\\n\\n### Security Best Practices\\n\\n#### \\u274c What NOT to Do\\n\\n```yaml\\n# DON\u0027T: Hardcode secrets\\nmodel_clients:\\n - id: insecure_example\\n provider: openai\\n config:\\n api_key: \\\"sk-actual-api-key-here\\\" # SECURITY RISK!\\n\\n# DON\u0027T: Put secrets in comments\\ntasks:\\n - id: bad_example\\n type: http\\n url: \\\"https://api.example.com\\\"\\n # API key is: sk-12345... DON\u0027T DO THIS!\\n headers:\\n Authorization: \\\"Bearer sk-real-token\\\" # SECURITY RISK!\\n```\\n\\n#### \\u2705 What TO Do\\n\\n```yaml\\n# DO: Use environment variables\\nmodel_clients:\\n - id: secure_example\\n provider: openai\\n config:\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\\n\\n# DO: Use different variables for different environments\\n- id: environment_aware_task\\n type: http\\n url: \\\"${API_BASE_URL}\\\" # Different URLs per environment\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Different tokens per environment\\n X-Environment: \\\"${ENVIRONMENT_NAME}\\\" # Track environment\\n\\n# DO: Use descriptive variable names\\nenvironment_vars:\\n CUSTOMER_API_TOKEN: \\\"${CUSTOMER_SERVICE_TOKEN}\\\"\\n BILLING_API_KEY: \\\"${BILLING_SERVICE_API_KEY}\\\"\\n NOTIFICATION_WEBHOOK_SECRET: \\\"${NOTIFICATION_SECRET}\\\"\\n```\\n\\n### Environment Variable Naming Conventions\\n\\n| Pattern | Example | Use Case |\\n|---------|---------|----------|\\n| `SERVICE_API_KEY` | `OPENAI_API_KEY` | Service API keys |\\n| `SERVICE_BASE_URL` | `CUSTOMER_API_BASE_URL` | Service endpoints |\\n| `DB_*` | `DB_HOST`, `DB_PASSWORD` | Database credentials |\\n| `WEBHOOK_*` | `WEBHOOK_SECRET_TOKEN` | Webhook authentication |\\n| `*_TIMEOUT` | `HTTP_REQUEST_TIMEOUT` | Timeout configurations |\\n\\n### Dynamic Environment Variables\\n\\nUse template resolution to create dynamic environment variable names:\\n\\n```yaml\\n# Different API keys per customer\\n- id: customer_specific_api\\n type: http\\n url: \\\"https://api.partner.com/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${PARTNER_API_KEY_${customer_tier}}\\\" # Resolves to PARTNER_API_KEY_PREMIUM\\n\\n# Environment-specific configurations\\n- id: env_specific_task\\n type: storage\\n config:\\n connection_string: \\\"${DATABASE_URL_${environment}}\\\" # DATABASE_URL_PRODUCTION\\n```\\n\\n### Secrets in Different Task Types\\n\\n```yaml\\n# AI Agent with multiple secure credentials\\n- id: secure_ai_task\\n type: ai_agent\\n config:\\n model_client_id: secure_model\\n system_message: \\\"You are a secure assistant with access to customer data.\\\"\\n user_message: \\\"Process customer ${customer_id}\\\"\\n environment_vars:\\n CUSTOMER_DB_TOKEN: \\\"${CUSTOMER_DATABASE_TOKEN}\\\"\\n AUDIT_LOG_KEY: \\\"${AUDIT_LOGGING_API_KEY}\\\"\\n\\n# Script task with secure file access\\n- id: secure_script_task\\n type: script\\n environment_vars:\\n AWS_ACCESS_KEY_ID: \\\"${AWS_ACCESS_KEY}\\\"\\n AWS_SECRET_ACCESS_KEY: \\\"${AWS_SECRET_KEY}\\\"\\n S3_BUCKET_NAME: \\\"${SECURE_BUCKET_NAME}\\\"\\n script: |\\n import boto3\\n import os\\n \\n # Credentials automatically available from environment\\n s3_client = boto3.client(\u0027s3\u0027)\\n bucket = os.environ[\u0027S3_BUCKET_NAME\u0027]\\n \\n # Process secure files...\\n\\n# Storage task with encrypted connections\\n- id: secure_storage_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${ENCRYPTED_DB_CONNECTION_STRING}\\\"\\n ssl_mode: \\\"require\\\"\\n ssl_cert: \\\"${SSL_CERT_PATH}\\\"\\n ssl_key: \\\"${SSL_KEY_PATH}\\\"\\n```\\n\\n### Environment Setup Examples\\n\\n#### Development Environment (.env file)\\n```bash\\n# Development API keys (non-production)\\nOPENAI_API_KEY=sk-dev-api-key-here\\nCUSTOMER_API_BASE_URL=https://api-dev.example.com\\nDATABASE_URL=postgresql://dev:pass@localhost:5432/workflow_dev\\nENVIRONMENT_NAME=development\\n```\\n\\n#### Production Environment\\n```bash\\n# Production API keys (secure)\\nOPENAI_API_KEY=sk-prod-api-key-here\\nCUSTOMER_API_BASE_URL=https://api.example.com\\nDATABASE_URL=postgresql://prod_user:secure_pass@prod-db:5432/workflow_prod\\nENVIRONMENT_NAME=production\\n```\\n\\n## Workflow Execution and Orchestration\\n\\nUnderstanding how workflows execute and how tasks are orchestrated is essential for building reliable, efficient workflows.\\n\\n### Execution Flow\\n\\nWorkflows follow a directed acyclic graph (DAG) execution model:\\n\\n1. **Initialization**: Workflow starts with inputs and model clients\\n2. **Task Scheduling**: Tasks are scheduled based on dependencies\\n3. **Parallel Execution**: Independent tasks run simultaneously\\n4. **Data Flow**: Outputs flow between tasks via template resolution\\n5. **Completion**: Workflow completes when all tasks finish\\n\\n```yaml\\n# Example execution flow\\ntasks:\\n # Stage 1: Independent tasks (run in parallel)\\n - id: fetch_customer # Runs immediately\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: fetch_products # Runs in parallel with fetch_customer\\n type: http\\n url: \\\"https://api.example.com/products\\\"\\n \\n # Stage 2: Dependent tasks (wait for Stage 1)\\n - id: validate_customer # Waits for fetch_customer\\n type: script\\n depends_on: [fetch_customer]\\n previous_node: fetch_customer\\n script: # validation logic...\\n \\n - id: filter_products # Waits for fetch_products\\n type: script\\n depends_on: [fetch_products]\\n previous_node: fetch_products\\n script: # filtering logic...\\n \\n # Stage 3: Final processing (waits for Stage 2)\\n - id: generate_recommendations # Waits for both validation and filtering\\n type: ai_agent\\n depends_on: [validate_customer, filter_products]\\n previous_node: validate_customer # UI hierarchy under customer validation\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate product recommendations for customer.\\\"\\n user_message: |\\n Customer: ${validate_customer}\\n Available products: ${filter_products}\\n```\\n\\n### Task States and Lifecycle\\n\\nEach task goes through these states during execution:\\n\\n| State | Description | Next States |\\n|-------|-------------|-------------|\\n| `PENDING` | Task created, waiting for dependencies | `RUNNING`, `SKIPPED` |\\n| `RUNNING` | Task is actively executing | `COMPLETED`, `FAILED` |\\n| `COMPLETED` | Task finished successfully | - |\\n| `FAILED` | Task encountered an error | `RUNNING` (if retries) |\\n| `SKIPPED` | Task skipped due to conditions | - |\\n| `TIMEOUT` | Task exceeded timeout limit | `RUNNING` (if retries) |\\n\\n### Conditional Execution\\n\\nTasks can be executed conditionally based on routes or conditions:\\n\\n```yaml\\n# Router-based conditional execution\\n- id: data_router\\n type: conditional_router\\n depends_on: [fetch_data]\\n previous_node: fetch_data\\n conditions:\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n default_route: \\\"standard_processing\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"batch_processing\\\"] # Only runs for large datasets\\n script: |\\n # Handle large dataset in batches\\n data = ${fetch_data}\\n process_in_batches(data, batch_size=1000)\\n\\n- id: direct_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"direct_processing\\\"] # Only runs for small datasets\\n script: |\\n # Process small dataset directly\\n data = ${fetch_data}\\n process_directly(data)\\n```\\n\\n### Error Handling and Retries\\n\\nConfigure retry policies for resilient workflow execution:\\n\\n```yaml\\n# Workflow-level retry policy\\nretry:\\n retryOn: [\\\"TEMPORARY_FAILURE\\\", \\\"TIMEOUT\\\", \\\"NETWORK_ERROR\\\"]\\n maxAttempts: 3\\n initialDelay: \\\"1s\\\"\\n maxDelay: \\\"60s\\\"\\n backoffMultiplier: 2.0\\n\\ntasks:\\n # Task-specific retry policy (overrides workflow-level)\\n - id: external_api_call\\n type: http\\n url: \\\"https://unreliable-api.example.com/data\\\"\\n retry:\\n maxAttempts: 5 # More retries for unreliable API\\n initialDelay: \\\"2s\\\"\\n maxDelay: \\\"120s\\\"\\n backoffMultiplier: 2.0\\n retryOn: [\\\"TIMEOUT\\\", \\\"HTTP_5XX\\\", \\\"NETWORK_ERROR\\\"]\\n \\n # Task with no retries\\n - id: critical_operation\\n type: script\\n retry:\\n maxAttempts: 1 # No retries for critical operations\\n script: |\\n # This operation should not be retried if it fails\\n perform_critical_database_operation()\\n```\\n\\n### Parallel Execution Patterns\\n\\n```yaml\\n# Fan-out pattern: One task triggers multiple parallel tasks\\n- id: data_source\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n\\n- id: process_customers # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process customer data from data_source\\n\\n- id: process_orders # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process order data from data_source\\n\\n- id: process_products # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process product data from data_source\\n\\n# Fan-in pattern: Multiple tasks feed into one task\\n- id: generate_report # Waits for all parallel tasks\\n type: ai_agent\\n depends_on: [process_customers, process_orders, process_products]\\n previous_node: process_customers # UI hierarchy\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate comprehensive report from processed data.\\\"\\n user_message: |\\n Customer data: ${process_customers}\\n Order data: ${process_orders}\\n Product data: ${process_products}\\n```\\n\\n### Resource Management\\n\\nControl resource usage and task distribution:\\n\\n```yaml\\n# Worker group targeting\\n- id: cpu_intensive_task\\n type: script\\n worker_group: \\\"high-cpu-workers\\\" # Target specific worker group\\n script: |\\n # CPU-intensive processing\\n perform_heavy_computation()\\n\\n- id: gpu_ml_task\\n type: ai_agent\\n worker_group: \\\"gpu-workers\\\" # Target GPU workers\\n config:\\n model_client_id: large_model\\n # Large model processing...\\n\\n# Timeout configuration\\n- id: long_running_task\\n type: script\\n timeout_seconds: 3600 # 1 hour timeout\\n script: |\\n # Long-running data processing\\n process_large_dataset()\\n\\n# Workflow-level timeout\\ntimeout_seconds: 7200 # 2 hour workflow timeout\\n```\\n\\n## Task Type Configurations\\n\\nThis section provides the complete YAML configuration for each supported task type.\\n\\n**Supported Task Types:**\\n- `script` - Execute Python code with full workflow context\\n- `http` - Make HTTP requests to external APIs\\n- `ai_agent` - Integrate with AI models for intelligent processing \\n- `loop` - Execute tasks repeatedly (for/while loops)\\n- `conditional_router` - Route execution based on conditions\\n- `mcp` - Execute tools via Model Context Protocol servers\\n- `storage` - Data persistence and retrieval operations\\n- `continuous_conversation` - Interactive AI conversations\\n- `human_input` - Tasks requiring human input or approval\\n- `chatbot` - Session-based chatbot conversations\\n\\n### Script Tasks\\n\\nExecute Python code with full workflow context access.\\n\\n```yaml\\n- id: script_task\\n name: \\\"Python Script Task\\\"\\n type: script\\n description: \\\"Execute Python code with dependencies and environment\\\"\\n \\n # Script configuration - script field is directly under task, NOT under config\\n script: | # REQUIRED: Python code to execute\\n import json\\n import requests\\n \\n # Access workflow inputs\\n input_value = \\\"${workflow_input}\\\"\\n \\n # Access previous task outputs\\n previous_result = ${previous_task}\\n \\n # Your processing logic\\n result = {\\\"processed\\\": True, \\\"value\\\": input_value}\\n \\n # Output results (must be JSON serializable)\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n # Alternative: use \u0027code\u0027 field instead of \u0027script\u0027\\n code: | # Alternative to \u0027script\u0027 field\\n print(\\\"Hello World\\\")\\n \\n # Package management\\n requirements: # Optional: Python packages\\n - requests==2.31.0\\n - pandas==1.5.3\\n - numpy==1.24.3\\n \\n # Environment configuration\\n environment: # Optional: Environment variables\\n API_KEY: \\\"${secrets.api_key}\\\"\\n DEBUG: \\\"true\\\"\\n CUSTOM_VAR: \\\"value\\\"\\n \\n environment_vars: # Alternative to \u0027environment\u0027\\n DATABASE_URL: \\\"${secrets.db_url}\\\"\\n \\n # Secrets configuration\\n secrets_config: # Optional: Secrets access\\n api_key: \\\"secret_name\\\"\\n db_password: \\\"db_secret\\\"\\n```\\n\\n### HTTP Tasks\\n\\nMake HTTP requests to external APIs and services.\\n\\n```yaml\\n- id: http_task\\n name: \\\"HTTP API Call\\\"\\n type: http\\n description: \\\"Call external REST API\\\"\\n \\n # HTTP configuration\\n method: POST # Default: GET\\n url: \\\"https://api.example.com/data\\\" # REQUIRED: Target URL\\n \\n # Headers configuration\\n headers: # Optional: HTTP headers\\n Content-Type: \\\"application/json\\\"\\n Authorization: \\\"Bearer ${secrets.api_token}\\\"\\n X-Custom-Header: \\\"workflow-engine\\\"\\n User-Agent: \\\"WorkflowEngine/1.0\\\"\\n \\n # Request body\\n body: | # Optional: Request body (for POST/PUT)\\n {\\n \\\"customer_id\\\": \\\"${customer_id}\\\",\\n \\\"timestamp\\\": \\\"${current_timestamp}\\\",\\n \\\"data\\\": ${previous_task}\\n }\\n \\n # Response validation\\n expected_status: [200, 201, 202] # Default: [200]\\n expectedStatus: [200, 201] # Alternative field name\\n \\n # Additional HTTP options\\n timeout_seconds: 30 # HTTP request timeout\\n follow_redirects: true # Follow HTTP redirects\\n verify_ssl: true # Verify SSL certificates\\n```\\n\\n### AI Agent Tasks\\n\\nIntegrate with AI models for intelligent processing.\\n\\n```yaml\\n- id: ai_agent_task\\n name: \\\"AI Processing Agent\\\"\\n type: ai_agent\\n description: \\\"Process data using AI model\\\"\\n \\n # AI configuration\\n config:\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # Optional: System prompt\\n You are a data analyst. Analyze the provided data and extract insights.\\n Return your analysis in JSON format with the following structure:\\n {\\n \\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n \\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n \\\"confidence_score\\\": 0.95\\n }\\n \\n user_message: | # REQUIRED: User prompt with template resolution\\n Please analyze this customer data:\\n ${previous_task}\\n \\n Customer ID: ${customer_id}\\n Date Range: ${date_range}\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1500 # Optional: Maximum response tokens\\n top_p: 1.0 # Optional: Nucleus sampling\\n frequency_penalty: 0.0 # Optional: Frequency penalty\\n presence_penalty: 0.0 # Optional: Presence penalty\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_api # Weather tools\\n - database_query # Database tools\\n - notification_service # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # Input/output format\\n input_format: \\\"text\\\" # Optional: text|json|structured\\n output_format: \\\"structured\\\" # Optional: text|json|structured\\n\\n### Important: AI Agent Output Access\\n\\nAI agent tasks return structured objects, not direct text responses. Always access the AI response using this pattern:\\n\\n```yaml\\n# In subsequent script tasks\\nscript: |\\n # CORRECT: Access AI response from structured output\\n agent_output = ${ai_agent_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n tokens_used = agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n cost_estimate = agent_output.get(\u0027estimated_cost\u0027, {}).get(\u0027amount\u0027, 0.0)\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n print(f\\\"AI Response: {ai_response}\\\")\\n print(f\\\"Tokens used: {tokens_used}\\\")\\n print(f\\\"Estimated cost: ${cost_estimate:.6f}\\\")\\n```\\n\\n```yaml\\n# In workflow outputs - specify the ai_response field\\noutputs:\\n ai_analysis:\\n type: string\\n source: ai_agent_task.ai_response # Access the ai_response field\\n description: \\\"AI-generated analysis text\\\"\\n \\n total_tokens:\\n type: integer \\n source: ai_agent_task.token_usage.total_tokens\\n description: \\\"Total tokens consumed\\\"\\n```\\n```\\n\\n### Loop Tasks\\n\\nExecute tasks repeatedly with for-loop and while-loop patterns.\\n\\n```yaml\\n# For-Loop (Fixed Iterations)\\n- id: for_loop_task\\n name: \\\"For Loop Processor\\\"\\n type: loop\\n description: \\\"Process data in fixed batches\\\"\\n \\n # Loop configuration\\n loop_type: for # REQUIRED: for|while\\n max_iterations: 10 # REQUIRED for \u0027for\u0027 loops\\n iteration_variable: batch_index # Default: iteration\\n \\n # State variables (persistent across iterations)\\n state_variables:\\n total_processed: 0\\n error_count: 0\\n batch_results: []\\n \\n # Tasks to execute in each iteration\\n loop_tasks: # REQUIRED: Tasks to run in loop\\n - id: process_batch\\n name: \\\"Process Current Batch\\\"\\n type: script\\n script: |\\n batch_idx = ${batch_index} # Access iteration variable\\n total = ${total_processed} # Access state variable\\n \\n # Process current batch\\n result = process_batch_data(batch_idx)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n - id: update_state\\n name: \\\"Update Loop State\\\"\\n type: script\\n depends_on: [process_batch]\\n script: |\\n # Update state variables\\n new_total = ${total_processed} + ${process_batch.items_processed}\\n \\n result = {\\\"total_processed\\\": new_total}\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n\\n# While-Loop (Conditional)\\n- id: while_loop_task\\n name: \\\"While Loop Quality Processor\\\"\\n type: loop\\n description: \\\"Process until quality threshold met\\\"\\n \\n # Loop configuration\\n loop_type: while # REQUIRED: for|while\\n exit_condition: \\\"${average_quality} \u003e= 85 || ${iteration_count} \u003e= 20\\\" # REQUIRED for \u0027while\u0027\\n max_iterations: 50 # Optional: Safety limit\\n iteration_variable: iteration_count # Default: iteration\\n \\n # State variables\\n state_variables:\\n average_quality: 0\\n items_processed: 0\\n quality_scores: []\\n \\n # Loop tasks\\n loop_tasks:\\n - id: assess_quality\\n type: script\\n script: |\\n current_quality = calculate_quality()\\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027quality\u0027: current_quality})}\\\")\\n```\\n\\n\\n### Conditional Router Tasks\\n\\nRoute execution to different paths based on conditions.\\n\\n```yaml\\n- id: conditional_router_task\\n name: \\\"Data Type Router\\\"\\n type: conditional_router\\n description: \\\"Route processing based on data characteristics\\\"\\n \\n # Router configuration\\n router_type: condition # Default: condition\\n \\n # Routing conditions\\n conditions: # REQUIRED: List of conditions\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${data_size} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\" \\n condition: \\\"${data_size} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n - name: \\\"medium_dataset\\\"\\n condition: \\\"${data_size} \u003e 1000 \u0026\u0026 ${data_size} \u003c= 10000\\\"\\n route: \\\"standard_processing\\\"\\n \\n default_route: \\\"standard_processing\\\" # REQUIRED: Default route if no conditions match\\n \\n # Alternative: evaluation script for complex routing\\n evaluation_script: | # Optional: Custom routing logic\\n if data_type == \\\"json\\\":\\n return \\\"json_processing\\\"\\n elif data_type == \\\"xml\\\":\\n return \\\"xml_processing\\\"\\n else:\\n return \\\"default_processing\\\"\\n \\n # Route mapping for complex scenarios\\n route_mapping: # Optional: Route name mappings\\n json_processing: \\\"specialized_json\\\"\\n xml_processing: \\\"specialized_xml\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_handler\\n name: \\\"Handle Large Dataset\\\"\\n type: script\\n execute_on_routes: [\\\"batch_processing\\\"] # REQUIRED: Routes this task runs on\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing large dataset in batches...\\\")\\n\\n- id: direct_handler\\n name: \\\"Handle Small Dataset\\\" \\n type: script\\n execute_on_routes: [\\\"direct_processing\\\"]\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing small dataset directly...\\\")\\n```\\n\\n### MCP Tasks\\n\\nExecute tools via Model Context Protocol servers.\\n\\n```yaml\\n- id: mcp_task\\n name: \\\"MCP Tool Execution\\\"\\n type: mcp\\n description: \\\"Execute external tool via MCP server\\\"\\n \\n # MCP server configuration\\n mcp_server: \\\"@agentic/mcp-notion-server\\\" # REQUIRED: MCP server package\\n tool_name: \\\"update_database\\\" # REQUIRED: Tool to execute\\n runtime: \\\"npx\\\" # REQUIRED: npx|uvx\\n \\n # Server startup\\n server_args: [] # Optional: Server startup arguments\\n server_startup_timeout: 30 # Optional: Server startup timeout (seconds)\\n \\n # Tool execution\\n tool_arguments: # REQUIRED: Arguments for the tool\\n database_id: \\\"${secrets.notion_database_id}\\\"\\n properties:\\n title: \\\"Workflow Results\\\"\\n status: \\\"Completed\\\"\\n processed_count: \\\"${previous_task.count}\\\"\\n timestamp: \\\"${current_timestamp}\\\"\\n \\n # Environment configuration\\n environment_vars: # Optional: Environment variables\\n NOTION_TOKEN: \\\"${secrets.notion_token}\\\"\\n DEBUG: \\\"true\\\"\\n \\n # Timeout configuration\\n timeout_seconds: 120 # Optional: Tool execution timeout\\n```\\n\\n### Storage Tasks\\n\\nManage data persistence and retrieval operations.\\n\\n```yaml\\n- id: storage_task\\n name: \\\"Data Storage Operation\\\"\\n type: storage\\n description: \\\"Save/load data to/from storage\\\"\\n \\n # Storage operation\\n operation: save # REQUIRED: save|load|delete|list\\n \\n # Storage configuration\\n storage_type: \\\"file\\\" # REQUIRED: file|database|cache\\n path: \\\"/data/results/${execution_id}.json\\\" # REQUIRED: Storage path\\n \\n # Data configuration\\n data: ${previous_task} # REQUIRED for \u0027save\u0027: Data to store\\n format: \\\"json\\\" # Optional: json|yaml|csv|parquet\\n compression: \\\"gzip\\\" # Optional: gzip|bz2|none\\n \\n # Metadata\\n metadata: # Optional: Additional metadata\\n workflow_id: \\\"${workflow_id}\\\"\\n created_at: \\\"${current_timestamp}\\\"\\n source_task: \\\"previous_task\\\"\\n \\n # File options\\n create_directories: true # Optional: Create parent directories\\n overwrite: true # Optional: Overwrite existing files\\n backup: false # Optional: Create backup before overwrite\\n \\n # Database options (when storage_type: \\\"database\\\")\\n table_name: \\\"workflow_results\\\" # Required for database storage\\n connection_string: \\\"${secrets.db_url}\\\" # Required for database storage\\n \\n # Cache options (when storage_type: \\\"cache\\\")\\n cache_key: \\\"result_${execution_id}\\\" # Required for cache storage\\n ttl: 3600 # Optional: Time to live (seconds)\\n```\\n\\n\\n### Continuous Conversation Tasks\\n\\nCreate interactive AI-powered conversations.\\n\\n```yaml\\n- id: conversation_task\\n name: \\\"AI Conversation Interface\\\"\\n type: continuous_conversation\\n description: \\\"Interactive conversation with AI assistant\\\"\\n \\n # Conversation configuration\\n config:\\n # AI configuration\\n ai_config:\\n model_client_id: support_assistant # REQUIRED: AI model to use\\n system_prompt: | # REQUIRED: System instructions\\n You are a helpful customer support representative.\\n Be friendly, professional, and solution-oriented.\\n \\n Guidelines:\\n - Always be polite and respectful\\n - Ask clarifying questions when needed\\n - Provide clear and actionable solutions\\n \\n temperature: 0.7 # Optional: AI creativity\\n max_tokens: 1000 # Optional: Response length limit\\n \\n # UI configuration\\n ui_config:\\n title: \\\"Customer Support Chat\\\" # Optional: Interface title\\n placeholder: \\\"How can I help you today?\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_timestamp: true # Optional: Show message timestamps\\n \\n # Termination conditions\\n termination:\\n keywords: [\\\"goodbye\\\", \\\"bye\\\", \\\"thanks\\\", \\\"done\\\"] # Optional: End conversation keywords\\n max_turns: 50 # Optional: Maximum conversation turns\\n timeout_minutes: 30 # Optional: Conversation timeout\\n inactivity_timeout: 10 # Optional: Inactivity timeout (minutes)\\n \\n # Context configuration\\n context:\\n system_context: \\\"Customer support conversation\\\" # Optional: Context description\\n user_id: \\\"${customer_id}\\\" # Optional: User identifier\\n session_data: ${previous_task} # Optional: Additional context data\\n```\\n\\n### Human Input Tasks\\n\\nTasks requiring human input or approval.\\n\\n```yaml\\n- id: human_input_task\\n name: \\\"Human Input Required\\\"\\n type: human_input\\n description: \\\"Wait for human input or approval\\\"\\n \\n # Human input configuration\\n config:\\n prompt: \\\"Please review and approve the processed data\\\" # REQUIRED: Prompt for human\\n input_type: \\\"approval\\\" # REQUIRED: approval|text|selection\\n \\n # Input options (for selection type)\\n options: [\\\"approve\\\", \\\"reject\\\", \\\"request_changes\\\"] # Optional: For selection input\\n \\n # Timeout configuration\\n timeout_minutes: 60 # Optional: How long to wait for input (minutes)\\n \\n # Conversation settings\\n conversation_mode: false # Optional: Add to conversation history\\n next_task: \\\"process_approval\\\" # Optional: Task to trigger after input\\n end_condition: \\\"bye|exit|quit\\\" # Optional: Keywords to end conversation\\n \\n # Input validation\\n required: true # Optional: Whether input is required\\n \\n # Additional context\\n context_data: ${previous_task} # Optional: Data to show to human\\n```\\n\\n### ChatBot Tasks\\n\\nSession-based chatbot conversations with streaming support.\\n\\n```yaml\\n- id: chatbot_task\\n name: \\\"AI Chatbot\\\"\\n type: chatbot\\n description: \\\"Interactive chatbot with session management\\\"\\n \\n # Chatbot configuration\\n config:\\n # AI model configuration\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # REQUIRED: System prompt for chatbot\\n You are a helpful AI assistant. Be friendly, helpful, and concise.\\n Answer questions clearly and ask for clarification when needed.\\n \\n # Session management\\n session_id: \\\"${session_id}\\\" # Optional: Session identifier (auto-generated if not provided)\\n max_history_messages: 20 # Optional: Maximum conversation history to maintain\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1000 # Optional: Maximum response tokens\\n stream: true # Optional: Enable streaming responses\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - send_notification # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # UI configuration\\n ui_config:\\n title: \\\"AI Assistant\\\" # Optional: Chat interface title\\n placeholder: \\\"Type your message...\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_typing_indicator: true # Optional: Show typing animation\\n \\n # Response configuration\\n response_config:\\n max_retries: 3 # Optional: Retry failed requests\\n timeout_seconds: 30 # Optional: Response timeout\\n \\n # Input message (for single-turn mode)\\n user_message: \\\"${user_input}\\\" # Optional: Direct message input\\n \\n # Context data\\n context_data: ${previous_task} # Optional: Additional context\\n```\\n\\n---\\n\\n## Common Errors and Corrections\\n\\nThis section documents frequent mistakes encountered when creating workflows and their proper corrections.\\n\\n### 1. Missing Required `id` Field\\n\\n**\\u274c Error:**\\n```yaml\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow without id\\\"\\ntasks:\\n - name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nid: \\\"my_workflow\\\" # REQUIRED: Unique workflow identifier\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow with proper id\\\"\\ntasks:\\n - id: task_1 # REQUIRED: Unique task identifier\\n name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n### 2. Incorrect Task Structure (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n task_1: # Wrong: tasks as object mapping\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n task_2:\\n type: ai_agent\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n - id: task_1 # Correct: tasks as array with id field\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n - id: task_2\\n type: ai_agent\\n```\\n\\n### 3. Incorrect Inputs Format (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ninputs:\\n user_name: # Wrong: inputs as object mapping\\n type: string\\n required: true\\n age:\\n type: integer\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ninputs:\\n - name: user_name # Correct: inputs as array with name field\\n type: string\\n required: true\\n - name: age\\n type: integer\\n```\\n\\n### 4. Script Configuration Location Error\\n\\n**\\u274c Error:**\\n```yaml\\n- id: script_task\\n type: script\\n config: # Wrong: script under config\\n script: |\\n print(\\\"Hello\\\")\\n requirements:\\n - requests\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: script_task\\n type: script\\n script: | # Correct: script directly under task\\n print(\\\"Hello\\\")\\n # requirements can be at task level if needed\\n```\\n\\n### 5. Individual Task Inputs (Not Supported)\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_1\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n inputs: # Wrong: tasks don\u0027t have individual inputs\\n search_query: ${search_query}\\n prev_result: ${previous_task}\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_1\\n type: script\\n depends_on: [previous_task] # Use depends_on for data access\\n script: |\\n # Access workflow inputs directly\\n search_query = \\\"${search_query}\\\"\\n # Access previous task outputs via template resolution\\n prev_result = ${previous_task}\\n```\\n\\n### 6. Incorrect Script Output Handling\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n result = process_data()\\n outputs[\u0027result\u0027] = result # Wrong: missing __OUTPUTS__ pattern\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n result = process_data()\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker\\n outputs = {}\\n outputs[\u0027result\u0027] = result\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### 7. AI Agent Configuration Errors\\n\\n**\\u274c Error:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n prompt: \\\"Process this data: ${data}\\\" # Wrong: use user_message\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n system_message: \\\"You are a helpful assistant.\\\"\\n user_message: \\\"Process this data: ${data}\\\" # Correct field name\\n```\\n\\n### 8. Missing Dependencies for Template Resolution\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_2\\n type: script\\n # Missing depends_on\\n script: |\\n data = ${task_1} # Will become UNRESOLVED_task_1\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_2\\n type: script\\n depends_on: [task_1] # Required for template access\\n script: |\\n data = ${task_1} # Now resolves correctly\\n```\\n\\n### 9. Incorrect Model Client Configuration\\n\\n**\\u274c Error:**\\n```yaml\\nmodel_clients:\\n my_model: # Wrong: object mapping format\\n type: openai\\n api_key: \\\"sk-...\\\"\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nmodel_clients:\\n - id: my_model # Correct: array format with id field\\n provider: openai\\n config:\\n model: \\\"gpt-4\\\"\\n api_key: \\\"sk-...\\\"\\n```\\n\\n### 10. Empty String Literals Causing Python Syntax Errors\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n def process_data(path=\\\"\\\"): # Wrong: empty string can cause syntax issues\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n \\n # This can lead to: SyntaxError: unterminated string literal\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n def process_data(path=None): # Correct: use None instead of empty string\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n # Or use path = path or \\\"default_value\\\"\\n```\\n\\n### 11. YAML Indentation and Structure Issues\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\nscript: | # Wrong: inconsistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\n script: | # Correct: consistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n### Best Practices Summary\\n\\n1. **Always include required `id` fields** for workflows and tasks\\n2. **Use array format** for `tasks`, `inputs`, and `model_clients`\\n3. **Script field goes directly under task**, not under `config`\\n4. **No individual task inputs** - use template resolution instead\\n5. **Use `depends_on`** to access previous task outputs\\n6. **Print `__OUTPUTS__`** in scripts for proper output parsing\\n7. **Use `user_message`** (not `prompt`) for AI agent tasks\\n8. **Avoid empty string literals** - use `None` or meaningful defaults\\n9. **Follow consistent YAML indentation** throughout the file\\n\\n## MCP Tools and Function Calling\\n\\nAI agents and chatbots can use MCP (Model Context Protocol) tools for function calling, enabling them to execute external tools and services.\\n\\n### Tools Configuration\\n\\nBoth `ai_agent` and `chatbot` tasks support tools configuration:\\n\\n```yaml\\n# AI Agent with MCP tools\\n- id: ai_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools.\\n Use the available tools when needed to help users.\\n user_message: \\\"What time is it and what\u0027s the weather like?\\\"\\n \\n # Tools configuration\\n tools: # List of MCP tool names\\n - current_time # Time tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - database_query # Database tools\\n - send_notification # Communication tools\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n\\n# Chatbot with MCP tools\\n- id: chatbot_with_tools\\n type: chatbot\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful chatbot with access to tools.\\n Use tools when users ask for specific information or actions.\\n \\n # Tools configuration (same format as AI agent)\\n tools:\\n - current_time\\n - weather_api\\n - user_preferences\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n```\\n\\n### How Tools Work\\n\\n1. **Tool Resolution**: The workflow engine resolves tool names to actual MCP server deployments using the `tenant_id`\\n2. **Function Calling**: When the AI model decides to use a tool, it calls the `tool_executor` function\\n3. **Tool Execution**: The engine executes the specified tool via the appropriate MCP server\\n4. **Response Integration**: Tool results are fed back to the AI model for response generation\\n\\n### Tool Configuration Requirements\\n\\n| Field | Required | Description |\\n|-------|----------|-------------|\\n| `tools` | No | List of MCP tool names available to the AI |\\n| `tenant_id` | **Yes** (when using tools) | Tenant UUID for tool resolution |\\n\\n### Example: Complete AI Agent with Tools\\n\\n```yaml\\nid: intelligent_assistant\\nname: \\\"Intelligent Assistant with Tools\\\"\\n\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4o-mini\\n api_key: \\\"${OPENAI_API_KEY}\\\"\\n temperature: 0.3\\n\\n# Tenant ID can be set at workflow level for all tasks\\ntenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n\\ntasks:\\n - id: assistant_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools:\\n - Time-related tools: current_time, relative_time, days_in_month\\n - Weather tools: weather_forecast\\n - Communication: send_notification\\n \\n Use these tools when users ask for:\\n - Current time or date information\\n - Weather forecasts\\n - Sending notifications\\n \\n Always explain what tool you\u0027re using and why.\\n \\n user_message: \\\"${user_query}\\\"\\n \\n # MCP tools configuration\\n tools:\\n - current_time\\n - relative_time\\n - days_in_month\\n - weather_forecast\\n - send_notification\\n \\n # Tool resolution requires tenant_id\\n tenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n \\n temperature: 0.7\\n max_tokens: 1000\\n\\ninputs:\\n - name: user_query\\n type: string\\n required: true\\n default: \\\"What time is it and can you send a notification to user123?\\\"\\n\\noutputs:\\n ai_response:\\n type: string\\n source: assistant_with_tools.ai_response\\n description: \\\"AI response using tools when needed\\\"\\n```\\n\\n### Tool Execution Flow\\n\\n1. **User Input**: User provides a query that might require tool usage\\n2. **AI Analysis**: AI model analyzes the query and decides if tools are needed\\n3. **Tool Calling**: AI calls `tool_executor(deployment_id, tool_name, arguments)`\\n4. **Tool Resolution**: Engine resolves tool name to MCP server using tenant_id\\n5. **Tool Execution**: MCP server executes the requested tool\\n6. **Result Processing**: Tool results are returned to the AI model\\n7. **Response Generation**: AI generates final response incorporating tool results\\n\\n### Best Practices for Tools\\n\\n1. **Clear System Messages**: Explain available tools and when to use them\\n2. **Tool Categories**: Group related tools (time, weather, database, etc.)\\n3. **Error Handling**: AI models handle tool execution failures gracefully\\n4. **Tool Documentation**: Include tool descriptions in system messages\\n5. **Tenant Configuration**: Ensure tenant_id matches your MCP server setup\\n\\n---\\n\\n*This comprehensive reference covers all supported task types and their YAML configurations. Use this as a quick reference when building workflows with different task types.*\", \"format\": \"markdown\"}, \"status\": \"success\", \"successful_url\": \"http://172.17.0.1:5000/api/docs/yaml-reference\"}\n",
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"ai_response": "Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\n\n## Defining Model Clients\n\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\n\n### Basic Structure\n\nHere\u0027s the fundamental structure for defining model clients:\n\n```yaml\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\n config: # REQUIRED: Provider-specific config\n model: gpt-4 # REQUIRED: Model name\n api_key: your_api_key # REQUIRED: API key for the provider\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\n max_tokens: 1000 # Optional: Maximum tokens to generate\n timeout: 30 # Optional: Request timeout in seconds\n```\n\n### Complete Example with Multiple Model Clients\n\nYou can define multiple model clients for different use cases:\n\n```yaml\nmodel_clients:\n # Fast, cost-effective model for simple tasks\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n # High-quality model for complex analysis\n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n # Creative model for content generation\n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n## Using Model Clients in Tasks\n\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\n\n```yaml\n# AI configuration\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References \"smart_model\" from above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References \"creative_model\"\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n## Security Best Practice\n\nFor production deployments, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n- id: secure_example\n provider: openai\n config:\n model: gpt-4\n api_key: \"${OPENAI_API_KEY}\" # Secure: Reads from environment variable\n```\n\n### Configuration Summary\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\n- **Referenced by**: `model_client_id` field in task configurations\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\n\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.",
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"output": "Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\n\n## Defining Model Clients\n\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\n\n### Basic Structure\n\nHere\u0027s the fundamental structure for defining model clients:\n\n```yaml\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\n config: # REQUIRED: Provider-specific config\n model: gpt-4 # REQUIRED: Model name\n api_key: your_api_key # REQUIRED: API key for the provider\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\n max_tokens: 1000 # Optional: Maximum tokens to generate\n timeout: 30 # Optional: Request timeout in seconds\n```\n\n### Complete Example with Multiple Model Clients\n\nYou can define multiple model clients for different use cases:\n\n```yaml\nmodel_clients:\n # Fast, cost-effective model for simple tasks\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n # High-quality model for complex analysis\n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n # Creative model for content generation\n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n## Using Model Clients in Tasks\n\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\n\n```yaml\n# AI configuration\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References \"smart_model\" from above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References \"creative_model\"\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n## Security Best Practice\n\nFor production deployments, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n- id: secure_example\n provider: openai\n config:\n model: gpt-4\n api_key: \"${OPENAI_API_KEY}\" # Secure: Reads from environment variable\n```\n\n### Configuration Summary\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\n- **Referenced by**: `model_client_id` field in task configurations\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\n\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\n__OUTPUTS__ {\"ai_response\": \"Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\\n\\n## Defining Model Clients\\n\\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\\n\\n### Basic Structure\\n\\nHere\u0027s the fundamental structure for defining model clients:\\n\\n```yaml\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\\n config: # REQUIRED: Provider-specific config\\n model: gpt-4 # REQUIRED: Model name\\n api_key: your_api_key # REQUIRED: API key for the provider\\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\\n max_tokens: 1000 # Optional: Maximum tokens to generate\\n timeout: 30 # Optional: Request timeout in seconds\\n```\\n\\n### Complete Example with Multiple Model Clients\\n\\nYou can define multiple model clients for different use cases:\\n\\n```yaml\\nmodel_clients:\\n # Fast, cost-effective model for simple tasks\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n # High-quality model for complex analysis\\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n # Creative model for content generation\\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n## Using Model Clients in Tasks\\n\\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\\n\\n```yaml\\n# AI configuration\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References \\\"smart_model\\\" from above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References \\\"creative_model\\\"\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n## Security Best Practice\\n\\nFor production deployments, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n- id: secure_example\\n provider: openai\\n config:\\n model: gpt-4\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure: Reads from environment variable\\n```\\n\\n### Configuration Summary\\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\\n- **Referenced by**: `model_client_id` field in task configurations\\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\\n\\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\", \"model_client_id\": \"openrouter_kimi\", \"agent_type\": \"assistant\", \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\", \"status\": \"completed\", \"token_usage\": {\"prompt_tokens\": 1867, \"completion_tokens\": 745, \"total_tokens\": 2612, \"estimated_cost\": {\"amount\": 0.10071, \"currency\": \"USD\"}, \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\"}}",
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"output": "Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\n\n## Defining Model Clients\n\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\n\n### Basic Structure\n\nHere\u0027s the fundamental structure for defining model clients:\n\n```yaml\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\n config: # REQUIRED: Provider-specific config\n model: gpt-4 # REQUIRED: Model name\n api_key: your_api_key # REQUIRED: API key for the provider\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\n max_tokens: 1000 # Optional: Maximum tokens to generate\n timeout: 30 # Optional: Request timeout in seconds\n```\n\n### Complete Example with Multiple Model Clients\n\nYou can define multiple model clients for different use cases:\n\n```yaml\nmodel_clients:\n # Fast, cost-effective model for simple tasks\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n # High-quality model for complex analysis\n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n # Creative model for content generation\n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n## Using Model Clients in Tasks\n\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\n\n```yaml\n# AI configuration\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References \"smart_model\" from above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References \"creative_model\"\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n## Security Best Practice\n\nFor production deployments, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n- id: secure_example\n provider: openai\n config:\n model: gpt-4\n api_key: \"${OPENAI_API_KEY}\" # Secure: Reads from environment variable\n```\n\n### Configuration Summary\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\n- **Referenced by**: `model_client_id` field in task configurations\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\n\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\n__OUTPUTS__ {\"ai_response\": \"Based on the workflow engine documentation, here\u0027s how to define model model_clients and configure it:\\n\\n## Defining Model Clients\\n\\nModel clients are declared under the `model_clients` field in your workflow configuration and define which AI models to use with authentication details.\\n\\n### Basic Structure\\n\\nHere\u0027s the fundamental structure for defining model clients:\\n\\n```yaml\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: unique_identifier # REQUIRED: Unique identifier to reference in tasks\\n provider: openai # REQUIRED: openai|anthropic|openrouter|groq\\n config: # REQUIRED: Provider-specific config\\n model: gpt-4 # REQUIRED: Model name\\n api_key: your_api_key # REQUIRED: API key for the provider\\n temperature: 0.7 # Optional: 0.0-2.0, controls randomness\\n max_tokens: 1000 # Optional: Maximum tokens to generate\\n timeout: 30 # Optional: Request timeout in seconds\\n```\\n\\n### Complete Example with Multiple Model Clients\\n\\nYou can define multiple model clients for different use cases:\\n\\n```yaml\\nmodel_clients:\\n # Fast, cost-effective model for simple tasks\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n # High-quality model for complex analysis\\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n # Creative model for content generation\\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n## Using Model Clients in Tasks\\n\\nOnce defined, model clients are referenced in AI tasks using the `model_client_id` field:\\n\\n```yaml\\n# AI configuration\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References \\\"smart_model\\\" from above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References \\\"creative_model\\\"\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n## Security Best Practice\\n\\nFor production deployments, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n- id: secure_example\\n provider: openai\\n config:\\n model: gpt-4\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure: Reads from environment variable\\n```\\n\\n### Configuration Summary\\n- **Required fields**: `id`, `provider`, `config.model`, `config.api_key`\\n- **Used in**: ai_agent, chatbot, continuous_conversation tasks\\n- **Referenced by**: `model_client_id` field in task configurations\\n- **Security**: Use `${ENV_VARIABLE}` syntax for sensitive data like API keys\\n\\nThis configuration approach allows you to centrally manage AI model settings and easily switch between different models or providers across your entire workflow.\", \"model_client_id\": \"openrouter_kimi\", \"agent_type\": \"assistant\", \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\", \"status\": \"completed\", \"token_usage\": {\"prompt_tokens\": 1867, \"completion_tokens\": 745, \"total_tokens\": 2612, \"estimated_cost\": {\"amount\": 0.10071, \"currency\": \"USD\"}, \"model\": \"moonshotai/kimi-k2\", \"provider\": \"openrouter\"}}",
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"content": "# YAML Workflow Structure Reference\n\nComplete reference for the YAML workflow definition structure supported by the Assistants Workflow Engine.\n\n## Table of Contents\n\n1. [Complete Schema Overview](#complete-schema-overview)\n2. [Required vs Optional Fields](#required-vs-optional-fields)\n3. [Data Types Reference](#data-types-reference)\n4. [Field Validation Rules](#field-validation-rules)\n5. [Parser Behavior](#parser-behavior)\n6. [Migration Guide](#migration-guide)\n\n## Complete Schema Overview\n\n```yaml\n# === WORKFLOW METADATA (Required) ===\nid: string # REQUIRED: Unique workflow identifier\nname: string # REQUIRED: Human-readable workflow name\n\n# === WORKFLOW METADATA (Optional) ===\nversion: string | integer # Default: 1\nnamespace: string # Default: \"default\"\ndescription: string # Default: \"\"\ntenant_id: string # Default: null\n\n# === EXECUTION CONFIGURATION ===\ntimeout_seconds: integer|string # Default: 3600 (1 hour) - Workflow-level timeout (supports duration strings like \"1h\", \"30m\")\nlabels: object # Default: {}\nmetadata: object # Default: {}\n\n# === DATA FLOW DEFINITIONS ===\ninputs: # Default: []\n - name: string # REQUIRED\n type: string # REQUIRED: string|integer|boolean|object|array\n description: string # Optional\n required: boolean # Default: false\n default: any # Optional default value\n\noutputs: object # Default: {}\n output_name:\n type: string # REQUIRED\n source: string # REQUIRED: task_id or task_id.field\n description: string # Optional\n\nvariables: object # Default: {}\n\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n - id: string # REQUIRED: Unique identifier to reference in tasks\n provider: string # REQUIRED: openai|anthropic|openrouter|groq\n config: object # REQUIRED: Provider-specific config\n model: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\n api_key: string # REQUIRED: API key for the provider\n temperature: number # Optional: 0.0-2.0, controls randomness\n max_tokens: integer # Optional: Maximum tokens to generate\n timeout: integer # Optional: Request timeout in seconds\n base_url: string # Optional: Custom API base URL\n\n# Model Client Examples:\n# OpenAI Configuration:\n# model_clients:\n# - id: gpt4_model\n# provider: openai\n# config:\n# model: gpt-4-turbo-preview\n# api_key: \"sk-your-openai-api-key\"\n# temperature: 0.7\n# max_tokens: 2000\n#\n# Anthropic Configuration:\n# model_clients:\n# - id: claude_model\n# provider: anthropic\n# config:\n# model: claude-3-sonnet-20240229\n# api_key: \"sk-ant-your-anthropic-api-key\"\n# temperature: 0.7\n# max_tokens: 1500\n#\n# Multiple Models:\n# model_clients:\n# - id: gpt4_model\n# provider: openai\n# config:\n# model: gpt-4-turbo-preview\n# api_key: \"sk-your-openai-api-key\"\n# - id: claude_model\n# provider: anthropic\n# config:\n# model: claude-3-sonnet-20240229\n# api_key: \"sk-ant-your-anthropic-api-key\"\n\n# === TASK DEFINITIONS (Required) ===\ntasks: # REQUIRED: List of tasks\n - id: string # REQUIRED: Unique task identifier\n name: string # Optional: Human-readable name\n type: string # REQUIRED: Task type\n description: string # Optional\n \n # DEPENDENCY CONFIGURATION\n depends_on: array # Optional: Execution dependencies and template resolution\n previous_node: string # REQUIRED: Task connectivity and UI hierarchy (essential for proper workflow execution)\n \n timeout_seconds: integer|string # Optional: Task-specific timeout (supports duration strings like \"5m\", \"30s\")\n worker_group: string # Optional: Target worker group\n conditions: array # Optional: Execution conditions\n execute_on_routes: array # Optional: Routes for conditional execution\n \n # Input/Output mapping\n input_mapping: object # Optional: Map workflow context to task inputs\n output_mapping: object # Optional: Map task outputs to workflow context\n \n # Retry configuration\n retry: object # Optional: Task-specific retry policy\n maxAttempts: integer # Default: 1\n initialDelay: string|integer # Default: \"1s\"\n maxDelay: string|integer # Default: \"300s\"\n backoffMultiplier: number # Default: 2.0\n retryOn: array # Default: [\"TEMPORARY_FAILURE\"]\n \n # === TASK TYPE SPECIFIC CONFIGURATIONS ===\n # See detailed sections below for each task type\n\n# === WORKFLOW-LEVEL RETRY POLICY ===\nretry: object # Optional: Global retry policy\n retryOn: array # Default: [\"TEMPORARY_FAILURE\"]\n maxAttempts: integer # Default: 1\n initialDelay: string|integer # Default: \"1s\"\n maxDelay: string|integer # Default: \"300s\"\n backoffMultiplier: number # Default: 2.0\n\n# === TRIGGERS ===\ntriggers: # Optional: List of triggers\n - name: string # REQUIRED\n type: string # REQUIRED: webhook|schedule|event\n description: string # Optional\n config: object # REQUIRED: Trigger-specific config\n input_mapping: array # Optional: Map trigger data to workflow inputs\n - webhook_field: string # Source field\n workflow_input: string # Target workflow input\n```\n\n## Required vs Optional Fields\n\n### Required Fields (Workflow Level)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `id` | string | Unique identifier for the workflow |\n| `tasks` | array | List of task definitions (can be empty) |\n\n### Required Fields (Task Level)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `id` | string | Unique identifier for the task |\n| `type` | string | Task type (script, http, ai_agent, etc.) |\n| `previous_node` | string | Reference to previous task for UI connectivity (required for proper task linking, except for the first task) |\n\n### Required Fields (Input Definition)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `name` | string | Input parameter name |\n| `type` | string | Data type (string, integer, boolean, object, array) |\n\n### Required Fields (Output Definition)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `type` | string | Output data type |\n| `source` | string | Source task ID or task_id.field_name |\n\n## Data Types Reference\n\n### Primitive Types\n\n```yaml\n# String type\ninputs:\n - name: customer_name\n type: string\n default: \"John Doe\"\n description: \"Customer full name\"\n\n# Integer type \ninputs:\n - name: batch_size\n type: integer\n default: 100\n description: \"Number of records per batch\"\n\n# Boolean type\ninputs:\n - name: include_archived\n type: boolean\n default: false\n description: \"Include archived records\"\n```\n\n### Complex Types\n\n```yaml\n# Object type\ninputs:\n - name: config_params\n type: object\n default:\n timeout: 30\n retries: 3\n debug: false\n description: \"Configuration parameters\"\n\n# Array type\ninputs:\n - name: customer_ids\n type: array\n default: [\"cust_001\", \"cust_002\"]\n description: \"List of customer identifiers\"\n```\n\n### Duration Types\n\nDuration values can be specified as strings or integers:\n\n```yaml\n# String format (recommended)\ntimeout_seconds: \"1h\" # 1 hour\ntimeout_seconds: \"30m\" # 30 minutes \ntimeout_seconds: \"45s\" # 45 seconds\ntimeout_seconds: \"2d\" # 2 days\n\n# Integer format (seconds)\ntimeout_seconds: 3600 # 1 hour in seconds\n\n# Supported units\n# s, sec, second, seconds\n# m, min, minute, minutes \n# h, hour, hours\n# d, day, days\n```\n\n### Timeout Configuration and Execution Behavior\n\nThe workflow engine supports configurable timeouts at both workflow and task levels with intelligent timeout-free communication for long-running tasks.\n\n#### Timeout Hierarchy\n\nTimeout values are resolved in this order of precedence:\n1. **Task-level `timeout_seconds`**: Specific timeout for individual tasks\n2. **Workflow-level `timeout_seconds`**: Default for all tasks in the workflow\n3. **System default**: 300 seconds (5 minutes) for tasks, 3600 seconds (1 hour) for workflows\n\n```yaml\n# Workflow-level timeout (applies to all tasks by default)\ntimeout_seconds: \"2h\" # 2 hour workflow timeout\n\ntasks:\n - id: quick_task\n type: script\n # Uses workflow-level timeout (2h)\n script: \"print(\u0027Quick task\u0027)\"\n \n - id: custom_timeout_task\n type: script\n timeout_seconds: \"30m\" # Override with 30 minute timeout\n script: \"print(\u0027Custom timeout task\u0027)\"\n \n - id: long_running_task\n type: script\n timeout_seconds: 600 # 10 minutes (integer format)\n script: |\n import time\n time.sleep(300) # 5 minute processing\n print(\"Long task completed\")\n```\n\n#### Timeout-Free Communication\n\nThe workflow engine automatically uses **timeout-free communication** for long-running tasks to prevent timeout-related failures:\n\n- **Loop tasks**: Always use timeout-free communication (event-driven execution)\n- **Script tasks**: Use timeout-free communication when `timeout_seconds \u003e 180` (host timeout threshold)\n- **Other task types**: Use standard timeout-based communication\n\n```yaml\ntasks:\n - id: short_script\n type: script\n timeout_seconds: 60 # Uses standard timeout-based communication\n script: \"print(\u0027Short script\u0027)\"\n \n - id: long_script\n type: script\n timeout_seconds: 600 # Uses timeout-free communication (600 \u003e 180)\n script: |\n import time\n print(\"\ud83d\ude80 This uses timeout-free communication\")\n time.sleep(300) # Won\u0027t cause timeout issues\n print(\"Long script completed\")\n \n - id: data_processing_loop\n type: loop\n timeout_seconds: \"1h\" # Always uses timeout-free communication\n loop_type: for\n max_iterations: 10\n # Loop tasks automatically use timeout-free communication\n```\n\n#### Loop Task Timeout Inheritance\n\nLoop tasks can pass timeout configuration to their inner script tasks:\n\n```yaml\n- id: batch_processing_loop\n type: loop\n timeout_seconds: \"30m\" # Loop-level timeout\n loop_type: for\n max_iterations: 5\n loop_tasks:\n - id: process_batch\n type: script\n # Inherits 30-minute timeout from parent loop\n script: |\n print(\"Processing batch with inherited timeout\")\n # This script will use the 30-minute timeout from the loop\n```\n\n#### Best Practices for Timeout Configuration\n\n1. **Use duration strings** for readability: `\"5m\"` vs `300`\n2. **Set appropriate timeouts** based on expected execution time plus buffer\n3. **Long-running tasks**: Set `timeout_seconds \u003e 180` to enable timeout-free communication\n4. **Loop tasks**: Always get timeout-free communication automatically\n5. **Critical tasks**: Use shorter timeouts for fail-fast behavior\n6. **External API calls**: Use longer timeouts to account for network latency\n\n```yaml\n# Example: Well-configured timeout workflow\ntimeout_seconds: \"1h\" # Reasonable workflow timeout\n\ntasks:\n - id: api_call\n type: http\n timeout_seconds: \"30s\" # Quick timeout for API calls\n url: \"https://api.example.com/data\"\n \n - id: data_processing\n type: script\n timeout_seconds: \"10m\" # Longer timeout for processing (uses timeout-free)\n script: |\n # Process large dataset\n process_data()\n \n - id: batch_processing\n type: loop\n timeout_seconds: \"45m\" # Generous timeout for loop (timeout-free)\n loop_type: for\n max_iterations: 100\n # Inner tasks inherit the 45-minute timeout\n```\n\n## Field Validation Rules\n\n### ID Fields\n\n```yaml\n# Valid IDs\nid: \"customer_processor\" # \u2705 Valid\nid: \"process-data\" # \u2705 Valid\nid: \"task_001\" # \u2705 Valid\n\n# Invalid IDs\nid: \"process data\" # \u274c Spaces not allowed\nid: \"123_start\" # \u274c Cannot start with number\nid: \"\" # \u274c Cannot be empty\n```\n\n### Timeout Values\n\n```yaml\n# Valid timeouts\ntimeout_seconds: 300 # \u2705 Positive integer\ntimeout_seconds: \"5m\" # \u2705 Duration string\ntimeout_seconds: \"1h30m\" # \u2705 Complex duration\n\n# Invalid timeouts\ntimeout_seconds: 0 # \u274c Must be positive\ntimeout_seconds: -60 # \u274c Cannot be negative\ntimeout_seconds: \"invalid\" # \u274c Invalid duration format\n```\n\n### Dependency Types and Validation\n\n#### `depends_on` vs `previous_node`\n\n**`depends_on` - Execution Dependencies:**\n- Controls actual task execution order\n- Enables template resolution and data access\n- Required for accessing outputs from other tasks\n- Validates that referenced tasks exist\n- Multiple tasks can be specified\n\n**`previous_node` - Task Connectivity and UI Hierarchy:**\n- **REQUIRED for proper task connectivity in UI**\n- Essential for workflow visualization and task linking\n- Critical for execution flow representation\n- Single task reference only\n- **Without this field, tasks won\u0027t be properly connected in the UI**\n\n```yaml\ntasks:\n - id: fetch_data\n type: http\n url: \"https://api.example.com/data\"\n \n - id: validate_data\n type: script\n depends_on: [fetch_data] # \u2705 EXECUTION: wait for fetch_data to complete\n previous_node: fetch_data # \u2705 UI: show as child of fetch_data in hierarchy\n script: |\n data = ${fetch_data} # \u2705 Can access because of depends_on\n if not validate(data):\n raise ValueError(\"Invalid data\")\n \n # Required: Use outputs dict and __OUTPUTS__ marker for proper parsing\n outputs[\u0027status\u0027] = \u0027valid\u0027\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n - id: process_data\n type: script\n depends_on: [validate_data] # \u2705 EXECUTION: wait for validation\n previous_node: validate_data # \u2705 UI: show as child of validate_data\n script: |\n validation_result = ${validate_data} # \u2705 Accessible via depends_on\n # Process data...\n\n - id: separate_task\n type: script\n previous_node: process_data # \u2705 UI: appears under process_data in hierarchy\n # NO depends_on means this can run immediately (parallel to other tasks)\n script: |\n # This task runs independently but appears under process_data in UI\n # Cannot access ${process_data} because no depends_on relationship\n print(\"Independent task\")\n\n### Complete Example with Proper Hierarchy\n\n```yaml\ntasks:\n - id: fetch_customer_data\n name: \"Fetch Customer Data\"\n type: http\n url: \"https://api.example.com/customers/${customer_id}\"\n \n - id: validate_customer\n name: \"Validate Customer Data\"\n type: script\n depends_on: [fetch_customer_data] # \u2705 EXECUTION: Wait for data fetch\n previous_node: fetch_customer_data # \u2705 UI: Show under fetch_customer_data\n script: |\n customer = ${fetch_customer_data} # \u2705 Can access because of depends_on\n if not customer.get(\u0027email\u0027):\n raise ValueError(\"Invalid customer data\")\n print(\"__OUTPUTS__ {\u0027status\u0027: \u0027valid\u0027}\")\n \n - id: process_customer\n name: \"Process Customer\"\n type: ai_agent\n depends_on: [validate_customer] # \u2705 EXECUTION: Wait for validation\n previous_node: validate_customer # \u2705 UI: Show under validate_customer\n config:\n model_client_id: gpt4_model\n user_message: \"Process customer: ${fetch_customer_data}\"\n \n - id: send_notification\n name: \"Send Notification\"\n type: script\n depends_on: [process_customer] # \u2705 EXECUTION: Wait for processing\n previous_node: process_customer # \u2705 UI: Show under process_customer\n script: |\n result = ${process_customer}\n print(f\"Notification sent for: ${fetch_customer_data.name}\")\n \n - id: parallel_audit_log\n name: \"Audit Log (Parallel)\"\n type: script\n depends_on: [fetch_customer_data] # \u2705 EXECUTION: Only needs initial data\n previous_node: fetch_customer_data # \u2705 UI: Show under fetch_customer_data\n script: |\n # This runs in parallel with validation/processing\n customer = ${fetch_customer_data}\n print(f\"Audit: Customer {customer.id} accessed at ${current_timestamp}\")\n```\n\n # Invalid examples\n - id: invalid_task\n type: script\n depends_on: [nonexistent_task] # \u274c VALIDATION ERROR: task doesn\u0027t exist\n previous_node: another_missing # \u26a0\ufe0f UI WARNING: task doesn\u0027t exist (but not fatal)\n script: \"print(\u0027Invalid\u0027)\"\n```\n\n#### Key Differences:\n\n| Aspect | `depends_on` | `previous_node` |\n|--------|--------------|-----------------|\n| **Purpose** | Execution control | Task connectivity \u0026 UI organization |\n| **Impact** | Blocks task until dependencies complete | **REQUIRED for UI task linking** |\n| **Template Access** | \u2705 Enables `${task_id}` resolution | \u274c No template access |\n| **Validation** | \u274c Parser error if task missing | \u26a0\ufe0f Warning if task missing |\n| **Multiple References** | \u2705 Array of task IDs | \u274c Single task ID only |\n| **Execution Order** | \u2705 Enforced | \u274c No execution impact |\n| **UI Connectivity** | \u274c No UI impact | \u2705 **ESSENTIAL for proper task connection** |\n\n### Circular Dependency Detection\n\n```yaml\n# This will be rejected by the parser\ntasks:\n - id: task_a\n depends_on: [task_b] # \u274c Circular dependency\n \n - id: task_b \n depends_on: [task_a] # \u274c Circular dependency\n```\n\n## Parser Behavior\n\n### Field Name Compatibility\n\nThe parser supports both camelCase and snake_case for field names:\n\n```yaml\ntasks:\n - id: flexible_task\n # Both formats are accepted\n dependsOn: [other_task] # camelCase\n depends_on: [other_task] # snake_case\n \n inputMapping: # camelCase\n source_field: \"target_field\"\n input_mapping: # snake_case \n source_field: \"target_field\"\n \n outputMapping: {} # camelCase\n output_mapping: {} # snake_case\n \n workerGroup: \"gpu-workers\" # camelCase\n worker_group: \"gpu-workers\" # snake_case\n```\n\n### Default Value Assignment\n\n```yaml\n# Fields with defaults are automatically populated\nid: my_workflow\n# name defaults to id value: \"my_workflow\"\n# version defaults to: 1\n# namespace defaults to: \"default\" \n# timeout_seconds defaults to: 3600\n# description defaults to: \"\"\n\ntasks:\n - id: simple_task\n type: script\n # name defaults to id: \"simple_task\"\n # timeout_seconds defaults to: 300\n # retry.maxAttempts defaults to: 1\n```\n\n### Template Resolution Context\n\nThe parser provides these built-in variables for template resolution:\n\n```yaml\ntasks:\n - id: context_aware_task\n type: script\n script: |\n # Workflow context\n workflow_id = \"${workflow_id}\" # Current workflow ID\n execution_id = \"${execution_id}\" # Unique execution ID\n namespace = \"${namespace}\" # Workflow namespace\n \n # Timing context\n current_timestamp = \"${current_timestamp}\" # ISO timestamp\n started_at = \"${started_at}\" # Workflow start time\n \n # Input context\n user_input = \"${input_parameter}\" # Workflow inputs\n \n # Task context\n previous_result = ${previous_task} # Previous task output\n specific_field = ${task_id.field_name} # Specific output field\n \n # Route context (for conditional workflows)\n route_info = ${_route_context} # Current route information\n```\n\n### Error Handling During Parsing\n\n```yaml\n# The parser will validate and report these errors:\n\n# 1. Missing required fields\n# ERROR: Missing required field: \u0027id\u0027\n\n# 2. Invalid YAML syntax \n# ERROR: Invalid YAML: mapping values are not allowed here\n\n# 3. Circular dependencies\n# ERROR: Circular dependency detected involving task \u0027task_a\u0027\n\n# 4. Invalid task references\n# ERROR: Task \u0027task_b\u0027 depends on non-existent task \u0027invalid_task\u0027\n\n# 5. Invalid duration formats\n# ERROR: Invalid duration: \u0027not_a_duration\u0027\n\n# 6. Invalid field values\n# ERROR: Timeout must be positive, got -10\n```\n\n## Migration Guide\n\n### From Version 1.0 to 2.0\n\n```yaml\n# OLD FORMAT (v1.0)\nworkflow:\n id: my_workflow\n tasks:\n - task_id: process_data\n task_type: python_script\n \n# NEW FORMAT (v2.0) \nid: my_workflow\ntasks:\n - id: process_data\n type: script\n language: python\n```\n\n### Deprecated Fields\n\n| Old Field | New Field | Status |\n|-----------|-----------|--------|\n| `task_type` | `type` | Deprecated in v2.0 |\n| `task_id` | `id` | Deprecated in v2.0 |\n| `python_script` | `script` (type) | Deprecated in v2.0 |\n| `plugin` | `mcp` (type) | Deprecated in v2.1 |\n\n### Backward Compatibility\n\nThe parser maintains backward compatibility for deprecated fields but will show warnings:\n\n```yaml\n# This will work but show a warning\ntasks:\n - task_id: old_style_task # \u26a0\ufe0f Warning: Use \u0027id\u0027 instead\n task_type: python_script # \u26a0\ufe0f Warning: Use \u0027type: script\u0027 instead\n```\n\n## Advanced Schema Features\n\n### Conditional Field Requirements\n\nSome fields become required based on task type:\n\n```yaml\n# HTTP tasks require URL\n- id: api_call\n type: http\n url: \"https://api.example.com\" # \u2705 Required for HTTP tasks\n \n# AI agent tasks require model_client_id\n- id: ai_task\n type: ai_agent\n config:\n model_client_id: gpt4 # \u2705 Required for AI agent tasks\n```\n\n### Dynamic Field Validation\n\nField validation can depend on other field values:\n\n```yaml\n# Loop tasks require loop-specific fields\n- id: data_loop\n type: loop\n loop_type: \"for\" # Determines required fields\n max_iterations: 10 # \u2705 Required for \u0027for\u0027 loops\n # exit_condition: not needed for \u0027for\u0027 loops\n \n- id: condition_loop \n type: loop\n loop_type: \"while\" # Determines required fields\n exit_condition: \"${count} \u003e= 10\" # \u2705 Required for \u0027while\u0027 loops\n # max_iterations: optional for \u0027while\u0027 loops\n```\n\n## Template Resolution and Data Flow\n\nTemplate resolution is a core feature that allows tasks to access data from previous tasks, workflow inputs, and built-in variables using the `${variable_name}` syntax. This is essential for creating dynamic workflows where tasks pass data to each other.\n\n### Basic Template Syntax\n\n```yaml\n# Access workflow inputs\nscript: |\n customer_name = \"${customer_name}\" # From workflow inputs\n \n# Access previous task outputs\nscript: |\n previous_result = ${data_fetch_task} # Entire output from data_fetch_task\n specific_field = ${data_fetch_task.customer_id} # Specific field\n \n# Access built-in variables\nscript: |\n workflow_id = \"${workflow_id}\"\n execution_id = \"${execution_id}\"\n timestamp = \"${current_timestamp}\"\n```\n\n### Available Built-in Variables\n\n| Variable | Description | Example Value |\n|----------|-------------|---------------|\n| `${workflow_id}` | Current workflow identifier | `customer_processor` |\n| `${execution_id}` | Unique execution identifier | `exec_12345_abcde` |\n| `${namespace}` | Workflow namespace | `production` |\n| `${current_timestamp}` | ISO timestamp | `2024-01-15T10:30:00Z` |\n| `${started_at}` | Workflow start time | `2024-01-15T10:29:45Z` |\n\n### Task Output Resolution\n\nTasks can access outputs from other tasks they depend on:\n\n```yaml\ntasks:\n - id: fetch_customer\n type: http\n url: \"https://api.example.com/customers/${customer_id}\"\n \n - id: process_customer\n type: script\n depends_on: [fetch_customer] # Required for template access\n previous_node: fetch_customer\n script: |\n # Access entire response\n customer_data = ${fetch_customer}\n \n # Access specific fields\n customer_name = \"${fetch_customer.name}\"\n customer_email = \"${fetch_customer.email}\"\n \n # Process the data\n result = {\n \"processed_name\": customer_name.upper(),\n \"domain\": customer_email.split(\"@\")[1]\n }\n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n```\n\n### AI Agent Output Resolution\n\nAI agent tasks return structured output that requires specific access patterns:\n\n```yaml\n# AI Agent task\n- id: ai_analysis_task\n type: ai_agent\n config:\n model_client_id: gpt4_model\n system_message: \"Analyze the customer data and provide insights.\"\n user_message: \"Customer data: ${customer_data}\"\n\n# Accessing AI agent output in subsequent tasks\n- id: process_ai_response\n type: script\n depends_on: [ai_analysis_task]\n previous_node: ai_analysis_task\n script: |\n # CORRECT: AI agents return structured output\n agent_output = ${ai_analysis_task}\n ai_response = agent_output[\u0027ai_response\u0027].strip()\n \n # Additional metadata available\n token_usage = agent_output.get(\u0027token_usage\u0027, {})\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\n \n # Process the AI response\n processed_analysis = {\n \"analysis\": ai_response,\n \"tokens_used\": token_usage.get(\u0027total_tokens\u0027, 0),\n \"model\": model_used,\n \"processed_at\": \"${current_timestamp}\"\n }\n \n print(f\"__OUTPUTS__ {json.dumps(processed_analysis)}\")\n\n# INCORRECT: Direct access won\u0027t work\n- id: incorrect_ai_access\n type: script\n depends_on: [ai_analysis_task]\n script: |\n # \u274c This will not work correctly\n ai_response = ${ai_analysis_task} # Returns full agent object\n # ai_response is now a dict, not the actual response text\n```\n\n### AI Agent Response Structure\n\nAI agent tasks return a structured object with the following format:\n\n```json\n{\n \"ai_response\": \"The actual AI-generated text response\",\n \"model\": \"gpt-4-turbo-preview\",\n \"token_usage\": {\n \"prompt_tokens\": 150,\n \"completion_tokens\": 75,\n \"total_tokens\": 225\n },\n \"estimated_cost\": {\n \"amount\": 0.00337,\n \"currency\": \"USD\"\n },\n \"provider\": \"openai\",\n \"timestamp\": \"2024-01-15T10:30:00Z\"\n}\n```\n\n### Loop Task Data Access and Dependencies\n\nLoop tasks have a special execution context that differs from regular workflow tasks. Understanding how to access data within loops is crucial for building functional workflows.\n\n#### Loop Task Internal Data Access\n\nWithin loop tasks, data access works through injected context files rather than template resolution:\n\n```yaml\n# Loop task with state variables and internal task dependencies\n- id: data_processing_loop\n type: loop\n loop_type: for\n max_iterations: 3\n state_variables:\n processed_items: []\n total_processed: 0\n error_count: 0\n loop_tasks:\n # First task in loop\n - id: fetch_data\n type: script\n script: |\n # Access loop state variables through injected context\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\n processed_items = loop_state.get(\u0027processed_items\u0027, [])\n \n # Access workflow inputs and external task outputs\n external_data = inputs.get(\u0027previous_workflow_task\u0027, {})\n iteration_num = inputs.get(\u0027iteration\u0027, 0) # Loop iteration variable\n \n # Process data\n batch_data = fetch_batch_data(iteration_num)\n \n outputs = {\"batch_data\": batch_data, \"batch_size\": len(batch_data)}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n # Second task depends on first task in same loop\n - id: process_data\n type: script\n depends_on: [fetch_data] # \u2705 CORRECT: Dependencies within loop tasks work normally\n script: |\n # \u2705 CORRECT: Access previous loop task through inputs context\n fetch_result = inputs.get(\u0027fetch_data\u0027, {})\n batch_data = fetch_result.get(\u0027batch_data\u0027, [])\n \n # \u274c INCORRECT: This won\u0027t work in loop context\n # batch_data = ${fetch_data.batch_data} # Template resolution fails in loops\n \n # Access and update state variables\n current_processed = loop_state.get(\u0027processed_items\u0027, [])\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\n \n # Process the batch\n processed_batch = process_batch(batch_data)\n current_processed.extend(processed_batch)\n \n # Update state variables using state_updates\n state_updates[\u0027processed_items\u0027] = current_processed\n state_updates[\u0027total_processed\u0027] = current_total + len(processed_batch)\n \n outputs = {\"processed_count\": len(processed_batch)}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n print(f\"__STATE_UPDATES__ {json.dumps(state_updates)}\") # \u2705 REQUIRED for state updates\n```\n\n#### Key Differences in Loop Task Data Access\n\n| Access Pattern | Regular Tasks | Loop Tasks |\n|---------------|---------------|------------|\n| **Previous Task Output** | `${previous_task}` | `inputs.get(\u0027field_name\u0027, default)` (flattened) |\n| **State Variables** | N/A | `loop_state.get(\u0027variable_name\u0027, default)` |\n| **State Updates** | N/A | `state_updates[\u0027variable_name\u0027] = value` |\n| **Workflow Inputs** | `\"${input_name}\"` | `inputs.get(\u0027input_name\u0027, \u0027default\u0027)` |\n| **Dependencies** | `depends_on: [task_id]` | Same: `depends_on: [task_id]` |\n\n#### Critical: Loop Task Output Flattening\n\n**Important:** The loop executor flattens task outputs into individual fields in the `inputs` dictionary, rather than storing them as nested objects under the task name.\n\n```yaml\n# First loop task outputs:\noutputs = {\"company_name\": \"ACME Corp\", \"company_id\": \"123\", \"status\": \"success\"}\nprint(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n\n# Second loop task accessing the data:\nscript: |\n # \u274c INCORRECT: This won\u0027t work - no nested object\n # company_data = inputs.get(\u0027first_task\u0027, {})\n # company_name = company_data.get(\u0027company_name\u0027)\n \n # \u2705 CORRECT: Access flattened fields directly\n company_name = inputs.get(\u0027company_name\u0027, \u0027\u0027)\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\n \n print(f\"Company: {company_name}, ID: {company_id}, Status: {task_status}\")\n```\n\n#### Complete Loop Task Example\n\n```yaml\n- id: financial_processing_loop\n type: loop\n loop_type: for\n max_iterations: 3\n iteration_variable: company_index\n depends_on: [list_companies] # External dependency\n \n state_variables:\n processed_companies: []\n total_extractions: 0\n successful_extractions: 0\n \n loop_tasks:\n - id: process_current_company\n type: script\n script: |\n # Access external workflow task data\n companies_data = inputs.get(\u0027list_companies\u0027, {})\n company_idx = inputs.get(\u0027company_index\u0027, 0)\n \n # Access state variables\n processed = loop_state.get(\u0027processed_companies\u0027, [])\n \n # Process company\n company = companies_data[\u0027companies\u0027][company_idx]\n result = process_company(company)\n \n outputs = {\"company_name\": company[\u0027name\u0027], \"status\": \"success\"}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n - id: extract_financial_data\n type: script\n depends_on: [process_current_company] # Depend on previous loop task\n requirements:\n - requests\u003e=2.28.0\n - some-ai-library\u003e=1.0.0\n script: |\n # \u2705 CORRECT: Access flattened outputs from previous loop task\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\n \n # \u274c INCORRECT: Loop executor flattens outputs, no nested access\n # company_data = inputs.get(\u0027process_current_company\u0027, {})\n # company_name = company_data.get(\u0027company_name\u0027, \u0027Unknown\u0027)\n \n # \u274c INCORRECT: Template resolution doesn\u0027t work in loops\n # company_data = ${process_current_company}\n \n # Access external data\n companies_list = inputs.get(\u0027list_companies\u0027, {})\n \n # Perform AI extraction\n extraction_result = extract_with_ai({\"company_name\": company_name, \"company_id\": company_id})\n \n # Update state variables\n current_processed = loop_state.get(\u0027processed_companies\u0027, [])\n current_total = loop_state.get(\u0027total_extractions\u0027, 0)\n current_successful = loop_state.get(\u0027successful_extractions\u0027, 0)\n \n current_processed.append({\n \"company_name\": company_name,\n \"extraction_result\": extraction_result\n })\n \n # State updates are required for persistence\n state_updates[\u0027processed_companies\u0027] = current_processed\n state_updates[\u0027total_extractions\u0027] = current_total + 1\n state_updates[\u0027successful_extractions\u0027] = current_successful + (1 if extraction_result else 0)\n \n outputs = {\"extracted\": bool(extraction_result)}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n print(f\"__STATE_UPDATES__ {json.dumps(state_updates)}\") # \u2705 REQUIRED\n\n# Accessing loop task outputs in subsequent workflow tasks\n- id: process_loop_results\n type: script\n depends_on: [financial_processing_loop]\n previous_node: financial_processing_loop\n script: |\n # \u2705 CORRECT: Access final state of loop\n loop_result = ${financial_processing_loop}\n loop_final_state = loop_result.get(\u0027final_state\u0027, {})\n \n # Access specific state variables from final state\n processed_companies = loop_final_state.get(\u0027processed_companies\u0027, [])\n total_processed = loop_final_state.get(\u0027total_extractions\u0027, 0)\n successful_count = loop_final_state.get(\u0027successful_extractions\u0027, 0)\n \n # Access loop execution metadata\n iterations_completed = loop_result.get(\u0027iterations_completed\u0027, 0)\n \n print(f\"Loop completed {iterations_completed} iterations\")\n print(f\"Processed {len(processed_companies)} companies\")\n print(f\"Success rate: {successful_count}/{total_processed}\")\n \n outputs = {\n \"companies_processed\": len(processed_companies),\n \"success_rate\": f\"{successful_count}/{total_processed}\",\n \"iterations\": iterations_completed\n }\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n#### Loop Task Context Injection and Output Flattening\n\nThe loop executor injects context through temporary files and flattens task outputs for easier access:\n\n```python\n# Auto-injected at the start of each loop task script:\nimport json\nimport os\n\n# Load context from temporary files (auto-generated)\nwith open(\u0027/tmp/loop_context_task_iter_N/inputs.json\u0027, \u0027r\u0027) as f:\n inputs = json.load(f) # Contains all workflow data + previous loop task outputs (FLATTENED)\n\nwith open(\u0027/tmp/loop_context_task_iter_N/loop_state.json\u0027, \u0027r\u0027) as f:\n loop_state = json.load(f) # Contains current state variables\n\noutputs = {}\nstate_updates = {}\n\n# Your script code follows...\n```\n\n#### Debugging Loop Task Data Access\n\nWhen debugging loop tasks, always print available keys to understand what data is accessible:\n\n```yaml\n- id: debug_loop_task\n type: script\n depends_on: [previous_loop_task]\n script: |\n # Debug: Print all available data\n print(f\"\ud83d\udd0d Available inputs: {list(inputs.keys())}\")\n print(f\"\ud83d\udd0d Loop state keys: {list(loop_state.keys())}\")\n \n # Debug: Print specific values\n for key in [\u0027company_name\u0027, \u0027company_id\u0027, \u0027status\u0027, \u0027fy_folders\u0027]:\n value = inputs.get(key, \u0027NOT_FOUND\u0027)\n print(f\"\ud83d\udd0d {key}: {value}\")\n \n # Now access the flattened data\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\n \n # Process your data...\n outputs = {\"processed\": f\"Company {company_name}\"}\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n#### Real Example: Task Output Flattening\n\n```yaml\n# Task 1 outputs this structure:\n__OUTPUTS__ {\"company_name\": \"LTTS\", \"company_id\": \"123\", \"fy_folders\": [{\"id\": \"456\", \"name\": \"FY24\"}], \"status\": \"success\"}\n\n# Task 2 receives flattened inputs:\ninputs = {\n \"folder_id\": \"...\", # Workflow input\n \"gemini_api_key\": \"...\", # Workflow input \n \"list_company_folders\": {...}, # External task output\n \"company_index\": 0, # Loop iteration variable\n \"company_name\": \"LTTS\", # \u2705 Flattened from Task 1\n \"company_id\": \"123\", # \u2705 Flattened from Task 1\n \"fy_folders\": [{\"id\": \"456\", \"name\": \"FY24\"}], # \u2705 Flattened from Task 1\n \"status\": \"success\", # \u2705 Flattened from Task 1\n \"total_extractions\": 0, # Loop state variable\n \"processed_companies\": [] # Loop state variable\n}\n```\n\n#### Loop Task Special Output Fields\n\n| Field | Description | Example Access |\n|-------|-------------|----------------|\n| `final_state` | Final values of all state variables | `${loop_task.final_state}` |\n| `final_state[\"variable_name\"]` | Specific state variable from final state | `${loop_task.final_state[\"total_processed\"]}` |\n| `iterations_completed` | Number of iterations executed | `${loop_task.iterations_completed}` |\n\n#### Safe Template Resolution Pattern for Loop States\n\nWhen accessing loop final state fields, use this safe parsing pattern:\n\n```yaml\nscript: |\n try:\n # Template resolution with safety checks\n final_state_str = \u0027\u0027\u0027${loop_task.final_state}\u0027\u0027\u0027\n processed_count_str = \u0027\u0027\u0027${loop_task.final_state[\"processed_count\"]}\u0027\u0027\u0027\n items_str = \u0027\u0027\u0027${loop_task.final_state[\"processed_items\"]}\u0027\u0027\u0027\n iterations_str = \u0027\u0027\u0027${loop_task.iterations_completed}\u0027\u0027\u0027\n \n # Safe parsing with fallbacks\n final_state = json.loads(final_state_str) if not final_state_str.startswith(\u0027UNRESOLVED_\u0027) else {}\n processed_count = int(processed_count_str) if processed_count_str.isdigit() else 0\n processed_items = json.loads(items_str) if not items_str.startswith(\u0027UNRESOLVED_\u0027) else []\n iterations = int(iterations_str) if iterations_str.isdigit() else 0\n \n print(f\"Successfully processed {processed_count} items in {iterations} iterations\")\n \n except Exception as e:\n print(f\"Error accessing loop results: {str(e)}\")\n # Handle error case\n```\n```\n\n### Real-World AI Agent Example\n\nBased on actual WhatsApp workflow patterns:\n\n```yaml\n# AI greeting agent\n- id: greeting_agent\n type: ai_agent\n config:\n model_client_id: assistant_model\n system_message: \"You are a helpful assistant. Generate a friendly greeting.\"\n user_message: \"User said: ${user_message}\"\n\n# Send the AI response via API\n- id: send_greeting\n type: script\n depends_on: [greeting_agent]\n previous_node: greeting_agent\n script: |\n import requests\n import json\n \n # CORRECT: Access AI response from structured output\n agent_output = ${greeting_agent}\n ai_greeting = agent_output[\u0027ai_response\u0027].strip()\n \n phone_number = \"${phone_number}\"\n api_token = \"${API_TOKEN}\"\n \n print(f\"\ud83e\udd16 AI generated greeting: {ai_greeting}\")\n \n # Send via API\n url = f\"https://api.example.com/messages/{phone_number}\"\n headers = {\"Authorization\": f\"Bearer {api_token}\"}\n \n response = requests.post(url, headers=headers, json={\n \"message\": ai_greeting\n })\n \n result = {\n \"status\": \"sent\",\n \"ai_response\": ai_greeting,\n \"api_response\": response.json(),\n \"tokens_used\": agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\n }\n \n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n```\n\n### Complex Template Patterns\n\n```yaml\n# Conditional templates with router outputs\n- id: router_task\n type: conditional_router\n conditions:\n - name: \"premium_customer\"\n condition: \"${customer_data.tier} == \u0027premium\u0027\"\n route: \"premium_processing\"\n\n- id: premium_handler\n type: script\n depends_on: [router_task]\n previous_node: router_task\n execute_on_routes: [\"premium_processing\"]\n script: |\n # Access router decision\n selected_route = \"${router_task.selected_route}\"\n route_data = ${router_task.route_data}\n \n # Process premium customer\n customer = ${customer_data}\n premium_benefits = calculate_benefits(customer)\n\n# Loop through arrays from previous tasks\n- id: process_items\n type: script\n depends_on: [fetch_items]\n previous_node: fetch_items\n script: |\n items = ${fetch_items.data}\n processed_items = []\n \n for item in items:\n processed_item = {\n \"id\": item[\"id\"],\n \"processed_at\": \"${current_timestamp}\",\n \"status\": \"processed\"\n }\n processed_items.append(processed_item)\n \n print(f\"__OUTPUTS__ {json.dumps({\u0027processed_items\u0027: processed_items})}\")\n\n# Nested object access\n- id: user_preferences\n type: script\n depends_on: [fetch_user]\n previous_node: fetch_user\n script: |\n # Access nested user preferences\n email_notifications = ${fetch_user.preferences.notifications.email}\n theme_setting = \"${fetch_user.preferences.ui.theme}\"\n language = \"${fetch_user.profile.language}\"\n \n settings = {\n \"notifications\": email_notifications,\n \"theme\": theme_setting,\n \"locale\": language\n }\n print(f\"__OUTPUTS__ {json.dumps(settings)}\")\n```\n\n### Template Resolution Rules\n\n1. **Dependency Requirement**: Tasks must have `depends_on` relationship to access other task outputs\n2. **String vs Object**: Use quotes for string values, no quotes for objects/arrays\n3. **Field Navigation**: Use dot notation for nested field access\n4. **Error Handling**: Unresolved templates become `UNRESOLVED_variable_name`\n\n```yaml\n# \u2705 Correct template usage\n- id: correct_task\n depends_on: [previous_task]\n previous_node: previous_task\n script: |\n string_value = \"${previous_task.name}\" # String with quotes\n object_value = ${previous_task.data} # Object without quotes\n nested_value = \"${previous_task.user.email}\" # Nested field\n\n# \u274c Incorrect template usage \n- id: incorrect_task\n # Missing depends_on - template won\u0027t resolve\n script: |\n value = ${previous_task.name} # Will become UNRESOLVED_previous_task.name\n```\n\n## Script Task Output Handling\n\nScript tasks must use the `__OUTPUTS__` marker pattern for proper output parsing by the workflow engine.\n\n### Required Output Pattern\n\n```yaml\ntasks:\n - id: example_script\n type: script\n script: |\n # Your script logic here\n data = process_data()\n \n # Required: Store outputs in outputs dict\n outputs = {}\n outputs[\u0027result\u0027] = data\n outputs[\u0027status\u0027] = \u0027success\u0027\n outputs[\u0027count\u0027] = len(data)\n \n # Required: Print with __OUTPUTS__ marker for engine parsing\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n### Output Access in Subsequent Tasks\n\n```yaml\ntasks:\n - id: data_processor\n type: script\n script: |\n # Process and store outputs\n results = {\"processed_count\": 100, \"errors\": []}\n \n outputs = {}\n outputs[\u0027results\u0027] = results\n outputs[\u0027status\u0027] = \u0027completed\u0027\n \n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n \n - id: result_handler\n type: script\n depends_on: [data_processor]\n script: |\n # Access previous task outputs\n processor_output = ${data_processor}\n processed_count = processor_output[\u0027results\u0027][\u0027processed_count\u0027]\n status = processor_output[\u0027status\u0027]\n \n print(f\"Previous task processed {processed_count} items with status: {status}\")\n \n # Store new outputs\n outputs = {\u0027message\u0027: f\u0027Handled {processed_count} results\u0027}\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n### Error Handling in Scripts\n\n```yaml\ntasks:\n - id: safe_script\n type: script\n script: |\n try:\n result = risky_operation()\n outputs = {\u0027result\u0027: result, \u0027status\u0027: \u0027success\u0027}\n except Exception as e:\n outputs = {\u0027error\u0027: str(e), \u0027status\u0027: \u0027failed\u0027}\n \n # Always print outputs, even for errors\n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n## Triggers and Workflow Activation\n\nTriggers define how workflows are started automatically based on external events. They enable event-driven workflow execution.\n\n### Webhook Triggers\n\nWebhook triggers allow external systems to start workflows by sending HTTP requests.\n\n```yaml\ntriggers:\n - name: customer_webhook\n type: webhook\n description: \"Triggered when customer data is updated\"\n config:\n path: \"/webhooks/customer-updated\" # Webhook endpoint path\n method: POST # HTTP method (GET, POST, PUT, etc.)\n authentication:\n type: token # none|token|signature|basic\n token: \"${WEBHOOK_SECRET}\" # Required if type is token\n header_name: \"X-Webhook-Token\" # Optional: custom header name\n validation:\n required_fields: [\"customer.id\", \"customer.email\"] # Required payload fields\n content_type: \"application/json\" # Expected content type\n input_mapping:\n - webhook_field: customer.id # Field from webhook payload\n workflow_input: customer_id # Map to workflow input\n - webhook_field: customer.email\n workflow_input: customer_email\n - webhook_field: customer.updated_at\n workflow_input: update_timestamp\n```\n\n### Scheduled Triggers\n\nSchedule workflows to run at specific times or intervals.\n\n```yaml\ntriggers:\n - name: daily_report\n type: schedule\n description: \"Generate daily customer report\"\n config:\n cron: \"0 9 * * *\" # Daily at 9 AM (cron format)\n timezone: \"UTC\" # Optional: timezone\n enabled: true # Optional: enable/disable\n max_instances: 1 # Optional: max concurrent instances\n input_mapping:\n - workflow_input: report_date\n default_value: \"${current_date}\" # Use built-in variable\n - workflow_input: report_type\n default_value: \"daily\"\n\n - name: weekly_cleanup\n type: schedule\n description: \"Weekly database cleanup\"\n config:\n cron: \"0 2 * * 0\" # Weekly on Sunday at 2 AM\n timezone: \"America/New_York\"\n input_mapping:\n - workflow_input: cleanup_before_date\n default_value: \"${date_minus_7_days}\"\n```\n\n### Event Triggers\n\nEvent triggers respond to system events from various sources.\n\n```yaml\ntriggers:\n - name: file_uploaded\n type: event\n description: \"Process uploaded files\"\n config:\n event_type: \"file.uploaded\" # Event type to listen for\n source: \"storage_service\" # Event source system\n filter: # Optional: event filtering\n file_type: [\"csv\", \"json\", \"xlsx\"]\n file_size_max: \"100MB\"\n path_pattern: \"/uploads/customers/*\"\n retry: # Optional: retry configuration\n max_attempts: 3\n initial_delay: \"5s\"\n input_mapping:\n - webhook_field: file.path\n workflow_input: file_path\n - webhook_field: file.size\n workflow_input: file_size\n - webhook_field: file.type\n workflow_input: file_type\n\n - name: order_status_changed\n type: event\n description: \"Handle order status changes\"\n config:\n event_type: \"order.status_changed\"\n source: \"order_management\"\n filter:\n status: [\"completed\", \"cancelled\", \"refunded\"]\n priority: [\"high\", \"urgent\"]\n input_mapping:\n - webhook_field: order.id\n workflow_input: order_id\n - webhook_field: order.status\n workflow_input: new_status\n - webhook_field: order.customer_id\n workflow_input: customer_id\n```\n\n### Dependency Triggers\n\nDependency triggers start workflows when other workflows complete.\n\n```yaml\ntriggers:\n - name: post_processing\n type: dependency\n description: \"Run after main processing completes\"\n config:\n depends_on_workflow: \"main_data_processing\" # Workflow to depend on\n depends_on_status: \"SUCCESS\" # SUCCESS|FAILED|COMPLETED\n namespace: \"production\" # Optional: workflow namespace\n timeout_minutes: 60 # Optional: wait timeout\n input_mapping:\n - webhook_field: execution_id # From completed workflow\n workflow_input: source_execution_id\n - webhook_field: workflow_outputs.processed_count\n workflow_input: items_processed\n```\n\n## Model Client Configuration\n\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\n\n### Basic Structure\n\n```yaml\nmodel_clients:\n - id: unique_identifier # Used to reference this model in tasks\n provider: provider_name # AI provider (openai, anthropic, etc.)\n config:\n model: model_name # Specific model to use\n api_key: your_api_key # Authentication key\n # Additional provider-specific options...\n```\n\n### Supported Providers\n\n#### OpenAI Provider\n\n```yaml\nmodel_clients:\n - id: gpt4_model\n provider: openai\n config:\n model: gpt-4-turbo-preview # Available: gpt-4, gpt-4-turbo, gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.7 # Optional: 0.0-2.0\n max_tokens: 2000 # Optional: Maximum response tokens\n timeout: 30 # Optional: Request timeout (seconds)\n```\n\n#### Anthropic Provider\n\n```yaml\nmodel_clients:\n - id: claude_model\n provider: anthropic\n config:\n model: claude-3-sonnet-20240229 # Available: claude-3-opus, claude-3-sonnet, claude-3-haiku\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7 # Optional: 0.0-1.0\n max_tokens: 1500 # Optional: Maximum response tokens\n timeout: 30 # Optional: Request timeout (seconds)\n```\n\n#### OpenRouter Provider\n\n```yaml\nmodel_clients:\n - id: openrouter_model\n provider: openrouter\n config:\n model: anthropic/claude-3-sonnet # Available: Various models via OpenRouter\n api_key: \"sk-or-your-openrouter-api-key\"\n base_url: \"https://openrouter.ai/api/v1\" # Optional: Custom endpoint\n temperature: 0.7\n max_tokens: 2000\n```\n\n#### Multiple Model Clients\n\nYou can define multiple model clients and reference them by their `id` in tasks:\n\n```yaml\nmodel_clients:\n - id: fast_model\n provider: openai\n config:\n model: gpt-3.5-turbo\n api_key: \"sk-your-openai-api-key\"\n temperature: 0.3\n \n - id: smart_model\n provider: anthropic\n config:\n model: claude-3-opus-20240229\n api_key: \"sk-ant-your-anthropic-api-key\"\n temperature: 0.7\n \n - id: creative_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"sk-your-openai-api-key\"\n temperature: 1.2\n```\n\n### Using Model Clients in Tasks\n\nReference model clients in AI tasks using the `model_client_id` field:\n\n```yaml\ntasks:\n - id: analysis_task\n type: ai_agent\n config:\n model_client_id: smart_model # References the claude model above\n system_message: \"You are a data analyst...\"\n user_message: \"Analyze this data: ${input_data}\"\n \n - id: creative_task\n type: ai_agent\n config:\n model_client_id: creative_model # References the creative GPT-4 model\n system_message: \"You are a creative writer...\"\n user_message: \"Write a story about ${topic}\"\n```\n\n### Environment Variables for API Keys\n\nFor security, use environment variables instead of hardcoding API keys:\n\n```yaml\nmodel_clients:\n - id: secure_model\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"${OPENAI_API_KEY}\" # References environment variable\n temperature: 0.7\n```\n\n### Common Configuration Options\n\n| Field | Description | Required | Default |\n|-------|-------------|----------|---------|\n| `id` | Unique identifier for referencing in tasks | Yes | - |\n| `provider` | AI provider name | Yes | - |\n| `config.model` | Specific model name | Yes | - |\n| `config.api_key` | Authentication API key | Yes | - |\n| `config.temperature` | Response randomness (0.0-2.0) | No | 0.7 |\n| `config.max_tokens` | Maximum response tokens | No | Provider default |\n| `config.timeout` | Request timeout in seconds | No | 30 |\n| `config.base_url` | Custom API endpoint | No | Provider default |\n\n## Secrets Management and Environment Variables\n\nProper secrets management is crucial for security. Never hardcode sensitive information like API keys, passwords, or tokens directly in workflow YAML files.\n\n### Using Environment Variables\n\nThe recommended approach is to use environment variables for all sensitive data:\n\n```yaml\n# \u2705 RECOMMENDED: Use environment variables\nmodel_clients:\n - id: secure_openai\n provider: openai\n config:\n model: gpt-4-turbo-preview\n api_key: \"${OPENAI_API_KEY}\" # References environment variable\n\n# HTTP tasks with environment variables\ntasks:\n - id: secure_api_call\n type: http\n url: \"${API_BASE_URL}/customers\" # Environment variable for URL\n headers:\n Authorization: \"Bearer ${API_TOKEN}\" # Environment variable for token\n X-API-Key: \"${EXTERNAL_API_KEY}\" # Environment variable for API key\n```\n\n### Environment Variable Patterns\n\n```yaml\n# Database connections\n- id: database_task\n type: storage\n config:\n storage_type: database\n connection_string: \"${DATABASE_URL}\" # Full connection string\n # or individual components:\n host: \"${DB_HOST}\"\n port: \"${DB_PORT}\"\n database: \"${DB_NAME}\"\n username: \"${DB_USER}\"\n password: \"${DB_PASSWORD}\"\n\n# Third-party service credentials\n- id: external_service\n type: http\n url: \"${SERVICE_BASE_URL}/api/v1/data\"\n headers:\n Authorization: \"Bearer ${SERVICE_TOKEN}\"\n X-Client-ID: \"${SERVICE_CLIENT_ID}\"\n X-Client-Secret: \"${SERVICE_CLIENT_SECRET}\"\n\n# MCP server credentials\n- id: mcp_task\n type: mcp\n mcp_server: \"@agentic/mcp-notion-server\"\n environment_vars:\n NOTION_TOKEN: \"${NOTION_API_TOKEN}\"\n WORKSPACE_ID: \"${NOTION_WORKSPACE_ID}\"\n```\n\n### Security Best Practices\n\n#### \u274c What NOT to Do\n\n```yaml\n# DON\u0027T: Hardcode secrets\nmodel_clients:\n - id: insecure_example\n provider: openai\n config:\n api_key: \"sk-actual-api-key-here\" # SECURITY RISK!\n\n# DON\u0027T: Put secrets in comments\ntasks:\n - id: bad_example\n type: http\n url: \"https://api.example.com\"\n # API key is: sk-12345... DON\u0027T DO THIS!\n headers:\n Authorization: \"Bearer sk-real-token\" # SECURITY RISK!\n```\n\n#### \u2705 What TO Do\n\n```yaml\n# DO: Use environment variables\nmodel_clients:\n - id: secure_example\n provider: openai\n config:\n api_key: \"${OPENAI_API_KEY}\" # Secure approach\n\n# DO: Use different variables for different environments\n- id: environment_aware_task\n type: http\n url: \"${API_BASE_URL}\" # Different URLs per environment\n headers:\n Authorization: \"Bearer ${API_TOKEN}\" # Different tokens per environment\n X-Environment: \"${ENVIRONMENT_NAME}\" # Track environment\n\n# DO: Use descriptive variable names\nenvironment_vars:\n CUSTOMER_API_TOKEN: \"${CUSTOMER_SERVICE_TOKEN}\"\n BILLING_API_KEY: \"${BILLING_SERVICE_API_KEY}\"\n NOTIFICATION_WEBHOOK_SECRET: \"${NOTIFICATION_SECRET}\"\n```\n\n### Environment Variable Naming Conventions\n\n| Pattern | Example | Use Case |\n|---------|---------|----------|\n| `SERVICE_API_KEY` | `OPENAI_API_KEY` | Service API keys |\n| `SERVICE_BASE_URL` | `CUSTOMER_API_BASE_URL` | Service endpoints |\n| `DB_*` | `DB_HOST`, `DB_PASSWORD` | Database credentials |\n| `WEBHOOK_*` | `WEBHOOK_SECRET_TOKEN` | Webhook authentication |\n| `*_TIMEOUT` | `HTTP_REQUEST_TIMEOUT` | Timeout configurations |\n\n### Dynamic Environment Variables\n\nUse template resolution to create dynamic environment variable names:\n\n```yaml\n# Different API keys per customer\n- id: customer_specific_api\n type: http\n url: \"https://api.partner.com/data\"\n headers:\n Authorization: \"Bearer ${PARTNER_API_KEY_${customer_tier}}\" # Resolves to PARTNER_API_KEY_PREMIUM\n\n# Environment-specific configurations\n- id: env_specific_task\n type: storage\n config:\n connection_string: \"${DATABASE_URL_${environment}}\" # DATABASE_URL_PRODUCTION\n```\n\n### Secrets in Different Task Types\n\n```yaml\n# AI Agent with multiple secure credentials\n- id: secure_ai_task\n type: ai_agent\n config:\n model_client_id: secure_model\n system_message: \"You are a secure assistant with access to customer data.\"\n user_message: \"Process customer ${customer_id}\"\n environment_vars:\n CUSTOMER_DB_TOKEN: \"${CUSTOMER_DATABASE_TOKEN}\"\n AUDIT_LOG_KEY: \"${AUDIT_LOGGING_API_KEY}\"\n\n# Script task with secure file access\n- id: secure_script_task\n type: script\n environment_vars:\n AWS_ACCESS_KEY_ID: \"${AWS_ACCESS_KEY}\"\n AWS_SECRET_ACCESS_KEY: \"${AWS_SECRET_KEY}\"\n S3_BUCKET_NAME: \"${SECURE_BUCKET_NAME}\"\n script: |\n import boto3\n import os\n \n # Credentials automatically available from environment\n s3_client = boto3.client(\u0027s3\u0027)\n bucket = os.environ[\u0027S3_BUCKET_NAME\u0027]\n \n # Process secure files...\n\n# Storage task with encrypted connections\n- id: secure_storage_task\n type: storage\n config:\n storage_type: database\n connection_string: \"${ENCRYPTED_DB_CONNECTION_STRING}\"\n ssl_mode: \"require\"\n ssl_cert: \"${SSL_CERT_PATH}\"\n ssl_key: \"${SSL_KEY_PATH}\"\n```\n\n### Environment Setup Examples\n\n#### Development Environment (.env file)\n```bash\n# Development API keys (non-production)\nOPENAI_API_KEY=sk-dev-api-key-here\nCUSTOMER_API_BASE_URL=https://api-dev.example.com\nDATABASE_URL=postgresql://dev:pass@localhost:5432/workflow_dev\nENVIRONMENT_NAME=development\n```\n\n#### Production Environment\n```bash\n# Production API keys (secure)\nOPENAI_API_KEY=sk-prod-api-key-here\nCUSTOMER_API_BASE_URL=https://api.example.com\nDATABASE_URL=postgresql://prod_user:secure_pass@prod-db:5432/workflow_prod\nENVIRONMENT_NAME=production\n```\n\n## Workflow Execution and Orchestration\n\nUnderstanding how workflows execute and how tasks are orchestrated is essential for building reliable, efficient workflows.\n\n### Execution Flow\n\nWorkflows follow a directed acyclic graph (DAG) execution model:\n\n1. **Initialization**: Workflow starts with inputs and model clients\n2. **Task Scheduling**: Tasks are scheduled based on dependencies\n3. **Parallel Execution**: Independent tasks run simultaneously\n4. **Data Flow**: Outputs flow between tasks via template resolution\n5. **Completion**: Workflow completes when all tasks finish\n\n```yaml\n# Example execution flow\ntasks:\n # Stage 1: Independent tasks (run in parallel)\n - id: fetch_customer # Runs immediately\n type: http\n url: \"https://api.example.com/customers/${customer_id}\"\n \n - id: fetch_products # Runs in parallel with fetch_customer\n type: http\n url: \"https://api.example.com/products\"\n \n # Stage 2: Dependent tasks (wait for Stage 1)\n - id: validate_customer # Waits for fetch_customer\n type: script\n depends_on: [fetch_customer]\n previous_node: fetch_customer\n script: # validation logic...\n \n - id: filter_products # Waits for fetch_products\n type: script\n depends_on: [fetch_products]\n previous_node: fetch_products\n script: # filtering logic...\n \n # Stage 3: Final processing (waits for Stage 2)\n - id: generate_recommendations # Waits for both validation and filtering\n type: ai_agent\n depends_on: [validate_customer, filter_products]\n previous_node: validate_customer # UI hierarchy under customer validation\n config:\n model_client_id: gpt4_model\n system_message: \"Generate product recommendations for customer.\"\n user_message: |\n Customer: ${validate_customer}\n Available products: ${filter_products}\n```\n\n### Task States and Lifecycle\n\nEach task goes through these states during execution:\n\n| State | Description | Next States |\n|-------|-------------|-------------|\n| `PENDING` | Task created, waiting for dependencies | `RUNNING`, `SKIPPED` |\n| `RUNNING` | Task is actively executing | `COMPLETED`, `FAILED` |\n| `COMPLETED` | Task finished successfully | - |\n| `FAILED` | Task encountered an error | `RUNNING` (if retries) |\n| `SKIPPED` | Task skipped due to conditions | - |\n| `TIMEOUT` | Task exceeded timeout limit | `RUNNING` (if retries) |\n\n### Conditional Execution\n\nTasks can be executed conditionally based on routes or conditions:\n\n```yaml\n# Router-based conditional execution\n- id: data_router\n type: conditional_router\n depends_on: [fetch_data]\n previous_node: fetch_data\n conditions:\n - name: \"large_dataset\"\n condition: \"${fetch_data.record_count} \u003e 10000\"\n route: \"batch_processing\"\n - name: \"small_dataset\"\n condition: \"${fetch_data.record_count} \u003c= 1000\"\n route: \"direct_processing\"\n default_route: \"standard_processing\"\n\n# Tasks that execute on specific routes\n- id: batch_processor\n type: script\n depends_on: [data_router]\n previous_node: data_router\n execute_on_routes: [\"batch_processing\"] # Only runs for large datasets\n script: |\n # Handle large dataset in batches\n data = ${fetch_data}\n process_in_batches(data, batch_size=1000)\n\n- id: direct_processor\n type: script\n depends_on: [data_router]\n previous_node: data_router\n execute_on_routes: [\"direct_processing\"] # Only runs for small datasets\n script: |\n # Process small dataset directly\n data = ${fetch_data}\n process_directly(data)\n```\n\n### Error Handling and Retries\n\nConfigure retry policies for resilient workflow execution:\n\n```yaml\n# Workflow-level retry policy\nretry:\n retryOn: [\"TEMPORARY_FAILURE\", \"TIMEOUT\", \"NETWORK_ERROR\"]\n maxAttempts: 3\n initialDelay: \"1s\"\n maxDelay: \"60s\"\n backoffMultiplier: 2.0\n\ntasks:\n # Task-specific retry policy (overrides workflow-level)\n - id: external_api_call\n type: http\n url: \"https://unreliable-api.example.com/data\"\n retry:\n maxAttempts: 5 # More retries for unreliable API\n initialDelay: \"2s\"\n maxDelay: \"120s\"\n backoffMultiplier: 2.0\n retryOn: [\"TIMEOUT\", \"HTTP_5XX\", \"NETWORK_ERROR\"]\n \n # Task with no retries\n - id: critical_operation\n type: script\n retry:\n maxAttempts: 1 # No retries for critical operations\n script: |\n # This operation should not be retried if it fails\n perform_critical_database_operation()\n```\n\n### Parallel Execution Patterns\n\n```yaml\n# Fan-out pattern: One task triggers multiple parallel tasks\n- id: data_source\n type: http\n url: \"https://api.example.com/data\"\n\n- id: process_customers # Parallel processing\n type: script\n depends_on: [data_source]\n previous_node: data_source\n script: # process customer data from data_source\n\n- id: process_orders # Parallel processing\n type: script\n depends_on: [data_source]\n previous_node: data_source\n script: # process order data from data_source\n\n- id: process_products # Parallel processing\n type: script\n depends_on: [data_source]\n previous_node: data_source\n script: # process product data from data_source\n\n# Fan-in pattern: Multiple tasks feed into one task\n- id: generate_report # Waits for all parallel tasks\n type: ai_agent\n depends_on: [process_customers, process_orders, process_products]\n previous_node: process_customers # UI hierarchy\n config:\n model_client_id: gpt4_model\n system_message: \"Generate comprehensive report from processed data.\"\n user_message: |\n Customer data: ${process_customers}\n Order data: ${process_orders}\n Product data: ${process_products}\n```\n\n### Resource Management\n\nControl resource usage and task distribution:\n\n```yaml\n# Worker group targeting\n- id: cpu_intensive_task\n type: script\n worker_group: \"high-cpu-workers\" # Target specific worker group\n script: |\n # CPU-intensive processing\n perform_heavy_computation()\n\n- id: gpu_ml_task\n type: ai_agent\n worker_group: \"gpu-workers\" # Target GPU workers\n config:\n model_client_id: large_model\n # Large model processing...\n\n# Timeout configuration\n- id: long_running_task\n type: script\n timeout_seconds: 3600 # 1 hour timeout\n script: |\n # Long-running data processing\n process_large_dataset()\n\n# Workflow-level timeout\ntimeout_seconds: 7200 # 2 hour workflow timeout\n```\n\n## Task Type Configurations\n\nThis section provides the complete YAML configuration for each supported task type.\n\n**Supported Task Types:**\n- `script` - Execute Python code with full workflow context\n- `http` - Make HTTP requests to external APIs\n- `ai_agent` - Integrate with AI models for intelligent processing \n- `loop` - Execute tasks repeatedly (for/while loops)\n- `conditional_router` - Route execution based on conditions\n- `mcp` - Execute tools via Model Context Protocol servers\n- `storage` - Data persistence and retrieval operations\n- `continuous_conversation` - Interactive AI conversations\n- `human_input` - Tasks requiring human input or approval\n- `chatbot` - Session-based chatbot conversations\n\n### Script Tasks\n\nExecute Python code with full workflow context access.\n\n```yaml\n- id: script_task\n name: \"Python Script Task\"\n type: script\n description: \"Execute Python code with dependencies and environment\"\n \n # Script configuration - script field is directly under task, NOT under config\n script: | # REQUIRED: Python code to execute\n import json\n import requests\n \n # Access workflow inputs\n input_value = \"${workflow_input}\"\n \n # Access previous task outputs\n previous_result = ${previous_task}\n \n # Your processing logic\n result = {\"processed\": True, \"value\": input_value}\n \n # Output results (must be JSON serializable)\n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n \n # Alternative: use \u0027code\u0027 field instead of \u0027script\u0027\n code: | # Alternative to \u0027script\u0027 field\n print(\"Hello World\")\n \n # Package management\n requirements: # Optional: Python packages\n - requests==2.31.0\n - pandas==1.5.3\n - numpy==1.24.3\n \n # Environment configuration\n environment: # Optional: Environment variables\n API_KEY: \"${secrets.api_key}\"\n DEBUG: \"true\"\n CUSTOM_VAR: \"value\"\n \n environment_vars: # Alternative to \u0027environment\u0027\n DATABASE_URL: \"${secrets.db_url}\"\n \n # Secrets configuration\n secrets_config: # Optional: Secrets access\n api_key: \"secret_name\"\n db_password: \"db_secret\"\n```\n\n### HTTP Tasks\n\nMake HTTP requests to external APIs and services.\n\n```yaml\n- id: http_task\n name: \"HTTP API Call\"\n type: http\n description: \"Call external REST API\"\n \n # HTTP configuration\n method: POST # Default: GET\n url: \"https://api.example.com/data\" # REQUIRED: Target URL\n \n # Headers configuration\n headers: # Optional: HTTP headers\n Content-Type: \"application/json\"\n Authorization: \"Bearer ${secrets.api_token}\"\n X-Custom-Header: \"workflow-engine\"\n User-Agent: \"WorkflowEngine/1.0\"\n \n # Request body\n body: | # Optional: Request body (for POST/PUT)\n {\n \"customer_id\": \"${customer_id}\",\n \"timestamp\": \"${current_timestamp}\",\n \"data\": ${previous_task}\n }\n \n # Response validation\n expected_status: [200, 201, 202] # Default: [200]\n expectedStatus: [200, 201] # Alternative field name\n \n # Additional HTTP options\n timeout_seconds: 30 # HTTP request timeout\n follow_redirects: true # Follow HTTP redirects\n verify_ssl: true # Verify SSL certificates\n```\n\n### AI Agent Tasks\n\nIntegrate with AI models for intelligent processing.\n\n```yaml\n- id: ai_agent_task\n name: \"AI Processing Agent\"\n type: ai_agent\n description: \"Process data using AI model\"\n \n # AI configuration\n config:\n model_client_id: gpt4_model # REQUIRED: Reference to model client\n \n system_message: | # Optional: System prompt\n You are a data analyst. Analyze the provided data and extract insights.\n Return your analysis in JSON format with the following structure:\n {\n \"insights\": [\"insight1\", \"insight2\"],\n \"recommendations\": [\"rec1\", \"rec2\"],\n \"confidence_score\": 0.95\n }\n \n user_message: | # REQUIRED: User prompt with template resolution\n Please analyze this customer data:\n ${previous_task}\n \n Customer ID: ${customer_id}\n Date Range: ${date_range}\n \n # Model parameters\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\n max_tokens: 1500 # Optional: Maximum response tokens\n top_p: 1.0 # Optional: Nucleus sampling\n frequency_penalty: 0.0 # Optional: Frequency penalty\n presence_penalty: 0.0 # Optional: Presence penalty\n \n # Tools configuration for function calling\n tools: # Optional: List of MCP tool names\n - current_time # Time-related tools\n - relative_time\n - days_in_month\n - weather_api # Weather tools\n - database_query # Database tools\n - notification_service # Communication tools\n \n # Required for tool resolution\n tenant_id: \"your-tenant-uuid\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\n \n # Input/output format\n input_format: \"text\" # Optional: text|json|structured\n output_format: \"structured\" # Optional: text|json|structured\n\n### Important: AI Agent Output Access\n\nAI agent tasks return structured objects, not direct text responses. Always access the AI response using this pattern:\n\n```yaml\n# In subsequent script tasks\nscript: |\n # CORRECT: Access AI response from structured output\n agent_output = ${ai_agent_task}\n ai_response = agent_output[\u0027ai_response\u0027].strip()\n \n # Additional metadata available\n tokens_used = agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\n cost_estimate = agent_output.get(\u0027estimated_cost\u0027, {}).get(\u0027amount\u0027, 0.0)\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\n \n print(f\"AI Response: {ai_response}\")\n print(f\"Tokens used: {tokens_used}\")\n print(f\"Estimated cost: ${cost_estimate:.6f}\")\n```\n\n```yaml\n# In workflow outputs - specify the ai_response field\noutputs:\n ai_analysis:\n type: string\n source: ai_agent_task.ai_response # Access the ai_response field\n description: \"AI-generated analysis text\"\n \n total_tokens:\n type: integer \n source: ai_agent_task.token_usage.total_tokens\n description: \"Total tokens consumed\"\n```\n```\n\n### Loop Tasks\n\nExecute tasks repeatedly with for-loop and while-loop patterns.\n\n```yaml\n# For-Loop (Fixed Iterations)\n- id: for_loop_task\n name: \"For Loop Processor\"\n type: loop\n description: \"Process data in fixed batches\"\n \n # Loop configuration\n loop_type: for # REQUIRED: for|while\n max_iterations: 10 # REQUIRED for \u0027for\u0027 loops\n iteration_variable: batch_index # Default: iteration\n \n # State variables (persistent across iterations)\n state_variables:\n total_processed: 0\n error_count: 0\n batch_results: []\n \n # Tasks to execute in each iteration\n loop_tasks: # REQUIRED: Tasks to run in loop\n - id: process_batch\n name: \"Process Current Batch\"\n type: script\n script: |\n batch_idx = ${batch_index} # Access iteration variable\n total = ${total_processed} # Access state variable\n \n # Process current batch\n result = process_batch_data(batch_idx)\n \n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n \n - id: update_state\n name: \"Update Loop State\"\n type: script\n depends_on: [process_batch]\n script: |\n # Update state variables\n new_total = ${total_processed} + ${process_batch.items_processed}\n \n result = {\"total_processed\": new_total}\n print(f\"__OUTPUTS__ {json.dumps(result)}\")\n\n# While-Loop (Conditional)\n- id: while_loop_task\n name: \"While Loop Quality Processor\"\n type: loop\n description: \"Process until quality threshold met\"\n \n # Loop configuration\n loop_type: while # REQUIRED: for|while\n exit_condition: \"${average_quality} \u003e= 85 || ${iteration_count} \u003e= 20\" # REQUIRED for \u0027while\u0027\n max_iterations: 50 # Optional: Safety limit\n iteration_variable: iteration_count # Default: iteration\n \n # State variables\n state_variables:\n average_quality: 0\n items_processed: 0\n quality_scores: []\n \n # Loop tasks\n loop_tasks:\n - id: assess_quality\n type: script\n script: |\n current_quality = calculate_quality()\n print(f\"__OUTPUTS__ {json.dumps({\u0027quality\u0027: current_quality})}\")\n```\n\n\n### Conditional Router Tasks\n\nRoute execution to different paths based on conditions.\n\n```yaml\n- id: conditional_router_task\n name: \"Data Type Router\"\n type: conditional_router\n description: \"Route processing based on data characteristics\"\n \n # Router configuration\n router_type: condition # Default: condition\n \n # Routing conditions\n conditions: # REQUIRED: List of conditions\n - name: \"large_dataset\"\n condition: \"${data_size} \u003e 10000\"\n route: \"batch_processing\"\n - name: \"small_dataset\" \n condition: \"${data_size} \u003c= 1000\"\n route: \"direct_processing\"\n - name: \"medium_dataset\"\n condition: \"${data_size} \u003e 1000 \u0026\u0026 ${data_size} \u003c= 10000\"\n route: \"standard_processing\"\n \n default_route: \"standard_processing\" # REQUIRED: Default route if no conditions match\n \n # Alternative: evaluation script for complex routing\n evaluation_script: | # Optional: Custom routing logic\n if data_type == \"json\":\n return \"json_processing\"\n elif data_type == \"xml\":\n return \"xml_processing\"\n else:\n return \"default_processing\"\n \n # Route mapping for complex scenarios\n route_mapping: # Optional: Route name mappings\n json_processing: \"specialized_json\"\n xml_processing: \"specialized_xml\"\n\n# Tasks that execute on specific routes\n- id: batch_handler\n name: \"Handle Large Dataset\"\n type: script\n execute_on_routes: [\"batch_processing\"] # REQUIRED: Routes this task runs on\n depends_on: [conditional_router_task]\n script: |\n print(\"Processing large dataset in batches...\")\n\n- id: direct_handler\n name: \"Handle Small Dataset\" \n type: script\n execute_on_routes: [\"direct_processing\"]\n depends_on: [conditional_router_task]\n script: |\n print(\"Processing small dataset directly...\")\n```\n\n### MCP Tasks\n\nExecute tools via Model Context Protocol servers.\n\n```yaml\n- id: mcp_task\n name: \"MCP Tool Execution\"\n type: mcp\n description: \"Execute external tool via MCP server\"\n \n # MCP server configuration\n mcp_server: \"@agentic/mcp-notion-server\" # REQUIRED: MCP server package\n tool_name: \"update_database\" # REQUIRED: Tool to execute\n runtime: \"npx\" # REQUIRED: npx|uvx\n \n # Server startup\n server_args: [] # Optional: Server startup arguments\n server_startup_timeout: 30 # Optional: Server startup timeout (seconds)\n \n # Tool execution\n tool_arguments: # REQUIRED: Arguments for the tool\n database_id: \"${secrets.notion_database_id}\"\n properties:\n title: \"Workflow Results\"\n status: \"Completed\"\n processed_count: \"${previous_task.count}\"\n timestamp: \"${current_timestamp}\"\n \n # Environment configuration\n environment_vars: # Optional: Environment variables\n NOTION_TOKEN: \"${secrets.notion_token}\"\n DEBUG: \"true\"\n \n # Timeout configuration\n timeout_seconds: 120 # Optional: Tool execution timeout\n```\n\n### Storage Tasks\n\nManage data persistence and retrieval operations.\n\n```yaml\n- id: storage_task\n name: \"Data Storage Operation\"\n type: storage\n description: \"Save/load data to/from storage\"\n \n # Storage operation\n operation: save # REQUIRED: save|load|delete|list\n \n # Storage configuration\n storage_type: \"file\" # REQUIRED: file|database|cache\n path: \"/data/results/${execution_id}.json\" # REQUIRED: Storage path\n \n # Data configuration\n data: ${previous_task} # REQUIRED for \u0027save\u0027: Data to store\n format: \"json\" # Optional: json|yaml|csv|parquet\n compression: \"gzip\" # Optional: gzip|bz2|none\n \n # Metadata\n metadata: # Optional: Additional metadata\n workflow_id: \"${workflow_id}\"\n created_at: \"${current_timestamp}\"\n source_task: \"previous_task\"\n \n # File options\n create_directories: true # Optional: Create parent directories\n overwrite: true # Optional: Overwrite existing files\n backup: false # Optional: Create backup before overwrite\n \n # Database options (when storage_type: \"database\")\n table_name: \"workflow_results\" # Required for database storage\n connection_string: \"${secrets.db_url}\" # Required for database storage\n \n # Cache options (when storage_type: \"cache\")\n cache_key: \"result_${execution_id}\" # Required for cache storage\n ttl: 3600 # Optional: Time to live (seconds)\n```\n\n\n### Continuous Conversation Tasks\n\nCreate interactive AI-powered conversations.\n\n```yaml\n- id: conversation_task\n name: \"AI Conversation Interface\"\n type: continuous_conversation\n description: \"Interactive conversation with AI assistant\"\n \n # Conversation configuration\n config:\n # AI configuration\n ai_config:\n model_client_id: support_assistant # REQUIRED: AI model to use\n system_prompt: | # REQUIRED: System instructions\n You are a helpful customer support representative.\n Be friendly, professional, and solution-oriented.\n \n Guidelines:\n - Always be polite and respectful\n - Ask clarifying questions when needed\n - Provide clear and actionable solutions\n \n temperature: 0.7 # Optional: AI creativity\n max_tokens: 1000 # Optional: Response length limit\n \n # UI configuration\n ui_config:\n title: \"Customer Support Chat\" # Optional: Interface title\n placeholder: \"How can I help you today?\" # Optional: Input placeholder\n theme: \"light\" # Optional: light|dark\n show_timestamp: true # Optional: Show message timestamps\n \n # Termination conditions\n termination:\n keywords: [\"goodbye\", \"bye\", \"thanks\", \"done\"] # Optional: End conversation keywords\n max_turns: 50 # Optional: Maximum conversation turns\n timeout_minutes: 30 # Optional: Conversation timeout\n inactivity_timeout: 10 # Optional: Inactivity timeout (minutes)\n \n # Context configuration\n context:\n system_context: \"Customer support conversation\" # Optional: Context description\n user_id: \"${customer_id}\" # Optional: User identifier\n session_data: ${previous_task} # Optional: Additional context data\n```\n\n### Human Input Tasks\n\nTasks requiring human input or approval.\n\n```yaml\n- id: human_input_task\n name: \"Human Input Required\"\n type: human_input\n description: \"Wait for human input or approval\"\n \n # Human input configuration\n config:\n prompt: \"Please review and approve the processed data\" # REQUIRED: Prompt for human\n input_type: \"approval\" # REQUIRED: approval|text|selection\n \n # Input options (for selection type)\n options: [\"approve\", \"reject\", \"request_changes\"] # Optional: For selection input\n \n # Timeout configuration\n timeout_minutes: 60 # Optional: How long to wait for input (minutes)\n \n # Conversation settings\n conversation_mode: false # Optional: Add to conversation history\n next_task: \"process_approval\" # Optional: Task to trigger after input\n end_condition: \"bye|exit|quit\" # Optional: Keywords to end conversation\n \n # Input validation\n required: true # Optional: Whether input is required\n \n # Additional context\n context_data: ${previous_task} # Optional: Data to show to human\n```\n\n### ChatBot Tasks\n\nSession-based chatbot conversations with streaming support.\n\n```yaml\n- id: chatbot_task\n name: \"AI Chatbot\"\n type: chatbot\n description: \"Interactive chatbot with session management\"\n \n # Chatbot configuration\n config:\n # AI model configuration\n model_client_id: gpt4_model # REQUIRED: Reference to model client\n \n system_message: | # REQUIRED: System prompt for chatbot\n You are a helpful AI assistant. Be friendly, helpful, and concise.\n Answer questions clearly and ask for clarification when needed.\n \n # Session management\n session_id: \"${session_id}\" # Optional: Session identifier (auto-generated if not provided)\n max_history_messages: 20 # Optional: Maximum conversation history to maintain\n \n # Model parameters\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\n max_tokens: 1000 # Optional: Maximum response tokens\n stream: true # Optional: Enable streaming responses\n \n # Tools configuration for function calling\n tools: # Optional: List of MCP tool names\n - current_time # Time-related tools\n - relative_time\n - days_in_month\n - weather_forecast # Weather tools\n - send_notification # Communication tools\n \n # Required for tool resolution\n tenant_id: \"your-tenant-uuid\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\n \n # UI configuration\n ui_config:\n title: \"AI Assistant\" # Optional: Chat interface title\n placeholder: \"Type your message...\" # Optional: Input placeholder\n theme: \"light\" # Optional: light|dark\n show_typing_indicator: true # Optional: Show typing animation\n \n # Response configuration\n response_config:\n max_retries: 3 # Optional: Retry failed requests\n timeout_seconds: 30 # Optional: Response timeout\n \n # Input message (for single-turn mode)\n user_message: \"${user_input}\" # Optional: Direct message input\n \n # Context data\n context_data: ${previous_task} # Optional: Additional context\n```\n\n---\n\n## Common Errors and Corrections\n\nThis section documents frequent mistakes encountered when creating workflows and their proper corrections.\n\n### 1. Missing Required `id` Field\n\n**\u274c Error:**\n```yaml\nname: \"My Workflow\"\ndescription: \"A workflow without id\"\ntasks:\n - name: \"Task 1\"\n type: script\n```\n\n**\u2705 Correct:**\n```yaml\nid: \"my_workflow\" # REQUIRED: Unique workflow identifier\nname: \"My Workflow\"\ndescription: \"A workflow with proper id\"\ntasks:\n - id: task_1 # REQUIRED: Unique task identifier\n name: \"Task 1\"\n type: script\n```\n\n### 2. Incorrect Task Structure (Object vs Array)\n\n**\u274c Error:**\n```yaml\ntasks:\n task_1: # Wrong: tasks as object mapping\n type: script\n script: \"print(\u0027hello\u0027)\"\n task_2:\n type: ai_agent\n```\n\n**\u2705 Correct:**\n```yaml\ntasks:\n - id: task_1 # Correct: tasks as array with id field\n type: script\n script: \"print(\u0027hello\u0027)\"\n - id: task_2\n type: ai_agent\n```\n\n### 3. Incorrect Inputs Format (Object vs Array)\n\n**\u274c Error:**\n```yaml\ninputs:\n user_name: # Wrong: inputs as object mapping\n type: string\n required: true\n age:\n type: integer\n```\n\n**\u2705 Correct:**\n```yaml\ninputs:\n - name: user_name # Correct: inputs as array with name field\n type: string\n required: true\n - name: age\n type: integer\n```\n\n### 4. Script Configuration Location Error\n\n**\u274c Error:**\n```yaml\n- id: script_task\n type: script\n config: # Wrong: script under config\n script: |\n print(\"Hello\")\n requirements:\n - requests\n```\n\n**\u2705 Correct:**\n```yaml\n- id: script_task\n type: script\n script: | # Correct: script directly under task\n print(\"Hello\")\n # requirements can be at task level if needed\n```\n\n### 5. Individual Task Inputs (Not Supported)\n\n**\u274c Error:**\n```yaml\n- id: task_1\n type: script\n script: \"print(\u0027hello\u0027)\"\n inputs: # Wrong: tasks don\u0027t have individual inputs\n search_query: ${search_query}\n prev_result: ${previous_task}\n```\n\n**\u2705 Correct:**\n```yaml\n- id: task_1\n type: script\n depends_on: [previous_task] # Use depends_on for data access\n script: |\n # Access workflow inputs directly\n search_query = \"${search_query}\"\n # Access previous task outputs via template resolution\n prev_result = ${previous_task}\n```\n\n### 6. Incorrect Script Output Handling\n\n**\u274c Error:**\n```yaml\nscript: |\n result = process_data()\n outputs[\u0027result\u0027] = result # Wrong: missing __OUTPUTS__ pattern\n```\n\n**\u2705 Correct:**\n```yaml\nscript: |\n result = process_data()\n \n # Required: Use outputs dict and __OUTPUTS__ marker\n outputs = {}\n outputs[\u0027result\u0027] = result\n outputs[\u0027status\u0027] = \u0027success\u0027\n \n import json\n print(f\"__OUTPUTS__ {json.dumps(outputs)}\")\n```\n\n### 7. AI Agent Configuration Errors\n\n**\u274c Error:**\n```yaml\n- id: ai_task\n type: ai_agent\n config:\n model_client_id: my_model\n prompt: \"Process this data: ${data}\" # Wrong: use user_message\n```\n\n**\u2705 Correct:**\n```yaml\n- id: ai_task\n type: ai_agent\n config:\n model_client_id: my_model\n system_message: \"You are a helpful assistant.\"\n user_message: \"Process this data: ${data}\" # Correct field name\n```\n\n### 8. Missing Dependencies for Template Resolution\n\n**\u274c Error:**\n```yaml\n- id: task_2\n type: script\n # Missing depends_on\n script: |\n data = ${task_1} # Will become UNRESOLVED_task_1\n```\n\n**\u2705 Correct:**\n```yaml\n- id: task_2\n type: script\n depends_on: [task_1] # Required for template access\n script: |\n data = ${task_1} # Now resolves correctly\n```\n\n### 9. Incorrect Model Client Configuration\n\n**\u274c Error:**\n```yaml\nmodel_clients:\n my_model: # Wrong: object mapping format\n type: openai\n api_key: \"sk-...\"\n```\n\n**\u2705 Correct:**\n```yaml\nmodel_clients:\n - id: my_model # Correct: array format with id field\n provider: openai\n config:\n model: \"gpt-4\"\n api_key: \"sk-...\"\n```\n\n### 10. Empty String Literals Causing Python Syntax Errors\n\n**\u274c Error:**\n```yaml\nscript: |\n def process_data(path=\"\"): # Wrong: empty string can cause syntax issues\n current_path = f\"{path}.{key}\" if path else key\n \n # This can lead to: SyntaxError: unterminated string literal\n```\n\n**\u2705 Correct:**\n```yaml\nscript: |\n def process_data(path=None): # Correct: use None instead of empty string\n current_path = f\"{path}.{key}\" if path else key\n # Or use path = path or \"default_value\"\n```\n\n### 11. YAML Indentation and Structure Issues\n\n**\u274c Error:**\n```yaml\ntasks:\n- id: task_1\n type: script\nscript: | # Wrong: inconsistent indentation\n print(\"hello\")\n```\n\n**\u2705 Correct:**\n```yaml\ntasks:\n- id: task_1\n type: script\n script: | # Correct: consistent indentation\n print(\"hello\")\n```\n\n### Best Practices Summary\n\n1. **Always include required `id` fields** for workflows and tasks\n2. **Use array format** for `tasks`, `inputs`, and `model_clients`\n3. **Script field goes directly under task**, not under `config`\n4. **No individual task inputs** - use template resolution instead\n5. **Use `depends_on`** to access previous task outputs\n6. **Print `__OUTPUTS__`** in scripts for proper output parsing\n7. **Use `user_message`** (not `prompt`) for AI agent tasks\n8. **Avoid empty string literals** - use `None` or meaningful defaults\n9. **Follow consistent YAML indentation** throughout the file\n\n## MCP Tools and Function Calling\n\nAI agents and chatbots can use MCP (Model Context Protocol) tools for function calling, enabling them to execute external tools and services.\n\n### Tools Configuration\n\nBoth `ai_agent` and `chatbot` tasks support tools configuration:\n\n```yaml\n# AI Agent with MCP tools\n- id: ai_with_tools\n type: ai_agent\n config:\n model_client_id: gpt4_model\n system_message: |\n You are a helpful assistant with access to various tools.\n Use the available tools when needed to help users.\n user_message: \"What time is it and what\u0027s the weather like?\"\n \n # Tools configuration\n tools: # List of MCP tool names\n - current_time # Time tools\n - relative_time\n - days_in_month\n - weather_forecast # Weather tools\n - database_query # Database tools\n - send_notification # Communication tools\n \n tenant_id: \"550e8400-e29b-41d4-a716-446655440000\" # Required for tool resolution\n\n# Chatbot with MCP tools\n- id: chatbot_with_tools\n type: chatbot\n config:\n model_client_id: gpt4_model\n system_message: |\n You are a helpful chatbot with access to tools.\n Use tools when users ask for specific information or actions.\n \n # Tools configuration (same format as AI agent)\n tools:\n - current_time\n - weather_api\n - user_preferences\n \n tenant_id: \"550e8400-e29b-41d4-a716-446655440000\" # Required for tool resolution\n```\n\n### How Tools Work\n\n1. **Tool Resolution**: The workflow engine resolves tool names to actual MCP server deployments using the `tenant_id`\n2. **Function Calling**: When the AI model decides to use a tool, it calls the `tool_executor` function\n3. **Tool Execution**: The engine executes the specified tool via the appropriate MCP server\n4. **Response Integration**: Tool results are fed back to the AI model for response generation\n\n### Tool Configuration Requirements\n\n| Field | Required | Description |\n|-------|----------|-------------|\n| `tools` | No | List of MCP tool names available to the AI |\n| `tenant_id` | **Yes** (when using tools) | Tenant UUID for tool resolution |\n\n### Example: Complete AI Agent with Tools\n\n```yaml\nid: intelligent_assistant\nname: \"Intelligent Assistant with Tools\"\n\nmodel_clients:\n - id: gpt4_model\n provider: openai\n config:\n model: gpt-4o-mini\n api_key: \"${OPENAI_API_KEY}\"\n temperature: 0.3\n\n# Tenant ID can be set at workflow level for all tasks\ntenant_id: \"0572fa8d-d7c3-47db-9219-ef40b80d42b7\"\n\ntasks:\n - id: assistant_with_tools\n type: ai_agent\n config:\n model_client_id: gpt4_model\n system_message: |\n You are a helpful assistant with access to various tools:\n - Time-related tools: current_time, relative_time, days_in_month\n - Weather tools: weather_forecast\n - Communication: send_notification\n \n Use these tools when users ask for:\n - Current time or date information\n - Weather forecasts\n - Sending notifications\n \n Always explain what tool you\u0027re using and why.\n \n user_message: \"${user_query}\"\n \n # MCP tools configuration\n tools:\n - current_time\n - relative_time\n - days_in_month\n - weather_forecast\n - send_notification\n \n # Tool resolution requires tenant_id\n tenant_id: \"0572fa8d-d7c3-47db-9219-ef40b80d42b7\"\n \n temperature: 0.7\n max_tokens: 1000\n\ninputs:\n - name: user_query\n type: string\n required: true\n default: \"What time is it and can you send a notification to user123?\"\n\noutputs:\n ai_response:\n type: string\n source: assistant_with_tools.ai_response\n description: \"AI response using tools when needed\"\n```\n\n### Tool Execution Flow\n\n1. **User Input**: User provides a query that might require tool usage\n2. **AI Analysis**: AI model analyzes the query and decides if tools are needed\n3. **Tool Calling**: AI calls `tool_executor(deployment_id, tool_name, arguments)`\n4. **Tool Resolution**: Engine resolves tool name to MCP server using tenant_id\n5. **Tool Execution**: MCP server executes the requested tool\n6. **Result Processing**: Tool results are returned to the AI model\n7. **Response Generation**: AI generates final response incorporating tool results\n\n### Best Practices for Tools\n\n1. **Clear System Messages**: Explain available tools and when to use them\n2. **Tool Categories**: Group related tools (time, weather, database, etc.)\n3. **Error Handling**: AI models handle tool execution failures gracefully\n4. **Tool Documentation**: Include tool descriptions in system messages\n5. **Tenant Configuration**: Ensure tenant_id matches your MCP server setup\n\n---\n\n*This comprehensive reference covers all supported task types and their YAML configurations. Use this as a quick reference when building workflows with different task types.*",
"format": "markdown"
},
"execution_details": {
"actual_result": {
"output": "Trying to fetch documentation from: http://host.docker.internal:5000/api/docs/yaml-reference\n\u274c Connection failed: http://host.docker.internal:5000/api/docs/yaml-reference - HTTPConnectionPool(host=\u0027host.docker.internal\u0027, port=5000): Max retries exceeded with url: /api/docs/yaml-reference (Caused by NameResolutionError(\"\u003curllib3.connection.HTTPConnection object at 0x75595e911cd0\u003e: Failed to resolve \u0027host.docker.internal\u0027 ([Errno -2] Name or service not known)\"))\nTrying to fetch documentation from: http://172.17.0.1:5000/api/docs/yaml-reference\n\u2705 Successfully connected to: http://172.17.0.1:5000/api/docs/yaml-reference\nSuccessfully fetched documentation from http://172.17.0.1:5000/api/docs/yaml-reference: 97047 characters\n__OUTPUTS__ {\"documentation\": {\"content\": \"# YAML Workflow Structure Reference\\n\\nComplete reference for the YAML workflow definition structure supported by the Assistants Workflow Engine.\\n\\n## Table of Contents\\n\\n1. [Complete Schema Overview](#complete-schema-overview)\\n2. [Required vs Optional Fields](#required-vs-optional-fields)\\n3. [Data Types Reference](#data-types-reference)\\n4. [Field Validation Rules](#field-validation-rules)\\n5. [Parser Behavior](#parser-behavior)\\n6. [Migration Guide](#migration-guide)\\n\\n## Complete Schema Overview\\n\\n```yaml\\n# === WORKFLOW METADATA (Required) ===\\nid: string # REQUIRED: Unique workflow identifier\\nname: string # REQUIRED: Human-readable workflow name\\n\\n# === WORKFLOW METADATA (Optional) ===\\nversion: string | integer # Default: 1\\nnamespace: string # Default: \\\"default\\\"\\ndescription: string # Default: \\\"\\\"\\ntenant_id: string # Default: null\\n\\n# === EXECUTION CONFIGURATION ===\\ntimeout_seconds: integer|string # Default: 3600 (1 hour) - Workflow-level timeout (supports duration strings like \\\"1h\\\", \\\"30m\\\")\\nlabels: object # Default: {}\\nmetadata: object # Default: {}\\n\\n# === DATA FLOW DEFINITIONS ===\\ninputs: # Default: []\\n - name: string # REQUIRED\\n type: string # REQUIRED: string|integer|boolean|object|array\\n description: string # Optional\\n required: boolean # Default: false\\n default: any # Optional default value\\n\\noutputs: object # Default: {}\\n output_name:\\n type: string # REQUIRED\\n source: string # REQUIRED: task_id or task_id.field\\n description: string # Optional\\n\\nvariables: object # Default: {}\\n\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n - id: string # REQUIRED: Unique identifier to reference in tasks\\n provider: string # REQUIRED: openai|anthropic|openrouter|groq\\n config: object # REQUIRED: Provider-specific config\\n model: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\n api_key: string # REQUIRED: API key for the provider\\n temperature: number # Optional: 0.0-2.0, controls randomness\\n max_tokens: integer # Optional: Maximum tokens to generate\\n timeout: integer # Optional: Request timeout in seconds\\n base_url: string # Optional: Custom API base URL\\n\\n# Model Client Examples:\\n# OpenAI Configuration:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 2000\\n#\\n# Anthropic Configuration:\\n# model_clients:\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 1500\\n#\\n# Multiple Models:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n\\n# === TASK DEFINITIONS (Required) ===\\ntasks: # REQUIRED: List of tasks\\n - id: string # REQUIRED: Unique task identifier\\n name: string # Optional: Human-readable name\\n type: string # REQUIRED: Task type\\n description: string # Optional\\n \\n # DEPENDENCY CONFIGURATION\\n depends_on: array # Optional: Execution dependencies and template resolution\\n previous_node: string # REQUIRED: Task connectivity and UI hierarchy (essential for proper workflow execution)\\n \\n timeout_seconds: integer|string # Optional: Task-specific timeout (supports duration strings like \\\"5m\\\", \\\"30s\\\")\\n worker_group: string # Optional: Target worker group\\n conditions: array # Optional: Execution conditions\\n execute_on_routes: array # Optional: Routes for conditional execution\\n \\n # Input/Output mapping\\n input_mapping: object # Optional: Map workflow context to task inputs\\n output_mapping: object # Optional: Map task outputs to workflow context\\n \\n # Retry configuration\\n retry: object # Optional: Task-specific retry policy\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n \\n # === TASK TYPE SPECIFIC CONFIGURATIONS ===\\n # See detailed sections below for each task type\\n\\n# === WORKFLOW-LEVEL RETRY POLICY ===\\nretry: object # Optional: Global retry policy\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n\\n# === TRIGGERS ===\\ntriggers: # Optional: List of triggers\\n - name: string # REQUIRED\\n type: string # REQUIRED: webhook|schedule|event\\n description: string # Optional\\n config: object # REQUIRED: Trigger-specific config\\n input_mapping: array # Optional: Map trigger data to workflow inputs\\n - webhook_field: string # Source field\\n workflow_input: string # Target workflow input\\n```\\n\\n## Required vs Optional Fields\\n\\n### Required Fields (Workflow Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the workflow |\\n| `tasks` | array | List of task definitions (can be empty) |\\n\\n### Required Fields (Task Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the task |\\n| `type` | string | Task type (script, http, ai_agent, etc.) |\\n| `previous_node` | string | Reference to previous task for UI connectivity (required for proper task linking, except for the first task) |\\n\\n### Required Fields (Input Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `name` | string | Input parameter name |\\n| `type` | string | Data type (string, integer, boolean, object, array) |\\n\\n### Required Fields (Output Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `type` | string | Output data type |\\n| `source` | string | Source task ID or task_id.field_name |\\n\\n## Data Types Reference\\n\\n### Primitive Types\\n\\n```yaml\\n# String type\\ninputs:\\n - name: customer_name\\n type: string\\n default: \\\"John Doe\\\"\\n description: \\\"Customer full name\\\"\\n\\n# Integer type \\ninputs:\\n - name: batch_size\\n type: integer\\n default: 100\\n description: \\\"Number of records per batch\\\"\\n\\n# Boolean type\\ninputs:\\n - name: include_archived\\n type: boolean\\n default: false\\n description: \\\"Include archived records\\\"\\n```\\n\\n### Complex Types\\n\\n```yaml\\n# Object type\\ninputs:\\n - name: config_params\\n type: object\\n default:\\n timeout: 30\\n retries: 3\\n debug: false\\n description: \\\"Configuration parameters\\\"\\n\\n# Array type\\ninputs:\\n - name: customer_ids\\n type: array\\n default: [\\\"cust_001\\\", \\\"cust_002\\\"]\\n description: \\\"List of customer identifiers\\\"\\n```\\n\\n### Duration Types\\n\\nDuration values can be specified as strings or integers:\\n\\n```yaml\\n# String format (recommended)\\ntimeout_seconds: \\\"1h\\\" # 1 hour\\ntimeout_seconds: \\\"30m\\\" # 30 minutes \\ntimeout_seconds: \\\"45s\\\" # 45 seconds\\ntimeout_seconds: \\\"2d\\\" # 2 days\\n\\n# Integer format (seconds)\\ntimeout_seconds: 3600 # 1 hour in seconds\\n\\n# Supported units\\n# s, sec, second, seconds\\n# m, min, minute, minutes \\n# h, hour, hours\\n# d, day, days\\n```\\n\\n### Timeout Configuration and Execution Behavior\\n\\nThe workflow engine supports configurable timeouts at both workflow and task levels with intelligent timeout-free communication for long-running tasks.\\n\\n#### Timeout Hierarchy\\n\\nTimeout values are resolved in this order of precedence:\\n1. **Task-level `timeout_seconds`**: Specific timeout for individual tasks\\n2. **Workflow-level `timeout_seconds`**: Default for all tasks in the workflow\\n3. **System default**: 300 seconds (5 minutes) for tasks, 3600 seconds (1 hour) for workflows\\n\\n```yaml\\n# Workflow-level timeout (applies to all tasks by default)\\ntimeout_seconds: \\\"2h\\\" # 2 hour workflow timeout\\n\\ntasks:\\n - id: quick_task\\n type: script\\n # Uses workflow-level timeout (2h)\\n script: \\\"print(\u0027Quick task\u0027)\\\"\\n \\n - id: custom_timeout_task\\n type: script\\n timeout_seconds: \\\"30m\\\" # Override with 30 minute timeout\\n script: \\\"print(\u0027Custom timeout task\u0027)\\\"\\n \\n - id: long_running_task\\n type: script\\n timeout_seconds: 600 # 10 minutes (integer format)\\n script: |\\n import time\\n time.sleep(300) # 5 minute processing\\n print(\\\"Long task completed\\\")\\n```\\n\\n#### Timeout-Free Communication\\n\\nThe workflow engine automatically uses **timeout-free communication** for long-running tasks to prevent timeout-related failures:\\n\\n- **Loop tasks**: Always use timeout-free communication (event-driven execution)\\n- **Script tasks**: Use timeout-free communication when `timeout_seconds \u003e 180` (host timeout threshold)\\n- **Other task types**: Use standard timeout-based communication\\n\\n```yaml\\ntasks:\\n - id: short_script\\n type: script\\n timeout_seconds: 60 # Uses standard timeout-based communication\\n script: \\\"print(\u0027Short script\u0027)\\\"\\n \\n - id: long_script\\n type: script\\n timeout_seconds: 600 # Uses timeout-free communication (600 \u003e 180)\\n script: |\\n import time\\n print(\\\"\\ud83d\\ude80 This uses timeout-free communication\\\")\\n time.sleep(300) # Won\u0027t cause timeout issues\\n print(\\\"Long script completed\\\")\\n \\n - id: data_processing_loop\\n type: loop\\n timeout_seconds: \\\"1h\\\" # Always uses timeout-free communication\\n loop_type: for\\n max_iterations: 10\\n # Loop tasks automatically use timeout-free communication\\n```\\n\\n#### Loop Task Timeout Inheritance\\n\\nLoop tasks can pass timeout configuration to their inner script tasks:\\n\\n```yaml\\n- id: batch_processing_loop\\n type: loop\\n timeout_seconds: \\\"30m\\\" # Loop-level timeout\\n loop_type: for\\n max_iterations: 5\\n loop_tasks:\\n - id: process_batch\\n type: script\\n # Inherits 30-minute timeout from parent loop\\n script: |\\n print(\\\"Processing batch with inherited timeout\\\")\\n # This script will use the 30-minute timeout from the loop\\n```\\n\\n#### Best Practices for Timeout Configuration\\n\\n1. **Use duration strings** for readability: `\\\"5m\\\"` vs `300`\\n2. **Set appropriate timeouts** based on expected execution time plus buffer\\n3. **Long-running tasks**: Set `timeout_seconds \u003e 180` to enable timeout-free communication\\n4. **Loop tasks**: Always get timeout-free communication automatically\\n5. **Critical tasks**: Use shorter timeouts for fail-fast behavior\\n6. **External API calls**: Use longer timeouts to account for network latency\\n\\n```yaml\\n# Example: Well-configured timeout workflow\\ntimeout_seconds: \\\"1h\\\" # Reasonable workflow timeout\\n\\ntasks:\\n - id: api_call\\n type: http\\n timeout_seconds: \\\"30s\\\" # Quick timeout for API calls\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: data_processing\\n type: script\\n timeout_seconds: \\\"10m\\\" # Longer timeout for processing (uses timeout-free)\\n script: |\\n # Process large dataset\\n process_data()\\n \\n - id: batch_processing\\n type: loop\\n timeout_seconds: \\\"45m\\\" # Generous timeout for loop (timeout-free)\\n loop_type: for\\n max_iterations: 100\\n # Inner tasks inherit the 45-minute timeout\\n```\\n\\n## Field Validation Rules\\n\\n### ID Fields\\n\\n```yaml\\n# Valid IDs\\nid: \\\"customer_processor\\\" # \\u2705 Valid\\nid: \\\"process-data\\\" # \\u2705 Valid\\nid: \\\"task_001\\\" # \\u2705 Valid\\n\\n# Invalid IDs\\nid: \\\"process data\\\" # \\u274c Spaces not allowed\\nid: \\\"123_start\\\" # \\u274c Cannot start with number\\nid: \\\"\\\" # \\u274c Cannot be empty\\n```\\n\\n### Timeout Values\\n\\n```yaml\\n# Valid timeouts\\ntimeout_seconds: 300 # \\u2705 Positive integer\\ntimeout_seconds: \\\"5m\\\" # \\u2705 Duration string\\ntimeout_seconds: \\\"1h30m\\\" # \\u2705 Complex duration\\n\\n# Invalid timeouts\\ntimeout_seconds: 0 # \\u274c Must be positive\\ntimeout_seconds: -60 # \\u274c Cannot be negative\\ntimeout_seconds: \\\"invalid\\\" # \\u274c Invalid duration format\\n```\\n\\n### Dependency Types and Validation\\n\\n#### `depends_on` vs `previous_node`\\n\\n**`depends_on` - Execution Dependencies:**\\n- Controls actual task execution order\\n- Enables template resolution and data access\\n- Required for accessing outputs from other tasks\\n- Validates that referenced tasks exist\\n- Multiple tasks can be specified\\n\\n**`previous_node` - Task Connectivity and UI Hierarchy:**\\n- **REQUIRED for proper task connectivity in UI**\\n- Essential for workflow visualization and task linking\\n- Critical for execution flow representation\\n- Single task reference only\\n- **Without this field, tasks won\u0027t be properly connected in the UI**\\n\\n```yaml\\ntasks:\\n - id: fetch_data\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: validate_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 EXECUTION: wait for fetch_data to complete\\n previous_node: fetch_data # \\u2705 UI: show as child of fetch_data in hierarchy\\n script: |\\n data = ${fetch_data} # \\u2705 Can access because of depends_on\\n if not validate(data):\\n raise ValueError(\\\"Invalid data\\\")\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker for proper parsing\\n outputs[\u0027status\u0027] = \u0027valid\u0027\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: process_data\\n type: script\\n depends_on: [validate_data] # \\u2705 EXECUTION: wait for validation\\n previous_node: validate_data # \\u2705 UI: show as child of validate_data\\n script: |\\n validation_result = ${validate_data} # \\u2705 Accessible via depends_on\\n # Process data...\\n\\n - id: separate_task\\n type: script\\n previous_node: process_data # \\u2705 UI: appears under process_data in hierarchy\\n # NO depends_on means this can run immediately (parallel to other tasks)\\n script: |\\n # This task runs independently but appears under process_data in UI\\n # Cannot access ${process_data} because no depends_on relationship\\n print(\\\"Independent task\\\")\\n\\n### Complete Example with Proper Hierarchy\\n\\n```yaml\\ntasks:\\n - id: fetch_customer_data\\n name: \\\"Fetch Customer Data\\\"\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: validate_customer\\n name: \\\"Validate Customer Data\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Wait for data fetch\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n customer = ${fetch_customer_data} # \\u2705 Can access because of depends_on\\n if not customer.get(\u0027email\u0027):\\n raise ValueError(\\\"Invalid customer data\\\")\\n print(\\\"__OUTPUTS__ {\u0027status\u0027: \u0027valid\u0027}\\\")\\n \\n - id: process_customer\\n name: \\\"Process Customer\\\"\\n type: ai_agent\\n depends_on: [validate_customer] # \\u2705 EXECUTION: Wait for validation\\n previous_node: validate_customer # \\u2705 UI: Show under validate_customer\\n config:\\n model_client_id: gpt4_model\\n user_message: \\\"Process customer: ${fetch_customer_data}\\\"\\n \\n - id: send_notification\\n name: \\\"Send Notification\\\"\\n type: script\\n depends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\n previous_node: process_customer # \\u2705 UI: Show under process_customer\\n script: |\\n result = ${process_customer}\\n print(f\\\"Notification sent for: ${fetch_customer_data.name}\\\")\\n \\n - id: parallel_audit_log\\n name: \\\"Audit Log (Parallel)\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n # This runs in parallel with validation/processing\\n customer = ${fetch_customer_data}\\n print(f\\\"Audit: Customer {customer.id} accessed at ${current_timestamp}\\\")\\n```\\n\\n # Invalid examples\\n - id: invalid_task\\n type: script\\n depends_on: [nonexistent_task] # \\u274c VALIDATION ERROR: task doesn\u0027t exist\\n previous_node: another_missing # \\u26a0\\ufe0f UI WARNING: task doesn\u0027t exist (but not fatal)\\n script: \\\"print(\u0027Invalid\u0027)\\\"\\n```\\n\\n#### Key Differences:\\n\\n| Aspect | `depends_on` | `previous_node` |\\n|--------|--------------|-----------------|\\n| **Purpose** | Execution control | Task connectivity \u0026 UI organization |\\n| **Impact** | Blocks task until dependencies complete | **REQUIRED for UI task linking** |\\n| **Template Access** | \\u2705 Enables `${task_id}` resolution | \\u274c No template access |\\n| **Validation** | \\u274c Parser error if task missing | \\u26a0\\ufe0f Warning if task missing |\\n| **Multiple References** | \\u2705 Array of task IDs | \\u274c Single task ID only |\\n| **Execution Order** | \\u2705 Enforced | \\u274c No execution impact |\\n| **UI Connectivity** | \\u274c No UI impact | \\u2705 **ESSENTIAL for proper task connection** |\\n\\n### Circular Dependency Detection\\n\\n```yaml\\n# This will be rejected by the parser\\ntasks:\\n - id: task_a\\n depends_on: [task_b] # \\u274c Circular dependency\\n \\n - id: task_b \\n depends_on: [task_a] # \\u274c Circular dependency\\n```\\n\\n## Parser Behavior\\n\\n### Field Name Compatibility\\n\\nThe parser supports both camelCase and snake_case for field names:\\n\\n```yaml\\ntasks:\\n - id: flexible_task\\n # Both formats are accepted\\n dependsOn: [other_task] # camelCase\\n depends_on: [other_task] # snake_case\\n \\n inputMapping: # camelCase\\n source_field: \\\"target_field\\\"\\n input_mapping: # snake_case \\n source_field: \\\"target_field\\\"\\n \\n outputMapping: {} # camelCase\\n output_mapping: {} # snake_case\\n \\n workerGroup: \\\"gpu-workers\\\" # camelCase\\n worker_group: \\\"gpu-workers\\\" # snake_case\\n```\\n\\n### Default Value Assignment\\n\\n```yaml\\n# Fields with defaults are automatically populated\\nid: my_workflow\\n# name defaults to id value: \\\"my_workflow\\\"\\n# version defaults to: 1\\n# namespace defaults to: \\\"default\\\" \\n# timeout_seconds defaults to: 3600\\n# description defaults to: \\\"\\\"\\n\\ntasks:\\n - id: simple_task\\n type: script\\n # name defaults to id: \\\"simple_task\\\"\\n # timeout_seconds defaults to: 300\\n # retry.maxAttempts defaults to: 1\\n```\\n\\n### Template Resolution Context\\n\\nThe parser provides these built-in variables for template resolution:\\n\\n```yaml\\ntasks:\\n - id: context_aware_task\\n type: script\\n script: |\\n # Workflow context\\n workflow_id = \\\"${workflow_id}\\\" # Current workflow ID\\n execution_id = \\\"${execution_id}\\\" # Unique execution ID\\n namespace = \\\"${namespace}\\\" # Workflow namespace\\n \\n # Timing context\\n current_timestamp = \\\"${current_timestamp}\\\" # ISO timestamp\\n started_at = \\\"${started_at}\\\" # Workflow start time\\n \\n # Input context\\n user_input = \\\"${input_parameter}\\\" # Workflow inputs\\n \\n # Task context\\n previous_result = ${previous_task} # Previous task output\\n specific_field = ${task_id.field_name} # Specific output field\\n \\n # Route context (for conditional workflows)\\n route_info = ${_route_context} # Current route information\\n```\\n\\n### Error Handling During Parsing\\n\\n```yaml\\n# The parser will validate and report these errors:\\n\\n# 1. Missing required fields\\n# ERROR: Missing required field: \u0027id\u0027\\n\\n# 2. Invalid YAML syntax \\n# ERROR: Invalid YAML: mapping values are not allowed here\\n\\n# 3. Circular dependencies\\n# ERROR: Circular dependency detected involving task \u0027task_a\u0027\\n\\n# 4. Invalid task references\\n# ERROR: Task \u0027task_b\u0027 depends on non-existent task \u0027invalid_task\u0027\\n\\n# 5. Invalid duration formats\\n# ERROR: Invalid duration: \u0027not_a_duration\u0027\\n\\n# 6. Invalid field values\\n# ERROR: Timeout must be positive, got -10\\n```\\n\\n## Migration Guide\\n\\n### From Version 1.0 to 2.0\\n\\n```yaml\\n# OLD FORMAT (v1.0)\\nworkflow:\\n id: my_workflow\\n tasks:\\n - task_id: process_data\\n task_type: python_script\\n \\n# NEW FORMAT (v2.0) \\nid: my_workflow\\ntasks:\\n - id: process_data\\n type: script\\n language: python\\n```\\n\\n### Deprecated Fields\\n\\n| Old Field | New Field | Status |\\n|-----------|-----------|--------|\\n| `task_type` | `type` | Deprecated in v2.0 |\\n| `task_id` | `id` | Deprecated in v2.0 |\\n| `python_script` | `script` (type) | Deprecated in v2.0 |\\n| `plugin` | `mcp` (type) | Deprecated in v2.1 |\\n\\n### Backward Compatibility\\n\\nThe parser maintains backward compatibility for deprecated fields but will show warnings:\\n\\n```yaml\\n# This will work but show a warning\\ntasks:\\n - task_id: old_style_task # \\u26a0\\ufe0f Warning: Use \u0027id\u0027 instead\\n task_type: python_script # \\u26a0\\ufe0f Warning: Use \u0027type: script\u0027 instead\\n```\\n\\n## Advanced Schema Features\\n\\n### Conditional Field Requirements\\n\\nSome fields become required based on task type:\\n\\n```yaml\\n# HTTP tasks require URL\\n- id: api_call\\n type: http\\n url: \\\"https://api.example.com\\\" # \\u2705 Required for HTTP tasks\\n \\n# AI agent tasks require model_client_id\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4 # \\u2705 Required for AI agent tasks\\n```\\n\\n### Dynamic Field Validation\\n\\nField validation can depend on other field values:\\n\\n```yaml\\n# Loop tasks require loop-specific fields\\n- id: data_loop\\n type: loop\\n loop_type: \\\"for\\\" # Determines required fields\\n max_iterations: 10 # \\u2705 Required for \u0027for\u0027 loops\\n # exit_condition: not needed for \u0027for\u0027 loops\\n \\n- id: condition_loop \\n type: loop\\n loop_type: \\\"while\\\" # Determines required fields\\n exit_condition: \\\"${count} \u003e= 10\\\" # \\u2705 Required for \u0027while\u0027 loops\\n # max_iterations: optional for \u0027while\u0027 loops\\n```\\n\\n## Template Resolution and Data Flow\\n\\nTemplate resolution is a core feature that allows tasks to access data from previous tasks, workflow inputs, and built-in variables using the `${variable_name}` syntax. This is essential for creating dynamic workflows where tasks pass data to each other.\\n\\n### Basic Template Syntax\\n\\n```yaml\\n# Access workflow inputs\\nscript: |\\n customer_name = \\\"${customer_name}\\\" # From workflow inputs\\n \\n# Access previous task outputs\\nscript: |\\n previous_result = ${data_fetch_task} # Entire output from data_fetch_task\\n specific_field = ${data_fetch_task.customer_id} # Specific field\\n \\n# Access built-in variables\\nscript: |\\n workflow_id = \\\"${workflow_id}\\\"\\n execution_id = \\\"${execution_id}\\\"\\n timestamp = \\\"${current_timestamp}\\\"\\n```\\n\\n### Available Built-in Variables\\n\\n| Variable | Description | Example Value |\\n|----------|-------------|---------------|\\n| `${workflow_id}` | Current workflow identifier | `customer_processor` |\\n| `${execution_id}` | Unique execution identifier | `exec_12345_abcde` |\\n| `${namespace}` | Workflow namespace | `production` |\\n| `${current_timestamp}` | ISO timestamp | `2024-01-15T10:30:00Z` |\\n| `${started_at}` | Workflow start time | `2024-01-15T10:29:45Z` |\\n\\n### Task Output Resolution\\n\\nTasks can access outputs from other tasks they depend on:\\n\\n```yaml\\ntasks:\\n - id: fetch_customer\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: process_customer\\n type: script\\n depends_on: [fetch_customer] # Required for template access\\n previous_node: fetch_customer\\n script: |\\n # Access entire response\\n customer_data = ${fetch_customer}\\n \\n # Access specific fields\\n customer_name = \\\"${fetch_customer.name}\\\"\\n customer_email = \\\"${fetch_customer.email}\\\"\\n \\n # Process the data\\n result = {\\n \\\"processed_name\\\": customer_name.upper(),\\n \\\"domain\\\": customer_email.split(\\\"@\\\")[1]\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### AI Agent Output Resolution\\n\\nAI agent tasks return structured output that requires specific access patterns:\\n\\n```yaml\\n# AI Agent task\\n- id: ai_analysis_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Analyze the customer data and provide insights.\\\"\\n user_message: \\\"Customer data: ${customer_data}\\\"\\n\\n# Accessing AI agent output in subsequent tasks\\n- id: process_ai_response\\n type: script\\n depends_on: [ai_analysis_task]\\n previous_node: ai_analysis_task\\n script: |\\n # CORRECT: AI agents return structured output\\n agent_output = ${ai_analysis_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n token_usage = agent_output.get(\u0027token_usage\u0027, {})\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n # Process the AI response\\n processed_analysis = {\\n \\\"analysis\\\": ai_response,\\n \\\"tokens_used\\\": token_usage.get(\u0027total_tokens\u0027, 0),\\n \\\"model\\\": model_used,\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\"\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(processed_analysis)}\\\")\\n\\n# INCORRECT: Direct access won\u0027t work\\n- id: incorrect_ai_access\\n type: script\\n depends_on: [ai_analysis_task]\\n script: |\\n # \\u274c This will not work correctly\\n ai_response = ${ai_analysis_task} # Returns full agent object\\n # ai_response is now a dict, not the actual response text\\n```\\n\\n### AI Agent Response Structure\\n\\nAI agent tasks return a structured object with the following format:\\n\\n```json\\n{\\n \\\"ai_response\\\": \\\"The actual AI-generated text response\\\",\\n \\\"model\\\": \\\"gpt-4-turbo-preview\\\",\\n \\\"token_usage\\\": {\\n \\\"prompt_tokens\\\": 150,\\n \\\"completion_tokens\\\": 75,\\n \\\"total_tokens\\\": 225\\n },\\n \\\"estimated_cost\\\": {\\n \\\"amount\\\": 0.00337,\\n \\\"currency\\\": \\\"USD\\\"\\n },\\n \\\"provider\\\": \\\"openai\\\",\\n \\\"timestamp\\\": \\\"2024-01-15T10:30:00Z\\\"\\n}\\n```\\n\\n### Loop Task Data Access and Dependencies\\n\\nLoop tasks have a special execution context that differs from regular workflow tasks. Understanding how to access data within loops is crucial for building functional workflows.\\n\\n#### Loop Task Internal Data Access\\n\\nWithin loop tasks, data access works through injected context files rather than template resolution:\\n\\n```yaml\\n# Loop task with state variables and internal task dependencies\\n- id: data_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n state_variables:\\n processed_items: []\\n total_processed: 0\\n error_count: 0\\n loop_tasks:\\n # First task in loop\\n - id: fetch_data\\n type: script\\n script: |\\n # Access loop state variables through injected context\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n processed_items = loop_state.get(\u0027processed_items\u0027, [])\\n \\n # Access workflow inputs and external task outputs\\n external_data = inputs.get(\u0027previous_workflow_task\u0027, {})\\n iteration_num = inputs.get(\u0027iteration\u0027, 0) # Loop iteration variable\\n \\n # Process data\\n batch_data = fetch_batch_data(iteration_num)\\n \\n outputs = {\\\"batch_data\\\": batch_data, \\\"batch_size\\\": len(batch_data)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n # Second task depends on first task in same loop\\n - id: process_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 CORRECT: Dependencies within loop tasks work normally\\n script: |\\n # \\u2705 CORRECT: Access previous loop task through inputs context\\n fetch_result = inputs.get(\u0027fetch_data\u0027, {})\\n batch_data = fetch_result.get(\u0027batch_data\u0027, [])\\n \\n # \\u274c INCORRECT: This won\u0027t work in loop context\\n # batch_data = ${fetch_data.batch_data} # Template resolution fails in loops\\n \\n # Access and update state variables\\n current_processed = loop_state.get(\u0027processed_items\u0027, [])\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n \\n # Process the batch\\n processed_batch = process_batch(batch_data)\\n current_processed.extend(processed_batch)\\n \\n # Update state variables using state_updates\\n state_updates[\u0027processed_items\u0027] = current_processed\\n state_updates[\u0027total_processed\u0027] = current_total + len(processed_batch)\\n \\n outputs = {\\\"processed_count\\\": len(processed_batch)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED for state updates\\n```\\n\\n#### Key Differences in Loop Task Data Access\\n\\n| Access Pattern | Regular Tasks | Loop Tasks |\\n|---------------|---------------|------------|\\n| **Previous Task Output** | `${previous_task}` | `inputs.get(\u0027field_name\u0027, default)` (flattened) |\\n| **State Variables** | N/A | `loop_state.get(\u0027variable_name\u0027, default)` |\\n| **State Updates** | N/A | `state_updates[\u0027variable_name\u0027] = value` |\\n| **Workflow Inputs** | `\\\"${input_name}\\\"` | `inputs.get(\u0027input_name\u0027, \u0027default\u0027)` |\\n| **Dependencies** | `depends_on: [task_id]` | Same: `depends_on: [task_id]` |\\n\\n#### Critical: Loop Task Output Flattening\\n\\n**Important:** The loop executor flattens task outputs into individual fields in the `inputs` dictionary, rather than storing them as nested objects under the task name.\\n\\n```yaml\\n# First loop task outputs:\\noutputs = {\\\"company_name\\\": \\\"ACME Corp\\\", \\\"company_id\\\": \\\"123\\\", \\\"status\\\": \\\"success\\\"}\\nprint(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n\\n# Second loop task accessing the data:\\nscript: |\\n # \\u274c INCORRECT: This won\u0027t work - no nested object\\n # company_data = inputs.get(\u0027first_task\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027)\\n \\n # \\u2705 CORRECT: Access flattened fields directly\\n company_name = inputs.get(\u0027company_name\u0027, \u0027\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n print(f\\\"Company: {company_name}, ID: {company_id}, Status: {task_status}\\\")\\n```\\n\\n#### Complete Loop Task Example\\n\\n```yaml\\n- id: financial_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n iteration_variable: company_index\\n depends_on: [list_companies] # External dependency\\n \\n state_variables:\\n processed_companies: []\\n total_extractions: 0\\n successful_extractions: 0\\n \\n loop_tasks:\\n - id: process_current_company\\n type: script\\n script: |\\n # Access external workflow task data\\n companies_data = inputs.get(\u0027list_companies\u0027, {})\\n company_idx = inputs.get(\u0027company_index\u0027, 0)\\n \\n # Access state variables\\n processed = loop_state.get(\u0027processed_companies\u0027, [])\\n \\n # Process company\\n company = companies_data[\u0027companies\u0027][company_idx]\\n result = process_company(company)\\n \\n outputs = {\\\"company_name\\\": company[\u0027name\u0027], \\\"status\\\": \\\"success\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: extract_financial_data\\n type: script\\n depends_on: [process_current_company] # Depend on previous loop task\\n requirements:\\n - requests\u003e=2.28.0\\n - some-ai-library\u003e=1.0.0\\n script: |\\n # \\u2705 CORRECT: Access flattened outputs from previous loop task\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n # \\u274c INCORRECT: Loop executor flattens outputs, no nested access\\n # company_data = inputs.get(\u0027process_current_company\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n \\n # \\u274c INCORRECT: Template resolution doesn\u0027t work in loops\\n # company_data = ${process_current_company}\\n \\n # Access external data\\n companies_list = inputs.get(\u0027list_companies\u0027, {})\\n \\n # Perform AI extraction\\n extraction_result = extract_with_ai({\\\"company_name\\\": company_name, \\\"company_id\\\": company_id})\\n \\n # Update state variables\\n current_processed = loop_state.get(\u0027processed_companies\u0027, [])\\n current_total = loop_state.get(\u0027total_extractions\u0027, 0)\\n current_successful = loop_state.get(\u0027successful_extractions\u0027, 0)\\n \\n current_processed.append({\\n \\\"company_name\\\": company_name,\\n \\\"extraction_result\\\": extraction_result\\n })\\n \\n # State updates are required for persistence\\n state_updates[\u0027processed_companies\u0027] = current_processed\\n state_updates[\u0027total_extractions\u0027] = current_total + 1\\n state_updates[\u0027successful_extractions\u0027] = current_successful + (1 if extraction_result else 0)\\n \\n outputs = {\\\"extracted\\\": bool(extraction_result)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED\\n\\n# Accessing loop task outputs in subsequent workflow tasks\\n- id: process_loop_results\\n type: script\\n depends_on: [financial_processing_loop]\\n previous_node: financial_processing_loop\\n script: |\\n # \\u2705 CORRECT: Access final state of loop\\n loop_result = ${financial_processing_loop}\\n loop_final_state = loop_result.get(\u0027final_state\u0027, {})\\n \\n # Access specific state variables from final state\\n processed_companies = loop_final_state.get(\u0027processed_companies\u0027, [])\\n total_processed = loop_final_state.get(\u0027total_extractions\u0027, 0)\\n successful_count = loop_final_state.get(\u0027successful_extractions\u0027, 0)\\n \\n # Access loop execution metadata\\n iterations_completed = loop_result.get(\u0027iterations_completed\u0027, 0)\\n \\n print(f\\\"Loop completed {iterations_completed} iterations\\\")\\n print(f\\\"Processed {len(processed_companies)} companies\\\")\\n print(f\\\"Success rate: {successful_count}/{total_processed}\\\")\\n \\n outputs = {\\n \\\"companies_processed\\\": len(processed_companies),\\n \\\"success_rate\\\": f\\\"{successful_count}/{total_processed}\\\",\\n \\\"iterations\\\": iterations_completed\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Loop Task Context Injection and Output Flattening\\n\\nThe loop executor injects context through temporary files and flattens task outputs for easier access:\\n\\n```python\\n# Auto-injected at the start of each loop task script:\\nimport json\\nimport os\\n\\n# Load context from temporary files (auto-generated)\\nwith open(\u0027/tmp/loop_context_task_iter_N/inputs.json\u0027, \u0027r\u0027) as f:\\n inputs = json.load(f) # Contains all workflow data + previous loop task outputs (FLATTENED)\\n\\nwith open(\u0027/tmp/loop_context_task_iter_N/loop_state.json\u0027, \u0027r\u0027) as f:\\n loop_state = json.load(f) # Contains current state variables\\n\\noutputs = {}\\nstate_updates = {}\\n\\n# Your script code follows...\\n```\\n\\n#### Debugging Loop Task Data Access\\n\\nWhen debugging loop tasks, always print available keys to understand what data is accessible:\\n\\n```yaml\\n- id: debug_loop_task\\n type: script\\n depends_on: [previous_loop_task]\\n script: |\\n # Debug: Print all available data\\n print(f\\\"\\ud83d\\udd0d Available inputs: {list(inputs.keys())}\\\")\\n print(f\\\"\\ud83d\\udd0d Loop state keys: {list(loop_state.keys())}\\\")\\n \\n # Debug: Print specific values\\n for key in [\u0027company_name\u0027, \u0027company_id\u0027, \u0027status\u0027, \u0027fy_folders\u0027]:\\n value = inputs.get(key, \u0027NOT_FOUND\u0027)\\n print(f\\\"\\ud83d\\udd0d {key}: {value}\\\")\\n \\n # Now access the flattened data\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n \\n # Process your data...\\n outputs = {\\\"processed\\\": f\\\"Company {company_name}\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Real Example: Task Output Flattening\\n\\n```yaml\\n# Task 1 outputs this structure:\\n__OUTPUTS__ {\\\"company_name\\\": \\\"LTTS\\\", \\\"company_id\\\": \\\"123\\\", \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], \\\"status\\\": \\\"success\\\"}\\n\\n# Task 2 receives flattened inputs:\\ninputs = {\\n \\\"folder_id\\\": \\\"...\\\", # Workflow input\\n \\\"gemini_api_key\\\": \\\"...\\\", # Workflow input \\n \\\"list_company_folders\\\": {...}, # External task output\\n \\\"company_index\\\": 0, # Loop iteration variable\\n \\\"company_name\\\": \\\"LTTS\\\", # \\u2705 Flattened from Task 1\\n \\\"company_id\\\": \\\"123\\\", # \\u2705 Flattened from Task 1\\n \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], # \\u2705 Flattened from Task 1\\n \\\"status\\\": \\\"success\\\", # \\u2705 Flattened from Task 1\\n \\\"total_extractions\\\": 0, # Loop state variable\\n \\\"processed_companies\\\": [] # Loop state variable\\n}\\n```\\n\\n#### Loop Task Special Output Fields\\n\\n| Field | Description | Example Access |\\n|-------|-------------|----------------|\\n| `final_state` | Final values of all state variables | `${loop_task.final_state}` |\\n| `final_state[\\\"variable_name\\\"]` | Specific state variable from final state | `${loop_task.final_state[\\\"total_processed\\\"]}` |\\n| `iterations_completed` | Number of iterations executed | `${loop_task.iterations_completed}` |\\n\\n#### Safe Template Resolution Pattern for Loop States\\n\\nWhen accessing loop final state fields, use this safe parsing pattern:\\n\\n```yaml\\nscript: |\\n try:\\n # Template resolution with safety checks\\n final_state_str = \u0027\u0027\u0027${loop_task.final_state}\u0027\u0027\u0027\\n processed_count_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_count\\\"]}\u0027\u0027\u0027\\n items_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_items\\\"]}\u0027\u0027\u0027\\n iterations_str = \u0027\u0027\u0027${loop_task.iterations_completed}\u0027\u0027\u0027\\n \\n # Safe parsing with fallbacks\\n final_state = json.loads(final_state_str) if not final_state_str.startswith(\u0027UNRESOLVED_\u0027) else {}\\n processed_count = int(processed_count_str) if processed_count_str.isdigit() else 0\\n processed_items = json.loads(items_str) if not items_str.startswith(\u0027UNRESOLVED_\u0027) else []\\n iterations = int(iterations_str) if iterations_str.isdigit() else 0\\n \\n print(f\\\"Successfully processed {processed_count} items in {iterations} iterations\\\")\\n \\n except Exception as e:\\n print(f\\\"Error accessing loop results: {str(e)}\\\")\\n # Handle error case\\n```\\n```\\n\\n### Real-World AI Agent Example\\n\\nBased on actual WhatsApp workflow patterns:\\n\\n```yaml\\n# AI greeting agent\\n- id: greeting_agent\\n type: ai_agent\\n config:\\n model_client_id: assistant_model\\n system_message: \\\"You are a helpful assistant. Generate a friendly greeting.\\\"\\n user_message: \\\"User said: ${user_message}\\\"\\n\\n# Send the AI response via API\\n- id: send_greeting\\n type: script\\n depends_on: [greeting_agent]\\n previous_node: greeting_agent\\n script: |\\n import requests\\n import json\\n \\n # CORRECT: Access AI response from structured output\\n agent_output = ${greeting_agent}\\n ai_greeting = agent_output[\u0027ai_response\u0027].strip()\\n \\n phone_number = \\\"${phone_number}\\\"\\n api_token = \\\"${API_TOKEN}\\\"\\n \\n print(f\\\"\\ud83e\\udd16 AI generated greeting: {ai_greeting}\\\")\\n \\n # Send via API\\n url = f\\\"https://api.example.com/messages/{phone_number}\\\"\\n headers = {\\\"Authorization\\\": f\\\"Bearer {api_token}\\\"}\\n \\n response = requests.post(url, headers=headers, json={\\n \\\"message\\\": ai_greeting\\n })\\n \\n result = {\\n \\\"status\\\": \\\"sent\\\",\\n \\\"ai_response\\\": ai_greeting,\\n \\\"api_response\\\": response.json(),\\n \\\"tokens_used\\\": agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### Complex Template Patterns\\n\\n```yaml\\n# Conditional templates with router outputs\\n- id: router_task\\n type: conditional_router\\n conditions:\\n - name: \\\"premium_customer\\\"\\n condition: \\\"${customer_data.tier} == \u0027premium\u0027\\\"\\n route: \\\"premium_processing\\\"\\n\\n- id: premium_handler\\n type: script\\n depends_on: [router_task]\\n previous_node: router_task\\n execute_on_routes: [\\\"premium_processing\\\"]\\n script: |\\n # Access router decision\\n selected_route = \\\"${router_task.selected_route}\\\"\\n route_data = ${router_task.route_data}\\n \\n # Process premium customer\\n customer = ${customer_data}\\n premium_benefits = calculate_benefits(customer)\\n\\n# Loop through arrays from previous tasks\\n- id: process_items\\n type: script\\n depends_on: [fetch_items]\\n previous_node: fetch_items\\n script: |\\n items = ${fetch_items.data}\\n processed_items = []\\n \\n for item in items:\\n processed_item = {\\n \\\"id\\\": item[\\\"id\\\"],\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\",\\n \\\"status\\\": \\\"processed\\\"\\n }\\n processed_items.append(processed_item)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027processed_items\u0027: processed_items})}\\\")\\n\\n# Nested object access\\n- id: user_preferences\\n type: script\\n depends_on: [fetch_user]\\n previous_node: fetch_user\\n script: |\\n # Access nested user preferences\\n email_notifications = ${fetch_user.preferences.notifications.email}\\n theme_setting = \\\"${fetch_user.preferences.ui.theme}\\\"\\n language = \\\"${fetch_user.profile.language}\\\"\\n \\n settings = {\\n \\\"notifications\\\": email_notifications,\\n \\\"theme\\\": theme_setting,\\n \\\"locale\\\": language\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(settings)}\\\")\\n```\\n\\n### Template Resolution Rules\\n\\n1. **Dependency Requirement**: Tasks must have `depends_on` relationship to access other task outputs\\n2. **String vs Object**: Use quotes for string values, no quotes for objects/arrays\\n3. **Field Navigation**: Use dot notation for nested field access\\n4. **Error Handling**: Unresolved templates become `UNRESOLVED_variable_name`\\n\\n```yaml\\n# \\u2705 Correct template usage\\n- id: correct_task\\n depends_on: [previous_task]\\n previous_node: previous_task\\n script: |\\n string_value = \\\"${previous_task.name}\\\" # String with quotes\\n object_value = ${previous_task.data} # Object without quotes\\n nested_value = \\\"${previous_task.user.email}\\\" # Nested field\\n\\n# \\u274c Incorrect template usage \\n- id: incorrect_task\\n # Missing depends_on - template won\u0027t resolve\\n script: |\\n value = ${previous_task.name} # Will become UNRESOLVED_previous_task.name\\n```\\n\\n## Script Task Output Handling\\n\\nScript tasks must use the `__OUTPUTS__` marker pattern for proper output parsing by the workflow engine.\\n\\n### Required Output Pattern\\n\\n```yaml\\ntasks:\\n - id: example_script\\n type: script\\n script: |\\n # Your script logic here\\n data = process_data()\\n \\n # Required: Store outputs in outputs dict\\n outputs = {}\\n outputs[\u0027result\u0027] = data\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n outputs[\u0027count\u0027] = len(data)\\n \\n # Required: Print with __OUTPUTS__ marker for engine parsing\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Output Access in Subsequent Tasks\\n\\n```yaml\\ntasks:\\n - id: data_processor\\n type: script\\n script: |\\n # Process and store outputs\\n results = {\\\"processed_count\\\": 100, \\\"errors\\\": []}\\n \\n outputs = {}\\n outputs[\u0027results\u0027] = results\\n outputs[\u0027status\u0027] = \u0027completed\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: result_handler\\n type: script\\n depends_on: [data_processor]\\n script: |\\n # Access previous task outputs\\n processor_output = ${data_processor}\\n processed_count = processor_output[\u0027results\u0027][\u0027processed_count\u0027]\\n status = processor_output[\u0027status\u0027]\\n \\n print(f\\\"Previous task processed {processed_count} items with status: {status}\\\")\\n \\n # Store new outputs\\n outputs = {\u0027message\u0027: f\u0027Handled {processed_count} results\u0027}\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Error Handling in Scripts\\n\\n```yaml\\ntasks:\\n - id: safe_script\\n type: script\\n script: |\\n try:\\n result = risky_operation()\\n outputs = {\u0027result\u0027: result, \u0027status\u0027: \u0027success\u0027}\\n except Exception as e:\\n outputs = {\u0027error\u0027: str(e), \u0027status\u0027: \u0027failed\u0027}\\n \\n # Always print outputs, even for errors\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n## Triggers and Workflow Activation\\n\\nTriggers define how workflows are started automatically based on external events. They enable event-driven workflow execution.\\n\\n### Webhook Triggers\\n\\nWebhook triggers allow external systems to start workflows by sending HTTP requests.\\n\\n```yaml\\ntriggers:\\n - name: customer_webhook\\n type: webhook\\n description: \\\"Triggered when customer data is updated\\\"\\n config:\\n path: \\\"/webhooks/customer-updated\\\" # Webhook endpoint path\\n method: POST # HTTP method (GET, POST, PUT, etc.)\\n authentication:\\n type: token # none|token|signature|basic\\n token: \\\"${WEBHOOK_SECRET}\\\" # Required if type is token\\n header_name: \\\"X-Webhook-Token\\\" # Optional: custom header name\\n validation:\\n required_fields: [\\\"customer.id\\\", \\\"customer.email\\\"] # Required payload fields\\n content_type: \\\"application/json\\\" # Expected content type\\n input_mapping:\\n - webhook_field: customer.id # Field from webhook payload\\n workflow_input: customer_id # Map to workflow input\\n - webhook_field: customer.email\\n workflow_input: customer_email\\n - webhook_field: customer.updated_at\\n workflow_input: update_timestamp\\n```\\n\\n### Scheduled Triggers\\n\\nSchedule workflows to run at specific times or intervals.\\n\\n```yaml\\ntriggers:\\n - name: daily_report\\n type: schedule\\n description: \\\"Generate daily customer report\\\"\\n config:\\n cron: \\\"0 9 * * *\\\" # Daily at 9 AM (cron format)\\n timezone: \\\"UTC\\\" # Optional: timezone\\n enabled: true # Optional: enable/disable\\n max_instances: 1 # Optional: max concurrent instances\\n input_mapping:\\n - workflow_input: report_date\\n default_value: \\\"${current_date}\\\" # Use built-in variable\\n - workflow_input: report_type\\n default_value: \\\"daily\\\"\\n\\n - name: weekly_cleanup\\n type: schedule\\n description: \\\"Weekly database cleanup\\\"\\n config:\\n cron: \\\"0 2 * * 0\\\" # Weekly on Sunday at 2 AM\\n timezone: \\\"America/New_York\\\"\\n input_mapping:\\n - workflow_input: cleanup_before_date\\n default_value: \\\"${date_minus_7_days}\\\"\\n```\\n\\n### Event Triggers\\n\\nEvent triggers respond to system events from various sources.\\n\\n```yaml\\ntriggers:\\n - name: file_uploaded\\n type: event\\n description: \\\"Process uploaded files\\\"\\n config:\\n event_type: \\\"file.uploaded\\\" # Event type to listen for\\n source: \\\"storage_service\\\" # Event source system\\n filter: # Optional: event filtering\\n file_type: [\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"]\\n file_size_max: \\\"100MB\\\"\\n path_pattern: \\\"/uploads/customers/*\\\"\\n retry: # Optional: retry configuration\\n max_attempts: 3\\n initial_delay: \\\"5s\\\"\\n input_mapping:\\n - webhook_field: file.path\\n workflow_input: file_path\\n - webhook_field: file.size\\n workflow_input: file_size\\n - webhook_field: file.type\\n workflow_input: file_type\\n\\n - name: order_status_changed\\n type: event\\n description: \\\"Handle order status changes\\\"\\n config:\\n event_type: \\\"order.status_changed\\\"\\n source: \\\"order_management\\\"\\n filter:\\n status: [\\\"completed\\\", \\\"cancelled\\\", \\\"refunded\\\"]\\n priority: [\\\"high\\\", \\\"urgent\\\"]\\n input_mapping:\\n - webhook_field: order.id\\n workflow_input: order_id\\n - webhook_field: order.status\\n workflow_input: new_status\\n - webhook_field: order.customer_id\\n workflow_input: customer_id\\n```\\n\\n### Dependency Triggers\\n\\nDependency triggers start workflows when other workflows complete.\\n\\n```yaml\\ntriggers:\\n - name: post_processing\\n type: dependency\\n description: \\\"Run after main processing completes\\\"\\n config:\\n depends_on_workflow: \\\"main_data_processing\\\" # Workflow to depend on\\n depends_on_status: \\\"SUCCESS\\\" # SUCCESS|FAILED|COMPLETED\\n namespace: \\\"production\\\" # Optional: workflow namespace\\n timeout_minutes: 60 # Optional: wait timeout\\n input_mapping:\\n - webhook_field: execution_id # From completed workflow\\n workflow_input: source_execution_id\\n - webhook_field: workflow_outputs.processed_count\\n workflow_input: items_processed\\n```\\n\\n## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n - id: unique_identifier # Used to reference this model in tasks\\n provider: provider_name # AI provider (openai, anthropic, etc.)\\n config:\\n model: model_name # Specific model to use\\n api_key: your_api_key # Authentication key\\n # Additional provider-specific options...\\n```\\n\\n### Supported Providers\\n\\n#### OpenAI Provider\\n\\n```yaml\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview # Available: gpt-4, gpt-4-turbo, gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-2.0\\n max_tokens: 2000 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### Anthropic Provider\\n\\n```yaml\\nmodel_clients:\\n - id: claude_model\\n provider: anthropic\\n config:\\n model: claude-3-sonnet-20240229 # Available: claude-3-opus, claude-3-sonnet, claude-3-haiku\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-1.0\\n max_tokens: 1500 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### OpenRouter Provider\\n\\n```yaml\\nmodel_clients:\\n - id: openrouter_model\\n provider: openrouter\\n config:\\n model: anthropic/claude-3-sonnet # Available: Various models via OpenRouter\\n api_key: \\\"sk-or-your-openrouter-api-key\\\"\\n base_url: \\\"https://openrouter.ai/api/v1\\\" # Optional: Custom endpoint\\n temperature: 0.7\\n max_tokens: 2000\\n```\\n\\n#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References the claude model above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References the creative GPT-4 model\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n - id: secure_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n temperature: 0.7\\n```\\n\\n### Common Configuration Options\\n\\n| Field | Description | Required | Default |\\n|-------|-------------|----------|---------|\\n| `id` | Unique identifier for referencing in tasks | Yes | - |\\n| `provider` | AI provider name | Yes | - |\\n| `config.model` | Specific model name | Yes | - |\\n| `config.api_key` | Authentication API key | Yes | - |\\n| `config.temperature` | Response randomness (0.0-2.0) | No | 0.7 |\\n| `config.max_tokens` | Maximum response tokens | No | Provider default |\\n| `config.timeout` | Request timeout in seconds | No | 30 |\\n| `config.base_url` | Custom API endpoint | No | Provider default |\\n\\n## Secrets Management and Environment Variables\\n\\nProper secrets management is crucial for security. Never hardcode sensitive information like API keys, passwords, or tokens directly in workflow YAML files.\\n\\n### Using Environment Variables\\n\\nThe recommended approach is to use environment variables for all sensitive data:\\n\\n```yaml\\n# \\u2705 RECOMMENDED: Use environment variables\\nmodel_clients:\\n - id: secure_openai\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n\\n# HTTP tasks with environment variables\\ntasks:\\n - id: secure_api_call\\n type: http\\n url: \\\"${API_BASE_URL}/customers\\\" # Environment variable for URL\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Environment variable for token\\n X-API-Key: \\\"${EXTERNAL_API_KEY}\\\" # Environment variable for API key\\n```\\n\\n### Environment Variable Patterns\\n\\n```yaml\\n# Database connections\\n- id: database_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${DATABASE_URL}\\\" # Full connection string\\n # or individual components:\\n host: \\\"${DB_HOST}\\\"\\n port: \\\"${DB_PORT}\\\"\\n database: \\\"${DB_NAME}\\\"\\n username: \\\"${DB_USER}\\\"\\n password: \\\"${DB_PASSWORD}\\\"\\n\\n# Third-party service credentials\\n- id: external_service\\n type: http\\n url: \\\"${SERVICE_BASE_URL}/api/v1/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${SERVICE_TOKEN}\\\"\\n X-Client-ID: \\\"${SERVICE_CLIENT_ID}\\\"\\n X-Client-Secret: \\\"${SERVICE_CLIENT_SECRET}\\\"\\n\\n# MCP server credentials\\n- id: mcp_task\\n type: mcp\\n mcp_server: \\\"@agentic/mcp-notion-server\\\"\\n environment_vars:\\n NOTION_TOKEN: \\\"${NOTION_API_TOKEN}\\\"\\n WORKSPACE_ID: \\\"${NOTION_WORKSPACE_ID}\\\"\\n```\\n\\n### Security Best Practices\\n\\n#### \\u274c What NOT to Do\\n\\n```yaml\\n# DON\u0027T: Hardcode secrets\\nmodel_clients:\\n - id: insecure_example\\n provider: openai\\n config:\\n api_key: \\\"sk-actual-api-key-here\\\" # SECURITY RISK!\\n\\n# DON\u0027T: Put secrets in comments\\ntasks:\\n - id: bad_example\\n type: http\\n url: \\\"https://api.example.com\\\"\\n # API key is: sk-12345... DON\u0027T DO THIS!\\n headers:\\n Authorization: \\\"Bearer sk-real-token\\\" # SECURITY RISK!\\n```\\n\\n#### \\u2705 What TO Do\\n\\n```yaml\\n# DO: Use environment variables\\nmodel_clients:\\n - id: secure_example\\n provider: openai\\n config:\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\\n\\n# DO: Use different variables for different environments\\n- id: environment_aware_task\\n type: http\\n url: \\\"${API_BASE_URL}\\\" # Different URLs per environment\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Different tokens per environment\\n X-Environment: \\\"${ENVIRONMENT_NAME}\\\" # Track environment\\n\\n# DO: Use descriptive variable names\\nenvironment_vars:\\n CUSTOMER_API_TOKEN: \\\"${CUSTOMER_SERVICE_TOKEN}\\\"\\n BILLING_API_KEY: \\\"${BILLING_SERVICE_API_KEY}\\\"\\n NOTIFICATION_WEBHOOK_SECRET: \\\"${NOTIFICATION_SECRET}\\\"\\n```\\n\\n### Environment Variable Naming Conventions\\n\\n| Pattern | Example | Use Case |\\n|---------|---------|----------|\\n| `SERVICE_API_KEY` | `OPENAI_API_KEY` | Service API keys |\\n| `SERVICE_BASE_URL` | `CUSTOMER_API_BASE_URL` | Service endpoints |\\n| `DB_*` | `DB_HOST`, `DB_PASSWORD` | Database credentials |\\n| `WEBHOOK_*` | `WEBHOOK_SECRET_TOKEN` | Webhook authentication |\\n| `*_TIMEOUT` | `HTTP_REQUEST_TIMEOUT` | Timeout configurations |\\n\\n### Dynamic Environment Variables\\n\\nUse template resolution to create dynamic environment variable names:\\n\\n```yaml\\n# Different API keys per customer\\n- id: customer_specific_api\\n type: http\\n url: \\\"https://api.partner.com/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${PARTNER_API_KEY_${customer_tier}}\\\" # Resolves to PARTNER_API_KEY_PREMIUM\\n\\n# Environment-specific configurations\\n- id: env_specific_task\\n type: storage\\n config:\\n connection_string: \\\"${DATABASE_URL_${environment}}\\\" # DATABASE_URL_PRODUCTION\\n```\\n\\n### Secrets in Different Task Types\\n\\n```yaml\\n# AI Agent with multiple secure credentials\\n- id: secure_ai_task\\n type: ai_agent\\n config:\\n model_client_id: secure_model\\n system_message: \\\"You are a secure assistant with access to customer data.\\\"\\n user_message: \\\"Process customer ${customer_id}\\\"\\n environment_vars:\\n CUSTOMER_DB_TOKEN: \\\"${CUSTOMER_DATABASE_TOKEN}\\\"\\n AUDIT_LOG_KEY: \\\"${AUDIT_LOGGING_API_KEY}\\\"\\n\\n# Script task with secure file access\\n- id: secure_script_task\\n type: script\\n environment_vars:\\n AWS_ACCESS_KEY_ID: \\\"${AWS_ACCESS_KEY}\\\"\\n AWS_SECRET_ACCESS_KEY: \\\"${AWS_SECRET_KEY}\\\"\\n S3_BUCKET_NAME: \\\"${SECURE_BUCKET_NAME}\\\"\\n script: |\\n import boto3\\n import os\\n \\n # Credentials automatically available from environment\\n s3_client = boto3.client(\u0027s3\u0027)\\n bucket = os.environ[\u0027S3_BUCKET_NAME\u0027]\\n \\n # Process secure files...\\n\\n# Storage task with encrypted connections\\n- id: secure_storage_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${ENCRYPTED_DB_CONNECTION_STRING}\\\"\\n ssl_mode: \\\"require\\\"\\n ssl_cert: \\\"${SSL_CERT_PATH}\\\"\\n ssl_key: \\\"${SSL_KEY_PATH}\\\"\\n```\\n\\n### Environment Setup Examples\\n\\n#### Development Environment (.env file)\\n```bash\\n# Development API keys (non-production)\\nOPENAI_API_KEY=sk-dev-api-key-here\\nCUSTOMER_API_BASE_URL=https://api-dev.example.com\\nDATABASE_URL=postgresql://dev:pass@localhost:5432/workflow_dev\\nENVIRONMENT_NAME=development\\n```\\n\\n#### Production Environment\\n```bash\\n# Production API keys (secure)\\nOPENAI_API_KEY=sk-prod-api-key-here\\nCUSTOMER_API_BASE_URL=https://api.example.com\\nDATABASE_URL=postgresql://prod_user:secure_pass@prod-db:5432/workflow_prod\\nENVIRONMENT_NAME=production\\n```\\n\\n## Workflow Execution and Orchestration\\n\\nUnderstanding how workflows execute and how tasks are orchestrated is essential for building reliable, efficient workflows.\\n\\n### Execution Flow\\n\\nWorkflows follow a directed acyclic graph (DAG) execution model:\\n\\n1. **Initialization**: Workflow starts with inputs and model clients\\n2. **Task Scheduling**: Tasks are scheduled based on dependencies\\n3. **Parallel Execution**: Independent tasks run simultaneously\\n4. **Data Flow**: Outputs flow between tasks via template resolution\\n5. **Completion**: Workflow completes when all tasks finish\\n\\n```yaml\\n# Example execution flow\\ntasks:\\n # Stage 1: Independent tasks (run in parallel)\\n - id: fetch_customer # Runs immediately\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: fetch_products # Runs in parallel with fetch_customer\\n type: http\\n url: \\\"https://api.example.com/products\\\"\\n \\n # Stage 2: Dependent tasks (wait for Stage 1)\\n - id: validate_customer # Waits for fetch_customer\\n type: script\\n depends_on: [fetch_customer]\\n previous_node: fetch_customer\\n script: # validation logic...\\n \\n - id: filter_products # Waits for fetch_products\\n type: script\\n depends_on: [fetch_products]\\n previous_node: fetch_products\\n script: # filtering logic...\\n \\n # Stage 3: Final processing (waits for Stage 2)\\n - id: generate_recommendations # Waits for both validation and filtering\\n type: ai_agent\\n depends_on: [validate_customer, filter_products]\\n previous_node: validate_customer # UI hierarchy under customer validation\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate product recommendations for customer.\\\"\\n user_message: |\\n Customer: ${validate_customer}\\n Available products: ${filter_products}\\n```\\n\\n### Task States and Lifecycle\\n\\nEach task goes through these states during execution:\\n\\n| State | Description | Next States |\\n|-------|-------------|-------------|\\n| `PENDING` | Task created, waiting for dependencies | `RUNNING`, `SKIPPED` |\\n| `RUNNING` | Task is actively executing | `COMPLETED`, `FAILED` |\\n| `COMPLETED` | Task finished successfully | - |\\n| `FAILED` | Task encountered an error | `RUNNING` (if retries) |\\n| `SKIPPED` | Task skipped due to conditions | - |\\n| `TIMEOUT` | Task exceeded timeout limit | `RUNNING` (if retries) |\\n\\n### Conditional Execution\\n\\nTasks can be executed conditionally based on routes or conditions:\\n\\n```yaml\\n# Router-based conditional execution\\n- id: data_router\\n type: conditional_router\\n depends_on: [fetch_data]\\n previous_node: fetch_data\\n conditions:\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n default_route: \\\"standard_processing\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"batch_processing\\\"] # Only runs for large datasets\\n script: |\\n # Handle large dataset in batches\\n data = ${fetch_data}\\n process_in_batches(data, batch_size=1000)\\n\\n- id: direct_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"direct_processing\\\"] # Only runs for small datasets\\n script: |\\n # Process small dataset directly\\n data = ${fetch_data}\\n process_directly(data)\\n```\\n\\n### Error Handling and Retries\\n\\nConfigure retry policies for resilient workflow execution:\\n\\n```yaml\\n# Workflow-level retry policy\\nretry:\\n retryOn: [\\\"TEMPORARY_FAILURE\\\", \\\"TIMEOUT\\\", \\\"NETWORK_ERROR\\\"]\\n maxAttempts: 3\\n initialDelay: \\\"1s\\\"\\n maxDelay: \\\"60s\\\"\\n backoffMultiplier: 2.0\\n\\ntasks:\\n # Task-specific retry policy (overrides workflow-level)\\n - id: external_api_call\\n type: http\\n url: \\\"https://unreliable-api.example.com/data\\\"\\n retry:\\n maxAttempts: 5 # More retries for unreliable API\\n initialDelay: \\\"2s\\\"\\n maxDelay: \\\"120s\\\"\\n backoffMultiplier: 2.0\\n retryOn: [\\\"TIMEOUT\\\", \\\"HTTP_5XX\\\", \\\"NETWORK_ERROR\\\"]\\n \\n # Task with no retries\\n - id: critical_operation\\n type: script\\n retry:\\n maxAttempts: 1 # No retries for critical operations\\n script: |\\n # This operation should not be retried if it fails\\n perform_critical_database_operation()\\n```\\n\\n### Parallel Execution Patterns\\n\\n```yaml\\n# Fan-out pattern: One task triggers multiple parallel tasks\\n- id: data_source\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n\\n- id: process_customers # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process customer data from data_source\\n\\n- id: process_orders # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process order data from data_source\\n\\n- id: process_products # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process product data from data_source\\n\\n# Fan-in pattern: Multiple tasks feed into one task\\n- id: generate_report # Waits for all parallel tasks\\n type: ai_agent\\n depends_on: [process_customers, process_orders, process_products]\\n previous_node: process_customers # UI hierarchy\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate comprehensive report from processed data.\\\"\\n user_message: |\\n Customer data: ${process_customers}\\n Order data: ${process_orders}\\n Product data: ${process_products}\\n```\\n\\n### Resource Management\\n\\nControl resource usage and task distribution:\\n\\n```yaml\\n# Worker group targeting\\n- id: cpu_intensive_task\\n type: script\\n worker_group: \\\"high-cpu-workers\\\" # Target specific worker group\\n script: |\\n # CPU-intensive processing\\n perform_heavy_computation()\\n\\n- id: gpu_ml_task\\n type: ai_agent\\n worker_group: \\\"gpu-workers\\\" # Target GPU workers\\n config:\\n model_client_id: large_model\\n # Large model processing...\\n\\n# Timeout configuration\\n- id: long_running_task\\n type: script\\n timeout_seconds: 3600 # 1 hour timeout\\n script: |\\n # Long-running data processing\\n process_large_dataset()\\n\\n# Workflow-level timeout\\ntimeout_seconds: 7200 # 2 hour workflow timeout\\n```\\n\\n## Task Type Configurations\\n\\nThis section provides the complete YAML configuration for each supported task type.\\n\\n**Supported Task Types:**\\n- `script` - Execute Python code with full workflow context\\n- `http` - Make HTTP requests to external APIs\\n- `ai_agent` - Integrate with AI models for intelligent processing \\n- `loop` - Execute tasks repeatedly (for/while loops)\\n- `conditional_router` - Route execution based on conditions\\n- `mcp` - Execute tools via Model Context Protocol servers\\n- `storage` - Data persistence and retrieval operations\\n- `continuous_conversation` - Interactive AI conversations\\n- `human_input` - Tasks requiring human input or approval\\n- `chatbot` - Session-based chatbot conversations\\n\\n### Script Tasks\\n\\nExecute Python code with full workflow context access.\\n\\n```yaml\\n- id: script_task\\n name: \\\"Python Script Task\\\"\\n type: script\\n description: \\\"Execute Python code with dependencies and environment\\\"\\n \\n # Script configuration - script field is directly under task, NOT under config\\n script: | # REQUIRED: Python code to execute\\n import json\\n import requests\\n \\n # Access workflow inputs\\n input_value = \\\"${workflow_input}\\\"\\n \\n # Access previous task outputs\\n previous_result = ${previous_task}\\n \\n # Your processing logic\\n result = {\\\"processed\\\": True, \\\"value\\\": input_value}\\n \\n # Output results (must be JSON serializable)\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n # Alternative: use \u0027code\u0027 field instead of \u0027script\u0027\\n code: | # Alternative to \u0027script\u0027 field\\n print(\\\"Hello World\\\")\\n \\n # Package management\\n requirements: # Optional: Python packages\\n - requests==2.31.0\\n - pandas==1.5.3\\n - numpy==1.24.3\\n \\n # Environment configuration\\n environment: # Optional: Environment variables\\n API_KEY: \\\"${secrets.api_key}\\\"\\n DEBUG: \\\"true\\\"\\n CUSTOM_VAR: \\\"value\\\"\\n \\n environment_vars: # Alternative to \u0027environment\u0027\\n DATABASE_URL: \\\"${secrets.db_url}\\\"\\n \\n # Secrets configuration\\n secrets_config: # Optional: Secrets access\\n api_key: \\\"secret_name\\\"\\n db_password: \\\"db_secret\\\"\\n```\\n\\n### HTTP Tasks\\n\\nMake HTTP requests to external APIs and services.\\n\\n```yaml\\n- id: http_task\\n name: \\\"HTTP API Call\\\"\\n type: http\\n description: \\\"Call external REST API\\\"\\n \\n # HTTP configuration\\n method: POST # Default: GET\\n url: \\\"https://api.example.com/data\\\" # REQUIRED: Target URL\\n \\n # Headers configuration\\n headers: # Optional: HTTP headers\\n Content-Type: \\\"application/json\\\"\\n Authorization: \\\"Bearer ${secrets.api_token}\\\"\\n X-Custom-Header: \\\"workflow-engine\\\"\\n User-Agent: \\\"WorkflowEngine/1.0\\\"\\n \\n # Request body\\n body: | # Optional: Request body (for POST/PUT)\\n {\\n \\\"customer_id\\\": \\\"${customer_id}\\\",\\n \\\"timestamp\\\": \\\"${current_timestamp}\\\",\\n \\\"data\\\": ${previous_task}\\n }\\n \\n # Response validation\\n expected_status: [200, 201, 202] # Default: [200]\\n expectedStatus: [200, 201] # Alternative field name\\n \\n # Additional HTTP options\\n timeout_seconds: 30 # HTTP request timeout\\n follow_redirects: true # Follow HTTP redirects\\n verify_ssl: true # Verify SSL certificates\\n```\\n\\n### AI Agent Tasks\\n\\nIntegrate with AI models for intelligent processing.\\n\\n```yaml\\n- id: ai_agent_task\\n name: \\\"AI Processing Agent\\\"\\n type: ai_agent\\n description: \\\"Process data using AI model\\\"\\n \\n # AI configuration\\n config:\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # Optional: System prompt\\n You are a data analyst. Analyze the provided data and extract insights.\\n Return your analysis in JSON format with the following structure:\\n {\\n \\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n \\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n \\\"confidence_score\\\": 0.95\\n }\\n \\n user_message: | # REQUIRED: User prompt with template resolution\\n Please analyze this customer data:\\n ${previous_task}\\n \\n Customer ID: ${customer_id}\\n Date Range: ${date_range}\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1500 # Optional: Maximum response tokens\\n top_p: 1.0 # Optional: Nucleus sampling\\n frequency_penalty: 0.0 # Optional: Frequency penalty\\n presence_penalty: 0.0 # Optional: Presence penalty\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_api # Weather tools\\n - database_query # Database tools\\n - notification_service # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # Input/output format\\n input_format: \\\"text\\\" # Optional: text|json|structured\\n output_format: \\\"structured\\\" # Optional: text|json|structured\\n\\n### Important: AI Agent Output Access\\n\\nAI agent tasks return structured objects, not direct text responses. Always access the AI response using this pattern:\\n\\n```yaml\\n# In subsequent script tasks\\nscript: |\\n # CORRECT: Access AI response from structured output\\n agent_output = ${ai_agent_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n tokens_used = agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n cost_estimate = agent_output.get(\u0027estimated_cost\u0027, {}).get(\u0027amount\u0027, 0.0)\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n print(f\\\"AI Response: {ai_response}\\\")\\n print(f\\\"Tokens used: {tokens_used}\\\")\\n print(f\\\"Estimated cost: ${cost_estimate:.6f}\\\")\\n```\\n\\n```yaml\\n# In workflow outputs - specify the ai_response field\\noutputs:\\n ai_analysis:\\n type: string\\n source: ai_agent_task.ai_response # Access the ai_response field\\n description: \\\"AI-generated analysis text\\\"\\n \\n total_tokens:\\n type: integer \\n source: ai_agent_task.token_usage.total_tokens\\n description: \\\"Total tokens consumed\\\"\\n```\\n```\\n\\n### Loop Tasks\\n\\nExecute tasks repeatedly with for-loop and while-loop patterns.\\n\\n```yaml\\n# For-Loop (Fixed Iterations)\\n- id: for_loop_task\\n name: \\\"For Loop Processor\\\"\\n type: loop\\n description: \\\"Process data in fixed batches\\\"\\n \\n # Loop configuration\\n loop_type: for # REQUIRED: for|while\\n max_iterations: 10 # REQUIRED for \u0027for\u0027 loops\\n iteration_variable: batch_index # Default: iteration\\n \\n # State variables (persistent across iterations)\\n state_variables:\\n total_processed: 0\\n error_count: 0\\n batch_results: []\\n \\n # Tasks to execute in each iteration\\n loop_tasks: # REQUIRED: Tasks to run in loop\\n - id: process_batch\\n name: \\\"Process Current Batch\\\"\\n type: script\\n script: |\\n batch_idx = ${batch_index} # Access iteration variable\\n total = ${total_processed} # Access state variable\\n \\n # Process current batch\\n result = process_batch_data(batch_idx)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n - id: update_state\\n name: \\\"Update Loop State\\\"\\n type: script\\n depends_on: [process_batch]\\n script: |\\n # Update state variables\\n new_total = ${total_processed} + ${process_batch.items_processed}\\n \\n result = {\\\"total_processed\\\": new_total}\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n\\n# While-Loop (Conditional)\\n- id: while_loop_task\\n name: \\\"While Loop Quality Processor\\\"\\n type: loop\\n description: \\\"Process until quality threshold met\\\"\\n \\n # Loop configuration\\n loop_type: while # REQUIRED: for|while\\n exit_condition: \\\"${average_quality} \u003e= 85 || ${iteration_count} \u003e= 20\\\" # REQUIRED for \u0027while\u0027\\n max_iterations: 50 # Optional: Safety limit\\n iteration_variable: iteration_count # Default: iteration\\n \\n # State variables\\n state_variables:\\n average_quality: 0\\n items_processed: 0\\n quality_scores: []\\n \\n # Loop tasks\\n loop_tasks:\\n - id: assess_quality\\n type: script\\n script: |\\n current_quality = calculate_quality()\\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027quality\u0027: current_quality})}\\\")\\n```\\n\\n\\n### Conditional Router Tasks\\n\\nRoute execution to different paths based on conditions.\\n\\n```yaml\\n- id: conditional_router_task\\n name: \\\"Data Type Router\\\"\\n type: conditional_router\\n description: \\\"Route processing based on data characteristics\\\"\\n \\n # Router configuration\\n router_type: condition # Default: condition\\n \\n # Routing conditions\\n conditions: # REQUIRED: List of conditions\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${data_size} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\" \\n condition: \\\"${data_size} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n - name: \\\"medium_dataset\\\"\\n condition: \\\"${data_size} \u003e 1000 \u0026\u0026 ${data_size} \u003c= 10000\\\"\\n route: \\\"standard_processing\\\"\\n \\n default_route: \\\"standard_processing\\\" # REQUIRED: Default route if no conditions match\\n \\n # Alternative: evaluation script for complex routing\\n evaluation_script: | # Optional: Custom routing logic\\n if data_type == \\\"json\\\":\\n return \\\"json_processing\\\"\\n elif data_type == \\\"xml\\\":\\n return \\\"xml_processing\\\"\\n else:\\n return \\\"default_processing\\\"\\n \\n # Route mapping for complex scenarios\\n route_mapping: # Optional: Route name mappings\\n json_processing: \\\"specialized_json\\\"\\n xml_processing: \\\"specialized_xml\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_handler\\n name: \\\"Handle Large Dataset\\\"\\n type: script\\n execute_on_routes: [\\\"batch_processing\\\"] # REQUIRED: Routes this task runs on\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing large dataset in batches...\\\")\\n\\n- id: direct_handler\\n name: \\\"Handle Small Dataset\\\" \\n type: script\\n execute_on_routes: [\\\"direct_processing\\\"]\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing small dataset directly...\\\")\\n```\\n\\n### MCP Tasks\\n\\nExecute tools via Model Context Protocol servers.\\n\\n```yaml\\n- id: mcp_task\\n name: \\\"MCP Tool Execution\\\"\\n type: mcp\\n description: \\\"Execute external tool via MCP server\\\"\\n \\n # MCP server configuration\\n mcp_server: \\\"@agentic/mcp-notion-server\\\" # REQUIRED: MCP server package\\n tool_name: \\\"update_database\\\" # REQUIRED: Tool to execute\\n runtime: \\\"npx\\\" # REQUIRED: npx|uvx\\n \\n # Server startup\\n server_args: [] # Optional: Server startup arguments\\n server_startup_timeout: 30 # Optional: Server startup timeout (seconds)\\n \\n # Tool execution\\n tool_arguments: # REQUIRED: Arguments for the tool\\n database_id: \\\"${secrets.notion_database_id}\\\"\\n properties:\\n title: \\\"Workflow Results\\\"\\n status: \\\"Completed\\\"\\n processed_count: \\\"${previous_task.count}\\\"\\n timestamp: \\\"${current_timestamp}\\\"\\n \\n # Environment configuration\\n environment_vars: # Optional: Environment variables\\n NOTION_TOKEN: \\\"${secrets.notion_token}\\\"\\n DEBUG: \\\"true\\\"\\n \\n # Timeout configuration\\n timeout_seconds: 120 # Optional: Tool execution timeout\\n```\\n\\n### Storage Tasks\\n\\nManage data persistence and retrieval operations.\\n\\n```yaml\\n- id: storage_task\\n name: \\\"Data Storage Operation\\\"\\n type: storage\\n description: \\\"Save/load data to/from storage\\\"\\n \\n # Storage operation\\n operation: save # REQUIRED: save|load|delete|list\\n \\n # Storage configuration\\n storage_type: \\\"file\\\" # REQUIRED: file|database|cache\\n path: \\\"/data/results/${execution_id}.json\\\" # REQUIRED: Storage path\\n \\n # Data configuration\\n data: ${previous_task} # REQUIRED for \u0027save\u0027: Data to store\\n format: \\\"json\\\" # Optional: json|yaml|csv|parquet\\n compression: \\\"gzip\\\" # Optional: gzip|bz2|none\\n \\n # Metadata\\n metadata: # Optional: Additional metadata\\n workflow_id: \\\"${workflow_id}\\\"\\n created_at: \\\"${current_timestamp}\\\"\\n source_task: \\\"previous_task\\\"\\n \\n # File options\\n create_directories: true # Optional: Create parent directories\\n overwrite: true # Optional: Overwrite existing files\\n backup: false # Optional: Create backup before overwrite\\n \\n # Database options (when storage_type: \\\"database\\\")\\n table_name: \\\"workflow_results\\\" # Required for database storage\\n connection_string: \\\"${secrets.db_url}\\\" # Required for database storage\\n \\n # Cache options (when storage_type: \\\"cache\\\")\\n cache_key: \\\"result_${execution_id}\\\" # Required for cache storage\\n ttl: 3600 # Optional: Time to live (seconds)\\n```\\n\\n\\n### Continuous Conversation Tasks\\n\\nCreate interactive AI-powered conversations.\\n\\n```yaml\\n- id: conversation_task\\n name: \\\"AI Conversation Interface\\\"\\n type: continuous_conversation\\n description: \\\"Interactive conversation with AI assistant\\\"\\n \\n # Conversation configuration\\n config:\\n # AI configuration\\n ai_config:\\n model_client_id: support_assistant # REQUIRED: AI model to use\\n system_prompt: | # REQUIRED: System instructions\\n You are a helpful customer support representative.\\n Be friendly, professional, and solution-oriented.\\n \\n Guidelines:\\n - Always be polite and respectful\\n - Ask clarifying questions when needed\\n - Provide clear and actionable solutions\\n \\n temperature: 0.7 # Optional: AI creativity\\n max_tokens: 1000 # Optional: Response length limit\\n \\n # UI configuration\\n ui_config:\\n title: \\\"Customer Support Chat\\\" # Optional: Interface title\\n placeholder: \\\"How can I help you today?\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_timestamp: true # Optional: Show message timestamps\\n \\n # Termination conditions\\n termination:\\n keywords: [\\\"goodbye\\\", \\\"bye\\\", \\\"thanks\\\", \\\"done\\\"] # Optional: End conversation keywords\\n max_turns: 50 # Optional: Maximum conversation turns\\n timeout_minutes: 30 # Optional: Conversation timeout\\n inactivity_timeout: 10 # Optional: Inactivity timeout (minutes)\\n \\n # Context configuration\\n context:\\n system_context: \\\"Customer support conversation\\\" # Optional: Context description\\n user_id: \\\"${customer_id}\\\" # Optional: User identifier\\n session_data: ${previous_task} # Optional: Additional context data\\n```\\n\\n### Human Input Tasks\\n\\nTasks requiring human input or approval.\\n\\n```yaml\\n- id: human_input_task\\n name: \\\"Human Input Required\\\"\\n type: human_input\\n description: \\\"Wait for human input or approval\\\"\\n \\n # Human input configuration\\n config:\\n prompt: \\\"Please review and approve the processed data\\\" # REQUIRED: Prompt for human\\n input_type: \\\"approval\\\" # REQUIRED: approval|text|selection\\n \\n # Input options (for selection type)\\n options: [\\\"approve\\\", \\\"reject\\\", \\\"request_changes\\\"] # Optional: For selection input\\n \\n # Timeout configuration\\n timeout_minutes: 60 # Optional: How long to wait for input (minutes)\\n \\n # Conversation settings\\n conversation_mode: false # Optional: Add to conversation history\\n next_task: \\\"process_approval\\\" # Optional: Task to trigger after input\\n end_condition: \\\"bye|exit|quit\\\" # Optional: Keywords to end conversation\\n \\n # Input validation\\n required: true # Optional: Whether input is required\\n \\n # Additional context\\n context_data: ${previous_task} # Optional: Data to show to human\\n```\\n\\n### ChatBot Tasks\\n\\nSession-based chatbot conversations with streaming support.\\n\\n```yaml\\n- id: chatbot_task\\n name: \\\"AI Chatbot\\\"\\n type: chatbot\\n description: \\\"Interactive chatbot with session management\\\"\\n \\n # Chatbot configuration\\n config:\\n # AI model configuration\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # REQUIRED: System prompt for chatbot\\n You are a helpful AI assistant. Be friendly, helpful, and concise.\\n Answer questions clearly and ask for clarification when needed.\\n \\n # Session management\\n session_id: \\\"${session_id}\\\" # Optional: Session identifier (auto-generated if not provided)\\n max_history_messages: 20 # Optional: Maximum conversation history to maintain\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1000 # Optional: Maximum response tokens\\n stream: true # Optional: Enable streaming responses\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - send_notification # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # UI configuration\\n ui_config:\\n title: \\\"AI Assistant\\\" # Optional: Chat interface title\\n placeholder: \\\"Type your message...\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_typing_indicator: true # Optional: Show typing animation\\n \\n # Response configuration\\n response_config:\\n max_retries: 3 # Optional: Retry failed requests\\n timeout_seconds: 30 # Optional: Response timeout\\n \\n # Input message (for single-turn mode)\\n user_message: \\\"${user_input}\\\" # Optional: Direct message input\\n \\n # Context data\\n context_data: ${previous_task} # Optional: Additional context\\n```\\n\\n---\\n\\n## Common Errors and Corrections\\n\\nThis section documents frequent mistakes encountered when creating workflows and their proper corrections.\\n\\n### 1. Missing Required `id` Field\\n\\n**\\u274c Error:**\\n```yaml\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow without id\\\"\\ntasks:\\n - name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nid: \\\"my_workflow\\\" # REQUIRED: Unique workflow identifier\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow with proper id\\\"\\ntasks:\\n - id: task_1 # REQUIRED: Unique task identifier\\n name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n### 2. Incorrect Task Structure (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n task_1: # Wrong: tasks as object mapping\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n task_2:\\n type: ai_agent\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n - id: task_1 # Correct: tasks as array with id field\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n - id: task_2\\n type: ai_agent\\n```\\n\\n### 3. Incorrect Inputs Format (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ninputs:\\n user_name: # Wrong: inputs as object mapping\\n type: string\\n required: true\\n age:\\n type: integer\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ninputs:\\n - name: user_name # Correct: inputs as array with name field\\n type: string\\n required: true\\n - name: age\\n type: integer\\n```\\n\\n### 4. Script Configuration Location Error\\n\\n**\\u274c Error:**\\n```yaml\\n- id: script_task\\n type: script\\n config: # Wrong: script under config\\n script: |\\n print(\\\"Hello\\\")\\n requirements:\\n - requests\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: script_task\\n type: script\\n script: | # Correct: script directly under task\\n print(\\\"Hello\\\")\\n # requirements can be at task level if needed\\n```\\n\\n### 5. Individual Task Inputs (Not Supported)\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_1\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n inputs: # Wrong: tasks don\u0027t have individual inputs\\n search_query: ${search_query}\\n prev_result: ${previous_task}\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_1\\n type: script\\n depends_on: [previous_task] # Use depends_on for data access\\n script: |\\n # Access workflow inputs directly\\n search_query = \\\"${search_query}\\\"\\n # Access previous task outputs via template resolution\\n prev_result = ${previous_task}\\n```\\n\\n### 6. Incorrect Script Output Handling\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n result = process_data()\\n outputs[\u0027result\u0027] = result # Wrong: missing __OUTPUTS__ pattern\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n result = process_data()\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker\\n outputs = {}\\n outputs[\u0027result\u0027] = result\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### 7. AI Agent Configuration Errors\\n\\n**\\u274c Error:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n prompt: \\\"Process this data: ${data}\\\" # Wrong: use user_message\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n system_message: \\\"You are a helpful assistant.\\\"\\n user_message: \\\"Process this data: ${data}\\\" # Correct field name\\n```\\n\\n### 8. Missing Dependencies for Template Resolution\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_2\\n type: script\\n # Missing depends_on\\n script: |\\n data = ${task_1} # Will become UNRESOLVED_task_1\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_2\\n type: script\\n depends_on: [task_1] # Required for template access\\n script: |\\n data = ${task_1} # Now resolves correctly\\n```\\n\\n### 9. Incorrect Model Client Configuration\\n\\n**\\u274c Error:**\\n```yaml\\nmodel_clients:\\n my_model: # Wrong: object mapping format\\n type: openai\\n api_key: \\\"sk-...\\\"\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nmodel_clients:\\n - id: my_model # Correct: array format with id field\\n provider: openai\\n config:\\n model: \\\"gpt-4\\\"\\n api_key: \\\"sk-...\\\"\\n```\\n\\n### 10. Empty String Literals Causing Python Syntax Errors\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n def process_data(path=\\\"\\\"): # Wrong: empty string can cause syntax issues\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n \\n # This can lead to: SyntaxError: unterminated string literal\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n def process_data(path=None): # Correct: use None instead of empty string\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n # Or use path = path or \\\"default_value\\\"\\n```\\n\\n### 11. YAML Indentation and Structure Issues\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\nscript: | # Wrong: inconsistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\n script: | # Correct: consistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n### Best Practices Summary\\n\\n1. **Always include required `id` fields** for workflows and tasks\\n2. **Use array format** for `tasks`, `inputs`, and `model_clients`\\n3. **Script field goes directly under task**, not under `config`\\n4. **No individual task inputs** - use template resolution instead\\n5. **Use `depends_on`** to access previous task outputs\\n6. **Print `__OUTPUTS__`** in scripts for proper output parsing\\n7. **Use `user_message`** (not `prompt`) for AI agent tasks\\n8. **Avoid empty string literals** - use `None` or meaningful defaults\\n9. **Follow consistent YAML indentation** throughout the file\\n\\n## MCP Tools and Function Calling\\n\\nAI agents and chatbots can use MCP (Model Context Protocol) tools for function calling, enabling them to execute external tools and services.\\n\\n### Tools Configuration\\n\\nBoth `ai_agent` and `chatbot` tasks support tools configuration:\\n\\n```yaml\\n# AI Agent with MCP tools\\n- id: ai_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools.\\n Use the available tools when needed to help users.\\n user_message: \\\"What time is it and what\u0027s the weather like?\\\"\\n \\n # Tools configuration\\n tools: # List of MCP tool names\\n - current_time # Time tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - database_query # Database tools\\n - send_notification # Communication tools\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n\\n# Chatbot with MCP tools\\n- id: chatbot_with_tools\\n type: chatbot\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful chatbot with access to tools.\\n Use tools when users ask for specific information or actions.\\n \\n # Tools configuration (same format as AI agent)\\n tools:\\n - current_time\\n - weather_api\\n - user_preferences\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n```\\n\\n### How Tools Work\\n\\n1. **Tool Resolution**: The workflow engine resolves tool names to actual MCP server deployments using the `tenant_id`\\n2. **Function Calling**: When the AI model decides to use a tool, it calls the `tool_executor` function\\n3. **Tool Execution**: The engine executes the specified tool via the appropriate MCP server\\n4. **Response Integration**: Tool results are fed back to the AI model for response generation\\n\\n### Tool Configuration Requirements\\n\\n| Field | Required | Description |\\n|-------|----------|-------------|\\n| `tools` | No | List of MCP tool names available to the AI |\\n| `tenant_id` | **Yes** (when using tools) | Tenant UUID for tool resolution |\\n\\n### Example: Complete AI Agent with Tools\\n\\n```yaml\\nid: intelligent_assistant\\nname: \\\"Intelligent Assistant with Tools\\\"\\n\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4o-mini\\n api_key: \\\"${OPENAI_API_KEY}\\\"\\n temperature: 0.3\\n\\n# Tenant ID can be set at workflow level for all tasks\\ntenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n\\ntasks:\\n - id: assistant_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools:\\n - Time-related tools: current_time, relative_time, days_in_month\\n - Weather tools: weather_forecast\\n - Communication: send_notification\\n \\n Use these tools when users ask for:\\n - Current time or date information\\n - Weather forecasts\\n - Sending notifications\\n \\n Always explain what tool you\u0027re using and why.\\n \\n user_message: \\\"${user_query}\\\"\\n \\n # MCP tools configuration\\n tools:\\n - current_time\\n - relative_time\\n - days_in_month\\n - weather_forecast\\n - send_notification\\n \\n # Tool resolution requires tenant_id\\n tenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n \\n temperature: 0.7\\n max_tokens: 1000\\n\\ninputs:\\n - name: user_query\\n type: string\\n required: true\\n default: \\\"What time is it and can you send a notification to user123?\\\"\\n\\noutputs:\\n ai_response:\\n type: string\\n source: assistant_with_tools.ai_response\\n description: \\\"AI response using tools when needed\\\"\\n```\\n\\n### Tool Execution Flow\\n\\n1. **User Input**: User provides a query that might require tool usage\\n2. **AI Analysis**: AI model analyzes the query and decides if tools are needed\\n3. **Tool Calling**: AI calls `tool_executor(deployment_id, tool_name, arguments)`\\n4. **Tool Resolution**: Engine resolves tool name to MCP server using tenant_id\\n5. **Tool Execution**: MCP server executes the requested tool\\n6. **Result Processing**: Tool results are returned to the AI model\\n7. **Response Generation**: AI generates final response incorporating tool results\\n\\n### Best Practices for Tools\\n\\n1. **Clear System Messages**: Explain available tools and when to use them\\n2. **Tool Categories**: Group related tools (time, weather, database, etc.)\\n3. **Error Handling**: AI models handle tool execution failures gracefully\\n4. **Tool Documentation**: Include tool descriptions in system messages\\n5. **Tenant Configuration**: Ensure tenant_id matches your MCP server setup\\n\\n---\\n\\n*This comprehensive reference covers all supported task types and their YAML configurations. Use this as a quick reference when building workflows with different task types.*\", \"format\": \"markdown\"}, \"status\": \"success\", \"successful_url\": \"http://172.17.0.1:5000/api/docs/yaml-reference\"}\n",
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"output": "Trying to fetch documentation from: http://host.docker.internal:5000/api/docs/yaml-reference\n\u274c Connection failed: http://host.docker.internal:5000/api/docs/yaml-reference - HTTPConnectionPool(host=\u0027host.docker.internal\u0027, port=5000): Max retries exceeded with url: /api/docs/yaml-reference (Caused by NameResolutionError(\"\u003curllib3.connection.HTTPConnection object at 0x75595e911cd0\u003e: Failed to resolve \u0027host.docker.internal\u0027 ([Errno -2] Name or service not known)\"))\nTrying to fetch documentation from: http://172.17.0.1:5000/api/docs/yaml-reference\n\u2705 Successfully connected to: http://172.17.0.1:5000/api/docs/yaml-reference\nSuccessfully fetched documentation from http://172.17.0.1:5000/api/docs/yaml-reference: 97047 characters\n__OUTPUTS__ {\"documentation\": {\"content\": \"# YAML Workflow Structure Reference\\n\\nComplete reference for the YAML workflow definition structure supported by the Assistants Workflow Engine.\\n\\n## Table of Contents\\n\\n1. [Complete Schema Overview](#complete-schema-overview)\\n2. [Required vs Optional Fields](#required-vs-optional-fields)\\n3. [Data Types Reference](#data-types-reference)\\n4. [Field Validation Rules](#field-validation-rules)\\n5. [Parser Behavior](#parser-behavior)\\n6. [Migration Guide](#migration-guide)\\n\\n## Complete Schema Overview\\n\\n```yaml\\n# === WORKFLOW METADATA (Required) ===\\nid: string # REQUIRED: Unique workflow identifier\\nname: string # REQUIRED: Human-readable workflow name\\n\\n# === WORKFLOW METADATA (Optional) ===\\nversion: string | integer # Default: 1\\nnamespace: string # Default: \\\"default\\\"\\ndescription: string # Default: \\\"\\\"\\ntenant_id: string # Default: null\\n\\n# === EXECUTION CONFIGURATION ===\\ntimeout_seconds: integer|string # Default: 3600 (1 hour) - Workflow-level timeout (supports duration strings like \\\"1h\\\", \\\"30m\\\")\\nlabels: object # Default: {}\\nmetadata: object # Default: {}\\n\\n# === DATA FLOW DEFINITIONS ===\\ninputs: # Default: []\\n - name: string # REQUIRED\\n type: string # REQUIRED: string|integer|boolean|object|array\\n description: string # Optional\\n required: boolean # Default: false\\n default: any # Optional default value\\n\\noutputs: object # Default: {}\\n output_name:\\n type: string # REQUIRED\\n source: string # REQUIRED: task_id or task_id.field\\n description: string # Optional\\n\\nvariables: object # Default: {}\\n\\n# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n - id: string # REQUIRED: Unique identifier to reference in tasks\\n provider: string # REQUIRED: openai|anthropic|openrouter|groq\\n config: object # REQUIRED: Provider-specific config\\n model: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\n api_key: string # REQUIRED: API key for the provider\\n temperature: number # Optional: 0.0-2.0, controls randomness\\n max_tokens: integer # Optional: Maximum tokens to generate\\n timeout: integer # Optional: Request timeout in seconds\\n base_url: string # Optional: Custom API base URL\\n\\n# Model Client Examples:\\n# OpenAI Configuration:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 2000\\n#\\n# Anthropic Configuration:\\n# model_clients:\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n# temperature: 0.7\\n# max_tokens: 1500\\n#\\n# Multiple Models:\\n# model_clients:\\n# - id: gpt4_model\\n# provider: openai\\n# config:\\n# model: gpt-4-turbo-preview\\n# api_key: \\\"sk-your-openai-api-key\\\"\\n# - id: claude_model\\n# provider: anthropic\\n# config:\\n# model: claude-3-sonnet-20240229\\n# api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n\\n# === TASK DEFINITIONS (Required) ===\\ntasks: # REQUIRED: List of tasks\\n - id: string # REQUIRED: Unique task identifier\\n name: string # Optional: Human-readable name\\n type: string # REQUIRED: Task type\\n description: string # Optional\\n \\n # DEPENDENCY CONFIGURATION\\n depends_on: array # Optional: Execution dependencies and template resolution\\n previous_node: string # REQUIRED: Task connectivity and UI hierarchy (essential for proper workflow execution)\\n \\n timeout_seconds: integer|string # Optional: Task-specific timeout (supports duration strings like \\\"5m\\\", \\\"30s\\\")\\n worker_group: string # Optional: Target worker group\\n conditions: array # Optional: Execution conditions\\n execute_on_routes: array # Optional: Routes for conditional execution\\n \\n # Input/Output mapping\\n input_mapping: object # Optional: Map workflow context to task inputs\\n output_mapping: object # Optional: Map task outputs to workflow context\\n \\n # Retry configuration\\n retry: object # Optional: Task-specific retry policy\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n \\n # === TASK TYPE SPECIFIC CONFIGURATIONS ===\\n # See detailed sections below for each task type\\n\\n# === WORKFLOW-LEVEL RETRY POLICY ===\\nretry: object # Optional: Global retry policy\\n retryOn: array # Default: [\\\"TEMPORARY_FAILURE\\\"]\\n maxAttempts: integer # Default: 1\\n initialDelay: string|integer # Default: \\\"1s\\\"\\n maxDelay: string|integer # Default: \\\"300s\\\"\\n backoffMultiplier: number # Default: 2.0\\n\\n# === TRIGGERS ===\\ntriggers: # Optional: List of triggers\\n - name: string # REQUIRED\\n type: string # REQUIRED: webhook|schedule|event\\n description: string # Optional\\n config: object # REQUIRED: Trigger-specific config\\n input_mapping: array # Optional: Map trigger data to workflow inputs\\n - webhook_field: string # Source field\\n workflow_input: string # Target workflow input\\n```\\n\\n## Required vs Optional Fields\\n\\n### Required Fields (Workflow Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the workflow |\\n| `tasks` | array | List of task definitions (can be empty) |\\n\\n### Required Fields (Task Level)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `id` | string | Unique identifier for the task |\\n| `type` | string | Task type (script, http, ai_agent, etc.) |\\n| `previous_node` | string | Reference to previous task for UI connectivity (required for proper task linking, except for the first task) |\\n\\n### Required Fields (Input Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `name` | string | Input parameter name |\\n| `type` | string | Data type (string, integer, boolean, object, array) |\\n\\n### Required Fields (Output Definition)\\n\\n| Field | Type | Description |\\n|-------|------|-------------|\\n| `type` | string | Output data type |\\n| `source` | string | Source task ID or task_id.field_name |\\n\\n## Data Types Reference\\n\\n### Primitive Types\\n\\n```yaml\\n# String type\\ninputs:\\n - name: customer_name\\n type: string\\n default: \\\"John Doe\\\"\\n description: \\\"Customer full name\\\"\\n\\n# Integer type \\ninputs:\\n - name: batch_size\\n type: integer\\n default: 100\\n description: \\\"Number of records per batch\\\"\\n\\n# Boolean type\\ninputs:\\n - name: include_archived\\n type: boolean\\n default: false\\n description: \\\"Include archived records\\\"\\n```\\n\\n### Complex Types\\n\\n```yaml\\n# Object type\\ninputs:\\n - name: config_params\\n type: object\\n default:\\n timeout: 30\\n retries: 3\\n debug: false\\n description: \\\"Configuration parameters\\\"\\n\\n# Array type\\ninputs:\\n - name: customer_ids\\n type: array\\n default: [\\\"cust_001\\\", \\\"cust_002\\\"]\\n description: \\\"List of customer identifiers\\\"\\n```\\n\\n### Duration Types\\n\\nDuration values can be specified as strings or integers:\\n\\n```yaml\\n# String format (recommended)\\ntimeout_seconds: \\\"1h\\\" # 1 hour\\ntimeout_seconds: \\\"30m\\\" # 30 minutes \\ntimeout_seconds: \\\"45s\\\" # 45 seconds\\ntimeout_seconds: \\\"2d\\\" # 2 days\\n\\n# Integer format (seconds)\\ntimeout_seconds: 3600 # 1 hour in seconds\\n\\n# Supported units\\n# s, sec, second, seconds\\n# m, min, minute, minutes \\n# h, hour, hours\\n# d, day, days\\n```\\n\\n### Timeout Configuration and Execution Behavior\\n\\nThe workflow engine supports configurable timeouts at both workflow and task levels with intelligent timeout-free communication for long-running tasks.\\n\\n#### Timeout Hierarchy\\n\\nTimeout values are resolved in this order of precedence:\\n1. **Task-level `timeout_seconds`**: Specific timeout for individual tasks\\n2. **Workflow-level `timeout_seconds`**: Default for all tasks in the workflow\\n3. **System default**: 300 seconds (5 minutes) for tasks, 3600 seconds (1 hour) for workflows\\n\\n```yaml\\n# Workflow-level timeout (applies to all tasks by default)\\ntimeout_seconds: \\\"2h\\\" # 2 hour workflow timeout\\n\\ntasks:\\n - id: quick_task\\n type: script\\n # Uses workflow-level timeout (2h)\\n script: \\\"print(\u0027Quick task\u0027)\\\"\\n \\n - id: custom_timeout_task\\n type: script\\n timeout_seconds: \\\"30m\\\" # Override with 30 minute timeout\\n script: \\\"print(\u0027Custom timeout task\u0027)\\\"\\n \\n - id: long_running_task\\n type: script\\n timeout_seconds: 600 # 10 minutes (integer format)\\n script: |\\n import time\\n time.sleep(300) # 5 minute processing\\n print(\\\"Long task completed\\\")\\n```\\n\\n#### Timeout-Free Communication\\n\\nThe workflow engine automatically uses **timeout-free communication** for long-running tasks to prevent timeout-related failures:\\n\\n- **Loop tasks**: Always use timeout-free communication (event-driven execution)\\n- **Script tasks**: Use timeout-free communication when `timeout_seconds \u003e 180` (host timeout threshold)\\n- **Other task types**: Use standard timeout-based communication\\n\\n```yaml\\ntasks:\\n - id: short_script\\n type: script\\n timeout_seconds: 60 # Uses standard timeout-based communication\\n script: \\\"print(\u0027Short script\u0027)\\\"\\n \\n - id: long_script\\n type: script\\n timeout_seconds: 600 # Uses timeout-free communication (600 \u003e 180)\\n script: |\\n import time\\n print(\\\"\\ud83d\\ude80 This uses timeout-free communication\\\")\\n time.sleep(300) # Won\u0027t cause timeout issues\\n print(\\\"Long script completed\\\")\\n \\n - id: data_processing_loop\\n type: loop\\n timeout_seconds: \\\"1h\\\" # Always uses timeout-free communication\\n loop_type: for\\n max_iterations: 10\\n # Loop tasks automatically use timeout-free communication\\n```\\n\\n#### Loop Task Timeout Inheritance\\n\\nLoop tasks can pass timeout configuration to their inner script tasks:\\n\\n```yaml\\n- id: batch_processing_loop\\n type: loop\\n timeout_seconds: \\\"30m\\\" # Loop-level timeout\\n loop_type: for\\n max_iterations: 5\\n loop_tasks:\\n - id: process_batch\\n type: script\\n # Inherits 30-minute timeout from parent loop\\n script: |\\n print(\\\"Processing batch with inherited timeout\\\")\\n # This script will use the 30-minute timeout from the loop\\n```\\n\\n#### Best Practices for Timeout Configuration\\n\\n1. **Use duration strings** for readability: `\\\"5m\\\"` vs `300`\\n2. **Set appropriate timeouts** based on expected execution time plus buffer\\n3. **Long-running tasks**: Set `timeout_seconds \u003e 180` to enable timeout-free communication\\n4. **Loop tasks**: Always get timeout-free communication automatically\\n5. **Critical tasks**: Use shorter timeouts for fail-fast behavior\\n6. **External API calls**: Use longer timeouts to account for network latency\\n\\n```yaml\\n# Example: Well-configured timeout workflow\\ntimeout_seconds: \\\"1h\\\" # Reasonable workflow timeout\\n\\ntasks:\\n - id: api_call\\n type: http\\n timeout_seconds: \\\"30s\\\" # Quick timeout for API calls\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: data_processing\\n type: script\\n timeout_seconds: \\\"10m\\\" # Longer timeout for processing (uses timeout-free)\\n script: |\\n # Process large dataset\\n process_data()\\n \\n - id: batch_processing\\n type: loop\\n timeout_seconds: \\\"45m\\\" # Generous timeout for loop (timeout-free)\\n loop_type: for\\n max_iterations: 100\\n # Inner tasks inherit the 45-minute timeout\\n```\\n\\n## Field Validation Rules\\n\\n### ID Fields\\n\\n```yaml\\n# Valid IDs\\nid: \\\"customer_processor\\\" # \\u2705 Valid\\nid: \\\"process-data\\\" # \\u2705 Valid\\nid: \\\"task_001\\\" # \\u2705 Valid\\n\\n# Invalid IDs\\nid: \\\"process data\\\" # \\u274c Spaces not allowed\\nid: \\\"123_start\\\" # \\u274c Cannot start with number\\nid: \\\"\\\" # \\u274c Cannot be empty\\n```\\n\\n### Timeout Values\\n\\n```yaml\\n# Valid timeouts\\ntimeout_seconds: 300 # \\u2705 Positive integer\\ntimeout_seconds: \\\"5m\\\" # \\u2705 Duration string\\ntimeout_seconds: \\\"1h30m\\\" # \\u2705 Complex duration\\n\\n# Invalid timeouts\\ntimeout_seconds: 0 # \\u274c Must be positive\\ntimeout_seconds: -60 # \\u274c Cannot be negative\\ntimeout_seconds: \\\"invalid\\\" # \\u274c Invalid duration format\\n```\\n\\n### Dependency Types and Validation\\n\\n#### `depends_on` vs `previous_node`\\n\\n**`depends_on` - Execution Dependencies:**\\n- Controls actual task execution order\\n- Enables template resolution and data access\\n- Required for accessing outputs from other tasks\\n- Validates that referenced tasks exist\\n- Multiple tasks can be specified\\n\\n**`previous_node` - Task Connectivity and UI Hierarchy:**\\n- **REQUIRED for proper task connectivity in UI**\\n- Essential for workflow visualization and task linking\\n- Critical for execution flow representation\\n- Single task reference only\\n- **Without this field, tasks won\u0027t be properly connected in the UI**\\n\\n```yaml\\ntasks:\\n - id: fetch_data\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n \\n - id: validate_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 EXECUTION: wait for fetch_data to complete\\n previous_node: fetch_data # \\u2705 UI: show as child of fetch_data in hierarchy\\n script: |\\n data = ${fetch_data} # \\u2705 Can access because of depends_on\\n if not validate(data):\\n raise ValueError(\\\"Invalid data\\\")\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker for proper parsing\\n outputs[\u0027status\u0027] = \u0027valid\u0027\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: process_data\\n type: script\\n depends_on: [validate_data] # \\u2705 EXECUTION: wait for validation\\n previous_node: validate_data # \\u2705 UI: show as child of validate_data\\n script: |\\n validation_result = ${validate_data} # \\u2705 Accessible via depends_on\\n # Process data...\\n\\n - id: separate_task\\n type: script\\n previous_node: process_data # \\u2705 UI: appears under process_data in hierarchy\\n # NO depends_on means this can run immediately (parallel to other tasks)\\n script: |\\n # This task runs independently but appears under process_data in UI\\n # Cannot access ${process_data} because no depends_on relationship\\n print(\\\"Independent task\\\")\\n\\n### Complete Example with Proper Hierarchy\\n\\n```yaml\\ntasks:\\n - id: fetch_customer_data\\n name: \\\"Fetch Customer Data\\\"\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: validate_customer\\n name: \\\"Validate Customer Data\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Wait for data fetch\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n customer = ${fetch_customer_data} # \\u2705 Can access because of depends_on\\n if not customer.get(\u0027email\u0027):\\n raise ValueError(\\\"Invalid customer data\\\")\\n print(\\\"__OUTPUTS__ {\u0027status\u0027: \u0027valid\u0027}\\\")\\n \\n - id: process_customer\\n name: \\\"Process Customer\\\"\\n type: ai_agent\\n depends_on: [validate_customer] # \\u2705 EXECUTION: Wait for validation\\n previous_node: validate_customer # \\u2705 UI: Show under validate_customer\\n config:\\n model_client_id: gpt4_model\\n user_message: \\\"Process customer: ${fetch_customer_data}\\\"\\n \\n - id: send_notification\\n name: \\\"Send Notification\\\"\\n type: script\\n depends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\n previous_node: process_customer # \\u2705 UI: Show under process_customer\\n script: |\\n result = ${process_customer}\\n print(f\\\"Notification sent for: ${fetch_customer_data.name}\\\")\\n \\n - id: parallel_audit_log\\n name: \\\"Audit Log (Parallel)\\\"\\n type: script\\n depends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\n previous_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\n script: |\\n # This runs in parallel with validation/processing\\n customer = ${fetch_customer_data}\\n print(f\\\"Audit: Customer {customer.id} accessed at ${current_timestamp}\\\")\\n```\\n\\n # Invalid examples\\n - id: invalid_task\\n type: script\\n depends_on: [nonexistent_task] # \\u274c VALIDATION ERROR: task doesn\u0027t exist\\n previous_node: another_missing # \\u26a0\\ufe0f UI WARNING: task doesn\u0027t exist (but not fatal)\\n script: \\\"print(\u0027Invalid\u0027)\\\"\\n```\\n\\n#### Key Differences:\\n\\n| Aspect | `depends_on` | `previous_node` |\\n|--------|--------------|-----------------|\\n| **Purpose** | Execution control | Task connectivity \u0026 UI organization |\\n| **Impact** | Blocks task until dependencies complete | **REQUIRED for UI task linking** |\\n| **Template Access** | \\u2705 Enables `${task_id}` resolution | \\u274c No template access |\\n| **Validation** | \\u274c Parser error if task missing | \\u26a0\\ufe0f Warning if task missing |\\n| **Multiple References** | \\u2705 Array of task IDs | \\u274c Single task ID only |\\n| **Execution Order** | \\u2705 Enforced | \\u274c No execution impact |\\n| **UI Connectivity** | \\u274c No UI impact | \\u2705 **ESSENTIAL for proper task connection** |\\n\\n### Circular Dependency Detection\\n\\n```yaml\\n# This will be rejected by the parser\\ntasks:\\n - id: task_a\\n depends_on: [task_b] # \\u274c Circular dependency\\n \\n - id: task_b \\n depends_on: [task_a] # \\u274c Circular dependency\\n```\\n\\n## Parser Behavior\\n\\n### Field Name Compatibility\\n\\nThe parser supports both camelCase and snake_case for field names:\\n\\n```yaml\\ntasks:\\n - id: flexible_task\\n # Both formats are accepted\\n dependsOn: [other_task] # camelCase\\n depends_on: [other_task] # snake_case\\n \\n inputMapping: # camelCase\\n source_field: \\\"target_field\\\"\\n input_mapping: # snake_case \\n source_field: \\\"target_field\\\"\\n \\n outputMapping: {} # camelCase\\n output_mapping: {} # snake_case\\n \\n workerGroup: \\\"gpu-workers\\\" # camelCase\\n worker_group: \\\"gpu-workers\\\" # snake_case\\n```\\n\\n### Default Value Assignment\\n\\n```yaml\\n# Fields with defaults are automatically populated\\nid: my_workflow\\n# name defaults to id value: \\\"my_workflow\\\"\\n# version defaults to: 1\\n# namespace defaults to: \\\"default\\\" \\n# timeout_seconds defaults to: 3600\\n# description defaults to: \\\"\\\"\\n\\ntasks:\\n - id: simple_task\\n type: script\\n # name defaults to id: \\\"simple_task\\\"\\n # timeout_seconds defaults to: 300\\n # retry.maxAttempts defaults to: 1\\n```\\n\\n### Template Resolution Context\\n\\nThe parser provides these built-in variables for template resolution:\\n\\n```yaml\\ntasks:\\n - id: context_aware_task\\n type: script\\n script: |\\n # Workflow context\\n workflow_id = \\\"${workflow_id}\\\" # Current workflow ID\\n execution_id = \\\"${execution_id}\\\" # Unique execution ID\\n namespace = \\\"${namespace}\\\" # Workflow namespace\\n \\n # Timing context\\n current_timestamp = \\\"${current_timestamp}\\\" # ISO timestamp\\n started_at = \\\"${started_at}\\\" # Workflow start time\\n \\n # Input context\\n user_input = \\\"${input_parameter}\\\" # Workflow inputs\\n \\n # Task context\\n previous_result = ${previous_task} # Previous task output\\n specific_field = ${task_id.field_name} # Specific output field\\n \\n # Route context (for conditional workflows)\\n route_info = ${_route_context} # Current route information\\n```\\n\\n### Error Handling During Parsing\\n\\n```yaml\\n# The parser will validate and report these errors:\\n\\n# 1. Missing required fields\\n# ERROR: Missing required field: \u0027id\u0027\\n\\n# 2. Invalid YAML syntax \\n# ERROR: Invalid YAML: mapping values are not allowed here\\n\\n# 3. Circular dependencies\\n# ERROR: Circular dependency detected involving task \u0027task_a\u0027\\n\\n# 4. Invalid task references\\n# ERROR: Task \u0027task_b\u0027 depends on non-existent task \u0027invalid_task\u0027\\n\\n# 5. Invalid duration formats\\n# ERROR: Invalid duration: \u0027not_a_duration\u0027\\n\\n# 6. Invalid field values\\n# ERROR: Timeout must be positive, got -10\\n```\\n\\n## Migration Guide\\n\\n### From Version 1.0 to 2.0\\n\\n```yaml\\n# OLD FORMAT (v1.0)\\nworkflow:\\n id: my_workflow\\n tasks:\\n - task_id: process_data\\n task_type: python_script\\n \\n# NEW FORMAT (v2.0) \\nid: my_workflow\\ntasks:\\n - id: process_data\\n type: script\\n language: python\\n```\\n\\n### Deprecated Fields\\n\\n| Old Field | New Field | Status |\\n|-----------|-----------|--------|\\n| `task_type` | `type` | Deprecated in v2.0 |\\n| `task_id` | `id` | Deprecated in v2.0 |\\n| `python_script` | `script` (type) | Deprecated in v2.0 |\\n| `plugin` | `mcp` (type) | Deprecated in v2.1 |\\n\\n### Backward Compatibility\\n\\nThe parser maintains backward compatibility for deprecated fields but will show warnings:\\n\\n```yaml\\n# This will work but show a warning\\ntasks:\\n - task_id: old_style_task # \\u26a0\\ufe0f Warning: Use \u0027id\u0027 instead\\n task_type: python_script # \\u26a0\\ufe0f Warning: Use \u0027type: script\u0027 instead\\n```\\n\\n## Advanced Schema Features\\n\\n### Conditional Field Requirements\\n\\nSome fields become required based on task type:\\n\\n```yaml\\n# HTTP tasks require URL\\n- id: api_call\\n type: http\\n url: \\\"https://api.example.com\\\" # \\u2705 Required for HTTP tasks\\n \\n# AI agent tasks require model_client_id\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4 # \\u2705 Required for AI agent tasks\\n```\\n\\n### Dynamic Field Validation\\n\\nField validation can depend on other field values:\\n\\n```yaml\\n# Loop tasks require loop-specific fields\\n- id: data_loop\\n type: loop\\n loop_type: \\\"for\\\" # Determines required fields\\n max_iterations: 10 # \\u2705 Required for \u0027for\u0027 loops\\n # exit_condition: not needed for \u0027for\u0027 loops\\n \\n- id: condition_loop \\n type: loop\\n loop_type: \\\"while\\\" # Determines required fields\\n exit_condition: \\\"${count} \u003e= 10\\\" # \\u2705 Required for \u0027while\u0027 loops\\n # max_iterations: optional for \u0027while\u0027 loops\\n```\\n\\n## Template Resolution and Data Flow\\n\\nTemplate resolution is a core feature that allows tasks to access data from previous tasks, workflow inputs, and built-in variables using the `${variable_name}` syntax. This is essential for creating dynamic workflows where tasks pass data to each other.\\n\\n### Basic Template Syntax\\n\\n```yaml\\n# Access workflow inputs\\nscript: |\\n customer_name = \\\"${customer_name}\\\" # From workflow inputs\\n \\n# Access previous task outputs\\nscript: |\\n previous_result = ${data_fetch_task} # Entire output from data_fetch_task\\n specific_field = ${data_fetch_task.customer_id} # Specific field\\n \\n# Access built-in variables\\nscript: |\\n workflow_id = \\\"${workflow_id}\\\"\\n execution_id = \\\"${execution_id}\\\"\\n timestamp = \\\"${current_timestamp}\\\"\\n```\\n\\n### Available Built-in Variables\\n\\n| Variable | Description | Example Value |\\n|----------|-------------|---------------|\\n| `${workflow_id}` | Current workflow identifier | `customer_processor` |\\n| `${execution_id}` | Unique execution identifier | `exec_12345_abcde` |\\n| `${namespace}` | Workflow namespace | `production` |\\n| `${current_timestamp}` | ISO timestamp | `2024-01-15T10:30:00Z` |\\n| `${started_at}` | Workflow start time | `2024-01-15T10:29:45Z` |\\n\\n### Task Output Resolution\\n\\nTasks can access outputs from other tasks they depend on:\\n\\n```yaml\\ntasks:\\n - id: fetch_customer\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: process_customer\\n type: script\\n depends_on: [fetch_customer] # Required for template access\\n previous_node: fetch_customer\\n script: |\\n # Access entire response\\n customer_data = ${fetch_customer}\\n \\n # Access specific fields\\n customer_name = \\\"${fetch_customer.name}\\\"\\n customer_email = \\\"${fetch_customer.email}\\\"\\n \\n # Process the data\\n result = {\\n \\\"processed_name\\\": customer_name.upper(),\\n \\\"domain\\\": customer_email.split(\\\"@\\\")[1]\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### AI Agent Output Resolution\\n\\nAI agent tasks return structured output that requires specific access patterns:\\n\\n```yaml\\n# AI Agent task\\n- id: ai_analysis_task\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Analyze the customer data and provide insights.\\\"\\n user_message: \\\"Customer data: ${customer_data}\\\"\\n\\n# Accessing AI agent output in subsequent tasks\\n- id: process_ai_response\\n type: script\\n depends_on: [ai_analysis_task]\\n previous_node: ai_analysis_task\\n script: |\\n # CORRECT: AI agents return structured output\\n agent_output = ${ai_analysis_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n token_usage = agent_output.get(\u0027token_usage\u0027, {})\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n # Process the AI response\\n processed_analysis = {\\n \\\"analysis\\\": ai_response,\\n \\\"tokens_used\\\": token_usage.get(\u0027total_tokens\u0027, 0),\\n \\\"model\\\": model_used,\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\"\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(processed_analysis)}\\\")\\n\\n# INCORRECT: Direct access won\u0027t work\\n- id: incorrect_ai_access\\n type: script\\n depends_on: [ai_analysis_task]\\n script: |\\n # \\u274c This will not work correctly\\n ai_response = ${ai_analysis_task} # Returns full agent object\\n # ai_response is now a dict, not the actual response text\\n```\\n\\n### AI Agent Response Structure\\n\\nAI agent tasks return a structured object with the following format:\\n\\n```json\\n{\\n \\\"ai_response\\\": \\\"The actual AI-generated text response\\\",\\n \\\"model\\\": \\\"gpt-4-turbo-preview\\\",\\n \\\"token_usage\\\": {\\n \\\"prompt_tokens\\\": 150,\\n \\\"completion_tokens\\\": 75,\\n \\\"total_tokens\\\": 225\\n },\\n \\\"estimated_cost\\\": {\\n \\\"amount\\\": 0.00337,\\n \\\"currency\\\": \\\"USD\\\"\\n },\\n \\\"provider\\\": \\\"openai\\\",\\n \\\"timestamp\\\": \\\"2024-01-15T10:30:00Z\\\"\\n}\\n```\\n\\n### Loop Task Data Access and Dependencies\\n\\nLoop tasks have a special execution context that differs from regular workflow tasks. Understanding how to access data within loops is crucial for building functional workflows.\\n\\n#### Loop Task Internal Data Access\\n\\nWithin loop tasks, data access works through injected context files rather than template resolution:\\n\\n```yaml\\n# Loop task with state variables and internal task dependencies\\n- id: data_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n state_variables:\\n processed_items: []\\n total_processed: 0\\n error_count: 0\\n loop_tasks:\\n # First task in loop\\n - id: fetch_data\\n type: script\\n script: |\\n # Access loop state variables through injected context\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n processed_items = loop_state.get(\u0027processed_items\u0027, [])\\n \\n # Access workflow inputs and external task outputs\\n external_data = inputs.get(\u0027previous_workflow_task\u0027, {})\\n iteration_num = inputs.get(\u0027iteration\u0027, 0) # Loop iteration variable\\n \\n # Process data\\n batch_data = fetch_batch_data(iteration_num)\\n \\n outputs = {\\\"batch_data\\\": batch_data, \\\"batch_size\\\": len(batch_data)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n # Second task depends on first task in same loop\\n - id: process_data\\n type: script\\n depends_on: [fetch_data] # \\u2705 CORRECT: Dependencies within loop tasks work normally\\n script: |\\n # \\u2705 CORRECT: Access previous loop task through inputs context\\n fetch_result = inputs.get(\u0027fetch_data\u0027, {})\\n batch_data = fetch_result.get(\u0027batch_data\u0027, [])\\n \\n # \\u274c INCORRECT: This won\u0027t work in loop context\\n # batch_data = ${fetch_data.batch_data} # Template resolution fails in loops\\n \\n # Access and update state variables\\n current_processed = loop_state.get(\u0027processed_items\u0027, [])\\n current_total = loop_state.get(\u0027total_processed\u0027, 0)\\n \\n # Process the batch\\n processed_batch = process_batch(batch_data)\\n current_processed.extend(processed_batch)\\n \\n # Update state variables using state_updates\\n state_updates[\u0027processed_items\u0027] = current_processed\\n state_updates[\u0027total_processed\u0027] = current_total + len(processed_batch)\\n \\n outputs = {\\\"processed_count\\\": len(processed_batch)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED for state updates\\n```\\n\\n#### Key Differences in Loop Task Data Access\\n\\n| Access Pattern | Regular Tasks | Loop Tasks |\\n|---------------|---------------|------------|\\n| **Previous Task Output** | `${previous_task}` | `inputs.get(\u0027field_name\u0027, default)` (flattened) |\\n| **State Variables** | N/A | `loop_state.get(\u0027variable_name\u0027, default)` |\\n| **State Updates** | N/A | `state_updates[\u0027variable_name\u0027] = value` |\\n| **Workflow Inputs** | `\\\"${input_name}\\\"` | `inputs.get(\u0027input_name\u0027, \u0027default\u0027)` |\\n| **Dependencies** | `depends_on: [task_id]` | Same: `depends_on: [task_id]` |\\n\\n#### Critical: Loop Task Output Flattening\\n\\n**Important:** The loop executor flattens task outputs into individual fields in the `inputs` dictionary, rather than storing them as nested objects under the task name.\\n\\n```yaml\\n# First loop task outputs:\\noutputs = {\\\"company_name\\\": \\\"ACME Corp\\\", \\\"company_id\\\": \\\"123\\\", \\\"status\\\": \\\"success\\\"}\\nprint(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n\\n# Second loop task accessing the data:\\nscript: |\\n # \\u274c INCORRECT: This won\u0027t work - no nested object\\n # company_data = inputs.get(\u0027first_task\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027)\\n \\n # \\u2705 CORRECT: Access flattened fields directly\\n company_name = inputs.get(\u0027company_name\u0027, \u0027\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n print(f\\\"Company: {company_name}, ID: {company_id}, Status: {task_status}\\\")\\n```\\n\\n#### Complete Loop Task Example\\n\\n```yaml\\n- id: financial_processing_loop\\n type: loop\\n loop_type: for\\n max_iterations: 3\\n iteration_variable: company_index\\n depends_on: [list_companies] # External dependency\\n \\n state_variables:\\n processed_companies: []\\n total_extractions: 0\\n successful_extractions: 0\\n \\n loop_tasks:\\n - id: process_current_company\\n type: script\\n script: |\\n # Access external workflow task data\\n companies_data = inputs.get(\u0027list_companies\u0027, {})\\n company_idx = inputs.get(\u0027company_index\u0027, 0)\\n \\n # Access state variables\\n processed = loop_state.get(\u0027processed_companies\u0027, [])\\n \\n # Process company\\n company = companies_data[\u0027companies\u0027][company_idx]\\n result = process_company(company)\\n \\n outputs = {\\\"company_name\\\": company[\u0027name\u0027], \\\"status\\\": \\\"success\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: extract_financial_data\\n type: script\\n depends_on: [process_current_company] # Depend on previous loop task\\n requirements:\\n - requests\u003e=2.28.0\\n - some-ai-library\u003e=1.0.0\\n script: |\\n # \\u2705 CORRECT: Access flattened outputs from previous loop task\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n task_status = inputs.get(\u0027status\u0027, \u0027\u0027)\\n \\n # \\u274c INCORRECT: Loop executor flattens outputs, no nested access\\n # company_data = inputs.get(\u0027process_current_company\u0027, {})\\n # company_name = company_data.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n \\n # \\u274c INCORRECT: Template resolution doesn\u0027t work in loops\\n # company_data = ${process_current_company}\\n \\n # Access external data\\n companies_list = inputs.get(\u0027list_companies\u0027, {})\\n \\n # Perform AI extraction\\n extraction_result = extract_with_ai({\\\"company_name\\\": company_name, \\\"company_id\\\": company_id})\\n \\n # Update state variables\\n current_processed = loop_state.get(\u0027processed_companies\u0027, [])\\n current_total = loop_state.get(\u0027total_extractions\u0027, 0)\\n current_successful = loop_state.get(\u0027successful_extractions\u0027, 0)\\n \\n current_processed.append({\\n \\\"company_name\\\": company_name,\\n \\\"extraction_result\\\": extraction_result\\n })\\n \\n # State updates are required for persistence\\n state_updates[\u0027processed_companies\u0027] = current_processed\\n state_updates[\u0027total_extractions\u0027] = current_total + 1\\n state_updates[\u0027successful_extractions\u0027] = current_successful + (1 if extraction_result else 0)\\n \\n outputs = {\\\"extracted\\\": bool(extraction_result)}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n print(f\\\"__STATE_UPDATES__ {json.dumps(state_updates)}\\\") # \\u2705 REQUIRED\\n\\n# Accessing loop task outputs in subsequent workflow tasks\\n- id: process_loop_results\\n type: script\\n depends_on: [financial_processing_loop]\\n previous_node: financial_processing_loop\\n script: |\\n # \\u2705 CORRECT: Access final state of loop\\n loop_result = ${financial_processing_loop}\\n loop_final_state = loop_result.get(\u0027final_state\u0027, {})\\n \\n # Access specific state variables from final state\\n processed_companies = loop_final_state.get(\u0027processed_companies\u0027, [])\\n total_processed = loop_final_state.get(\u0027total_extractions\u0027, 0)\\n successful_count = loop_final_state.get(\u0027successful_extractions\u0027, 0)\\n \\n # Access loop execution metadata\\n iterations_completed = loop_result.get(\u0027iterations_completed\u0027, 0)\\n \\n print(f\\\"Loop completed {iterations_completed} iterations\\\")\\n print(f\\\"Processed {len(processed_companies)} companies\\\")\\n print(f\\\"Success rate: {successful_count}/{total_processed}\\\")\\n \\n outputs = {\\n \\\"companies_processed\\\": len(processed_companies),\\n \\\"success_rate\\\": f\\\"{successful_count}/{total_processed}\\\",\\n \\\"iterations\\\": iterations_completed\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Loop Task Context Injection and Output Flattening\\n\\nThe loop executor injects context through temporary files and flattens task outputs for easier access:\\n\\n```python\\n# Auto-injected at the start of each loop task script:\\nimport json\\nimport os\\n\\n# Load context from temporary files (auto-generated)\\nwith open(\u0027/tmp/loop_context_task_iter_N/inputs.json\u0027, \u0027r\u0027) as f:\\n inputs = json.load(f) # Contains all workflow data + previous loop task outputs (FLATTENED)\\n\\nwith open(\u0027/tmp/loop_context_task_iter_N/loop_state.json\u0027, \u0027r\u0027) as f:\\n loop_state = json.load(f) # Contains current state variables\\n\\noutputs = {}\\nstate_updates = {}\\n\\n# Your script code follows...\\n```\\n\\n#### Debugging Loop Task Data Access\\n\\nWhen debugging loop tasks, always print available keys to understand what data is accessible:\\n\\n```yaml\\n- id: debug_loop_task\\n type: script\\n depends_on: [previous_loop_task]\\n script: |\\n # Debug: Print all available data\\n print(f\\\"\\ud83d\\udd0d Available inputs: {list(inputs.keys())}\\\")\\n print(f\\\"\\ud83d\\udd0d Loop state keys: {list(loop_state.keys())}\\\")\\n \\n # Debug: Print specific values\\n for key in [\u0027company_name\u0027, \u0027company_id\u0027, \u0027status\u0027, \u0027fy_folders\u0027]:\\n value = inputs.get(key, \u0027NOT_FOUND\u0027)\\n print(f\\\"\\ud83d\\udd0d {key}: {value}\\\")\\n \\n # Now access the flattened data\\n company_name = inputs.get(\u0027company_name\u0027, \u0027Unknown\u0027)\\n company_id = inputs.get(\u0027company_id\u0027, \u0027\u0027)\\n \\n # Process your data...\\n outputs = {\\\"processed\\\": f\\\"Company {company_name}\\\"}\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n#### Real Example: Task Output Flattening\\n\\n```yaml\\n# Task 1 outputs this structure:\\n__OUTPUTS__ {\\\"company_name\\\": \\\"LTTS\\\", \\\"company_id\\\": \\\"123\\\", \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], \\\"status\\\": \\\"success\\\"}\\n\\n# Task 2 receives flattened inputs:\\ninputs = {\\n \\\"folder_id\\\": \\\"...\\\", # Workflow input\\n \\\"gemini_api_key\\\": \\\"...\\\", # Workflow input \\n \\\"list_company_folders\\\": {...}, # External task output\\n \\\"company_index\\\": 0, # Loop iteration variable\\n \\\"company_name\\\": \\\"LTTS\\\", # \\u2705 Flattened from Task 1\\n \\\"company_id\\\": \\\"123\\\", # \\u2705 Flattened from Task 1\\n \\\"fy_folders\\\": [{\\\"id\\\": \\\"456\\\", \\\"name\\\": \\\"FY24\\\"}], # \\u2705 Flattened from Task 1\\n \\\"status\\\": \\\"success\\\", # \\u2705 Flattened from Task 1\\n \\\"total_extractions\\\": 0, # Loop state variable\\n \\\"processed_companies\\\": [] # Loop state variable\\n}\\n```\\n\\n#### Loop Task Special Output Fields\\n\\n| Field | Description | Example Access |\\n|-------|-------------|----------------|\\n| `final_state` | Final values of all state variables | `${loop_task.final_state}` |\\n| `final_state[\\\"variable_name\\\"]` | Specific state variable from final state | `${loop_task.final_state[\\\"total_processed\\\"]}` |\\n| `iterations_completed` | Number of iterations executed | `${loop_task.iterations_completed}` |\\n\\n#### Safe Template Resolution Pattern for Loop States\\n\\nWhen accessing loop final state fields, use this safe parsing pattern:\\n\\n```yaml\\nscript: |\\n try:\\n # Template resolution with safety checks\\n final_state_str = \u0027\u0027\u0027${loop_task.final_state}\u0027\u0027\u0027\\n processed_count_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_count\\\"]}\u0027\u0027\u0027\\n items_str = \u0027\u0027\u0027${loop_task.final_state[\\\"processed_items\\\"]}\u0027\u0027\u0027\\n iterations_str = \u0027\u0027\u0027${loop_task.iterations_completed}\u0027\u0027\u0027\\n \\n # Safe parsing with fallbacks\\n final_state = json.loads(final_state_str) if not final_state_str.startswith(\u0027UNRESOLVED_\u0027) else {}\\n processed_count = int(processed_count_str) if processed_count_str.isdigit() else 0\\n processed_items = json.loads(items_str) if not items_str.startswith(\u0027UNRESOLVED_\u0027) else []\\n iterations = int(iterations_str) if iterations_str.isdigit() else 0\\n \\n print(f\\\"Successfully processed {processed_count} items in {iterations} iterations\\\")\\n \\n except Exception as e:\\n print(f\\\"Error accessing loop results: {str(e)}\\\")\\n # Handle error case\\n```\\n```\\n\\n### Real-World AI Agent Example\\n\\nBased on actual WhatsApp workflow patterns:\\n\\n```yaml\\n# AI greeting agent\\n- id: greeting_agent\\n type: ai_agent\\n config:\\n model_client_id: assistant_model\\n system_message: \\\"You are a helpful assistant. Generate a friendly greeting.\\\"\\n user_message: \\\"User said: ${user_message}\\\"\\n\\n# Send the AI response via API\\n- id: send_greeting\\n type: script\\n depends_on: [greeting_agent]\\n previous_node: greeting_agent\\n script: |\\n import requests\\n import json\\n \\n # CORRECT: Access AI response from structured output\\n agent_output = ${greeting_agent}\\n ai_greeting = agent_output[\u0027ai_response\u0027].strip()\\n \\n phone_number = \\\"${phone_number}\\\"\\n api_token = \\\"${API_TOKEN}\\\"\\n \\n print(f\\\"\\ud83e\\udd16 AI generated greeting: {ai_greeting}\\\")\\n \\n # Send via API\\n url = f\\\"https://api.example.com/messages/{phone_number}\\\"\\n headers = {\\\"Authorization\\\": f\\\"Bearer {api_token}\\\"}\\n \\n response = requests.post(url, headers=headers, json={\\n \\\"message\\\": ai_greeting\\n })\\n \\n result = {\\n \\\"status\\\": \\\"sent\\\",\\n \\\"ai_response\\\": ai_greeting,\\n \\\"api_response\\\": response.json(),\\n \\\"tokens_used\\\": agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n }\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n```\\n\\n### Complex Template Patterns\\n\\n```yaml\\n# Conditional templates with router outputs\\n- id: router_task\\n type: conditional_router\\n conditions:\\n - name: \\\"premium_customer\\\"\\n condition: \\\"${customer_data.tier} == \u0027premium\u0027\\\"\\n route: \\\"premium_processing\\\"\\n\\n- id: premium_handler\\n type: script\\n depends_on: [router_task]\\n previous_node: router_task\\n execute_on_routes: [\\\"premium_processing\\\"]\\n script: |\\n # Access router decision\\n selected_route = \\\"${router_task.selected_route}\\\"\\n route_data = ${router_task.route_data}\\n \\n # Process premium customer\\n customer = ${customer_data}\\n premium_benefits = calculate_benefits(customer)\\n\\n# Loop through arrays from previous tasks\\n- id: process_items\\n type: script\\n depends_on: [fetch_items]\\n previous_node: fetch_items\\n script: |\\n items = ${fetch_items.data}\\n processed_items = []\\n \\n for item in items:\\n processed_item = {\\n \\\"id\\\": item[\\\"id\\\"],\\n \\\"processed_at\\\": \\\"${current_timestamp}\\\",\\n \\\"status\\\": \\\"processed\\\"\\n }\\n processed_items.append(processed_item)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027processed_items\u0027: processed_items})}\\\")\\n\\n# Nested object access\\n- id: user_preferences\\n type: script\\n depends_on: [fetch_user]\\n previous_node: fetch_user\\n script: |\\n # Access nested user preferences\\n email_notifications = ${fetch_user.preferences.notifications.email}\\n theme_setting = \\\"${fetch_user.preferences.ui.theme}\\\"\\n language = \\\"${fetch_user.profile.language}\\\"\\n \\n settings = {\\n \\\"notifications\\\": email_notifications,\\n \\\"theme\\\": theme_setting,\\n \\\"locale\\\": language\\n }\\n print(f\\\"__OUTPUTS__ {json.dumps(settings)}\\\")\\n```\\n\\n### Template Resolution Rules\\n\\n1. **Dependency Requirement**: Tasks must have `depends_on` relationship to access other task outputs\\n2. **String vs Object**: Use quotes for string values, no quotes for objects/arrays\\n3. **Field Navigation**: Use dot notation for nested field access\\n4. **Error Handling**: Unresolved templates become `UNRESOLVED_variable_name`\\n\\n```yaml\\n# \\u2705 Correct template usage\\n- id: correct_task\\n depends_on: [previous_task]\\n previous_node: previous_task\\n script: |\\n string_value = \\\"${previous_task.name}\\\" # String with quotes\\n object_value = ${previous_task.data} # Object without quotes\\n nested_value = \\\"${previous_task.user.email}\\\" # Nested field\\n\\n# \\u274c Incorrect template usage \\n- id: incorrect_task\\n # Missing depends_on - template won\u0027t resolve\\n script: |\\n value = ${previous_task.name} # Will become UNRESOLVED_previous_task.name\\n```\\n\\n## Script Task Output Handling\\n\\nScript tasks must use the `__OUTPUTS__` marker pattern for proper output parsing by the workflow engine.\\n\\n### Required Output Pattern\\n\\n```yaml\\ntasks:\\n - id: example_script\\n type: script\\n script: |\\n # Your script logic here\\n data = process_data()\\n \\n # Required: Store outputs in outputs dict\\n outputs = {}\\n outputs[\u0027result\u0027] = data\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n outputs[\u0027count\u0027] = len(data)\\n \\n # Required: Print with __OUTPUTS__ marker for engine parsing\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Output Access in Subsequent Tasks\\n\\n```yaml\\ntasks:\\n - id: data_processor\\n type: script\\n script: |\\n # Process and store outputs\\n results = {\\\"processed_count\\\": 100, \\\"errors\\\": []}\\n \\n outputs = {}\\n outputs[\u0027results\u0027] = results\\n outputs[\u0027status\u0027] = \u0027completed\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n \\n - id: result_handler\\n type: script\\n depends_on: [data_processor]\\n script: |\\n # Access previous task outputs\\n processor_output = ${data_processor}\\n processed_count = processor_output[\u0027results\u0027][\u0027processed_count\u0027]\\n status = processor_output[\u0027status\u0027]\\n \\n print(f\\\"Previous task processed {processed_count} items with status: {status}\\\")\\n \\n # Store new outputs\\n outputs = {\u0027message\u0027: f\u0027Handled {processed_count} results\u0027}\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### Error Handling in Scripts\\n\\n```yaml\\ntasks:\\n - id: safe_script\\n type: script\\n script: |\\n try:\\n result = risky_operation()\\n outputs = {\u0027result\u0027: result, \u0027status\u0027: \u0027success\u0027}\\n except Exception as e:\\n outputs = {\u0027error\u0027: str(e), \u0027status\u0027: \u0027failed\u0027}\\n \\n # Always print outputs, even for errors\\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n## Triggers and Workflow Activation\\n\\nTriggers define how workflows are started automatically based on external events. They enable event-driven workflow execution.\\n\\n### Webhook Triggers\\n\\nWebhook triggers allow external systems to start workflows by sending HTTP requests.\\n\\n```yaml\\ntriggers:\\n - name: customer_webhook\\n type: webhook\\n description: \\\"Triggered when customer data is updated\\\"\\n config:\\n path: \\\"/webhooks/customer-updated\\\" # Webhook endpoint path\\n method: POST # HTTP method (GET, POST, PUT, etc.)\\n authentication:\\n type: token # none|token|signature|basic\\n token: \\\"${WEBHOOK_SECRET}\\\" # Required if type is token\\n header_name: \\\"X-Webhook-Token\\\" # Optional: custom header name\\n validation:\\n required_fields: [\\\"customer.id\\\", \\\"customer.email\\\"] # Required payload fields\\n content_type: \\\"application/json\\\" # Expected content type\\n input_mapping:\\n - webhook_field: customer.id # Field from webhook payload\\n workflow_input: customer_id # Map to workflow input\\n - webhook_field: customer.email\\n workflow_input: customer_email\\n - webhook_field: customer.updated_at\\n workflow_input: update_timestamp\\n```\\n\\n### Scheduled Triggers\\n\\nSchedule workflows to run at specific times or intervals.\\n\\n```yaml\\ntriggers:\\n - name: daily_report\\n type: schedule\\n description: \\\"Generate daily customer report\\\"\\n config:\\n cron: \\\"0 9 * * *\\\" # Daily at 9 AM (cron format)\\n timezone: \\\"UTC\\\" # Optional: timezone\\n enabled: true # Optional: enable/disable\\n max_instances: 1 # Optional: max concurrent instances\\n input_mapping:\\n - workflow_input: report_date\\n default_value: \\\"${current_date}\\\" # Use built-in variable\\n - workflow_input: report_type\\n default_value: \\\"daily\\\"\\n\\n - name: weekly_cleanup\\n type: schedule\\n description: \\\"Weekly database cleanup\\\"\\n config:\\n cron: \\\"0 2 * * 0\\\" # Weekly on Sunday at 2 AM\\n timezone: \\\"America/New_York\\\"\\n input_mapping:\\n - workflow_input: cleanup_before_date\\n default_value: \\\"${date_minus_7_days}\\\"\\n```\\n\\n### Event Triggers\\n\\nEvent triggers respond to system events from various sources.\\n\\n```yaml\\ntriggers:\\n - name: file_uploaded\\n type: event\\n description: \\\"Process uploaded files\\\"\\n config:\\n event_type: \\\"file.uploaded\\\" # Event type to listen for\\n source: \\\"storage_service\\\" # Event source system\\n filter: # Optional: event filtering\\n file_type: [\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"]\\n file_size_max: \\\"100MB\\\"\\n path_pattern: \\\"/uploads/customers/*\\\"\\n retry: # Optional: retry configuration\\n max_attempts: 3\\n initial_delay: \\\"5s\\\"\\n input_mapping:\\n - webhook_field: file.path\\n workflow_input: file_path\\n - webhook_field: file.size\\n workflow_input: file_size\\n - webhook_field: file.type\\n workflow_input: file_type\\n\\n - name: order_status_changed\\n type: event\\n description: \\\"Handle order status changes\\\"\\n config:\\n event_type: \\\"order.status_changed\\\"\\n source: \\\"order_management\\\"\\n filter:\\n status: [\\\"completed\\\", \\\"cancelled\\\", \\\"refunded\\\"]\\n priority: [\\\"high\\\", \\\"urgent\\\"]\\n input_mapping:\\n - webhook_field: order.id\\n workflow_input: order_id\\n - webhook_field: order.status\\n workflow_input: new_status\\n - webhook_field: order.customer_id\\n workflow_input: customer_id\\n```\\n\\n### Dependency Triggers\\n\\nDependency triggers start workflows when other workflows complete.\\n\\n```yaml\\ntriggers:\\n - name: post_processing\\n type: dependency\\n description: \\\"Run after main processing completes\\\"\\n config:\\n depends_on_workflow: \\\"main_data_processing\\\" # Workflow to depend on\\n depends_on_status: \\\"SUCCESS\\\" # SUCCESS|FAILED|COMPLETED\\n namespace: \\\"production\\\" # Optional: workflow namespace\\n timeout_minutes: 60 # Optional: wait timeout\\n input_mapping:\\n - webhook_field: execution_id # From completed workflow\\n workflow_input: source_execution_id\\n - webhook_field: workflow_outputs.processed_count\\n workflow_input: items_processed\\n```\\n\\n## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n - id: unique_identifier # Used to reference this model in tasks\\n provider: provider_name # AI provider (openai, anthropic, etc.)\\n config:\\n model: model_name # Specific model to use\\n api_key: your_api_key # Authentication key\\n # Additional provider-specific options...\\n```\\n\\n### Supported Providers\\n\\n#### OpenAI Provider\\n\\n```yaml\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview # Available: gpt-4, gpt-4-turbo, gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-2.0\\n max_tokens: 2000 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### Anthropic Provider\\n\\n```yaml\\nmodel_clients:\\n - id: claude_model\\n provider: anthropic\\n config:\\n model: claude-3-sonnet-20240229 # Available: claude-3-opus, claude-3-sonnet, claude-3-haiku\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7 # Optional: 0.0-1.0\\n max_tokens: 1500 # Optional: Maximum response tokens\\n timeout: 30 # Optional: Request timeout (seconds)\\n```\\n\\n#### OpenRouter Provider\\n\\n```yaml\\nmodel_clients:\\n - id: openrouter_model\\n provider: openrouter\\n config:\\n model: anthropic/claude-3-sonnet # Available: Various models via OpenRouter\\n api_key: \\\"sk-or-your-openrouter-api-key\\\"\\n base_url: \\\"https://openrouter.ai/api/v1\\\" # Optional: Custom endpoint\\n temperature: 0.7\\n max_tokens: 2000\\n```\\n\\n#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n - id: fast_model\\n provider: openai\\n config:\\n model: gpt-3.5-turbo\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 0.3\\n \\n - id: smart_model\\n provider: anthropic\\n config:\\n model: claude-3-opus-20240229\\n api_key: \\\"sk-ant-your-anthropic-api-key\\\"\\n temperature: 0.7\\n \\n - id: creative_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"sk-your-openai-api-key\\\"\\n temperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n - id: analysis_task\\n type: ai_agent\\n config:\\n model_client_id: smart_model # References the claude model above\\n system_message: \\\"You are a data analyst...\\\"\\n user_message: \\\"Analyze this data: ${input_data}\\\"\\n \\n - id: creative_task\\n type: ai_agent\\n config:\\n model_client_id: creative_model # References the creative GPT-4 model\\n system_message: \\\"You are a creative writer...\\\"\\n user_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\\n\\n```yaml\\nmodel_clients:\\n - id: secure_model\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n temperature: 0.7\\n```\\n\\n### Common Configuration Options\\n\\n| Field | Description | Required | Default |\\n|-------|-------------|----------|---------|\\n| `id` | Unique identifier for referencing in tasks | Yes | - |\\n| `provider` | AI provider name | Yes | - |\\n| `config.model` | Specific model name | Yes | - |\\n| `config.api_key` | Authentication API key | Yes | - |\\n| `config.temperature` | Response randomness (0.0-2.0) | No | 0.7 |\\n| `config.max_tokens` | Maximum response tokens | No | Provider default |\\n| `config.timeout` | Request timeout in seconds | No | 30 |\\n| `config.base_url` | Custom API endpoint | No | Provider default |\\n\\n## Secrets Management and Environment Variables\\n\\nProper secrets management is crucial for security. Never hardcode sensitive information like API keys, passwords, or tokens directly in workflow YAML files.\\n\\n### Using Environment Variables\\n\\nThe recommended approach is to use environment variables for all sensitive data:\\n\\n```yaml\\n# \\u2705 RECOMMENDED: Use environment variables\\nmodel_clients:\\n - id: secure_openai\\n provider: openai\\n config:\\n model: gpt-4-turbo-preview\\n api_key: \\\"${OPENAI_API_KEY}\\\" # References environment variable\\n\\n# HTTP tasks with environment variables\\ntasks:\\n - id: secure_api_call\\n type: http\\n url: \\\"${API_BASE_URL}/customers\\\" # Environment variable for URL\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Environment variable for token\\n X-API-Key: \\\"${EXTERNAL_API_KEY}\\\" # Environment variable for API key\\n```\\n\\n### Environment Variable Patterns\\n\\n```yaml\\n# Database connections\\n- id: database_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${DATABASE_URL}\\\" # Full connection string\\n # or individual components:\\n host: \\\"${DB_HOST}\\\"\\n port: \\\"${DB_PORT}\\\"\\n database: \\\"${DB_NAME}\\\"\\n username: \\\"${DB_USER}\\\"\\n password: \\\"${DB_PASSWORD}\\\"\\n\\n# Third-party service credentials\\n- id: external_service\\n type: http\\n url: \\\"${SERVICE_BASE_URL}/api/v1/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${SERVICE_TOKEN}\\\"\\n X-Client-ID: \\\"${SERVICE_CLIENT_ID}\\\"\\n X-Client-Secret: \\\"${SERVICE_CLIENT_SECRET}\\\"\\n\\n# MCP server credentials\\n- id: mcp_task\\n type: mcp\\n mcp_server: \\\"@agentic/mcp-notion-server\\\"\\n environment_vars:\\n NOTION_TOKEN: \\\"${NOTION_API_TOKEN}\\\"\\n WORKSPACE_ID: \\\"${NOTION_WORKSPACE_ID}\\\"\\n```\\n\\n### Security Best Practices\\n\\n#### \\u274c What NOT to Do\\n\\n```yaml\\n# DON\u0027T: Hardcode secrets\\nmodel_clients:\\n - id: insecure_example\\n provider: openai\\n config:\\n api_key: \\\"sk-actual-api-key-here\\\" # SECURITY RISK!\\n\\n# DON\u0027T: Put secrets in comments\\ntasks:\\n - id: bad_example\\n type: http\\n url: \\\"https://api.example.com\\\"\\n # API key is: sk-12345... DON\u0027T DO THIS!\\n headers:\\n Authorization: \\\"Bearer sk-real-token\\\" # SECURITY RISK!\\n```\\n\\n#### \\u2705 What TO Do\\n\\n```yaml\\n# DO: Use environment variables\\nmodel_clients:\\n - id: secure_example\\n provider: openai\\n config:\\n api_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\\n\\n# DO: Use different variables for different environments\\n- id: environment_aware_task\\n type: http\\n url: \\\"${API_BASE_URL}\\\" # Different URLs per environment\\n headers:\\n Authorization: \\\"Bearer ${API_TOKEN}\\\" # Different tokens per environment\\n X-Environment: \\\"${ENVIRONMENT_NAME}\\\" # Track environment\\n\\n# DO: Use descriptive variable names\\nenvironment_vars:\\n CUSTOMER_API_TOKEN: \\\"${CUSTOMER_SERVICE_TOKEN}\\\"\\n BILLING_API_KEY: \\\"${BILLING_SERVICE_API_KEY}\\\"\\n NOTIFICATION_WEBHOOK_SECRET: \\\"${NOTIFICATION_SECRET}\\\"\\n```\\n\\n### Environment Variable Naming Conventions\\n\\n| Pattern | Example | Use Case |\\n|---------|---------|----------|\\n| `SERVICE_API_KEY` | `OPENAI_API_KEY` | Service API keys |\\n| `SERVICE_BASE_URL` | `CUSTOMER_API_BASE_URL` | Service endpoints |\\n| `DB_*` | `DB_HOST`, `DB_PASSWORD` | Database credentials |\\n| `WEBHOOK_*` | `WEBHOOK_SECRET_TOKEN` | Webhook authentication |\\n| `*_TIMEOUT` | `HTTP_REQUEST_TIMEOUT` | Timeout configurations |\\n\\n### Dynamic Environment Variables\\n\\nUse template resolution to create dynamic environment variable names:\\n\\n```yaml\\n# Different API keys per customer\\n- id: customer_specific_api\\n type: http\\n url: \\\"https://api.partner.com/data\\\"\\n headers:\\n Authorization: \\\"Bearer ${PARTNER_API_KEY_${customer_tier}}\\\" # Resolves to PARTNER_API_KEY_PREMIUM\\n\\n# Environment-specific configurations\\n- id: env_specific_task\\n type: storage\\n config:\\n connection_string: \\\"${DATABASE_URL_${environment}}\\\" # DATABASE_URL_PRODUCTION\\n```\\n\\n### Secrets in Different Task Types\\n\\n```yaml\\n# AI Agent with multiple secure credentials\\n- id: secure_ai_task\\n type: ai_agent\\n config:\\n model_client_id: secure_model\\n system_message: \\\"You are a secure assistant with access to customer data.\\\"\\n user_message: \\\"Process customer ${customer_id}\\\"\\n environment_vars:\\n CUSTOMER_DB_TOKEN: \\\"${CUSTOMER_DATABASE_TOKEN}\\\"\\n AUDIT_LOG_KEY: \\\"${AUDIT_LOGGING_API_KEY}\\\"\\n\\n# Script task with secure file access\\n- id: secure_script_task\\n type: script\\n environment_vars:\\n AWS_ACCESS_KEY_ID: \\\"${AWS_ACCESS_KEY}\\\"\\n AWS_SECRET_ACCESS_KEY: \\\"${AWS_SECRET_KEY}\\\"\\n S3_BUCKET_NAME: \\\"${SECURE_BUCKET_NAME}\\\"\\n script: |\\n import boto3\\n import os\\n \\n # Credentials automatically available from environment\\n s3_client = boto3.client(\u0027s3\u0027)\\n bucket = os.environ[\u0027S3_BUCKET_NAME\u0027]\\n \\n # Process secure files...\\n\\n# Storage task with encrypted connections\\n- id: secure_storage_task\\n type: storage\\n config:\\n storage_type: database\\n connection_string: \\\"${ENCRYPTED_DB_CONNECTION_STRING}\\\"\\n ssl_mode: \\\"require\\\"\\n ssl_cert: \\\"${SSL_CERT_PATH}\\\"\\n ssl_key: \\\"${SSL_KEY_PATH}\\\"\\n```\\n\\n### Environment Setup Examples\\n\\n#### Development Environment (.env file)\\n```bash\\n# Development API keys (non-production)\\nOPENAI_API_KEY=sk-dev-api-key-here\\nCUSTOMER_API_BASE_URL=https://api-dev.example.com\\nDATABASE_URL=postgresql://dev:pass@localhost:5432/workflow_dev\\nENVIRONMENT_NAME=development\\n```\\n\\n#### Production Environment\\n```bash\\n# Production API keys (secure)\\nOPENAI_API_KEY=sk-prod-api-key-here\\nCUSTOMER_API_BASE_URL=https://api.example.com\\nDATABASE_URL=postgresql://prod_user:secure_pass@prod-db:5432/workflow_prod\\nENVIRONMENT_NAME=production\\n```\\n\\n## Workflow Execution and Orchestration\\n\\nUnderstanding how workflows execute and how tasks are orchestrated is essential for building reliable, efficient workflows.\\n\\n### Execution Flow\\n\\nWorkflows follow a directed acyclic graph (DAG) execution model:\\n\\n1. **Initialization**: Workflow starts with inputs and model clients\\n2. **Task Scheduling**: Tasks are scheduled based on dependencies\\n3. **Parallel Execution**: Independent tasks run simultaneously\\n4. **Data Flow**: Outputs flow between tasks via template resolution\\n5. **Completion**: Workflow completes when all tasks finish\\n\\n```yaml\\n# Example execution flow\\ntasks:\\n # Stage 1: Independent tasks (run in parallel)\\n - id: fetch_customer # Runs immediately\\n type: http\\n url: \\\"https://api.example.com/customers/${customer_id}\\\"\\n \\n - id: fetch_products # Runs in parallel with fetch_customer\\n type: http\\n url: \\\"https://api.example.com/products\\\"\\n \\n # Stage 2: Dependent tasks (wait for Stage 1)\\n - id: validate_customer # Waits for fetch_customer\\n type: script\\n depends_on: [fetch_customer]\\n previous_node: fetch_customer\\n script: # validation logic...\\n \\n - id: filter_products # Waits for fetch_products\\n type: script\\n depends_on: [fetch_products]\\n previous_node: fetch_products\\n script: # filtering logic...\\n \\n # Stage 3: Final processing (waits for Stage 2)\\n - id: generate_recommendations # Waits for both validation and filtering\\n type: ai_agent\\n depends_on: [validate_customer, filter_products]\\n previous_node: validate_customer # UI hierarchy under customer validation\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate product recommendations for customer.\\\"\\n user_message: |\\n Customer: ${validate_customer}\\n Available products: ${filter_products}\\n```\\n\\n### Task States and Lifecycle\\n\\nEach task goes through these states during execution:\\n\\n| State | Description | Next States |\\n|-------|-------------|-------------|\\n| `PENDING` | Task created, waiting for dependencies | `RUNNING`, `SKIPPED` |\\n| `RUNNING` | Task is actively executing | `COMPLETED`, `FAILED` |\\n| `COMPLETED` | Task finished successfully | - |\\n| `FAILED` | Task encountered an error | `RUNNING` (if retries) |\\n| `SKIPPED` | Task skipped due to conditions | - |\\n| `TIMEOUT` | Task exceeded timeout limit | `RUNNING` (if retries) |\\n\\n### Conditional Execution\\n\\nTasks can be executed conditionally based on routes or conditions:\\n\\n```yaml\\n# Router-based conditional execution\\n- id: data_router\\n type: conditional_router\\n depends_on: [fetch_data]\\n previous_node: fetch_data\\n conditions:\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\"\\n condition: \\\"${fetch_data.record_count} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n default_route: \\\"standard_processing\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"batch_processing\\\"] # Only runs for large datasets\\n script: |\\n # Handle large dataset in batches\\n data = ${fetch_data}\\n process_in_batches(data, batch_size=1000)\\n\\n- id: direct_processor\\n type: script\\n depends_on: [data_router]\\n previous_node: data_router\\n execute_on_routes: [\\\"direct_processing\\\"] # Only runs for small datasets\\n script: |\\n # Process small dataset directly\\n data = ${fetch_data}\\n process_directly(data)\\n```\\n\\n### Error Handling and Retries\\n\\nConfigure retry policies for resilient workflow execution:\\n\\n```yaml\\n# Workflow-level retry policy\\nretry:\\n retryOn: [\\\"TEMPORARY_FAILURE\\\", \\\"TIMEOUT\\\", \\\"NETWORK_ERROR\\\"]\\n maxAttempts: 3\\n initialDelay: \\\"1s\\\"\\n maxDelay: \\\"60s\\\"\\n backoffMultiplier: 2.0\\n\\ntasks:\\n # Task-specific retry policy (overrides workflow-level)\\n - id: external_api_call\\n type: http\\n url: \\\"https://unreliable-api.example.com/data\\\"\\n retry:\\n maxAttempts: 5 # More retries for unreliable API\\n initialDelay: \\\"2s\\\"\\n maxDelay: \\\"120s\\\"\\n backoffMultiplier: 2.0\\n retryOn: [\\\"TIMEOUT\\\", \\\"HTTP_5XX\\\", \\\"NETWORK_ERROR\\\"]\\n \\n # Task with no retries\\n - id: critical_operation\\n type: script\\n retry:\\n maxAttempts: 1 # No retries for critical operations\\n script: |\\n # This operation should not be retried if it fails\\n perform_critical_database_operation()\\n```\\n\\n### Parallel Execution Patterns\\n\\n```yaml\\n# Fan-out pattern: One task triggers multiple parallel tasks\\n- id: data_source\\n type: http\\n url: \\\"https://api.example.com/data\\\"\\n\\n- id: process_customers # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process customer data from data_source\\n\\n- id: process_orders # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process order data from data_source\\n\\n- id: process_products # Parallel processing\\n type: script\\n depends_on: [data_source]\\n previous_node: data_source\\n script: # process product data from data_source\\n\\n# Fan-in pattern: Multiple tasks feed into one task\\n- id: generate_report # Waits for all parallel tasks\\n type: ai_agent\\n depends_on: [process_customers, process_orders, process_products]\\n previous_node: process_customers # UI hierarchy\\n config:\\n model_client_id: gpt4_model\\n system_message: \\\"Generate comprehensive report from processed data.\\\"\\n user_message: |\\n Customer data: ${process_customers}\\n Order data: ${process_orders}\\n Product data: ${process_products}\\n```\\n\\n### Resource Management\\n\\nControl resource usage and task distribution:\\n\\n```yaml\\n# Worker group targeting\\n- id: cpu_intensive_task\\n type: script\\n worker_group: \\\"high-cpu-workers\\\" # Target specific worker group\\n script: |\\n # CPU-intensive processing\\n perform_heavy_computation()\\n\\n- id: gpu_ml_task\\n type: ai_agent\\n worker_group: \\\"gpu-workers\\\" # Target GPU workers\\n config:\\n model_client_id: large_model\\n # Large model processing...\\n\\n# Timeout configuration\\n- id: long_running_task\\n type: script\\n timeout_seconds: 3600 # 1 hour timeout\\n script: |\\n # Long-running data processing\\n process_large_dataset()\\n\\n# Workflow-level timeout\\ntimeout_seconds: 7200 # 2 hour workflow timeout\\n```\\n\\n## Task Type Configurations\\n\\nThis section provides the complete YAML configuration for each supported task type.\\n\\n**Supported Task Types:**\\n- `script` - Execute Python code with full workflow context\\n- `http` - Make HTTP requests to external APIs\\n- `ai_agent` - Integrate with AI models for intelligent processing \\n- `loop` - Execute tasks repeatedly (for/while loops)\\n- `conditional_router` - Route execution based on conditions\\n- `mcp` - Execute tools via Model Context Protocol servers\\n- `storage` - Data persistence and retrieval operations\\n- `continuous_conversation` - Interactive AI conversations\\n- `human_input` - Tasks requiring human input or approval\\n- `chatbot` - Session-based chatbot conversations\\n\\n### Script Tasks\\n\\nExecute Python code with full workflow context access.\\n\\n```yaml\\n- id: script_task\\n name: \\\"Python Script Task\\\"\\n type: script\\n description: \\\"Execute Python code with dependencies and environment\\\"\\n \\n # Script configuration - script field is directly under task, NOT under config\\n script: | # REQUIRED: Python code to execute\\n import json\\n import requests\\n \\n # Access workflow inputs\\n input_value = \\\"${workflow_input}\\\"\\n \\n # Access previous task outputs\\n previous_result = ${previous_task}\\n \\n # Your processing logic\\n result = {\\\"processed\\\": True, \\\"value\\\": input_value}\\n \\n # Output results (must be JSON serializable)\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n # Alternative: use \u0027code\u0027 field instead of \u0027script\u0027\\n code: | # Alternative to \u0027script\u0027 field\\n print(\\\"Hello World\\\")\\n \\n # Package management\\n requirements: # Optional: Python packages\\n - requests==2.31.0\\n - pandas==1.5.3\\n - numpy==1.24.3\\n \\n # Environment configuration\\n environment: # Optional: Environment variables\\n API_KEY: \\\"${secrets.api_key}\\\"\\n DEBUG: \\\"true\\\"\\n CUSTOM_VAR: \\\"value\\\"\\n \\n environment_vars: # Alternative to \u0027environment\u0027\\n DATABASE_URL: \\\"${secrets.db_url}\\\"\\n \\n # Secrets configuration\\n secrets_config: # Optional: Secrets access\\n api_key: \\\"secret_name\\\"\\n db_password: \\\"db_secret\\\"\\n```\\n\\n### HTTP Tasks\\n\\nMake HTTP requests to external APIs and services.\\n\\n```yaml\\n- id: http_task\\n name: \\\"HTTP API Call\\\"\\n type: http\\n description: \\\"Call external REST API\\\"\\n \\n # HTTP configuration\\n method: POST # Default: GET\\n url: \\\"https://api.example.com/data\\\" # REQUIRED: Target URL\\n \\n # Headers configuration\\n headers: # Optional: HTTP headers\\n Content-Type: \\\"application/json\\\"\\n Authorization: \\\"Bearer ${secrets.api_token}\\\"\\n X-Custom-Header: \\\"workflow-engine\\\"\\n User-Agent: \\\"WorkflowEngine/1.0\\\"\\n \\n # Request body\\n body: | # Optional: Request body (for POST/PUT)\\n {\\n \\\"customer_id\\\": \\\"${customer_id}\\\",\\n \\\"timestamp\\\": \\\"${current_timestamp}\\\",\\n \\\"data\\\": ${previous_task}\\n }\\n \\n # Response validation\\n expected_status: [200, 201, 202] # Default: [200]\\n expectedStatus: [200, 201] # Alternative field name\\n \\n # Additional HTTP options\\n timeout_seconds: 30 # HTTP request timeout\\n follow_redirects: true # Follow HTTP redirects\\n verify_ssl: true # Verify SSL certificates\\n```\\n\\n### AI Agent Tasks\\n\\nIntegrate with AI models for intelligent processing.\\n\\n```yaml\\n- id: ai_agent_task\\n name: \\\"AI Processing Agent\\\"\\n type: ai_agent\\n description: \\\"Process data using AI model\\\"\\n \\n # AI configuration\\n config:\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # Optional: System prompt\\n You are a data analyst. Analyze the provided data and extract insights.\\n Return your analysis in JSON format with the following structure:\\n {\\n \\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n \\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n \\\"confidence_score\\\": 0.95\\n }\\n \\n user_message: | # REQUIRED: User prompt with template resolution\\n Please analyze this customer data:\\n ${previous_task}\\n \\n Customer ID: ${customer_id}\\n Date Range: ${date_range}\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1500 # Optional: Maximum response tokens\\n top_p: 1.0 # Optional: Nucleus sampling\\n frequency_penalty: 0.0 # Optional: Frequency penalty\\n presence_penalty: 0.0 # Optional: Presence penalty\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_api # Weather tools\\n - database_query # Database tools\\n - notification_service # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # Input/output format\\n input_format: \\\"text\\\" # Optional: text|json|structured\\n output_format: \\\"structured\\\" # Optional: text|json|structured\\n\\n### Important: AI Agent Output Access\\n\\nAI agent tasks return structured objects, not direct text responses. Always access the AI response using this pattern:\\n\\n```yaml\\n# In subsequent script tasks\\nscript: |\\n # CORRECT: Access AI response from structured output\\n agent_output = ${ai_agent_task}\\n ai_response = agent_output[\u0027ai_response\u0027].strip()\\n \\n # Additional metadata available\\n tokens_used = agent_output.get(\u0027token_usage\u0027, {}).get(\u0027total_tokens\u0027, 0)\\n cost_estimate = agent_output.get(\u0027estimated_cost\u0027, {}).get(\u0027amount\u0027, 0.0)\\n model_used = agent_output.get(\u0027model\u0027, \u0027unknown\u0027)\\n \\n print(f\\\"AI Response: {ai_response}\\\")\\n print(f\\\"Tokens used: {tokens_used}\\\")\\n print(f\\\"Estimated cost: ${cost_estimate:.6f}\\\")\\n```\\n\\n```yaml\\n# In workflow outputs - specify the ai_response field\\noutputs:\\n ai_analysis:\\n type: string\\n source: ai_agent_task.ai_response # Access the ai_response field\\n description: \\\"AI-generated analysis text\\\"\\n \\n total_tokens:\\n type: integer \\n source: ai_agent_task.token_usage.total_tokens\\n description: \\\"Total tokens consumed\\\"\\n```\\n```\\n\\n### Loop Tasks\\n\\nExecute tasks repeatedly with for-loop and while-loop patterns.\\n\\n```yaml\\n# For-Loop (Fixed Iterations)\\n- id: for_loop_task\\n name: \\\"For Loop Processor\\\"\\n type: loop\\n description: \\\"Process data in fixed batches\\\"\\n \\n # Loop configuration\\n loop_type: for # REQUIRED: for|while\\n max_iterations: 10 # REQUIRED for \u0027for\u0027 loops\\n iteration_variable: batch_index # Default: iteration\\n \\n # State variables (persistent across iterations)\\n state_variables:\\n total_processed: 0\\n error_count: 0\\n batch_results: []\\n \\n # Tasks to execute in each iteration\\n loop_tasks: # REQUIRED: Tasks to run in loop\\n - id: process_batch\\n name: \\\"Process Current Batch\\\"\\n type: script\\n script: |\\n batch_idx = ${batch_index} # Access iteration variable\\n total = ${total_processed} # Access state variable\\n \\n # Process current batch\\n result = process_batch_data(batch_idx)\\n \\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n \\n - id: update_state\\n name: \\\"Update Loop State\\\"\\n type: script\\n depends_on: [process_batch]\\n script: |\\n # Update state variables\\n new_total = ${total_processed} + ${process_batch.items_processed}\\n \\n result = {\\\"total_processed\\\": new_total}\\n print(f\\\"__OUTPUTS__ {json.dumps(result)}\\\")\\n\\n# While-Loop (Conditional)\\n- id: while_loop_task\\n name: \\\"While Loop Quality Processor\\\"\\n type: loop\\n description: \\\"Process until quality threshold met\\\"\\n \\n # Loop configuration\\n loop_type: while # REQUIRED: for|while\\n exit_condition: \\\"${average_quality} \u003e= 85 || ${iteration_count} \u003e= 20\\\" # REQUIRED for \u0027while\u0027\\n max_iterations: 50 # Optional: Safety limit\\n iteration_variable: iteration_count # Default: iteration\\n \\n # State variables\\n state_variables:\\n average_quality: 0\\n items_processed: 0\\n quality_scores: []\\n \\n # Loop tasks\\n loop_tasks:\\n - id: assess_quality\\n type: script\\n script: |\\n current_quality = calculate_quality()\\n print(f\\\"__OUTPUTS__ {json.dumps({\u0027quality\u0027: current_quality})}\\\")\\n```\\n\\n\\n### Conditional Router Tasks\\n\\nRoute execution to different paths based on conditions.\\n\\n```yaml\\n- id: conditional_router_task\\n name: \\\"Data Type Router\\\"\\n type: conditional_router\\n description: \\\"Route processing based on data characteristics\\\"\\n \\n # Router configuration\\n router_type: condition # Default: condition\\n \\n # Routing conditions\\n conditions: # REQUIRED: List of conditions\\n - name: \\\"large_dataset\\\"\\n condition: \\\"${data_size} \u003e 10000\\\"\\n route: \\\"batch_processing\\\"\\n - name: \\\"small_dataset\\\" \\n condition: \\\"${data_size} \u003c= 1000\\\"\\n route: \\\"direct_processing\\\"\\n - name: \\\"medium_dataset\\\"\\n condition: \\\"${data_size} \u003e 1000 \u0026\u0026 ${data_size} \u003c= 10000\\\"\\n route: \\\"standard_processing\\\"\\n \\n default_route: \\\"standard_processing\\\" # REQUIRED: Default route if no conditions match\\n \\n # Alternative: evaluation script for complex routing\\n evaluation_script: | # Optional: Custom routing logic\\n if data_type == \\\"json\\\":\\n return \\\"json_processing\\\"\\n elif data_type == \\\"xml\\\":\\n return \\\"xml_processing\\\"\\n else:\\n return \\\"default_processing\\\"\\n \\n # Route mapping for complex scenarios\\n route_mapping: # Optional: Route name mappings\\n json_processing: \\\"specialized_json\\\"\\n xml_processing: \\\"specialized_xml\\\"\\n\\n# Tasks that execute on specific routes\\n- id: batch_handler\\n name: \\\"Handle Large Dataset\\\"\\n type: script\\n execute_on_routes: [\\\"batch_processing\\\"] # REQUIRED: Routes this task runs on\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing large dataset in batches...\\\")\\n\\n- id: direct_handler\\n name: \\\"Handle Small Dataset\\\" \\n type: script\\n execute_on_routes: [\\\"direct_processing\\\"]\\n depends_on: [conditional_router_task]\\n script: |\\n print(\\\"Processing small dataset directly...\\\")\\n```\\n\\n### MCP Tasks\\n\\nExecute tools via Model Context Protocol servers.\\n\\n```yaml\\n- id: mcp_task\\n name: \\\"MCP Tool Execution\\\"\\n type: mcp\\n description: \\\"Execute external tool via MCP server\\\"\\n \\n # MCP server configuration\\n mcp_server: \\\"@agentic/mcp-notion-server\\\" # REQUIRED: MCP server package\\n tool_name: \\\"update_database\\\" # REQUIRED: Tool to execute\\n runtime: \\\"npx\\\" # REQUIRED: npx|uvx\\n \\n # Server startup\\n server_args: [] # Optional: Server startup arguments\\n server_startup_timeout: 30 # Optional: Server startup timeout (seconds)\\n \\n # Tool execution\\n tool_arguments: # REQUIRED: Arguments for the tool\\n database_id: \\\"${secrets.notion_database_id}\\\"\\n properties:\\n title: \\\"Workflow Results\\\"\\n status: \\\"Completed\\\"\\n processed_count: \\\"${previous_task.count}\\\"\\n timestamp: \\\"${current_timestamp}\\\"\\n \\n # Environment configuration\\n environment_vars: # Optional: Environment variables\\n NOTION_TOKEN: \\\"${secrets.notion_token}\\\"\\n DEBUG: \\\"true\\\"\\n \\n # Timeout configuration\\n timeout_seconds: 120 # Optional: Tool execution timeout\\n```\\n\\n### Storage Tasks\\n\\nManage data persistence and retrieval operations.\\n\\n```yaml\\n- id: storage_task\\n name: \\\"Data Storage Operation\\\"\\n type: storage\\n description: \\\"Save/load data to/from storage\\\"\\n \\n # Storage operation\\n operation: save # REQUIRED: save|load|delete|list\\n \\n # Storage configuration\\n storage_type: \\\"file\\\" # REQUIRED: file|database|cache\\n path: \\\"/data/results/${execution_id}.json\\\" # REQUIRED: Storage path\\n \\n # Data configuration\\n data: ${previous_task} # REQUIRED for \u0027save\u0027: Data to store\\n format: \\\"json\\\" # Optional: json|yaml|csv|parquet\\n compression: \\\"gzip\\\" # Optional: gzip|bz2|none\\n \\n # Metadata\\n metadata: # Optional: Additional metadata\\n workflow_id: \\\"${workflow_id}\\\"\\n created_at: \\\"${current_timestamp}\\\"\\n source_task: \\\"previous_task\\\"\\n \\n # File options\\n create_directories: true # Optional: Create parent directories\\n overwrite: true # Optional: Overwrite existing files\\n backup: false # Optional: Create backup before overwrite\\n \\n # Database options (when storage_type: \\\"database\\\")\\n table_name: \\\"workflow_results\\\" # Required for database storage\\n connection_string: \\\"${secrets.db_url}\\\" # Required for database storage\\n \\n # Cache options (when storage_type: \\\"cache\\\")\\n cache_key: \\\"result_${execution_id}\\\" # Required for cache storage\\n ttl: 3600 # Optional: Time to live (seconds)\\n```\\n\\n\\n### Continuous Conversation Tasks\\n\\nCreate interactive AI-powered conversations.\\n\\n```yaml\\n- id: conversation_task\\n name: \\\"AI Conversation Interface\\\"\\n type: continuous_conversation\\n description: \\\"Interactive conversation with AI assistant\\\"\\n \\n # Conversation configuration\\n config:\\n # AI configuration\\n ai_config:\\n model_client_id: support_assistant # REQUIRED: AI model to use\\n system_prompt: | # REQUIRED: System instructions\\n You are a helpful customer support representative.\\n Be friendly, professional, and solution-oriented.\\n \\n Guidelines:\\n - Always be polite and respectful\\n - Ask clarifying questions when needed\\n - Provide clear and actionable solutions\\n \\n temperature: 0.7 # Optional: AI creativity\\n max_tokens: 1000 # Optional: Response length limit\\n \\n # UI configuration\\n ui_config:\\n title: \\\"Customer Support Chat\\\" # Optional: Interface title\\n placeholder: \\\"How can I help you today?\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_timestamp: true # Optional: Show message timestamps\\n \\n # Termination conditions\\n termination:\\n keywords: [\\\"goodbye\\\", \\\"bye\\\", \\\"thanks\\\", \\\"done\\\"] # Optional: End conversation keywords\\n max_turns: 50 # Optional: Maximum conversation turns\\n timeout_minutes: 30 # Optional: Conversation timeout\\n inactivity_timeout: 10 # Optional: Inactivity timeout (minutes)\\n \\n # Context configuration\\n context:\\n system_context: \\\"Customer support conversation\\\" # Optional: Context description\\n user_id: \\\"${customer_id}\\\" # Optional: User identifier\\n session_data: ${previous_task} # Optional: Additional context data\\n```\\n\\n### Human Input Tasks\\n\\nTasks requiring human input or approval.\\n\\n```yaml\\n- id: human_input_task\\n name: \\\"Human Input Required\\\"\\n type: human_input\\n description: \\\"Wait for human input or approval\\\"\\n \\n # Human input configuration\\n config:\\n prompt: \\\"Please review and approve the processed data\\\" # REQUIRED: Prompt for human\\n input_type: \\\"approval\\\" # REQUIRED: approval|text|selection\\n \\n # Input options (for selection type)\\n options: [\\\"approve\\\", \\\"reject\\\", \\\"request_changes\\\"] # Optional: For selection input\\n \\n # Timeout configuration\\n timeout_minutes: 60 # Optional: How long to wait for input (minutes)\\n \\n # Conversation settings\\n conversation_mode: false # Optional: Add to conversation history\\n next_task: \\\"process_approval\\\" # Optional: Task to trigger after input\\n end_condition: \\\"bye|exit|quit\\\" # Optional: Keywords to end conversation\\n \\n # Input validation\\n required: true # Optional: Whether input is required\\n \\n # Additional context\\n context_data: ${previous_task} # Optional: Data to show to human\\n```\\n\\n### ChatBot Tasks\\n\\nSession-based chatbot conversations with streaming support.\\n\\n```yaml\\n- id: chatbot_task\\n name: \\\"AI Chatbot\\\"\\n type: chatbot\\n description: \\\"Interactive chatbot with session management\\\"\\n \\n # Chatbot configuration\\n config:\\n # AI model configuration\\n model_client_id: gpt4_model # REQUIRED: Reference to model client\\n \\n system_message: | # REQUIRED: System prompt for chatbot\\n You are a helpful AI assistant. Be friendly, helpful, and concise.\\n Answer questions clearly and ask for clarification when needed.\\n \\n # Session management\\n session_id: \\\"${session_id}\\\" # Optional: Session identifier (auto-generated if not provided)\\n max_history_messages: 20 # Optional: Maximum conversation history to maintain\\n \\n # Model parameters\\n temperature: 0.7 # Optional: Model creativity (0.0-2.0)\\n max_tokens: 1000 # Optional: Maximum response tokens\\n stream: true # Optional: Enable streaming responses\\n \\n # Tools configuration for function calling\\n tools: # Optional: List of MCP tool names\\n - current_time # Time-related tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - send_notification # Communication tools\\n \\n # Required for tool resolution\\n tenant_id: \\\"your-tenant-uuid\\\" # REQUIRED when using tools: Tenant ID for MCP tool resolution\\n \\n # UI configuration\\n ui_config:\\n title: \\\"AI Assistant\\\" # Optional: Chat interface title\\n placeholder: \\\"Type your message...\\\" # Optional: Input placeholder\\n theme: \\\"light\\\" # Optional: light|dark\\n show_typing_indicator: true # Optional: Show typing animation\\n \\n # Response configuration\\n response_config:\\n max_retries: 3 # Optional: Retry failed requests\\n timeout_seconds: 30 # Optional: Response timeout\\n \\n # Input message (for single-turn mode)\\n user_message: \\\"${user_input}\\\" # Optional: Direct message input\\n \\n # Context data\\n context_data: ${previous_task} # Optional: Additional context\\n```\\n\\n---\\n\\n## Common Errors and Corrections\\n\\nThis section documents frequent mistakes encountered when creating workflows and their proper corrections.\\n\\n### 1. Missing Required `id` Field\\n\\n**\\u274c Error:**\\n```yaml\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow without id\\\"\\ntasks:\\n - name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nid: \\\"my_workflow\\\" # REQUIRED: Unique workflow identifier\\nname: \\\"My Workflow\\\"\\ndescription: \\\"A workflow with proper id\\\"\\ntasks:\\n - id: task_1 # REQUIRED: Unique task identifier\\n name: \\\"Task 1\\\"\\n type: script\\n```\\n\\n### 2. Incorrect Task Structure (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n task_1: # Wrong: tasks as object mapping\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n task_2:\\n type: ai_agent\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n - id: task_1 # Correct: tasks as array with id field\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n - id: task_2\\n type: ai_agent\\n```\\n\\n### 3. Incorrect Inputs Format (Object vs Array)\\n\\n**\\u274c Error:**\\n```yaml\\ninputs:\\n user_name: # Wrong: inputs as object mapping\\n type: string\\n required: true\\n age:\\n type: integer\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ninputs:\\n - name: user_name # Correct: inputs as array with name field\\n type: string\\n required: true\\n - name: age\\n type: integer\\n```\\n\\n### 4. Script Configuration Location Error\\n\\n**\\u274c Error:**\\n```yaml\\n- id: script_task\\n type: script\\n config: # Wrong: script under config\\n script: |\\n print(\\\"Hello\\\")\\n requirements:\\n - requests\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: script_task\\n type: script\\n script: | # Correct: script directly under task\\n print(\\\"Hello\\\")\\n # requirements can be at task level if needed\\n```\\n\\n### 5. Individual Task Inputs (Not Supported)\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_1\\n type: script\\n script: \\\"print(\u0027hello\u0027)\\\"\\n inputs: # Wrong: tasks don\u0027t have individual inputs\\n search_query: ${search_query}\\n prev_result: ${previous_task}\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_1\\n type: script\\n depends_on: [previous_task] # Use depends_on for data access\\n script: |\\n # Access workflow inputs directly\\n search_query = \\\"${search_query}\\\"\\n # Access previous task outputs via template resolution\\n prev_result = ${previous_task}\\n```\\n\\n### 6. Incorrect Script Output Handling\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n result = process_data()\\n outputs[\u0027result\u0027] = result # Wrong: missing __OUTPUTS__ pattern\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n result = process_data()\\n \\n # Required: Use outputs dict and __OUTPUTS__ marker\\n outputs = {}\\n outputs[\u0027result\u0027] = result\\n outputs[\u0027status\u0027] = \u0027success\u0027\\n \\n import json\\n print(f\\\"__OUTPUTS__ {json.dumps(outputs)}\\\")\\n```\\n\\n### 7. AI Agent Configuration Errors\\n\\n**\\u274c Error:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n prompt: \\\"Process this data: ${data}\\\" # Wrong: use user_message\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: ai_task\\n type: ai_agent\\n config:\\n model_client_id: my_model\\n system_message: \\\"You are a helpful assistant.\\\"\\n user_message: \\\"Process this data: ${data}\\\" # Correct field name\\n```\\n\\n### 8. Missing Dependencies for Template Resolution\\n\\n**\\u274c Error:**\\n```yaml\\n- id: task_2\\n type: script\\n # Missing depends_on\\n script: |\\n data = ${task_1} # Will become UNRESOLVED_task_1\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\n- id: task_2\\n type: script\\n depends_on: [task_1] # Required for template access\\n script: |\\n data = ${task_1} # Now resolves correctly\\n```\\n\\n### 9. Incorrect Model Client Configuration\\n\\n**\\u274c Error:**\\n```yaml\\nmodel_clients:\\n my_model: # Wrong: object mapping format\\n type: openai\\n api_key: \\\"sk-...\\\"\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nmodel_clients:\\n - id: my_model # Correct: array format with id field\\n provider: openai\\n config:\\n model: \\\"gpt-4\\\"\\n api_key: \\\"sk-...\\\"\\n```\\n\\n### 10. Empty String Literals Causing Python Syntax Errors\\n\\n**\\u274c Error:**\\n```yaml\\nscript: |\\n def process_data(path=\\\"\\\"): # Wrong: empty string can cause syntax issues\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n \\n # This can lead to: SyntaxError: unterminated string literal\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\nscript: |\\n def process_data(path=None): # Correct: use None instead of empty string\\n current_path = f\\\"{path}.{key}\\\" if path else key\\n # Or use path = path or \\\"default_value\\\"\\n```\\n\\n### 11. YAML Indentation and Structure Issues\\n\\n**\\u274c Error:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\nscript: | # Wrong: inconsistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n**\\u2705 Correct:**\\n```yaml\\ntasks:\\n- id: task_1\\n type: script\\n script: | # Correct: consistent indentation\\n print(\\\"hello\\\")\\n```\\n\\n### Best Practices Summary\\n\\n1. **Always include required `id` fields** for workflows and tasks\\n2. **Use array format** for `tasks`, `inputs`, and `model_clients`\\n3. **Script field goes directly under task**, not under `config`\\n4. **No individual task inputs** - use template resolution instead\\n5. **Use `depends_on`** to access previous task outputs\\n6. **Print `__OUTPUTS__`** in scripts for proper output parsing\\n7. **Use `user_message`** (not `prompt`) for AI agent tasks\\n8. **Avoid empty string literals** - use `None` or meaningful defaults\\n9. **Follow consistent YAML indentation** throughout the file\\n\\n## MCP Tools and Function Calling\\n\\nAI agents and chatbots can use MCP (Model Context Protocol) tools for function calling, enabling them to execute external tools and services.\\n\\n### Tools Configuration\\n\\nBoth `ai_agent` and `chatbot` tasks support tools configuration:\\n\\n```yaml\\n# AI Agent with MCP tools\\n- id: ai_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools.\\n Use the available tools when needed to help users.\\n user_message: \\\"What time is it and what\u0027s the weather like?\\\"\\n \\n # Tools configuration\\n tools: # List of MCP tool names\\n - current_time # Time tools\\n - relative_time\\n - days_in_month\\n - weather_forecast # Weather tools\\n - database_query # Database tools\\n - send_notification # Communication tools\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n\\n# Chatbot with MCP tools\\n- id: chatbot_with_tools\\n type: chatbot\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful chatbot with access to tools.\\n Use tools when users ask for specific information or actions.\\n \\n # Tools configuration (same format as AI agent)\\n tools:\\n - current_time\\n - weather_api\\n - user_preferences\\n \\n tenant_id: \\\"550e8400-e29b-41d4-a716-446655440000\\\" # Required for tool resolution\\n```\\n\\n### How Tools Work\\n\\n1. **Tool Resolution**: The workflow engine resolves tool names to actual MCP server deployments using the `tenant_id`\\n2. **Function Calling**: When the AI model decides to use a tool, it calls the `tool_executor` function\\n3. **Tool Execution**: The engine executes the specified tool via the appropriate MCP server\\n4. **Response Integration**: Tool results are fed back to the AI model for response generation\\n\\n### Tool Configuration Requirements\\n\\n| Field | Required | Description |\\n|-------|----------|-------------|\\n| `tools` | No | List of MCP tool names available to the AI |\\n| `tenant_id` | **Yes** (when using tools) | Tenant UUID for tool resolution |\\n\\n### Example: Complete AI Agent with Tools\\n\\n```yaml\\nid: intelligent_assistant\\nname: \\\"Intelligent Assistant with Tools\\\"\\n\\nmodel_clients:\\n - id: gpt4_model\\n provider: openai\\n config:\\n model: gpt-4o-mini\\n api_key: \\\"${OPENAI_API_KEY}\\\"\\n temperature: 0.3\\n\\n# Tenant ID can be set at workflow level for all tasks\\ntenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n\\ntasks:\\n - id: assistant_with_tools\\n type: ai_agent\\n config:\\n model_client_id: gpt4_model\\n system_message: |\\n You are a helpful assistant with access to various tools:\\n - Time-related tools: current_time, relative_time, days_in_month\\n - Weather tools: weather_forecast\\n - Communication: send_notification\\n \\n Use these tools when users ask for:\\n - Current time or date information\\n - Weather forecasts\\n - Sending notifications\\n \\n Always explain what tool you\u0027re using and why.\\n \\n user_message: \\\"${user_query}\\\"\\n \\n # MCP tools configuration\\n tools:\\n - current_time\\n - relative_time\\n - days_in_month\\n - weather_forecast\\n - send_notification\\n \\n # Tool resolution requires tenant_id\\n tenant_id: \\\"0572fa8d-d7c3-47db-9219-ef40b80d42b7\\\"\\n \\n temperature: 0.7\\n max_tokens: 1000\\n\\ninputs:\\n - name: user_query\\n type: string\\n required: true\\n default: \\\"What time is it and can you send a notification to user123?\\\"\\n\\noutputs:\\n ai_response:\\n type: string\\n source: assistant_with_tools.ai_response\\n description: \\\"AI response using tools when needed\\\"\\n```\\n\\n### Tool Execution Flow\\n\\n1. **User Input**: User provides a query that might require tool usage\\n2. **AI Analysis**: AI model analyzes the query and decides if tools are needed\\n3. **Tool Calling**: AI calls `tool_executor(deployment_id, tool_name, arguments)`\\n4. **Tool Resolution**: Engine resolves tool name to MCP server using tenant_id\\n5. **Tool Execution**: MCP server executes the requested tool\\n6. **Result Processing**: Tool results are returned to the AI model\\n7. **Response Generation**: AI generates final response incorporating tool results\\n\\n### Best Practices for Tools\\n\\n1. **Clear System Messages**: Explain available tools and when to use them\\n2. **Tool Categories**: Group related tools (time, weather, database, etc.)\\n3. **Error Handling**: AI models handle tool execution failures gracefully\\n4. **Tool Documentation**: Include tool descriptions in system messages\\n5. **Tenant Configuration**: Ensure tenant_id matches your MCP server setup\\n\\n---\\n\\n*This comprehensive reference covers all supported task types and their YAML configurations. Use this as a quick reference when building workflows with different task types.*\", \"format\": \"markdown\"}, \"status\": \"success\", \"successful_url\": \"http://172.17.0.1:5000/api/docs/yaml-reference\"}\n",
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[Header 1: Additional provider-specific options...] (score: 0.751)\n__OUTPUTS__ {\"search_results\": [{\"path\": \"content.chunk_205\", \"content\": \"# AI configuration\\nai_config:\\nmodel_client_id: support_assistant # REQUIRED: AI model to use\\nsystem_prompt: | # REQUIRED: System instructions\\nYou are a helpful customer support representative.\\nBe friendly, professional, and solution-oriented. \\nGuidelines:\\n- Always be polite and respectful\\n- Ask clarifying questions when needed\\n- Provide clear and actionable solutions \\ntemperature: 0.7 # Optional: AI creativity\\nmax_tokens: 1000 # Optional: Response length limit\", \"relevance_score\": 0.7550173331782846, \"distance_score\": 0.32447290420532227, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 532}, {\"path\": \"content.chunk_103\", \"content\": \"## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n- id: unique_identifier # Used to reference this model in tasks\\nprovider: provider_name # AI provider (openai, anthropic, etc.)\\nconfig:\\nmodel: model_name # Specific model to use\\napi_key: your_api_key # Authentication key\", \"relevance_score\": 0.7524179642555167, \"distance_score\": 0.3290485441684723, \"headers\": \"Header 1: Always print outputs, even for errors\", \"source\": \"content\", \"content_length\": 537}, {\"path\": \"content.chunk_106\", \"content\": \"#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n- id: fast_model\\nprovider: openai\\nconfig:\\nmodel: gpt-3.5-turbo\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 0.3 \\n- id: smart_model\\nprovider: anthropic\\nconfig:\\nmodel: claude-3-opus-20240229\\napi_key: \\\"sk-ant-your-anthropic-api-key\\\"\\ntemperature: 0.7 \\n- id: creative_model\\nprovider: openai\\nconfig:\\nmodel: gpt-4-turbo-preview\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\", \"relevance_score\": 0.7509286171129537, \"distance_score\": 0.33168449997901917, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 619}, {\"path\": \"content.chunk_34\", \"content\": \"config:\\nmodel_client_id: gpt4_model\\nuser_message: \\\"Process customer: ${fetch_customer_data}\\\" \\n- id: send_notification\\nname: \\\"Send Notification\\\"\\ntype: script\\ndepends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\nprevious_node: process_customer # \\u2705 UI: Show under process_customer\\nscript: |\\nresult = ${process_customer}\\nprint(f\\\"Notification sent for: ${fetch_customer_data.name}\\\") \\n- id: parallel_audit_log\\nname: \\\"Audit Log (Parallel)\\\"\\ntype: script\\ndepends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\nprevious_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\nscript: |\", \"relevance_score\": 0.7497532788435617, \"distance_score\": 0.33377209305763245, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Field Validation Rules \u003e Header 3: Dependency Types and Validation \u003e Header 4: `depends_on` vs `previous_node`\", \"source\": \"content\", \"content_length\": 633}, {\"path\": \"content.chunk_4\", \"content\": \"# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: string # REQUIRED: Unique identifier to reference in tasks\\nprovider: string # REQUIRED: openai|anthropic|openrouter|groq\\nconfig: object # REQUIRED: Provider-specific config\\nmodel: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\napi_key: string # REQUIRED: API key for the provider\\ntemperature: number # Optional: 0.0-2.0, controls randomness\\nmax_tokens: integer # Optional: Maximum tokens to generate\\ntimeout: integer # Optional: Request timeout in seconds\", \"relevance_score\": 0.7407043943200355, \"distance_score\": 0.35006624460220337, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Complete Schema Overview\", \"source\": \"content\", \"content_length\": 788}, {\"path\": \"content.chunk_170\", \"content\": \"# AI configuration\\nconfig:\\nmodel_client_id: gpt4_model # REQUIRED: Reference to model client \\nsystem_message: | # Optional: System prompt\\nYou are a data analyst. Analyze the provided data and extract insights.\\nReturn your analysis in JSON format with the following structure:\\n{\\n\\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n\\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n\\\"confidence_score\\\": 0.95\\n} \\nuser_message: | # REQUIRED: User prompt with template resolution\\nPlease analyze this customer data:\\n${previous_task} \\nCustomer ID: ${customer_id}\\nDate Range: ${date_range}\", \"relevance_score\": 0.7399492168589372, \"distance_score\": 0.3514440953731537, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 595}, {\"path\": \"content.chunk_107\", \"content\": \"Reference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n- id: analysis_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: smart_model # References the claude model above\\nsystem_message: \\\"You are a data analyst...\\\"\\nuser_message: \\\"Analyze this data: ${input_data}\\\" \\n- id: creative_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: creative_model # References the creative GPT-4 model\\nsystem_message: \\\"You are a creative writer...\\\"\\nuser_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\", \"relevance_score\": 0.7395209017974772, \"distance_score\": 0.3522268235683441, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 616}, {\"path\": \"content.chunk_120\", \"content\": \"# DO: Use environment variables\\nmodel_clients:\\n- id: secure_example\\nprovider: openai\\nconfig:\\napi_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\", \"relevance_score\": 0.7384198111915177, \"distance_score\": 0.3542431890964508, \"headers\": \"Header 1: DO: Use environment variables\", \"source\": \"content\", \"content_length\": 147}], \"search_query\": \"how to defince model model_clients and configure it\", \"total_results\": 12, \"total_chunks\": 246, \"status\": \"success\"}\n",
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[Header 1: Additional provider-specific options...] (score: 0.751)\n__OUTPUTS__ {\"search_results\": [{\"path\": \"content.chunk_205\", \"content\": \"# AI configuration\\nai_config:\\nmodel_client_id: support_assistant # REQUIRED: AI model to use\\nsystem_prompt: | # REQUIRED: System instructions\\nYou are a helpful customer support representative.\\nBe friendly, professional, and solution-oriented. \\nGuidelines:\\n- Always be polite and respectful\\n- Ask clarifying questions when needed\\n- Provide clear and actionable solutions \\ntemperature: 0.7 # Optional: AI creativity\\nmax_tokens: 1000 # Optional: Response length limit\", \"relevance_score\": 0.7550173331782846, \"distance_score\": 0.32447290420532227, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 532}, {\"path\": \"content.chunk_103\", \"content\": \"## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n- id: unique_identifier # Used to reference this model in tasks\\nprovider: provider_name # AI provider (openai, anthropic, etc.)\\nconfig:\\nmodel: model_name # Specific model to use\\napi_key: your_api_key # Authentication key\", \"relevance_score\": 0.7524179642555167, \"distance_score\": 0.3290485441684723, \"headers\": \"Header 1: Always print outputs, even for errors\", \"source\": \"content\", \"content_length\": 537}, {\"path\": \"content.chunk_106\", \"content\": \"#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n- id: fast_model\\nprovider: openai\\nconfig:\\nmodel: gpt-3.5-turbo\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 0.3 \\n- id: smart_model\\nprovider: anthropic\\nconfig:\\nmodel: claude-3-opus-20240229\\napi_key: \\\"sk-ant-your-anthropic-api-key\\\"\\ntemperature: 0.7 \\n- id: creative_model\\nprovider: openai\\nconfig:\\nmodel: gpt-4-turbo-preview\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\", \"relevance_score\": 0.7509286171129537, \"distance_score\": 0.33168449997901917, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 619}, {\"path\": \"content.chunk_34\", \"content\": \"config:\\nmodel_client_id: gpt4_model\\nuser_message: \\\"Process customer: ${fetch_customer_data}\\\" \\n- id: send_notification\\nname: \\\"Send Notification\\\"\\ntype: script\\ndepends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\nprevious_node: process_customer # \\u2705 UI: Show under process_customer\\nscript: |\\nresult = ${process_customer}\\nprint(f\\\"Notification sent for: ${fetch_customer_data.name}\\\") \\n- id: parallel_audit_log\\nname: \\\"Audit Log (Parallel)\\\"\\ntype: script\\ndepends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\nprevious_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\nscript: |\", \"relevance_score\": 0.7497532788435617, \"distance_score\": 0.33377209305763245, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Field Validation Rules \u003e Header 3: Dependency Types and Validation \u003e Header 4: `depends_on` vs `previous_node`\", \"source\": \"content\", \"content_length\": 633}, {\"path\": \"content.chunk_4\", \"content\": \"# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: string # REQUIRED: Unique identifier to reference in tasks\\nprovider: string # REQUIRED: openai|anthropic|openrouter|groq\\nconfig: object # REQUIRED: Provider-specific config\\nmodel: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\napi_key: string # REQUIRED: API key for the provider\\ntemperature: number # Optional: 0.0-2.0, controls randomness\\nmax_tokens: integer # Optional: Maximum tokens to generate\\ntimeout: integer # Optional: Request timeout in seconds\", \"relevance_score\": 0.7407043943200355, \"distance_score\": 0.35006624460220337, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Complete Schema Overview\", \"source\": \"content\", \"content_length\": 788}, {\"path\": \"content.chunk_170\", \"content\": \"# AI configuration\\nconfig:\\nmodel_client_id: gpt4_model # REQUIRED: Reference to model client \\nsystem_message: | # Optional: System prompt\\nYou are a data analyst. Analyze the provided data and extract insights.\\nReturn your analysis in JSON format with the following structure:\\n{\\n\\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n\\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n\\\"confidence_score\\\": 0.95\\n} \\nuser_message: | # REQUIRED: User prompt with template resolution\\nPlease analyze this customer data:\\n${previous_task} \\nCustomer ID: ${customer_id}\\nDate Range: ${date_range}\", \"relevance_score\": 0.7399492168589372, \"distance_score\": 0.3514440953731537, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 595}, {\"path\": \"content.chunk_107\", \"content\": \"Reference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n- id: analysis_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: smart_model # References the claude model above\\nsystem_message: \\\"You are a data analyst...\\\"\\nuser_message: \\\"Analyze this data: ${input_data}\\\" \\n- id: creative_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: creative_model # References the creative GPT-4 model\\nsystem_message: \\\"You are a creative writer...\\\"\\nuser_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\", \"relevance_score\": 0.7395209017974772, \"distance_score\": 0.3522268235683441, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 616}, {\"path\": \"content.chunk_120\", \"content\": \"# DO: Use environment variables\\nmodel_clients:\\n- id: secure_example\\nprovider: openai\\nconfig:\\napi_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\", \"relevance_score\": 0.7384198111915177, \"distance_score\": 0.3542431890964508, \"headers\": \"Header 1: DO: Use environment variables\", \"source\": \"content\", \"content_length\": 147}], \"search_query\": \"how to defince model model_clients and configure it\", \"total_results\": 12, \"total_chunks\": 246, \"status\": \"success\"}\n",
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"content": "## Model Client Configuration\n\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\n\n### Basic Structure\n\n```yaml\nmodel_clients:\n- id: unique_identifier # Used to reference this model in tasks\nprovider: provider_name # AI provider (openai, anthropic, etc.)\nconfig:\nmodel: model_name # Specific model to use\napi_key: your_api_key # Authentication key",
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"content": "#### Multiple Model Clients\n\nYou can define multiple model clients and reference them by their `id` in tasks:\n\n```yaml\nmodel_clients:\n- id: fast_model\nprovider: openai\nconfig:\nmodel: gpt-3.5-turbo\napi_key: \"sk-your-openai-api-key\"\ntemperature: 0.3 \n- id: smart_model\nprovider: anthropic\nconfig:\nmodel: claude-3-opus-20240229\napi_key: \"sk-ant-your-anthropic-api-key\"\ntemperature: 0.7 \n- id: creative_model\nprovider: openai\nconfig:\nmodel: gpt-4-turbo-preview\napi_key: \"sk-your-openai-api-key\"\ntemperature: 1.2\n```\n\n### Using Model Clients in Tasks\n\nReference model clients in AI tasks using the `model_client_id` field:",
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"content": "# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: string # REQUIRED: Unique identifier to reference in tasks\nprovider: string # REQUIRED: openai|anthropic|openrouter|groq\nconfig: object # REQUIRED: Provider-specific config\nmodel: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\napi_key: string # REQUIRED: API key for the provider\ntemperature: number # Optional: 0.0-2.0, controls randomness\nmax_tokens: integer # Optional: Maximum tokens to generate\ntimeout: integer # Optional: Request timeout in seconds",
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"content": "# AI configuration\nconfig:\nmodel_client_id: gpt4_model # REQUIRED: Reference to model client \nsystem_message: | # Optional: System prompt\nYou are a data analyst. Analyze the provided data and extract insights.\nReturn your analysis in JSON format with the following structure:\n{\n\"insights\": [\"insight1\", \"insight2\"],\n\"recommendations\": [\"rec1\", \"rec2\"],\n\"confidence_score\": 0.95\n} \nuser_message: | # REQUIRED: User prompt with template resolution\nPlease analyze this customer data:\n${previous_task} \nCustomer ID: ${customer_id}\nDate Range: ${date_range}",
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"content": "Reference model clients in AI tasks using the `model_client_id` field:\n\n```yaml\ntasks:\n- id: analysis_task\ntype: ai_agent\nconfig:\nmodel_client_id: smart_model # References the claude model above\nsystem_message: \"You are a data analyst...\"\nuser_message: \"Analyze this data: ${input_data}\" \n- id: creative_task\ntype: ai_agent\nconfig:\nmodel_client_id: creative_model # References the creative GPT-4 model\nsystem_message: \"You are a creative writer...\"\nuser_message: \"Write a story about ${topic}\"\n```\n\n### Environment Variables for API Keys\n\nFor security, use environment variables instead of hardcoding API keys:",
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[Header 1: Additional provider-specific options...] (score: 0.751)\n__OUTPUTS__ {\"search_results\": [{\"path\": \"content.chunk_205\", \"content\": \"# AI configuration\\nai_config:\\nmodel_client_id: support_assistant # REQUIRED: AI model to use\\nsystem_prompt: | # REQUIRED: System instructions\\nYou are a helpful customer support representative.\\nBe friendly, professional, and solution-oriented. \\nGuidelines:\\n- Always be polite and respectful\\n- Ask clarifying questions when needed\\n- Provide clear and actionable solutions \\ntemperature: 0.7 # Optional: AI creativity\\nmax_tokens: 1000 # Optional: Response length limit\", \"relevance_score\": 0.7550173331782846, \"distance_score\": 0.32447290420532227, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 532}, {\"path\": \"content.chunk_103\", \"content\": \"## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n- id: unique_identifier # Used to reference this model in tasks\\nprovider: provider_name # AI provider (openai, anthropic, etc.)\\nconfig:\\nmodel: model_name # Specific model to use\\napi_key: your_api_key # Authentication key\", \"relevance_score\": 0.7524179642555167, \"distance_score\": 0.3290485441684723, \"headers\": \"Header 1: Always print outputs, even for errors\", \"source\": \"content\", \"content_length\": 537}, {\"path\": \"content.chunk_106\", \"content\": \"#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n- id: fast_model\\nprovider: openai\\nconfig:\\nmodel: gpt-3.5-turbo\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 0.3 \\n- id: smart_model\\nprovider: anthropic\\nconfig:\\nmodel: claude-3-opus-20240229\\napi_key: \\\"sk-ant-your-anthropic-api-key\\\"\\ntemperature: 0.7 \\n- id: creative_model\\nprovider: openai\\nconfig:\\nmodel: gpt-4-turbo-preview\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\", \"relevance_score\": 0.7509286171129537, \"distance_score\": 0.33168449997901917, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 619}, {\"path\": \"content.chunk_34\", \"content\": \"config:\\nmodel_client_id: gpt4_model\\nuser_message: \\\"Process customer: ${fetch_customer_data}\\\" \\n- id: send_notification\\nname: \\\"Send Notification\\\"\\ntype: script\\ndepends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\nprevious_node: process_customer # \\u2705 UI: Show under process_customer\\nscript: |\\nresult = ${process_customer}\\nprint(f\\\"Notification sent for: ${fetch_customer_data.name}\\\") \\n- id: parallel_audit_log\\nname: \\\"Audit Log (Parallel)\\\"\\ntype: script\\ndepends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\nprevious_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\nscript: |\", \"relevance_score\": 0.7497532788435617, \"distance_score\": 0.33377209305763245, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Field Validation Rules \u003e Header 3: Dependency Types and Validation \u003e Header 4: `depends_on` vs `previous_node`\", \"source\": \"content\", \"content_length\": 633}, {\"path\": \"content.chunk_4\", \"content\": \"# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: string # REQUIRED: Unique identifier to reference in tasks\\nprovider: string # REQUIRED: openai|anthropic|openrouter|groq\\nconfig: object # REQUIRED: Provider-specific config\\nmodel: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\napi_key: string # REQUIRED: API key for the provider\\ntemperature: number # Optional: 0.0-2.0, controls randomness\\nmax_tokens: integer # Optional: Maximum tokens to generate\\ntimeout: integer # Optional: Request timeout in seconds\", \"relevance_score\": 0.7407043943200355, \"distance_score\": 0.35006624460220337, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Complete Schema Overview\", \"source\": \"content\", \"content_length\": 788}, {\"path\": \"content.chunk_170\", \"content\": \"# AI configuration\\nconfig:\\nmodel_client_id: gpt4_model # REQUIRED: Reference to model client \\nsystem_message: | # Optional: System prompt\\nYou are a data analyst. Analyze the provided data and extract insights.\\nReturn your analysis in JSON format with the following structure:\\n{\\n\\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n\\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n\\\"confidence_score\\\": 0.95\\n} \\nuser_message: | # REQUIRED: User prompt with template resolution\\nPlease analyze this customer data:\\n${previous_task} \\nCustomer ID: ${customer_id}\\nDate Range: ${date_range}\", \"relevance_score\": 0.7399492168589372, \"distance_score\": 0.3514440953731537, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 595}, {\"path\": \"content.chunk_107\", \"content\": \"Reference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n- id: analysis_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: smart_model # References the claude model above\\nsystem_message: \\\"You are a data analyst...\\\"\\nuser_message: \\\"Analyze this data: ${input_data}\\\" \\n- id: creative_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: creative_model # References the creative GPT-4 model\\nsystem_message: \\\"You are a creative writer...\\\"\\nuser_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\", \"relevance_score\": 0.7395209017974772, \"distance_score\": 0.3522268235683441, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 616}, {\"path\": \"content.chunk_120\", \"content\": \"# DO: Use environment variables\\nmodel_clients:\\n- id: secure_example\\nprovider: openai\\nconfig:\\napi_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\", \"relevance_score\": 0.7384198111915177, \"distance_score\": 0.3542431890964508, \"headers\": \"Header 1: DO: Use environment variables\", \"source\": \"content\", \"content_length\": 147}], \"search_query\": \"how to defince model model_clients and configure it\", \"total_results\": 12, \"total_chunks\": 246, \"status\": \"success\"}\n",
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[Header 1: Additional provider-specific options...] (score: 0.751)\n__OUTPUTS__ {\"search_results\": [{\"path\": \"content.chunk_205\", \"content\": \"# AI configuration\\nai_config:\\nmodel_client_id: support_assistant # REQUIRED: AI model to use\\nsystem_prompt: | # REQUIRED: System instructions\\nYou are a helpful customer support representative.\\nBe friendly, professional, and solution-oriented. \\nGuidelines:\\n- Always be polite and respectful\\n- Ask clarifying questions when needed\\n- Provide clear and actionable solutions \\ntemperature: 0.7 # Optional: AI creativity\\nmax_tokens: 1000 # Optional: Response length limit\", \"relevance_score\": 0.7550173331782846, \"distance_score\": 0.32447290420532227, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 532}, {\"path\": \"content.chunk_103\", \"content\": \"## Model Client Configuration\\n\\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\\n\\n### Basic Structure\\n\\n```yaml\\nmodel_clients:\\n- id: unique_identifier # Used to reference this model in tasks\\nprovider: provider_name # AI provider (openai, anthropic, etc.)\\nconfig:\\nmodel: model_name # Specific model to use\\napi_key: your_api_key # Authentication key\", \"relevance_score\": 0.7524179642555167, \"distance_score\": 0.3290485441684723, \"headers\": \"Header 1: Always print outputs, even for errors\", \"source\": \"content\", \"content_length\": 537}, {\"path\": \"content.chunk_106\", \"content\": \"#### Multiple Model Clients\\n\\nYou can define multiple model clients and reference them by their `id` in tasks:\\n\\n```yaml\\nmodel_clients:\\n- id: fast_model\\nprovider: openai\\nconfig:\\nmodel: gpt-3.5-turbo\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 0.3 \\n- id: smart_model\\nprovider: anthropic\\nconfig:\\nmodel: claude-3-opus-20240229\\napi_key: \\\"sk-ant-your-anthropic-api-key\\\"\\ntemperature: 0.7 \\n- id: creative_model\\nprovider: openai\\nconfig:\\nmodel: gpt-4-turbo-preview\\napi_key: \\\"sk-your-openai-api-key\\\"\\ntemperature: 1.2\\n```\\n\\n### Using Model Clients in Tasks\\n\\nReference model clients in AI tasks using the `model_client_id` field:\", \"relevance_score\": 0.7509286171129537, \"distance_score\": 0.33168449997901917, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 619}, {\"path\": \"content.chunk_34\", \"content\": \"config:\\nmodel_client_id: gpt4_model\\nuser_message: \\\"Process customer: ${fetch_customer_data}\\\" \\n- id: send_notification\\nname: \\\"Send Notification\\\"\\ntype: script\\ndepends_on: [process_customer] # \\u2705 EXECUTION: Wait for processing\\nprevious_node: process_customer # \\u2705 UI: Show under process_customer\\nscript: |\\nresult = ${process_customer}\\nprint(f\\\"Notification sent for: ${fetch_customer_data.name}\\\") \\n- id: parallel_audit_log\\nname: \\\"Audit Log (Parallel)\\\"\\ntype: script\\ndepends_on: [fetch_customer_data] # \\u2705 EXECUTION: Only needs initial data\\nprevious_node: fetch_customer_data # \\u2705 UI: Show under fetch_customer_data\\nscript: |\", \"relevance_score\": 0.7497532788435617, \"distance_score\": 0.33377209305763245, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Field Validation Rules \u003e Header 3: Dependency Types and Validation \u003e Header 4: `depends_on` vs `previous_node`\", \"source\": \"content\", \"content_length\": 633}, {\"path\": \"content.chunk_4\", \"content\": \"# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\\n- id: string # REQUIRED: Unique identifier to reference in tasks\\nprovider: string # REQUIRED: openai|anthropic|openrouter|groq\\nconfig: object # REQUIRED: Provider-specific config\\nmodel: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\\napi_key: string # REQUIRED: API key for the provider\\ntemperature: number # Optional: 0.0-2.0, controls randomness\\nmax_tokens: integer # Optional: Maximum tokens to generate\\ntimeout: integer # Optional: Request timeout in seconds\", \"relevance_score\": 0.7407043943200355, \"distance_score\": 0.35006624460220337, \"headers\": \"Header 1: YAML Workflow Structure Reference \u003e Header 2: Complete Schema Overview\", \"source\": \"content\", \"content_length\": 788}, {\"path\": \"content.chunk_170\", \"content\": \"# AI configuration\\nconfig:\\nmodel_client_id: gpt4_model # REQUIRED: Reference to model client \\nsystem_message: | # Optional: System prompt\\nYou are a data analyst. Analyze the provided data and extract insights.\\nReturn your analysis in JSON format with the following structure:\\n{\\n\\\"insights\\\": [\\\"insight1\\\", \\\"insight2\\\"],\\n\\\"recommendations\\\": [\\\"rec1\\\", \\\"rec2\\\"],\\n\\\"confidence_score\\\": 0.95\\n} \\nuser_message: | # REQUIRED: User prompt with template resolution\\nPlease analyze this customer data:\\n${previous_task} \\nCustomer ID: ${customer_id}\\nDate Range: ${date_range}\", \"relevance_score\": 0.7399492168589372, \"distance_score\": 0.3514440953731537, \"headers\": \"Header 1: AI configuration\", \"source\": \"content\", \"content_length\": 595}, {\"path\": \"content.chunk_107\", \"content\": \"Reference model clients in AI tasks using the `model_client_id` field:\\n\\n```yaml\\ntasks:\\n- id: analysis_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: smart_model # References the claude model above\\nsystem_message: \\\"You are a data analyst...\\\"\\nuser_message: \\\"Analyze this data: ${input_data}\\\" \\n- id: creative_task\\ntype: ai_agent\\nconfig:\\nmodel_client_id: creative_model # References the creative GPT-4 model\\nsystem_message: \\\"You are a creative writer...\\\"\\nuser_message: \\\"Write a story about ${topic}\\\"\\n```\\n\\n### Environment Variables for API Keys\\n\\nFor security, use environment variables instead of hardcoding API keys:\", \"relevance_score\": 0.7395209017974772, \"distance_score\": 0.3522268235683441, \"headers\": \"Header 1: Additional provider-specific options...\", \"source\": \"content\", \"content_length\": 616}, {\"path\": \"content.chunk_120\", \"content\": \"# DO: Use environment variables\\nmodel_clients:\\n- id: secure_example\\nprovider: openai\\nconfig:\\napi_key: \\\"${OPENAI_API_KEY}\\\" # Secure approach\", \"relevance_score\": 0.7384198111915177, \"distance_score\": 0.3542431890964508, \"headers\": \"Header 1: DO: Use environment variables\", \"source\": \"content\", \"content_length\": 147}], \"search_query\": \"how to defince model model_clients and configure it\", \"total_results\": 12, \"total_chunks\": 246, \"status\": \"success\"}\n",
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"content": "# AI configuration\nai_config:\nmodel_client_id: support_assistant # REQUIRED: AI model to use\nsystem_prompt: | # REQUIRED: System instructions\nYou are a helpful customer support representative.\nBe friendly, professional, and solution-oriented. \nGuidelines:\n- Always be polite and respectful\n- Ask clarifying questions when needed\n- Provide clear and actionable solutions \ntemperature: 0.7 # Optional: AI creativity\nmax_tokens: 1000 # Optional: Response length limit",
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"content": "## Model Client Configuration\n\nBefore defining AI-related tasks (ai_agent, chatbot, continuous_conversation), you must configure model clients. Model clients define which AI models to use and how to authenticate with them.\n\n### Basic Structure\n\n```yaml\nmodel_clients:\n- id: unique_identifier # Used to reference this model in tasks\nprovider: provider_name # AI provider (openai, anthropic, etc.)\nconfig:\nmodel: model_name # Specific model to use\napi_key: your_api_key # Authentication key",
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"content": "#### Multiple Model Clients\n\nYou can define multiple model clients and reference them by their `id` in tasks:\n\n```yaml\nmodel_clients:\n- id: fast_model\nprovider: openai\nconfig:\nmodel: gpt-3.5-turbo\napi_key: \"sk-your-openai-api-key\"\ntemperature: 0.3 \n- id: smart_model\nprovider: anthropic\nconfig:\nmodel: claude-3-opus-20240229\napi_key: \"sk-ant-your-anthropic-api-key\"\ntemperature: 0.7 \n- id: creative_model\nprovider: openai\nconfig:\nmodel: gpt-4-turbo-preview\napi_key: \"sk-your-openai-api-key\"\ntemperature: 1.2\n```\n\n### Using Model Clients in Tasks\n\nReference model clients in AI tasks using the `model_client_id` field:",
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"content": "config:\nmodel_client_id: gpt4_model\nuser_message: \"Process customer: ${fetch_customer_data}\" \n- id: send_notification\nname: \"Send Notification\"\ntype: script\ndepends_on: [process_customer] # \u2705 EXECUTION: Wait for processing\nprevious_node: process_customer # \u2705 UI: Show under process_customer\nscript: |\nresult = ${process_customer}\nprint(f\"Notification sent for: ${fetch_customer_data.name}\") \n- id: parallel_audit_log\nname: \"Audit Log (Parallel)\"\ntype: script\ndepends_on: [fetch_customer_data] # \u2705 EXECUTION: Only needs initial data\nprevious_node: fetch_customer_data # \u2705 UI: Show under fetch_customer_data\nscript: |",
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"content": "# === MODEL CLIENT CONFIGURATION (Required for AI tasks) ===\nmodel_clients: # REQUIRED: For ai_agent, chatbot, continuous_conversation tasks\n- id: string # REQUIRED: Unique identifier to reference in tasks\nprovider: string # REQUIRED: openai|anthropic|openrouter|groq\nconfig: object # REQUIRED: Provider-specific config\nmodel: string # REQUIRED: Model name (e.g., gpt-4, claude-3-sonnet-20240229)\napi_key: string # REQUIRED: API key for the provider\ntemperature: number # Optional: 0.0-2.0, controls randomness\nmax_tokens: integer # Optional: Maximum tokens to generate\ntimeout: integer # Optional: Request timeout in seconds",
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"content": "# AI configuration\nconfig:\nmodel_client_id: gpt4_model # REQUIRED: Reference to model client \nsystem_message: | # Optional: System prompt\nYou are a data analyst. Analyze the provided data and extract insights.\nReturn your analysis in JSON format with the following structure:\n{\n\"insights\": [\"insight1\", \"insight2\"],\n\"recommendations\": [\"rec1\", \"rec2\"],\n\"confidence_score\": 0.95\n} \nuser_message: | # REQUIRED: User prompt with template resolution\nPlease analyze this customer data:\n${previous_task} \nCustomer ID: ${customer_id}\nDate Range: ${date_range}",
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"content": "Reference model clients in AI tasks using the `model_client_id` field:\n\n```yaml\ntasks:\n- id: analysis_task\ntype: ai_agent\nconfig:\nmodel_client_id: smart_model # References the claude model above\nsystem_message: \"You are a data analyst...\"\nuser_message: \"Analyze this data: ${input_data}\" \n- id: creative_task\ntype: ai_agent\nconfig:\nmodel_client_id: creative_model # References the creative GPT-4 model\nsystem_message: \"You are a creative writer...\"\nuser_message: \"Write a story about ${topic}\"\n```\n\n### Environment Variables for API Keys\n\nFor security, use environment variables instead of hardcoding API keys:",
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"content": "# DO: Use environment variables\nmodel_clients:\n- id: secure_example\nprovider: openai\nconfig:\napi_key: \"${OPENAI_API_KEY}\" # Secure approach",
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}
Task Executions
fetch_documentation
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vector_search
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ai_assistant
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