YAML Workflows

Define complex multi-agent workflows in YAML files with support for advanced patterns like routing, parallel execution, loops, and more.

Minimum Required Fields

The absolute minimum to run a workflow:

agents:
  my_agent:
    role: Assistant        # Required: Agent's job title

steps:
  - agent: my_agent        # Required: Which agent executes

Practical minimum (recommended):

name: My Workflow
input: "Your input here"   # The data passed INTO the workflow (accessed via {{input}})

agents:
  my_agent:
    role: Assistant
    goal: Help with the task
    instructions: "You are a helpful assistant"

steps:
  - agent: my_agent
    action: "Process this: {{input}}"

Field	Required	Default	Description
`agents`	✅	-	At least one agent definition
`agents.*.role`	✅	-	Agent’s job title
`steps`	✅	-	At least one step
`steps.*.agent`	✅	-	Agent to execute the step
`name`	❌	“Workflow”	Workflow identifier
`input`	❌	""	Data passed INTO the workflow (accessed via `{{input}}`)
`goal`	❌	“Complete the task”	Agent’s objective
`instructions`	❌	Generic	Agent behavior/persona
`action`	❌	`{{input}}`	What the step does

input vs topic: Use input (canonical) for clarity. topic still works for backward compatibility but input better conveys that this is the data going INTO your workflow.

Use {{input}} to reference the workflow input and {{previous_output}} to get the result from the previous step.

Field Names Reference (A-I-G-S)

PraisonAI accepts both old (agents.yaml) and new (workflow.yaml) field names. Use the canonical names for new projects:

Canonical (Recommended)	Alias (Also Works)	Purpose
`agents`	`roles`	Define agent personas
`instructions`	`backstory`	Agent behavior/persona
`action`	`description`	What the step does
`steps`	`tasks` (nested)	Define work items
`name`	-	Workflow identifier
`input`	`topic`	Data passed INTO the workflow

A-I-G-S Mnemonic - Easy to remember:

Agents - Who does the work
Instructions - How they behave
Goal - What they achieve
Steps - What they do

# Quick Reference - Canonical Format
name: My Workflow              # Workflow name
input: What to process         # Data going INTO the workflow (not 'topic')
agents:                        # Define agents (not 'roles')
  my_agent:
    role: Job Title            # Agent's role
    goal: What to achieve      # Agent's goal
    instructions: How to act   # Agent's behavior (not 'backstory')
    
steps:                         # Define steps (not 'tasks')
  - agent: my_agent
    action: "Process: {{input}}"  # Step action (not 'description')

The parser accepts both old and new names. Run praisonai workflow validate <file.yaml> to see suggestions for canonical names.

Feature Parity

Both agents.yaml and workflow.yaml now support the same features:

Feature	agents.yaml	workflow.yaml
Workflow patterns (route, parallel, loop, repeat)	✅	✅
All agent fields	✅	✅
All step/task fields	✅	✅
Framework support (praisonai, crewai, autogen)	✅	✅
Process types (sequential, hierarchical, workflow)	✅	✅
Planning & Reasoning	✅	✅

Quick Start

# Run a YAML workflow
praisonai workflow run research.yaml

# Run with variables
praisonai workflow run research.yaml --var topic="AI trends"

# Validate a workflow
praisonai workflow validate research.yaml

# Create from template
praisonai workflow template routing --output my_workflow.yaml

# Auto-generate a workflow
praisonai workflow auto "Research AI trends" --pattern parallel

Programmatic Routing: The AgentsGenerator wrapper auto-detects workflow YAML and routes to the appropriate engine:

Discriminator in YAML	Routed to
`process: workflow`	`YAMLWorkflowParser`
`steps:` + `workflow:`	`YAMLWorkflowParser`
`type: job`	`YAMLWorkflowParser`
`type: hybrid`	`YAMLWorkflowParser`
(none of the above)	Framework adapter

This means calling AgentsGenerator(agent_file="release.yaml").generate_crew_and_kickoff() with a job/hybrid YAML now routes correctly to the workflow runner instead of falling through to the framework adapter.

Async parity: agenerate_crew_and_kickoff() now initializes observability and adapter setup the same way generate_crew_and_kickoff() does — Langfuse / AgentOps traces are wired on both paths.

Complete workflow.yaml Reference

# workflow.yaml - Full feature reference
name: Complete Workflow
description: Demonstrates all workflow.yaml features
framework: praisonai  # praisonai, crewai, autogen
process: workflow     # sequential, hierarchical, workflow
input: "Your input data here"  # Data passed into workflow (accessed via {{input}})

# ============================================================================
# WORKFLOW SETTINGS
# ============================================================================
workflow:
  planning: true                      # Enable planning mode
  planning_llm: gpt-4o                # LLM for planning
  reasoning: true                     # Enable reasoning mode
  verbose: true                       # Verbose output
  router: true                        # Enable model routing
  routing_strategy: cost-optimized    # auto, cost-optimized, performance-optimized
  memory_config:
    provider: chroma
    persist: true

# ============================================================================
# CONTEXT MANAGEMENT (Prevent Token Overflow)
# ============================================================================
context: true  # Enable auto-compaction (recommended for workflows with tools)

# ============================================================================
# CUSTOM MODELS (Optional - for model routing)
# ============================================================================
models:
  cheap-fast:
    provider: openai
    complexity: [simple]              # simple, moderate, complex, very_complex
    cost_per_1k: 0.0001
    capabilities: [text]
    context_window: 16000
  
  balanced:
    provider: openai
    complexity: [moderate]
    cost_per_1k: 0.001
    capabilities: [text, function-calling]
    context_window: 128000
  
  premium:
    provider: anthropic
    complexity: [complex, very_complex]
    cost_per_1k: 0.015
    capabilities: [text, vision, function-calling]
    context_window: 200000
    supports_tools: true
    supports_streaming: true
    strengths: [reasoning, analysis, code-generation]

# ============================================================================
# VARIABLES
# ============================================================================
variables:
  topic: AI trends
  items: [ML, NLP, Vision]

# ============================================================================
# AGENTS
# ============================================================================
agents:
  researcher:
    name: Researcher                  # Display name
    role: Research Analyst            # Required: Agent's job title
    goal: Research topics thoroughly  # Agent's objective
    instructions: "Provide detailed research findings"  # Agent behavior/persona
    
    # LLM Configuration
    llm: gpt-4o-mini                  # Model to use
    llm_routing: auto                 # Enable auto model selection
    llm_models: [balanced, premium]   # Models for auto-routing
    function_calling_llm: gpt-4o      # Model for tool calls
    reflect_llm: gpt-4o               # Model for self-reflection
    
    # Rate Limiting & Timeouts
    max_rpm: 10                       # Max requests per minute
    max_execution_time: 300           # Timeout in seconds
    
    # Self-Reflection
    min_iterations: 1                    # Minimum reflection iterations
    max_iterations: 3                    # Maximum reflection iterations
    
    # System Prompt
    system_template: "You are a helpful assistant"
    
    # Tools
    tools:
      - tavily_search
      - wikipedia_search

  writer:
    name: Writer
    role: Content Writer
    goal: Write clear content
    instructions: "Write engaging content"
    llm: premium                      # Use premium model for quality

# ============================================================================
# STEPS
# ============================================================================
steps:
  # Basic step
  - name: research_step
    agent: researcher
    action: "Research {{input}}"      # Use {{input}} for workflow input
    expected_output: "Comprehensive research report"
    output_file: "output/research.md"
    create_directory: true
    
  # Step with context dependency
  - name: writing_step
    agent: writer
    action: "Write article based on: {{previous_output}}"
    context:                          # Task dependencies
      - research_step
    output_json:                      # Structured output
      type: object
      properties:
        title: { type: string }
        content: { type: string }
  
  # Parallel step
  - name: parallel_research
    parallel:
      - agent: researcher
        action: "Research market trends"
      - agent: researcher
        action: "Research competitors"
  
  # Routing step
  - name: routing
    route:
      technical: [tech_agent]
      creative: [creative_agent]
      default: [researcher]
  
  # Loop step
  - agent: researcher
    action: "Research {{item}}"
    loop:
      over: items                     # Variable to iterate
  
  # Repeat step (evaluator-optimizer)
  - agent: writer
    action: "Write and improve"
    repeat:
      until: "approved"
      max_iterations: 3

# ============================================================================
# CALLBACKS (Optional)
# ============================================================================
callbacks:
  on_workflow_start: log_start
  on_step_complete: log_step
  on_workflow_complete: log_complete

Agent Fields Reference

Field	Required	Default	Description
`role`	✅	-	Agent’s job title
`name`	❌	Agent ID	Display name
`goal`	❌	“Complete the task”	Agent’s objective
`instructions`	❌	Generic	Agent behavior/persona (alias: `backstory`)
`llm`	❌	`gpt-4o-mini`	Model to use
`llm_routing`	❌	-	Enable auto model selection (`auto`)
`llm_models`	❌	-	Models for auto-routing
`function_calling_llm`	❌	Same as `llm`	Model for tool calls
`reflect_llm`	❌	Same as `llm`	Model for self-reflection
`max_rpm`	❌	Unlimited	Max requests per minute
`max_execution_time`	❌	300	Timeout in seconds
`min_iterations`	❌	0	Minimum reflection iterations
`max_iterations`	❌	3	Maximum reflection iterations
`system_template`	❌	-	Custom system prompt
`tools`	❌	[]	List of tools

Step Fields Reference

Field	Required	Default	Description
`agent`	✅*	-	Agent to execute (*not needed for parallel/route/include)
`action`	❌	`{{input}}`	What the step does
`name`	❌	Auto-generated	Step identifier
`expected_output`	❌	-	Description of expected output
`output_file`	❌	-	Save output to file
`create_directory`	❌	false	Create output directory
`context`	❌	-	List of dependent step names
`output_json`	❌	-	JSON schema for structured output (inline schema)
`output_pydantic`	❌	-	Pydantic model name from tools.py for structured output
`output_variable`	❌	-	Store output in named variable for use in subsequent steps
`parallel`	❌	-	List of parallel sub-steps
`route`	❌	-	Routing configuration
`loop`	❌	-	Loop configuration (`over: variable`)
`repeat`	❌	-	Repeat configuration (`until`, `max_iterations`)
`include`	❌	-	Include another recipe (`recipe`, `input`, `variables`)

Models Fields Reference

Field	Required	Default	Description
`provider`	✅	-	Provider: `openai`, `anthropic`, `google`, `openrouter`
`complexity`	✅	-	List: `simple`, `moderate`, `complex`, `very_complex`
`cost_per_1k`	✅	-	Cost per 1,000 tokens in USD
`capabilities`	❌	`[text]`	List: `text`, `vision`, `function-calling`, `streaming`
`context_window`	❌	128000	Max context window in tokens
`supports_tools`	❌	true	Supports tool/function calling
`supports_streaming`	❌	true	Supports streaming responses
`strengths`	❌	-	List: `reasoning`, `code-generation`, `analysis`, etc.

Context Management (Token Overflow Prevention)

For tool-heavy workflows (search, crawl, etc.): Always enable context: true to prevent token overflow errors.

Enable automatic context optimization to prevent “context length exceeded” errors:

# Simple: Enable with defaults
context: true

# Or detailed configuration
context:
  auto_compact: true       # Automatically compact when threshold reached
  compact_threshold: 0.8   # Trigger at 80% of context window
  strategy: smart          # smart | truncate | sliding_window | summarize | prune_tools
  tool_output_max: 10000   # Max tokens per tool output (e.g., search results)

What Context Management Does

When context: true is enabled:

Auto-compaction: Automatically compresses history when approaching token limits
Tool Output Truncation: Limits large tool results (e.g., full web page content)
Smart Strategy: Prioritizes recent/important messages, prunes tool outputs first
Overflow Prevention: Prevents “context_length_exceeded” errors

Best Practices

# Recommended for workflows with search/crawl tools
name: Research Workflow
context: true  # ← Enable this!

agents:
  researcher:
    role: Researcher
    tools:
      - tavily_search
      - crawl_url

steps:
  - agent: researcher
    action: "Search for {{topic}}"

Without context: true: Tool outputs (especially search with full page content) can easily exceed 128K tokens, causing API errors.

Context Strategies

Strategy	Description	Best For
`smart`	Adaptive strategy (default)	General use
`truncate`	Remove oldest messages	Speed-critical
`sliding_window`	Keep last N turns	Conversation agents
`summarize`	LLM summarizes old context	Quality preservation
`prune_tools`	Truncate tool outputs first	Tool-heavy workflows

For more details, see Context Management.

Workflow Patterns

Sequential (Default)

Agents execute one after another, passing context.

name: Sequential Workflow
agents:
  researcher:
    name: Researcher
    role: Research Analyst
    goal: Research topics
    instructions: "Provide research findings"
  writer:
    name: Writer
    role: Content Writer
    goal: Write content
    instructions: "Write based on research"

steps:
  - agent: researcher
    action: "Research {{topic}}"
  - agent: writer
    action: "Write summary based on: {{previous_output}}"

Parallel

Multiple agents work concurrently.

name: Parallel Workflow
agents:
  market_analyst:
    name: MarketAnalyst
    role: Market Researcher
    goal: Research market trends
    instructions: "Provide market insights"
  tech_analyst:
    name: TechAnalyst
    role: Technology Researcher
    goal: Research technology
    instructions: "Provide tech insights"
  aggregator:
    name: Aggregator
    role: Synthesizer
    goal: Combine findings
    instructions: "Synthesize all research"

steps:
  - name: parallel_research
    parallel:
      - agent: market_analyst
        action: "Research market trends for {{topic}}"
      - agent: tech_analyst
        action: "Research technology trends for {{topic}}"
  - agent: aggregator
    action: "Combine all findings into a report"

Routing

Classifier routes to specialized agents.

name: Routing Workflow
agents:
  classifier:
    name: Classifier
    role: Request Classifier
    goal: Classify requests
    instructions: "Respond with ONLY: 'technical', 'creative', or 'general'"
  tech_expert:
    name: TechExpert
    role: Technical Expert
    goal: Handle technical questions
    instructions: "Provide technical answers"
  creative_expert:
    name: CreativeExpert
    role: Creative Expert
    goal: Handle creative requests
    instructions: "Provide creative responses"

steps:
  - agent: classifier
    action: "Classify: {{input}}"
  - name: routing
    route:
      technical: [tech_expert]
      creative: [creative_expert]
      default: [tech_expert]

Loop

Iterate over a list of items.

name: Loop Workflow
variables:
  topics:
    - Machine Learning
    - Natural Language Processing
    - Computer Vision

agents:
  researcher:
    name: Researcher
    role: Research Analyst
    goal: Research topics
    instructions: "Provide brief research on each topic"

steps:
  - agent: researcher
    action: "Research {{item}}"
    loop:
      over: topics

Multi-Step Loop

Execute multiple steps sequentially for each item in the loop. This is perfect for pipelines where each item needs to go through several processing stages (e.g., research → write → publish).

Context Isolation: Each loop iteration is isolated - context doesn’t leak between iterations. Within an iteration, {{previous_output}} chains between steps.

name: Multi-Step Loop Workflow
variables:
  topics:
    - Machine Learning
    - Natural Language Processing
    - Computer Vision

agents:
  researcher:
    role: Research Analyst
    goal: Research topics thoroughly
    instructions: "Provide comprehensive research"
    tools:
      - tavily_search
  
  writer:
    role: Content Writer
    goal: Write engaging articles
    instructions: "Write in British English, include code examples"
  
  publisher:
    role: WordPress Publisher
    goal: Publish content to WordPress
    instructions: "Validate content before publishing"
    tools:
      - create_wp_post

steps:
  # Multi-step loop: research → write → publish for EACH topic
  - loop:
      over: topics
      parallel: true      # Process topics in parallel
      max_workers: 4      # Limit concurrent executions
    steps:
      - agent: researcher
        action: "Research {{item}} thoroughly"
      - agent: writer
        action: "Write article based on: {{previous_output}}"
      - agent: publisher
        action: "Publish: {{previous_output}}"

# Enable context management to prevent token overflow
context:
  enabled: true
  max_tool_output_tokens: 5000

Multi-Step Loop Features

Feature	Description
`steps:`	Array of steps to execute for each item
`{{item}}`	Access current loop item in first step
`{{previous_output}}`	Chain outputs between steps in iteration
`parallel: true`	Execute iterations concurrently
`max_workers: N`	Limit parallel workers
`output_variable`	Store final outputs in variable

Within vs Between Iterations:

Within iteration: Steps run sequentially (step1 → step2 → step3)
Between iterations: Can run in parallel with parallel: true

Structured Output

Get structured JSON responses from agents using output_json (inline schema) or output_pydantic (reference to Pydantic model in tools.py).

Inline JSON Schema (Option A)
Pydantic Reference (Option B)

name: Structured Output Workflow
agents:
  topic_finder:
    role: Topic Finder
    goal: Find AI topics
    instructions: "Return topics as structured JSON"

steps:
  - agent: topic_finder
    action: "Find 3 AI topics"
    output_json:
      type: array
      items:
        type: object
        properties:
          title:
            type: string
          url:
            type: string
        required:
          - title
          - url
    output_variable: topics
  
  # Loop over the structured output
  - agent: researcher
    action: "Research: {{item.title}}"
    loop:
      over: topics

# agents.yaml
name: Structured Output Workflow
agents:
  topic_finder:
    role: Topic Finder
    goal: Find AI topics
    instructions: "Return topics as structured JSON"

steps:
  - agent: topic_finder
    action: "Find 3 AI topics"
    output_pydantic: TopicList  # Reference to class in tools.py
    output_variable: topics

# tools.py (in same directory)
from pydantic import BaseModel
from typing import List

class Topic(BaseModel):
    title: str
    url: str

class TopicList(BaseModel):
    items: List[Topic]

How it works: When output_json or output_pydantic is specified, PraisonAI automatically uses the LLM’s native structured output feature (response_format) for supported models.Supported Models: GPT-4o, GPT-4o-mini, Claude 3.5 Sonnet, Gemini 2.0 FlashFlow:

Agent checks if model supports native structured output
If supported → uses response_format with JSON schema (clean output)
If not supported → falls back to prompt injection (schema in prompt)

Force Native Mode: In Python, use native_structured_output=True on the Agent to force native mode:

agent = Agent(llm="custom-model", native_structured_output=True)

Repeat (Evaluator-Optimizer)

Repeat until a condition is met.

name: Repeat Workflow
agents:
  writer:
    name: Writer
    role: Content Writer
    goal: Write high-quality content
    instructions: "Write and improve content"
  evaluator:
    name: Evaluator
    role: Quality Checker
    goal: Evaluate content quality
    instructions: "Rate content 1-10. Say 'approved' if >= 8"

steps:
  - agent: writer
    action: "Write article about {{topic}}"
    repeat:
      until: "approved"
      max_iterations: 3

Include (Modular Recipes)

Include reusable recipe files in your workflow.

name: Parent Workflow
topic: "AI Code & Tools 2026"

variables:
  today: "January 2026"

agents:
  writer:
    role: Content Writer
    goal: Write content
    instructions: "Write engaging content"

steps:
  # Include a modular recipe - it receives parent's variables
  - include: ai-topic-gatherer
  
  # Or with explicit configuration
  - include:
      recipe: ai-research-pipeline
      input: "{{previous_output}}"
  
  # Continue with local steps
  - agent: writer
    action: "Write blog post about: {{previous_output}}"

Variable Passing: When you include a recipe, the parent’s topic, variables, and other fields are automatically passed to the child recipe. The child can use {{topic}} in its actions.

Variables

Define reusable variables for use throughout your workflow.

name: Variables Workflow
topic: "AI Development Tools"  # Automatically added to variables

variables:
  today: "January 2026"
  max_results: 5
  categories:
    - "Coding Assistants"
    - "Testing Tools"
    - "Documentation"

agents:
  researcher:
    role: Research Analyst
    goal: Research topics
    instructions: "Research AI development tools"

steps:
  # Use topic variable (from topic: field)
  - agent: researcher
    action: |
      Today is: {{today}}
      Research: {{topic}}
      Find up to {{max_results}} results
    
  # Loop over variable list
  - agent: researcher
    action: "Research {{item}}"
    loop:
      over: categories

Topic Propagation: The topic: field at the root of your YAML is automatically added to the variables dict. This means:

Use topic: for the main subject
Use variables: for additional reusable values
Both are available as {{topic}} and {{variable_name}} in actions

Variable Substitution Examples:

Pattern	Description	Example
`{{input}}`	Workflow input	`"Process: {{input}}"`
`{{topic}}`	Topic field value	`"Research {{topic}}"`
`{{previous_output}}`	Previous step result	`"Expand on: {{previous_output}}"`
`{{variable_name}}`	Custom variable	`"Date: {{today}}"`
`{{item}}`	Current loop item	`"Process {{item}}"`
`{{item.field}}`	Field in loop item	`"Title: {{item.title}}"`

Extended agents.yaml

Use workflow patterns in agents.yaml with process: workflow:

# agents.yaml with workflow patterns
framework: praisonai
process: workflow  # Enables workflow mode
topic: "Research AI trends"

workflow:
  planning: true
  reasoning: true
  verbose: true

agents:  # Canonical: use 'agents' instead of 'roles'
  classifier:
    role: Request Classifier
    instructions:  # Canonical: use 'instructions' instead of 'backstory' "Classify requests into categories"
    goal: Classify requests
    
  researcher:
    role: Research Analyst
    instructions:  # Canonical: use 'instructions' instead of 'backstory' "Expert researcher"
    goal: Research topics
    tools:
      - tavily_search

steps:
  - agent: classifier
    action: "Classify: {{topic}}"
    
  - name: routing
    route:
      technical: [tech_expert]
      default: [researcher]
      
  - name: parallel_research
    parallel:
      - agent: researcher
        action: "Research market trends"
      - agent: researcher
        action: "Research competitors"

Run with:

praisonai agents.yaml

Auto-Generate Workflows

Generate workflows automatically from a topic description:

# Sequential workflow (default)
praisonai workflow auto "Research AI trends"

# Parallel workflow
praisonai workflow auto "Research AI trends" --pattern parallel

# Routing workflow
praisonai workflow auto "Build a chatbot" --pattern routing

# Specify output file
praisonai workflow auto "Research AI" --output my_workflow.yaml

CLI Commands

Command	Description
`praisonai workflow run <file.yaml>`	Run a YAML workflow
`praisonai workflow run <file.yaml> --var key=value`	Run with variables
`praisonai workflow validate <file.yaml>`	Validate a workflow
`praisonai workflow template <name>`	Create from template
`praisonai workflow auto "topic"`	Auto-generate workflow
`praisonai workflow list`	List workflows
`praisonai workflow help`	Show help

CLI Options

Flag	Description
`--var key=value`	Set variable for YAML workflows
`--pattern <pattern>`	Pattern for auto-generation (sequential, parallel, routing, loop)
`--output <file>`	Output file for auto-generation
`--planning`	Enable planning mode
`--reasoning`	Enable reasoning mode
`--verbose`	Enable verbose output
`--save`	Save output to file

Progress Indicators

When running workflows, you’ll see clear progress indicators:

Running YAML workflow: research.yaml
 Workflow: Research Workflow  
┏━━━━━━━━━━━┳━━━━━━━┓
┃ Property  ┃ Value ┃
┡━━━━━━━━━━━╇━━━━━━━┩
│ Steps     │ 3     │
│ Planning  │ True  │
│ Reasoning │ False │
└───────────┴───────┘

Executing workflow...

📋 Execution Plan: [plan description]
⚡ Running 2 steps in parallel...
✅ Parallel complete: 2 results
🔀 Routing to: technical
✅ AgentName: [output preview]

✅ Workflow completed successfully!

Debug Mode

Enable debug logging to see detailed execution:

LOGLEVEL=debug praisonai workflow run research.yaml

This shows:

Agent parameters (prompt, temperature, tools)
Messages sent to LLM
HTTP requests to API
Full agent/role/goal context

​YAML Workflows

​Minimum Required Fields

​Field Names Reference (A-I-G-S)

​Feature Parity

​Quick Start

​Complete workflow.yaml Reference

​Agent Fields Reference

​Step Fields Reference

​Models Fields Reference

​Context Management (Token Overflow Prevention)

​What Context Management Does

​Best Practices

​Context Strategies

​Workflow Patterns

​Sequential (Default)

​Parallel

​Routing

​Loop

​Multi-Step Loop

​Multi-Step Loop Features

​Structured Output

​Repeat (Evaluator-Optimizer)

​Include (Modular Recipes)

​Variables

​Extended agents.yaml

​Auto-Generate Workflows

​CLI Commands

​CLI Options

​Progress Indicators

​Debug Mode

YAML Workflows

Minimum Required Fields

Field Names Reference (A-I-G-S)

Feature Parity

Quick Start

Complete workflow.yaml Reference

Agent Fields Reference

Step Fields Reference

Models Fields Reference

Context Management (Token Overflow Prevention)

What Context Management Does

Best Practices

Context Strategies

Workflow Patterns

Sequential (Default)

Parallel

Routing

Loop

Multi-Step Loop

Multi-Step Loop Features

Structured Output

Repeat (Evaluator-Optimizer)

Include (Modular Recipes)

Variables

Extended agents.yaml

Auto-Generate Workflows

CLI Commands

CLI Options

Progress Indicators

Debug Mode