CrewAI with PraisonAI

CrewAI framework integration with PraisonAI provides collaborative multi-agent workflows with advanced configuration options for agents, tasks, and execution.

Need a framework that isn’t listed here? See Framework Adapter Plugins to register your own via Python entry points.

Quick Start

Install

pip install "praisonai[crewai]"

Create YAML file

framework: crewai
topic: Create Movie Script About Cat in Mars

roles:
  researcher:
    role: Research Analyst
    goal: Gather information about Mars and cats
    backstory: Skilled in research, with a focus on gathering accurate and relevant information.
    tasks:
      research_task:
        description: Research about Mars environment and cat behavior for the movie concept
        expected_output: Research findings document with key facts

Run

export OPENAI_API_KEY=your-key
praisonai agents.yaml --framework crewai

How CrewAI Works

YAML Format for CrewAI

CrewAI requires the roles format YAML (not the steps workflow format):

framework: crewai
topic: Create Movie Script About Cat in Mars

roles:
  researcher:
    role: Research Analyst
    goal: Gather information about Mars and cats
    backstory: Skilled in research, with a focus on gathering accurate and relevant information.
    tasks:
      research_task:
        description: Research about Mars environment and cat behavior
        expected_output: Research findings document with key facts

The --framework crewai flag only works with YAML files using the roles format. The newer steps + agents workflow format only supports the praisonai framework.

Advanced YAML Fields

PraisonAI’s CrewAI adapter supports extensive configuration options for both agents and tasks:

Agent-Level Fields

Configure advanced agent behavior under each roles.<name>:

YAML Key	Type	Default	Description	Maps to CrewAI
`llm`	object	inherits dispatcher	LLM configuration via `PraisonAIModel`	`llm=`
`function_calling_llm`	object	inherits dispatcher	Separate LLM for function calls	`function_calling_llm=`
`allow_delegation`	bool	`False`	Allow agent to delegate tasks	`allow_delegation=`
`max_iter`	int	`15`	Maximum iterations per agent	`max_iter=`
`max_rpm`	int	`None`	Requests per minute throttle	`max_rpm=`
`max_execution_time`	int	`None`	Execution timeout in seconds	`max_execution_time=`
`verbose`	bool	`True`	Enable verbose output	`verbose=`
`cache`	bool	`True`	Enable LLM response caching	`cache=`
`system_template`	str	`None`	Override system prompt template	`system_template=`
`prompt_template`	str	`None`	Override prompt template	`prompt_template=`
`response_template`	str	`None`	Override response template	`response_template=`

Task-Level Fields

Configure advanced task behavior under each roles.<name>.tasks.<task_name>:

YAML Key	Type	Default	Description	Maps to CrewAI
`description`	str	required	Task description	`description=`
`expected_output`	str	required	Expected output format	`expected_output=`
`tools`	list[str]	`[]`	Tool names from `tools_dict`	`tools=`
`async_execution`	bool	`False`	Execute task asynchronously	`async_execution=`
`context`	list[str]	`[]`	Task dependencies by name	`context=` (resolved to Task objects)
`config`	object	`{}`	Free-form task configuration	`config=`
`output_json`	type	`None`	Pydantic schema for JSON output	`output_json=`
`output_pydantic`	type	`None`	Pydantic model class	`output_pydantic=`
`output_file`	str	`""`	Output file path	`output_file=`
`callback`	callable	`None`	Task completion callback	`callback=`
`human_input`	bool	`False`	Require human input	`human_input=`
`create_directory`	bool	`False`	Create parent directories	`create_directory=`

Advanced Configuration Example

framework: crewai
topic: Quarterly report

roles:
  analyst:
    role: Financial Analyst
    goal: Extract Q3 numbers from the input docs
    backstory: Senior analyst, CFA, 10y experience.
    llm:
      model: openai/gpt-4o
    function_calling_llm:
      model: openai/gpt-4o-mini
    max_iter: 20
    verbose: true
    cache: true
    system_template: "You are a precise financial analyst. Never invent figures."
    tasks:
      extract_numbers:
        description: Pull revenue, COGS, opex for Q3 from {topic}
        expected_output: A JSON table with revenue, cogs, opex.
        tools: ["file_reader", "calculator"]
        async_execution: false

  writer:
    role: Report Writer
    goal: Write a narrative summary
    backstory: Equity-research writer.
    llm:
      model: openai/gpt-4o
    max_iter: 10
    verbose: false
    system_template: "You are a precise, hedge-friendly equity research writer."
    tasks:
      draft_summary:
        description: Draft a 200-word summary of the extracted numbers.
        expected_output: A 200-word markdown summary.
        context: [extract_numbers]
        output_file: out/q3_summary.md
        create_directory: true
        human_input: false

LLM Configuration

Model Resolution

The CrewAI adapter resolves LLM configuration in this order:

Agent-level llm: roles.<agent>.llm.*
Global llm_config: From CLI dispatcher
Environment: MODEL_NAME environment variable
Default: openai/gpt-4o-mini

Function Calling LLM

Use function_calling_llm to specify a different model for tool calls:

roles:
  researcher:
    role: Research Analyst
    llm:
      model: openai/gpt-4o          # For reasoning
    function_calling_llm:
      model: openai/gpt-4o-mini     # For tool calls (cheaper/faster)

Task Dependencies

Use the context field to create task dependencies. Reference tasks by name:

roles:
  researcher:
    tasks:
      research_phase:
        description: Research the topic thoroughly
        expected_output: Research findings

  analyst:
    tasks:
      analysis_phase:
        description: Analyze the research findings
        expected_output: Analysis report
        context: [research_phase]  # Wait for research_phase to complete

The context field accepts a list of task names (strings), not Task objects. The adapter resolves them automatically after all tasks are created.

File Output

Use output_file with create_directory to save task results:

tasks:
  report_task:
    description: Generate final report
    expected_output: Comprehensive report
    output_file: reports/final_report.md
    create_directory: true  # Creates 'reports/' directory if needed

AgentOps Integration

When agentops is installed, the CrewAI adapter automatically calls agentops.end_session("Success") after crew execution completes. No configuration required.

pip install agentops
# AgentOps tracking is automatically enabled

Framework Selection Priority

CLI flag (--framework crewai) takes precedence
YAML file (framework: crewai) is used if no CLI flag
Default: praisonai framework

Auto Mode

praisonai --framework crewai --auto "Create a Movie Script About Cat in Mars"

Troubleshooting

Common Issues

Task context not found: If context: [some_task] fails, ensure some_task exists as a key under any role’s tasks: block in the same YAML file. Function calling LLM inheritance: function_calling_llm falls back to MODEL_NAME environment variable or openai/gpt-4o-mini, not the agent’s main llm configuration. Direct prompt limitation: Direct prompts (praisonai "question" --framework crewai) always use the praisonai framework. Use YAML files with roles format for CrewAI. Missing installation error: If the framework is not installed, PraisonAI now fails fast at CLI entry with:

Framework 'crewai' was requested but is not installed.
Install it with:
    pip install 'praisonai[crewai]'   # or: pip install crewai

The error appears immediately, before YAML parsing — so a typo in --framework is caught before any expensive setup runs.

Installation Errors

Missing CrewAI: pip install "praisonai[crewai]"
Tool resolution issues: Ensure tool names in tools: [] exist in the global tools_dict

Best Practices

Use function_calling_llm for cost optimization

Set a cheaper/faster model for function_calling_llm while using a more capable model for reasoning tasks.

Structure task dependencies clearly

Use descriptive task names in context: [] lists. The order doesn’t matter, but referenced tasks must exist.

Leverage output files for complex workflows

Use output_file with create_directory: true for tasks that produce artifacts other agents need to reference.

Configure timeouts appropriately

Set max_execution_time for long-running tasks and max_iter to prevent infinite loops.

AutoGen

AutoGen framework integration

PraisonAI Agents

PraisonAI native agents framework

Documentation Index

​Quick Start

​How CrewAI Works

​YAML Format for CrewAI

​Advanced YAML Fields

​Agent-Level Fields

​Task-Level Fields

​Advanced Configuration Example

​LLM Configuration

​Model Resolution

​Function Calling LLM

​Task Dependencies

​File Output

​AgentOps Integration

​Framework Selection Priority

​Auto Mode

​Troubleshooting

​Common Issues

​Installation Errors

​Best Practices

​Related

AutoGen

PraisonAI Agents

Quick Start

How CrewAI Works

YAML Format for CrewAI

Advanced YAML Fields

Agent-Level Fields

Task-Level Fields

Advanced Configuration Example

LLM Configuration

Model Resolution

Function Calling LLM

Task Dependencies

File Output

AgentOps Integration

Framework Selection Priority

Auto Mode

Troubleshooting

Common Issues

Installation Errors

Best Practices

Related