Agents

Overview of an Agent

In the CrewAI framework, an Agent is an autonomous unit that can:

Perform specific tasks
Make decisions based on its role and goal
Use tools to accomplish objectives
Communicate and collaborate with other agents
Maintain memory of interactions
Delegate tasks when allowed

Agent Attributes

Attribute	Parameter	Type	Description
Role	`role`	`str`	Defines the agent’s function and expertise within the crew.
Goal	`goal`	`str`	The individual objective that guides the agent’s decision-making.
Backstory	`backstory`	`str`	Provides context and personality to the agent, enriching interactions.
LLM (optional)	`llm`	`Union[str, LLM, Any]`	Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or “gpt-4”.
Tools (optional)	`tools`	`List[BaseTool]`	Capabilities or functions available to the agent. Defaults to an empty list.
Function Calling LLM (optional)	`function_calling_llm`	`Optional[Any]`	Language model for tool calling, overrides crew’s LLM if specified.
Max Iterations (optional)	`max_iter`	`int`	Maximum iterations before the agent must provide its best answer. Default is 20.
Max RPM (optional)	`max_rpm`	`Optional[int]`	Maximum requests per minute to avoid rate limits.
Max Execution Time (optional)	`max_execution_time`	`Optional[int]`	Maximum time (in seconds) for task execution.
Verbose (optional)	`verbose`	`bool`	Enable detailed execution logs for debugging. Default is False.
Allow Delegation (optional)	`allow_delegation`	`bool`	Allow the agent to delegate tasks to other agents. Default is False.
Step Callback (optional)	`step_callback`	`Optional[Any]`	Function called after each agent step, overrides crew callback.
Cache (optional)	`cache`	`bool`	Enable caching for tool usage. Default is True.
System Template (optional)	`system_template`	`Optional[str]`	Custom system prompt template for the agent.
Prompt Template (optional)	`prompt_template`	`Optional[str]`	Custom prompt template for the agent.
Response Template (optional)	`response_template`	`Optional[str]`	Custom response template for the agent.
Allow Code Execution (optional)	`allow_code_execution`	`Optional[bool]`	Enable code execution for the agent. Default is False.
Max Retry Limit (optional)	`max_retry_limit`	`int`	Maximum number of retries when an error occurs. Default is 2.
Respect Context Window (optional)	`respect_context_window`	`bool`	Keep messages under context window size by summarizing. Default is True.
Code Execution Mode (optional)	`code_execution_mode`	`Literal["safe", "unsafe"]`	Mode for code execution: ‘safe’ (using Docker) or ‘unsafe’ (direct). Default is ‘safe’.
Multimodal (optional)	`multimodal`	`bool`	Whether the agent supports multimodal capabilities. Default is False.
Inject Date (optional)	`inject_date`	`bool`	Whether to automatically inject the current date into tasks. Default is False.
Date Format (optional)	`date_format`	`str`	Format string for date when inject_date is enabled. Default is “%Y-%m-%d” (ISO format).
Reasoning (optional)	`reasoning`	`bool`	Whether the agent should reflect and create a plan before executing a task. Default is False.
Max Reasoning Attempts (optional)	`max_reasoning_attempts`	`Optional[int]`	Maximum number of reasoning attempts before executing the task. If None, will try until ready.
Embedder (optional)	`embedder`	`Optional[Dict[str, Any]]`	Configuration for the embedder used by the agent.
Knowledge Sources (optional)	`knowledge_sources`	`Optional[List[BaseKnowledgeSource]]`	Knowledge sources available to the agent.
Use System Prompt (optional)	`use_system_prompt`	`Optional[bool]`	Whether to use system prompt (for o1 model support). Default is True.

Creating Agents

There are two common ways to create agents in CrewAI: using JSONC project configuration (recommended for new crews) or defining them directly in code.

JSONC Configuration (Recommended)

New projects created with crewai create crew <name> use JSON-first configuration. Each agent is defined in agents/<agent_name>.jsonc, and crew.jsonc lists which agents are part of the crew.

After creating your CrewAI project as outlined in the Installation section, edit the generated files in agents/.

Here’s an example agents/researcher.jsonc file:

{
  "role": "{topic} Senior Data Researcher",
  "goal": "Uncover cutting-edge developments in {topic}",
  "backstory": "You find the most relevant information and present it clearly.",
  "llm": "openai/gpt-4o",
  "tools": ["SerperDevTool"],
  "settings": {
    "verbose": true,
    "allow_delegation": false,
    "max_iter": 20
  }
}

Then include that agent from crew.jsonc:

{
  "name": "Research Crew",
  "agents": ["researcher"],
  "tasks": [
    {
      "name": "research_task",
      "description": "Research {topic}",
      "expected_output": "A concise briefing about {topic}",
      "agent": "researcher"
    }
  ],
  "inputs": {
    "topic": "AI Agents"
  }
}

Agent files support any public Agent field. Common fields include role, goal, backstory, llm, tools, function_calling_llm, guardrail, step_callback, and settings. Behavior options such as verbose, allow_delegation, max_iter, max_rpm, memory, cache, planning_config, and use_system_prompt can be placed at the top level or under settings; values in settings take precedence.

Classic YAML Configuration

Classic projects created with crewai create crew <name> --classic use config/agents.yaml and a @CrewBase class in crew.py. This remains supported for teams that want Python decorators or existing YAML projects.

Direct Code Definition

You can create agents directly in code by instantiating the Agent class. Here’s a comprehensive example showing all available parameters:

from crewai import Agent
from crewai_tools import SerperDevTool

# Create an agent with all available parameters
agent = Agent(
    role="Senior Data Scientist",
    goal="Analyze and interpret complex datasets to provide actionable insights",
    backstory="With over 10 years of experience in data science and machine learning, "
              "you excel at finding patterns in complex datasets.",
    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
    function_calling_llm=None,  # Optional: Separate LLM for tool calling
    verbose=False,  # Default: False
    allow_delegation=False,  # Default: False
    max_iter=20,  # Default: 20 iterations
    max_rpm=None,  # Optional: Rate limit for API calls
    max_execution_time=None,  # Optional: Maximum execution time in seconds
    max_retry_limit=2,  # Default: 2 retries on error
    allow_code_execution=False,  # Default: False
    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
    respect_context_window=True,  # Default: True
    use_system_prompt=True,  # Default: True
    multimodal=False,  # Default: False
    inject_date=False,  # Default: False
    date_format="%Y-%m-%d",  # Default: ISO format
    reasoning=False,  # Default: False
    max_reasoning_attempts=None,  # Default: None
    tools=[SerperDevTool()],  # Optional: List of tools
    knowledge_sources=None,  # Optional: List of knowledge sources
    embedder=None,  # Optional: Custom embedder configuration
    system_template=None,  # Optional: Custom system prompt template
    prompt_template=None,  # Optional: Custom prompt template
    response_template=None,  # Optional: Custom response template
    step_callback=None,  # Optional: Callback function for monitoring
)

Let’s break down some key parameter combinations for common use cases:

Basic Research Agent

research_agent = Agent(
    role="Research Analyst",
    goal="Find and summarize information about specific topics",
    backstory="You are an experienced researcher with attention to detail",
    tools=[SerperDevTool()],
    verbose=True  # Enable logging for debugging
)

Code Development Agent

dev_agent = Agent(
    role="Senior Python Developer",
    goal="Write and debug Python code",
    backstory="Expert Python developer with 10 years of experience",
    allow_code_execution=True,
    code_execution_mode="safe",  # Uses Docker for safety
    max_execution_time=300,  # 5-minute timeout
    max_retry_limit=3  # More retries for complex code tasks
)

Long-Running Analysis Agent

analysis_agent = Agent(
    role="Data Analyst",
    goal="Perform deep analysis of large datasets",
    backstory="Specialized in big data analysis and pattern recognition",
    memory=True,
    respect_context_window=True,
    max_rpm=10,  # Limit API calls
    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
)

Custom Template Agent

custom_agent = Agent(
    role="Customer Service Representative",
    goal="Assist customers with their inquiries",
    backstory="Experienced in customer support with a focus on satisfaction",
    system_template="""<|start_header_id|>system<|end_header_id|>
                        {{ .System }}<|eot_id|>""",
    prompt_template="""<|start_header_id|>user<|end_header_id|>
                        {{ .Prompt }}<|eot_id|>""",
    response_template="""<|start_header_id|>assistant<|end_header_id|>
                        {{ .Response }}<|eot_id|>""",
)

Date-Aware Agent with Reasoning

strategic_agent = Agent(
    role="Market Analyst",
    goal="Track market movements with precise date references and strategic planning",
    backstory="Expert in time-sensitive financial analysis and strategic reporting",
    inject_date=True,  # Automatically inject current date into tasks
    date_format="%B %d, %Y",  # Format as "May 21, 2025"
    reasoning=True,  # Enable strategic planning
    max_reasoning_attempts=2,  # Limit planning iterations
    verbose=True
)

Reasoning Agent

reasoning_agent = Agent(
    role="Strategic Planner",
    goal="Analyze complex problems and create detailed execution plans",
    backstory="Expert strategic planner who methodically breaks down complex challenges",
    reasoning=True,  # Enable reasoning and planning
    max_reasoning_attempts=3,  # Limit reasoning attempts
    max_iter=30,  # Allow more iterations for complex planning
    verbose=True
)

Multimodal Agent

multimodal_agent = Agent(
    role="Visual Content Analyst",
    goal="Analyze and process both text and visual content",
    backstory="Specialized in multimodal analysis combining text and image understanding",
    multimodal=True,  # Enable multimodal capabilities
    verbose=True
)

Parameter Details

Critical Parameters

role, goal, and backstory are required and shape the agent’s behavior
llm determines the language model used (default: OpenAI’s GPT-4)

Memory and Context

memory: Enable to maintain conversation history
respect_context_window: Prevents token limit issues
knowledge_sources: Add domain-specific knowledge bases

Execution Control

max_iter: Maximum attempts before giving best answer
max_execution_time: Timeout in seconds
max_rpm: Rate limiting for API calls
max_retry_limit: Retries on error

Code Execution

allow_code_execution (deprecated): Previously enabled built-in code execution via CodeInterpreterTool.
code_execution_mode (deprecated): Previously controlled execution mode ("safe" for Docker, "unsafe" for direct execution).

Advanced Features

multimodal: Enable multimodal capabilities for processing text and visual content
reasoning: Enable agent to reflect and create plans before executing tasks
inject_date: Automatically inject current date into task descriptions

Templates

system_template: Defines agent’s core behavior
prompt_template: Structures input format
response_template: Formats agent responses

Agent Tools

Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:

Here’s how to add tools to an agent:

from crewai import Agent
from crewai_tools import SerperDevTool, WikipediaTools

# Create tools
search_tool = SerperDevTool()
wiki_tool = WikipediaTools()

# Add tools to agent
researcher = Agent(
    role="AI Technology Researcher",
    goal="Research the latest AI developments",
    tools=[search_tool, wiki_tool],
    verbose=True
)

Agent Memory and Context

Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.

from crewai import Agent

analyst = Agent(
    role="Data Analyst",
    goal="Analyze and remember complex data patterns",
    memory=True,  # Enable memory
    verbose=True
)

Context Window Management

CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model’s token limits. This powerful feature is controlled by the respect_context_window parameter.

How Context Window Management Works

When an agent’s conversation history grows too large for the LLM’s context window, CrewAI automatically detects this situation and can either:

Automatically summarize content (when respect_context_window=True)
Stop execution with an error (when respect_context_window=False)

Automatic Context Handling (`respect_context_window=True`)

This is the default and recommended setting for most use cases. When enabled, CrewAI will:

# Agent with automatic context management (default)
smart_agent = Agent(
    role="Research Analyst",
    goal="Analyze large documents and datasets",
    backstory="Expert at processing extensive information",
    respect_context_window=True,  # 🔑 Default: auto-handle context limits
    verbose=True
)

What happens when context limits are exceeded:

⚠️ Warning message: "Context length exceeded. Summarizing content to fit the model context window."
🔄 Automatic summarization: CrewAI intelligently summarizes the conversation history
✅ Continued execution: Task execution continues seamlessly with the summarized context
📝 Preserved information: Key information is retained while reducing token count

Strict Context Limits (`respect_context_window=False`)

When you need precise control and prefer execution to stop rather than lose any information:

# Agent with strict context limits
strict_agent = Agent(
    role="Legal Document Reviewer",
    goal="Provide precise legal analysis without information loss",
    backstory="Legal expert requiring complete context for accurate analysis",
    respect_context_window=False,  # ❌ Stop execution on context limit
    verbose=True
)

What happens when context limits are exceeded:

❌ Error message: "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."
🛑 Execution stops: Task execution halts immediately
🔧 Manual intervention required: You need to modify your approach

Choosing the Right Setting

Use `respect_context_window=True` (Default) when:

Processing large documents that might exceed context limits
Long-running conversations where some summarization is acceptable
Research tasks where general context is more important than exact details
Prototyping and development where you want robust execution

# Perfect for document processing
document_processor = Agent(
    role="Document Analyst",
    goal="Extract insights from large research papers",
    backstory="Expert at analyzing extensive documentation",
    respect_context_window=True,  # Handle large documents gracefully
    max_iter=50,  # Allow more iterations for complex analysis
    verbose=True
)

Use `respect_context_window=False` when:

Precision is critical and information loss is unacceptable
Legal or medical tasks requiring complete context
Code review where missing details could introduce bugs
Financial analysis where accuracy is paramount

# Perfect for precision tasks
precision_agent = Agent(
    role="Code Security Auditor",
    goal="Identify security vulnerabilities in code",
    backstory="Security expert requiring complete code context",
    respect_context_window=False,  # Prefer failure over incomplete analysis
    max_retry_limit=1,  # Fail fast on context issues
    verbose=True
)

Alternative Approaches for Large Data

When dealing with very large datasets, consider these strategies:

1. Use RAG Tools

from crewai_tools import RagTool

# Create RAG tool for large document processing
rag_tool = RagTool()

rag_agent = Agent(
    role="Research Assistant",
    goal="Query large knowledge bases efficiently",
    backstory="Expert at using RAG tools for information retrieval",
    tools=[rag_tool],  # Use RAG instead of large context windows
    respect_context_window=True,
    verbose=True
)

2. Use Knowledge Sources

# Use knowledge sources instead of large prompts
knowledge_agent = Agent(
    role="Knowledge Expert",
    goal="Answer questions using curated knowledge",
    backstory="Expert at leveraging structured knowledge sources",
    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
    respect_context_window=True,
    verbose=True
)

Context Window Best Practices

Monitor Context Usage: Enable verbose=True to see context management in action
Design for Efficiency: Structure tasks to minimize context accumulation
Use Appropriate Models: Choose LLMs with context windows suitable for your tasks
Test Both Settings: Try both True and False to see which works better for your use case
Combine with RAG: Use RAG tools for very large datasets instead of relying solely on context windows

Troubleshooting Context Issues

If you’re getting context limit errors:

# Quick fix: Enable automatic handling
agent.respect_context_window = True

# Better solution: Use RAG tools for large data
from crewai_tools import RagTool
agent.tools = [RagTool()]

# Alternative: Break tasks into smaller pieces
# Or use knowledge sources instead of large prompts

If automatic summarization loses important information:

# Disable auto-summarization and use RAG instead
agent = Agent(
    role="Detailed Analyst",
    goal="Maintain complete information accuracy",
    backstory="Expert requiring full context",
    respect_context_window=False,  # No summarization
    tools=[RagTool()],  # Use RAG for large data
    verbose=True
)

Direct Agent Interaction with `kickoff()`

Agents can be used directly without going through a task or crew workflow using the kickoff() method. This provides a simpler way to interact with an agent when you don’t need the full crew orchestration capabilities.

How `kickoff()` Works

The kickoff() method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent’s capabilities (tools, reasoning, etc.).

from crewai import Agent
from crewai_tools import SerperDevTool

# Create an agent
researcher = Agent(
    role="AI Technology Researcher",
    goal="Research the latest AI developments",
    tools=[SerperDevTool()],
    verbose=True
)

# Use kickoff() to interact directly with the agent
result = researcher.kickoff("What are the latest developments in language models?")

# Access the raw response
print(result.raw)

Parameters and Return Values

Parameter	Type	Description
`messages`	`Union[str, List[Dict[str, str]]]`	Either a string query or a list of message dictionaries with role/content
`response_format`	`Optional[Type[Any]]`	Optional Pydantic model for structured output

The method returns a LiteAgentOutput object with the following properties:

raw: String containing the raw output text
pydantic: Parsed Pydantic model (if a response_format was provided)
agent_role: Role of the agent that produced the output
usage_metrics: Token usage metrics for the execution

Structured Output

You can get structured output by providing a Pydantic model as the response_format:

from pydantic import BaseModel
from typing import List

class ResearchFindings(BaseModel):
    main_points: List[str]
    key_technologies: List[str]
    future_predictions: str

# Get structured output
result = researcher.kickoff(
    "Summarize the latest developments in AI for 2025",
    response_format=ResearchFindings
)

# Access structured data
print(result.pydantic.main_points)
print(result.pydantic.future_predictions)

Multiple Messages

You can also provide a conversation history as a list of message dictionaries:

messages = [
    {"role": "user", "content": "I need information about large language models"},
    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
    {"role": "user", "content": "What are the latest developments in 2025?"}
]

result = researcher.kickoff(messages)

Async Support

An asynchronous version is available via kickoff_async() with the same parameters:

import asyncio

async def main():
    result = await researcher.kickoff_async("What are the latest developments in AI?")
    print(result.raw)

asyncio.run(main())

Important Considerations and Best Practices

Security and Code Execution

Performance Optimization

Use respect_context_window: true to prevent token limit issues
Set appropriate max_rpm to avoid rate limiting
Enable cache: true to improve performance for repetitive tasks
Adjust max_iter and max_retry_limit based on task complexity

Memory and Context Management

Leverage knowledge_sources for domain-specific information
Configure embedder when using custom embedding models
Use custom templates (system_template, prompt_template, response_template) for fine-grained control over agent behavior

Advanced Features

Enable reasoning: true for agents that need to plan and reflect before executing complex tasks
Set appropriate max_reasoning_attempts to control planning iterations (None for unlimited attempts)
Use inject_date: true to provide agents with current date awareness for time-sensitive tasks
Customize the date format with date_format using standard Python datetime format codes
Enable multimodal: true for agents that need to process both text and visual content

Agent Collaboration

Enable allow_delegation: true when agents need to work together
Use step_callback to monitor and log agent interactions
Consider using different LLMs for different purposes:
- Main llm for complex reasoning
- function_calling_llm for efficient tool usage

Date Awareness and Reasoning

Use inject_date: true to provide agents with current date awareness for time-sensitive tasks
Customize the date format with date_format using standard Python datetime format codes
Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
Invalid date formats will be logged as warnings and will not modify the task description
Enable reasoning: true for complex tasks that benefit from upfront planning and reflection

Model Compatibility

Set use_system_prompt: false for older models that don’t support system messages
Ensure your chosen llm supports the features you need (like function calling)

Troubleshooting Common Issues

Rate Limiting: If you’re hitting API rate limits:
- Implement appropriate max_rpm
- Use caching for repetitive operations
- Consider batching requests
Context Window Errors: If you’re exceeding context limits:
- Enable respect_context_window
- Use more efficient prompts
- Clear agent memory periodically
Code Execution Issues: If code execution fails:
- Verify Docker is installed for safe mode
- Check execution permissions
- Review code sandbox settings
Memory Issues: If agent responses seem inconsistent:
- Check knowledge source configuration
- Review conversation history management

Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.

Agents

Overview of an Agent

Agent Attributes

Creating Agents

JSONC Configuration (Recommended)

Classic YAML Configuration

Direct Code Definition

Basic Research Agent

Code Development Agent

Long-Running Analysis Agent

Custom Template Agent

Date-Aware Agent with Reasoning

Reasoning Agent

Multimodal Agent

Parameter Details

Critical Parameters

Memory and Context

Execution Control

Code Execution

Advanced Features

Templates

Agent Tools

Agent Memory and Context

Context Window Management

How Context Window Management Works

Automatic Context Handling (respect_context_window=True)

Strict Context Limits (respect_context_window=False)

Choosing the Right Setting

Use respect_context_window=True (Default) when:

Use respect_context_window=False when:

Alternative Approaches for Large Data

1. Use RAG Tools

2. Use Knowledge Sources

Context Window Best Practices

Troubleshooting Context Issues

Direct Agent Interaction with kickoff()

How kickoff() Works

Parameters and Return Values

Structured Output

Multiple Messages

Async Support

Important Considerations and Best Practices

Security and Code Execution

Performance Optimization

Memory and Context Management

Advanced Features

Agent Collaboration

Date Awareness and Reasoning

Model Compatibility

Troubleshooting Common Issues

Automatic Context Handling (`respect_context_window=True`)

Strict Context Limits (`respect_context_window=False`)

Use `respect_context_window=True` (Default) when:

Use `respect_context_window=False` when:

Direct Agent Interaction with `kickoff()`

How `kickoff()` Works