Crews

Overview

A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.

Crew Attributes

Attribute	Parameters	Description
Tasks	`tasks`	A list of tasks assigned to the crew.
Agents	`agents`	A list of agents that are part of the crew.
Process (optional)	`process`	The process flow (e.g., sequential, hierarchical) the crew follows. Default is `sequential`.
Verbose (optional)	`verbose`	The verbosity level for logging during execution. Defaults to `False`.
Manager LLM (optional)	`manager_llm`	The language model used by the manager agent in a hierarchical process. Required when using a hierarchical process.
Function Calling LLM (optional)	`function_calling_llm`	If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew’s LLM for function calling.
Config (optional)	`config`	Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.
Max RPM (optional)	`max_rpm`	Maximum requests per minute the crew adheres to during execution. Defaults to `None`.
Memory (optional)	`memory`	Utilized for storing execution memories (short-term, long-term, entity memory).
Cache (optional)	`cache`	Specifies whether to use a cache for storing the results of tools’ execution. Defaults to `True`.
Embedder (optional)	`embedder`	Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.
Step Callback (optional)	`step_callback`	A function that is called after each step of every agent. This can be used to log the agent’s actions or to perform other operations; it won’t override the agent-specific `step_callback`.
Task Callback (optional)	`task_callback`	A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.
Share Crew (optional)	`share_crew`	Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.
Output Log File (optional)	`output_log_file`	Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.
Manager Agent (optional)	`manager_agent`	`manager` sets a custom agent that will be used as a manager.
Prompt File (optional)	`prompt_file`	Path to the prompt JSON file to be used for the crew.
Planning (optional)	`planning`	Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.
Planning LLM (optional)	`planning_llm`	The language model used by the AgentPlanner in a planning process.
Knowledge Sources (optional)	`knowledge_sources`	Knowledge sources available at the crew level, accessible to all the agents.
Stream (optional)	`stream`	Enable streaming output to receive real-time updates during crew execution. Returns a `CrewStreamingOutput` object that can be iterated for chunks. Defaults to `False`.
Chat LLM (optional)	`chat_llm`	The language model used to orchestrate `crewai chat` CLI interactions with the crew. Accepts a model name string or `LLM` instance. Defaults to `None`.
Before Kickoff Callbacks (optional)	`before_kickoff_callbacks`	A list of callable functions executed before the crew starts. Each callback receives and can modify the inputs dict. Distinct from the `@before_kickoff` decorator. Defaults to `[]`.
After Kickoff Callbacks (optional)	`after_kickoff_callbacks`	A list of callable functions executed after the crew finishes. Each callback receives and can modify the `CrewOutput`. Distinct from the `@after_kickoff` decorator. Defaults to `[]`.
Tracing (optional)	`tracing`	Controls OpenTelemetry tracing for the crew. `True` = always enable, `False` = always disable, `None` = inherit from environment / user settings. Defaults to `None`.
Skills (optional)	`skills`	A list of `Path` objects (skill search directories) or pre-loaded `Skill` objects applied to all agents in the crew. Defaults to `None`.
Security Config (optional)	`security_config`	A `SecurityConfig` instance managing crew fingerprinting and identity. Defaults to `SecurityConfig()`.
Checkpoint (optional)	`checkpoint`	Enables automatic checkpointing. Pass `True` for sensible defaults, a `CheckpointConfig` for full control, `False` to opt out, or `None` to inherit. See the Checkpointing section below. Defaults to `None`.

Creating Crews

There are two common ways to create crews 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 crew.jsonc for crew-level settings and tasks, plus one file per agent in agents/.

crewai run automatically detects crew.jsonc or crew.json, loads the referenced agents, prompts for missing placeholders, and kicks off the crew.

Example `crew.jsonc`

{
  "name": "Market Research Crew",
  "agents": ["researcher", "analyst"],
  "tasks": [
    {
      "name": "research",
      "description": "Research {topic} and collect the most relevant facts.",
      "expected_output": "Structured research notes about {topic}.",
      "agent": "researcher"
    },
    {
      "name": "analysis",
      "description": "Analyze the research and write a concise report.",
      "expected_output": "A markdown report with findings and recommendations.",
      "agent": "analyst",
      "context": ["research"],
      "output_file": "output/report.md"
    }
  ],
  "process": "sequential",
  "verbose": true,
  "memory": true,
  "inputs": {
    "topic": "AI Agents"
  }
}

Each string in agents resolves to agents/<name>.jsonc first, then agents/<name>.json.

{
  "role": "{topic} Senior Researcher",
  "goal": "Find accurate and current information about {topic}.",
  "backstory": "You are a careful researcher who cites clear evidence.",
  "llm": "openai/gpt-4o",
  "tools": ["SerperDevTool"]
}

For hierarchical crews, set "process": "hierarchical" and provide either manager_llm or manager_agent. A manager_agent can reference an agents/<name>.jsonc file that is not included in the top-level agents list.

JSON crew definitions support crew-level fields such as process, verbose, memory, cache, max_rpm, planning, planning_llm, manager_llm, manager_agent, function_calling_llm, output_log_file, stream, tracing, before_kickoff_callbacks, and after_kickoff_callbacks.

Python callbacks and custom classes use {"python": "module.attribute"}. Custom tools use "custom:<name>" and load tools/<name>.py at runtime.

Classic Python/YAML Configuration

Classic projects created with crewai create crew <name> --classic use crew.py, config/agents.yaml, config/tasks.yaml, and the @CrewBase, @agent, @task, and @crew decorators. That pattern remains supported and is documented in Using Annotations.

Direct Code Definition (Alternative)

Alternatively, you can define the crew directly in code without using YAML configuration files.

from crewai import Agent, Crew, Task, Process
from crewai_tools import YourCustomTool

class YourCrewName:
    def agent_one(self) -> Agent:
        return Agent(
            role="Data Analyst",
            goal="Analyze data trends in the market",
            backstory="An experienced data analyst with a background in economics",
            verbose=True,
            tools=[YourCustomTool()]
        )

    def agent_two(self) -> Agent:
        return Agent(
            role="Market Researcher",
            goal="Gather information on market dynamics",
            backstory="A diligent researcher with a keen eye for detail",
            verbose=True
        )

    def task_one(self) -> Task:
        return Task(
            description="Collect recent market data and identify trends.",
            expected_output="A report summarizing key trends in the market.",
            agent=self.agent_one()
        )

    def task_two(self) -> Task:
        return Task(
            description="Research factors affecting market dynamics.",
            expected_output="An analysis of factors influencing the market.",
            agent=self.agent_two()
        )

    def crew(self) -> Crew:
        return Crew(
            agents=[self.agent_one(), self.agent_two()],
            tasks=[self.task_one(), self.task_two()],
            process=Process.sequential,
            verbose=True
        )

How to run the above code:

YourCrewName().crew().kickoff(inputs={})

In this example:

Agents and tasks are defined directly within the class without decorators.
We manually create and manage the list of agents and tasks.
This approach provides more control but can be less maintainable for larger projects.

Crew Output

The output of a crew in the CrewAI framework is encapsulated within the CrewOutput class. This class provides a structured way to access results of the crew’s execution, including various formats such as raw strings, JSON, and Pydantic models. The CrewOutput includes the results from the final task output, token usage, and individual task outputs.

Crew Output Attributes

Attribute	Parameters	Type	Description
Raw	`raw`	`str`	The raw output of the crew. This is the default format for the output.
Pydantic	`pydantic`	`Optional[BaseModel]`	A Pydantic model object representing the structured output of the crew.
JSON Dict	`json_dict`	`Optional[Dict[str, Any]]`	A dictionary representing the JSON output of the crew.
Tasks Output	`tasks_output`	`List[TaskOutput]`	A list of `TaskOutput` objects, each representing the output of a task in the crew.
Token Usage	`token_usage`	`Dict[str, Any]`	A summary of token usage, providing insights into the language model’s performance during execution.

Crew Output Methods and Properties

Method/Property	Description
json	Returns the JSON string representation of the crew output if the output format is JSON.
to_dict	Converts the JSON and Pydantic outputs to a dictionary.
**str**	Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw.

Accessing Crew Outputs

Once a crew has been executed, its output can be accessed through the output attribute of the Crew object. The CrewOutput class provides various ways to interact with and present this output.

Example

# Example crew execution
crew = Crew(
    agents=[research_agent, writer_agent],
    tasks=[research_task, write_article_task],
    verbose=True
)

crew_output = crew.kickoff()

# Accessing the crew output
print(f"Raw Output: {crew_output.raw}")
if crew_output.json_dict:
    print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
if crew_output.pydantic:
    print(f"Pydantic Output: {crew_output.pydantic}")
print(f"Tasks Output: {crew_output.tasks_output}")
print(f"Token Usage: {crew_output.token_usage}")

Accessing Crew Logs

You can see real time log of the crew execution, by setting output_log_file as a True(Boolean) or a file_name(str). Supports logging of events as both file_name.txt and file_name.json. In case of True(Boolean) will save as logs.txt.

In case of output_log_file is set as False(Boolean) or None, the logs will not be populated.

# Save crew logs
crew = Crew(output_log_file = True)  # Logs will be saved as logs.txt
crew = Crew(output_log_file = file_name)  # Logs will be saved as file_name.txt
crew = Crew(output_log_file = file_name.txt)  # Logs will be saved as file_name.txt
crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name.json

Checkpointing

Checkpointing lets a crew automatically save its state after key events (e.g. task completion) so that long-running or interrupted runs can be resumed exactly where they left off without re-executing completed tasks.

Quick Start

Pass checkpoint=True to enable checkpointing with sensible defaults (saves to .checkpoints/ after every task):

from crewai import Crew, Process

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    checkpoint=True,  # saves to .checkpoints/ after every task
)

crew.kickoff(inputs={"topic": "AI trends"})

Full Control with `CheckpointConfig`

Use CheckpointConfig for fine-grained control over location, trigger events, storage backend, and retention:

from crewai import Crew, Process
from crewai.state.checkpoint_config import CheckpointConfig

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    checkpoint=CheckpointConfig(
        location="./.checkpoints",       # directory for JSON files (default)
        on_events=["task_completed"],    # trigger after each task (default)
        max_checkpoints=5,               # keep only the 5 most recent checkpoints
    ),
)

crew.kickoff(inputs={"topic": "AI trends"})

Resuming from a Checkpoint

Use Crew.from_checkpoint() to restore a crew from a saved checkpoint file, then call kickoff() to resume:

# Resume from the most recent checkpoint
crew = Crew.from_checkpoint(".checkpoints/latest.json")
crew.kickoff()

`CheckpointConfig` Attributes

Attribute	Type	Default	Description
`location`	`str`	`"./.checkpoints"`	Storage destination. For `JsonProvider` this is a directory path; for `SqliteProvider` a database file path.
`on_events`	`list[str]`	`["task_completed"]`	Event types that trigger a checkpoint write. Use `["*"]` to checkpoint on every event.
`provider`	`JsonProvider \| SqliteProvider`	`JsonProvider()`	Storage backend. Defaults to `JsonProvider` (plain JSON files).
`max_checkpoints`	`int \| None`	`None`	Maximum checkpoints to keep. Oldest are pruned after each write. `None` keeps all.

Memory Utilization

Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.

Cache Utilization

Caches can be employed to store the results of tools’ execution, making the process more efficient by reducing the need to re-execute identical tasks.

Crew Usage Metrics

After the crew execution, you can access the usage_metrics attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.

# Access the crew's usage metrics
crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
crew.kickoff()
print(crew.usage_metrics)

Crew Execution Process

Sequential Process: Tasks are executed one after another, allowing for a linear flow of work.
Hierarchical Process: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. Note: A manager_llm or manager_agent is required for this process and it’s essential for validating the process flow.

Kicking Off a Crew

Once your crew is assembled, initiate the workflow with the kickoff() method. This starts the execution process according to the defined process flow.

# Start the crew's task execution
result = my_crew.kickoff()
print(result)

Different Ways to Kick Off a Crew

Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process.

Synchronous Methods

kickoff(): Starts the execution process according to the defined process flow.
kickoff_for_each(): Executes tasks sequentially for each provided input event or item in the collection.

Asynchronous Methods

CrewAI offers two approaches for async execution:

Method	Type	Description
`akickoff()`	Native async	True async/await throughout the entire execution chain
`akickoff_for_each()`	Native async	Native async execution for each input in a list
`kickoff_async()`	Thread-based	Wraps synchronous execution in `asyncio.to_thread`
`kickoff_for_each_async()`	Thread-based	Thread-based async for each input in a list

# Start the crew's task execution
result = my_crew.kickoff()
print(result)

# Example of using kickoff_for_each
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
results = my_crew.kickoff_for_each(inputs=inputs_array)
for result in results:
    print(result)

# Example of using native async with akickoff
inputs = {'topic': 'AI in healthcare'}
async_result = await my_crew.akickoff(inputs=inputs)
print(async_result)

# Example of using native async with akickoff_for_each
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
async_results = await my_crew.akickoff_for_each(inputs=inputs_array)
for async_result in async_results:
    print(async_result)

# Example of using thread-based kickoff_async
inputs = {'topic': 'AI in healthcare'}
async_result = await my_crew.kickoff_async(inputs=inputs)
print(async_result)

# Example of using thread-based kickoff_for_each_async
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
for async_result in async_results:
    print(async_result)

These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs. For detailed async examples, see the Kickoff Crew Asynchronously guide.

Streaming Crew Execution

For real-time visibility into crew execution, you can enable streaming to receive output as it’s generated:

# Enable streaming
crew = Crew(
    agents=[researcher],
    tasks=[task],
    stream=True
)

# Iterate over streaming output
streaming = crew.kickoff(inputs={"topic": "AI"})
for chunk in streaming:
    print(chunk.content, end="", flush=True)

# Access final result
result = streaming.result

Learn more about streaming in the Streaming Crew Execution guide.

Replaying from a Specific Task

You can now replay from a specific task using our CLI command replay.

The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command crewai replay -t <task_id>, you can specify the task_id for the replay process.

Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.

Replaying from a Specific Task Using the CLI

To use the replay feature, follow these steps:

Open your terminal or command prompt.
Navigate to the directory where your CrewAI project is located.
Run the following command:

To view the latest kickoff task IDs, use:

crewai log-tasks-outputs

Then, to replay from a specific task, use:

crewai replay -t <task_id>

These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.