Crews
What is a Crew?¶
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 | Description |
|---|---|
| Tasks | A list of tasks assigned to the crew. |
| Agents | A list of agents that are part of the crew. |
| Process (optional) | The process flow (e.g., sequential, hierarchical) the crew follows. |
| Verbose (optional) | The verbosity level for logging during execution. |
| Manager LLM (optional) | The language model used by the manager agent in a hierarchical process. Required when using a hierarchical process. |
| Function Calling LLM (optional) | 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) | Optional configuration settings for the crew, in Json or Dict[str, Any] format. |
| Max RPM (optional) | Maximum requests per minute the crew adheres to during execution. |
| Language (optional) | Language used for the crew, defaults to English. |
| Language File (optional) | Path to the language file to be used for the crew. |
| Memory (optional) | Utilized for storing execution memories (short-term, long-term, entity memory). |
| Cache (optional) | Specifies whether to use a cache for storing the results of tools' execution. |
| Embedder (optional) | Configuration for the embedder to be used by the crew. mostly used by memory for now |
| Full Output (optional) | Whether the crew should return the full output with all tasks outputs or just the final output. |
| Step Callback (optional) | 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) | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
| Share Crew (optional) | 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) | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently and it will be called logs.txt or passing a string with the full path and name of the file. |
Crew Max RPM
The max_rpm attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' max_rpm settings if you set it.
Creating a Crew¶
When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
Example: Assembling a Crew¶
from crewai import Crew, Agent, Task, Process
from langchain_community.tools import DuckDuckGoSearchRun
# Define agents with specific roles and tools
researcher = Agent(
role='Senior Research Analyst',
goal='Discover innovative AI technologies',
tools=[DuckDuckGoSearchRun()]
)
writer = Agent(
role='Content Writer',
goal='Write engaging articles on AI discoveries',
verbose=True
)
# Create tasks for the agents
research_task = Task(
description='Identify breakthrough AI technologies',
agent=researcher
)
write_article_task = Task(
description='Draft an article on the latest AI technologies',
agent=writer
)
# Assemble the crew with a sequential process
my_crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_article_task],
process=Process.sequential,
full_output=True,
verbose=True,
)
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_llmis 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.