Agents
What is an Agent?¶
What is an Agent?
An agent is an autonomous unit programmed to:
- Perform tasks
- Make decisions
- Communicate with other agents
Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
Agent Attributes¶
| Attribute | Description |
|---|---|
| Role | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| Goal | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| Backstory | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| LLM (optional) | Represents the language model that will run the agent. It dynamically fetches the model name from the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4" if not specified. |
| Tools (optional) | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. |
| Function Calling LLM (optional) | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is None. |
| Max Iter (optional) | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is 25. |
| Max RPM (optional) | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of None. |
| max_execution_time (optional) | Maximum execution time for an agent to execute a task It's optional and can be left unspecified, with a default value of None, menaning no max execution time |
| Verbose (optional) | Setting this to True configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is False. |
| Allow Delegation (optional) | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is True. |
| Step Callback (optional) | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew step_callback. |
| Cache (optional) | Indicates if the agent should use a cache for tool usage. Default is True. |
Creating an Agent¶
Agent Interaction
Agents can interact with each other using crewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
To create an agent, you would typically initialize an instance of the Agent class with the desired properties. Here's a conceptual example including all attributes:
# Example: Creating an agent with all attributes
from crewai import Agent
agent = Agent(
role='Data Analyst',
goal='Extract actionable insights',
backstory="""You're a data analyst at a large company.
You're responsible for analyzing data and providing insights
to the business.
You're currently working on a project to analyze the
performance of our marketing campaigns.""",
tools=[my_tool1, my_tool2], # Optional, defaults to an empty list
llm=my_llm, # Optional
function_calling_llm=my_llm, # Optional
max_iter=15, # Optional
max_rpm=None, # Optional
verbose=True, # Optional
allow_delegation=True, # Optional
step_callback=my_intermediate_step_callback, # Optional
cache=True # Optional
)
Conclusion¶
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.