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* support unsafe code execution. add in docker install and running checks. * Update return type
162 lines
12 KiB
Plaintext
162 lines
12 KiB
Plaintext
---
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title: Agents
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description: What are CrewAI Agents and how to use them.
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icon: robot
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---
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## What is an agent?
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An agent is an **autonomous unit** programmed to:
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<ul>
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<li class='leading-3'>Perform tasks</li>
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<li class='leading-3'>Make decisions</li>
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<li class='leading-3'>Communicate with other agents</li>
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</ul>
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<Tip>
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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.
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</Tip>
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## Agent attributes
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| Attribute | Parameter | Description |
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| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| **Role** | `role` | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
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| **Goal** | `goal` | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
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| **Backstory** | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
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| **LLM** *(optional)* | `llm` | 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. |
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| **Tools** *(optional)* | `tools` | 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. |
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| **Function Calling LLM** *(optional)* | `function_calling_llm` | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. |
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| **Max Iter** *(optional)* | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`. |
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| **Max RPM** *(optional)* | `max_rpm` | Max RPM is 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`. |
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| **Max Execution Time** *(optional)* | `max_execution_time` | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time. |
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| **Verbose** *(optional)* | `verbose` | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. |
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| **Allow Delegation** *(optional)* | `allow_delegation` | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`. |
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| **Step Callback** *(optional)* | `step_callback` | 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`. |
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| **Cache** *(optional)* | `cache` | Indicates if the agent should use a cache for tool usage. Default is `True`. |
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| **System Template** *(optional)* | `system_template` | Specifies the system format for the agent. Default is `None`. |
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| **Prompt Template** *(optional)* | `prompt_template` | Specifies the prompt format for the agent. Default is `None`. |
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| **Response Template** *(optional)* | `response_template` | Specifies the response format for the agent. Default is `None`. |
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| **Allow Code Execution** *(optional)* | `allow_code_execution` | Enable code execution for the agent. Default is `False`. |
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| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`. |
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| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. |
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| **Respect Context Window** *(optional)* | `respect_context_window` | Summary strategy to avoid overflowing the context window. Default is `True`. |
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| **Code Execution Mode** *(optional)* | `code_execution_mode` | Determines the mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution on the host machine). Default is `safe`. |
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## Creating an agent
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<Note>
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**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.
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</Note>
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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:
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```python Code example
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from crewai import Agent
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agent = Agent(
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role='Data Analyst',
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goal='Extract actionable insights',
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backstory="""You're a data analyst at a large company.
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You're responsible for analyzing data and providing insights
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to the business.
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You're currently working on a project to analyze the
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performance of our marketing campaigns.""",
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tools=[my_tool1, my_tool2], # Optional, defaults to an empty list
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llm=my_llm, # Optional
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function_calling_llm=my_llm, # Optional
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max_iter=15, # Optional
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max_rpm=None, # Optional
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max_execution_time=None, # Optional
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verbose=True, # Optional
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allow_delegation=False, # Optional
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step_callback=my_intermediate_step_callback, # Optional
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cache=True, # Optional
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system_template=my_system_template, # Optional
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prompt_template=my_prompt_template, # Optional
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response_template=my_response_template, # Optional
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config=my_config, # Optional
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crew=my_crew, # Optional
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tools_handler=my_tools_handler, # Optional
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cache_handler=my_cache_handler, # Optional
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callbacks=[callback1, callback2], # Optional
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allow_code_execution=True, # Optional
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max_retry_limit=2, # Optional
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use_system_prompt=True, # Optional
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respect_context_window=True, # Optional
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code_execution_mode='safe', # Optional, defaults to 'safe'
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)
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```
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## Setting prompt templates
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Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates:
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```python Code example
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agent = Agent(
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role="{topic} specialist",
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goal="Figure {goal} out",
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backstory="I am the master of {role}",
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system_template="""<|start_header_id|>system<|end_header_id|>
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{{ .System }}<|eot_id|>""",
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prompt_template="""<|start_header_id|>user<|end_header_id|>
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{{ .Prompt }}<|eot_id|>""",
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response_template="""<|start_header_id|>assistant<|end_header_id|>
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{{ .Response }}<|eot_id|>""",
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)
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```
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## Bring your third-party agents
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Extend your third-party agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the CrewAI's `BaseAgent` class.
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<Note>
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**BaseAgent** includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
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</Note>
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CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
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```python Code example
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from crewai import Agent, Task, Crew
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from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
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from langchain.agents import load_tools
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langchain_tools = load_tools(["google-serper"], llm=llm)
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agent1 = CustomAgent(
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role="agent role",
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goal="who is {input}?",
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backstory="agent backstory",
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verbose=True,
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)
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task1 = Task(
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expected_output="a short biography of {input}",
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description="a short biography of {input}",
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agent=agent1,
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)
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agent2 = Agent(
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role="agent role",
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goal="summarize the short bio for {input} and if needed do more research",
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backstory="agent backstory",
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verbose=True,
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)
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task2 = Task(
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description="a tldr summary of the short biography",
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expected_output="5 bullet point summary of the biography",
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agent=agent2,
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context=[task1],
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)
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my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
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crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
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```
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## Conclusion
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Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents,
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you can create sophisticated AI systems that leverage the power of collaborative intelligence. The `code_execution_mode` attribute provides flexibility in how agents execute code, allowing for both secure and direct execution options.
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