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CodeInterpreterTool

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Description

The CodeInterpreterTool enables CrewAI agents to execute Python 3 code that they generate autonomously. The code is run in a secure, isolated Docker container, ensuring safety regardless of the content. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.

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Requirements

  • Docker must be installed and running on your system. If you don’t have it, you can install it from here.

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Installation

To use this tool, you need to install the CrewAI tools package:

pip install 'crewai[tools]'

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Example

The following example demonstrates how to use the CodeInterpreterTool with a CrewAI agent:

Code
from crewai import Agent, Task, Crew, Process
from crewai_tools import CodeInterpreterTool

# Initialize the tool
code_interpreter = CodeInterpreterTool()

# Define an agent that uses the tool
programmer_agent = Agent(
    role="Python Programmer",
    goal="Write and execute Python code to solve problems",
    backstory="An expert Python programmer who can write efficient code to solve complex problems.",
    tools=[code_interpreter],
    verbose=True,
)

# Example task to generate and execute code
coding_task = Task(
    description="Write a Python function to calculate the Fibonacci sequence up to the 10th number and print the result.",
    expected_output="The Fibonacci sequence up to the 10th number.",
    agent=programmer_agent,
)

# Create and run the crew
crew = Crew(
    agents=[programmer_agent],
    tasks=[coding_task],
    verbose=True,
    process=Process.sequential,
)
result = crew.kickoff()

You can also enable code execution directly when creating an agent:

Code
from crewai import Agent

# Create an agent with code execution enabled
programmer_agent = Agent(
    role="Python Programmer",
    goal="Write and execute Python code to solve problems",
    backstory="An expert Python programmer who can write efficient code to solve complex problems.",
    allow_code_execution=True,  # This automatically adds the CodeInterpreterTool
    verbose=True,
)

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Parameters

The CodeInterpreterTool accepts the following parameters during initialization:

  • user_dockerfile_path: Optional. Path to a custom Dockerfile to use for the code interpreter container.
  • user_docker_base_url: Optional. URL to the Docker daemon to use for running the container.
  • unsafe_mode: Optional. Whether to run code directly on the host machine instead of in a Docker container. Default is False. Use with caution!

When using the tool with an agent, the agent will need to provide:

  • code: Required. The Python 3 code to execute.
  • libraries_used: Required. A list of libraries used in the code that need to be installed.

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Agent Integration Example

Here’s a more detailed example of how to integrate the CodeInterpreterTool with a CrewAI agent:

Code
from crewai import Agent, Task, Crew
from crewai_tools import CodeInterpreterTool

# Initialize the tool
code_interpreter = CodeInterpreterTool()

# Define an agent that uses the tool
data_analyst = Agent(
    role="Data Analyst",
    goal="Analyze data using Python code",
    backstory="""You are an expert data analyst who specializes in using Python 
    to analyze and visualize data. You can write efficient code to process 
    large datasets and extract meaningful insights.""",
    tools=[code_interpreter],
    verbose=True,
)

# Create a task for the agent
analysis_task = Task(
    description="""
    Write Python code to:
    1. Generate a random dataset of 100 points with x and y coordinates
    2. Calculate the correlation coefficient between x and y
    3. Create a scatter plot of the data
    4. Print the correlation coefficient and save the plot as 'scatter.png'
    
    Make sure to handle any necessary imports and print the results.
    """,
    expected_output="The correlation coefficient and confirmation that the scatter plot has been saved.",
    agent=data_analyst,
)

# Run the task
crew = Crew(
    agents=[data_analyst],
    tasks=[analysis_task],
    verbose=True,
    process=Process.sequential,
)
result = crew.kickoff()

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Implementation Details

The CodeInterpreterTool uses Docker to create a secure environment for code execution:

Code
class CodeInterpreterTool(BaseTool):
    name: str = "Code Interpreter"
    description: str = "Interprets Python3 code strings with a final print statement."
    args_schema: Type[BaseModel] = CodeInterpreterSchema
    default_image_tag: str = "code-interpreter:latest"
    
    def _run(self, **kwargs) -> str:
        code = kwargs.get("code", self.code)
        libraries_used = kwargs.get("libraries_used", [])

        if self.unsafe_mode:
            return self.run_code_unsafe(code, libraries_used)
        else:
            return self.run_code_in_docker(code, libraries_used)

The tool performs the following steps:

  1. Verifies that the Docker image exists or builds it if necessary
  2. Creates a Docker container with the current working directory mounted
  3. Installs any required libraries specified by the agent
  4. Executes the Python code in the container
  5. Returns the output of the code execution
  6. Cleans up by stopping and removing the container

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Security Considerations

By default, the CodeInterpreterTool runs code in an isolated Docker container, which provides a layer of security. However, there are still some security considerations to keep in mind:

  1. The Docker container has access to the current working directory, so sensitive files could potentially be accessed.
  2. The unsafe_mode parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
  3. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.

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Conclusion

The CodeInterpreterTool provides a powerful way for CrewAI agents to execute Python code in a relatively secure environment. By enabling agents to write and run code, it significantly expands their problem-solving capabilities, especially for tasks involving data analysis, calculations, or other computational work. This tool is particularly useful for agents that need to perform complex operations that are more efficiently expressed in code than in natural language.

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