This agent provides a general-purpose, sandboxed environment for executing Python code to accomplish user-defined tasks.
It leverages a Large Language Model (LLM) to interpret a natural language task, generate Python code, and execute it within a Docker container.
The agent operates by creating an interactive session with a Jupyter kernel running inside the container, allowing it to iteratively write code, execute it, and use the output to inform its next steps until the task is complete.
To run this agent, a Docker daemon must be available and running on the host machine.
The agent itself is a Python command-line application.
It pulls a specified Docker image (defaulting to jupyter/datascience-notebook:latest) to create the execution environment.
Sandboxed Execution: All code is executed within a secure and isolated Docker container, preventing unintended side effects on the host machine.
Customizable Environment: Users can specify any Docker image for the execution environment and mount local directories as volumes into the container.
LLM-Powered Task Resolution: The agent takes a high-level, natural language task and intelligently generates and executes the code needed to complete it.
Interactive Code Execution: Provides tools for the LLM to execute_code and restart_kernel, allowing for an interactive and stateful problem-solving process.
Task Completion Reporting: The agent can explicitly mark a task as complete with a success or failure status and a final summary.
Step-by-Step Iteration: The agent operates within a defined loop with a maximum number of steps (max_steps) to ensure termination.
Artifact Logging: Upon completion, the agent can log the entire working directory as an artifact to Dreadnode, preserving any generated files.
