This framework provides a Pythonic interface for building AI agents capable of complex tasks like deep research and browser interaction. It targets developers and researchers looking to leverage large language models with advanced tooling and planning capabilities. The primary benefit is a structured approach to agent development, abstracting away much of the underlying LLM orchestration.

How It Works

Minion Agent utilizes a modular design, allowing agents to be configured with various LLM backends (OpenAI, Azure OpenAI, LiteLLM) and a flexible tool system. It supports the Model Context Protocol (MCP) for integrating external tools and services, including filesystem access and SSE-based communication for remote servers. Agents can be equipped with planning capabilities, enabling them to break down tasks and re-evaluate their strategy over multiple steps.

Quick Start & Requirements

• Install via pip: pip install minion-agent-x
• Requires Python 3.7+ and an OpenAI or Azure OpenAI API key set as environment variables.
• Development setup involves cloning the repository and installing dev dependencies: pip install -e ".[dev]".
• Example usage and detailed documentation are available via provided links.

Highlighted Details

• Supports Model Context Protocol (MCP) for tool integration.
• Features automatic planning with configurable intervals.
• Offers flexibility in LLM backend selection (OpenAI, Azure, LiteLLM).
• Includes examples for browser use, deep research, and managed agents.

Maintenance & Community

The project is licensed under the MIT License. Community support is available via a WeChat discussion group.

Licensing & Compatibility

MIT License. Permissive for commercial use and integration with closed-source projects.

Limitations & Caveats

The framework relies heavily on external LLM providers, and its performance is directly tied to the quality and availability of these services. Security warnings are present for SSE-based MCP tools, emphasizing the need to connect only to trusted servers.