LLM File System Agent

About

This project provides a minimal example of automating file system tasks by integrating a Large Language Model (LLM) like Ollama with a Multi-Agent Communication Protocol (MCP) tool via a Python agent bridge. It establishes a pipeline where user's natural language commands are processed by the LLM, translated into structured tool calls, executed by an MCP server, and finally performed by a backend REST API that handles actual file system operations. This architecture allows for natural language control over file and directory management.

Key Features

• Automates file system operations using natural language commands
• 0 GitHub stars
• Implements MCP protocol for file system tool orchestration
• Supports common file operations like copy, read, write, delete, and directory creation
• Provides a backend REST API for core file/directory management
• Integrates with LLMs (e.g., Ollama) via a Python agent bridge

Use Cases

• Building LLM-powered agents for interactive file system control
• Developing a foundational framework for more complex conversational file manipulation tools
• Automating routine file and directory management tasks with natural language