CodeMind
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CodeMindShow dependencies of the calculate_total function"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CodeMind
Persistent codebase memory and semantic context engine for AI development agents.
CodeMind provides AI agents and LLMs with a persistent, real-time memory layer over large codebases. Instead of relying on raw token dumps or simple keyword searches, CodeMind indexes software architecture into symbol graphs and Abstract Syntax Tree (AST) chunks. This allows AI tools to analyze dependencies, track code changes, and navigate complex structures effortlessly.
Features
AST-Aware Chunking: Parses source files intelligently by structure (classes, functions, methods) rather than arbitrary line counts.
Symbol Graph Store: Maps relationships, cross-references, and dependencies between codebase entities.
Model Context Protocol (MCP) Integration: Directly connects into MCP-compatible clients like Claude Desktop, Cursor, or custom agent frameworks.
Automated File Scanner: Keeps track of codebase changes and keeps indexing pipelines up to date.
Related MCP server: hive-memory
Prerequisites
Before installing CodeMind, ensure you have the following setup on your machine:
Python 3.10+
Git
Virtual environment tool (venv or conda)
Installation Steps
1. Clone the Repository
git clone https://github.com/twly7ho2/codemind.git cd codemind
2. Set Up a Virtual Environment
It is recommended to use a virtual environment to manage dependencies isolated from your main system.
On Windows: python -m venv venv venv\Scripts\activate
On macOS / Linux: python3 -m venv venv source venv/bin/activate
3. Install Package and Dependencies
Install CodeMind in editable mode along with required packages using pyproject.toml:
pip install --upgrade pip pip install -e .
Usage and Execution
Running the MCP Server
CodeMind exposes an MCP server interface to communicate directly with AI agent clients.
To launch the server directly from terminal:
python -m codemind.mcp.server
Local MCP Configuration Example
To connect CodeMind to Claude Desktop or another MCP client, add the following entry to your claude_desktop_config.json:
{ "mcpServers": { "codemind": { "command": "python", "args": [ "-m", "codemind.mcp.server" ], "env": { "PYTHONPATH": "C:\Users\YOUR_USERNAME\codemind" } } } }
Repository Structure
codemind/
|-- codemind/
| |-- indexer/
| | |-- engine.py # Main indexing orchestration engine
| | -- scanner.py # Codebase file crawler and change detection | |-- parser/ | | -- chunker.py # AST-based syntax chunking logic
| |-- storage/
| | -- graph_store.py # Symbol graph and dependency storage | -- mcp/
| -- server.py # Model Context Protocol server endpoints |-- pyproject.toml # Build system and metadata configuration |-- .gitignore # Ignored files (venv, caches, indexes) -- README.md # Project documentation
Contributing
Contributions, issues, and feature requests are welcome!
Fork the project.
Create your feature branch (git checkout -b feature/AmazingFeature).
Commit your changes (git commit -m 'Add some AmazingFeature').
Push to the branch (git push origin feature/AmazingFeature).
Open a Pull Request.
License
Distributed under the MIT License. See LICENSE for more information.
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