Persistent, project-aware, long-term memory for AI coding assistants
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# Heimdall MCP Server - Your AI Coding Assistant's Long-Term Memory [](https://pypi.org/project/heimdall-mcp/) [](hhttps://github.com/lcbcFoo/heimdall-mcp-server/blob/main/README.mdttps://opensource.org/licenses/Apache-2.0) [](https://modelcontextprotocol.io/) [](https://youtu.be/7X1gntAXsao)  **The Problem:** Your AI coding assistant has short-lived memory. Every chat session starts from a blank slate. **The Solution:** Heimdall gives your LLM a persistent, growing, cognitive memory of your specific codebase, lessons and memories carry over time. https://github.com/user-attachments/assets/120b3d32-72d1-4d42-b3ab-285e8a711981 ## Key Features - 🧠 **Context-Rich Memory**: Heimdall learns from your documentation, session insights, and development history, allowing your LLM to recall specific solutions and architectural patterns across conversations. - 📚 **Git-Aware Context**: It indexes your project's entire git history, understanding not just what changed, but also who changed it, when, and context. - 🔗 **Isolated & Organized**: Each project gets its own isolated memory space, ensuring that context from one project doesn't leak into another. - ⚡ **Efficient Integration**: Built on the Model Context Protocol (MCP), it provides a standardized, low-overhead way for LLMs to access this powerful memory. ## 🚀 Getting Started **Prerequisites**: Python 3.11+ and Docker (for Qdrant vector database). Heimdall provides a unified `heimdall` CLI that manages everything from project setup to MCP integration. ### 1. Install Heimdall ```bash pip install heimdall-mcp ``` This installs the `heimdall` command-line tool with all …
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