@bsmi021
基本情報
A powerful, production-ready context management system for Large Language Models (LLMs). Built with ChromaDB and modern embedding technologies, it provides persistent, project-specific memory capabilities that enhance your AI's understanding and response quality.
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Deploy using Docker Compose. Run docker-compose up --build -d to build and start the application and its required ChromaDB vector database.
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Default environment variables include `CHROMADB_URL`, `TRANSPORT`, `HTTP_PORT`, `MCP_MEMBANK_EMBEDDING_MODEL`, `NODE_ENV`, and `NODE_OPTIONS`. They can be overridden in your environment or in `docker-compose.yml`.
The default embedding model is `Xenova/all-MiniLM-L6-v2`, configurable via `MCP_MEMBANK_EMBEDDING_MODEL`.
Yes, ChromaDB data is persisted in the named Docker volume `chromadb-data`.
Port 3000 for the main application HTTP API, and port 8000 for ChromaDB.
No, the application runs inside the container as a non-root user.
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 162 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
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One permissioned folder your team and your AI agents both log into.
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Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,
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