semantic-memory-mcp

# Semantic Memory MCP **Persistent, semantic memory for AI coding agents — a single Python file, entirely local.** `semantic-memory-mcp` is a self-hosted [Model Context Protocol](https://modelcontextprotocol.io) server that gives AI coding agents a durable, searchable memory across sessions. It is built as one Python file, stores everything in a single local SQLite database, and needs no cloud service and no API key. ## What it is - **Cross-session memory:** notes survive between sessions; the agent searches its own memory at the start of every task instead of relying on the user to re-explain context. - **Hybrid retrieval:** keyword full-text search (SQLite FTS5) and semantic vector search (sqlite-vec) are combined through Reciprocal Rank Fusion — a note can be found by exact wording *or* by meaning. - **Multilingual embeddings:** quantized ONNX model (`Xenova/multilingual-e5-small`, ~113 MB) runs locally on CPU. English and German work equally well; no OpenAI key required. - **Trivially auditable:** the whole server is a single readable file (`memory_server.py`, ~1,100 lines). There is no hidden state beyond the SQLite file. ### Tools | Tool | Purpose | |------|---------| | `memory_store` | Save a note (with tags and source) | | `memory_search` | Hybrid search (semantic + full-text), optional token budget | | `memory_get` / `memory_recent` | Read one note by ID / list recent notes | | `memory_update` / `memory_delete` | Edit or remove a note | | `memory_status` | Database statistics | | `memory_zotero_sync` | Optional: back up memory content into a Zotero library | ## Why AI coding agents forget everything between sessions. File-based memory (markdown indexes that get reloaded) is keyword-based: it has no semantic recall and no ranking — a note saved as *"embedding model comparison"* will not be found when the agent later asks *"which embedding did we test?"*. This server was built as a working reference implementation and **measured, not assumed** — see…