mienetic/mnema

🧠 Mnema — Long-term memory for AI via MCP × Vector DB. Pluggable backends, hybrid search, Auto Dream consolidation, CLI + REST API + SDK.

About mnema

Give your AI agents persistent, searchable memory. Solve the context-window problem with MCP × Vector DB.

Mnema (μνῆμα — Greek for "memory") is an open-source Model Context Protocol server that gives language-model agents a long-term memory layer. Instead of stuffing every relevant fact into a single conversation (and paying for it in tokens, latency, and lost context), store durable facts once and recall them later — by meaning, not by keyword.

- 🔌 MCP-native — drop it into Claude Desktop, Claude Code, Cursor, Zed, Cline, Continue, Windsurf, ZCode, or any MCP-compatible client. - 🗄️ Pluggable vector backends — ChromaDB (embedded, default), Qdrant (local or remote), or sqlite-vec (pure-SQLite, zero-dep). - 🧠 Pluggable embeddings — sentence-transformers (offline, default), OpenAI, or Ollama (local server). - 🔍 Hybrid search — combines semantic similarity + tag overlap + decay scoring into a single ranked score. - ⏳ Memory decay — a forgetting curve (recency × frequency × importance) so the store stays focused on what matters. - 📝 Summarization — plans how to condense many memories into a few high-level ones; the calling AI executes the plan (Mnema never calls an LLM on its own). - 👥 Multi-user / multi-session — scope-based namespace isolation (user:alice, session:abc, agent:bot-1). - 🔧 Offline by default — local sentence-transformers embeddings; no API keys required to start. - 📦 Programmatic SDK — use Mnema from Python without standing up an MCP server. - 💻 CLI — mnema add, mnema recall, mnema stats… for terminal-first workflows. - 🧪 Well-tested — 86 tests across pure-fun

From the project README.

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