mnemory

Give your AI agents persistent memory. mnemory is a self-hosted MCP server that adds personalization and long-term memory to any AI assistant — Claude Code, ChatGPT, Open WebUI, Cursor, or any MCP-compatible client.

Plug and play. Connect mnemory and your agent immediately starts remembering user preferences, facts, decisions, and context across conversations. No system prompt changes needed.

Self-hosted and secure. Your data stays on your infrastructure. No cloud dependencies, no third-party access to your memories.

Intelligent. Uses a unified LLM pipeline for fact extraction, deduplication, and contradiction resolution in a single call. Memories are semantically searchable, automatically categorized, and expire naturally when no longer relevant.

Features

• Zero config — uvx mnemory, connect your MCP client, done. Works out of the box with any OpenAI-compatible API.
• Intelligent extraction — A single LLM call extracts facts, classifies metadata, and deduplicates against existing memories.
• Contradiction resolution — "I drive a Skoda" + later "I bought a Tesla" = automatic update, not a duplicate.
• Two-tier memory — Fast searchable summaries in a vector store + detailed artifact storage (reports, code, research) retrieved on demand.
• AI-powered search — Multi-query semantic search with temporal awareness. Ask "What did I decide last week about the database?" and it finds the right memories.
• Memory health checks — Built-in three-phase consistency checker (fsck) detects duplicates, contradictions, quality issues, and prompt injection. Run manually or on a schedule with auto-fix.
• 10+ client support — Claude Code, ChatGPT, Open WebUI, OpenClaw, Cursor, Windsurf, Cline, OpenCode, and more. Native plugins available for automatic recall/remember.
• Built-in management UI — Dashboard, semantic search, memory browser with full CRUD, relationship graph visualization, and health check interface. No extra tools needed.
• Production ready — Qdrant for vectors, S3/MinIO for artifacts, API key or Cognis JWT authentication, per-user isolation, Kubernetes-friendly stateless HTTP.
• Secure by default — API key or Cognis JWT authentication with session-level identity binding, per-user memory isolation, anti-injection safeguards in extraction prompts.
• REST API + MCP — Dual interface with the same backend. 16 MCP tools + full REST API with OpenAPI spec. Build plugins, integrations, or use directly.
• Prometheus monitoring — Built-in /metrics endpoint with operation counters and memory gauges. Pre-built Grafana dashboard included.

Quick Start

mnemory needs an OpenAI-compatible API key for LLM and embeddings. It picks up OPENAI_API_KEY from your environment automatically.

uvx mnemory

That's it. mnemory starts on http://localhost:8050/mcp, stores data in ~/.mnemory/.

Now connect your client — for Claude Code, add to your MCP config:

{
  "mcpServers": {
    "mnemory": {
      "type": "streamable-http",
      "url": "http://localhost:8050/mcp",
      "headers": {
        "X-Agent-Id": "claude-code"
      }
    }
  }
}

Start a new conversation. Memory works automatically.

Also available via Docker, pip, or production setup with Qdrant + S3. See the full quick start guide for more clients and options.

Screenshots

Dashboard with memory breakdowns by type, category, and role

Semantic search and AI-powered find with filters

Memory relationship graph visualization

See all screenshots and UI features including memory browser, health checks, and artifact management.

Supported Clients

mnemory works with any MCP-compatible client. Some clients also have dedicated plugins for automatic recall/remember.

MCP = works via Model Context Protocol (LLM-driven tool calls). Plugin = dedicated integration with automatic recall/remember (no LLM tool-calling needed).

How It Works

Storing: You share information naturally. mnemory extracts individual facts, classifies them (type, category, importance), checks for duplicates and contradictions against existing memories, and stores them as searchable vectors — all in a single LLM call.

Searching: Ask a question and mnemory generates multiple search queries covering different angles and associations, runs them in parallel, and reranks results by relevance. Temporal-aware — "what did I decide last week?" just works.

Recalling: At conversation start, your agent loads pinned memories (core facts, preferences, identity) plus recent context. During conversation, relevant memories are found automatically based on what you're discussing.

Maintaining: Memories have configurable TTL — context expires in 7 days, episodic memories in 90. Frequently accessed memories stay alive (reinforcement). The built-in health checker detects and fixes duplicates, contradictions, and quality issues.

Learn more in the architecture docs.

Benchmark

Evaluated on the LoCoMo benchmark — 10 multi-session dialogues with 1540 QA questions across 4 categories:

Configuration: gpt-5-mini for extraction, text-embedding-3-small for vectors. gpt-oss-120b via Groq is a budget alternative at ~5x lower cost with comparable quality. See configuration docs for model options and benchmarks/ for reproduction.

Documentation

License

Apache 2.0