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

<img src="files/screenshots/ui-dashboard.jpg" alt="Dashboard — memory totals, breakdowns, and operation counts" width="800" /> Dashboard with memory breakdowns by type, category, and role <img src="files/screenshots/ui-search.jpg" alt="Search — semantic and AI-powered search with filters" width="800" /> Semantic search and AI-powered find with filters <img src="files/screenshots/ui-graph.jpg" alt="Graph — D3.js force-directed memory relationship visualization" width="800" /> 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.

| Client | MCP | Plugin | Setup Guide | |---|---|---|---| | Claude Code | Yes | Yes (hooks) | Guide | | ChatGPT | Yes (MCP connector) | -- | Guide | | Claude Desktop | Yes | -- | Guide | | Hermes Agent | Yes | Yes (plugin) | Guide | | Open WebUI | Yes | Yes (filter) | Guide | | OpenCode | Yes | Yes (plugin) | Guide | | OpenClaw | Yes | Yes (plugin) | Guide | | Cursor | Yes | -- | Guide | | Windsurf | Yes | -- | Guide | | Cline | Yes | -- | Guide | | Continue.dev | Yes | -- | Guide | | Codex CLI | Yes | -- | Guide |

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:

| System | single_hop | multi_hop | temporal | open_domain | Overall | |---|---|---|---|---|---| | mnemory | 63.1 | 53.1 | 74.8 | 78.2 | 73.2 | | mnemory (gpt-oss-120b) | 66.3 | 59.4 | 68.5 | 73.8 | 70.5 | | Memobase | 70.9 | 52.1 | 85.0 | 77.2 | 75.8 | | Mem0-Graph | 65.7 | 47.2 | 58.1 | 75.7 | 68.4 | | Mem0 | 67.1 | 51.2 | 55.5 | 72.9 | 66.9 | | Zep | 61.7 | 41.4 | 49.3 | 76.6 | 66.0 | | LangMem | 62.2 | 47.9 | 23.4 | 71.1 | 58.1 |

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

| Document | Description | |---|---| | Quick Start | Get running in 5 minutes with any client | | Configuration | All environment variables — LLM, storage, server, memory behavior | | Memory Model | Types, categories, importance, TTL, roles, scoping, sub-agents | | MCP Tools | 16 MCP tools — memory CRUD, search, artifacts | | REST API | Full REST API, fsck pipeline, recall/remember endpoints | | Architecture | System diagram, detailed flows for storing/searching/recalling | | Management UI | Screenshots, features, access, UI development | | Monitoring | Prometheus metrics, Grafana dashboard | | Deployment | Production setup, Docker, authentication, Kubernetes | | Development | Building, testing, linting, contributing | | Client Guides | Per-client setup instructions (10 clients) | | System Prompts | Templates for personality agents and custom setups |

License

Apache 2.0

Mnemory

Install / Use

README