mnemory
Memory server
Created by Filip Pytloun
Published in fpytloun/mnemory. Publisher page
283 GitHub stars
What it is
A self-hosted, secure, feature-rich memory system for AI agents and assistants. Provides intelligent fact extraction and deduplication, with an artifact store for detailed content.
AutoGineer overview, from the brain’s own files
mnemory is a self-hosted memory system designed to provide AI agents and assistants with persistent, long-term memory. It functions as an MCP server, allowing it to integrate seamlessly with various AI clients like Claude Code, Cursor, and Open WebUI. The system uses a two-tier architecture: a vector store for fast, semantically searchable summaries and an artifact store for detailed content. It features an intelligent pipeline that performs fact extraction, deduplication, and contradiction resolution in a single LLM call. mnemory also includes a built-in management UI for monitoring and memory maintenance, along with health checks to ensure data quality. It is built for production environments with support for Qdrant, S3/MinIO, and Prometheus monitoring.
How it works
Memory model: Vector store
AutoGineer overview, from the brain’s own files
mnemory operates by intercepting information shared during agent conversations. It uses an LLM pipeline to extract facts, classify them by importance and category, and resolve contradictions against existing data. These facts are stored in a vector database for semantic search, while detailed artifacts are kept in a separate store. During interaction, the system retrieves relevant memories based on the current context and user queries. It also manages memory lifecycles using configurable time-to-live (TTL) settings and provides a health-checking mechanism to detect and fix data inconsistencies.
| Folder | Role | Files |
|---|---|---|
| .github | agent-config | 4 |
| docs | docs | 31 |
| tests | tests | 42 |
| benchmarks | folder | 16 |
| files | folder | 8 |
| integrations | folder | 40 |
| mnemory | folder | 59 |
The agent reads
- get_core_memories evidence: AGENTS.md
The agent writes
- docs/mcp-tools.md evidence: AGENTS.md
- pyproject.toml evidence: AGENTS.md
- docs/configuration.md evidence: AGENTS.md
- add_memory evidence: AGENTS.md
Requirements
Runtimes: Python >=3.11
Databases: Qdrant, Redis
API keys (by name): OPENAI_API_KEY
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)
Set it up
- Not scanned by AutoGineerIts dependencies are installed by pip or uv when you run it and were not scanned by AutoGineer (9 declared). https://pypi.org/project/mnemory/
Created by Filip Pytloun. Published in fpytloun/mnemory. Licence: Apache License 2.0.
Usage
AutoGineer overview, from the brain’s own files
- Connects to AI agents via the Model Context Protocol (MCP).
- Provides a REST API for custom integrations and plugin development.
- Includes a web-based dashboard for memory management and visualization.
- Supports automatic recall and memory persistence across sessions.
- Offers built-in health checks and maintenance tools for memory quality.
- Integrates with monitoring tools like Prometheus and Grafana.
- “What did I decide last week about the database?”
- The agent performs a temporal-aware semantic search across stored memories to retrieve the specific decision made.
- “I drive a Skoda.”
- The system extracts this fact and stores it; if a later statement says 'I bought a Tesla', it updates the record instead of creating a duplicate.
Documentation by Filip Pytloun
<p align="center"> <img src="files/banner.png" alt="mnemory banner" /> </p>
mnemory
Looking for a complete Agent OS? Meet Cognis — a cloud-native, self-hosted workspace where AI agents do real work. Bring together conversations, tools, workflows, and remote executors, with long-term memory powered by mnemory and guardrails provided by Intaris.
Deletion is intentionally best effort, not a verified-erasure or compliance guarantee. Normal deletion retracts and preserves history; explicit privacy erasure attempts physical cleanup. See deletion limits.
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
/metricsendpoint 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 mnemoryIf uvx reports No module named 'mcp.server.fastmcp', update uv. Older uv
versions on Alpine can ignore mnemory's MCP v1 dependency constraint. Until
you update uv, run:
uvx --with "mcp<2" mnemoryThat'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
<p align="center"> <img src="files/screenshots/ui-dashboard.jpg" alt="Dashboard — memory totals, breakdowns, and operation counts" width="800" /> <br><em>Dashboard with memory breakdowns by type, category, and role</em> </p>
<p align="center"> <img src="files/screenshots/ui-search.jpg" alt="Search — semantic and AI-powered search with filters" width="800" /> <br><em>Semantic search and AI-powered find with filters</em> </p>
<p align="center"> <img src="files/screenshots/ui-graph.jpg" alt="Graph — D3.js force-directed memory relationship visualization" width="800" /> <br><em>Memory relationship graph visualization</em> </p>
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
LICENSE
Shown as plain text.
Apache License
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Advantages
- Intelligent extraction and contradiction resolution ensure memory accuracy and prevent duplicate entries. evidence: README.md
- Self-hosted architecture ensures data privacy with no third-party access to stored memories. evidence: README.md
- Supports a wide range of clients through the Model Context Protocol and dedicated plugins. evidence: README.md
Limitations
- Deletion is best-effort and does not guarantee verified erasure or compliance. evidence: README.md
- Most MCP clients require a specific system prompt instruction to initialize memory correctly. evidence: docs/README.md
Security
Risk level: Low risk
This brain runs
Agent instructions that run or fetch something (8)
AGENTS.md:docker buildx build --platform linux/amd64,linux/arm64 -t ghcr.io/fpytloun/mnemory:latest .AGENTS.md:docker buildx build --platform linux/amd64,linux/arm64 --push -t ghcr.io/fpytloun/mnemory:latest .AGENTS.md:# Unit tests (fast, no API key needed, default pytest run)AGENTS.md:They are **excluded from the default `pytest` run** via `addopts = "-m 'not e2e'"` in `pyproject.toml`.AGENTS.md:**When to run e2e tests:**AGENTS.md:Migrations run automatically on startup before the server accepts requests.AGENTS.md:Each migration has a unique ID (e.g., `001_add_sparse_vectors`), a `description`, and a `run()` methodAGENTS.md:Migrations must be **idempotent** — safe to re-run if interrupted mid-way or if multiple instances start simultaneously
MCP servers (1)
README.md(mnemory):http://localhost:8050/mcp· uses the network
- Low risk: network-instructions (AGENTS.md:88)
Attribution and licence
- Created by
- Filip Pytloun
- Declared authors
- Filip Pytloun (declared in pyproject.toml)
- Publisher
- Filip Pytloun (Community)
- Source
- fpytloun/mnemory · commit 2d83c47
- Licence
- Apache License 2.0 (files served here)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Filip Pytloun.
Install from source
196 of its files are not served here, so there is no ZIP. fpytloun/mnemory at 2d83c47