by fpytloun · MCP Server · ★ 148
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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 — , 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.
| Stars | 148 |
| Forks | 12 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 61.420504439387/100 |
| Last Updated | 2026-06-09 |
| Created | 2026-02-16 |
| Platforms | mcp, python |
| Est. Tokens | ~281k |
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mnemory 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.. It is categorized as a MCP Server with 148 GitHub stars.
mnemory is primarily written in Python. It covers topics such as agent-memory, agents-memory, ai.
You can find installation instructions and usage details in the mnemory GitHub repository at github.com/fpytloun/mnemory. The project has 148 stars and 12 forks, indicating an active community.
The top alternatives to mnemory on Agent Skills Hub include Ori-Mnemos, yantrikdb-server, swarmclaw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.