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s60yucca/mnemos

by Various

s60yucca/mnemos — indexed from awesome-mcp-servers-punkpeye

S

MCP

s60yucca/mnemos

Added 1 June 2026

Overview

Mnemos is a Go-based MCP server that provides memory and knowledge graph capabilities for AI agents. It stores and retrieves structured information using a graph database, enabling persistent context across sessions.

Best for

Best for
Developers experimenting with persistent memory for MCP-based AI agents

Use cases

  • Give an AI agent long-term memory across conversations
  • Store and query structured knowledge as a graph
  • Integrate persistent context into MCP-compatible tools

How to use

Install

npx -y @s60yucca/mnemos setup claude

Tools exposed

  • MCP-native
  • Auto-summarization
  • mnemos_store
  • mnemos_search
  • mnemos_context
  • mnemos_get
  • mnemos_update
  • mnemos_delete
  • mnemos_relate
  • mnemos_maintain
  • mnemos_runtime
  • short_term
  • long_term

Tested with

Claude Desktop, Claude Code, Cursor, VS Code, ChatGPT

Notes

Mnemos is a Go-based MCP server that provides memory and knowledge graph capabilities for AI agents. It stores and retrieves structured information using a graph database, enabling persistent context across sessions.

9 stars on GitHub. Last updated 2026-05-19. Licensed MIT.

Use cases

  • Give an AI agent long-term memory across conversations
  • Store and query structured knowledge as a graph
  • Integrate persistent context into MCP-compatible tools

Pros

  • Lightweight Go implementation with minimal dependencies
  • Graph-based storage enables rich relational queries
  • Open source with a permissive license

Cons

  • Very early stage with only 9 GitHub stars and limited community
  • No documentation beyond the repository README
  • Requires manual setup and integration effort

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Lightweight Go implementation with minimal dependencies
  • Graph-based storage enables rich relational queries
  • Open source with a permissive license

Cons

  • Very early stage with only 9 GitHub stars and limited community
  • No documentation beyond the repository README
  • Requires manual setup and integration effort
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