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mnemoverse/mcp-memory-server

by Various

Hosted memory for AI agents that learns and forgets — feedback reranks what helps, recall fades by recency. One key across Claude, Cursor, VS Code & ChatGPT. Official MCP Registry.

M

MCP

mnemoverse/mcp-memory-server

Added 18 June 2026

#ai-memory #anthropic #chatgpt #claude #cross-tool #cursor #forgetting #hosted-memory

Overview

A hosted memory server for AI agents that learns from feedback and fades recall by recency. It provides a shared memory layer accessible across Claude, Cursor, VS Code, and ChatGPT via a single API key. Listed on the official MCP Registry.

Best for

Best for
Developers building multi-tool AI workflows that need persistent, feedback-aware memory

Use cases

  • Persist conversation context across different AI tools for continuity
  • Rerank memory importance based on user feedback
  • Manage agent recall in multi-session workflows

Notes

A hosted memory server for AI agents that learns from feedback and fades recall by recency. It provides a shared memory layer accessible across Claude, Cursor, VS Code, and ChatGPT via a single API key. Listed on the official MCP Registry.

1 stars on GitHub. Last updated 2026-06-18. Licensed MIT.

Use cases

  • Persist conversation context across different AI tools for continuity
  • Rerank memory importance based on user feedback
  • Manage agent recall in multi-session workflows

Pros

  • Cross-platform memory sharing with one key reduces setup overhead
  • Feedback-driven reranking improves relevance over static storage
  • Official MCP listing suggests community vetting

Cons

  • Early-stage project with only 1 star on GitHub
  • Dependence on hosted service which may have uptime or latency
  • Unclear documentation for use beyond basic integration

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

Pros

  • Cross-platform memory sharing with one key reduces setup overhead
  • Feedback-driven reranking improves relevance over static storage
  • Official MCP listing suggests community vetting

Cons

  • Early-stage project with only 1 star on GitHub
  • Dependence on hosted service which may have uptime or latency
  • Unclear documentation for use beyond basic integration