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yubinkim444/repo-memory

by Various

Shared, git-tracked memory for AI agents on the same codebase. What one Claude/Cursor/Cline learns, the next one picks up. No DB, no SaaS — just files in your repo. CLI + MCP serve

MCP

yubinkim444/repo-memory

Added 16 Sept 2026

#ai-agents #claude #cline #cursor #developer-tools #knowledge-base #llm #mcp

Overview

Shared, git-tracked memory for AI agents working on the same codebase. Knowledge learned by one Claude, Cursor, or Cline instance is picked up by the next. Uses plain files in the repository instead of a database or SaaS, with a CLI and MCP server.

Best for

Best for
Developers using multiple AI agents on a shared codebase who want lightweight, git-versioned memory.

Use cases

  • Persisting agent context across sessions
  • Sharing learned conventions among team agents
  • Tracking agent memory changes via git history

How to use

Install

uvx repo-memory-mcp --help

Tools exposed

  • get_repo_memory
  • add_fact
  • add_decision
  • add_gotcha
  • list_facts

Tested with

Claude Desktop, Claude Code, Cursor, Cline

Notes

Shared, git-tracked memory for AI agents working on the same codebase. Knowledge learned by one Claude, Cursor, or Cline instance is picked up by the next. Uses plain files in the repository instead of a database or SaaS, with a CLI and MCP server.

1 stars on GitHub. Last updated 2026-08-26. Licensed MIT.

Use cases

  • Persisting agent context across sessions
  • Sharing learned conventions among team agents
  • Tracking agent memory changes via git history

Pros

  • No external infrastructure required
  • Leverages existing git workflow for versioning
  • Works across multiple agent tools

Cons

  • Very early stage with only 1 star
  • Requires committing memory files to the repo
  • Limited to Python-based tooling

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

Pros

  • No external infrastructure required
  • Leverages existing git workflow for versioning
  • Works across multiple agent tools

Cons

  • Very early stage with only 1 star
  • Requires committing memory files to the repo
  • Limited to Python-based tooling
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