Repo Memory MCP Server

@yubinkim444

Basic information

About Repo Memory MCP Server

A different approach from typical persistent-memory MCPs. Instead of a local

#memory #knowledge-base #ai-agents #claude-code #cursor #cline

Repo Memory MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "repo-memory": {
      "command": "uvx",
      "args": [
        "repo-memory-mcp",
        "--repo",
        "/path/to/your/repo"
      ]
    }
  }
}

Repo Memory MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

What is Repo Memory?

Repo Memory is a shared, git-tracked working memory for AI agents that share a codebase. It stores structured facts, decisions, and gotchas in plain files under a .ai-memory/ directory, synchronized via git. It is designed for developers using AI coding assistants such as Claude, Cursor, or Cline.

How to use Repo Memory?

Install with pip install repo-memory or run via uvx. Initialize memory in your repo with repo-memory init, then use commands such as repo-memory add-fact, add-decision, or add-gotcha. For AI agent integration, add an MCP server entry in your client config pointing to repo-memory-mcp with the repo path.

Key features of Repo Memory

Git-tracked, append-only facts with evidence
No database, no SaaS, no daemon; works offline
Structured facts, decisions, and gotchas as plain files
Per-repo scope – memory stays local to the codebase
MCP server with 5 tools for read/write access
Automatic discovery hint in CLAUDE.md or AGENTS.md

Use cases of Repo Memory

Sharing known facts across AI agent sessions on the same repository
Recording architecture decisions and non-obvious conventions
Avoiding repeated grep or re-discovery of codebase structure
Multi-user or multi-tool collaboration on a shared codebase

Frequently asked questions

How is Repo Memory different from `CLAUDE.md` or `.cursorrules`?

`CLAUDE.md` is for rules that a human writes; Repo Memory is for facts that an agent verifies – locations, behaviors, and decisions discovered during work.

What are the runtime requirements?

Python 3.10 or later. The only dependency is the official MCP SDK.

Where does Repo Memory store data?

In a `.ai-memory/` directory inside the repository. The data is plain files (facts.jsonl, decisions/, gotchas.md) and is synchronized via normal git commits and pushes.

What transport does the MCP server use?

It uses stdio transport. The server is launched via `repo-memory-mcp` with a `--repo` argument or the `REPO_MEMORY_ROOT` environment variable.

How do multiple agents avoid conflicting facts?

Facts are append-only; stale entries remain. Readers check `verified_at` to re-verify if needed. Suspicious facts are filtered through normal code review via git.

Comments

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