repo-memory-mcp server

repo-memory-mcp

mcp-name: io.github.Umarjaum/repo-memory-mcp

Persistent memory for AI coding agents

AI coding agents are powerful, but they forget repository-specific corrections, conventions, constraints, and architectural decisions. repo-memory-mcp gives MCP-compatible coding agents a persistent, local, repository-aware memory layer.

Teach your coding agent once. Let your repository remember.

What it does

MCP hosts decide when tools are called; MCP does not automatically intercept every conversation. This project exposes explicit tools that let an agent load important context at session startup, search relevant memories, store corrections and decisions, update stale guidance, and archive obsolete memories.

The memory database lives in .repo-memory/memory.db inside the detected repository. It uses SQLite, deterministic local ranking, and no cloud service.

Why developers use it

• Persistent project memory: corrections, rules, conventions, constraints, decisions, and bug-fix lessons survive sessions.
• Repository-aware isolation: unrelated repositories do not share memory.
• Local-first privacy: no API keys, remote database, external embeddings, telemetry, or background upload.
• Explicit MCP workflow: the agent chooses when to remember and recall; there is no claim of automatic conversation interception.
• Fast and inspectable: SQLite storage, JSON export, CLI diagnostics, and human-readable records.
• Security-minded: likely API keys, bearer tokens, passwords, cloud credentials, and private keys are rejected by default.

Install and use in two minutes

The public PyPI distribution is named repo-memory-mcp-ai because the shorter repo-memory-mcp name is already owned by another project. The MCP server and GitHub project identity remain repo-memory-mcp.

pip install repo-memory-mcp-ai
repo-memory doctor

Start the MCP server directly:

repo-memory-mcp

Or run it without a permanent installation:

uvx --from repo-memory-mcp-ai repo-memory-mcp

The server is local and communicates over stdio. It does not need an API key.

Configure an MCP client

The common stdio configuration is:

{
  "mcpServers": {
    "repo-memory": {
      "command": "uvx",
      "args": ["--from", "repo-memory-mcp-ai", "repo-memory-mcp"]
    }
  }
}

Use the client-specific configuration location and format documented by your MCP host. Ready-to-copy examples are in examples/:

• claude-desktop.json
• cursor.json
• workflow.md

The project is vendor-neutral and is intended for Claude Code, Cursor, Cline, Windsurf, and other MCP-compatible coding agents. Compatibility depends on the host’s MCP support and configuration format.

Recommended agent workflow

At the beginning of a coding session, call getstartupcontext(). When the developer corrects a repeated mistake, call remembercorrection(topic, lesson). When a relevant question arises, call recallmemory(query). When guidance becomes stale, call updatememory() or archive it with forgetmemory().

Example instruction to an agent:

At the start of this session, call get_startup_context. If I correct a repeated mistake or establish a durable project rule, store it with the appropriate repo-memory tool. Do not store credentials or transient chat context.

Available MCP tools

Shortened. The full README is on GitHub.