repo-memory-mcp

Official registry Developer tools

Persistent local memory for MCP-compatible AI coding agents, stored per repository in SQLite.

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

PYPI package repo-memory-mcp-ai
Claude Code
claude mcp add repo-memory-mcp -- uvx repo-memory-mcp-ai==0.2.0
JSON configuration (Claude Desktop, Cursor, VS Code)
{
    "mcpServers": {
        "repo-memory-mcp": {
            "command": "uvx",
            "args": [
                "repo-memory-mcp-ai==0.2.0"
            ]
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. repo-memory-mcp
  2. Persistent memory for AI coding agents
  3. What it does
  4. Why developers use it
  5. Install and use in two minutes
  6. Configure an MCP client
  7. Recommended agent workflow
  8. Available MCP tools
  9. CLI
  10. Privacy and security boundaries
  11. Development
  12. Discovery and publishing
  13. Developer
  14. Roadmap
  15. Companion extensions
  16. License

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/:

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.

At the beginning of a coding session, call get_startup_context(). When the developer corrects a repeated mistake, call remember_correction(topic, lesson). When a relevant question arises, call recall_memory(query). When guidance becomes stale, call update_memory() or archive it with forget_memory().

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

ToolUse it for
get_startup_contextLoad concise, high-value repository guidance at session start
remember_correctionStore a lesson learned from a correction or bug fix
remember_ruleStore a durable project rule or convention
remember_decisionStore an architectural decision and rationale
recall_memorySearch active memory with deterministic local scoring
list_project_rulesList active project rules
list_memoriesFilter memories by type or status
update_memoryCorrect an existing memory without creating a replacement
forget_memoryArchive obsolete memory for audit-friendly retention
export_memory / import_memoryMove validated memory between safe copies of the same repository

CLI

repo-memory init
repo-memory list
repo-memory rules
repo-memory search "database migrations"
repo-memory export ./memory-backup.json
repo-memory import ./memory-backup.json
repo-memory doctor

Privacy and security boundaries

Memories stay on the machine running the server. This implementation does not require API keys, cloud accounts, remote databases, external embeddings, or telemetry. It only inspects text explicitly submitted to memory tools. Your operating system, package manager, backups, and MCP host may have separate behavior.

Likely secrets are rejected by default. Do not submit passwords, tokens, private keys, or credentials to memory tools. The .repo-memory/ directory is ignored by Git so private memory is not accidentally committed.

Development

git clone https://github.com/Umarjaum/repo-memory-mcp.git
cd repo-memory-mcp
python -m venv .venv
. .venv/bin/activate
pip install -e '.[dev]'
pytest

Discovery and publishing

Developer

Muhammad Umar Jabbar — Khanewal, Punjab, Pakistan. Personal website: umarjaum.netlify.app.

Roadmap

Future work may improve ranking, add confidence feedback, provide optional local embeddings, generate project context, add a memory inspection UI, and publish the companion extensions to official marketplaces. Team/shared memory remains explicitly opt-in.

Companion extensions

  • extensions/vscode provides local setup commands for VS Code.
  • extensions/bing provides a privacy-safe Manifest V3 context-menu helper for turning selected Bing research into a repo-memory prompt.

The browser companion intentionally does not invoke the local stdio MCP process. Direct browser-to-MCP integration would require a separately reviewed native-messaging bridge.

License

MIT. See LICENSE.

Frequently asked questions

What is repo-memory-mcp?

Persistent local memory for MCP-compatible AI coding agents, stored per repository in SQLite.

How do I install repo-memory-mcp?

It runs on your machine. In Claude Code: claude mcp add repo-memory-mcp -- uvx repo-memory-mcp-ai==0.2.0. For other clients, paste the JSON configuration from this page into their MCP settings.

Does repo-memory-mcp need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is repo-memory-mcp open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains repo-memory-mcp?

It is published by Umarjaum. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.