
repo-memory-mcp
io.github.UmarjaumPublished by GitHub account Umarjaumv0.2.0Updated Oct 24, 2026
Persistent local memory for MCP-compatible AI coding agents, stored per repository in SQLite.
The publisher's own server: Memory.fit
Overview
A local MCP server that gives coding agents persistent, per-repository memory in SQLite, so corrections, rules and decisions survive sessions.
- What you can do
- Load startup context and search active memories with local scoring, filtered by type or status, and list project rules or memories by type or status, and store new lessons, durable rules and architectural decisions, and update existing memories in place or archive obsolete ones, and export and import validated memory between safe copies of the same repository.
- When to use it
- Useful when a coding agent keeps repeating the same mistakes across sessions and you want repository-specific conventions, constraints and decisions remembered locally, without cloud services or embeddings.
- Requirements
- Runs locally over stdio as a Python package (PyPI distribution repo-memory-mcp-ai), installed with pip or run via uvx; no API key, cloud account or remote database is needed. Memory is stored in .repo-memory/memory.db inside the detected repository.
Installation
In SourceWeft
- Open repo-memory-mcp in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
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.
Start the MCP server directly:
Or run it without a permanent installation:
The server is local and communicates over stdio. It does not need an API key.
Configure an MCP client
The common stdio configuration is:
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.
Recommended agent workflow
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:
Available MCP tools
CLI
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
Discovery and publishing
- Project site: repo-memory-mcp.negoshades.workers.dev
- Documentation: quickstart and tools
- Client guides: Claude, Cursor, VS Code, Cline, and Windsurf
- Extensions: VS Code and Bing-compatible browser companion
- Contributions: open-source contribution guide
- Source: GitHub
- Package: PyPI
- Registry metadata:
server.json - Publishing guide:
PUBLISHING.md
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/vscodeprovides local setup commands for VS Code.extensions/bingprovides 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.
Source: README.md at commit 4e3e3d5
Tools
0Version history
1- v0.2.0LatestSep 25, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts
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