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

VerifiedSTDIODesktop onlyKnowledge & MemoryDeveloper Tools

Overview

AI-generated 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.
Before you install
The agent decides when to store or recall; nothing is captured automatically. Likely secrets are rejected by default, but do not submit passwords, tokens, private keys or credentials to memory tools. Storing, updating and archiving memories write to the local database, and import overwrites or merges memory, so keep backups.

Installation

In SourceWeft

  1. Open repo-memory-mcp in the dashboard and add it to a workspace.
  2. 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.

[Tests] [PyPI] [License]

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.

bash
pip install repo-memory-mcp-airepo-memory doctor

Start the MCP server directly:

bash
repo-memory-mcp

Or run it without a permanent installation:

bash
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:

json
{  "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.

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:

text
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

bash
repo-memory initrepo-memory listrepo-memory rulesrepo-memory search "database migrations"repo-memory export ./memory-backup.jsonrepo-memory import ./memory-backup.jsonrepo-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

bash
git clone https://github.com/Umarjaum/repo-memory-mcp.gitcd repo-memory-mcppython -m venv .venv. .venv/bin/activatepip 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.

Source: README.md at commit 4e3e3d5

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  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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