Potencia tu agente
El comando propio del publicador. No es un endpoint de PlugMCP.
Host a copy with our keys and logs.
uvx repo-memory-mcp-ai==0.2.0Claude Code no tiene enlace de instalación para un comando local. Copia el comando de abajo y ejecútalo en una terminal.
claude mcp add io.github.umarjaum-repo-memory-mcp -- uvx repo-memory-mcp-ai==0.2.0Ejecútalo en una terminal.
{ "mcpServers": { "io.github.umarjaum-repo-memory-mcp": { "command": "uvx", "args": [ "repo-memory-mcp-ai==0.2.0" ] } }}claude_desktop_config.json
¿URL remota o un comando en tu máquina? En qué se diferencian las dos instalaciones
Ejecutar en PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
AbrirDel publicador
mcp-name: io.github.Umarjaum/repo-memory-mcp
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.
Persistent local memory for MCP-compatible AI coding agents, stored per repository in SQLite.
Teach your coding agent once. Let your repository remember.
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.
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.
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.
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
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.
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
server.jsonPUBLISHING.mdMuhammad Umar Jabbar — Khanewal, Punjab, Pakistan. Personal website: umarjaum.netlify.app.
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.
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.
MIT. See LICENSE.
Persistent local memory for MCP-compatible AI coding agents, stored per repository in SQLite.
{ "mcpServers": { "io.github.umarjaum-repo-memory-mcp": { "command": "uvx", "args": [ "repo-memory-mcp-ai==0.2.0" ] } }}Herramientas
Aún no hay lista de herramientas. Los servidores remotos la reciben tras la primera comprobación de estado.
Preguntas frecuentes
Copia en esta página la configuración stdio para Cursor, Claude Desktop, Claude Code o VS Code. Usa la instalación del publicador, no un proxy de PlugMCP.
Este registro no indica ninguna variable de entorno obligatoria.
Repo Memory se ejecuta localmente por stdio.
Última comprobación: 2026-11-09. Persistent local memory for MCP-compatible AI coding agents, stored per repository in SQLite.