repo-memory
A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep
README
<!-- mcp-name: io.github.yubinkim444/repo-memory -->
repo-memory
Shared, git-tracked working memory for AI agents that share a codebase. What one Claude / Cursor / Cline learns about your repo, the next one picks up automatically. No database. No SaaS. Just files in your repo.
The problem
Every AI session that touches your repo starts from zero. It re-greps the same files. It re-discovers the same conventions. It re-asks the same questions you already answered three sessions ago. Multi-user / multi-tool makes it worse: your teammate's Cursor and your Claude Code learn the same codebase independently.
There is CLAUDE.md / .cursorrules for rules the human writes. But
there is nothing for facts an agent verified — "the auth middleware
lives at src/auth/middleware.py:42", "PR #387 chose httpx over requests
because of HTTP/2", "don't run migrations during peak hours".
repo-memory is that nothing. A .ai-memory/ directory you commit to
your repo. Every AI tool reads from it, writes to it. Git is the database.
Layout
your-repo/
├── .ai-memory/
│ ├── README.md # explains the convention
│ ├── facts.jsonl # append-only structured facts with evidence
│ ├── decisions/ # one markdown per non-trivial decision
│ │ └── 2026-05-19-pick-httpx.md
│ └── gotchas.md # one-line "watch out for X" notes
├── src/
└── README.md
Pure files. Sync over git. No daemon, no DB, no auth. Works offline.
Install
pip install repo-memory
# or
uvx repo-memory --help
Python ≥3.10. Depends only on the official mcp SDK (for the MCP server).
Quick start
cd your-repo
repo-memory init # creates .ai-memory/ + adds CLAUDE.md hint
repo-memory add-fact "Auth middleware lives in src/auth/middleware.py" \
--file src/auth/middleware.py --lines 42-180 --tool grep
repo-memory add-decision "Use httpx not requests" \
--body "HTTP/2 + async support; we already use anyio."
repo-memory add-gotcha "Don't run migrations during 9-11am UTC peak"
repo-memory show # render everything as Markdown
repo-memory list-facts --tag auth
Then commit .ai-memory/ like any other file:
git add .ai-memory/
git commit -m "memory: record auth middleware location + httpx decision"
git push
Anyone else (human or AI) who pulls the repo now sees those facts/decisions immediately.
LLM & AI Agent Readable Specification
For agents discovering this repo through search or MCP indexing.
Identity
- name:
repo-memory - purpose: read/write a structured, git-tracked memory store about the current codebase so multiple AI agents (and humans) don't re-learn it.
- transport: CLI (
repo-memory) and MCP stdio (repo-memory-mcp). - storage: plain files under
.ai-memory/in the repo. Sync = git.
When to call which tool
| Tool | When |
|---|---|
get_repo_memory |
At the start of any task on this repo. |
add_fact |
After you verify a non-obvious fact (location, behavior, convention). Include evidence so the next agent can re-verify cheaply. |
add_decision |
After a non-trivial choice (architecture, library, trade-off). Body should explain why, not just what. |
add_gotcha |
After a surprise that wasted your time. |
list_facts |
When you want only facts in a specific area (tag, source_file). |
Recommended agent workflow
1. agent.call("get_repo_memory") -> absorb prior context
2. ...do task, run tools, verify things...
3. agent.call("add_fact", claim, evidence) -> for each new fact
4. agent.call("add_decision", title, body) -> if a choice was made
5. session ends, human commits .ai-memory/ -> shared via git
MCP server install
Add to your client config (Claude Desktop / Cursor / Cline):
{
"mcpServers": {
"repo-memory": {
"command": "uvx",
"args": ["repo-memory-mcp", "--repo", "/abs/path/to/the/repo"]
}
}
}
Or set REPO_MEMORY_ROOT env var instead of --repo.
Exposes 5 tools: get_repo_memory, add_fact, list_facts,
add_decision, add_gotcha.
Why git, not a database
- Zero infra. No service to host, no account to create, no API key to rotate.
- Already authoritative. Git history is the single source of truth.
git blametells you which agent added which fact and when. - Works offline. Plane, train, conference WiFi — all fine.
- PR review. Suspicious or wrong facts get filtered through normal code review.
- Per-repo scope. A fact about repo A doesn't leak into repo B; the store is local to the repo.
Schema (for tooling authors)
facts.jsonl — one JSON object per line:
{
"id": "abc123def456",
"ts": "2026-05-19T18:00:00Z",
"claim": "Auth middleware lives in src/auth/middleware.py",
"evidence": {
"file": "src/auth/middleware.py",
"lines": "42-180",
"tool": "grep",
"command": "rg 'def authenticate' src/",
"verified_at": "2026-05-19T18:00:00Z"
},
"tags": ["auth"],
"added_by": "claude-opus-4.7"
}
Append-only. Stale entries stay. Readers consult verified_at and
re-verify if they want.
Automatic discovery hint
repo-memory init also appends a short discoverability section to your
repo's CLAUDE.md (or AGENTS.md if you already have one) telling any
AI agent that enters the repo to check .ai-memory/ first and to record
new findings back into it. Idempotent — re-running won't duplicate.
Opt out with --no-claude-md.
The appended block is delimited by <!-- BEGIN: repo-memory --> and
<!-- END: repo-memory -->, so you can hand-edit other parts of your
CLAUDE.md freely.
CLI reference
| Command | Effect |
|---|---|
repo-memory init [--no-claude-md] |
Create .ai-memory/ skeleton + (default) update CLAUDE.md/AGENTS.md. |
repo-memory show [--limit N] |
Print everything as one Markdown doc. |
repo-memory add-fact "<claim>" [--file F --lines L --tool T --command C --tag T --by AGENT] |
Append a fact. |
repo-memory list-facts [--tag T] [--source-file F] [--since ISO] [--limit N] [--json] |
List/filter facts. |
repo-memory add-decision "<title>" [--body MD] |
Write a decision file. |
repo-memory list-decisions |
List decision file paths. |
repo-memory add-gotcha "<note>" |
Append a one-line gotcha. |
All commands take --root PATH if your CWD isn't the repo root.
License
MIT © yubinkim444
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.