repo-graph

Structural graph memory for AI coding assistants. Map your codebase. Navigate by structure. Read only what matters.

repo-graph gives LLMs a map of your codebase — entities, relationships, and flows — so they can navigate to the right files without reading everything first.

Instead of flooding an LLM's context window with your entire codebase (or hoping it guesses right), repo-graph builds a lightweight graph of what exists, how things connect, and where the entry points are. The LLM queries the graph, finds the minimal set of files it needs, and reads only those.

It pays off most where that's hardest to do by hand: large repos, monorepos that span several languages, and multi-service systems where a feature's path crosses files, stacks, and service boundaries. On a small single-language project a model can just read the files — see Where it fits best for the honest sweet spot.

Install in one click:

Or one command in your terminal wires up every agent you have: uvx mcp-repo-graph install (see Install).

Demo

https://github.com/user-attachments/assets/a1e4171b-b225-40d4-9210-39453e14b76a

https://github.com/user-attachments/assets/fc3191e5-fc35-4bd7-8372-72af55995883

Same bug, same model, same prompt — the only difference is whether repo-graph is installed.

The task: fix a reversed comparison operator in a Go + Angular monorepo (566 nodes, 620 edges).

2.5x fewer tokens. ~9x faster. Same correct fix.

How the test was run

Both runs used identical conditions to keep the comparison fair:

• Same model: Claude Opus, 100% (no Haiku routing)

• Same prompt: "Groups that were created recently are showing as closed, and old groups show as open. This is backwards — new groups should be open for members to join. Find and fix the bug."

• Fresh context: each run started from /clear with no prior conversation

• No other tools: CLAUDE.md, plugins, hooks, and all other MCP servers were removed for both runs — the only variable was whether repo-graph was installed

• No hints: the prompt describes the symptom, not the location — Claude has to find group_controller.go:57 on its own

Without repo-graph, Claude greps for keywords, reads files, greps again, reads more files, and eventually narrows down to the bug. With repo-graph, Claude calls trace("groups"), gets back the exact handler function and file, reads it, and fixes it.

Browse pre-generated examples for FastAPI, Gin, Hono, and NestJS — real graph output you can inspect without installing anything.

Related MCP server: Serena

The problem

LLMs working on code waste most of their context on orientation:

• Reading files that turn out to be irrelevant

• Missing connections between components in different languages

• Not knowing where a feature starts or what it touches

• Loading 50 files when 5 would do

This is expensive, slow, and gets worse as codebases grow.

How repo-graph solves it

repo-graph scans your codebase once and builds a graph of:

• Entities: modules, packages, classes, functions, routes, services, components

• Relationships: imports, calls, handles, defines, contains, cross-stack HTTP

• Flows: end-to-end paths from entry point to data layer

Then it exposes 6 MCP tools that let the LLM:

1. Orient — "What languages are in this repo? What are the main features? Where is the graph blind?"

2. Navigate — "Trace the login flow from route to database" / "What's the shortest path between UserService and the payments API?"

3. Scope — "Which nodes matter for this bug?" / "Give me just the files I need for this fix"

4. Assess — "What's the blast radius of changing this function?" / "What here is dead code?"

The LLM gets structural context in a few hundred tokens instead of reading thousands of lines.

Where it fits best

repo-graph earns its keep when a codebase is bigger or more tangled than the model can hold in its head at once. The payoff scales with three things:

• Size — enough files that reading the relevant ones blows the context budget.

• Complexity — rules, indirection, and layers, so "just read it" stops working.

• Cross-boundary reach — the answer spans files, languages, or services that a text search can't link.

Strong fits:

• Monorepos — a frontend calling a backend across a language boundary. repo-graph links the HTTP call to the route it hits and the handler behind it — the one thing grep structurally can't do. Point --repo at the monorepo root and a single graph spans every project. (The demo above is exactly this: Go + Angular in one repo.)

• Multi-service / polyrepo systems — drop the services under one directory and point --repo at it; the graph traces a feature across service boundaries in one call.

• Large single codebases — thousands of files where orientation itself is the cost.

• Unfamiliar or legacy code — where you don't yet know what touches what.

Where it doesn't pull its weight: a small, single-language repo with a clear task. The model can just read the files — grep wins and the graph is overhead. Don't reach for it to shave tokens, either: the MCP layer is a fixed per-turn cost, so on easy tasks it can cost more. The token win shows up only when it heads off a grep-read-grep spiral (like the demo above). What it reliably buys you is correct, complete, cross-boundary answers in a few calls on code too big or too interconnected to fit in context — yours or the model's. (Don't want the MCP layer at all? Skip it and call the engine directly.)

Use it without MCP

The MCP server is the zero-config path, but the graph isn't tied to it. The engine ships as a plain Python wheel — pip install repo-graph-py — so you can build the graph and call the same answer primitives directly, from a script or your own tooling, with none of the per-turn MCP cost:

import repo_graph_py as rg

g = rg.generate(".")                            # or rg.load_from_gmap(rg.default_gmap_dir("."))
print(g.blast_radius("checkout", "both"))       # ranked, located, live-filtered — JSON
print(g.cross_stack_trace("notifications"))     # feature path across the stack, mechanism-labelled
print(g.resolve(open("error.log").read()))      # stacktrace / test / diff → the nodes that matter
print(g.coverage())                             # where extraction is partial (grep those)

Same graph, same answers — just without the tool schemas in your context. It's the same Rust engine (glia) the MCP server wraps; repo-graph-py is its published wheel. Good for CI checks, batch analysis, or wiring the graph into your own agent.

Supported languages

Cross-cutting extractors (work across all languages):

• Data sources — DB/cache/queue/blob/search/email client detection

• CLI entrypoints — Python click, JS commander/yargs, Go cobra, Rust clap

• gRPC — service/method definitions from .proto files

• Queue consumers — Celery, Dramatiq, BullMQ, Sidekiq, Oban, NATS

• Cross-stack HTTP — frontend fetch/axios calls linked to backend routes

Multiple languages can match one repo (e.g., Go backend + Angular frontend + SCSS). Each contributes its nodes and edges into a single unified graph.

Install

One command

uvx mcp-repo-graph install

This detects the AI coding agents you have installed (Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, Codex, Gemini CLI, opencode, Kiro), writes each one's MCP config, and adds a short usage block to its instructions file so the agent reaches for the graph before it greps. Where the agent supports it, it also grants auto-allow so repo-graph tools don't prompt on every call.

It's safe to re-run, and uvx mcp-repo-graph uninstall reverses everything (config, instructions, permissions) while leaving your graph data in place.

uvx mcp-repo-graph install --agents all          # every supported agent, not just detected
uvx mcp-repo-graph install --scope user          # your global config, not this project
uvx mcp-repo-graph install --dry-run             # show what it would write, change nothing
uvx mcp-repo-graph install --yes                 # no prompt (scripts and CI)
uvx mcp-repo-graph install --print-config cursor # print one agent's config, write nothing

Manual, per client

If you'd rather wire it up yourself, the package name is the run command. uvx mcp-repo-graph just works. No prior pip install, nothing to keep on PATH. This is the same command VS Code, Cursor, and the MCP registry use under the hood.

Requirements: Python 3.11+, and uv if you use the uvx path. Prebuilt wheels ship for the Rust engine on Linux (x86_64, aarch64), macOS (Intel + Apple Silicon), and Windows (x86_64) — no Rust toolchain needed.

Claude Code

claude mcp add repo-graph -- uvx mcp-repo-graph --repo .

(--repo . points the graph at the current project; use an absolute path to pin it.)

VS Code

One command — adds the server to your user config:

code --add-mcp '{"name":"repo-graph","command":"uvx","args":["mcp-repo-graph","--repo","${workspaceFolder}"]}'

Or click Install on the MCP gallery entry, or add it to .vscode/mcp.json manually (see below).

Cursor / any MCP client — manual config

Add this to your client's MCP config (.mcp.json, .cursor/mcp.json, .vscode/mcp.json, or ~/.claude.json):

{
  "mcpServers": {
    "repo-graph": {
      "command": "uvx",
      "args": ["mcp-repo-graph", "--repo", "/path/to/your/project"]
    }
  }
}

Prefer a persistent install? pip install mcp-repo-graph (or uv tool install mcp-repo-graph) puts a mcp-repo-graph / repo-graph command on your PATH; then use "command": "mcp-repo-graph" in the config above.

--repo also accepts a git URL. Point it at any public repo without cloning first — it shallow-clones and maps it (requires git):

uvx mcp-repo-graph --repo https://github.com/org/repo

Quick start

1. Initialise the target repo (optional)

uvx --from mcp-repo-graph repo-graph-init --repo /path/to/your/project
# or, if installed:  repo-graph-init --repo /path/to/your/project

This generates the graph, writes .mcp.json and CLAUDE.md instructions, and gets your AI assistant ready to use repo-graph. If you used the one-liners above, you can skip this — the server builds the graph on first connect.

2. Use it

The AI assistant now has access to all 6 tools. Example queries it can answer:

• "What does this codebase do?" → orient tool

• "Trace the checkout flow" → trace tool

• "What would break if I change UserService?" → impact tool

• "Which nodes are relevant to this bug?" / "Here's a stacktrace — where do I look?" → find tool

• "Show me that function's source" → read tool

• "Give me the full graph context cheaply" → orient full=true

• "Rebuild after a big refactor" → refresh tool

3. Freshness (automatic)

The graph stays current on its own. While the server is running it watches the repo and does an incremental rebuild a moment after you save, so a structural question right after an edit reflects the change with no manual refresh. On top of that, the graph refreshes on cold start whenever the source tree changed since the cached .gmap was written, so it's never stale when your assistant connects.

The watcher is on by default. Set REPO_GRAPH_WATCH=0 to disable it (the cold-start refresh still applies). It needs the watchdog package, which ships as a dependency.

Want the cache pre-built and committed so teammates and CI get it too? Add the pre-commit hook automatically:

uvx mcp-repo-graph install --agents none --git-hook

That installs a marker-fenced pre-commit hook that refreshes the graph and stages .ai/repo-graph/ on every commit. uvx mcp-repo-graph uninstall removes it again.

Tip: If you don't want graph data in version control, add .ai/repo-graph/ to .gitignore and skip the hook — the watcher and cold-start refresh keep it fresh locally.

MCP tools reference

repo-graph exposes 6 tools — one natural verb each, backed by a Rust engine primitive.

Most tools also take a budget (max chars) so a result fits a small-model context window.

These 6 collapsed from an earlier 13 once the engine (v0.4.18) grew answer-shaped primitives — blast_radius, cross_stack_trace, resolve, coverage — that return complete, ranked, located, live-filtered results in one call. Fewer tools = less fixed per-turn overhead and less agent confusion.

How it works

mcp-repo-graph is a thin Python MCP server that wraps glia, a Rust engine.

1. Parse — per-language tree-sitter parsers extract raw nodes and unresolved references

2. Extract — cross-cutting extractors layer on HTTP routes, data sources, CLI entrypoints, gRPC services, queue consumers

3. Resolve — graph builder resolves intra-repo references; cross-graph resolvers link stacks (frontend HTTP calls → backend routes, etc.)

4. Store — merged graph lands in .ai/repo-graph/ as a zero-copy .gmap (rkyv + mmap) plus JSON projections for portability

5. Serve — the MCP server loads the graph into memory and exposes the 6 tools

The Rust engine lives in its own glia repo; mcp-repo-graph is the MCP-facing thin wrapper.

Config (optional escape hatch)

If auto-detection misses a weird layout, drop .ai/repo-graph/config.yaml in the target repo:

skip:
  - legacy       # directory basenames excluded from the walk
  - scratch

roots:           # explicit roots heuristics miss — added on top of auto-detection
  - path: apps/weird-layout
    kind: python
  - path: services/custom
    kind: go

kind values: go, rust, python, typescript, react, vue, angular, java, scala, clojure, csharp, ruby, php, swift, c_cpp, dart, elixir, solidity, terraform. config.json works too if you prefer.

Graph data format

Generated files live in .ai/repo-graph/ inside the target repo:

• nodes.json — [{id, type, name, file_path, confidence, ...}, ...]

• edges.json — [{from, to, type}, ...]

• flows/*.yaml — named feature flows with ordered step sequences and kind (http/page/cli/grpc/queue)

• state.md — human-readable snapshot for quick orientation

Common edge types: imports, defines, contains, uses, calls, handles, handled_by, exports, includes, tests, cross-stack HTTP links.

Privacy Policy

repo-graph runs on your machine and is built to keep your code there. Full text: PRIVACY.md.

• Telemetry / analytics: None. No tracking, no update checks, no phone-home.

• Data collection & sharing: None. Your source code and graph data are never sent to repo-graph, its author, or any third party.

• Local processing & storage: Scanning and graph-building happen locally; the graph is cached in your project's .ai/repo-graph/ directory and stays on your device.

• Network access — only two cases, both user-initiated:

  1. Installation — uvx/pip downloads the package and its prebuilt engine wheel from PyPI.

  2. Git-URL targets — if you pass a git URL to --repo, repo-graph runs git clone against the URL you specified; nothing is sent to repo-graph or its author. A local --repo path (the default) makes zero network calls.

• Data retention: The local cache persists until you delete it — fully under your control.

• Contact: GitHub issues

License

MIT

Support

If repo-graph saved you time, consider buying me a coffee.