Repo Graph

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).

Upgrading to 0.5.2

Most people don't have to do anything. uvx and pip install mcp-repo-graph pull in the new engine (glia 0.5.1, pinned exactly). The cached graph in .glia/graph/ is in a new format, so the first start in each repo rebuilds it once. After that it's back to normal.

You only need to act if you also run glia's own CLI hooks (glia install-hooks) in the same repo. Reinstall the CLI from the v0.5.1 tag too. If the CLI and the server are on different glia versions, they rebuild each other's cache on every commit.

The same goes for a team that commits the cache with repo-graph's pre-commit hook: the next commit re-adds it once in the new format, and anyone whose server still runs the glia 0.5.0 engine rebuilds it on every load until they upgrade (uv tool upgrade mcp-repo-graph if you installed it as a uv tool).

What's better: a richer graph (mounted Go routes carry their prefix, frontend endpoints pair through their base URL, many more calls resolve), read shows the host a WebSocket / gRPC / GraphQL client dials, and the server no longer creeps up in memory over long sessions.

⚠️ Upgrading to 0.5.0

The engine package was renamed repo-graph-py to glia-py. If you install with uvx or pip install mcp-repo-graph, you don't have to do anything. The new engine is pulled in for you.

You only need to act if you import the engine directly:

- import repo_graph_py as rg
+ import glia_py as rg

Three other things changed in that same release:

• The answers are Python objects, not JSON strings. find, resolve, blast_radius, cross_stack_trace and governing_docs return a {"results": [...], "absence": {...}} dict. If you were doing json.loads(...) on a result, drop it.
• find_node and find_nodes_by_qname are gone. One find(query, top_k) replaces both.
• The graph cache moved from .ai/repo-graph/ to .glia/graph/. It's rebuilt automatically, so you can delete the old directory. The new one ignores itself and never shows up in .

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.

(Recorded on an earlier engine, which mapped that repo to 566 nodes / 620 edges. The glia 0.5.1 engine extracts far more from the same code, 3,466 nodes and 6,869 edges (measured 2026-10-02), so the graph the model gets today is richer than the one in the video. The token and time figures are from the recorded run and are left as measured.)

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.

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, which a text search can't do because the two sides share no string. Point --repo at the monorepo root and a single graph spans every project. (The demo above is exactly this: Go + Angular in one repo.)

  Other tools link fetch/axios to Express, Fastify and Koa routes. What differs here is reach: the same pairing runs across Go, Python, Java, C#, Rust, PHP, Ruby and the rest, in one graph, and covers gRPC, GraphQL, WebSockets, queues and events on the same footing. That breadth is not benchmarked against them, so take it as a design difference rather than a measured win.

• 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 glia-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 glia_py as rg

g = rg.load_from_gmap(rg.default_gmap_dir("."), ".")   # builds it if there's no cache yet

g.find("checkout")                              # ranked, located nodes for a name
g.blast_radius(["checkout", "Cart.add"], "both")  # many seeds, one walk, one ranking
g.cross_stack_trace("notifications")            # feature path across the stack, mechanism-labelled
g.resolve(open("error.log").read())             # stacktrace / test / diff → the nodes that matter
g.coverage()                                    # where extraction is partial (grep those)

Each of these returns plain Python — a {"results": [...], "absence": {...}} dict for the lookups, a list for coverage(). When results is empty, absence says why, so a script can branch on it instead of guessing.

Same graph, same answers — just without the tool schemas in your context. It's the same Rust engine (glia) the MCP server wraps; glia-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):

• Cross-stack HTTP: frontend fetch/axios calls linked to the backend route they hit, and the handler behind it
• WebSockets: handlers and clients (gorilla, browser, Python, Java, C#), paired by path
• gRPC: services, methods and message types from .proto, plus client stubs and server implementations
• GraphQL: resolvers and the operations that call them
• Queues: consumers and producers (Celery, Dramatiq, BullMQ, Sidekiq, Oban, NATS), with const-resolved topics
• Events: emitters and handlers
• Cron jobs: scheduled entry points
• Page navigation: frontend routes and the links between them (NAVIGATES_TO)
• Data sources: DB / cache / queue / blob / search / email clients, and which code touches which
• Data entities: shared schemas and the code that reads or writes them
• CLI entrypoints: Python click, JS commander/yargs, Go cobra, Rust clap, Java picocli, C# System.CommandLine and Spectre
• Contracts: OpenAPI / AsyncAPI / Pact operations linked to the routes that implement them
• Dependency injection: constructor injection wired to the thing injected
• Config keys: where a key is defined and everywhere that reads it
• Infra: Terraform and Kubernetes resources
• Docs: doc sections linked to the symbols they govern (what read surfaces as governed by)
• Tests: which tests cover which code

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 heals itself on cold start — if the cached .gmap is stale, an old format, or missing, the engine rebuilds it and writes it back, 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 .glia/graph/ on every commit. uvx mcp-repo-graph uninstall removes it again.

Tip: The graph dir ignores itself (.glia/graph/.gitignore), so it never shows up in git status unless the hook stages it with git add -f. Skip the hook and the watcher plus 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 grew answer-shaped primitives — find, 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.

It tells you when it doesn't know

Most tools answer an empty query with nothing, which leaves the assistant to guess whether that means "no such edge exists" or "I couldn't see it". Since 0.5.0 an empty result comes back as a structured absence: the reason, whether that reason is a FACT or a HEURISTIC, and which extractions are partial for the mechanism that came up empty.

  No answer (no_edges, FACT): no carry edge touches `backend::server::server` in this graph;
  `backend::server::server` is a container: structural IMPORTS/CONTAINS/DEFINES are not carry
  edges, so seed a symbol inside it
    looked over: CALLS
    blind spots (grep to confirm):
      ⚠ CALLS (*): calls through reflection, dynamic dispatch, or higher-order indirection are
        not resolved — verify: grep the callee name
    searched 26 nodes

orient surfaces the same blind spots up front, so the model knows when to grep instead of trusting a gap.

What we measured. 14 symbols across FastAPI, Gin, Hono and NestJS, each one a case where the graph honestly has no edge but real callers exist. Same agent, same task, the only difference being whether an empty answer explained itself. The structured absence cut cost about 3x on identical tasks, in 28 of 28 matched pairs, at the same number of turns: told why the answer is empty, the agent stops re-querying. It did not change correctness. Sonnet 5 answered "not safe to delete" in all 56 runs, so if you were hoping this stops an agent deleting live code, we have no evidence of that and some evidence against it on a careful model. Harness and raw results are in bench/absence/. One model, n=14, our own harness: treat it as directional.

0.5.0 also adds whole-diff impact in one call (unresolved names are reported, not dropped), ranked distinct cross-stack paths in trace, and role-aware labels so a declared component or service isn't flattened to "class".

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 .glia/graph/ as a zero-copy sharded .gmap (rkyv + mmap) plus a manifest
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.

Layout detection

repo-graph finds your projects itself — go.mod, package.json, pyproject.toml, Cargo.toml, pom.xml, build.gradle, composer.json, pubspec.yaml, mix.exs, Package.swift, CMakeLists.txt and friends. Point --repo at a monorepo root and every project under it lands in one graph.

It also prunes on its own: node_modules, vendor, .venv, site-packages, build output (dist, target, .next, .nuxt, .angular, coverage) and hashed bundle directories collapse to a single region anchor instead of being parsed file by file, and .gitignore is honoured.

Note: earlier versions documented a config.yaml escape hatch with skip: / roots: keys. That was a Python-era feature and the Rust engine does not read it — if you have one, it is being ignored. If auto-detection misses your layout, please open an issue with the shape of the repo; that's more useful than a config file nobody can see.

Graph data format

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

• repo-<id>.gmap: the graph itself, sharded, as zero-copy rkyv (mmap'd on load)
• manifest.json: format version, repo labels, roots and parse errors, so a warm load equals a fresh generate
• parse_cache.bin: per-file content-hashed parse cache, so an incremental rebuild only re-parses edited files
• .gitignore: the dir ignores itself, so it never shows up in git status

The whole directory is regenerated: delete it and the next call rebuilds it.

The engine carries 36 edge categories. The ones you'll see most: CALLS, IMPORTS, CONTAINS, DEFINES, USES, HANDLED_BY, HTTP_CALLS, IMPLEMENTS, INHERITS_FROM, INJECTS, ACCESSES_DATA, TESTS, DOCUMENTS, NAVIGATES_TO, plus the channel ones (GRPC_CALLS, QUEUE_FLOWS, WS_CONNECTS, EVENT_FLOWS, GRAPHQL_CALLS, RPC_CALLS). There are 49 node kinds. Both tables come from the engine, so orient always reports the set your version actually has.

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 .glia/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.