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Structural graph memory for AI coding assistants — MCP server for codebase navigation

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  • Open-source license (MIT)
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Last scanned: 9/16/2026
Install in Claude Code / Claude Desktop
Method: UVX (Python) · mcp-repo-graph
Claude Code CLI
claude mcp add repo-graph -- uvx mcp-repo-graph
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "repo-graph": {
      "command": "uvx",
      "args": ["mcp-repo-graph"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
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Resumen de MCP Servers

# repo-graph

[![repo-graph MCP server](https://glama.ai/mcp/servers/James-Chahwan/repo-graph/badges/card.svg)](https://glama.ai/mcp/servers/James-Chahwan/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](#where-it-fits-best) for the honest sweet spot.

**Install in one click:**

[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://vscode.dev/redirect/mcp/install?name=repo-graph&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-repo-graph%22%2C%22--repo%22%2C%22.%22%5D%7D)
[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=repo-graph&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-repo-graph%22%2C%22--repo%22%2C%22.%22%5D%7D&quality=insiders)
[![Add to Cursor](https://cursor.com/deeplink/mcp-install-dark.svg)](https://cursor.com/install-mcp?name=repo-graph&config=eyJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJtY3AtcmVwby1ncmFwaCIsIi0tcmVwbyIsIi4iXX0%3D)

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

| | Without repo-graph | With repo-graph |
|---|---|---|
| **Tokens used** | 75,308 | 29,838 |
| **Time to fix** | 4m 36s | ~30s |
| **Files explored** | ~15 (grep, read, grep, read...) | 2 (trace lookup + handler file) |
| **Outcome** | Found and fixed the bug | Found and fixed the bug |

**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](examples/) for [FastAPI](examples/fastapi/), [Gin](examples/gin/), [Hono](examples/hono/), and [NestJS](examples/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 — 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](#use-it-without-mcp) 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**:

```python
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](https://github.com/James-Chahwan/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

| Language | Detection | What it extracts |
|----------|-----------|-----------------|
| **Go** | `go.mod` | Packages, functions, HTTP routes (gin/echo/chi/stdlib), imports |
| **Rust** | `Cargo.toml` | Crates, modules, structs, traits, functions, routes (Actix/Rocket/Axum) |
| **TypeScript** | `tsconfig.json` / `package.json` | Modules, classes, functions, import relationships |
| **React** | `react` in `package.json` | Components, hooks, context providers, React Router routes, fetch/axios calls, flows |
| **Angular** | `@angular/core` in `package.json` | Components, services, guards, DI injection, HTTP calls, feature flows |
| **Vue** | `vue` in `package.json` | SFCs, composables, Vue Router routes, fetch/axios calls |
| **Python** | `pyproject.toml` / `setup.py` / `requirements.txt` | Packages, modules, classes, functions, routes (Flask/FastAPI/Django) |
| **Java/Kotlin** | `pom.xml` / `build.gradle` | Packages, classes, routes (Spring/JAX-RS/Ktor/WebFlux/Micronaut) |
| **Scala** | `build.sbt` | Packages, objects/classes/traits, routes (Play/Akka HTTP/http4s) |
| **Clojure** | `project.clj` / `deps.edn` | Namespaces, defn/defprotocol/defrecord, routes (Compojure/Reitit) |
| **C#/.NET** | `.csproj` / `.sln` | Namespaces, classes, routes (ASP.NET/Minimal API) |
| **Ruby** | `Gemfile` / `.gemspec` | Files, classes, modules, Rails routes |
| **PHP** | `composer.json` | Namespaces, classes, interfaces, routes (Laravel/Symfony) |
| **Swift** | `Package.swift` / `.xcodeproj` | Files, types (class/struct/enum/protocol/actor), Vapor routes |
| **C/C++** | `CMakeLists.txt` / `Makefile` / `meson.build` | Sources, headers, classes, structs, enums, namespaces, includes |
| **Dart/Flutter** | `pubspec.yaml` | Modules, classes, widgets, go_router/shelf routes |
| **Elixir/Phoenix** | `mix.exs` | Modules, functions, Phoenix router scopes + rou

Lo que la gente pregunta sobre repo-graph

¿Qué es James-Chahwan/repo-graph?

+

James-Chahwan/repo-graph es mcp servers para el ecosistema de Claude AI. Structural graph memory for AI coding assistants — MCP server for codebase navigation Tiene 15 estrellas en GitHub y su última actualización registrada es del 2026-09-15.

¿Cómo se instala repo-graph?

+

Puedes instalar repo-graph clonando el repositorio (https://github.com/James-Chahwan/repo-graph) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar James-Chahwan/repo-graph?

+

Nuestro agente de seguridad ha analizado James-Chahwan/repo-graph y le ha asignado un Trust Score de 87/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.

¿Quién mantiene James-Chahwan/repo-graph?

+

James-Chahwan/repo-graph es mantenido por James-Chahwan. La última actividad registrada en GitHub es del 2026-09-15, con 0 issues abiertos.

¿Hay alternativas a repo-graph?

+

Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.

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