Repo Graph FAQs

Question 1

What is Repo Graph and how does it help AI coding assistants?

Accepted Answer

Repo Graph provides AI coding assistants with a structural graph memory of your codebase, mapping entities, relationships, and flows. This allows LLMs to navigate efficiently, understand code structure, and focus on only the relevant files for a task, significantly improving performance and reducing token usage.

Question 2

How does Repo Graph reduce token usage for Large Language Models (LLMs)?

Accepted Answer

Instead of flooding an LLM's context window with an entire codebase, Repo Graph enables intelligent context budgeting. LLMs query the generated graph to find the minimal set of files required for a specific task, reducing token consumption by only reading what matters.

Question 3

What programming languages does Repo Graph support?

Accepted Answer

Repo Graph supports a broad range of programming languages and frameworks, including Go, Rust, TypeScript, Python, Java, C#, PHP, Ruby, Swift, C/C++, SCSS, and frontend frameworks like React and Angular. It generates a unified graph across multiple languages in a monorepo.

Question 4

What kind of tools does Repo Graph offer to AI assistants?

Accepted Answer

Repo Graph exposes 12 distinct MCP tools that allow AI assistants to orient themselves in a codebase, navigate complex flows, scope tasks to minimal file sets, and assess the impact of changes. Tools include `flow`, `trace`, `impact`, `minimal_read`, and `split_plan`.

Question 5

How can I ensure the Repo Graph data is always up-to-date?

Accepted Answer

Repo Graph automates graph regeneration with git hooks. By adding `repo-graph-generate` to your pre-commit hook, the graph stays fresh with every commit, ensuring your AI assistant always has an accurate map of the codebase without any manual intervention or LLM context overhead.

Repo Graph

Repo Graph

Key Features

Use Cases

Key Features

Use Cases