repobrain

RepoBrain

Cross-IDE repository knowledge engine for grounded codebase Q&A.

Formerly known as Antigravity Workspace Template — same project, new name.

English · 中文 · Español

[License] [Python] [CI] [DeepWiki] [NLPM] [Stars]

RepoBrain helps you understand an unfamiliar repository, trace an implementation, and ask questions with source-file references. It keeps project knowledge in .repobrain/, shared across supported IDEs and agents.

Project Philosophy

Cross-IDE repository knowledge engine for grounded codebase Q&A. Same .repobrain/ knowledge layer reads in every IDE; one engine, every host.

An AI Agent's capability ceiling = the quality of context it can read.

rb-refresh deploys a multi-agent cluster that autonomously reads your code — each module gets its own Agent that generates a knowledge doc. rb-ask routes questions to the right Agent, grounded in real code with file paths and line numbers.

Instead of handing Claude Code / Codex a repo-wide grep and making it hunt on its own, give it a ChatGPT for your repository.

Traditional approach:              RepoBrain approach:
  CLAUDE.md = 5000 lines of docs     Claude Code calls ask_project("how does auth work?")
  Agent reads it all, forgets most   Router → ModuleAgent reads actual source, returns exact answer
  Hallucination rate stays high      Grounded in real code, file paths, and git history

Architecture is files + a live Q&A engine, not plugins. Portable across any IDE, any LLM, zero vendor lock-in.

Read the full project philosophy.

Quick Start

Ask an AI assistant that can run commands in your project to install RepoBrain:

Read AI_INSTALL.md and follow it to install RepoBrain in this project.

The guide installs the tools, configures a model backend, then builds the knowledge base and checks an answer. A logged-in headless CLI such as Trae can serve as the backend without an API key; an OpenAI-compatible API is also supported. Requirements: Python 3.10+, Git with at least one commit, and a clean worktree before refresh.

For platform-specific installation or manual configuration, see the installation guide and the quick-start guide. Installing RepoBrain into an existing project does not require rb init or rb-init.

Everyday Use

Run these commands in your project directory after installation and configuration:

rb-refresh
rb-ask "How does authentication work in this project?"

rb-refresh automatically builds the first knowledge base, updates affected groups after committed changes, or continues a matching interrupted task. If knowledge is already current, it skips without calling a model. After a failure, run the same command again to continue.

Use this only when you want to regenerate all knowledge:

rb-refresh --full

rb-ask reads the existing knowledge base and answers with source evidence. It reminds you when committed code is newer; it does not refresh during a question.

Examples of useful questions:

• Where is this API implemented, and what calls it?
• How does data move from the UI to the backend?
• Which modules are affected by changing this function?

How It Works

Source code → rb-refresh → .repobrain/ knowledge → rb-ask → answer with code references

RepoBrain groups related code, generates module knowledge, and routes each question to relevant context. Knowledge lives in your project, so supported IDEs share the same base. Updates are staged separately and become active only when complete; a failed update preserves the existing usable generation.

The first build can take several minutes or longer on large repositories and uses your configured model. Commit or stash local changes before refreshing. IDE rules and new-project scaffolding are optional; see the usage reference.

Support Matrix

Platform-specific commands, JSON output, and MCP registration are documented in the usage reference and INSTALL.md.

Evaluation

A locked comparison on Flask, ripgrep, Vite, and Prometheus used the same source access and model route for RepoBrain and CodeGraph + Trae. The full report includes accuracy, query time, tokens, and cold-build cost. Results apply to that experiment; AI knowledge generation and static graph indexing are different workloads.

Documentation

• Installation and troubleshooting
• Commands, JSON, and optional integrations
• Knowledge generation and Q&A architecture
• External MCP tools · Sandbox configuration
• Release changes
• Full documentation: English · 中文 · Español

NLPM Audit Feedback — Improved by NLPM, a natural-language programming linter by xiaolai.

Contributing

Ideas are contributions too! Open an issue to report bugs, suggest features, or propose architecture.

Contributors

Star History

[Star History Chart]

License

MIT License. See LICENSE for details.

📚 Full Documentation →

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