RepoBrain

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

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

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🚀 实测推荐: TeamoRouter —— 非广告，我自己在 Codex、Claude Code 等 AI 编程工具里高强度使用：一个 API Key 接入 Claude、GPT-5.5 等，模型价格方便调整，价格优惠，并没有掺水的情况。接入指南见 openrouter-api-key。

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

Four concrete failure modes RepoBrain fixes — click to expand

Problem	Without RepoBrain	With RepoBrain
Agent forgets coding style	Repeats the same corrections	Reads .repobrain/conventions.md — gets it right the first time
Onboarding a new codebase	Agent guesses at architecture	rb-refresh → ModuleAgents self-learn each module
Switching between IDEs	Different rules everywhere	One .repobrain/ folder — every IDE reads it
Asking "how does X work?"	Agent reads random files	ask_project MCP → Router routes to the responsible ModuleAgent

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?"