RepoBrain

Give your repo a brain 🧠 — ChatGPT for your codebase, works in Claude Code, Cursor, Codex, Windsurf & 4 more.

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。

# 1 — Install (Claude Code plugin marketplace)
/plugin marketplace add study8677/repobrain
/plugin install repobrain@repobrain

# 2 — Configure a backend (logged-in local CLI = no key, or paste an API key), build the knowledge base
/repobrain:rb-setup
/repobrain:rb-refresh

# 3 — Ask anything, grounded in real code with file paths + line numbers
/repobrain:rb-ask "How does auth work?"

95.83% weighted semantic accuracy · 4.04× faster scored queries than CodeGraph + Trae on the main track. Benchmark results ↓ Codex CLI users — drop the repobrain: prefix; the same four slash commands ship there too.

🧠 Fastest start — let your AI install it for you (no API key)

Already in a logged-in AI IDE (Trae / Cursor / Claude Code / Codex)? Don't touch pip or an API key — paste this one line to your AI assistant and it does the rest (detects your logged-in CLI, wires up a zero-key backend, initializes the project, self-tests):

Read https://github.com/study8677/repobrain/blob/main/AI_INSTALL.md and follow it to install RepoBrain in this project.

Then just ask your AI anything about your codebase.

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Why RepoBrain?

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.

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RepoBrain vs CodeGraph + Trae (2026-09-09)

Flask, ripgrep, Vite, Prometheus · 20 questions × 3 repeats per product · same source access and model route (Seed-2.1-Turbo → seed-code-pro).

Main-track metric	RepoBrain	CodeGraph + Trae
Weighted semantic accuracy	95.83%	84.17%
Successful queries	60/60	53/60
Scored query time	5,106.67 s	20,621.14 s
Query tokens	22,326,315	86,027,974
Cold build time	7,420.63 s	9.58 s
Cold build + scored queries	12,527.30 s	20,630.72 s

Failures count as incorrect. Cold builds compare full AI knowledge generation with static code-graph indexing, not equivalent workloads. Results apply to this locked experiment only.

Full results and methodology

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Quick Start

Plugin install for Claude Code / Codex CLI (recommended — the rb CLI and engine auto-install together on Claude's first session):

# Claude Code
/plugin marketplace add study8677/repobrain
/plugin install repobrain@repobrain
/repobrain:rb-setup            # interactive: use a logged-in local CLI (Codex/Trae/Claude, no key) or paste an API key; writes .env
/repobrain:rb-refresh          # first refresh auto-creates .repobrain/
/repobrain:rb-ask "How does this project work?"

# Codex CLI (manual engine install — Codex hooks are not yet supported)
pipx install "git+https://github.com/study8677/repobrain.git#subdirectory=engine"
pipx inject --force --include-apps repobrain-engine "git+https://github.com/study8677/repobrain.git#subdirectory=cli"
codex plugin marketplace add study8677/repobrain
/rb-setup
/rb-refresh
/rb-ask "How does this project work?"

Codex auto-discovers slash commands from the plugin's commands/ directory, so the same four commands work without the repobrain: namespace prefix. The raw CLI calls (rb-refresh --workspace ., rb-ask "..." --workspace .) also still work. If your Codex build supports MCP, register rb-mcp --workspace separately.

Option B — Manual install: engine + CLI via pip

# 1. Install engine + CLI
pip install "git+https://github.com/study8677/repobrain.git#subdirectory=cli"
pip install "git+https://github.com/study8677/repobrain.git#subdirectory=engine"

# 2. Configure .env with any OpenAI-compatible API key
cd my-project
cat > .env <<EOF
OPENAI_BASE_URL=https://your-endpoint/v1
OPENAI_API_KEY=your-key
OPENAI_MODEL=your-model
RB_ASK_TIMEOUT_SECONDS=120
EOF

# 3. Build knowledge base (ModuleAgents self-learn each module)
rb-refresh --workspace .

# 4. Ask anything
rb-as