Code Review Graph

安装配置

{
    "mcpServers": {
        "code-review-graph": {
            "command": "code-review-graph",
            "args": [
                "serve",
                "--tools",
                "query_graph_tool,semantic_search_nodes_tool,detect_changes_tool,get_review_context_tool"
            ]
        }
    }
}

README 摘要

code-review-graph Stop burning tokens. Start reviewing smarter. English | 简体中文 | 日本語 | 한국어 | हिन्दी AI coding tools can end up re-reading large parts of your codebase on review tasks. `code-review-graph` fixes that. It builds a structural map of your code with [Tree-sitter](https://tree-sitter.github.io/tree-sitter/), tracks changes incrementally, and gives your AI assistant precise context via [MCP](https://modelcontextprotocol.io/) so it reads only what matters. --- ## Quick Start ```bash pip install code-review-graph # or: pipx install code-review-graph code-review-graph install # auto-detects and configures all supported platforms code-review-graph build # parse your codebase ``` One command sets up everything. `install` detects which AI coding tools you have, writes the correct MCP configuration for each one, installs platform-native hooks/skills where supported, and injects graph-aware instructions into your platform rules. It auto-detects whether you installed via `uvx` or `pip`/`pipx` and generates the right config. Restart your editor/tool after installing. To target a specific platform: ```bash code-review-graph install --platform codex # configure only Codex code-review-graph install --platform cursor # configure only Cursor code-review-graph install --platform claude-code # configure only Claude Code code-review-graph install --platform gemini-cli # configure only Gemini CLI code-review-graph install --platform kiro # configure only Kiro code-review-graph install --platform copilot # configure only GitHub Copilot (VS Code) code-review-graph install --platform copilot-cli # configure only GitHub Copilot CLI ``` Requires Python 3.10+. For the best experience, install [uv](https://docs.astral.sh/uv/) (the MCP config will use `uvx` if available, otherwise falls back to the `code-review-graph` command directly). Then open your project and ask your AI assistant: ``` Build the code review graph for this project ``` The initial build takes ~10 seconds for a 500-file project. After that, watch mode and supported hooks can keep the graph updated automatically. ## How It Works Your repository is parsed into an AST with Tree-sitter, stored as a graph of nodes (functions, classes, imports) and edges (calls, inheritance, test coverage), then queried at review time to compute the minimal set of files your AI assistant needs to read. ### Blast-radius analysis When a file changes, the graph traces every caller, dependent, and test that could be affected. This is the "blast radius" of the change. Your AI reads only these files instead of scanning the whole project. ### Incremental updates in < 2 seconds When hooks or watch mode are enabled, file saves and supported commit hooks trigger incremental updates. The graph diffs changed files, finds their dependents via SHA-256 hash checks, and re-parses only what changed. A 2,900-file project re-indexes in under 2 seconds. ### The monorepo problem, solved Large monorepos are where token waste is most painful. The graph cuts through the noise — 27,700+ files excluded from review context, only ~15 files actually read. ### Broad language coverage + Jupyter notebooks Parser support covers functions, classes, imports, call sites, inheritance, and test detection across the current parser surface, using Tree-si...