by tirth8205
Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
# Add to your Claude Code skills
git clone https://github.com/tirth8205/code-review-graphGuides for using mcp servers skills like code-review-graph.
Last scanned: 4/17/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-04-17T06:07:13.106Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}See how code-review-graph compares with popular alternatives.
code-review-graph is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by tirth8205. Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows. It has 31,364 GitHub stars.
Yes. code-review-graph passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/tirth8205/code-review-graph" and add it to your Claude Code skills directory (see the Installation section above).
code-review-graph is primarily written in Python. It is open-source under tirth8205 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh code-review-graph against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
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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, tracks changes incrementally, and gives your AI assistant precise context via MCP so it reads only what matters.
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:
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 antigravity # configure only Antigravity
code-review-graph install --platform windsurf # configure only Windsurf
code-review-graph install --platform zed # configure only Zed
code-review-graph install --platform continue # configure only Continue
code-review-graph install --platform opencode # configure only OpenCode
code-review-graph install --platform qwen # configure only Qwen
code-review-graph install --platform qoder # configure only Qoder
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
code-review-graph install --platform codebuddy # configure only CodeBuddy Code
code-review-graph install --platform hermes # configure only Hermes Agent
Requires Python 3.10+. For the best experience, install uv (the MCP config will use uvx if available, otherwise falls back to the code-review-graph command directly).
To remove CRG from a Git or SVN project, use the symmetric uninstall command from anywhere inside its working tree. The target is normalized to the working tree root, and non-repository directories are refused. It removes only CRG-owned files and entries; unrelated MCP servers, hooks, skills, and JSONC comments remain untouched. Shared configuration changes use atomic replacement so a failed write leaves the original file intact.
code-review-graph uninstall --dry-run # preview every action; write nothing
code-review-graph uninstall # preview, ask for confirmation, then apply
code-review-graph uninstall --yes # apply without prompting
code-review-graph uninstall --all-repos # also clean every registered repository
code-review-graph uninstall --keep-data # remove integrations but keep graph databases
code-review-graph uninstall --keep-user-configs --repo . # clean this project only
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.
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.
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.
When hooks or watch mode are enabled, file saves and supported commit hooks trigger incremental updates. The graph diffs changed files, finds their dependents through the graph's own import and call edges, and re-parses only the files whose SHA-256 hash actually changed. On a ~3,000-file project (django) a two-file edit re-indexes in about 2.5 seconds on the path the hooks use, of which ~1.4 s is process start-up; a no-op update costs only that start-up. See Incremental update latency for the full measurement.
The bigger the repository, the more token waste hurts. Instead of feeding a whole corpus to the model, the graph returns an answer-shaped slice of it: on this repository, 208,821 source tokens become ~3,190 tokens per question.
Parser support covers functions, classes, imports, call sites, inheritance, and test detection across the current parser surface, using Tree-sitter where available and targeted fallbacks where needed. Current support includes Python, JavaScript/TypeScript/TSX, Go, Rust, Java, C/C++, C#, VB.NET, Ruby, Kotlin, Swift, PHP, Scala, Solidity, Dart, R, Perl, Lua/Luau