tirth8205/code-review-graph
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.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
2 releases in the last 90 days, the latest 48 days ago, with 128 contributors on GitHub.
Peaked at #2 in the weekly ranking the week of Jul 19, its best in the past year.
Use Cases & Benefits
- Builds a persistent, local-first code intelligence graph that incrementally maps codebases for efficient AI-assisted code reviews and analysis.
- Reduces AI token usage drastically by providing minimal, precise context through blast-radius impact analysis and incremental updates, optimizing large-repo workflows.
- Use for accelerating multi-file code reviews in large monorepos by focusing AI attention only on affected code and dependencies.
- Use for integrating with AI coding assistants across 23+ languages and Jupyter notebooks to improve static analysis and semantic search precision.
- Use for generating interactive visualizations, architecture maps, and automated markdown wikis to understand and maintain complex codebases.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Stop burning tokens. Start reviewing smarter.
AI coding tools re-read your entire codebase on every task.
code-review-graphfixes 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.One command sets up everything.
installdetects which AI coding tools you have, writes the correct MCP configuration for each one, and injects graph-aware instructions into your platform rules. It auto-detects whether you installed viauvxorpip/pipxand generates the right config. Restart your editor/tool after installing.Requires Python 3.10+. For the best experience, install uv (the MCP config will use
uvxif available, otherwise falls back to thecode-review-graphcommand directly).Then open your project and ask your AI assistant:
The initial build takes ~10 seconds for a 500-file project. After that, the graph updates automatically on every file edit and git commit.
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.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+3,171 stars over 90 days across 21 snapshot days; best day +143 on Aug 15.
Forks
3.0K+266
Open issues
197+115
Compared with peers in LLM
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| NVIDIA/auto-ontology | 11.4K | +8.5K | 75 | — |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| DietrichGebert/ponytail | 188.4K | +4.4K | 75 | 12 days ago |
| affaan-m/ECC | 300.8K | +4.3K | 75 | 40 days ago |
| Graphify-Labs/graphify | 145.7K | +3.8K | 75 | today |
| tirth8205/code-review-graph | 32.6K | — | 62 | 48 days ago |
| Category median | 145.7K | +4.4K | 75 | — |
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