Code Review Graph

code-review-graph

Stop burning tokens. Start reviewing smarter.

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Usage · Commands · FAQ · Troubleshooting · GitHub Action · Reproducing the benchmarks · Roadmap

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.

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Quick Start

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 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 (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

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-sitter where available and targeted fallbacks where needed. Current support includes Python, JavaScript/TypeScript/TSX, Go, Rust, Java, C/C++, C#, Ruby, Kotlin, Swift, PHP, Scala, Solidity, Dart, R, Perl, Lua/Luau, Objective-C, shell scripts, Elixir, Zig, PowerShell, Julia, ReScript, GDScript, Nix, Verilog/SystemVerilog, SQL, Vue/Svelte SFCs, Astro files parsed through the TypeScript parser, Jupyter/Databricks notebooks (.ipynb), and Perl XS files (.xs).

Add your own language (no fork needed)

If your repo uses a language the parser does not cover yet, drop a languages.toml into .code-review-graph/ mapping file extensions to any grammar bundled in tree_sitter_language_pack, plus the tree-sitter node types for functions, classes, imports, and calls:

[languages.erlang]
extensions = [".erl"]
grammar = "erlang"
function_node_types = ["function_clause"]
class_node_types = ["record_decl"]
import_node_types = ["import_attribute"]
call_node_types = ["call"]

The generic tree-sitter walker handles extraction from there — no code changes, and built-in languages can never be overridden. See [docs/CUSTOMLANGUAGES.md](docs/CUSTOMLANGUAGES.md) for the schema reference, validation rules, and a worked end-to-end example.

Risk-scored PR reviews in CI (GitHub Action)

The same analysis runs as a composite GitHub Action — and it stays local-first: the knowledge graph is built and queried entirely on your CI runner, with no source code sent to any external service. On each pull request the action posts a single sticky comment with risk-scored functions, affected execution flows, and test gaps, updated in place on every push. An optional fail-on-risk input turns the review into a merge gate.

# .github/workflows/code-review-graph.yml
on:
  pull_request:

permissions:
  contents: read
  pull-requests: write

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: tirth8205/code-review-graph@v2.3.6
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}

See [docs/GITHUBACTION.md](docs/GITHUBACTION.md) for inputs, risk levels, and caching details, or the dogfood workflow this repo runs on itself in [.github/workflows/pr-review.yml](.github/workflows/pr-review.yml).

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Benchmarks

Headline number: the median per-question token reduction across the 6 repos is ~82x (whole-corpus baseline vs graph query). The frequently quoted 528x is the maximum — a single best-case repo (fastapi) — not the typical result.

All numbers come from the automated evaluation runner against 6 real open-source repositories (13 commits total). Every config pins an upstream SHA, the Leiden community detector runs with a fixed seed, and embeddings are deterministic on CPU — so two runs on different machines produce identical numbers. The full reproduction recipe with expected outputs is in [docs/REPRODUCING.md](docs/REPRODUCING.md). A weekly report-only run on the two smallest configs lives in [.github/workflows/eval.yml](.github/workflows/eval.yml).

Token efficiency: ~82x median per-question reduction (range 38x – 528x; whole-corpus vs graph query)

For a typical agent question ("how does authentication work", "what is the main entry point", etc.), the graph returns ~2,000–3,500 tokens of targeted search hits + neighbor edges instead of forcing the agent to read every source file. The table below averages over the 5 sample questions defined in code_review_graph/token_benchmark.py.

| Repo | Snapshot SHA | naivecorpustokens | avg graph_tokens | Reduction | |------|---|-----------------:|----------------:|----------:| | fastapi | 0227991a | 951,071 | 2,169 | 528.4x | | code-review-graph | 84bde354 | 208,821 | 2,495 | 93.0x | | gin | 5c00df8a | 166,868 | 1,990 | 91.8x | | flask | a29f88ce | 125,022 | 1,986 | 71.4x | | express | b4ab7d65 | 135,955 | 3,465 | 40.6x | | httpx | b55d4635 | 89,492 | 2,438 | 38.0x |

Median per-question reduction across the 6 repos: ~82x. The range is 38x – 528x, where 528x is the best case (fastapi, the largest corpus), not the headline.

The whole-corpus baseline above is an upper bound no real agent pays: a competent agent greps for identifiers and reads only the best-matching files. The agent_baseline eval benchmark measures that realistic baseline — a pure-python grep over the corpus, top-3 files by match count, token-counted and compared to the graph query cost (evaluate/results/_agent_baseline_*.csv).

The formal eval/benchmarks/token_efficiency.py benchmark measures a different scenario — full get_review_context() JSON versus just the changed-file content of a commit — and reports ratios below 1 for small commits, because the review-context response carries impact-radius edges plus source snippets that exceed a tiny single-file diff. That is not a bug; the two benchmarks answer different questions. See [docs/REPRODUCING.md](docs/REPRODUCING.md) for the full methodology.

Since v2.3.4, review and impact tools attach a compact context_savings estimate so MCP clients can see the approximate context saved per call. In v2.3.5 the CLI surfaces this as the boxed Token Savings panel shown above (see "Token Savings panel" in the Usage section) and adds --verify to cross-check against OpenAI's cl100k_base tokenizer. Calibration data in [docs/REPRODUCING.md](docs/REPRODUCING.md) shows the estimate is within ~1% of real GPT-4 tokens in aggregate across 222 sample files.

Impact accuracy: 0.71 average F1 against graph-derived ground truth (recall 1.0 is a circular upper bound, not "100% recall")

Blast-radius analysis recovers every file in the ground truth on all 13 evaluation commits — but read that as an upper bound, not as "100% recall": in this mode the ground truth (changed files + files with call/import edges into them) is derived from the same graph the predictor traverses, so it is circular by construction. The over-prediction visible in the precision column is a deliberate trade-off: better to flag too many files than miss a broken dependency.

| Repo | Commits | Avg F1 | Avg Precision | Recall (graph-derived upper bound) | |------|--------:|-------:|--------------:|-------:| | httpx | 2 | 0.864 | 0.786 | 1.0 | | fastapi | 2 | 0.834 | 0.750 | 1.0 | | code-review-graph | 2 | 0.734 | 0.584 | 1.0 | | express | 2 | 0.667 | 0.500 | 1.0 | | flask | 2 | 0.628 | 0.481 | 1.0 | | gin | 3 | 0.609 | 0.439 | 1.0 | | Average | 13 | 0.714 | 0.578 | 1.000 |

The benchmark also runs an honest co-change mode: the predictor is seeded with a single changed file and graded against the other files the author actually touched in the same commit — independent-ish evidence from git history, not from the graph. Both modes appear side by side in the result CSVs (ground_truth_mode column). Co-change numbers will be added to the canonical stats once captured by the eval runner; we do not quote them before measuring.

Build performance

| Repo | Files | Nodes | Edges | Flow Detection | Search Latency | |------|------:|------:|------:|---------------:|---------------:| | express | 141 | 1,910 | 17,553 | 106ms | 0.7ms | | fastapi | 1,122 | 6,285 | 27,117 | 128ms | 1.5ms | | flask | 83 | 1,446 | 7,974 | 95ms | 0.7ms | | gin | 99 | 1,286 | 16,762 | 111ms | 0.5ms | | httpx | 60 | 1,253 | 7,896 | 96ms | 0.4ms |

Limitations and known weaknesses

• Impact "recall 1.0" is graph-derived and circular: the historical ground truth comes from the same graph edges the predictor walks, so it is an upper bound by construction. The honest co-change mode (grade against files actually co-changed in the same commit) is measured alongside it; expect those numbers to be substantially lower.
• Small single-file changes: Graph context can exceed naive file reads for trivial edits (see express results above). The overhead is the structural metadata that enables multi-file analysis.
• Search quality (MRR 0.35): Keyword search finds the right result in the top-4 for most queries, but ranking needs improvement. Express queries return 0 hits due to module-pattern naming.
• Flow detection (33% recall): Only reliably detects entry points in Python repos (fastapi, httpx) where framework patterns are recognized. JavaScript and Go flow detection needs work.
• Precision vs recall trade-off: Impact analysis is deliberately conservative. It flags files that might be affected, which means some false positives in large dependency graphs.

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Features

| Feature | Details | |---------|---------| | Incremental updates | Re-parses only changed files. Subsequent updates complete in under 2 seconds. | | Broad language + notebook support | Python, JavaScript/TypeScript/TSX, Go, Rust, Java, C/C++, C#, Ruby, Kotlin, Swift, PHP, Scala, Solidity, Dart, R, Perl, Lua/Luau, Objective-C, shell scripts, Elixir, Zig, PowerShell, Julia, ReScript, GDScript, Nix, Verilog/SystemVerilog, SQL, Vue/Svelte SFCs, Astro files parsed through the TypeScript parser, Jupyter/Databricks (.ipynb), and Perl XS (.xs) | | Blast-radius analysis | Shows which functions, classes, and files are likely affected by a change | | Auto-update hooks | Hooks and watch mode can update the graph on file saves and supported commit hooks | | Semantic search | Optional vector embeddings via sentence-transformers, Google Gemini, MiniMax, or any OpenAI-compatible endpoint (real OpenAI, Azure, new-api, LiteLLM, vLLM, LocalAI) | | Interactive visualisation | D3.js force-directed graph with search, community legend toggles, and degree-scaled nodes | | Hub & bridge detection | Find most-connected nodes and architectural chokepoints via betweenness centrality | | Surprise scoring | Detect unexpected coupling: cross-community, cross-language, peripheral-to-hub edges | | Knowledge gap analysis | Identify isolated nodes, untested hotspots, thin communities, and structural weaknesses | | Suggested questions | Auto-generated review questions from graph analysis (bridges, hubs, surprises) | | Edge confidence | Three-tier confidence scoring (EXTRACTED/INFERRED/AMBIGUOUS) with float scores on edges | | Graph traversal | Free-form BFS/DFS exploration from any node with configurable depth and token budget | | Export formats | GraphML (Gephi/yEd), Neo4j Cypher, Obsidian vault with wikilinks, SVG static graph | | Graph diff | Compare graph snapshots over time: new/removed nodes, edges, community changes | | Token benchmarking | Measure naive full-corpus tokens vs graph query tokens with per-question ratios | | Estimated context savings | Compact context_savings metadata on relevant MCP/CLI review outputs, labelled as estimated and kept to three small fields | | Memory loop | Persist Q&A results as markdown for re-ingestion, so the graph grows from queries | | Community auto-split | Oversized communities (>25% of graph) are recursively split via Leiden | | Execution flows | Trace call chains from entry points, sorted by weighted criticality | | Community detection | Cluster related code via Leiden algorithm with resolution scaling for large graphs | | Architecture overview | Auto-generated architecture map with coupling warnings | | Risk-scored reviews | detect_changes maps diffs to affected functions, flows, and test gaps | | Custom languages | Add new languages via .code-review-graph/languages.toml — no fork or code changes needed | | GitHub Action | Sticky risk-scored PR review comments in CI, with an optional fail-on-risk merge gate | | Refactoring tools | Rename preview, framework-aware dead code detection, community-driven suggestions | | Wiki generation | Auto-generate markdown wiki from community structure | | Multi-repo registry | Register multiple repos, search across all of them | | Multi-repo daemon | crg-daemon watches multiple repos as child processes, with health checks and auto-restart | | MCP prompts | 5 workflow templates: review, architecture, debug, onboard, pre-merge | | Full-text search | FTS5-powered hybrid search combining keyword and vector similarity | | Local storage | SQLite file in .code-review-graph/. Core graph storage needs no external database or cloud service. | | Watch mode | Continuous graph updates as you work |

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Usage

Slash commands

| Command | Description | |---------|-------------| | /code-review-graph:build-graph | Build or rebuild the code graph | | /code-review-graph:review-delta | Review changes since last commit | | /code-review-graph:review-pr | Full PR review wit

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Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tirth8205
• Source: tirth8205/code-review-graph
• License: MIT
• Homepage: https://code-review-graph.com

Install and usage instructions live in the source repository linked above.