code-review-graph

name	code-review-graph
description	Token-efficient code review using Tree-sitter AST graphs and MCP. Reduces AI assistant token usage by 6.8–49x by computing blast radius of changes instead of reading entire codebases. Uses SQLite graph database for structural analysis.
when_to_use	When reviewing code in large codebases (500+ files), when token costs are high, when making multi-file changes with cross-module dependencies, or when working with monorepos. Also for dead code detection, architecture visualization, and refactoring previews. NOT for small projects under 200 files with isolated single-file changes.
allowed-tools	Read, Grep, Glob, Bash
effort	medium

Code Review Graph — Token-Efficient Codebase Context via MCP

Reduce AI token usage by 6.8x average (up to 49x on monorepos) by giving the AI a structural map of your codebase instead of letting it read everything.

Overview

code-review-graph is an MCP server that uses Tree-sitter to parse your codebase into an AST graph stored in SQLite. When your AI assistant needs context for a task, it queries the graph first — getting only the files in the blast radius of your change — instead of reading every file in the directory.

Token Impact:

Codebase Type	Without Graph	With Graph	Reduction
FastAPI (3K files)	138,585 tokens	37,217 tokens	3.7x
httpx	64,666 tokens	14,090 tokens	4.6x
Next.js monorepo (27K files)	739,352 tokens	15,049 tokens	49.1x
Express.js (small)	Less benefit	Graph overhead > savings	~1x
Average across 6 repos	—	—	8.2x

Quality also improves: Graph-assisted reviews score 8.8/10 vs 7.2/10 for naive reviews. Less noise = better signal = more accurate output.

🛑 Auto-Bootstrap Protocol (MANDATORY TOOL USE For AI Agents)

When invoked during /plan or standard usage on a mid-to-large project, proactively check if the user can benefit from graph analysis:

1. Step 1: You MUST explicitly use your terminal/bash execution tool to run a quick check: Get-Command code-review-graph (Windows) or which code-review-graph (macOS/Linux).
2. Step 2: Check if .code-review-graph/ directory exists in the workspace.
3. Step 3: If the exit code is 0 (INSTALLED) but the index does not exist, you MUST use your terminal tool to auto-execute code-review-graph build to proactively optimize your token usage before reading multi-file contexts.
4. Step 4: If exit code is non-zero (NOT INSTALLED) and the project is large, proactively ask the user: "Would you like me to run pip install code-review-graph to build a local map and optimize your token usage by ~8x for this project?"

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When to Use vs When to Skip

✅ Install it if:

• Codebase is 500+ files
• You make multi-file changes with cross-module dependencies
• You spend $20+/month on AI assistant tokens
• You work with monorepos, microservices, or cross-package TypeScript
• You want better review quality in addition to cost savings

❌ Skip it if:

• Codebase is under ~200 files with isolated single-file changes
• Heavy use of dynamic patterns (reflection, runtime code gen, dynamic imports)
• You want zero-maintenance — the graph needs to stay in sync
• Team hasn't standardized on an AI coding tool yet

⚠️ Evaluate first if:

• Codebase is 200–500 files — benchmark before committing
• Mix of static and dynamic patterns — test on representative commits

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How It Works (4 Layers)

Layer 1: PARSE    → Tree-sitter builds ASTs from 19 languages
Layer 2: STORE    → Nodes + edges saved in SQLite graph
Layer 3: TRACE    → BFS computes blast radius of changes
Layer 4: SERVE    → MCP exposes graph to AI assistants

What the Graph Contains

• Nodes: Files, functions, methods, classes, imports, tests
• Edges: "A calls B", "X imports Y", "TestZ covers FunctionW", "ClassA extends ClassB"
• Metadata: Name, type, file path, line range per node
• Privacy: Structural metadata only — NO source code content in the graph

Supported Languages (19)

Python, TypeScript, JavaScript, Go, Rust, Java, C#, Ruby, Kotlin, Swift, PHP, C/C++, Vue SFC, Solidity, Dart, R, Perl, Lua, Jupyter/Databricks notebooks.

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Installation

Prerequisites

• Python 3.9+ (python3 --version)
• pip or pipx installed
• An MCP-compatible AI client (AG Kit, Claude Code, Cursor, Windsurf, Zed)
• Git-tracked codebase (for incremental updates)

Step 1: Install the Package

# Recommended: isolated environment
pipx install code-review-graph

# Alternative: fastest, no permanent install
uvx code-review-graph install

# Alternative: global pip
pip install code-review-graph

Step 2: Configure MCP Client

# Auto-detect all supported tools
code-review-graph install

# Or target a specific platform
code-review-graph install --platform claude-code
code-review-graph install --platform cursor
code-review-graph install --platform windsurf

Restart your editor after this step. The MCP server activates on restart.

Step 3: Build the Initial Graph

cd /your/project
code-review-graph build

Codebase Size	Expected Build Time
500 files	10–30 seconds
5,000 files	2–5 minutes
27,000 files	5–10 minutes

Step 4: Enable Watch Mode (Recommended)

# Keep graph current as you work
code-review-graph watch

Incremental updates complete in under 2 seconds. If you prefer manual updates:

code-review-graph update

Step 5: Verify Integration

Open your AI client and check MCP connection. For Claude Code: run /mcp and confirm code-review-graph appears.

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Configuration

Ignore File

Create .code-review-graphignore at project root (uses .gitignore syntax):

# Build artifacts
dist/**
.next/**
build/**

# Dependencies
node_modules/**
vendor/**

# Generated files
generated/**
*.generated.ts
*.min.js

# Test fixtures (if large)
__fixtures__/**

Excluding generated files and build artifacts is critical — they inflate the graph with meaningless nodes.

Multi-Repo Setup

For microservice architectures:

# Register additional repos
code-review-graph register /path/to/other/repo

# List all registered repos
code-review-graph repos

The MCP server serves context across all registered repositories.

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Core Workflows

1. Blast Radius Analysis (Primary Use)

This is automatic when the MCP server is active. Your AI assistant queries the graph before reading files, getting only the impacted files instead of everything.

Without graph:  Changed auth/middleware.py → AI reads 200+ files → 8,200 tokens
With graph:     Changed auth/middleware.py → Graph returns 12 impacted files → 1,000 tokens

2. Risk-Scored Change Analysis

code-review-graph detect-changes

Scores each uncommitted change by risk level:

• Number of dependents
• Test coverage gaps
• Whether changed functions are on critical paths
• High-risk changes flagged before you ask for review

3. Dead Code Detection

The graph finds nodes with no incoming edges — no callers, no importers, no test coverage:

# Surfaces functions/classes that are candidates for removal
# Useful on mature codebases to reduce cruft

4. Refactoring Preview

code-review-graph rename preview --from OldClassName --to NewClassName

Shows every file affected by a rename, and flags edge cases (dynamic string references that static analysis can't catch).

5. Architecture Visualization

code-review-graph visualize

Generates interactive visualization showing module clusters using community detection (Leiden algorithm). Useful for:

• Onboarding new contributors
• Identifying architectural drift
• Spotting overly-coupled modules

6. Wiki Generation

code-review-graph wiki

Generates markdown wiki of codebase structure — every module, its public API, dependencies, and test coverage.

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Known Limitations

Limitation	Impact	Mitigation
Dynamic imports (require(variable), import(buildPath()))	Dependencies invisible to parser	Manually note in .code-review-graphignore or accept over-prediction
Reflection-based calls (Django signals, getattr(), Java reflection)	Missed edges in graph	Serena (LSP-based) is better for these codebases
Runtime-generated code (eval, template engines)	Not parseable at static time	Accept limitation or exclude from graph
Cross-language boundaries (Python calling TypeScript API)	No edges between language runtimes	Use multi-repo registration as partial workaround
Stale graph (without watch mode)	Claude queries outdated relationships	Always run code-review-graph update before tasks, or use watch mode
TypeScript path aliases (@/components/...)	May require tsconfig resolution config	Check tsconfig_resolver.py handles your setup

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Alternatives Comparison

Tool	Approach	Pros	Cons	Best For
code-review-graph	Tree-sitter + SQLite	Fast, 19 languages, local, no deps	Static analysis only	General use, large codebases
Claudette	Go rewrite	Single binary, no Python	Fewer languages, simpler	Python-averse teams
Serena	LSP-based	Deep semantic precision, type resolution	Heavy setup, slower	Polymorphism-heavy codebases
code-graph-rag	RAG + vector search	Natural language queries	Complex setup	Codebase exploration
Native IDE context	Editor built-in	Zero setup	No explicit blast radius	Simple projects

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Integration with AG Kit

Complementary Skills

AG Kit Skill	How It Complements
context-compression	Graph reduces input context; compression reduces output verbosity
coordinator-mode	Graph-aware workers can be dispatched with precise file lists
verify-changes	After graph-informed review, verify changes via execution
batch-operations	Graph's blast radius informs which files need batch updates

Recommended Session Architecture

1. Start fresh session for each distinct task
2. Graph pre-filters context → AI reads only blast radius
3. Context compression summarizes completed phases
4. Memory system saves key decisions for next session
5. Result: minimum tokens, maximum quality

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Best Practices

1. Always run watch mode in development — stale graphs produce stale context
2. Exclude generated files — they inflate the graph with noise
3. Benchmark first — measure token usage for 1 week without, 1 week with
4. Combine with output constraints — graph reduces input, prompt engineering reduces output
5. Use .code-review-graphignore for build artifacts, node_modules, dist/
6. Keep sessions short — fresh sessions + graph = optimal token efficiency
7. Multi-repo registration for microservice architectures