Stop burning tokens. Start reviewing smarter.
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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 codebaseOne 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 CLIRequires 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.
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 via SHA-256 hash checks, and re-parses only what changed. A 2,900-file project re-indexes in under 2 seconds.
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.
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).
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.
Token efficiency: 38x – 528x fewer tokens per question (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 | naive_corpus_tokens | 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 |
Range across the 6 repos: 38x – 528x (median per-question reduction ~82x).
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 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 shows the estimate is within ~1% of real GPT-4 tokens in aggregate across 222 sample files.
Impact accuracy: 100% recall, 0.71 average F1
Blast-radius analysis reaches 100% recall on every one of the 13 evaluation commits. It over-predicts in some cases, which is a conservative trade-off: better to flag too many files than miss a broken dependency.
| Repo | Commits | Avg F1 | Avg Precision | Recall |
|---|---|---|---|---|
| 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 |
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
- 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.
| 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 |
| 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 |
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 with blast-radius analysis |
CLI reference
code-review-graph install # Auto-detect and configure all platforms
code-review-graph install --platform <name> # Target a specific platform
code-review-graph build # Parse entire codebase
code-review-graph update # Incremental update (changed files only)
code-review-graph status # Graph statistics
code-review-graph watch # Auto-update on file changes
code-review-graph visualize # Generate interactive HTML graph
code-review-graph visualize --format graphml # Export as GraphML
code-review-graph visualize --format svg # Export as SVG
code-review-graph visualize --format obsidian # Export as Obsidian vault
code-review-graph visualize --format cypher # Export as Neo4j Cypher
code-review-graph wiki # Generate markdown wiki from communities
code-review-graph detect-changes --brief # Risk panel + token savings (read-only)
code-review-graph update --brief # Refresh graph + same panel
code-review-graph detect-changes --brief --verify # Cross-check vs tiktoken
code-review-graph register <path> # Register repo in multi-repo registry
code-review-graph unregister <id> # Remove repo from registry
code-review-graph repos # List registered repositories
code-review-graph daemon start # Start multi-repo watch daemon
code-review-graph daemon stop # Stop the daemon
code-review-graph daemon status # Show daemon status and repos
code-review-graph eval # Run evaluation benchmarks
code-review-graph serve # Start MCP serverToken Savings panel: detect-changes --brief vs update --brief
Both commands print the same compact panel showing how many tokens the graph saved you compared to handing the changed files to an agent raw. They differ in one thing: whether the graph gets refreshed first.
┌─────────────────────── Token Savings ────────────────────────┐
│ Full context would be: 12,921 tokens │
│ Graph context used: 762 tokens │
│ Saved: 12,159 tokens (~94%) │
│ Breakdown: Functions 244 · Tests 191 · Risk 244 · Other 83 │
└──────────────────────────────────────────────────────────────┘
| Command | What it does | When to use |
|---|---|---|
detect-changes --brief |
Read-only. Looks at your current changes, queries the existing graph, prints the panel. ~1 sec. | Most of the time — the hooks (or crg-daemon) keep the graph fresh in the background, so this is enough. |
update --brief |
Re-parses your changed files into the graph first, then prints the same panel. ~5 sec. | After a rebase, a large change set, or any time you suspect the graph is stale. |
Both end with the same panel because both call the same analyze_changes() step at the end. The difference is whether the graph itself got refreshed before that analysis ran.
Add --verify to either command to cross-check the displayed numbers against OpenAI's cl100k_base tokenizer (the GPT-4 family). Requires pip install tiktoken. The estimate stays within ~1% of real tokens on a typical change set — see docs/REPRODUCING.md for the calibration data.
The same context_savings metadata is also attached automatically to the JSON responses of get_impact_radius, get_review_context, detect_changes, and get_architecture_overview MCP tools, so AI agents can surface the savings to humans in chat without any extra prompting.
Multi-repo daemon
If your editor doesn't support hooks (e.g. Cursor, OpenCode), or you just want your
graph to stay fresh in the background without any editor integration, the daemon is
for you. It watches your repos for file changes and automatically rebuilds the graph
— no manual build or update commands needed.
The daemon is included with code-review-graph — no separate install required.
Quick setup:
# 1. Register the repos you want to watch
crg-daemon add ~/project-a --alias proj-a
crg-daemon add ~/project-b
# 2. Start the daemon (runs in the background)
crg-daemon start
# 3. That's it — graphs stay up to date automatically
crg-daemon status # check daemon and per-repo watcher status
crg-daemon logs --repo proj-a -f # tail logs for a specific repo
crg-daemon stop # stop daemon and all watcher processesAlso available as code-review-graph daemon start|stop|status|....
Under the hood, crg-daemon add writes to a TOML config file at
~/.code-review-graph/watch.toml. You can also edit this file directly:
[[repos]]
path = "/home/user/project-a"
alias = "proj-a"
[[repos]]
path = "/home/user/project-b"
alias = "project-b"The daemon monitors this config file for changes and automatically starts/stops watcher processes as repos are added or removed. Health checks every 30 seconds restart dead watchers. No external dependencies required.
See docs/COMMANDS.md for the full config reference and all available options.
30 MCP tools
Your AI assistant uses these automatically once the graph is built.
| Tool | Description |
|---|---|
build_or_update_graph_tool |
Build or incrementally update the graph |
run_postprocess_tool |
Re-run flow detection, community detection, and FTS indexing |
get_minimal_context_tool |
Ultra-compact context (~100 tokens) — call this first |
get_impact_radius_tool |
Blast radius of changed files |
get_review_context_tool |
Token-optimised review context with structural summary |
query_graph_tool |
Callers, callees, tests, imports, inheritance queries |
traverse_graph_tool |
BFS/DFS traversal from any node with token budget |
semantic_search_nodes_tool |
Search code entities by name or meaning |
embed_graph_tool |
Compute vector embeddings for semantic search |
list_graph_stats_tool |
Graph size and health |
get_docs_section_tool |
Retrieve documentation sections |
find_large_functions_tool |
Find functions/classes exceeding a line-count threshold |
list_flows_tool |
List execution flows sorted by criticality |
get_flow_tool |
Get details of a single execution flow |
get_affected_flows_tool |
Find flows affected by changed files |
list_communities_tool |
List detected code communities |
get_community_tool |
Get details of a single community |
get_architecture_overview_tool |
Architecture overview from community structure |
detect_changes_tool |
Risk-scored change impact analysis for code review |
get_hub_nodes_tool |
Find most-connected nodes (architectural hotspots) |
get_bridge_nodes_tool |
Find chokepoints via betweenness centrality |
get_knowledge_gaps_tool |
Identify structural weaknesses and untested hotspots |
get_surprising_connections_tool |
Detect unexpected cross-community coupling |
get_suggested_questions_tool |
Auto-generated review questions from analysis |
refactor_tool |
Rename preview, dead code detection, suggestions |
apply_refactor_tool |
Apply a previously previewed refactoring |
generate_wiki_tool |
Generate markdown wiki from communities |
get_wiki_page_tool |
Retrieve a specific wiki page |
list_repos_tool |
List registered repositories |
cross_repo_search_tool |
Search across all registered repositories |
MCP Prompts (5 workflow templates):
review_changes, architecture_map, debug_issue, onboard_developer, pre_merge_check
Configuration
To exclude paths from indexing, create a .code-review-graphignore file in your repository root:
generated/**
*.generated.ts
vendor/**
node_modules/**
Note: in git repos, only tracked files are indexed (git ls-files), so gitignored files are skipped automatically. Use .code-review-graphignore to exclude tracked files or when git isn't available.
Optional dependency groups:
pip install code-review-graph[embeddings] # Local vector embeddings (sentence-transformers)
pip install code-review-graph[google-embeddings] # Google Gemini embeddings
pip install code-review-graph[communities] # Community detection (igraph)
pip install code-review-graph[enrichment] # Python call-resolution enrichment (Jedi)
pip install code-review-graph[eval] # Evaluation benchmarks (matplotlib)
pip install code-review-graph[wiki] # Wiki generation with LLM summaries (ollama)
pip install code-review-graph[all] # All optional dependencies| Variable | Description | Default |
|---|---|---|
CRG_GIT_TIMEOUT |
Timeout in seconds for Git operations | 30 |
CRG_DATA_DIR |
Override directory for graph databases and generated graph artefacts | - |
CRG_EMBEDDING_MODEL |
Default model for vector embeddings | all-MiniLM-L6-v2 |
CRG_ACCEPT_CLOUD_EMBEDDINGS |
Suppress the cloud embedding egress warning after explicit acknowledgement | - |
CRG_ALLOW_REMOTE_CODE |
Allow HuggingFace models that require trust_remote_code=True |
0 |
CRG_MAX_IMPACT_NODES |
Maximum nodes to include in impact analysis | 500 |
CRG_MAX_IMPACT_DEPTH |
Search depth for blast-radius analysis | 2 |
CRG_MAX_BFS_DEPTH |
Maximum depth for graph traversal | 15 |
CRG_MAX_CHANGED_FUNCS |
Maximum changed functions analysed in one change report | 500 |
CRG_MAX_TRANSITIVE_FRONTIER |
Maximum frontier size for transitive caller/callee expansion | 50 |
CRG_TOOL_TIMEOUT |
Optional timeout in seconds for bounded MCP tools (0 disables timeout) |
0 |
CRG_RECURSE_SUBMODULES |
Include git submodules in file collection when set to 1, true, or yes |
- |
CRG_TOOLS |
Comma-separated allowlist of MCP tools to expose when serving | - |
GOOGLE_API_KEY |
API key for Google Gemini embeddings | - |
MINIMAX_API_KEY |
API key for MiniMax embeddings | - |
CRG_OPENAI_BASE_URL |
OpenAI-compatible embeddings endpoint | - |
CRG_OPENAI_API_KEY |
API key for OpenAI-compatible embeddings | - |
CRG_OPENAI_MODEL |
Model name for OpenAI-compatible embeddings | - |
CRG_OPENAI_DIMENSION |
Pin embedding dimension (v3 models support reduction) | - |
NO_COLOR |
If set, disables ANSI colors in terminal | - |
CRG_SERIAL_PARSE |
If 1, disables parallel parsing (use for debugging) |
- |
OpenAI-compatible embeddings (real OpenAI, Azure, or any self-hosted gateway like
new-api / LiteLLM / vLLM / LocalAI / Ollama in openai mode) need no extra install —
just set the environment variables and pass provider="openai" to embed_graph:
export CRG_OPENAI_BASE_URL=http://127.0.0.1:3000/v1 # or https://api.openai.com/v1
export CRG_OPENAI_API_KEY=sk-...
export CRG_OPENAI_MODEL=text-embedding-3-small # whatever your gateway serves
# optional:
export CRG_OPENAI_DIMENSION=1536 # pin dim (v3 models support reduction)
export CRG_OPENAI_BATCH_SIZE=100 # lower for gateways with tight limits
# (e.g. Qwen text-embedding-v4 caps at 10)The cloud-egress warning is auto-skipped when the base URL points to localhost
(127.0.0.1, localhost, 0.0.0.0, ::1).
Model selection tip. Avoid
-preview/-beta/-expmodel IDs (e.g.google/gemini-embedding-2-preview) for anything you plan to keep long-term — preview models can change weights (different dimension → full re-embed required) or be deprecated without notice. Prefer stable GA releases such astext-embedding-3-small/text-embedding-3-large(OpenAI),Qwen/Qwen3-Embedding-8B(via self-hosted vLLM / LocalAI), orgemini-embedding-001(via the native Gemini provider, which requiresGOOGLE_API_KEYinstead of the OpenAI-compatible path).Also note:
code-review-graphcurrently embeds function signatures only (~10 tokens per node, e.g."parse_file function (path: str) returns Tree"). Models whose headline quality comes from long-context body understanding (such as Gemini 2 or Qwen3-8B at their MTEB-code SOTA scores) will see a much narrower quality gap against smaller models at this input length. Body/docstring embedding is tracked as a follow-up enhancement.
CRG exposes 30 MCP tools by default. In token-constrained environments, you can
limit the server to a subset of tools using --tools or the CRG_TOOLS
environment variable:
# Via CLI flag
code-review-graph serve --tools query_graph_tool,semantic_search_nodes_tool,detect_changes_tool
# Via environment variable
CRG_TOOLS=query_graph_tool,semantic_search_nodes_tool code-review-graph serveThe CLI flag takes precedence over the environment variable. When neither is set, all tools are available. This is especially useful for MCP client configurations:
{
"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"]
}
}
}Installing from a source tree (for example pipx install .) needs build dependencies from PyPI (for example hatchling). If you see Could not find a version that satisfies the requirement hatchling after connection warnings, the Python/pip in that terminal may not be able to open an HTTPS client to pypi.org (sometimes seen in an integrated editor terminal; less often system-wide with VPN, firewall, or proxy).
Options:
-
Run the same command from macOS Terminal.app (or iTerm) instead of the IDE’s terminal, then retry
pipx install .orpipx install "git+https://...". -
Use uv to install the CLI from a checkout (uses different download machinery than
pipin many cases):cd /path/to/code-review-graph uv tool install . --force
-
For development in a clone without a global install, use
uv syncanduv run code-review-graph …(or activate.venvafteruv sync).
Diagnose (optional): python3 scripts/diagnose_pypi_connectivity.py — if it prints FAILED, the issue is environment/network, not a wrong package name in this repo.
If you are using Windows and encounter Invalid JSON: EOF while parsing or MCP error -32000: Connection closed when connecting via Claude Code, do not use the cmd /c wrapper in your config.
Ensure fastmcp is updated to at least 3.2.4+. Then, configure your ~/.claude.json to execute the .exe directly and pass the UTF-8 environment variable via the config:
"code-review-graph": {
"command": "C:\\path\\to\\your\\venv\\Scripts\\code-review-graph.exe",
"args": ["serve", "--repo", "C:\\path\\to\\your\\project"],
"env": { "PYTHONUTF8": "1" }
}git clone https://github.com/tirth8205/code-review-graph.git
cd code-review-graph
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytestAdding a new language
Edit code_review_graph/parser.py and add your extension to EXTENSION_TO_LANGUAGE along with node type mappings in _CLASS_TYPES, _FUNCTION_TYPES, _IMPORT_TYPES, and _CALL_TYPES. Include a test fixture and open a PR.
MIT. See LICENSE.
code-review-graph.com
pip install code-review-graph && code-review-graph install
Works with Codex, Claude Code, Cursor, Windsurf, Zed, Continue, OpenCode, Antigravity, Gemini CLI, Qwen, Qoder, Kiro, GitHub Copilot, and GitHub Copilot CLI