tirth8205/code-review-graph

tirth8205-Profil

Letzte erfasste Quellaktivität: 13. September 2026 um 23:59

SkillsMP-Katalog aktualisiert: 17. Oktober 2026 um 00:09

gesammelte Skills: 7

GitHub-Stars: 32.179
GitHub-Forks: 2.954

README des Repositorys

README-Quelldatei: README.md

README-Quelldatei lesen

Vollständige README anzeigen

<h1 align="center">code-review-graph</h1> <a href="https://trendshift.io/repositories/23329?utm_source=repository-badge&utm_medium=badge&utm_campaign=badge-repository-23329" target="_blank" rel="noopener noreferrer"> <img src="https://trendshift.io/api/badge/repositories/23329" alt="tirth8205%2Fcode-review-graph | Trendshift" width="250" height="55" /> </a> A local code knowledge graph that gives AI coding tools precise review context over MCP. <a href="README.md">English</a> | <a href="README.zh-CN.md">简体中文</a> | <a href="README.ja-JP.md">日本語</a> | <a href="README.ko-KR.md">한국어</a> | <a href="README.hi-IN.md">हिन्दी</a> <a href="https://pypi.org/project/code-review-graph/"><img src="https://img.shields.io/pypi/v/code-review-graph?style=flat-square&color=blue" alt="PyPI"></a> <a href="https://pepy.tech/project/code-review-graph"><img src="https://img.shields.io/pepy/dt/code-review-graph?style=flat-square" alt="Downloads"></a> <a href="https://github.com/tirth8205/code-review-graph/stargazers"><img src="https://img.shields.io/github/stars/tirth8205/code-review-graph?style=flat-square" alt="Stars"></a> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square" alt="MIT Licence"></a> <a href="https://github.com/tirth8205/code-review-graph/actions/workflows/ci.yml"><img src="https://github.com/tirth8205/code-review-graph/actions/workflows/ci.yml/badge.svg?branch=staging" alt="CI"></a> <a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.10%2B-blue.svg?style=flat-square" alt="Python 3.10+"></a> <a href="https://modelcontextprotocol.io/"><img src="https://img.shields.io/badge/MCP-compatible-green.svg?style=flat-square" alt="MCP"></a> <a href="https://code-review-graph.com"><img src="https://img.shields.io/badge/website-code--review--graph.com-blue?style=flat-square" alt="Website"></a> <a href="https://discord.gg/3p58KXqGFN"><img src="https://img.shields.io/badge/discord-join-5865F2?style=flat-square&logo=discord&logoColor=white" alt="Discord"></a> <a href="docs/USAGE.md">Usage</a> · <a href="docs/COMMANDS.md">Commands</a> · <a href="docs/FAQ.md">FAQ</a> · <a href="docs/TROUBLESHOOTING.md">Troubleshooting</a> · <a href="docs/GITHUB_ACTION.md">GitHub Action</a> · <a href="docs/REPRODUCING.md">Reproducing the benchmarks</a> · <a href="docs/ROADMAP.md">Roadmap</a> AI coding tools often re-read large parts of a codebase to review a change. `code-review-graph` builds a structural map of the code with [Tree-sitter](https://tree-sitter.github.io/tree-sitter/), keeps it updated incrementally, and serves compact context over [MCP](https://modelcontextprotocol.io/), so the assistant reads only the files a change touches. <img src="diagrams/diagram1_before_vs_after.png" alt="The Token Problem: reading flask's whole corpus costs 143,594 tokens, a graph answer costs 2,196 (65x fewer)" width="85%" /> --- ## Quick Start ```bash pip install code-review-graph # or: pipx install code-review-graph code-review-graph install # detect installed AI coding tools and configure each one code-review-graph build # parse the codebase ``` `install` detects which AI coding tools you have, writes an MCP server entry for each, installs hooks and skills where the platform supports them, and adds graph instructions to the platform's rules file. The MCP entry uses `poetry run` or `uv run` inside a Poetry or uv project environment, `uvx code-review-graph serve` when `uvx` is on PATH, and otherwise the current Python interpreter. Restart the editor or tool afterwards. <img src="diagrams/diagram8_supported_platforms.png" alt="One install, every platform: detects Codex, Claude Code, CodeBuddy Code, Cursor, Windsurf, Zed, Continue, OpenCode, Antigravity, Gemini CLI, Qwen, Qoder, Kiro, GitHub Copilot, GitHub Copilot CLI, and Hermes Agent" width="85%" /> To configure one platform, pass `--platform` with one of `codex`, `claude-code`, `cursor`, `windsurf`, `zed`, `continue`, `opencode`, `antigravity`, `gemini-cli`, `qwen`, `kiro`, `qoder`, `copilot`, `copilot-cli`, `codebuddy`, or `hermes`: ```bash code-review-graph install --platform cursor code-review-graph install --platform codebuddy ``` Config file locations are listed in [docs/USAGE.md](docs/USAGE.md#supported-platforms). Requires Python 3.10+. `uninstall` removes CRG-owned files and entries from a Git or SVN working tree and leaves other MCP servers, hooks, skills and JSONC comments alone. Run it from anywhere inside the tree. Shared config files are replaced atomically, so a failed write leaves the original intact. ```bash code-review-graph uninstall --dry-run # preview only code-review-graph uninstall # preview, confirm, 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, keep graph databases code-review-graph uninstall --keep-user-configs --repo . # this project only ``` Then open the project and ask the assistant: ``` Build the code review graph for this project ``` Build time scales with repository size; a cold build of a ~3,000-file repository took about 40 seconds ([measured](docs/REPRODUCING.md#incremental-update-latency)). After that, hooks and watch mode keep the graph updated. If some files fail to parse, the result has status `partial` and names them in its summary; the CLI also prints a `Warning:` line on stderr, and those files keep their previous graph rows. ## How It Works <img src="diagrams/diagram7_mcp_integration_flow.png" alt="How the assistant uses the graph: the user asks for a review, the assistant calls MCP tools, the graph returns blast radius and risk scores, the assistant reads only the affected files" width="80%" /> The repository is parsed into ASTs with Tree-sitter and stored as a graph of nodes (functions, classes, imports) and edges (calls, inheritance, test coverage). At review time the graph is queried for the smallest set of files the assistant needs to read. <img src="diagrams/diagram2_architecture_pipeline.png" alt="Architecture pipeline: Repository to Tree-sitter parser to SQLite graph to blast radius to minimal review set" width="100%" /> ### Blast-radius analysis When a file changes, the graph traces every caller, dependent and test that could be affected. The assistant reads those files instead of scanning the whole project. <img src="diagrams/diagram3_blast_radius.png" alt="Blast radius: a change to login() propagates to callers, dependents, and tests" width="70%" /> ### Incremental updates Hooks, the pre-commit hook and watch mode trigger incremental updates. The update diffs changed files, finds their dependents through the graph's import and call edges, and re-parses only the files whose SHA-256 hash 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](docs/REPRODUCING.md#incremental-update-latency). <img src="diagrams/diagram4_incremental_update.png" alt="Incremental update flow: a hook or watch update triggers a git diff, dependents are found through graph edges, and only files whose SHA-256 hash changed are re-parsed" width="90%" /> ### Whole codebase or targeted answer? Instead of feeding a whole corpus to the model, the graph returns a slice shaped to the question. In the 2026-08-02 capture of this repository at `84bde354`, 208,821 source tokens became ~3,190 tokens per question. The repository has grown a lot since that snapshot, so both numbers are larger today. <img src="diagrams/diagram6_monorepo_funnel.png" alt="code-review-graph at the 84bde354 snapshot: 208,821 source tokens funnel down to ~3,190 token graph responses, about 65x fewer tokens per question" width="80%" /> ### Language coverage and notebooks <img src="diagrams/diagram9_language_coverage.png" alt="Language coverage by category: Web, Backend, Systems, Mobile, Scripting, Shells, Domain, and Other, plus Jupyter and Databricks notebooks" width="90%" /> The parser extracts functions, classes, imports, call sites, inheritance and tests, using Tree-sitter where a grammar exists and targeted fallbacks elsewhere. Supported: Python, JavaScript/TypeScript/TSX, Go, Rust, Java, C/C++, C#, VB.NET, 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, Terraform/OpenTofu (`.tf`; other `.hcl` files become file nodes only), Ansible YAML (playbooks, roles, tasks), Spring Boot application config (`application.properties`, `application.yml`, `application.yaml` and their `application-<profile>` variants; key names and value types only, never values), Vue/Svelte SFCs, Astro files (parsed with the TypeScript grammar), Jupyter and Databricks notebooks (`.ipynb`), and Perl XS files (`.xs`). Other YAML and other `.properties` files are not treated as source code. PHP projects also get repository-bounded Composer PSR-4 resolution, Blade template references, and Laravel Route and Eloquent edges when the source shows explicit framework imports, model inheritance and receiver evidence. Java projects get Spring dependency-injection call resolution, request endpoints and WebFlux routes, scheduled triggers, application-event publisher-to-listener edges, and Temporal workflow and activity edges. Each resolver runs after the parse and needs the injected field, published event or workflow stub to be visible in the repository. ### Add your own language If your repository uses a language the parser does not cover, add a `languages.toml` to `.code-review-graph/` that maps file extensions to any grammar bundled in `tree_sitter_language_pack`, plus the node types for functions, classes, imports and calls: ```toml [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 does the extraction. Built-in languages cannot be overridden. See [docs/CUSTOM_LANGUAGES.md](docs/CUSTOM_LANGUAGES.md) for the schema, validation rules and a worked example. ### Risk-scored PR reviews in CI (GitHub Action) The same analysis runs as a composite GitHub Action. The graph is built and queried on your CI runner; no source code is sent to an external service. On each pull request the action posts one sticky comment with risk-scored functions, affected execution flows and test gaps, updated in place on every push. The optional `fail-on-risk` input turns it into a merge gate. ```yaml # .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@v7 - uses: tirth8205/code-review-graph@v2.3.9 with: github-token: ${{ secrets.GITHUB_TOKEN }} ``` See [docs/GITHUB_ACTION.md](docs/GITHUB_ACTION.md) for inputs, risk levels and caching, or the workflow this repository runs on itself in [`.github/workflows/pr-review.yml`](.github/workflows/pr-review.yml). --- ## Benchmarks <img src="diagrams/diagram5_benchmark_board.png" alt="Benchmarks across 6 repositories: ~63x median per-question token reduction (358x max), 0.69 average impact F1 against graph-derived ground truth" width="85%" /> The median per-question token reduction across the 6 repositories is about **63x** (whole-corpus baseline vs graph query). The **358x** maximum is one repository (fastapi, the largest corpus), not the typical result. All numbers come from the evaluation runner against 6 open-source repositories (13 commits). Every config pins an upstream SHA, Leiden runs with a fixed seed, and embeddings are deterministic on CPU, so two runs on different machines produce the same numbers. The reproduction recipe 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). <details> <summary>Token efficiency: ~63x median per-question reduction (range 35x to 358x; whole-corpus vs graph query)</summary> For a typical agent question (`"how does authentication work"`, `"what is the main entry point"`, and so on), the graph returns ~2,200 to 3,900 tokens of search hits plus neighbour edges instead of every source file. The table averages the 5 sample questions defined in `code_review_graph/token_benchmark.py`. | Repo | Snapshot SHA | naive_corpus_tokens | avg graph_tokens | Reduction | |------|---|-----------------:|----------------:|----------:| | fastapi | `22381558` | 948,793 | 2,653 | **357.6x** | | flask | `a29f88ce` | 143,594 | 2,196 | **65.4x** | | code-review-graph | `84bde354` | 208,821 | 3,190 | **65.5x** | | gin | `5c00df8a` | 166,868 | 2,766 | **60.3x** | | httpx | `b55d4635` | 142,356 | 2,661 | **53.5x** | | express | `b4ab7d65` | 136,052 | 3,936 | **34.6x** | > Captured 2026-08-02 from clean clones at the pinned SHAs (crg 2.3.7, local `all-MiniLM-L6-v2` embeddings). These numbers are lower than the 2026-05-25 capture they replace: node embedding text became richer, so `avg graph_tokens` rose in every repo. fastapi is measured at its current pin `22381558` rather than the retired `0227991a`. > > The Reduction column is `naive_corpus_tokens / avg graph_tokens`, so it divides out from the two columns beside it. The benchmark's own `average_reduction_ratio` averages the five per-question ratios instead, which always reads higher; those per-question figures are in [`docs/REPRODUCING.md`](docs/REPRODUCING.md#standalone-token-benchmark-code_review_graphtoken_benchmarkpy). > > The `code-review-graph` row is a snapshot, not a current measurement. The repository has grown since `84bde354`, so its corpus and graph are both much larger today. The whole-corpus baseline is an upper bound no real agent pays; an agent greps for identifiers and reads the best-matching files. The `agent_baseline` eval benchmark measures that case (a pure-Python grep over the corpus, top-3 files by match count, token-counted against the graph query cost). It writes `evaluate/results/<repo>_agent_baseline_<date>.csv`; no canonical capture has been published yet. The formal `token_efficiency` benchmark measures a different scenario, the full `get_review_context()` JSON against only the changed-file content of a commit, and reports ratios below 1 for small commits because the response carries impact-radius edges and source snippets. The two benchmarks answer different questions; see [`docs/REPRODUCING.md`](docs/REPRODUCING.md#which-benchmark-measures-what). Review and impact tools attach a compact `context_savings` estimate to their responses. The CLI shows the same figures in the `Token Savings` panel (see Usage below) and `--verify` compares them with OpenAI's `cl100k_base` tokenizer. Calibration across 222 sample files puts the estimate within about 1% of real tokens in aggregate ([data](docs/REPRODUCING.md#calibration-table)). </details> <details> <summary>Impact accuracy: 0.69 average F1 against graph-derived ground truth (recall 1.0 is a circular upper bound)</summary> Blast-radius analysis recovers every file in the ground truth on all 13 evaluation commits. Read that as an upper bound, not as "100% recall": the ground truth (changed files plus files with call or import edges into them) comes from the same graph the predictor traverses. The lower precision is deliberate; flagging an extra file costs less than missing a broken dependency. | Repo | Commits | Avg F1 | Avg Precision | Recall (graph-derived upper bound) | |------|--------:|-------:|--------------:|-------:| | httpx | 2 | 0.863 | 0.785 | 1.0 | | code-review-graph | 2 | 0.734 | 0.584 | 1.0 | | fastapi | 2 | 0.697 | 0.539 | 1.0 | | express | 2 | 0.667 | 0.500 | 1.0 | | flask | 2 | 0.633 | 0.485 | 1.0 | | gin | 3 | 0.609 | 0.439 | 1.0 | | **Average** | **13** | **0.693** | **0.546** | **1.000** | The benchmark also runs a **co-change mode**: the predictor is seeded with one changed file and graded against the other files the author touched in the same commit, which is evidence from git history rather than from the graph. Both modes appear in the result CSVs (`ground_truth_mode` column). In the 2026-08-02 capture co-change mode returned `predicted_files = 0` on every graded commit, so it is not yet a usable measurement and no co-change number is quoted. </details> <details> <summary>Build stats</summary> From the same 2026-08-02 clean-room build at the pinned SHAs above. Embedding counts are lower than node counts because File nodes are not embedded. The `code-review-graph` row is that snapshot, not the repository as it stands now. | Repo | Nodes | Edges | Embeddings | |------|------:|------:|-----------:| | fastapi | 6,287 | 32,036 | 5,159 | | express | 1,990 | 19,492 | 1,849 | | gin | 1,589 | 17,237 | 1,491 | | code-review-graph | 1,446 | 9,094 | 1,354 | | flask | 1,415 | 8,259 | 1,329 | | httpx | 1,263 | 8,236 | 1,193 | </details> ### Limitations - **Impact "recall 1.0" is circular.** The historical ground truth comes from the same graph edges the predictor walks, so it is an upper bound by construction. The co-change mode is not yet a usable measurement. - **Small single-file changes.** Graph context can exceed a plain file read for trivial edits. The overhead is the structural metadata that makes multi-file analysis possible. - **Search ranking.** Keyword search usually finds the right result near the top, but ranking needs work. Express queries can return no hits because of module-pattern naming. - **Flow detection.** Entry-point detection is strongest for Python and PHP/Laravel. JavaScript and Go flow detection needs work. - **Precision vs recall.** Impact analysis is conservative. It flags files that might be affected, which means false positives in large dependency graphs. --- ## Features | Feature | Details | |---------|---------| | **Incremental updates** | Re-parses only files whose hash changed. On a ~3,000-file repo a two-file edit takes ~2.5 s on the hook path ([measured](docs/REPRODUCING.md#incremental-update-latency)). | | **Language and notebook support** | See [Language coverage](#language-coverage-and-notebooks) above. | | **Framework-aware PHP parsing** | Repository-bounded Composer PSR-4 imports, Blade template references, evidence-gated Laravel Route-to-controller and Eloquent relationship edges | | **Framework-aware Java parsing** | Spring dependency-injection call resolution, request endpoints and WebFlux routes, scheduled triggers, application-event publisher-to-listener edges, Temporal workflow and activity edges, and Spring Boot config keys indexed without their values | | **Blast-radius analysis** | Which functions, classes and files are likely affected by a change | | **Auto-update hooks** | Editor hooks, a git pre-commit hook and watch mode update the graph as you work | | **Semantic search** | Optional vector embeddings via sentence-transformers, Google Gemini, MiniMax, Voyage AI, or any OpenAI-compatible endpoint (OpenAI, Azure, new-api, LiteLLM, vLLM, LocalAI) | | **Interactive visualisation** | D3.js force-directed graph with search, community legend toggles and degree-scaled nodes | | **Hub and bridge detection** | Most-connected nodes and chokepoints (betweenness centrality) | | **Surprise scoring** | Unexpected coupling: cross-community, cross-language, peripheral-to-hub edges | | **Knowledge gap analysis** | Isolated nodes, untested hotspots, thin communities | | **Suggested questions** | Review questions generated from bridges, hubs and surprises | | **Edge confidence** | Two-tier confidence (EXTRACTED/INFERRED) with float scores on edges | | **Graph traversal** | BFS/DFS from any node with configurable depth and token budget | | **Export formats** | GraphML (Gephi/yEd), Neo4j Cypher, Obsidian vault, JSON, and SVG (SVG needs matplotlib from the `eval` extra) | | **Token benchmarking** | `code_review_graph/token_benchmark.py` measures whole-corpus tokens against graph query tokens per question | | **Estimated context savings** | `context_savings` metadata (`estimated`, `saved_tokens`, `saved_percent`) on review, impact, detect-changes and architecture responses | | **Community auto-split** | Communities above 25% of the graph are split recursively with Leiden | | **Execution flows** | Call chains from entry points, sorted by weighted criticality | | **Community detection** | Leiden clustering with resolution scaled to graph size | | **Architecture overview** | Community-based architecture map with coupling warnings | | **Risk-scored reviews** | `detect_changes` maps diffs to affected functions, flows and test gaps | | **Custom languages** | New languages via `.code-review-graph/languages.toml`, no fork 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** | Markdown wiki from community structure | | **Multi-repo registry** | Register several repos and search across them | | **Multi-repo daemon** | `crg-daemon` watches several repos as child processes, with health checks and restart | | **MCP prompts** | 5 workflow templates: review, architecture, debug, onboard, pre-merge | | **Full-text search** | FTS5 hybrid search combining keyword and vector similarity | | **Local storage** | One SQLite file in `.code-review-graph/`; no external database or cloud service | --- ## Usage <details> <summary>Skills</summary> `install` writes these four skills for the platforms that support them (Claude Code, Gemini CLI, CodeBuddy Code, Hermes Agent and Qoder). Ask for one by name. | Skill | Description | |-------|-------------| | `explore-codebase` | Navigate and understand codebase structure using the knowledge graph | | `review-changes` | Perform a structured code review using change detection and impact | | `debug-issue` | Systematically debug issues using graph-powered code navigation | | `refactor-safely` | Plan and execute safe refactoring using dependency analysis | Qoder also gets `build-graph`, `review-delta` and `review-pr` from the repository's `skills/` directory. </details> <details> <summary>CLI reference</summary> ```bash code-review-graph install # Detect and configure all platforms code-review-graph install --platform <name> # One platform code-review-graph uninstall --dry-run # Preview removal of installed artifacts code-review-graph build # Parse the whole codebase code-review-graph update # Incremental update (changed files only) code-review-graph status # Graph statistics code-review-graph watch # Update on file changes code-review-graph forget <path> # Drop already-parsed files from the graph code-review-graph dead-code # Functions and classes with no callers or tests code-review-graph visualize # Interactive HTML graph code-review-graph visualize --format json # Export graph data as JSON code-review-graph visualize --format graphml # Export as GraphML code-review-graph visualize --format svg # Export as SVG (needs matplotlib) code-review-graph visualize --format obsidian # Export as Obsidian vault code-review-graph visualize --format cypher # Export as Neo4j Cypher code-review-graph wiki # Markdown wiki from communities code-review-graph detect-changes --brief # Risk panel + token savings (read-only) code-review-graph detect-changes --brief --base main # Against the merge base of main and HEAD code-review-graph update --brief # Refresh graph + same panel code-review-graph detect-changes --brief --verify # Cross-check against tiktoken code-review-graph register <path> # Register repo in the multi-repo registry code-review-graph unregister <path|alias> # Remove repo from the registry code-review-graph repos # List registered repositories code-review-graph daemon start # Start the multi-repo watch daemon code-review-graph daemon stop # Stop the daemon code-review-graph daemon status # Daemon status and repos code-review-graph eval # Run evaluation benchmarks code-review-graph serve # Start the MCP server (stdio) code-review-graph serve --http # MCP over Streamable HTTP on localhost:5555 ``` This is a selection. `code-review-graph --help` lists every command, and [docs/COMMANDS.md](docs/COMMANDS.md) documents their flags. When `detect-changes --base` names a branch, the diff runs against the merge base of that branch and HEAD. Commit hashes and other revisions are used as given. `visualize --format svg` needs matplotlib, which ships in the `eval` extra (`pip install "code-review-graph[eval]"`). The other export formats need no extra install. JSON exports are written inside the local graph data directory, which Git ignores by default. They can contain absolute paths and code-structure metadata, so inspect an export before publishing it. </details> <details> <summary>Token Savings panel: <code>detect-changes --brief</code> vs <code>update --brief</code></summary> Both commands print the same panel showing how many tokens the graph saved compared with handing the changed files to an agent raw. They differ in one thing: whether the graph is refreshed first. ```text ┌─────────────────────── 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. Queries the existing graph for the current changes and prints the panel. | Most of the time; hooks or `crg-daemon` keep the graph fresh. | | `update --brief` | Re-parses the changed files into the graph first, then prints the same panel. | After a rebase, a large change set, or whenever the graph may be stale. | Add `--verify` to either command to compare the figures with OpenAI's `cl100k_base` tokenizer (needs `pip install tiktoken`). The estimate is within about 1% of real tokens in aggregate; see [`docs/REPRODUCING.md`](docs/REPRODUCING.md#calibration-table). The same `context_savings` metadata is attached to the JSON responses of the `get_impact_radius`, `get_review_context`, `detect_changes` and `get_architecture_overview` MCP tools. </details> <details> <summary>Multi-repo daemon</summary> If your editor does not support hooks (for example Cursor or OpenCode), or you want the graph kept fresh without editor integration, the daemon watches your repositories and updates their graphs. It ships with `code-review-graph`; no separate install. ```bash # 1. Register the repos 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. Check on it crg-daemon status # daemon and per-repo watcher status crg-daemon logs --repo proj-a -f # tail logs for one repo crg-daemon stop # stop the daemon and all watchers ``` Also available as `code-review-graph daemon start|stop|status|...`. `crg-daemon add` writes to `~/.code-review-graph/watch.toml`, which you can also edit directly: ```toml [[repos]] path = "/home/user/project-a" alias = "proj-a" [[repos]] path = "/home/user/project-b" alias = "project-b" ``` The daemon watches this file and starts or stops watcher processes as repos are added or removed. A health check every 30 seconds restarts dead watchers. See [docs/COMMANDS.md](docs/COMMANDS.md#standalone-daemon-cli-crg-daemon) for the full config reference. </details> <details> <summary>30 MCP tools</summary> The assistant uses these 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` | Compact context (~100 tokens); call this first | | `get_impact_radius_tool` | Blast radius of changed files | | `get_review_context_tool` | Review context with structural summary | | `query_graph_tool` | Callers, callees, tests, imports, inheritance queries | | `traverse_graph_tool` | BFS/DFS traversal from any node with a 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` | Functions, classes or files above a line-count threshold | | `list_flows_tool` | Execution flows sorted by criticality | | `get_flow_tool` | One execution flow | | `get_affected_flows_tool` | Flows affected by changed files | | `list_communities_tool` | Detected code communities | | `get_community_tool` | One community | | `get_architecture_overview_tool` | Architecture overview from community structure | | `detect_changes_tool` | Risk-scored change impact analysis | | `get_hub_nodes_tool` | Most-connected nodes | | `get_bridge_nodes_tool` | Chokepoints by betweenness centrality | | `get_knowledge_gaps_tool` | Structural weaknesses and untested hotspots | | `get_surprising_connections_tool` | Unexpected cross-community coupling | | `get_suggested_questions_tool` | Review questions generated from the analysis | | `refactor_tool` | Rename preview, dead code detection, suggestions | | `apply_refactor_tool` | Apply a previously previewed refactoring | | `generate_wiki_tool` | Markdown wiki from communities | | `get_wiki_page_tool` | One wiki page | | `list_repos_tool` | Registered repositories | | `cross_repo_search_tool` | Search registered repositories; `repos` limits the search to a subset | **MCP prompts** (5 workflow templates): `review_changes`, `architecture_map`, `debug_issue`, `onboard_developer`, `pre_merge_check` </details> <details> <summary>Configuration</summary> To exclude paths from indexing, create a `.code-review-graphignore` file in the repository root: ``` generated/** *.generated.ts vendor/** node_modules/** ``` In git repositories only tracked files are indexed (`git ls-files`), so gitignored files are skipped. Use `.code-review-graphignore` to exclude tracked files or when git is not available. The default ignore list is in [docs/USAGE.md](docs/USAGE.md#ignore-patterns). Optional dependency groups: ```bash 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 and SVG export (matplotlib) pip install "code-review-graph[wiki]" # ollama client (not used by the current wiki generator) pip install "code-review-graph[all]" # All optional dependencies ``` ### Environment Variables | Variable | Description | Default | |----------|-------------|---------| | `CRG_GIT_TIMEOUT` | Timeout in seconds for Git operations (build, update, watch) | `30` | | `CRG_DISCOVERY_TIMEOUT` | Timeout in seconds for each Git command that discovers what changed, when a review tool or command was not given an explicit file list. Running out reports an error, never "no changes" | `5`, or `CRG_GIT_TIMEOUT` when you set that explicitly | | `CRG_DATA_DIR` | Directory for graph databases and generated artefacts | - | | `CRG_HOOK_WORKTREES` | Set to `1` to let the pre-commit hook run in linked git worktrees | - | | `CRG_EMBEDDING_MODEL` | Default model for local vector embeddings | `all-MiniLM-L6-v2` | | `CRG_ACCEPT_CLOUD_EMBEDDINGS` | Set to `1` to suppress the cloud embedding egress warning | - | | `CRG_ALLOW_REMOTE_CODE` | Allow HuggingFace models that require `trust_remote_code=True` | `0` | | `CRG_MAX_IMPACT_NODES` | Maximum nodes 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_CALLER_TEST_ROUTE_DEPTH` | Hops to walk up incoming `CALLS` when looking for a tested caller of an untested changed symbol. Reported as a note, never as coverage | `2` | | `CRG_CALLER_TEST_ROUTE_MAX_CALLERS` | Maximum callers examined while looking for that tested caller | `500` | | `CRG_TOOL_TIMEOUT` | Timeout in seconds for read-only MCP tools (`0` disables). Does not bound the tools that write: build, postprocess, embed, wiki and apply-refactor | `0` | | `CRG_CHURN_WINDOW_DAYS` | Window for `detect-changes --churn` commit counts | `90` | | `CRG_LEIDEN_SEED` | Seed for Leiden community detection | `42` | | `CRG_RECURSE_SUBMODULES` | Include git submodules 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 | - | | `VOYAGE_API_KEY` | API key for Voyage embeddings | - | | `CRG_VOYAGE_MODEL` | Model for Voyage embeddings | `voyage-code-3` | | `CRG_VOYAGE_OUTPUT_DIMENSION` | Output dimension for Voyage embeddings | `1024` | | `CRG_VOYAGE_OUTPUT_DTYPE` | Output dtype for Voyage embeddings | `float` | | `CRG_VOYAGE_BASE_URL` | Voyage embeddings endpoint | `https://api.voyageai.com/v1` | | `CRG_VOYAGE_BATCH_SIZE` | Batch size for Voyage requests | `100` | | `CRG_VOYAGE_MIN_INTERVAL_SEC` | Minimum delay between Voyage requests | `0` | | `CRG_OPENAI_BASE_URL` | OpenAI-compatible embeddings endpoint | - | | `CRG_OPENAI_API_KEY` | API key for OpenAI-compatible embeddings | - | | `CRG_OPENAI_MODEL` | Model for OpenAI-compatible embeddings | - | | `CRG_OPENAI_DIMENSION` | Pin the embedding dimension (v3 models support reduction) | - | | `CRG_OPENAI_BATCH_SIZE` | Batch size for OpenAI-compatible requests | `100` | | `NO_COLOR` | Disable ANSI colours in the terminal | - | | `CRG_SERIAL_PARSE` | Set to `1` to disable parallel parsing (for debugging) | - | OpenAI-compatible embeddings (OpenAI, Azure, or a self-hosted gateway such as new-api, LiteLLM, vLLM, LocalAI, or Ollama in OpenAI mode) need no extra install. Set the variables and pass `provider="openai"` to `embed_graph`: ```bash 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 skipped when the base URL points at localhost (`127.0.0.1`, `localhost`, `0.0.0.0`, `::1`). Voyage embeddings need no extra install. Set `VOYAGE_API_KEY` and pass `provider="voyage"` to `embed_graph`; the default model is `voyage-code-3`: ```bash export VOYAGE_API_KEY=pa-... export CRG_ACCEPT_CLOUD_EMBEDDINGS=1 code-review-graph embed --provider voyage --model voyage-code-3 ``` > **Model selection.** Avoid `-preview`, `-beta` or `-exp` model IDs for an index you plan to keep; preview models can change weights (a different dimension forces a full re-embed) or be withdrawn. Prefer GA releases such as `text-embedding-3-small` / `text-embedding-3-large` (OpenAI), `Qwen/Qwen3-Embedding-8B` (self-hosted vLLM or LocalAI), or `gemini-embedding-001` (native Gemini provider, which needs `GOOGLE_API_KEY`). > > The embedding text is identifiers, signatures, structural context, and a bounded first-paragraph docstring or doc-comment summary. Function bodies are not sent. Graphs created before documentation extraction was added need one full `code-review-graph build` before re-embedding. Routine builds never refresh embeddings; to refresh after a build, pass both `--embedding-provider` and `--embedding-model`. Cloud providers receive this source-derived text and may charge for it. #### Tool Filtering CRG exposes 30 MCP tools by default. To limit the server to a subset, use `--tools` or the `CRG_TOOLS` environment variable: ```bash # CLI flag code-review-graph serve --tools query_graph_tool,semantic_search_nodes_tool,detect_changes_tool # Environment variable CRG_TOOLS=query_graph_tool,semantic_search_nodes_tool code-review-graph serve ``` The flag takes precedence over the variable. When neither is set, all tools are available. In an MCP client config: ```json { "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"] } } } ``` </details> --- ## FAQ and comparisons Answers in [docs/FAQ.md](docs/FAQ.md): - [vs LSP / language servers](docs/FAQ.md#how-is-this-different-from-lsp-and-language-servers): one persistent cross-language graph instead of per-language daemons; LSP stays more precise per symbol. - [vs RAG / embeddings](docs/FAQ.md#isnt-this-just-rag): structural edges parsed from the AST, not similarity chunks; embeddings are optional and only assist search. - [vs grep / agentic search](docs/FAQ.md#why-not-just-grep): grep wins on one-hop lookups; the graph wins on multi-hop questions (impact radius, callers-of-callers, tests-for, affected flows). - [vs Serena, codegraph, claude-context, repomix](docs/FAQ.md#how-does-it-compare-to-serena-codegraph-claude-context-and-repomix): comparison table. - [When not to use it](docs/FAQ.md#when-should-i-not-use-it): small repos, trivial single-file diffs, one-off questions. - [Does it phone home?](docs/FAQ.md#does-it-phone-home): no telemetry; cloud embeddings are opt-in. - [How do I verify it is working?](docs/FAQ.md#how-do-i-verify-it-is-working): `status`, `detect-changes --brief`, `/mcp`. ## Troubleshooting More cases, including Windows/WSL, are in [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md). ### `pip` / `pipx` cannot download `hatchling` (or `Errno 9` / `Bad file descriptor` to PyPI) Installing from a source tree (for example `pipx install .`) needs build dependencies from PyPI. If you see `Could not find a version that satisfies the requirement hatchling` after connection warnings, the Python in that terminal may not be able to open an HTTPS connection to `pypi.org`. This is seen most often in an editor's integrated terminal, and sometimes with a VPN, firewall or proxy. 1. Run the same command from Terminal.app or iTerm instead of the editor's terminal. 2. Install from a checkout with [uv](https://docs.astral.sh/uv/), which uses different download machinery: ```bash cd /path/to/code-review-graph uv tool install . --force ``` 3. For development in a clone, use `uv sync` and `uv run code-review-graph ...`. To diagnose: `python3 scripts/diagnose_pypi_connectivity.py`. If it prints `FAILED`, the problem is the network environment, not the package name. ### Windows: `Invalid JSON: EOF while parsing` or `MCP error -32000: Connection closed` Do not use a `cmd /c` wrapper in the Claude Code config. Point `~/.claude.json` at the `.exe` directly and set UTF-8 through the config: ```json "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" } } ``` ## Contributing ```bash 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]" pytest ``` Pull requests target `staging` (the default branch). Changes are promoted `staging` → `testing` → `main`, and releases are tagged from `main`. The first step runs once a day by itself when `staging` is green; promotion to `main` is never automatic. See [CONTRIBUTING.md](CONTRIBUTING.md#branching-and-promotion) for the full flow. To add a built-in language, edit `code_review_graph/parser.py`: add the extension to `EXTENSION_TO_LANGUAGE` and node type mappings to `_CLASS_TYPES`, `_FUNCTION_TYPES`, `_IMPORT_TYPES` and `_CALL_TYPES`. Include a test fixture and open a PR. For a language you only need in one repository, use [`languages.toml`](docs/CUSTOM_LANGUAGES.md) instead. ## Licence MIT. See [LICENSE](LICENSE). <a href="https://code-review-graph.com">code-review-graph.com</a> <code>pip install code-review-graph && code-review-graph install</code>

README des Repositorys wird geladen…

Skills in diesem Repository

Softwarequalitätssicherungsanalysten und -tester Softwareentwickler

2 Berufskategorien · 100% klassifiziert

Skill

Beruf

Beschreibung

Aktualisiert

build-graph

Softwarequalitätssicherungsanalysten und -tester

Build or update the code review knowledge graph. Run this first to initialize, or let hooks keep it updated automatically.

Quellsprache: Englisch

2026-09-13

debug-issue

Softwarequalitätssicherungsanalysten und -tester

Systematically debug issues using graph-powered code navigation

Quellsprache: Englisch

2026-09-13

explore-codebase

Softwareentwickler

Navigate and understand codebase structure using the knowledge graph

Quellsprache: Englisch

2026-09-13

refactor-safely

Softwareentwickler

Plan and execute safe refactoring using dependency analysis

Quellsprache: Englisch

2026-09-13

review-changes

Softwarequalitätssicherungsanalysten und -tester

Perform a structured code review using change detection and impact

Quellsprache: Englisch

2026-09-13

review-delta

Softwarequalitätssicherungsanalysten und -tester

Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.

Quellsprache: Englisch

2026-09-13

review-pr

Softwarequalitätssicherungsanalysten und -tester

Review a PR or branch diff using the knowledge graph for full structural context. Outputs a structured review with blast-radius analysis.

Quellsprache: Englisch

2026-09-13

Es werden 7 von 7 gesammelten Skills angezeigt.

Mit einem KI-Assistenten installieren

Diesen Prompt kopieren und in den verwendeten KI-Assistenten einfügen.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/tirth8205/code-review-graph.