CodeGraph

Cross-language code intelligence for AI agents and developers.

CodeGraph builds a semantic graph of your codebase — functions, classes, imports, call chains — and exposes it through 42 MCP tools, a VS Code extension, a JetBrains IDE plugin, and a persistent memory layer. Parses 38 languages via tree-sitter. AI agents get structured code understanding instead of grepping through files.

Quick Start

MCP Server (Claude Code, Cursor, any MCP client)

Add to ~/.claude.json (or your MCP client config):

{
  "mcpServers": {
    "codegraph": {
      "command": "/path/to/codegraph-server",
      "args": ["--mcp"]
    }
  }
}

The server indexes the current working directory automatically.

VS Code Extension

Install the VSIX:

code --install-extension codegraph-0.20.1.vsix

One VSIX serves every platform. The analysis engine is not bundled: on first activation the extension offers to download the engine built for your platform, verifies it against the published checksum, and installs it into ~/.codegraph/bin - the same location the JetBrains plugin uses, so one download serves both. The download is offered rather than performed automatically, because it is a native binary that runs with your permissions. Decline it and run CodeGraph: Download Analysis Engine from the command palette whenever you are ready.

Once an engine is present, the extension starts it automatically and registers all tools as Language Model Tools for Copilot.

JetBrains IDEs

A plugin for IntelliJ IDEA, PyCharm, GoLand, Android Studio and the rest of the family drives the same engine over LSP: Code Vision, Symbols and Memories tool windows, a graph panel, and one-click MCP registration for the AI Assistant. It resolves or downloads the engine the same way the VS Code extension does, sharing ~/.codegraph/bin.

→ jetbrains/README.md for surfaces, engine resolution order, and building from source.

Rules for AI agents

Pre-configured rule files that teach AI coding agents (Claude, Cursor, Windsurf, Codex, Cline) to use CodeGraph MCP tools before falling back to grep / multi-file reads. Maps natural-language intent to the right codegraph_* tool.

→ codegraph-ai/codegraph-rules-for-agents

Setup is cp <agent>/codegraph.md ~/<agent>/ (one line per agent — see the rules repo's README).

GitHub Action — PR review in CI

Drop a workflow into your repo to get an automatic code-graph analysis comment on every PR — blast radius, test gaps, stale docs, suggested reviewers. Runs graph-only (no embeddings, no ONNX model), so it's fast and needs no API keys — just the built-in GITHUB_TOKEN.

Copy .github/workflows/codegraph-pr.yml into your repo. The core invocation is a single command:

codegraph-server --graph-only \
  --run-tool codegraph_pr_context \
  --tool-args '{"baseBranch":"main","format":"markdown"}'

This prints a ready-to-post markdown comment. The --graph-only flag skips embedding generation (10-50× faster indexing); --run-tool runs one tool and exits without the MCP stdio handshake — ideal for scripting.

Configuration

MCP Server flags

--embedding-model static — model2vec fast indexing

Static (model2vec) embeddings replace the ONNX transformer with a token→vector lookup table: indexing is ~100× faster (this repo's 5,873 symbols embed in ~1 s vs ~3.4 min with BGE) and there's no ONNX runtime or 1.5 GB RAM gate. Retrieval stays hybrid (BM25 + semantic), so end-to-end quality is ~90% of BGE. The model is not bundled with any client — it needs a local model directory (config.json + tokenizer.json + model.safetensors) at ~/.codegraph/static_models/jina-code-static-256, or wherever CODEGRAPH_STATIC_MODEL points:

• Installing @astudioplus/codegraph-mcp from npm downloads it into that default location for you (best-effort; set CODEGRAPH_SKIP_MODEL_FETCH=1 to skip, and the install never fails over it).
• Otherwise fetch the prebuilt one with scripts/fetch-static-model.sh, or distill your own from any sentence-transformer (Apache-2.0 Jina-Code by default) in ~30 s on CPU: python scripts/distill_static_model.py.
• A model in the default location needs no IDE setting: both IDE clients leave CODEGRAPH_STATIC_MODEL unset and let the engine resolve it. To use a model kept somewhere else, set codegraph.staticModelPath in VS Code, or Settings → Tools → CodeGraph → Embeddings → Static model directory in JetBrains; each client then passes that path as CODEGRAPH_STATIC_MODEL.

CODEGRAPH_SKIP_MEMORY_CHECK — force the embedding model past the RAM gate

Before loading the ONNX model, the server checks available memory and, if under ~1.5 GB, skips the model to avoid an OOM-kill (running graph-only instead). Set CODEGRAPH_SKIP_MEMORY_CHECK=1 (also accepts true/yes) to bypass that check and always load the model.

Use it if embeddings are disabled even though the machine has plenty of free RAM. A reading of 0 MB available is treated as a detection failure and the model loads anyway (macOS parks reclaimable memory in inactive/speculative pages that some memory readers do not count as free), so this override is mainly for other cases where the reported figure is low but wrong. It works in both MCP and one-shot --run-tool modes.

--profile — narrow the MCP tool surface

The full 42-tool surface is convenient but inflates the agent's prompt-context cost. A profile exposes only the slice you need (also settable via the CODEGRAPH_TOOL_PROFILE env var):

VS Code settings

The codegraph.* settings are documented once, next to the extension that reads them:

→ vscode/README.md — Configuration

Full-body embeddings are enabled by default. Function body text is captured at parse time with zero I/O overhead.

Built-in exclusions (always skipped) cover ~47 directories across three categories:

• Build / cache: node_modules, target, dist, build, out, .git, __pycache__, vendor, .venv, venv, .tox, .pytest_cache, .mypy_cache, .ruff_cache, .next, .nuxt, .svelte-kit, .parcel-cache, .npm, .yarn, .pnpm-store, .cache, .cargo, .bundle, .gradle, DerivedData, Pods, xcuserdata, cmake-build-*
• IDE / IaC state: .idea, .vscode-test, .fleet, .terraform, .terragrunt-cache, .serverless
• Sensitive credential dirs: .aws, .ssh, .gnupg, .kube, .docker

Plus glob patterns for binary archives, native libraries, OS metadata, and secret file extensions (*.pem, *.key, *.p12, *.pfx, *.crt, *.gpg, *.kdbx, SSH key conventions like id_rsa, etc.) — defense in depth against accidentally embedding credentials.

Indexing produced zero files, or something else looks wrong? See docs/troubleshooting.md.

Tools

42 community tools, plus 27 more (17 of them security analyzers) in CodeGraph Pro.

Code Analysis (11)

Code Navigation (13)

Indexing (3)

Memory (7)

Persistent AI context across sessions — debugging insights, architectural decisions, known issues.

Pairs well with Tempera — an episodic memory system that captures transferable debugging strategies and solutions across projects. CodeGraph's memory tools store project-scoped notes; Tempera captures cross-project BKMs (best-known methods) that improve over time.

PR / Change Analysis (1)

Documentation (7)

Persistent project documentation — index design docs, search them semantically, verify code matches the design, generate architecture docs from the code graph.

All tool names are prefixed with codegraph_ (e.g. codegraph_get_ai_context). Tools that target a specific symbol accept uri + line or nodeId from symbol_search results.

Usage examples

Index a design doc and search it:

codegraph_index_markdown(path: "/projects/myapp/docs/ARCHITECTURE.md")
codegraph_search_docs(query: "how does the auth module handle JWT refresh?")

Check if the code matches the design:

codegraph_verify_design(source: "/projects/myapp/docs/ARCHITECTURE.md", direction: "forward")
// → "132/132 identifiers verified, 0 gaps"

Find what's described in docs but not yet implemented:

codegraph_design_gaps(source: "/projects/myapp/docs/API_DESIGN.md")
// → "4 of 12 identifiers not found in code: PaymentService, RefundHandler, ..."

Generate architecture docs from the code graph:

codegraph_generate_architecture_doc(scope: "src/", topN: 5)
// → Markdown with modules, complexity hotspots, hot paths, circular deps

Save a debugging insight for future sessions:

codegraph_memory_store(kind: "debug_context", title: "Nginx body size limit",
  content: "The /upload endpoint fails on payloads > 1MB...",
  problem: "API returns 500 on large uploads",
  solution: "Increase nginx client_max_body_size to 10M",
  agentSource: "claude")

Get AI context with graph compression stats + design doc augmentation:

codegraph_get_ai_context(uri: "file:///projects/myapp/src/auth.rs", line: 42, intent: "modify")
// → Code context + graphStats: {entitiesInGraph: 13555, entitiesTraversed: 47, entitiesKept: 8}
// → design_context section from indexed docs mentioning "auth"

Review a PR — blast radius, test gaps, stale docs, reviewers in one call:

codegraph_pr_context(baseBranch: "main")
// → "PR changes 4 files (+263/-77, 12 functions). 37 direct callers, 8 tests, 3 untested. Risk: medium."
// → test_gaps: [refresh_token, revoke_session] — functions with 0 test callers
// → stale_docs: ["auth.rs described in ARCHITECTURE.md > Authentication — doc may need updating"]
// → suggested_reviewers: [{author: "anvanster", lines_owned: 3200}]
// → commit_hint: "feat(mcp): <describe the change>"

Narrow the tool surface for chatty sessions:

codegraph-server --mcp --profile=core  # Only 8 tools: search + symbol info + AI context

CodeGraph Pro

Additional tools available in CodeGraph Pro:

Cross-cutting features (all security_check_* tools):

• include_tests / treat_as_production — first-class skip for tests/samples/vendored
• check_compile_gates — C/C++ findings inside #ifdef X are marked DEFENSIVE_GATED_OFF when X isn't defined by CMake/Cargo/Makefile
• 25-marker suppression honoring (# nosec, // NOLINT, // codeql[ignore], # rubocop:disable, etc.) at line and function level
• Telemetry blocks per scan: path_filter (examined/matched/skipped) + compile_gate (gated_off count)

Languages

38 languages parsed via tree-sitter — functions, classes, imports, call graph, complexity metrics, dependency graphs, symbol search, and impact analysis:

HTTP handler detection: Python (FastAPI/Flask/Django), TypeScript (NestJS), Java (Spring/JAX-RS), Go (stdlib/Gin/Echo/Fiber), C# (ASP.NET), Ruby (Rails), PHP (Laravel/Symfony).

Community vs full builds: COBOL, Fortran, Perl, Dart, Zig, and R are compiled only with --features extra-languages. The default community binary omits them — they had zero usage in telemetry and their tree-sitter grammars add ~25 MB (COBOL's parse tables alone are 30 MB). The other 32 languages are always available.

Architecture

MCP Client (Claude, Cursor, ...)   VS Code Extension   JetBrains Plugin
        |                                  |                  |
    MCP (stdio)                       LSP Protocol       LSP Protocol
        |                                  |                  |
        └───────────┐               ┌──────┴──────────────────┘
                    ▼               ▼
            ┌─────────────────────────────┐
            │       codegraph-server      │
            ├─────────────────────────────┤
            │  38 tree-sitter parsers     │
            │  Semantic graph engine      │
            │  AI query engine (BM25)     │
            │  Memory layer (RocksDB)     │
            │  Docs store (RocksDB+HNSW)  │
            │  Full-body embeddings (BGE) │
            │  HNSW vector index          │
            └─────────────────────────────┘

A single Rust binary serves both MCP and LSP protocols.

• Indexing: ~60 files/sec. Incremental re-indexing on file changes via FNV-1a content hashing.
• Persistence: Graph and embeddings persist to ~/.codegraph/graph.db (RocksDB). Instant startup on restart — no re-parsing, no re-embedding.
• Queries: Sub-100ms. Cross-file import and call resolution at index time.
• Embeddings: Full-body (function bodies captured at parse time, zero disk I/O). Vectors stored in RocksDB alongside the graph. Auto-downloads model on first run.

Supported platforms

The engine is a native binary, downloaded for your platform on first run.

Linux requires glibc 2.30 or newer and a libstdc++ from GCC 11 or newer (GLIBCXX_3.4.29). The second requirement is the binding one, and it is not implied by the first — the engine embeds ONNX Runtime, which is built with GCC 11.

Both Linux architectures have identical requirements. If the engine exits immediately with a message like

version `GLIBCXX_3.4.29' not found (required by codegraph-server)

the distribution's C++ runtime is older than the engine needs; installing a newer libstdc++ (for example RHEL 8's gcc-toolset-11) resolves it without upgrading the distribution.

Building from Source

git clone https://github.com/codegraph-ai/codegraph
cd codegraph
cargo build --release -p codegraph-server    # Rust server
cd vscode && npm install && npm run esbuild  # VS Code extension
npx @vscode/vsce package                     # VSIX

Requires Rust stable, Node.js 18+, VS Code 1.90+.

Support the project

CodeGraph is free, open-source, and maintained by a solo developer. If it saves you time, consider sponsoring on GitHub — it helps keep the project alive and growing.

License

Apache-2.0