hedwig-cg 0.14.1

Local-first code graph builder with hybrid vector + graph search

hedwig-cg

"With hedwig-cg, your coding agent knows what to read."
Quick Start · 한국어 · 日本語 · 中文 · Deutsch

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Why hedwig-cg?

raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki - Andrej Karpathy

hedwig-cg builds a queryable code graph and knowledge base from codebases with 10,000+ files and knowledge documents, powered by lightweight local LLM models. Hybrid vector + keyword search with subgraph response (vector + keyword → RRF fusion with MST subgraph) lets coding agents truly understand your entire project, not just search keywords. Install it, and Claude Code sees the full picture — no extra tokens, no extra commands, everything runs 100% locally.

Quick Start

pip install hedwig-cg

cd your-project/
hedwig-cg claude install

Then tell Claude Code:

"Build a code graph for this project"

That's it. Claude Code will build the graph, and from then on, consult it before every search. The graph auto-rebuilds when your session ends.

AI Agent Integrations

hedwig-cg integrates with major AI coding agents in one command:

Agent	Install	What it does
Claude Code	hedwig-cg claude install	Skill + CLAUDE.md + PreToolUse hook
Codex CLI	hedwig-cg codex install	AGENTS.md + PreToolUse hook
Gemini CLI	hedwig-cg gemini install	GEMINI.md + BeforeTool hook
Cursor IDE	hedwig-cg cursor install	.cursor/rules/ rule file
Windsurf IDE	hedwig-cg windsurf install	.windsurf/rules/ rule file
Cline	hedwig-cg cline install	.clinerules file
Aider CLI	hedwig-cg aider install	CONVENTIONS.md + .aider.conf.yml
MCP Server	claude mcp add hedwig-cg -- hedwig-cg mcp	5 tools over Model Context Protocol

Each install does two things: writes a context file with rules, and (where supported) registers a hook that fires before tool calls. To remove: hedwig-cg <platform> uninstall.

Supported Languages

Structural Extraction (20+ languages)

hedwig-cg extracts functions, classes, methods, calls, imports, and inheritance from source code using tree-sitter and native parsers.

Python	JavaScript	TypeScript	Go
Rust	Java	C	C++
C#	Ruby	Swift	Scala
Lua	PHP	Elixir	Kotlin
Objective-C	Terraform/HCL

Also extracts structure from config and document formats: YAML, JSON, TOML, Markdown, PDF, HTML, CSV, Shell, R, and more.

Multilingual Natural Language

Text nodes (docs, comments, markdown) are embedded with intfloat/multilingual-e5-small supporting 100+ natural languages — Korean, Japanese, Chinese, German, French, and more. Search in your language, find results in any language.

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Features

Auto-Rebuild

When integrated with AI coding agents (Claude Code, Codex, etc.), hedwig-cg automatically rebuilds the graph when code changes. The Stop/SessionEnd hook detects modified files via git diff and triggers an incremental rebuild in the background — zero manual intervention.

Smart Ignore

hedwig-cg respects ignore patterns from three sources, all using full gitignore spec (negation !, ** globs, directory-only patterns):

Source	Description
Built-in	.git, node_modules, __pycache__, dist, build, etc.
.gitignore	Auto-read from project root — your existing git ignores just work
.hedwig-cg-ignore	Project-specific overrides for the code graph

Incremental Builds

SHA-256 content hashing per file. Only changed files are re-extracted and re-embedded. Unchanged files are merged from the existing graph — typically 95%+ faster than a full rebuild.

Memory Management

4GB memory budget with stage-wise release. The pipeline generates → stores → frees at each stage: extraction results are freed after graph build, embeddings are streamed in batches and freed after DB write, and the full graph is released after persistence. GC triggers proactively at 75% threshold.

100% Local

No cloud services, no API keys, no telemetry. SQLite + FAISS for storage, sentence-transformers for embeddings. All data stays on your machine.

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Hybrid Search with Subgraph Response

Every query returns seed nodes and a subgraph showing how they connect:

Search Pipeline

Signal	What it finds
Vector Search	Semantically similar code and documents (dual-model: code + text)
Keyword Search	Exact name matches via FTS5 (BM25)

Results are fused via Weighted Reciprocal Rank Fusion (RRF), then connected through MST-based shortest paths to reveal how seed nodes relate.

Response Format

seeds:
hedwig_cg/core/pipeline.py:71
hedwig_cg/query/embeddings.py:70

edges:
hedwig_cg/core/pipeline.py:71 -calls-> hedwig_cg/core/extract.py:747
hedwig_cg/core/pipeline.py:0 -co_change-> hedwig_cg/query/embeddings.py:0

• seeds: Node IDs (file:line) found by search
• edges: Subgraph connecting seeds through shortest paths (intermediate nodes appear in edges)

CLI Reference

All commands output compact text by default (designed for AI agent consumption).

Command	Description
build <dir>	Build code graph (--incremental)
search <query>	Hybrid vector + keyword search with subgraph (--top-k, --fast)
search-vector <query>	Vector similarity only (code + text dual model)
search-keyword <query>	FTS5 keyword matching only (BM25 ranking)
query	Interactive search REPL
communities	List and search communities (--search, --level)
stats	Graph statistics
node <id>	Node details with fuzzy matching
export	Export as JSON, GraphML, or D3.js
visualize	Interactive HTML visualization
clean	Remove .hedwig-cg/ database
doctor	Check installation health
mcp	Start MCP server (stdio)
claude install|uninstall	Manage Claude Code integration
codex install|uninstall	Manage Codex CLI integration
gemini install|uninstall	Manage Gemini CLI integration
cursor install|uninstall	Manage Cursor IDE integration
windsurf install|uninstall	Manage Windsurf IDE integration
cline install|uninstall	Manage Cline integration
aider install|uninstall	Manage Aider CLI integration

Performance

Benchmarks on hedwig-cg's own codebase (~3,500 lines, 90 files, 1,300 nodes):

Operation	Time
Full build	~14s
Incremental (changes)	~4s
Incremental (no changes)	~0.4s
Cold search (dual model)	~2.8s
Cold search (--fast)	~0.2s
Warm search	~0.08s
Cached search	<1ms

• Embedding models: ~180MB, downloaded once to ~/.hedwig-cg/models/
• Database: ~2MB (SQLite + FTS5 + FAISS indices)
• Incremental builds: SHA-256 hashing, 95%+ faster than full rebuild

Requirements

• Python 3.10+
• ~180MB disk for embedding models (cached on first use)

# Optional: PDF extraction
pip install hedwig-cg[docs]

Development

pip install -e ".[dev]"
pytest
ruff check hedwig_cg/

License

MIT License. See LICENSE for details.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.