Using Graphify to Map Codebases for Claude Code

The Breakthrough

Graphify replaces standard grep-based file searching in AI coding agents with a deterministic knowledge graph that maps code structure, dependencies, and documentation, allowing agents to query the repository architecture directly rather than scanning raw files.

What Actually Worked

• Deterministic Parsing: The tool uses Tree-sitter to parse code locally without an LLM, extracting classes, functions, imports, and call graphs to build the initial graph structure.
• Multi-Pass Extraction: The system executes three distinct passes: a structural code analysis (pass one), audio/video transcription via Faster-Whisper (pass two), and semantic analysis of documentation and images (pass three).
• Persistent Hooking: Running graphify hook install enables automatic graph rebuilding after each git commit, ensuring the knowledge map stays synchronized with repository changes without additional API costs.
• Agent Integration: The tool installs a custom skill for Claude Code, allowing users to trigger queries via /graphify or set the agent to use the graph automatically for all context retrieval.

Before / After

• Token Usage: In a test tracing a design request flow through the Open Design repository, the standard grep-based approach consumed approximately 200,000 tokens, while the Graphify-enabled approach consumed 80,000 tokens, representing a 60% reduction in token cost.

Context

AI coding assistants often struggle with large repositories because they rely on searching through individual files, which is inefficient and token-heavy. Graphify acts as a middle ground between simple note-taking tools like Obsidian and complex RAG infrastructure. By building a persistent map of nodes and edges, it provides the agent with a structural understanding of the codebase, which remains useful across multiple sessions and team-based workflows.

Content References

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