Codebase Architecture Mapping: Meet graphify
AI coding agents (such as Claude Code, Cursor, and GitHub Copilot) excel at generating code snippets, but they often struggle to maintain full architectural context in large software repositories. When answering multi-file questions, traditional agents repeatedly search and re-read raw source files—wasting thousands of context window tokens and introducing latency.
graphify is an open-source tool developed by Graphify-Labs to solve codebase context fragmentation. By converting entire repositories into structured, queryable knowledge graphs, graphify provides AI agents with instant architectural awareness while reducing token consumption by up to 71.5×.
What is graphify?
graphify bridges the gap between deterministic static analysis and semantic AI comprehension. It acts as an architectural layer that maps relationships between functions, classes, modules, and documentation across your entire project.
Key Core Features
1. Zero-Token Local Tree-Sitter AST Parsing
The structural analysis phase of graphify uses tree-sitter to parse your code into an Abstract Syntax Tree (AST). It extracts exact function calls, class inheritance, and import dependencies locally—requiring zero LLM tokens and sending zero data to external servers.
2. Hybrid Semantic Layer
Beyond static code structure, graphify uses your AI assistant to perform a semantic pass on documentation, Markdown files, and architectural notes. It links high-level design concepts directly to the underlying source code nodes.
3. 71.5× Token Cost Reduction
By providing pre-computed graph queries rather than forcing the LLM to grep through hundreds of raw files, graphify dramatically reduces prompt token volume, enabling faster responses and lower API bills.
4. Rich Interactive Outputs
Running /graphify generates multiple architectural artifacts inside your workspace:
-
graph.html: An interactive D3 visual map of your repository. -
GRAPH_REPORT.md: A structured markdown summary of codebase architecture. -
graph.json: A structured graph format compatible with GraphRAG pipelines.
5. Seamless AI Client & Git Integration
graphify integrates with popular CLI agents and IDEs, and can be configured as a Git pre-commit hook to keep your knowledge graph continuously synchronized.
# Install Graphify
uv tool install graphify-labs
# Generate codebase knowledge graph
graphify
Conclusion
By shifting AI codebase analysis from brute-force file reading to structured knowledge graph querying, graphify sets a new standard for developer productivity. It provides AI agents with the deep architectural awareness required for large-scale refactoring and system design.
Want to map your codebase? Check out the graphify GitHub Repository.
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