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Graphify: Turn Codebases into Knowledge Graphs to Slash AI Token Costs

Graphify-Labs has released graphify, an open-source tool that converts entire codebases into structured knowledge graphs to reduce AI token consumption by up to 71.5x. The tool uses tree-sitter for local static analysis and an AI semantic pass on documentation, providing agents with instant architectural awareness and integrating with CLI agents and IDEs.

read2 min views4 publishedAug 19, 2026

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×.

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.

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.

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.

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.

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.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.

uv tool install graphify-labs

graphify

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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