Imagine watching your AI coding agent burn through 200,000 tokens just blindly clicking through folders trying to figure out how your app is wired. Graphify just solved this massive "amnesia" problem by giving Claude Code a permanent, living knowledge graph of your entire repository before it even starts thinking.

We’ll talk about:

The 3-Pass Mapping System: How Graphify turns not just code, but meeting transcripts, architecture PDFs, and images into a highly structured knowledge graph.
The 120,000-Token Wipeout: The surprising benchmark test where pre-mapping a repo dropped Claude Code's query cost from 200k tokens down to just 80k—without losing accuracy.
Graphify vs. Graph RAG: Breaking down why you shouldn't use traditional embeddings for structured codebases, and why deterministic local parsing (tree-sitter) is the smarter play.
The "Living Repo" Strategy: How to set up an automated git hook so your AI’s memory updates itself with every single commit—plus a slick trick to export it all directly into an Obsidian vault.

Keywords: Graphify, Claude Code, Anthropic, AI coding agents, knowledge graph, Graph RAG, token optimization, tree-sitter, LLM memory, codebase mapping, developer tools, Obsidian vault.

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#48 Robin: Stop Wasting Tokens - How Graphify Gives Claude Code a Brain and Slashes API Costs by 60% 💸

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#48 Robin: Stop Wasting Tokens - How Graphify Gives Claude Code a Brain and Slashes API Costs by 60% 💸