How I Cut My AI Coding Agent's Token Usage by 120x With a Code Knowledge Graph

AI coding agents are powerful — but they're also blind. Every time Claude Code, Codex, or Gemini CLI needs to understand your codebase, they explore it file by file. Grep here, read there, grep again. For a simple question like "what calls ProcessOrder?", an agent might burn through 45,000 tokens just opening files and scanning for matches. I built codebase-memory-mcp to fix this. It parses your codebase into a persistent knowledge graph — functions, classes, call chains, imports, HTTP routes — and exposes it through 14 MCP tools. The same question now costs ~200 tokens and answers in under 1ms. The Problem: File-by-File Exploration Doesn't Scale Here's what actually happens when you ask an AI agent "trace the callers of ProcessOrder": Agent greps for ProcessOrder across all files (~15,000 tokens) Reads each matching file to understand context (~25,000 tokens) Follows imports to find indirect callers (~20,000 tokens) Gives up after hitting context limits, missing half the call chain Mu