Index Large Codebases for AI Agents in Minutes to Cut Token and Call Costs
A local, open-source tool uses code-intelligence graphs to dramatically reduce the token and tool-call overhead for AI agents, with evidence from a 31-repo evaluation.
Practical Summary
Replace slow, one-by-one file reading by AI agents with a pre-built knowledge graph of your codebase. This approach, implemented in the tool 'codebase-memory-mcp', has been shown to reduce token usage for structural queries by 99% and cut the number of tool calls by 2.1x, directly lowering operational costs and latency.
Why It Matters
For development teams using AI agents for code analysis or generation, token consumption and tool calls are major cost drivers. This workflow offers a method to index code locally, enabling agents to query a graph instead of scanning files. The result is faster, cheaper agent operations with research-backed quality (83% answer quality) and no external API dependencies, supporting cost reduction and security.
Understanding the Cost Problem with AI Agents
When an AI agent analyzes a large codebase, it often reads files sequentially or searches text. This process consumes many tokens (the units you pay for with LLM APIs) and requires numerous tool calls, increasing latency and cost. For a 28-million-line codebase like the Linux kernel, this approach is prohibitively slow and expensive.
The Solution: A Code Intelligence Knowledge Graph
The open-source tool 'codebase-memory-mcp' creates an Abstract Syntax Tree (AST) knowledge graph. It maps functions, classes, routes, and dependencies. Instead of reading files, the agent queries this graph directly for context, which is a structural lookup rather than a text search.
This architectural shift is evaluated in the paper arXiv:2603.27277 across 31 real-world repositories. The research reports: 99% reduction in tokens for structural queries, 83% answer quality on complex tasks, and 2.1x fewer tool calls required.
Key Benefits for Cost and Workflow Optimization
1. Direct Cost Reduction: Lower token usage means lower API bills when using paid LLMs. 2. Speed: Fewer tool calls and direct graph queries reduce agent response time. 3. Local & Secure: The tool runs as a single binary on your machine with no Docker or API keys, keeping code private and eliminating external service costs.
