Faber
AI coding agent with code-graph search and lower token cost
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Faber - AI coding agent with code-graph search and lower token cost

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Faber is an open-source AI coding agent for the terminal that uses a code graph to find relevant files and symbols before loading context. It reduces unnecessary context and repeated API cost through code-graph-guided search, prompt caching, and concise output. It supports Claude, OpenAI Codex, and local models, plus Git-aware workflows, undo/redo, memory, and detailed token and cost tracking.

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I built Faber because I kept seeing coding agents read far more of a repository than they actually needed for focused tasks, which slows things down and drives up token costs. Faber is free and open source; model API charges, if any, are billed directly by the provider you choose. The core idea was to use a code graph to identify the files, symbols, callers, and dependencies that matter first, so the model gets less unnecessary context and can respond faster. I then added prompt caching, token-conscious output, model switching between Claude, OpenAI Codex and local models, Git-aware workflows, memory, and a /usage dashboard that shows exactly where tokens, cache savings, and cost are going. My goal with Faber is simple: make AI-assisted coding much faster and significantly cheaper by reading less, reusing more context, and only sending the model what actually matters.