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Faber
AI coding agent with code-graph search and lower token cost
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.
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.
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About Faber on Product Hunt
“AI coding agent with code-graph search and lower token cost”
Faber was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #150 on the daily leaderboard. 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.
Faber was featured in Open Source (68.8k followers), Developer Tools (519k followers), Artificial Intelligence (478.1k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 246.4k products, making this a competitive space to launch in.
Who hunted Faber?
Faber was hunted by Jiban Shial. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Faber stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Jiban Shial