Tiny Tool Use is a minimal, open-source library for LLMs to make reliable, auditable tool calls. Supports SFT, DPO, and synthetic data — all driven by simple JSON config. Fast setup, strong evals, and ready for real-world prototyping.
Achieve tool use with open-source LLMs, made simple
Tiny Tool Use is a minimal, open-source library for LLMs to make reliable, auditable tool calls. Supports SFT, DPO, and synthetic data — all driven by simple JSON config. Fast setup, strong evals, and ready for real-world prototyping.
👋 Hey Product Hunt! At Bagel Labs, we believe the future of advanced AI systems depends on their ability to reason with external tools, APIs, and data sources. But when we tried building with existing “tool-use” stacks, we ran into the same issues every time: brittle code, no reproducibility, and zero shared benchmarks. So we built Tiny Tool Use — a minimal, MIT-licensed open-source library that turns adapting LLMs for robust, auditable tool calls into a config-only workflow. No fragile scaffold
Can’t wait to see it all in action so I think I will start reading all the documentation
This launch is a dream come true for developers
This is the future of AI systems!
Clean utility 🔧🧠 Simplifying tool use with open-source LLMs hits the sweet spot for devs and tinkerers.
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