What is Tiny Tool Use by Bagel Labs?
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.
Problem
Users previously had to manually integrate tools with open-source LLMs, facing challenges with complex setup, unreliable tool calls, and lack of standardization.
Solution
An open-source library that enables developers to configure tool calls via JSON, supporting supervised fine-tuning (SFT), DPO, and synthetic data generation — simplifying LLM integration and evaluation.
Customers
Machine learning engineers and developers building LLM-powered applications, researchers prototyping tool-assisted AI workflows, and startups prioritizing auditable AI solutions.
Unique Features
Combines SFT, DPO, and synthetic data workflows with a JSON-driven setup, emphasizing reliability, auditability, and fast prototyping for real-world use cases.
User Comments
Saves weeks of custom code for tool integration
Simplifies complex LLM workflows
Transparent JSON configs boost trust
Enables rapid iteration for startups
Strong evaluations prevent production risks
Traction
Launched on Product Hunt (date unspecified); no public revenue or user metrics. GitHub repository likely active (details unconfirmed due to restricted access).
Market Size
The global machine learning market is projected to reach $209.91 billion by 2029 (Fortune Business Insights, 2023), with open-source LLM tooling as a key growth segment.
