Build Agents That Can Learn Like Humans

ART (Agent Reinforcement Trainer) is an open-source framework that simplifies reinforcement learning for LLMs by eliminating manual reward function engineering. It uses GRPO (Group Relative Policy Optimization) where agents attempt tasks multiple times, an LLM judge compares attempts, and the model learns from relative performance. Unlike traditional RL frameworks limited to simple chatbot interactions, ART supports multi-turn conversations, tool calls, and integrates with LangGraph, CrewAI, and ADK. It combines vLLM for model serving and Unsloth for GRPO training, enabling developers to fine-tune small open-source models to outperform larger closed-source alternatives on specific tasks.