Eval Protocol: RL on your agents, in any environment

Eval Protocol (EP) is an open-source, language-agnostic framework designed to facilitate reinforcement fine-tuning on agents, making it adaptable across various frameworks, environments, and trainers. EP aims to address the challenges of applying reinforcement learning (RL) in real-world, messy production environments by providing a standard interface that integrates seamlessly with existing agent systems. With a focus on production RL, EP prioritizes trace-based evaluation, allowing users to observe and iterate on agent performance within their actual environments. This approach contrasts with traditional RL frameworks that often operate in sanitized, academic settings. EP's growing integration ecosystem supports multiple trainers and environments, enabling users to transition from local testing to remote training while maintaining consistent evaluation criteria. The framework is designed to make RL more accessible and practical, emphasizing the importance of using real-world interactions as the basis for scoring and evaluation, thereby helping users refine their evaluators based on authentic user experiences.

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