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Eval Protocol (EP) is an open solution for doing reinforcement learning fine-tuning on existing agents — across any language, container, or framework.
Most teams already have complex agents running in production — often across remote services with heavy dependencies, Docker containers, or TypeScript backends deployed on Vercel. When they try to train or fine-tune these agents with reinforcement learning, connecting them to a trainer quickly becomes painful. Eval Protocol makes this possible in two ways:
  1. Expose Your Agent Through a Simple API Wrap your existing agent (Python, TypeScript, Docker, etc.) in a simple HTTP service using EP’s rollout interface. EP handles the rollout orchestration, metadata passing, and trace storage automatically.
  2. Connect With Any Trainer Once your agent speaks the EP standard, it can be fine-tuned or evaluated with any supported trainer — Fireworks RFT, TRL, Unsloth, or your own — with no environment rewrites.
The result: RL that works out-of-the-box for existing production agents.

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Who This Is For

  • Applied AI teams adding RL to existing production agents.
  • Research engineers experimenting with fine-tuning complex, multi-turn or tool-using agents.
  • MLOps teams building reproducible, language-agnostic rollout pipelines.

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Getting Started

Try our Quickstart to see how we built and trained an SVGAgent end-to-end using the RemoteRolloutProcessor — including full Fireworks Tracing integration.