Eval Protocol
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:
- 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.
- 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.
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.
Quickstart
- See the Quickstart repository: eval-protocol/quickstart
Resources
- Documentation – Guides and API reference
- Discord – Community
- GitHub – Source and examples
License
4 maintainers
Release files for eval-protocol 0.3.32
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