Your Inference Server is Secretly a Learner: Reef Infrastructure for Continual Self-Improving Agents

Reef is an open-source infrastructure project for continual self-improving AI agents, treating live inference not as a final deployment stage but as a source of experience that can improve future agent behavior. It collects structured records of inference traces, execution results, and feedback through standard serving endpoints, then lets configurable learning recipes use this data to update agent components asynchronously. Unlike systems focused solely on model training, Reef is designed to evolve both model weights and the broader agent harness, including prompts, memory, tools, skills, routing, and orchestration logic. Candidate updates are evaluated before release, versioned as managed artifacts using an append-only release process, and can be deployed without interrupting service. The project supports or plans to support methods such as online reinforcement learning, test-time training, skill and harness evolution, and self-play, with examples including OpenClaw-RL and TTT-Discover, and its developers invite the community to contribute recipes and integrations through its public GitHub repository.

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