March 27, 2026 · The Harbor Team
If you’ve built a Harbor task before, you’ve probably spent time figuring out the same things everyone else does: multi-container setups, simulated users, adding MCP tools, implementing computer use environments, and more. We built Harbor to make agent evals simple. That’s why we’re releasing the Harbor Cookbook. It contains a collection of realistic, ready-to-run examples of how to build evals and optimize agents with Harbor. What’s inside We recommend you give your coding agent context on the one closest to what you’re building and adapt from there.
Beyond evals: optimizing agents
Harbor tasks produce a reward, which means the same datasets you use for evals can also serve as training environments. The cookbook includes two recipes that demonstrate this: one example pairs Harbor with GEPA to optimize an agent harness on MedAgentBench. The other is the Harbor integration contributed by Thinking Machines, which uses Harbor tasks as RL environments through the Tinker SDK.
We welcome feedback on which examples to build next and how to improve Harbor. We’re actively developing the Harbor framework to improve its ability to integrate into optimization loops.
Get started
Clone the cookbook and then run: