GitHub - harbor-framework/harbor-cookbook: Realistic examples of building evals and optimizing agents with Harbor (original) (raw)
Realistic examples of building evals and optimizing agents using Harbor.
Getting Started
Install Harbor:
Run any task recipe:
harbor run -p harbor_cookbook/recipes/ -a claude-code -m anthropic/claude-opus-4-6
Task Recipes
| Name | Description |
|---|---|
| simple‑task | Minimal single-container task. |
| multi‑container | Docker Compose task where the agent interacts with a locally hosted REST API. |
| mcp‑tools | Giving the agent custom tools via a locally hosted FastMCP server. |
| multi‑reward | Multiple independent verifiers each producing their own score. |
| simulated‑user | Agent discovers requirements by talking to a simulated user. |
| computer‑use‑ubuntu | Computer use reference implementation on an Ubuntu virtual desktop. |
| computer‑use‑windows | Computer use reference implementation on a remote Windows desktop (Daytona). |
| dns‑blacklisting | Network-level hostname blacklisting with exact, wildcard, and regex rules. |
| skills | Giving agents access to custom skills. |
| multi‑step | Ordered multi-step task with per-step instructions, tests, workdir uploads, healthcheck, early stopping, and per-step artifacts. |
Optimization Examples
| Name | Description |
|---|---|
| harbor‑rl | RL training on Harbor tasks using harbor.rl + Tinker. |
| gepa | Agent harness optimization for MedAgentBench using Harbor+GEPA. |
| tinker‑rl | RL training on Harbor tasks using the Tinker Cookbook integration. |
| prime‑rl | RL training on Harbor tasks using Prime RL and Verifiers. |
| sky‑rl | RL training on Harbor tasks using SkyRL. |