HarborRL
HarborRL is a framework that trains tool-using AI agents with reinforcement learning on sandboxed coding and terminal tasks for developers.
What it is good for
- Training AI agents to complete coding tasks through trial and reward
- Running reinforcement learning on agents inside isolated sandboxes
- Comparing reinforcement learning methods for tool-using agents
At a glance
- Who it is for
- Setup
- Requires programming
Level 5 of 5. You need to write code to use it.
- Cost
- Free and open source
- Type
- Framework or SDK
- Works with
- Claude Code, Codex, LangGraph, SWE-agent, Slime, OpenSandbox
Momentum
- Rank this week
- Not ranked yet
- Best rank
- None yet
- GitHub stars
- 82
GitHub numbers as of Nov 2, 2026. Updated weekly.
Official links
Setup steps are on the official pages. We do not repeat them here, since they change.
Questions
- Is HarborRL free?
- Yes. It is free and open source (MIT license).
- Do I need to know how to code to use HarborRL?
- Yes. You need to write code to use it.
- What does HarborRL work with?
- According to its own documentation, it works with Claude Code, Codex, LangGraph, SWE-agent, Slime, and OpenSandbox.
- What is HarborRL good for?
- Training AI agents to complete coding tasks through trial and reward, running reinforcement learning on agents inside isolated sandboxes, and comparing reinforcement learning methods for tool-using agents.
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