Harbor is a framework for running agent evaluations and creating and using RL environments.
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Updated
Jun 14, 2026 - Python
Harbor is a framework for running agent evaluations and creating and using RL environments.
A Universal Platform for Training and Evaluation of Mobile Interaction
A graphical interface for reinforcement learning and gym-based environments.
Interoperating between (Deep) Reiforcement Learning libraries
Gymnasium-style API standard for RL environment creation in JAX
Create new gridworld gym environments easily
Workspace manager for coding agents. Interactively solve and develop Harbor tasks.
Surge AI — large-scale human-labeled data for LLM training
Outcome-verified agent trajectories, benchmarks, and RL environments — with a live leaderboard and a CI gate for your agents. Offline-first, MIT.
Foundry Lite: a public runnable sample of Veyl’s local environment harness for software-engineering agent evals.
A lightweight, open-source framework that turns historical GitHub pull requests into reproducible, verifiable software-engineering tasks for training and evaluating coding agents.
Pure Go implementation of the Gymnasium RL environment API. ×ばつ faster than Python.
Open-source SDK (Apache-2.0): RL environments, conformal calibration, a TRL-compatible reward function, the Lean 4 formal track, and the verifiable/vlabs CLI.
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