Rl Environments Python Packages

harbor-rewardkit

Framework for evaluating and improving agents

harbor-langsmith

Framework for evaluating and improving agents

agentsynth-ai

Outcome-verified agent trajectories, benchmarks, and RL environments — with a live leaderboard and a CI gate for your agents. Offline-first, MIT.

vlabs-calibrate

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.

envrax

Gymnasium-style API standard for RL environment creation in JAX

verifiable-labs-envs

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.

helx

Interoperate among reinforcement learning libraries with jax, pytorch, gym and dm_env

mobile-env-rl

A Universal Platform for Training and Evaluation of Mobile Interaction

verifiable-labs

Python SDK + CLI for Verifiable Labs — evaluate frontier LLMs on conformal-calibrated scientific RL environments.

verifiable-labs-code-humaneval

Single-turn procedural code-execution RL environment with sandboxed pytest scoring and conformal coverage

verifiable-labs-code-humaneval-multiturn

Multi-turn procedural code-execution RL environment with test-feedback rollouts and conformal coverage

helx-base

Interoperate among reinforcement learning libraries with jax, pytorch, gym and dm_env