AgentGym

Overview

AgentGym is a broad multi-environment framework aimed at evolving LLM-based agents across diverse tasks rather than isolating them in one domain. It bundles environments, trajectories, and a self-evolution method under one roof.

Why it matters

It matters because it treats agent improvement as an across-environment problem. That makes it one of the better references for a harness that aspires to generalize beyond a single benchmark world.

Distinctive trait

Its distinctive trait is breadth: the environment layer, trajectory layer, and evolution method are designed to work together as a unified generalist substrate.

Relationships

Read AgentGym with rl-gyms-and-executable-environments-for-ai-harnesses, self-evolving-workflows, evaluation-and-review-loops, and compare it with mlgym plus atropos.