hermes-atropos-environments

To build a reinforcement learning environment for tool-using agents, use a scaffold that integrates multi-turn agent loops with tool resolution, reward computation, and evaluation workflows. It supports serve, process, and evaluate modes for end-to-end agent training.

An Atropos RL environment evaluates agent performance by executing multi-turn loops with tool calls, computing rewards through tool integration, and logging evaluation metrics with wandb across sandboxed tasks.

You can integrate Hermes tooling with your reinforcement learning training loop by extending HermesAgentBaseEnv, which provides tool resolution and execution context for evaluating tool integration within agent experiments.

Start an agent-loop evaluation by running serve mode with your OpenAI base URL and model name, deploying the end-to-end agent loop to connect the environment to the model for multi-turn tool usage and reward evaluation.

Running sandboxed agent experiments requires the Hermes Atropos ecosystem and proper tooling interfaces to execute end-to-end multi-turn loops, tool calls, and reward computation workflows.

Limitations when debugging multi-turn agent loops include strict dependency on the Hermes Atropos ecosystem and proper tooling interfaces; without them, the environment cannot run end-to-end experiments or resolve tool calls.