hermes-atropos-environments

To build and test Hermes Agent RL environments for Atropos training, you use the HermesAgentBaseEnv interface to construct multi-turn agent loops with tool calling, then validate your integration using the evaluate CLI mode for end-to-end benchmarking.

The HermesAgentBaseEnv interface provides the foundational structure for multi-turn agent loops with tool calling within Atropos RL environments. It standardizes how agent interactions, reward design, and evaluation workflows integrate with the Atropos training framework.

To evaluate and log RL agent performance during Atropos training, run the evaluate CLI mode to execute evaluation workflows, which automatically track progress and metrics using wandb logging integrated directly into the Hermes environment.

You do not strictly need a local model server to run Hermes Atropos RL environments. The setup requires a Python environment with Atropos and access to a model provider, allowing you to use OpenRouter, OpenAI, or a local server for full agent loops.

The serve, process, and evaluate CLI modes manage Atropos environments across different stages. Serve handles development, process manages testing, and evaluate executes benchmarking workflows, ensuring production-ready RL setups for Hermes Agent.

Hermes Agent tool calling in Atropos environments may fail if the Python environment lacks proper Atropos integration or if model provider access via OpenRouter, OpenAI, or a local server is misconfigured. Debug by validating the agent loop in the process CLI mode.