hermes-atropos-environments

To build an RL environment for agent loop training, you implement the HermesAgentBaseEnv interface alongside reward functions and tool resolution. This provides a structured blueprint for the full agent loop with tool usage and decision making.

The Atropos framework is a training system for RL environments that supports CLI modes like serve, process, and evaluate. It standardizes workflows by requiring specific methods including setup, get_next_item, format_prompt, and compute_reward.

You can test and debug an Atropos environment by running a quick test that processes a small trajectory with a single item. This validates your setup and environment implementation before running full training loops.

Yes, the Atropos training framework supports wandb logging for evaluation. The environment interface includes a specific wandb_log method to track evaluation metrics and tool usage during the agent loop.

Yes, you can use this environment setup to benchmark tool usage and decision making under deterministic prompts. It is specifically designed to validate tool integration and evaluate agent performance in the hermes-agent repository.

The required methods to implement an RL environment for Atropos are setup, get_next_item, format_prompt, compute_reward, evaluate, and wandb_log. These satisfy the interface needed for the serve, process, and evaluate CLI modes.