hermes-atropos-environments

To build a custom Hermes Agent reinforcement learning environment, implement the HermesAgentBaseEnv interface. You must define setup, item selection, prompt formatting, reward, evaluation, and logging methods to ensure correct Atropos training workflows.

Evaluate multi-turn tool-using tasks by running a full HermesAgentLoop evaluation. This measures real agent task performance across multiple turns instead of single-turn completions, requiring correct AgentResult handling and ToolContext verification.

Design reward logic for a Hermes Agent Atropos environment using LLM judges, heuristic fallbacks, or ToolContext-based sandbox checks. These methods validate terminal and file outputs to accurately score agent rollouts during training.

Configure provider-specific inference for OpenRouter, VLLM, and other OpenAI-compatible endpoints within your Atropos environment setup. This ensures correct API routing and model serving during agent evaluation and offline processing workflows.

Yes, Atropos environment validation requires wandb logging. You must implement logging methods within the HermesAgentBaseEnv interface to track evaluation runs, reward metrics, and agent performance during reinforcement learning training.

Hermes Agent rollout failures during Atropos evaluation often stem from incorrect AgentResult handling or missing ToolContext verification. Validate your environment implementation and ensure full HermesAgentLoop evaluation runs correctly across all turns.