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hermes-atropos-environments

Implements Hermes-AgentRL environments integrating multi-turn tool-calling loops with the Atropos training framework.

19|4|Updated Apr 22, 2026

Search Tags:#tool calling #reward functions #agent evaluation #wandb logging #rl environments #atopos training #cli modes

Ready to run

npx skills add https://github.com/carterwayneskhizeine/hermes-agent-windows-R --skill hermes-atropos-environments-carterwayneskhizeine

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hermes-atropos-environments
Source: https://github.com/carterwayneskhizeine/hermes-agent-windows-R/tree/main/optional-skills/mlops/hermes-atropos-environments
Command: npx skills add https://github.com/carterwayneskhizeine/hermes-agent-windows-R --skill hermes-atropos-environments-carterwayneskhizeine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you design and debug Hermes Agent RL environments that integrate with the Atropos training framework, so you can score multi-turn tool-using agent rollouts reliably instead of guessing at evaluation logic.

Core Features & Use Cases

Helps you implement HermesAgentBaseEnv: guides you to wire the required methods (setup, get_next_item, format_prompt, compute_reward, evaluate, wandb_log) so Atropos can train against your environment.
Agentic evaluation with tools: ensures evaluate() runs the full HermesAgentLoop (tools, tool parsing, ToolContext verification) rather than single-turn chat completions.
Practical reward verification patterns: covers LLM-judge scoring and deterministic sandbox verification via ToolContext terminal execution, including how to correctly extract final responses from AgentResult.messages.
Covers three operational CLI modes: serve (training loop), process (offline JSONL generation), and evaluate (tool-using benchmark runs), with guidance to save results in eval mode and avoid polluting training metrics.

Quick Start

Tell the AI which inference backend you will use, then implement your environment by extending HermesAgentBaseEnv with setup, format_prompt, compute_reward, and an evaluate() that runs HermesAgentLoop with tools, following the SKILL.md guidance.

Frequently Asked Questions about hermes-atropos-environments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build an RL environment for multi-turn tool-calling agent training?▼

To build an RL environment for multi-turn tool-calling agent training, you extend HermesAgentBaseEnv and implement required methods like setup, get_next_item, format_prompt, compute_reward, and evaluate so Atropos can train against your environment.

How does evaluate() work for agentic rollouts in Atropos training?▼

The evaluate() method runs the full HermesAgentLoop, executing tools, tool parsing, and ToolContext verification instead of single-turn chat completions. You extract final responses from AgentResult.messages to score multi-turn rollouts accurately.

What reward functions can I use for agent evaluation in RL environments?▼

You can use LLM-judge scoring or deterministic sandbox verification via ToolContext terminal execution for agent evaluation. The environment extracts final responses from AgentResult.messages and wires reward logging with wandb metrics correctly.

Does Atropos support offline trajectory generation for tool-enabled rollouts?▼

Atropos supports offline trajectory generation through its process CLI mode, which outputs JSONL files. It also offers serve mode for the training loop and evaluate mode for tool-using benchmark runs, keeping results separate from training metrics.

How do I log reward metrics to wandb during agent training?▼

You log reward metrics to wandb by implementing the wandb_log method required by HermesAgentBaseEnv. This ensures Atropos correctly tracks and records training metrics during tool-enabled rollouts.

Why should I not use single-turn chat completions for agent evaluation?▼

Single-turn chat completions skip the HermesAgentLoop, failing to execute tools or verify ToolContext. Multi-turn tool-enabled rollouts are required to properly evaluate agent behavior and compute accurate rewards for Atropos training.