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hermes-atropos-environments

Build, test, and debug Zeus Agent RL environments for Atropos training.

Updated May 5, 2026
Search Tags:#evaluation#tool calling#reward functions#atropos#hermes-agent#wandb logging#rl environments
One-click install
npx skills add https://github.com/Z43L/zeus-agent --skill hermes-atropos-environments-z43l
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hermes-atropos-environments
Source: https://github.com/Z43L/zeus-agent/tree/main/optional-skills/mlops/hermes-atropos-environments
Command: npx skills add https://github.com/Z43L/zeus-agent --skill hermes-atropos-environments-z43l

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you build and troubleshoot Zeus Agent RL environments for the Atropos training framework, especially when you need a full multi-turn Hermes-style agent loop with tool calling and reward verification.

Core Features & Use Cases

  • HermesAgentBaseEnv integration: Guides you on implementing only the required environment methods while the base env orchestrates the agent loop and tool resolution.
  • Reward computation patterns: Shows how to score rollouts via LLM judgment, sandbox/terminal verification, or multi-signal reward composition.
  • Tool-aware evaluation loop: Ensures evaluation uses the HermesAgentLoop (not single-turn chat completion) and supports deterministic evaluation settings.
  • End-to-end CLI modes: Covers serve, process (offline JSONL generation), and evaluate (periodic/standalone evaluation) workflows including logging (wandb) guidance.

Quick Start

Ask what inference provider you want to use, then implement your environment in environments/your_env.py by wiring up setup(), get_next_item(), format_prompt(), compute_reward(), evaluate(), and wandb_log() following the HermesAgentBaseEnv architecture.

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 multi-turn tool calling RL environments for Atropos training?▼

To build multi-turn tool calling RL environments for Atropos training, you implement required methods like setup, get_next_item, and compute_reward while HermesAgentBaseEnv orchestrates the agent loop and tool resolution.

How does reward computation work in Zeus Agent RL environments?▼

Reward computation in Zeus Agent RL environments works by scoring rollouts through LLM judgment, sandbox or terminal verification, or multi-signal reward composition, using data extracted from AgentResult.messages.

Can I use a configurable inference endpoint with HermesAgentBaseEnv evaluation?▼

Yes, HermesAgentBaseEnv supports configurable inference endpoints without assuming specific providers, and evaluation must use the HermesAgentLoop rather than single-turn chat completions for deterministic results.

What CLI modes are supported for Atropos environment processing and evaluation?▼

Atropos environment processing and evaluation support three CLI modes: serve for live interaction, process for offline JSONL generation, and evaluate for periodic or standalone evaluation with wandb metric logging.

Why is my Atropos agent evaluation not extracting tool usage correctly?▼

Atropos agent evaluation fails to extract tool usage correctly when not using HermesAgentLoop for multi-turn rollouts, as final responses and tool context must be properly extracted from AgentResult.messages.

Do I need wandb logging for RL environment evaluation in Atropos?▼

Wandb logging is integrated into the evaluate CLI mode for RL environments in Atropos, requiring you to implement wandb_log alongside evaluate and compute_reward methods within HermesAgentBaseEnv.

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