zorro-atropos-environments

Zorro Agent Atropos Environments

Guide for building RL environments in the zorro-agent repo that integrate with the Atropos training framework.

Architecture Overview

Atropos BaseEnv (atroposlib/envs/base.py)
    └── ZorroAgentBaseEnv (environments/zorro_base_env.py)
            ├── Handles agent loop orchestration
            ├── Handles tool resolution per group
            ├── Handles ToolContext for reward verification
            └── YOUR ENVIRONMENT (environments/your_env.py)
                    Only implements: setup, get_next_item, format_prompt,
                                    compute_reward, evaluate, wandb_log

Zorro environments are special because they run a multi-turn agent loop with tool calling — not just single-turn completions. The base env handles the loop; you implement the task and scoring.

File Locations

Inference Setup — Ask the User First

IMPORTANT: Before running any test, evaluation, or data generation command, always ask the user how they want to handle inference. Do NOT assume OpenRouter or any specific endpoint. Present these options:

1. OpenRouter — Ask which model they want to use (e.g., anthropic/claude-sonnet-4.5, google/gemini-2.5-pro, meta-llama/llama-3.3-70b-instruct, etc.). Requires OPENROUTER_API_KEY in environment.
2. Self-hosted VLLM endpoint — Ask for their base URL (e.g., http://localhost:8000/v1) and model name. Set --openai.server_type vllm.
3. Other OpenAI-compatible API — Ask for the base URL, model name, and any required API key. Set --openai.server_type openai and --openai.health_check false.
4. Local Atropos training server — For serve mode with a live training loop. Default http://localhost:8000/v1.