Agents

The agents/ folder contains reference agents and the programs used to run them.

You only need it if:

• you want to run one of the reference agents
• you want a starting point for your own agent
• you want to evaluate an agent across benchmark tasks

The Two Main Parts

Most of this section comes down to two folders:

What Lives In agents/agents/

agents/agents/ contains the agent classes.

An agent is the code that looks at the current observation and decides what actions to take next.

We currently ship these agents:

• ClaudeAgent
• Gemini3Agent
• Qwen3VLAgent
• KimiAzureAgent

These classes are exported through agents.agents, which is why the evaluation commands refer to names such as --agent ClaudeAgent.

What Lives In agents/evaluation/

agents/evaluation/ contains the programs that actually run an agent against environments and tasks.

The two main ones are:

• run_single.py: run one agent on one task
• run_batch.py: run one agent across many tasks

If you only want to try an agent once, start with run_single.py.

What An Agent Receives

Our current agent interface is built around four methods:

• __init__
• init(task_description, display_resolution, save_path)
• step(obs, action_outputs)
• finish(...)

The important one is step(...).

That method receives:

• the latest observation from the environment
• the outputs from the previous actions

and it returns one or more action groups.

Each action group contains:

• tool_id
• actions

The actions list can contain normal environment actions such as mouse and keyboard input. It can also contain built-in control actions such as:

• {"action": "screenshot"}
• {"action": "wait", "time": 1.5}

Those control actions are handled by the environment layer, not by the evaluation loop.

The Fastest Way To Try One

gym-anything benchmark moodle --task enroll_student --agent ClaudeAgent --model claude-opus-4

That command loads the environment, resets it, creates the agent, lets it act until the run finishes, and writes run artifacts.

If You Want To Add Your Own Agent

The simplest workflow is:

1. copy a nearby file in agents/agents/
2. implement the same basic methods
3. export the new class from agents/agents/__init__.py
4. run it with gym-anything benchmark

If you're starting from scratch, read:

1. agents/agents/base.py
2. one concrete agent such as agents/agents/claude.py
3. agents/evaluation/run_single.py

That gives you the agent interface first, then one implementation, then the program that drives it.