thetabench 0.1.0

Gymnasium-compatible SDK for ThetaBench web agent training & evaluation

ThetaBench Python SDK

Gymnasium-compatible SDK for training and evaluating web agents on ThetaBench environments.

Installation

pip install thetabench

# With browser mode support (Playwright)
pip install 'thetabench[browser]'
playwright install chromium

Quick Start

REST Mode (Fast Training — 1-5ms/step)

import thetabench

env = thetabench.make("shopify-admin", task_id="prod-001")
obs, info = env.reset()

print(f"Task: {info['task_goal']}")
print(f"Max steps: {info['max_steps']}")

done = False
while not done:
    # Your agent decides what to do
    action = {"action": "update_product", "productId": "1", "fields": {"price": "34.99"}}
    obs, reward, terminated, truncated, info = env.step(action)
    done = terminated or truncated

result = env.finish()
print(f"Score: {result['score']:.0%} | Steps: {result['steps']} | Reward: {result['total_reward']:.2f}")
env.close()

Browser Mode (Realistic Evaluation)

import thetabench

env = thetabench.make("shopify-admin", task_id="prod-001", mode="browser")
obs, info = env.reset()

# obs includes screenshot, accessibility tree, and URL
print(f"URL: {obs['url']}")

action = {"type": "navigate", "url": "/admin/products/1"}
obs, reward, terminated, truncated, info = env.step(action)

action = {"type": "fill", "selector": "#price", "value": "34.99"}
obs, reward, terminated, truncated, info = env.step(action)

action = {"type": "click", "selector": "button:has-text('Save')"}
obs, reward, terminated, truncated, info = env.step(action)

result = env.finish()
env.close()

Curriculum Training

import thetabench

def my_agent(obs, info):
    # Your agent logic here
    return {"action": "navigate", "target": "/admin/products"}

runner = thetabench.CurriculumRunner(
    base_url="http://localhost:3000",
    mastery_threshold=0.8,
)

for stage_result in runner.run(my_agent):
    print(f"Stage {stage_result['stage']}: {stage_result['title']}")
    print(f"  Score: {stage_result['avg_score']:.0%}")
    print(f"  Mastery: {'Yes' if stage_result['mastery_achieved'] else 'No'}")

runner.close()

Browse Tasks

from thetabench import ThetaBenchClient

client = ThetaBenchClient("http://localhost:3000")

# List all tasks
tasks = client.list_tasks()
print(f"Total tasks: {tasks.total}")

# Filter by domain and difficulty
easy_product_tasks = client.list_tasks(domain="products", difficulty="easy")
for t in easy_product_tasks.tasks:
    print(f"  {t.id}: {t.title}")

# Get curriculum
curriculum = client.get_curriculum()
for stage in curriculum.stages:
    print(f"Stage {stage.stage}: {stage.title} ({len(stage.taskIds)} tasks)")

client.close()

API Reference

thetabench.make(site, task_id, mode, **kwargs)

Factory function for creating environments.

Param	Type	Default	Description
site	str	"shopify-admin"	Site identifier
task_id	str	"prod-001"	Task to run
base_url	str	"http://localhost:3000"	ThetaBench server URL
mode	str	"rest"	"rest" or "browser"

ThetaBenchEnv (Gymnasium)

Method	Returns	Description
reset()	(obs, info)	Start new episode
step(action)	(obs, reward, terminated, truncated, info)	Execute action
evaluate()	dict	Mid-episode score check
finish(response?)	dict	End episode, get final score
close()	None	Cleanup

CurriculumRunner

Method	Description
run(agent_fn)	Generator yielding stage results
run_stage(stage, agent_fn)	Run all tasks in one stage
run_task(task_id, agent_fn)	Run a single task