evalstone/evalscope/docs/en/benchmarks/miniwob.md

MiniWoB

Overview

MiniWoB evaluates whether a multimodal agent can complete short browser tasks such as clicking buttons, filling forms, scrolling and dragging items.

Task Description

• Task Type: Interactive browser tasks
• Input: A task goal, an accessibility tree and a screenshot
• Output: Browser actions selected through function calling
• Dataset: 125 MiniWoB tasks
• Metrics: success_rate for completed tasks and error_rate for environment failures

Evaluation Notes

• The default run evaluates one deterministic episode per task.
• Set repeats=5 for the five-episode schedule.
• Each episode allows up to 10 model/tool turns by default.
• The model must support image input and function calling.
• See the MiniWoB usage guide for installation and examples.

Properties

Property	Value
Benchmark Name	miniwob
Dataset ID	BrowserGym
Paper	N/A
Tags	Agent, FunctionCalling, MultiModal, MultiTurn
Metrics	success_rate, error_rate
Default Shots	0-shot
Evaluation Split	test

Data Statistics

Metric	Value
Total Samples	125
Prompt Length (Mean)	77 chars
Prompt Length (Min/Max)	77 / 77 chars

Sample Example

Subset: default

{
  "input": [
    {
      "id": "4b7219db",
      "content": "The task goal and browser observation are supplied when the episode is reset."
    }
  ],
  "target": "1",
  "id": 0,
  "group_id": 0,
  "tools": [
    {
      "name": "browser_action",
      "description": "Execute exactly one BrowserGym MiniWoB action. Supported signatures: noop(wait_ms=1000), mouse_move(x, y), mouse_click(x, y, button=\"left\"), mouse_dblclick(x, y, button=\"left\"), mouse_down(x, y, button=\"left\"), mouse_up(x, y, button=\"left\"),  ... [TRUNCATED 44 chars] ... \"left\"), keyboard_press(key), keyboard_type(text), fill(bid, value). click accepts a string BID, for example click(\"13\"); use mouse_click(x, y) for visual targets. Coordinates are absolute screenshot pixels, not normalized 0-1000 coordinates.",
      "parameters": {
        "properties": {
          "action": {
            "type": "string",
            "description": "One BrowserGym function-call expression."
          }
        },
        "required": [
          "action"
        ]
      }
    }
  ],
  "metadata": {
    "task_name": "miniwob.ascending-numbers",
    "miniwob_category": "hidden test",
    "comment": "",
    "webgum_subset": "False",
    "similarity_group": "0",
    "browsergym_split": "test",
    "task_id": "miniwob.ascending-numbers",
    "seed": 1608637542,
    "repeat": 0
  }
}

Note: Some content was truncated for display.

Prompt Template

Prompt Template:

{question}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets miniwob \
    --agent-config '{"mode":"native","strategy":"function_calling","max_steps":10}' \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import TaskConfig, run_task
from evalscope.api.agent import NativeAgentConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['miniwob'],
    agent_config=NativeAgentConfig(
        strategy='function_calling',
        max_steps=10,
    ),
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)