Browser-Use Integration

The BrowserUse runner wraps browser-use with ACE learning. The agent automates browser tasks and learns strategies from each run — improving navigation, element selection, and error recovery over time.

Installation

uv add ace-framework[browser-use]

Quick Start

from ace import BrowserUse
from langchain_openai import ChatOpenAI

runner = BrowserUse.from_model(
    browser_llm=ChatOpenAI(model="gpt-4o"),
    ace_model="gpt-4o-mini",
)

results = runner.run("Find the top post on Hacker News")
runner.save("browser_expert.json")

Parameters

from_model()

Parameter	Type	Default	Description
`browser_llm`	`Any`	—	LLM for browser-use execution
`ace_model`	`str`	`"gpt-4o-mini"`	Model for Reflector + SkillManager
`ace_max_tokens`	`int`	`2048`	Max tokens for ACE LLM responses
`ace_temperature`	`float`	`0.0`	Sampling temperature for ACE roles

from_roles()

Parameter	Type	Default	Description
`browser_llm`	`Any`	—	LLM for browser-use execution
`reflector`	`ReflectorLike`	—	Reflector instance
`skill_manager`	`SkillManagerLike`	—	SkillManager instance
`skillbook_path`	`str`	`None`	Load saved skillbook
`browser`	`Browser`	`None`	browser-use Browser instance
`agent_kwargs`	`dict`	`None`	Extra kwargs for browser-use Agent
`dedup_config`	`DeduplicationConfig`	`None`	Deduplication config
`checkpoint_dir`	`str`	`None`	Checkpoint directory

Methods

results = runner.run(tasks, epochs=1)       # Run with learning
runner.save("path.json")                    # Save skillbook
runner.wait_for_background()                # Wait for async learning
runner.get_strategies()                     # View learned strategies

How It Works

INJECT — Skillbook strategies are added to the task prompt
EXECUTE — browser-use runs the task (navigation, clicks, form fills)
Extract trace — ACE extracts a chronological trace of agent thoughts, actions, and results
LEARN — Reflector analyzes the full trace, SkillManager updates the skillbook

The extracted trace includes:

Agent reasoning at each step
Browser actions taken (click, type, navigate)
Page observations
Success/failure of each action

Running Multiple Tasks

results = runner.run([
    "Find the top post on Hacker News",
    "Search for ACE framework on GitHub",
    "Check the weather in NYC",
])

Example: Domain Checker

from ace import BrowserUse
from langchain_openai import ChatOpenAI

runner = BrowserUse.from_model(
    browser_llm=ChatOpenAI(model="gpt-4o"),
    ace_model="gpt-4o-mini",
)

domains = ["example.com", "test.org", "sample.net"]
for domain in domains:
    runner.run(f"Check if {domain} is available for registration")

# After several runs, the agent learns:
# - Which registrar sites to use
# - How to navigate the domain search UI
# - How to interpret availability results
runner.save("domain_checker.json")

Resuming from a Saved Skillbook

runner = BrowserUse.from_model(
    browser_llm=ChatOpenAI(model="gpt-4o"),
    ace_model="gpt-4o-mini",
    skillbook_path="browser_expert.json",
)