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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

pip install ace-framework[browser-use]

Quick Start

from ace_next 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
ace_llm LLMClientLike None Pre-built LLM 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

  1. INJECT — Skillbook strategies are added to the task prompt
  2. EXECUTE — browser-use runs the task (navigation, clicks, form fills)
  3. Extract trace — ACE extracts a chronological trace of agent thoughts, actions, and results
  4. 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_next 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",
)