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116524e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # Browser-Use Integration
The `BrowserUse` runner wraps [browser-use](https://github.com/browser-use/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
```bash
uv add ace-framework[browser-use]
```
## Quick Start
```python
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
```python
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
```python
results = runner.run([
"Find the top post on Hacker News",
"Search for ACE framework on GitHub",
"Check the weather in NYC",
])
```
## Example: Domain Checker
```python
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
```python
runner = BrowserUse.from_model(
browser_llm=ChatOpenAI(model="gpt-4o"),
ace_model="gpt-4o-mini",
skillbook_path="browser_expert.json",
)
```
## What to Read Next
- [Integration Pattern](../guides/integration.md) — how the INJECT/EXECUTE/LEARN pattern works
- [The Skillbook](../concepts/skillbook.md) — how learned strategies are stored
- [Opik Observability](opik.md) — monitor browser automation costs
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