mirror of
https://github.com/j93es/browser-use-oauth.git
synced 2026-06-04 08:31:51 +09:00
imnyang
638a3d47ce
- Introduced custom output format instructions with example code. - Detailed connection methods for launching and connecting to browsers, including local and remote options. - Provided guidelines for handling sensitive data securely, including best practices and examples. - Documented supported LangChain chat models with setup instructions and environment variable requirements. - Added instructions for customizing the system prompt to control agent behavior.
82 lines
No EOL
2.3 KiB
Markdown
82 lines
No EOL
2.3 KiB
Markdown
---
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applyTo: '**'
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---
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## 🧠 General Guidelines for Contributing to `browser-use`
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**Browser-Use** is an AI agent that autonomously interacts with the web. It takes a user-defined task, navigates web pages using Chromium via Playwright, processes HTML, and repeatedly queries a language model (like `gpt-4o`) to decide the next action—until the task is completed.
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### 🗂️ File Documentation
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When you create a **new file**:
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* **For humans**: At the top of the file, include a docstring in natural language explaining:
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* What this file does.
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* How it fits into the browser-use system.
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* If it introduces a new abstraction or replaces an old one.
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* **For LLMs/AI**: Include structured metadata using standardized comments such as:
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```python
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# @file purpose: Defines <purpose>
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```
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---
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### 🧰 Development Rules
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* ✅ **Always use [`uv`](mdc:https:/github.com/astral-sh/uv) instead of `pip`**
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For deterministic and fast dependency installs.
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```bash
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uv venv --python 3.11
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source .venv/bin/activate
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uv sync
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```
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* ✅ **Use real model names**
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Do **not** replace `gpt-4o` with `gpt-4`. The model `gpt-4o` is a distinct release and supported.
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* ✅ **Type-safe coding**
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Use **Pydantic v2 models** for all internal action schemas, task inputs/outputs, and controller I/O. This ensures robust validation and LLM-call integrity.
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---
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## ⚙️ Adding New Actions
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To add a new action that your browser agent can execute:
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```python
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from playwright.async_api import Page
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from browser_use.core.controller import Controller, ActionResult
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controller = Controller()
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@controller.registry.action("Search the web for a specific query")
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async def search_web(query: str, page: Page):
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# Implement your logic here, e.g., query a search engine and return results
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result = ...
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return ActionResult(extracted_content=result, include_in_memory=True)
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```
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### Notes:
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* Use descriptive names and docstrings for each action.
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* Prefer returning `ActionResult` with structured content to help the agent reason better.
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---
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## 🧠 Creating and Running an Agent
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To define a task and run a browser-use agent:
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```python
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from browser_use import Agent
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from langchain.chat_models import ChatOpenAI
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task = "Find the CEO of OpenAI and return their name"
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model = ChatOpenAI(model="gpt-4o")
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agent = Agent(task=task, llm=model, controller=controller)
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history = await agent.run()
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``` |