cyysky · April 11, 2025 04:41 · Apr 11, 2025
diff --git a/README.md b/README.md
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+# Browser-use: Connecting AI Agents with the Browser
+
+🌐 Browser-use is the easiest way to connect your AI agents with the browser.  With pip (Python>=3.11):
+
+```bash
+pip install browser-use
+```
+
+Install Playwright:
+
+```bash
+playwright install chromium
+```
+
+## Getting Started
+
+This project demonstrates a simple example of using `browser-use` to interact with an AI model.
+
+### Requirements
+
+*   Python 3.11 or higher
+*   `browser-use`
+*   Playwright (Chromium)
+
+## Example Usage
+
+The following code demonstrates a basic example:
+
+### `browser_agent.py`
+
+```python src/main.py
+from langchain_ollama import ChatOllama
+from browser_use import Agent
+import asyncio
+from dotenv import load_dotenv
+load_dotenv()
+
+# Initialize the model
+llm=ChatOllama(model="gemma3:4b", num_ctx=32000,base_url="http://localhost:11434")
+
+async def main():
+    # Create agent with the model
+    agent = Agent(
+        task="Compare the price of gpt-4o and DeepSeek-V3",
+        llm=llm
+    )
+    await agent.run()
+
+asyncio.run(main())
+```
+
+This script initializes a `ChatOllama` model and creates an `Agent` object. The agent is configured to compare the prices of `gpt-4o` and `DeepSeek-V3`.  It then runs the agent, which will interact with the model to perform the comparison.
+
+### `langchain_ollama_sample.py`
+
+```python langchain_ollama_sample.py (1-17)
+from langchain_ollama import ChatOllama
+
+llm = ChatOllama(
+    model="gemma3:4b",
+    temperature=1,
+    base_url="http://127.0.0.1:11434",
+    #Other args
+)
+
+messages = [
+    (
+        "system",
+        "You are a helpful assistant that translates English to French. Translate the user sentence.",
+    ),
+    ("human", "I love programming."),
+]
+ai_msg = llm.invoke(messages)
+print(ai_msg)
+```
+
+This script demonstrates a simple translation task using `ChatOllama`. It sets up a system message to define the assistant's role, provides a user sentence, and then invokes the model to translate the sentence. The translated message is then printed to the console.