ChatOllama

from browser_use import Agent, ChatOllama
import asyncio

async def main():
    llm = ChatOllama(model='llama3.2')
    agent = Agent(
        task="Find the number 1 post on Show HN",
        llm=llm,
    )
    await agent.run()

if __name__ == "__main__":
    asyncio.run(main())

# Pull recommended models
ollama pull llama3.2
ollama pull llama3.2:70b
ollama pull qwen2.5-coder:32b

# Verify Ollama is running
curl http://localhost:11434

from browser_use import Agent, ChatOllama

# Connect to remote Ollama instance
llm = ChatOllama(
    model='llama3.2',
    host='http://192.168.1.100:11434',
)

agent = Agent(task="Your task", llm=llm)

from browser_use import Agent, ChatOllama
from ollama import Options

llm = ChatOllama(
    model='llama3.2',
    ollama_options=Options(
        temperature=0.7,
        num_predict=2048,
        top_k=40,
        top_p=0.9,
        repeat_penalty=1.1,
    ),
)

agent = Agent(task="Your task", llm=llm)

from browser_use import Agent, ChatOllama
from pydantic import BaseModel

class SearchResult(BaseModel):
    title: str
    description: str
    url: str

llm = ChatOllama(model='llama3.2')

agent = Agent(
    task="Extract search result",
    llm=llm,
    output_model_schema=SearchResult,
)

result = await agent.run()
print(result.structured_output)  # SearchResult instance

from browser_use import Agent, ChatOllama
import httpx

llm = ChatOllama(
    model='llama3.2:70b',
    timeout=300.0,  # 5 minutes for large model
)

agent = Agent(task="Complex task", llm=llm)

from browser_use import Agent, ChatOllama

llm = ChatOllama(
    model='qwen2.5-coder:32b',
    ollama_options={
        'temperature': 0.2,
        'num_predict': 4096,
        'top_p': 0.95,
    },
)

agent = Agent(task="Your task", llm=llm)

# Install Ollama
brew install ollama

# Or download from ollama.com
curl -fsSL https://ollama.com/install.sh | sh

# Start service
ollama serve

# Pull model
ollama pull llama3.2

# Install
curl -fsSL https://ollama.com/install.sh | sh

# Start service (usually auto-starts)
sudo systemctl start ollama

# Pull model
ollama pull llama3.2

# Run Ollama in Docker
docker run -d \
  --name ollama \
  -p 11434:11434 \
  -v ollama:/root/.ollama \
  ollama/ollama

# Pull model
docker exec ollama ollama pull llama3.2

from browser_use import Agent, ChatOllama
from browser_use.llm.exceptions import ModelProviderError
import httpx

try:
    llm = ChatOllama(model='llama3.2')
    agent = Agent(task="Your task", llm=llm)
    result = await agent.run()
except ModelProviderError as e:
    print(f"Ollama error: {e.message}")
    print("Make sure Ollama is running: ollama serve")
except httpx.ConnectError:
    print("Cannot connect to Ollama. Is it running?")
    print("Start with: ollama serve")

llm = ChatOllama(model='llama3.2')
print(llm.provider)  # "ollama"

llm = ChatOllama(model='llama3.2')
print(llm.name)  # "llama3.2"

llm = ChatOllama(model='llama3.2')
client = llm.get_client()
# Use client directly for advanced operations

from browser_use.llm.messages import SystemMessage, UserMessage

llm = ChatOllama(model='llama3.2')

messages = [
    SystemMessage(content="You are a helpful assistant"),
    UserMessage(content="What is Browser Use?")
]

response = await llm.ainvoke(messages)
print(response.completion)  # String response

# Check model sizes
ollama list

# Remove unused models
ollama rm model-name

# Optimize for speed
llm = ChatOllama(
    model='llama3.2',
    ollama_options={
        'num_predict': 512,  # Limit output length
        'num_ctx': 2048,     # Smaller context window
    },
)

# Check if Ollama is running
curl http://localhost:11434

# Start Ollama
ollama serve

# Or on Linux with systemd
sudo systemctl start ollama
sudo systemctl status ollama

# List installed models
ollama list

# Pull missing model
ollama pull llama3.2

# Verify correct host
llm = ChatOllama(
    model='llama3.2',
    host='http://localhost:11434',  # Default
)

# Use smaller model
ollama pull llama3.2:3b

# Check GPU usage
nvidia-smi  # For NVIDIA GPUs

# Reduce context size in options