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"""
Example of using Cerebras with browser-use.
To use this example:
1. Set your CEREBRAS_API_KEY environment variable
2. Run this script
Cerebras integration is working great for:
- Direct text generation
- Simple tasks without complex structured output
- Fast inference for web automation
Available Cerebras models (9 total):
Small/Fast models (8B-32B):
- cerebras_llama3_1_8b (8B parameters, fast)
- cerebras_llama_4_scout_17b_16e_instruct (17B, instruction-tuned)
- cerebras_llama_4_maverick_17b_128e_instruct (17B, extended context)
- cerebras_qwen_3_32b (32B parameters)
Large/Capable models (70B-480B):
- cerebras_llama3_3_70b (70B parameters, latest version)
- cerebras_gpt_oss_120b (120B parameters, OpenAI's model)
- cerebras_qwen_3_235b_a22b_instruct_2507 (235B, instruction-tuned)
- cerebras_qwen_3_235b_a22b_thinking_2507 (235B, complex reasoning)
- cerebras_qwen_3_coder_480b (480B, code generation)
Note: Cerebras has some limitations with complex structured output due to JSON schema compatibility.
"""
import asyncio
import os
from browser_use import Agent
async def main():
# Set your API key (recommended to use environment variable)
api_key = os.getenv('CEREBRAS_API_KEY')
if not api_key:
raise ValueError('Please set CEREBRAS_API_KEY environment variable')
# Option 1: Use the pre-configured model instance (recommended)
from browser_use import llm
# Choose your model:
# Small/Fast models:
# model = llm.cerebras_llama3_1_8b # 8B, fast
# model = llm.cerebras_llama_4_scout_17b_16e_instruct # 17B, instruction-tuned
# model = llm.cerebras_llama_4_maverick_17b_128e_instruct # 17B, extended context
# model = llm.cerebras_qwen_3_32b # 32B
# Large/Capable models:
# model = llm.cerebras_llama3_3_70b # 70B, latest
# model = llm.cerebras_gpt_oss_120b # 120B, OpenAI's model
# model = llm.cerebras_qwen_3_235b_a22b_instruct_2507 # 235B, instruction-tuned
model = llm.cerebras_qwen_3_235b_a22b_thinking_2507 # 235B, complex reasoning
# model = llm.cerebras_qwen_3_coder_480b # 480B, code generation
# Option 2: Create the model instance directly
# model = ChatCerebras(
# model="qwen-3-coder-480b", # or any other model ID
# api_key=os.getenv("CEREBRAS_API_KEY"),
# temperature=0.2,
# max_tokens=4096,
# )
# Create and run the agent with a simple task
task = 'Explain the concept of quantum entanglement in simple terms.'
agent = Agent(task=task, llm=model)
print(f'Running task with Cerebras {model.name} (ID: {model.model}): {task}')
history = await agent.run(max_steps=3)
result = history.final_result()
print(f'Result: {result}')
if __name__ == '__main__':
asyncio.run(main())
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