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126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
"""Example demonstrating the ComputerAgent capabilities with the Omni provider."""
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import asyncio
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import logging
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import signal
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import traceback
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# Import the unified agent class and types
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from agent import ComputerAgent
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from computer import Computer, VMProviderType
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# Import utility functions
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from utils import handle_sigint, load_dotenv_files
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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async def run_agent_example():
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"""Run example of using the ComputerAgent with different models."""
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print("\n=== Example: ComputerAgent with different models ===")
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try:
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# Create a local macOS computer
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computer = Computer(
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os_type="macos",
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verbosity=logging.DEBUG,
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)
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# Create a remote Linux computer with Cua
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# computer = Computer(
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# os_type="linux",
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# api_key=os.getenv("CUA_API_KEY"),
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# name=os.getenv("CUA_CONTAINER_NAME"),
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# provider_type=VMProviderType.CLOUD,
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# )
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# Create ComputerAgent with new API
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agent = ComputerAgent(
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# Supported models:
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# == OpenAI CUA (computer-use-preview) ==
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model="openai/computer-use-preview",
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# == Anthropic CUA (Claude > 3.5) ==
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# model="anthropic/claude-opus-4-20250514",
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# model="anthropic/claude-sonnet-4-20250514",
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# model="anthropic/claude-3-7-sonnet-20250219",
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# model="anthropic/claude-sonnet-4-5-20250929",
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# == UI-TARS ==
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# model="huggingface-local/ByteDance-Seed/UI-TARS-1.5-7B",
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# model="mlx/mlx-community/UI-TARS-1.5-7B-6bit",
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# model="ollama_chat/0000/ui-tars-1.5-7b",
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# == Omniparser + Any LLM ==
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# model="omniparser+anthropic/claude-opus-4-20250514",
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# model="omniparser+ollama_chat/gemma3:12b-it-q4_K_M",
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tools=[computer],
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only_n_most_recent_images=3,
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verbosity=logging.DEBUG,
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trajectory_dir="trajectories",
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use_prompt_caching=True,
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max_trajectory_budget=1.0,
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)
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# Example tasks to demonstrate the agent
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tasks = [
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"Look for a repository named trycua/cua on GitHub.",
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"Check the open issues, open the most recent one and read it.",
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"Clone the repository in users/lume/projects if it doesn't exist yet.",
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"Open the repository with an app named Cursor (on the dock, black background and white cube icon).",
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"From Cursor, open Composer if not already open.",
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"Focus on the Composer text area, then write and submit a task to help resolve the GitHub issue.",
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]
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# Use message-based conversation history
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history = []
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for i, task in enumerate(tasks):
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print(f"\nExecuting task {i+1}/{len(tasks)}: {task}")
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# Add user message to history
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history.append({"role": "user", "content": task})
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# Run agent with conversation history
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async for result in agent.run(history, stream=False):
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# Add agent outputs to history
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history += result.get("output", [])
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# Print output for debugging
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for item in result.get("output", []):
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if item.get("type") == "message":
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content = item.get("content", [])
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for content_part in content:
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if content_part.get("text"):
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print(f"Agent: {content_part.get('text')}")
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elif item.get("type") == "computer_call":
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action = item.get("action", {})
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action_type = action.get("type", "")
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print(f"Computer Action: {action_type}({action})")
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elif item.get("type") == "computer_call_output":
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print("Computer Output: [Screenshot/Result]")
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print(f"✅ Task {i+1}/{len(tasks)} completed: {task}")
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except Exception as e:
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logger.error(f"Error in run_agent_example: {e}")
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traceback.print_exc()
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raise
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def main():
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"""Run the Anthropic agent example."""
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try:
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load_dotenv_files()
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# Register signal handler for graceful exit
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signal.signal(signal.SIGINT, handle_sigint)
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asyncio.run(run_agent_example())
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except Exception as e:
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print(f"Error running example: {e}")
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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