README.md

cua-agent is a general Computer-Use framework for running multi-app agentic workflows targeting macOS and Linux sandbox created with Cua, supporting local (Ollama) and cloud model providers (OpenAI, Anthropic, Groq, DeepSeek, Qwen).

Get started with Agent

Install

pip install "cua-agent[all]"

# or install specific loop providers
pip install "cua-agent[openai]" # OpenAI Cua Loop
pip install "cua-agent[anthropic]" # Anthropic Cua Loop
pip install "cua-agent[omni]" # Cua Loop based on OmniParser (includes Ollama for local models)
pip install "cua-agent[ui]" # Gradio UI for the agent

Run

async with Computer() as macos_computer:
  # Create agent with loop and provider
  agent = ComputerAgent(
      computer=macos_computer,
      loop=AgentLoop.OPENAI,
      model=LLM(provider=LLMProvider.OPENAI)
      # or
      # loop=AgentLoop.ANTHROPIC,
      # model=LLM(provider=LLMProvider.ANTHROPIC)
      # or
      # loop=AgentLoop.OMNI,
      # model=LLM(provider=LLMProvider.OLLAMA, model="gemma3")
  )

  tasks = [
      "Look for a repository named trycua/cua on GitHub.",
      "Check the open issues, open the most recent one and read it.",
      "Clone the repository in users/lume/projects if it doesn't exist yet.",
      "Open the repository with an app named Cursor (on the dock, black background and white cube icon).",
      "From Cursor, open Composer if not already open.",
      "Focus on the Composer text area, then write and submit a task to help resolve the GitHub issue.",
  ]

  for i, task in enumerate(tasks):
      print(f"\nExecuting task {i}/{len(tasks)}: {task}")
      async for result in agent.run(task):
          print(result)

      print(f"\n✅ Task {i+1}/{len(tasks)} completed: {task}")

Refer to these notebooks for step-by-step guides on how to use the Computer-Use Agent (CUA):

• Agent Notebook - Complete examples and workflows

Using the Gradio UI

The agent includes a Gradio-based user interface for easy interaction. To use it:

# Install with Gradio support
pip install "cua-agent[ui]"

Create a simple launcher script

# launch_ui.py
from agent.ui.gradio.app import create_gradio_ui

app = create_gradio_ui()
app.launch(share=False)

Setting up API Keys

For the Gradio UI to show available models, you need to set API keys as environment variables:

# For OpenAI models
export OPENAI_API_KEY=your_openai_key_here

# For Anthropic models
export ANTHROPIC_API_KEY=your_anthropic_key_here

# Launch with both keys set
OPENAI_API_KEY=your_key ANTHROPIC_API_KEY=your_key python launch_ui.py

Without these environment variables, the UI will show "No models available" for the corresponding providers, but you can still use local models with the OMNI loop provider.

Using Local Models

You can use local models with the OMNI loop provider by selecting "Custom model..." from the dropdown. The default provider URL is set to http://localhost:1234/v1 which works with LM Studio.

If you're using a different local model server:

• vLLM: http://localhost:8000/v1
• LocalAI: http://localhost:8080/v1
• Ollama with OpenAI compat API: http://localhost:11434/v1

The Gradio UI provides:

• Selection of different agent loops (OpenAI, Anthropic, OMNI)
• Model selection for each provider
• Configuration of agent parameters
• Chat interface for interacting with the agent

Agent Loops

The cua-agent package provides three agent loops variations, based on different CUA models providers and techniques:

Agent Loop	Supported Models	Description	Set-Of-Marks
AgentLoop.OPENAI	• computer_use_preview	Use OpenAI Operator CUA model	Not Required
AgentLoop.ANTHROPIC	• claude-3-5-sonnet-20240620 • claude-3-7-sonnet-20250219	Use Anthropic Computer-Use	Not Required
AgentLoop.OMNI	• claude-3-5-sonnet-20240620 • claude-3-7-sonnet-20250219 • gpt-4.5-preview • gpt-4o • gpt-4 • phi4 • phi4-mini • gemma3 • ... • Any Ollama or OpenAI-compatible model	Use OmniParser for element pixel-detection (SoM) and any VLMs for UI Grounding and Reasoning	OmniParser

AgentResponse

The AgentResponse class represents the structured output returned after each agent turn. It contains the agent's response, reasoning, tool usage, and other metadata. The response format aligns with the new OpenAI Agent SDK specification for better consistency across different agent loops.

async for result in agent.run(task):
  print("Response ID: ", result.get("id"))

  # Print detailed usage information
  usage = result.get("usage")
  if usage:
      print("\nUsage Details:")
      print(f"  Input Tokens: {usage.get('input_tokens')}")
      if "input_tokens_details" in usage:
          print(f"  Input Tokens Details: {usage.get('input_tokens_details')}")
      print(f"  Output Tokens: {usage.get('output_tokens')}")
      if "output_tokens_details" in usage:
          print(f"  Output Tokens Details: {usage.get('output_tokens_details')}")
      print(f"  Total Tokens: {usage.get('total_tokens')}")

  print("Response Text: ", result.get("text"))

  # Print tools information
  tools = result.get("tools")
  if tools:
      print("\nTools:")
      print(tools)

  # Print reasoning and tool call outputs
  outputs = result.get("output", [])
  for output in outputs:
      output_type = output.get("type")
      if output_type == "reasoning":
          print("\nReasoning Output:")
          print(output)
      elif output_type == "computer_call":
          print("\nTool Call Output:")
          print(output)