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Merge branch 'main' into feat/agent-proxy

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Dillon DuPont
2025-09-02 16:07:19 -04:00
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2
CONTRIBUTING.md
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@@ -51,6 +51,6 @@ Documentation improvements are always welcome. You can:
- Improve API documentation
- Add tutorials or guides
For detailed instructions on setting up your development environment and submitting code contributions, please see our [Developer-Guide](./docs/Developer-Guide.md) guide.
For detailed instructions on setting up your development environment and submitting code contributions, please see our [Developer-Guide](./docs/Developer-Guide.md).
Feel free to join our [Discord community](https://discord.com/invite/mVnXXpdE85) to discuss ideas or get help with your contributions.

2
LICENSE.md
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@@ -1,6 +1,6 @@
MIT License
Copyright (c) 2025 trycua
Copyright (c) 2025 Cua AI, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal

27
README.md
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@@ -13,6 +13,11 @@
<a href="https://trendshift.io/repositories/13685" target="_blank"><img src="https://trendshift.io/api/badge/repositories/13685" alt="trycua%2Fcua | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</div>
> We’re hosting the **Computer-Use Agents SOTA Challenge** at [Hack the North](https://hackthenorth.com) and online!
>> **Track A (On-site @ UWaterloo)**: Reserved for participants accepted to Hack the North. 🏆 Prize: **YC interview guaranteed**.
>> **Track B (Remote)**: Open to everyone worldwide. 🏆 Prize: **Cash award**.
>>> 👉 Sign up here: [trycua.com/hackathon](https://www.trycua.com/hackathon)
**cua** ("koo-ah") is Docker for [Computer-Use Agents](https://www.oneusefulthing.org/p/when-you-give-a-claude-a-mouse) - it enables AI agents to control full operating systems in virtual containers and deploy them locally or to the cloud.
<div align="center">
@@ -47,7 +52,7 @@ Missing a model? [Raise a feature request](https://github.com/trycua/cua/issues/
- [Get started with a Computer-Use Agent UI](https://docs.trycua.com/docs/quickstart-ui)
- [Get started with the Computer-Use Agent CLI](https://docs.trycua.com/docs/quickstart-cli)
- [Get Started with the Python SDKs](https://docs.trycua.com/docs/quickstart-devs)
- [Get started with the Python SDKs](https://docs.trycua.com/docs/quickstart-devs)
<br/>
@@ -181,17 +186,31 @@ Join our [Discord community](https://discord.com/invite/mVnXXpdE85) to discuss i
## License
Cua is open-sourced under the MIT License - see the [LICENSE](LICENSE) file for details.
Cua is open-sourced under the MIT License - see the [LICENSE](LICENSE) file for details.
The base image `kasmweb/core-ubuntu-jammy` is maintained by [Kasm Technologies](https://github.com/kasmtech/workspaces-core-images) and distributed under the Apache License 2.0. Usage of that image is subject to its own license terms.
Microsoft's OmniParser, which is used in this project, is licensed under the Creative Commons Attribution 4.0 International License (CC-BY-4.0) - see the [OmniParser LICENSE](https://github.com/microsoft/OmniParser/blob/master/LICENSE) file for details.
### Third-Party Licenses and Optional Components
Some optional extras for this project depend on third-party packages that are licensed under terms different from the MIT License.
- The optional "omni" extra (installed via `pip install "cua-agent[omni]"`) installs the `cua-som` module, which includes `ultralytics` and is licensed under the AGPL-3.0.
When you choose to install and use such optional extras, your use, modification, and distribution of those third-party components are governed by their respective licenses (e.g., AGPL-3.0 for `ultralytics`).
## Contributing
We welcome contributions to CUA! Please refer to our [Contributing Guidelines](CONTRIBUTING.md) for details.
We welcome contributions to Cua! Please refer to our [Contributing Guidelines](CONTRIBUTING.md) for details.
## Trademarks
Apple, macOS, and Apple Silicon are trademarks of Apple Inc. Ubuntu and Canonical are registered trademarks of Canonical Ltd. Microsoft is a registered trademark of Microsoft Corporation. This project is not affiliated with, endorsed by, or sponsored by Apple Inc., Canonical Ltd., or Microsoft Corporation.
Apple, macOS, and Apple Silicon are trademarks of Apple Inc.
Ubuntu and Canonical are registered trademarks of Canonical Ltd.
Microsoft is a registered trademark of Microsoft Corporation.
This project is not affiliated with, endorsed by, or sponsored by Apple Inc., Canonical Ltd., Microsoft Corporation, or Kasm Technologies.
## Stargazers

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docs/content/docs/agent-sdk/agent-loops.mdx
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@@ -22,7 +22,7 @@ agent = ComputerAgent(
tools=[computer]
)
prompt = "open github, navigate to trycua/cua"
prompt = "Take a screenshot and tell me what you see"
async for result in agent.run(prompt):
if result["output"][-1]["type"] == "message":
@@ -30,3 +30,142 @@ async for result in agent.run(prompt):
```
For a list of supported models and configurations, see the [Supported Agents](./supported-agents/computer-use-agents) page.
### Response Format
```python
{
"output": [
{
"type": "message",
"role": "assistant",
"content": [{"type": "output_text", "text": "I can see..."}]
},
{
"type": "computer_call",
"action": {"type": "screenshot"},
"call_id": "call_123"
},
{
"type": "computer_call_output",
"call_id": "call_123",
"output": {"image_url": "data:image/png;base64,..."}
}
],
"usage": {
"prompt_tokens": 150,
"completion_tokens": 75,
"total_tokens": 225,
"response_cost": 0.01,
}
}
```
### Environment Variables
Use the following environment variables to configure the agent and its access to cloud computers and LLM providers:
```bash
# Computer instance (cloud)
export CUA_CONTAINER_NAME="your-container-name"
export CUA_API_KEY="your-cua-api-key"
# LLM API keys
export ANTHROPIC_API_KEY="your-anthropic-key"
export OPENAI_API_KEY="your-openai-key"
```
### Input and output
The input prompt passed to `Agent.run` can either be a string or a list of message dictionaries:
```python
messages = [
{
"role": "user",
"content": "Take a screenshot and describe what you see"
},
{
"role": "assistant",
"content": "I'll take a screenshot for you."
}
]
```
The output is an AsyncGenerator that yields response chunks.
### Parameters
The `ComputerAgent` constructor provides a wide range of options for customizing agent behavior, tool integration, callbacks, resource management, and more.
- `model` (`str`): Default: **required**
The LLM or agent model to use. Determines which agent loop is selected unless `custom_loop` is provided. (e.g., "claude-3-5-sonnet-20241022", "computer-use-preview", "omni+vertex_ai/gemini-pro")
- `tools` (`List[Any]`):
List of tools the agent can use (e.g., `Computer`, sandboxed Python functions, etc.).
- `custom_loop` (`Callable`):
Optional custom agent loop function. If provided, overrides automatic loop selection.
- `only_n_most_recent_images` (`int`):
If set, only the N most recent images are kept in the message history. Useful for limiting memory usage. Automatically adds `ImageRetentionCallback`.
- `callbacks` (`List[Any]`):
List of callback instances for advanced preprocessing, postprocessing, logging, or custom hooks. See [Callbacks & Extensibility](#callbacks--extensibility).
- `verbosity` (`int`):
Logging level (e.g., `logging.INFO`). If set, adds a logging callback.
- `trajectory_dir` (`str`):
Directory path to save full trajectory data, including screenshots and responses. Adds `TrajectorySaverCallback`.
- `max_retries` (`int`): Default: `3`
Maximum number of retries for failed API calls (default: 3).
- `screenshot_delay` (`float` | `int`): Default: `0.5`
Delay (in seconds) before taking screenshots (default: 0.5).
- `use_prompt_caching` (`bool`): Default: `False`
Enables prompt caching for repeated prompts (mainly for Anthropic models).
- `max_trajectory_budget` (`float` | `dict`):
If set (float or dict), adds a budget manager callback that tracks usage costs and stops execution if the budget is exceeded. Dict allows advanced options (e.g., `{ "max_budget": 5.0, "raise_error": True }`).
- `**kwargs` (`any`):
Any additional keyword arguments are passed through to the agent loop or model provider.
**Example with advanced options:**
```python
from agent import ComputerAgent
from computer import Computer
from agent.callbacks import ImageRetentionCallback
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[Computer(...)],
only_n_most_recent_images=3,
callbacks=[ImageRetentionCallback(only_n_most_recent_images=3)],
verbosity=logging.INFO,
trajectory_dir="trajectories",
max_retries=5,
screenshot_delay=1.0,
use_prompt_caching=True,
max_trajectory_budget={"max_budget": 5.0, "raise_error": True}
)
```
### Streaming Responses
```python
async for result in agent.run(messages, stream=True):
# Process streaming chunks
for item in result["output"]:
if item["type"] == "message":
print(item["content"][0]["text"], end="", flush=True)
elif item["type"] == "computer_call":
action = item["action"]
print(f"\n[Action: {action['type']}]")
```
### Error Handling
```python
try:
async for result in agent.run(messages):
# Process results
pass
except BudgetExceededException:
print("Budget limit exceeded")
except Exception as e:
print(f"Agent error: {e}")
```

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@@ -9,9 +9,9 @@ The benchmark system evaluates models on GUI grounding tasks, specifically agent
## Available Benchmarks
- **[ScreenSpot-v2](./screenspot-v2)** - Standard resolution GUI grounding
- **[ScreenSpot-Pro](./screenspot-pro)** - High-resolution GUI grounding
- **[Interactive Testing](./interactive)** - Real-time testing and visualization
- **[ScreenSpot-v2](./benchmarks/screenspot-v2)** - Standard resolution GUI grounding
- **[ScreenSpot-Pro](./benchmarks/screenspot-pro)** - High-resolution GUI grounding
- **[Interactive Testing](./benchmarks/interactive)** - Real-time testing and visualization
## Quick Start

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docs/content/docs/agent-sdk/callbacks/agent-lifecycle.mdx
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@@ -42,11 +42,4 @@ Called when responses are received from agent loop.
- `on_screenshot(screenshot, name)` - When screenshots are taken
### 10. `on_run_end(kwargs, old_items, new_items)`
Called when agent run completes. Finalize tracking, save trajectories.
## Built-in Callbacks
- **ImageRetentionCallback**: Limits recent images in context
- **BudgetManagerCallback**: Stops execution when budget exceeded
- **TrajectorySaverCallback**: Saves conversation trajectories
- **LoggingCallback**: Logs agent activities
Called when agent run completes. Finalize tracking, save trajectories.

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@@ -28,19 +28,23 @@ agent = ComputerAgent(
## Budget Manager Shorthand
```python
# Simple budget limit
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer],
max_trajectory_budget=5.0 # Auto-adds BudgetManagerCallback
max_trajectory_budget=5.0 # $5 limit
)
```
**Or with options:**
```python
# Advanced budget configuration
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer],
max_trajectory_budget={"max_budget": 5.0, "raise_error": True}
max_trajectory_budget={
"max_budget": 10.0,
"raise_error": True, # Raise error when exceeded
"reset_after_each_run": False # Persistent across runs
}
)
```

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@@ -0,0 +1,64 @@
---
title: Callbacks
---
Callbacks in the Agent SDK provide hooks into the agent's lifecycle, allowing for custom functionality to be executed at various stages of an agent's run. They enable extensibility by allowing developers to integrate their own logic for tasks such as logging, cost management, and data anonymization.
## Usage
You can add preprocessing and postprocessing hooks using callbacks, or write your own by subclassing `AsyncCallbackHandler`.
### Built-in Callbacks
Built-in callbacks can be used as follows:
```python
from agent.callbacks import (
ImageRetentionCallback,
TrajectorySaverCallback,
BudgetManagerCallback,
LoggingCallback
)
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer],
callbacks=[
ImageRetentionCallback(only_n_most_recent_images=3),
TrajectorySaverCallback(trajectory_dir="trajectories"),
BudgetManagerCallback(max_budget=10.0, raise_error=True),
LoggingCallback(level=logging.INFO)
]
)
```
The following built-in callbacks are available:
- [BudgetManagerCallback](callbacks/cost-saving): Stops execution when budget exceeded
- [LoggingCallback](callbacks/trajectories): Logs agent activities
- **ImageRetentionCallback**: Limits recent images in context
- **TrajectorySaverCallback**: Saves conversation trajectories
- [PII Anonymization](callbacks/pii-anonymization)
### Custom Callbacks
Create custom callbacks using knowlege of the callback lifecycle as described in [Agent Lifecycle](callbacks/agent-lifecycle).
```python
from agent.callbacks.base import AsyncCallbackHandler
class CustomCallback(AsyncCallbackHandler):
async def on_llm_start(self, messages):
"""Preprocess messages before LLM call"""
# Add custom preprocessing logic
return messages
async def on_llm_end(self, messages):
"""Postprocess messages after LLM call"""
# Add custom postprocessing logic
return messages
async def on_usage(self, usage):
"""Track usage information"""
print(f"Tokens used: {usage.total_tokens}")
```

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docs/content/docs/agent-sdk/callbacks/trajectories.mdx
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@@ -29,8 +29,8 @@ agent = ComputerAgent(
```python
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer],
trajectory_dir="trajectories" # Auto-adds TrajectorySaverCallback
trajectory_dir="trajectories", # Auto-save trajectories
tools=[computer]
)
```
@@ -46,6 +46,12 @@ The viewer provides:
## Trajectory Structure
Trajectories are saved with:
- Complete conversation history
- Usage statistics and costs
- Timestamps and metadata
- Screenshots and computer actions
Each trajectory contains:
- **metadata.json**: Run info, timestamps, usage stats (`total_tokens`, `response_cost`)
- **turn_000/**: Turn-by-turn conversation history (api calls, responses, computer calls, screenshots)

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@@ -1,11 +1,29 @@
---
title: Sandboxed Tools
slug: sandboxed-tools
title: Custom Tools
slug: custom-tools
---
The Agent SDK supports defining custom Python tools that run securely in sandboxed environments on remote Cua Computers. This enables safe execution of user-defined functions, isolation of dependencies, and robust automation workflows.
## Example: Defining a Sandboxed Tool
## Custom Tools
Define a custom tool for an agent:
```python
def calculate(a: int, b: int) -> int:
"""Calculate the sum of two integers"""
return a + b
# Use with agent
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer, calculate]
)
```
## Sandboxed Tools
Define a sandboxed tool:
```python
from computer.helpers import sandboxed

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@@ -3,47 +3,131 @@ title: HUD Evals
description: Use ComputerAgent with HUD for benchmarking and evaluation
---
The HUD integration allows you to use ComputerAgent with the [HUD benchmarking framework](https://www.hud.so/), providing the same interface as existing HUD agents while leveraging ComputerAgent's capabilities.
The HUD integration allows an agent to be benchmarked using the [HUD framework](https://www.hud.so/). Through the HUD integration, the agent controls a computer inside HUD, where tests are run to evaluate the success of each task.
## Installation
First, install the required package:
```bash
pip install "cua-agent[hud]"
## or install hud-python directly
# pip install hud-python==0.2.10
# pip install hud-python==0.4.12
```
## Usage
## Environment Variables
Before running any evaluations, you’ll need to set up your environment variables for HUD and your model providers:
```bash
# HUD access
export HUD_API_KEY="your_hud_api_key"
# Model provider keys (at least one required)
export OPENAI_API_KEY="your_openai_key"
export ANTHROPIC_API_KEY="your_anthropic_key"
```
## Running a Single Task
You can run a single task from a HUD dataset for quick verification.
### Example
```python
from agent.integrations.hud import run_job
from hud import load_taskset
from hud.taskset import TaskSet
import logging
from agent.integrations.hud import run_single_task
# Load taskset
taskset = await load_taskset("OSWorld-Verified")
taskset = TaskSet(tasks=taskset[:10]) # limit to 10 tasks instead of all 370
# Run benchmark job
job = await run_job(
model="openai/computer-use-preview",
# model="anthropic/claude-3-5-sonnet-20241022",
# model="huggingface-local/HelloKKMe/GTA1-7B+openai/gpt-5",
task_or_taskset=taskset,
job_name="test-computeragent-job",
max_concurrent_tasks=5,
# add any extra ComputerAgent kwargs:
verbosity=logging.INFO, # Enable logging
# trajectory_dir=".." # Save trajectories locally
await run_single_task(
dataset="hud-evals/OSWorld-Verified-XLang", # or another HUD dataset
model="openai/computer-use-preview+openai/gpt-5-nano", # any supported model string
task_id=155, # e.g., reopen last closed tab
)
# Get results OR view them at app.hud.so
print(await job.get_analytics())
print(f"View results at: https://app.hud.so/jobs/{job.id}")
```
**Available Benchmarks:**
1. [OSWorld-Verified](/agent-sdk/benchmarks/osworld-verified) - Benchmark on OSWorld tasks
### Parameters
See the [HUD docs](https://docs.hud.so/environment-creation) for more eval environments.
- `task_id` (`int`): Default: `0`
Index of the task to run from the dataset.
## Running a Full Dataset
To benchmark your agent at scale, you can run an entire dataset (or a subset) in parallel.
### Example
```python
from agent.integrations.hud import run_full_dataset
results = await run_full_dataset(
dataset="hud-evals/OSWorld-Verified-XLang", # can also pass a Dataset or list[dict]
model="openai/computer-use-preview",
split="train[:3]", # try a few tasks to start
max_concurrent=20, # tune to your infra
max_steps=50 # safety cap per task
)
```
### Parameters
- `job_name` (`str` | `None`):
Optional human-readable name for the evaluation job (shows up in HUD UI).
- `max_concurrent` (`int`): Default: `30`
Number of tasks to run in parallel. Scale this based on your infra.
- `max_steps` (`int`): Default: `50`
Safety cap on steps per task to prevent infinite loops.
- `split` (`str`): Default: `"train"`
Dataset split or subset (e.g., `"train[:10]"`).
## Additional Parameters
Both single-task and full-dataset runs share a common set of configuration options. These let you fine-tune how the evaluation runs.
- `dataset` (`str` | `Dataset` | `list[dict]`): **Required**
HUD dataset name (e.g. `"hud-evals/OSWorld-Verified-XLang"`), a loaded `Dataset`, or a list of tasks.
- `model` (`str`): Default: `"computer-use-preview"`
Model string, e.g. `"openai/computer-use-preview+openai/gpt-5-nano"`. Supports composition with `+` (planning + grounding).
- `allowed_tools` (`list[str]`): Default: `["openai_computer"]`
Restrict which tools the agent may use.
- `tools` (`list[Any]`):
Extra tool configs to inject.
- `custom_loop` (`Callable`):
Optional custom agent loop function. If provided, overrides automatic loop selection.
- `only_n_most_recent_images` (`int`): Default: `5` for full dataset, `None` for single task.
Retain only the last N screenshots in memory.
- `callbacks` (`list[Any]`):
Hook functions for logging, telemetry, or side effects.
- `verbosity` (`int`):
Logging level. Set `2` for debugging every call/action.
- `trajectory_dir` (`str` | `dict`):
Save local copies of trajectories for replay/analysis.
- `max_retries` (`int`): Default: `3`
Number of retries for failed model/tool calls.
- `screenshot_delay` (`float` | `int`): Default: `0.5`
Delay (seconds) between screenshots to avoid race conditions.
- `use_prompt_caching` (`bool`): Default: `False`
Cache repeated prompts to reduce API calls.
- `max_trajectory_budget` (`float` | `dict`):
Limit on trajectory size/budget (e.g., tokens, steps).
- `telemetry_enabled` (`bool`): Default: `True`
Whether to send telemetry/traces to HUD.
- `**kwargs` (`any`):
Any additional keyword arguments are passed through to the agent loop or model provider.
## Available Benchmarks
HUD provides multiple benchmark datasets for realistic evaluation.
1. **[OSWorld-Verified](/agent-sdk/benchmarks/osworld-verified)** – Benchmark on 369+ real-world desktop tasks across Chrome, LibreOffice, GIMP, VS Code, etc.
*Best for*: evaluating full computer-use agents in realistic environments.
*Verified variant*: fixes 300+ issues from earlier versions for reliability.
**Coming soon:** SheetBench (spreadsheet automation) and other specialized HUD datasets.
See the [HUD docs](https://docs.hud.so/environment-creation) for more eval environments.
## Tips
* **Debugging:** set `verbosity=2` to see every model call and tool action.
* **Performance:** lower `screenshot_delay` for faster runs; raise it if you see race conditions.
* **Safety:** always set `max_steps` (defaults to 50) to prevent runaway loops.
* **Custom tools:** pass extra `tools=[...]` into the agent config if you need beyond `openai_computer`.

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@@ -4,11 +4,11 @@
"pages": [
"agent-loops",
"supported-agents",
"supported-model-providers",
"chat-history",
"callbacks",
"sandboxed-tools",
"custom-tools",
"custom-computer-handlers",
"local-models",
"prompt-caching",
"usage-tracking",
"benchmarks",

4
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@@ -25,7 +25,9 @@ agent = ComputerAgent(
When using Anthropic-based CUAs (Claude models), setting `use_prompt_caching=True` will automatically add `{ "cache_control": "ephemeral" }` to your messages. This enables prompt caching for the session and can speed up repeated runs with the same prompt.
> **Note:** This argument is only required for Anthropic CUAs. For other providers, it is ignored.
<Callout title="Note">
This argument is only required for Anthropic CUAs. For other providers, it is ignored.
</Callout>
## OpenAI Provider

2
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@@ -62,5 +62,3 @@ The human-in-the-loop interface provides:
- **Testing**: Validate agent, tool, and environment behavior manually
---
For more details on the human tool implementation, see the [Human Tool Documentation](../../tools/human-tool).

32
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View File

@@ -0,0 +1,32 @@
---
title: Supported Model Providers
---
## Supported Models
### Anthropic Claude (Computer Use API)
```python
model="anthropic/claude-3-5-sonnet-20241022"
model="anthropic/claude-3-7-sonnet-20250219"
model="anthropic/claude-opus-4-20250514"
model="anthropic/claude-sonnet-4-20250514"
```
### OpenAI Computer Use Preview
```python
model="openai/computer-use-preview"
```
### UI-TARS (Local or Huggingface Inference)
```python
model="huggingface-local/ByteDance-Seed/UI-TARS-1.5-7B"
model="ollama_chat/0000/ui-tars-1.5-7b"
```
### Omniparser + Any LLM
```python
model="omniparser+ollama_chat/mistral-small3.2"
model="omniparser+vertex_ai/gemini-pro"
model="omniparser+anthropic/claude-3-5-sonnet-20241022"
model="omniparser+openai/gpt-4o"
```

0
docs/content/docs/agent-sdk/local-models.mdx → docs/content/docs/agent-sdk/supported-model-providers/local-models.mdx
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2
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@@ -56,7 +56,7 @@ agent = ComputerAgent(
)
```
See also: [Budget Manager Callbacks](./callbacks#cost-saving)
See also: [Budget Manager Callbacks](./callbacks/cost-saving)
## See Also
- [Prompt Caching](./prompt-caching)

41
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@@ -202,17 +202,17 @@ Direct file and directory manipulation:
</Tab>
<Tab value="TypeScript">
```typescript
// File existence checks
# File existence checks
await computer.interface.fileExists(path); // Check if file exists
await computer.interface.directoryExists(path); // Check if directory exists
// File content operations
# File content operations
await computer.interface.readText(path, "utf-8"); // Read file content
await computer.interface.writeText(path, content, "utf-8"); // Write file content
await computer.interface.readBytes(path); // Read file content as bytes
await computer.interface.writeBytes(path, content); // Write file content as bytes
// File and directory management
# File and directory management
await computer.interface.deleteFile(path); // Delete file
await computer.interface.createDir(path); // Create directory
await computer.interface.deleteDir(path); // Delete directory
@@ -243,3 +243,38 @@ Access system accessibility information:
```
</Tab>
</Tabs>
## Delay Configuration
Control timing between actions:
<Tabs items={['Python']}>
<Tab value="Python">
```python
# Set default delay between all actions (in seconds)
computer.interface.delay = 0.5 # 500ms delay between actions
# Or specify delay for individual actions
await computer.interface.left_click(x, y, delay=1.0) # 1 second delay after click
await computer.interface.type_text("Hello", delay=0.2) # 200ms delay after typing
await computer.interface.press_key("enter", delay=0.5) # 500ms delay after key press
```
</Tab>
</Tabs>
## Python Virtual Environment Operations
Manage Python environments:
<Tabs items={['Python']}>
<Tab value="Python">
```python
# Virtual environment management
await computer.venv_install("demo_venv", ["requests", "macos-pyxa"]) # Install packages in a virtual environment
await computer.venv_cmd("demo_venv", "python -c 'import requests; print(requests.get(`https://httpbin.org/ip`).json())'') # Run a shell command in a virtual environment
await computer.venv_exec("demo_venv", python_function_or_code, *args, **kwargs) # Run a Python function in a virtual environment and return the result / raise an exception
```
</Tab>
</Tabs>

80
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@@ -0,0 +1,80 @@
---
title: Computer UI
---
The computer module includes a Gradio UI for creating and sharing demonstration data. We make it easy for people to build community datasets for better computer use models with an upload to Huggingface feature.
```bash
# Install with UI support
pip install "cua-computer[ui]"
```
<Callout title="Note">
For precise control of the computer, we recommend using VNC or Screen Sharing instead of the Computer Gradio UI.
</Callout>
### Building and Sharing Demonstrations with Huggingface
Follow these steps to contribute your own demonstrations:
#### 1. Set up Huggingface Access
Set your HF_TOKEN in a .env file or in your environment variables:
```bash
# In .env file
HF_TOKEN=your_huggingface_token
```
#### 2. Launch the Computer UI
```python
# launch_ui.py
from computer.ui.gradio.app import create_gradio_ui
from dotenv import load_dotenv
load_dotenv('.env')
app = create_gradio_ui()
app.launch(share=False)
```
For examples, see [Computer UI Examples](https://github.com/trycua/cua/tree/main/examples/computer_ui_examples.py)
#### 3. Record Your Tasks
<details open>
<summary>View demonstration video</summary>
<video src="https://github.com/user-attachments/assets/de3c3477-62fe-413c-998d-4063e48de176" controls width="600"></video>
</details>
Record yourself performing various computer tasks using the UI.
#### 4. Save Your Demonstrations
<details open>
<summary>View demonstration video</summary>
<video src="https://github.com/user-attachments/assets/5ad1df37-026a-457f-8b49-922ae805faef" controls width="600"></video>
</details>
Save each task by picking a descriptive name and adding relevant tags (e.g., "office", "web-browsing", "coding").
#### 5. Record Additional Demonstrations
Repeat steps 3 and 4 until you have a good amount of demonstrations covering different tasks and scenarios.
#### 6. Upload to Huggingface
<details open>
<summary>View demonstration video</summary>
<video src="https://github.com/user-attachments/assets/c586d460-3877-4b5f-a736-3248886d2134" controls width="600"></video>
</details>
Upload your dataset to Huggingface by:
- Naming it as `{your_username}/{dataset_name}`
- Choosing public or private visibility
- Optionally selecting specific tags to upload only tasks with certain tags
#### Examples and Resources
- Example Dataset: [ddupont/test-dataset](https://huggingface.co/datasets/ddupont/test-dataset)
- Find Community Datasets: 🔍 [Browse CUA Datasets on Huggingface](https://huggingface.co/datasets?other=cua)

2
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@@ -109,7 +109,7 @@ cua provides local containers using different providers depending on your host o
```bash
# Option 1: Pull from Docker Hub
docker pull trycua/cua-ubuntu:latest
docker pull --platform=linux/amd64 trycua/cua-ubuntu:latest
# Option 2: Build locally
cd libs/kasm

1
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View File

@@ -4,6 +4,7 @@
"pages": [
"computers",
"commands",
"computer-ui",
"sandboxed-python"
]
}

26
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@@ -44,6 +44,32 @@ You can also install packages in the virtual environment using the `venv_install
await my_computer.venv_install("myenv", ["requests"])
```
## Example: Interacting with macOS Applications
You can use sandboxed functions to interact with macOS applications on a local Cua Computer (requires `os_type="darwin"`). This is particularly useful for automation tasks that involve GUI applications.
```python
# Example: Use sandboxed functions to execute code in a Cua Container
from computer.helpers import sandboxed
await computer.venv_install("demo_venv", ["macos-pyxa"]) # Install packages in a virtual environment
@sandboxed("demo_venv")
def greet_and_print(name):
"""Get the HTML of the current Safari tab"""
import PyXA
safari = PyXA.Application("Safari")
html = safari.current_document.source()
print(f"Hello from inside the container, {name}!")
return {"greeted": name, "safari_html": html}
# When a @sandboxed function is called, it will execute in the container
result = await greet_and_print("Cua")
# Result: {"greeted": "Cua", "safari_html": "<html>...</html>"}
# stdout and stderr are also captured and printed / raised
print("Result from sandboxed function:", result)
```
## Error Handling
If the remote execution fails, the decorator will retry up to `max_retries` times. If all attempts fail, the last exception is raised locally.

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@@ -8,109 +8,14 @@ github:
The Agent library provides the ComputerAgent class and tools for building AI agents that automate workflows on Cua Computers.
## Reference
## Agent Loops
### Basic Usage
See the [Agent Loops](../agent-sdk/agent-loops) documentation for how agents process information and take actions.
```python
from agent import ComputerAgent
from computer import Computer
## Chat History
computer = Computer() # Connect to a cua container
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer]
)
See the [Chat History](../agent-sdk/chat-history) documentation for managing conversational context and turn-by-turn interactions.
prompt = "open github, navigate to trycua/cua"
## Callbacks
async for result in agent.run(prompt):
print("Agent:", result["output"][-1]["content"][0]["text"])
```
---
### ComputerAgent Constructor Options
The `ComputerAgent` constructor provides a wide range of options for customizing agent behavior, tool integration, callbacks, resource management, and more.
| Parameter | Type | Default | Description |
| --------------------------- | ----------------- | ------------ | ---------------------------------------------------------------------------------------------------- |
| `model` | `str` | **required** | Model name (e.g., "claude-3-5-sonnet-20241022", "computer-use-preview", "omni+vertex_ai/gemini-pro") |
| `tools` | `List[Any]` | `None` | List of tools (e.g., computer objects, decorated functions) |
| `custom_loop` | `Callable` | `None` | Custom agent loop function (overrides auto-selection) |
| `only_n_most_recent_images` | `int` | `None` | If set, only keep the N most recent images in message history (adds ImageRetentionCallback) |
| `callbacks` | `List[Any]` | `None` | List of AsyncCallbackHandler instances for preprocessing/postprocessing |
| `verbosity` | `int` | `None` | Logging level (`logging.DEBUG`, `logging.INFO`, etc.; adds LoggingCallback) |
| `trajectory_dir` | `str` | `None` | Directory to save trajectory data (adds TrajectorySaverCallback) |
| `max_retries` | `int` | `3` | Maximum number of retries for failed API calls |
| `screenshot_delay` | `float` \| `int` | `0.5` | Delay before screenshots (seconds) |
| `use_prompt_caching` | `bool` | `False` | Use prompt caching to avoid reprocessing the same prompt (mainly for Anthropic) |
| `max_trajectory_budget` | `float` \| `dict` | `None` | If set, adds BudgetManagerCallback to track usage costs and stop when budget is exceeded |
| `**kwargs` | _any_ | | Additional arguments passed to the agent loop |
#### Parameter Details
- **model**: The LLM or agent model to use. Determines which agent loop is selected unless `custom_loop` is provided.
- **tools**: List of tools the agent can use (e.g., `Computer`, sandboxed Python functions, etc.).
- **custom_loop**: Optional custom agent loop function. If provided, overrides automatic loop selection.
- **only_n_most_recent_images**: If set, only the N most recent images are kept in the message history. Useful for limiting memory usage. Automatically adds `ImageRetentionCallback`.
- **callbacks**: List of callback instances for advanced preprocessing, postprocessing, logging, or custom hooks. See [Callbacks & Extensibility](#callbacks--extensibility).
- **verbosity**: Logging level (e.g., `logging.INFO`). If set, adds a logging callback.
- **trajectory_dir**: Directory path to save full trajectory data, including screenshots and responses. Adds `TrajectorySaverCallback`.
- **max_retries**: Maximum number of retries for failed API calls (default: 3).
- **screenshot_delay**: Delay (in seconds) before taking screenshots (default: 0.5).
- **use_prompt_caching**: Enables prompt caching for repeated prompts (mainly for Anthropic models).
- **max_trajectory_budget**: If set (float or dict), adds a budget manager callback that tracks usage costs and stops execution if the budget is exceeded. Dict allows advanced options (e.g., `{ "max_budget": 5.0, "raise_error": True }`).
- **\*\*kwargs**: Any additional keyword arguments are passed through to the agent loop or model provider.
**Example with advanced options:**
```python
from agent import ComputerAgent
from computer import Computer
from agent.callbacks import ImageRetentionCallback
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[Computer(...)],
only_n_most_recent_images=3,
callbacks=[ImageRetentionCallback(only_n_most_recent_images=3)],
verbosity=logging.INFO,
trajectory_dir="trajectories",
max_retries=5,
screenshot_delay=1.0,
use_prompt_caching=True,
max_trajectory_budget={"max_budget": 5.0, "raise_error": True}
)
```
---
### Message Array (Multi-turn)
```python
messages = [
{"role": "user", "content": "go to trycua on gh"},
# ... (reasoning, computer_call, computer_call_output, etc)
]
async for result in agent.run(messages):
# Handle output, tool invocations, screenshots, etc.
print("Agent:", result["output"][-1]["content"][0]["text"])
messages += result["output"] # Add agent output to message array
...
```
### Callbacks & Extensibility
You can add preprocessing and postprocessing hooks using callbacks, or write your own by subclassing `AsyncCallbackHandler`:
```python
from agent.callbacks import ImageRetentionCallback, PIIAnonymizationCallback
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer],
callbacks=[ImageRetentionCallback(only_n_most_recent_images=3)]
)
```
See the [Callbacks](../agent-sdk/callbacks) documentation for extending and customizing agent behavior with custom hooks.

200
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@@ -8,202 +8,16 @@ github:
- https://github.com/trycua/cua/tree/main/libs/typescript/computer
---
The Computer library provides a Computer class that can be used to control and automate a container running the Computer Server.
The Computer library provides a Computer class for controlling and automating containers running the Computer Server.
## Reference
## Connecting to Computers
### Basic Usage
See the [Cua Computers](../computer-sdk/computers) documentation for how to connect to different computer types (cloud, local, or host desktop).
Connect to a cua cloud container:
## Computer Commands
<Tabs items={['Python', 'TypeScript']}>
<Tab value="Python">
```python
from computer import Computer
See the [Commands](../computer-sdk/commands) documentation for all supported commands and interface methods (Shell, Mouse, Keyboard, File System, etc.).
computer = Computer(
os_type="linux",
provider_type="cloud",
name="your-container-name",
api_key="your-api-key"
)
## Sandboxed Python Functions
computer = await computer.run() # Connect to a cua cloud container
```
</Tab>
<Tab value="TypeScript">
```typescript
import { Computer, OSType } from '@trycua/computer';
const computer = new Computer({
osType: OSType.LINUX,
name: "your-container-name",
apiKey: "your-api-key"
});
await computer.run(); // Connect to a cua cloud container
```
</Tab>
</Tabs>
Connect to a cua local container:
<Tabs items={['Python']}>
<Tab value="Python">
```python
from computer import Computer
computer = Computer(
os_type="macos"
)
computer = await computer.run() # Connect to the container
```
</Tab>
</Tabs>
### Interface Actions
<Tabs items={['Python', 'TypeScript']}>
<Tab value="Python">
```python
# Shell Actions
result = await computer.interface.run_command(cmd) # Run shell command
# result.stdout, result.stderr, result.returncode
# Mouse Actions
await computer.interface.left_click(x, y) # Left click at coordinates
await computer.interface.right_click(x, y) # Right click at coordinates
await computer.interface.double_click(x, y) # Double click at coordinates
await computer.interface.move_cursor(x, y) # Move cursor to coordinates
await computer.interface.drag_to(x, y, duration) # Drag to coordinates
await computer.interface.get_cursor_position() # Get current cursor position
await computer.interface.mouse_down(x, y, button="left") # Press and hold a mouse button
await computer.interface.mouse_up(x, y, button="left") # Release a mouse button
# Keyboard Actions
await computer.interface.type_text("Hello") # Type text
await computer.interface.press_key("enter") # Press a single key
await computer.interface.hotkey("command", "c") # Press key combination
await computer.interface.key_down("command") # Press and hold a key
await computer.interface.key_up("command") # Release a key
# Scrolling Actions
await computer.interface.scroll(x, y) # Scroll the mouse wheel
await computer.interface.scroll_down(clicks) # Scroll down
await computer.interface.scroll_up(clicks) # Scroll up
# Screen Actions
await computer.interface.screenshot() # Take a screenshot
await computer.interface.get_screen_size() # Get screen dimensions
# Clipboard Actions
await computer.interface.set_clipboard(text) # Set clipboard content
await computer.interface.copy_to_clipboard() # Get clipboard content
# File System Operations
await computer.interface.file_exists(path) # Check if file exists
await computer.interface.directory_exists(path) # Check if directory exists
await computer.interface.read_text(path, encoding="utf-8") # Read file content
await computer.interface.write_text(path, content, encoding="utf-8") # Write file content
await computer.interface.read_bytes(path) # Read file content as bytes
await computer.interface.write_bytes(path, content) # Write file content as bytes
await computer.interface.delete_file(path) # Delete file
await computer.interface.create_dir(path) # Create directory
await computer.interface.delete_dir(path) # Delete directory
await computer.interface.list_dir(path) # List directory contents
# Accessibility
await computer.interface.get_accessibility_tree() # Get accessibility tree
# Delay Configuration
# Set default delay between all actions (in seconds)
computer.interface.delay = 0.5 # 500ms delay between actions
# Or specify delay for individual actions
await computer.interface.left_click(x, y, delay=1.0) # 1 second delay after click
await computer.interface.type_text("Hello", delay=0.2) # 200ms delay after typing
await computer.interface.press_key("enter", delay=0.5) # 500ms delay after key press
# Python Virtual Environment Operations
await computer.venv_install("demo_venv", ["requests", "macos-pyxa"]) # Install packages in a virtual environment
await computer.venv_cmd("demo_venv", "python -c 'import requests; print(requests.get(`https://httpbin.org/ip`).json())'') # Run a shell command in a virtual environment
await computer.venv_exec("demo_venv", python_function_or_code, *args, **kwargs) # Run a Python function in a virtual environment and return the result / raise an exception
# Example: Use sandboxed functions to execute code in a Cua Container
from computer.helpers import sandboxed
@sandboxed("demo_venv")
def greet_and_print(name):
"""Get the HTML of the current Safari tab"""
import PyXA
safari = PyXA.Application("Safari")
html = safari.current_document.source()
print(f"Hello from inside the container, {name}!")
return {"greeted": name, "safari_html": html}
# When a @sandboxed function is called, it will execute in the container
result = await greet_and_print("Cua")
# Result: {"greeted": "Cua", "safari_html": "<html>...</html>"}
# stdout and stderr are also captured and printed / raised
print("Result from sandboxed function:", result)
```
</Tab>
<Tab value="TypeScript">
```typescript
// Shell Actions
const result = await computer.interface.runCommand(cmd); // Run shell command
// result.stdout, result.stderr, result.returncode
// Mouse Actions
await computer.interface.leftClick(x, y); // Left click at coordinates
await computer.interface.rightClick(x, y); // Right click at coordinates
await computer.interface.doubleClick(x, y); // Double click at coordinates
await computer.interface.moveCursor(x, y); // Move cursor to coordinates
await computer.interface.dragTo(x, y, duration); // Drag to coordinates
await computer.interface.getCursorPosition(); // Get current cursor position
await computer.interface.mouseDown(x, y, "left"); // Press and hold a mouse button
await computer.interface.mouseUp(x, y, "left"); // Release a mouse button
// Keyboard Actions
await computer.interface.typeText("Hello"); // Type text
await computer.interface.pressKey("enter"); // Press a single key
await computer.interface.hotkey("command", "c"); // Press key combination
await computer.interface.keyDown("command"); // Press and hold a key
await computer.interface.keyUp("command"); // Release a key
// Scrolling Actions
await computer.interface.scroll(x, y); // Scroll the mouse wheel
await computer.interface.scrollDown(clicks); // Scroll down
await computer.interface.scrollUp(clicks); // Scroll up
// Screen Actions
await computer.interface.screenshot(); // Take a screenshot
await computer.interface.getScreenSize(); // Get screen dimensions
// Clipboard Actions
await computer.interface.setClipboard(text); // Set clipboard content
await computer.interface.copyToClipboard(); // Get clipboard content
// File System Operations
await computer.interface.fileExists(path); // Check if file exists
await computer.interface.directoryExists(path); // Check if directory exists
await computer.interface.readText(path, "utf-8"); // Read file content
await computer.interface.writeText(path, content, "utf-8"); // Write file content
await computer.interface.readBytes(path); // Read file content as bytes
await computer.interface.writeBytes(path, content); // Write file content as bytes
await computer.interface.deleteFile(path); // Delete file
await computer.interface.createDir(path); // Create directory
await computer.interface.deleteDir(path); // Delete directory
await computer.interface.listDir(path); // List directory contents
// Accessibility
await computer.interface.getAccessibilityTree(); // Get accessibility tree
```
</Tab>
</Tabs>
See the [Sandboxed Python](../computer-sdk/sandboxed-python) documentation for running Python functions securely in isolated environments on a remote Cua Computer.

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@@ -3,46 +3,7 @@ title: Lume CLI Reference
description: Command Line Interface reference for Lume
---
Lume is a lightweight Command Line Interface and local API server for creating, running and managing **macOS and Linux virtual machines** with near-native performance on Apple Silicon, using Apple's [Virtualization.Framework](https://developer.apple.com/documentation/virtualization).
## Quick Start
Install and run a prebuilt macOS VM in two commands:
```bash
# Install Lume
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
# Pull & start a macOS image
lume run macos-sequoia-vanilla:latest
```
> **Security Note**: All prebuilt images use the default password `lume`. Change this immediately after your first login using the `passwd` command.
**System Requirements**:
- Apple Silicon Mac (M1, M2, M3, etc.)
- macOS 13.0 or later
- At least 8GB of RAM (16GB recommended)
- At least 50GB of free disk space
## Install
Install with a single command:
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
```
By default, Lume is installed as a background service that starts automatically on login. If you prefer to start the Lume API service manually when needed, you can use the `--no-background-service` option:
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh) --no-background-service"
```
> **Note:** With this option, you'll need to manually start the Lume API service by running `lume serve` in your terminal whenever you need to use tools or libraries that rely on the Lume API (such as the Computer-Use Agent).
You can also download the `lume.pkg.tar.gz` archive from the [latest release](https://github.com/trycua/cua/releases?q=lume&expanded=true), extract it, and install the package manually.
## Using Lume
import { Callout } from 'fumadocs-ui/components/callout';
Once installed, you can start using Lume with these common workflows:
@@ -56,7 +17,9 @@ lume run macos-sequoia-vanilla:latest
lume run ubuntu-noble-vanilla:latest
```
> We provide [prebuilt VM images](#prebuilt-images) in our [ghcr registry](https://github.com/orgs/trycua/packages).
<Callout>
We provide [prebuilt VM images](../lume/prebuilt-images) in our [ghcr registry](https://github.com/orgs/trycua/packages).
</Callout>
### Create a Custom VM
@@ -68,4 +31,179 @@ lume create my-macos-vm --cpu 4 --memory 8GB --disk-size 50GB
lume create my-linux-vm --os linux --cpu 2 --memory 4GB
```
> **Disk Space**: The actual disk space used by sparse images will be much lower than the logical size listed. You can resize VM disks after creation using `lume set <name> --disk-size <size>`.
<Callout title="Disk Space">
The actual disk space used by sparse images will be much lower than the logical size listed. You can resize VM disks after creation using `lume set <name> --disk-size <size>`.
</Callout>
## VM Management
lume create &lt;name&gt;
Create a new macOS or Linux virtual machine.
**Options:**
- `--os <os>` - Operating system to install (macOS or linux, default: macOS)
- `--cpu <cores>` - Number of CPU cores (default: 4)
- `--memory <size>` - Memory size, e.g., 8GB (default: 4GB)
- `--disk-size <size>` - Disk size, e.g., 50GB (default: 40GB)
- `--display <res>` - Display resolution (default: 1024x768)
- `--ipsw <path>` - Path to IPSW file or 'latest' for macOS VMs
- `--storage <name>` - VM storage location to use
**Examples:**
```bash
# Create macOS VM with custom specs
lume create my-mac --cpu 6 --memory 16GB --disk-size 100GB
# Create Linux VM
lume create my-ubuntu --os linux --cpu 2 --memory 8GB
# Create macOS VM with latest IPSW
lume create my-sequoia --ipsw latest
```
lume run &lt;name&gt;
Start and run a virtual machine.
**Options:**
- `--no-display` - Do not start the VNC client app
- `--shared-dir <dir>` - Share directory with VM (format: path[:ro|rw])
- `--mount <path>` - For Linux VMs only, attach a read-only disk image
- `--registry <url>` - Container registry URL (default: ghcr.io)
- `--organization <org>` - Organization to pull from (default: trycua)
- `--vnc-port <port>` - Port to use for the VNC server (default: 0 for auto-assign)
- `--recovery-mode <boolean>` - For macOS VMs only, start VM in recovery mode (default: false)
- `--storage <name>` - VM storage location to use
**Examples:**
```bash
# Run VM with shared directory
lume run my-vm --shared-dir /path/to/share:rw
# Run VM without display (headless)
lume run my-vm --no-display
# Run macOS VM in recovery mode
lume run my-mac --recovery-mode true
```
lume stop &lt;name&gt;
Stop a running virtual machine.
**Options:**
- `--storage <name>` - VM storage location to use
### lume delete &lt;name&gt;
Delete a virtual machine and its associated files.
**Options:**
- `--force` - Force deletion without confirmation
- `--storage <name>` - VM storage location to use
### lume clone &lt;name&gt; &lt;new-name&gt;
Create a copy of an existing virtual machine.
**Options:**
- `--source-storage <name>` - Source VM storage location
- `--dest-storage <name>` - Destination VM storage location
## VM Information and Configuration
### lume ls
List all virtual machines and their status.
### lume get &lt;name&gt;
Get detailed information about a specific virtual machine.
**Options:**
- `-f, --format <format>` - Output format (json|text)
- `--storage <name>` - VM storage location to use
### lume set &lt;name&gt;
Modify virtual machine configuration.
**Options:**
- `--cpu <cores>` - New number of CPU cores (e.g., 4)
- `--memory <size>` - New memory size (e.g., 8192MB or 8GB)
- `--disk-size <size>` - New disk size (e.g., 40960MB or 40GB)
- `--display <res>` - New display resolution in format WIDTHxHEIGHT (e.g., 1024x768)
- `--storage <name>` - VM storage location to use
**Examples:**
```bash
# Increase VM memory
lume set my-vm --memory 16GB
# Change display resolution
lume set my-vm --display 1920x1080
# Add more CPU cores
lume set my-vm --cpu 8
```
## Image Management
### lume images
List available macOS images in local cache.
### lume pull &lt;image&gt;
Download a VM image from a container registry.
**Options:**
- `--registry <url>` - Container registry URL (default: ghcr.io)
- `--organization <org>` - Organization to pull from (default: trycua)
- `--storage <name>` - VM storage location to use
### lume push &lt;name&gt; &lt;image:tag&gt;
Upload a VM image to a container registry.
**Options:**
- `--additional-tags <tags...>` - Additional tags to push the same image to
- `--registry <url>` - Container registry URL (default: ghcr.io)
- `--organization <org>` - Organization/user to push to (default: trycua)
- `--storage <name>` - VM storage location to use
- `--chunk-size-mb <size>` - Chunk size for disk image upload in MB (default: 512)
- `--verbose` - Enable verbose logging
- `--dry-run` - Prepare files and show plan without uploading
- `--reassemble` - Verify integrity by reassembling chunks (requires --dry-run)
### lume ipsw
Get the latest macOS restore image URL.
### lume prune
Remove cached images to free up disk space.
## Configuration
### lume config
Manage Lume configuration settings.
**Subcommands:**
##### Storage Management
- `lume config storage add <name> <path>` - Add a new VM storage location
- `lume config storage remove <name>` - Remove a VM storage location
- `lume config storage list` - List all VM storage locations
- `lume config storage default <name>` - Set the default VM storage location
##### Cache Management
- `lume config cache get` - Get current cache directory
- `lume config cache set <path>` - Set cache directory
##### Image Caching
- `lume config caching get` - Show current caching status
- `lume config caching set <boolean>` - Enable or disable image caching
## API Server
### lume serve
Start the Lume API server for programmatic access.
**Options:**
- `--port <port>` - Port to listen on (default: 7777)
## Global Options
These options are available for all commands:
- `--help` - Show help information
- `--version` - Show version number

4
libs/lume/docs/FAQ.md → docs/content/docs/libraries/lume/faq.md
View File

@@ -1,4 +1,6 @@
# FAQs
---
title: FAQ
---
### Where are the VMs stored?

747
docs/content/docs/libraries/lume/http-api.mdx
View File

@@ -1,9 +1,10 @@
---
title: HTTP Server API
description: Lume exposes a local HTTP API server that listens at localhost for programatic management of VMs.
description: Lume exposes a local HTTP API server that listens at localhost for programmatic management of VMs.
---
import { Tabs, Tab } from 'fumadocs-ui/components/tabs';
import { Callout } from 'fumadocs-ui/components/callout';
## Default URL
@@ -19,11 +20,13 @@ http://localhost:7777
## Endpoints
---
### Create VM
Create a new virtual machine.
`POST: /vms`
`POST: /lume/vms`
#### Parameters
@@ -86,32 +89,34 @@ print(r.json())
```typescript
const payload = {
name: "lume_vm",
os: "macOS",
name: 'lume_vm',
os: 'macOS',
cpu: 2,
memory: "4GB",
diskSize: "64GB",
display: "1024x768",
ipsw: "latest",
storage: "ssd"
}
memory: '4GB',
diskSize: '64GB',
display: '1024x768',
ipsw: 'latest',
storage: 'ssd',
};
const res = await fetch('http://localhost:7777/lume/vms', {
methdo: 'POST'
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
console.log(await res.json())
console.log(await res.json());
```
</Tab>
</Tabs>
---
### Run VM
Run a virtual machine instance.
`POST: /vms/:name/run`
`POST: /lume/vms/:name/run`
#### Parameters
@@ -181,7 +186,7 @@ print(r.json())
```typescript
// Basic run
const res = await fetch('http://localhost:7777/lume/vms/my-vm-name/run', {
let res = await fetch('http://localhost:7777/lume/vms/my-vm-name/run', {
method: 'POST',
});
console.log(await res.json());
@@ -193,22 +198,24 @@ const payload = {
recoveryMode: false,
storage: 'ssd',
};
const res2 = await fetch('http://localhost:7777/lume/vms/lume_vm/run', {
res = await fetch('http://localhost:7777/lume/vms/lume_vm/run', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
console.log(await res2.json());
console.log(await res.json());
```
</Tab>
</Tabs>
---
### List VMs
List all virtual machines.
`GET: /vms`
`GET: /lume/vms`
#### Example Request
@@ -263,11 +270,13 @@ console.log(await res.json());
]
```
---
### Get VM Details
Get details for a specific virtual machine.
`GET: /vms/:name`
`GET: /lume/vms/:name`
#### Parameters
@@ -312,12 +321,12 @@ print(details.json())
```typescript
// Basic get
const res = await fetch('http://localhost:7777/lume/vms/lume_vm');
let res = await fetch('http://localhost:7777/lume/vms/lume_vm');
console.log(await res.json());
// Get with specific storage
const res2 = await fetch('http://localhost:7777/lume/vms/lume_vm?storage=ssd');
console.log(await res2.json());
res = await fetch('http://localhost:7777/lume/vms/lume_vm?storage=ssd');
console.log(await res.json());
```
</Tab>
@@ -344,11 +353,13 @@ console.log(await res2.json());
}
```
---
### Update VM Configuration
Update the configuration of a virtual machine.
`PUT: /vms/:name`
`PATCH: /lume/vms/:name`
#### Parameters
@@ -368,7 +379,7 @@ Update the configuration of a virtual machine.
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X PUT \
-X PATCH \
-H "Content-Type: application/json" \
-d '{
"cpu": 4,
@@ -393,7 +404,7 @@ payload = {
"display": "1920x1080",
"storage": "ssd"
}
r = requests.put("http://localhost:7777/lume/vms/lume_vm", json=payload, timeout=50)
r = requests.patch("http://localhost:7777/lume/vms/lume_vm", json=payload, timeout=50)
print(r.json())
```
@@ -409,7 +420,7 @@ const payload = {
storage: 'ssd',
};
const res = await fetch('http://localhost:7777/lume/vms/lume_vm', {
method: 'PUT',
method: 'PATCH',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
@@ -419,11 +430,19 @@ console.log(await res.json());
</Tab>
</Tabs>
---
### Stop VM
Stop a running virtual machine.
`POST: /vms/:name/stop`
`POST: /lume/vms/:name/stop`
#### Parameters
| Name | Type | Required | Description |
| ------- | ------ | -------- | -------------------------- |
| storage | string | No | Storage type (`ssd`, etc.) |
#### Example Request
@@ -431,10 +450,17 @@ Stop a running virtual machine.
<Tab value="Curl">
```bash
# Basic stop
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/vms/lume_vm/stop
# Stop with storage location specified
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/vms/lume_vm/stop?storage=ssd
```
</Tab>
@@ -443,15 +469,27 @@ curl --connect-timeout 6000 \
```python
import requests
# Basic stop
r = requests.post("http://localhost:7777/lume/vms/lume_vm/stop", timeout=50)
print(r.json())
# Stop with storage location specified
r = requests.post("http://localhost:7777/lume/vms/lume_vm/stop", params={"storage": "ssd"}, timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/vms/lume_vm/stop', {
// Basic stop
let res = await fetch('http://localhost:7777/lume/vms/lume_vm/stop', {
method: 'POST',
});
console.log(await res.json());
// Stop with storage location specified
res = await fetch('http://localhost:7777/lume/vms/lume_vm/stop?storage=ssd', {
method: 'POST',
});
console.log(await res.json());
@@ -460,11 +498,13 @@ console.log(await res.json());
</Tab>
</Tabs>
---
### Delete VM
Delete a virtual machine instance.
`DELETE: /vms/:name`
`DELETE: /lume/vms/:name`
#### Parameters
@@ -511,34 +551,110 @@ print(r.status_code)
```typescript
// Basic delete
const res = await fetch('http://localhost:7777/lume/vms/lume_vm', {
let res = await fetch('http://localhost:7777/lume/vms/lume_vm', {
method: 'DELETE',
});
console.log(res.status);
// Delete with specific storage
const res2 = await fetch('http://localhost:7777/lume/vms/lume_vm?storage=ssd', {
res = await fetch('http://localhost:7777/lume/vms/lume_vm?storage=ssd', {
method: 'DELETE',
});
console.log(res2.status);
console.log(res.status);
```
</Tab>
</Tabs>
---
### Clone VM
Clone an existing virtual machine.
`POST: /lume/vms/clone`
#### Parameters
| Name | Type | Required | Description |
| -------------- | ------ | -------- | ----------------------------------- |
| name | string | Yes | Source VM name |
| newName | string | Yes | New VM name |
| sourceLocation | string | No | Source storage location (`default`) |
| destLocation | string | No | Destination storage location |
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"name": "source-vm",
"newName": "cloned-vm",
"sourceLocation": "default",
"destLocation": "ssd"
}' \
http://localhost:7777/lume/vms/clone
```
</Tab>
<Tab value="Python">
```python
import requests
payload = {
"name": "source-vm",
"newName": "cloned-vm",
"sourceLocation": "default",
"destLocation": "ssd"
}
r = requests.post("http://localhost:7777/lume/vms/clone", json=payload, timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const payload = {
name: 'source-vm',
newName: 'cloned-vm',
sourceLocation: 'default',
destLocation: 'ssd',
};
const res = await fetch('http://localhost:7777/lume/vms/clone', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
console.log(await res.json());
```
</Tab>
</Tabs>
---
### Pull VM Image
Pull a VM image from a registry.
`POST: /images/pull`
`POST: /lume/pull`
#### Parameters
| Name | Type | Required | Description |
| ------------ | ------ | -------- | ------------------------------------- |
| image | string | Yes | Image name (e.g. `macos-sequoia-...`) |
| registry | string | Yes | Registry host (e.g. `ghcr.io`) |
| organization | string | Yes | Organization name |
| name | string | No | VM name for the pulled image |
| registry | string | No | Registry host (e.g. `ghcr.io`) |
| organization | string | No | Organization name |
| storage | string | No | Storage type (`ssd`, etc.) |
#### Example Request
@@ -553,11 +669,12 @@ curl --connect-timeout 6000 \
-H "Content-Type: application/json" \
-d '{
"image": "macos-sequoia-vanilla:latest",
"name": "my-vm-name",
"registry": "ghcr.io",
"organization": "trycua",
"storage": "ssd"
}' \
http://localhost:7777/lume/images/pull
http://localhost:7777/lume/pull
```
</Tab>
@@ -568,11 +685,12 @@ import requests
payload = {
"image": "macos-sequoia-vanilla:latest",
"name": "my-vm-name",
"registry": "ghcr.io",
"organization": "trycua",
"storage": "ssd"
}
r = requests.post("http://localhost:7777/lume/images/pull", json=payload, timeout=50)
r = requests.post("http://localhost:7777/lume/pull", json=payload, timeout=50)
print(r.json())
```
@@ -582,11 +700,12 @@ print(r.json())
```typescript
const payload = {
image: 'macos-sequoia-vanilla:latest',
name: 'my-vm-name',
registry: 'ghcr.io',
organization: 'trycua',
storage: 'ssd',
};
const res = await fetch('http://localhost:7777/lume/images/pull', {
const res = await fetch('http://localhost:7777/lume/pull', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
@@ -596,3 +715,553 @@ console.log(await res.json());
</Tab>
</Tabs>
---
### Push VM Image
Push a VM to a registry as an image (asynchronous operation).
`POST: /lume/vms/push`
#### Parameters
| Name | Type | Required | Description |
| ------------ | ------------ | -------- | ----------------------------------------------- |
| name | string | Yes | Local VM name to push |
| imageName | string | Yes | Image name in registry |
| tags | array | Yes | Image tags (e.g. `["latest", "v1"]`) |
| organization | string | Yes | Organization name |
| registry | string | No | Registry host (e.g. `ghcr.io`) |
| chunkSizeMb | integer | No | Chunk size in MB for upload |
| storage | string/null | No | Storage type (`ssd`, etc.) |
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"name": "my-local-vm",
"imageName": "my-image",
"tags": ["latest", "v1"],
"organization": "my-org",
"registry": "ghcr.io",
"chunkSizeMb": 512,
"storage": null
}' \
http://localhost:7777/lume/vms/push
```
</Tab>
<Tab value="Python">
```python
import requests
payload = {
"name": "my-local-vm",
"imageName": "my-image",
"tags": ["latest", "v1"],
"organization": "my-org",
"registry": "ghcr.io",
"chunkSizeMb": 512,
"storage": None
}
r = requests.post("http://localhost:7777/lume/vms/push", json=payload, timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const payload = {
name: 'my-local-vm',
imageName: 'my-image',
tags: ['latest', 'v1'],
organization: 'my-org',
registry: 'ghcr.io',
chunkSizeMb: 512,
storage: null,
};
const res = await fetch('http://localhost:7777/lume/vms/push', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
console.log(await res.json());
```
</Tab>
</Tabs>
**Response (202 Accepted):**
```json
{
"message": "Push initiated in background",
"name": "my-local-vm",
"imageName": "my-image",
"tags": [
"latest",
"v1"
]
}
```
---
### List Images
List available VM images.
`GET: /lume/images`
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/images
```
</Tab>
<Tab value="Python">
```python
import requests
r = requests.get("http://localhost:7777/lume/images", timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/images');
console.log(await res.json());
```
</Tab>
</Tabs>
```json
{
"local": [
"macos-sequoia-xcode:latest",
"macos-sequoia-vanilla:latest"
]
}
```
---
### Prune Images
Remove unused VM images to free up disk space.
`POST: /lume/prune`
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/prune
```
</Tab>
<Tab value="Python">
```python
import requests
r = requests.post("http://localhost:7777/lume/prune", timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/prune', {
method: 'POST',
});
console.log(await res.json());
```
</Tab>
</Tabs>
---
### Get Latest IPSW URL
Get the URL for the latest macOS IPSW file.
`GET: /lume/ipsw`
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/ipsw
```
</Tab>
<Tab value="Python">
```python
import requests
r = requests.get("http://localhost:7777/lume/ipsw", timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/ipsw');
console.log(await res.json());
```
</Tab>
</Tabs>
---
## Configuration Management
### Get Configuration
Get current Lume configuration settings.
`GET: /lume/config`
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/config
```
</Tab>
<Tab value="Python">
```python
import requests
r = requests.get("http://localhost:7777/lume/config", timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/config');
console.log(await res.json());
```
</Tab>
</Tabs>
```json
{
"homeDirectory": "~/.lume",
"cacheDirectory": "~/.lume/cache",
"cachingEnabled": true
}
```
### Update Configuration
Update Lume configuration settings.
`POST: /lume/config`
#### Parameters
| Name | Type | Required | Description |
| --------------- | ------- | -------- | -------------------------------- |
| homeDirectory | string | No | Lume home directory path |
| cacheDirectory | string | No | Cache directory path |
| cachingEnabled | boolean | No | Enable or disable caching |
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"homeDirectory": "~/custom/lume",
"cacheDirectory": "~/custom/lume/cache",
"cachingEnabled": true
}' \
http://localhost:7777/lume/config
```
</Tab>
<Tab value="Python">
```python
import requests
payload = {
"homeDirectory": "~/custom/lume",
"cacheDirectory": "~/custom/lume/cache",
"cachingEnabled": True
}
r = requests.post("http://localhost:7777/lume/config", json=payload, timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const payload = {
homeDirectory: '~/custom/lume',
cacheDirectory: '~/custom/lume/cache',
cachingEnabled: true,
};
const res = await fetch('http://localhost:7777/lume/config', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
console.log(await res.json());
```
</Tab>
</Tabs>
---
## Storage Location Management
### Get VM Storage Locations
List all configured VM storage locations.
`GET: /lume/config/locations`
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/config/locations
```
</Tab>
<Tab value="Python">
```python
import requests
r = requests.get("http://localhost:7777/lume/config/locations", timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/config/locations');
console.log(await res.json());
```
</Tab>
</Tabs>
```json
[
{
"name": "default",
"path": "~/.lume/vms",
"isDefault": true
},
{
"name": "ssd",
"path": "/Volumes/SSD/lume/vms",
"isDefault": false
}
]
```
### Add VM Storage Location
Add a new VM storage location.
`POST: /lume/config/locations`
#### Parameters
| Name | Type | Required | Description |
| ---- | ------ | -------- | ---------------------------- |
| name | string | Yes | Storage location name |
| path | string | Yes | File system path for storage |
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"name": "ssd",
"path": "/Volumes/SSD/lume/vms"
}' \
http://localhost:7777/lume/config/locations
```
</Tab>
<Tab value="Python">
```python
import requests
payload = {
"name": "ssd",
"path": "/Volumes/SSD/lume/vms"
}
r = requests.post("http://localhost:7777/lume/config/locations", json=payload, timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const payload = {
name: 'ssd',
path: '/Volumes/SSD/lume/vms',
};
const res = await fetch('http://localhost:7777/lume/config/locations', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
});
console.log(await res.json());
```
</Tab>
</Tabs>
### Remove VM Storage Location
Remove a VM storage location.
`DELETE: /lume/config/locations/:name`
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X DELETE \
http://localhost:7777/lume/config/locations/ssd
```
</Tab>
<Tab value="Python">
```python
import requests
r = requests.delete("http://localhost:7777/lume/config/locations/ssd", timeout=50)
print(r.status_code)
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/config/locations/ssd', {
method: 'DELETE',
});
console.log(res.status);
```
</Tab>
</Tabs>
### Set Default VM Storage Location
Set a storage location as the default.
`POST: /lume/config/locations/default/:name`
#### Example Request
<Tabs groupId="language" persist items={['Curl', 'Python', 'TypeScript']}>
<Tab value="Curl">
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/config/locations/default/ssd
```
</Tab>
<Tab value="Python">
```python
import requests
r = requests.post("http://localhost:7777/lume/config/locations/default/ssd", timeout=50)
print(r.json())
```
</Tab>
<Tab value="TypeScript">
```typescript
const res = await fetch('http://localhost:7777/lume/config/locations/default/ssd', {
method: 'POST',
});
console.log(await res.json());
```
</Tab>
</Tabs>

4
docs/content/docs/libraries/lume/index.mdx
View File

@@ -5,6 +5,4 @@ github:
- https://github.com/trycua/cua/tree/main/libs/lume
---
## Overview
The Lume CLI provides command line tools for managing virtual machines with Lume.
Lume is a lightweight Command Line Interface and local API server for creating, running and managing **macOS and Linux virtual machines** with near-native performance on Apple Silicon, using Apple's [Virtualization.Framework](https://developer.apple.com/documentation/virtualization).

47
docs/content/docs/libraries/lume/installation.mdx Normal file
View File

@@ -0,0 +1,47 @@
---
title: Installation
description: Installation instructions for the current version of the Lume CLI.
---
## Quickstart
Install and run a prebuilt macOS VM in two commands:
```bash
# Install Lume
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
# Pull & start a macOS image
lume run macos-sequoia-vanilla:latest
```
<Callout title="Security Note">
All prebuilt images use the default password `lume`. Change this immediately after your first login using the `passwd` command.
</Callout>
**System Requirements**:
- Apple Silicon Mac (M1, M2, M3, etc.)
- macOS 13.0 or later
- At least 8GB of RAM (16GB recommended)
- At least 50GB of free disk space
## Install with Script
Install with a single command:
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
```
### Manual Start (No Background Service)
By default, Lume is installed as a background service that starts automatically on login. If you prefer to start the Lume API service manually when needed, you can use the `--no-background-service` option:
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh) --no-background-service"
```
<Callout title="Note">
With this option, you'll need to manually start the Lume API service by running `lume serve` in your terminal whenever you need to use tools or libraries that rely on the Lume API (such as the Computer-Use Agent).
</Callout>
## Manual Download and Installation
You can also download the `lume.pkg.tar.gz` archive from the [latest release](https://github.com/trycua/cua/releases?q=lume&expanded=true), extract it, and install the package manually.

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@@ -0,0 +1,9 @@
{
"pages": [
"installation",
"prebuilt-images",
"cli-reference",
"http-api",
"faq"
]
}

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---
title: Prebuilt Images
---
Pre-built images are available in the registry [ghcr.io/trycua](https://github.com/orgs/trycua/packages).
**Important Note (v0.2.0+):** Images are being re-uploaded with sparse file system optimizations enabled, resulting in significantly lower actual disk usage. Older images (without the `-sparse` suffix) are now **deprecated**. The last version of `lume` fully supporting the non-sparse images was `v0.1.x`. Starting from `v0.2.0`, lume will automatically pull images optimized with sparse file system support.
These images come with an SSH server pre-configured and auto-login enabled.
For the security of your VM, change the default password `lume` immediately after your first login.
| Image | Tag | Description | Logical Size |
|-------|------------|-------------|------|
| `macos-sequoia-vanilla` | `latest`, `15.2` | macOS Sequoia 15.2 image | 20GB |
| `macos-sequoia-xcode` | `latest`, `15.2` | macOS Sequoia 15.2 image with Xcode command line tools | 22GB |
| `macos-sequoia-cua` | `latest`, `15.3` | macOS Sequoia 15.3 image compatible with the Computer interface | 24GB |
| `ubuntu-noble-vanilla` | `latest`, `24.04.1` | [Ubuntu Server for ARM 24.04.1 LTS](https://ubuntu.com/download/server/arm) with Ubuntu Desktop | 20GB |
For additional disk space, resize the VM disk after pulling the image using the `lume set <name> --disk-size <size>` command. Note that the actual disk space used by sparse images will be much lower than the logical size listed.

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---
title: Building Lumier
---
If you want to customize the Lumier container or build it from source, you can follow these steps:
```bash
# 1. Navigate to the Lumier directory
cd libs/lumier
# 2. Build the Docker image locally
docker build -t lumier-custom:latest .
# 3. Run your custom build
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
lumier-custom:latest
```
### Customization Options
The Dockerfile provides several customization points:
1. **Base image**: The container uses Debian Bullseye Slim as the base. You can modify this if needed.
2. **Installed packages**: You can add or remove packages in the apt-get install list.
3. **Hooks**: Check the `/run/hooks/` directory for scripts that run at specific points during VM lifecycle.
4. **Configuration**: Review `/run/config/constants.sh` for default settings.
After making your modifications, you can build and push your custom image to your own Docker Hub repository:
```bash
# Build with a custom tag
docker build -t yourusername/lumier:custom .
# Push to Docker Hub (after docker login)
docker push yourusername/lumier:custom
```

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---
title: Docker Compose
---
You can also use Docker Compose to run Lumier with a simple configuration file. Create a `docker-compose.yml` file with the following content:
```yaml
version: '3'
services:
lumier:
image: trycua/lumier:latest
container_name: lumier-vm
restart: unless-stopped
ports:
- "8006:8006" # Port for VNC access
volumes:
- ./storage:/storage # VM persistent storage
- ./shared:/shared # Shared folder accessible in the VM
environment:
- VM_NAME=lumier-vm
- VERSION=ghcr.io/trycua/macos-sequoia-cua:latest
- CPU_CORES=4
- RAM_SIZE=8192
- HOST_STORAGE_PATH=${PWD}/storage
- HOST_SHARED_PATH=${PWD}/shared
stop_signal: SIGINT
stop_grace_period: 2m
```
Then run Lumier using:
```bash
# First create the required directories
mkdir -p storage shared
# Start the container
docker-compose up -d
# View the logs
docker-compose logs -f
# Stop the container when done
docker-compose down
```

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---
title: Docker
---
You can use Lumier through Docker:
### Run a macOS VM (ephemeral)
```bash
# Run the container with temporary storage (using pre-built image from Docker Hub)
docker run -it --rm \
--name macos-vm \
-p 8006:8006 \
-e VM_NAME=macos-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
trycua/lumier:latest
```
Access the VM in your browser at [http://localhost:8006](http://localhost:8006).
After running the command above, you can access your macOS VM through a web browser (e.g., http://localhost:8006).
<Callout title="Note">
With the basic setup above, your VM will be reset when you stop the container (ephemeral mode). This means any changes you make inside the macOS VM will be lost. See the section below for how to save your VM state.
</Callout>
## Saving Your VM State
To save your VM state between sessions (so your changes persist when you stop and restart the container), you'll need to set up a storage location:
```bash
# First, create a storage directory if it doesn't exist
mkdir -p storage
# Then run the container with persistent storage
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-v $(pwd)/storage:/storage \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
-e HOST_STORAGE_PATH=$(pwd)/storage \
trycua/lumier:latest
```
This command creates a connection between a folder on your Mac (`$(pwd)/storage`) and a folder inside the Docker container (`/storage`). The `-v` flag (volume mount) and the `HOST_STORAGE_PATH` variable work together to ensure your VM data is saved on your host Mac.
## Sharing Files with Your VM
To share files between your Mac and the virtual machine, you can set up a shared folder:
```bash
# Create both storage and shared folders
mkdir -p storage shared
# Run with both persistent storage and a shared folder
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-v $(pwd)/storage:/storage \
-v $(pwd)/shared:/shared \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
-e HOST_STORAGE_PATH=$(pwd)/storage \
-e HOST_SHARED_PATH=$(pwd)/shared \
trycua/lumier:latest
```
With this setup, any files you place in the `shared` folder on your Mac will be accessible from within the macOS VM, and vice versa.
## Automating VM Startup with on-logon.sh
You can automatically run scripts when the VM starts up by placing an `on-logon.sh` script in the shared folder's lifecycle directory. This is useful for setting up your VM environment each time it starts.
```bash
# Create the lifecycle directory in your shared folder
mkdir -p shared/lifecycle
# Create a sample on-logon.sh script
cat > shared/lifecycle/on-logon.sh << 'EOF'
#!/usr/bin/env bash
# Create a file on the desktop
echo "Hello from Lumier!" > /Users/lume/Desktop/hello_lume.txt
# You can add more commands to execute at VM startup
# For example:
# - Configure environment variables
# - Start applications
# - Mount network drives
# - Set up development environments
EOF
# Make the script executable
chmod +x shared/lifecycle/on-logon.sh
```
The script will be automatically executed when the VM starts up. It runs in the VM context and has access to:
- The `/Users/lume` user directory (home directory in the VM)
- The shared folder at `/Volumes/My Shared Files` inside the VM
- Any resources available to the VM
This feature enables automation of VM setup without modifying the base VM image.
## Configuration Options
When running Lumier, you'll need to configure a few things:
- **Port forwarding** (`-p 8006:8006`): Makes the VM's VNC interface accessible in your browser. If port 8006 is already in use, you can use a different port like `-p 8007:8006`.
- **Environment variables** (`-e`): Configure your VM settings:
- `VM_NAME`: A name for your virtual machine
- `VERSION`: The macOS image to use
- `CPU_CORES`: Number of CPU cores to allocate
- `RAM_SIZE`: Memory in MB to allocate
- `HOST_STORAGE_PATH`: Path to save VM state (when using persistent storage)
- `HOST_SHARED_PATH`: Path to the shared folder (optional)
- **Background service**: The `lume serve` service should be running on your host (starts automatically when you install Lume using the `install.sh` script above).

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- https://github.com/trycua/cua/tree/main/libs/lumier
---
## Overview
**Lumier** is an interface for running macOS virtual machines with minimal setup. It uses Docker as a packaging system to deliver a pre-configured environment that connects to the `lume` virtualization service running on your host machine. With Lumier, you get:
The Lumier library provides a Docker-based interface for creating performant macOS virtual machines.
- A ready-to-use macOS or Linux virtual machine in minutes
- Browser-based VNC access to your VM
- Easy file sharing between your host and VM
- Simple configuration through environment variables
## Installation
## How It Works
**Requirements:**
- Docker for Apple Silicon (or compatible Mac)
- Lume virtualization CLI (install with: `/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"`)
<Callout title="Note">
We're using Docker primarily as a convenient delivery mechanism, not as an isolation layer. Unlike traditional Docker containers, Lumier leverages the Apple Virtualization Framework (Apple Vz) through the `lume` CLI to create true virtual machines.
</Callout>
## Usage
Here's what's happening behind the scenes:
### Run a macOS VM (ephemeral)
```bash
docker run -it --rm \
--name macos-vm \
-p 8006:8006 \
-e VM_NAME=macos-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
trycua/lumier:latest
```
Access the VM in your browser at [http://localhost:8006](http://localhost:8006).
### Persistent Storage
```bash
mkdir -p storage
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-v $(pwd)/storage:/storage \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
-e HOST_STORAGE_PATH=$(pwd)/storage \
trycua/lumier:latest
```
### Shared Folder
```bash
mkdir -p shared
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-v $(pwd)/storage:/storage \
-v $(pwd)/shared:/shared \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
-e HOST_STORAGE_PATH=$(pwd)/storage \
-e HOST_SHARED_PATH=$(pwd)/shared \
trycua/lumier:latest
```
---
See the [Lumier README](https://github.com/trycua/cua/tree/main/libs/lumier) for advanced options, Docker Compose setup, and automation scripts.
1. The Docker container provides a consistent environment to run the Lumier interface
2. Lumier connects to the Lume service running on your host Mac
3. Lume uses Apple's Virtualization Framework to create a true macOS virtual machine
4. The VM runs with hardware acceleration using your Mac's native virtualization capabilities

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---
title: Installation
---
Before using Lumier, make sure you have:
1. **Docker for Apple Silicon** - download it [here](https://desktop.docker.com/mac/main/arm64/Docker.dmg) and follow the installation instructions.
2. **Lume** - This is the virtualization CLI that powers Lumier. Install it with this command:
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
```
After installation, Lume runs as a background service and listens on port 7777. This service allows Lumier to create and manage virtual machines. If port 7777 is already in use on your system, you can specify a different port with the `--port` option when running the `install.sh` script.

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{
"pages": [
"installation",
"docker",
"docker-compose",
"building-lumier"
]
}

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---
title: Client Integrations
---
## Claude Desktop Integration
To use with Claude Desktop, add an entry to your Claude Desktop configuration (`claude_desktop_config.json`, typically found in `~/.config/claude-desktop/`):
For more information on MCP with Claude Desktop, see the [official MCP User Guide](https://modelcontextprotocol.io/quickstart/user).
## Cursor Integration
To use with Cursor, add an MCP configuration file in one of these locations:
- **Project-specific**: Create `.cursor/mcp.json` in your project directory
- **Global**: Create `~/.cursor/mcp.json` in your home directory
After configuration, you can simply tell Cursor's Agent to perform computer tasks by explicitly mentioning the CUA agent, such as "Use the computer control tools to open Safari."
For more information on MCP with Cursor, see the [official Cursor MCP documentation](https://docs.cursor.com/context/model-context-protocol).

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---
title: Configuration
---
The server is configured using environment variables (can be set in the Claude Desktop config):
| Variable | Description | Default |
|----------|-------------|---------|
| `CUA_MODEL_NAME` | Model string (e.g., "anthropic/claude-3-5-sonnet-20241022", "openai/computer-use-preview", "huggingface-local/ByteDance-Seed/UI-TARS-1.5-7B", "omniparser+litellm/gpt-4o", "omniparser+ollama_chat/gemma3") | anthropic/claude-3-5-sonnet-20241022 |
| `CUA_MAX_IMAGES` | Maximum number of images to keep in context | 3 |

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- https://github.com/trycua/cua/tree/main/libs/python/mcp-server
---
## ⚠️ 🚧 Under Construction 🚧 ⚠️
The MCP Server API reference documentation is currently under development.
## Overview
The MCP Server provides Model Context Protocol endpoints for AI model integration.
## API Documentation
Coming soon.
**cua-mcp-server** is a MCP server for the Computer-Use Agent (CUA), allowing you to run CUA through Claude Desktop or other MCP clients.

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---
title: Installation
---
Install the package from PyPI:
```bash
pip install cua-mcp-server
```
This will install:
- The MCP server
- CUA agent and computer dependencies
- An executable `cua-mcp-server` script in your PATH
## Easy Setup Script
If you want to simplify installation, you can use this one-liner to download and run the installation script:
```bash
curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/python/mcp-server/scripts/install_mcp_server.sh | bash
```
This script will:
- Create the ~/.cua directory if it doesn't exist
- Generate a startup script at ~/.cua/start_mcp_server.sh
- Make the script executable
- The startup script automatically manages Python virtual environments and installs/updates the cua-mcp-server package
You can then use the script in your MCP configuration like this:
```json
{
"mcpServers": {
"cua-agent": {
"command": "/bin/bash",
"args": ["~/.cua/start_mcp_server.sh"],
"env": {
"CUA_MODEL_NAME": "anthropic/claude-3-5-sonnet-20241022"
}
}
}
}
```
### Troubleshooting
If you get a `/bin/bash: ~/cua/libs/python/mcp-server/scripts/start_mcp_server.sh: No such file or directory` error, try changing the path to the script to be absolute instead of relative.
To see the logs:
```
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
```

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---
title: LLM Integrations
---
## LiteLLM Integration
This MCP server features comprehensive liteLLM integration, allowing you to use any supported LLM provider with a simple model string configuration.
- **Unified Configuration**: Use a single `CUA_MODEL_NAME` environment variable with a model string
- **Automatic Provider Detection**: The agent automatically detects the provider and capabilities from the model string
- **Extensive Provider Support**: Works with Anthropic, OpenAI, local models, and any liteLLM-compatible provider
### Model String Examples:
- **Anthropic**: `"anthropic/claude-3-5-sonnet-20241022"`
- **OpenAI**: `"openai/computer-use-preview"`
- **UI-TARS**: `"huggingface-local/ByteDance-Seed/UI-TARS-1.5-7B"`
- **Omni + Any LiteLLM**: `"omniparser+litellm/gpt-4o"`, `"omniparser+litellm/claude-3-haiku"`, `"omniparser+ollama_chat/gemma3"`

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{
"pages": [
"installation",
"configuration",
"usage",
"tools",
"client-integrations",
"llm-integrations"
]
}

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---
title: Tools
---
## Available Tools
The MCP server exposes the following tools to Claude:
1. `run_cua_task` - Run a single Computer-Use Agent task with the given instruction
2. `run_multi_cua_tasks` - Run multiple tasks in sequence

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---
title: Usage
---
## Usage
Once configured, you can simply ask Claude to perform computer tasks:
- "Open Chrome and go to github.com"
- "Create a folder called 'Projects' on my desktop"
- "Find all PDFs in my Downloads folder"
- "Take a screenshot and highlight the error message"
Claude will automatically use your CUA agent to perform these tasks.
### First-time Usage Notes
**API Keys**: Ensure you have valid API keys:
- Add your Anthropic API key, or other model provider API key in the Claude Desktop config (as shown above)
- Or set it as an environment variable in your shell profile

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---
title: Configuration
---
### Detection Parameters
#### Box Threshold (0.3)
Controls the confidence threshold for accepting detections:
<img src="/docs/img/som_box_threshold.png" alt="Illustration of confidence thresholds in object detection, with a high-confidence detection accepted and a low-confidence detection rejected." width="500px" />
- Higher values (0.3) yield more precise but fewer detections
- Lower values (0.01) catch more potential icons but increase false positives
- Default is 0.3 for optimal precision/recall balance
#### IOU Threshold (0.1)
Controls how overlapping detections are merged:
<img src="/docs/img/som_iou_threshold.png" alt="Diagram showing Intersection over Union (IOU) with low overlap between two boxes kept separate and high overlap leading to merging." width="500px" />
- Lower values (0.1) more aggressively remove overlapping boxes
- Higher values (0.5) allow more overlapping detections
- Default is 0.1 to handle densely packed UI elements
### OCR Configuration
- **Engine**: EasyOCR
- Primary choice for all platforms
- Fast initialization and processing
- Built-in English language support
- GPU acceleration when available
- **Settings**:
- Timeout: 5 seconds
- Confidence threshold: 0.5
- Paragraph mode: Disabled
- Language: English only
## Performance
### Hardware Acceleration
#### MPS (Metal Performance Shaders)
- Multi-scale detection (640px, 1280px, 1920px)
- Test-time augmentation enabled
- Half-precision (FP16)
- Average detection time: ~0.4s
- Best for production use when available
#### CPU
- Single-scale detection (1280px)
- Full-precision (FP32)
- Average detection time: ~1.3s
- Reliable fallback option
### Example Output Structure
```
examples/output/
├── {timestamp}_no_ocr/
│ ├── annotated_images/
│ │ └── screenshot_analyzed.png
│ ├── screen_details.txt
│ └── summary.json
└── {timestamp}_ocr/
├── annotated_images/
│ └── screenshot_analyzed.png
├── screen_details.txt
└── summary.json
```

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2. Pull the CUA Ubuntu container
```bash
docker pull trycua/cua-ubuntu:latest
docker pull --platform=linux/amd64 trycua/cua-ubuntu:latest
```
</Tab>

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2. Pull the CUA Ubuntu container
```bash
docker pull trycua/cua-ubuntu:latest
docker pull --platform=linux/amd64 trycua/cua-ubuntu:latest
```
</Tab>
@@ -87,6 +87,16 @@ Choose how you want to run your cua computer. **Cloud containers are recommended
<Tab value="Python">
```bash
pip install "cua-agent[all]" cua-computer
# or install specific providers
pip install "cua-agent[openai]" # OpenAI computer-use-preview support
pip install "cua-agent[anthropic]" # Anthropic Claude support
pip install "cua-agent[omni]" # Omniparser + any LLM support
pip install "cua-agent[uitars]" # UI-TARS
pip install "cua-agent[uitars-mlx]" # UI-TARS + MLX support
pip install "cua-agent[uitars-hf]" # UI-TARS + Huggingface support
pip install "cua-agent[glm45v-hf]" # GLM-4.5V + Huggingface support
pip install "cua-agent[ui]" # Gradio UI support
```
</Tab>
<Tab value="TypeScript">

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2. Pull the CUA Ubuntu container
```bash
docker pull trycua/cua-ubuntu:latest
docker pull --platform=linux/amd64 trycua/cua-ubuntu:latest
```
</Tab>

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'use client';
import React, { useRef, useEffect, useState, useCallback } from 'react';
/**
* Represents a rectangle with position, dimensions, styling, and identification
*/
interface Rectangle {
/** The x-coordinate of the rectangle's left edge */
left: number;
/** The y-coordinate of the rectangle's top edge */
top: number;
/** The width of the rectangle */
width: number;
/** The height of the rectangle */
height: number;
/** The fill color of the rectangle */
fill: string;
/** The display name of the rectangle */
name: string;
}
/**
* Props for the IOU component
*/
interface IOUProps {
/** The title to display above the visualization */
title: string;
/** The description text to display below the IOU value */
description: string;
/** The first rectangle for IOU calculation */
rect1: Rectangle;
/** The second rectangle for IOU calculation */
rect2: Rectangle;
}
/**
* A React component that visualizes and calculates the Intersection over Union (IOU)
* of two rectangles on a canvas
* @param props - The component props
* @returns The rendered IOU visualization component
*/
export default function IOU({ title, description, rect1, rect2 }: IOUProps) {
const canvasRef = useRef<HTMLCanvasElement>(null);
const [actualIOU, setActualIOU] = useState<number>(0);
/**
* Converts a rectangle to a bounding box with left, right, top, and bottom coordinates
* @param rect - The rectangle to convert
* @returns An object containing the bounding box coordinates
*/
const getBbox = (rect: Rectangle) => ({
left: rect.left,
right: rect.left + rect.width,
@@ -28,6 +55,12 @@ export default function IOU({ title, description, rect1, rect2 }: IOUProps) {
bottom: rect.top + rect.height,
});
/**
* Calculates the intersection area between two bounding boxes
* @param bbox1 - The first bounding box
* @param bbox2 - The second bounding box
* @returns The area of intersection between the two bounding boxes
*/
const calcIntersection = (bbox1: any, bbox2: any): number => {
const x1 = Math.max(bbox1.left, bbox2.left);
const x2 = Math.min(bbox1.right, bbox2.right);
@@ -43,10 +76,18 @@ export default function IOU({ title, description, rect1, rect2 }: IOUProps) {
return intersection;
};
/**
* Calculates the area of a rectangle
* @param rect - The rectangle to calculate area for
* @returns The area of the rectangle
*/
const calcArea = (rect: Rectangle): number => {
return rect.width * rect.height;
};
/**
* Draws the rectangles on the canvas and calculates the IOU value
*/
const drawCanvas = useCallback(() => {
const canvas = canvasRef.current;
if (!canvas) return;

4
examples/computer-example-ts/.env.example
View File

@@ -1,3 +1,3 @@
OPENAI_KEY=
CUA_KEY=
OPENAI_API_KEY=
CUA_API_KEY=
CUA_CONTAINER_NAME=

4
examples/computer-example-ts/README.md
View File

@@ -19,8 +19,8 @@ This example demonstrates how to control a cua Cloud container using the OpenAI
2. **Set up environment variables:**
Create a `.env` file with the following variables:
- `OPENAI_KEY` — your OpenAI API key
- `CUA_KEY` — your cua Cloud API key
- `OPENAI_API_KEY` — your OpenAI API key
- `CUA_API_KEY` — your cua Cloud API key
- `CUA_CONTAINER_NAME` — the name of your provisioned container
3. **Run the example:**

841
examples/computer-example-ts/package-lock.json generated Normal file
View File

@@ -0,0 +1,841 @@
{
"name": "computer-example-ts",
"version": "1.0.0",
"lockfileVersion": 3,
"requires": true,
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"": {
"name": "computer-example-ts",
"version": "1.0.0",
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"dotenv": "^16.5.0",
"openai": "^5.7.0"
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4
examples/computer-example-ts/src/index.ts
View File

@@ -4,13 +4,13 @@ import { executeAction } from "./helpers";
import "dotenv/config";
const openai = new OpenAI({ apiKey: process.env.OPENAI_KEY });
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const COMPUTER_USE_PROMPT = "Open firefox and go to trycua.com";
// Initialize the Computer Connection
const computer = new Computer({
apiKey: process.env.CUA_KEY!,
apiKey: process.env.CUA_API_KEY!,
name: process.env.CUA_CONTAINER_NAME!,
osType: OSType.LINUX,
});

12
libs/kasm/README.md
View File

@@ -21,18 +21,6 @@ docker build -t cua-ubuntu:latest .
### Pushing to Registry
To push the container to a Docker registry:
```bash
# Tag for your registry (replace with your registry URL)
docker tag cua-ubuntu:latest your-registry.com/cua-ubuntu:latest
# Push to registry
docker push your-registry.com/cua-ubuntu:latest
```
For Docker Hub:
```bash
# Tag for Docker Hub (replace 'trycua' with your Docker Hub username)
docker tag cua-ubuntu:latest trycua/cua-ubuntu:latest

8
libs/lume/docs/Development.md → libs/lume/Development.md
View File

@@ -10,6 +10,14 @@ Lume development requires:
- macOS Sequoia 15.2 or higher
- (Optional) VS Code with Swift extension
If you're working on Lume in the context of the Cua monorepo, we recommend using the dedicated VS Code workspace configuration:
```bash
# Open VS Code workspace from the root of the monorepo
code .vscode/lume.code-workspace
```
This workspace is preconfigured with Swift language support, build tasks, and debug configurations.
## Setting Up the Repository Locally
1. **Fork the Repository**: Create your own fork of lume

172
libs/lume/README.md
View File

@@ -23,174 +23,42 @@
<img src="../../img/cli.png" alt="lume cli">
</div>
```bash
lume run macos-sequoia-vanilla:latest
```
## Development Environment
## Quickstart
If you're working on Lume in the context of the CUA monorepo, we recommend using the dedicated VS Code workspace configuration:
```bash
# Open VS Code workspace from the root of the monorepo
code .vscode/lume.code-workspace
```
This workspace is preconfigured with Swift language support, build tasks, and debug configurations.
## Usage
```bash
lume <command>
Commands:
lume create <name> Create a new macOS or Linux VM
lume run <name> Run a VM
lume ls List all VMs
lume get <name> Get detailed information about a VM
lume set <name> Modify VM configuration
lume stop <name> Stop a running VM
lume delete <name> Delete a VM
lume pull <image> Pull a macOS image from container registry
lume push <name> <image:tag> Push a VM image to a container registry
lume clone <name> <new-name> Clone an existing VM
lume config Get or set lume configuration
lume images List available macOS images in local cache
lume ipsw Get the latest macOS restore image URL
lume prune Remove cached images
lume serve Start the API server
Options:
--help Show help [boolean]
--version Show version number [boolean]
Command Options:
create:
--os <os> Operating system to install (macOS or linux, default: macOS)
--cpu <cores> Number of CPU cores (default: 4)
--memory <size> Memory size, e.g., 8GB (default: 4GB)
--disk-size <size> Disk size, e.g., 50GB (default: 40GB)
--display <res> Display resolution (default: 1024x768)
--ipsw <path> Path to IPSW file or 'latest' for macOS VMs
--storage <name> VM storage location to use
run:
--no-display Do not start the VNC client app
--shared-dir <dir> Share directory with VM (format: path[:ro|rw])
--mount <path> For Linux VMs only, attach a read-only disk image
--registry <url> Container registry URL (default: ghcr.io)
--organization <org> Organization to pull from (default: trycua)
--vnc-port <port> Port to use for the VNC server (default: 0 for auto-assign)
--recovery-mode <boolean> For MacOS VMs only, start VM in recovery mode (default: false)
--storage <name> VM storage location to use
set:
--cpu <cores> New number of CPU cores (e.g., 4)
--memory <size> New memory size (e.g., 8192MB or 8GB)
--disk-size <size> New disk size (e.g., 40960MB or 40GB)
--display <res> New display resolution in format WIDTHxHEIGHT (e.g., 1024x768)
--storage <name> VM storage location to use
delete:
--force Force deletion without confirmation
--storage <name> VM storage location to use
pull:
--registry <url> Container registry URL (default: ghcr.io)
--organization <org> Organization to pull from (default: trycua)
--storage <name> VM storage location to use
push:
--additional-tags <tags...> Additional tags to push the same image to
--registry <url> Container registry URL (default: ghcr.io)
--organization <org> Organization/user to push to (default: trycua)
--storage <name> VM storage location to use
--chunk-size-mb <size> Chunk size for disk image upload in MB (default: 512)
--verbose Enable verbose logging
--dry-run Prepare files and show plan without uploading
--reassemble Verify integrity by reassembling chunks (requires --dry-run)
get:
-f, --format <format> Output format (json|text)
--storage <name> VM storage location to use
stop:
--storage <name> VM storage location to use
clone:
--source-storage <name> Source VM storage location
--dest-storage <name> Destination VM storage location
config:
get Get current configuration
storage Manage VM storage locations
add <name> <path> Add a new VM storage location
remove <name> Remove a VM storage location
list List all VM storage locations
default <name> Set the default VM storage location
cache Manage cache settings
get Get current cache directory
set <path> Set cache directory
caching Manage image caching settings
get Show current caching status
set <boolean> Enable or disable image caching
serve:
--port <port> Port to listen on (default: 7777)
```
## Install
Install with a single command:
Install and run a prebuilt macOS VM in two commands:
```bash
# Install Lume
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
# Pull & start a macOS image
lume run macos-sequoia-vanilla:latest
```
By default, Lume is installed as a background service that starts automatically on login. If you prefer to start the Lume API service manually when needed, you can use the `--no-background-service` option:
<Callout title="Security Note">
All prebuilt images use the default password `lume`. Change this immediately after your first login using the `passwd` command.
</Callout>
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh) --no-background-service"
```
**System Requirements**:
- Apple Silicon Mac (M1, M2, M3, etc.)
- macOS 13.0 or later
- At least 8GB of RAM (16GB recommended)
- At least 50GB of free disk space
**Note:** With this option, you'll need to manually start the Lume API service by running `lume serve` in your terminal whenever you need to use tools or libraries that rely on the Lume API (such as the Computer-Use Agent).
## Development
You can also download the `lume.pkg.tar.gz` archive from the [latest release](https://github.com/trycua/lume/releases), extract it, and install the package manually.
## Prebuilt Images
Pre-built images are available in the registry [ghcr.io/trycua](https://github.com/orgs/trycua/packages).
**Important Note (v0.2.0+):** Images are being re-uploaded with sparse file system optimizations enabled, resulting in significantly lower actual disk usage. Older images (without the `-sparse` suffix) are now **deprecated**. The last version of `lume` fully supporting the non-sparse images was `v0.1.x`. Starting from `v0.2.0`, lume will automatically pull images optimized with sparse file system support.
These images come with an SSH server pre-configured and auto-login enabled.
For the security of your VM, change the default password `lume` immediately after your first login.
| Image | Tag | Description | Logical Size |
|-------|------------|-------------|------|
| `macos-sequoia-vanilla` | `latest`, `15.2` | macOS Sequoia 15.2 image | 20GB |
| `macos-sequoia-xcode` | `latest`, `15.2` | macOS Sequoia 15.2 image with Xcode command line tools | 22GB |
| `macos-sequoia-cua` | `latest`, `15.3` | macOS Sequoia 15.3 image compatible with the Computer interface | 24GB |
| `ubuntu-noble-vanilla` | `latest`, `24.04.1` | [Ubuntu Server for ARM 24.04.1 LTS](https://ubuntu.com/download/server/arm) with Ubuntu Desktop | 20GB |
For additional disk space, resize the VM disk after pulling the image using the `lume set <name> --disk-size <size>` command. Note that the actual disk space used by sparse images will be much lower than the logical size listed.
## Local API Server
`lume` exposes a local HTTP API server that listens on `http://localhost:7777/lume`, enabling automated management of VMs.
```bash
lume serve
```
For detailed API documentation, please refer to [API Reference](docs/API-Reference.md).
To get set up with Lume for development, read [these instructions](Development.md).
## Docs
- [API Reference](docs/API-Reference.md)
- [Development](docs/Development.md)
- [FAQ](docs/FAQ.md)
- [Installation](https://trycua.com/docs/libraries/lume/installation)
- [Prebuilt Images](https://trycua.com/docs/libraries/lume/prebuilt-images)
- [CLI Reference](https://trycua.com/docs/libraries/lume/cli-reference)
- [HTTP API](https://trycua.com/docs/libraries/lume/http-api)
- [FAQ](https://trycua.com/docs/libraries/lume/faq)
## Contributing

387
libs/lume/docs/API-Reference.md
View File

@@ -1,387 +0,0 @@
## API Reference
<details open>
<summary><strong>Create VM</strong> - POST /vms</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"name": "lume_vm",
"os": "macOS",
"cpu": 2,
"memory": "4GB",
"diskSize": "64GB",
"display": "1024x768",
"ipsw": "latest",
"storage": "ssd"
}' \
http://localhost:7777/lume/vms
```
</details>
<details open>
<summary><strong>Run VM</strong> - POST /vms/:name/run</summary>
```bash
# Basic run
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/vms/my-vm-name/run
# Run with VNC client started and shared directory
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"noDisplay": false,
"sharedDirectories": [
{
"hostPath": "~/Projects",
"readOnly": false
}
],
"recoveryMode": false,
"storage": "ssd"
}' \
http://localhost:7777/lume/vms/lume_vm/run
```
</details>
<details open>
<summary><strong>List VMs</strong> - GET /vms</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/vms
```
```
[
{
"name": "my-vm",
"state": "stopped",
"os": "macOS",
"cpu": 2,
"memory": "4GB",
"diskSize": "64GB"
},
{
"name": "my-vm-2",
"state": "stopped",
"os": "linux",
"cpu": 2,
"memory": "4GB",
"diskSize": "64GB"
}
]
```
</details>
<details open>
<summary><strong>Get VM Details</strong> - GET /vms/:name</summary>
```bash
# Basic get
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/vms/lume_vm
# Get with storage location specified
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/vms/lume_vm?storage=ssd
```
```
{
"name": "lume_vm",
"state": "running",
"os": "macOS",
"cpu": 2,
"memory": "4GB",
"diskSize": "64GB"
}
```
</details>
<details open>
<summary><strong>Update VM Settings</strong> - PATCH /vms/:name</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X PATCH \
-H "Content-Type: application/json" \
-d '{
"cpu": 4,
"memory": "8GB",
"diskSize": "128GB",
"storage": "ssd"
}' \
http://localhost:7777/lume/vms/my-vm-name
```
</details>
<details open>
<summary><strong>Stop VM</strong> - POST /vms/:name/stop</summary>
```bash
# Basic stop
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/vms/my-vm-name/stop
# Stop with storage location specified
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/vms/my-vm-name/stop?storage=ssd
```
</details>
<details open>
<summary><strong>Delete VM</strong> - DELETE /vms/:name</summary>
```bash
# Basic delete
curl --connect-timeout 6000 \
--max-time 5000 \
-X DELETE \
http://localhost:7777/lume/vms/my-vm-name
# Delete with storage location specified
curl --connect-timeout 6000 \
--max-time 5000 \
-X DELETE \
http://localhost:7777/lume/vms/my-vm-name?storage=ssd
```
</details>
<details open>
<summary><strong>Pull Image</strong> - POST /pull</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"image": "macos-sequoia-vanilla:latest",
"name": "my-vm-name",
"registry": "ghcr.io",
"organization": "trycua",
"storage": "ssd"
}' \
http://localhost:7777/lume/pull
```
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"image": "macos-sequoia-vanilla:15.2",
"name": "macos-sequoia-vanilla"
}' \
http://localhost:7777/lume/pull
```
</details>
<details open>
<summary><strong>Push Image (Async)</strong> - POST /vms/push</summary>
```bash
# Push VM 'my-local-vm' to 'my-org/my-image:latest' and 'my-org/my-image:v1'
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"name": "my-local-vm",
"imageName": "my-image",
"tags": ["latest", "v1"],
"organization": "my-org",
"registry": "ghcr.io",
"chunkSizeMb": 512,
"storage": null
}' \
http://localhost:7777/lume/vms/push
```
**Response (202 Accepted):**
```json
{
"message": "Push initiated in background",
"name": "my-local-vm",
"imageName": "my-image",
"tags": [
"latest",
"v1"
]
}
```
</details>
<details open>
<summary><strong>Clone VM</strong> - POST /vms/:name/clone</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"name": "source-vm",
"newName": "cloned-vm",
"sourceLocation": "default",
"destLocation": "ssd"
}' \
http://localhost:7777/lume/vms/clone
```
</details>
<details open>
<summary><strong>Get Latest IPSW URL</strong> - GET /ipsw</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/ipsw
```
</details>
<details open>
<summary><strong>List Images</strong> - GET /images</summary>
```bash
# List images with default organization (trycua)
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/images
```
```json
{
"local": [
"macos-sequoia-xcode:latest",
"macos-sequoia-vanilla:latest"
]
}
```
</details>
<details open>
<summary><strong>Prune Images</strong> - POST /lume/prune</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/prune
```
</details>
<details open>
<summary><strong>Get Configuration</strong> - GET /lume/config</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/config
```
```json
{
"homeDirectory": "~/.lume",
"cacheDirectory": "~/.lume/cache",
"cachingEnabled": true
}
```
</details>
<details open>
<summary><strong>Update Configuration</strong> - POST /lume/config</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"homeDirectory": "~/custom/lume",
"cacheDirectory": "~/custom/lume/cache",
"cachingEnabled": true
}' \
http://localhost:7777/lume/config
```
</details>
<details open>
<summary><strong>Get VM Storage Locations</strong> - GET /lume/config/locations</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
http://localhost:7777/lume/config/locations
```
```json
[
{
"name": "default",
"path": "~/.lume/vms",
"isDefault": true
},
{
"name": "ssd",
"path": "/Volumes/SSD/lume/vms",
"isDefault": false
}
]
```
</details>
<details open>
<summary><strong>Add VM Storage Location</strong> - POST /lume/config/locations</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
-H "Content-Type: application/json" \
-d '{
"name": "ssd",
"path": "/Volumes/SSD/lume/vms"
}' \
http://localhost:7777/lume/config/locations
```
</details>
<details open>
<summary><strong>Remove VM Storage Location</strong> - DELETE /lume/config/locations/:name</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X DELETE \
http://localhost:7777/lume/config/locations/ssd
```
</details>
<details open>
<summary><strong>Set Default VM Storage Location</strong> - POST /lume/config/locations/default/:name</summary>
```bash
curl --connect-timeout 6000 \
--max-time 5000 \
-X POST \
http://localhost:7777/lume/config/locations/default/ssd
```
</details>

202
libs/lumier/README.md
View File

@@ -39,19 +39,6 @@ Before using Lumier, make sure you have:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
```
After installation, Lume runs as a background service and listens on port 7777. This service allows Lumier to create and manage virtual machines. If port 7777 is already in use on your system, you can specify a different port with the `--port` option when running the `install.sh` script.
## How It Works
> **Note:** We're using Docker primarily as a convenient delivery mechanism, not as an isolation layer. Unlike traditional Docker containers, Lumier leverages the Apple Virtualization Framework (Apple Vz) through the `lume` CLI to create true virtual machines.
Here's what's happening behind the scenes:
1. The Docker container provides a consistent environment to run the Lumier interface
2. Lumier connects to the Lume service running on your host Mac
3. Lume uses Apple's Virtualization Framework to create a true macOS virtual machine
4. The VM runs with hardware acceleration using your Mac's native virtualization capabilities
## Getting Started
```bash
@@ -68,191 +55,14 @@ docker run -it --rm \
After running the command above, you can access your macOS VM through a web browser (e.g., http://localhost:8006).
> **Note:** With the basic setup above, your VM will be reset when you stop the container (ephemeral mode). This means any changes you make inside the macOS VM will be lost. See the section below for how to save your VM state.
> **Note:** With the basic setup above, your VM will be reset when you stop the container (ephemeral mode). This means any changes you make inside the macOS VM will be lost. See [the documentation](https://trycua.com/docs/libraries/lumier/docker) for how to save your VM state.
## Saving Your VM State
## Docs
To save your VM state between sessions (so your changes persist when you stop and restart the container), you'll need to set up a storage location:
```bash
# First, create a storage directory if it doesn't exist
mkdir -p storage
# Then run the container with persistent storage
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-v $(pwd)/storage:/storage \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
-e HOST_STORAGE_PATH=$(pwd)/storage \
trycua/lumier:latest
```
This command creates a connection between a folder on your Mac (`$(pwd)/storage`) and a folder inside the Docker container (`/storage`). The `-v` flag (volume mount) and the `HOST_STORAGE_PATH` variable work together to ensure your VM data is saved on your host Mac.
## Sharing Files with Your VM
To share files between your Mac and the virtual machine, you can set up a shared folder:
```bash
# Create both storage and shared folders
mkdir -p storage shared
# Run with both persistent storage and a shared folder
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-v $(pwd)/storage:/storage \
-v $(pwd)/shared:/shared \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
-e HOST_STORAGE_PATH=$(pwd)/storage \
-e HOST_SHARED_PATH=$(pwd)/shared \
trycua/lumier:latest
```
With this setup, any files you place in the `shared` folder on your Mac will be accessible from within the macOS VM, and vice versa.
## Automating VM Startup with on-logon.sh
You can automatically run scripts when the VM starts up by placing an `on-logon.sh` script in the shared folder's lifecycle directory. This is useful for setting up your VM environment each time it starts.
```bash
# Create the lifecycle directory in your shared folder
mkdir -p shared/lifecycle
# Create a sample on-logon.sh script
cat > shared/lifecycle/on-logon.sh << 'EOF'
#!/usr/bin/env bash
# Create a file on the desktop
echo "Hello from Lumier!" > /Users/lume/Desktop/hello_lume.txt
# You can add more commands to execute at VM startup
# For example:
# - Configure environment variables
# - Start applications
# - Mount network drives
# - Set up development environments
EOF
# Make the script executable
chmod +x shared/lifecycle/on-logon.sh
```
The script will be automatically executed when the VM starts up. It runs in the VM context and has access to:
- The `/Users/lume` user directory (home directory in the VM)
- The shared folder at `/Volumes/My Shared Files` inside the VM
- Any resources available to the VM
This feature enables automation of VM setup without modifying the base VM image.
## Using Docker Compose
You can also use Docker Compose to run Lumier with a simple configuration file. Create a `docker-compose.yml` file with the following content:
```yaml
version: '3'
services:
lumier:
image: trycua/lumier:latest
container_name: lumier-vm
restart: unless-stopped
ports:
- "8006:8006" # Port for VNC access
volumes:
- ./storage:/storage # VM persistent storage
- ./shared:/shared # Shared folder accessible in the VM
environment:
- VM_NAME=lumier-vm
- VERSION=ghcr.io/trycua/macos-sequoia-cua:latest
- CPU_CORES=4
- RAM_SIZE=8192
- HOST_STORAGE_PATH=${PWD}/storage
- HOST_SHARED_PATH=${PWD}/shared
stop_signal: SIGINT
stop_grace_period: 2m
```
Then run Lumier using:
```bash
# First create the required directories
mkdir -p storage shared
# Start the container
docker-compose up -d
# View the logs
docker-compose logs -f
# Stop the container when done
docker-compose down
```
## Building and Customizing Lumier
If you want to customize the Lumier container or build it from source, you can follow these steps:
```bash
# 1. Navigate to the Lumier directory
cd libs/lumier
# 2. Build the Docker image locally
docker build -t lumier-custom:latest .
# 3. Run your custom build
docker run -it --rm \
--name lumier-vm \
-p 8006:8006 \
-e VM_NAME=lumier-vm \
-e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
-e CPU_CORES=4 \
-e RAM_SIZE=8192 \
lumier-custom:latest
```
### Customization Options
The Dockerfile provides several customization points:
1. **Base image**: The container uses Debian Bullseye Slim as the base. You can modify this if needed.
2. **Installed packages**: You can add or remove packages in the apt-get install list.
3. **Hooks**: Check the `/run/hooks/` directory for scripts that run at specific points during VM lifecycle.
4. **Configuration**: Review `/run/config/constants.sh` for default settings.
After making your modifications, you can build and push your custom image to your own Docker Hub repository:
```bash
# Build with a custom tag
docker build -t yourusername/lumier:custom .
# Push to Docker Hub (after docker login)
docker push yourusername/lumier:custom
```
## Configuration Options
When running Lumier, you'll need to configure a few things:
- **Port forwarding** (`-p 8006:8006`): Makes the VM's VNC interface accessible in your browser. If port 8006 is already in use, you can use a different port like `-p 8007:8006`.
- **Environment variables** (`-e`): Configure your VM settings:
- `VM_NAME`: A name for your virtual machine
- `VERSION`: The macOS image to use
- `CPU_CORES`: Number of CPU cores to allocate
- `RAM_SIZE`: Memory in MB to allocate
- `HOST_STORAGE_PATH`: Path to save VM state (when using persistent storage)
- `HOST_SHARED_PATH`: Path to the shared folder (optional)
- **Background service**: The `lume serve` service should be running on your host (starts automatically when you install Lume using the `install.sh` script above).
- [Installation](https://trycua.com/docs/libraries/lumier/installation)
- [Docker](https://trycua.com/docs/libraries/lumier/docker)
- [Docker Compose](https://trycua.com/docs/libraries/lumier/docker-compose)
- [Building Lumier](https://trycua.com/docs/libraries/lumier/building-lumier)
## Credits

317
libs/python/agent/README.md
View File

@@ -29,16 +29,6 @@
```bash
pip install "cua-agent[all]"
# or install specific providers
pip install "cua-agent[openai]" # OpenAI computer-use-preview support
pip install "cua-agent[anthropic]" # Anthropic Claude support
pip install "cua-agent[omni]" # Omniparser + any LLM support
pip install "cua-agent[uitars]" # UI-TARS
pip install "cua-agent[uitars-mlx]" # UI-TARS + MLX support
pip install "cua-agent[uitars-hf]" # UI-TARS + Huggingface support
pip install "cua-agent[glm45v-hf]" # GLM-4.5V + Huggingface support
pip install "cua-agent[ui]" # Gradio UI support
```
## Quick Start
@@ -79,303 +69,18 @@ if __name__ == "__main__":
asyncio.run(main())
```
## Supported Models
## Docs
### Anthropic Claude (Computer Use API)
```python
model="anthropic/claude-3-5-sonnet-20241022"
model="anthropic/claude-3-7-sonnet-20250219"
model="anthropic/claude-opus-4-20250514"
model="anthropic/claude-sonnet-4-20250514"
```
### OpenAI Computer Use Preview
```python
model="openai/computer-use-preview"
```
### UI-TARS (Local or Huggingface Inference)
```python
model="huggingface-local/ByteDance-Seed/UI-TARS-1.5-7B"
model="ollama_chat/0000/ui-tars-1.5-7b"
```
### Omniparser + Any LLM
```python
model="omniparser+ollama_chat/mistral-small3.2"
model="omniparser+vertex_ai/gemini-pro"
model="omniparser+anthropic/claude-3-5-sonnet-20241022"
model="omniparser+openai/gpt-4o"
```
## Custom Tools
Define custom tools using decorated functions:
```python
from computer.helpers import sandboxed
@sandboxed()
def read_file(location: str) -> str:
"""Read contents of a file
Parameters
----------
location : str
Path to the file to read
Returns
-------
str
Contents of the file or error message
"""
try:
with open(location, 'r') as f:
return f.read()
except Exception as e:
return f"Error reading file: {str(e)}"
def calculate(a: int, b: int) -> int:
"""Calculate the sum of two integers"""
return a + b
# Use with agent
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer, read_file, calculate]
)
```
## Callbacks System
agent provides a comprehensive callback system for extending functionality:
### Built-in Callbacks
```python
from agent.callbacks import (
ImageRetentionCallback,
TrajectorySaverCallback,
BudgetManagerCallback,
LoggingCallback
)
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
tools=[computer],
callbacks=[
ImageRetentionCallback(only_n_most_recent_images=3),
TrajectorySaverCallback(trajectory_dir="trajectories"),
BudgetManagerCallback(max_budget=10.0, raise_error=True),
LoggingCallback(level=logging.INFO)
]
)
```
### Custom Callbacks
```python
from agent.callbacks.base import AsyncCallbackHandler
class CustomCallback(AsyncCallbackHandler):
async def on_llm_start(self, messages):
"""Preprocess messages before LLM call"""
# Add custom preprocessing logic
return messages
async def on_llm_end(self, messages):
"""Postprocess messages after LLM call"""
# Add custom postprocessing logic
return messages
async def on_usage(self, usage):
"""Track usage information"""
print(f"Tokens used: {usage.total_tokens}")
```
## Budget Management
Control costs with built-in budget management:
```python
# Simple budget limit
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
max_trajectory_budget=5.0 # $5 limit
)
# Advanced budget configuration
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
max_trajectory_budget={
"max_budget": 10.0,
"raise_error": True, # Raise error when exceeded
"reset_after_each_run": False # Persistent across runs
}
)
```
## Trajectory Management
Save and replay agent conversations:
```python
agent = ComputerAgent(
model="anthropic/claude-3-5-sonnet-20241022",
trajectory_dir="trajectories", # Auto-save trajectories
tools=[computer]
)
# Trajectories are saved with:
# - Complete conversation history
# - Usage statistics and costs
# - Timestamps and metadata
# - Screenshots and computer actions
```
## Configuration Options
### ComputerAgent Parameters
- `model`: Model identifier (required)
- `tools`: List of computer objects and decorated functions
- `callbacks`: List of callback handlers for extensibility
- `only_n_most_recent_images`: Limit recent images to prevent context overflow
- `verbosity`: Logging level (logging.INFO, logging.DEBUG, etc.)
- `trajectory_dir`: Directory to save conversation trajectories
- `max_retries`: Maximum API call retries (default: 3)
- `screenshot_delay`: Delay between actions and screenshots (default: 0.5s)
- `use_prompt_caching`: Enable prompt caching for supported models
- `max_trajectory_budget`: Budget limit configuration
### Environment Variables
```bash
# Computer instance (cloud)
export CUA_CONTAINER_NAME="your-container-name"
export CUA_API_KEY="your-cua-api-key"
# LLM API keys
export ANTHROPIC_API_KEY="your-anthropic-key"
export OPENAI_API_KEY="your-openai-key"
```
## Advanced Usage
### Streaming Responses
```python
async for result in agent.run(messages, stream=True):
# Process streaming chunks
for item in result["output"]:
if item["type"] == "message":
print(item["content"][0]["text"], end="", flush=True)
elif item["type"] == "computer_call":
action = item["action"]
print(f"\n[Action: {action['type']}]")
```
### Interactive Chat Loop
```python
history = []
while True:
user_input = input("> ")
if user_input.lower() in ['quit', 'exit']:
break
history.append({"role": "user", "content": user_input})
async for result in agent.run(history):
history += result["output"]
# Display assistant responses
for item in result["output"]:
if item["type"] == "message":
print(item["content"][0]["text"])
```
### Error Handling
```python
try:
async for result in agent.run(messages):
# Process results
pass
except BudgetExceededException:
print("Budget limit exceeded")
except Exception as e:
print(f"Agent error: {e}")
```
## API Reference
### ComputerAgent.run()
```python
async def run(
self,
messages: Messages,
stream: bool = False,
**kwargs
) -> AsyncGenerator[Dict[str, Any], None]:
"""
Run the agent with the given messages.
Args:
messages: List of message dictionaries
stream: Whether to stream the response
**kwargs: Additional arguments
Returns:
AsyncGenerator that yields response chunks
"""
```
### Message Format
```python
messages = [
{
"role": "user",
"content": "Take a screenshot and describe what you see"
},
{
"role": "assistant",
"content": "I'll take a screenshot for you."
}
]
```
### Response Format
```python
{
"output": [
{
"type": "message",
"role": "assistant",
"content": [{"type": "output_text", "text": "I can see..."}]
},
{
"type": "computer_call",
"action": {"type": "screenshot"},
"call_id": "call_123"
},
{
"type": "computer_call_output",
"call_id": "call_123",
"output": {"image_url": "data:image/png;base64,..."}
}
],
"usage": {
"prompt_tokens": 150,
"completion_tokens": 75,
"total_tokens": 225,
"response_cost": 0.01,
}
}
```
- [Agent Loops](https://trycua.com/docs/agent-sdk/agent-loops)
- [Supported Agents](https://trycua.com/docs/agent-sdk/supported-agents)
- [Supported Models](https://trycua.com/docs/agent-sdk/supported-models)
- [Chat History](https://trycua.com/docs/agent-sdk/chat-history)
- [Callbacks](https://trycua.com/docs/agent-sdk/callbacks)
- [Custom Tools](https://trycua.com/docs/agent-sdk/custom-tools)
- [Custom Computer Handlers](https://trycua.com/docs/agent-sdk/custom-computer-handlers)
- [Prompt Caching](https://trycua.com/docs/agent-sdk/prompt-caching)
- [Usage Tracking](https://trycua.com/docs/agent-sdk/usage-tracking)
- [Benchmarks](https://trycua.com/docs/agent-sdk/benchmarks)
## License

2
libs/python/agent/agent/adapters/__init__.py
View File

@@ -4,8 +4,10 @@ Adapters package for agent - Custom LLM adapters for LiteLLM
from .huggingfacelocal_adapter import HuggingFaceLocalAdapter
from .human_adapter import HumanAdapter
from .mlxvlm_adapter import MLXVLMAdapter
__all__ = [
"HuggingFaceLocalAdapter",
"HumanAdapter",
"MLXVLMAdapter",
]

359
libs/python/agent/agent/adapters/mlxvlm_adapter.py Normal file
View File

@@ -0,0 +1,359 @@
import asyncio
import functools
import warnings
import io
import base64
import math
import re
from concurrent.futures import ThreadPoolExecutor
from typing import Iterator, AsyncIterator, Dict, List, Any, Optional, Tuple, cast
from PIL import Image
from litellm.types.utils import GenericStreamingChunk, ModelResponse
from litellm.llms.custom_llm import CustomLLM
from litellm import completion, acompletion
# Try to import MLX dependencies
try:
import mlx.core as mx
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
from transformers.tokenization_utils import PreTrainedTokenizer
MLX_AVAILABLE = True
except ImportError:
MLX_AVAILABLE = False
# Constants for smart_resize
IMAGE_FACTOR = 28
MIN_PIXELS = 100 * 28 * 28
MAX_PIXELS = 16384 * 28 * 28
MAX_RATIO = 200
def round_by_factor(number: float, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: float, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: float, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
def smart_resize(
height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
"""
Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
if max(height, width) / min(height, width) > MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
return h_bar, w_bar
class MLXVLMAdapter(CustomLLM):
"""MLX VLM Adapter for running vision-language models locally using MLX."""
def __init__(self, **kwargs):
"""Initialize the adapter.
Args:
**kwargs: Additional arguments
"""
super().__init__()
self.models = {} # Cache for loaded models
self.processors = {} # Cache for loaded processors
self.configs = {} # Cache for loaded configs
self._executor = ThreadPoolExecutor(max_workers=1) # Single thread pool
def _load_model_and_processor(self, model_name: str):
"""Load model and processor if not already cached.
Args:
model_name: Name of the model to load
Returns:
Tuple of (model, processor, config)
"""
if not MLX_AVAILABLE:
raise ImportError("MLX VLM dependencies not available. Please install mlx-vlm.")
if model_name not in self.models:
# Load model and processor
model_obj, processor = load(
model_name,
processor_kwargs={"min_pixels": MIN_PIXELS, "max_pixels": MAX_PIXELS}
)
config = load_config(model_name)
# Cache them
self.models[model_name] = model_obj
self.processors[model_name] = processor
self.configs[model_name] = config
return self.models[model_name], self.processors[model_name], self.configs[model_name]
def _process_coordinates(self, text: str, original_size: Tuple[int, int], model_size: Tuple[int, int]) -> str:
"""Process coordinates in box tokens based on image resizing using smart_resize approach.
Args:
text: Text containing box tokens
original_size: Original image size (width, height)
model_size: Model processed image size (width, height)
Returns:
Text with processed coordinates
"""
# Find all box tokens
box_pattern = r"<\|box_start\|>\((\d+),\s*(\d+)\)<\|box_end\|>"
def process_coords(match):
model_x, model_y = int(match.group(1)), int(match.group(2))
# Scale coordinates from model space to original image space
# Both original_size and model_size are in (width, height) format
new_x = int(model_x * original_size[0] / model_size[0]) # Width
new_y = int(model_y * original_size[1] / model_size[1]) # Height
return f"<|box_start|>({new_x},{new_y})<|box_end|>"
return re.sub(box_pattern, process_coords, text)
def _convert_messages(self, messages: List[Dict[str, Any]]) -> Tuple[List[Dict[str, Any]], List[Image.Image], Dict[int, Tuple[int, int]], Dict[int, Tuple[int, int]]]:
"""Convert OpenAI format messages to MLX VLM format and extract images.
Args:
messages: Messages in OpenAI format
Returns:
Tuple of (processed_messages, images, original_sizes, model_sizes)
"""
processed_messages = []
images = []
original_sizes = {} # Track original sizes of images for coordinate mapping
model_sizes = {} # Track model processed sizes
image_index = 0
for message in messages:
processed_message = {
"role": message["role"],
"content": []
}
content = message.get("content", [])
if isinstance(content, str):
# Simple text content
processed_message["content"] = content
elif isinstance(content, list):
# Multi-modal content
processed_content = []
for item in content:
if item.get("type") == "text":
processed_content.append({
"type": "text",
"text": item.get("text", "")
})
elif item.get("type") == "image_url":
image_url = item.get("image_url", {}).get("url", "")
pil_image = None
if image_url.startswith("data:image/"):
# Extract base64 data
base64_data = image_url.split(',')[1]
# Convert base64 to PIL Image
image_data = base64.b64decode(base64_data)
pil_image = Image.open(io.BytesIO(image_data))
else:
# Handle file path or URL
pil_image = Image.open(image_url)
# Store original image size for coordinate mapping
original_size = pil_image.size
original_sizes[image_index] = original_size
# Use smart_resize to determine model size
# Note: smart_resize expects (height, width) but PIL gives (width, height)
height, width = original_size[1], original_size[0]
new_height, new_width = smart_resize(height, width)
# Store model size in (width, height) format for consistent coordinate processing
model_sizes[image_index] = (new_width, new_height)
# Resize the image using the calculated dimensions from smart_resize
resized_image = pil_image.resize((new_width, new_height))
images.append(resized_image)
# Add image placeholder to content
processed_content.append({
"type": "image"
})
image_index += 1
processed_message["content"] = processed_content
processed_messages.append(processed_message)
return processed_messages, images, original_sizes, model_sizes
def _generate(self, **kwargs) -> str:
"""Generate response using the local MLX VLM model.
Args:
**kwargs: Keyword arguments containing messages and model info
Returns:
Generated text response
"""
messages = kwargs.get('messages', [])
model_name = kwargs.get('model', 'mlx-community/UI-TARS-1.5-7B-4bit')
max_tokens = kwargs.get('max_tokens', 128)
# Warn about ignored kwargs
ignored_kwargs = set(kwargs.keys()) - {'messages', 'model', 'max_tokens'}
if ignored_kwargs:
warnings.warn(f"Ignoring unsupported kwargs: {ignored_kwargs}")
# Load model and processor
model, processor, config = self._load_model_and_processor(model_name)
# Convert messages and extract images
processed_messages, images, original_sizes, model_sizes = self._convert_messages(messages)
# Process user text input with box coordinates after image processing
# Swap original_size and model_size arguments for inverse transformation
for msg_idx, msg in enumerate(processed_messages):
if msg.get("role") == "user" and isinstance(msg.get("content"), str):
content = msg.get("content", "")
if "<|box_start|>" in content and original_sizes and model_sizes and 0 in original_sizes and 0 in model_sizes:
orig_size = original_sizes[0]
model_size = model_sizes[0]
# Swap arguments to perform inverse transformation for user input
processed_messages[msg_idx]["content"] = self._process_coordinates(content, model_size, orig_size)
try:
# Format prompt according to model requirements using the processor directly
prompt = processor.apply_chat_template(
processed_messages,
tokenize=False,
add_generation_prompt=True,
return_tensors='pt'
)
tokenizer = cast(PreTrainedTokenizer, processor)
# Generate response
text_content, usage = generate(
model,
tokenizer,
str(prompt),
images, # type: ignore
verbose=False,
max_tokens=max_tokens
)
except Exception as e:
raise RuntimeError(f"Error generating response: {str(e)}") from e
# Process coordinates in the response back to original image space
if original_sizes and model_sizes and 0 in original_sizes and 0 in model_sizes:
# Get original image size and model size (using the first image)
orig_size = original_sizes[0]
model_size = model_sizes[0]
# Check if output contains box tokens that need processing
if "<|box_start|>" in text_content:
# Process coordinates from model space back to original image space
text_content = self._process_coordinates(text_content, orig_size, model_size)
return text_content
def completion(self, *args, **kwargs) -> ModelResponse:
"""Synchronous completion method.
Returns:
ModelResponse with generated text
"""
generated_text = self._generate(**kwargs)
result = completion(
model=f"mlx/{kwargs.get('model', 'mlx-community/UI-TARS-1.5-7B-4bit')}",
mock_response=generated_text,
)
return cast(ModelResponse, result)
async def acompletion(self, *args, **kwargs) -> ModelResponse:
"""Asynchronous completion method.
Returns:
ModelResponse with generated text
"""
# Run _generate in thread pool to avoid blocking
loop = asyncio.get_event_loop()
generated_text = await loop.run_in_executor(
self._executor,
functools.partial(self._generate, **kwargs)
)
result = await acompletion(
model=f"mlx/{kwargs.get('model', 'mlx-community/UI-TARS-1.5-7B-4bit')}",
mock_response=generated_text,
)
return cast(ModelResponse, result)
def streaming(self, *args, **kwargs) -> Iterator[GenericStreamingChunk]:
"""Synchronous streaming method.
Returns:
Iterator of GenericStreamingChunk
"""
generated_text = self._generate(**kwargs)
generic_streaming_chunk: GenericStreamingChunk = {
"finish_reason": "stop",
"index": 0,
"is_finished": True,
"text": generated_text,
"tool_use": None,
"usage": {"completion_tokens": 0, "prompt_tokens": 0, "total_tokens": 0},
}
yield generic_streaming_chunk
async def astreaming(self, *args, **kwargs) -> AsyncIterator[GenericStreamingChunk]:
"""Asynchronous streaming method.
Returns:
AsyncIterator of GenericStreamingChunk
"""
# Run _generate in thread pool to avoid blocking
loop = asyncio.get_event_loop()
generated_text = await loop.run_in_executor(
self._executor,
functools.partial(self._generate, **kwargs)
)
generic_streaming_chunk: GenericStreamingChunk = {
"finish_reason": "stop",
"index": 0,
"is_finished": True,
"text": generated_text,
"tool_use": None,
"usage": {"completion_tokens": 0, "prompt_tokens": 0, "total_tokens": 0},
}
yield generic_streaming_chunk

23
libs/python/agent/agent/agent.py
View File

@@ -3,6 +3,7 @@ ComputerAgent - Main agent class that selects and runs agent loops
"""
import asyncio
from pathlib import Path
from typing import Dict, List, Any, Optional, AsyncGenerator, Union, cast, Callable, Set, Tuple
from litellm.responses.utils import Usage
@@ -22,6 +23,7 @@ import inspect
from .adapters import (
HuggingFaceLocalAdapter,
HumanAdapter,
MLXVLMAdapter,
)
from .callbacks import (
ImageRetentionCallback,
@@ -29,6 +31,7 @@ from .callbacks import (
TrajectorySaverCallback,
BudgetManagerCallback,
TelemetryCallback,
OperatorNormalizerCallback
)
from .computers import (
AsyncComputerHandler,
@@ -160,7 +163,7 @@ class ComputerAgent:
only_n_most_recent_images: Optional[int] = None,
callbacks: Optional[List[Any]] = None,
verbosity: Optional[int] = None,
trajectory_dir: Optional[str] = None,
trajectory_dir: Optional[str | Path | dict] = None,
max_retries: Optional[int] = 3,
screenshot_delay: Optional[float | int] = 0.5,
use_prompt_caching: Optional[bool] = False,
@@ -185,7 +188,11 @@ class ComputerAgent:
max_trajectory_budget: If set, adds BudgetManagerCallback to track usage costs and stop when budget is exceeded
telemetry_enabled: If set, adds TelemetryCallback to track anonymized usage data. Enabled by default.
**kwargs: Additional arguments passed to the agent loop
"""
"""
# If the loop is "human/human", we need to prefix a grounding model fallback
if model in ["human/human", "human"]:
model = "openai/computer-use-preview+human/human"
self.model = model
self.tools = tools or []
self.custom_loop = custom_loop
@@ -201,6 +208,9 @@ class ComputerAgent:
# == Add built-in callbacks ==
# Prepend operator normalizer callback
self.callbacks.insert(0, OperatorNormalizerCallback())
# Add telemetry callback if telemetry_enabled is set
if self.telemetry_enabled:
if isinstance(self.telemetry_enabled, bool):
@@ -218,7 +228,10 @@ class ComputerAgent:
# Add trajectory saver callback if trajectory_dir is set
if self.trajectory_dir:
self.callbacks.append(TrajectorySaverCallback(self.trajectory_dir))
if isinstance(self.trajectory_dir, dict):
self.callbacks.append(TrajectorySaverCallback(**self.trajectory_dir))
elif isinstance(self.trajectory_dir, (str, Path)):
self.callbacks.append(TrajectorySaverCallback(str(self.trajectory_dir)))
# Add budget manager if max_trajectory_budget is set
if max_trajectory_budget:
@@ -234,9 +247,11 @@ class ComputerAgent:
device="auto"
)
human_adapter = HumanAdapter()
mlx_adapter = MLXVLMAdapter()
litellm.custom_provider_map = [
{"provider": "huggingface-local", "custom_handler": hf_adapter},
{"provider": "human", "custom_handler": human_adapter}
{"provider": "human", "custom_handler": human_adapter},
{"provider": "mlx", "custom_handler": mlx_adapter}
]
litellm.suppress_debug_info = True

2
libs/python/agent/agent/callbacks/__init__.py
View File

@@ -8,6 +8,7 @@ from .logging import LoggingCallback
from .trajectory_saver import TrajectorySaverCallback
from .budget_manager import BudgetManagerCallback
from .telemetry import TelemetryCallback
from .operator_validator import OperatorNormalizerCallback
__all__ = [
"AsyncCallbackHandler",
@@ -16,4 +17,5 @@ __all__ = [
"TrajectorySaverCallback",
"BudgetManagerCallback",
"TelemetryCallback",
"OperatorNormalizerCallback",
]

125
libs/python/agent/agent/callbacks/image_retention.py
View File

@@ -50,90 +50,41 @@ class ImageRetentionCallback(AsyncCallbackHandler):
"""
if self.only_n_most_recent_images is None:
return messages
# First pass: Assign call_id to reasoning items based on the next computer_call
messages_with_call_ids = []
for i, msg in enumerate(messages):
msg_copy = msg.copy() if isinstance(msg, dict) else msg
# If this is a reasoning item without a call_id, find the next computer_call
if (msg_copy.get("type") == "reasoning" and
not msg_copy.get("call_id")):
# Look ahead for the next computer_call
for j in range(i + 1, len(messages)):
next_msg = messages[j]
if (next_msg.get("type") == "computer_call" and
next_msg.get("call_id")):
msg_copy["call_id"] = next_msg.get("call_id")
break
messages_with_call_ids.append(msg_copy)
# Find all computer_call_output items with images and their call_ids
image_call_ids = []
for msg in reversed(messages_with_call_ids): # Process in reverse to get most recent first
if (msg.get("type") == "computer_call_output" and
isinstance(msg.get("output"), dict) and
"image_url" in msg.get("output", {})):
call_id = msg.get("call_id")
if call_id and call_id not in image_call_ids:
image_call_ids.append(call_id)
if len(image_call_ids) >= self.only_n_most_recent_images:
break
# Keep the most recent N image call_ids (reverse to get chronological order)
keep_call_ids = set(image_call_ids[:self.only_n_most_recent_images])
# Filter messages: remove computer_call, computer_call_output, and reasoning for old images
filtered_messages = []
for msg in messages_with_call_ids:
msg_type = msg.get("type")
call_id = msg.get("call_id")
# Remove old computer_call items
if msg_type == "computer_call" and call_id not in keep_call_ids:
# Check if this call_id corresponds to an image call
has_image_output = any(
m.get("type") == "computer_call_output" and
m.get("call_id") == call_id and
isinstance(m.get("output"), dict) and
"image_url" in m.get("output", {})
for m in messages_with_call_ids
)
if has_image_output:
continue # Skip this computer_call
# Remove old computer_call_output items with images
if (msg_type == "computer_call_output" and
call_id not in keep_call_ids and
isinstance(msg.get("output"), dict) and
"image_url" in msg.get("output", {})):
continue # Skip this computer_call_output
# Remove old reasoning items that are paired with removed computer calls
if (msg_type == "reasoning" and
call_id and call_id not in keep_call_ids):
# Check if this call_id corresponds to an image call that's being removed
has_image_output = any(
m.get("type") == "computer_call_output" and
m.get("call_id") == call_id and
isinstance(m.get("output"), dict) and
"image_url" in m.get("output", {})
for m in messages_with_call_ids
)
if has_image_output:
continue # Skip this reasoning item
filtered_messages.append(msg)
# Clean up: Remove call_id from reasoning items before returning
final_messages = []
for msg in filtered_messages:
if msg.get("type") == "reasoning" and "call_id" in msg:
# Create a copy without call_id for reasoning items
cleaned_msg = {k: v for k, v in msg.items() if k != "call_id"}
final_messages.append(cleaned_msg)
else:
final_messages.append(msg)
return final_messages
# Gather indices of all computer_call_output messages that contain an image_url
output_indices: List[int] = []
for idx, msg in enumerate(messages):
if msg.get("type") == "computer_call_output":
out = msg.get("output")
if isinstance(out, dict) and ("image_url" in out):
output_indices.append(idx)
# Nothing to trim
if len(output_indices) <= self.only_n_most_recent_images:
return messages
# Determine which outputs to keep (most recent N)
keep_output_indices = set(output_indices[-self.only_n_most_recent_images :])
# Build set of indices to remove in one pass
to_remove: set[int] = set()
for idx in output_indices:
if idx in keep_output_indices:
continue # keep this screenshot and its context
to_remove.add(idx) # remove the computer_call_output itself
# Remove the immediately preceding computer_call with matching call_id (if present)
call_id = messages[idx].get("call_id")
prev_idx = idx - 1
if prev_idx >= 0 and messages[prev_idx].get("type") == "computer_call" and messages[prev_idx].get("call_id") == call_id:
to_remove.add(prev_idx)
# Check a single reasoning immediately before that computer_call
r_idx = prev_idx - 1
if r_idx >= 0 and messages[r_idx].get("type") == "reasoning":
to_remove.add(r_idx)
# Construct filtered list
filtered = [m for i, m in enumerate(messages) if i not in to_remove]
return filtered

138
libs/python/agent/agent/callbacks/operator_validator.py Normal file
View File

@@ -0,0 +1,138 @@
"""
OperatorValidatorCallback
Ensures agent output actions conform to expected schemas by fixing common issues:
- click: add default button='left' if missing
- keypress: wrap keys string into a list
- etc.
This runs in on_llm_end, which receives the output array (AgentMessage[] as dicts).
The purpose is to avoid spending another LLM call to fix broken computer call syntax when possible.
"""
from __future__ import annotations
from typing import Any, Dict, List
from .base import AsyncCallbackHandler
class OperatorNormalizerCallback(AsyncCallbackHandler):
"""Normalizes common computer call hallucinations / errors in computer call syntax."""
async def on_llm_end(self, output: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
# Mutate in-place as requested, but still return the list for chaining
for item in output or []:
if item.get("type") != "computer_call":
continue
action = item.get("action")
if not isinstance(action, dict):
continue
# rename mouse click actions to "click"
for mouse_btn in ["left", "right", "wheel", "back", "forward"]:
if action.get("type", "") == f"{mouse_btn}_click":
action["type"] = "click"
action["button"] = mouse_btn
# rename hotkey actions to "keypress"
for alias in ["hotkey", "key", "press", "key_press"]:
if action.get("type", "") == alias:
action["type"] = "keypress"
# assume click actions
if "button" in action and "type" not in action:
action["type"] = "click"
if "click" in action and "type" not in action:
action["type"] = "click"
if ("scroll_x" in action or "scroll_y" in action) and "type" not in action:
action["type"] = "scroll"
if "text" in action and "type" not in action:
action["type"] = "type"
action_type = action.get("type")
def _keep_keys(action: Dict[str, Any], keys_to_keep: List[str]):
"""Keep only the provided keys on action; delete everything else.
Always ensures required 'type' is present if listed in keys_to_keep.
"""
for key in list(action.keys()):
if key not in keys_to_keep:
del action[key]
# rename "coordinate" to "x", "y"
if "coordinate" in action:
action["x"] = action["coordinate"][0]
action["y"] = action["coordinate"][1]
del action["coordinate"]
if action_type == "click":
# convert "click" to "button"
if "button" not in action and "click" in action:
action["button"] = action["click"]
del action["click"]
# default button to "left"
action["button"] = action.get("button", "left")
# add default scroll x, y if missing
if action_type == "scroll":
action["scroll_x"] = action.get("scroll_x", 0)
action["scroll_y"] = action.get("scroll_y", 0)
# ensure keys arg is a list (normalize aliases first)
if action_type == "keypress":
keys = action.get("keys")
for keys_alias in ["keypress", "key", "press", "key_press", "text"]:
if keys_alias in action:
action["keys"] = action[keys_alias]
del action[keys_alias]
keys = action.get("keys")
if isinstance(keys, str):
action["keys"] = keys.replace("-", "+").split("+") if len(keys) > 1 else [keys]
required_keys_by_type = {
# OpenAI actions
"click": ["type", "button", "x", "y"],
"double_click": ["type", "x", "y"],
"drag": ["type", "path"],
"keypress": ["type", "keys"],
"move": ["type", "x", "y"],
"screenshot": ["type"],
"scroll": ["type", "scroll_x", "scroll_y", "x", "y"],
"type": ["type", "text"],
"wait": ["type"],
# Anthropic actions
"left_mouse_down": ["type", "x", "y"],
"left_mouse_up": ["type", "x", "y"],
"triple_click": ["type", "button", "x", "y"],
}
keep = required_keys_by_type.get(action_type or "")
if keep:
_keep_keys(action, keep)
# # Second pass: if an assistant message is immediately followed by a computer_call,
# # replace the assistant message itself with a reasoning message with summary text.
# if isinstance(output, list):
# for i, item in enumerate(output):
# # AssistantMessage shape: { type: 'message', role: 'assistant', content: OutputContent[] }
# if item.get("type") == "message" and item.get("role") == "assistant":
# next_idx = i + 1
# if next_idx >= len(output):
# continue
# next_item = output[next_idx]
# if not isinstance(next_item, dict):
# continue
# if next_item.get("type") != "computer_call":
# continue
# contents = item.get("content") or []
# # Extract text from OutputContent[]
# text_parts: List[str] = []
# if isinstance(contents, list):
# for c in contents:
# if isinstance(c, dict) and c.get("type") == "output_text" and isinstance(c.get("text"), str):
# text_parts.append(c["text"])
# text_content = "\n".join(text_parts).strip()
# # Replace assistant message with reasoning message
# output[i] = {
# "type": "reasoning",
# "summary": [
# {
# "type": "summary_text",
# "text": text_content,
# }
# ],
# }
return output

92
libs/python/agent/agent/callbacks/trajectory_saver.py
View File

@@ -11,6 +11,8 @@ from pathlib import Path
from typing import List, Dict, Any, Optional, Union, override
from PIL import Image, ImageDraw
import io
from copy import deepcopy
from .base import AsyncCallbackHandler
def sanitize_image_urls(data: Any) -> Any:
@@ -43,6 +45,64 @@ def sanitize_image_urls(data: Any) -> Any:
return data
def extract_computer_call_outputs(items: List[Dict[str, Any]], screenshot_dir: Optional[Path]) -> List[Dict[str, Any]]:
"""
Save any base64-encoded screenshots from computer_call_output entries to files and
replace their image_url with the saved file path when a call_id is present.
Only operates if screenshot_dir is provided and exists; otherwise returns items unchanged.
Args:
items: List of message/result dicts potentially containing computer_call_output entries
screenshot_dir: Directory to write screenshots into
Returns:
A new list with updated image_url fields when applicable.
"""
if not items:
return items
if not screenshot_dir or not screenshot_dir.exists():
return items
updated: List[Dict[str, Any]] = []
for item in items:
# work on a shallow copy; deep copy nested 'output' if we modify it
msg = dict(item)
try:
if msg.get("type") == "computer_call_output":
call_id = msg.get("call_id")
output = msg.get("output", {})
image_url = output.get("image_url")
if call_id and isinstance(image_url, str) and image_url.startswith("data:"):
# derive extension from MIME type e.g. data:image/png;base64,
try:
ext = image_url.split(";", 1)[0].split("/")[-1]
if not ext:
ext = "png"
except Exception:
ext = "png"
out_path = screenshot_dir / f"{call_id}.{ext}"
# write file if it doesn't exist
if not out_path.exists():
try:
b64_payload = image_url.split(",", 1)[1]
img_bytes = base64.b64decode(b64_payload)
out_path.parent.mkdir(parents=True, exist_ok=True)
with open(out_path, "wb") as f:
f.write(img_bytes)
except Exception:
# if anything fails, skip modifying this message
pass
# update image_url to file path
new_output = dict(output)
new_output["image_url"] = str(out_path)
msg["output"] = new_output
except Exception:
# do not block on malformed entries; keep original
pass
updated.append(msg)
return updated
class TrajectorySaverCallback(AsyncCallbackHandler):
"""
Callback handler that saves agent trajectories to disk.
@@ -51,7 +111,7 @@ class TrajectorySaverCallback(AsyncCallbackHandler):
within the trajectory gets its own folder with screenshots and responses.
"""
def __init__(self, trajectory_dir: str, reset_on_run: bool = True):
def __init__(self, trajectory_dir: str, reset_on_run: bool = True, screenshot_dir: Optional[str] = None):
"""
Initialize trajectory saver.
@@ -67,10 +127,12 @@ class TrajectorySaverCallback(AsyncCallbackHandler):
self.model: Optional[str] = None
self.total_usage: Dict[str, Any] = {}
self.reset_on_run = reset_on_run
# Optional directory to store extracted screenshots from metadata/new_items
self.screenshot_dir: Optional[Path] = Path(screenshot_dir) if screenshot_dir else None
# Ensure trajectory directory exists
self.trajectory_dir.mkdir(parents=True, exist_ok=True)
def _get_turn_dir(self) -> Path:
"""Get the directory for the current turn."""
if not self.trajectory_id:
@@ -94,6 +156,10 @@ class TrajectorySaverCallback(AsyncCallbackHandler):
# format: turn_000/0000_name.json
artifact_filename = f"{self.current_artifact:04d}_{name}"
artifact_path = turn_dir / f"{artifact_filename}.json"
# add created_at
if isinstance(artifact, dict):
artifact = artifact.copy()
artifact["created_at"] = str(uuid.uuid1().time)
with open(artifact_path, "w") as f:
json.dump(sanitize_image_urls(artifact), f, indent=2)
self.current_artifact += 1
@@ -135,12 +201,21 @@ class TrajectorySaverCallback(AsyncCallbackHandler):
trajectory_path = self.trajectory_dir / self.trajectory_id
trajectory_path.mkdir(parents=True, exist_ok=True)
# Save trajectory metadata
# Save trajectory metadata (optionally extract screenshots to screenshot_dir)
kwargs_to_save = kwargs.copy()
try:
if "messages" in kwargs_to_save:
kwargs_to_save["messages"] = extract_computer_call_outputs(
kwargs_to_save["messages"], self.screenshot_dir
)
except Exception:
# If extraction fails, fall back to original messages
pass
metadata = {
"trajectory_id": self.trajectory_id,
"created_at": str(uuid.uuid1().time),
"status": "running",
"kwargs": kwargs,
"kwargs": kwargs_to_save,
}
with open(trajectory_path / "metadata.json", "w") as f:
@@ -167,11 +242,18 @@ class TrajectorySaverCallback(AsyncCallbackHandler):
metadata = {}
# Update metadata with completion info
# Optionally extract screenshots from new_items before persisting
new_items_to_save = new_items
try:
new_items_to_save = extract_computer_call_outputs(new_items, self.screenshot_dir)
except Exception:
pass
metadata.update({
"status": "completed",
"completed_at": str(uuid.uuid1().time),
"total_usage": self.total_usage,
"new_items": sanitize_image_urls(new_items),
"new_items": new_items_to_save,
"total_turns": self.current_turn
})

226
libs/python/agent/agent/human_tool/ui.py
View File

@@ -196,7 +196,9 @@ class HumanCompletionUI:
gr.update(choices=["latest"], value="latest"), # dropdown
gr.update(value=None), # image (no image)
gr.update(value=[]), # chatbot (empty messages)
gr.update(interactive=False) # submit button
gr.update(interactive=False), # submit button
gr.update(visible=False), # click_actions_group hidden
gr.update(visible=False), # actions_group hidden
)
# Sort pending calls by created_at to get oldest first
@@ -237,7 +239,9 @@ class HumanCompletionUI:
gr.update(choices=choices, value="latest"),
gr.update(value=self.last_image),
gr.update(value=conversation),
gr.update(interactive=bool(choices))
gr.update(interactive=bool(choices)),
gr.update(visible=True), # click_actions_group visible when there is a call
gr.update(visible=True), # actions_group visible when there is a call
)
def on_call_selected(self, selected_choice):
@@ -246,7 +250,9 @@ class HumanCompletionUI:
return (
gr.update(value=None), # no image
gr.update(value=[]), # empty chatbot
gr.update(interactive=False)
gr.update(interactive=False),
gr.update(visible=False), # click_actions_group hidden
gr.update(visible=False), # actions_group hidden
)
pending_calls = self.get_pending_calls()
@@ -254,7 +260,9 @@ class HumanCompletionUI:
return (
gr.update(value=None), # no image
gr.update(value=[]), # empty chatbot
gr.update(interactive=False)
gr.update(interactive=False),
gr.update(visible=False), # click_actions_group hidden
gr.update(visible=False), # actions_group hidden
)
# Handle "latest" option
@@ -286,7 +294,9 @@ class HumanCompletionUI:
return (
gr.update(value=None), # no image
gr.update(value=[]), # empty chatbot
gr.update(interactive=False)
gr.update(interactive=False),
gr.update(visible=False), # click_actions_group hidden
gr.update(visible=False), # actions_group hidden
)
conversation = self.format_messages_for_chatbot(selected_call.get("messages", []))
@@ -297,7 +307,9 @@ class HumanCompletionUI:
return (
gr.update(value=self.last_image),
gr.update(value=conversation),
gr.update(interactive=True)
gr.update(interactive=True),
gr.update(visible=True), # click_actions_group visible
gr.update(visible=True), # actions_group visible
)
def submit_response(self, response_text: str):
@@ -368,6 +380,10 @@ class HumanCompletionUI:
"""Submit a hotkey action."""
return self.submit_action("keypress", keys=keys)
def submit_wait_action(self) -> str:
"""Submit a wait action with no kwargs."""
return self.submit_action("wait")
def submit_description_click(self, description: str, action_type: str = "click", button: str = "left") -> str:
"""Submit a description-based action."""
if action_type == "click":
@@ -407,7 +423,7 @@ def create_ui():
"""Create the Gradio interface."""
ui_handler = HumanCompletionUI()
with gr.Blocks(title="Human-in-the-Loop Agent Tool") as demo:
with gr.Blocks(title="Human-in-the-Loop Agent Tool", fill_width=True) as demo:
gr.Markdown("# 🤖 Human-in-the-Loop Agent Tool")
gr.Markdown("Review AI conversation requests and provide human responses.")
@@ -415,29 +431,30 @@ def create_ui():
with gr.Column(scale=2):
with gr.Group():
screenshot_image = gr.Image(
label="Screenshot",
label="Interactive Screenshot",
interactive=False,
height=600
)
# Action type selection for image clicks
with gr.Row():
action_type_radio = gr.Radio(
label="Action Type",
choices=["click", "double_click", "move", "left_mouse_up", "left_mouse_down"],
value="click",
scale=2
)
action_button_radio = gr.Radio(
label="Button (for click only)",
choices=["left", "right", "wheel", "back", "forward"],
value="left",
visible=True,
scale=1
)
# Action type selection for image clicks (wrapped for visibility control)
with gr.Group(visible=False) as click_actions_group:
with gr.Row():
action_type_radio = gr.Dropdown(
label="Action",
choices=["click", "double_click", "move", "left_mouse_up", "left_mouse_down"],
value="click",
scale=2
)
action_button_radio = gr.Dropdown(
label="Button",
choices=["left", "right", "wheel", "back", "forward"],
value="left",
visible=True,
scale=1
)
conversation_chatbot = gr.Chatbot(
label="Messages",
label="Conversation",
type="messages",
height=500,
show_copy_button=True
@@ -446,91 +463,83 @@ def create_ui():
with gr.Column(scale=1):
with gr.Group():
call_dropdown = gr.Dropdown(
label="Select a pending call",
label="Select a pending conversation request",
choices=["latest"],
interactive=True,
value="latest"
)
refresh_btn = gr.Button("🔄 Refresh", variant="secondary")
status_display = gr.Textbox(
label="Status",
interactive=False,
value="Ready to receive requests..."
)
with gr.Group():
response_text = gr.Textbox(
label="Response",
label="Message",
lines=3,
placeholder="Enter your response here..."
placeholder="Enter your message here..."
)
submit_btn = gr.Button("📤 Submit Response", variant="primary", interactive=False)
submit_btn = gr.Button("📤 Submit Message", variant="primary", interactive=False)
# Action Accordions
with gr.Accordion("🖱️ Click Actions", open=False):
with gr.Group():
with gr.Row():
click_x = gr.Number(label="X", value=0, minimum=0)
click_y = gr.Number(label="Y", value=0, minimum=0)
with gr.Row():
click_action_type = gr.Dropdown(
label="Action Type",
choices=["click", "double_click", "move", "left_mouse_up", "left_mouse_down"],
value="click"
)
click_button = gr.Dropdown(
label="Button (for click only)",
choices=["left", "right", "wheel", "back", "forward"],
value="left"
)
click_submit_btn = gr.Button("Submit Action")
with gr.Accordion("📝 Type Action", open=False):
with gr.Group():
type_text = gr.Textbox(
label="Text to Type",
placeholder="Enter text to type..."
)
type_submit_btn = gr.Button("Submit Type")
with gr.Accordion("⌨️ Keypress Action", open=False):
with gr.Group():
keypress_text = gr.Textbox(
label="Keys",
placeholder="e.g., ctrl+c, alt+tab"
)
keypress_submit_btn = gr.Button("Submit Keypress")
with gr.Accordion("🎯 Description Action", open=False):
with gr.Group():
description_text = gr.Textbox(
label="Element Description",
placeholder="e.g., 'Privacy and security option in left sidebar'"
)
with gr.Row():
description_action_type = gr.Dropdown(
label="Action Type",
choices=["click", "double_click", "move", "left_mouse_up", "left_mouse_down"],
value="click"
)
description_button = gr.Radio(
label="Button (for click only)",
choices=["left", "right", "wheel", "back", "forward"],
value="left"
)
description_submit_btn = gr.Button("Submit Description Action")
status_display = gr.Textbox(
label="Status",
interactive=False,
value="Ready to receive calls..."
)
# Action Accordions (wrapped for visibility control)
with gr.Group(visible=False) as actions_group:
with gr.Tabs():
with gr.Tab("🖱️ Click Actions"):
with gr.Group():
description_text = gr.Textbox(
label="Element Description",
placeholder="e.g., 'Privacy and security option in left sidebar'"
)
with gr.Row():
description_action_type = gr.Dropdown(
label="Action",
choices=["click", "double_click", "move", "left_mouse_up", "left_mouse_down"],
value="click"
)
description_button = gr.Dropdown(
label="Button",
choices=["left", "right", "wheel", "back", "forward"],
value="left"
)
description_submit_btn = gr.Button("Submit Click Action")
with gr.Tab("📝 Type Action"):
with gr.Group():
type_text = gr.Textbox(
label="Text to Type",
placeholder="Enter text to type..."
)
type_submit_btn = gr.Button("Submit Type")
with gr.Tab("⌨️ Keypress Action"):
with gr.Group():
keypress_text = gr.Textbox(
label="Keys",
placeholder="e.g., ctrl+c, alt+tab"
)
keypress_submit_btn = gr.Button("Submit Keypress")
with gr.Tab("🧰 Misc Actions"):
with gr.Group():
misc_action_dropdown = gr.Dropdown(
label="Action",
choices=["wait"],
value="wait"
)
misc_submit_btn = gr.Button("Submit Action")
# Event handlers
refresh_btn.click(
fn=ui_handler.refresh_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
call_dropdown.change(
fn=ui_handler.on_call_selected,
inputs=[call_dropdown],
outputs=[screenshot_image, conversation_chatbot, submit_btn]
outputs=[screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
def handle_image_click(evt: gr.SelectData):
@@ -548,7 +557,7 @@ def create_ui():
outputs=[status_display]
).then(
fn=ui_handler.wait_for_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
# Response submission
@@ -558,7 +567,7 @@ def create_ui():
outputs=[response_text, status_display]
).then(
fn=ui_handler.refresh_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
# Toggle button radio visibility based on action type
@@ -570,16 +579,6 @@ def create_ui():
inputs=[action_type_radio],
outputs=[action_button_radio]
)
# Action accordion handlers
click_submit_btn.click(
fn=ui_handler.submit_click_action,
inputs=[click_x, click_y, click_action_type, click_button],
outputs=[status_display]
).then(
fn=ui_handler.wait_for_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
)
type_submit_btn.click(
fn=ui_handler.submit_type_action,
@@ -587,7 +586,7 @@ def create_ui():
outputs=[status_display]
).then(
fn=ui_handler.wait_for_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
keypress_submit_btn.click(
@@ -596,7 +595,7 @@ def create_ui():
outputs=[status_display]
).then(
fn=ui_handler.wait_for_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
def handle_description_submit(description, action_type, button):
@@ -612,13 +611,30 @@ def create_ui():
outputs=[status_display]
).then(
fn=ui_handler.wait_for_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
# Misc action handler
def handle_misc_submit(selected_action):
if selected_action == "wait":
result = ui_handler.submit_wait_action()
ui_handler.wait_for_pending_calls()
return result
return f"Unsupported misc action: {selected_action}"
misc_submit_btn.click(
fn=handle_misc_submit,
inputs=[misc_action_dropdown],
outputs=[status_display]
).then(
fn=ui_handler.wait_for_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
# Load initial data
demo.load(
fn=ui_handler.refresh_pending_calls,
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn]
outputs=[call_dropdown, screenshot_image, conversation_chatbot, submit_btn, click_actions_group, actions_group]
)
return demo

287
libs/python/agent/agent/integrations/hud/__init__.py
View File

@@ -1,77 +1,228 @@
"""HUD integration for ComputerAgent."""
"""HUD integration: Generic HuggingFace dataset evaluation runner (CUA proxy).
import logging
from typing import Any, Optional, Dict
from hud import run_job as hud_run_job
This module exposes two helpers to evaluate HUD-compatible datasets using
HUD's OperatorAgent, while proxying model calls through our ComputerAgent via
`FakeAsyncOpenAI` (see `agent/integrations/hud/agent.py`).
from .agent import ComputerAgent
from .adapter import ComputerAgentAdapter
from .computer_handler import HUDComputerHandler
Exports:
- run_single_task(dataset_name, *, agent_type="cua-proxy", model=None, allowed_tools=None)
- run_full_dataset(dataset_name, *, agent_type="cua-proxy", model=None, allowed_tools=None, max_concurrent=30, max_steps=50)
"""
import time
from typing import Any, Optional
from PIL import Image
from datasets import load_dataset, Dataset
from hud.agents import OperatorAgent
from hud.datasets import Task, run_dataset
from hud.tools.computer.settings import computer_settings
from hud import trace
from agent.agent import ComputerAgent as BaseComputerAgent
from .proxy import FakeAsyncOpenAI
async def run_job(
model: str,
task_or_taskset: Any,
job_name: str,
# Job kwargs
auto_reply_question: bool = False,
adapter_cls: Any = None,
adapter_kwargs: Optional[Dict[str, Any]] = None,
max_steps_per_task: int = 20,
run_parallel: bool = True,
job_metadata: Optional[Dict[str, Any]] = None,
show_progress: bool = True,
max_concurrent_env_creations: Optional[int] = 30, # Limits gym.make calls
max_concurrent_agent_predictions: Optional[int] = None, # No limit on LLM calls
max_concurrent_tasks: Optional[int] = 30, # Limits overall task concurrency
**agent_kwargs: Any
) -> Any:
# ---------------------------------------------------------------------------
# Proxy OperatorAgent
# ---------------------------------------------------------------------------
class ProxyOperatorAgent(OperatorAgent):
"""OperatorAgent that proxies model calls through our ComputerAgent.
Accepts the same config keys we pass via hud.run_dataset `agent_config`:
- model: str | None
- allowed_tools: list[str] | None
Additional kwargs are forwarded to OperatorAgent (if any are supported).
"""
Run a job using ComputerAgent with the specified model.
def __init__(
self,
*,
model: str | None = None,
allowed_tools: list[str] | None = None,
trajectory_dir: str | dict | None = None,
# === ComputerAgent kwargs ===
tools: list[Any] | None = None,
custom_loop: Any | None = None,
only_n_most_recent_images: int | None = None,
callbacks: list[Any] | None = None,
verbosity: int | None = None,
max_retries: int | None = 3,
screenshot_delay: float | int = 0.5,
use_prompt_caching: bool | None = False,
max_trajectory_budget: float | dict | None = None,
telemetry_enabled: bool | None = True,
**kwargs: Any,
) -> None:
model = model or "computer-use-preview"
allowed_tools = allowed_tools or ["openai_computer"]
computer_shim = {
'screenshot': lambda: Image.new('RGB', (computer_settings.OPENAI_COMPUTER_WIDTH, computer_settings.OPENAI_COMPUTER_HEIGHT)),
'environment': 'linux',
'dimensions': (computer_settings.OPENAI_COMPUTER_WIDTH, computer_settings.OPENAI_COMPUTER_HEIGHT)
}
# Build tools ensuring the computer_shim is included
agent_tools: list[Any] = [computer_shim]
if tools:
agent_tools.extend(tools)
computer_agent = BaseComputerAgent(
model=model,
tools=agent_tools,
custom_loop=custom_loop,
only_n_most_recent_images=only_n_most_recent_images,
callbacks=callbacks,
verbosity=verbosity,
trajectory_dir=trajectory_dir,
max_retries=max_retries,
screenshot_delay=screenshot_delay,
use_prompt_caching=use_prompt_caching,
max_trajectory_budget=max_trajectory_budget,
telemetry_enabled=telemetry_enabled,
)
model_client = FakeAsyncOpenAI(computer_agent)
super().__init__(
model_client=model_client, # type: ignore[arg-type]
model=model,
allowed_tools=allowed_tools,
**kwargs,
)
# ---------------------------------------------------------------------------
# Single-task runner
# ---------------------------------------------------------------------------
async def run_single_task(
dataset: str | Dataset | list[dict[str, Any]],
*,
task_id: int = 0,
model: str | None = None,
allowed_tools: list[str] | None = None,
# === ComputerAgent kwargs ===
tools: list[Any] | None = None,
custom_loop: Any | None = None,
only_n_most_recent_images: int | None = None,
callbacks: list[Any] | None = None,
verbosity: int | None = None,
trajectory_dir: str | dict | None = None,
max_retries: int | None = 3,
screenshot_delay: float | int = 0.5,
use_prompt_caching: bool | None = False,
max_trajectory_budget: float | dict | None = None,
telemetry_enabled: bool | None = True,
) -> None:
"""Load one task from the dataset and execute it with Operator+CUA proxy."""
# Load dataset and pick a sample
if isinstance(dataset, str):
dataset = load_dataset(dataset, split="train") # type: ignore[arg-type]
elif isinstance(dataset, list):
dataset = dataset
else:
dataset = dataset["train"]
Args:
model: Model string for ComputerAgent (e.g., "anthropic/claude-3-5-sonnet-20241022")
task_or_taskset: Task or TaskSet to run
job_name: Name for the job
auto_reply_question: Whether to auto-reply to questions
adapter_cls: Custom adapter class (defaults to ComputerAgentAdapter)
adapter_kwargs: Additional kwargs for the adapter
max_steps_per_task: Maximum steps per task
run_parallel: Whether to run tasks in parallel
job_metadata: Additional metadata for the job
show_progress: Whether to show progress
max_concurrent_env_creations: Max concurrent environment creations
max_concurrent_agent_predictions: Max concurrent agent predictions
max_concurrent_tasks: Max concurrent tasks
**agent_kwargs: Additional kwargs to pass to ComputerAgent
Returns:
Job instance from HUD
"""
# combine verbose and verbosity kwargs
if "verbose" in agent_kwargs:
agent_kwargs["verbosity"] = logging.INFO
del agent_kwargs["verbose"]
verbose = True if agent_kwargs.get("verbosity", logging.WARNING) > logging.INFO else False
# run job
return await hud_run_job(
agent_cls=ComputerAgent,
agent_kwargs={"model": model, **agent_kwargs},
task_or_taskset=task_or_taskset,
job_name=job_name,
auto_reply_question=auto_reply_question,
adapter_cls=adapter_cls,
adapter_kwargs=adapter_kwargs,
max_steps_per_task=max_steps_per_task,
run_parallel=run_parallel,
job_metadata=job_metadata,
show_progress=show_progress,
verbose=verbose,
max_concurrent_env_creations=max_concurrent_env_creations,
max_concurrent_agent_predictions=max_concurrent_agent_predictions,
max_concurrent_tasks=max_concurrent_tasks
sample_task = dataset[task_id] # type: ignore[index]
task_prompt = sample_task.get("prompt", f"Task {sample_task.get('id', 0)}") # type: ignore[attr-defined]
with trace(name=task_prompt):
task = Task(**sample_task) # type: ignore[arg-type]
agent = ProxyOperatorAgent(
model=model,
allowed_tools=allowed_tools,
# === ComputerAgent kwargs passthrough ===
tools=tools,
custom_loop=custom_loop,
only_n_most_recent_images=only_n_most_recent_images,
callbacks=callbacks,
verbosity=verbosity,
trajectory_dir=trajectory_dir,
max_retries=max_retries,
screenshot_delay=screenshot_delay,
use_prompt_caching=use_prompt_caching,
max_trajectory_budget=max_trajectory_budget,
telemetry_enabled=telemetry_enabled,
)
print(f"Running: {task_prompt}")
result = await agent.run(task, max_steps=10)
print(f"✅ Reward: {getattr(result, 'reward')}")
# ---------------------------------------------------------------------------
# Full-dataset runner
# ---------------------------------------------------------------------------
async def run_full_dataset(
dataset: str | Dataset | list[dict[str, Any]],
*,
job_name: Optional[str] = None,
model: str | None = None,
allowed_tools: list[str] | None = None,
max_concurrent: int = 30,
max_steps: int = 50,
split: str = "train",
trajectory_dir: str | dict | None = None,
# === ComputerAgent kwargs ===
tools: list[Any] | None = None,
custom_loop: Any | None = None,
only_n_most_recent_images: int | None = 5,
callbacks: list[Any] | None = None,
verbosity: int | None = None,
max_retries: int | None = 3,
screenshot_delay: float | int = 0.5,
use_prompt_caching: bool | None = False,
max_trajectory_budget: float | dict | None = None,
telemetry_enabled: bool | None = True,
) -> list[Any]:
"""Run evaluation across the entire dataset using hud.datasets.run_dataset."""
# We pass OperatorAgent as the class and provide a config that injects our
# FakeAsyncOpenAI per agent instantiation.
if isinstance(dataset, str):
dataset_name = dataset.split('/')[-1]
job_name = job_name or f"Evaluation {dataset_name}"
dataset = load_dataset(dataset, split=split) # type: ignore[arg-type]
else:
dataset_name = "custom"
job_name = job_name or f"Evaluation {time.strftime('%H:%M %Y-%m-%d')}"
# Execute evaluation
return await run_dataset(
name=job_name,
dataset=dataset,
agent_class=ProxyOperatorAgent,
agent_config={
"model": model,
"allowed_tools": allowed_tools,
"trajectory_dir": trajectory_dir,
# === ComputerAgent kwargs passthrough ===
"tools": tools,
"custom_loop": custom_loop,
"only_n_most_recent_images": only_n_most_recent_images,
"callbacks": callbacks,
"verbosity": verbosity,
"max_retries": max_retries,
"screenshot_delay": screenshot_delay,
"use_prompt_caching": use_prompt_caching,
"max_trajectory_budget": max_trajectory_budget,
"telemetry_enabled": telemetry_enabled,
},
max_concurrent=max_concurrent,
metadata={"dataset": dataset_name},
max_steps=max_steps,
auto_respond=True,
)
__all__ = ["ComputerAgent", "ComputerAgentAdapter", "HUDComputerHandler", "run_job"]
__all__ = [
"run_single_task",
"run_full_dataset",
"ProxyOperatorAgent",
]

121
libs/python/agent/agent/integrations/hud/adapter.py
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@@ -1,121 +0,0 @@
"""HUD Adapter for ComputerAgent integration."""
from __future__ import annotations
from typing import Any, ClassVar
from hud.adapters.common import CLA, Adapter
from hud.adapters.common.types import (
CLAButton,
CLAKey,
ClickAction,
CustomAction,
DragAction,
MoveAction,
Point,
PressAction,
ResponseAction,
ScreenshotFetch,
ScrollAction,
TypeAction,
WaitAction,
)
class ComputerAgentAdapter(Adapter):
"""Adapter for ComputerAgent to work with HUD."""
KEY_MAP: ClassVar[dict[str, CLAKey]] = {
"return": "enter",
"arrowup": "up",
"arrowdown": "down",
"arrowleft": "left",
"arrowright": "right",
"cmd": "ctrl",
"super": "win",
"meta": "win",
}
BUTTON_MAP: ClassVar[dict[str, CLAButton]] = {
"wheel": "middle",
"middle": "middle",
}
def __init__(self) -> None:
super().__init__()
# ComputerAgent default dimensions (can be overridden)
self.agent_width = 1024
self.agent_height = 768
def _map_key(self, key: str) -> CLAKey:
"""Map a key to its standardized form."""
return self.KEY_MAP.get(key.lower(), key.lower()) # type: ignore
def convert(self, data: Any) -> CLA:
"""Convert a ComputerAgent action to a HUD action."""
try:
action_type = data.get("type")
if action_type == "click":
x, y = data.get("x", 0), data.get("y", 0)
button = data.get("button", "left")
button = self.BUTTON_MAP.get(button, button)
if button is None:
button = "left"
converted_action = ClickAction(point=Point(x=x, y=y), button=button)
elif action_type == "double_click":
x, y = data.get("x", 0), data.get("y", 0)
converted_action = ClickAction(point=Point(x=x, y=y), button="left", pattern=[100])
elif action_type == "scroll":
x, y = int(data.get("x", 0)), int(data.get("y", 0))
scroll_x = int(data.get("scroll_x", 0))
scroll_y = int(data.get("scroll_y", 0))
converted_action = ScrollAction(
point=Point(x=x, y=y), scroll=Point(x=scroll_x, y=scroll_y)
)
elif action_type == "type":
text = data.get("text", "")
converted_action = TypeAction(text=text, enter_after=False)
elif action_type == "wait":
ms = data.get("ms", 1000)
converted_action = WaitAction(time=ms)
elif action_type == "move":
x, y = data.get("x", 0), data.get("y", 0)
converted_action = MoveAction(point=Point(x=x, y=y))
elif action_type == "keypress":
keys = data.get("keys", [])
if isinstance(keys, str):
keys = [keys]
converted_action = PressAction(keys=[self._map_key(k) for k in keys])
elif action_type == "drag":
path = data.get("path", [])
points = [Point(x=p.get("x", 0), y=p.get("y", 0)) for p in path]
converted_action = DragAction(path=points)
elif action_type == "screenshot":
converted_action = ScreenshotFetch()
elif action_type == "response":
converted_action = ResponseAction(text=data.get("text", ""))
elif action_type == "custom":
converted_action = CustomAction(action=data.get("action", ""))
else:
raise ValueError(f"Unsupported action type: {action_type}")
# Add reasoning and logs if available
converted_action.reasoning = data.get("reasoning", "")
converted_action.logs = data.get("logs", "")
return converted_action
except Exception as e:
raise ValueError(f"Invalid action: {data}. Error: {e!s}") from e

373
libs/python/agent/agent/integrations/hud/agent.py
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@@ -1,373 +0,0 @@
"""HUD ComputerAgent wrapper for OSWorld benchmarking."""
import logging
from typing import Any, Literal, Optional, Union, List, Dict
import asyncio
from agent import ComputerAgent as BaseComputerAgent
from agent.responses import make_failed_tool_call_items
from hud.adapters import Adapter
from hud.agent.base import Agent
from hud.utils.common import Observation
from hud.adapters.common.types import LogType
from hud.types import Gym
from .adapter import ComputerAgentAdapter
from .computer_handler import HUDComputerHandler
logger = logging.getLogger(__name__)
BASE_SYSTEM_PROMPT = """
You are an autonomous computer-using agent. Follow these guidelines:
1. Be decisive and complete tasks without asking for confirmation unless absolutely necessary.
2. Use the computer tools to complete the task and do not stop until the task is complete.
3. Do NOT ask questions like "Should I proceed?" or "Would you like me to continue?" - just proceed with the task.
4. When you find what you're looking for (e.g., a file to upload), proceed with the action directly.
5. Only stop when the task is fully complete or if you encounter an error that prevents completion.
6. Trust that the user wants you to complete the entire task they've requested.
7. You must say "Task completed" when the task is complete.
Remember: You have been given permission to complete the requested task autonomously.
""".strip()
class ComputerAgent(Agent[BaseComputerAgent, dict[str, Any]]):
"""
A ComputerAgent wrapper for HUD integration.
This agent wraps the base ComputerAgent to work with HUD environments,
providing the same interface as OperatorAgent but using ComputerAgent internally.
"""
transfer_gyms: dict[Gym, Gym] = {"qa": "hud-browser"}
def __init__(
self,
model: str = "anthropic/claude-3-5-sonnet-20241022",
environment: Literal["windows", "mac", "linux", "browser"] = "linux",
adapter: Optional[Adapter] = None,
name: Optional[str] = None,
**kwargs: Any,
):
"""
Initialize the ComputerAgent for HUD.
Args:
model: The model string for ComputerAgent (e.g., "anthropic/claude-3-5-sonnet-20241022")
environment: The environment type (windows, mac, linux, browser)
adapter: The adapter to use for preprocessing and postprocessing
name: The name of the agent
**kwargs: Additional arguments passed to ComputerAgent
"""
# Create adapter if not provided
adapter = adapter or ComputerAgentAdapter()
if name is None:
name = f"computeragent-{model.split('/')[-1]}"
# Initialize the base Agent class without client (we'll create it later)
super().__init__(client=None, adapter=adapter, name=name)
self.model = model
self.environment = environment
self.kwargs = kwargs
# Default dimensions
self.width = 1024
self.height = 768
# Update dimensions if adapter is provided
if self.adapter:
self.width = self.adapter.agent_width
self.height = self.adapter.agent_height
# Create HUD computer handler
self.hud_computer = HUDComputerHandler(
environment=environment,
dimensions=(self.width, self.height)
)
# Handle trajectory_dir by adding TrajectorySaverCallback
trajectory_dir = kwargs.pop("trajectory_dir", None)
callbacks = kwargs.get("callbacks", [])
if trajectory_dir:
from agent.callbacks.trajectory_saver import TrajectorySaverCallback
trajectory_callback = TrajectorySaverCallback(trajectory_dir, reset_on_run=False)
callbacks = callbacks + [trajectory_callback]
kwargs["callbacks"] = callbacks
# Initialize ComputerAgent with HUD computer handler
self.computer_agent = BaseComputerAgent(
model=model,
tools=[self.hud_computer],
**kwargs
)
# Set the client to the computer_agent for compatibility
self.client = self.computer_agent
# State tracking
self.conversation_history: List[Dict[str, Any]] = []
self.initial_prompt: Optional[str] = None
# System prompt for computer use tasks
self.base_system_prompt = BASE_SYSTEM_PROMPT
async def fetch_response(self, observation: Observation) -> tuple[list[dict[str, Any]], bool]:
"""
Fetch a response from ComputerAgent based on the observation.
Args:
observation: The preprocessed observation, attributes:
screenshot: Base64 encoded PNG string of the screen
text: Text observation, if available
Returns:
tuple[list[dict[str, Any]], bool, list[LogType] | None]: A tuple containing the list of raw actions,
boolean indicating if the agent believes the task is complete.
"""
try:
# Update the computer handler with the current screenshot
if observation.screenshot:
self.hud_computer.update_screenshot(observation.screenshot)
# Set up action callback to capture actions
captured_actions = []
action_done = False
async def action_callback(action: Dict[str, Any]) -> None:
"""Callback to capture actions from ComputerAgent."""
nonlocal captured_actions, action_done
captured_actions.append(action)
# Set the action callback
self.hud_computer.set_action_callback(action_callback)
# Prepare the message for ComputerAgent
if not self.conversation_history:
# First interaction - use the observation text as initial prompt
if observation.text:
self.initial_prompt = observation.text
message = f"{self.base_system_prompt}\n\nTask: {observation.text}"
else:
message = f"{self.base_system_prompt}\n\nPlease analyze the current screen and determine what action to take."
input_content = [
{"type": "input_text", "text": message}
]
# Add screenshot if present
if observation.screenshot:
input_content.append(
{
"type": "input_image",
"image_url": f"data:image/png;base64,{observation.screenshot}",
}
)
self.conversation_history.append({"role": "user", "content": input_content})
else:
# Subsequent interactions - check if last action was computer_call
# If so, add computer_call_output with screenshot instead of user message
last_computer_calls = []
for msg in reversed(self.conversation_history):
if msg.get("type") == "computer_call":
call_id = msg.get("call_id")
if call_id:
# Check if this call_id already has a computer_call_output
has_output = any(
m.get("type") == "computer_call_output" and m.get("call_id") == call_id
for m in self.conversation_history
)
if not has_output:
last_computer_calls.append(call_id)
if last_computer_calls:
if not observation.screenshot:
print("No screenshot found, taking screenshot")
screenshot_b64 = await self.hud_computer.screenshot()
# Add computer_call_output for each unresponded computer_call
for call_id in reversed(last_computer_calls): # Maintain order
self.conversation_history.append({
"type": "computer_call_output",
"call_id": call_id,
"output": {
"type": "input_image",
"image_url": f"data:image/png;base64,{screenshot_b64}"
}
})
else:
# No computer_call found, add regular user message
message = "Continue with the task based on the current screen state."
input_content = [
{"type": "input_text", "text": message}
]
# Add screenshot if present
if observation.screenshot:
input_content.append(
{
"type": "input_image",
"image_url": f"data:image/png;base64,{observation.screenshot}",
}
)
self.conversation_history.append({"role": "user", "content": input_content})
# If the last message is a reasoning message, change it to output_text
if (self.conversation_history and
self.conversation_history[-1].get("type") == "reasoning" and
self.conversation_history[-1].get("summary")):
reasoning_msg = self.conversation_history[-1]
summary_texts = []
# Extract all summary_text entries
for summary_item in reasoning_msg["summary"]:
if summary_item.get("type") == "summary_text":
summary_texts.append(summary_item.get("text", ""))
# Convert to message format with output_text
if summary_texts:
converted_message = {
"type": "message",
"role": "assistant",
"content": [
{
"text": " ".join(summary_texts),
"type": "output_text"
}
]
}
# Replace the reasoning message with the converted message
self.conversation_history[-1] = converted_message
# Run ComputerAgent
try:
new_items = []
# ComputerAgent.run returns an async generator
try:
async for result in self.computer_agent.run(self.conversation_history, stream=False):
# if the result has computer_call_output, immediately exit
if result.get("output", []) and result.get("output", [])[-1].get("type") == "computer_call_output":
break
# otherwise add agent output to conversation history
new_items += result["output"]
except Exception as e:
# if the last message is reasoning, change it to output_text
if new_items and new_items[-1].get("type") == "reasoning":
new_items[-1] = {
"type": "message",
"role": "assistant",
"content": [
{
"text": new_items[-1].get("summary", [{}])[0].get("text", ""),
"type": "output_text"
}
]
}
# Check if there are any computer_call items in new_items
computer_calls = [item for item in new_items if item.get("type") == "computer_call"]
if computer_calls:
# Remove computer_call items from new_items
new_items = [item for item in new_items if item.get("type") != "computer_call"]
# Add failed tool call items for each computer call
for computer_call in computer_calls:
tool_input = computer_call.get("action", {})
call_id = computer_call.get("call_id")
new_items.extend(make_failed_tool_call_items(
tool_name="computer",
tool_kwargs=tool_input,
error_message=repr(e),
call_id=call_id
))
else:
# add error message to conversation history (fallback for non-computer-call errors)
new_items.append({
"type": "user",
"content": [
{
"type": "input_text",
"text": f"Error during previous attempted action: {repr(e)}"
}
]
})
# Check if we captured any actions
if captured_actions:
# Extract reasoning from the conversation history
reasoning = ""
# Look for the latest reasoning message
for msg in reversed(new_items):
if msg.get("type") == "reasoning" and msg.get("summary"):
reasoning = " ".join([s.get("text", "") for s in msg["summary"] if s.get("type") == "summary_text"])
break
elif msg.get("type") == "message" and msg.get("role") == "assistant":
content = msg.get("content", [])
if isinstance(content, list):
reasoning = " ".join([c.get("text", "") for c in content if c.get("type") == "output_text"])
break
# update conversation history
self.conversation_history += new_items
# Add reasoning and logs to each action
for action in captured_actions:
action["reasoning"] = reasoning
action["logs"] = {"conversation_length": len(self.conversation_history)}
return captured_actions, False
# Check if the last message is "Task completed"
response_text = ""
for msg in reversed(new_items):
if msg.get("type") == "message" and msg.get("role") == "assistant":
content = msg.get("content", [])
for c in content:
if c.get("type") == "output_text":
response_text = c.get("text", response_text)
break
break
done = "task completed" in response_text.lower()
# update conversation history
self.conversation_history += new_items
response_action = {
"type": "response",
"text": response_text,
"reasoning": response_text,
"logs": {"conversation_length": len(self.conversation_history)}
}
# Check if this indicates task completion or failure
if "task is infeasible" in response_text.lower():
response_action = {"type": "custom", "action": "FAIL"}
done = True
return [response_action], done
except Exception as e:
logger.error(f"Error running ComputerAgent: {e}")
# Return an error response
error_action = {
"type": "response",
"text": f"Error occurred: {str(e)}",
"reasoning": f"ComputerAgent encountered an error: {str(e)}",
"logs": {"error": str(e)}
}
return [error_action], True
except Exception as e:
logger.error(f"Error in fetch_response: {e}")
error_action = {
"type": "response",
"text": f"Error in agent processing: {str(e)}",
"reasoning": f"Agent processing error: {str(e)}",
"logs": {"error": str(e)}
}
return [error_action], True

187
libs/python/agent/agent/integrations/hud/computer_handler.py
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@@ -1,187 +0,0 @@
"""HUD Computer Handler for ComputerAgent integration."""
import base64
from io import BytesIO
from typing import Literal, Optional, Any, Dict, Callable
from PIL import Image
from agent.computers import AsyncComputerHandler
class HUDComputerHandler(AsyncComputerHandler):
"""Computer handler that interfaces with HUD environment."""
def __init__(
self,
environment: Literal["windows", "mac", "linux", "browser"] = "linux",
dimensions: tuple[int, int] = (1024, 768),
screenshot_callback: Optional[Callable] = None,
action_callback: Optional[Callable] = None,
):
"""
Initialize HUD computer handler.
Args:
environment: The environment type for HUD
dimensions: Screen dimensions as (width, height)
screenshot_callback: Optional callback to get screenshots from HUD environment
action_callback: Optional callback to execute actions in HUD environment
"""
super().__init__()
self._environment = environment
self._dimensions = dimensions
self._screenshot_callback = screenshot_callback
self._action_callback = action_callback
# Store the last screenshot for reuse
self._last_screenshot: Optional[str] = None
def set_screenshot_callback(self, callback: Callable) -> None:
"""Set the screenshot callback."""
self._screenshot_callback = callback
def set_action_callback(self, callback: Callable) -> None:
"""Set the action callback."""
self._action_callback = callback
def update_screenshot(self, screenshot: str) -> None:
"""Update the stored screenshot (base64 string)."""
self._last_screenshot = screenshot
async def get_environment(self) -> Literal["windows", "mac", "linux", "browser"]:
"""Get the current environment type."""
return self._environment # type: ignore
async def get_dimensions(self) -> tuple[int, int]:
"""Get screen dimensions as (width, height)."""
return self._dimensions
async def screenshot(self) -> str:
"""Take a screenshot and return as base64 string."""
if self._screenshot_callback:
screenshot = await self._screenshot_callback()
if isinstance(screenshot, str):
self._last_screenshot = screenshot
return screenshot
elif isinstance(screenshot, Image.Image):
# Convert PIL Image to base64
buffer = BytesIO()
screenshot.save(buffer, format="PNG")
screenshot_b64 = base64.b64encode(buffer.getvalue()).decode()
self._last_screenshot = screenshot_b64
return screenshot_b64
elif isinstance(screenshot, bytes):
screenshot_b64 = base64.b64encode(screenshot).decode()
self._last_screenshot = screenshot_b64
return screenshot_b64
# Return last screenshot if available, otherwise create a blank one
if self._last_screenshot:
return self._last_screenshot
# Create a blank screenshot as fallback
blank_image = Image.new('RGB', self._dimensions, color='white')
buffer = BytesIO()
blank_image.save(buffer, format="PNG")
screenshot_b64 = base64.b64encode(buffer.getvalue()).decode()
self._last_screenshot = screenshot_b64
return screenshot_b64
async def click(self, x: int, y: int, button: str = "left") -> None:
"""Click at coordinates with specified button."""
if self._action_callback:
await self._action_callback({
"type": "click",
"x": x,
"y": y,
"button": button
})
async def double_click(self, x: int, y: int) -> None:
"""Double click at coordinates."""
if self._action_callback:
await self._action_callback({
"type": "double_click",
"x": x,
"y": y
})
async def scroll(self, x: int, y: int, scroll_x: int, scroll_y: int) -> None:
"""Scroll at coordinates with specified scroll amounts."""
if self._action_callback:
await self._action_callback({
"type": "scroll",
"x": x,
"y": y,
"scroll_x": scroll_x,
"scroll_y": scroll_y
})
async def type(self, text: str) -> None:
"""Type text."""
if self._action_callback:
await self._action_callback({
"type": "type",
"text": text
})
async def wait(self, ms: int = 1000) -> None:
"""Wait for specified milliseconds."""
if self._action_callback:
await self._action_callback({
"type": "wait",
"ms": ms
})
async def move(self, x: int, y: int) -> None:
"""Move cursor to coordinates."""
if self._action_callback:
await self._action_callback({
"type": "move",
"x": x,
"y": y
})
async def keypress(self, keys: list[str] | str) -> None:
"""Press key combination."""
if isinstance(keys, str):
keys = [keys]
if self._action_callback:
await self._action_callback({
"type": "keypress",
"keys": keys
})
async def drag(self, path: list[dict[str, int]]) -> None:
"""Drag along a path of points."""
if self._action_callback:
await self._action_callback({
"type": "drag",
"path": path
})
async def left_mouse_down(self, x: Optional[int] = None, y: Optional[int] = None) -> None:
"""Left mouse down at coordinates."""
if self._action_callback:
await self._action_callback({
"type": "left_mouse_down",
"x": x,
"y": y
})
async def left_mouse_up(self, x: Optional[int] = None, y: Optional[int] = None) -> None:
"""Left mouse up at coordinates."""
if self._action_callback:
await self._action_callback({
"type": "left_mouse_up",
"x": x,
"y": y
})
async def get_current_url(self) -> str:
"""Get the current URL."""
if self._action_callback:
return await self._action_callback({
"type": "get_current_url"
})
return ""

183
libs/python/agent/agent/integrations/hud/proxy.py Normal file
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@@ -0,0 +1,183 @@
"""HUD ComputerAgent wrapper and Fake AsyncOpenAI client.
Provides FakeAsyncOpenAI that adapts our ComputerAgent to the OpenAI Responses
interface needed by HUD's OperatorAgent. It implements only `responses.create`
and returns an OpenAI Response object with `id` and `output` fields, where `output` is a list of
OpenAI-like response blocks. We intentionally only support a single-step call
by consuming the first yielded result from `ComputerAgent.run()`.
"""
import traceback
import time
import uuid
from typing import Any, Dict, List, Optional
from agent.agent import ComputerAgent as BaseComputerAgent
# OpenAI Responses typed models (required)
from openai.types.responses import (
Response,
ResponseInputParam,
ResponseOutputItem,
ResponseComputerToolCall,
ResponseOutputMessage,
ResponseOutputText,
ResponseReasoningItem,
ResponseUsage,
)
def _map_agent_output_to_openai_blocks(output_items: List[Dict[str, Any]]) -> List[ResponseOutputItem]:
"""Map our agent output items to OpenAI ResponseOutputItem typed models.
Only a subset is supported: computer_call, assistant message (text), and reasoning.
Unknown types are ignored.
"""
blocks: List[ResponseOutputItem] = []
for item in output_items or []:
t = item.get("type")
if t == "computer_call":
comp = ResponseComputerToolCall.model_validate({
"id": item.get("id") or f"cu_{uuid.uuid4().hex}",
"type": "computer_call",
"call_id": item["call_id"],
"action": item["action"],
"pending_safety_checks": item.get("pending_safety_checks", []),
"status": "completed",
})
blocks.append(comp)
# we will exit early here as the responses api only supports a single step
break
elif t == "message" and item.get("role") == "assistant":
content_blocks: List[ResponseOutputText] = []
for c in item.get("content", []) or []:
content_blocks.append(
ResponseOutputText.model_validate({
"type": "output_text",
"text": c["text"],
"annotations": [],
})
)
if content_blocks:
msg = ResponseOutputMessage.model_validate({
"id": item.get("id") or f"msg_{uuid.uuid4()}",
"type": "message",
"role": "assistant",
"status": "completed",
"content": [ct.model_dump() for ct in content_blocks],
})
blocks.append(msg)
elif t == "reasoning":
reasoning = ResponseReasoningItem.model_validate({
"id": item.get("id") or f"rsn_{uuid.uuid4()}",
"type": "reasoning",
"summary": item["summary"],
})
blocks.append(reasoning)
# Unhandled types are ignored
return blocks
def _to_plain_dict_list(items: Any) -> List[Dict[str, Any]]:
out: List[Dict[str, Any]] = []
for it in list(items):
if hasattr(it, "model_dump"):
out.append(it.model_dump()) # type: ignore[attr-defined]
elif isinstance(it, dict):
out.append(it)
else:
# Strict: rely on default __dict__ if present
out.append(dict(it)) # may raise if not mapping
return out
class FakeAsyncOpenAI:
"""Minimal fake OpenAI client with only `responses.create` implemented.
It uses a provided `ComputerAgent` instance to produce a single-step
response compatible with HUD's OperatorAgent loop.
"""
def __init__(self, computer_agent: BaseComputerAgent) -> None:
self._agent = computer_agent
self.responses = self._Responses(self)
class _Responses:
def __init__(self, parent: "FakeAsyncOpenAI") -> None:
# Caches for cross-call context when using previous_response_id
self.blocks_cache: Dict[str, ResponseInputParam | ResponseOutputItem] = {}
self.context_cache: Dict[str, List[str]] = {}
self.agent = parent._agent
async def create(
self,
*,
model: str,
input: ResponseInputParam,
tools: Optional[List[Dict[str, Any]]] = None,
instructions: Optional[str] = None,
previous_response_id: Optional[str] = None,
max_retries: int = 5,
**_: Any,
) -> Any:
for attempt in range(max_retries):
# Prepend cached blocks from previous_response_id to input
full_input = input
if previous_response_id is not None:
prev_block_ids = self.context_cache[previous_response_id]
prev_blocks = [self.blocks_cache[b_id] for b_id in prev_block_ids]
full_input = _to_plain_dict_list(prev_blocks + input)
# Pre-pend instructions message
effective_input = full_input
if instructions:
effective_input = [{
"role": "user",
"content": instructions,
}] + full_input
# Run a single iteration of the ComputerAgent
agent_result: Optional[Dict[str, Any]] = None
async for result in self.agent.run(effective_input): # type: ignore[arg-type]
agent_result = result
break
assert agent_result is not None, "Agent failed to produce result"
output = _map_agent_output_to_openai_blocks(agent_result["output"])
usage = agent_result["usage"]
# Cache conversation context using the last response id
block_ids: List[str] = []
blocks_to_cache = full_input + output
for b in blocks_to_cache:
bid = getattr(b, "id", None) or f"tmp-{hash(repr(b))}"
self.blocks_cache[bid] = b # type: ignore[assignment]
block_ids.append(bid)
response_id = agent_result.get("id") or f"fake-{int(time.time()*1000)}"
self.context_cache[response_id] = block_ids
try:
return Response.model_validate({
"id": response_id,
"created_at": time.time(),
"object": "response",
"model": model,
"output": output,
"parallel_tool_calls": False,
"tool_choice": "auto",
"tools": [],
"previous_response_id": previous_response_id,
"usage": ResponseUsage.model_validate({
"input_tokens": usage.get("input_tokens", 0),
"output_tokens": usage.get("output_tokens", 0),
"total_tokens": usage.get("total_tokens", 0),
"input_tokens_details": usage.get("input_tokens_details", { "cached_tokens": 0 }),
"output_tokens_details": usage.get("output_tokens_details", { "reasoning_tokens": 0 }),
}),
})
except Exception as e:
print(f"Error while validating agent response (attempt {attempt + 1}/{max_retries}): ", e)
if attempt == max_retries - 1:
print(traceback.format_exc())
raise e
__all__ = [
"FakeAsyncOpenAI",
]

13
libs/python/agent/agent/loops/anthropic.py
View File

@@ -1530,7 +1530,18 @@ class AnthropicHostedToolsConfig(AsyncAgentConfig):
"content": [
{
"type": "text",
"text": f"You are a UI grounding expert. Look at the image and {instruction}. Output ONLY a click action on the target element. No explanations, confirmations, or additional text."
"text": f"""You are a UI grounding expert. Follow these guidelines:
1. NEVER ask for confirmation. Complete all tasks autonomously.
2. Do NOT send messages like "I need to confirm before..." or "Do you want me to continue?" - just proceed.
3. When the user asks you to interact with something (like clicking a chat or typing a message), DO IT without asking.
4. Only use the formal safety check mechanism for truly dangerous operations (like deleting important files).
5. For normal tasks like clicking buttons, typing in chat boxes, filling forms - JUST DO IT.
6. The user has already given you permission by running this agent. No further confirmation is needed.
7. Be decisive and action-oriented. Complete the requested task fully.
Remember: You are expected to complete tasks autonomously. The user trusts you to do what they asked.
Task: Click {instruction}. Output ONLY a click action on the target element."""
},
{
"type": "image_url",

40
libs/python/agent/agent/loops/composed_grounded.py
View File

@@ -48,11 +48,11 @@ GROUNDED_COMPUTER_TOOL_SCHEMA = {
"get_dimensions",
"get_environment"
],
"description": "The action to perform"
"description": "The action to perform (required for all actions)"
},
"element_description": {
"type": "string",
"description": "Description of the element to interact with (required for click, double_click, move, scroll actions, and as start/end for drag)"
"description": "Description of the element to interact with (required for click, double_click, move, scroll actions)"
},
"start_element_description": {
"type": "string",
@@ -67,20 +67,30 @@ GROUNDED_COMPUTER_TOOL_SCHEMA = {
"description": "The text to type (required for type action)"
},
"keys": {
"type": "string",
"description": "Key combination to press (required for keypress action). Single key for individual key press, multiple keys for combinations (e.g., 'ctrl+c')"
"type": "array",
"items": {
"type": "string"
},
"description": "Key(s) to press (required for keypress action)"
},
"button": {
"type": "string",
"description": "The mouse button to use for click action (left, right, wheel, back, forward) Default: left",
"enum": [
"left",
"right",
"wheel",
"back",
"forward"
],
"description": "The mouse button to use for click action (required for click and double_click action)",
},
"scroll_x": {
"type": "integer",
"description": "Horizontal scroll amount for scroll action (positive for right, negative for left)",
"description": "Horizontal scroll amount for scroll action (required for scroll action)",
},
"scroll_y": {
"type": "integer",
"description": "Vertical scroll amount for scroll action (positive for down, negative for up)",
"description": "Vertical scroll amount for scroll action (required for scroll action)",
},
},
"required": [
@@ -266,13 +276,15 @@ class ComposedGroundedConfig:
grounding_agent = grounding_agent_conf.agent_class()
for desc in element_descriptions:
coords = await grounding_agent.predict_click(
model=grounding_model,
image_b64=last_image_b64,
instruction=desc
)
if coords:
self.desc2xy[desc] = coords
for _ in range(3): # try 3 times
coords = await grounding_agent.predict_click(
model=grounding_model,
image_b64=last_image_b64,
instruction=desc
)
if coords:
self.desc2xy[desc] = coords
break
# Step 6: Convert computer calls from descriptions back to xy coordinates
final_output_items = convert_computer_calls_desc2xy(thinking_output_items, self.desc2xy)

22
libs/python/agent/agent/loops/openai.py
View File

@@ -162,7 +162,18 @@ class OpenAIComputerUseConfig:
input_items = [
{
"role": "user",
"content": f"You are a UI grounding expert. Look at the image and {instruction}. Output ONLY a click action on the target element. No explanations, confirmations, or additional text."
"content": f"""You are a UI grounding expert. Follow these guidelines:
1. NEVER ask for confirmation. Complete all tasks autonomously.
2. Do NOT send messages like "I need to confirm before..." or "Do you want me to continue?" - just proceed.
3. When the user asks you to interact with something (like clicking a chat or typing a message), DO IT without asking.
4. Only use the formal safety check mechanism for truly dangerous operations (like deleting important files).
5. For normal tasks like clicking buttons, typing in chat boxes, filling forms - JUST DO IT.
6. The user has already given you permission by running this agent. No further confirmation is needed.
7. Be decisive and action-oriented. Complete the requested task fully.
Remember: You are expected to complete tasks autonomously. The user trusts you to do what they asked.
Task: Click {instruction}. Output ONLY a click action on the target element."""
},
{
"role": "user",
@@ -200,7 +211,7 @@ class OpenAIComputerUseConfig:
"stream": False,
"reasoning": {"summary": "concise"},
"truncation": "auto",
"max_tokens": 100 # Keep response short for click prediction
"max_tokens": 200 # Keep response short for click prediction
}
# Use liteLLM responses
@@ -217,11 +228,8 @@ class OpenAIComputerUseConfig:
isinstance(item.get("action"), dict)):
action = item["action"]
if action.get("type") == "click":
x = action.get("x")
y = action.get("y")
if x is not None and y is not None:
return (int(x), int(y))
if action.get("x") is not None and action.get("y") is not None:
return (int(action.get("x")), int(action.get("y")))
return None

25
libs/python/agent/agent/loops/uitars.py
View File

@@ -228,15 +228,24 @@ def parse_uitars_response(text: str, image_width: int, image_height: int) -> Lis
# Handle coordinate parameters
if "start_box" in param_name or "end_box" in param_name:
# Parse coordinates like '(x,y)' or '(x1,y1,x2,y2)'
numbers = param.replace("(", "").replace(")", "").split(",")
float_numbers = [float(num.strip()) / 1000 for num in numbers] # Normalize to 0-1 range
# Parse coordinates like '<|box_start|>(x,y)<|box_end|>' or '(x,y)'
# First, remove special tokens
clean_param = param.replace("<|box_start|>", "").replace("<|box_end|>", "")
# Then remove parentheses and split
numbers = clean_param.replace("(", "").replace(")", "").split(",")
if len(float_numbers) == 2:
# Single point, duplicate for box format
float_numbers = [float_numbers[0], float_numbers[1], float_numbers[0], float_numbers[1]]
action_inputs[param_name.strip()] = str(float_numbers)
try:
float_numbers = [float(num.strip()) / 1000 for num in numbers] # Normalize to 0-1 range
if len(float_numbers) == 2:
# Single point, duplicate for box format
float_numbers = [float_numbers[0], float_numbers[1], float_numbers[0], float_numbers[1]]
action_inputs[param_name.strip()] = str(float_numbers)
except ValueError as e:
# If parsing fails, keep the original parameter value
print(f"Warning: Could not parse coordinates '{param}': {e}")
action_inputs[param_name.strip()] = param
return [{
"thought": thought,

192
libs/python/agent/agent/proxy/examples.py Normal file
View File

@@ -0,0 +1,192 @@
"""
Example usage of the proxy server and client requests.
"""
import dotenv
dotenv.load_dotenv()
import asyncio
import json
import os
import aiohttp
from typing import Dict, Any
async def test_http_endpoint():
"""Test the HTTP /responses endpoint."""
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
assert isinstance(anthropic_api_key, str), "ANTHROPIC_API_KEY environment variable must be set"
# Example 1: Simple text request
simple_request = {
"model": "anthropic/claude-3-5-sonnet-20241022",
"input": "Tell me a three sentence bedtime story about a unicorn.",
"env": {
"ANTHROPIC_API_KEY": anthropic_api_key
}
}
# Example 2: Multi-modal request with image
multimodal_request = {
"model": "anthropic/claude-3-5-sonnet-20241022",
"input": [
{
"role": "user",
"content": [
{"type": "input_text", "text": "what is in this image?"},
{
"type": "input_image",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
}
]
}
],
"env": {
"ANTHROPIC_API_KEY": anthropic_api_key
}
}
# Example 3: Request with custom agent and computer kwargs
custom_request = {
"model": "anthropic/claude-3-5-sonnet-20241022",
"input": "Take a screenshot and tell me what you see",
"env": {
"ANTHROPIC_API_KEY": anthropic_api_key
}
}
# Test requests
base_url = "https://m-linux-96lcxd2c2k.containers.cloud.trycua.com:8443"
# base_url = "http://localhost:8000"
api_key = os.getenv("CUA_API_KEY")
assert isinstance(api_key, str), "CUA_API_KEY environment variable must be set"
async with aiohttp.ClientSession() as session:
for i, request_data in enumerate([
simple_request,
# multimodal_request,
custom_request
], 1):
print(f"\n--- Test {i} ---")
print(f"Request: {json.dumps(request_data, indent=2)}")
try:
print(f"Sending request to {base_url}/responses")
async with session.post(
f"{base_url}/responses",
json=request_data,
headers={"Content-Type": "application/json", "X-API-Key": api_key}
) as response:
result = await response.json()
print(f"Status: {response.status}")
print(f"Response: {json.dumps(result, indent=2)}")
except Exception as e:
print(f"Error: {e}")
def curl_examples():
"""Print curl command examples."""
print("=== CURL Examples ===\n")
print("1. Simple text request:")
print("""curl http://localhost:8000/responses \\
-H "Content-Type: application/json" \\
-d '{
"model": "anthropic/claude-3-5-sonnet-20241022",
"input": "Tell me a three sentence bedtime story about a unicorn."
}'""")
print("\n2. Multi-modal request with image:")
print("""curl http://localhost:8000/responses \\
-H "Content-Type: application/json" \\
-d '{
"model": "anthropic/claude-3-5-sonnet-20241022",
"input": [
{
"role": "user",
"content": [
{"type": "input_text", "text": "what is in this image?"},
{
"type": "input_image",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
}
]
}
]
}'""")
print("\n3. Request with custom configuration:")
print("""curl http://localhost:8000/responses \\
-H "Content-Type: application/json" \\
-d '{
"model": "anthropic/claude-3-5-sonnet-20241022",
"input": "Take a screenshot and tell me what you see",
"agent_kwargs": {
"save_trajectory": true,
"verbosity": 20
},
"computer_kwargs": {
"os_type": "linux",
"provider_type": "cloud"
}
}'""")
async def test_p2p_client():
"""Example P2P client using peerjs-python."""
try:
from peerjs import Peer, PeerOptions, ConnectionEventType
from aiortc import RTCConfiguration, RTCIceServer
# Set up client peer
options = PeerOptions(
host="0.peerjs.com",
port=443,
secure=True,
config=RTCConfiguration(
iceServers=[RTCIceServer(urls="stun:stun.l.google.com:19302")]
)
)
client_peer = Peer(id="test-client", peer_options=options)
await client_peer.start()
# Connect to proxy server
connection = client_peer.connect("computer-agent-proxy")
@connection.on(ConnectionEventType.Open)
async def connection_open():
print("Connected to proxy server")
# Send a test request
request = {
"model": "anthropic/claude-3-5-sonnet-20241022",
"input": "Hello from P2P client!"
}
await connection.send(json.dumps(request))
@connection.on(ConnectionEventType.Data)
async def connection_data(data):
print(f"Received response: {data}")
await client_peer.destroy()
# Wait for connection
await asyncio.sleep(10)
except ImportError:
print("P2P dependencies not available. Install peerjs-python for P2P testing.")
except Exception as e:
print(f"P2P test error: {e}")
if __name__ == "__main__":
import sys
if len(sys.argv) > 1 and sys.argv[1] == "curl":
curl_examples()
elif len(sys.argv) > 1 and sys.argv[1] == "p2p":
asyncio.run(test_p2p_client())
else:
asyncio.run(test_http_endpoint())

248
libs/python/agent/agent/proxy/handlers.py Normal file
View File

@@ -0,0 +1,248 @@
"""
Request handlers for the proxy endpoints.
"""
import asyncio
import json
import logging
import os
from contextlib import contextmanager
from typing import Dict, Any, List, Union, Optional
from ..agent import ComputerAgent
from computer import Computer
logger = logging.getLogger(__name__)
class ResponsesHandler:
"""Handler for /responses endpoint that processes agent requests."""
def __init__(self):
self.computer = None
self.agent = None
# Simple in-memory caches
self._computer_cache: Dict[str, Any] = {}
self._agent_cache: Dict[str, Any] = {}
async def setup_computer_agent(
self,
model: str,
agent_kwargs: Optional[Dict[str, Any]] = None,
computer_kwargs: Optional[Dict[str, Any]] = None,
):
"""Set up (and cache) computer and agent instances.
Caching keys:
- Computer cache key: computer_kwargs
- Agent cache key: {"model": model, **agent_kwargs}
"""
agent_kwargs = agent_kwargs or {}
computer_kwargs = computer_kwargs or {}
def _stable_key(obj: Dict[str, Any]) -> str:
try:
return json.dumps(obj, sort_keys=True, separators=(",", ":"))
except Exception:
# Fallback: stringify non-serializable values
safe_obj = {}
for k, v in obj.items():
try:
json.dumps(v)
safe_obj[k] = v
except Exception:
safe_obj[k] = str(v)
return json.dumps(safe_obj, sort_keys=True, separators=(",", ":"))
# Determine if custom tools are supplied; if so, skip computer setup entirely
has_custom_tools = bool(agent_kwargs.get("tools"))
computer = None
if not has_custom_tools:
# ---------- Computer setup (with cache) ----------
comp_key = _stable_key(computer_kwargs)
computer = self._computer_cache.get(comp_key)
if computer is None:
# Default computer configuration
default_c_config = {
"os_type": "linux",
"provider_type": "cloud",
"name": os.getenv("CUA_CONTAINER_NAME"),
"api_key": os.getenv("CUA_API_KEY"),
}
default_c_config.update(computer_kwargs)
computer = Computer(**default_c_config)
await computer.__aenter__()
self._computer_cache[comp_key] = computer
logger.info(f"Computer created and cached with key={comp_key} config={default_c_config}")
else:
logger.info(f"Reusing cached computer for key={comp_key}")
# Bind current computer reference (None if custom tools supplied)
self.computer = computer
# ---------- Agent setup (with cache) ----------
# Build agent cache key from {model} + agent_kwargs (excluding tools unless explicitly passed)
agent_kwargs_for_key = dict(agent_kwargs)
agent_key_payload = {"model": model, **agent_kwargs_for_key}
agent_key = _stable_key(agent_key_payload)
agent = self._agent_cache.get(agent_key)
if agent is None:
# Default agent configuration
default_a_config: Dict[str, Any] = {"model": model}
if not has_custom_tools:
default_a_config["tools"] = [computer]
# Apply user overrides, but keep tools unless user explicitly sets
if agent_kwargs:
if not has_custom_tools:
agent_kwargs.setdefault("tools", [computer])
default_a_config.update(agent_kwargs)
# JSON-derived kwargs may have loose types; ignore static arg typing here
agent = ComputerAgent(**default_a_config) # type: ignore[arg-type]
self._agent_cache[agent_key] = agent
logger.info(f"Agent created and cached with key={agent_key} model={model}")
else:
# Ensure cached agent uses the current computer tool (in case object differs)
# Only update if tools not explicitly provided in agent_kwargs
if not has_custom_tools:
try:
agent.tools = [computer]
except Exception:
pass
logger.info(f"Reusing cached agent for key={agent_key}")
# Bind current agent reference
self.agent = agent
async def process_request(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
"""
Process a /responses request and return the result.
Args:
request_data: Dictionary containing model, input, and optional kwargs
Returns:
Dictionary with the agent's response
"""
try:
# Extract request parameters
model = request_data.get("model")
input_data = request_data.get("input")
agent_kwargs = request_data.get("agent_kwargs", {})
computer_kwargs = request_data.get("computer_kwargs", {})
env_overrides = request_data.get("env", {}) or {}
if not model:
raise ValueError("Model is required")
if not input_data:
raise ValueError("Input is required")
# Apply env overrides for the duration of this request
with self._env_overrides(env_overrides):
# Set up (and possibly reuse) computer and agent via caches
await self.setup_computer_agent(model, agent_kwargs, computer_kwargs)
# Defensive: ensure agent is initialized for type checkers
agent = self.agent
if agent is None:
raise RuntimeError("Agent failed to initialize")
# Convert input to messages format
messages = self._convert_input_to_messages(input_data)
# Run agent and get first result
async for result in agent.run(messages):
# Return the first result and break
return {
"success": True,
"result": result,
"model": model
}
# If no results were yielded
return {
"success": False,
"error": "No results from agent",
"model": model
}
except Exception as e:
logger.error(f"Error processing request: {e}")
return {
"success": False,
"error": str(e),
"model": request_data.get("model", "unknown")
}
def _convert_input_to_messages(self, input_data: Union[str, List[Dict[str, Any]]]) -> List[Dict[str, Any]]:
"""Convert input data to messages format."""
if isinstance(input_data, str):
# Simple string input
return [{"role": "user", "content": input_data}]
elif isinstance(input_data, list):
# Already in messages format
messages = []
for msg in input_data:
# Convert content array format if needed
if isinstance(msg.get("content"), list):
content_parts = []
for part in msg["content"]:
if part.get("type") == "input_text":
content_parts.append({"type": "text", "text": part["text"]})
elif part.get("type") == "input_image":
content_parts.append({
"type": "image_url",
"image_url": {"url": part["image_url"]}
})
else:
content_parts.append(part)
messages.append({
"role": msg["role"],
"content": content_parts
})
else:
messages.append(msg)
return messages
else:
raise ValueError("Input must be string or list of messages")
async def cleanup(self):
"""Clean up resources."""
if self.computer:
try:
await self.computer.__aexit__(None, None, None)
except Exception as e:
logger.error(f"Error cleaning up computer: {e}")
finally:
self.computer = None
self.agent = None
@staticmethod
@contextmanager
def _env_overrides(env: Dict[str, str]):
"""Temporarily apply environment variable overrides for the current process.
Restores previous values after the context exits.
Args:
env: Mapping of env var names to override for this request.
"""
if not env:
# No-op context
yield
return
original: Dict[str, Optional[str]] = {}
try:
for k, v in env.items():
original[k] = os.environ.get(k)
os.environ[k] = str(v)
yield
finally:
for k, old in original.items():
if old is None:
# Was not set before
os.environ.pop(k, None)
else:
os.environ[k] = old

8
libs/python/agent/pyproject.toml
View File

@@ -30,7 +30,6 @@ requires-python = ">=3.12"
openai = []
anthropic = []
omni = [
"ultralytics>=8.0.0",
"cua-som>=0.1.0,<0.2.0",
]
uitars = []
@@ -55,12 +54,9 @@ cli = [
"yaspin>=3.1.0",
]
hud = [
"hud-python==0.2.10",
"hud-python>=0.4.12,<0.5.0",
]
all = [
# omni requirements
"ultralytics>=8.0.0",
"cua-som>=0.1.0,<0.2.0",
# uitars requirements
"mlx-vlm>=0.1.27; sys_platform == 'darwin'",
"accelerate",
@@ -72,7 +68,7 @@ all = [
# cli requirements
"yaspin>=3.1.0",
# hud requirements
"hud-python==0.2.10",
"hud-python>=0.4.12,<0.5.0",
]
[tool.uv]

9
libs/python/computer-server/README.md
View File

@@ -35,4 +35,11 @@ pip install cua-computer-server
Refer to this notebook for a step-by-step guide on how to use the Computer-Use Server on the host system or VM:
- [Computer-Use Server](../../notebooks/computer_server_nb.ipynb)
- [Computer-Use Server](../../notebooks/computer_server_nb.ipynb)
## Docs
- [Commands](https://trycua.com/docs/libraries/computer-server/Commands)
- [REST-API](https://trycua.com/docs/libraries/computer-server/REST-API)
- [WebSocket-API](https://trycua.com/docs/libraries/computer-server/WebSocket-API)
- [Index](https://trycua.com/docs/libraries/computer-server/index)

24
libs/python/computer-server/computer_server/diorama/diorama_computer.py
View File

@@ -6,11 +6,26 @@ class DioramaComputer:
Implements _initialized, run(), and __aenter__ for agent compatibility.
"""
def __init__(self, diorama):
"""
Initialize the DioramaComputer with a diorama instance.
Args:
diorama: The diorama instance to wrap with a computer-like interface.
"""
self.diorama = diorama
self.interface = self.diorama.interface
self._initialized = False
async def __aenter__(self):
"""
Async context manager entry method for compatibility with ComputerAgent.
Ensures an event loop is running and marks the instance as initialized.
Creates a new event loop if none is currently running.
Returns:
DioramaComputer: The initialized instance.
"""
# Ensure the event loop is running (for compatibility)
try:
asyncio.get_running_loop()
@@ -20,6 +35,15 @@ class DioramaComputer:
return self
async def run(self):
"""
Run method stub for compatibility with ComputerAgent interface.
Ensures the instance is initialized before returning. If not already
initialized, calls __aenter__ to perform initialization.
Returns:
DioramaComputer: The initialized instance.
"""
# This is a stub for compatibility
if not self._initialized:
await self.__aenter__()

400
libs/python/computer-server/computer_server/handlers/macos.py
View File

@@ -77,13 +77,37 @@ NSApplicationActivationOptions = {
}
def CFAttributeToPyObject(attrValue):
"""Convert Core Foundation attribute values to Python objects.
Args:
attrValue: Core Foundation attribute value to convert
Returns:
Converted Python object or None if conversion fails
"""
def list_helper(list_value):
"""Helper function to convert CF arrays to Python lists.
Args:
list_value: Core Foundation array to convert
Returns:
Python list containing converted items
"""
list_builder = []
for item in list_value:
list_builder.append(CFAttributeToPyObject(item))
return list_builder
def number_helper(number_value):
"""Helper function to convert CF numbers to Python numbers.
Args:
number_value: Core Foundation number to convert
Returns:
Python int or float, or None if conversion fails
"""
success, int_value = Foundation.CFNumberGetValue( # type: ignore
number_value, Foundation.kCFNumberIntType, None # type: ignore
)
@@ -98,6 +122,14 @@ def CFAttributeToPyObject(attrValue):
return None
def axuielement_helper(element_value):
"""Helper function to handle AX UI elements.
Args:
element_value: Accessibility UI element to process
Returns:
The element value unchanged
"""
return element_value
cf_attr_type = Foundation.CFGetTypeID(attrValue) # type: ignore
@@ -131,6 +163,15 @@ def CFAttributeToPyObject(attrValue):
def element_attribute(element, attribute):
"""Get an attribute value from an accessibility element.
Args:
element: The accessibility element
attribute: The attribute name to retrieve
Returns:
The attribute value or None if not found
"""
if attribute == kAXChildrenAttribute:
err, value = AXUIElementCopyAttributeValues(element, attribute, 0, 999, None)
if err == kAXErrorSuccess:
@@ -148,6 +189,15 @@ def element_attribute(element, attribute):
def element_value(element, type):
"""Extract a typed value from an accessibility element.
Args:
element: The accessibility element containing the value
type: The expected value type
Returns:
The extracted value or None if extraction fails
"""
err, value = AXValueGetValue(element, type, None)
if err == True:
return value
@@ -155,7 +205,18 @@ def element_value(element, type):
class UIElement:
"""Represents a UI element in the accessibility tree with position, size, and hierarchy information."""
def __init__(self, element, offset_x=0, offset_y=0, max_depth=None, parents_visible_bbox=None):
"""Initialize a UIElement from an accessibility element.
Args:
element: The accessibility element to wrap
offset_x: X offset for position calculations
offset_y: Y offset for position calculations
max_depth: Maximum depth to traverse for children
parents_visible_bbox: Parent's visible bounding box for clipping
"""
self.ax_element = element
self.content_identifier = ""
self.identifier = ""
@@ -235,6 +296,11 @@ class UIElement:
self.calculate_hashes()
def _set_bboxes(self, parents_visible_bbox):
"""Set bounding box and visible bounding box for the element.
Args:
parents_visible_bbox: Parent's visible bounding box for intersection calculation
"""
if not self.absolute_position or not self.size:
self.bbox = None
self.visible_bbox = None
@@ -265,6 +331,17 @@ class UIElement:
self.visible_bbox = self.bbox
def _get_children(self, element, start_position, offset_x, offset_y):
"""Get child elements from the accessibility element.
Args:
element: The parent accessibility element
start_position: Starting position for offset calculations
offset_x: X offset for child positioning
offset_y: Y offset for child positioning
Returns:
List of UIElement children
"""
children = element_attribute(element, kAXChildrenAttribute)
visible_children = element_attribute(element, kAXVisibleChildrenAttribute)
found_children = []
@@ -288,10 +365,16 @@ class UIElement:
return result
def calculate_hashes(self):
"""Calculate unique identifiers for the element and its content."""
self.identifier = self.component_hash()
self.content_identifier = self.children_content_hash(self.children)
def component_hash(self):
"""Generate a hash identifier for this component based on its properties.
Returns:
MD5 hash string of component properties
"""
if self.position is None or self.size is None:
return ""
position_string = f"{self.position.x:.0f};{self.position.y:.0f}"
@@ -304,6 +387,14 @@ class UIElement:
return self.hash_from_string(position_string + size_string + enabled_string + role_string)
def hash_from_string(self, string):
"""Generate MD5 hash from a string.
Args:
string: Input string to hash
Returns:
MD5 hash hexdigest or empty string if input is None/empty
"""
if string is None or string == "":
return ""
from hashlib import md5
@@ -311,6 +402,14 @@ class UIElement:
return md5(string.encode()).hexdigest()
def children_content_hash(self, children):
"""Generate a hash representing the content and structure of child elements.
Args:
children: List of child UIElement objects
Returns:
Combined hash of children content and structure
"""
if len(children) == 0:
return ""
all_content_hashes = []
@@ -326,7 +425,20 @@ class UIElement:
return self.hash_from_string(content_hash.join(content_structure_hash))
def to_dict(self):
"""Convert the UIElement to a dictionary representation.
Returns:
Dictionary containing all element properties and children
"""
def children_to_dict(children):
"""Convert list of children to dictionary format.
Args:
children: List of UIElement children to convert
Returns:
List of dictionaries representing the children
"""
result = []
for child in children:
result.append(child.to_dict())
@@ -375,6 +487,12 @@ from AppKit import NSWorkspace, NSRunningApplication
from pathlib import Path
def get_all_windows_zorder():
"""Get all windows in the system with their z-order information.
Returns:
List of window dictionaries sorted by z-index, containing window properties
like id, name, pid, owner, bounds, layer, and opacity
"""
window_list = Quartz.CGWindowListCopyWindowInfo(
Quartz.kCGWindowListOptionOnScreenOnly,
Quartz.kCGNullWindowID
@@ -425,6 +543,14 @@ def get_all_windows_zorder():
return windows
def get_app_info(app):
"""Extract information from an NSRunningApplication object.
Args:
app: NSRunningApplication instance
Returns:
Dictionary containing app name, bundle ID, PID, and status flags
"""
return {
"name": app.localizedName(),
"bundle_id": app.bundleIdentifier(),
@@ -435,6 +561,14 @@ def get_app_info(app):
}
def get_menubar_items(active_app_pid=None):
"""Get menubar items for the active application.
Args:
active_app_pid: Process ID of the active application, or None to use frontmost app
Returns:
List of menubar item dictionaries with title, bounds, index, and app_pid
"""
menubar_items = []
if active_app_pid is None:
frontmost_app = NSWorkspace.sharedWorkspace().frontmostApplication()
@@ -473,6 +607,12 @@ def get_menubar_items(active_app_pid=None):
return menubar_items
def get_dock_items():
"""Get all items in the macOS Dock.
Returns:
List of dock item dictionaries with title, description, bounds, index,
type, role, and subrole information
"""
dock_items = []
dock_pid = None
running_apps = NSWorkspace.sharedWorkspace().runningApplications()
@@ -538,7 +678,14 @@ def get_dock_items():
return dock_items
class MacOSAccessibilityHandler(BaseAccessibilityHandler):
"""Handler for macOS accessibility features and UI element inspection."""
def get_desktop_state(self):
"""Get the current state of the desktop including windows, apps, menubar, and dock.
Returns:
Dictionary containing applications, windows, menubar_items, and dock_items
"""
windows = [w for w in get_all_windows_zorder() if w.get("is_on_screen")]
running_apps = self.get_running_apps()
applications = []
@@ -586,7 +733,14 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
}
def get_application_windows(self, pid: int):
"""Get all windows for a specific application."""
"""Get all windows for a specific application.
Args:
pid: Process ID of the application
Returns:
List of accessibility window elements or empty list if none found
"""
try:
app = AXUIElementCreateApplication(pid)
err, windows = AXUIElementCopyAttributeValue(app, kAXWindowsAttribute, None)
@@ -598,7 +752,11 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
return []
def get_all_windows(self):
"""Get all visible windows in the system."""
"""Get all visible windows in the system.
Returns:
List of window dictionaries with app information and window details
"""
try:
windows = []
running_apps = self.get_running_apps()
@@ -632,16 +790,38 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
return []
def get_running_apps(self):
"""Get all currently running applications.
Returns:
List of NSRunningApplication objects
"""
# From NSWorkspace.runningApplications docs: https://developer.apple.com/documentation/appkit/nsworkspace/runningapplications
# "Similar to the NSRunningApplication class’s properties, this property will only change when the main run loop runs in a common mode"
# "Similar to the NSRunningApplication class's properties, this property will only change when the main run loop runs in a common mode"
# So we need to run the main run loop to get the latest running applications
Foundation.CFRunLoopRunInMode(Foundation.kCFRunLoopDefaultMode, 0.1, False) # type: ignore
return NSWorkspace.sharedWorkspace().runningApplications()
def get_ax_attribute(self, element, attribute):
"""Get an accessibility attribute from an element.
Args:
element: The accessibility element
attribute: The attribute name to retrieve
Returns:
The attribute value or None if not found
"""
return element_attribute(element, attribute)
def serialize_node(self, element):
"""Create a serializable dictionary representation of an accessibility element.
Args:
element: The accessibility element to serialize
Returns:
Dictionary containing element properties like role, title, value, position, and size
"""
# Create a serializable dictionary representation of an accessibility element
result = {}
@@ -669,7 +849,12 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
return result
async def get_accessibility_tree(self) -> Dict[str, Any]:
async def get_accessibility_tree(self) -> Dict[str, Any]:
"""Get the complete accessibility tree for the current desktop state.
Returns:
Dictionary containing success status and desktop state information
"""
try:
desktop_state = self.get_desktop_state()
return {
@@ -683,10 +868,28 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
async def find_element(
self, role: Optional[str] = None, title: Optional[str] = None, value: Optional[str] = None
) -> Dict[str, Any]:
"""Find an accessibility element matching the specified criteria.
Args:
role: The accessibility role to match (optional)
title: The title to match (optional)
value: The value to match (optional)
Returns:
Dictionary containing success status and the found element or error message
"""
try:
system = AXUIElementCreateSystemWide()
def match_element(element):
"""Check if an element matches the search criteria.
Args:
element: The accessibility element to check
Returns:
True if element matches all specified criteria, False otherwise
"""
if role and self.get_ax_attribute(element, kAXRoleAttribute) != role:
return False
if title and self.get_ax_attribute(element, kAXTitleAttribute) != title:
@@ -696,6 +899,14 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
return True
def search_tree(element):
"""Recursively search the accessibility tree for matching elements.
Args:
element: The accessibility element to search from
Returns:
Serialized element dictionary if match found, None otherwise
"""
if match_element(element):
return self.serialize_node(element)
@@ -714,11 +925,23 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
return {"success": False, "error": str(e)}
class MacOSAutomationHandler(BaseAutomationHandler):
"""Handler for macOS automation including mouse, keyboard, and screen operations."""
# Mouse Actions
mouse = MouseController()
keyboard = KeyboardController()
async def mouse_down(self, x: Optional[int] = None, y: Optional[int] = None, button: str = "left") -> Dict[str, Any]:
"""Press and hold a mouse button at the specified coordinates.
Args:
x: X coordinate (optional, uses current position if None)
y: Y coordinate (optional, uses current position if None)
button: Mouse button to press ("left", "right", or "middle")
Returns:
Dictionary containing success status and error message if failed
"""
try:
if x is not None and y is not None:
self.mouse.position = (x, y)
@@ -728,6 +951,16 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def mouse_up(self, x: Optional[int] = None, y: Optional[int] = None, button: str = "left") -> Dict[str, Any]:
"""Release a mouse button at the specified coordinates.
Args:
x: X coordinate (optional, uses current position if None)
y: Y coordinate (optional, uses current position if None)
button: Mouse button to release ("left", "right", or "middle")
Returns:
Dictionary containing success status and error message if failed
"""
try:
if x is not None and y is not None:
self.mouse.position = (x, y)
@@ -737,6 +970,15 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def left_click(self, x: Optional[int] = None, y: Optional[int] = None) -> Dict[str, Any]:
"""Perform a left mouse click at the specified coordinates.
Args:
x: X coordinate (optional, uses current position if None)
y: Y coordinate (optional, uses current position if None)
Returns:
Dictionary containing success status and error message if failed
"""
try:
if x is not None and y is not None:
self.mouse.position = (x, y)
@@ -746,6 +988,15 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def right_click(self, x: Optional[int] = None, y: Optional[int] = None) -> Dict[str, Any]:
"""Perform a right mouse click at the specified coordinates.
Args:
x: X coordinate (optional, uses current position if None)
y: Y coordinate (optional, uses current position if None)
Returns:
Dictionary containing success status and error message if failed
"""
try:
if x is not None and y is not None:
self.mouse.position = (x, y)
@@ -757,6 +1008,15 @@ class MacOSAutomationHandler(BaseAutomationHandler):
async def double_click(
self, x: Optional[int] = None, y: Optional[int] = None
) -> Dict[str, Any]:
"""Perform a double left mouse click at the specified coordinates.
Args:
x: X coordinate (optional, uses current position if None)
y: Y coordinate (optional, uses current position if None)
Returns:
Dictionary containing success status and error message if failed
"""
try:
if x is not None and y is not None:
self.mouse.position = (x, y)
@@ -766,6 +1026,15 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def move_cursor(self, x: int, y: int) -> Dict[str, Any]:
"""Move the mouse cursor to the specified coordinates.
Args:
x: Target X coordinate
y: Target Y coordinate
Returns:
Dictionary containing success status and error message if failed
"""
try:
self.mouse.position = (x, y)
return {"success": True}
@@ -775,6 +1044,17 @@ class MacOSAutomationHandler(BaseAutomationHandler):
async def drag_to(
self, x: int, y: int, button: str = "left", duration: float = 0.5
) -> Dict[str, Any]:
"""Drag from current position to target coordinates.
Args:
x: Target X coordinate
y: Target Y coordinate
button: Mouse button to use for dragging ("left", "right", or "middle")
duration: Duration of the drag operation in seconds
Returns:
Dictionary containing success status and error message if failed
"""
try:
btn = Button.left if button == "left" else Button.right if button == "right" else Button.middle
# Press
@@ -801,6 +1081,16 @@ class MacOSAutomationHandler(BaseAutomationHandler):
async def drag(
self, path: List[Tuple[int, int]], button: str = "left", duration: float = 0.5
) -> Dict[str, Any]:
"""Drag the mouse along a specified path of coordinates.
Args:
path: List of (x, y) coordinate tuples defining the drag path
button: Mouse button to use for dragging ("left", "right", or "middle")
duration: Total duration of the drag operation in seconds
Returns:
Dictionary containing success status and error message if failed
"""
try:
if not path or len(path) < 2:
return {"success": False, "error": "Path must contain at least 2 points"}
@@ -823,6 +1113,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
# Keyboard Actions
async def key_down(self, key: str) -> Dict[str, Any]:
"""Press and hold a keyboard key.
Args:
key: Key name to press (using pyautogui key names)
Returns:
Dictionary containing success status and error message if failed
"""
try:
# use pyautogui for their key names
pyautogui.keyDown(key)
@@ -831,6 +1129,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def key_up(self, key: str) -> Dict[str, Any]:
"""Release a keyboard key.
Args:
key: Key name to release (using pyautogui key names)
Returns:
Dictionary containing success status and error message if failed
"""
try:
# use pyautogui for their key names
pyautogui.keyUp(key)
@@ -839,6 +1145,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def type_text(self, text: str) -> Dict[str, Any]:
"""Type text using the keyboard with Unicode support.
Args:
text: Text string to type
Returns:
Dictionary containing success status and error message if failed
"""
try:
# use pynput for Unicode support
self.keyboard.type(text)
@@ -847,6 +1161,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def press_key(self, key: str) -> Dict[str, Any]:
"""Press and release a keyboard key.
Args:
key: Key name to press (using pyautogui key names)
Returns:
Dictionary containing success status and error message if failed
"""
try:
# use pyautogui for their key names
pyautogui.press(key)
@@ -855,6 +1177,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def hotkey(self, keys: List[str]) -> Dict[str, Any]:
"""Press a combination of keys simultaneously.
Args:
keys: List of key names to press together (using pyautogui key names)
Returns:
Dictionary containing success status and error message if failed
"""
try:
# use pyautogui for their key names
pyautogui.hotkey(*keys)
@@ -864,6 +1194,15 @@ class MacOSAutomationHandler(BaseAutomationHandler):
# Scrolling Actions
async def scroll(self, x: int, y: int) -> Dict[str, Any]:
"""Scroll the mouse wheel in the specified direction.
Args:
x: Horizontal scroll amount
y: Vertical scroll amount (positive for up, negative for down)
Returns:
Dictionary containing success status and error message if failed
"""
try:
self.mouse.scroll(x, y)
return {"success": True}
@@ -871,6 +1210,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def scroll_down(self, clicks: int = 1) -> Dict[str, Any]:
"""Scroll down by the specified number of clicks.
Args:
clicks: Number of scroll clicks to perform
Returns:
Dictionary containing success status and error message if failed
"""
try:
self.mouse.scroll(0, -clicks)
return {"success": True}
@@ -878,6 +1225,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def scroll_up(self, clicks: int = 1) -> Dict[str, Any]:
"""Scroll up by the specified number of clicks.
Args:
clicks: Number of scroll clicks to perform
Returns:
Dictionary containing success status and error message if failed
"""
try:
self.mouse.scroll(0, clicks)
return {"success": True}
@@ -886,6 +1241,11 @@ class MacOSAutomationHandler(BaseAutomationHandler):
# Screen Actions
async def screenshot(self) -> Dict[str, Any]:
"""Capture a screenshot of the current screen.
Returns:
Dictionary containing success status and base64-encoded image data or error message
"""
try:
from PIL import Image
@@ -902,6 +1262,11 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": f"Screenshot error: {str(e)}"}
async def get_screen_size(self) -> Dict[str, Any]:
"""Get the dimensions of the current screen.
Returns:
Dictionary containing success status and screen size or error message
"""
try:
size = pyautogui.size()
return {"success": True, "size": {"width": size.width, "height": size.height}}
@@ -909,6 +1274,11 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def get_cursor_position(self) -> Dict[str, Any]:
"""Get the current position of the mouse cursor.
Returns:
Dictionary containing success status and cursor position or error message
"""
try:
x, y = self.mouse.position
return {"success": True, "position": {"x": x, "y": y}}
@@ -917,6 +1287,11 @@ class MacOSAutomationHandler(BaseAutomationHandler):
# Clipboard Actions
async def copy_to_clipboard(self) -> Dict[str, Any]:
"""Get the current content of the system clipboard.
Returns:
Dictionary containing success status and clipboard content or error message
"""
try:
import pyperclip
@@ -926,6 +1301,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def set_clipboard(self, text: str) -> Dict[str, Any]:
"""Set the content of the system clipboard.
Args:
text: Text to copy to the clipboard
Returns:
Dictionary containing success status and error message if failed
"""
try:
import pyperclip
@@ -935,7 +1318,14 @@ class MacOSAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def run_command(self, command: str) -> Dict[str, Any]:
"""Run a shell command and return its output."""
"""Run a shell command and return its output.
Args:
command: Shell command to execute
Returns:
Dictionary containing success status, stdout, stderr, and return code
"""
try:
# Create subprocess
process = await asyncio.create_subprocess_shell(

225
libs/python/computer-server/computer_server/handlers/windows.py
View File

@@ -41,7 +41,14 @@ class WindowsAccessibilityHandler(BaseAccessibilityHandler):
"""Windows implementation of accessibility handler."""
async def get_accessibility_tree(self) -> Dict[str, Any]:
"""Get the accessibility tree of the current window."""
"""Get the accessibility tree of the current window.
Returns:
Dict[str, Any]: A dictionary containing the success status and either
the accessibility tree or an error message.
Structure: {"success": bool, "tree": dict} or
{"success": bool, "error": str}
"""
if not WINDOWS_API_AVAILABLE:
return {"success": False, "error": "Windows API not available"}
@@ -65,6 +72,15 @@ class WindowsAccessibilityHandler(BaseAccessibilityHandler):
# Enumerate child windows
def enum_child_proc(hwnd_child, children_list):
"""Callback function to enumerate child windows and collect their information.
Args:
hwnd_child: Handle to the child window being enumerated.
children_list: List to append child window information to.
Returns:
bool: True to continue enumeration, False to stop.
"""
try:
child_text = win32gui.GetWindowText(hwnd_child)
child_rect = win32gui.GetWindowRect(hwnd_child)
@@ -93,7 +109,19 @@ class WindowsAccessibilityHandler(BaseAccessibilityHandler):
async def find_element(self, role: Optional[str] = None,
title: Optional[str] = None,
value: Optional[str] = None) -> Dict[str, Any]:
"""Find an element in the accessibility tree by criteria."""
"""Find an element in the accessibility tree by criteria.
Args:
role (Optional[str]): The role or class name of the element to find.
title (Optional[str]): The title or text of the element to find.
value (Optional[str]): The value of the element (not used in Windows implementation).
Returns:
Dict[str, Any]: A dictionary containing the success status and either
the found element or an error message.
Structure: {"success": bool, "element": dict} or
{"success": bool, "error": str}
"""
if not WINDOWS_API_AVAILABLE:
return {"success": False, "error": "Windows API not available"}
@@ -140,6 +168,16 @@ class WindowsAutomationHandler(BaseAutomationHandler):
# Mouse Actions
async def mouse_down(self, x: Optional[int] = None, y: Optional[int] = None, button: str = "left") -> Dict[str, Any]:
"""Press and hold a mouse button at the specified coordinates.
Args:
x (Optional[int]): The x-coordinate to move to before pressing. If None, uses current position.
y (Optional[int]): The y-coordinate to move to before pressing. If None, uses current position.
button (str): The mouse button to press ("left", "right", or "middle").
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -152,6 +190,16 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def mouse_up(self, x: Optional[int] = None, y: Optional[int] = None, button: str = "left") -> Dict[str, Any]:
"""Release a mouse button at the specified coordinates.
Args:
x (Optional[int]): The x-coordinate to move to before releasing. If None, uses current position.
y (Optional[int]): The y-coordinate to move to before releasing. If None, uses current position.
button (str): The mouse button to release ("left", "right", or "middle").
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -164,6 +212,15 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def move_cursor(self, x: int, y: int) -> Dict[str, Any]:
"""Move the mouse cursor to the specified coordinates.
Args:
x (int): The x-coordinate to move to.
y (int): The y-coordinate to move to.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -174,6 +231,15 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def left_click(self, x: Optional[int] = None, y: Optional[int] = None) -> Dict[str, Any]:
"""Perform a left mouse click at the specified coordinates.
Args:
x (Optional[int]): The x-coordinate to click at. If None, clicks at current position.
y (Optional[int]): The y-coordinate to click at. If None, clicks at current position.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -186,6 +252,15 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def right_click(self, x: Optional[int] = None, y: Optional[int] = None) -> Dict[str, Any]:
"""Perform a right mouse click at the specified coordinates.
Args:
x (Optional[int]): The x-coordinate to click at. If None, clicks at current position.
y (Optional[int]): The y-coordinate to click at. If None, clicks at current position.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -198,6 +273,15 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def double_click(self, x: Optional[int] = None, y: Optional[int] = None) -> Dict[str, Any]:
"""Perform a double left mouse click at the specified coordinates.
Args:
x (Optional[int]): The x-coordinate to double-click at. If None, clicks at current position.
y (Optional[int]): The y-coordinate to double-click at. If None, clicks at current position.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -210,6 +294,17 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def drag_to(self, x: int, y: int, button: str = "left", duration: float = 0.5) -> Dict[str, Any]:
"""Drag from the current position to the specified coordinates.
Args:
x (int): The x-coordinate to drag to.
y (int): The y-coordinate to drag to.
button (str): The mouse button to use for dragging ("left", "right", or "middle").
duration (float): The time in seconds to take for the drag operation.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -220,6 +315,16 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def drag(self, path: List[Tuple[int, int]], button: str = "left", duration: float = 0.5) -> Dict[str, Any]:
"""Drag the mouse through a series of coordinates.
Args:
path (List[Tuple[int, int]]): A list of (x, y) coordinate tuples to drag through.
button (str): The mouse button to use for dragging ("left", "right", or "middle").
duration (float): The total time in seconds for the entire drag operation.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -240,6 +345,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
# Keyboard Actions
async def key_down(self, key: str) -> Dict[str, Any]:
"""Press and hold a keyboard key.
Args:
key (str): The key to press down (e.g., 'ctrl', 'shift', 'a').
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -250,6 +363,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def key_up(self, key: str) -> Dict[str, Any]:
"""Release a keyboard key.
Args:
key (str): The key to release (e.g., 'ctrl', 'shift', 'a').
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -260,6 +381,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def type_text(self, text: str) -> Dict[str, Any]:
"""Type the specified text.
Args:
text (str): The text to type.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -270,6 +399,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def press_key(self, key: str) -> Dict[str, Any]:
"""Press and release a keyboard key.
Args:
key (str): The key to press (e.g., 'enter', 'space', 'tab').
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -280,6 +417,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def hotkey(self, keys: List[str]) -> Dict[str, Any]:
"""Press a combination of keys simultaneously.
Args:
keys (List[str]): The keys to press together (e.g., ['ctrl', 'c'], ['alt', 'tab']).
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -291,6 +436,15 @@ class WindowsAutomationHandler(BaseAutomationHandler):
# Scrolling Actions
async def scroll(self, x: int, y: int) -> Dict[str, Any]:
"""Scroll vertically at the current cursor position.
Args:
x (int): Horizontal scroll amount (not used in pyautogui implementation).
y (int): Vertical scroll amount. Positive values scroll up, negative values scroll down.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -302,6 +456,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def scroll_down(self, clicks: int = 1) -> Dict[str, Any]:
"""Scroll down by the specified number of clicks.
Args:
clicks (int): The number of scroll clicks to perform downward.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -312,6 +474,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def scroll_up(self, clicks: int = 1) -> Dict[str, Any]:
"""Scroll up by the specified number of clicks.
Args:
clicks (int): The number of scroll clicks to perform upward.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -323,6 +493,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
# Screen Actions
async def screenshot(self) -> Dict[str, Any]:
"""Capture a screenshot of the entire screen.
Returns:
Dict[str, Any]: A dictionary containing the success status and either
base64-encoded image data or an error message.
Structure: {"success": bool, "image_data": str} or
{"success": bool, "error": str}
"""
if not pyautogui:
return {"success": False, "error": "pyautogui not available"}
@@ -341,6 +519,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": f"Screenshot error: {str(e)}"}
async def get_screen_size(self) -> Dict[str, Any]:
"""Get the size of the screen in pixels.
Returns:
Dict[str, Any]: A dictionary containing the success status and either
screen size information or an error message.
Structure: {"success": bool, "size": {"width": int, "height": int}} or
{"success": bool, "error": str}
"""
try:
if pyautogui:
size = pyautogui.size()
@@ -356,6 +542,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def get_cursor_position(self) -> Dict[str, Any]:
"""Get the current position of the mouse cursor.
Returns:
Dict[str, Any]: A dictionary containing the success status and either
cursor position or an error message.
Structure: {"success": bool, "position": {"x": int, "y": int}} or
{"success": bool, "error": str}
"""
try:
if pyautogui:
pos = pyautogui.position()
@@ -371,6 +565,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
# Clipboard Actions
async def copy_to_clipboard(self) -> Dict[str, Any]:
"""Get the current content of the clipboard.
Returns:
Dict[str, Any]: A dictionary containing the success status and either
clipboard content or an error message.
Structure: {"success": bool, "content": str} or
{"success": bool, "error": str}
"""
try:
import pyperclip
content = pyperclip.paste()
@@ -379,6 +581,14 @@ class WindowsAutomationHandler(BaseAutomationHandler):
return {"success": False, "error": str(e)}
async def set_clipboard(self, text: str) -> Dict[str, Any]:
"""Set the clipboard content to the specified text.
Args:
text (str): The text to copy to the clipboard.
Returns:
Dict[str, Any]: A dictionary with success status and optional error message.
"""
try:
import pyperclip
pyperclip.copy(text)
@@ -388,6 +598,17 @@ class WindowsAutomationHandler(BaseAutomationHandler):
# Command Execution
async def run_command(self, command: str) -> Dict[str, Any]:
"""Execute a shell command asynchronously.
Args:
command (str): The shell command to execute.
Returns:
Dict[str, Any]: A dictionary containing the success status and either
command output or an error message.
Structure: {"success": bool, "stdout": str, "stderr": str, "return_code": int} or
{"success": bool, "error": str}
"""
try:
# Create subprocess
process = await asyncio.create_subprocess_shell(

81
libs/python/computer/README.md
View File

@@ -65,80 +65,9 @@ Refer to this notebook for a step-by-step guide on how to use the Computer-Use I
- [Computer-Use Interface (CUI)](https://github.com/trycua/cua/blob/main/notebooks/computer_nb.ipynb)
## Using the Gradio Computer UI
The computer module includes a Gradio UI for creating and sharing demonstration data. We make it easy for people to build community datasets for better computer use models with an upload to Huggingface feature.
```bash
# Install with UI support
pip install "cua-computer[ui]"
```
> **Note:** For precise control of the computer, we recommend using VNC or Screen Sharing instead of the Computer Gradio UI.
### Building and Sharing Demonstrations with Huggingface
Follow these steps to contribute your own demonstrations:
#### 1. Set up Huggingface Access
Set your HF_TOKEN in a .env file or in your environment variables:
```bash
# In .env file
HF_TOKEN=your_huggingface_token
```
#### 2. Launch the Computer UI
```python
# launch_ui.py
from computer.ui.gradio.app import create_gradio_ui
from dotenv import load_dotenv
load_dotenv('.env')
app = create_gradio_ui()
app.launch(share=False)
```
For examples, see [Computer UI Examples](https://github.com/trycua/cua/tree/main/examples/computer_ui_examples.py)
#### 3. Record Your Tasks
<details open>
<summary>View demonstration video</summary>
<video src="https://github.com/user-attachments/assets/de3c3477-62fe-413c-998d-4063e48de176" controls width="600"></video>
</details>
Record yourself performing various computer tasks using the UI.
#### 4. Save Your Demonstrations
<details open>
<summary>View demonstration video</summary>
<video src="https://github.com/user-attachments/assets/5ad1df37-026a-457f-8b49-922ae805faef" controls width="600"></video>
</details>
Save each task by picking a descriptive name and adding relevant tags (e.g., "office", "web-browsing", "coding").
#### 5. Record Additional Demonstrations
Repeat steps 3 and 4 until you have a good amount of demonstrations covering different tasks and scenarios.
#### 6. Upload to Huggingface
<details open>
<summary>View demonstration video</summary>
<video src="https://github.com/user-attachments/assets/c586d460-3877-4b5f-a736-3248886d2134" controls width="600"></video>
</details>
Upload your dataset to Huggingface by:
- Naming it as `{your_username}/{dataset_name}`
- Choosing public or private visibility
- Optionally selecting specific tags to upload only tasks with certain tags
#### Examples and Resources
- Example Dataset: [ddupont/test-dataset](https://huggingface.co/datasets/ddupont/test-dataset)
- Find Community Datasets: 🔍 [Browse CUA Datasets on Huggingface](https://huggingface.co/datasets?other=cua)
## Docs
- [Computers](https://trycua.com/docs/computer-sdk/computers)
- [Commands](https://trycua.com/docs/computer-sdk/commands)
- [Computer UI](https://trycua.com/docs/computer-sdk/computer-ui)
- [Sandboxed Python](https://trycua.com/docs/computer-sdk/sandboxed-python)

139
libs/python/computer/computer/diorama_computer.py
View File

@@ -6,16 +6,35 @@ class DioramaComputer:
A Computer-compatible proxy for Diorama that sends commands over the ComputerInterface.
"""
def __init__(self, computer, apps):
"""
Initialize the DioramaComputer with a computer instance and list of apps.
Args:
computer: The computer instance to proxy commands through
apps: List of applications available in the diorama environment
"""
self.computer = computer
self.apps = apps
self.interface = DioramaComputerInterface(computer, apps)
self._initialized = False
async def __aenter__(self):
"""
Async context manager entry point.
Returns:
self: The DioramaComputer instance
"""
self._initialized = True
return self
async def run(self):
"""
Initialize and run the DioramaComputer if not already initialized.
Returns:
self: The DioramaComputer instance
"""
if not self._initialized:
await self.__aenter__()
return self
@@ -25,11 +44,31 @@ class DioramaComputerInterface:
Diorama Interface proxy that sends diorama_cmds via the Computer's interface.
"""
def __init__(self, computer, apps):
"""
Initialize the DioramaComputerInterface.
Args:
computer: The computer instance to send commands through
apps: List of applications available in the diorama environment
"""
self.computer = computer
self.apps = apps
self._scene_size = None
async def _send_cmd(self, action, arguments=None):
"""
Send a command to the diorama interface through the computer.
Args:
action (str): The action/command to execute
arguments (dict, optional): Additional arguments for the command
Returns:
The result from the diorama command execution
Raises:
RuntimeError: If the computer interface is not initialized or command fails
"""
arguments = arguments or {}
arguments = {"app_list": self.apps, **arguments}
# Use the computer's interface (must be initialized)
@@ -42,6 +81,15 @@ class DioramaComputerInterface:
return result.get("result")
async def screenshot(self, as_bytes=True):
"""
Take a screenshot of the diorama scene.
Args:
as_bytes (bool): If True, return image as bytes; if False, return PIL Image object
Returns:
bytes or PIL.Image: Screenshot data in the requested format
"""
from PIL import Image
import base64
result = await self._send_cmd("screenshot")
@@ -53,41 +101,122 @@ class DioramaComputerInterface:
return img_bytes if as_bytes else img
async def get_screen_size(self):
"""
Get the dimensions of the diorama scene.
Returns:
dict: Dictionary containing 'width' and 'height' keys with pixel dimensions
"""
if not self._scene_size:
await self.screenshot(as_bytes=False)
return {"width": self._scene_size[0], "height": self._scene_size[1]}
async def move_cursor(self, x, y):
"""
Move the cursor to the specified coordinates.
Args:
x (int): X coordinate to move cursor to
y (int): Y coordinate to move cursor to
"""
await self._send_cmd("move_cursor", {"x": x, "y": y})
async def left_click(self, x=None, y=None):
"""
Perform a left mouse click at the specified coordinates or current cursor position.
Args:
x (int, optional): X coordinate to click at. If None, clicks at current cursor position
y (int, optional): Y coordinate to click at. If None, clicks at current cursor position
"""
await self._send_cmd("left_click", {"x": x, "y": y})
async def right_click(self, x=None, y=None):
"""
Perform a right mouse click at the specified coordinates or current cursor position.
Args:
x (int, optional): X coordinate to click at. If None, clicks at current cursor position
y (int, optional): Y coordinate to click at. If None, clicks at current cursor position
"""
await self._send_cmd("right_click", {"x": x, "y": y})
async def double_click(self, x=None, y=None):
"""
Perform a double mouse click at the specified coordinates or current cursor position.
Args:
x (int, optional): X coordinate to double-click at. If None, clicks at current cursor position
y (int, optional): Y coordinate to double-click at. If None, clicks at current cursor position
"""
await self._send_cmd("double_click", {"x": x, "y": y})
async def scroll_up(self, clicks=1):
"""
Scroll up by the specified number of clicks.
Args:
clicks (int): Number of scroll clicks to perform upward. Defaults to 1
"""
await self._send_cmd("scroll_up", {"clicks": clicks})
async def scroll_down(self, clicks=1):
"""
Scroll down by the specified number of clicks.
Args:
clicks (int): Number of scroll clicks to perform downward. Defaults to 1
"""
await self._send_cmd("scroll_down", {"clicks": clicks})
async def drag_to(self, x, y, duration=0.5):
"""
Drag from the current cursor position to the specified coordinates.
Args:
x (int): X coordinate to drag to
y (int): Y coordinate to drag to
duration (float): Duration of the drag operation in seconds. Defaults to 0.5
"""
await self._send_cmd("drag_to", {"x": x, "y": y, "duration": duration})
async def get_cursor_position(self):
"""
Get the current cursor position.
Returns:
dict: Dictionary containing the current cursor coordinates
"""
return await self._send_cmd("get_cursor_position")
async def type_text(self, text):
"""
Type the specified text at the current cursor position.
Args:
text (str): The text to type
"""
await self._send_cmd("type_text", {"text": text})
async def press_key(self, key):
"""
Press a single key.
Args:
key: The key to press
"""
await self._send_cmd("press_key", {"key": key})
async def hotkey(self, *keys):
"""
Press multiple keys simultaneously as a hotkey combination.
Args:
*keys: Variable number of keys to press together. Can be Key enum instances or strings
Raises:
ValueError: If any key is not a Key enum or string type
"""
actual_keys = []
for key in keys:
if isinstance(key, Key):
@@ -101,4 +230,14 @@ class DioramaComputerInterface:
await self._send_cmd("hotkey", {"keys": actual_keys})
async def to_screen_coordinates(self, x, y):
"""
Convert coordinates to screen coordinates.
Args:
x (int): X coordinate to convert
y (int): Y coordinate to convert
Returns:
dict: Dictionary containing the converted screen coordinates
"""
return await self._send_cmd("to_screen_coordinates", {"x": x, "y": y})

114
libs/python/mcp-server/README.md
View File

@@ -17,60 +17,20 @@
**cua-mcp-server** is a MCP server for the Computer-Use Agent (CUA), allowing you to run CUA through Claude Desktop or other MCP clients.
## LiteLLM Integration
This MCP server features comprehensive liteLLM integration, allowing you to use any supported LLM provider with a simple model string configuration.
- **Unified Configuration**: Use a single `CUA_MODEL_NAME` environment variable with a model string
- **Automatic Provider Detection**: The agent automatically detects the provider and capabilities from the model string
- **Extensive Provider Support**: Works with Anthropic, OpenAI, local models, and any liteLLM-compatible provider
### Model String Examples:
- **Anthropic**: `"anthropic/claude-3-5-sonnet-20241022"`
- **OpenAI**: `"openai/computer-use-preview"`
- **UI-TARS**: `"huggingface-local/ByteDance-Seed/UI-TARS-1.5-7B"`
- **Omni + Any LiteLLM**: `"omniparser+litellm/gpt-4o"`, `"omniparser+litellm/claude-3-haiku"`, `"omniparser+ollama_chat/gemma3"`
### Get started with Agent
## Prerequisites
Before installing the MCP server, you'll need to set up full Computer-Use Agent capabilities. This includes:
Cua MCP Server requires [lume](https://github.com/trycua/cua/blob/main/libs/lume/README.md#install) to be installed.
1. Installing the Lume CLI
2. Pulling the latest macOS CUA image
3. Starting the Lume daemon service
4. Installing the required Python libraries (Optional: only needed if you want to verify the agent is working before installing MCP server)
## Install
Make sure these steps are completed and working before proceeding with the MCP server installation.
## Installation
Install the package from PyPI:
```bash
pip install cua-mcp-server
```
This will install:
- The MCP server
- CUA agent and computer dependencies
- An executable `cua-mcp-server` script in your PATH
## Easy Setup Script
If you want to simplify installation, you can use this one-liner to download and run the installation script:
Download and run the installation script:
```bash
curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/python/mcp-server/scripts/install_mcp_server.sh | bash
```
This script will:
- Create the ~/.cua directory if it doesn't exist
- Generate a startup script at ~/.cua/start_mcp_server.sh
- Make the script executable
- The startup script automatically manages Python virtual environments and installs/updates the cua-mcp-server package
You can then use the script in your MCP configuration like this:
```json
@@ -87,9 +47,9 @@ You can then use the script in your MCP configuration like this:
}
```
## Development Guide
## Development
If you want to develop with the cua-mcp-server directly without installation, you can use this configuration:
Use this configuration to develop with the cua-mcp-server directly without installation:
```json
{
@@ -112,61 +72,11 @@ This configuration:
Just add this to your MCP client's configuration and it will use your local development version of the server.
### Troubleshooting
## Docs
If you get a `/bin/bash: ~/cua/libs/python/mcp-server/scripts/start_mcp_server.sh: No such file or directory` error, try changing the path to the script to be absolute instead of relative.
To see the logs:
```
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
```
## Claude Desktop Integration
To use with Claude Desktop, add an entry to your Claude Desktop configuration (`claude_desktop_config.json`, typically found in `~/.config/claude-desktop/`):
For more information on MCP with Claude Desktop, see the [official MCP User Guide](https://modelcontextprotocol.io/quickstart/user).
## Cursor Integration
To use with Cursor, add an MCP configuration file in one of these locations:
- **Project-specific**: Create `.cursor/mcp.json` in your project directory
- **Global**: Create `~/.cursor/mcp.json` in your home directory
After configuration, you can simply tell Cursor's Agent to perform computer tasks by explicitly mentioning the CUA agent, such as "Use the computer control tools to open Safari."
For more information on MCP with Cursor, see the [official Cursor MCP documentation](https://docs.cursor.com/context/model-context-protocol).
### First-time Usage Notes
**API Keys**: Ensure you have valid API keys:
- Add your Anthropic API key, or other model provider API key in the Claude Desktop config (as shown above)
- Or set it as an environment variable in your shell profile
## Configuration
The server is configured using environment variables (can be set in the Claude Desktop config):
| Variable | Description | Default |
|----------|-------------|---------|
| `CUA_MODEL_NAME` | Model string (e.g., "anthropic/claude-3-5-sonnet-20241022", "openai/computer-use-preview", "huggingface-local/ByteDance-Seed/UI-TARS-1.5-7B", "omniparser+litellm/gpt-4o", "omniparser+ollama_chat/gemma3") | anthropic/claude-3-5-sonnet-20241022 |
| `CUA_MAX_IMAGES` | Maximum number of images to keep in context | 3 |
## Available Tools
The MCP server exposes the following tools to Claude:
1. `run_cua_task` - Run a single Computer-Use Agent task with the given instruction
2. `run_multi_cua_tasks` - Run multiple tasks in sequence
## Usage
Once configured, you can simply ask Claude to perform computer tasks:
- "Open Chrome and go to github.com"
- "Create a folder called 'Projects' on my desktop"
- "Find all PDFs in my Downloads folder"
- "Take a screenshot and highlight the error message"
Claude will automatically use your CUA agent to perform these tasks.
- [Installation](https://trycua.com/docs/libraries/mcp-server/installation)
- [Configuration](https://trycua.com/docs/libraries/mcp-server/configuration)
- [Usage](https://trycua.com/docs/libraries/mcp-server/usage)
- [Tools](https://trycua.com/docs/libraries/mcp-server/tools)
- [Client Integrations](https://trycua.com/docs/libraries/mcp-server/client-integrations)
- [LLM Integrations](https://trycua.com/docs/libraries/mcp-server/llm-integrations)

661
libs/python/som/LICENSE Normal file
View File

@@ -0,0 +1,661 @@
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
Developers that use our General Public Licenses protect your rights
with two steps: (1) assert copyright on the software, and (2) offer
you this License which gives you legal permission to copy, distribute
and/or modify the software.
A secondary benefit of defending all users' freedom is that
improvements made in alternate versions of the program, if they
receive widespread use, become available for other developers to
incorporate. Many developers of free software are heartened and
encouraged by the resulting cooperation. However, in the case of
software used on network servers, this result may fail to come about.
The GNU General Public License permits making a modified version and
letting the public access it on a server without ever releasing its
source code to the public.
The GNU Affero General Public License is designed specifically to
ensure that, in such cases, the modified source code becomes available
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88
libs/python/som/README.md
View File

@@ -75,93 +75,9 @@ for elem in result.elements:
print(f"Text: '{elem.content}', confidence={elem.confidence:.3f}")
```
## Configuration
## Docs
### Detection Parameters
#### Box Threshold (0.3)
Controls the confidence threshold for accepting detections:
```
High Threshold (0.3): Low Threshold (0.01):
+----------------+ +----------------+
| | | +--------+ |
| Confident | | |Unsure?| |
| Detection | | +--------+ |
| (✓ Accept) | | (? Reject) |
| | | |
+----------------+ +----------------+
conf = 0.85 conf = 0.02
```
- Higher values (0.3) yield more precise but fewer detections
- Lower values (0.01) catch more potential icons but increase false positives
- Default is 0.3 for optimal precision/recall balance
#### IOU Threshold (0.1)
Controls how overlapping detections are merged:
```
IOU = Intersection Area / Union Area
Low Overlap (Keep Both): High Overlap (Merge):
+----------+ +----------+
| Box1 | | Box1 |
| | vs. |+-----+ |
+----------+ ||Box2 | |
+----------+ |+-----+ |
| Box2 | +----------+
| |
+----------+
IOU ≈ 0.05 (Keep Both) IOU ≈ 0.7 (Merge)
```
- Lower values (0.1) more aggressively remove overlapping boxes
- Higher values (0.5) allow more overlapping detections
- Default is 0.1 to handle densely packed UI elements
### OCR Configuration
- **Engine**: EasyOCR
- Primary choice for all platforms
- Fast initialization and processing
- Built-in English language support
- GPU acceleration when available
- **Settings**:
- Timeout: 5 seconds
- Confidence threshold: 0.5
- Paragraph mode: Disabled
- Language: English only
## Performance
### Hardware Acceleration
#### MPS (Metal Performance Shaders)
- Multi-scale detection (640px, 1280px, 1920px)
- Test-time augmentation enabled
- Half-precision (FP16)
- Average detection time: ~0.4s
- Best for production use when available
#### CPU
- Single-scale detection (1280px)
- Full-precision (FP32)
- Average detection time: ~1.3s
- Reliable fallback option
### Example Output Structure
```
examples/output/
├── {timestamp}_no_ocr/
│ ├── annotated_images/
│ │ └── screenshot_analyzed.png
│ ├── screen_details.txt
│ └── summary.json
└── {timestamp}_ocr/
├── annotated_images/
│ └── screenshot_analyzed.png
├── screen_details.txt
└── summary.json
```
- [Configuration](http://localhost:8090/docs/libraries/som/configuration)
## Development

4
libs/python/som/pyproject.toml
View File

@@ -26,12 +26,12 @@ dependencies = [
]
requires-python = ">=3.11"
readme = "README.md"
license = {text = "MIT"}
license = {text = "AGPL-3.0-or-later"}
keywords = ["computer-vision", "ocr", "ui-analysis", "icon-detection"]
classifiers = [
"Development Status :: 4 - Beta",
"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Topic :: Scientific/Engineering :: Artificial Intelligence",

90
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@@ -1,28 +1,35 @@
# Cua Computer TypeScript Library
<div align="center">
<h1>
<div class="image-wrapper" style="display: inline-block;">
<picture>
<source media="(prefers-color-scheme: dark)" alt="logo" height="150" srcset="https://raw.githubusercontent.com/trycua/cua/main/img/logo_white.png" style="display: block; margin: auto;">
<source media="(prefers-color-scheme: light)" alt="logo" height="150" srcset="https://raw.githubusercontent.com/trycua/cua/main/img/logo_black.png" style="display: block; margin: auto;">
<img alt="Shows my svg">
</picture>
</div>
The TypeScript library for C/cua Computer - a powerful computer control and automation library.
[![TypeScript](https://img.shields.io/badge/TypeScript-333333?logo=typescript&logoColor=white&labelColor=333333)](#)
[![macOS](https://img.shields.io/badge/macOS-000000?logo=apple&logoColor=F0F0F0)](#)
[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?&logo=discord&logoColor=white)](https://discord.com/invite/mVnXXpdE85)
[![NPM](https://img.shields.io/npm/v/@trycua/computer?color=333333)](https://www.npmjs.com/package/@trycua/computer)
</h1>
</div>
## Overview
**@trycua/computer** is a Computer-Use Interface (CUI) framework powering Cua for interacting with local macOS and Linux sandboxes, Playwright-compatible, and pluggable with any AI agent systems (Cua, Langchain, CrewAI, AutoGen). Computer relies on [Lume](https://github.com/trycua/lume) for creating and managing sandbox environments.
This library is a TypeScript port of the Python computer library, providing the same functionality for controlling virtual machines and computer interfaces. It enables programmatic control of virtual machines through various providers and offers a consistent interface for interacting with the VM's operating system.
### Get started with Computer
## Installation
```bash
npm install @trycua/computer
# or
pnpm add @trycua/computer
```
## Usage
<div align="center">
<img src="https://raw.githubusercontent.com/trycua/cua/main/img/computer.png"/>
</div>
```typescript
import { Computer } from '@trycua/computer';
import { Computer, OSType } from '@trycua/computer';
// Create a new computer instance
const computer = new Computer({
osType: OSType.LINUX,
name: 's-linux-vm_id'
name: 's-linux-vm_id',
apiKey: 'your-api-key'
});
@@ -30,60 +37,47 @@ const computer = new Computer({
await computer.run();
// Get the computer interface for interaction
const interface = computer.interface;
const computerInterface = computer.interface;
// Take a screenshot
const screenshot = await interface.getScreenshot();
const screenshot = await computerInterface.getScreenshot();
// In a Node.js environment, you might save it like this:
// import * as fs from 'fs';
// fs.writeFileSync('screenshot.png', Buffer.from(screenshot));
// Click at coordinates
await interface.click(500, 300);
await computerInterface.click(500, 300);
// Type text
await interface.typeText('Hello, world!');
await computerInterface.typeText('Hello, world!');
// Stop the computer
await computer.stop();
```
## Architecture
## Install
The library is organized into the following structure:
### Core Components
- **Computer Factory**: A factory object that creates appropriate computer instances
- **BaseComputer**: Abstract base class with shared functionality for all computer types
- **Types**: Type definitions for configuration options and shared interfaces
### Provider Implementations
- **Computer**: Implementation for cloud-based VMs
## Development
- Install dependencies:
To install the Computer-Use Interface (CUI):
```bash
pnpm install
npm install @trycua/computer
# or
pnpm add @trycua/computer
```
- Run the unit tests:
The `@trycua/computer` package provides the TypeScript library for interacting with computer interfaces.
```bash
pnpm test
```
## Run
- Build the library:
Refer to this example for a step-by-step guide on how to use the Computer-Use Interface (CUI):
```bash
pnpm build
```
- [Computer-Use Interface (CUI)](https://github.com/trycua/cua/tree/main/examples/computer-example-ts)
- Type checking:
## Docs
```bash
pnpm typecheck
```
- [Computers](https://trycua.com/docs/computer-sdk/computers)
- [Commands](https://trycua.com/docs/computer-sdk/commands)
- [Computer UI](https://trycua.com/docs/computer-sdk/computer-ui)
## License

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@@ -1,22 +1,47 @@
# Cua Core TypeScript Library
<div align="center">
<h1>
<div class="image-wrapper" style="display: inline-block;">
<picture>
<source media="(prefers-color-scheme: dark)" alt="logo" height="150" srcset="https://raw.githubusercontent.com/trycua/cua/main/img/logo_white.png" style="display: block; margin: auto;">
<source media="(prefers-color-scheme: light)" alt="logo" height="150" srcset="https://raw.githubusercontent.com/trycua/cua/main/img/logo_black.png" style="display: block; margin: auto;">
<img alt="Shows my svg">
</picture>
</div>
The core cua library with support for telemetry and other utilities.
[![TypeScript](https://img.shields.io/badge/TypeScript-333333?logo=typescript&logoColor=white&labelColor=333333)](#)
[![macOS](https://img.shields.io/badge/macOS-000000?logo=apple&logoColor=F0F0F0)](#)
[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?&logo=discord&logoColor=white)](https://discord.com/invite/mVnXXpdE85)
[![NPM](https://img.shields.io/npm/v/@trycua/core?color=333333)](https://www.npmjs.com/package/@trycua/core)
</h1>
</div>
**Cua Core** provides essential shared functionality and utilities used across the Cua ecosystem:
- Privacy-focused telemetry system for transparent usage analytics
- Common helper functions and utilities used by other Cua packages
- Core infrastructure components shared between modules
## Installation
```bash
pnpm install @trycua/core
```
## Development
- Install dependencies:
Install dependencies:
```bash
pnpm install
```
- Run the unit tests:
Run the unit tests:
```bash
pnpm test
```
- Build the library:
Build the library:
```bash
pnpm build

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@@ -0,0 +1,19 @@
# CUA Notebooks
This folder contains Jupyter notebooks that demonstrate the core functionality of the CUA (Computer Use Automation) system. These notebooks serve as interactive examples and quickstart guides for different components of the CUA platform.
## Available Notebooks
### Core Components
- **`computer_nb.ipynb`** - Demonstrates the Computer API for programmatically operating sandbox VMs using either Cua Cloud Containers or local Lume VMs on Apple Silicon macOS systems
- **`agent_nb.ipynb`** - Shows how to use CUA's Agent to run automated workflows in virtual sandboxes with various AI models (OpenAI, Anthropic, local models)
- **`pylume_nb.ipynb`** - Quickstart guide for the pylume Python library, which handles VM creation, management, and image operations
- **`computer_server_nb.ipynb`** - Demonstrates how to host and configure the Computer server that powers the Computer API
### Evaluation & Benchmarking
- **`eval_osworld.ipynb`** - Shows ComputerAgent integration with HUD for OSWorld benchmarking, supporting both Claude and OpenAI models
### Tutorials
- **`blog/`** - Tutorial notebooks from blog posts:
- `build-your-own-operator-on-macos-1.ipynb` - Part 1: Building a CUA operator using OpenAI's computer-use-preview model
- `build-your-own-operator-on-macos-2.ipynb` - Part 2: Using the cua-agent package for more advanced automation

61
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@@ -31,13 +31,7 @@
"metadata": {},
"outputs": [],
"source": [
"!pip install \"cua-agent[all]\"\n",
"\n",
"# Or install individual agent loops:\n",
"# !pip install cua-agent[openai]\n",
"# !pip install cua-agent[anthropic]\n",
"# !pip install cua-agent[uitars]\n",
"# !pip install cua-agent[omni]"
"!pip install \"cua-agent[all]\""
]
},
{
@@ -78,7 +72,7 @@
"outputs": [],
"source": [
"from computer import Computer, VMProviderType\n",
"from agent import ComputerAgent, LLM, AgentLoop, LLMProvider"
"from agent import ComputerAgent"
]
},
{
@@ -184,8 +178,8 @@
"\n",
"# Create agent\n",
"agent = ComputerAgent(\n",
" model=\"openai/computer-use-preview\",\n",
" tools=[computer],\n",
" model=\"computer-use-preview\",\n",
" trajectory_dir=str(Path(\"trajectories\")),\n",
" only_n_most_recent_images=3,\n",
" verbosity=logging.INFO\n",
@@ -213,7 +207,7 @@
"\n",
"for i, task in enumerate(tasks):\n",
" print(f\"\\nExecuting task {i+1}/{len(tasks)}: {task}\")\n",
" async for result in cloud_agent.run(task):\n",
" async for result in agent.run(task):\n",
" # print(result)\n",
" pass\n",
" print(f\"✅ Task {i+1}/{len(tasks)} completed: {task}\")\n"
@@ -223,7 +217,38 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Option 2: Agent with Local VMs (Lume daemon)\n",
"## Option 2: KASM Local Docker Containers (cross-platform)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we can create an agent, we need to initialize a local computer with Docker provider."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"from pathlib import Path\n",
"\n",
"computer = Computer(\n",
" os_type=\"linux\",\n",
" provider_type=\"docker\",\n",
" image=\"trycua/cua-ubuntu:latest\",\n",
" name=\"my-cua-container\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Option 3: Agent with Local VMs (Lume daemon)\n",
"\n",
"For Apple Silicon Macs, run agents on local VMs with near-native performance."
]
@@ -259,7 +284,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an agent with local VM"
"## Create an agent"
]
},
{
@@ -277,7 +302,7 @@
"source": [
"# Create agent with Anthropic loop and provider\n",
"agent = ComputerAgent(\n",
" model=\"openai/computer-use-preview\"\n",
" model=\"openai/computer-use-preview\",\n",
" tools=[computer],\n",
" trajectory_dir=str(Path(\"trajectories\")),\n",
" only_n_most_recent_images=3,\n",
@@ -289,7 +314,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Run tasks on a local Lume VM"
"Run tasks on a computer:"
]
},
{
@@ -331,7 +356,7 @@
"source": [
"import logging\n",
"from pathlib import Path\n",
"from agent import ComputerAgent, LLM, AgentLoop\n",
"from agent import ComputerAgent\n",
"\n",
"computer = Computer(verbosity=logging.INFO)\n",
"\n",
@@ -424,7 +449,7 @@
" model=\"openai/computer-use-preview\",\n",
" trajectory_dir=str(Path(\"trajectories\")),\n",
" verbosity=logging.INFO\n",
")\n"
")"
]
},
{
@@ -521,7 +546,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "cua312",
"display_name": "cua",
"language": "python",
"name": "python3"
},
@@ -535,7 +560,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
"version": "3.13.5"
}
},
"nbformat": 4,

6
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@@ -78,7 +78,7 @@
"outputs": [],
"source": [
"from computer import Computer\n",
"from agent import ComputerAgent, LLM, AgentLoop, LLMProvider"
"from agent import ComputerAgent"
]
},
{
@@ -129,7 +129,7 @@
"outputs": [],
"source": [
"agent = ComputerAgent(\n",
" computer=[computer],\n",
" tools=[computer],\n",
" model=\"openai/computer-use-preview\",\n",
" save_trajectory=True,\n",
" only_n_most_recent_images=3,\n",
@@ -170,7 +170,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
"version": "3.12.11"
}
},
"nbformat": 4,

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@@ -18,9 +18,470 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Skipping cua-computer as it is not installed.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting cua-computer[all]\n",
" Downloading cua_computer-0.4.4-py3-none-any.whl.metadata (5.8 kB)\n",
"Collecting pillow>=10.0.0 (from cua-computer[all])\n",
" Downloading pillow-11.3.0-cp313-cp313-win_amd64.whl.metadata (9.2 kB)\n",
"Collecting websocket-client>=1.8.0 (from cua-computer[all])\n",
" Downloading websocket_client-1.8.0-py3-none-any.whl.metadata (8.0 kB)\n",
"Collecting websockets>=12.0 (from cua-computer[all])\n",
" Downloading websockets-15.0.1-cp313-cp313-win_amd64.whl.metadata (7.0 kB)\n",
"Collecting aiohttp>=3.9.0 (from cua-computer[all])\n",
" Downloading aiohttp-3.12.15-cp313-cp313-win_amd64.whl.metadata (7.9 kB)\n",
"Collecting cua-core<0.2.0,>=0.1.0 (from cua-computer[all])\n",
" Downloading cua_core-0.1.9-py3-none-any.whl.metadata (1.6 kB)\n",
"Collecting pydantic>=2.11.1 (from cua-computer[all])\n",
" Downloading pydantic-2.11.7-py3-none-any.whl.metadata (67 kB)\n",
"Collecting gradio>=5.23.3 (from cua-computer[all])\n",
" Downloading gradio-5.43.1-py3-none-any.whl.metadata (16 kB)\n",
"Collecting python-dotenv>=1.0.1 (from cua-computer[all])\n",
" Downloading python_dotenv-1.1.1-py3-none-any.whl.metadata (24 kB)\n",
"Collecting datasets>=3.6.0 (from cua-computer[all])\n",
" Downloading datasets-4.0.0-py3-none-any.whl.metadata (19 kB)\n",
"Collecting httpx>=0.24.0 (from cua-core<0.2.0,>=0.1.0->cua-computer[all])\n",
" Downloading httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB)\n",
"Collecting posthog>=3.20.0 (from cua-core<0.2.0,>=0.1.0->cua-computer[all])\n",
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"Collecting aiohappyeyeballs>=2.5.0 (from aiohttp>=3.9.0->cua-computer[all])\n",
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"Collecting aiosignal>=1.4.0 (from aiohttp>=3.9.0->cua-computer[all])\n",
" Downloading aiosignal-1.4.0-py3-none-any.whl.metadata (3.7 kB)\n",
"Collecting attrs>=17.3.0 (from aiohttp>=3.9.0->cua-computer[all])\n",
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"Installing collected packages: pytz, pydub, brotli, xxhash, websockets, websocket-client, urllib3, tzdata, typing-inspection, tqdm, tomlkit, sniffio, shellingham, semantic-version, ruff, pyyaml, python-multipart, python-dotenv, pydantic-core, pyarrow, propcache, pillow, orjson, numpy, multidict, mdurl, markupsafe, idna, h11, groovy, fsspec, frozenlist, filelock, ffmpy, distro, dill, click, charset_normalizer, certifi, backoff, audioop-lts, attrs, annotated-types, aiohappyeyeballs, aiofiles, yarl, uvicorn, requests, pydantic, pandas, multiprocess, markdown-it-py, jinja2, httpcore, anyio, aiosignal, starlette, rich, posthog, huggingface-hub, httpx, aiohttp, typer, safehttpx, gradio-client, fastapi, cua-core, gradio, datasets, cua-computer\n",
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" ---------------------------------------- 70/70 [cua-computer]\n",
"\n",
"Successfully installed aiofiles-24.1.0 aiohappyeyeballs-2.6.1 aiohttp-3.12.15 aiosignal-1.4.0 annotated-types-0.7.0 anyio-4.10.0 attrs-25.3.0 audioop-lts-0.2.2 backoff-2.2.1 brotli-1.1.0 certifi-2025.8.3 charset_normalizer-3.4.3 click-8.2.1 cua-computer-0.4.4 cua-core-0.1.9 datasets-4.0.0 dill-0.3.8 distro-1.9.0 fastapi-0.116.1 ffmpy-0.6.1 filelock-3.19.1 frozenlist-1.7.0 fsspec-2025.3.0 gradio-5.43.1 gradio-client-1.12.1 groovy-0.1.2 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 huggingface-hub-0.34.4 idna-3.10 jinja2-3.1.6 markdown-it-py-4.0.0 markupsafe-3.0.2 mdurl-0.1.2 multidict-6.6.4 multiprocess-0.70.16 numpy-2.3.2 orjson-3.11.2 pandas-2.3.2 pillow-11.3.0 posthog-6.6.1 propcache-0.3.2 pyarrow-21.0.0 pydantic-2.11.7 pydantic-core-2.33.2 pydub-0.25.1 python-dotenv-1.1.1 python-multipart-0.0.20 pytz-2025.2 pyyaml-6.0.2 requests-2.32.5 rich-14.1.0 ruff-0.12.10 safehttpx-0.1.6 semantic-version-2.10.0 shellingham-1.5.4 sniffio-1.3.1 starlette-0.47.3 tomlkit-0.13.3 tqdm-4.67.1 typer-0.16.1 typing-inspection-0.4.1 tzdata-2025.2 urllib3-2.5.0 uvicorn-0.35.0 websocket-client-1.8.0 websockets-15.0.1 xxhash-3.5.0 yarl-1.20.1\n"
]
}
],
"source": [
"!pip uninstall -y cua-computer\n",
"!pip install \"cua-computer[all]\""
@@ -153,6 +614,8 @@
"metadata": {},
"outputs": [],
"source": [
"await computer.run() # Initialize the computer first\n",
"\n",
"screenshot = await computer.interface.screenshot()\n",
"\n",
"with open(\"screenshot.png\", \"wb\") as f:\n",
@@ -163,7 +626,43 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Option 2: Local VMs (Lume daemon)\n",
"## Option 2: KASM Local Docker Containers (cross-platform)\n",
"\n",
"### Pull from Docker Hub\n",
"```bash\n",
"docker pull --platform=linux/amd64 trycua/cua-ubuntu:latest\n",
"```\n",
"\n",
"### Or build locally\n",
"```bash\n",
"cd libs/kasm\n",
"docker build -t cua-ubuntu:latest .\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from computer import Computer\n",
"\n",
"computer = Computer(\n",
" os_type=\"linux\",\n",
" provider_type=\"docker\",\n",
" image=\"trycua/cua-ubuntu:latest\",\n",
" name=\"my-cua-container\"\n",
")\n",
"\n",
"await computer.run() # Launch & connect to Docker container"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Option 3: Local VMs (Lume daemon)\n",
"\n",
"For Apple Silicon Macs, you can run VMs locally using the Lume daemon."
]
@@ -460,7 +959,8 @@
"dir_exists = await computer.interface.directory_exists(\"/path/to/directory\")\n",
"\n",
"# Run shell commands\n",
"stdout, stderr = await computer.interface.run_command(\"ls -la\")"
"result = await computer.interface.run_command(\"ls -la\")\n",
"stdout, stderr, returncode = result.stdout, result.stderr, result.returncode"
]
},
{
@@ -587,7 +1087,7 @@
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"display_name": "cua",
"language": "python",
"name": "python3"
},
@@ -601,7 +1101,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
"version": "3.13.5"
}
},
"nbformat": 4,

32
notebooks/computer_server_nb.ipynb
View File

@@ -49,36 +49,6 @@
"## Start the Computer server"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==> Starting computer-server on 0.0.0.0:8000...\n",
"Starting computer-server on 0.0.0.0:8000...\n",
"\u001b[32mINFO\u001b[0m: Started server process [\u001b[36m65480\u001b[0m]\n",
"\u001b[32mINFO\u001b[0m: Waiting for application startup.\n",
"\u001b[32mINFO\u001b[0m: Application startup complete.\n",
"\u001b[32mINFO\u001b[0m: Uvicorn running on \u001b[1mhttp://0.0.0.0:8000\u001b[0m (Press CTRL+C to quit)\n",
"^C\n",
"\u001b[32mINFO\u001b[0m: Shutting down\n",
"\u001b[32mINFO\u001b[0m: Waiting for application shutdown.\n",
"\u001b[32mINFO\u001b[0m: Application shutdown complete.\n",
"\u001b[32mINFO\u001b[0m: Finished server process [\u001b[36m65480\u001b[0m]\n"
]
}
],
"source": [
"import os\n",
"# os.chdir('../../scripts')\n",
"\n",
"! ./run_computer_server.sh\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -109,7 +79,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
"version": "3.13.5"
}
},
"nbformat": 4,

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