Open World Agents Documentation¶
A comprehensive framework for building AI agents that interact with desktop applications through vision, keyboard, and mouse control.
Open World Agents (OWA) is a monorepo containing the complete toolkit for multimodal desktop agent development. From high-performance data capture to model training and real-time evaluation, everything is designed for flexibility and performance.
Architecture Overview¶
OWA consists of four core components:
🌍 Environment (Env) - Asynchronous, event-driven interface for real-time agent interactions 📊 Data - High-performance recording, storage, and analysis of multimodal desktop data 📝 Messages - Centralized message definitions with automatic discovery and registry system 🤖 Examples - Complete implementations and training pipelines for multimodal agents
Quick Navigation¶
🌍 Environment Framework¶
Build reactive desktop agents with our asynchronous environment system.
| Component | Description |
|---|---|
| Core Concepts | Callables, Listeners, and Runnables for real-time processing |
| Environment Guide | Zero-configuration plugin system and CLI tools |
| Custom Plugins | Create your own environment extensions |
Built-in Plugins:
- Desktop Environment - Mouse, keyboard, and window event handling
- GStreamer Environment - High-performance screen capture (6x faster than alternatives)
- Standard Environment - Basic utilities and timing functions
📊 Data Infrastructure¶
Capture, store, and analyze multimodal desktop interaction data.
| Component | Description |
|---|---|
| Data Overview | Complete data pipeline for desktop agents |
| OWAMcap Format | Specialized format capturing complete desktop interactions (screen + events) with nanosecond precision |
| Desktop Recorder (ocap) | High-performance desktop recording tool |
| Data Viewer | Visualize and analyze recorded sessions |
| Data Explorer | Tools for data exploration and editing |
🤖 Agent Examples¶
Learn from complete implementations and training pipelines.
| Example | Description | Status |
|---|---|---|
| Multimodal Game Agent | Vision-based game playing agent | 🚧 In Progress |
| GUI Agent | General desktop application automation | 🚧 In Progress |
| Interactive World Model | Predictive modeling of desktop environments | 🚧 In Progress |
| Usage with LLMs | Integration with large language models | 🚧 In Progress |
| Usage with Transformers | Vision transformer implementations | 🚧 In Progress |
Community & Ecosystem¶
🌱 Growing Ecosystem: OWA is designed for extensibility. Community contributions include:
- Custom environment plugins (
owa.env.minecraft,owa.env.web, etc.) - Specialized data processors and analyzers
- Novel agent architectures and training methods
🤗 HuggingFace Integration: Upload and share datasets created with ocap. Preview datasets at HuggingFace Spaces.
Development Resources¶
| Resource | Description |
|---|---|
| Installation Guide | Detailed installation instructions |
| Contributing Guide | Development setup, bug reports, feature proposals |
| FAQ | Common questions and troubleshooting |
What Can You Build?¶
Anything that runs on desktop. If a human can do it on a computer, you can build an AI agent to automate it:
🤖 Desktop Automation - Navigate applications, automate workflows, interact with any software
🎮 Game AI - Master complex games through visual understanding and real-time decision making
📊 Training Datasets - Capture high-quality human-computer interaction data for foundation models
📈 Benchmarks - Create and evaluate desktop agent performance across diverse tasks
License¶
This project is released under the MIT License. See the LICENSE file for details.