Open‐Source Frameworks for AI‐Driven Computer Control - FlexNetOS/MicroAgentStack GitHub Wiki

Self-Operating Computer Framework: Multimodal Integration & Vision-Based Models - Datatunnel

Here's an evaluation of open-source, multimodal AI frameworks designed to autonomously operate computers. These tools enable AI agents to interact with graphical user interfaces (GUIs) using vision, language, and action inputs, simulating human-like computer usage.(HyperWrite, TechRadar)

🔧 Top Open-Source Frameworks for AI-Driven Computer Control

1. Self-Operating Computer Framework (OthersideAI / HyperWrite)

  • Overview: A pioneering framework allowing multimodal models to control a computer by interpreting screen content and executing mouse and keyboard actions.

  • Key Features:

    • Model Compatibility: Supports GPT-4o, Gemini Pro Vision, Claude 3, LLaVa, and Qwen-VL.

    • Cross-Platform: Operates on macOS, Windows, and Linux (with X server).

    • Enhanced Interaction: Incorporates Optical Character Recognition (OCR) and Set-of-Mark (SoM) prompting for improved visual grounding.

  • License: MIT License.

  • Resources: GitHub Repository | HyperWrite AI(SourceForge, GitHub, HyperWrite)

2. Agent S (Simular AI)

  • Overview: An open agentic framework enabling autonomous GUI interactions through hierarchical planning and experience augmentation.

  • Key Features:

    • Hierarchical Planning: Utilizes experience-augmented hierarchical planning for efficient task execution.

    • Agent-Computer Interface (ACI): Facilitates reasoning and control capabilities in GUI agents.

    • Benchmark Performance: Achieved state-of-the-art results on the OSWorld benchmark.

  • License: Open-source (specific license details in the repository).

  • Resources: GitHub Repository | Research Paper(arXiv)

3. ScreenAgent

  • Overview: A vision-language model-driven agent that controls computers by observing screenshots and performing GUI actions.

  • Key Features:

    • Automated Control Pipeline: Includes planning, acting, and reflecting phases for task completion.

    • ScreenAgent Dataset: Provides a dataset of screenshots and action sequences for training.

    • Performance: Demonstrated capabilities comparable to GPT-4V with precise UI positioning.

  • License: Open-source (specific license details in the repository).

  • Resources: GitHub Repository | Research Paper(arXiv)

4. Open Computer Agent (Hugging Face)

  • Overview: A semi-autonomous web assistant capable of interacting with websites and applications using simulated mouse and keyboard actions.

  • Key Features:

    • Web Interaction: Performs tasks like form filling, ticket booking, and navigation within a browser.

    • Vision-Language Models: Leverages models like Qwen-VL for element detection and interaction.

    • Open-Source: Part of Hugging Face's "smolagents" project, emphasizing flexibility and transparency.

  • License: Open-source (specific license details in the repository).

  • Resources: TechRadar Article(TechRadar)

5. OSCAR (Operating System Control via State-Aware Reasoning and Re-Planning)

  • Overview: A generalist agent designed to autonomously navigate and interact with various desktop and mobile applications through standardized controls.

  • Key Features:

    • State-Aware Reasoning: Translates human instructions into executable Python code for precise GUI control.

    • Dynamic Re-Planning: Equipped with error-handling mechanisms and the ability to adjust tasks based on real-time feedback.

    • Cross-Platform: Demonstrated effectiveness across diverse benchmarks on desktop and mobile platforms.

  • License: Open-source (specific license details in the repository).

  • Resources: Research Paper(arXiv)

🧠 Summary Comparison

Framework Model Compatibility Key Features License
Self-Operating Computer GPT-4o, Gemini Pro Vision, Claude 3, LLaVa OCR, SoM prompting, cross-platform support MIT
Agent S Multimodal LLMs Hierarchical planning, ACI, benchmark leader Open-source
ScreenAgent Vision-Language Models Automated control pipeline, ScreenAgent Dataset Open-source
Open Computer Agent Qwen-VL Web interaction, simulated GUI actions Open-source
OSCAR Multimodal LLMs State-aware reasoning, dynamic re-planning Open-source

🧭 Recommendations

  • For General-Purpose Automation: The Self-Operating Computer Framework offers a robust, cross-platform solution with broad model compatibility and enhanced interaction features.

  • For Complex Task Planning: Agent S provides advanced hierarchical planning capabilities and has demonstrated superior performance on benchmarks.

  • For Web-Based Tasks: The Open Computer Agent is ideal for automating web interactions, such as form filling and navigation, directly within a browser.(TechRadar)

  • For Research and Development: ScreenAgent and OSCAR offer cutting-edge approaches to GUI automation, suitable for experimental applications and further development.

These open-source frameworks provide diverse options for implementing AI-driven computer control. Depending on your specific use case—be it general automation, complex task execution, or web interaction—you can select the framework that best aligns with your objectives.

[![Self-Operating Computer Framework: Multimodal Integration & Vision-Based Models - Datatunnel](https://tse1.mm.bing.net/th?id=OIP.-JSP9i135HFtInu72Pr2MgAAAA\&pid=Api)](https://datatunnel.io/tldr_listing/self-operating-computer-framework-multimodal-integration-vision-based-models-3/)

Here's an evaluation of open-source, multimodal AI frameworks designed to autonomously operate computers. These tools enable AI agents to interact with graphical user interfaces (GUIs) using vision, language, and action inputs, simulating human-like computer usage.([HyperWrite]1, [TechRadar]2)

🔧 Top Open-Source Frameworks for AI-Driven Computer Control

1. Self-Operating Computer Framework (OthersideAI / HyperWrite)

2. Agent S (Simular AI)

3. ScreenAgent

4. Open Computer Agent (Hugging Face)

5. OSCAR (Operating System Control via State-Aware Reasoning and Re-Planning)

  • Overview: A generalist agent designed to autonomously navigate and interact with various desktop and mobile applications through standardized controls.

  • Key Features:

    • State-Aware Reasoning: Translates human instructions into executable Python code for precise GUI control.
    • Dynamic Re-Planning: Equipped with error-handling mechanisms and the ability to adjust tasks based on real-time feedback.
    • Cross-Platform: Demonstrated effectiveness across diverse benchmarks on desktop and mobile platforms.

  • License: Open-source (specific license details in the repository).

  • Resources: [Research Paper](https://arxiv.org/abs/2410.18963)([[arXiv](https://arxiv.org/abs/2410.18963?utm_source=chatgpt.com)][7])

🧠 Summary Comparison

Framework Model Compatibility Key Features License
Self-Operating Computer GPT-4o, Gemini Pro Vision, Claude 3, LLaVa OCR, SoM prompting, cross-platform support MIT
Agent S Multimodal LLMs Hierarchical planning, ACI, benchmark leader Open-source
ScreenAgent Vision-Language Models Automated control pipeline, ScreenAgent Dataset Open-source
Open Computer Agent Qwen-VL Web interaction, simulated GUI actions Open-source
OSCAR Multimodal LLMs State-aware reasoning, dynamic re-planning Open-source (SourceForge, arXiv, NVIDIA Developer, Reddit, arXiv, WIRED, [TechRadar]2, HyperWrite, arXiv, Tom's Hardware)

🧭 Recommendations

  • For General-Purpose Automation: The Self-Operating Computer Framework offers a robust, cross-platform solution with broad model compatibility and enhanced interaction features.

  • For Complex Task Planning: Agent S provides advanced hierarchical planning capabilities and has demonstrated superior performance on benchmarks.

  • For Web-Based Tasks: The Open Computer Agent is ideal for automating web interactions, such as form filling and navigation, directly within a browser.(TechRadar)

  • For Research and Development: ScreenAgent and OSCAR offer cutting-edge approaches to GUI automation, suitable for experimental applications and further development.

These open-source frameworks provide diverse options for implementing AI-driven computer control. Depending on your specific use case—be it general automation, complex task execution, or web interaction—you can select the framework that best aligns with your objectives.

ANTHROPIC_Computer use (beta) Spongecake_Open Source Operator for Computer Use A natural language interface for computers HyperWrite_Self-Operating Computer A framework to enable multimodal models to operate a computer. Agent S: an open agentic framework that uses computers like a human Qwen2.5 Alibaba’s Qwen team releases AI models that can control PCs and phones

This AI Paper Introduces Agents: An Open-Source Python Framework for Autonomous Language Agents ...

Integrating Agent S, Open Computer Agent, ScreenAgent, and OSCAR into a cohesive AI-driven system can create a robust, multimodal computer control framework. Here's a detailed plan to achieve this integration:

🧩 Component Overview

  1. Agent S: An open-source framework enabling autonomous interaction with computers through a Graphical User Interface (GUI), designed to perform complex tasks by learning from past experiences. ([GitHub]1)

  2. Open Computer Agent: A free, cloud-based AI agent by Hugging Face that simulates mouse and keyboard actions to interact with websites and applications. It leverages vision-language models like Qwen-VL for element detection and interaction. ([TechRadar]2)

  3. ScreenAgent: A vision-language model-driven agent that controls computers by observing screenshots and performing GUI actions. It includes a dataset of screenshots and action sequences for training.

  4. OSCAR: An open-source agent system developed by Université de Montréal & Mila that achieves strong performance using screenshot-based interaction. ([GitHub]3)

🛠️ Integration Framework

1. Central Orchestrator

  • Function: Acts as the command center, managing task assignments and coordinating between agents.([AI Agent Store]4)

  • Implementation: Develop a scheduler that assigns tasks based on agent capabilities and current workload.

2. Agent Integration

  • Agent S: Handles complex, multi-step tasks requiring learning from past experiences.([Hugging Face]5)

  • Open Computer Agent: Manages web-based interactions, such as form filling and navigation.

  • ScreenAgent: Executes tasks requiring precise GUI interactions based on visual inputs.

  • OSCAR: Focuses on tasks that benefit from screenshot-based analysis and interaction.

3. Communication Protocol

  • Standardization: Establish a common API or messaging protocol (e.g., RESTful API, gRPC) for inter-agent communication.

  • Data Exchange: Define data formats for task descriptions, status updates, and results to ensure interoperability.

4. Task Management

  • Queue System: Implement a task queue that prioritizes and assigns tasks to appropriate agents.

  • Monitoring: Track task progress, handle retries on failures, and log outcomes for analysis.

🧪 Implementation Steps

  1. Environment Setup:

    • Agent S: Clone the repository and follow the setup instructions. ([GitHub]6)

    • Open Computer Agent: Access the Hugging Face Space and integrate it into your system. ([Hugging Face]7)

    • ScreenAgent: Clone the repository and set up the environment as per the guidelines.

    • OSCAR: Clone the repository and configure it according to the documentation.

  2. Develop Central Orchestrator:

    • Language: Choose a programming language (e.g., Python) for developing the orchestrator.

    • Functionality: Implement task scheduling, agent communication, and monitoring features.

  3. Define Communication Protocol:

    • API Design: Design RESTful APIs or use messaging queues (e.g., RabbitMQ) for communication.

    • Data Formats: Use JSON or Protocol Buffers for structured data exchange.

  4. Testing and Validation:

    • Unit Tests: Write tests for individual components to ensure they function correctly.

    • Integration Tests: Test the interaction between agents and the orchestrator.

    • Performance Testing: Assess the system's performance under various workloads.

🔒 Security and Maintenance

  • Authentication: Implement authentication mechanisms to secure agent communications.

  • Logging: Maintain logs for auditing and debugging purposes.

  • Updates: Regularly update agents and dependencies to incorporate improvements and security patches.

By following this framework, you can effectively integrate Agent S, Open Computer Agent, ScreenAgent, and OSCAR into a unified system that leverages their individual strengths for comprehensive AI-driven computer control.

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