In December 2025, something significant happened in AI research: an open-source agent became the first to surpass human-level performance on OSWorld, the standard benchmark for computer-use automation. Agent S3, built by Simular AI, scored 72.60% — just 0.24 percentage points above the human baseline of ~72.36%.

The margin is narrow. But the direction is clear.

This guide covers how to install and run Agent S3 using the official gui-agents Python package. All commands are sourced directly from the Simular AI GitHub README.

⚠️ Context note: The OSWorld 72.60% figure was achieved in December 2025 using Behavior Best-of-N scaling, not the base model alone. The base Agent S3 model reaches ~66% in the 100-step setting; the 72.60% result uses Best-of-N selection across multiple rollouts. Both figures outperform all previous published benchmarks. The brightcoding.dev tutorial (May 2026) covers the same framework — this guide uses the official source.

What Is Agent S3?

Agent S3 is the third generation of Simular AI’s open-source computer-use agent framework. Unlike traditional automation tools that use brittle CSS selectors or hardcoded coordinates, Agent S3 uses computer vision and large language models to perceive and manipulate screen elements dynamically — the same way a human does.

Key capabilities:

  • Works across Windows, macOS, and Linux
  • Interacts with any GUI application without requiring API access
  • Integrates with OpenAI, Anthropic, Gemini, Azure OpenAI, and vLLM inference
  • Ships as the PyPI package gui-agents

The same codebase that achieves benchmark-leading scores is available to any developer with a pip command.

Prerequisites

  • A single-monitor setup (Agent S3 is designed for single-screen use)
  • Python 3.x environment
  • An API key for at least one supported LLM provider (OpenAI, Anthropic, Gemini, etc.)
  • Tesseract OCR installed on your system
  • A grounding model endpoint (see below)

⚠️ Security warning from the official README: Agent S3 runs Python code to control your computer. Use with care. Only run the agent in trusted environments and with trusted inputs. The --enable_local_env flag allows the agent to execute arbitrary Python and Bash code locally — treat this flag with extra caution.

Step 1: Install gui-agents

pip install gui-agents

If you want to run Agent S3 while making changes to the source code:

git clone https://github.com/simular-ai/Agent-S
cd Agent-S
pip install -e .

Step 2: Install Tesseract OCR

Agent S3 uses Pytesseract for text recognition, which requires Tesseract as a system dependency:

macOS:

brew install tesseract

For Linux and Windows, refer to the Tesseract installation guide for your platform.

Step 3: Configure API Keys

Option 1: Environment variables (recommended for development)

Add to your .bashrc (Linux) or .zshrc (macOS):

export OPENAI_API_KEY=your_openai_key_here
export ANTHROPIC_API_KEY=your_anthropic_key_here
export HF_TOKEN=your_huggingface_token_here

Option 2: In your Python script

import os
os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"

Step 4: Set Up a Grounding Model

A grounding model handles the visual perception side of computer-use — identifying UI elements by their position and type on screen. This is a required component for running Agent S3.

The official README recommends UI-TARS-1.5-7B, hosted on Hugging Face Inference Endpoints or another provider.

See the Hugging Face Inference Endpoints documentation for setup instructions on deploying a hosted grounding model endpoint.

Step 5: Run Agent S3

Once your grounding model endpoint is running (e.g., at http://localhost:8080), launch Agent S3:

agent_s \
    --provider openai \
    --model gpt-5-2025-08-07 \
    --ground_provider huggingface \
    --ground_url http://localhost:8080 \
    --ground_model ui-tars-1.5-7b \
    --grounding_width 1920 \
    --grounding_height 1080

Required Parameters

Parameter Description
--provider Main LLM provider (openai, anthropic, etc.)
--model Main generation model name
--ground_provider Provider for the grounding model
--ground_url URL of the grounding model endpoint
--ground_model Name of the grounding model
--grounding_width Output coordinate resolution width from grounding model
--grounding_height Output coordinate resolution height from grounding model

Enable Local Code Execution (Optional)

For tasks involving code execution, file manipulation, or system automation, add --enable_local_env:

agent_s \
    --provider openai \
    --model gpt-5-2025-08-07 \
    --ground_provider huggingface \
    --ground_url http://localhost:8080 \
    --ground_model ui-tars-1.5-7b \
    --grounding_width 1920 \
    --grounding_height 1080 \
    --enable_local_env

⚠️ This flag enables execution of arbitrary Python and Bash code on your local machine. Only use in trusted environments.

Practical Use Cases

Agent S3 is particularly well-suited for:

  • Legacy system automation: Any application that lacks a modern API but has a UI
  • Cross-application workflows: Tasks that span multiple applications (e.g., copy data from a spreadsheet, paste into a web form, download a confirmation)
  • Automated QA testing: UI testing without brittle selectors
  • Repetitive desktop tasks: Anything a human would do by clicking and typing repeatedly

The framework works across Windows, macOS, and Linux, making it broadly applicable for enterprise workflows.

Don’t Want to Self-Host?

Simular AI offers a managed cloud deployment at cloud.simular.ai if you’d rather try the capability without setting up local infrastructure.

Additional Resources

Sources

  1. Simular AI / Agent-S — Official GitHub README
  2. BrightCoding — Agent S: The AI That Controls Computers Like Humans
  3. Simular AI — Agent S3 Blog Post
  4. OSWorld Benchmark Leaderboard

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260531-2000

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