Agent S
A GUI automation framework that turns screenshots and model decisions into computer actions.
Per-dimension scores and reasoning
Evidence: README explicitly warns that the local coding environment executes arbitrary code and recommends use in trusted environments, showing limited consideration of least privilege (least_privilege=1). However, no user confirmation mechanism (user_confirmation=0), data flow transparency (data_flow_transparency=0), sensitive data handling (sensitive_data_handling=0), or dependency security (dependency_security=0). External effects: README warns about code execution risks but no rollback mechanism (rollback=0). Source attribution: README provides paper citations and author info (source_attribution=1). Deductions: lack of security mechanisms and transparency.
Evidence: README and code examples are consistent, providing CLI and SDK usage, good self-consistency (self_consistency=2). Dependency availability: requirements.txt lists dependencies but no version pinning or integrity checks (dependency_availability=1). Failure messages: test_providers.py checks config errors but not runtime failures (failure_messages=1). Deductions: dependencies not pinned, limited failure handling.
Evidence: README identifies target audience (researchers, developers) and use cases (OSWorld, WindowsAgentArena, AndroidWorld), with multi-platform support (audience_and_scenarios=2). Capability boundaries: mentions single monitor limitation and local env risks (capability_boundaries=2). Trigger precision: detailed CLI parameters but no explicit trigger conditions (trigger_precision=1). Environment fit: supports Linux, macOS, Windows with platform-specific deps (environment_fit=2). Deductions: trigger conditions unclear.
Evidence: README has clear structure with TOC, installation, usage, citations (information_architecture=2). Install notes detailed with pip and extra deps (install_notes=2). Naming stability: version history but no naming conventions (naming_stability=1). Examples and FAQ: CLI and SDK examples but no FAQ (examples_and_faq=2). Known limitations: mentions single monitor and local env risks but not comprehensive (known_limitations=1). License: Apache-2.0 full text (license=3). Versioning/changelog: README updates but no separate CHANGELOG (versioning_changelog=2). Maintenance responsibility: no maintainers or contribution guidelines (maintenance_responsibility=1). Deductions: missing FAQ, full limitations, maintenance info.
Evidence: Output usability: CLI and SDK usage, output is executable code (output_usability=2). Marginal value: claims to surpass human performance but no independent verification (marginal_value=2). Cost-benefit: requires multiple API keys and grounding model, high cost, no cost analysis (cost_benefit=1). Deductions: cost-benefit not quantified.
Evidence: README provides paper links and citations, traceable (claim_traceability=2). Cross-source corroboration: multiple benchmark results but no independent reproduction (cross_source_corroboration=1). Fact-inference separation: README distinguishes results and inferences but some claims not clearly labeled (fact_inference_separation=1). Deductions: lack of independent verification and clear separation.
- The local coding environment executes arbitrary code; use only in trusted environments and consider sandboxing.
- Requires multiple API keys and a grounding model, which can be costly; evaluate budget.
- Dependencies are not pinned, posing supply chain risks.
- No user confirmation mechanism; automated actions may cause unintended changes.
What does this agent do, and when should you use it?
Agent S is an open-source framework for autonomous computer interaction, with Agent S3 presented as the current version in the README. Its documented setup combines a main generation model, an OSWorldACI grounding agent, and an optional LocalEnv. The main model plans actions while the grounding model maps them to executable screen coordinates. It exposes both the `agent_s` CLI and a Python SDK centered on `AgentS3` and `OSWorldACI`; the SDK example captures a screenshot with PyAutoGUI and executes returned Python action code. The project states support for Linux, macOS, and Windows, but is designed for a single-monitor display. It documents main-model paths for Azure OpenAI, Anthropic, Gemini, OpenRouter, and vLLM, while requiring users to configure a separate grounding endpoint.
A caller supplies a task instruction and a screenshot. In the SDK example, pyautogui.screenshot() captures the display, the image is converted to PNG bytes, and AgentS3.predict(instruction, observation) receives those bytes in observation["screenshot"]. AgentS3 uses the configured generation engine, while OSWorldACI uses a separate grounding engine plus grounding_width and grounding_height to produce executable actions; the example runs the result with exec(action[0]). The agent_s CLI accepts the main-model provider and model plus the grounding provider, URL, model, and coordinate resolution. With --enable_local_env, the agent can use call_code_agent to run Python and Bash locally for file, data, system-automation, or coding tasks.
- A developer automating multi-step GUI work on a single-monitor Linux, macOS, or Windows machine.
- A researcher deploying Agent S3 in OSWorld to evaluate computer-use tasks.
- A team with an OpenAI, Anthropic, Gemini, OpenRouter, or vLLM inference path that needs a separate UI grounding model for screen-coordinate actions.
- An application developer who needs to capture the current screen in Python, send an instruction such as “Close VS Code,” and run the returned action.
- A user in a trusted environment who wants the agent to process CSVs, files, or scripts through the optional local coding environment.
What are this agent's strengths and limitations?
- It separates the main reasoning model from the grounding model, allowing a general model to be paired with a dedicated UI coordinate model.
- It offers both the
agent_sCLI and a Python SDK usingAgentS3andOSWorldACI. - The README explicitly documents Linux, macOS, and Windows support, plus an OSWorld deployment path.
- The optional
LocalEnvextends GUI interaction with local code, file, and data operations.
- A working deployment needs a separate grounding provider, endpoint, model, and coordinate resolution; a main-model API key alone is insufficient.
- The documented design assumes a single monitor, so multi-monitor use is outside the stated setup.
- LocalEnv executes arbitrary Python and Bash with the launching user's permissions, and the README limits it to trusted environments.
- Although several main-model providers are listed, the recommended configuration uses a specific main model and UI-TARS grounding model; alternate combinations require user validation.
How do you install or deploy this agent?
Install the released package with pip install gui-agents. For editable development after cloning the repository, run pip install -e .. Install Tesseract; the macOS command shown is brew install tesseract, and the README says PyTesseract requires it. Configure at least one main-model credential, for example export OPENAI_API_KEY=<YOUR_API_KEY>; ANTHROPIC_API_KEY and HF_TOKEN are also documented. You must additionally provide a grounding-model provider, URL, model name, and output coordinate resolution.
How do you use this agent?
For UI-TARS-1.5-7B grounding, run: 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. UI-TARS-72B uses --grounding_width 1000 --grounding_height 1000. For Python integration, construct OSWorldACI(env=local_env, platform=current_platform, engine_params_for_generation=engine_params, engine_params_for_grounding=engine_params_for_grounding), then AgentS3(engine_params, grounding_agent, platform=current_platform), and call agent.predict(). Add --enable_local_env or create LocalEnv() only in a trusted environment, because it enables arbitrary local Python and Bash execution.
How does this agent compare with similar options?
The README claims that Agent S2 outperformed OpenAI CUA/Operator and Anthropic Claude 3.7 Sonnet Computer-Use on the benchmarks it names, and that Agent S3 reached 72.6% on OSWorld with Behavior Best-of-N. It does not provide a workflow-, deployment-, or cost-level comparison with those alternatives.
FAQ
What credentials and services are required?
OPENAI_API_KEY, ANTHROPIC_API_KEY, and HF_TOKEN.Can it execute code on my machine?
--enable_local_env is enabled or LocalEnv is used in the SDK. That mode can run Python and Bash with the same permissions as the user who launched the agent.