Agent-S
Agent S is an open-source Python framework for building autonomous GUI agents that interact with computers like humans would. It achieves state-of-the-art performance on benchmarks like OSWorld (72.6% accuracy, surpassing human-level) and supports Windows, macOS, and Linux through a vision-language model pipeline with grounding components.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | simular-ai/Agent-S |
| Owner | simular-ai |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 12k |
| Forks | 1.4k |
| Open issues | 36 |
| Latest release | v0.3.2 (2025-12-16) |
| Last updated | 2026-05-13 |
| Source | https://github.com/simular-ai/Agent-S |
What Agent-S is
Agent S uses multimodal LLMs (gpt-5, Claude, Gemini) paired with grounding models (UI-TARS-1.5-7B) to perform GUI automation via screenshot analysis and action prediction. The framework includes in-context reinforcement learning, memory systems, RAG, and optional local code execution; requires external LLM API keys and grounding model hosting.
Get the Agent-S source
Clone the repository and explore it locally.
git clone https://github.com/simular-ai/Agent-S.gitcd Agent-S# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires API keys for at least one LLM provider (OpenAI, Anthropic, Gemini, etc.) and separate grounding model hosting (Hugging Face Inference Endpoints recommended); no free tier clearly stated.
- Security warning in README: agent executes Python/Bash code when local_env is enabled—only use in trusted environments with trusted inputs. Audit code paths carefully.
- Installation requires system dependency (tesseract via `brew install tesseract` on macOS); similar OS-level packages may be needed on Linux/Windows.
- Grounding model (UI-TARS-1.5-7B) must be self-hosted or accessed via third-party inference service; no SaaS option documented.
- Single-monitor limitation and lack of multi-window orchestration documentation may require custom wrappers for complex enterprise workflows.
When to avoid it — and what to weigh
- Fully Offline or Air-Gapped Environments — Agent S requires external LLM APIs (OpenAI, Anthropic, Gemini) and optionally Hugging Face Inference Endpoints. No bundled local model; running entirely offline is not straightforward.
- Deterministic, Auditable Action Logs Required — LLM-based agents are inherently non-deterministic. If compliance or repeatability auditing is critical, consider rule-based RPA tools instead.
- Multi-Monitor or Complex Display Setups — The framework explicitly targets single-monitor environments. Multi-monitor automation would require custom extensions not documented in the README.
- Strict Real-Time Latency Requirements — API call round-trips and grounding model inference add latency unsuitable for subsecond response requirements or high-throughput transaction processing.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license permitting commercial use, modification, and distribution under stated terms.
Apache-2.0 permits commercial use without explicit license fees. However, commercial deployment depends heavily on third-party LLM API costs (OpenAI GPT-5, Anthropic, Gemini) and grounding model hosting. No warranty or support guarantees are provided by the license; production use should be reviewed with legal counsel regarding liability, especially given the local code execution capability.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
The framework executes arbitrary Python and Bash code when local_env is enabled, creating a significant attack surface if inputs are untrusted. API keys for LLMs must be protected. No explicit authentication, encryption, or audit logging is documented. Running GUI agents on production machines grants the agent control over all visible applications and data. Network communication to external LLM/grounding services is required; TLS/HTTPS security depends on the provider. No formal security audit or vulnerability disclosure policy is mentioned.
Alternatives to consider
Anthropic Claude 3.5 Sonnet (Computer-Use Beta)
Proprietary, closed-source alternative with similar GUI automation capabilities; fully managed by Anthropic with different performance/cost trade-offs and no local control.
OpenAI Operator / CUA (Closed Beta)
OpenAI's computer-use agent; proprietary but integrated into OpenAI's ecosystem; different benchmarking baseline and no open-source access for customization.
UiPath / Automation Anywhere
Enterprise RPA platforms with visual workflow builders, multi-monitor support, and vendor support; deterministic rule-based, not LLM-based; mature for production but less flexible for new task types.
Build on Agent-S with DEV.co software developers
Agent S is a powerful open-source framework for autonomous computer tasks. Start with pip install gui-agents and explore agentic AI—or contact our team to evaluate it for your enterprise workflow automation needs.
Talk to DEV.coRelated on DEV.co
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Agent-S FAQ
Can Agent S run fully offline?
What are the costs of running Agent S in production?
Does Agent S support multi-monitor setups?
How do I deploy Agent S in a containerized environment?
Custom software development services
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Ready to Build Intelligent Automation?
Agent S is a powerful open-source framework for autonomous computer tasks. Start with pip install gui-agents and explore agentic AI—or contact our team to evaluate it for your enterprise workflow automation needs.