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Skyvern is an AI-powered browser automation framework that uses LLMs and computer vision to automate web workflows without brittle selectors. It provides both a Python SDK extending Playwright and a cloud-hosted service with a no-code workflow builder.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | Skyvern-AI/skyvern |
| Owner | Skyvern-AI |
| Primary language | Python |
| License | AGPL-3.0 — OSI-approved |
| Stars | 22.1k |
| Forks | 2.1k |
| Open issues | 216 |
| Latest release | v1.0.45 (2026-07-07) |
| Last updated | 2026-07-08 |
| Source | https://github.com/Skyvern-AI/skyvern |
What skyvern is
Built in Python, Skyvern augments Playwright with vision-based agent capabilities for autonomous task execution. It employs multi-agent reasoning to comprehend web pages visually and execute actions via natural language prompts, replacing XPath-dependent automation.
Get the skyvern source
Clone the repository and explore it locally.
git clone https://github.com/Skyvern-AI/skyvern.gitcd skyvern# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Configure LLM credentials (.env) before deployment; Skyvern Cloud offers managed infrastructure with bundled CAPTCHA solvers and anti-bot detection.
- Database choice impacts scale: default SQLite for development, Postgres required for production multi-instance deployments.
- Python 3.11–3.13 required locally; Windows needs Rust and C++ dev tools. Docker Compose setup available for containerized deployment.
- LLM cost per automation step can accumulate; test and validate prompts on representative pages before full-scale rollout.
- Vision-based interaction may fail on heavily obfuscated, JavaScript-heavy, or CAPTCHA-protected sites despite built-in CAPTCHA solver claims.
When to avoid it — and what to weigh
- Real-time Performance Requirements — LLM inference adds latency; unsuitable for latency-critical or high-throughput automation tasks where sub-second response times are required.
- Cost-Sensitive Low-Volume Automation — LLM API calls per workflow step increase operational costs significantly compared to selector-based solutions; not economical for simple, high-repetition tasks.
- Proprietary/Closed-Source LLM Requirement — Skyvern relies on external LLM providers (OpenAI, etc.) or self-hosted models; if your security policy forbids cloud LLM calls, deployment is complex.
- AGPL Copyleft Incompatibility — AGPL-3.0 license requires derivative works and network services to open-source their code; unsuitable if you need to keep proprietary automation logic private.
License & commercial use
AGPL-3.0 (GNU Affero General Public License v3.0). This is a copyleft license requiring source code disclosure of derivative works and network services that use the software. Any modifications or usage in a networked/SaaS context require source disclosure.
Requires careful review. AGPL-3.0 is not a permissive license; commercial use is permitted only if you comply with source-code disclosure requirements. If your automation logic or proprietary modifications must remain private, AGPL-3.0 poses significant risk. Consider: (1) using Skyvern Cloud (managed service, licensing may differ), (2) licensing exceptions from maintainers, or (3) legal review before commercial deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
LLM inference sends page content (HTML, visual data) to external LLM providers or local models—sensitive data exposure risk. CAPTCHA solving capability suggests anti-bot resilience, but relies on third-party CAPTCHA services. No explicit security audit or threat model documented. Self-hosted deployments require LLM endpoint security hardening. Credential storage (Bitwarden, 1Password integration) offloads secrets but adds supply-chain dependency.
Alternatives to consider
Selenium + Custom Vision Logic
Lower cost, permissive licensing, full control over logic; requires engineering effort to build LLM integration layer.
UiPath or Blue Prism (RPA Platforms)
Enterprise-grade RPA with visual recognition built-in, stronger maintenance SLAs; significantly higher licensing and operational cost.
Playwright (vanilla) + Manual Selectors
Simple, zero LLM dependency, permissive MIT license; breaks on layout changes, unsuitable for cross-site workflows.
Build on skyvern with DEV.co software developers
Skyvern simplifies cross-site workflow automation using vision-based agents. Review AGPL-3.0 licensing implications, validate LLM costs, and test on representative sites before production rollout.
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skyvern FAQ
Can I use Skyvern commercially?
What LLM models are supported?
How much does LLM inference cost per workflow?
Does Skyvern work offline?
Build it with a software development company
DEV.co is a software development company with a dedicated AI development practice. Whether skyvern is a component or the centerpiece, we design the surrounding ai frameworks architecture and ship it.
Ready to Deploy AI-Powered Automation?
Skyvern simplifies cross-site workflow automation using vision-based agents. Review AGPL-3.0 licensing implications, validate LLM costs, and test on representative sites before production rollout.