Jev Ultrafast vs Stagehand
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A browser agent that picks one action and element per step, using a single model call per decision.
Open source · MIT
TypeScript, Python and Go SDK for letting AI control a web browser to act on and extract data from sites.
The short answer
Choose Jev Ultrafast if you need…
- Developers experimenting with fast, low-cost browser automation driven by natural-language goals
- Researchers studying structured action spaces for web agents
- People who want to read a compact codebase to understand how a browser agent loop works
- Anyone who can run Python and has API keys for the required model services
Choose Stagehand if you need…
- Developers building browser agents that must work on sites they do not control
- Teams extracting structured data from web pages that lack an API
- Engineers who already know Playwright and want AI-assisted steps
- Projects needing TypeScript, Python or Go bindings of the same workflow
Side-by-side
| Jev Ultrafast | Stagehand | |
|---|---|---|
| Type | AI model / agent | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 24.1k | 25.9k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Python | TypeScript |
| Last code update | 5 days ago | 6 days ago |
What each one is
Jev Ultrafast
Jev Ultrafast is a Python browser agent. You give it a goal in plain language, and it reads the visible controls on a web page, chooses an operation such as clicking or typing, and carries it out in Chrome. A small language model writes text only when the chosen operation is typing. The README reports a 7.1-second Google Flights search, but this is a single demo run, not a general benchmark.
Stagehand
Stagehand is an SDK that lets code drive a real web browser using plain-language instructions alongside Playwright-style methods. You can ask it to observe elements, act on a page (such as clicking a button), or extract data into a schema you define. Sessions such as login cookies can persist between runs.
Best for
Jev Ultrafast
- Developers experimenting with fast, low-cost browser automation driven by natural-language goals
- Researchers studying structured action spaces for web agents
- People who want to read a compact codebase to understand how a browser agent loop works
- Anyone who can run Python and has API keys for the required model services
Stagehand
- Developers building browser agents that must work on sites they do not control
- Teams extracting structured data from web pages that lack an API
- Engineers who already know Playwright and want AI-assisted steps
- Projects needing TypeScript, Python or Go bindings of the same workflow
Strengths
Jev Ultrafast
- The README describes one model round trip per decision, which it says cut browser protocol calls from 1,092 to 101 in its tests
- Code is organized into a few small files, so the agent loop is readable
- MIT license and a public repository make it free to use and modify
Stagehand
- Familiar Playwright-style API for developers already using it
- Returns schema-validated data from extract() calls
- Available in TypeScript, Python and Go with MIT license
Not ideal for
Jev Ultrafast
- Users who need a finished no-code tool, since setup requires API keys and a command-line workflow
- Tasks needing shadow DOM, iframes, canvas, file uploads, or pop-up tabs, which the README says are outside this MVP
- Production workloads needing proven reliability, since the README says its benchmark covers one task on one browser profile
Stagehand
- People who only need fixed scripted browser tests with no AI, since the README centers on model-driven steps
- Non-programmers, because the examples require writing code and an API key for a model provider
- Users without Chrome installed for local runs, since the README says local runs need it
Trade-offs
Jev Ultrafast
- The README's evidence is limited to three repeats of one task, and the authors say it is not a general reliability benchmark
- Requires paid API keys (TypeSafe and an LLM provider) and a local Chrome with remote debugging enabled
- Parts of the web, such as shadow roots, frames, and canvas, are explicitly not supported
Stagehand
- Relies on a third-party model provider and its API key, so usage has external cost and data flow
- README is truncated, so full feature and configuration details could not be verified here
- Model-driven actions can behave differently across sites and runs
Getting started
Jev Ultrafast
Clone the repository
git clone https://github.com/browser-use/jev-ultrafast.gitEnter the project folder
cd jev-ultrafastStagehand
Install TypeScript package
pnpm add @browserbasehq/stagehand 'zod@~4.4.3'Install Python package
pip install stagehandFrequently asked questions
What is the difference between Jev Ultrafast and Stagehand?
Jev Ultrafast: A browser agent that picks one action and element per step, using a single model call per decision. Stagehand: TypeScript, Python and Go SDK for letting AI control a web browser to act on and extract data from sites. Jev Ultrafast suits developers experimenting with fast, low-cost browser automation driven by natural-language goals, while Stagehand suits developers building browser agents that must work on sites they do not control.
Are Jev Ultrafast and Stagehand free?
Jev Ultrafast: Free and open source. Stagehand: Free and open source.
Is Jev Ultrafast or Stagehand more popular?
Stagehand has more GitHub stars (25.9k) than Jev Ultrafast (24.1k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Jev Ultrafast and Stagehand?
Other options people compare them with include Browser Use, CloakBrowser, Page Agent, CrewAI, LangGraph.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.