Stagehand vs Browser Use: AI Browser Agent Guide

Stagehand and Browser Use are common AI browser automation frameworks. Both let you control a browser with natural language instructions powered by LLMs. But the choice between them isn't obvious. Stagehand is TypeScript-first and favors hybrid control where you mix AI with traditional code. Browser Use is Python-first and favors full autonomy where you describe a goal and the agent figures out every step.

In this article, we'll compare both frameworks across language ecosystem, API design, LLM support, and community. You'll get a clear decision framework for picking the right tool, plus guidance on running either framework in production. Let's get started!

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Quick Comparison Table

Here's a side-by-side look at both frameworks to help you decide fast:

The quick answer: if you're a Python developer who wants full autonomy, go with Browser Use. If you're a TypeScript developer who wants hybrid control over each step, go with Stagehand. The sections below explain why.

What Is Browser Use?

Browser Use is an open-source Python library that lets LLMs control web browsers. You describe a goal in plain language, and Browser Use's agent figures out the clicks, typing, navigation, and data extraction needed to complete the task.

Browser Use runs on Playwright under the hood and takes a screenshot-based approach. The AI sees what a human would see on the screen, then decides what action to take next. The agent loop continues until the task completes or hits a step limit.

Browser Use's biggest strengths are:

• Full autonomy: Give the agent a goal like "find the cheapest flight from NYC to London next Friday" and Browser Use handles every step without you writing navigation code
• Local LLM support via Ollama: Run models locally for free, private inference. No API costs, no data leaving your machine
• Large community: With 80k+ GitHub stars and an active contributor base, Browser Use has extensive third-party tutorials and integrations
• Cloud platform: Browser Use also offers a cloud platform for running agents at scale without managing browser infrastructure

Here's a basic Browser Use agent:

import os
import asyncio

from browser_use import Agent, ChatGoogle

# get a free Google API key for Gemini
# https://aistudio.google.com/

os.environ["GOOGLE_API_KEY"] = "Your Google API KEY"

async def main():
    llm = ChatGoogle(model="gemini-flash-latest")

    agent = Agent(
        task=(
            "Find the top 3 trending repositories on GitHub today"
        ),
        llm=llm,
    )

    result = await agent.run()
    print(result.final_result())

if __name__ == "__main__":
    asyncio.run(main())

The code creates an agent with a natural language task and a default LLM. The agent.run() method starts the autonomous loop where Browser Use navigates to GitHub, finds the trending page, and extracts the top repositories. You don't write any selectors or navigation logic.

Now let's look at the other side of the comparison.

What Is Stagehand?

Stagehand is an open-source browser automation framework built by Browserbase. Stagehand takes a hybrid approach that combines AI-powered actions with traditional code-based control. Instead of handing full control to an AI agent, you write the automation flow and use AI for the parts that need flexibility.

Stagehand v3 moved to a CDP-native architecture that talks directly to the browser through the Chrome DevTools Protocol. The v3 update removed the Playwright dependency, cutting round-trip time and improving performance by 44% on complex DOM interactions.

Stagehand's key strengths include:

• Hybrid AI + code control: Combine deterministic code for reliable steps with AI for changing elements. Use act() for clicking, extract() for pulling data, and observe() for finding elements
• Performance: Element caching reduces repeated LLM calls, and the CDP-native driver minimizes overhead
• Self-healing execution: When DOM structures change, Stagehand's AI adapts without breaking your automation
• iframe and shadow DOM support: v3 handles deeply nested iframes and shadow DOMs that trip up other tools

Here's a basic Stagehand example:

// get a free Google API key for Gemini
// https://aistudio.google.com/
process.env.GOOGLE_GENERATIVE_AI_API_KEY = "Your Google API key";

import { Stagehand } from "@browserbasehq/stagehand";

async function main() {
  const stagehand = new Stagehand({
    env: "LOCAL",
    model: "google/gemini-2.5-flash",
  });

  await stagehand.init();

  const page = stagehand.context.pages()[0];
  await page.goto("https://web-scraping.dev/products");

  const data = await stagehand.extract(
    "Extract all product names and prices from this page."
  );
  console.log(data);

  await stagehand.act("Click on the next page button.");
  await stagehand.close();
}

main();

The code initializes Stagehand with a local browser, navigates to a page, extracts product data using natural language, and then performs a click action. Notice how you control the flow (navigation, when to extract, when to click) while AI handles the flexible parts (finding elements, parsing content).

Head-to-Head Comparison

Language and Ecosystem

Browser Use lives in the Python ecosystem. If your team builds data pipelines, ML workflows, or backend services in Python, Browser Use drops right in. Python's async support through asyncio powers the agent loop, and the entire data science toolkit (pandas, numpy, scikit-learn) works alongside Browser Use for processing extracted data.

Stagehand lives in the TypeScript/JavaScript ecosystem. If your team builds web applications, Node.js services, or full-stack TypeScript projects, Stagehand fits naturally. Stagehand v3 also ships Python and Go SDKs, but TypeScript remains the primary and best-supported language.

Pick the framework that matches your team's existing stack. Switching languages adds friction that outweighs any feature difference.

API Design Philosophy

Browser Use follows a high-autonomy design. You give the agent a goal, and the agent decides every step:

agent = Agent(task="Book the cheapest hotel in Paris for March 15-17", llm=llm)
await agent.run()

Stagehand follows a step-by-step control design. You define the workflow and use AI for individual steps:

await page.goto("https://hotels.example.com");
await stagehand.act("Set destination to Paris");
await stagehand.act("Select check-in March 15 and check-out March 17");
await stagehand.act("Sort by price low to high");
const hotels = await stagehand.extract("Extract the first 3 hotel names and prices");

The trade-off is autonomy vs. predictability. Browser Use handles complex, multi-step workflows with less code. Stagehand gives you control over each step, making automation easier to debug and reproduce. For production scraping where you need consistent, repeatable results, Stagehand's fine-grained control reduces unexpected behavior.

LLM Support and Costs

Both frameworks support the major LLM providers:

• OpenAI: GPT-4o, GPT-4o-mini
• Anthropic: Claude Sonnet, Claude Haiku
• Google: Gemini 2.5 Flash, Gemini 2.5 Pro

Browser Use has one big advantage: Ollama support for local LLMs. You can run models like Qwen, DeepSeek, or Llama locally through Ollama with zero API costs. Local inference also keeps your data private since nothing leaves your machine.

Stagehand doesn't recommend local models and works best with cloud-hosted LLMs. Each act() or extract() call consumes LLM tokens, and costs scale with the model you choose. Autonomous goals through the agent API use more tokens than individual atomic operations.

For cost-sensitive projects or privacy-focused use cases, Browser Use with Ollama offers a truly free option.

Community and Documentation

Browser Use has the larger community with 80k+ GitHub stars compared to Stagehand's ~21k. A larger community means more third-party tutorials, GitHub threads, and community-built integrations.

Stagehand has a smaller but focused community backed by Browserbase. Stagehand's official documentation covers migration guides (v2 to v3), working examples, and full API references. Both frameworks have responsive maintainers who address issues quickly.

When to Choose Browser Use

Browser Use is the right choice when your requirements match these scenarios:

• You work in Python and want to stay in the Python ecosystem for your automation pipeline
• You want full agent autonomy where you describe a goal and the AI handles every navigation step
• You need local LLM support for privacy, cost reduction, or offline operation through Ollama
• You're building data pipelines or ML workflows where Browser Use feeds directly into pandas, scikit-learn, or other Python tools
• You prefer a larger community with more third-party resources and examples
• You want free inference using Ollama with open-source models or Gemini's free tier

When to Choose Stagehand

Stagehand is the right choice when your requirements match these scenarios:

• You work in TypeScript/JavaScript and want native integration with your web development stack
• You want hybrid AI + code control for predictable, debuggable automation
• You need repeatable, production-grade workflows where each step produces consistent results
• You're building scraping or testing automation where precise element targeting matters more than full autonomy
• You value Browserbase's commercial backing and enterprise support options
• You need iframe and shadow DOM handling that v3's CDP-native architecture handles well

Running Both in Production with Scrapfly

Both Browser Use and Stagehand face the same scaling challenges in production:

• Single machine bottleneck: Running multiple browser instances on one server limits throughput
• IP blocking: Protected sites detect and block repeated requests from the same IP
• Browser fingerprinting: Default browser configurations get flagged by anti-bot systems
• Session management: Handling cookies, authentication, and state across distributed browsers adds complexity

Scrapfly's Cloud Browser works with both frameworks. You keep your existing Browser Use or Stagehand code and swap the browser endpoint to Scrapfly. The same automation logic runs on Scrapfly's infrastructure with built-in proxy rotation, anti-fingerprinting, and session management.

import asyncio

from browser_use import Agent, Browser, ChatBrowserUse, ChatOpenAI

# connect to Scrapfly's cloud browser via CDP
browser = Browser(
    cdp_url="wss://browser.scrapfly.io?key=YOUR_API_KEY"  # Get a CDP URL from any provider
)

# define the LLM model to use for the agent, use in-house browser-use LLM model
llm_model = ChatBrowserUse()

# or use an  OpenAI model
# llm_model = ChatOpenAI(model="gpt-4o-mini") # OpenAI model

agent = Agent(
    # define the task to be performed by the agent
    task="scrape the product data from https://web-scraping.dev/products",
    llm=ChatBrowserUse(),
    browser=browser,
)

async def main():
    # run the agent
    await agent.run()

asyncio.run(main())

import { Stagehand } from "@browserbasehq/stagehand";

async function main() {
  // initialize Stagehand with external CDP browser connection
  const stagehand = new Stagehand({
    env: "LOCAL",  // local means are we aren't using browserbase, we are using a Scrapfly's cloud browser over cdp
    localBrowserLaunchOptions: {
      // connect to Scrapfly's cloud browser via CDP
      cdpUrl: "wss://browser.scrapfly.io?key=YOUR_API_KEY"
    }
  });

  // initialize the stagehand
  await stagehand.init();

  // create an agent with specific model configuration
  const agent = stagehand.agent({
    model: {
      // choose the llm model to use and add its api key
      modelName: "openai/computer-use-preview",
      apiKey: "YOUR_API_KEY"
    },
    // add a system prompt to the agent
    systemPrompt: "You are an AI browser automation agent. Follow instructions precisely."
  });

  // define the task to be performed by the agent
  const task = `
    Go to https://web-scraping.dev/products
    Extract the product names and prices
    Return the data as JSON
  `;

  // execute the agent workflow
  const result = await agent.execute(task);

  console.log("Agent workflow result:", result);

  // close the connection
  await stagehand.close();
}

main();

The advantage is flexibility. You don't need to pick a cloud provider based on which framework you use. Try both Browser Use and Stagehand, switch between them, or run both in different parts of your pipeline. Scrapfly's Cloud Browser connects through standard CDP or Playwright protocols that both frameworks support. Check the Stagehand integration docs and Browser Use integration docs for setup details.

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Summary

Stagehand and Browser Use solve the same problem with different philosophies. Browser Use gives Python developers full agent autonomy. Stagehand gives TypeScript developers hybrid control where AI and code work together.

The decision comes down to your context. Choose Browser Use if you work in Python, want autonomous agents, or need local LLM support through Ollama. Choose Stagehand if you work in TypeScript, want predictable step-by-step automation, or need fine-grained control over each action.

Both frameworks are excellent tools. Start with the one that matches your language and workflow. When you're ready to scale, Scrapfly's Cloud Browser supports both without locking you into a single framework.

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