MemetikEdition 2026-09

Lists / AI infra

Best browser automation tools for AI agents (2026): What ChatGPT, Claude & Gemini Recommend

Playwright is named in 48 of 50 AI answers on browser automation for agents. See 15 tools ranked by how often 10 AI models name them.

Playwright is the browser automation tool AI models name most often for agents. It appears in 48 of 50 recorded answers and comes first in 21. Stagehand and Browserbase follow, each named in 41 of 50, then Browser Use at 40 with 15 first mentions. This list ranks 15 tools by how often ten AI models name them in answer to five fixed buyer questions. It measures visibility in AI answers, not product quality.

TL;DR

1. Playwright

Pick Playwright if your team wants deterministic browser control that an agent calls as a tool, and wants the name every model in this panel reaches for.

Measured: named 48 of 50 (Playwright 96%), first 21 of 50 (Playwright 42%), average position 2.58.

Playwright is the layer most answers build on, not the agent itself. GPT-5.6 Terra puts it plainly: “Use Playwright as your foundation.” Claude Sonnet 5 says: “Most AI agent tools are built on top of it.” That is the trade. Playwright gives an agent predictable actions, and the reasoning has to come from a model or a framework on top. Some answers open with that agent layer and mention Playwright later. GPT-5.6 Terra’s answer to the first question leads with Browserbase and Stagehand, for example.

Mastra’s guide says traditional browser automation tools such as Playwright and Selenium work well when every step can be defined in advance, while AI agents often need more flexibility. It describes Stagehand as an open-source browser automation framework built on Playwright.

Pros

Cons

Pricing: No public pricing is recorded.

2. Stagehand

Pick Stagehand if you already write Playwright code and want to hand only the unpredictable steps to a model.

Measured: named 41 of 50 (Stagehand 82%), first 7 of 50 (Stagehand 14%), average position 2.9.

Mastra’s guide describes Stagehand as an open-source browser automation framework built on Playwright. Its four primitives are act, extract, observe and agent. Browserbase develops it. That link is why some answers present the two as one stack. GPT-5.6 Terra’s answer to the first question does exactly that, with Stagehand on top and Browserbase underneath. Both are named in 41 of 50 answers, although the sheet does not count how often they share an answer. Claude Fable 5 is the one model where Stagehand is the most-named tool. The weak spot is Gemini 3.5 Flash, which names it in 2 of 5 answers while its sibling Gemini 3.6 Flash names it in all five.

Pros

Cons

Pricing: No public pricing is recorded.

Best for: developers who want to combine AI-native browser automation with deterministic Playwright code.

3. Browserbase

Pick Browserbase if you need hosted browsers for agents in production and want to keep your choice of framework.

Measured: named 41 of 50 (Browserbase 82%), first 1 of 50 (Browserbase 2%), average position 4.27.

Mastra’s guide says Browserbase provides managed cloud browser infrastructure for browser automation and AI agents. Developers launch isolated Browserbase sessions and connect using Playwright, Puppeteer, Selenium or Stagehand without managing their own browser fleet.

That position shapes its counts. Nine of ten models name it in at least four of five answers, yet it comes first once. Its average position of 4.27 is the latest of the top four. GPT-5.6 Terra is the outlier, naming it in 2 of 5 answers. For infrastructure products, answer share is the fairer comparison than first place.

Pros

Cons

Pricing: No public pricing is recorded.

Best for: teams that need managed browser infrastructure for AI agents and browser automation running in production, according to Mastra’s Browserbase entry.

4. Browser Use

Pick Browser Use if you want an agent that takes a goal in plain language and works out the steps, with the option to run it on your own machine.

Measured: named 40 of 50 (Browser Use 80%), first 15 of 50 (Browser Use 30%), average position 2.8.

Browser Use is the agent-first entry near the top of the count. Six models name it in all five answers, and Sonar Reasoning Pro names it more than any other tool. GPT-5.6 Terra and Gemini 3.6 Flash name it in 2 of 5 each. Its 15 first mentions are second only to Playwright’s. Stagehand, the next framework on the first-place count, has 7. Both Perplexity models name it in every answer (Browser Use 100% of Perplexity answers).

Mastra’s guide describes Browser Use as an open-source framework that lets agents interpret webpages and work out how to accomplish many tasks from a high-level objective.

Its own homepage says it is MIT licensed and runs in the cloud or on your own machine.

Pros

Cons

Pricing: pay as you go on credits, with no subscription, according to its homepage.

Best for: developers building browser agents that can complete tasks from high-level goals while remaining programmable through code.

5. Skyvern

Pick Skyvern if the sites your agent visits change often and you would rather not maintain selectors for them.

Measured: named 27 of 50 (Skyvern 54%), first 0 of 50 (Skyvern 0%), average position 5.26.

Mastra’s guide describes Skyvern as an AI workflow automation platform that uses real browsers and adapts as interfaces change, rather than depending entirely on predefined selectors. Skyvern is available as both a managed service and a self-hosted platform.

The models treat it as a specialist. It appears in more than half the answers and never first. Its named-first gap of 54 points equals Playwright’s, but Playwright turns 21 of its mentions into first place and Skyvern turns none. Claude Sonnet 5, Claude Fable 5 and Sonar Reasoning Pro name it in 4 of 5 answers. GPT-5.6 Terra and GPT-5.6 Luna name it once each. Its own site, skyvern.com, is one of five vendor-owned hosts cited in the recorded answers.

Pros

Cons

Pricing: No public pricing is recorded.

Best for: teams automating browser workflows across websites that change frequently or cannot be reliably scripted.

6. Selenium

Pick Selenium if your team already runs Selenium scripts and wants agent infrastructure that accepts them.

Measured: named 24 of 50 (Selenium 48%), first 1 of 50 (Selenium 2%), average position 3.88.

Selenium is named more as a compatibility target than as an agent tool. The model split is wide. GPT-5.6 Luna, Claude Sonnet 5 and Gemini 3.5 Flash name it in 4 of 5 answers. GPT-5.6 Sol, Gemini 3.6 Flash, Sonar Pro and Sonar Reasoning Pro name it once. Several answers to the comparison question, the one prompt that does not mention agents, list it beside Playwright, Cypress and Puppeteer as a test framework. That context lifts its count without saying much about agent use.

Mastra’s guide calls Playwright and Selenium traditional browser automation tools that work well when every step can be defined in advance. Its Browserbase entry lists Selenium among the frameworks developers can connect to Browserbase sessions.

Pros

Cons

Pricing: No public pricing is recorded.

7. Puppeteer

Pick Puppeteer if your automation already runs on it and you want a cloud browser provider that accepts it.

Measured: named 24 of 50 (Puppeteer 48%), first 0 of 50 (Puppeteer 0%), average position 4.29.

Puppeteer ties Selenium on mentions and trails it on first place and position. It was never named first. Google’s two models name it most (Puppeteer 70% of Google answers), with GPT-5.6 Luna and Gemini 3.5 Flash at 4 of 5 each. GPT-5.6 Sol, Sonar Pro and Sonar Reasoning Pro name it once each. In the comparison answers from GPT-5.6 Terra, GPT-5.6 Sol and GPT-5.6 Luna, it sits in a list of test and scripting frameworks rather than agent tools. For an agent buyer, its count mainly signals which existing scripts a cloud browser will run.

Mastra’s guide mentions Puppeteer as a way to connect to cloud browsers: developers can connect to Browserbase sessions using Playwright, Puppeteer, Selenium or Stagehand. Browserless also lists Playwright, Puppeteer and Selenium connections.

Pros

Cons

Pricing: No public pricing is recorded.

8. Bright Data

Consider Bright Data if you are an enterprise team whose agents hit sites that block bots, and you want the browser provider to handle that.

Measured: named 21 of 50 (Bright Data 42%), first 2 of 50 (Bright Data 4%), average position 6.05.

None of the captured guides covers Bright Data, so its record here is the recorded answers. Claude Sonnet 5 lists its Agent Browser under managed cloud browser infrastructure and describes it as suited to “enterprise teams needing production-ready infrastructure with built-in unlocking”. The model split carries the rest. Claude Sonnet 5 names it in all five answers and Claude Opus 5 in four. The three OpenAI models never name it. Its own domain, brightdata.com, is the third most-cited host in the answers’ sources and one of five vendor-owned hosts on that list. A buyer who asks only an OpenAI model will not see it.

Pros

Cons

Pricing: No public pricing is recorded.

9. Steel

Pick Steel if you want managed or self-hosted browser infrastructure that any framework, including Browser Use, can drive.

Measured: named 17 of 50 (Steel 34%), first 0 of 50 (Steel 0%), average position 6.06.

Mastra’s guide says Steel provides managed browser infrastructure, and developers can connect using Playwright, Puppeteer, Selenium, Browser Use or other CDP-compatible tools. Its capabilities include persistent browser sessions, browser profiles, proxy support and CAPTCHA handling. Steel is available as both a managed cloud service and a self-hosted deployment.

In the counts, Steel is an Anthropic name. Claude Opus 5 and Claude Sonnet 5 name it in 4 of 5 answers each (Steel 73.3% of Anthropic answers). The three OpenAI models and Sonar Pro never name it. It was never named first, like Kernel, Hyperbrowser and Anchor.

Pros

Cons

Pricing: No public pricing is recorded.

Best for: teams that want managed browser infrastructure compatible with multiple browser automation frameworks and AI agents.

10. Firecrawl

Consider Firecrawl if you want a managed browser sandbox for agents and your buyers research tools in Perplexity, where it is named most.

Measured: named 14 of 50 (Firecrawl 28%), first 1 of 50 (Firecrawl 2%), average position 6.

Most of Firecrawl’s mentions come from one model family. Sonar Pro names it in all five answers and Sonar Reasoning Pro in four (Firecrawl 90% of Perplexity answers). The three OpenAI models and Gemini 3.5 Flash never name it. Claude Sonnet 5 describes its Browser Sandbox as a fully managed browser environment for agents. Its own domain, firecrawl.dev, is the second most-cited host in the answers’ sources and the most-cited vendor-owned host. Firecrawl also publishes a ranked guide to browser agents that appears in the search results for this query. That guide was not used as a source for this page. A buyer should read Firecrawl’s Perplexity count alongside that citation footprint.

Pros

Cons

Pricing: No public pricing is recorded.

11. Cypress

Pick Cypress only if you want a front-end test framework, because every mention of it here comes from answers about testing.

Measured: named 9 of 50 (Cypress 18%), first 0 of 50 (Cypress 0%), average position 3.56.

Cypress is the clearest case of prompt wording moving a count. All nine mentions come from answers to “Compare the top browser automation tool options right now.” That is the only one of the five questions that does not mention AI agents. Nine of ten models named it there and nowhere else. Claude Sonnet 5 describes it as the tool that “Rounds out the testing-focused category alongside the above”. Its average position of 3.56 comes from those comparison answers, where the test frameworks sit near the top of the list. For an agent buyer, the count is evidence about test tooling, not agent tooling.

Pros

Cons

Pricing: No public pricing is recorded.

12. BrowserAct

Put BrowserAct on a long list rather than a shortlist, unless a model your team trusts named it for your use case, because only three models name it at all.

Measured: named 8 of 50 (BrowserAct 16%), first 2 of 50 (BrowserAct 4%), average position 3.13.

BrowserAct has the oddest profile on the sheet. It is named in only eight answers but first in two of them, and its average position of 3.13 is earlier than Browserbase’s. Every mention comes from three models. Claude Opus 5 and Sonar Pro name it in 3 of 5 answers, and Sonar Reasoning Pro in 2. The other seven models never name it. Its own domain, browseract.com, is one of five vendor-owned hosts cited in the answers, with 20 citations. No captured guide covers it, so this page holds no product claims about it. Treat it as a name to check, not a default.

Pros

Cons

Pricing: No public pricing is recorded.

13. Kernel

Pick Kernel if you already run an agent framework and want browser infrastructure underneath it without rewriting agent logic for a new SDK.

Measured: named 6 of 50 (Kernel 12%), first 0 of 50 (Kernel 0%), average position 6.67.

Kernel shows the distance between a guide’s ranking and the models’ answers. The panel names it in six answers and never first. Claude Fable 5 and Sonar Reasoning Pro name it twice each, Claude Sonnet 5 and Gemini 3.6 Flash once each. Claude Sonnet 5 cites the Mastra guide in one of its answers, and mastra.ai is cited 60 times across the answers’ sources. The guide’s top pick has not carried into the models’ lists.

Mastra’s guide lists Kernel first among its best AI browser automation platforms. It calls Kernel browser infrastructure, not a framework, that works underneath whatever agent stack a team already runs.

Pros

Cons

Pricing: No public pricing is recorded.

Best for: teams building production browser AI agents on any framework that need speed, reliability and infrastructure-level bot handling.

14. Hyperbrowser

Pick Hyperbrowser if you want cloud browsers and a Playwright-based agent framework from one vendor.

Measured: named 4 of 50 (Hyperbrowser 8%), first 0 of 50 (Hyperbrowser 0%), average position 7.25.

Mastra’s guide describes Hyperbrowser as a cloud browser platform for running browser automation and AI agents at scale. It is closely integrated with HyperAgent, an open-source framework that extends Playwright with AI capabilities. It also supports Browser Use, Claude Computer Use and OpenAI’s Computer-Using Agent. That bundle is the decision a buyer makes here: one vendor for both the browser and the agent layer. The models barely register it. Claude Fable 5 names it twice, Claude Sonnet 5 and Gemini 3.5 Flash once each. Its average position of 7.25 means it arrives late in the few answers that include it.

Pros

Cons

Pricing: No public pricing is recorded.

Best for: teams that want managed cloud browsers with built-in support for AI browser agents and browser automation.

15. Anchor

Pick Anchor if your agents log into the same applications again and again and need persistent, authenticated browser identities.

Measured: named 3 of 50 (Anchor 6%), first 0 of 50 (Anchor 0%), average position 9.

Mastra’s guide lists it as Anchor Browser, which provides managed cloud browser infrastructure for AI agents and browser automation. Its authentication and identity system lets teams create reusable browser profiles and persistent identities, so agents return to authenticated applications without repeating the login process.

The panel counts both “Anchor Browser” and “Anchor” as this vendor. Three answers name it: two from Claude Fable 5 and one from Claude Sonnet 5. No other model names it, and its average position of 9 puts it near the end of those lists. It is the least-named tool that still makes the ranking, which says more about its reach in AI answers than about its fit for login-heavy agents.

Pros

Cons

Pricing: No public pricing is recorded.

Best for: teams building production computer-use agents that require secure, authenticated browser sessions.

What counts as a browser automation tool for AI agents?

It is any product an AI agent uses to open, read and act on web pages. Mastra’s guide splits the category into three implementation models. Browser agents receive a goal and decide how to complete it. Hybrid frameworks combine AI-assisted actions with deterministic code. Browser infrastructure platforms supply the cloud browsers, sessions and operational tooling. The same guide notes that these approaches are often used together. This list covers all three, plus the classic browser libraries the models still name, because a buyer usually needs one product from the control side and sometimes one from the infrastructure side.

How the tools compare

Playwright leads, named in 48 of 50 answers. Below it sits a tight second tier: Stagehand and Browserbase at 41 each, Browser Use at 40. Then the count drops to Skyvern at 27 and to Selenium and Puppeteer at 24. The bottom five are each named in nine answers or fewer. Answer share is the share of recorded answers that name a tool. Named first is the share where it appears before any other tracked tool. Average position is where a tool falls in the order of tracked names, across the answers that name it, and lower is earlier.

Rank Vendor Named Answer share Named first First share Avg position
1 Playwright 48/50 Playwright 96% 21/50 Playwright 42% 2.58
2 Stagehand 41/50 Stagehand 82% 7/50 Stagehand 14% 2.9
3 Browserbase 41/50 Browserbase 82% 1/50 Browserbase 2% 4.27
4 Browser Use 40/50 Browser Use 80% 15/50 Browser Use 30% 2.8
5 Skyvern 27/50 Skyvern 54% 0/50 Skyvern 0% 5.26
6 Selenium 24/50 Selenium 48% 1/50 Selenium 2% 3.88
7 Puppeteer 24/50 Puppeteer 48% 0/50 Puppeteer 0% 4.29
8 Bright Data 21/50 Bright Data 42% 2/50 Bright Data 4% 6.05
9 Steel 17/50 Steel 34% 0/50 Steel 0% 6.06
10 Firecrawl 14/50 Firecrawl 28% 1/50 Firecrawl 2% 6
11 Cypress 9/50 Cypress 18% 0/50 Cypress 0% 3.56
12 BrowserAct 8/50 BrowserAct 16% 2/50 BrowserAct 4% 3.13
13 Kernel 6/50 Kernel 12% 0/50 Kernel 0% 6.67
14 Hyperbrowser 4/50 Hyperbrowser 8% 0/50 Hyperbrowser 0% 7.25
15 Anchor 3/50 Anchor 6% 0/50 Anchor 0% 9

Stagehand and Browserbase tie on mentions, and Stagehand has more first mentions, 7 against 1. Selenium and Puppeteer also tie on mentions, and Selenium has the one first mention between them. Apify and Notte were tracked and never named, so they carry no row.

Why is Browserbase named so often but almost never first?

Because the models describe a stack, and Browserbase is the second layer of it. Browserbase is named in 41 of 50 answers and first in 1, a gap of 80 points, the widest on the sheet. The answers show the reason. GPT-5.6 Terra tells the agent builder to “add a managed browser provider such as Browserbase only when you need cloud scale, isolation, persistent sessions, or operational tooling”. Infrastructure arrives as a conditional second step, after the framework.

The pattern holds across the infrastructure products. Steel, Kernel, Hyperbrowser and Anchor were never named first. Browserbase was first once. Bright Data, which Claude Sonnet 5 files under managed cloud browser infrastructure, was first twice. The first slot goes to the tool the agent drives: Playwright in 21 answers, Browser Use in 15, Stagehand in 7.

Mastra’s guide draws the same line. It groups Kernel, Browserbase, Steel, Hyperbrowser, Browserless and Anchor Browser as browser infrastructure platforms that provide cloud browsers, session management and operational tooling.

This changes how a buyer should read the table. Named first is a measure of which control layer the models default to. For infrastructure products it is close to meaningless, because the models almost never open with one. Compare infrastructure products with each other on answer share and on the model split, not on first place. On that basis Browserbase sits in a tier of its own, named in 41 answers against Bright Data’s 21 and Steel’s 17.

Where the models disagree

The models agree on the leader less than the totals suggest. Eight of ten models name Playwright more than any other tool. Claude Fable 5 names Stagehand in all five answers and Playwright in four. Sonar Reasoning Pro does the same with Browser Use.

The family split is sharper than the model split. The three OpenAI models, GPT-5.6 Sol, GPT-5.6 Terra and GPT-5.6 Luna, never name Bright Data, Steel, Firecrawl, BrowserAct, Kernel, Hyperbrowser or Anchor. Their answers stay with the frameworks and Browserbase, with Browserbase 66.7% and Browser Use 53.3% of OpenAI answers. The three Anthropic models are the opposite. They name Stagehand 100%, Browserbase 100% and Browser Use 100% of Anthropic answers, and they are the main source of Steel’s count.

Perplexity’s two Sonar models are the only ones that treat Firecrawl as a core name, at Firecrawl 90% of Perplexity answers. Google’s two models disagree with each other. Gemini 3.6 Flash names Stagehand in 5 of 5 answers and Browser Use in 2 of 5. Gemini 3.5 Flash reverses it, with Stagehand in 2 of 5 and Browser Use in 5 of 5.

The practical reading: the shortlist a team sees depends on the assistant it asks. Each cell in the per-model table is the number of that model’s five answers that named the tool.

Vendor GPT-5.6 Sol GPT-5.6 Terra GPT-5.6 Luna Claude Opus 5 Claude Sonnet 5 Claude Fable 5 Gemini 3.6 Flash Gemini 3.5 Flash Sonar Pro Sonar Reasoning Pro
Playwright 5/5 5/5 5/5 5/5 5/5 4/5 5/5 5/5 5/5 4/5
Stagehand 4/5 4/5 4/5 5/5 5/5 5/5 5/5 2/5 3/5 4/5
Browserbase 4/5 2/5 4/5 5/5 5/5 5/5 4/5 4/5 4/5 4/5
Browser Use 3/5 2/5 3/5 5/5 5/5 5/5 2/5 5/5 5/5 5/5
Skyvern 3/5 1/5 1/5 2/5 4/5 4/5 3/5 2/5 3/5 4/5
Selenium 1/5 2/5 4/5 3/5 4/5 3/5 1/5 4/5 1/5 1/5
Puppeteer 1/5 2/5 4/5 2/5 3/5 3/5 3/5 4/5 1/5 1/5
Bright Data 0/5 0/5 0/5 4/5 5/5 1/5 3/5 2/5 2/5 4/5
Steel 0/5 0/5 0/5 4/5 4/5 3/5 2/5 2/5 0/5 2/5
Firecrawl 0/5 0/5 0/5 2/5 1/5 1/5 1/5 0/5 5/5 4/5
Cypress 1/5 1/5 1/5 1/5 1/5 1/5 1/5 0/5 1/5 1/5
BrowserAct 0/5 0/5 0/5 3/5 0/5 0/5 0/5 0/5 3/5 2/5
Kernel 0/5 0/5 0/5 0/5 1/5 2/5 1/5 0/5 0/5 2/5
Hyperbrowser 0/5 0/5 0/5 0/5 1/5 2/5 0/5 1/5 0/5 0/5
Anchor 0/5 0/5 0/5 0/5 1/5 2/5 0/5 0/5 0/5 0/5

How the sample was built

The sample is fixed: 10 models x 5 fixed prompts = 50 recorded answers. It belongs to edition 2026-09. Each model answered each question once, and every answer was recorded. The five questions, verbatim:

  1. What is the best browser automation tool for AI agents? Name specific products.
  2. Which browser automation tool would you recommend to AI agents in 2026?
  3. Compare the top browser automation tool options right now.
  4. I’m AI agents and I need a browser automation tool. What should I use and why?
  5. Best browser automation tool for AI agents to let an agent use websites?

The models come from four families. OpenAI supplies 15 answers from GPT-5.6 Sol, GPT-5.6 Terra and GPT-5.6 Luna. Anthropic supplies 15 from Claude Opus 5, Claude Sonnet 5 and Claude Fable 5. Google supplies 10 from Gemini 3.6 Flash and Gemini 3.5 Flash. Perplexity supplies 10 from Sonar Pro and Sonar Reasoning Pro. The panel tracks 17 vendors in this category. Fifteen were named at least once. Apify and Notte were never named. The full method is on the method page, and every answer, split and cited source is in the browser automation category record.

How this sits against the browser agent guides

The pages that rank for this question sell or rank products. None of them counts what AI models say.

Mastra’s guide is written by Sam Bhagwat, the founder and CEO of Mastra.

Its “Choose … if” guidance runs Browserbase, Steel, Hyperbrowser and Browserless in that order, and says to choose Browserbase if you want managed browser infrastructure designed for production browser automation while remaining compatible with Playwright, Puppeteer, Selenium, and Stagehand.

It explains the category well. Browser agents receive a goal and determine how to complete it, hybrid frameworks combine AI-assisted actions with deterministic code, and infrastructure platforms provide the cloud browsers. Mastra itself appears in the Kernel entry, among the agent frameworks Kernel works with.

Playwright appears there as a traditional browser automation tool that works well when every step can be defined in advance, not as one of the ranked platforms.

The panel’s counts invert parts of that order. Playwright is the most-named tool in the recorded answers, and Kernel is named in six.

Browser Use’s homepage also ranks for the question. It describes Browser Use Agents and Browser Infrastructure as its two products. It links to its own benchmarks comparing Browser Use with other models and browser providers.

Those benchmark results stay out of the ranking, because product quality is outside what the panel measures.

A YouTube video in the same results is sponsored by AWS. It presents Amazon Nova Act as a browser automation system built for reliability at scale. Its description says Selenium breaks when sites update and Puppeteer needs constant babysitting.

Amazon Nova Act is not among the 17 vendors the panel tracks. Firecrawl, which is on the list, publishes its own ranked guide that also appears in the results, and it was not used as a source. A Reddit thread in the results could not be read.

The measurement is what those pages lack: which tools ten AI models name, how often, in what order, and where the models disagree. Use the guides for product detail and the counts to see which names an AI assistant will put in front of a buyer.

What these counts cannot tell you

These counts measure presence in AI answers. They say nothing about uptime, reliability, support, price fairness or fit with a particular stack. Being named is not the same as being recommended, because an answer can list a tool only to warn against it. Each model answered each question once, so a single answer can move a count by one. The sample is one dated snapshot, edition 2026-09. Names are matched as strings, so a tool the panel does not track is not counted. Claude Sonnet 5 names Browserless and Vercel Agent Browser in its answers, and neither is tracked here. Answers came through model APIs, which can differ from the consumer chat apps. The questions are in English. Wording matters too, as Cypress shows: the one question without the word agents pulled test frameworks into the counts. Citation counts reflect citations returned in the recorded API responses, and coverage varies by model.

What should a buyer do with this list?

Use the ranking to build a shortlist, then test the shortlist yourself.

  1. Decide the layer first. A control framework decides what the agent does. Infrastructure decides where the browser runs.
  2. For the control layer, start with the three frameworks at the top of the count: Playwright for deterministic control, Stagehand to mix code and model steps, Browser Use for goal-driven agents.
  3. Add infrastructure only if the agent needs hosted sessions, logins that persist, or scale. Browserbase is the most-named option. Steel, Kernel, Hyperbrowser and Anchor are named far less, so check them against your own needs rather than their counts.
  4. If your own customers find tools through one assistant, read that model’s column in the split. A tool absent from OpenAI answers will be invisible to buyers who only ask an OpenAI model.
  5. Treat counts as a starting point. Run each finalist against your own sites before you commit.

Frequently asked questions

Which AI browser automation tool is the best?

Playwright is the tool AI models name most for browser automation in this panel, and eight of ten models name it more than any other. That measures visibility, not quality. For agents, most answers pair Playwright with an agent layer such as Stagehand or Browser Use, and add hosted browsers such as Browserbase when the agent needs them.

Which AI agent is best for automation?

Among browser agent frameworks, Browser Use is the one the models most often name first, in 15 of 50 answers. It takes a goal in plain language and works out the steps. Stagehand is the most-named framework that mixes model steps with deterministic Playwright code. Neither count says which performs better on your tasks.

What is the best web search tool for AI agents?

Web search tools are outside this ranking. The panel tracks web search and scraping APIs for AI as a separate category, and none of its figures appear here. Firecrawl, which also appears on this list, is named in this category mostly by Perplexity’s models.

Can AI agent use browser?

Yes. An AI agent can use a browser through a browser automation tool. Mastra’s guide describes AI browser automation as letting AI applications interact with websites through a browser, interpreting pages and deciding what to do. In practice the agent calls a library such as Playwright, a framework such as Browser Use or Stagehand, or a hosted browser service such as Browserbase.

Should an AI agent use Playwright or Selenium?

In this panel Playwright is named far more often than Selenium, and it is named first 21 times against Selenium’s once.

Mastra’s guide treats the two alike: traditional browser automation tools that work well when every step can be defined in advance. If your team already runs Selenium, Browserbase accepts connections using Playwright, Puppeteer, Selenium or Stagehand.

Can a vendor pay to rank higher on this list?

No. No position is sold, sponsored or influenced, and vendors cannot pay to appear, be reordered or be removed. The order comes only from how often the models named each tool in the recorded answers.

How this list is ordered

The order is the measurement, not an assessment of the products. Answer share is the share of recorded answers that named the tool. Named first is the share where it appeared before any other tracked tool. Both are counts from one dated edition and are published in full on the category page.

A tool appears here only if it was named in the edition and its record carries a sourced claim. A product that was never named is not listed, and no position is sold.

Where to check it