Browser automation tools help software navigate pages, enter information, and inspect results. An AI agent adds a decision-making layer: it can interpret a task and choose which browser actions to take.
The best browser automation tools by use case
| Tool | Approach | Where we would start |
|---|---|---|
| Playwright | Scripted automation and testing | Repeatable workflows with explicit checks |
| Puppeteer | JavaScript browser control | Browser scripts in an existing JavaScript stack |
| Selenium | WebDriver-based automation | Existing WebDriver infrastructure and test suites |
| Stagehand | AI-assisted browser SDK | Combining natural-language actions with code |
| Browser Use | Browser-agent framework | Letting an agent work through a multi-step task |
| Skyvern | AI browser workflows | Business processes involving forms and documents |
| agent-browser | Browser CLI for agents | Giving a terminal-based agent a browser interface |
These tools sit at different layers. Playwright, Puppeteer, and Selenium give you browser control. Stagehand adds AI-assisted operations. Browser Use and Skyvern take on more of the workflow. agent-browser exposes browser actions through a command-line interface.
Browser hosting is another decision. A service such as Browserbase can provide a remote browser session for an automation framework. That does not, by itself, determine the agent's reasoning or success criteria.
Playwright
Our starting point for repeatable workflows you can describe precisely.
Playwright supports browser automation and testing across Chromium, Firefox, and WebKit. Its testing tools give you a way to express expected behavior and check the result of an interaction.
Consider a settings flow: open the page, locate a labeled control, change its value, save, reload, and verify that the value persisted. When those steps are stable, explicit code makes the behavior easier to review and rerun.
An agent can help write and repair that code. It can also use Playwright MCP to interact with the browser through tools. These are different operating modes: generating a test is not the same as asking a model to choose every click at runtime.
Puppeteer
A useful choice for browser scripts in JavaScript.
Puppeteer provides a JavaScript API for controlling Chrome and Firefox. It fits tasks such as navigating pages, interacting with the UI, capturing screenshots, and generating PDFs.
If your application already uses Puppeteer to generate reports, adding a separate automation framework solely because an agent is involved may not help. Give the agent a well-defined operation and return enough information to verify it.
For example: “Open this report URL, wait for the report content, and save a PDF.” The implementation should check for an authentication screen or error state before treating the exported file as success.
Selenium
A strong candidate when you already have WebDriver infrastructure.
Selenium WebDriver drives browsers through their automation interfaces. It has an established ecosystem around browser testing, with tooling for running sessions locally or remotely.
An existing Selenium suite can be useful context for an agent. Rather than reconstructing an entire flow from a screenshot, the agent can work with known page interactions and expected outcomes.
A first task might be: “Investigate why this checkout test fails, using the existing test and its captured evidence.” That keeps the agent's work grounded in a workflow your team already maintains.
Stagehand
Our shortlist choice for mixing code with AI-assisted actions.
Stagehand is a browser-agent SDK with operations for actions, observation, and extraction. It lets a developer describe parts of a browser task in natural language while keeping the surrounding program explicit.
This is useful when most of a workflow is stable but one part varies. Your code might open a known page and validate a final result, while an AI-assisted step locates the relevant control or extracts information.
Try a contained task: “Read the available subscription plans and return their names and limits.” Validate the returned fields before using them in another operation.
Browser Use
Our shortlist choice when you want an agent to work through the browser task.
Browser Use provides a framework for agents that interact with websites. Its examples center on giving an agent a task and a model, then letting it navigate toward the result.
That fits work where you cannot list every click in advance. For example: “Find the export settings in this application and explain the available formats.” Begin with an inspection task before moving to a workflow that changes data.
Skyvern
Our shortlist choice for business workflows across web interfaces.
Skyvern focuses on automating browser workflows with AI, including processes involving forms and documents. It is worth considering when the workflow is larger than one browser action.
Think about preparing a vendor-portal submission: gather the input, navigate the form, populate fields, attach the appropriate document, and stop for review before submission. Each stage has a result that can be checked independently.
agent-browser
Our shortlist choice for agents that already work through a terminal.
agent-browser is a browser automation CLI from Vercel Labs. It offers commands for opening pages, inspecting snapshots, and interacting with elements through references.
This can be a direct fit for a coding agent that already executes commands. The agent can inspect a page, choose an element from the current snapshot, act, and inspect the resulting state without building a new application around a browser SDK.
A useful task is: “Open the local preview and verify that the new navigation link reaches the expected page.” Ask for the observed destination and page content as evidence.
Published browser-agent results
Skyvern’s published WebVoyager evaluation shows how its agent changed as planning and validation were added:
| Configuration | Reported task success |
|---|---|
| Single actor loop | About 45% |
| Planner and actor | About 68.7% |
| Skyvern 2.0: planner, actor, validator | 85.85% |
These vendor-run results are from January 2025, using GPT-4o and GPT-4o-mini in Skyvern Cloud. The report was updated in July 2026. It measures the complete agent on WebVoyager’s web-navigation tasks. Source: Skyvern’s evaluation and run artifacts.
The practical lesson is to verify the result of each action: after saving a setting, reload it; after exporting a file, open it. In this evaluation, adding a validator increased reported success from about 68.7% to 85.85%.
Playwright vs Stagehand vs Browser Use
We would start with Playwright when the sequence and expected outcome are known. It gives you a straightforward place to express repeatable behavior.
We would evaluate Stagehand when a mostly controlled program needs a few steps that benefit from interpretation, such as identifying a control or extracting differently arranged content.
We would evaluate Browser Use when the agent needs to decide more of the navigation sequence itself. That flexibility changes what you need to measure: successful completion, unnecessary actions, recovery behavior, and when it asks for help.
What should you test before choosing?
Run the same small set of tasks with each shortlisted approach. Include conditions that resemble production, not just the easiest path through a public page.
| Task | What to verify |
|---|---|
| Navigate an authenticated app | Correct account and page; no accidental login-screen success |
| Change a reversible setting | Value persists after reload |
| Extract a table | Units, empty cells, and footnotes remain meaningful |
| Work across several pages | No skipped records or duplicated actions |
| Encounter an expired session | Clear stop or recovery rather than a fabricated result |
| Prepare a submission | Review point occurs before the consequential action |
Record completion rate, total elapsed time, model and browser costs, and how much human intervention was needed. Keep screenshots, traces, or structured observations that explain failures. An attractive success-rate claim from a different task set cannot answer these questions for your application.
A useful instruction is: “After each important action, inspect the result. If the expected state is missing, explain what you observed before retrying.” This makes the outcome easier to audit and reduces blind repetition.
When should you use an API or MCP server instead?
If a service exposes the exact operation you need through a documented API or an MCP server, we would evaluate that first. It can give the agent structured inputs and results without requiring it to navigate presentation details.
Browser automation becomes useful when the work depends on the interface itself, when you are testing a user journey, or when the operation is only available through the website. Our MCP server shortlist covers several alternatives for connected services.
Similarly, use a search API to discover sources when that is the task. Use browser interaction when the agent needs to navigate or operate the page. Searching, reading, and acting are related steps, but they have different requirements.
Use browser automation in Dexto
Ask Dexto to check a local app preview, collect information from a website, or work through a browser task. It can use the page alongside files, code, and your project context.
Save recurring steps in a skill: which pages to visit, what to extract, and how to check the result. For example, a weekly reporting skill can collect the latest figures and turn them into a spreadsheet you can review.