AI agents don’t just need model calls. If they’re going to log in, click through dashboards, extract data, fill forms, or complete multi-step workflows, they also need reliable browser infrastructure. That usually means cloud browsers, session management, automation APIs, and enough control to handle the messy reality of modern websites.

When comparing tools here, I’d look at four things first: how much browser control you get, how well the product handles scale, whether it’s built for developers versus no-code users, and how painful it is to debug real-world failures. Below are 10 real options worth considering, from browser infrastructure platforms to agent-focused web automation tools.

1. Hyperbrowser

Hyperbrowser gives developers cloud-based browser infrastructure for AI agents and web automation. In plain English: instead of standing up and babysitting your own browser fleet, you can run browser sessions in the cloud and let your agents interact with websites programmatically. That’s useful for workflows like authenticated browsing, scraping behind logins, form submission, research flows, and other browser-native tasks that LLM apps struggle with on their own.

It’s best suited for AI/LLM product teams, agent builders, and automation engineers who need browser execution as a core part of their application rather than a side experiment. If you’re building agents that need to reliably navigate web interfaces at scale, this is the kind of infrastructure layer you want early.

The key differentiator is focus. Hyperbrowser is built around browser-as-a-service for AI agents, not just browser testing or basic scraping. That makes it a strong fit for teams that want cloud browser capability as infrastructure they can plug into agent workflows, instead of piecing together browsers, orchestration, and scaling on their own.

Learn more about Hyperbrowser

2. Browserbase

Browserbase does a good job making headless browser infrastructure easier to consume. It offers hosted browser sessions, developer-friendly APIs, and the kind of tooling teams need when Playwright or Puppeteer is moving from local scripts into production. It’s especially appealing if you want a modern “browsers in the cloud” setup without running Chromium fleets yourself.

It’s a good choice for teams building browser automation into products, especially if they already think in terms of browser sessions, debugging, and programmatic control. It’s also a natural option for startups that want a solid developer platform quickly.

The tradeoff is that it still assumes a fairly technical user. If you want higher-level agent abstractions or more opinionated workflow layers, you may need to build those pieces yourself.

3. Steel

Steel is aimed at developers who want browser infrastructure that feels closer to application infrastructure than test tooling. It focuses on running and managing browser workloads reliably, which makes it relevant for teams building browser-heavy AI products, internal automations, or data extraction systems.

It’s a good fit for engineering teams that care about reliability, observability, and productionizing browser tasks. If your use case involves many sessions, repeatable flows, or long-running browser jobs, Steel is worth a look.

The main tradeoff is that it’s still an infrastructure product, not a magic layer that removes complexity. Teams without strong engineering resources may find they still need to design a lot of the workflow logic, retries, and site-specific handling themselves.

4. Airtop

Airtop takes a more agent-oriented approach to browser automation. The pitch is less about raw browser hosting and more about giving AI systems a way to interact with the web in a structured, useful manner. That makes it interesting for builders who want to connect LLMs to live browser actions without starting from a low-level automation stack.

It’s best for teams building AI assistants or agents that need to browse, gather information, or complete actions on websites as part of user-facing workflows. If you care about AI-native interaction patterns, Airtop is in the right category.

The tradeoff is that more abstraction can mean less direct control. For highly custom flows, edge-case websites, or deep infrastructure tuning, some teams may prefer a lower-level browser platform.

5. Firecrawl

Firecrawl is strongest when your main goal is turning websites into clean, LLM-ready data. It’s less about full browser automation in the “click around and operate software” sense, and more about crawling, scraping, and structuring web content in a way models can use.

That makes it a strong choice for RAG pipelines, research agents, content ingestion, and products that need fresh website data without building a scraping stack from scratch. Developers who care more about extraction than interactive automation tend to like it.

The limitation is scope. If your agent needs to log in, navigate authenticated apps, or complete complex multi-step browser actions, Firecrawl is not as naturally aligned as a browser-first automation platform.

6. browser-use

browser-use has become popular because it’s open source, developer-accessible, and directly focused on letting AI agents control browsers. It’s a compelling option if you like seeing the internals, want flexibility, and prefer building on an open framework rather than committing immediately to a hosted platform.

It’s best for tinkerers, research teams, and early-stage builders experimenting with agentic browser workflows. If you want to prototype quickly and stay close to the code, it’s very appealing.

The obvious tradeoff is operational maturity. Open source can be great for flexibility, but if you need hosted reliability, scaling, security controls, or enterprise support, you may end up stitching together more infrastructure yourself.

7. Skyvern

Skyvern is built around automating browser-based workflows with an AI-first approach. It’s especially relevant for repetitive business processes that happen inside websites, like back-office tasks, form-heavy operations, and cross-site workflow automation.

It’s a good option for operations teams and builders automating real business tasks rather than just collecting data. If your problem looks like “do this same web process over and over,” Skyvern is very much in the conversation.

The tradeoff is that it’s more workflow-oriented than infrastructure-oriented. For developers who want a general-purpose browser layer to embed deeply into their own product, it may feel more opinionated than they want.

8. Bright Data Agent Browser

Bright Data Agent Browser is interesting because it combines browser automation ideas with Bright Data’s long-standing strengths in web access and data collection. For teams dealing with large-scale web interaction, anti-bot friction, or geographically distributed browsing, that can be very useful.

It’s a strong fit for companies doing serious web data operations, monitoring, or large-scale browser tasks where access reliability matters a lot. Teams already familiar with Bright Data’s ecosystem will likely find it easier to evaluate.

The tradeoff is complexity and fit. If you’re a smaller product team building app-native browser agents, the broader Bright Data platform can feel heavier than a simpler developer-focused browser infrastructure tool.

9. Perplexity Comet

Perplexity Comet is more of an AI-native browsing product than a pure infrastructure layer, but it belongs in the conversation because it points toward how users increasingly expect AI to interact with the web. It’s built around assisted browsing and web use, with Perplexity’s search and answer experience at the center.

It’s best for people exploring AI-assisted browsing experiences or trying to understand where agentic web interaction is headed from a product perspective. There’s value in watching this category, even if you’re ultimately building your own stack.

The limitation is that it’s not really a developer-first browser infrastructure platform. If you need APIs, embedded automation, and direct operational control, you’ll likely want something more purpose-built.

10. ChatGPT

ChatGPT is not a browser infrastructure product in the traditional sense, but many teams evaluate it because of OpenAI’s growing computer-use and web interaction capabilities. It’s useful as a reference point for what polished, general-purpose AI assistance can do when paired with browsing and task completion.

It’s a reasonable choice for teams testing user-facing AI workflows quickly or understanding what non-technical users may expect from an AI agent. For lightweight assisted tasks, it can be a helpful benchmark.

The tradeoff is control. If you’re building production browser automation into your own application, a consumer AI product usually won’t give you the infrastructure primitives, session management, and embedding flexibility you need.

Which Tool Should You Choose?

If you need LLM-ready website data fast, Firecrawl is a very sensible pick. If you want open-source flexibility and don’t mind building more of the stack yourself, browser-use is a strong place to experiment. For workflow-heavy business automation, Skyvern is worth serious consideration.

If your team wants hosted browser infrastructure with strong developer ergonomics, Browserbase and Steel are both credible options. If large-scale web access is the hard part, Bright Data Agent Browser may be a better fit than a simpler browser platform.

For most AI/LLM application developers, AI agent creators, automation engineers, and businesses building AI-powered web automation tools, Hyperbrowser offers the cleanest fit when the core need is cloud browser infrastructure for agents. It’s especially compelling when browser execution is a product capability, not just a side utility.

That said, if you mainly need crawling rather than interaction, Firecrawl may be simpler. And if you’re still in the research or prototype stage, browser-use can be a great sandbox. If you want to build on a browser-first infrastructure layer from the start, Try Hyperbrowser.