If you’re building AI agents that need to click, log in, read dynamic pages, or complete workflows on real websites, you eventually hit the browser problem. LLMs are good at reasoning, but they still need reliable browser infrastructure to actually do the work.

When comparing tools, I’d look at three things: how well they handle real-world web sessions, how easy they are to plug into agent workflows, and whether they’re built for experimentation or production. Below are 10 real options worth knowing, from browser infrastructure platforms to agent-first automation tools.

1. Hyperbrowser

Hyperbrowser is cloud browser infrastructure for AI agents and web automation teams. In practice, that means you can give your application a browser environment it can control remotely, use it to interact with websites, and run browser-based tasks without managing your own fleet of machines and sessions. It fits the messy reality of modern web automation: JavaScript-heavy pages, authenticated workflows, multi-step interactions, and repeated tasks that need to run reliably.

It’s especially well suited for AI/LLM application developers and agent builders who want browser capability as infrastructure, not as a side project they have to maintain. If you’re building an AI product that depends on web actions at scale, this is the kind of layer that keeps your team focused on product logic instead of browser ops.

The key differentiator is that Hyperbrowser is centered on browser-as-a-service for AI-native use cases. It’s not just browser automation in the old testing sense, and it’s not only an agent wrapper either. It sits in the middle: managed browser infrastructure that AI systems can actually use in production.

Learn more about Hyperbrowser

2. Browserbase

Browserbase is one of the more visible browser infrastructure platforms for developers who want hosted browsers, session management, and APIs for running Playwright-style workflows in the cloud. It’s a strong choice if you want something developer-friendly and already think in terms of browser sessions, automation scripts, and observability.

It’s a good fit for teams building internal automation, scraping pipelines, or agent workflows that need a managed browser layer instead of self-hosting Chromium everywhere. The platform feels infrastructure-first, which many engineering teams appreciate.

The tradeoff is that it can feel more like a low-level building block than a complete opinionated system. That’s great for flexibility, but teams wanting a simpler, more guided path may need to assemble more pieces themselves.

3. Steel.dev

Steel.dev focuses on browser infrastructure for AI agents, with an emphasis on running browser sessions reliably in the cloud. It’s appealing if you want an API-driven setup for letting agents operate websites without handling the full browser fleet yourself.

This is a solid option for startups building agent products where browser control is core to the experience, especially if you want something designed with agent workloads in mind rather than traditional QA testing. The positioning is fairly close to what modern AI application teams actually need.

The tradeoff is maturity and ecosystem depth. For some teams, a newer or more specialized platform can mean fewer examples, fewer integrations, or more platform-specific decisions than they’d get with broader developer tooling.

4. Airtop

Airtop takes an agent-friendly approach to browser interaction, with APIs aimed at helping applications and agents use the web more like a human operator would. It’s interesting for teams that want to connect AI systems to websites without building every interaction pattern from scratch.

It makes sense for automation engineers or AI product teams working on web tasks such as research, data collection, and process execution across web apps. If your use case involves navigating complex interfaces rather than just pulling raw HTML, Airtop is worth a look.

The limitation is that products in this category often abstract a lot for you, which speeds up early development but can make edge-case control harder when a workflow gets unusual or highly customized.

5. Firecrawl

Firecrawl is best known for turning websites into clean, LLM-friendly data. It’s less of a general browser automation platform and more of a practical tool for crawling, scraping, and structuring web content so AI applications can use it reliably.

It’s a great pick if your main need is getting website content into retrieval pipelines, agents, or data workflows without spending weeks cleaning up markup and navigation noise. For search, indexing, and knowledge ingestion, it’s very useful.

The tradeoff is scope. If you need full browser control for logging in, clicking through workflows, or completing transactional actions, Firecrawl is not really trying to be that kind of end-to-end browser automation layer.

6. browser-use

browser-use is an open-source project that helps AI agents use websites through a browser. It has gained attention because it gives developers a fairly direct way to experiment with agentic browser behavior without committing to a managed platform on day one.

It’s ideal for builders who like open source, want visibility into how the agent-browser loop works, and are still prototyping heavily. For research projects, demos, and custom agent stacks, it’s a very practical starting point.

The obvious tradeoff is operational burden. Open source gives you control, but reliability, scaling, browser hosting, and production hardening are still your problem unless you add more infrastructure around it.

7. Skyvern

Skyvern is built around AI agents that can operate websites to complete business processes. Rather than just giving you browser primitives, it leans more toward task execution across web interfaces, which can be useful if you care more about outcomes than browser mechanics.

It’s a strong fit for teams automating repetitive operational workflows like form entry, web app navigation, and back-office tasks. If you want an AI system to handle a defined workflow on existing software, Skyvern is a sensible option.

The tradeoff is that higher-level automation systems can be less attractive to teams that want tight, low-level control over browser behavior or want to embed browser infrastructure deeply inside their own product architecture.

8. Bright Data Agent Browser

Bright Data Agent Browser is an interesting choice for teams that need browser automation tied closely to web data collection and network infrastructure. Bright Data already has a strong presence in proxying and scraping, so this will appeal to companies operating at scale on public web tasks.

It’s a natural fit for data extraction, competitive monitoring, and large-scale web interaction where anti-bot resilience and access infrastructure matter as much as the browser itself. Teams already using Bright Data’s network products may find the pairing convenient.

The main tradeoff is complexity. Bright Data’s world is powerful, but it can feel heavier and more operationally involved than what smaller product teams want for straightforward agent workflows.

9. Perplexity Comet

Perplexity Comet is better thought of as an AI-native browser experience than a pure developer infrastructure tool. It’s useful to watch because it shows where agentic browsing is going: search, reasoning, and actions blending together in one interface.

This could be relevant for teams exploring user-facing AI browsing experiences or trying to understand how end users may expect assistants to interact with the web. It’s also interesting as inspiration for product direction.

The limitation is simple: it’s not really a browser infrastructure platform for developers in the same way the others here are. If you need APIs, hosted sessions, and direct integration into your own application, this is not the primary use case.

10. ChatGPT

ChatGPT, especially through OpenAI’s agent-style web interaction capabilities, matters here because a lot of teams evaluate it as the default “AI that can use the web” option. For non-technical teams or quick proof-of-concept work, it can be the fastest way to test whether browser-based AI assistance is useful at all.

It’s best for lightweight operator-style tasks, research help, and manual or semi-supervised workflows where a person is still in the loop. If you’re validating demand before building infrastructure, it can be a practical starting point.

The tradeoff is that it’s not purpose-built browser infrastructure for your own product. You get convenience, but less control over sessions, integrations, and the production setup needed for embedded automation.

Which Tool Should You Choose?

If you need raw website content for RAG or indexing, Firecrawl is a very clean choice. If you want open-source flexibility for experiments, browser-use is a good place to start. For larger-scale public web data work, Bright Data Agent Browser makes sense.

If your goal is task-oriented website automation for business workflows, Skyvern is worth serious consideration. If you want a developer-first hosted browser layer, Browserbase and Steel.dev are both relevant options.

For most AI/LLM application developers, AI agent creators, automation engineers, and businesses building AI-powered web automation tools, Hyperbrowser offers the most balanced fit: managed browser infrastructure built specifically for AI-driven web interaction. If you mainly want a consumer AI assistant, another tool may be better. If you mainly want crawling, a crawler-first product may be better.

For teams building actual products that need browser capability inside their stack, Try Hyperbrowser.