Your agent has just told a user something that stopped being true weeks ago, because it can't see the web. A web search API fixes that, and which one fits turns on whether you need raw Google results, a separate index, agent-ready answers or search plus scraping. Trial two on the same real questions before you wire one in.
This page is for developers and technical founders giving an AI agent or LLM app live access to the web. Ten search APIs follow, in no particular order, grouped by the job each one says it does, with none placed above the rest. Every description comes from what the vendor says about its own product, so treat those claims as theirs until your own test agrees with them.
Trial two search APIs in one afternoon
Before you read a single homepage, set up a small test you can rerun. It's the quickest way to see past the marketing, and it takes less time than comparing feature pages.
- Write down ten questions your agent gets asked for real, including two about something that happened in the past week and one with an awkward spelling.
- Choose two APIs from different groups below, say one that returns search engine results and one built for agents, and get a key for each.
- Send the same ten questions through both with matching settings, and save the raw responses rather than only the answer your model writes.
- Read what came back: whether the results point at pages you'd trust, whether page text arrives with them or needs a second call, and how much of the response is noise your model will have to read.
- Feed both sets to your agent, check its answers against what you know is true, and keep the API that needed less cleanup code.
Keep that question file. You'll want to rerun it whenever a vendor changes something, and they do change.
What your agent needs back from a search call
A person scanning search results can ignore the junk. An agent can't, or rather it pays for the junk in context space and in wrong answers. So it helps to know what shape of response you want before you compare anything at all.
Some APIs give you a list of links, titles and snippets, close to what you'd see on a results page. That's fine when your agent only needs to know which pages exist, or when it'll open them in a second step. Others return the content of the pages, cleaned up, so the model can read it straight away. A few go further and run several searches, read what they find and hand back something closer to a research brief.
None of those is better in the abstract. A shopping assistant checking whether a product page exists wants speed and a tidy list. A research agent summarising a niche topic wants the text, and plenty of it. Decide which of those your agent is before you read another homepage, because the homepages all sound alike.
It's also worth deciding how attached you want to be. A plain results API is simple to swap out later. Something that searches, extracts and monitors for you is harder to leave, since more of your own logic ends up depending on how it behaves.
Is it a search engine or a window onto one?
This is the first thing to test, and it's easy to miss on the vendor pages. Not every search API for agents searches its own index. Two of the ten describe themselves plainly as APIs for Google search results. Brave's own page says its API accesses an index of 40+ billion pages, which is the vendor's own claim. Several more pitch search built for agents without saying much about where the results come from.
Why does that matter? If you're buying a wrapper around one search engine, your agent sees roughly what that engine shows, filtered through someone else's parsing. If you're buying a separate search product, results may differ from what you'd get in a browser, which can help or hurt depending on your users. Ask each vendor where its results come from, and read the documentation for anything that describes the index behind it.
Then run three of your test questions through an ordinary search engine in your browser. If one API's results match it almost line for line, you've learned what you're buying.
Four jobs these ten APIs do
Search built for agents
Tavily, Exa and Linkup each describe search made for AI applications and agents. Parallel goes wider, calling itself web infrastructure for AI to search, extract, monitor and reason. You.com Web Search API sits close to this group too, pitched at grounding LLMs and agents in live web and news results.
Google results as data
Serper and SerpApi both return Google search results to developers. The second adds, in its own description, that it handles proxies, captchas and the parsing of rich structured data, which is plumbing you'd otherwise build and maintain yourself.
A separate index
Brave Search API is the one here that leads with its own index, and it lists specialised endpoints for training models as well as for powering search.
Search with the page content attached
Firecrawl describes itself as a web data API that gives agents searched and scraped content, and says it's open source. Perplexity Search API returns ranked results with domain filtering, multi-query search and content extraction, so it belongs in this conversation when your agent needs page text rather than links.
Ten search APIs side by side
| Tool | Kind of API | Who it suits |
|---|---|---|
| Brave Search API | Independent index API | Searching an independent web index |
| Exa | Agent search API | Real-time data and deep research |
| Firecrawl | Search and scrape API | Search plus scraped page content |
| Linkup | Agent search API | AI apps wanting a search API made for them |
| Parallel | Agent web infrastructure | Search, extraction and monitoring for agents |
| Perplexity Search API | LLM-vendor search API | Filtered, multi-query search with extraction |
| SerpApi | Google SERP API | Google results with the parsing handled |
| Serper | Google SERP API | Plain Google results as data |
| Tavily | Agent search API | Search plus content extraction |
| You.com Web Search API | LLM search API | Live web and news results |
All ten, A to Z
Alphabetical, so the order says nothing about merit. Each line sums up what the maker says its own API does.
Brave Search API
Best for: Teams who'd rather query Brave's own index than a wrapper, with the specialised endpoints the vendor lists for training models and powering search.
Exa
Best for: Agents that need, in the vendor's words, real-time web data, deep research and structured content from one endpoint.
Firecrawl
Best for: Developers wanting searched and scraped page content from one web data API; the vendor's claim is fast, reliable, token-efficient and open source.
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Linkup
Best for: AI applications and agents that need a web search API built for them, which is exactly how the vendor describes its product.
Parallel
Best for: Agents that have to search, extract, monitor and reason over web information, which the vendor calls web infrastructure for AI.
Perplexity Search API
Best for: Developers after real-time results with domain filtering, multi-query search and content extraction, all features the vendor names in its documentation.
SerpApi
Best for: Apps that want Google results without building the plumbing; the vendor claims it handles proxies, solves captchas and parses rich structured data.
Serper
Best for: Projects that only need Google search results returned through an API, which is the whole of the vendor's own pitch.
Tavily
Best for: Builders who need web search and content extraction side by side; the vendor's claim is fast and secure APIs for both.
You.com Web Search API
Best for: LLMs and agents that need live web and news results, which the vendor says ground your AI in the live web.
Where a small agent team should begin
If you're one or two developers with an agent that needs the web next week, don't overthink the shortlist. Take one API from the agent-first group and one from a different group, run the afternoon test above, and let your own questions decide.
Whatever you choose, put it behind a small function of your own, so swapping later is an hour's work rather than a rewrite. If your agent mostly needs search engine result pages as data, my list of SERP APIs goes deeper on that one job, while these web scraping APIs cover the case where you already know the URLs and just need clean page text.
Do you make a web search API for AI agents? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What's the difference between a SERP API and a search API for AI agents?
A SERP API returns what a search engine shows on its results page, usually as structured data. A search API pitched at agents tends to return content shaped for a model to read, sometimes with the page text included. Some tools sit between the two.
Does my agent need page content or just links?
Links are enough if your agent only checks that something exists or opens pages in a later step. If it has to answer from what the pages say, you want content back, either from the search call or from a second extraction call.
Can one agent use more than one search API?
Yes. Some teams send news questions to one provider and deeper research to another. Begin with one, wrap it in your own function, and add a second only when your tests show a gap.
How can I tell whether results are fresh?
Put questions about events from the past few days in your test set and see which API finds them. Repeat the test now and then, because freshness can change without notice.
Is there an open source option here?
One tool on this list describes itself as open source. Read its repository and licence before you plan to host it yourself, and confirm which parts are open.
