Quick answer: There is no single best search API for AI agents, because "search" hides three different jobs: getting ranked links, getting Google's exact SERP, and getting full page content your model can actually read. Tavily and Exa are strong for agent tool-calling, Serper wins for raw Google SERP data, Brave is a solid cheap general index, and link.sc is built for the case where your agent needs the full content of the results, not just snippets. Pick by job, not by brand.
I've wired most of these into agents at this point, so this is a comparison from the trenches rather than a feature-page summary.
What an Agent Actually Needs From Search
Before comparing products, be clear about what your agent does with the results. In my experience there are four distinct needs:
- Tool-calling: the agent asks a question mid-task and needs a short, relevant answer with sources.
- RAG ingestion: you need lots of clean text to chunk, embed, and cite.
- SERP data: you care about Google's actual rankings, ads, and People Also Ask boxes.
- Full-content retrieval: snippets are not enough, you need the whole page as markdown.
Most disappointment with search APIs comes from buying a tool built for one of these and using it for another. Snippet-oriented APIs are genuinely bad at RAG, and I wrote about why in search results with full page content.
The Contenders
Tavily
Tavily was designed for LLM agents from day one, and it shows. Results come back pre-filtered for relevance, there's an option to include a short generated answer, and it's the default search tool in a lot of LangChain and LlamaIndex examples.
The trade-off: you're getting Tavily's opinion of what's relevant, and content extraction is partial. For quick agent lookups that's fine. For deep research it can feel shallow.
Exa
Exa is the odd one out: it's a neural search engine, not a wrapper around a traditional index. You can search by meaning ("startups building open-source vector databases") rather than keywords, and it has a genuinely useful "find similar to this URL" mode.
It shines for discovery and semantic queries. It's weaker when you want exhaustive coverage of a keyword or anything resembling Google's view of the web.
Brave Search API
Brave runs its own independent index and sells access at prices that are, qualitatively, among the lowest in the category, with a free tier for small projects. If you want plain web results without touching Google, it's a sensible default, and setup is quick (we have a Brave Search API key walkthrough).
The catch: results are links, titles, and snippets. Content extraction is your problem. Index depth is good but not Google-good on long-tail queries.
Serper
Serper does one thing: it gives you Google's SERP as JSON, fast. Organic results, People Also Ask, knowledge panels, news tabs. If your product is about what Google ranks (SEO tooling, rank tracking, SERP analysis), this category is the right answer.
But it's SERP data, not content. And you inherit Google's consent pages, locale quirks, and result volatility as features of your data.
Perplexity Sonar
Sonar is different in kind: you send a question, and you get back a generated answer with citations. The search and synthesis happen inside their black box.
That's great when you want an answer and don't want to build the pipeline. It's limiting when you need the raw sources, want your own model to do the reasoning, or need to control cost per token. You're buying a finished meal, not groceries.
link.sc
This is us, so discount accordingly. link.sc pairs a search endpoint with a fetch endpoint that turns any result URL into the full page as clean markdown, so an agent goes from query to readable content in two calls against one API. It exists because we kept building agents that needed the whole page, and stapling a scraper onto a snippet API is miserable.
Where we're weaker: we don't give you Google's exact SERP layout, and we're not a semantic index like Exa. We're built for agents and RAG pipelines that need readable content.
Comparison Table
| API | Best at | Content included | Own index | Weak spot |
|---|---|---|---|---|
| Tavily | Agent tool-calling | Partial extracts | No (aggregates) | Depth of content |
| Exa | Semantic discovery | Optional page text | Yes (neural) | Keyword coverage |
| Brave | Cheap general search | Snippets only | Yes | You extract content |
| Serper | Google SERP data | Snippets only | No (Google) | Not built for content |
| Perplexity Sonar | Ready-made answers | Citations, not raw text | No | Black box, less control |
| link.sc | Search plus full content | Full markdown | Hybrid | Not a SERP mirror |
Decision Table by Use Case
| Your use case | Pick first | Reasonable alternative |
|---|---|---|
| Agent tool-calling, quick lookups | Tavily | link.sc, Brave |
| RAG ingestion of live web data | link.sc | Exa with content option |
| SEO / rank tracking / SERP features | Serper | Other SERP APIs |
| Semantic "find things like this" | Exa | None comparable |
| Answer engine without building one | Perplexity Sonar | Build it yourself |
| Full-page content behind search | link.sc | Search API + separate scraper |
What the Full-Content Flow Looks Like
Here's the shape of the pipeline: one call to get ranked results, then a fetch per page you actually want to read.
curl -X POST https://api.link.sc/v1/search \
-H "x-api-key: lsc_your_key" \
-H "Content-Type: application/json" \
-d '{
"q": "vector database benchmarks 2026",
"engine": "google"
}'
The response's serpData.results array gives each hit's title, targetUrl, description, and realPosition. Take as many as you need (slice client-side, there's no limit parameter) and fetch each one as markdown:
curl -X POST https://api.link.sc/v1/fetch \
-H "x-api-key: lsc_your_key" \
-H "Content-Type: application/json" \
-d '{
"url": "https://example.com/benchmark-post",
"format": "markdown"
}'
That returns the page body as content, ready to chunk or drop straight into a context window. The quickstart covers both endpoints.
Honest Trade-Offs Nobody Puts on Pricing Pages
A few things I wish someone had told me earlier:
- Per-search pricing lies to you. A cheap snippet API that forces a second scraping service is often more expensive per usable document than a pricier full-content call.
- Rate limits bite agents harder than apps. Agents burst. Check what happens at the limit before you commit, because a retry loop against a 429 wall wrecks latency budgets.
- Freshness varies wildly. Independent indexes lag on breaking news. If freshness matters, test with today's news, not evergreen queries.
- "AI-optimized" is marketing until proven. Run your own eval: ten real queries from your agent, score the results yourself.
If you're deciding between search styles more broadly, what is agentic search covers how agents use search differently from humans.
My Actual Recommendation
Start from the job. SERP analysis: Serper. Semantic discovery: Exa. Cheap general lookups: Brave or Tavily. Content-hungry agents and RAG: that's the gap link.sc was built to fill, and the free tier's 500 credits a month are enough to run the eval yourself.
Whatever you pick, benchmark on your queries. Every one of these APIs looks great on its own demo queries.
Ready to give your agent search results it can actually read? Sign up for link.sc and get 500 free credits a month.