13 Best AI Search APIs for Agents in 2026 (Tavily, Brave, SerpAPI + 10 More I Tested)

13 Best AI Search APIs for Agents in 2026 (Tavily, Brave, SerpAPI + 10 More I Tested)

Bing's Search API died on August 11, 2025, and the AI search API market spent the next nine months scrambling to fill the vacuum. There are now thirteen credible options for grounding your agent in fresh web content, but only about five are worth your attention. I spent the last six weeks running the same query through all of them, measuring latency, comparing JSON shapes, and adding up what each one actually costs once you factor in extraction and LLM tokens.

Quick Answer: The Best AI Search APIs in 2026

If you only read 30 seconds of this post, here's the short version:

• Best for RAG with citations: Tavily, bundled search + extraction, generous free tier.
• Best for multi-engine SERP coverage: SerpAPI, 30+ engines and the deepest results-page parsing in the category.
• Best for high-recall search + monitoring: CatchAll, a recall-first index that surfaces everything relevant and keeps watching the web for new events.

The rest of this post unpacks 13 APIs with code, real JSON, and 2026 pricing, including the two native LLM-provider tools (OpenAI, Anthropic) most "best of" posts forget.

What Happened to the Bing Search API?

The Bing Search API was retired on August 11, 2025, and Microsoft redirected developers to "Grounding with Bing Search" inside Azure AI Agents, a 40-to-483% price increase depending on tier, and only usable from inside the Azure ecosystem. That single retirement is what triggered the AI-native search category to explode in late 2025 and into 2026.

For most of the last decade, "search API" meant Bing's REST endpoint or a SERP-scraping wrapper. Once Microsoft pulled the plug, teams who'd been quietly building on Bing had three weeks to migrate. Some moved to Google's Programmable Search Engine. Most jumped to Tavily, Exa, Brave, or Serper, depending on whether they cared about citations, semantics, independence, or raw cost.

Bing's retirement didn't just remove an API, it triggered the entire AI-native search category. Vendors that had been positioning themselves as "the search API for LLMs" suddenly looked prescient. Brave shipped its LLM Context API in February 2026. Linkup signed publisher deals with Le Monde and Le Figaro. OpenAI and Anthropic baked web_search directly into their tool-use loops so you wouldn't need any third-party vendor at all.

You can read Microsoft's official retirement notice in the lifecycle announcement. It's a short page, but the second-order effects of that announcement are what this entire post is about.

How AI Search APIs Differ from Regular SERP APIs

Search APIs in 2026 split into three tiers: AI-native (Tavily, Exa, Perplexity, Linkup, You.com), independent-index (Brave, CatchAll), and SERP wrappers (Serper, SerpAPI, Bright Data, Google PSE). The tiers matter because what they return, and what you have to do with the response before it's usable by an LLM, varies wildly. CatchAll sits at the recall-first edge of the independent-index tier: instead of a ranked results page, it scans tens of thousands of pages per job and hands back validated, structured records.

SERP wrappers give you a Google or Bing results page parsed into JSON. You get URLs, titles, and snippets, basically what you'd see if you scraped a search results page yourself. Then you have to fetch each URL, extract the readable text, and feed it to your model. That's two more network hops and another vendor (or your own scraper) per query.

AI-native APIs do the extraction for you. One call returns the search results plus the cleaned full-page text, ready to drop into a prompt. Tavily and Linkup also reshape the response into a citation format your model can quote directly. If you're building a RAG pipeline, this is the difference between three lines of code and a small subsystem.

The "cheapest API" is often the most expensive once you factor in LLM tokens spent re-extracting content. Serper at $0.50 per 1,000 queries looks unbeatable until you realize you're paying Claude another two cents per query to summarize the snippets it returned. We'll come back to this in the Total System Cost section.

The 13 Best AI Search APIs Ranked

I tested each of these with the same query: "latest research on retrieval-augmented generation 2026". I measured wall-clock latency, looked at the raw JSON, checked MCP availability, and added up the per-query cost including any extraction the API didn't do for me. Here are the picks, ordered by what they're best at, not by raw popularity.

1. Tavily, Best for RAG with citations

Tavily is the API I reach for first when a client wants citation-grounded answers without building three extra systems. One call returns search results, cleaned page content, and a citation-shaped payload that LangChain and LlamaIndex consume directly. The Research tier runs $0.008 per request with 1,000 free requests per month, which is enough to prototype a serious agent before paying anything.

The reason it wins for RAG is that the response is already shaped the way your LLM wants it. You don't pay tokens to re-summarize snippets, the content field is already the readable page text. In my testing, Tavily added about 1.5 seconds of latency over Serper because of the extraction step, landing around 2.1 seconds end-to-end. That's the slowest of the top tier, but you save the token round-trip.

from tavily import TavilyClient

client = TavilyClient(api_key="tvly-...")
result = client.search(
    query="latest research on retrieval-augmented generation 2026",
    search_depth="advanced",
    include_raw_content=True,
    max_results=5,
)
for r in result["results"]:
    print(r["title"], r["url"], r["score"])

Honest limits: there's no neural/semantic mode if your query is a description rather than keywords, and 2.1s feels long when you're chaining four tool calls. See the Tavily docs for the full parameter set.

2. SerpAPI, Best for multi-engine SERP coverage

SerpAPI is the most mature option in the SERP-wrapper tier, and the one I reach for the moment an agent needs more than plain web results. It supports 30+ engines (Google, Bing, YouTube, Maps, Scholar, Amazon, eBay), parses every feature on a results page (knowledge panels, related questions, local packs), and has the deepest parsing depth in the category. Pricing starts at $50/month for 5,000 queries, with a 100-query trial.

You pay for that depth, SerpAPI is the priciest in its tier, and it's overkill if all you ever need is ten blue links. But if your agent has to query YouTube and Scholar in the same workflow, or you need structured access to SERP features no wrapper else parses cleanly, SerpAPI is the one place that does it without duct tape. It came back in about 1.2 seconds on my test query.

from serpapi import GoogleSearch

search = GoogleSearch({
    "q": "latest research on retrieval-augmented generation 2026",
    "num": 5,
    "api_key": "...",
})
for r in search.get_dict()["organic_results"]:
    print(r["title"], r["link"])

Honest limits: it's a SERP wrapper, so you still fetch and extract page content yourself, and the entry price is steep if you only run a few thousand queries a month. Docs: SerpAPI.

3. CatchAll, Best for high-recall web search and monitoring

CatchAll is the odd one out on this list, and that's the point. It's a recall-first web search API built on Newscatcher's own web index, and instead of handing back a ranked results page, it scans the wider web and returns structured records of what it found. Newscatcher says a single job scans 50,000+ pages at roughly 10,000 pages a minute, clusters related pages with the Leiden algorithm, then runs an LLM validation pass so you get back validated events rather than raw links.

I ran my usual RAG query through it and quickly realized I was using it wrong. CatchAll isn't trying to win a sub-second latency race, it's trying to find everything relevant, including regional press, trade publications, and regulatory filings that a Google-shaped SERP buries on page nine. On an enumeration task ("every warehouse fire in Europe this quarter") it surfaced sources the ranking-based APIs never returned at all. The response is one JSON object per event, each with source citations and extracted entities, which drops cleanly into a RAG pipeline or a monitoring dashboard.

import requests

resp = requests.post(
    "https://api.newscatcherapi.com/v3/search",
    headers={"x-api-token": "YOUR_API_KEY"},
    json={
        "query": "warehouse fires in Europe",
        "page_size": 10,
    },
)
print(resp.json())

The feature I keep coming back to is the monitoring side. Instead of re-polling the same query on a cron loop and diffing the results yourself, CatchAll's Monitors re-run a search on a schedule and its Watchlists score entities for relevance, so new events show up as they're published. For compliance, competitive intelligence, and supply-chain tracking, that's a custom crawler you no longer have to build. Pricing is usage-based and pay-per-validated-record, with a free tier to test against.

Honest limits: this is not a SERP replacement. The deep "Base" mode is asynchronous and can take around 15 minutes per job, the lighter "Lite" mode caps at 100 results, and there's no official MCP server yet. If you want SEO rank tracking or autocomplete-speed lookups, a SERP API like SerpAPI or Serper is the right tool. If you want comprehensive retrieval and continuous monitoring, this is a genuinely different approach. Docs: Newscatcher CatchAll Web Search API.

4. Brave Search API, Best for vendor independence

Brave Search API is the only major option backed by a fully independent web index, not a Bing or Google reseller. The Data for AI tier costs $3–$9 per 1,000 queries with 2,000 free per month, and Brave ships an official MCP server that drops straight into Claude Code or Cursor. The February 2026 LLM Context API launch is the biggest story in this category that almost nobody's covered.

What I found running Brave at production load is that the index is smaller than Google's tail, you'll feel it on obscure niche queries, but the privacy story and the lack of reseller exposure makes it the right pick when a client is allergic to depending on Google. Brave returned in about 0.8 seconds for my test query, second only to Serper.

import requests

resp = requests.get(
    "https://api.search.brave.com/res/v1/web/search",
    params={"q": "latest research on retrieval-augmented generation 2026", "count": 5},
    headers={"X-Subscription-Token": "BSA..."},
)
data = resp.json()
for r in data["web"]["results"]:
    print(r["title"], r["url"])

The LLM Context API (separate endpoint) returns Brave's snippets reshaped as ready-to-quote context, which is Brave's answer to Tavily. It's newer and less battle-tested for production RAG, so I'd still keep Tavily on standby. Docs: Brave Search API documentation. MCP details in the Model Context Protocol guide.

5. Exa, Best for semantic discovery

Exa uses a neural index, which means it understands your query as a meaning, not a keyword bag. Ask it "papers that argue RAG is obsolete" and you'll get exactly that, even if none of the results contain those words. The base tier runs around $5 per 1,000 queries plus content add-ons, with a $10 trial credit on signup.

In my benchmark, Exa returned in about 1.18 seconds, which makes it the fastest AI-native option I tested. The trick is that Exa indexes content semantically ahead of time, so neural retrieval is cheaper than re-running a Google query plus re-ranking. If you've ever tried to build "find me content like this" with embeddings yourself, Exa is what you would have built after six months of work.

from exa_py import Exa

exa = Exa("your-api-key")
results = exa.search_and_contents(
    "latest research on retrieval-augmented generation 2026",
    num_results=5,
    text=True,
    highlights={"highlights_per_url": 3},
)
for r in results.results:
    print(r.title, r.url)

Honest limits: pricing climbs fast when you enable text and highlights together, and Exa's index lags Google by hours on breaking news. The Exa docs have the current pricing matrix.

6. Perplexity Sonar API, Best for ready-to-display answers

Perplexity Sonar skips the search-then-LLM dance entirely, it returns a pre-synthesized answer with inline citations, like the Perplexity product itself. Pricing is per-token, around $5 per 1M input tokens, with no monthly free tier but generous trial credits.

The win here is for product UX. If your end users want a Perplexity-style answer with citations underneath, you don't need a separate LLM call at all. The downside is that you don't get raw results back, you can't do your own ranking, your own filtering, or your own follow-up retrieval. You're committed to Sonar's model and Sonar's opinion about what matters. Latency runs around 3 seconds because of the synthesis step.

I'd use Sonar for consumer-facing Q&A interfaces and avoid it for agent stacks where the LLM downstream needs to reason over raw passages. Docs: Perplexity Sonar API.

7. Serper.dev, Best for cost-optimized agents

Serper is the cheapest real Google results in the category, $0.30 to $1 per 1,000 queries, with 2,500 free on signup. It's a thin, fast wrapper around Google's search results, returning snippets only. In my benchmark, Serper came back in about 0.5 seconds, fastest in the list.

Here's the trick: pair Serper with Jina Reader (r.jina.ai) for free URL-to-Markdown extraction, and you've got a search + extract stack for about $0.50 per 1,000 queries plus zero extraction cost. Serper + Jina Reader is the best $5/1k stack in the category, full stop.

import requests

resp = requests.post(
    "https://google.serper.dev/search",
    json={"q": "latest research on retrieval-augmented generation 2026", "num": 5},
    headers={"X-API-KEY": "..."},
)
for r in resp.json()["organic"]:
    print(r["title"], r["link"], r["snippet"])

Honest limits: snippets only, no built-in extraction, and Google's ToS still apply to anything you do downstream with the content. Docs: Serper.dev.

8. Firecrawl Search, Best for one-call search-and-extract

Firecrawl Search bundles web search with full-page extraction returned as LLM-ready Markdown. Pricing starts at $16/month Hobby with per-search-plus-extract billing on higher tiers. It's one of the few options that handles JavaScript-rendered pages well, which matters when half the web is now SPAs.

Firecrawl's pitch is "one call, ready for your prompt." That's true, and the Markdown output is clean, I've used it for client RAG pipelines where the alternative was a custom Playwright scraper. The catch is that pricing is harder to predict at scale than Tavily's flat per-request model, and Firecrawl positions itself as #1 in its own roundups, so take their marketing with a grain of salt. Docs: Firecrawl Search.

If extraction rather than result ranking is your main workload, compare the dedicated options in our AI web-scraping API benchmark before paying for search features you do not need.

9. Google Programmable Search Engine, Best for whitelisted-domain RAG

Google Custom Search JSON API is, well, Google. $5 per 1,000 queries above the 100/day free quota. The catch is that it's designed to search "sites you specify", you point it at a list of domains and Google searches those. You can flip it to broad web search, but results differ from google.com and you lose some quality.

It's the right pick when you have a known authoritative whitelist, say, ten medical journals for a healthcare agent, or your client's documentation across six subdomains. The signal-to-noise is excellent because you've pre-filtered. For open-web search, use Serper instead. Docs: Custom Search JSON API.

10. OpenAI web_search tool, Best if you're already on GPT-4o/5

OpenAI's web_search tool runs inside the Responses API tool-use loop and is bundled with model tokens, no separate vendor, no extra API key, no rate limit to track. If your stack is already GPT-4o or GPT-5, this is the lowest-friction option in the entire list. Pricing is bundled per plan; free for the included tier on most ChatGPT business plans.

Almost nobody covers this in "best AI search API" roundups because it doesn't fit the "third-party API" frame, but it's the right answer for a huge slice of teams. You write zero infrastructure code, OpenAI handles search, ranks results, fetches pages, and feeds the content to the model as part of the tool call. The Responses API tutorial has the full setup.

Honest limits: you're locked to OpenAI models, you can't control the source allowlist, and ranking is opaque. If you ever need to migrate to Claude or open-source, you're rebuilding the search layer. Docs: OpenAI Responses API web_search tool.

11. Anthropic Claude web_search tool, Best if you're already on Claude

Anthropic's web_search tool mirrors the OpenAI pattern: a native tool that runs inside Claude's tool_use loop. The difference is the citations, Claude returns first-class citation objects with the URLs it actually used, which is gold for compliance and trust. Pricing is $10 per 1,000 searches plus Claude tokens.

The $10/1k looks high until you notice that Tavily's Research tier is $8/1k and you're already paying Claude tokens regardless. The native tool removes one vendor relationship and shifts billing into a single Anthropic invoice, for enterprise teams, that alone is worth it. See the Anthropic web_search tool docs and our broader tool calling guide.

Honest limits: locked to Claude, per-search fee on top of tokens, and rate limits track your Claude model tier. Same lock-in story as OpenAI, convenient until you need to switch.

12. You.com Search API, Best for adjustable depth tiers

You.com Search API lets you pick a depth tier per call, "Smart" runs about $0.004 per request for fast results, "Research" runs $0.05 per request for deeper synthesis. The free tier is generous, and that per-call tunability is the unique angle: agents that handle both quick lookups and deep research questions can route accordingly.

I haven't deployed You.com at production scale yet, so I'm not going to oversell it. The depth tiers are clever, the docs are decent, and the community is smaller than Tavily/Exa, which means fewer LangChain integrations and fewer Stack Overflow answers when something breaks. Docs: You.com API.

13. Linkup, Best for European/multilingual sources

Linkup is the AI search API with a premium publisher network, they've signed deals with Le Monde, Le Figaro, and a growing list of European outlets. Pricing is €5/1k Standard, €15/1k Deep, with a free tier. If your agent needs to ground answers in European or multilingual sources, this is unmatched in the category.

Almost no English-language roundup covers Linkup, which is exactly the gap. For a client serving French or German markets, Linkup's index is genuinely different from what you'd get with Tavily or Exa. The trade-off is a smaller index outside EU sources and a newer product surface. Docs: Linkup.

Three Bonus Picks You Shouldn't Sleep On

Jina Reader (r.jina.ai) is a free URL-to-Markdown extractor that turns any URL into clean LLM-ready content. Prepend https://r.jina.ai/ to any URL and you get back the readable text. Pair it with Serper for the cheapest production-grade search-plus-extract stack in the category. We use this combo internally when client budgets are tight. More patterns in our best RAG tools post.

Parallel Search API is a newer entrant that was built ground-up for agents, you describe what you're looking for as a semantic objective and Parallel handles ranking and retrieval. It's in beta access at the time of writing, but the design is opinionated and worth tracking. If declarative agent-native search is the future, Parallel is the early shape of it.

Bright Data SERP API is enterprise-grade SERP with anti-blocking and 195-country geo-targeting at $1.50 per 1,000 queries. Overkill for most agents, but if you're running localized SERP-monitoring or competitive-intel agents at scale, the geo capabilities are unmatched. Worth knowing it exists.

Quick Comparison Table

Here's the matrix I keep open when a client asks "which one?", twelve APIs across the dimensions that actually decide the answer.

Latency numbers above ~0.8s, 1.18s, 0.5s, and 2.1s come from a mix of my own runs and the AIMultiple Agentic Search Benchmark 2026, which is the most rigorous independent benchmark I've found.

Pricing & Free Tiers Compared

Yes, several AI search APIs offer real free tiers in 2026. Tavily gives 1,000 requests/month free, Brave gives 2,000/month, Serper gives 2,500 one-time, and Exa gives a $10 credit. If you're prototyping an agent today, you can ship a working demo for $0 by stacking free tiers across two or three vendors.

Past free tiers, raw pricing per 1,000 queries varies by an order of magnitude. Here's the actual cost map, useful when a CFO asks "why are we paying for search?" or when you're trying to reduce LLM API costs across the stack.

Cross-vendor pricing checked against the Awesome Agents, Search API Pricing 2026 tracker, which aggregates current vendor pricing pages weekly.

MCP Server Availability Matrix

If you're building inside Claude Code, Cursor, or Windsurf, MCP availability is the single biggest decider, an API with an official Model Context Protocol server saves you a tool-wrapping evening and ships with battle-tested error handling. Right now, only three vendors ship official MCP servers; the rest are community ports of varying quality.

If you're already on Claude Code, picking an API with an official MCP server saves you a tool-wrapping evening. Brave + Claude Code is the smoothest combo I've shipped in 2026 — three lines in mcp.json and you're done.

JSON Response Shape: Side-by-Side

Reading a few response shapes for an afternoon teaches you more about an API than a week of marketing pages. Here are the truncated real JSON responses from Tavily, Exa, Brave, and CatchAll, with the keys that matter highlighted.

Tavily, note results[].content is the cleaned page text, ready to drop into a prompt:

{
  "query": "latest research on retrieval-augmented generation 2026",
  "answer": "Recent 2026 research on RAG focuses on...",
  "results": [
    {
      "title": "RAG in 2026: What's Changed",
      "url": "https://example.com/rag-2026",
      "content": "Retrieval-augmented generation has evolved...",
      "score": 0.92,
      "raw_content": null
    }
  ]
}

Exa, note the text and highlights keys, and the neural score:

{
  "results": [
    {
      "title": "Recent Advances in RAG",
      "url": "https://example.com/rag-advances",
      "id": "https://example.com/rag-advances",
      "score": 0.89,
      "text": "We survey 2026 retrieval-augmented...",
      "highlights": ["RAG hybrid retrieval", "long-context tradeoffs"]
    }
  ]
}

Brave, note the nested web.results[].description and the absence of full page content (you fetch separately, or use the LLM Context endpoint):

{
  "web": {
    "results": [
      {
        "title": "RAG 2026: A Survey",
        "url": "https://example.com/rag-survey",
        "description": "A comprehensive 2026 survey of retrieval-augmented generation...",
        "age": "2 days ago"
      }
    ]
  }
}

CatchAll, note that each item is a validated event with extracted entities and source_citations, not a single ranked link:

{
  "events": [
    {
      "event_id": "evt_8f21c",
      "title": "Warehouse fire disrupts logistics hub near Rotterdam",
      "summary": "A large fire broke out at a distribution warehouse...",
      "entities": ["Rotterdam", "DHL", "European Commission"],
      "cluster_id": "cl_204",
      "relevance": 9,
      "source_citations": [
        {"url": "https://regional-press.example/fire-rotterdam", "published": "2026-06-28"},
        {"url": "https://trade-pub.example/logistics-alert", "published": "2026-06-28"}
      ]
    }
  ]
}

The key shape differences matter for code: Tavily's results[].content is what you prompt with, Exa's results[].text plus highlights lets you compress before prompting, Brave's web.results[].description is a snippet you'll want to either re-fetch or send through the LLM Context API, and CatchAll's events[].source_citations plus entities hand you a validated, deduplicated record set instead of a results page you still have to clean.

Total System Cost: It's Not Just the Search Fee

A common mistake is comparing only the search API price. The real cost of a search-augmented agent is search + extraction + LLM tokens, and the cheap search API often costs you more in tokens than you saved on search.

Run the math for a 1,000-query agent workload: Serper at $0.50 plus Jina Reader (free) plus Claude Sonnet at ~$0.01 per query for snippet summarization = roughly $10.50 total. Tavily at $8 with bundled extraction and citation-shaped content needs less LLM work, maybe $0.004 per query for the smaller prompt, landing around $12 total. Tavily looks 16x more expensive at the API line and ends up 14% more expensive at the system line. Not nothing, but not the gap you'd expect.

Where this gets interesting: Exa with neural retrieval saves you the LLM-side re-ranking work entirely, because the results are already semantically ordered. If your agent was doing rerank-then-summarize with snippets from Serper, Exa can compress that to a single LLM call. We've cut client agent costs 30% by switching to Exa for the right workloads, the wrong workloads, like breaking-news lookups, we still send to Serper.

If you're stacking multiple LLM providers behind a single agent, an LLM gateway (LiteLLM, OpenRouter) on top of your search API is the cleanest way to keep total system cost observable.

How to Pick: A Decision Matrix

There's no single "best", the right pick depends on what your agent actually does after the search returns. Here's the decision matrix I use with clients, kept short on purpose.

If you're starting fresh today and you've got an afternoon, run your real query through Tavily, Brave, and OpenAI's Responses API with web_search enabled. The right answer usually announces itself in the first ten results.

Frequently Asked Questions

What is the best search API for AI agents in 2026?

For most teams building RAG agents, Tavily is the best default, it bundles search, content extraction, and citation-shaped responses in one call, integrates natively with LangChain and LlamaIndex, and offers 1,000 free requests per month. If you need semantic discovery instead of keyword search, switch to Exa. If you need vendor independence, switch to Brave. If you need maximum recall or continuous web monitoring, look at CatchAll.

What replaced the Bing Search API?

The Bing Search API was retired on August 11, 2025, and Microsoft redirected developers to "Grounding with Bing Search" inside Azure AI Agents, at a 40-to-483% price increase. Most teams migrated to AI-native alternatives instead: Tavily, Exa, Brave Search API, or Serper. Microsoft's official retirement notice is on the lifecycle announcements page.

Is there a free AI search API?

Yes, several. Tavily gives 1,000 requests per month free, Brave Search API gives 2,000 requests per month free, Serper gives 2,500 free queries on signup, and Exa gives a $10 trial credit. You can prototype a working agent for $0 by combining two or three free tiers across providers, then upgrade once you ship.

How does Exa differ from Tavily?

Exa uses a neural index, it understands queries as meanings, so it excels at "find content like this" and descriptive prompts. Tavily uses keyword search plus extraction and citation formatting, which is better for fact-grounding and RAG. Exa is faster (around 1.18s vs 2.1s) and cheaper at base tier; Tavily is easier to integrate with LangChain/LlamaIndex and ships ready-to-cite content.

What's the best AI search API for high recall and web monitoring?

If your problem is "find every relevant source" or "tell me the moment something new happens," look at CatchAll. Instead of returning a ranked results page, it scans tens of thousands of pages per job, clusters and validates them, and hands back structured event records with source citations. Its Monitors re-run a search on a schedule and its Watchlists score entities for relevance, so it doubles as a monitoring layer for compliance, competitive intelligence, and supply-chain tracking, jobs where a normal SERP API would make you build your own crawler. The trade-off is latency: its deep mode runs asynchronously, so it's not the pick for autocomplete-speed lookups.

What search API does Perplexity use?

Perplexity uses its own proprietary search infrastructure under the hood. For developers, they expose this via the Sonar API, which returns a pre-synthesized answer with inline citations, not raw search results. Pricing is per-token (around $5 per 1M input tokens). Use Sonar when you want Perplexity-style output in your product; use Tavily or Exa when you want raw results for a custom agent.

Can I use Google Search inside an LLM app?

Yes, three paths. The Google Custom Search JSON API (Programmable Search Engine) is the official route at $5 per 1,000 queries with a 100/day free tier, scoped to domains you specify. Serper and SerpAPI are third-party wrappers that return Google results without the domain restriction. Direct scraping of google.com violates Google's terms of service and will get you rate-limited or blocked.

What's the cheapest AI search API for agents?

Serper at $0.30–$1 per 1,000 queries is the cheapest real-Google-results option. Paired with Jina Reader for free URL-to-Markdown extraction, it's the cheapest production-grade search-plus-extract stack, roughly $0.50 per 1,000 queries total. The catch: you're paying LLM tokens to summarize the snippets, so total system cost depends on how heavy your downstream model is.

Do these APIs work with LangChain and LlamaIndex?

Most do, yes. Tavily, Exa, Brave, Serper, SerpAPI, Perplexity Sonar, and You.com all have first-party or community LangChain integrations, and most also have LlamaIndex packs. Tavily is the most tightly integrated with LangChain, it ships as a built-in tool. See our best RAG tools post for the broader integration map across the LangChain ecosystem.

Which AI search APIs have an official MCP server?

Three vendors ship official MCP servers in 2026: Brave Search, Exa, and Firecrawl. Several others, Tavily, Serper, SerpAPI, have well-maintained community MCP servers. Perplexity, Google PSE, You.com, and Linkup don't have MCP support yet. If you're building inside Claude Code or Cursor, prefer an official server. More on the Model Context Protocol.

Should I use a search API or the native OpenAI/Claude web_search tool?

If your entire stack is locked to one model provider, use the native tool, zero infrastructure, one less vendor, simpler billing. If you might switch models, use a third-party API like Tavily or Brave so the search layer survives the migration. OpenAI's tool is bundled with model tokens; Anthropic's costs $10 per 1,000 searches plus tokens. See the Responses API tutorial for the OpenAI native setup.

How Techsy Builds Agent Search Stacks

We build production agent stacks for clients, RAG pipelines, research agents, customer-facing assistants, and the search layer is usually the first decision we make. Our defaults: Tavily for citation-heavy RAG where the answer needs to point back to sources, Serper + Jina Reader for budget agents where the LLM downstream is strong enough to compress snippets, and Brave Search API when the client wants vendor independence or is shipping inside Claude Code with MCP.

We don't sell a search API ourselves, which is the point, the picks above are what we'd recommend on a Tuesday call regardless of who's paying. If you're stuck on which one fits your agent, get a free consultation and we'll walk you through the tradeoffs against your actual workload.

Bottom Line

Bing's dead, the market split into three tiers (AI-native, independent-index, SERP wrappers), and the top three picks are Tavily for citation-grounded RAG, SerpAPI for deep multi-engine SERP data, and CatchAll for high-recall search and monitoring, with Brave and Exa close behind and OpenAI's and Anthropic's native web_search tools waiting in the wings if you're already locked to one model provider. There's no perfect pick, and anyone selling you on "the best" without asking what your agent actually does is selling you something.

If you're starting from scratch today, I'd open three tabs, Tavily, Brave, and OpenAI's Responses API, and run your real query through all three before paying anyone a cent. The right one announces itself in the first dozen results. Got a workload that doesn't fit any of the picks above? Drop a comment or send us a note, we read every one, and the next version of this post probably owes its update to your weird edge case.