9 Agentic Search Tools Ranked by Index Points per Dollar

Until a few years ago, global web traffic was mostly humans.

But the 2026 Thales Bad Bot report found that bots now account for 53% of all web traffic, putting humans at 47%. Meaning, there are more bots using the web than humans at this point. And they need to pull web pages without polluting their context windows.

That's why we launched TinyFish in 2025: an agentic search tool that makes the web accessible to agents without burning a hole in your pocket, even if your agent makes thousands of web queries.

In this piece, we'll cover 9 agentic search tools you can pick from based on your use-case. While I am biased towards TinyFish, everything mentioned is based on pure facts. So stay with me here.

What are agentic search tools and how they work

Agentic search tools are search APIs for AI agents, built to be called by a large language model mid-conversation through tool calling, also known as function calling, to fetch information. The agent writes the query in natural language, reads the results as text, and searches again until it can answer a user's question.

That loop, where the model decides its own next step, is what people mean by agentic AI, as opposed to artificial intelligence that answers once and stops. The APIs built for that loop are the agentic AI tools this piece compares.

The agent decides what to search, reads results, and searches again

In an agentic workflow, the intelligent agent runs search as a loop, and the loop ends when the agent judges it can answer. This could be one web search or a thousand at the agent's discretion. The only goal is to provide the user with a valid response.

Every search call costs money on most APIs. And because you don't know how many searches the agent will make beforehand, you don't really know what a task will cost from the API price alone.

Results come back as model-readable text, and every token costs money

Every result a web search API for LLM use returns, whether plain snippets or structured outputs in JSON, lands in the model's context window. The model provider then bills you for those input tokens.

So if one tool returns three times as much text as another, the cheaper one can still make the overall task more expensive. Failed searches have the same problem. The agent searches, reads a bunch of irrelevant text, searches again, and keeps paying for everything it has already pulled into context.

A good agentic search tool gives the agent enough information to make the next decision without dumping unnecessary page content into the prompt.

Agentic search tools vs. RAG and SERP scrapers

Agentic search, retrieval-augmented generation or RAG, and SERP scrapers can all get information into a model, but they solve different problems. The first of those is the agentic AI tool that reaches the live web. So the agent reads what is on the web now rather than what was indexed when the corpus was built. Basically, the agentic search vs RAG choice comes down to whether you already own the information.

• RAG uses semantic search over a corpus you prepared beforehand, which means running an ingestion pipeline and an embedding model before the first query.
• SERP scrapers return search-result data that the agent still has to work through.
• Agentic search queries the live web for model-readable results.

RAG makes sense when you already own the information. SERP APIs make sense when the search results themselves are what you need. Agentic search makes sense when the agent needs to go out onto the web and answer something.

Per-query pricing of agentic search tools

While API price is one part of the cost equation (there's the number of queries the agent makes and how much the model itself costs for input and output tokens), it's a good gauge for picking between agentic AI tools.

TinyFish is the only free web search API on the list with no metered price at all. Most of the others offer a free tier or credit before billing starts, which is what free usually means in practice.

How I'm comparing the agentic search tools in this list

I'm using the Artificial Analysis Search Index for the benchmark numbers.

Artificial Analysis runs the search providers through the same GPT-5.6 Luna agent across DeepSearchQA, OpenAI's BrowseComp and AA-Omniscience. It also reports the total cost of completing a task, including both search calls and model tokens.

You can read their full methodology here if you want the details.

For this piece, I've used a simpler, combined score with numbers from the benchmark:

Index score (from benchmark) / total task cost = Index points per dollar

Top 9 agentic search tools in 2026

Ranked by index points per dollar, TinyFish comes first at 2,060 and Tavily last at 340, so a point of benchmark quality costs six times more at the bottom of the list than at the top.

1. TinyFish Search

TinyFish Search scores 71.2 on the Index. Its total cost is $0.0345 per task, the lowest of the nine. That works out to 2,060 Index points per dollar, almost twice Parallel turbo.

Search itself costs $0, so the whole task cost is basically just your model tokens. The model also read less text per task, so the remaining model bill is quite small.

Now, I work on TinyFish and I know it's a great product, but we can’t claim we win on every quality metric.

TinyFish performed slower for the assigned benchmark task, taking 59.4 seconds to complete, so a user waiting on the answer waits close to a minute.

But that said, this benchmark result reflects the kinds of tasks where information is not cleanly available and the agent needs more time to search across the web. On faster, more direct searches, the difference may be much smaller.

Features:

• Free Search and Fetch is capped at 500 requests an hour, so search volume never draws from the wallet.
• One domain_type parameter switches a query between the general web, news articles with publisher and date fields, and research papers. So the agent picks the kind of source it needs without changing endpoints.
• recency_minutes takes any window from one minute to ten years, or you pass after_date and before_date instead. So the agent only reads pages published inside the window it cares about.
• An optional purpose field takes a sentence or two of free text that tells the search what the agent is trying to accomplish with the query.
• Fetch renders each URL in a real browser with JavaScript, so single-page apps and dynamic content come back as clean Markdown, JSON, or HTML.
• TinyFish ships a Model Context Protocol server and listed integrations for Claude Code, LangChain, CrewAI and the workflow automation tool n8n. So agents on those stacks call Search without writing their own tool definition.

Pros:

• Search costs nothing per request, so the bill stops growing with search volume and only your model tokens remain
• Low model-token use keeps that remaining bill small, both in the benchmark and through our extensive internal testing
• Obscure questions and factual recall both hold up, with strong BrowseComp and AA-Omniscience results, so cheap does not mean inaccurate
• A CrewAI crew calls Search through the listed integration, so there is no custom tool definition to write before the first query

Cons:

• Weakest on deep-research questions, with the lowest DeepSearchQA score of the nine, so research-heavy agents lose some accuracy here
• Tasks take longer to finish, the second-slowest total time on the benchmark, so users waiting on an answer wait longer
• The agent needed more searches per task than with any other provider, and each extra round trip adds to the wait, though not the bill

Pricing:

• Search and Fetch: $0 per request
• Standard limits: 30 requests per minute and 500 per hour
• New accounts receive $8 in wallet credit for other paid TinyFish products
• Search and Fetch do not use wallet credit

Verdict: I'd recommend TinyFish for any agent that searches many times per task. At $0 a search, the bill stops growing with search volume and only the model tokens remain. In exchange you accept slower tasks and our weaker DeepSearchQA result, and past 500 requests an hour you need to talk to us about an enterprise plan.

2. Parallel Search

Parallel Search accepts a natural-language objective and returns ranked URLs with excerpts compressed for a model.

Parallel turbo scores 67.1 on the Index. Each task cost $0.0602 in total. That works out to 1,114 Index points per dollar, second only to TinyFish. The catch is its AA-Omniscience score of 56.5, the lowest of any tool on the board, so it answers plain factual questions correctly less often than any other tool here.

Parallel's other modes offer better quality for higher pricing.

• Fast raises the Index score to 73.1. The task cost rises only to $0.0681, a small step up from turbo. It also finished the benchmark in 18.9 seconds, the fastest time of any variant on the board, which means the least waiting for a user of any tool tested.
• Advanced reaches 74.8 on the Index. The task cost climbs to $0.0835 for it, so you pay more per point of quality than with fast.

Features:

• The request takes a natural-language objective describing what the agent wants to know, with search_queries as an optional list alongside it. So the agent can describe what it needs instead of guessing keywords.
• The mode parameter picks between turbo, fast, basic, and advanced, with stated latency running from a fraction of a second on turbo to about 3 seconds on advanced. So you trade speed for quality by changing one value.
• Excerpts come back compressed for a model, and on fast the agent made fewer searches per task than with any other variant on the board. So it found what it needed in fewer round trips.
• Basic returns longer snippets per result and is documented to work best with two or three focused search_queries.
• Turbo handles English and Japanese only, with basic or advanced as the documented route for other languages, so agents working in those languages pay the higher per-request rate.

Pros:

• Cheapest paid search on the board at $0.001 per query, so heavy search volume barely moves the bill
• Switching to fast buys a sizeable accuracy gain, six points on the Index, for a small rise in task cost
• Fast finishes tasks quickest of anything tested, 18.9 seconds in the benchmark, so users wait the least for an answer

Cons:

• Turbo gets plain factual questions wrong more often than any other tool here, with the lowest AA-Omniscience score on the board
• Turbo only handles English and Japanese, so other languages need the pricier modes, and the ranking figures here apply to turbo alone

Pricing:

• Turbo and fast: $1 per 1,000 requests
• Basic and advanced: $5 per 1,000 requests
• Free allowance: 5,000 requests per month
• Rate limit: 600 requests per minute

Verdict: Pick Parallel turbo if you need cheap, high-volume English search and can accept the lower factual score. I would recommend using fast if you can justify a slightly higher task cost because the quality jump is substantial.

3. Firecrawl Search

Firecrawl Search combines web search and page scraping under the same API and credit system.

Firecrawl scores 73.5 on the Index. Each task cost $0.0754 in total. That works out to 974 Index points per dollar. Its AA-Omniscience score of 72.8 is the highest on the board, so it answers plain factual questions correctly more often than any other tool here.

Firecrawl is also the slowest tool in this ranking at 62.4 seconds per task, so a user waits over a minute for an answer. Each search query took nearly 3 seconds, the slowest query latency on the board. The agent also searched more often than with most tools here, so that waiting added up.

The benchmark used Firecrawl without full-page scraping, so the agent received titles, URLs and query-relevant highlights. You can also ask Firecrawl to return the full markdown or HTML behind the search results.

Features:

• Adding scrapeOptions to a search returns the full page behind each result as markdown, HTML, links, or a screenshot in the same call. So the agent skips a second fetch step.
• Highlights matched to the query come back by default, and this snippet-only mode is what the benchmark ran with scrape_format=none.
• categories restricts a search to research sites, PDFs, or developer documentation and code repositories, with the limit applied per category. So the agent skips general-web noise when it wants papers or code.
• sources can request web, news, and image results in one call, and tbs filters by past hour through past year or a custom date range. So the agent skips results older than it needs.
• Search draws from the same credit pool as scrape, map, and crawl, so one balance covers finding pages and reading them.

Pros:

• Most reliable on plain factual questions, with the top AA-Omniscience score on the benchmark, so straightforward lookups come back right more often
• You can run it yourself, since the code is open source under AGPL, instead of depending on the hosted service

Cons:

• Users wait longest for an answer here, since Firecrawl posted the slowest total task time among the nine tools
• Each query takes nearly 3 seconds to return, the slowest on the board, so agents that search in tight loops mostly wait
• The agent needed more searches per task than with most tools here, so those slow queries stack up and each one draws down more credits

Pricing:

• Search: 2 credits for 10 results
• Free allowance: 1,000 credits per month
• Standard plan: $83 for 100,000 credits
• Roughly $1.66 per 1,000 ten-result searches on Standard
• Full-page scraping adds 1 credit per page

Verdict: Pick Firecrawl if your agent usually needs to scrape the pages it finds. If you only need search, there are faster and cheaper options in this list.

4. Perplexity Search

The Perplexity Search API returns the ranked search results behind Perplexity without generating a Perplexity answer on top.

Perplexity's medium context mode scores 80.0 on the Index, the highest raw score on the board, meaning the most accurate answers overall of any tool tested. Total cost came to $0.0914 per task. That works out to 875 Index points per dollar. Its BrowseComp score of 87.0 is also the highest of any tool, so obscure facts buried deep in the web get found more often.

The API lets you control how much extracted text comes back with search_context_size, so you can trade token spend against answer quality.

Features:

• search_context_size sets how much text is extracted from each result page. Medium scored 80.0 on the Index, while high, the default, read more text per result and scored about a point lower. That's why medium is the better setting.
• One request carries up to five queries and bills as a single unit, so an agent can fan out sub-questions without multiplying the search cost.
• Each result includes a date field and a snippet that runs to a long markdown passage rather than a one-line description. So the agent can check how fresh a source is before it reads the passage.
• max_results caps how many results come back per request, and domain and language filters apply to every query in the batch. So a fanned-out request stays inside the sites and languages you allow.

Pros:

• Most accurate overall, with the top Search Index score in the comparison, so agents that cannot afford wrong answers start here
• Best at hard fact-finding on the web, with the top BrowseComp score on the board, so obscure questions get answered more often
• The model reads little text per task, with the second-lowest model-token bill in the benchmark, so most of what you pay is the search fee

Cons:

• You pay more per unit of quality than with Firecrawl, because Perplexity's search fee makes up about two thirds of its task cost and outweighs the higher score
• You cannot buy more accuracy by turning context up, since neither high nor low beat medium in the benchmark

Pricing:

• $5 per 1,000 requests
• Up to five queries can be included in one billed request
• No separate Perplexity token charge for search responses
• No free tier listed

Verdict: Pick Perplexity medium if answer quality matters more than getting the lowest possible task cost. You pay roughly nine cents a task for it, and most of that is Perplexity's search fee rather than model tokens. That's because the agent searched about a dozen times per task and each search bills at the list price.

5. Keenable Search

Keenable Search runs on its own search index and offers realtime and pro modes.

Keenable's realtime mode scores 66.5 on the Index. Each task cost $0.0905 in total. That works out to 735 Index points per dollar, a middling result because the model bill outweighs its cheap search by more than two to one.

Each Keenable search took 0.34 seconds in the benchmark, the fastest query latency on the board. That makes it the closest thing here to the best real-time search API for AI agents. Its full task time was 21.8 seconds, second fastest among the nine variants in this ranking, so the per-query speed carries through to the finished answer.

Each result carries both publication and acquisition timestamps, so the agent can tell how stale a source is. The API can also run point-in-time searches through query_time, so a search can be replayed against what the index held on a given day.

Features:

• Every result carries published_at and acquired_at, so the agent can tell when a page was written and when the index picked it up.
• query_time searches the index as it stood at a chosen moment and excludes pages acquired after it, so nothing the index learned afterwards leaks into the results.
• The mode parameter picks realtime or pro. Pro scored only half a point higher on the Index, and for that each query took more than three times as long, so realtime is the better default.
• snippet_max_length caps how long each snippet can run, which matters here because the model bill outweighs the search bill.
• site restricts a search to one domain, and separate filters cover both published and acquired date ranges. So the agent can stay inside a trusted site and a time window.
• Its MCP server is the recommended way to connect, so agents skip a hand-written tool definition.

Pros:

• Each query returns in 0.34 seconds, so agents that search in tight loops spend the least time waiting
• Whole tasks finish quickly too, second fastest in this ranking, so the per-query speed reaches the user as a faster answer

Cons:

• Answers are less accurate than with most tools here, with a 66.5 Index score, so you trade correctness for speed
• Less time in the market than the established providers, so you have fewer independent reports to check before committing

Pricing:

• First 100,000 requests per month: free
• Pay as you go: $4 per 1,000 requests
• High-volume tier at 100 requests per second or more: $1 per 1,000 requests
• Artificial Analysis used $2 per 1,000 queries in its benchmark cost calculation, below the current pay-as-you-go price

Verdict: Pick Keenable if search latency and freshness controls matter more than having the highest benchmark score. Its large free allowance also makes it practical to test against your own workload before committing.

6. You.com Search

The You.com Web Search API lets you choose between short snippets and longer highlights extracted from each result page.

You.com's highlights mode scores 74.1 on the Index. Each task cost $0.1175 in total. That works out to 631 Index points per dollar.

It completed a benchmark task in 20.2 seconds, the fastest among the nine variants in this ranking, so users wait the least for an answer. But the model read more text per task than with any other variant on the board. So the model bill ends up the largest in this ranking and outweighs what You.com itself charges for the searches.

The shorter snippets mode reduces how much text comes back but scored more than six points lower on the Index, so trimming the token bill costs you accuracy.

Features:

• extraction_mode set to highlights pulls longer passages out of each result page; leaving it off returns the default short snippets[] per result.
• The same parameter also accepts full_page, so the agent can move from snippets to whole pages without changing endpoints.
• One response is sectioned into web and news results, so the agent reads both from a single call.
• count sets how many results one call returns, and the per-call price does not change however many you ask for.
• A SKILL.md file at you.com lets agents that support skills load the API's usage rules directly.

Pros:

• Users wait the least for an answer, since it posted the fastest total task time among the nine selected variants
• Highlights mode buys a clear accuracy gain over snippets, more than six points on the Index, at the same per-call price

Cons:

• Long highlights make the model read more text per task than with any other variant here, so your token bill roughly doubles against snippet-only tools
• Most of what you pay goes to your model provider, not You.com, since model tokens make up well over half of the task cost

Pricing:

• Web Search: $5 per 1,000 calls
• Same call price for 1 to 100 results
• News endpoint: no additional charge
• New accounts receive $100 in credit
• Keyless free plan: 100 queries per day

Verdict: Pick You.com highlights if task speed matters and you're comfortable paying the model-token cost of richer search results.

7. Exa Search

Exa Search focuses on semantic search over the web and can return page contents alongside the results.

It is also available as a Claude connector and a ChatGPT plugin, so either chatbot can search through it without custom tool code.

Exa's auto mode scores 73.7 on the Index. Each task cost $0.1272 in total. That works out to 579 Index points per dollar. Its DeepSearchQA score of 77.8 is third among these nine tools, behind Perplexity and Brave, which makes deep-research questions its strongest area.

The agent made fewer searches per task with Exa than with any other tool here. Search still made up about half of the task cost, so fewer calls did not make it one of the cheaper options.

Exa can return highlights, full text or summaries, and can combine the result content into one context string for the model. So the agent gets page content without a separate fetch step.

Features:

• The type parameter selects instant, fast, auto, or the deep modes, and the benchmark's auto mode scored more than five points higher than instant and fast. So the two faster modes give up noticeable accuracy.
• contents returns text, highlights[] with highlightScores, or a summary per result in the same call, with a JSON schema option so summaries come back as structured outputs.
• Highlights are the passages the model judges most relevant on each page, and contents={"highlights": true} is what the benchmark ran.
• maxAgeHours sets how old a cached page may be before Exa fetches it live, with zero forcing a fresh crawl.
• Each result includes a publishedDate parsed from the page and an author field when one is available, so the agent can cite who wrote a source and when.

Pros:

• Strong on deep-research questions, with a DeepSearchQA score behind only Perplexity and Brave here, so multi-step lookups come back more complete
• The agent finds what it needs in fewer round trips, the fewest searches per task of the nine, so less time is lost re-searching

Cons:

• Fewer calls do not make it cheap, because each request costs $7 per 1,000 and the model still reads long highlights
• The faster modes lose accuracy, with instant and fast both scoring more than five points below auto, so speed costs you correctness

Pricing:

• Up to 10 results: $7 per 1,000 requests
• Each additional result: $1 per 1,000 requests
• Page contents: $1 per 1,000 pages
• New accounts receive $20 at signup and $10 per month after that

Verdict: Pick Exa if semantic search and getting page content in the same call are more important than minimizing task cost.

8. Brave Search

The Brave Search API runs on Brave's own independent web index, which Brave markets to chatbots as well as agents. For agents, its LLM Context endpoint returns page chunks rather than the conventional snippets from its Web Search endpoint.

Brave's LLM Context endpoint scores 74.6 on the Index. Total cost per task was $0.1295. That works out to 576 Index points per dollar, almost level with Exa. Its DeepSearchQA score of 78.1 is second only to Perplexity among these nine tools, so deep-research questions are its strongest area.

Search latency is low at 0.44 seconds per query, so the agent spends little time waiting on each call. But those page chunks are large, so the model reads far more text per task than with snippet tools. That pushes the model bill past half of the task cost.

Brave's regular Web Search endpoint scored ten points lower in the same benchmark. It also cost more per task, so for agent use the older endpoint loses on both quality and price.

Features:

• LLM Context returns chunks of actual page content, including tables and code as structured data, in place of the Web Search endpoint's description snippets. So the model can read a table or code block without fetching the page.
• maximum_number_of_tokens caps how much of that page text lands in the prompt, and maximum_number_of_urls caps how many pages it draws from. So you can keep the model bill in check.
• Web Search is built for human readers, by Brave's own description, and agent use belongs on the newer LLM Context endpoint, which scored ten points higher.
• Web Search can add up to five extra_snippets per result on top of the description when you do need the conventional endpoint.
• freshness takes pd, pw, pm, or py for the past day, week, month, or year, or a custom date range, so stale pages drop out of the results.
• A Brave Search MCP server lets agents call the API without a hand-written tool definition.

Pros:

• Answers come back accurate, with a 74.6 Index score near the top of this list, so the token cost buys real quality
• Deep-research questions are a strength, with the second-highest DeepSearchQA score of the nine, so multi-step lookups come back more complete
• Each query returns in 0.44 seconds, so the agent spends little of its task time waiting on search

Cons:

• Big page chunks make the model read far more text per task, so your model bill ends up higher than with snippet-based APIs
• The older Web Search endpoint is a worse buy for agents, scoring ten points lower at a higher task cost

Pricing:

• Search plan: $5 per 1,000 requests
• Both endpoints bill at the same request price
• Rate limit: 50 requests per second
• Brave is free up to $5 of monthly credit, with Brave's attribution requirement and a card on file

Verdict: Pick Brave LLM Context if you want an independent web index and page-level context returned directly to the agent. Expect to spend more on model input tokens than with snippet-heavy APIs.

9. Tavily Search

The Tavily API returns scored snippets from Tavily Search and is already integrated into a lot of agent frameworks.

Tavily's basic search scores 65.6 on the Index, the lowest of the nine, so its answers were the least accurate in this comparison. Each task cost $0.1930 in total, also the highest here. That works out to 340 Index points per dollar.

Search alone made up about two thirds of that task cost, the highest search bill on the board. That comes from a list price of $0.008 per search, the highest here, multiplied by an agent that searched more often than with most tools.

Its BrowseComp score of 58.5 is also the lowest among these nine variants, so hard-to-find facts get missed more often than with any other tool here.

Please note, Artificial Analysis did not benchmark its advanced search depth, so the numbers here apply only to basic.

Features:

• Every result carries a relevance score, so an agent can do its own reranking or drop low-scoring hits before spending tokens on them.
• search_depth picks basic or advanced, and chunks_per_source sets how many content chunks each source contributes, so you control how much page text each result adds to the prompt.
• include_answer returns a generated answer to the query alongside the results, with basic for a quick one and advanced for a detailed one.
• include_raw_content returns the cleaned page text as markdown or plain text, so search and page reading happen in one request.
• topic switches between general and news search, and date and domain filters narrow either one.

Pros:

• Little integration work, because a lot of agent frameworks already ship Tavily support alongside MCP and agent skills
• Trying it costs nothing up front, since the free plan gives 1,000 credits a month without a card

Cons:

• Each query costs $0.008, the highest list price on the board, so every extra search hurts more here than anywhere else
• The agent also searched more often here than with most tools, so the priciest query multiplies into the largest search bill in the benchmark
• Hard-to-find facts get missed most often, with the lowest BrowseComp score among the nine selected variants
• Advanced depth was never benchmarked, so you cannot tell from these numbers whether paying double per search buys better answers

Pricing:

• Basic search: 1 credit
• Advanced search: 2 credits
• Pay as you go: $0.008 per credit
• Larger plans go as low as $0.005 per credit
• Free plan: 1,000 credits per month with no card required

Verdict: Pick Tavily if your framework already supports it and you value integration convenience over the benchmark economics here. I would test advanced separately before judging the whole product from its basic-mode score.

Which agentic search tool should you pick?

Cost points at TinyFish, raw benchmark quality at Perplexity medium, and per-query latency at Keenable. The other choices come down to what your agent already integrates with.