10 best web search APIs for AI agents in 2026

An AI agent can reason about information, but it still needs a way to find what's happening outside its training data. If someone asks it to research a company, compare recently released software, or find the latest documentation for a library, it needs access to current web sources.

A web search API provides that access. Instead of opening a browser and manually searching, an agent can send a query and receive structured results containing links, descriptions, source information, and sometimes the actual page content.

The challenge is choosing an API that returns the kind of information your agent needs. Some provide traditional search engine results, others return content prepared for language models, and some generate answers with citations.

This guide compares 10 web search APIs for AI agents in 2026, including their capabilities, pricing, and the workflows they're best suited for.

What makes a good web search API for AI agents?

A traditional search engine is designed to help people find websites. It presents a list of results, and the person decides which pages to open.

An AI agent needs to do more of that work itself. When an agent receives search results, it must determine which sources are relevant, whether the available information is sufficient, and which pages it needs to read before answering.

That makes the structure and content of the API response just as important as the search results themselves.

A useful way to compare providers is by looking at what they return.

These categories overlap. A single provider may offer several capabilities through different endpoints.

When evaluating an API, consider how well it handles relevance, fresh information, source attribution, geographic targeting, pagination, and cost.

For an agent that performs several searches before answering, you should also pay attention to response size. Returning entire webpages when the agent only needs a few sentences can increase token usage and processing time.

Quick comparison: 10 web search APIs for AI agents

Prices are in USD and reflect published entry rates. They aren't directly equivalent. Some providers charge per request, others per result or credit, and services that generate AI answers may also charge for model tokens or additional processing.

1. Spidra

Spidra is a web data API that combines search, scraping, crawling, and structured extraction.

Its Search API lets applications and AI agents discover relevant content across different types of sources without managing search infrastructure.

Beyond ordinary web results, Spidra supports searches across news, images, videos, research publications, and developer sources such as GitHub issues and pull requests.

Each source returns structured information appropriate to its content. Web results include titles, URLs, descriptions, and positions, while research results can include authors, dates, and publication metadata.

Spidra also supports domain filtering, geographic preferences, pagination, and PDF-focused searches.

Search and retrieve the content you need

Spidra separates finding information from extracting it. A search can return URLs and descriptions for an agent to evaluate. If the agent needs additional context, it can request the Markdown content of selected search results.

You can also use Spidra's Scrape, Batch, or Crawl tools to collect information after discovery.

For example, a research agent could search for open-source vector databases, select relevant comparison pages, and extract the information needed for a structured report.

The process stays within the same platform.

Example: Search the web with Python

Install the Spidra SDK:

pip install spidra

Then create a file named search.py:

# search.py
import os
from spidra import Spidra

client = Spidra(api_key=os.environ["SPIDRA_API_KEY"])

results = client.search(
    "open source vector database",
    sources=["web"],
    limit=5
)

for result in results.data.web[:3]:
    print(result.position, result.title)

A completed search returned:

1 Best Open-Source Vector Database In 2026: 10 Options Compared
2 8 Best Open Source Vector Database Tools for 2026
3 Comparing the best open source vector databases

The response also contains the source URLs and descriptions, which the agent can use to decide what to retrieve next. Search results may change over time.

If the application needs the full text, you can request Markdown from selected results or scrape their URLs separately.

For an agent, this allows a useful distinction: search broadly to discover sources, then retrieve detailed information only from the ones worth reading.

Key features

• Web, news, image, video, research, and developer search.
• Structured results with source-specific metadata.
• Domain filtering and geographic search preferences.
• PDF discovery.
• Optional Markdown extraction from search results.
• Additional Scrape, Batch, Map, and Crawl tools.
• SDKs and MCP support for AI agents.

Pricing

Spidra offers 300 one-time free credits, with no credit card required. Paid plans start at $19/month for 5,000 credits.

Search typically costs one credit per 10 results per source. Fetching the content of search results adds the cost of the pages retrieved.

Failed jobs aren't charged.

Best for: Developers building AI agents that need to search different types of web sources, inspect results, and extract structured information within the same workflow.

2. Tavily

Tavily is a web search API designed for AI applications and agents.

It focuses on returning search information in a format that language models can use directly.

Rather than providing only the title and link for each result, Tavily returns content excerpts relevant to the query. Developers can configure search depth, result counts, domains, and other options.

Its Search API can also provide an AI-generated answer when requested.

Tavily offers additional tools for extracting webpage content, crawling websites, and conducting more involved research.

Key features

• Search results prepared for LLM consumption.
• Basic and advanced search depths.
• Query-relevant content excerpts.
• Optional generated answers.
• Domain and date controls.
• Separate extraction, crawling, and research capabilities.
• SDKs and agent framework support.

Pricing

Tavily provides 1,000 free API credits per month without requiring a credit card.

Pay-as-you-go pricing starts at $0.008 per credit, while its Project plan starts at $30/month for 4,000 credits.

Credit usage varies by endpoint and search configuration.

Best for: Developers who want to add web search to an AI assistant or RAG application without building a separate content-processing layer for every query.

3. Exa

Exa is a search API built around semantic discovery and retrieval. Traditional keyword search works well when the query contains recognizable names, topics, or phrases.

But AI agents often work with broader instructions. For example, an agent may need to find companies developing a particular kind of software without knowing their names.

Exa supports search based on semantic relevance, allowing applications to retrieve information using descriptions of what they're looking for.

It also offers content retrieval, search highlights, deep search, and agent-oriented research tools.

Key features

• Semantic and keyword-oriented search.
• Search modes with different retrieval depths.
• Webpage text and relevant highlights.
• Company, people, and research-oriented data.
• Content retrieval from discovered URLs.
• Answer and research capabilities.
• Structured results for AI applications.

Pricing

Exa provides $10 in free credits every month, plus an onboarding bonus.

Its Lite Search starts at $4 per 1,000 searches, Auto Search at $7 per 1,000, and Deep Search at $12 per 1,000, for searches returning up to 10 results.

Additional content retrieval and some enhanced features have separate charges.

Best for: Research agents, company discovery applications, and RAG systems that benefit from semantic search.

4. Brave Search API

Brave Search API provides access to Brave's independent web search index. It offers conventional web search results, along with specialized search capabilities for news, images, videos, and other content.

For AI applications, Brave also provides an LLM Context endpoint. Instead of returning only short snippets, LLM Context supplies extracted passages and source metadata prepared for a language model. Developers can control the amount of context returned using token budgets.

Brave also has a separate Answers API for generating responses grounded in web search results.

Key features

• Independent web search index.
• Web, news, image, and video search.
• LLM Context for relevant webpage excerpts.
• Configurable context token budgets.
• Domain filtering and result reranking through Goggles.
• Geographic and freshness controls.
• Optional AI-generated answers with citations.

Pricing

Brave Search costs $5 per 1,000 requests. Its Answers API costs $4 per 1,000 requests, plus token-based charges.

Brave includes $5 in monthly credits for each activated plan. A payment card is required to activate the plan, even when you intend to use only the free allowance.

Best for: Agents that need a general-purpose web search index, relevant context, and control over how information is passed to the language model.

5. Parallel

Parallel provides web search and retrieval infrastructure designed for AI agents.

Its Search API accepts keywords or a research objective and returns ranked URLs with compressed excerpts.

The goal is to give an agent useful information without requiring it to process unnecessary webpage content.

Parallel offers different search modes that balance cost, response time, and the amount of retrieval work performed.

It also provides an Extract API for known URLs and separate APIs for more involved research, monitoring, and structured data collection.

Key features

• Search based on keywords or natural-language objectives.
• Ranked results with compressed excerpts.
• Multiple search modes.
• Optional full-page extraction through a separate API.
• Domain filtering and freshness controls.
• Research and structured-output capabilities.
• APIs designed for agent workflows.

Pricing

Parallel Search starts at $1 per 1,000 requests for Turbo and Fast modes. Basic and Advanced search cost $5 per 1,000 requests.

Additional extraction and research operations have their own rates.

Best for: Agents that perform frequent searches and need a balance between response size, retrieval depth, and cost.

6. Linkup

Linkup is a web search API focused on retrieving source-grounded information for AI applications.

It supports several output formats, including ordinary search results, sourced answers, and structured information. This gives developers flexibility over what happens after retrieval.

For example, a research assistant may need a sourced answer to a question. A data enrichment application, on the other hand, may need specific fields such as a company's revenue, headquarters, or product category.

Linkup also supports different search depths and offers enterprise deployment options for teams with stricter privacy or infrastructure requirements.

Key features

• Search results with sources and relevant excerpts.
• Answers grounded in retrieved web information.
• Structured output for defined fields.
• Search modes for different levels of research.
• Domain controls.
• Separate webpage fetching and research products.
• Enterprise privacy and deployment options.

Pricing

Linkup uses pay-as-you-go pricing. Its standard search options start at approximately $0.005 to $0.006 per request, depending on the search configuration and output type.

Deeper research options cost more. Linkup also advertises free initial usage for developers exploring the platform.

Best for: AI research assistants and applications that need cited answers or structured facts rather than only links.

7. Firecrawl

Firecrawl is a web data API that combines search with scraping, crawling, and content extraction.

Its Search API returns web results with query-relevant excerpts. Developers can also request the full Markdown content of search results through the same API call. This is useful for agents that need to discover sources and then read the underlying information.

Firecrawl also provides tools for scraping individual URLs, crawling websites, mapping site URLs, and interacting with pages.

Its search capabilities include web, news, and image sources, along with specialized content categories.

Key features

• Web search with query-relevant excerpts.
• Optional Markdown extraction from results.
• Search filters for domains, dates, and locations.
• PDF, developer, and research-oriented discovery.
• Separate webpage scraping and crawling.
• Structured extraction options.
• SDKs and MCP server support.

Pricing

Firecrawl offers 1,000 free credits per month. Its Hobby plan starts at $19/month, or $16/month when billed annually, for 5,000 credits.

Search costs two credits per 10 results. Retrieving full-page content adds the cost of scraping those pages.

Best for: AI agents and RAG pipelines that need search results and webpage content together.

8. Perplexity Search API

Perplexity provides a Search API that returns structured, ranked results from its web search infrastructure.

The Search API is distinct from Perplexity's AI-powered answer products. It returns search results, snippets, URLs, and metadata without generating an answer through a language model.

Developers can use those results in their own agent pipelines, with controls for domains, languages, countries, and search recency. Perplexity also supports multiple queries in a request and configurable limits on the amount of extracted text returned.

For applications that need the provider to generate an answer, Perplexity offers separate model-powered agent capabilities.

Key features

• Ranked web search results.
• Structured URLs, titles, and extracted snippets.
• Domain, language, and geographic controls.
• Date filtering.
• Multi-query search.
• Configurable content limits.
• Separate agent and answer-generation capabilities.

Pricing

Perplexity's standard Search API costs $5 per 1,000 successful requests. Its Fast Search option costs $1 per 1,000 requests.

A successful Search API request can include multiple queries and does not incur additional model-token charges. Model-powered agent operations have separate pricing.

Best for: Developers who want to use Perplexity's retrieval infrastructure while maintaining control over their own reasoning model.

9. Serper

Serper provides structured Google search results through an API. It's a different type of service from semantic search or answer-generation APIs.

Serper focuses on retrieving search engine result page information, including organic results and other available Google search features.

Developers can use it to discover URLs, retrieve search metadata, or support workflows that depend on Google search results.

For an AI agent, Serper can provide the discovery layer. However, if the agent needs the full content of a result, that generally requires a separate extraction step.

Key features

• Google search results as structured JSON.
• Organic results and SERP metadata.
• Geographic search targeting.
• Support for different Google search result types.
• API access for automated discovery.
• Top-up pricing without a recurring subscription requirement.

Pricing

Serper provides 2,500 free queries for new users. Its Starter package costs $50 for 50,000 queries, equivalent to $1 per 1,000 searches.

Larger packages reduce the per-query price. Purchased credits are valid for six months.

Best for: Developers who need economical Google search results and already have a way to retrieve or process webpage content.

10. SerpApi

SerpApi provides APIs for retrieving search engine results from Google and other major search services.

It supports a broad range of search engines and specialized result types, including web search, news, images, shopping, maps, and scholarly search.

Rather than focusing exclusively on AI agents, SerpApi provides detailed search data that developers can incorporate into many kinds of applications.

Its responses can include organic results, answer boxes, knowledge graph data, and other structured information when available.

For an AI agent, this can be valuable when the details of a search engine result page matter.

Key features

• Search results from multiple search engines.
• Organic results and structured SERP features.
• News, image, shopping, and other specialized searches.
• Geographic and language targeting.
• API and SDK support.
• Structured JSON and other supported response formats.

Pricing

SerpApi offers 250 free searches per month. Paid plans start at $25/month for 1,000 searches, with higher plans supporting additional volume and throughput.

Its pricing is based on successful search requests.

Best for: Applications that require detailed search engine data, regional search results, or multiple SERP features.

Which web search API should you choose?

These APIs overlap, but their strengths point toward different applications.

A provider can be excellent for one workflow and less appropriate for another. For example, Serper may be sufficient for an application that needs Google results and already has its own scraper.

A research agent that needs source text for reasoning may benefit more from Tavily, Exa, Brave LLM Context, Parallel, or Firecrawl.

And an application that needs to monitor how ChatGPT or Google AI Overview responds to particular queries has a different requirement altogether.

How should you evaluate a search API for an AI agent?

A feature list doesn't tell you whether an API will work well for your application. The better approach is to evaluate providers using the kinds of queries your agent will actually receive.

Start with a representative set of questions. For example:

A current-information query

Find the latest release of a software library and summarize the changes using its official documentation.

This tests whether search results favor current and relevant sources rather than older articles.

A discovery query

Find companies offering open-source observability tools for Kubernetes.

This tests whether the API can discover relevant companies when the query describes a category rather than naming a specific product.

A technical research query

Find documentation explaining how to configure distributed tracing in a particular framework.

This tests whether results include useful documentation and whether the agent can retrieve the relevant technical details.

A multi-source question

Compare the pricing, deployment options, and security documentation of several database providers.

This tests how well the search API supports an agent that needs information from multiple websites.

For each query, examine the following:

Use the same questions and similar output budgets across providers.

If one API returns only titles and snippets while another supplies full-page text, comparing only the search request price won't tell you the actual cost of answering the question.

The complete agent workflow matters more than a single API call.

Search results are not the same as verified answers

Adding web search to an agent doesn't automatically make its answers correct. A relevant result can still contain outdated information, incomplete context, or inaccurate claims.

The agent needs to evaluate that information before using it. For source-grounded answers, a practical workflow is:

1. Search for relevant sources.
2. Prefer authoritative or primary sources where appropriate.
3. Retrieve additional content when the search result doesn't provide enough evidence.
4. Extract the facts required to answer the question.
5. Generate an answer that preserves source attribution.
6. Check that cited sources actually support the claims being made.
7. Not every question needs all these steps.

A question asking for the URL of a project's documentation may be answered directly from search results.

A question comparing security guarantees across cloud providers will usually require reading the relevant documentation rather than relying on snippets alone.

For a deeper explanation of this distinction, see our guide to search APIs versus scraping APIs.

Building agents with search and extraction

Web search is often only the first part of what an agent needs. Once it discovers a relevant page, it may need to read it, extract specific fields, or follow additional links.

Spidra supports this through separate Search, Scrape, Batch, Map, and Crawl tools.

For example, you can use Search to discover companies in a particular category, Scrape to extract details from the most relevant company websites, and structured output to prepare the information for further processing.

If you already know the website but need to discover pages inside it, Crawl or Map may be more appropriate than searching the wider web.

You can use these tools through the API, SDKs, or the Spidra MCP server.

For an application that already has its own search provider, you can also use Spidra only for webpage extraction.

The important decision is to separate discovery from retrieval and use each operation where it adds value.

Choosing the right web search API

The best web search API for your agent depends on what happens after the query.

Serper and SerpApi provide structured search engine results. Tavily, Exa, Brave, and Parallel focus on retrieving information suitable for AI applications. Linkup supports source-grounded and structured answers, while Perplexity offers search retrieval alongside separate agent capabilities.

Firecrawl and Spidra combine search with broader web data extraction. Rather than choosing based on a feature list alone, start with your agent's actual questions. Check whether the returned information is relevant, whether it contains enough context to answer, and how much the complete process costs.

If your application needs both web discovery and structured extraction, try Spidra Search or create a free account to experiment with your own queries.