Agentic Search in 2026: Benchmark 8 Search APIs for Agents

Agentic search plays a crucial role in bridging the gap between traditional search engines and AI search capabilities. These systems enable AI agents to autonomously find, retrieve, and structure relevant information, powering applications from research assistance to real-time monitoring and multi-step reasoning.

Search APIs are the first layer of an agentic search tool where performance directly impacts the quality and reliability of AI outputs. We benchmarked 8 search APIs across 100 real-world AI/LLM queries, evaluating 4,000 retrieved results using an LLM judge.

Compare top agentic search tools and AI web data capabilities:

Benchmark results

Agent Score = Mean Relevant × Quality (higher is better)

Metrics explained

• Mean Relevant: Average number of relevant results per query (out of 5 retrieved)
• Quality: Mean quality score (1-5 scale) where 5 = authoritative, directly answers query
• Agent Score: Mean Relevant × Quality, which rewards high-quality results with low noise

Key findings

• Top 4 APIs perform equally well. Brave Search leads with 14.89, but Firecrawl, Exa, and Parallel Search Pro are so close that the differences could be random variation.
• Only one clear winner: Brave consistently outperformed Tavily by about 1 point, a gap large enough to be meaningful rather than random chance.
• See statistical methodology for confidence intervals and detailed analysis.

Latency varies 20× across APIs, from 669ms (Brave) to 13.6 seconds (Parallel Pro). When quality is similar, speed becomes the deciding factor.

Latency in agentic workflows

In multi-step agent tasks, search latency compounds. Consider a research agent that:

1. Searches for background information
2. Finds relevant sources
3. Verifies claims across multiple queries
4. Synthesizes findings

With 5 search calls, total wait time ranges from 3 seconds (Brave) to 68 seconds (Parallel Pro). For real-time applications like customer support bots or coding assistants, sub-second latency is essential.

Agentic search tools

Agentic search ecosystems rely on three layers, each serving a distinct purpose:

Layer 1: Agentic web search & retrieval providers

These tools interact directly with the open web to discover, retrieve, and preprocess live data from search engines, websites, and external sources. In an agentic system, they form the information acquisition layer, supplying structured and machine-readable inputs to downstream reasoning, planning, or automation components.

This layer includes multiple capability types:

• Search APIs, which help agents discover where relevant information exists
• Scraping and crawling infrastructure, which reliably retrieves content at scale
• Automation platforms, which package scraping logic into reusable execution units
• Semantic retrieval layers, which optimize retrieved data for LLM reasoning and RAG pipelines

Here are some tools:

Brave Search

Brave Search is a privacy-focused web search engine offering an API for programmatic access to indexed web results. It operates its own search index rather than relying on Google or Bing, making it attractive for agentic systems seeking independence from major search engine providers. The API returns structured search results suitable for downstream LLM processing.

Benchmark observations

• Achieved the highest Agent Score (14.89) among all evaluated APIs.
• Ranked in the top tier, with no statistically significant difference compared to Firecrawl, Exa, or Parallel Search Pro.
• Was the only API to reliably outperform Tavily, the ~1 point gap held up across repeated statistical tests.
• Demonstrated the lowest average latency in the benchmark (669 ms).
• Performed consistently well across all query categories, including research, factual verification, and tool discovery.

Pricing

• Free AI: $0, limited usage for evaluation. 1 query/second, up to 2,000 queries/month. No commercial usage rights.
• Base AI: $5 per 1,000 requests, usage-based pricing. Up to 20 queries/second, up to 20 million queries/month. Includes rights for use in AI applications.
• Pro AI: $9 per 1,000 requests, usage-based pricing. Up to 50 queries/second, unlimited monthly queries. Includes rights for use in AI applications.

Firecrawl

Firecrawl is a web crawling and data extraction API that converts live web pages into clean, structured formats optimized for LLM use. Instead of SERP-style ranking, it focuses on rendering and parsing full-page content, including dynamic sites, making it suitable for agentic workflows that require complete document context rather than link lists.

Benchmark observations

• Achieved the second-highest Agent Score (14.58) in the benchmark.
• Placed in the top performance tier, with no meaningful gap versus Brave Search, Exa, or Parallel Search Pro.
• Recorded the highest Mean Relevance score (4.30) across all evaluated tools.
• Delivered solid quality scores (3.39), within the same band as other top performers.
• Showed moderate latency (1,335 ms), slower than Brave Search and Tavily, but significantly faster than Parallel Search Pro and Perplexity.
• Performed best on deep content retrieval tasks where full-page context was critical.

Pricing

• Free Plan: €0 one-time, 500 pages, 2 concurrent requests, low rate limits.
• Hobby: €14/month (billed yearly), 3,000 pages, 5 concurrent requests, basic support. Extra 1k credits €8.
• Standard (Most popular): €71/month (billed yearly), 100,000 pages, 50 concurrent requests, standard support. Extra 35k credits €40.
• Growth: €286/month (billed yearly), 500,000 pages, 100 concurrent requests, priority support. Extra 175k credits €152.

Exa AI

Exa AI provides a semantic search API optimized for agentic research and retrieval tasks. Unlike scraping platforms, it focuses on document discovery and relevance, returning contextually meaningful sources rather than raw web pages.

Benchmark observations

• Ranked third overall with an Agent Score of 14.39, statistically tied with the top tier.
• Showed strong performance on technical documentation queries, achieving the highest quality score in that category.
• Delivered solid relevance across research-oriented queries, though differences versus peers were within statistical noise.
• Latency was moderate (~1.2 s), slower than Brave but faster than Parallel Search Pro and Perplexity.

Pricing

• API (Pay-as-you-go): $5–$15 per 1k requests/pages, $5–$10 per 1k agent tasks, custom enterprise plans available
• Websets:
• Starter: $49/month
  • 8,000 credits, up to 100 results per Webset, 2 seats, 10 enrichment columns, 2 concurrent searches, import up to 1,000 CSV rows.
• Pro: $449/month
  • 100,000 credits, up to 1,000 results per Webset, 10 seats, 50 enrichment columns, 5 concurrent searches, import up to 10,000 CSV rows.
• Enterprise: Custom pricing
  • Custom credits, 5,000+ results per Webset, unlimited seats and enrichment columns, custom concurrent searches and CSV import limits, enterprise support, and volume credit discounts.

Parallel Search Pro

Parallel Search Pro is a high-capacity search API designed for large-scale, parallelized querying. It is positioned for workloads that require broad retrieval across many sources rather than interactive, low-latency use. The Pro tier emphasizes throughput and depth over speed.

Benchmark observations

• Ranked fourth overall with an Agent Score of 14.21, statistically indistinguishable from the top three.
• Quality and relevance metrics were comparable to Brave, Firecrawl, and Exa.
• Exhibited very high latency (13.6 seconds on average), the slowest among top-tier tools.
• Performed well on real-time and comparative queries but with significant response delays.

Parallel Search Base

Parallel Search Base is the lower-tier offering of Parallel Search, intended for lighter workloads with reduced capacity and cost compared to the Pro tier. It targets general-purpose search use cases without the full throughput guarantees of Pro.

Benchmark observations

• Ranked sixth overall with an Agent Score of 13.5.
• Performed below the top tier but above Perplexity and SerpAPI.
• Quality scores were close to Tavily, though relevance was slightly lower.
• Latency (~2.9 s) was significantly better than Pro but still slower than Brave, Exa, and Tavily.

Tavily

Tavily is a web search and extraction API designed for integration with AI agents, supporting agentic search workflows by delivering structured, ready-to-use data.

Benchmark observations 

• Ranked fifth overall with an Agent Score of 13.67.
• Performed slightly below the top tier. The gap versus Brave (~1 point) was the only statistically meaningful difference in the benchmark.
• Latency was relatively low (998 ms), suitable for interactive agents.
• Quality and relevance were consistent but marginally lower across most categories.

Pricing

• Researcher Plan: Free, 1,000 API credits per month, suitable for experimentation or new users.
• Project Plan: $30/month, 4,000 API credits, higher rate limits for small projects.
• Pay-As-You-Go: $0.008 per credit, flexible usage without long-term commitment.
• Enterprise Plan: Custom pricing, includes enterprise-grade SLAs, security, support, and adjustable API limits.

SerpAPI

SerpAPI provides programmatic access to major search engines through a unified API, returning structured search results without managing scraping infrastructure. It is optimized for AI agents that need autonomous, real-time search access across geographies and sources.

Benchmark observations

• Ranked eighth overall with an Agent Score of 12.28.
• Showed high quality for relevant results but low mean relevance, meaning many queries returned irrelevant hits.
• Latency averaged 2.4 s, faster than some slow-tier competitors but still less optimal for interactive loops.
• Stronger on comparative and tool discovery queries but weaker on real-time and research queries.

Pricing

• Free: 250 searches/month, $0
• Developer: 5,000 searches/month, $75/month
• Production: 15,000 searches/month, $150/month
• Big Data: 30,000 searches/month, $275/month.

Perplexity

Perplexity provides programmatic access to search results backed by its search and answer engine. It is often associated with conversational search experiences and synthesis-oriented retrieval rather than raw document discovery.

Benchmark observations 

• Ranked seventh overall with an Agent Score of 12.96.
• Showed reasonable quality when results were relevant, but lower mean relevance than most competitors.
• Exhibited very high latency (11+ seconds on average).
• Performed relatively well on factual verification queries but inconsistently elsewhere.

Pricing

Search API: $5 per 1,000 requests. Returns raw web search results with advanced filtering. Request-based pricing only; no token costs.

Which API Should You Use?

For production AI agents with balanced requirements, Brave Search offers a strong combination of quality (Agent Score 14.89) and speed (669ms). When quality differences aren’t statistically significant, latency and reliability become the deciding factors.

For prototyping and cost-sensitive development, Tavily is a practical option. It performs slightly below Brave (Agent Score 13.67) but offers a generous free tier and fast responses (998ms). The quality gap is small enough that it won’t affect your development workflow.

If your agent primarily searches for technical documentation, Exa is worth considering. It showed a slight edge on API docs and configuration queries (Quality 3.16 vs Brave’s 3.02), though this category had only 20 queries so the difference may be noise.

For latency-sensitive applications, Perplexity may not be the right fit. Despite decent quality, its 11+ second average response time limits its use in interactive agents. It may be more appropriate for batch processing or async workflows where latency is less critical.

Layer 2: Agentic search frameworks & orchestration tools

Agentic frameworks or agentic orchestration tools do not retrieve web data themselves. Instead, they coordinate reasoning, planning, and tool execution. These frameworks decide time to search, specific tools to call, and order of sequence actions to solve complex, multi-step tasks. They are the backbone of agentic search behavior. Some of these tools include:

Explore more on agentic frameworks: 

• Agentic analytics
• Agentic RAG frameworks
• Agentic AI design patterns
• Agentic monitoring tools

Layer 3: Reasoning & generation

This is the model layer where AI models perform reasoning, synthesis, and response generation. These models interpret information retrieved from the web and orchestrated by agent frameworks to produce final outputs. On their own, they do not guarantee access to current or external data.

• Proprietary LLMs: These models provide strong reasoning capabilities, long-context handling, and natural language generation. In agentic search systems, they are typically responsible for query interpretation, multi-step reasoning, and producing final answers.
• Open-weight models: Open-weight models are often used in environments that require data control or self-hosting. While they may require more engineering effort, they allow enterprises to customize and deploy agentic search systems within controlled infrastructures.

Benchmark methodology

Query selection

Queries were selected from AIMultiple.com’s top 500 organic search queries in the AI/LLM domain to ensure real-world relevance.

Selection process:

• Source: Top 500 queries from AIMultiple.com organic search traffic (Dec 2024 to Jan 2025)
• Filtering: Removed non-English queries, proxy-related queries, and spam
• Categorization: Organized into 6 categories representing AI agent use cases

Query distribution:

• Research (24 queries): Deep exploration of technical topics
• Factual Verification (20 queries): Finding empirical data and expert consensus
• Technical Documentation (20 queries): Finding API docs, configuration guides
• Real-time Events (10 queries): Current news and recent developments
• Comparative (16 queries): Product/service comparisons
• Tool Discovery (10 queries): Finding tools for specific tasks

Example queries:

• Research: “agentic ai frameworks 2025”, “llm orchestration frameworks”
• Factual: “llm hallucination rates comparison”, “agi timeline expert predictions”
• Technical: “vllm speculative decoding”, “llm vram calculator”
• Real-time: “recent ai model releases benchmarks”, “ai regulation autonomous agents”
• Comparative: “cline vs claude code”, “qdrant vs weaviate”
• Tool Discovery: “best agentic ai framework”, “gpu cloud providers llm”

Hardware & software

• Server: Contabo VPS (France datacenter)
• Operating System: Ubuntu 24.04.3 LTS
• Runtime: Python 3.11+ with asyncio for concurrent API calls
• HTTP Client: httpx with connection pooling
• LLM Judge: GPT-5.2 via OpenRouter with temperature=0

APIs evaluated

We tested 8 search APIs, retrieving 5 results per query from each: Brave Search, Tavily, Exa, Firecrawl, SerpAPI, Perplexity, Parallel Search (Base), and Parallel Search (Pro). All APIs were called with default settings except the result count.

Evaluation protocol

1. Query execution: All 100 queries sent to all 8 APIs with rate limiting (1 req/sec for Brave free tier)
2. Result collection: Top 5 results per query per API (~4,000 total results)
3. LLM evaluation: Each result judged for relevance (boolean), quality (1-5), noise (boolean), and source type
4. Human verification: 10% of LLM judgments (~400 results) manually reviewed to validate rating accuracy
5. Retry logic: Failed requests retried up to 3 times with exponential backoff; 30-second timeout per request
6. Execution time: ~3.5 hours (rate limiting for Brave API was the bottleneck)

LLM Judge Criteria

Each search result was evaluated using a structured prompt with the following criteria:

• Relevant (boolean): Does this result help answer the query?
• Quality Score (1-5 scale):
  • 1: Completely useless, wrong topic
  • 2: Tangentially related but doesn’t answer the query
  • 3: Somewhat relevant but incomplete or low quality source
  • 4: Good result, addresses the query well
  • 5: Excellent result, authoritative source, directly answers query
• Noisy (boolean): Is this SEO spam, AI-generated fluff, or clickbait?
• Source Type: academic, official_docs, news, blog, forum, commercial, or other

Statistical methodology

Bootstrap confidence intervals

We use bootstrap resampling to calculate 95% confidence intervals. This method doesn’t assume any particular distribution shape, making it suitable for our data.

How it works:

1. Start with the original dataset of 100 queries tested with each API
2. Create 10,000 new datasets by randomly sampling 100 queries with replacement
3. Recalculate all metrics (Mean Relevant, Quality, Agent Score) for each resample
4. The 95% CI is the range from the 2.5th to 97.5th percentile of the 10,000 values

Paired Bootstrap Difference Tests

To compare APIs, we use paired bootstrap tests. Since all APIs were evaluated on the same 100 queries, we can measure differences query-by-query, which provides more statistical power than comparing independent groups.

How it works:

1. For each bootstrap resample, calculate the difference in Agent Score between two APIs
2. Repeat 10,000 times to get a distribution of differences
3. Calculate the 95% CI of the difference
4. If the CI includes 0, the difference is not statistically significant
5. P-value equals the proportion of bootstrap samples where the difference is ≤ 0

Why Bootstrap?

Our Agent Score (Mean Relevant × Quality) is a product of two metrics, creating a non-normal distribution. Bootstrap handles this well because it makes no assumptions about distribution shape and works for any metric type. It is more robust than traditional parametric tests like t-tests or ANOVA.

Statistical results

Full results with 95% bootstrap confidence intervals (10,000 resamples):

Interpreting overlapping CIs: When confidence intervals overlap substantially (e.g., Brave 13.80-15.93 vs Exa 13.25-15.50), the difference is not statistically significant. This is why we report “top 4 APIs are statistically indistinguishable” despite raw score differences.

Limitations

• Domain-specific: All queries are AI/LLM-related. Results don’t generalize to medical, legal, e-commerce, or general domains.
• Single time point: APIs improve continuously. This reflects December 2025 snapshot only.
• LLM judge bias: Quality ratings depend on GPT-5.2’s preferences and prompt design. While 10% of judgments were human-verified, systematic biases may remain in the unverified portion.

What is agent search? 

Agentic search retrieves and analyzes information where AI agents perform tasks autonomously, going beyond the capabilities of traditional search engines. Unlike conventional systems that respond to individual queries, an agentic search system can interpret user intent, break it down into multiple multi-step tasks, and leverage external tools to deliver a comprehensive response. This represents a fundamental shift from simple keyword matching to AI that reasons, plans, and executes actions independently.

Agentic AI combines the power of large language models (LLMs) with retrieval augmented generation (RAG) to access live information from multiple sources, including structured data, websites, and enterprise knowledge bases. In this approach, AI agents not only retrieve information but also synthesize it to provide direct answers and comprehensive answers for complex queries.

Some defining characteristics of agentic AI systems include:

• Autonomous decision-making: AI agents can independently determine which external tools or data sources to use.
• Iterative reasoning loop: By reviewing chat history and previous steps, agents refine results in a continuous iterative loop.
• Multi-tool integration: The system combines AI models with APIs, scrapers, and analysis platforms to generate actionable outputs.
• Natural language understanding: Enables agents to interpret user questions and convert them into focused subqueries for higher precision.

How search AI agents work

At the core of agentic AI are AI agents designed to perform complex tasks using multiple tools and reasoning capabilities. These agents are capable of:

• Planning multi-step reasoning for complex queries
• Generating detailed plans to navigate through multiple subqueries
• Using tool calling or function calling to interact with other tools
• Combining information from multiple sources to produce final answers

The decision-making process of these agents involves several steps:

1. Original query analysis: AI interprets user intent beyond the literal text.
2. Query planning: The agent designs a sequence of focused subqueries for a comprehensive answer.
3. Tool selection and execution: AI decides which external tools or agent types are best for retrieving relevant data.
4. Data gathering and synthesis: The gathered information from relevant sources is structured and combined.
5. Answer generation: A large language model compiles a complete answer considering previous steps and context.

Key features of agentic search systems

A well-designed agentic search system relies on several core features:

• Integration with multiple tools: Supports tool calling for scraping, database queries, and API interactions.
• Multi-step tasks: Agents break down complex tasks into focused subqueries.
• Natural language query support: Enables conversational agents to interpret user questions and user intent.
• Iterative loop reasoning: Ensures reinforcement learning helps agents improve results over time.
• Comprehensive response generation: Combines multiple sources to provide a complete answer

The use of RAG pipelines ensures retrieval augmented generation can deliver direct answers rather than just links or indexed content, bridging the gap between traditional search and AI-powered search.

Choosing the Right Agentic AI Tool

The best agentic AI systems balance autonomy, integration with other tools, and the ability to answer questions while providing comprehensive answers for complex tasks. While selecting a fitting solution, evaluate these factors: 

• Scope of tasks: Are you solving complex challenges or simple searches?
• Integration needs: Do agents require multiple tools and external tools?
• User experience: Should users interact via conversational agents or dashboards?
• Content goals: Are you optimizing content marketing, technical SEO, or research workflows?
• Compliance: Ensure enterprise AI systems meet legal and ethical standards.

Agentic search use cases

Agentic search has transformed how AI interacts with the web and other structured/unstructured data sources. Below are some of the main use cases:

1. Web scraping and data extraction

Traditional web scraping requires rigid, rule-based scripts, which often break when websites update their layouts. Agentic AI agents, however, can interpret natural language instructions, allowing dynamic adaptation to changing web pages. For example:

• An agent can receive a prompt like: “Extract all product names, prices, and ratings from this e-commerce site”
• It can navigate the site, handle pagination, and collect structured data without human intervention
• Multi-agent systems allow specialized scraping agents to serve other agents, creating reusable, modular workflows.

2. Real-time market and trend analysis 

Agentic AI can monitor open web data to track pricing, product launches, and trend analysis. By synthesizing gathered information from multiple sources, companies can generate relevant content for marketing campaigns or content strategy improvements.

• Price fluctuations across competitors’ websites
• Trending products or services
• News or regulatory updates relevant to the business
• Automates people search for industry influencers
• Provides relevant results for technical SEO and content marketing
• Reduces time spent on visit fewer websites approach.

3. Content marketing

AI-powered agents help teams develop content strategy and content generation by using multiple queries to retrieve relevant sources and create structured summaries.

• Identifies relevant content from diverse data sources
• Optimizes content marketing campaigns using direct answers to user questions
• Supports multi-step reasoning to align content with business goals

4. Automated research and reporting 

Agentic AI enables research across multiple sources, producing comprehensive answers for complex challenges. Using multi-step reasoning and iterative loops, agents handle tasks like:

• Academic, patent or IP research: compiling summaries from multiple papers and sources
• Financial research: aggregating earnings reports, news, and analyst opinions
• Policy monitoring: synthesizing legislative updates from official government portals.

5. Interactive Web Automation

Some websites require user interactions like clicks, scrolling, or form submissions to reveal information. Tools integrated with agentic search, such as browser-use, allow AI agents to:

• Simulate human browsing behavior (scrolling, clicking links, filling forms)
• Extract dynamic content generated by JavaScript or interactive elements
• Perform complex, multi-step automated actions across sites.

6. Enterprise Knowledge Management

Companies increasingly deploy agentic AI systems to extract insights from structured data, internal documents, and external tools. This allows users to interact with AI agents as conversational agents to quickly access comprehensive answers without manual searches.

• Query multi-departmental data using natural language
• Extract structured insights from documents, reports, or spreadsheets
• Reduce manual data aggregation, improving decision-making speed
• Reduces reliance on traditional search engines
• Allows AI agents to visit fewer websites and retrieve relevant results
• Supports complex tasks such as combining multiple sources for reporting.

Further reading

Reference Links

1.

Brave Search API | Brave

2.

Exa | Web Search API, AI Search Engine, & Website Crawler

Exa Labs

3.

Tavily - The Web Access Layer for AI Agents

Industry Analyst

Hazal Şimşek

Industry Analyst

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Hazal is an industry analyst at AIMultiple, focusing on process mining and IT automation.

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