Agentic Search: 8 AI Web Data Tools in 2026

updated on Dec 25, 2025

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.

Explore top agentic search tools and AI web data capabilities:

Agentic search tools

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

Tool	Price	Key Features
Bright Data	Enterprise: Custom	Search APIs, crawling, proxy network, browser automation
Apify	Free: $0 Starter: $39/month Scale: $199/mont Business: $999/month	Scraping, calling support
Browse AI	Free with limitations Personal: $19/mo Professional: $69/mo Enterprise: Custom	Point-and-click robots, scheduled scrapes, anti-bot handling, app integrations
Exa AI	Starter $49/mo Pro $449/mo Enterprise custom	Structured answers, semantic ranking
Firecrawl	Free: €0 with limits Hobby: €14/mo Standard: €71/mo Growth: €286/mo	JS rendering, pagination handling, Markdown/JSON
SerpAPI	Free: $0 Developer: $75/mo Production: $150/mo Big Data: $275/mo	Google/Bing search APIs

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:

Bright Data

Bright Data is a web data platform that provides structured data access for AI agents and agentic search workflows. It enables automated retrieval from multiple sources while handling anti-bot and proxy requirements. Key features include:

Web scraping APIs: Pre-built and customizable endpoints for retrieving structured content from diverse sites.
Browser automation: Headless browser support for multi-step tasks and interactive websites.
Proxy network: Large-scale IP rotation to ensure reliable data access across regions.
Data delivery: Returns structured data in formats suitable for AI reasoning and multi-agent workflows.
Integration support: Connects with common agent frameworks and RAG pipelines.

Figure 1: Bright Data integration to Agent AI frameworks ¹

Apify

Apify is a cloud-based platform for web scraping and automation, providing agents with access to structured web data for multi-step workflows. Its features include:

Pre-built scraping: Ready-to-use scraping routines for major websites.
Structured outputs: Returns JSON and other formats optimized for AI ingestion.
Function calling support: Can be invoked dynamically by agentic AI systems during workflow execution.

Pricing:

Free: $0, $5 credit for Apify Store or own Actors, $0.3 per compute unit, community support.
Starter: $39/month + pay-as-you-go compute, $39 credit, chat support, bronze store discount.
Scale: $199/month + pay-as-you-go compute, $199 credit, priority chat support, silver store discount.
Business: $999/month + pay-as-you-go compute, $999 credit, account manager, gold store discount.

Figure 2: Apify architecture²

Browse AI

Browse AI is a no-code platform for web data extraction, enabling AI agents to retrieve structured data from websites. It supports automated workflows that convert website content into AI-ready datasets or APIs for agentic search and RAG pipelines. Key features include:

Automated web scraping: Handles dynamic websites, multi-step interactions (scrolling, clicking, form fills), and extracts structured data suitable for AI consumption.
Change detection & monitoring: Tracks website updates and delivers notifications or structured data updates, supporting iterative agentic workflows.
API & integration support: Turns scraped data into REST APIs or integrates with 7,000+ apps, making it directly usable by AI agents.
High-volume, scalable operations: Supports concurrent scraping of thousands of websites, with built-in bot evasion, proxy management, and retry logic to maintain data reliability.

Pricing:

Free: Limited to 2 websites, 3 users, unlimited robots, full platform access.
Personal: $19/month, 5 websites, 3 users, basic email support.
Professional: $69/month, 10 websites, 10 users, priority support, 60,000 credits/year.
Premium/Enterprise: Custom pricing, fully managed setup, unlimited websites and users, scalable infrastructure, and dedicated account manager.

Figure 3: Browse AI dashboard³

Exa AI

Exa AI provides a semantic search API designed 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. Key characteristics:

Semantic document retrieval: Returns contextually relevant documents instead of unstructured links.
Semantic search optimized for retrieval-augmented generation
Structured LLM-ready outputs: Provides data ready for multi-step reasoning and RAG pipelines.
Integration with AI Agents: Can be invoked by agentic AI workflows for autonomous research tasks.
Often combined with crawlers or scrapers when deeper content extraction is required.

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.

Firecrawl

Firecrawl is a web crawling and preprocessing tool that converts web pages into formats usable by LLMs and RAG systems. Top features include:

LLM-ready outputs: Returns structured data, Markdown, HTML, screenshots, links, and metadata suitable for language models.
Handles dynamic complex websites: Manages dynamic content (JS-rendered), proxies, anti-bot measures, and parsing/orchestration.
Customizable crawling: Supports auth-protected pages, tag exclusions, adjustable crawl depth, and input actions like click, scroll, and wait.
Media parsing & batching: Extracts PDFs, DOCX, images, and scrapes thousands of URLs concurrently via async endpoints.
Change detection: Monitors websites over time to detect and track content changes reliably.

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.

SerpAPI

SerpAPI provides programmatic access to major search engines via a unified API, enabling agentic systems to retrieve structured search results without managing scraping infrastructure. It is particularly suited for workflows where AI agents need to query traditional search engines autonomously.

Structured data outputs: Returns results in JSON for downstream agent processing.
Real-time retrieval: Delivers up-to-date search results suitable for AI workflows.
Global search coverage: Access search results from any location using geolocation parameters, supporting agents with global queries.
Full browser execution: Handles JavaScript-heavy pages and CAPTCHAs automatically.

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.

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. Its features include:

Plug & Play Integration: API key setup enables quick connection to AI systems.
Real-Time Web Access: Supports high-volume queries with minimal latency.
Structured Content Outputs: Provides clean, organized data suitable for AI reasoning and multi-step workflows.
Research Support: Supplies comprehensive web data for analytical tasks and reporting.
Data Enrichment: Converts unstructured web information into structured, actionable formats.
Chat Assistance: Allows conversational agents to access up-to-date, relevant information for responses.

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.

Figure : Tavily agentic search approach⁴

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:

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.

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:

Original query analysis: AI interprets user intent beyond the literal text.
Query planning: The agent designs a sequence of focused subqueries for a comprehensive answer.
Tool selection and execution: AI decides which external tools or agent types are best for retrieving relevant data.
Data gathering and synthesis: The gathered information from relevant sources is structured and combined.
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.