AI agents are everywhere these days, but how are these agents built? What tools and API's are being used? Well lets just quickly overview of what AI Agents are and then go through all the tools that are being used for building these. Think of them as your new digital teammates, quietly working away in the background. They’re researching, digging through data, scraping websites, or even tapping into APIs to get stuff done.

It’s pretty amazing to see how far they’ve come! But if you’re looking to build one of these clever, self-sufficient agents, whether it’s a research assistant, a market analyst bot, or a voice-powered helper, you’ll need the right set of tools.

Here’s a breakdown of key tools that can be used via API's, categorized by function—so you can build agents that aren’t just smart, but efficiently autonomous.

🔍 Web Search APIs: For Real-Time Intelligence

AI agents need access to up-to-date information. These search APIs help the agent to get fresh, real-time info to stay sharp:

• Tavily: Tailored for AI agents, Tavily offers structured search results with clean metadata—no fluff, just facts. Perfect for agents that need to synthesize or cite live data.
• Exa: Designed with AI workflows in mind, Exa goes beyond keyword search. It retrieves relevant documents and pages using semantic search, which pairs well with LLM-driven summarization.
• DuckDuckGo (DDG) API: A privacy-respecting search API that returns links, snippets, and instant answers without tracking. Simple and effective for lightweight lookups.

Use Case:

Note: If you are looking to automate your work using these tools in a no code way, we built Athina Flows for you to automate your linear tasks and Athina Sheets for you to automate your work at scale. Check out Flows and Sheets.

🕸 Web Scraping: Getting the Raw Web

Sometimes search results just don’t give you the full picture, your agent needs to dig a little deeper. That’s when scraping tools step in:

• Spidercrawl: A powerful crawler built for AI. It handles JavaScript-heavy pages and extracts structured data from deep links. Great for large-scale scraping tasks.
• Firecrawl: Built with AI in mind, Firecrawl doesn’t just fetch web pages—it preprocesses and summarizes content, reducing load on the LLM.

Use Case:

📄 Parsing and Structuring Data

Raw text isn’t enough. Agents need to understand and structure information.

• LlamaParse: Developed by the team behind LlamaIndex, this parser takes messy PDFs, HTML, or scraped text and turns it into structured chunks optimized for vector indexing or LLM prompts.
• Unstructured: Unstructured specializes in breaking down complex documents—like PDFs, Word files, or web pages—into clean, usable data. It’s a go-to for agents that need to process diverse file types with ease.