AgentQL Integration Week: Every Page, an Endpoint

Last week, we launched six major integrations—each one a step toward a fully accessible web, where data flows freely between people, AI agents, and smart workflows.

LLMs are usually limited to outdated training data or APIs that gate information. Last week, AgentQL entered the agentic world with six integrations bringing the web to AI agents and models that let them extract structured data from the web and interact with web pages.

This is what it looks like when every page becomes an endpoint.

Bringing the Web to Claude, Cursor, and Windsurf with MCP

AI assistants can’t browse the web by default—but with Model Context Protocol (MCP), they can access external tools that retrieve information for them. AgentQL is now that tool.

With the AgentQL MCP Server, assistants like Claude, Cursor, and Windsurf can now:

• Extract structured data from any website
• Retrieve live financial, research, or business intelligence data
• Pull in real-time search results or extract structured content from articles and reports

AI no longer needs to rely on pre-built APIs. It can access the open web.

Read about the AgentQL MCP Server →

AI Agents that navigate, retrieve, and act

Autonomous AI agents are only useful if they can see, retrieve, and act on the world around them. On their own, LLMs are limited to reasoning within their own training data and have no way to interact with web pages. But if you give that LLM tools, it can become an active Agent, fetching data and interacting on the user’s behalf.

Agents can now extract and analyze live web data with AgentQL and AI frameworks like Langchain, LlamaIndex, and AgentStack and low-code agentic flow composers like Dify.

What’s more, in Langchain and LlamaIndex, agents now have the power to navigate and interact with websites using AgentQL and Playwright-powered tools.

An agent that only predicts is limited. An agent that reacts to live web data is powerful. Make your agents powerful by integration AgentQL with LlamaIndex, Langchain, AgentStack, and Dify.

Retrieval-Augmented Generation (RAG) that stays current with LlamaIndex and Langchain

AI models are only as good as the data they can access. Most RAG systems rely on static snapshots of information, leaving them outdated the moment they are deployed.

With AgentQL WebReaders in LlamaIndex and DocumentLoader in Langchain, AI apps can now retrieve live, structured web data as part of its retrieval pipeline. Learn how with our LlamaIndex and Langchain integrations.

Automated Workflows that pull from the Web with Zapier

For businesses and automation experts, APIs have been the only way to connect structured data to workflows. But the web is full of unstructured data. Now, with Zapier + AgentQL, anyone can:

• Monitor markets and track price changes automatically
• Extract job postings and update Airtable or Notion in real time
• Pull breaking news or product listings into spreadsheets without manual copy-pasting

No code. No scraping scripts. Just structured data flowing seamlessly into your workflows. Read about Zapier + AgentQL →

What’s Next?

Integration Week was about granting AI access real-time structured web data.

Every one of these launches brings real-time web data into AI workflows. Every one of them moves AI closer to retrieving and interacting with information like a human.

AgentQL is now inside AI agents, retrieval models, no-code automation, and developer workflows.

Now that every page is an endpoint, the Agentic Web Era can begin.

—The TinyFish team building AgentQL

P.S. We have a few more surprises coming over the following weeks! If you're building with AgentQL, we want to hear from you! Find us on Discord, X, or Bluesky.

P.S.S. The team made and listened to this launch playlist over the past week. May it grant you perserverance in your own launches!