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I connected my Apify Actor to an AI agent over the Apify MCP server. It ran, returned data, and the data was garbage. Nothing was broken: every failure traced back to something my input schema never said. Four description rewrites later, the agent picks the right settings on its own.
Features, pricing, and crawl options side by side, plus a look at Firecrawl's new Alexandria library and when each platform is the better pick.
Your users know the answer is somewhere in the docs, but they’re still stuck. Build an AI chatbot that helps them find the right instructions and figure out what to do next.
One of my popular Actors is a LinkedIn Sales Navigator scraper. It has dozens of users, but is also completely unusable by an AI agent, and I did not know that until I checked.
Somewhere in the middle of building Apify's new office, Martina developed very serious opinions about millimeters, sockets, and which way doors should open. Her story of memories, mishaps, and a miracle she insists was planned.
Build a tracker that scrapes a category on a schedule and logs every run into Google Sheets, no code required.
Choose your AI agent’s web tools based on actual evidence. Compare what Apify and Exa returned, what they missed, how long each task took, and how much it cost.
We tested Tavily against RAG Web Browser on real research queries. One retrieved 8.4x more evidence for $0.115 per 4 queries. See the results.
You need the data, but collecting it has become a chore. Let ChatGPT and Apify handle the work for you.
The real choice is not marketplace vs. retrieval API, but whether your agent primarily needs fast, search-oriented web retrieval or direct, controllable extraction from the source.
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