Tinyfish provides exclusive AI agents that can actually surf websites and return structured outputs. They support different trigger modes, including API-triggered and headless modes, as well as geo-specific mode. There is also a silent crawling mode. These are goal-oriented agents, not just tools. Each search session is available and integratable via streamable APIs, and every session includes logs that can be pulled through separate APIs. If Tinyfish is used within other websites, we can be reassured that we don’t need to host LLMs or build and maintain a system for managing search sessions, history, and related details—Tinyfish takes care of all of that. Parallel sessions are also possible, which helps improve the speed of research. The UI has the sessions, it holds profiles and is sleek. The pricing is optimal. The communities are well present and active for any help and getting started is really easy.

What I like most about TinyFish is how it lets AI agents interact with real websites and complete tasks through the browser instead of being limited to APIs or static data. This makes it especially useful for automating workflows that would normally require manually navigating websites, extracting information, and taking actions across different web services. The biggest value for me is the combination of AI intelligence and browser automation. It allows me to turn a natural-language goal into an actionable workflow, which can significantly reduce repetitive work. I also like the potential for integrations because the same approach can be applied across websites without needing to build a custom integration for every individual service. The overall experience makes TinyFish feel less like a simple scraping tool and more like infrastructure for building practical AI agents that can actually operate on the web. On a daily basis, TinyFish is incredibly smooth to integrate into our automated pipelines. The interface—whether using their REST API, TypeScript SDK, or CLI—is intuitive and cuts out the huge headache of managing browser servers, proxies, and anti-bot bypass mechanisms manually. The sub-250ms cold starts mean our agents execute web actions instantly without hanging. It provides a seamless, developer-friendly experience that removes the clunkiness of old web scraping stacks.

TinyFish is the page-scrape layer I actually trust after a search API. I run Tavily for broad hits, then TinyFish run-sse against a specific URL with a goal — Gensyn docs, DoraHacks briefs, GitHub orgs, hackathon portals — and it returns extractable text instead of “10 blue links.” Header is just X-API-Key. That one-call shape is easy to drop into a Node agent: POST { url, goal }, parse SSE data: lines, keep the COMPLETE payload. On my research desk I credit-gate it (max per cycle/hour) so I don’t burn the key, and I only fire it when a question cites a primary URL. That’s the upside: it closes the loop from “I found a page” to “I have the words on that page,” which is what stops me inventing deadlines and prize amounts. The automated piece I use is the web agent over SSE, not a chat UI. I POST a live URL plus a goal (extract get-started network names, pull prize/deadline text, summarize this GitHub org page) and parse data: events until COMPLETE. That is what sits in my TypeScript desk: credit-gated, max per cycle/hour, only when a question already cites a primary URL. The AI is useful when the goal is tight and the page is official. It is weaker when the goal is fuzzy or the page is a listing dump — you still have to read the extract and mark thin results unverified. I do not let it auto-trade or auto-publish off the scrape.