TinyFish AI Web Agents

What do you like best about the product?

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 do you dislike about the product?

Tinyfish web sessions or instances are independent and do not share information with each other. So, if we need a parent-child relationship between browser sessions and want certain parts of a campaign to run in parallel mode, it becomes difficult. For example, if we want agents to trigger multiple parallel sub-sessions, each with its own browser, while still sharing the data collected across those browsers, that kind of feature is not available.

What problems is the product solving and how is that benefiting you?

Tinyfish solves the problem of web-related research agent. so in case we need a campaign-like feature in our application we can easily just integrate this rather than building a system to maintain and scale web agents. This also solves the step-by-step exploration of websites and automation testing. Here we do not need to have automation scripts made and maintained every time, since Tinyfish dynamically works with the websites, it can do testing with just natural language instructions as input and generate reports in any asked format.

What do you like best about the product?

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.

What do you dislike about the product?

The main area I think TinyFish could improve is the learning curve around building and debugging more complex browser-agent workflows. The basic concept is powerful, but when an agent encounters an unexpected website state or a workflow fails midway, it would be helpful to have even more detailed debugging information and clearer visibility into exactly what the agent attempted.

I’d also like to see broader integrations, more examples for advanced use cases, and clearer guidance around performance and pricing as workflows scale. These improvements would make it easier to move from experimenting with TinyFish to confidently using it in production.

What problems is the product solving and how is that benefiting you?

TinyFish solves the problem of automating web-based tasks that would normally require manually navigating websites or building separate integrations for every service. Instead of relying entirely on APIs, I can use AI agents to interact with real websites, retrieve information, and carry out multi-step workflows.

This benefits me because I work on AI and automation projects where connecting different web services can otherwise take significant development time. TinyFish reduces the amount of repetitive browser work and makes it easier to prototype practical AI agents that can operate on the web.

It also opens up use cases that would be difficult to automate through traditional APIs alone, allowing me to focus more on the product logic and user experience rather than maintaining custom integrations for every website.

What do you like best about the product?

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.

What do you dislike about the product?

It isn’t magic on hostile pages. Cloudflare-gated UIs and noisy listing pages (I burned a pass on a Reddit listing that came back generic/unrelated) still fail or waste a credit. I treat a thin extract as unverified, not as a fact. Credits mean you have to budget it; I wouldn’t fire TinyFish on every search hit.

What problems is the product solving and how is that benefiting you?

Builder/research agents that must quote primary sources. AFTERCUT and my scout stack: official docs, hackathon pages, GitHub. TinyFish is the “open this URL and pull the text” step. Search alone is not enough for that.

What do you like best about the product?

The Fetch API has been a game-changer for us. It quickly turns messy HTML into clean, LLM-ready Markdown, which has significantly reduced our token costs and improved AI accuracy. On top of that, the generous free tier for Search and Fetch makes it a low-risk, high-value addition to our stack. TinyFish integrates seamlessly with my stack. The native MCP support makes connecting to Claude Code and Cursor pretty easy.

What do you dislike about the product?

The Agent feature’s accuracy still feels like it’s maturing. On complex, dynamic pages it occasionally hallucinates data, which means I can’t fully trust it to run autonomously on production-critical tasks without manual validation. Also, the daily OAuth expiration for MCP usage is frustrating and disrupts workflow continuity.

What problems is the product solving and how is that benefiting you?

TinyFish addresses the issue of stale training data and noisy web pages. By delivering clean, up-to-date Markdown through Fetch, my AI models can pull in current, accurate information without having to sift through ads, pop-ups, or navigation bars. As a result, answer quality has improved and token usage is noticeably lower, which helps keep our application fast and reliable.

What do you like best about the product?

I built my own AI news-monitoring and research tool, and Fetch is the piece that made it viable. I hand it a URL and get back clean Markdown instead of a wall of nav bars, cookie banners and ads, which cuts my token cost on every LLM call downstream. Search and Fetch being free is genuinely rare — everyone else meters exactly this. Integration was trivial: one API key, plain REST, no SDK to adopt. Failed URLs not counting against quota is a small thing that matters a lot when you're pulling from news sites that move or drop content.
Over my last 100 fetches the success rate is 98%, with an average latency of 2.7 seconds. Most pages come back in under two seconds, which matters when I'm processing a batch of feed links in one go.
Onboarding was about as short as it gets: sign up, copy the key, first successful fetch inside five minutes, no credit card in the way. And the output is built for the job — Fetch returns content already shaped for an LLM to read, so the AI layer of my own tool gets a clean input instead of raw HTML it has to fight through.
The dashboard deserves a mention too. Success rate, average latency, extracted content size and credit usage are all on the landing page for each API — I went looking for my failure rate and had it in one click, without digging through logs. For a free tier that's more observability than I expected.

What do you dislike about the product?

Fetch is unreliable on paywalled and aggregator pages, and unpredictably so. My success rate is 98% over the last 100 fetches, but the failures aren't random: a paywalled article on The Athletic, and a press-review page on an Italian sports aggregator. What makes it awkward to design around is that four other articles from the same publication went through fine in the same window. So I can't route by domain — I have to handle failure per URL and decide at runtime whether a source is usable. Clearer signalling on why a fetch failed would help more than a higher success rate would.

Second, the free tier ends exactly where the platform gets interesting. Search and Fetch cost nothing, Agent and Browser need a funded wallet. There's no middle step, so one stubborn source pushes you from free to pay-per-step.

Related to that, I'm running production tooling on a free tier with nothing stated behind it — no SLA, no published commitment that Search and Fetch stay free. I'd rather see a policy than assume it holds.

What problems is the product solving and how is that benefiting you?

I pull transfer-market news links from RSS feeds on a set of selected sources — Italian, German and English sports sites — and then use Fetch to turn each article into clean content my LLM can actually reason over. Before, the choice was either hand-rolled scraping I had to maintain per site, or dumping raw HTML into the model and paying tokens for nav bars and cookie banners. Fetch removed both problems in one call, and it renders in a real browser so JS-heavy pages come through intact rather than half-empty.

The second use is research. I use Search plus Fetch instead of Perplexity for digging into a topic — same result, but inside my own pipeline, where I control the prompt, the sources and the output format, and with no API cost on the retrieval layer. That's the part that changed how I work: the retrieval layer stopped being a budget line I had to think about, so I run it on every article in the feed instead of picking which ones are worth the spend.

What do you like best about the product?

Through my brand I serve over 200,000 visitors a month, so I live inside research tools all day. TinyFish has become my default web layer inside Claude Code and OpenCode. The MCP connection was easy to set up, and I was running searches and fetches within minutes. Over the past two to three months it has handled my daily query volume without breaking a sweat. The dashboard is clean, the search results are relevant, and I like that I can jump from a quick search to a full browser session without leaving my coding environment.

What do you dislike about the product?

The dashboard is clean and easy to use, and the overall experience is solid. The only thing holding it back from a perfect score is the browser automation speed. It works well, but it can be slow and I have hit timeouts on longer verification runs. When it finishes the results are good, but faster and more reliable agent sessions would make a big difference for the amount of daily browser work I do. It is not a dealbreaker because search and fetch still cover most of my needs, but that is the main area I would like to see improved.

What problems is the product solving and how is that benefiting you?

TinyFish saves me from paying for multiple research subscriptions across my different projects. I need to confirm claims and sources before anything I publish goes live, and doing that manually at scale is not realistic. With TinyFish I can fact-check quickly and trust that what I am putting in front of my audience is accurate. The ROI is strong because the core features I use most are essentially free, and the time I save on research goes straight back into building my products and content.

What do you like best about the product?

I like that TinyFish is basically free when using it for personal purposes, which is a big plus for me. I also find it incredibly fast, which saves me time when I need to finish tasks quickly. Another feature I appreciate is its agent function, which helps in accessing websites with anti-bot techniques by launching an agent to fetch and view the content.

What do you dislike about the product?

There are two main issues for me. First, when I use TinyFish as an MCP, it can only be logged in through OAuth. It doesn't support the API key way to use as an MCP server. So, I have to log in to TinyFish every day because the OAuth seems to expire daily, which is quite troublesome. Secondly, TinyFish MSAP doesn't really work with OpenAI models. I face constant errors when trying to use a GPT model with TinyFish MCP, which is another problem.

What problems is the product solving and how is that benefiting you?

I use TinyFish to find best practices for app development and verify job information. It's fast and free, with an agent function that bypasses anti-bot techniques. However, OAuth logins are annoying, and it doesn't work well with OpenAI models, causing frequent errors.

What do you like best about the product?

I used TinyFish Agent and Fetch in July 2026 to collect a JavaScript-rendered RIDI webtoon bestseller list. Fetch is free and quick. Four pages came back in about 3.2 seconds, which is useful when I just need a first look at a public page. The product split is clear: Search and Fetch do not burn credits, Agent is 1 credit per step, and the docs are honest that COMPLETED means the browser finished, not that the goal succeeded.

What do you dislike about the product?

On that same RIDI list, Agent looped for about 20 minutes and 127 steps, then failed. A guided page-1 retry returned all 60 titles after about 11 minutes, but it fabricated episode and rating values for ranks 26-48. Episode accuracy was 37/60 and rating/count accuracy was 33/55. One checked row, rank 26, was actually 146 episodes and 28 ratings; Agent returned 100 episodes and 4,500 ratings. Fetch also dropped 28 of 200 titles. I cannot trust Agent output on a virtualized list without independent field checks.

What problems is the product solving and how is that benefiting you?

I needed a cheap way to pull live web pages for a Korean webtoon catalog research project, without paying per page for every test. TinyFish Fetch let me try public list pages quickly and compare them against Browser Use and Firecrawl. That saved setup time during the first benchmark, even though I later kept Firecrawl and a local browser for production collection.

What do you like best about the product?

For my brand-protection project, TinyFish became the browser layer for checking marketplace listings. I liked giving it a goal and required fields instead of maintaining selectors. The live browser preview helped with debugging and demos, and the HTTP API plus documentation made TypeScript integration quick.

What do you dislike about the product?

In my case, agent output still needs validation: a run can complete but return incomplete or inconsistent data if the goal is ambiguous. Concurrency and cost planning also matter as usage grows. Stronger schema guarantees and clearer capacity visibility would improve production use.

What problems is the product solving and how is that benefiting you?

TinyFish helped me automate marketplace monitoring across dynamic pages without building the browser, proxy, and anti-bot stack myself. It shortened the path from idea to a working brand-protection workflow, letting me focus on scoring suspicious listings and presenting results. The ROI is strongest for targeted, difficult web tasks rather than simple bulk crawling.

What do you like best about the product?

The configuration is very simple and it supports MCP, making it basically ready to use out of the box.

I have integrated web search capabilities into different coding agents and different harnesses. This way when using coding agents, I can have it directly search for the latest and most relevant information, rather than being limited by the training data cutoff date (which often results in outdated content being provided).

Because it is not limited to the services provided by a particular model provider, so it can be used with any harness paired with any open-source model. For me, its biggest advantage is its strong versatility.

What do you dislike about the product?

Not really, because it's a free service.

What problems is the product solving and how is that benefiting you?

The dependency versions that most models provide by default are often outdated. If I ask it to look up the latest versions, that largely resolves the issue.