Tabstack Web Research

What it does

/research gives your app or agent cited answers from the live web in one API call. Not a pre-indexed corpus: the actual live web. Every request comes back with source URL's users can verify. Source selection, synthesis, and citation formatting are all inside the call. You write or maintain none of that code. Built for legal, financial, and competitive intel, where a wrong answer is a liability. Free to try.

Run a research agent with cited answers in a single API call. Live-web sourcing, multi-source synthesis, and SSE streaming, without building or maintaining a research pipeline.

Ask one question and get cited answers back, so your users can verify every claim without building or maintaining a research pipeline. Regional banks face intensifying capital requirements under the revised Basel III “endgame” rules 1 , alongside heightened scrutiny of commercial real-estate concentration following recent supervisory guidance 2 . Liquidity-coverage expectations have also tightened for institutions above the $100B threshold 3 | Answers include cited source URLs your users can verify, so you avoid confident-sounding hallucinations and "summarized the first result" outputs. They can inspect where conclusions came from. import Tabstack from ' @tabstack/sdk ' const client = new…from tabstack.ai

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  Hi HN, My team and I are building Tabstack to handle the "web layer" for AI agents. Launch Post: https://tabstack.ai/blog/intro-browsing-infrastructure-ai-ag... Maintaining a complex infrastructure stack for web browsing is one of the biggest bottlenecks in building reliable agents. You start with a simple fetch, but quickly end up managing a complex stack of proxies, handling client-side hydration, and debugging brittle selectors. and writing custom parsing logic for every site. Tabstack is an API that abstracts that infrastructure. You send a URL and an intent; we…

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  Tabstack Research – An API for verified web research (by Mozilla)Feb 2026 · ▲10

  Hi HN, My team and I are building Tabstack to handle the web layer for AI agents. Today we are sharing Tabstack Research, an API for multi-step web discovery and synthesis. https://tabstack.ai/blog/tabstack-research-verified-answers In many agent systems, there is a clear distinction between extracting structured data from a single page and answering a question that requires reading across many sources. The first case is fairly well served today. The second usually is not. Most teams handle research by combining search, scraping, and summarization. This becomes brittle…

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  Web search using a ChatGPT-like model that can cite its sources2022 · beta.sayhello.so · ▲318

  We’ve trained a generative AI model to browse the web and answer questions/retrieve code snippets directly. Unlike ChatGPT, it has access to primary sources and is able to cite them when you hover over an answer (click on the text to go to the source being cited). We also show regular Bing results side-by-side with our AI answer. The model is an 11-billion parameter T5-derivative that has been fine-tuned on feedback given on hundreds of thousands of searches done (anonymously) on our platform. Giving the model web access lessens its burden to need to store a snapshot of human knowledge…

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