展示HN：Tabstack Research – 一个用于验证网络研究的API（由Mozilla提供）
嗨，HN，
我和我的团队正在构建 Tabstack，旨在处理 AI 代理的网络层。今天，我们分享了 Tabstack Research，这是一个用于多步骤网络发现和综合的 API。
在许多代理系统中，从单一页面提取结构化数据与回答需要跨多个来源阅读的问题之间存在明显的区别。第一种情况目前得到了相对较好的支持，而第二种情况通常不然。
大多数团队通过结合搜索、抓取和总结来处理研究。这在规模扩大时变得脆弱且成本高昂。你最终需要管理浏览器编排，移动大量原始文本仅仅是为了提取几个主张，并编写自定义逻辑来检查问题是否真正得到了回答。
我们构建了 Tabstack Research，将这一推理循环移入基础设施层。你只需发送一个目标，系统会：
- 将其分解为针对不同数据孤岛的子问题。
- 根据需要使用抓取或浏览器自动化进行网络导航。
- 在综合之前提取并验证主张，以保持上下文窗口专注于信号。
- 检查与原始意图的覆盖情况，并在检测到信息缺口时进行调整。
例如，如果搜索企业政策发现数据分散在多个子服务中（如 Teams 数据存储在 SharePoint 中），引擎会检测到这一缺口并自动调整以寻找缺失的文档。
我们的目标是返回应用程序可以直接依赖的内容：一个带有内联引用和直接链接到源文本的结构化对象，而不是一系列链接或一个黑箱摘要。
上面链接的博客文章详细介绍了引擎架构和扩展代理浏览的技术挑战。
我们提供一个免费层，每月包含 50,000 个积分，您可以在没有信用卡的情况下进行测试： [https://console.tabstack.ai/signup](https://console.tabstack.ai/signup)
我非常希望能听到您对这种方法的反馈，并回答您关于该技术栈的任何问题。
查看原文
Hi HN,<p>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.<p><a href="https://tabstack.ai/blog/tabstack-research-verified-answers" rel="nofollow">https://tabstack.ai/blog/tabstack-research-verified-answers</a><p>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.<p>Most teams handle research by combining search, scraping, and summarization. This becomes brittle and expensive at scale. You end up managing browser orchestration, moving large amounts of raw text just to extract a few claims, and writing custom logic to check if a question was actually answered.<p>We built Tabstack Research to move this reasoning loop into the infrastructure layer. You send a goal, and the system:<p>- Decomposes it into targeted sub-questions to hit different data silos.<p>- Navigates the web using fetches or browser automation as needed.<p>- Extracts and verifies claims before synthesis to keep the context window focused on signal.<p>- Checks coverage against the original intent and pivots if it detects information gaps.<p>For example, if a search for enterprise policies identifies that data is fragmented across multiple sub-services (like Teams data living in SharePoint), the engine detects that gap and automatically pivots to find the missing documentation.<p>The goal is to return something an application can rely on directly: a structured object with inline citations and direct links to the source text, rather than a list of links or a black-box summary.<p>The blog post linked above goes into more detail on the engine architecture and the technical challenges of scaling agentic browsing.<p>We have a free tier that includes 50,000 credits per month so you can test it without a credit card: <a href="https://console.tabstack.ai/signup" rel="nofollow">https://console.tabstack.ai/signup</a><p>I would love to get your feedback on the approach and answer any questions about the stack.