A high-level search agent

Hacker News

Hi HN — we’re building high-level capabilities for AI applications at Gensee: packaged tooling + infra that remove brittle low-level plumbing so teams can focus on their product’s real job. After speaking with many AI developers and experiencing it ourselves, we found that building agents requiring web content is often bottlenecked on the “search” part, as it involves iterations of search, crawl, extract, re-query, and error handling. We package all these search-related low-level details in an efficient and more intelligent way, so AI builders can get back to work on their core agent ideas. What Gensee Search Agent is behind the single API call: - Web searching + crawling + browsing - Built-in error handling + retries/fallbacks - Breadth-first search approach to search in parallel and rule out bad results early on - Goal-aware extraction that returns content closely related to your query and directly usable by downstream tasks Results: - Improved the GAIA benchmark accuracy for Owl (open-source implementation of Manus) by 23%. - Helped a San Diego developer boost his AI agent’s accuracy by 40%. What we’d love feedback on: - Corner cases where your agents struggle or customization needs for your agents - Output formats you want - How you’d like to control search quality vs. cost - Other features you care about (e.g., runtime monitoring, eval harnesses) We’ll stick around in the thread to answer questions and share implementation details. Thanks for taking a look!

Visit product View on Hacker NewsAug 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, single · Missing: mac, macos, cursor

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, builder · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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