AnySearch

Real-time structured search trusted by agents and developers

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As more AI agents rely on AnySearch, how do you prevent misinformation from propagating through automated workflows?

How does AnySearch evaluate the trustworthiness of sources and resolve conflicting information before returning structured results to AI agents?

The de-dup is the part I'd poke at. When two of your trusted sources report the same fact but genuinely conflict, does dedup collapse them into one clean answer, or does the agent still see they disagree? In my runs, the moment I hid source disagreement to save tokens the agent got more confident and more wrong at once. A structured 'these three agree, this one dissents' beats a single merged result for me. How do you decide what's a duplicate versus a real conflict?

The "searches trusted sources in parallel" detail is what caught my attention, most agent search tools are still doing sequential calls and the latency compounds fast in multi-step workflows. Curious how you handle source conflicts when parallel results return contradictory information on the same query: does AnySearch surface both versions with their respective sources, or does it resolve the conflict before handing structured output to the agent?

Looks great! One question: how do you measure whether AnySearch actually improves an agent's final answer compared to using Google Search or Tavily? Really curious about the benchmarks.

Interesting approach. Structured search that agents can actually work with is a real gap right now. Most search APIs return messy results that need a ton of post-processing before an agent can use them.

How are you handling schema consistency across different data sources? That's been one of the hardest parts in our experience.

The deduplication across parallel sources is the part I'm most curious about. In practice, the same story or data point gets syndicated everywhere, and agents end up with 4 near-identical chunks in context that all look authoritative. How are you handling that, string similarity or something semantic? And how do you manage source trust when a "trusted source" is just wrong about something recent?

Congrats for launching! This looks interesting, but since the normal search results are full of ads and SEO content. How do you decide the sources are trusted without filtering out useful information ?

searching multiple trusted sources in parallel and then deduping sounds great for accuracy but what does that do to latency and cost per query compared to a single search call. for an agent making dozens of tool calls in a session those add up fast, curious if there's a way to tune how many sources it hits per query or if that's fixed

Congrats on the launch @trahant! Filtering SEO spam before it hits the context window is the underrated part, most agents are one bad Reddit thread away from confidently hallucinating.

Congrats !!! Does AnySearch return citations, confidence scores, or relevance rankings so downstream agents can reason about the reliability of each result?

The de-dupe plus parallel trusted-source angle is the useful part here, since most agent failures I see are stale or duplicated context rather than missing data. One operator question: can I scope the trusted-source set per agent (e.g. pin a support bot to our own docs plus a couple of domains), or is the source list global across all calls? That is the line between this being safe for a customer-facing agent versus just research.

the bit about agents not being able to skim and decide like humans is exactly the right framing — what does the output structure look like, json schema or more like markdown blocks?

Congrats on the launch and the #1 spot! I run research agents internally and messy search results are genuinely my biggest reliability problem, so this is timely. Quick question: can I restrict searches to my own whitelist of domains per query? For things like competitive research I only trust maybe 15 sources, and “trusted sources” chosen by the tool is not the same as trusted by me.

the dedup and structuring part is the interesting bit to me, most "search for agents" pitches i've seen just wrap a regular search API. what happens when two trusted sources genuinely disagree on a fact, does anysearch pick one and present it as ground truth or does it surface both and let the agent reason about it

I noticed the promise of parallel searches across trusted sources. How much faster is it compared with a normal search workflow? A few real examples with measurable improvements would answer that quickly.

This is a good direction for agent tooling. The hard part is not just search quality, it is making the agent carry source confidence forward instead of turning a clean JSON result into false certainty. I would love to see provenance, freshness, and failure states treated as first-class fields in the response.