AnySearch - Real-time structured search trusted by agents and developers
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A search tool for agents, not a search box. AI agents are only as good as the information they receive. When connected to AnySearch, your agent gets filtered, de-duplicated, and structured information from trusted sources searched in parallel, helping it produce more reliable results. Free to start.
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AgentKey
AnySearch
@luki_notlowkey Thanks! Yep, that’s exactly the kind of downstream agent use case we care about.
AnySearch returns structured results with source attribution, so agents can cite where a claim came from instead of treating search as a black box. Results are also relevance-ranked, and we expose confidence/freshness-style signals where available so developers can decide how much weight to give each source.
We try to be careful with this though: confidence shouldn’t mean “the AI is definitely right.” It’s a signal for the agent to combine with its own policies, cross-checking, or human review when the stakes are higher.
Dial
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
AnySearch
@galdayan Great question — that tradeoff is very real.
Multi-source retrieval can increase cost versus a single-source call, but latency does not have to scale linearly because sources can be queried in parallel and the router does not need to hit every source for every query.
The key is source breadth control. For latency-sensitive agent steps, you want fewer sources and faster returns. For high-stakes or evidence-heavy steps, you may want broader retrieval plus dedupe and reranking.
Our direction with AnySearch is to make this query-aware rather than fixed: route based on domain, freshness needs, source type, and confidence requirements, while letting developers constrain scope with things like domain filters, source types, result limits, or lighter vs deeper retrieval modes.
The goal is not “always search everything,” but to let agents choose the right cost / latency / accuracy tradeoff per query, especially when a session may involve dozens of tool calls.
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.
AnySearch
@masato_furuno1 Data from different sources perform variations in format. By transforming them into structured contents that can be used by Agents, and by sinking the tasks of cleaning, and filtering to the infra layer, we make the data Agents receive is clean, referenced, and directly inferable.
@terence_lou Makes sense. Having each agent deal with messy data on its own is a pain, so sinking that to infra is smart. What happens when two sources define the same entity differently though? Like conflicting schemas for the same thing.
AnySearch
@terence_lou @masato_furuno1 Yeah, that’s exactly where we try not to be too clever.
When two sources describe the same entity differently, we don’t want to blindly squash them into one “clean” object and lose the disagreement. We normalize the common fields so agents can work with the result, but keep source-specific fields and provenance attached.
So if source A and source B disagree on an attribute, the agent should be able to see that as a conflict, not as a mysteriously averaged answer. In those cases, the useful output is often: “these are probably the same entity, here are the conflicting fields, here is where each version came from.”
Basically, normalize the shape, not the truth.
@terence_lou @ye_y "Normalize the shape, not the truth" - that's a really clean way to put it. Keeping the conflict visible instead of hiding it behind a merged object makes a lot more sense for agents that need to make judgment calls downstream. Nice design choice.
Great product
AnySearch
@madalina_barbu Thank you for comment, try AnySearch in your Agents or Workflows plz.
Timbal AI
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?
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?
How does AnySearch evaluate the trustworthiness of sources and resolve conflicting information before returning structured results to AI agents?
As more AI agents rely on AnySearch, how do you prevent misinformation from propagating through automated workflows?
AnySearch
@robert_dimla Preventing misinformation from propagating through AI workflows is exactly what we want to do and have been solving. We use exclusive algorithms and self built data sources on the search layer to filter out incorrect and outdated information, and improve the ranking of real-time and accurate information sources. We are calling on and inviting more AI developers, companies, or open source projects to use AnySearch for a better search experience and promote the development of the AI industry together.
We hit this with Tavily in a RAG pipeline: parallel source search was fine, but stable structured fields mattered more for tool calls. Less glue code if AnySearch ships that cleanly
Interesting project! I’m currently using Tavily for search in my AI research workflows, but I’m curious to see how AnySearch compares. I’ll give it a try!