Web Search Agents by Nimble
Self-learning agents automate web research + retrieval
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Web Search Agents by Nimble - Self-learning agents automate web research + retrieval

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Web Search Agents are expert web crawling and research agents for your specific domain (company enrichment, regulations research, etc.). They self-learn your use case to go deeper into the sources that matter most to you, giving your AI deeper and more relevant web context. To get started, give your AI this link:

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Maker

Hey! Alon here from Nimble. Super excited to tell you about our latest launch.

We built Web Search Agents to discover and retrieve everything your AI needs from the web, using less tokens.

Generic web search tools can discover basic information for your AI, but complex research tasks require expertise.

Web Search Agents are experts at researching specific domains - like company enrichment, news monitoring, financial analysis, and any other research task.

They go deeper into your domain than other web search tools by self-learning the best ways to retrieve the specific information you need. The result is more complete, accurate web context, using less tokens.

Learn how to get started with

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 Super neat concept.. is there any plans to add a direct integration for exporting straight to Google Sheets or Airtable?

Maker

   thats a great feature idea! Mind sharing what you had in mind for use cases? This would help us build something more relevant for you.

Hunter

S/O for the launch, ?makers! keep up the great work, looking forward to more launches on

 nice launch! the domain specific research angle are really interesting and also using the fewer token while getting deeper research sounds like a great swap.

how does that learning process are work in practice????

Maker

 Hey Amrita, thanks! The agent is a complete managed harness that persists learnings for its future runs, constantly driving itself to improve its own results. Troubles and issues it solved on its way to its data are documented for and learned from in future runs - gradually reducing the cost and effort needed to get this data again.

 i like the self learning approach and also using fewer are tokens for deeper research sounds useful.
congrats on the launch team!

“Wait, does it actually learn from how you use it?”

Maker

 
yep! each agent keeps memories from its previous runs, so that past experience is already part of what it knows and shapes how it acts next time.


A few examples:
a. If it tried fetching data a certain way, it remembers how that went and will lean towards that approach again if it worked well.
b. If it wrote and ran a script that worked, it can reuse that instead of starting from scratch.

tell us if you give it a go!

Hunter

had a blast collaborating on this launch.

are specialized web experts that execute complex web research, enrichment, and dataset building tasks for your specific use case. The product is used by more than 1.5M developers, and technical founders like from put it eloquently:

Nimble’s data platform met our massive data needs out of the box, feeding our large Language models with relevant, high-quality data. This scalability has been crucial in developing more robust and reliable AI systems.

Browse some example apps at and get started.

Hunter

one more thing - If you're launching today, you might find this launch monitoring tool helpful:

it searches the web across press, social, and developer communities for product launch signals, flagging mischaracterizations and competitor responses.

“News monitoring seems like a pretty natural fit for this.”

Maker

 Indeed, Nimble's web search agent is exceptionally good at wide range monitoring of news and other types of feeds; and you have great control over which types of sources it will go through.

Hunter

News monitoring seems like a pretty natural fit for this.

absolutely - the team actually built an example app for this exact use case. see here:

TL,DR: in this example, search the web across press, social, and developer communities for product launch signals, flagging mischaracterizations and competitor responses.

hope it inspires!

Using less tokens without losing the useful stuff is the part I’m most curious about.

Maker

 

This is driven by a few things: Your agents searches, finds and returns only the fields you asked for and not full page dumps you'd have to parse yourself. Additionally it pre-filters and dedupes across sources, so you're not paying token cost for nav menus, duplicate content or irrelevant information. You get structured outputs with trust scores

Hunter

Using less tokens without losing the useful stuff is the part I’m most curious about.

+1 towards responsible tokenmaxxing

“Researching companies usually turns into a million tabs for me. I can see the appeal here.”

Maker

 

actually it's one of the our common usecases, we even have it in our sample agents -

Hunter

any particular use case in mind? the team created a cookbook of example apps built with for inspiration:

clone them to get started quickly

“I wonder what happens when it picks the wrong source and keeps going with it.”

Maker

 

A few things guard against this:

  • The agent checks results against multiple sources, so a wrong or low confidence source gets cross checked against others instead of running blindly

  • Every piece of data has a trust score and the agent validates the trust before returning a result. If the trust is low it will go back to search for higher confidence sources

  • You have control over guiding the agent to sources to prioritize or ignore, allowing you to define what a "wrong source" looks like

  • Finally, each field is returned with the trust score so you or a downstream logic can set thresholds for trust scores, so even if it got returned you can surface it and see it instead of it silently tainting your results

Maker

   

one more thing on top of Adriel's answer: the agent also remembers when a source burned it. a domain that came back low trust or led nowhere gets deprioritized on future runs, so the "keeps going with it" part mostly fixes itself over time. and since every field links back to its source, you can always see where a bad value came from and block that domain for good!!

Would be cool to see how much better it gets after using it for the same kind of research over time.

Maker

 
same here honestly, it's one of the more fun things to watch.

Hunter

yes! and to get started, browse this collection of working example apps for inspiration here:

Maker

 let us know how it goes!

will try using this for best productivity resources' research use cases

Maker

 great thinking! To get a broader sense of the kind of use cases you can address with Web Search Agents, check out our cookbooks here (which include a few in the productivity category!):

Hunter

 looking forward to seeing what you'll be building with ! make sure to add your review here:

Congrats #makers and . Looks a great product, will give it a try. GL!
Maker

   Thanks! Let us know what you build with it and if you have any questions!

Hunter

yes, and make sure to add your review here:

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