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Web Search Agents by Nimble
Self-learning agents automate web research + retrieval
142 followers
Self-learning agents automate web research + retrieval
142 followers
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: https://docs.nimbleway.com/agent-onboarding.md
Mastra
had a blast collaborating on this launch.
Web Search Agents are specialized web experts that execute complex web research, enrichment, and dataset building tasks for your specific use case.
Browse some example apps at nimbleway.com/cookbooks and get started.
“Wait, does it actually learn from how you use it?”
Web Search Agents by Nimble
@aria_taylor
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!
“News monitoring seems like a pretty natural fit for this.”
Web Search Agents by Nimble
@evan_taft1 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.
“I wonder what happens when it picks the wrong source and keeps going with it.”
Web Search Agents by Nimble
@grant_w1
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
Web Search Agents by Nimble
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 our docs
Start for free here
@alon_bar_tzlil Super neat concept.. is there any plans to add a direct integration for exporting straight to Google Sheets or Airtable?
“Researching companies usually turns into a million tabs for me. I can see the appeal here.”
Web Search Agents by Nimble
@dylan_friddle12
actually it's one of the our common usecases, we even have it in our sample agents -
Using less tokens without losing the useful stuff is the part I’m most curious about.
Web Search Agents by Nimble
@ethan_blake27
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