Cookbooks & Examples Built with Nimble

A 4-phase pipeline that pulls live market statistics from 100 US cities via Redfin, uses Claude to rank and select the top 10 investment markets, then surfaces active Zillow listings and deep market reports from Perplexity for each selected city. The entire research workflow an analyst would spend days on, done in minutes.

A Snowflake-native data app that uses NIMBLE_AGENT_RUN to run one google_maps_search Web Search Agent per warehouse row. Start with a table of search queries like “coffee shops in Williamsburg Brooklyn” or “gyms in Austin Texas,” then generate a structured business universe with names, categories, addresses, ratings, review counts, phone numbers, websites, coordinates, and Google Maps URLs — all from SQL.

A pipeline that sends the same question to three major AI platforms simultaneously — ChatGPT, Perplexity, and Gemini — via Nimble’s AI agents, then uses Claude Haiku to read all three raw responses and judge whether they agree. Ships with 100 pre-loaded questions and a live tab for real-time queries.

A demonstration of Nimble Web Search Agents as LangChain tools inside a LangGraph ReAct agent. The agent receives a search query, calls Nimble’s Google Maps agent to find matching businesses, then visits every business website via Nimble Extract to pull contact emails, opening hours, and a short description. A scoring chain ranks every lead 1–10 on outreach potential. The full dataset is queryable via natural language chat.

Pulls live used EV listings from eBay Motors and Carvana, normalizes them into a single dataset, scores each listing on 5 weighted factors, and surfaces the best deals in a filterable dashboard with direct links to live listings.

Does where you live change what you pay on Amazon? Tracks 500 Amazon best-sellers across 10 US zip codes with hourly price snapshots for 4 days — 479,231 total observations. Answers two questions: does Amazon price dynamically by geography, and how much does any given product’s price move in 96 hours?