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Nimble Web Search Agents (WSA) are intelligent agents that search, extract, and structure real-time knowledge from any website. Describe what data you need, and Nimble builds an agent that delivers structured results at scale — no CSS selectors, no scraping expertise, no maintenance headaches. A growing library of pre-built agents covers popular sites like Amazon, Google, and Walmart with zero configuration. For any other website, create a custom agent in seconds using natural language.

Try Nimble Studio

Create a Web Search Agent for any website — no coding required

Install Plugin

Use Nimble directly in Claude Code or Cursor

Quick Start

Example Request

from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

result = nimble.agent.run(
    agent="amazon_pdp",
    params={
        "asin": "B0DLKFK6LR"
    }
)

parsed = result.data.parsing["parsed"]
print(f"Product: {parsed['product_title']}")
print(f"Price: ${parsed['web_price']}")

Example Response

{
  "url": "https://www.amazon.com/dp/B08N5WRWNW",
  "task_id": "b1fa7943-cba5-4ec2-a88c-4d2d6799c794",
  "status": "success",
  "data": {
    "html": "...",
    "parsing": {
      "asin": "B08N5WRWNW",
      "product_title": "Apple AirPods Pro (2nd Generation)",
      "brand": "Apple",
      "web_price": 249.0,
      "list_price": 279.0,
      "average_of_reviews": 4.7,
      "number_of_reviews": 125432,
      "availability": true
    }
  },
  "status_code": 200
}

How it works

1

Select an agent and provide inputs

Choose an agent by name (e.g., amazon_pdp, google_search) and pass the required parameters like product ID, search query, or URL
2

Nimble handles everything

The agent fetches the page, handles anti-bot protection, and extracts data using battle-tested selectors maintained by Nimble
3

Receive structured data

Get clean, normalized JSON with consistent field names - ready to use in your application immediately

Any website. Structured data. One API.

Every Web Search Agent — whether pre-built or custom — works through the same simple API. Pick a site, describe what you need, and get production-ready structured data.

Pre-Built Agents

\ A growing library of agents for popular websites — maintained by Nimble 24/7, auto-healing when sites change. Browse the Gallery.

Custom Agents

\ Create an agent for any website using Nimble Studio or the Nimble plugin in your IDE. Describe what you need in plain English.

Parameters

Supported input parameters:
agent
string
required
The name of the pre-built agent to use. Each agent is designed for a specific platform or data type.Popular agents:
  • amazon_pdp - Amazon product pages
  • amazon_serp - Amazon search results
  • google_search - Google search results
  • google_maps_search - Google Maps locations
  • walmart_pdp - Walmart products
  • chatgpt - ChatGPT prompt results
  • perplexity - Perplexity prompt results
Browse all agents →
params
object
required
Agent-specific parameters that tell the agent what data to fetch. Each agent has different requirements.Common param types:
  • Product IDs (ASINs, SKUs)
  • Search queries
  • URLs or usernames
  • Page numbers for pagination
Example:
{
  "asin": "B08N5WRWNW"
}
localization
boolean
default:"false"
Enable location-based pricing and availability. Required when passing zip_code or store_id in params.Only available for agents that support localization (check agent details).Example:
{
  "agent": "amazon_pdp",
  "localization": true,
  "params": {
    "asin": "B08N5WRWNW",
    "zip_code": "90210"
  }
}
Each agent has unique parameter requirements. Check the Agent Gallery for exact parameters for each agent.

Usage

E-commerce product extraction

Extract product data from Amazon, Walmart, and other retailers:
from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

# Amazon product
result = nimble.agent.run(
    agent="amazon_pdp",
    params={
        "asin": "B08N5WRWNW"
    }
)

parsed = result.data.parsing["parsed"]
print(f"Product: {parsed['product_title']}")
print(f"Price: ${parsed['web_price']}")
print(f"Rating: {parsed['average_of_reviews']}")

Search results extraction

Get search results from Google, Amazon, and other platforms:
from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

# Amazon search
result = nimble.agent.run(
    agent="amazon_serp",
    params={
        "keyword": "wireless headphones"
    }
)

products = result.data.parsing["parsed"]
print(f"Found {len(products)} products")
for product in products:
    print(f"- {product['product_name']}: ${product['price']}")

Google Maps extraction

Find businesses and locations:
from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

result = nimble.agent.run(
    agent="google_maps_search",
    params={
        "query": "coffee shops near Times Square"
    }
)

parsed = result.data.parsing["parsed"]
for place in parsed["entities"]["SearchResult"]:
    print(f"- {place['title']} ({place['rating']} stars)")

LLM platform extraction

Get responses from AI platforms like ChatGPT and Perplexity:
from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

result = nimble.agent.run(
    agent="chatgpt",
    params={
        "prompt": "What are the best practices for web scraping?"
    }
)

parsed = result.data.parsing["parsed"]
print(parsed["response"])

Localized extraction

Get location-specific pricing and availability:
from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

# Get New York pricing
result = nimble.agent.run(
    agent="amazon_pdp",
    localization=True,
    params={
        "asin": "B08N5WRWNW",
        "zip_code": "10001"
    }
)

parsed = result.data.parsing["parsed"]
print(f"Price in NYC: ${parsed['web_price']}")

Async extraction

Run agent extractions asynchronously:
from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

response = nimble.agent.run_async(
    agent="amazon_pdp",
    params={"asin": "B0DLKFK6LR"}
)

task_id = response.task_id
print(f"Task submitted: {task_id}")

# Poll for completion
import time

while True:
    my_task = nimble.tasks.get(task_id)
    print(f"Status: {my_task.task.state}")

    if my_task.state == "success":
        break
    elif my_task.state == "failed":
        print(f"Task failed: {status.error}")
        break

    time.sleep(15)
Async mode supports polling, callbacks and cloud storage
Browse pre-built agents maintained by Nimble for popular platforms:

Explore Full Gallery

Browse all agents with interactive documentation and live testing

E-commerce

AgentPlatformDescription
amazon_pdpAmazonProduct details, pricing, reviews
amazon_serpAmazonSearch results with products
walmart_pdpWalmartProduct details and pricing
walmart_searchWalmartSearch results
target_pdpTargetProduct details
best_buy_pdpBest BuyProduct details

Search Engines

AgentPlatformDescription
google_searchGoogleSearch results with snippets
google_maps_searchGoogle MapsBusiness listings and locations
google_search_aioGoogleAI Overview results

LLM Platforms

AgentPlatformDescription
chatgptChatGPTPrompt responses
perplexityPerplexitySearch + AI responses
geminiGoogle GeminiPrompt responses
grokGrokPrompt responses

Social Media

AgentPlatformDescription
tiktok_accountTikTokAccount profiles and videos
facebook_pageFacebookPage information
youtube_shortsYouTubeShort-form videos

Create Custom Agents

Can’t find an agent for your target website? Create your own using Nimble Studio - no coding required.
1

Open Nimble Studio

Go to the Nimble Studio in Nimble Platform
2

Provide URL and describe your needs

Enter the website URL and describe what data you need in plain English:“Extract product name, price, rating, and all review comments”
3

AI creates your agent

Our AI analyzes the page and builds an extraction agent automatically - no CSS selectors needed
4

Test and refine

Preview extracted data and refine your description if needed
5

Use via API

Your custom agent is available via the same Agent API with your chosen name

Custom agent example

from nimble_python import Nimble

nimble = Nimble(api_key="YOUR-API-KEY")

# Use your custom agent just like a public one
result = nimble.agent.run(
    agent="my_custom_store_pdp",  # Your custom agent name
    params={
        "url": "https://www.nike.com/t/air-max-90-mens-shoes"
    }
)

parsed = result.data.parsing["parsed"]
print(f"Product: {parsed['product_name']}")
print(f"Price: ${parsed['price']}")
Pre-built agents are maintained by Nimble 24/7 and auto-heal when sites change. Custom agents can be updated anytime in Nimble Studio. For popular sites, check the Gallery first — a pre-built agent may already exist.

Response Fields

FieldTypeDescription
urlstringThe URL that was extracted
task_idstringUnique identifier for the request
statusstringsuccess or failed
data.htmlstringRaw HTML content
data.parsingobjectStructured extracted data
status_codenumberHTTP status code from target

Use cases

Price Monitoring

Track prices across Amazon, Walmart, and other retailers with consistent data formats

Product Research

Gather comprehensive product data from major e-commerce platforms

Search Tracking

Monitor Google, Bing, and marketplace search results for SEO and visibility

Competitive Intelligence

Extract competitor data from any website using public or custom agents

Agents vs other tools

What you needUse
Data from popular sites (Amazon, Google, etc.)Public Agents - browse gallery
Data from sites not in the galleryCustom Agents - create in Studio
Data from specific URLs (expert users)Extract - full control with CSS selectors
Data from entire websiteCrawl
Search web + extract content from resultsSearch
Web Search Agents work with any website. Pre-built agents cover popular sites with zero configuration. For everything else, create a custom agent in seconds — or install the Nimble plugin and let your AI coding assistant handle it.

Next steps

Try Nimble Studio

Create a Web Search Agent for any website — see the value in minutes

Install Plugin

Use Nimble in Claude Code or Cursor — your AI assistant builds agents for you

Agent Gallery

Browse pre-built agents for popular sites

API Reference

Explore the Agents API for direct integration