Bright Data provides a powerful Web Scraper API that allows you to extract structured data from 100+ ppular domains, including Amazon product details, LinkedIn profiles, and more, making it particularly useful for AI agents requiring reliable structured web data feeds.

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Overview

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Integration details

ClassPackageSerializableJS supportPackage latest
BrightDataWebScraperAPIlangchain-brightdata✅❌PyPI - Version

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Tool features

Native asyncReturns artifactReturn dataPricing
❌❌Structured data from websites (Amazon products, LinkedIn profiles, etc.)Requires Bright Data account

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Setup

The integration lives in the langchain-brightdata package. 
pip install langchain-brightdata
You’ll need a Bright Data API key to use this tool. You can set it as an environment variable: 
import os

os.environ["BRIGHT_DATA_API_KEY"] = "your-api-key"
Or pass it directly when initializing the tool: 
from langchain_brightdata import BrightDataWebScraperAPI

scraper_tool = BrightDataWebScraperAPI(bright_data_api_key="your-api-key")

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Instantiation

Here we show how to instantiate an instance of the BrightDataWebScraperAPI tool. This tool allows you to extract structured data from various websites including Amazon product details, LinkedIn profiles, and more using Bright Data’s Dataset API. The tool accepts various parameters during instantiation:
  • bright_data_api_key (required, str): Your Bright Data API key for authentication.
  • dataset_mapping (optional, Dict[str, str]): A dictionary mapping dataset types to their corresponding Bright Data dataset IDs. The default mapping includes:
    • “amazon_product”: “gd_l7q7dkf244hwjntr0”
    • “amazon_product_reviews”: “gd_le8e811kzy4ggddlq”
    • “linkedin_person_profile”: “gd_l1viktl72bvl7bjuj0”
    • “linkedin_company_profile”: “gd_l1vikfnt1wgvvqz95w”

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Invocation

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Basic Usage

from langchain_brightdata import BrightDataWebScraperAPI

# Initialize the tool
scraper_tool = BrightDataWebScraperAPI(
    bright_data_api_key="your-api-key"  # Optional if set in environment variables
)

# Extract Amazon product data
results = scraper_tool.invoke(
    {"url": "https://www.amazon.com/dp/B08L5TNJHG", "dataset_type": "amazon_product"}
)

print(results)

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Advanced Usage with Parameters

from langchain_brightdata import BrightDataWebScraperAPI

# Initialize with default parameters
scraper_tool = BrightDataWebScraperAPI(bright_data_api_key="your-api-key")

# Extract Amazon product data with location-specific pricing
results = scraper_tool.invoke(
    {
        "url": "https://www.amazon.com/dp/B08L5TNJHG",
        "dataset_type": "amazon_product",
        "zipcode": "10001",  # Get pricing for New York City
    }
)

print(results)

# Extract LinkedIn profile data
linkedin_results = scraper_tool.invoke(
    {
        "url": "https://www.linkedin.com/in/satyanadella/",
        "dataset_type": "linkedin_person_profile",
    }
)

print(linkedin_results)

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Customization Options

The BrightDataWebScraperAPI tool accepts several parameters for customization:
ParameterTypeDescription
urlstrThe URL to extract data from
dataset_typestrType of dataset to use (e.g., “amazon_product”)
zipcodestrOptional zipcode for location-specific data

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Available Dataset Types

The tool supports the following dataset types for structured data extraction:
Dataset TypeDescription
amazon_productExtract detailed Amazon product data
amazon_product_reviewsExtract Amazon product reviews
linkedin_person_profileExtract LinkedIn person profile data
linkedin_company_profileExtract LinkedIn company profile data

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Use within an agent

from langchain_brightdata import BrightDataWebScraperAPI
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import create_react_agent

# Initialize the LLM
llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key="your-api-key")

# Initialize the Bright Data Web Scraper API tool
scraper_tool = BrightDataWebScraperAPI(bright_data_api_key="your-api-key")

# Create the agent with the tool
agent = create_react_agent(llm, [scraper_tool])

# Provide a user query
user_input = "Scrape Amazon product data for https://www.amazon.com/dp/B0D2Q9397Y?th=1 in New York (zipcode 10001)."

# Stream the agent's step-by-step output
for step in agent.stream(
    {"messages": user_input},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

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API reference