> ## Documentation Index
> Fetch the complete documentation index at: https://docs.brightdata.com/llms.txt
> Use this file to discover all available pages before exploring further.
# How to Set Up Bright Data With LangChain
> Integrate Bright Data with LangChain to give LLM-powered agents reliable, anonymous, and scalable web access for real-world data tasks. Spans 195 countries.
You might be eligible for our Startup Program. Get fully funded access to the infrastructure you're reading about right now (up to \$20K value).
Integrating Bright Data with LangChain enhances LLM-powered agents with reliable, anonymous, and scalable web access for real-world applications.\
You can do it either by using the `langchain-brightdata` Python package is the official LangChain integration for Bright Data, including support for:
* **BrightDataSERP** - Bright Data provides a powerful SERP API that allows you to query search engines (Google,Bing, DuckDuckGo,Yandex) with geo-targeting and advanced customization options, particularly useful for AI agents requiring real-time web information.
* **BrightDataUnlockerAPI** - Bright Data provides a powerful Unlocker API that allows you to access websites that might be protected by anti-bot measures, geo-restrictions, or other access limitations, making it particularly useful for AI agents requiring reliable web content extraction.
* **BrightDataWebScraperAPI** - Bright Data provides a powerful Scrapers 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.
Or by [**Bright Data’s MCP (Model Context Protocol)**](https://docs.brightdata.com/ai/mcp-server/overview#configuration) - a local server that exposes a wide range of scraping and automation tools. While not part of the `langchain-brightdata` package, it can be integrated manually using LangChain’s `Tool` or `RequestsWrapper`.
## How to Integrate Bright Data With LangChain
* Log in to your [Bright Data dashboard](https://brightdata.com/cp).
* Go to [Account Settings](https://brightdata.com/cp/setting/users).
* [Generate an API key](https://docs.brightdata.com/api-reference/authentication#how-do-i-authenticate-with-api-key%3F) if you haven't already done so.
Install the Bright Data integration package for LangChain by running the following command:
```bash theme={null}
pip install langchain-brightdata
```
Set your Bright Data API key as an environment variable:
```python theme={null}
import os
os.environ["BRIGHT_DATA_API_KEY"] = "your-api-key"
```
Or pass it directly when initializing tools:
```python theme={null}
from langchain_bright_data import BrightDataSERP
tool = BrightDataSERP(bright_data_api_key="your-api-key")
```
The Bright Data + LangChain integration currently supports:
**API reference**: [SERP API Documentation](https://docs.brightdata.com/scraping-automation/serp-api/introduction)
Collect search engine results with geo-targeting
```python Basic Usage theme={null}
from langchain_brightdata import BrightDataSERP
# Initialize the tool
serp_tool = BrightDataSERP(
bright_data_api_key="your-api-key" # Optional if set in environment variables
)
# Run a basic search
results = serp_tool.invoke("latest AI research papers")
print(results)
```
```python Advanced Usage with Parameters theme={null}
from langchain_brightdata import BrightDataSERP
# Initialize with default parameters
serp_tool = BrightDataSERP(
bright_data_api_key="your-api-key",
search_engine="google", # Default
country="us", # Default
language="en", # Default
results_count=10, # Default
parse_results=True, # Get structured JSON results
)
# Use with specific parameters for this search
results = serp_tool.invoke(
{
"query": "best electric vehicles",
"country": "de", # Get results as if searching from Germany
"language": "de", # Get results in German
"search_type": "shop", # Get shopping results
"device_type": "mobile", # Simulate a mobile device
"results_count": 15,
}
)
print(results)
```
```python Use within an agent theme={null}
from langchain_brightdata import BrightDataSERP
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import create_react_agent
# Initialize the LLM
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key="your-api-key")
# Initialize the Bright Data SERP tool
serp_tool = BrightDataSERP(
bright_data_api_key="your-api-key",
search_engine="google",
country="us",
language="en",
results_count=10,
parse_results=True,
)
# Create the agent
agent = create_react_agent(llm, [serp_tool])
# Provide a user query
user_input = "Search for 'best electric vehicles' shopping results in Germany in German using mobile."
# Stream the agent's output step-by-step
for step in agent.stream(
{"messages": user_input},
stream_mode="values",
):
step["messages"][-1].pretty_print()
```
**API reference**: [Unlocker API Documentation](https://docs.brightdata.com/scraping-automation/web-unlocker/introduction)
Access any public website, even if it’s bot-protected or geo-restricted.
```python Basic Usage theme={null}
from langchain_brightdata import BrightDataUnlocker
# Initialize the tool
unlocker_tool = BrightDataUnlocker(
bright_data_api_key="your-api-key" # Optional if set in environment variables
)
# Access a webpage
result = unlocker_tool.invoke("https://example.com")
print(result)
```
```python Advanced Usage with Parameters theme={null}
from langchain_brightdata import BrightDataUnlocker
unlocker_tool = BrightDataUnlocker(
bright_data_api_key="your-api-key",
)
# Access a webpage with specific parameters
result = unlocker_tool.invoke(
{
"url": "https://example.com/region-restricted-content",
"country": "gb", # Access as if from Great Britain
"data_format": "html", # Get content in markdown format
"zone": "unlocker", # Use the unlocker zone
}
)
print(result)
```
```python Use within an agent theme={null}
from langchain_brightdata import BrightDataUnlocker
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import create_react_agent
# Initialize the LLM
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key="your-api-key")
# Initialize the tool
bright_data_tool = BrightDataUnlocker(bright_data_api_key="your-api-key")
# Create the agent
agent = create_react_agent(llm, [bright_data_tool])
# Input URLs or prompt
user_input = "Get the content from https://example.com/region-restricted-page - access it from GB"
# Stream the agent's output step by step
for step in agent.stream(
{"messages": user_input},
stream_mode="values",
):
step["messages"][-1].pretty_print()
```
**API reference**: [Scrapers Documentation](https://docs.brightdata.com/datasets/scrapers/scrapers-library/overview)
Extract structured data from over 100 supported domains like Amazon, LinkedIn, and more.
```python Basic Usage theme={null}
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)
```
```python Advanced Usage with Parameters theme={null}
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)
```
```python Use within an agent theme={null}
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-2.0-flash", google_api_key="your-api-key")
# Initialize the Bright Data Scrapers 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()
```