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Strands Agents is an open-source Python library that provides a unified toolkit for developing autonomous AI agents. It bridges the gap between large language models and practical applications by offering ready-to-use tools for file operations, system execution, API interactions, mathematical operations, and more.

Steps to Get Started

Prerequisites

Bright Data API Key
Python 3.10+

Installation

Set up the strands-agents-tools package by either using the quick pip install or the full development installation with virtual environment and pre-commit hooks.

Quick Install

pip install strands-agents-tools

Set the environment variable

Set your Bright Data API key and unlocker zone as an environment variables:

export BRIGHT_DATA_API_KEY="your_api_key_here"
export BRIGHT_DATA_ZONE="your_unlocker_zone_here"

Bright Data Usage Examples

Using bright data tools within an agent
Direct tool invocation

from strands import Agent
from strands.models.litellm import LiteLLMModel
import strands_tools.bright_data as bright_data
import os
from dotenv import load_dotenv


load_dotenv()


def main():
load_dotenv()
print("🔍 Testing Bright Data Tool with OpenAI GPT-4o via LiteLLM")
print("=" * 50)

# Configure OpenAI model via LiteLLM
openai_model = LiteLLMModel(
    client_args={
        "api_key": os.getenv("OPENAI_API_KEY"), 
    },
    model_id="openai/gpt-4o",
)

# Create agent with LiteLLM model and bright_data tool
agent = Agent(
    model=openai_model,
    tools=[bright_data],
    system_prompt="you are a helpful Web Search assistant, whenever user asks you to search the web you will use avilable tools, always set zone name to 'unlocker'"
)

# Test the bright_data tool with a relevant query
print("Testing web scraping...")
result = agent("Please scrape the content from https://example.com and return it as markdown")
print(f'\n\nScraping Result:\n{result}')

print("\n" + "=" * 50)
print("Testing web search...")
result2 = agent("Please search Google for 'Python programming tutorials")
print(f'\n\nSearch Result:\n{result2}')


if __name__ == "__main__":
main()

from strands import Agent
from strands.models.litellm import LiteLLMModel
import strands_tools.bright_data as bright_data
import os
from dotenv import load_dotenv


load_dotenv()


def main():
  load_dotenv()
  print("🔍 Testing Bright Data Tool with OpenAI GPT-4o via LiteLLM")
  print("=" * 50)
  
  # Configure OpenAI model via LiteLLM
  openai_model = LiteLLMModel(
      client_args={
          "api_key": os.getenv("OPENAI_API_KEY"), 
      },
      model_id="openai/gpt-4o",
  )
  
  # Create agent with LiteLLM model and bright_data tool
  agent = Agent(
      model=openai_model,
      tools=[bright_data],
      system_prompt="you are a helpful Web Search assistant, whenever user asks you to search the web you will use avilable tools, always set zone name to 'unblocker'"
  )
  
  # Test the bright_data tool with a relevant query
  print("Testing web scraping...")
  result = agent.tool.bright_data(
  action="scrape_as_markdown",
  url="https://example.com",
  zone="unblocker"
)
  print(f'\n\nScraping Result:\n{result}')
  
  print("\n" + "=" * 50)
  print("Testing web search...")
  result2 = agent.tool.bright_data(
  action="search_engine",
  query="Python programming tutorials",
  engine="google",
  zone="unblocker"
  )
  print(f'\n\nSearch Result:\n{result2}')


  result5 = agent.tool.bright_data(
      action="web_data_feed",
      source_type="amazon_product",
      url="https://www.amazon.com/dp/B0D2Q9397Y?th=1"
  )
  print(f'\n\nAmazon Product Data:\n{result5}')


if __name__ == "__main__":
  main()

Building an AI startup?

​Steps to Get Started

Steps to Get Started