Build a Cost-Effective AI Search Agent Using OpenAI Agents SDK and Oxlabs WebScraper API

by

Build a Cost-Effective AI Search Agent Using OpenAI Agents SDK and Oxlabs WebScraper API

The cost of AI-powered search can quickly add up, especially when using OpenAI’s models for web data retrieval. A few weeks ago, an in-house AI search agent powered by OpenAI was delivering impressive results until the bill arrived. The solution? Replacing direct OpenAI calls with Oxlabs WebScraper API, delivering the same real-time web data for a fraction of the cost while eliminating context window limitations.

This practical integration combines two powerful tools: OpenAI Agents SDK and Oxlabs WebScraper API to create a more efficient and cost-effective solution.

Understanding OpenAI Agents SDK

The Agents SDK developed by OpenAI has been generating significant interest in tech communities. This toolkit enables developers to build autonomous, AI-powered agents capable of using tools, maintaining memory, and completing complex multi-step reasoning tasks. OpenAI also provides real-time monitoring and performance evaluation features to track and improve your agents.

Integration Overview

This implementation creates a basic AI agent that can scrape any provided web source using Oxlabs MCP server and answer user questions based on that data. While direct API calls are possible using function tool decorators, the MCP server provides a standardized framework for AI tools to access fresh web data, making the implementation faster and easier.

Required Tools

For this project, you’ll need just two key components:

  • Oxlabs WebScraper API (free trial available by registering on their dashboard)
  • OpenAI API

If you prefer not to use OpenAI models, you can utilize the Light LLM extension and connect any other supported LLM provider.

Implementation Steps

  1. Install the UV package for communicating with the Oxlabs MCP server. On macOS, you can use HomeBrew for installation.
  2. Verify UV installation by checking the version output.
  3. Create a new Python project and install the Agents SDK.
  4. Set up credentials by creating a .tml file in your project directory to store your WebScraper API credentials and OpenAI API key.
  5. Create a Python script that imports the necessary libraries.
  6. Initialize an MCP server connection using an Async context manager, with the UVX command serving as a shortened alias for the UV run tool command.
  7. Add a timeout parameter to override the default 5-second timeout.
  8. Create an AI agent that uses the MCP server tools to scrape web content and answer questions.
  9. Implement a simple CLI for communicating with the agent.

After executing the script, you can access the complete answer from the final output attribute. The system logs show when the agent calls the MCP server and when the server successfully processes the request.

Benefits of This Integration

This integration serves as an effective blueprint for building agent workflows with cost-effective, real-time access to web data. The approach offers several advantages:

  • Significantly reduced costs compared to direct OpenAI usage
  • No context window limitations cutting off answers
  • Real-time access to web data
  • Flexible implementation that can be adapted to various use cases

Whether you’re building a research assistant, content aggregator, or competitive analysis tool, this integration provides a solid foundation for web-data powered AI agents without breaking the bank.

Leave a Comment