How to Build a Research AI Agent with Vanilla Python in Less Than 2 Hours

What if I told you that you could build one yourself using vanilla Python in less than two hours?

In this blog post, we’ll explore how to create a powerful research AI agent using minimal dependencies and flexible tools. Let’s dive in!

Why Build a Custom Research Assistant?

Research and summarization are among the most common use cases for AI agents in industry. While there are existing solutions like LangChain’s tutorial for building a fully local research assistant, we’re going to take a different approach. Our goal is to recreate similar functionality while minimizing dependencies and using vanilla Python as much as possible.

The Benefits of Minimizing Dependencies

By reducing the number of third-party libraries and tools, we make our research assistant:

1. Less vulnerable to changes in external packages
2. Easier to maintain in the long term
3. More flexible and customizable to our specific needs

Core Components of Our Research AI Agent

Our research assistant will consist of several key components:

1. Search query generation
2. Web search functionality