Python search agent: connect the model and tool around one question

Set up the project and understand its components

The February 2025 tutorial describes a 38-line Python agent using LangChain, the OpenAI API and DuckDuckGo search. Its goal is a beginner-friendly internet-search assistant. The published description explains the components but does not reproduce the 38 lines or give a repository link, so no invented script is attributed to the lesson.

Core technologies begin at 02:06, the API key at 04:22 and the framework and environment at 10:33. A virtual environment keeps the project’s Python dependencies together, while the API key gives the selected client access to the model service. They solve different setup needs.

1. Follow the recorded project and environment setup.
2. Install the libraries through the route demonstrated in the lesson.
3. Keep the API key out of shared code and visible output.

Trace one question through the model and search tool

The agent uses search to gather information and returns an answer to the user. Keep the model configuration and available tool distinct so you can see whether a response used the intended information or simply sounded plausible.

The initial run and output analysis begin at 16:23. Use a small question whose answer and sources you can inspect. Read the returned result alongside the search evidence, noting missing information or a failed tool call before treating the program as working.

1. Run one question through the initial script.
2. Inspect errors, search output and the final answer.
3. Compare the answer with the retrieved material before changing more code.

Add interaction without losing the working baseline

The interactive chat and future-enhancements section begins at 19:42. Repeat the original question after adding the interface so you can distinguish an interaction problem from a model or search problem. A chat loop makes the program easier to use, but does not itself establish reliable memory or better factual accuracy.

The tutorial is an early-2025 framework example. Library APIs, model access and costs may have changed since then; keep the demonstrated route and your installed versions identifiable while following it. The useful outcome is an understandable program with a checkable tool-to-answer path, not the assumption that 38 lines solve every agent task.

The takeaway

Understand the path from input to search to answer, verify one run and preserve it when adding interactive chat.