Python Guide Builds AI Educational Agent With 2 Key Libraries via OpenRouter API

3 articles · Updated · Towards Data Science · May 24

A beginner-focused tutorial walks readers through building a Python AI educational agent, from installing Python and PyCharm to creating main.py and a .env file.
Two core packages drive the project: openai for model requests and python-dotenv for loading an API key stored in the environment file.
OpenRouter supplies the API access and model endpoint, with the guide using the free baidu/cobuddy:free model through base_url="https://openrouter.ai/api/v1".
A while True chat loop keeps the agent running, takes user prompts, shows a "Thinking..." status, sends system and user messages, and prints response.choices[0].message.content.
The tutorial frames the project as an entry-level example of how AI agents differ from basic chatbots by using tools, memory and autonomous task handling, while warning that free models can respond slowly.

As tutorials make building AI agents easy, what hidden complexities await beginners in real-world applications?

With Chinese models now dominating AI traffic, how will this global power shift impact the future of AI development?

If anyone can now build an autonomous AI, who becomes responsible when these agents inevitably make mistakes?