Quick Takeaways
- Build a simple AI agent in Python that combines a language model, persistent memory, and tools.
- Remember past chats: save messages with timestamps in a JSON file and recall the five most recent.
- Use tools when needed: the model can request a calculator or a basic sample web search to help answer.
- The takeaway: memory and tools make an agent more useful than a chatbot—but the search is simulated and can be upgraded with a real API.
Build the Agent in Three Parts
AI agents can do more than answer one question at a time. They can use past context, call tools and help people make decisions. Still, they do not replace expert judgment, especially in areas such as health care.
A simple Python project shows how the pieces fit. First, a memory class saves messages and timestamps in a JSON file. Next, a tools class offers a calculator and a sample search function. Finally, an agent class sends prompts to a language model, checks whether it should call a tool, and returns an answer.
Memory and Tools Add Useful Context
The agent recalls recent messages, so a user may not need to repeat details in every exchange. For example, it could remember a child’s age when a parent asks a follow-up question about phonics. The JSON file also preserves the conversation after the program closes.
Tools extend the agent beyond text generation. A calculator can handle arithmetic, while the search example returns results from a small, fixed dictionary. That distinction matters: The sample search does not reach the live web. A real search requires an external service.
Start Small, Then Set Limits
This design offers a clear starting point for learning how agents work. Developers can add more tools or improve how the system stores and retrieves information. However, a JSON file and a short memory window will not suit every project. They can also raise privacy concerns when conversations contain personal details.
The sample calculator uses Python’s eval() function, which can run code beyond basic math. Developers should replace it with a safer method before accepting untrusted input. Likewise, an agent can make mistakes, even when it recalls context or uses a tool. Build carefully, check its outputs and treat it as a helpful assistant—not an authority.
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