Three Python classes, an OpenRouter key, and gpt-4.1-mini give the agent a calculator, a stub web search, and conversation recall that survives restarts. The post, Give Your Python AI Agent Persistent Memory with One JSON File, appeared first on Towards Data Science. AI agents differ from chatbots by processing information and aiding decision-making.
Most chatbots work one message at a time, but agents can remember past conversations, decide when to use tools, and use results for final responses. This article builds a simple Python agent that stores past conversations in a JSON file, connects to a language model via API, and writes functions the agent can call to perform tasks. The implementation uses a JSON file for memory, integrates with a language model through an API, and provides a calculator and simple web-search tool.
The agent follows a basic flow of memory storage, tool use, and decision-making. Setting up the project involves creating a Python file in an IDE and installing required libraries like OpenAI and python-dotenv. The os module checks for existing memory files and reads API keys from environment variables.
The json module stores data in JSON format, while the datetime module records timestamps for conversation messages. The load_dotenv function reads variables from a .env file. Building the AI agent requires creating Python classes for memory, tools, and decision-making, then integrating them with the language model to enable persistent conversation recall across restarts.
Source: Towards Data Science · Summarized by HeadlinesBriefing