Tutorial: How to Build an AI Agent That Retains Memory Across Sessions

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A developer tutorial on DEV Community explains why AI agents lose context between sessions and how to fix it using a concept called Loop Engineering. The article outlines a four-stage evolution framework — Prompt, Context, Harness, and Loop Engineering — where each stage addresses the shortcomings of the previous one. The core problem is that most agents are built without persistent state or memory, so they effectively reset after each session ends. The tutorial provides a fully runnable Python script of roughly 50 lines that saves agent state and memory to local JSON files, allowing it to resume tasks after a restart. Readers need Python 3.10 or higher, an OpenAI API key, and the openai library to run the example.

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