-
How to Add Memory to an AI Agent Using Python
1. What Is Memory in an AI Agent? Memory allows an AI Agent to remember information from previous interactions and use that information in future conversations. Without memory, an AI Agent generally treats each interaction as a new conversation unless previous context is explicitly provided. For example: Later: This happens because the agent does not have access to the previous conversation context. With memory: Later: So, memory allows an AI Agent to retain and reuse relevant information across interactions. 2. Why Do We Need Memory in AI Agents? Explain the problems without memory: 3. How Does Memory Work in an AI Agent? Explain the basic flow: User → Agent →…
-
How to Build AI Agents Using OpenAI Agent SDK with Python
What is OpenAI Agents SDK? The OpenAI Agents SDK is a lightweight Python framework that helps developers build AI agents and Agentic AI applications. In simple words, an AI agent is a program that can understand a task, decide what to do, use tools, and complete the task with less human intervention. The Agents SDK is designed to make agent development simple and easy to understand. It provides a small number of important building blocks instead of adding many complex features. Why use the Agents SDK One of the main benefits of the Agents SDK is its simplicity. Developers do not need to learn a large number of abstractions before…
-
What is RAG?
RAG stands for Retrieval-Augmented Generation. RAG allows an LLM to search relevant information from an external knowledge source and use that information to generate an answer. Without RAG: With RAG: 1. Why do we need RAG? LLMs have several limitations. An LLM normally doesn’t know your company’s internal documents.Imagine you have an automotive company’s internal documents: Now a user asks: “What is the warranty period for a battery replacement?” A general LLM may not know your company’s specific warranty policy. Even if you provide the model with a huge prompt containing all documents, you have problems with: RAG solves this by retrieving only the relevant information. 2. RAG has two…