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  • Book Overview & Buying Building Agents with OpenAI  Agents SDK
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Building Agents with OpenAI  Agents SDK

Building Agents with OpenAI Agents SDK

By : Habib
4 (1)
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Building Agents with OpenAI  Agents SDK

Building Agents with OpenAI Agents SDK

4 (1)
By: Habib

Overview of this book

Everyone’s talking about AI agents, but how do you build one that works in the real world? Not a toy demo, but an agent that solves real problems, saves time, and integrates into workflows. With vague frameworks, fragmented tooling, and endless hype, most developers are left without a clear path. The hardest part isn’t technical; it is knowing where to start. This book gives you that starting point. It’s a complete guide to building intelligent AI agents and agentic systems using the official OpenAI Agents SDK. It begins by grounding you in the core concepts, design principles, and architecture of AI agents, how they differ from other traditional systems, their advantages, and why that matters. Through practical step-by-step projects, you’ll master every feature of the SDK—tools, memory, RAG, multi-agent orchestration, tracing, handoffs, and more—while contributing to an end-to-end agent system that grows in complexity. Projects include a custom support agent, invoice and inventory assistant, health advisor, sales trainer, and data analyst, giving you production-ready skills. By the end, you’ll know how to design, build, and deploy agentic systems that interact with APIs, query databases, hand off to external systems, and drive meaningful outcomes. You won’t just understand AI agents; you’ll be ready to ship them.
Table of Contents (15 chapters)
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1
Part 1: AI Agents
5
Part 2: OpenAI Agents SDK
11
Part 3: Build AI Agents
13
Other Books You May Enjoy
14
Index

Summary

In this chapter, we equipped agents with two useful skill sets to make them more intelligent: memory and knowledge.

We started by implementing short-term memory, manually tracking message history, and using the Sessions class to support multi-turn conversations. We addressed the challenge of growing context windows by introducing the sliding message window and message summarization techniques.

Next, we explored long-term memory. We made agent memory persistent across sessions using SQLiteSession with a file-backed database, and introduced structured memory recall through tool-calling. This approach enabled agents to store only the most relevant facts, reducing memory bloat while increasing recall precision.

We then looked at two distinct types of model knowledge:

  • Training knowledge, which is the static information “baked into” the model during its initial training.
  • Retrieved knowledge, which is the dynamic, context-specific data fetched...
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Building Agents with OpenAI  Agents SDK
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