Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Learn how to build hierarchical multi-agent systems where orchestrators manage sub-orchestrators, each leading specialized teams, enabling recursive task decomposition for large-scale workflows.
Implement competitive multi-agent patterns where agents debate, critique, and red-team each other's outputs to improve accuracy, catch errors, and stress-test AI-generated content before it reaches users.
Build a complete customer support multi-agent system with four specialized agents — a router, FAQ handler, billing specialist, and escalation agent — using the OpenAI Agents SDK with shared context and graceful fallbacks.
Learn systematic approaches for debugging multi-agent systems, including structured logging, trace visualization, identifying bottlenecks in agent chains, and replay testing to reproduce and fix failures.
Learn the three core strategies for managing conversation history in AI agents — sliding windows, summary-based compression, and compaction — to stay within context window limits while preserving critical information.
Explore four approaches to building agent memory — in-memory lists, file-based storage, relational databases, and vector stores — with practical Python implementations and guidance on when to use each.
Learn how to build a semantic memory system for AI agents using text embeddings, similarity thresholds, and memory consolidation to retrieve the most relevant facts from past interactions.
Discover how to implement episodic memory for AI agents — storing complete interaction episodes, retrieving similar past experiences, and creating feedback loops that improve agent performance over time.
