Building Multi-Agent Systems with Python: Orchestration Patterns That Work

Short summary

Learn how to build autonomous AI agents that plan multi-step workflows, use tools like APIs and code execution, and maintain memory across sessions. This tutorial covers the core components (reasoning engine, tools, memory, planning loop) with a working Python example using the ReAct pattern with OpenAI's API. Best practices: start with 3-5 well-defined tools on narrow problems and implement graceful failure handling.

• •Autonomous agents need an LLM, tools, memory, and a planning loop to perceive, decide, and act independently
• •This tutorial provides working Python code using OpenAI's API and the ReAct pattern for multi-step agent orchestration
• •Best practices: start with 3-5 tools on narrow problems, graceful failure handling, and layered memory (working + episodic + semantic)

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