doomL/langchain-langgraph-tutorial

The tutorials progressively build from LangChain fundamentals through LangGraph's state-machine graph architecture, covering agent types (ReAct, zero-shot, plan-and-execute), memory systems, and RAG implementations. It demonstrates practical patterns like integrating Pydantic for structured outputs, combining LangChain components within LangGraph workflows, and implementing production concerns including rate limiting, cost optimization, and monitoring. Real-world applications span content moderation, customer support automation, and semantic search pipelines, all accelerated by Groq's high-performance inference.

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