@DanKornas: Agent tutorials are noisy. This repo gives you the path. LangGraph 101 is a hands-on tutorial repo for learning LangCha…

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Summary

LangGraph 101 is an open-source tutorial repo for learning LangChain, LangGraph, and Deep Agents through notebooks and runnable agent examples, organized into fundamental and production-patterns tracks.

Agent tutorials are noisy. This repo gives you the path. LangGraph 101 is a hands-on tutorial repo for learning LangChain, LangGraph, and Deep Agents through notebooks and runnable agent examples. It helps you move from basic agent building to deeper patterns by organizing the material into a 101 fundamentals track and a 201 production-patterns track. Key features: • Two learning tracks – start with LangChain/LangGraph fundamentals, then move into multi-agent systems, deep agents, and production workflows • Notebook-first walkthroughs – build agents with models, tools, memory, streaming, middleware, human-in-the-loop, and guardrails • Concrete agent examples – includes email triage, multi-agent music store, researcher, and DeepAgents implementations • LangGraph Studio workflow – run agents locally with `langgraph dev` for a local API server, Studio UI, and hot reloading • Provider setup notes – centralized model config with guidance for OpenAI, Azure OpenAI, AWS Bedrock, and Google Vertex AI It’s open-source (MIT license). Link in the reply
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Cached at: 06/05/26, 01:14 PM

Agent tutorials are noisy. This repo gives you the path.

LangGraph 101 is a hands-on tutorial repo for learning LangChain, LangGraph, and Deep Agents through notebooks and runnable agent examples.

It helps you move from basic agent building to deeper patterns by organizing the material into a 101 fundamentals track and a 201 production-patterns track.

Key features:

• Two learning tracks – start with LangChain/LangGraph fundamentals, then move into multi-agent systems, deep agents, and production workflows • Notebook-first walkthroughs – build agents with models, tools, memory, streaming, middleware, human-in-the-loop, and guardrails • Concrete agent examples – includes email triage, multi-agent music store, researcher, and DeepAgents implementations • LangGraph Studio workflow – run agents locally with langgraph dev for a local API server, Studio UI, and hot reloading • Provider setup notes – centralized model config with guidance for OpenAI, Azure OpenAI, AWS Bedrock, and Google Vertex AI

It’s open-source (MIT license).

Link in the reply

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