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baem1n/langchain-langgraph-deepagents-notebooks

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LangChain · LangGraph · Deep Agents Notebooks

Overview

From introductory concepts to advanced frameworks, operations and evaluations, practical agent examples, and a provider integrated catalog, it is provided together with Jupyter Notebook, Typst Handbook, and Lecture Deck deliverables. - en/README.md - book/agent-handbook.pdf - en/book/agent-handbook-en.pdf - docs/releases/v1.2.0.md - decks/ — Location for storing lecture HTML/PDF/PPTX/Script deliverables This repository is for learning "how to actually create and operate LLM Agents" step by step. It's not just a collection of simple API call examples; it ties the following flow into a single learning path. 1. — Messages, prompts, tool calling, ReAct, memory 2. — create_agent, tools, middleware, MCP, guardrails, streaming 3. — StateGraph, persistence, interrupt, subgraph, durable execution 4. — backend, subagent, skill, memory, sandbox, async subagent 5. — tracing, dataset, evaluation, prompt hub, monitoring, Agent Evals 6. — RAG, SQL, data analysis, ML, deep research, multimodal PDF RAG 7.

Overview (machine translated)

LangChain · LangGraph · Deep Agents · An LLM Agent training repository where you can practice LangSmith in Korean to the end

From introductory concepts to advanced frameworks, operations and evaluation, practical agent examples, and a provider integrated catalog, Jupyter Notebook, Typst Handbook, and Lecture Deck deliverables are all provided together.

  • English README: en/README.md
  • Korean Handbook: book/agent-handbook.pdf
  • English Handbook: en/book/agent-handbook-en.pdf
  • Latest release note: docs/releases/v1.3.2.md
  • Lecture decks: decks/ — Location for storing HTML/PDF/PPTX/transcript deliverables for lectures. PPTX is an image-based presentation file tailored to the existing distribution format, and the editable source is the HTML/Python source of decks/source/.

What is this repository?

This repository is a training notebook curriculum designed to teach step-by-step how to actually create and operate LLM Agents. It’s not just a collection of simple API call examples; it ties the following flow into a single learning path.

  1. LLM & Agent Basics — Messages, prompts, tool calling, ReAct, memory
  2. LangChain v1 — create_agent, tools, middleware, MCP, guardrails, streaming
  3. LangGraph v1 — StateGraph, persistence, interrupt, subgraph, durable execution
  4. Deep Agents SDK — backend, subagent, skill, memory, sandbox, async subagent
  5. LangSmith Operation — Tracing, dataset, evaluation, prompt hub, monitoring, Agent Evals
  6. Practical Examples — RAG, SQL, data analysis, ML, deep research, multimodal PDF RAG

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Install

npx skillfish add baem1n/langchain-langgraph-deepagents-notebooks
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