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Learn LangGraph 1.0 concepts, workflow design, and practical application patterns.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "dive-into-langgraph" yet — see the docs or source repo.
Explain the core concepts of LangGraph 1.0, including State, Node, Edge, and workflow execution, and provide a minimal runnable example.
A clear beginner-friendly explanation with a basic LangGraph code example and execution flow walkthrough.
Using LangGraph 1.0, design an agent workflow with conditional branching and loops for question analysis, tool calling, and result summarization, and provide a Python example.
A complete LangGraph workflow design with node descriptions, flow structure, and Python implementation code.
Compare LangChain and LangGraph in terms of use cases, architectural differences, and development style, and explain when to choose LangGraph 1.0.
A developer-focused comparison that clarifies when LangGraph is the better choice for multi-step agents and controlled workflows.
Invoke deployed LLMGraph workflows as MCP tools for chat, RAG, and automation.
Search LangGraph docs semantically and get context-aware answers fast.
Use search, fetching, calculator, and notes tools with LangGraph agents.
Share semantic memory, reviews, and classifications across AI assistants.
Provides extensible MCP access to LangGraph documentation and resources.
Connect a local GraphRAG knowledge base for private offline document retrieval.