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  "description": "LangGraph is a versatile Python library designed for stateful, cyclic, and multi-actor Large Language Model (LLM) applications. This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph.",
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LangGraph is a Python library that helps you build stateful, cyclic, and multi-actor workflows for Large Language Model (LLM) applications, expanding upon LangChain’s capabilities.
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LangGraph builds on LangChain by providing tools to create complex workflows with state management, conditional logic, and cycles, making it ideal for handling real-world LLM application complexities.
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Yes, you can use LangGraph without LangChain, although they’re highly compatible and often used together to leverage LangChain’s foundational structures with LangGraph’s advanced capabilities.
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A state graph in LangGraph is a directed graph representation of LLM workflows, where nodes represent actions like function calls, and edges dictate the workflow’s sequence, enabling conditional and cyclic operations.
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You create conditional workflows in LangGraph by using conditional edges, which allow you to define different paths based on the evaluation of conditions within your graph’s state.
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You create a conditional edge by defining a function that determines the next node based on the current state. You then register this function with workflow.add_conditional_edges() to conditionally control the graph’s flow.
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A LangGraph agent consists of an LLM for decision-making and a set of tools for performing tasks. The agent’s architecture is often cyclic and conditional, allowing it to autonomously process and act on input data.
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