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Nodes are the "doers" of your LangGraph application—the functions that hold all the logic. But to build truly professional, production-ready agents, you need to know how these nodes access everything from the agent's memory to runtime configurations and tracing data.
In this essential tutorial, we dive deep into Nodes in LangGraph. We'll show you exactly what a node is, the critical arguments it can accept, and how to define a custom node function that leverages the agent's shared State, the execution Config, and the runtime Context. This is the key to decoupling your node logic from the rest of your graph, enabling better testing, debugging, and advanced features like human-in-the-loop.

In this video, you will learn:
What Nodes are: the fundamental unit of computation in a LangGraph workflow.
The three core arguments a custom node function can accept and why they matter:
state: Accessing and updating the agent's shared memory.
config: Reading runtime configurations like thread_id and tracing tags.
runtime: Accessing advanced context like the stream writer or execution store.
How to define a custom node in Python and use the return value to update the graph state.
A practical demo creating nodes that consume state, config, and runtime context.
The best practices for keeping your node logic clean, reusable, and testable.

Mastering node arguments is critical for any developer who wants to build robust, observable, and multi-tenant AI agents with LangGraph.

🔥 Ready to master the core logic of LangGraph? Dive into custom nodes and their arguments!
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