An introduction to LangGraph

What is LangGraph?

LangGraph is a Python-based framework that enables developers to create sophisticated, multi-step workflows for AI models. It focuses on building robust, graph-like structures that can handle a sequence of tasks and decision-making paths. These workflows are typically used in large-scale language models, automating processes that involve understanding and generating human-like text. LangGraph is particularly useful when a task requires multiple, distinct steps, where each step could either be a model's prediction or a procedural action.

State graph

A shared data structure that represents the current snapshot of your application. It can be any Python type but is typically a?TypedDict?Or Pydantic?BaseModel.

Node

Python functions that encode the logic of your agents. They receive the current?State?as input, perform some computation or side-effect, and return an updated?State.

Edges

Python functions that determine which?Node?to execute next based on the current?State. They can be conditional branches or fixed transitions.

Simple Example:

We want to build an AI-powered text processor system using LangGraph. Here's a basic structure of how it might look:

Use Case: Process the text to make it meaningful concerning the context

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Simple LangGraph

Little snippets that will give you quick glance.

How the graph is created with a node and edges

graph.stream() is one of the runnable in LangGraph to invoke the graph.

• stream: stream back chunks of the response
• invoke: call the chain on an input
• batch: call the chain on a list of inputs

Sample response

Input: te nme may be Ava and capable enough to do gymnastics. $tjgsl Need good guidance.

Output: Ava may be capable enough to do gymnastics and needs good guidance.

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