LangGraph

LangGraph is a robust framework for developing stateful, multi-actor applications leveraging Large Language Models (LLMs). It provides a graph-based execution model that enables structured, dynamic, and scalable workflows, making it a powerful tool for building multi-agent systems and conversational AI applications. LangGraph is designed to overcome the stateless nature of traditional LLM pipelines, allowing developers to persist state, control execution flow, and integrate human feedback seamlessly. Its flexibility makes it suitable for a variety of use cases, from chatbot orchestration to complex decision-making systems.

Core Features of LangGraph

  • Graph-Based Execution Model: LangGraph structures workflows as a directed graph, where nodes represent functions (or agents) and edges define the flow of execution. This allows for parallel execution, conditional branching, and dynamic state updates. It provides a structured alternative to unstructured LLM chaining, making debugging and monitoring more intuitive.

  • Stateful Processing with Persistence: Unlike traditional LLM applications that process inputs independently, LangGraph maintains state across multiple interactions. It supports long-running tasks, memory retention, and interruption-resumption workflows, ensuring continuity in interactions and decision-making processes.

  • Multi-Agent Collaboration: LangGraph makes it easy to design multi-agent environments, where multiple autonomous agents communicate and collaborate to complete complex tasks. It supports event-driven execution, allowing agents to operate asynchronously, respond to real-time events, and coordinate decisions dynamically.

  • Human-in-the-Loop Support:One of LangGraph’s key strengths is its built-in support for human intervention. Workflows can pause execution at critical decision points, request human validation or input, and then resume execution, making it ideal for applications requiring human oversight, such as legal document review, customer service escalation, or AI-assisted research.

Hands-On labs

Building a Graph with LangGraph

Before implementing something concrete, it is important to practice conceptualizing the graph flow and drawing it yourself. This exercise will help you understand the structure of the graph and the relationships between different nodes.

LangGraph Studio (MacOS Only)

LangGraph Studio is a visual development environment designed to help users build, test, and deploy stateful, multi-step AI workflows using LangGraph, an open-source framework built on top of LangChain. It allows users to create complex agent behaviors and conversational flows with memory and branching logic, using an easy-to-use visual interface. LangGraph Studio is especially useful for prototyping and debugging AI agents that require multiple steps, tools, and decision-making paths.

You need to install Docker and the latest .dmg file from the LangGraph Studio repository to run the LangGraph Studio. Currently, LangGraph Studio is only available for MacOS. You also need to login LangSmith or need to LangSmith API key.

  • https://github.com/langchain-ai/langgraph-studio

Distributed by an MIT license. This hands-on lab was developed by Microsoft AI GBB (Global Black Belt).