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AI Agents in LangGraph

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Multi-Agent Platforms & Frameworks
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Basic Information

This repository hosts course material for 'AI Agents in LangGraph', a deeplearning.ai short course that teaches how to build and enhance AI agents using the LangGraph extension of LangChain. It is intended as a learning resource that walks students through designing flow-based agent applications, understanding how responsibilities are divided between an LLM and surrounding code, and rebuilding agents using LangGraph components. The course covers topics such as agentic search, persistence for state management, human-in-the-loop integration, and a practical project to implement an essay writing agent. The README emphasizes hands-on experience and practical workflows and identifies instructors Harrison Chase and Rotem Weiss as the course leaders.

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App URL
https://github.com/ksm26/AI-Agents-in-LangGraph
Github Repository
https://github.com/ksm26/AI-Agents-in-LangGraph/blob/main/README.md

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App Details

Features
The repository emphasizes several concrete features of LangGraph-based agent development. It shows how to build an agent from scratch with Python and an LLM and then reimplement the same agent using LangGraph components to illustrate component composition. It introduces agentic search to retrieve multiple, structured answers and describes techniques for persistence to maintain state across threads and conversation switches. The materials cover human-in-the-loop designs for validation and safety and include an essay writing agent example that models a researcher workflow. The README highlights instructor-led lessons and visual diagrams illustrating key concepts.
Use Cases
This course repository is helpful for practitioners who want a practical introduction to building robust, flow-based AI agents with LangGraph. It teaches how to separate LLM reasoning from programmatic logic, how to use LangGraph components to construct and debug agent flows, and how to add agentic search to augment built-in knowledge. Guidance on persistence helps implement stateful conversations, thread management, and session reloads. Human-in-the-loop patterns are presented to improve accuracy and reliability. The essay writing agent example demonstrates applying these techniques to a real workflow, aiming to improve productivity and the quality of agent outputs. Overall it provides structured, instructor-led materials for hands-on learning.

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