A comprehensive learning path and practical guide for Generative AI development with hands-on LangChain implementations and detailed notes.

Practical step-by-step LangChain guides

This project offers hands-on examples for LangChain and LangGraph, complementing their textbooks with practical guides on workflows, tools, and agentic RAG techniques.

The Meghkosh Classroom Series – Agentic Cohort is a guided, follow-along codebase that teaches practical agentic AI development using LangChain, LangGraph, and Crew. Participants build step-by-step from fundamentals to a complete multi-agent workflow, culminating in a hands-on deployment within Meghkosh Azure Labs for real-world experience.

This series focuses on exploring LangChain and generative AI, providing practical guides and tutorials for building advanced AI applications. Each part covers key concepts, tools, and techniques to help you leverage LangChain for creating powerful, data-driven solutions. Ideal for developers looking to dive into AI and NLP development.

Learning LangChain with practical examples, theory notes, and code snippets. This repo is a complete guide for beginners to understand and build AI-powered apps using LangChain.

Practical guide to LangChain output parsers. Convert raw LLM text into structured, validated data (JSON, lists, tables) with schemas, robust error handling, and retries. Build reliable pipelines that downstream tools can trust and automate.

A comprehensive, hands-on tutorial repository for learning and mastering LangChain - the powerful framework for building applications with Large Language Models (LLMs). This codebase provides a structured learning path with practical examples covering everything from basic chat models to advanced AI agents, organized in a progressive curriculum.

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A complete, beginner-to-advanced practical guide to using LangChain with ChromaDB.

This repository provides a complete guide to Generative AI with LangChain, covering both theoretical concepts and practical implementations.

A beginner-friendly practical guide and codebase for learning the fundamentals of LangChain, including chat models, prompt templates, and basic chains.

Official repository for Udemy Course 'LangChain Mastery - The Most Practical Course on Developing Real World AI Apps', featuring complete code examples and assignments. Learn how to build AI-powered applications with LangChain through practical, step-by-step guides.

A comprehensive guide to advanced RAG techniques including reranking, deep memory, and vector store optimization. Includes practical implementations and best practices using LlamaIndex, Langchain, etc..

An end-to-end guide to building robust LLM pipelines with Hugging Face and LangChain. Explores Transformers pipelines, Hugging Face Hub integration, secure token handling, and practical access to both open-source and gated large language models.

A concise guide for using LangChain to build various AI applications. It includes examples for creating chat models, prompt templates, and complex chains, with a focus on Retrieval-Augmented Generation (RAG) and practical applications like trip planning bot with Crew AI.

LangGraph Lightning ⚡ - 基于 LangChain Academy 的深度解读与工程实战指南 | In-depth Guide and Practical Cases based on LangChain Academy

This repository demonstrates the modern Runnable architecture in LangChain using LCEL (LangChain Expression Language). It covers practical implementations of: RunnableSequence RunnableParallel RunnableBranch RunnablePassthrough The project focuses on understanding how LangChain builds execution graphs using composable Runnables.

A practical guide to structuring outputs in LangChain using TypedDict, Pydantic, and JSON Schema.

LangChain-Playground: A hands-on guide for GenerativeAI applications leveraging LangChain and LangGraph to explore agent-based application with practical examples and tutorials.

LangChain Project: A Comprehensive Guide and Practical Exercises for Utilizing Various GPT Applications and Implementing LLM Workflows

A practical, code-first guide to LangChain — self-contained tutorials from prompt templates to production-ready agents.

A hands-on guide to LangChain covering Models, Prompts, Chains, Runnables, RAG pipelines, and Tools with practical Python examples.

A practical guide to transforming raw LLM text into structured data (JSON, Pydantic objects) using various LangChain Output Parsers.

Prompt Engineering using LangChain Course is a practical guide showcasing how to build LLM applications with LangChain, including examples of prompt design, chaining, memory, and tool integration.

A practical guide to choosing between LangChain, CrewAI, and Pydantic for building AI agents. Includes code examples, comparisons, and best practices.

A practical guide to building powerful, multi-step LLM workflows with LangChain, from simple sequences to complex conditional and parallel chains.

A practical guide to getting reliable, structured JSON output from LLMs using LangChain, demonstrating schema enforcement with Pydantic, TypedDict, and JSON Schema.

This Repo provides practical examples and guides for building applications with language models using the LangChain framework. Explore middleware, message handling, streaming, batch processing, and more through organized notebooks and scripts. Ideal for learning, prototyping, and extending LangChain capabilities.

Implementation of "Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems" by Antonio Gullí. This repository explores 21 essential design patterns for building autonomous AI agents, featuring practical code examples using LangChain, LangGraph, and CrewAI.