Guardian is a production-ready AI-powered penetration testing automation CLI tool that leverages Google Gemini and LangChain to orchestrate intelligent, step-by-step penetration testing workflows while maintaining ethical hacking standards.

Step-by-Step Guide to Building a PDF-Chat App using LangChain, OpenAI API & Streamlit

Practical step-by-step LangChain guides

Step-by-step guides for migrating between AI agent frameworks. LangChain to CrewAI, AutoGen to LangGraph, and more. With code examples and gotchas.

An Agentic RAG (Retrieval-Augmented Generation) system powered by LangChain, enabling multi-step reasoning over documents using LLMs, ChromaDB, and Google Drive as a document source.

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.

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In this step-by-step guide, we will cover what is New in Claude 3, how to use Claude 3, why Claude 3 is so much better than GPT-4 and Gemini finally how to use Claude 3 with Langchain

A tool-augmented agent (LangChain, RAG) that functions as a hyper-personalization engine. It autonomously creates adaptive travel itineraries by integrating 5+ real-time APIs for complex, multi-step problem-solving.

ReAct Agent built upon LangChain and LangGraph to make custom and complex multi-step questions by combining retrieval and analyitical tools. Do SQL querying and/or RAG approaches to get the best answer

This is step by step guild to use gemini pro with langchain

Step-by-step LangChain-based LLM implementations and experiments

Step-by-Step Guide: Creating a Multi-PDF RAG Chatbot with Langchain and Streamlit

Build a ChatGPT-Powered PDF Assistant with Langchain and Streamlit | Step-by-Step Tutorial from Youtube

Step-by-step LangChain tutorials covering models, prompts, chains, retrievers, tools, and agents — theory to full implementation.

A Streamlit-based chatbot powered by Google Gemma 2 and LangChain that solves math problems with step-by-step reasoning and retrieves information using Wikipedia.

Langchain-Braintune Integration: A guide to help Python developers integrate the Braintune platform with the Langchain framework for building powerful, language model-driven applications. Includes step-by-step tutorials and examples.

Welcome to the LangChain Guide! This repository serves as a comprehensive resource for anyone looking to dive into the world of LangChain, offering a step-by-step journey from understanding the basics to crafting impactful projects.

This lecture presents a step-by-step guide to building a Python AI project for extracting structured data from PDFs, using OpenAI’s large language models (LLMs), LangChain, ChromaDB, and Docker.

10 Days of LangChain 2026 is a hands-on course that walks you step-by-step through building a fully agentic AI Learning Assistant using LangChain, LangGraph, and modern RAG techniques, from core agent logic to a complete Streamlit app.

A step-by-step guide to understanding and implementing Retrieval-Augmented Generation. This repository contains all the tutorial notebooks for the course, covering fundamental RAG techniques with LangChain and building advanced, agentic workflows with LangGraph.

A beginner-friendly yet industry-aligned repository explaining LangChain prompt templates. Covers PromptTemplate, ChatPromptTemplate, and FewShotChatPromptTemplate with clear concepts, real-world use cases, and step-by-step explanations to help build reliable LLM-powered applications.

A comprehensive repository to learn and implement Retrieval-Augmented Generation (RAG) from scratch using LangChain. It covers the full RAG pipeline including Document Loaders, Text Splitters, Embeddings, Vector Databases, and Retrievers with practical examples and step-by-step explanations.

**LangGraph Models** is a curated collection of modular LLM workflows built using [LangGraph](https://github.com/langchain-ai/langgraph). It features step-by-step examples—from basic agents to advanced multi-agent and RAG systems—designed for stateful, memory-aware, and tool-augmented applications.

Learn LangChain by building modular GenAI agents that think, remember, and evolve. This repo is your launchpad—where logic meets storytelling, and every chain is a step toward clarity, creativity, and cosmic understanding.

his is my own custom-built offline AI bot that lets you chat with PDFs and web pages using **local embeddings** and **local LLMs** like LLaMA 3. I built it step by step using LangChain, FAISS, HuggingFace, and Ollama — without relying on OpenAI or DeepSeek APIs anymore (they just kept failing or costing too much)

This project implements Anthropic's Contextual Retrieval technique using LangGraph and LangChain. Contextual Retrieval dramatically improves the retrieval step in RAG (Retrieval-Augmented Generation) by prepending chunk-specific explanatory context to each chunk before embedding.

An easy step-by-step guide. guide to Code Like a Genuine Prompt Engineer. Learn to seamlessly integrate the LangChain Framework with Vector Database. -First Edition by Amit Sarkar.

step by step guidance of langchain

Langchain: PDF Chat App (GUI) | ChatGPT for Your PDF FILES | Step-by-Step Tutorial