Let's learn about Langchain via these 59 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
LangChain is a framework designed to simplify the creation of applications using large language models (LLMs), providing tools to chain together LLMs with other components. It enables developers to build complex, context-aware LLM applications more efficiently, unlocking advanced AI capabilities.
1. A Practical 5-Step Guide to Do Semantic Search on Your Private Data With the Help of LLMs
In this practical guide, I will show you 5 simple steps to implement semantic search with help of LangChain, vector databases, and large language models.
2. Vector Databases - Basics of Vector Search and Langchain Package in Python
In this article, I will walk you through the basics of vector databases, vector search and Langchain package in python for storing and querying similar vectors.
3. Unlocking Structured JSON Data with LangChain and GPT: A Step-by-Step Tutorial
Manipulating Structured Data (from PDFs) with the Model behind ChatGPT, LangChain, and Python for Powerful AI-driven Applications.
4. Vector Databases: Getting Started With ChromaDB and More
In this article, we will explore another well-known vector store called ChromaDB. Chroma DB is a vector store that is open-source.
5. Managing Large Data Volumes With MinIO, Langchain and OpenAI
A practical guide to integrating MinIO, Langchain and OpenAI’s GPT-3.5 model focusing on summarizing documents stored in MinIO buckets.
6. How to Build a Web Page Summarization App With Next.js, OpenAI, LangChain, and Supabase
An app that can understand the context of any web page. We'll show you how to create a handy web app that can summarize the content of any web page
7. Improving Text-to-SQL with a Fine-Tuned 7B LLM for DB Interactions
A step-by-step guide to fine-tuning models for SQL generation on custom database structures.
8. Effortlessly Launch LangChain APIs with LangServe and MinIO Integration
Streamline LangChain app deployment with LangServe and MinIO, creating powerful, production-ready APIs for seamless data management.
9. Introducing LLM Sandbox: Securely Execute LLM-Generated Code with Ease
LLM Sandbox: a secure, isolated environment to run LLM-generated code using Docker. Ideal for AI researchers, developers, and hobbyists.
10. How to Turn Your OpenAPI Specification Into an AI Chatbot With RAG
Learn how to automate API documentation by integrating OpenAPI specifications with LLMs using Retrieval Augmented Generation (RAG), Langchain, and ChromaDB
11. AI Agents for Beginners: Building Your First AI Agent
Build your first real AI agent with this simple guide for beginners—learn, code, and create smart tools that take action.
12. How ChatGPT Can Learn to Use Tools and Plugins
Large Language Models (LLMs) like ChatGPT are super cool, and changed everything, although they have some very strong limitations.
13. AutoGPT — LangChain — Deep Lake — MetaGPT: Building the Ultimate LLM App
What is the future of the LLM technology? How do we convert today's LLMs to automated agents acting like human beings? You can find the answer in this article!
14. AI Agents Aren’t Production Ready - and Access Control Might Be the Reason
Learn how to implement proper access control for AI agents in applications for production-ready AI systems.
15. Building Knowledge Graphs for RAG: Exploring GraphRAG with Neo4j and LangChain
Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.
16. How to Add Real-Time Web Search to Your LLM
Learn how to connect Tavily Search so your AI can fetch real-time facts instead of guessing.
17. Building a Hybrid RAG Agent with Neo4j Graphs and Milvus Vector Search
Build a Graph-RAG agent using Neo4j and Milvus to combine graph and vector search, delivering accurate, context-rich answers to complex queries.
18. Prompt Chaining: Turn One Prompt Into a Reliable LLM Workflow
Prompt Chaining links prompts into workflows—linear, branching, looping—so LLM outputs are structured, debuggable, and production-ready.
19. Streamlining LLM Application Development and Deployment with LangChain, Heroku, and Python
In this tutorial, learn how to build and deploy LLM-based applications with ease using LangChain, Python, and Heroku for streamlined development and deployment.
20. How Will Software Engineers Lose Their Jobs Within the Next 5 Years?
Approximately three years ago, I was chatting with a dear friend on LinkedIn and predicted that in around 5-10 years we will have advanced systems that will gen
21. The New Data Engineering Landscape: DataOps, VectorOps, and LangChain
DataOps, VectorOps, and LangChain integration creates powerful applications that combine efficient data management, high-dimensional data processing.
22. Understanding the Magic Behind Langchain Autonomous Agents
Understand the mechanics behind how langchain autonomous agents work.
23. How to Extract and Generate JSON Data With GPTs, LangChain, and Node.js
Manipulating Structured Data (from PDFs) with the Model behind ChatGPT, LangChain, and Node.js for Powerful AI-driven Applications
24. Langchain: Explained and Getting Started
Langchain is a crucial component for developing LLM models. It helps in orchestration and act as building block
25. How I Built a Data Analysis Assistant with BigQuery and Langchain
Leveraging Generative AI for Data Analytics with Langchain and OpenAI
26. LangChain Promised an Easy AI Interface for MySQL—Here’s What It Really Took
Learn how I built a multi-stage Langchain agent for MySQL. This article details my journey, challenges, and key steps in creating an intelligent database intera
27. 100 Days of AI Day 7: Building Your Own ChatGPT with Langchain
Step by step process on how to build chat with you data application using Open AI & LangChain in Python.
28. You’re Building AI Agents Wrong. Here’s How to Fix That with AAC
Fragile, chaotic AI agents are everywhere. AAC is a simple yet powerful architecture that brings structure, scalability, and reliability to your agent systems.
29. From RAG to Agentic RAG: Building Agentic RAG system that runs completely offline.
This implementation demonstrates significant advancement from basic offline RAG to an intelligent offline based agentic system.
30. Taming LLMs with Langchain + Langgraph
How to fix LLMs and chat bots with Langchain and Langgraph.
31. The HackerNoon Newsletter: This Detective Game Helps Beginners Master SQL and Database Logic (4/14/2025)
4/14/2025: Top 5 stories on the HackerNoon homepage!
32. LangChain vs LangGraph: A Beginner’s Guide to Building Smarter AI Workflows
Learn how LangChain and LangGraph help you design intelligent, adaptive AI workflows that move from simple prompts to full applications.
33. Enhancing RAG with Knowledge Graphs: Integrating Llama 3.1, NVIDIA NIM, and LangChain for Dynamic AI
Use Llama 3.1 native function-calling capabilities to retrieve structured data from a knowledge graph to power your RAG applications.
34. Your AI Just Got a Brain (and Maybe a Will): A Friendly Intro to Agentic AI
A beginner-friendly intro to agentic AI—what it is, why it matters, and how you can start building your own autonomous AI assistants today. No jargon, just real
35. Unlocking Precision in RAG Applications: Harnessing Knowledge Graphs with Neo4j and LangChain
Explore a practical guide for constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain.
36. Streamlining LLM Implementation: How to Enhance Specific Business Solutions with RAG
Learn how to enhance your LLMs with retrieval-augmented generation, using LlamaIndex and LangChain for data context, deploying your application to Heroku.
37. Learning AI From Scratch: Streaming Output, the Secret Sauce Behind Real-Time LLMs
Learn how to build real-time AI experiences with LangChain’s streaming API. Stream tokens, enhance UX, and master LCEL for scalable LLM pipelines.
38. New Crash Course Promises to Help You Develop AI Applications with LangChain
LangChain is an open-source framework that simplifies building applications powered by Large Language Models (LLMs)
39. Agents 101 — Build and Deploy AI Agents to Production using LangChain
Learn how Langchain turns a simple prompt into a fully functional AI agent that can think, act and remember.
40. Traditional Keyword-Based Search vs Semantic Search: Which Is Best For You?
How to build keyword and semantic search in MariaDB using Python, LangChain, and AI embeddings
41. From Prototype to Production: Building Real World AI Systems That Actually Work
In this interview, Ademola Balogun, Founder of 180GIG Ltd, shares insights from building production ready AI systems across hiring, finance, and nutrition techn
42. How to Build Your First AI Agent and Deploy it to Sevalla
Learn how to build and deploy your first AI agent using Langchain and Sevalla.
43. How to Govern Agentic AI Before It Governs You
A practical framework for governing agentic AI systems with six principles, the ALRI metric, and ready-to-use audit controls for real compliance.
44. Why I Stopped Letting aI Agents Write Directly to my Database (and Built MemState)
Your agent needs a database, not just a chat log. Learn how to build reliable, type-safe agents with transactions and rollbacks using MemState.
45. How I Super-Charged My LangChain-MySQL Agent: Part 2
Enhanced your SQL workflows with LangChain and FAISS: learn how vector databases, foreign-key-aware retrieval, and AI-powered tests remove token bloat
46. The Future of News Broadcasting: How I Built an AI-Controlled Podcast
How I Built an AI-controlled AI podcast that also streams over RTMP to YouTube and Twitch.
47. Building a Local AI Chatbot with LangChain4J and Ollama
48. Build a Tool-Backed Market Brief Copilot (Not a Chat Demo)
Build a data-validated Market Copilot with LangChain, EODHD, and Streamlit. Automate auditable financial briefs and eliminate AI hallucinations.
49. LangGraph Beginner to Advance: Part 1: Introduction to LangGraph and Some Basic Concepts
LangGraph is a Python library designed for building advanced conversational AI workflows.
50. Here's Why Extraction Matters the Most
Experiment with various retrieval-augmented generation option shows extraction (getting information from PDFs) matters most.
51. Building Chatbots from Scratch: Understanding and Harnessing Large Language Models (LLMs)
Imagine having a super smart friend who has read every book, article, and blog post on the internet.
52. Master Innovative Prompt Engineering — AI for Web Devs
Prompt engineering lets you modify AI behavior without changing application code. This post covers tools and techniques for prompt engineering.
53. Behind Every Question-Answer AI Is a Data Pipeline Built for Scale — Here's How to Build Your Own
Explore how to build a data pipeline that continuously indexes document embedding into a Redis vector database.
54. The Noonification: BEP 341: Consecutive Block Production (7/7/2024)
7/7/2024: Top 5 stories on the HackerNoon homepage!
55. Transform Your Ops with a Unified Agent and SOP Structure
Reference architecture for LoA-3 agents on rails: SOP-first (YAML), UC-governed tools, LangChain agent, Assurance Gate, MLflow 3 + OTel, Databricks GPT-OSS.
56. Firecrawl Part 2: This Confidence Gate Decides When Bing Gets a Vote - How It Works
My enrichment pipeline shipped "Unknown" as a company name—silently. The fix: a confidence gate that checks usable fields, not return codes.
57. How to Build and Deploy a LogAnalyzer Agent using Langchain and Sevalla
Learn to build a log analyzing agent using Langchain and Sevalla
58. The Noonification: Should You Invest in Nvidia? (6/27/2024)
6/27/2024: Top 5 stories on the HackerNoon homepage!
59. The HackerNoon Newsletter: The Double Life of a TensorFlow Function (6/3/2025)
6/3/2025: Top 5 stories on the HackerNoon homepage!
Thank you for checking out the 59 most read blog posts about Langchain on HackerNoon.
Visit the /Learn Repo to find the most read blog posts about any technology.