LangChain & LangGraph
7 articles · written by Niraj Kumar
LangChain gets you to a working prototype fast and then asks harder questions: where does state live, what happens when a tool call fails halfway through, how do you put a human in the loop without blocking the process, and how do you debug a chain that produced the wrong answer three steps ago.
These posts focus on the answers. They cover LangGraph's graph model for multi-step agents, PostgreSQL checkpointers for durable state, human-in-the-loop approval gates, tool design patterns that keep agents from looping, and the structural choices that separate a demo agent from one you can leave running.
There is also a comparison with LlamaIndex for readers still choosing a framework, and several posts that use LangChain purely as the retrieval layer of a larger RAG system rather than as the whole application.
- GraphRAGLangChainOllama
How to Build a Local GraphRAG Pipeline with PostgreSQL, LangChain, and Ollama for Multi-Hop Reasoning
Build a fully local GraphRAG system with LangChain, Ollama and PostgreSQL — extract entity triplets, store a knowledge graph, run multi-hop queries.
- RAGOllamaLangChain
How to Build a Local RAG Pipeline Using Ollama, LangChain, and PostgreSQL
Set up a private, offline RAG system by connecting locally hosted LLMs via Ollama to a PostgreSQL pgvector database using LangChain.
- LangGraphAI AgentsLangChain
Beyond Basic Chatbots: Developing Advanced AI Agents with LangGraph
Build stateful, multi-step AI agents with LangGraph — core concepts, real architectures, code examples, and the pitfalls beyond simple chatbots.
- RAGLangChainNext.js
Building a Production-Ready RAG System with LangChain and Next.js
Architect and build a production RAG system with LangChain for orchestration and Next.js for the UI — ingestion, retrieval, streaming, deployment.
- LangChainOpenAIOllama
Building Your Own Chatbot with LangChain and OpenAI (or Ollama)
Build a production chatbot with LangChain and OpenAI or Ollama — document ingestion, vector stores, a RAG pipeline, and a deployable web interface.
- Graph-RAGKnowledge GraphsRAG
Graph‑RAG Explained: Building Smarter AI Agents with Knowledge Graphs
How Graph-RAG combines knowledge graphs with LLMs to improve multi-hop reasoning and accuracy — with code examples and enterprise use cases.
- LangChainLlamaIndexLLM
LangChain vs. LlamaIndex: Which One Should You Use in 2026?
LangChain vs LlamaIndex compared on architecture, use cases and developer experience — with code examples to help you pick an LLM framework.
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Production LangChain & LangGraph systems for RAG, agents, and AI workflows.
LangChain Developer Services →Related topics
- Retrieval-Augmented Generation (RAG)
- Large Language Models
- AI Agents
- Next.js
- React
- TypeScript
- Node.js
- PostgreSQL
- Docker & Containers
- DevOps
- CI/CD
- Application Security
- AWS
- n8n Automation