LangChain / LangGraph
Curated LangChain / LangGraph tutorials.
Advanced RAG: Complete Guide 2026 – Beyond Basic Retrieval to Build Production-Grade Knowledge Bases
Basic RAG systems are easy to set up, but making them stable and effective in production is hard. This article dives deep into advanced RAG techniques: hybrid retrieval, reranking, multi-query decomposition, query routing, and systematic evaluation to improve RAG performance.
AdvancedAI Agent Frameworks: LangChain, AutoGen & CrewAI for Production in 2025
AI agents go beyond chatbots—they use tools, maintain memory, plan multi-step tasks, and collaborate with other agents. This guide compares LangChain, LangGraph, AutoGen, and CrewAI for different use cases, covers reliable agent design patterns, tool calling best practices, memory architectures (short-term, long-term, episodic), handling errors and hallucinations, and deploying production agents with observability.
AdvancedAI Agents in Production: Architecture Patterns and Reliability Engineering
AI agents—autonomous systems that use tools and make decisions to complete multi-step tasks—are moving into production at enterprise scale. This guide covers reliable agent architecture: tool design and error handling, state management for long-running agents, human-in-the-loop patterns, observability and debugging agents, graceful failure modes, security considerations, and testing strategies for non-deterministic systems.
AdvancedBuilding Financial Analysis Agent with AI Agents: Complete Guide 2026
Building Financial Analysis Agent with AI Agents 2026 Introduction AI agents that can analyze financial data and generate investment reports are transforming how developers work. This guide shows you how to build a production-ready Financial Analys
AdvancedBuilding SQL Query Agent with AI Agents: Complete Guide 2026
Building SQL Query Agent with AI Agents 2026 Introduction AI agents that can translate natural language to SQL and execute queries are transforming how developers work. This guide shows you how to build a production-ready SQL Query Agent using Lang
AdvancedContextual Compression RAG: Implementation Guide with Pinecone 2026
Contextual Compression RAG: Complete Implementation 2026 Overview Contextual Compression RAG is a specialized retrieval pattern that focuses on compressing retrieved context to fit LLM window. This guide shows you how to build a production-ready sy
IntermediateCrewAI Tutorial 2026: Build Multi-Agent Systems That Work Together
Complete tutorial for building multi-agent AI systems using CrewAI in 2026. Covers agent role design, task delegation, crew orchestration, tool integration, and building production workflows where multiple specialized AI agents collaborate on complex tasks.
AdvancedGraph RAG: Implementation Guide with Neo4j 2026
Graph RAG: Complete Implementation 2026 Overview Graph RAG is a specialized retrieval pattern that focuses on knowledge graph traversal for multi-hop reasoning. This guide shows you how to build a production-ready system using Neo4j. Why Graph RAG
AdvancedHybrid Search RAG: Implementation Guide with Elasticsearch 2026
Hybrid Search RAG: Complete Implementation 2026 Overview Hybrid Search RAG is a specialized retrieval pattern that focuses on combining vector and keyword search for maximum recall. This guide shows you how to build a production-ready system using
AdvancedLangChain LCEL: Advanced Patterns for Production AI Applications
LangChain Expression Language (LCEL) is the modern way to build composable LLM pipelines. This guide covers advanced LCEL patterns: parallel execution, streaming, dynamic routing, conditional chains, retry and fallback logic, tool use orchestration, and testing strategies. Includes production patterns for RAG applications, multi-step agents, and complex data transformation pipelines with real performance benchmarks.
AdvancedLangChain vs LangGraph 2026 Deep Dive: When to Use Which Framework?
LangChain vs LangGraph comparison (2026): Chains handle linear flows, Graphs handle stateful Agents (loops/approval/persistence)—one rule of thumb: does the runtime need to change paths based on results? Includes decision table, combined usage (graph orchestration + chain for single steps), and AgentExecutor migration path.
IntermediateLangChain vs LlamaIndex: Which Framework to Choose in 2025?
Comprehensive comparison of LangChain and LlamaIndex for building LLM applications. Compare architecture, use cases, performance, and ecosystem to make the right choice for your project.
IntermediateLangChain vs LlamaIndex 2026: Which Framework Should You Use for RAG?
Detailed comparison of LangChain and LlamaIndex for building retrieval-augmented generation applications in 2026. Covers architecture differences, performance benchmarks, integration ecosystems, and specific use cases where each framework excels.
AdvancedLangChain vs LlamaIndex vs Haystack: RAG Framework 2026
Detailed comparison of LangChain, LlamaIndex, and Haystack for building RAG pipelines. Covers document processing, retrieval strategies, performance benchmarks, and production deployment for 2026.
AdvancedLangGraph Complete Guide 2026: Build Stateful AI Agents
LangGraph Complete Guide (2026): Build agents as state machines—State+reducer, conditional edges, checkpointer persistence (multi-turn memory/crash recovery/time travel), interrupt for human approval gates, multi-agent supervisor pattern. Includes an honest comparison of when to use it and when not to.
IntermediateLangSmith for LLM Evaluation: Building Systematic Feedback Loops
LangSmith LLM Evaluation Workflow (2026): Trace → Dataset → Evaluator (including LLM-as-judge) → Experiment — the four-piece suite that turns "feels better" into measurable progress. Includes @traceable code, weekly evaluation loops, bias calibration for LLM judges, and comparison vs Langfuse.
IntermediateLlamaIndex Practical Guide: RAG Application Development from Beginner to Production
LlamaIndex is purpose-built for RAG applications, making it the go-to framework for building enterprise knowledge base Q&A systems. This article covers the core architecture, key differences from LangChain, and 5 complete code examples from document loading to production deployment.
AdvancedLlamaIndex Tutorial 2026: Build Production RAG Applications
Complete LlamaIndex tutorial 2026. Covers VectorStoreIndex, persistent Qdrant storage, chat engines, sub-question decomposition, semantic chunking, metadata filtering, and streaming.
IntermediateLlamaIndex vs LangChain: Which One to Use for Building RAG (2026 Hands-On Comparison)
Everyone says LlamaIndex focuses on retrieval and LangChain leans toward orchestration, but when it comes to actual projects, you still get stuck. This article breaks it down by 'what you want to do,' with real code and pitfalls, helping you make a decision in 10 minutes.
AdvancedMulti-Vector RAG: Implementation Guide with Weaviate 2026
Multi-Vector RAG: Complete Implementation 2026 Overview Multi-Vector RAG is a specialized retrieval pattern that focuses on storing multiple embedding types per document. This guide shows you how to build a production-ready system using Weaviate.
IntermediateNode.js + LangChain.js: How to Build Node.js AI applications (2026)
Node.js + LangChain.js Integration Guide 2026 Overview This guide shows you exactly how to build Node.js AI applications using Node.js and LangChain.js. We cover setup, core integration, and production-ready patterns. Prerequisites - Node.js envi
AdvancedParent Document RAG: Implementation Guide with Chroma 2026
Parent Document RAG: Complete Implementation 2026 Overview Parent Document RAG is a specialized retrieval pattern that focuses on retrieving small chunks with large parent context. This guide shows you how to build a production-ready system using C
AdvancedPython AI Development Stack 2026: FastAPI + LangChain + Supabase
Complete guide to building production AI applications with FastAPI, LangChain, and Supabase in 2026. Covers project setup, async AI endpoints, RAG pipeline, vector search, and deployment.
AdvancedSelf-Query RAG: Implementation Guide with Qdrant 2026
Self-Query RAG: Complete Implementation 2026 Overview Self-Query RAG is a specialized retrieval pattern that focuses on AI-generated metadata filters for precise retrieval. This guide shows you how to build a production-ready system using Qdrant.