#LangChain
174 related articles
Building Enterprise-Grade Agents from Scratch: A Deep Dive into LangChain 1.0's Core Capabilities
A systematic breakdown of AI Agent concepts, software paradigm evolution, and LangChain 1.0's six major upgrades including the unified create_agent interface — ideal for Agent development beginners.
langchain-halu: Adding an LLM Hallucination Detection Layer to LangChain
langchain-halu is an open-source LangChain integration that embeds LLM hallucination detection into LCEL chains via annotate, gate, and auto-retry strategies, returning calibrated probabilities and error types.
LangChain Beginner's Guide: Core Concepts and Three Key Capabilities for AI Agent Development
LangChain is a beginner-friendly LLM app framework. Learn the Agent = Model + Harness formula and how LangChain solves knowledge cutoffs, private data access, and memory limitations.
LangChain + MCP Practical Guide: From Tool Calling to Building AI Agents
A beginner's guide to LangChain and MCP: how to bind tools to LLMs, how Agents truly execute tool calls, and why you should build on frameworks instead of reinventing the wheel.
How to Intercept LangChain Tool Calls by Argument Without Touching Agent Code
How to intercept LangChain/LangGraph tool calls by argument without modifying agent code. Analyzes a gateway-based policy layer, comparing interrupt() human approval vs. in-tool checks, and key limitations like streaming and fail open.
Adding Agent Skills to LangChain/LangGraph: Engineering Trade-offs Between Context, Caching, and Artifacts
An open-source developer shares three key architectural lessons from adding Agent Skills to a LangChain/LangGraph financial agent: prompt caching trade-offs, using Artifacts to bypass LLM data routing, and letting workflows define the runtime.
LangChain + Playwright: Building an Intelligent Test Agent Driven by Natural Language
Learn how to build an intelligent test agent with LangChain and Playwright, using a local LLM and browser toolkit to enable natural language-driven UI automation testing.
LangSmith Fine-Tuning Launches: One CLI to Rule the Entire Fine-Tuning Pipeline
LangChain launches LangSmith Fine-Tuning and smithtune CLI tool, with Fireworks as the first training partner. Go from LangSmith traces to hosted SFT, eval, and deployment in one command — no training infra required.
Why LangChain Is Essential for LLM Applications: A Guide to Core Concepts
Why learn LangChain if you can already call LLM APIs? This article explains LangChain's core value from an engineering perspective, covering Prompt Templates, Chains, Memory, RAG, and Agents.
AI Agent Development: A Beginner's Guide to the Next Big Tech Wave
AI Agents are the next paradigm shift in software. Learn how they differ from general LLMs, explore multi-agent systems, market growth data, and how to get started with LangChain.
LangChain Beginner's Guide: Building AI Agents from Scratch
A beginner's guide to LangChain: learn its core positioning, how the name originated, and the three key problems it solves — knowledge cutoff, external data, and memory management for AI Agent development.
LangChain September Update: Delegated Access and Agent Observability Upgrades
LangChain's September update brings delegated LangSmith access for sandboxes, stop_reason SDK tracing, ReDoS security fixes, and updates to OpenAI, Anthropic, and LangGraph CLI packages.
LangChain Getting Started: Why LLM Applications Can't Do Without It
A beginner's guide to LangChain: understand its role as an LLM application framework, the three core limitations of standalone LLMs, and how LangChain addresses them to power RAG and Agent applications.
Why LangChain Docs Drive Away Beginners: The Pain of Local Models and Product Promotion
A beginner's detailed critique of LangChain docs: local model (Ollama, llama.cpp) info is unfindable, LangSmith promotion clutters onboarding, and examples assume cloud APIs throughout.
AI Agent Development: A Complete Beginner's Guide from Concepts to Practice
A complete beginner's guide to AI Agent development: from chatbots to intelligent agents, Stanford Smallville & ChatDev, and a 3-phase LangChain learning path.
Getting Started with LangChain: From Core Libraries to AI Agent Development
LangChain is an open-source framework for building LLM-powered apps. Learn how LangChain Core, LangGraph, LangServe, and LangSmith work together for RAG and AI Agent development.
LangChain 1.3 Beginner's Guide: Core Concepts of LLMs and Agents Explained
LangChain 1.3 beginner's guide: understand the three core LLM limitations, unified interfaces, modular architecture, and the LLM → Agent → DeepAgent hierarchy.
LangChain Deep Agents vs. MDA: Clearing Up the Confusion for Developers
What's the difference between LangChain's Deep Agents and MDA (Managed Deep Agents)? We break down create_deep_agent vs. define_deep_agent and help developers choose.
LangChain Agent Web Scraping: DIY vs. Managed API — A Practical Comparison
DIY scraping vs. managed APIs for LangChain Agents: compare Playwright/bs4 self-builds against Firecrawl and context.dev on cost, anti-bot maintenance, and when to switch.
TutorialsGetting Started with RAG: What Is Retrieval-Augmented Generation and How Does It Solve LLM Hallucination?
RAG (Retrieval-Augmented Generation) is the core solution for LLM hallucination. Learn RAG concepts, how it works, three causes of hallucination, and the complete learning path from basics to Knowledge Graph RAG.