#LangChain
166 related articles
Ditching LangChain for Raw APIs: Why Developers Are Rethinking AI Framework Over-Abstraction
A developer spent 4 hours debugging LangChain, then deleted the framework and rewrote with native Python and FastAPI. This article analyzes the cost of AI framework over-abstraction and when to use frameworks vs. raw APIs with lightweight SDKs.
748-Episode AI Agent Development Course on Bilibili: A Complete Guide from Beginner to Enterprise-Level Practice
Breaking down the 748-episode AI Agent development course on Bilibili: Fundamentals covers LLM principles and API calls; Advanced covers RAG, Tool Use, memory management, and multi-agent collaboration; Practical covers intelligent customer service, office automation, and more—plus learning paths and pitfalls to avoid.
LangChain Agent Event Streaming Explained: A Practical Guide
A deep dive into LangChain 1.3's Event Streaming feature, covering the six event types—Raw, Messages, Tool Calls, Values, Custom, and Interleave—and key practical tips for Agent developers.
Bilibili DeepAgents Practical Tutorial: From LangChain Basics to Agent Development
Bilibili DeepAgents tutorial practical analysis: from LangChain framework basics to agent development, covering the core path of building large model applications, including code implementation and learning advice, suitable for AI application development beginners.
LangChain for Beginners: A Practical Guide to Structured Output and Agents
Starting from zero with LangChain: clarify the difference between low-code platforms and large model application development, covering structured output, tool calling, Agent practice, and multi-tenant RAG enterprise deployment—ideal for engineers with development experience looking to transition into AI application development.
Beyond LangChain: How to Choose Backend Infrastructure for AI Applications?
A Reddit discussion reveals backend infrastructure needs beyond LangChain: document normalization, vector storage, auth, and background tasks. Explore framework boundaries and production choices for scalable AI architecture.
The Most Comprehensive AI Agent Tutorial on Bilibili: From Zero to Practice in Seven Days
Comprehensive analysis of Bilibili's AI Agent tutorial: from ReAct framework, planning & reasoning, memory mechanisms in the cognition part to LangChain & AutoGen practice in advanced part, and enterprise project implementation. Is the seven-day learning path reliable? Who is it for? This article breaks down the course structure and design philosophy.
Getting Started with LangChain: From Framework Positioning to Agent in Practice
What is LangChain? This article starts from framework positioning, explaining how LangChain solves three major LLM shortcomings: limited knowledge, inability to call external tools, and lack of multi-turn memory.
Enterprise Agent Development with LangChain: From Framework Setup to Private Deployment
Based on a Bilibili LangChain tutorial: the three limitations of LLM APIs, LangChain's Components/Chains/Agents, private knowledge base Q&A and business automation, plus private deployment engineering.
LangChain for Absolute Beginners: Core Concepts and Practical Invocation Explained
A beginner's guide to LangChain: what it is, the core formula Agent = Model + Harness, the three key problems it solves (internet access, external data, multi-turn context), plus learning path advice.
Connecting LangChain Agents to Structured External Data: Three Patterns and Core Challenges
How do LangChain agents connect to structured external datasets like SEC EDGAR and Census? Community discussion reveals three patterns—custom tools, MCP servers, and retrieval pipelines—plus two core challenges: data normalization and freshness.
LangChain vs. LangGraph: How to Choose the Right Framework
LangChain and LangGraph are both open-source LLM frameworks—how do you choose? This article breaks down their differences in positioning, structure, state management, and use cases with concrete examples, offers practical advice based on workflow shape, and explains how the two complement each other in layers.
Bilibili's 748-Episode Agent Development Course: A Complete Breakdown from Zero to Enterprise-Level Practice
An in-depth look at Bilibili's 748-episode AI Agent beginner course: three-part structure covering foundational concepts, LangChain and AutoGen practice, and enterprise projects, assessing how it bridges the gap between theory and hands-on work.
Text-to-SQL Agent in Practice: Building Read-Only Safe Queries with LangChain and Gemini Free Tier
Explore a Text-to-SQL agent built with LangChain and Gemini free tier, featuring three custom tools, database-level read-only protection, and defense against prompt injection attacks.
LangChain in Practice: A Complete Path to Building a Production-Ready Agent from Scratch
Based on a Bilibili hands-on video, this article systematically covers building a production-ready Agent from scratch with LangChain: model integration, system prompts, tools and internet search, short-term and long-term memory, RAG knowledge base, FastAPI service exposure, and the key pitfall of memory persistence in multi-worker deployments.
LangChain Agent in Practice: A Zero-Base Introduction to AI Agent Development
Based on the project practice chapter of Bilibili's 348-episode AI large model course, this article breaks down the LangChain Agent development learning path: virtual project demo, product and technical architecture design, resource preparation, and environment setup — a reference for zero-base learners starting AI Agent development.
RAG in Production: Should You Really Use Frameworks Like LangChain?
A developer shares production-grade RAG engine experience: when to use LangChain/LlamaIndex frameworks and when to write native code. Document parsing, retrieval fusion, and observability each have their best choices.
LangChain Tutorial for Beginners: A Complete Guide from Principles to Practice in AI Application Development
What is LangChain? This article starts from principles: unified model interfaces, private data integration, context and memory management, agents and tool calling, plus comparisons with Ollama and Coze to help you quickly build an overall understanding of the AI application development framework.
LangChainJS Quickly Adapts to Gemini 4 Argon: Current Status and Future Plans
LangChainJS is now compatible with Gemini 4 Argon without requiring a new library. This article covers the current capabilities, the minimal thinking level, the 64k output limit, and follow-up support plans for Long Decode and the Interactions API.
AI Agent for Beginners: Building a Commercial-Grade Coding Agent from Scratch
A hands-on AI Agent tutorial covering the perception-decision-action loop, LangChain, LangGraph, and MCP, with a Vue project case study showing full commercial-grade coding agent development.