#LangChain
155 related articles
Full Breakdown of the LangChain + LangGraph + MCP + Agent Enterprise Development Learning Path
An analysis of Bilibili creator Loulan's LangChain+LangGraph+MCP+Agent enterprise development course, covering the four-stage learning path, framework selection logic, time travel, and token cost planning to help developers build a complete Agent development system.
LangChain Breaks Down Self-Improving Agents: The Triple Evolution of Models, Harnesses, and Context
LangChain breaks down the three dimensions of self-improving agents: models (SFT and RL), harness, and context—and how traces, evals, and red teaming form the flywheel.
Building Enterprise-Grade AI Agents from Scratch: A Three-Stage Learning Path from Bilibili's Comprehensive Tutorial
Analysis of Bilibili's 748-episode AI Agent beginner course: cognition section clarifies Agent vs. LLM differences, advanced covers LangChain, AutoGen, and multi-Agent collaboration, hands-on implements intelligent customer service and automated data analysis projects, with source code, courseware, and a learning roadmap.
Long-Running AI Agents Require Multi-Layer Feedback Loops
The LangChain Deep Agents course proposes a loop engineering framework that breaks AI agent feedback into execution, verification, and cross-run improvement loops to build reliable long-running agents.
LangChain and MCP Integration in Practice: From Framework to Agent Development
Explaining how LangChain and MCP fit together: LangChain as an AI app framework and MCP as a tool-calling protocol, enabling reusable, cross-framework agent tools. Covers agent wrapping, function calling vs. MCP, and practical learning advice.
LangChain Beginner's Guide: The Core Framework for Agent and RAG Development
What is LangChain? This article based on a Bilibili tutorial outlines LangChain's core positioning, the three major model limitations it solves (knowledge timeliness, external interaction, state persistence), and the official documentation learning path to help developers quickly get started with Agent and RAG development.
DeepAgent Sub-Agent Breakdown: Precompilation, Sync vs. Async, and Selection Pitfalls
An in-depth breakdown of DeepAgent sub-agents: LangChain ecosystem positioning, sync vs. async differences, precompilation reuse, LangGraph-Server hosting, and selection pitfalls.
The Most Comprehensive Agent Development Tutorial on Bilibili: From Zero to Expert in Seven Days
A Bilibili creator releases a 748-episode zero-foundation AI Agent tutorial in three parts: cognition, advanced, and hands-on, covering ReAct, LangChain, AutoGen, multi-Agent collaboration, and enterprise projects, with source code and a learning roadmap.
LangChain from Scratch: A Deep Dive into a Hands-On RAG and Agent Tutorial
A deep dive into a Bilibili zero-foundation LangChain tutorial: analyzing the learning path around RAG and Agent scenarios, from basic calls to complete deployable projects. Suitable for students, career changers, and side-project developers.
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.