#LangChain
164 related articles
LangChain Enterprise Development in Practice: A Complete Path from Zero to Agent Deployment
Analysis of the Bilibili LangChain enterprise development tutorial: environment setup, model invocation, Prompt management, Output Parser, LCEL, RAG, and enterprise Agent deployment, with a zero-foundation learning path and key knowledge points.
Bilibili MCP Tutorial in Practice: From Fundamentals to LangChain Integration
A breakdown of the knowledge framework in a Bilibili MCP tutorial: MCP architecture roles, LangChain Agent integration, authentication and security, tool interceptors, and interactive information gathering—helping you decide which chapters are worth your time.
Agent Development Learning Path: A Complete Guide to LangChain and LangGraph in Practice
Confused by LangChain, LangGraph, and MCP? This article distills a Bilibili hands-on course, covering environment setup, the create_agent core API, Agent Loop principles, and tool-calling demos to provide a complete Agent development learning path from model integration to multi-agent collaboration.
Getting Started with LangChain: Agent Development and RAG in Practice
A Bilibili LangChain tutorial breakdown: what LangChain is, its analogy to Spring, the three LLM limitations (knowledge cutoff, external interaction, statelessness) and how LangChain solves them, plus official docs learning path and Agent development tips.
Getting Started with LangChain: A Guide to the LLM Application Development Framework
An in-depth look at the origins and core features of the LangChain framework, the three major limitations of large model applications, and how LangChain solves them through chained construction, state management, and tool calling, plus the 1.0 architecture changes and its relationship with LangGraph.
LangChain's Default Config Poses a 10.4x Amplification Risk — How Can Public Chatbots Defend Themselves?
Red team researchers found that LangChain's default create_agent + SqliteSaver pattern amplifies untrusted input 10.4x on disk with quadratic growth. This is not a CVE but a design-level risk affecting every public chatbot. This article details the attack principles, experimental data, and five effective mitigations.
How a Small Team Built Agentic Workflows with LangChain: A Two-Month Retrospective
A 3-person team built 24 agentic workflows with the LangChain toolchain and deployed them on AWS AgentCore. Two months later, they share management challenges and lessons on mobile approvals, decoupled boundaries, and landing AI workflows in small teams.
LangChain Retry Middleware Configuration: Practices and Selection Guide
Based on Reddit community discussion, this article analyzes LangChain ModelRetryMiddleware retry parameter configuration practices, covering the selection logic and alternatives for key options like max_retries, backoff_factor, and jitter.
LangChain Adds SchemagateRetriever: On-Demand Table Retrieval Cuts Text-to-SQL Prompts by 92.9%
LangChain docs add SchemagateRetriever: retrieves only the tables needed for text-to-SQL with permission filtering, cutting median prompt size by 92.9% on Spider 2.0 large schemas via offline BM25 selection with no model calls.
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.