#LangChain beginner guide
133 related articles
·4 min
AI Agent Learning Roadmap: From Zero to Real-World Projects
A complete AI Agent learning roadmap covering agent principles, prompt engineering, RAG, multi-agent systems, and hands-on projects — from zero to real-world deployment.
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·4 min
AI Agent Development Deep Dive: Opportunities, Real Entry Barriers, and Learning Paths
A deep dive into AI Agent development: real architecture, entry barriers, and learning paths. From ReAct to multi-agent systems and LangChain — cut through the hype.
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·3 min
AI Application Development Learning Roadmap: From Agents and RAG to Real-World Career Success
Want to break into AI application development? This guide covers the full learning path — from Agents and RAG to Prompt Engineering — helping you master LLM engineering skills and land the job.
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·4 min
8-Step AI Agent Development Guide: From Zero to Production
Demo works but production fails? This guide covers the full AI Agent development path: when to use Agents, hand-writing ReAct loops, tool schemas, RAG, eval sets, and production fallback strategies.
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·6 min
A Three-Month AI Agent Learning Roadmap: From Zero to Hands-On Agent Development
A systematic breakdown of the complete AI Agent learning roadmap, covering prompt engineering, the ReAct paradigm, memory mechanisms, and multi-agent collaboration, with hands-on project advice.
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·6 min
Introduction to Prompt Engineering: Principles, Methods, and Practical Case Studies
Prompt Engineering is the core skill for harnessing LLMs. This article covers principles and design methods through real cases like translation role-setting and DeepSeek image generation.
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·6 min
Complete Guide to Deploying Dify Locally: Docker + Ollama + RAG Setup Tutorial
A detailed guide to deploying the Dify agent platform locally: from Docker setup and integrating Ollama + DeepSeek local LLMs to workflow orchestration and RAG knowledge base construction.
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·7 min
Zero-Basis Learning Path for AI Agents: A Three-Stage Journey from Beginner to Deployment
A systematic zero-basis learning path for AI Agent development, covering Python and LLM fundamentals, five core capabilities like task planning and RAG, and LangChain hands-on practice.
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·6 min
LangChain 1.3 in Practice: A Complete Guide to DeepAgent Architecture and the Harness Philosophy
An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.
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·7 min
Learning Python from Scratch: A Complete Guide to the Three-Stage Learning Path
Want to learn Python from scratch but don't know where to begin? This article breaks down three stages—basic syntax, advanced mastery, and hands-on practice—with real projects in crawling, automation, and data analysis to help you build programming thinking.
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·6 min
AI Agent Learning Roadmap: A Four-Stage Guide from Beginner to Practical Mastery
A systematic AI Agent learning roadmap in four progressive stages: fundamentals → ReAct core paradigm → memory & tools → multi-agent collaboration. Master LangChain, AutoGen, and more, growing from beginner to practical developer in three months.
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·5 min
A Programmer's Guide to Large Language Models: The Complete Path from Prompts to Agents
A tailored large-model learning path for ordinary programmers: from prompt engineering, API calls, and LangChain, to RAG, Agents, fine-tuning, and enterprise deployment—six steps to build AI application skills fast.
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·6 min
Claude Code Chinese Guide: A Complete Path from Zero to an Efficient Workflow
A systematic Claude Code learning guide built for Chinese developers, covering ten core modules including Slash Commands, Memory, MCP, and Hooks, with a three-tier path to build an AI coding workflow in 11–13 hours.
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·5 min
Introduction to Prompt Engineering: A Complete Guide to Core Concepts, Methodologies, and Limitations
Prompt engineering is a core skill in the AI era. This article breaks down the essential differences between prompts and prompt engineering, the six-step workflow, four evaluation criteria, and key limitations like context limits and hallucination.
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·7 min
Spring AI 2.0 in Practice: A Beginner's Guide for Java Developers Building AI Agents
A complete Spring AI 2.0 guide for Java developers covering unified API abstraction, RAG, tool calling, MCP protocol, and enterprise projects to build AI Agents.
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·3 min
LLM Development Learning Path: From Zero to Enterprise-Level Projects
A complete LLM development learning roadmap covering prompt engineering, RAG, AI Agents, and fine-tuning — helping beginners master LangChain, LlamaIndex, and more.
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·2 min
Coze 3.0 Multi-Agent Collaboration: A Complete Guide to Building Your AI Team
A hands-on guide to Coze 3.0 multi-user, multi-Agent projects: create projects, add members, toggle Agents, and use @ mentions to dispatch tasks efficiently.
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·2 min
Troubleshooting Dependency Conflicts in AI Agent Projects: A Hands-On Environment Setup Guide
Learn how to resolve dependency conflicts when setting up AI Agent projects — covering pip list audits, handling selenium version issues, and using Chinese PyPI mirrors.
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·3 min
Hermes Agent Setup in Practice: Run a Complete AI Agent Workflow in 3 Minutes
Step-by-step guide to installing Hermes Agent: no sudo required, single-command deployment, supports Ollama, Anthropic, OpenRouter and more. Includes verification steps, key commands, and beginner tips.
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·7 min
Getting Started with LangGraph Multi-Agent Development: Architecture Analysis and Three Practical Case Studies
Learn LangGraph multi-agent development covering Supervisor and Collaboration architectures, with three hands-on projects: code assistant, prompt assistant, and WebRTC digital human.
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