#LangChain4j tutorial
224 related articles
Tutorials·3 min
Hermes Agent Framework Practical Guide: Building AI Agents from Scratch
A detailed guide on ByteDance's Hermes Agent framework covering core features, deployment, custom tool development, and enterprise-grade AI Agent applications.
Read more →
Tutorials·3 min
LangChain Unified Interface in Practice: A Guide to Avoiding Model Invocation Pitfalls
A detailed comparison of LangChain's two model invocation approaches, focusing on init_chat_model unified interface usage and tips for avoiding DeepSeek V4 Pro Thinking Mode pitfalls in Agent scenarios.
Read more →
Tutorials·3 min
Deep Dive into a 198-Hour Python Zero-to-Hero Course: Is This Learning Path Worth Following?
In-depth analysis of a 198-hour Python+AI beginner course: breaking down its structure, learning path, and projects with honest pros, cons, and study tips.
Read more →
Tutorials·3 min
Can You Really Transition to AI/LLM in Three Months? A Deep Dive into the Learning Roadmap
Deep dive into a popular 3-month AI/LLM transition roadmap: from Python basics and Prompt engineering to LangChain, RAG, Agents, and hands-on projects, with realistic time estimates and pitfall warnings.
Read more →
Tutorials·3 min
Review of a 648-Episode Complete Python Tutorial: Is It Worth Learning from Zero to AI Applications?
In-depth review of a 648-episode Python tutorial on Bilibili, analyzing its three-stage course structure, resource quality, and learning effectiveness with practical study tips for beginners.
Read more →
Product Reviews·3 min
Claude Code with MiniMax M2: Testing a Low-Cost AI Coding Solution Across Three Real Projects
Real-world testing of MiniMax M2 as Claude Code's backend model across three projects: framework migration, iOS development, and full-stack MVP — at just 8% of Claude's price.
Read more →
Tutorials·3 min
The Complete Guide to Spring AI: A Full Learning Path for Java Engineers Building AI Applications
A comprehensive guide to Spring AI covering LLM integration, prompt engineering, RAG knowledge bases, and five AI Agent patterns, with three enterprise projects for Java engineers.
Read more →
Tutorials·3 min
LangGraph Multi-Agent Architecture: Core Principles and Enterprise-Level Implementation Guide
Deep dive into LangGraph multi-agent architecture covering Graph structure principles, MCP service integration, Time Travel debugging, and supervised multi-agent enterprise implementation patterns.
Read more →
Tutorials·3 min
Building a Financial Report Analysis AI Agent with Cursor + Skills: A Step-by-Step Tutorial from Scratch
A hands-on tutorial for building a financial report analysis AI Agent from scratch using Cursor editor, Skills definitions, and MiniMax M2.1. Covers setup, architecture, Skills methodology, and multi-language programming.
Read more →
Tutorials·2 min
Complete Breakdown of Andrew Ng's LangChain Course: Five Core Components and Hands-On Guide
Deep dive into Andrew Ng and Harrison Chase's LangChain course, covering the five core components—Models, Prompts, Indexes, Chains, and Agents—to help developers master LLM app development.
Read more →
Tutorials·3 min
Complete Breakdown of Andrew Ng's Agent Course: A Practical Guide to the Five Core Modules of AI Agent Development
Deep dive into Andrew Ng's viral AI Agent course covering five core modules: Reflection, Planning, Tool Use, Multi-Agent Collaboration, and Memory, with practical learning paths for LLM agent development.
Read more →
Tutorials·3 min
Getting Started with LLM Application Development from Scratch: A Complete Guide to Learning Paths and Career Directions
A complete beginner's guide to LLM application development: learn the three key directions (API calling, RAG, Agent), master frameworks like LangChain, and follow a step-by-step learning path to become an AI application developer.
Read more →
Tutorials·3 min
Learning LLM Application Development from Scratch: A Complete Roadmap from RAG to Agent
How to start LLM application development from scratch? A complete roadmap covering Python basics, RAG knowledge bases, and Agent development with LangChain.
Read more →
Tutorials·3 min
LLM Learning Roadmap: A Complete Guide from Beginner to Project Implementation Across Seven Core Modules
A systematic breakdown of seven core LLM learning modules covering environment setup, Prompt Engineering, RAG, Agents, dev frameworks, fine-tuning, and hands-on projects for developers.
Read more →
Tutorials·2 min
Hermes Agent Development in Practice: From Installation & Configuration to Lark Integration and Code Review
A practical guide to the Hermes enterprise AI Agent framework: installation, MCP protocol integration, Lark bots, news aggregation, and automated code review.
Read more →
Tutorials·1 min
MLflow Hands-On Tutorial: A Full-Stack MLOps Guide for GenAI Tracing + Classic ML Tuning
Learn MLflow's core features for GenAI and classic ML: auto tracing, model evaluation, Prompt versioning, hyperparameter tuning, and model deployment in just a few lines of code.
Read more →
Tutorials·2 min
Implementing Claude Code in Java: A Complete Beginner's Tutorial for AI Agent Development
An open-source project implementing Claude Code's core features in Java. Master AI Agent architecture, tool calling, task decomposition, and multi-Agent systems through 12 progressive lessons.
Read more →
Tutorials·2 min
Build an AI Agent in 200 Lines of Python: A Deep Dive into Five Core Modules
Build an AI Agent in 200 lines of Python from scratch. A deep dive into the five core modules: Prompt, Memory, Tool Use, RAG, and Skill, with progressive development workflow.
Read more →
Expert Opinions·4 min
Using AI All Day But Still Not More Productive? You're Chatting, Not Using an Agent
Using Doubao, Kimi, or ChatGPT daily but still overwhelmed? The problem isn't AI — it's how you use it. Learn the key difference between AI chat and AI Agents, and how to shift from talking to AI to having AI actually do the work.
Read more →
Tutorials·3 min
CLIProxyAPI Deployment Guide: Aggregate Multi-Platform AI Accounts for Unlimited Free Tokens
Complete Docker deployment guide for CLIProxyAPI (CPA) — aggregate OpenAI, Gemini, and CodeX accounts into one unified API with multi-account rotation to maximize free Token usage.
Read more →