#LangChain beginner guide
137 related articles
·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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·6 min
Getting Started with LangChain: A Deep Dive into Model and Agent Core Concepts
Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.
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·7 min
Complete Guide to OpenCode: Installation, Configuration, and Custom Commands in Practice
A systematic guide to OpenCode, the open-source terminal AI coding tool: installation methods (including WSL), model configuration, rules files, Agent types, custom commands, and MCP tool extensions.
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·4 min
Dify Local Deployment Tutorial: Build Your AI Application Development Platform from Scratch
Step-by-step guide to deploying Dify locally using BT Panel, covering VM setup, Ubuntu configuration, and Docker deployment for a private AI dev platform.
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·4 min
AI Agent Learning Roadmap for Beginners: A Three-Stage Journey from Fundamentals to Hands-On Practice
A systematic AI Agent learning roadmap for beginners covering core theory, the ReAct paradigm, and multi-agent collaboration, with hands-on project suggestions.
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·3 min
AI Agent Beginner's Guide: A Complete Four-Stage Learning Path
A complete learning path for AI Agent development from scratch, covering core theory, ReAct paradigm, multi-agent collaboration, Prompt optimization, and hands-on projects across four stages.
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·3 min
AI Agent Development: A Three-Stage Learning Roadmap from Zero to One
A systematic three-stage AI Agent development roadmap: from Python basics and LLM fundamentals, through five core capabilities like planning and tool use, to hands-on RAG projects for real-world deployment.
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·3 min
748-Episode AI Large Language Model Development Tutorial: Seven Core Modules from Zero to Project Deployment
Analysis of a 748-episode, 198-hour AI LLM development tutorial covering API integration, prompt engineering, RAG, AI Agents, fine-tuning, multimodal development, and deployment.
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·3 min
The Complete Skill Map for AI Application Engineers: A Progressive Roadmap from Fundamentals to Production
A systematic breakdown of the complete skill structure for AI application engineers, covering Python & deep learning fundamentals, small model engineering, LLM fine-tuning, Agent development, and enterprise projects.
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·4 min
Is the 748-Episode AI Large Language Model Tutorial on Bilibili Worth Watching? A Full Breakdown of Content Structure and Study Tips
In-depth analysis of Bilibili's 748-episode AI LLM tutorial covering RAG, Agent, and fine-tuning. Includes content structure breakdown and practical study tips for beginners.
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·4 min
Loop Engineering from Beginner to Expert: A Complete Guide to Agent Loop Development
A deep dive into Loop Engineering covering Agent Loop workflows, code implementation (While loops and Graph patterns), and how it differs from Prompt Engineering.
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·3 min
Enterprise-Grade RAG Full-Pipeline Optimization: Multi-Turn Conversations, Retrieval Tuning, and Production Engineering in Practice
A deep dive into full-pipeline optimization for enterprise RAG systems, covering multi-turn query rewriting, retrieval tuning, and quality evaluation to take RAG from demo to production.
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·3 min
AI Agent Hands-On Learning Path: A Complete Guide from Beginner to Enterprise-Level Development
A systematic AI Agent development learning roadmap covering prompt engineering, RAG, multi-Agent collaboration, tool calling, and more—with phased learning advice and 28 hands-on project references.
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·3 min
AI Agent Learning Roadmap: A Four-Step Practical Guide from Zero to Job-Ready
A proven AI Agent learning roadmap covering four core elements, mainstream architecture patterns, multi-agent collaboration, and hands-on projects to go from zero to job-ready in three months.
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·4 min
AI Agent Systematic Learning Path: From Zero to Independent Development
A systematic AI Agent learning path covering core principles, Prompt engineering, RAG, multi-Agent collaboration, and hands-on projects for beginners.
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·3 min
AI Large Model Learning Roadmap Breakdown: Three Stages from Application Development to Model Fine-Tuning
Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.
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·2 min
Hands-On Tutorial: Build an AI Agent from Scratch with 200 Lines of Python
Build an AI Agent from scratch with 200 lines of Python, covering prompts, memory, tool calling, RAG, and Skills — a practical guide for developers.
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