#Agent开发
128 related articles
TutorialsFrom Programmer to Agent Developer: A Detailed Four-Stage Learning Roadmap
A systematic four-stage learning roadmap for programmers transitioning to AI Agent development, covering core theory, ReAct and classic paradigms, Prompt engineering, and hands-on projects.
TutorialsThe Three-Layer Pyramid Model for Agent Development: From Beginner to Industrial-Grade Deployment
Deep dive into the Three-Layer Pyramid Model for Agent development, covering autonomous agents, collaborative multi-agent systems, and universal orchestration agents with a complete learning path from beginner to industrial-grade deployment.
TutorialsVibe Coding in Practice: Building a Mini-OpenClaw Agent from Scratch Without Writing Code
Learn how to build a Mini-OpenClaw Agent framework using Vibe Coding with zero code, covering OpenClaw's architecture, LangChain integration, and AI-assisted development workflows.
TutorialsOpenClaw: Building Industrial-Grade Agents with Zero Code — Deployment, Memory Management & Feishu Automation in Practice
A deep dive into OpenClaw's industrial-grade agent framework: local deployment, dual-layer memory management, Skills system design, and zero-code enterprise Agent building with Feishu automation.
TutorialsHermes 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.
TutorialsLangChain Unified Interface in Practice: Complete Guide from Model Invocation to Agent Development
A detailed guide to LangChain's unified interface, comparing model invocation methods, explaining LLM vs Agent differences, with complete Python walkthrough from setup to agent development.
TutorialsBest Practices for Claude Computer Use: A Complete Guide to Resolution, Click Accuracy, and Model Selection
In-depth guide on Anthropic's Claude Computer Use best practices covering screenshot resolution, click accuracy optimization, model selection, and small target strategies for AI Agent automation.
Tutorials2026 AI Large Language Model Development Learning Roadmap: From Zero to Enterprise-Level Implementation
Complete 2026 AI LLM development learning path covering Prompt Engineering, RAG, Agent development, and fine-tuning, with a phased plan from zero to enterprise-level implementation.
TutorialsAI Agent Acceptance Testing Anti-Pitfall Guide: Replace AI Self-Reports with Logs and Data
Don't trust AI's self-reported test results. Learn three-layer acceptance criteria using HTTP status, business logs, and database persistence to verify AI Agent outputs.
TutorialsVibe Coding Interview in Practice: Passing a Programming Interview with Claude Code in One Hour
Real Vibe Coding interview experience: using Claude Code and MiniMax M2.1 to build a smart outfit assistant Agent in one hour. Learn Plan Mode, MVP strategy, and AI collaboration tips.
TutorialsAnthropic's Official MCP Tutorial: A Complete Guide from Basics to Remote Deployment
A detailed guide to Anthropic's official MCP course covering client-server architecture, building MCP servers, third-party integrations, and remote deployment for standardized AI tool access.
TutorialsBuild 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.
TutorialsLLM Agent Development Learning Path: A Three-Step Practical Guide from Zero to Freelancing
A systematic three-step learning path for LLM Agent development: from Prompt Engineering and API calls, to RAG and vector databases, to ReAct and multi-agent systems.
TutorialsClaude Skills Tutorial: Building an AI Agent Skill System from Scratch
Learn to build AI Agent Skills for Claude Code: skill.md writing, script integration, image generation workflows, and reusable skill architecture from scratch.
TutorialsLangChain Agent Development Tutorial: RAG and Agent Hands-On Learning Path
A systematic learning path for LangChain Agent development covering RAG, autonomous Agent decision-making, and tool calling—from zero to production-ready projects.
TutorialsLangChain Beginner's Guide: A Complete Guide to LLM and Agent Development
A deep dive into LangChain framework essentials, clarifying the key differences between LLMs and Agents, covering Agent development, DeepAgent advanced topics, and learning paths.
TutorialsSpring AI in Practice: Building a Text-to-SQL Agent Tool Chain from Scratch
Build a Text-to-SQL assistant using Spring AI Alibaba Agent Framework with a four-step Tool chain, plus solving ThreadLocal failures in stream mode with RunnableConfig.
TutorialsLangChain Streaming Output & Async in Practice: Complete Implementation for Agent Scenarios
Deep dive into LangChain streaming output and async implementation, covering astream usage, custom AsyncQueueCallbackHandler, and Agent streaming executor architecture for production AI apps.
TutorialsPractical Guide to OpenAI Agents SDK Message Types and Conversation History Management
Deep dive into OpenAI Agents SDK's five message types, conversation history management, and common pitfalls including static/dynamic instructions and Function Call pairing.
Deep DivesHarness Engineering Explained: The Third Paradigm Shift in AI Engineering
Deep dive into Harness Engineering: the evolution from Prompt Engineering to Context Engineering to Harness Engineering, comparing Anthropic and OpenAI's different approaches to AI Agent development.