AI 开发 教程
系统化的 AI 开发学习路径。从提示词工程到模型微调，从 RAG 到 Agent 开发，覆盖 AI 应用开发全栈知识。共 142 篇教程，18 个分类，入门到高级全覆盖。
找到 20 篇教程
DeepSeek Agent Development Tutorial
Complete tutorial for building AI agents based on DeepSeek: ReAct mode, tool calling, memory management, and multi-agent collaboration. Includes practical code for LangChain Agent, AutoGPT-style autonomous agents, and Function Calling Agent.
阅读教程 →Introduction to the MCP Protocol: Building Standard Interfaces for AI Tool Calls
MCP (Model Context Protocol) is the hottest AI tool calling standard protocol in 2026. This article explains the MCP protocol principles from scratch, Server construction, Client integration, and a complete integration solution with DeepSeek API.
阅读教程 →Multi-Agent Collaboration Patterns in Practice: From Orchestration to Autonomy
Single agents have limited capabilities; multi-agent collaboration is key to unlocking complex tasks. This article starts from practical experience, compares mainstream collaboration modes such as Supervisor, GroupChat, and Sequential, and provides runnable code implementations.
阅读教程 →MCP Server Development in Practice: From Protocol to Production-Grade Tool Service
MCP is becoming the standard protocol for AI tool invocation. Starting from protocol principles, this article walks you through implementing a production-grade MCP server, covering tool definition, authentication, error handling, streaming responses, and deployment operations.
阅读教程 →Agent State Machine and Fault-Tolerant Orchestration: Building Reliable Agents
In real business, agents fail: tool timeouts, context overflow, loop deadlocks. This article models the agent lifecycle with a finite state machine and explains the complete implementation of fault-tolerant orchestration, including timeout circuit breaking, retry compensation, and session recovery.
阅读教程 →Agent Memory System Design: Short-Term, Long-Term, and Tool Memory
Memory is the foundation of Agent intelligence. This article models four layers: working, episodic, semantic, and tool memory, covering compression, retrieval, writing, and forgetting strategies. Implement a complete Agent memory subsystem with vector memory and reflection using DeepSeek API.
阅读教程 →Agent Loop Basics: Understanding AI Autonomous Workflows from Scratch
Agent Loop is the hottest AI development paradigm today. Starting from the ReAct pattern, this article helps you understand how AI achieves the autonomous loop of observation → planning → execution → feedback, and master the core concepts of Agent development.
阅读教程 →Tool Calling Loop: Enabling AI to Use External Tools
Function Calling is a key capability of Agent Loop. This article explains in depth how AI autonomously selects tools, calls APIs, and parses results to build truly capable AI assistants.
阅读教程 →Reflection and Self-Correction: Doubling AI Output Quality
Reflection is a key technique to improve the quality of AI output. This article explains how to enable AI to self-evaluate, discover errors, and iteratively correct them, significantly improving output quality through a reflection loop.
阅读教程 →Multi-Agent Collaboration: Building an AI Team with Skill Chains
A single AI has limited capabilities, but collaboration among multiple AI roles can solve complex problems. This article explains how to orchestrate multiple Agents with skill chains, building a collaborative team of planner → executor → reviewer.
阅读教程 →Hands-on: Build Your First Agent Loop Application
From theory to practice, this article teaches you step by step how to build a complete Agent Loop application using the DeepSeek API. It includes the complete process of requirements analysis, task planning, step-by-step execution, and result verification.
阅读教程 →Design of Agent Memory Systems
Memory is the key to transforming agents from one-time tools to long-term intelligent companions. This article deeply analyzes the architecture design of agent memory systems, covering short-term/long-term memory, RAG-enhanced memory, and hybrid retrieval strategies.
阅读教程 →In-Depth Implementation of ReAct Agents
ReAct (Reasoning + Acting) is one of the most classic paradigms in agent development. This article explains in depth how to build a production-grade ReAct agent, from principles to implementation, including prompt engineering, tool management, and error recovery.
阅读教程 →Design of Multi-Agent Communication Protocols
When multiple AI agents need to collaborate, communication protocols are the key link connecting them. This article explores the design principles, message formats, routing strategies, and consistency guarantees of multi-agent communication protocols.
阅读教程 →Agent Security and Permission Control
When AI agents can autonomously call tools, access databases, and execute code, security becomes critical. This article explains agent security architecture design, including sandbox isolation, permission models, audit logs, and injection protection.
阅读教程 →Agent Testing and Debugging Strategies
The non-deterministic behavior of agents makes traditional testing methods ineffective. This article introduces specialized testing strategies for agents, including evaluation-driven testing, observability debugging, and CI/CD integration solutions.
阅读教程 →AI Agent Tool Development in Practice: From Design to Deployment
Tools are the hands and feet of agents. This article provides an in-depth explanation of how to design, develop, test, and deploy high-quality AI agent tools, including tool interface design, error handling, performance optimization, and security protection.
阅读教程 →Multi-Agent Collaboration: Building an AI Team
A single Agent has limited capabilities; multi-Agent collaboration can solve more complex problems. This article explains the design patterns, communication mechanisms, and practical application cases of multi-Agent systems, teaching you to build AI teams that can work collaboratively.
阅读教程 →AI Agent Architecture Design: From ReAct to Plan-and-Execute
AI Agent is one of the hottest technology directions currently. This article provides an in-depth analysis of mainstream Agent architectures such as ReAct, Plan-and-Execute, and Multi-Agent, helping you understand the applicable scenarios and design principles of different architectures.
阅读教程 →Function Calling in Practice: Let AI Invoke External Tools
Function Calling is the bridge connecting AI to the real world. Starting from scratch, this article explains the principles, implementation methods, and common application scenarios of Function Calling, enabling AI to query databases, send emails, and call APIs.
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