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 9 篇教程
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 →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 →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 →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.
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