代理人工智能工程是一个生产级工程资源，用于构建现代代理人工智能系统，支持LangChain、LangGraph、RAG、MCP、本地模型和可部署的Python服务。

该仓库引导至第5章实现的架构：一个多节点的LangGraph助手，连接到独立的MCP服务器，具备意图路由、工具执行、响应摘要、电子邮件工具、数学工具、自动化测试和GitHub Actions CI。前几章构建了其底层所需层：提供者抽象、LCEL编排、矢量检索、内存、ReAct代理、路由器图、顺序工作流、多代理协作和人机环控。

这不是初学者聊天机器人的攻略。这是为开发者设计的结构化工程路径，帮助构建需要状态、工具、路由、检索、可观测性、模块化以及模型-提供者灵活性的系统。

你将要建造的东西

• 多节点LangGraph助手，具备路由器、执行、摘要和会话节点。
• MCP 工具服务器 在解耦服务器边界上暴露孤立的数学和电子邮件工具。

Agentic AI Engineering

Agentic AI Engineering is a production-grade engineering resource for building modern agentic AI systems with LangChain, LangGraph, RAG, MCP, local models, and deployable Python services.

The repository leads toward the architecture implemented in Chapter 5: a multi-node LangGraph assistant connected to a standalone MCP server, with intent routing, tool execution, response summarization, email tooling, math tools, automated tests, and GitHub Actions CI. Earlier chapters build the required layers underneath it: provider abstraction, LCEL orchestration, vector retrieval, memory, ReAct agents, router graphs, sequential workflows, multi-agent collaboration, and human-in-the-loop control.

This is not a beginner chatbot walkthrough. It is a structured engineering path for developers building systems that need state, tools, routing, retrieval, observability, modularity, and model-provider flexibility.

What You’ll Build

• Multi-node LangGraph assistant with router, execution, summarization, and conversation nodes.
• MCP tool server exposing isolated math and email tools over a decoupled server boundary.
• Tool-using ReAct workflows that call external capabilities through typed tool contracts.
• RAG pipelines using vector stores, embeddings, retrieval chains, and local document context.
• Provider-flexible LLM interfaces across OpenAI, Gemini, and Ollama.
• Agent routing systems for sequential, router-based, ReAct, and multi-agent workflows.
• Human-in-the-loop execution paths for safer agent behavior in production-style flows.
• Tested agent components with pytest coverage for Chapter 5 node behavior and tool contracts.
• CI-backed agent validation through GitHub Actions for repeatable checks on graph and MCP behavior.

Engineering Roadmap