本课程手把手教你用Python、LangChain与LangGraph开发具备推理、工具调用与记忆的自主AI智能体，涵盖ReAct架构、多智能体协同及FastAPI与Docker部署，适合Python基础学习者。
原始标题：Build Autonomous AI Agents with LangChain & LangGraph
本课程是一门从零开始、步骤导向的实战型 AI 智能体（Agents）开发课程。它依托于快速演进的 Agentic AI 行业背景，专注于教授如何使用 Python、LangChain 和 LangGraph 核心技术栈，将传统聊天机器人升级为具备独立推理、工具调用与上下文记忆能力的自主系统。学员将系统性地掌握 ReAct 架构、自定义工具集成、基于状态的持久化记忆，以及关键的人机协作（审批）工作流。此外，课程直击工业界落地痛点，涵盖了多智能体协同系统的设计，并指导学员使用 FastAPI、Docker 和 Streamlit 将智能体部署为可交付的实际应用。
课程只需具备 Python 基础编程能力，无需任何大模型开发经验，即可在全手写代码的演练中，跨越从理论到商业级智能体部署的鸿沟。
Published 7/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 36m | Size: 1.38 GB
Build intelligent AI agents using LangChain, LangGraph, Python, tools, memory, and multiagent architectures from scratch
What you’ll learn
Build intelligent AI agents using LangChain and LangGraph with Python.
Create autonomous AI agents with memory, tools, and reasoning capabilities using the ReAct architecture.
Design and implement multi-agent systems where specialized AI agents collaborate to solve complex tasks.
Integrate external tools, APIs, and custom Python functions into AI agent workflows.
Implement persistent memory, state management, and human-in-the-loop approval workflows in LangGraph.
Deploy AI agents using FastAPI, Docker, and Streamlit for real-world applications.
Requirements
Basic knowledge of Python programming is recommended.
Familiarity with fundamental programming concepts such as variables, functions, loops, and classes.
No prior experience with LangChain, LangGraph, or AI agents is required—everything is taught step by step.
Basic understanding of APIs and command-line tools is helpful but not mandatory.
Enthusiasm to learn and build real-world AI agent applications through hands-on coding.
Description This course contains the use of artificial intelligence.
Artificial Intelligence is rapidly evolving from simple chatbots to autonomous AI agents capable of reasoning, using tools, maintaining memory, and solving complex tasks with minimal human intervention. As businesses increasingly adopt Agentic AI, skills in frameworks likeLangChain andLangGraph have become highly valuable for developers and AI enthusiasts.
In this hands-on course, you’ll learn how to build intelligent AI agents from scratch usingPython,LangChain, andLangGraph. We’ll start with the fundamentals of Agentic AI, exploring how large language models, prompts, memory, and tools work together to create autonomous systems. You’ll then build LLM-powered applications, add conversational memory, integrate external tools, and create custom Python tools for automation.
Next, you’ll move beyond traditional AI chains and discover howLangGraph enables powerful graph-based workflows. Through practical coding demonstrations, you’ll build autonomous agents using the ReAct architecture, implement state management and persistent memory, create human-in-the-loop approval workflows, and develop collaborative multi-agent systems with supervisor and worker agents.
Finally, you’ll learn how to deploy your AI agents usingFastAPI, containerize them withDocker, and build a simpleStreamlit interface for interacting with your applications.
This course focuses on practical implementation rather than theory, with step-by-step demos that help you build real-world AI applications. Whether you’re a Python developer, AI engineer, machine learning enthusiast, or software developer looking to enter the world of Agentic AI, this course will give you the knowledge and hands-on experience needed to build, customize, and deploy modern AI agents with confidence.
Who this course is for
Python developers who want to build intelligent AI agents using LangChain and LangGraph.
AI and machine learning enthusiasts interested in Agentic AI and autonomous systems.
Software developers looking to integrate LLMs, tools, memory, and multi-agent workflows into their applications.
Students and professionals who want to learn practical AI agent development through hands-on projects.
Developers looking to deploy production-ready AI agents using FastAPI, Docker, and Streamlit.
Anyone who understands basic Python and wants to transition from building chatbots to creating autonomous AI agents.
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