掌握OpenClaw框架，构建自主AI智能体。本课程覆盖开发环境搭建、多模型网关、自定义工具、RAG记忆管理及多智能体协同架构，助你开发生产级AI系统。
原始标题：Master OpenClaw and Build Powerful AI Agents
本课程是一门面向开发者的自主 AI 智能体（AI Agent）全栈开发与部署实战指南。课程依托 OpenClaw 核心框架，带你从零基础搭建开发环境开始，逐步深入多模型网关（Gateway）配置，并赋予智能体调用自定义工具（Tools）与复用技能（Skills）的行动能力；通过融合长短期记忆管理与 RAG（检索增强生成）技术，让智能体具备精准的行业知识检索和上下文理解能力；最终，你将掌握主管-工人（Supervisor-Worker）等多智能体协同架构的设计方法，并嵌入安全护栏（Guardrails）机制，从而能够独立开发出安全、高可用且能解决复杂现实业务的生产级 AI 协作系统。
Published 7/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 0m | Size: 1.83 GB
Build Autonomous AI Agents with OpenClaw, Gateway, Skills, Tools, Memory, RAG & Multi-Agent Workflows
What you’ll learn
Build autonomous AI agents using OpenClaw from setup to deployment with real-world hands-on projects.
Create AI agents that use custom tools, Skills, memory, and multiple LLM providers to automate complex tasks.
Implement Retrieval-Augmented Generation (RAG) and context management to build accurate knowledge-based AI assistants.
Design and develop secure multi-agent workflows with supervisor-worker architectures, guardrails, and best practices.
Requirements
Basic computer skills and familiarity with using Windows, macOS, or Linux.
No prior experience with OpenClaw is required—we start from the fundamentals.
Basic knowledge of Python is recommended but not mandatory.
A computer with internet access to install OpenClaw and required development tools.
Description
This course contains the use of artificial intelligence.
Artificial Intelligence is rapidly evolving from simple chatbots intoautonomous AI agents capable of reasoning, using tools, remembering conversations, retrieving knowledge, and collaborating with other agents. OpenClaw is a powerful framework that enables developers to build these intelligent, production-ready AI systems with ease.
In this comprehensive, hands-on course, you’ll learn how to build powerful AI agents using OpenClaw from the ground up. We’ll begin with the fundamentals of AI agents, explore the OpenClaw architecture, and set up a complete development environment. You’ll then learn how to configure different LLM providers, create your first AI agent, define agent roles and system prompts, and understand the complete agent execution flow.
As you progress, you’ll connect agents to communication channels, buildcustom tools, work withreusable Skills, and learn advanced prompting techniques to create more reliable and intelligent agents. You’ll also implement short-term and long-term memory, manage conversation context, and buildRetrieval-Augmented Generation (RAG) applications that allow your agents to answer questions using external knowledge sources.
Finally, you’ll bring everything together by building collaborativemulti-agent workflows, where multiple AI agents work together to solve complex tasks. You’ll also learn important concepts such asguardrails, prompt injection protection, and error handling to make your AI agents more secure and reliable.
This course focuses on practical learning through step-by-step demonstrations rather than theory alone. Every major concept is reinforced with hands-on demos, helping you build real AI agents that can communicate, use tools, retain memory, retrieve knowledge, and automate workflows.
Whether you’re a software developer, AI engineer, automation enthusiast, or student looking to enter the world of Agentic AI, this course will provide the knowledge and practical skills needed to confidently build modern AI agent applications using OpenClaw.
Who this course is for
Software developers who want to build autonomous AI agents with OpenClaw.
Python developers looking to create tool-using, memory-enabled, and RAG-powered AI agents.
Students and professionals who want hands-on experience building real-world AI agent workflows.
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