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OpenClaw AI Agents: Build Tools, Memory, RAG & Multi-Agent
264 students
OpenClaw AI Agents: Build Tools, Memory, RAG & Multi-Agent
Set up OpenClaw, connect LLMs and channels, add custom tools, Skills, memory and RAG, then run multi-agent workflows
Created byYotta Academy
Last updated 10/2026
English
English [Auto],
What you'll learn
- Set up a complete OpenClaw development environment and understand its architecture and agent execution flow
- Configure different LLM providers and create your first agent with clear roles and system prompts
- Connect agents to communication channels and extend them with custom tools and reusable Skills
- Implement short-term and long-term memory and manage conversation context across interactions
- Build RAG applications so your agents answer questions from external knowledge sources
- Design multi-agent workflows with guardrails, prompt injection protection and error handling
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Course content
11 sections • 43 lectures • 2h 1m total length
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.
Chatbots answer. Agents act. They reason, call tools, remember, look things up and work with other agents. In this course you build those agents yourself, with OpenClaw, step by step.
No slide-deck theory marathon. Every major concept comes with a hands-on demo you follow along with.
Who this course is for:
- Developers who want to add real AI agents to their skill set
- Students looking to break into Agentic AI
- Freelancers and automation builders who want agents that use tools and automate workflows
- AI engineers who want a structured path through OpenClaw
What you'll build, in order:
- A complete OpenClaw development environment, plus a clear picture of the OpenClaw architecture
- Your first AI agent, with roles, system prompts and different LLM providers configured
- Agents connected to communication channels
- Custom tools and reusable Skills that let your agents actually do things
- Short-term and long-term memory, with proper conversation context management
- Retrieval-Augmented Generation (RAG) apps that answer from external knowledge sources
- Collaborative multi-agent workflows where several agents work together on complex tasks
What's inside:
- Agent fundamentals and the full agent execution flow
- Advanced prompting techniques for more reliable agents
- Guardrails and prompt injection protection to keep agents secure
- Error handling so your agents don't fall over in real use
How it's structured:
We start from the ground up. First the fundamentals and setup. Then one agent. Then tools, Skills, memory and RAG. Finally, everything comes together in multi-agent workflows. Each step builds on the last, so you always know why you're doing what you're doing.
By the end you'll be able to build modern AI agents with OpenClaw. Your agents will communicate, use tools, retain memory, retrieve knowledge and automate workflows, and you'll have the security basics in place.
Ready to build? Enrol now and let's ship your first OpenClaw agent together.
- Team Yotta Academy
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.