Free AI agents course for beginners: learn LLMs, tools, agent loops, memory, multi-agent systems, evals, and a real OpenClaw case study.
Chapter 1
Introduces the course path and the core architecture behind large language models, setting the base for everything agents do later.
In your own words, write a five-sentence explanation of how an LLM produces its next token.
AI Agents for Beginners is a free, 3-hour course that teaches you to build AI agents from first principles. It moves from LLM basics (transformers, tokens, context windows) to tool calling, agent loops, memory, multi-agent systems, and evaluation. It ends with a deep case study of OpenClaw, an open-source agent codebase. It suits developers who want to understand how agents work under the hood, not just use a framework.
The course has six sections. Sections 1–2 cover how LLMs work and how to use them through APIs. Sections 3–4 cover code setup, tool schemas, and when to use workflows instead of agents. Section 5 builds agents from scratch: the perceive–reason–act loop, guardrails, memory, multi-agent communication, error handling, and frameworks such as LangChain and Mastra. Section 6 applies it all to OpenClaw, including testing, observability, and security.
Build every lab yourself rather than just watching. Most of the learning happens in the agent loop code.
Many agent tutorials jump straight to a framework. This one builds the loop by hand first, then explains frameworks, evaluation, observability, and security, which are the topics that decide whether an agent survives in production. The OpenClaw case study gives rare, concrete exposure to a real agent codebase.
AI agent engineering is one of the fastest-growing areas in software. Employers value engineers who understand tool calling, memory, evaluation, and security, not just prompt writing. Building an evaluated, observable agent from this course makes a strong portfolio piece for AI engineer roles.
This course was created by KodeKloud and published by freeCodeCamp.org. Free Coding Academy curates and organises publicly available learning media for educational purposes and does not own, host, download, or re-upload the video.
Yes. It starts with how LLMs, tokens, and APIs work before moving to agents, so you do not need prior AI experience. Basic programming knowledge helps, especially for the hands-on labs where you build agent loops and a personal assistant.
An AI workflow follows predefined steps that you design in advance. An AI agent decides its own next step in a loop, choosing tools and actions based on what it observes. The course recommends the simplest approach that works, which is often a workflow.
OpenClaw is an open-source AI agent project used here as a real-world case study. The final section tours its codebase, terminal interface, concurrency handling, evaluations, observability, and security, showing how agent concepts appear in production code.
You make your first LLM API call, build a multi-turn chat, write an agent while-loop with tools and guardrails, and finish with a production-style personal assistant. The course describes four distinct agents that evolve into a multi-agent system.
Because agent output is non-deterministic, the course uses evaluation datasets and LLM-as-a-judge scoring instead of relying only on exact-match unit tests. It pairs this with observability, tracking latency, cost, and success metrics over time.
The video is about 3 hours 6 minutes. With the labs and practice tasks, plan for roughly 8 to 10 hours spread over a week or two.
The course was created by KodeKloud and published by freeCodeCamp.org. Free Coding Academy organises it into a structured learning page and links to the original lesson.
11 chapters Free 8-hour agentic AI course for beginners: generative AI, embeddings, LLMs, RAG agents, design patterns, agent frameworks, MCP, and guardrails.
Open structured course →
26 chapters Learn AI Foundations for Absolute Beginners with a structured free course featuring guided chapters, summaries, practice tasks, and career-focused notes.
Open structured course →
28 chapters Learn Artificial Intelligence | Complete AI Course 2026 | Intellipaat with guided chapters, summaries, practice tasks, and career-focused notes.
Open structured course →