5 No-Cost Learning Resources for LLM Agents

We all know that LLM agents are the new talk of the town and considering the speed with which the progress is being made, we all need to constantly upgrade ourselves to match with the needs of the industry. Finding the right resources for trendy topics might get difficult and often frustrating. I will be sharing a list of 5 resources that I recommend to people jumping into LLM agents. From blogs, playlists to masterclass, this will be all that you will need to get started with the LLM Agents.

1. Large Language Model Agents MOOC by UC Berkeley (State of the Art – Most Recommended)

 
Link to course

This course is a comprehensive overview of LLM agents, their foundational principles, and their capabilities in task automation and personalization. With lectures covering reasoning, agent infrastructure, retrieval-augmented generation, and multimodal agents, this MOOC is designed for anyone serious about learning LLM agents. Industry leaders like Google, Meta, and NVIDIA provide guest lectures, and the syllabus explores unique applications from code generation to robotics as well. The course also addresses privacy, safety, and human-agent interaction, making it an all-in-one learning experience. Completion certificates are awarded, with varying levels of achievement for participants.

Key Topics:

• Foundations of LLMs
• Reasoning, Planning, and Tool Use
• Infrastructure for LLM Agents
• Real-world Applications: Robotics, Medical, Web Automation
• Privacy, Ethics, and Human-Agent Interaction

2. AI Agent Mastery BootCamp by ArizeAI

 
Link to playlist

This self-paced YouTube playlist is perfect for those looking to understand the architecture and optimization of AI agents. From troubleshooting complex agent behavior to implementing effective performance evaluation methods, ArizeAI’s bootcamp provides practical insights for developing efficient and capable LLM agents. It includes sessions from notable experts, including Jerry Liu (LlamaIndex) and Chi Wang (AutoGen), who discuss real-world frameworks like LangGraph and LlamaIndex workflows. Whether you’re debugging agent loops or exploring framework comparisons, this bootcamp has you covered.

Key Topics:

• Agent Architectures and Core Concepts
• Comparing Frameworks: LangGraph, LlamaIndex
• Evaluating Agent Performance
• Resolving Agent Looping Issues
• Agents Masterclass

3. Multi AI Agent Systems with crewAI by DeepLearning.ai

 
Link to course

This course, offered by DeepLearning.AI and taught by João Moura, Founder and CEO of crewAI, provides training on building multi-agent systems to automate complex business processes. Using the open-source crewAI library, you will learn to design multiple agents with specific roles and tools, creating a collaborative AI team capable of handling tasks like resume tailoring, event planning, technical writing, customer support, and financial analysis.

Key Topics:

• Role-playing for Agents
• Memory management for Agents
• Tools & Guardrails
• Collaboration – Build agents to work together for improved workflow

4. LLM Agents: From Zero to One Roadmap

 
Link to roadmap

Created by Aishwarya N Reganti, GenAI Lead at AWS, this roadmap guides beginners through essential LLM agent concepts. It’s structured into daily learning modules for 5 days that cover everything from agent fundamentals to practical applications. Each day includes tutorials, case studies, and resources (blogs, research papers) for building your own agents with LangChain and other popular tools.

Roadmap Highlights:

• Basic Agent Terminology and Principles
• Core Components: Examples from NVIDIA and LangChain
• Evaluation Frameworks: AgentBench, ToolBench
• Case Studies: ChemCrow, BabyAGI
• Implementation Guides: LangChain tutorials

5. Starting with AI Agents: Insights for Newcomers

 
Link to resource

I found Tereza Tizkova’s blog post particularly valuable for beginners. It offers an approachable introduction to AI agents, making it especially helpful for those new to the field. This guide can be used alongside the courses mentioned earlier, providing a solid foundation for understanding AI agents. It also includes a wealth of resources for each subtopic, allowing readers to explore more. I usually recommend a top-to-bottom learning approach, and I believe this post serves as an excellent reference material to start with before exploring more advanced topics.

• Defining AI Agents
• Historical Context – Evolution of AI agents
• Advantages of AI Agents
• Challenges and Limitations
• Curated Resources of valuable Articles, Tools, and Expert Interviews

Kanwal Mehreen Kanwal is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.