I Took This Free AI Agents Crash Course—Here's What You Need to Know

By R. Shivakumar

Last week, I stumbled across a free mini crash course on AI agents from Daily Dose of Data Science. The timing couldn't have been better—I'd just finished writing about agent frameworks and YouTube tutorials, and here was a practical, hands-on course that promised to tie everything together.

I cleared my Saturday afternoon, grabbed coffee, and dove in. What I found was one of the most comprehensive free resources for understanding modern AI agents, complete with code, tools, and real-world implementation patterns.

Here's my honest breakdown of what works, what doesn't, and whether you should invest your time in this course.

TL;DR – Free AI Agents Crash Course

What it is: Hands-on, free crash course to build AI agents from scratch using open-source tools. No vendor lock-in.
Core concepts:
- AI agents that think, remember, and act
- Tool integration via MCP (Model Context Protocol)
- Persistent, graph-based memory with Zep Graphiti
- Observability and debugging using Opik
Why it’s valuable:
- Practical, production-ready patterns
- Clear visual explanations
- Complete code repository to run and modify
- Simple evaluation metrics (G-Eval)
Who it’s for: Developers, data scientists, or teams wanting hands-on AI agent experience. Not ideal for complete Python beginners.
Time commitment: 4–6 hours (video + coding + experimentation)
Pros: Open-source, practical, production-focused, modular, visual guides
Cons: Quick pace, some setup/troubleshooting challenges, Windows setup trickier, shallow MCP dive, no LLM cost discussion
Bottom line: Excellent free resource for learning modern AI agents with code you can deploy immediately.

What this crash course actually covers

The course builds a complete AI agent system from scratch using entirely open-source tools. No proprietary APIs, no vendor lock-in, just practical implementation you can run on your own machine.

The core curriculum:

What AI agents actually are (beyond the buzzwords)
Connecting agents to real-world tools
Introduction to MCP (Model Context Protocol)
Replacing traditional tools with MCP servers
Setting up observability and tracing for production

The tech stack (and why it matters):

CrewAI: Building MCP-ready agents
Zep Graphiti: Adding persistent memory to agents
Opik: Observability and tracing for debugging

This combination addresses three critical challenges I've seen developers struggle with: making agents reliable, giving them memory that persists, and debugging when things go wrong.

The system you'll build (and why it's clever)

The course walks you through building an agent system that actually does something useful. Here's the architecture:

User sends a query
Assistant agent runs a web search via MCP
Query and search results get sent to a Memory Manager
Memory Manager stores context in Graphiti (graph-based memory)
Response agent crafts the final answer using all available context

Why this architecture matters: Most tutorials build agents that forget everything between sessions. This one shows you how to build agents that remember previous interactions and get smarter over time.

The visual walkthrough in the course made the data flow crystal clear. I paused, sketched it out, and could immediately see how to adapt this pattern for my own projects.

What makes this course different

1. It's genuinely practical

The course doesn't waste time on theory you won't use. Every concept gets implemented immediately with code you can run. The GitHub repository contains everything, from setup instructions to complete working examples.

My experience: I had the basic agent running within 45 minutes. That's not just following along—that's understanding enough to modify behavior and experiment.

2. It tackles production concerns

Most tutorials stop at "look, it works!" This course goes further: observability, tracing, error handling, and evaluation metrics. These are the unglamorous topics that separate hobby projects from professional work.

The section on G-Eval particularly impressed me. It shows how to create evaluation metrics in plain English rather than writing complex scoring code. You define what "good output" means in natural language, and the system scores your agent's responses accordingly.

Real example from the course:

Define metric: "The output should be relevant to the context provided"
High relevance = high score
Low relevance = low score

Simple concept, powerful results. I've already started using this pattern in my own agent evaluation.

3. It explains MCP (Model Context Protocol)

MCP is still relatively new, and most developers don't fully understand it yet. This course demystifies it with clear explanations and practical examples.

What MCP solves: Instead of building custom tool integrations for every agent, MCP provides a standardized way for agents to interact with external systems. Think of it as USB-C for AI agents—one protocol, many uses.

The course shows you how to replace traditional tool connections with MCP servers, which makes your agents more flexible and maintainable. This clicked for me when I saw the Jupyter MCP server example—agents controlling Jupyter notebooks in real-time, creating and executing cells autonomously.

4. Memory that actually works

The Zep Graphiti integration is where this course shines. Most agent memory implementations are fragile or limited. Graphiti uses graph-based storage, which means agents can understand relationships between concepts, not just store conversation history.

Why this matters: An agent with graph-based memory can remember:

You prefer Python over JavaScript
You work in healthcare
You asked about HIPAA compliance last week
How these facts relate to your current question

Traditional memory systems struggle with these connections. Graph-based memory handles them naturally.

My test: I built an agent that remembered my coding preferences across sessions and automatically adjusted its suggestions. This wasn't possible with the simpler memory systems I'd tried before.

What you need before starting

Technical requirements:

Basic Python knowledge (you should be comfortable reading code)
Understanding of how APIs work
Familiarity with terminal/command line
Git for cloning the repository

Time commitment:

Video content: ~30-45 minutes
Following along with code: 2-3 hours
Experimenting and customizing: another 2-4 hours

Hardware:

Any modern laptop works
No GPU required for the basic examples
Cloud deployment is optional

Realistic expectations: If you're completely new to programming, this will be challenging. If you've built any Python applications before, you'll be fine.

My detailed walkthrough experience

Part 1: Understanding agents (minutes 0-10)

The course opens with a clear definition: An AI agent uses an LLM as its brain, has memory to retain context, and can take real-world actions through tools.

In short: it thinks, remembers, and acts.

This three-part framework helped me finally explain agents to non-technical colleagues. The visuals showing information flow between these components are worth the price of admission alone (which is free, so even better).

Part 2: Tool integration (minutes 10-25)

Here's where theory meets practice. The course shows you how agents connect to tools—web search in this case—and use results to inform their responses.

What I appreciated: They show you both the working code and common mistakes. When the tool integration failed during the demo, they debugged it on camera. That's where real learning happens.

Pro tip: Pause frequently during this section and run the code yourself. Don't just watch—type every line. My comprehension jumped when I started doing this.

Part 3: MCP implementation (minutes 25-35)

This section introduces MCP and shows you how to replace traditional tool integrations with MCP servers. The transition is surprisingly smooth.

The aha moment: When I saw the same agent working with different MCP servers (web search, then Jupyter notebooks) without changing core agent code, MCP's power became obvious.

Challenge I hit: MCP is still maturing, so documentation is scattered. The course provides enough context to get started, but expect to explore GitHub repos for advanced use cases.

Part 4: Memory integration (minutes 35-50)

Adding Graphiti for memory management was technically the most complex part, but the course breaks it down well.

Key insight: Memory isn't just storage—it's about retrieving relevant information at the right time. Graphiti's graph structure makes retrieval smarter because relationships between facts matter as much as the facts themselves.

My stumbling block: Setting up Graphiti required some dependency management. The course mentions this but doesn't hand-hold through every error. Budget extra time here if you're on Windows or have an older Python version.

Part 5: Observability and tracing (minutes 50-65)

The Opik integration for observability felt like leveling up from hobbyist to professional developer.

What you get:

Detailed logs of every agent action
Performance metrics for each step
Error tracking with full context
Token usage monitoring

Why it matters: Without observability, debugging agents is guessing in the dark. With it, you see exactly where things break and why.

My experience: I introduced a bug deliberately to test the tracing. Within seconds, Opik showed me the exact tool call that failed and the error message. In previous projects without tracing, this would have taken 30 minutes of print statement debugging.

The strengths of this course

1. Open-source everything

No hidden costs, no API rate limits (beyond what you set), no vendor dependency. You control the entire stack, which means you can modify anything and deploy anywhere.

2. Production-ready patterns

The course doesn't just make things work—it makes them work reliably. Error handling, logging, evaluation metrics, and monitoring are baked in from the start.

3. Visual explanations

The animated diagrams showing data flow between components are excellent. I screenshot several and reference them when explaining agent architecture to others.

4. Complete code repository

Every example is in the GitHub repo, well-commented and ready to run. No "left as an exercise for the reader" gaps.

5. Bonus content on evaluation

The G-Eval section on LLM evaluation deserves its own course. It solves a problem I've struggled with: how do you measure if an agent's response is good when there's no single correct answer?

The weaknesses (because nothing's perfect)

1. Assumes some context

The course moves quickly. If you've never seen agent code before, you might feel lost. I recommend watching some CrewAI basics first (see my previous article on YouTube channels).

2. Limited error troubleshooting

When things break, the course shows you the fix but doesn't always explain why it broke. For beginners, this can be frustrating.

3. Windows users may struggle

Most setup instructions assume Mac or Linux. Windows users will need WSL or extra patience with path configurations. Not a dealbreaker, but worth knowing.

4. Shallow dive on MCP

MCP gets introduced but not fully explored. You'll understand what it is and how to use it basically, but advanced MCP patterns require external research.

5. No discussion of costs

The course uses open-source tools, but running LLMs costs money (either API calls or compute). Budget considerations aren't addressed.

What I built using this course

After completing the course, I spent a weekend building a research assistant that:

Searches multiple sources (web, academic papers, documentation)
Maintains context across research sessions using Graphiti
Summarizes findings with citations
Tracks which sources were most useful over time

Development time: About 8 hours total, including debugging and customization.

What worked: The memory system was game-changing. My agent got noticeably better at finding relevant information as I used it more.

What I modified: Added PDF parsing and connected to Zotero for academic paper management. The modular architecture made adding features straightforward.

Production readiness: I've been using this daily for two weeks. With Opik monitoring, I caught and fixed three bugs that would have been nightmares without observability.

Who should take this course

Perfect for:

Developers who've built basic AI apps and want to level up
Data scientists curious about agent engineering
Engineers evaluating agent frameworks for production use
Teams considering open-source alternatives to proprietary agents
Anyone who learns best by building real systems

Skip if:

You've never written Python (start with Python basics first)
You want purely theoretical understanding (this is hands-on)
You prefer video lectures over coding along (this requires participation)
You need vendor support (open-source means community support)

How to get the most from this course

Based on my experience, here's my recommended approach:

Before starting:

Review basic CrewAI concepts (15-30 minutes)
Install Python 3.9+ and set up a virtual environment
Clone the GitHub repo and browse the code
Read the README completely before running anything

During the course:

Type every line of code yourself—no copy-paste
Pause after each section and experiment
Intentionally break things to test your understanding
Take notes on architectural decisions, not just syntax
Commit your code to Git with detailed messages

After completing:

Rebuild the entire system from memory
Modify it for a different use case
Join the CrewAI and Opik Discord communities
Share what you built and get feedback
Help other beginners working through the same content

Time-saving tip: Run the complete example first, see it work, then go back and understand each component. Starting with a working system gives you a north star when things break.

Additional resources that pair well

To maximize learning, combine this course with:

For CrewAI depth:

Official CrewAI documentation
CrewAI YouTube channel (covered in my previous article)

For MCP understanding:

Anthropic's MCP documentation
MCP server examples on GitHub

For memory systems:

Zep documentation on graph-based memory
Papers on knowledge graphs for LLMs

For observability:

Opik documentation and guides
Daily Dose of Data Science's practical guide on Opik

The bottom line

This free crash course delivers more practical value than some paid courses I've taken. It's comprehensive without being overwhelming, technical without being gatekeeping, and most importantly, it results in working code you can deploy.

What you'll gain:

Understanding of modern agent architecture
Hands-on experience with production patterns
A working agent system you can customize
Knowledge of MCP, memory systems, and observability
Confidence to build your own agents

The investment: 4-6 hours of focused work for a complete understanding of agent development. That's a bargain.

My recommendation: If you've been wanting to understand AI agents beyond surface-level explanations, take this course. If you learn by building, take this course. If you want to deploy agents in production, definitely take this course.

The field is moving fast, but the patterns taught here—agent architecture, tool integration, memory management, and observability—will remain relevant regardless of which framework dominates in two years.

I've already recommended this course to three colleagues. Two have completed it and are now building their own agents. The third is halfway through and texted me yesterday: "Why didn't we start here?"

Good question. Start here.

Access the course: Free Mini Crash Course on AI Agents
GitHub repository: ai-engineering-hub

About the Author

R. Shivakumar runs Agent-Kits.com, where he tests, reviews, and explains open-source AI frameworks. He's spent the past month building agent systems using various frameworks and believes the best learning happens when theory meets practical implementation. Currently experimenting with graph-based memory systems and wondering why he didn't discover them sooner.