Maarten Grootendorst

Why is Reinforcement Learning Important for LLMs? Join this free masterclass to learn the methods for fine-tuning LLMs, and what are their strengths and weaknesses. You’ll also learn why in order to mitigate these weaknesses, reinforcement learning m...

✅ Want in? Register here: www.ragpack.ai/masterclass

Announcing a free Masterclass on RL for LLMs!

This has been such a wonderful collaboration with some great people and personal heroes.

📅 Jan 15, 2026 (live) starting at 9 am PT / 12 pm ET Led by *The RAG Pack*: Jay Alammar, Maarten Grootendorst, Chris McCormick, and Joshua Starmer.

An Illustrated Guide to AI Agents Artificial intelligence is entering a new phase. No longer limited to answering prompts or completing simple writing tasks, AI agents can now reason, plan, and act with increasing... - Selection from ...

There are three chapters in Early Release on the O’Reilly Platform (Intro, Memory, and Tools): learning.oreilly.com/library/view...

And here you have it! The cover of our “An Illustrated Guide to AI Agents” book!

Why a Dolphin? The process of choosing cover animals is a closely guarded secret held deep within the legendary halls of O'Reilly. @jayalammar.bsky.social and I do not get to choose it, but we love it!

And here you have it! The cover of our “An Illustrated Guide to AI Agents” book!

Why a Dolphin? The process of choosing cover animals is a closely guarded secret held deep within the legendary halls of O'Reilly. @Jay and I do not get to choose it, but we love it!

An Illustrated Guide to AI Agents Artificial intelligence is entering a new phase. No longer limited to answering prompts or completing simple writing tasks, AI agents can now reason, plan, and act with increasing... - Selection from ...

The first two chapters are in early release: learning.oreilly.com/library/view...

Contest Rules 2:
* 7 lucky winners will receive digital copies of the book.
* We will close the contest on Monday 24th of November and share the cover!
* TIP - You can find all existing O’Reilly animal covers here: www.oreilly.com/animals.csp

Contest Rules 1:
* Each participant is allowed two guesses
* 3 lucky winners will be randomly selected from those who guessed correctly to receive a free print copy of our book when it hits the shelves (note: we cannot ship to Russia or Ukraine)

It’s time to guess the animal on the cover!

Our cover animal is here, and @jayalammar.bsky.social and I are thrilled to celebrate it with an exciting contest for our upcoming release, An Illustrated Guide to AI Agents!

Each person gets two guesses. Which animal do you think we got?

You should be able to access it through the platform. I believe even if you do not have a subscription, but I'm not sure 😅

An Illustrated Guide to AI Agents Artificial intelligence is entering a new phase. No longer limited to answering prompts or completing simple writing tasks, AI agents can now reason, plan, and act with increasing... - Selection from ...

www.oreilly.com/library/view...

@jayalammar.bsky.social and I cover memory (including context engineering) and tools (including MCP) in these chapters, along with many, many visuals. Every so often, we will release a new chapter and show you how they all fit within the framework(s) of AI Agent(s).

Drafts of the first two chapters of "An Illustrated Guide to AI Agents" are now available in Early Release on the O'Reilly platform!

The Illustrated Guide to AI Agents

New book announcement!

Thrilled that together with @maartengr.bsky.social , we're writing a new book titled “An Illustrated Guide to AI Agents” and published by @oreilly.bsky.social.

Excited to share that @jayalammar.bsky.social and I are writing the book “An Illustrated Guide to AI Agents” with @oreilly.bsky.social 🥳

Our new book will contain chapters on the fundamentals of agents (memory, tools, and planning), alongside more advanced concepts like RL and reasoning LLMs.

Hands-On-Large-Language-Models/bonus at main · HandsOnLLM/Hands-On-Large-Language-Models Official code repo for the O'Reilly Book - "Hands-On Large Language Models" - HandsOnLLM/Hands-On-Large-Language-Models

Building off the book's foundation, we created enough content to hopefully keep you busy for a while:

github.com/HandsOnLLM/H...

We've made so much extra (free!) content it could be its own book 😅

I counted more than 300 visuals across our illustrated and visual guides!

Intuition behind Mamba and State Space Models | Enhancing LLMs! YouTube video by Maarten Grootendorst

www.youtube.com/watch?v=BDTV...

Happy to introduce my video on this alternative LLM architecture, Mamba and State Space Models!

I wanted to do it for a while now and finally found the time to work on animating my visual guide. Expect to gain an intuitive understanding of this alternative LLM architecture.

Link below 👇

Release v0.17.0 · MaartenGr/BERTopic Highlights: Light-weight installation without UMAP and HDBSCAN by @MaartenGr in #2289 Add Model2Vec as an embedding backend by @MaartenGr in #2245 Add LiteLLM as a representation model by @Maarten...

You can find the full changelog here: github.com/MaartenGr/BE...

Announcing BERTopic v0.17 🥳

This is a feature-packed update that includes the amazing Model2Vec, more interactive DataMapPlot functionalities, a method for lightweight installation, and much more!

A Visual Guide to LLM Agents Exploring the main components of Single- and Multi-Agents

I might have gone a little overboard with the number of visuals... but this is such an exciting topic! Here's the article: newsletter.maartengrootendorst.com/p/a-visual-g...

Introducing "A Visual Guide to LLM Agents" 🤖

With over 60 (!) custom visuals, explore how Agents work, how they are created, their reasoning behavior, Model Context Protocol (MCP), Multi-Agent frameworks, and much more!

Link below 👇

Can you guess which visual guide is coming?

MCP is such an interesting protocol and a welcome addition to LLM-based frameworks.

I discuss Model Context Protocol (MCP) in an upcoming article and figured I would share a small video illustrating a simplified flow!

GitHub - MaartenGr/KeyBERT: Minimal keyword extraction with BERT Minimal keyword extraction with BERT. Contribute to MaartenGr/KeyBERT development by creating an account on GitHub.

You can find the repo here: github.com/MaartenGr/Ke...

As a bonus, I created a lightweight installation of KeyBERT using ONLY scikit-learn and Model2Vec! Installation is straightforward:

Blazingly fast keyword generation with KeyBERT v0.9 and Model2Vec 🔥

I have been a big fan of the amazing embedding models by The Minish Lab, so I had to integrate them with KeyBERT.

A release for the GPU-poor 😉

You can find everything at our repo: github.com/HandsOnLLM/H...