Unlock practical AI agents inside your database. In this live demo and deep dive, Oren Eini shows how to build real, production-ready AI agents directly in RavenDB that query your data, take actions, remember context, and stay inside strict security guardrails. You will see an agent defined in a few lines of code, connected to OpenAI or any LLM you choose, running vector search and RAG over your catalog, and safely executing business actions like “add to cart,” “find policies,” or “sign document,” all with parameters that are enforced by the database rather than trusted to the model. You will learn how RavenDB agents eliminate fragile glue code by giving the model explicit tools: data queries that return typed results and server-side actions you validate in your code.

Conversations are stored as documents, with automatic token-aware summarization to control latency and cost. The demo streams responses token by token for responsive UX, switches models without rewrites, and shows how scope parameters prevent data leaks even if the prompt is manipulated. You will also see a multi-tool HR assistant that chains tools, coordinates front end and back end, and persists state. The session closes with a look at the roadmap, including multi-agent orchestration and AI assist inside Studio.

Yesterday I gave a live talk about some of the re-design we did to the internals of RavenDB’s storage engine (Voron). I think it went pretty well, and the record is here.

Would love to hear your feedback!

Last week I did an hour long webinar showing AI integration in RavenDB. From vector search to RAG, from embedding generation to Gen AI inside of the database engine.

Most of those features are already released, but I would really love your feedback on the Gen AI integration story (starts at around to 30 minutes mark in the video).

Let me know what you think!

Watch Oren Eini, CEO of RavenDB, as he delves into the intricate process of constructing a database engine using C# and .NET. Uncover the unique features that make C# a robust system language for high-end system development. Learn how C# provides direct memory access and fine-grained control, enabling developers to seamlessly blend high-level concepts with intimate control over system operations within a single project. Embark on the journey of leveraging the power of C# and .NET to craft a potent and efficient database engine, unlocking new possibilities in system development.

I’m going deep into some of the cool stuff that you can do with C# and low level programming.

When Oren Eini originally developed RavenDB, he used the Lucene library to implement indexing. Eventually, his team encountered limitations with this strategy, so they created the Corax search engine, which improved query execution time significantly. Oren discusses the challenges involved in creating this engine and the approaches they took to overcome these challenges.

Part 1:

Part 2:

I spoke with Jaime recently in the Modern .NET Podcast:

In this episode of The Modern .NET Show podcast, Oren Eini, a seasoned developer with over 20 years of experience in the .NET field, discussed the evolution of the .NET framework and the complexities that come with it. Eini highlighted the rapid pace of change in the language, from the introduction of generics at version 2.0 to switch expressions and pattern matching in the latest versions. While these new features allow for more concise code, Eini acknowledged that they also increase the scope and complexity of learning C# from scratch.

Would love to hear your feedback.

This was actually released a while ago, I was occupied with other matters and missed that.

I had a blast talking with Carl & Richard about data sharding and how we implemented that in RavenDB.

What is data sharding, and why do you need it? Carl and Richard talk to Oren Eini about his latest work on RavenDB, including the new data sharding feature. Oren talks about the power of sharding a database across multiple servers to improve performance on massive data sets. While a sharded database is typically in a single data center, it is possible to distribute the shards across multiple locations. The conversation explores the advantages and disadvantages of the different approaches, including that you might not need it today, but it's great to know it's there when you do!

You can listen to the podcast here.

Jaime and I had a really good discussion about RavenDB, why I took the time to create my own NoSql database engine, and the fact that I built it using .NET Core before it was released (back in the pre-1.0 days, when it was known as dnx), and some of the optimisation stories that I worked on when creating RavenDB. Along the way, we cover what the GC (or garbage collector) is, performance issues to look out for when dealing with large JSON objects, and some tips for those who want to optimise their applications.

You can listen to it here.

Would love your feedback.

I spoke for over an hour about how you can build high-performance systems and how we utilize these techniques inside of RavenDB.