Agentic AI for the JVM

Embabel is an Agentic AI framework for the JVM. Python has long been the go-to for machine learning experiments, thanks to its ease, ecosystem richness, and data scientist-friendly tools. However, it often struggles when you try to move from experiment to real-world, production-scale AI.

As AI adoption becomes more mission critical, what matters is not just model training or computation. It is context, reliability, type safety, performance, observability, and integration with existing enterprise systems. Java and Kotlin offer these strengths. Strong typing, mature tooling, and proven track records in resilient, scalable systems make them a compelling choice for serious AI. It is time to move beyond proofs of concept.

As modern as Kotlin, as proven as Java

The JVM has evolved. It is now modern and feature-rich, while remaining one of the fastest platforms and carrying the reliability of decades of enterprise use. What's stopping your from writing applied AI code that look like this?

    @Action
    fun extractPerson(userInput: UserInput, context: OperationContext): Person? =
        // All prompts are typesafe
        context.ai().withDefaultLlm().createObjectIfPossible(
            """
            Create a person from this user input, extracting their name:
            ${userInput.content}
            """
        )

The Team Behind Embabel

Alongside Spring Framework founder Rod Johnson and other alumni, Embabel is built by a team of high-achieving engineers with a proven record not only in applied AI, but also in designing, scaling, and delivering large, complex systems.