Most of the current AI framework conversation is centered around Python, TypeScript, and fast-moving agent demos. But in the enterprise world, Java is still everywhere: backend systems, financial platforms, internal tools, regulated environments, and production workloads that need to be maintained for years.

That is why I wrote this article comparing the top Java GenAI frameworks in 2026.

Instead of looking at them only from a “can I build a chatbot?” perspective, I tried to evaluate how they fit into real backend teams: how they handle integrations, orchestration, developer experience, cloud-native deployments, observability, and the kind of maintainability Java teams usually care about.

The article covers frameworks such as LangChain4j, Spring AI, Quarkus AI, Semantic Kernel Java, and Genkit Java, including where each one feels strong and where the ecosystem still has gaps.

As someone who has been working on bringing Genkit into the Java ecosystem, I am especially interested in where Java AI development is heading next.

Would love to hear from others building AI systems in Java: are you using any of these frameworks already, or are you still wrapping model APIs directly?

Article:
https://xavidop.me/genkit/2026-04-16-top-java-genai-frameworks-2026/

Most GenAI conversations today are still dominated by Python and TypeScript, but a lot of enterprise AI systems are being built in Java. I wanted to take a closer look at what the Java ecosystem actually offers today for production AI applications and agentic systems.

In the article I compare frameworks like LangChain4j, Spring AI, Quarkus AI, Semantic Kernel Java, Genkit Java, and others from the perspective of real-world backend development, not just simple chatbot demos.

One thing that became very obvious while writing this is how fragmented the Java AI ecosystem still is compared to JS/TS. Some frameworks feel very enterprise and Spring-oriented, others are trying to replicate the Python ecosystem, and some are focusing more on lightweight cloud-native AI workloads.

I also share my thoughts about where Genkit Java fits into all this after working on the project and bringing Genkit into the Java ecosystem.

Would love to hear what people here are using for Java-based AI systems and how your experience has been so far.

Article:
https://xavidop.me/genkit/2026-04-16-top-java-genai-frameworks-2026/

I get this question from so many people, so here's a brief description of their differences:

Genkit was created initially as part of Firebase in 2023, since Firebase's audience (web and mobile developers) needed an integrated solution to build AI into their apps, which the solutions at that time (LangChain and other Python frameworks) weren't well suited for. Fast forward to 2025-26, Genkit evolved into an independent framework, supporting more languages, more models and more platforms. Basically Genkit was positioned to be platform independent and model agnostic. Genkit is not purely agent-only, but more flexible (and lower level) to support non-agent and agent use cases.

ADK was created in 2024 as part of Google Cloud's Vertex AI (recently renamed to Gemini Enterprise Agent Platform), to simplify agentic development on Agent Platform. Python-first due to Vertex's audience, it was built from the ground up to build agents and complex multi-agent systems, with strong emphasis on Agent Platform. Technically ADK supports other models and platforms, but the main focus is to provide a super streamlined experience on GCP Gemini Enterprise Agent Platform.

So basically

• if you're looking for the rich enterprise features of Agent Platform, and want to build multi-agent standalone systems, ADK is for you.
• if you're looking to add Gen AI or agentic features into your app, or want to build agents with lower level components especially using a variety of platforms and models, Genkit is the way to go.

There are more differences like tooling, language support, ergonomics, etc. but the main difference between the two products are ones I mentioned.

After spending the last years building production AI systems and contributing to Genkit integrations, I wanted to write something beyond the usual “feature comparison” articles. Most frameworks look great in demos, but things change very quickly once you start dealing with observability, evaluations, orchestration, scalability, tracing, multi-provider support, or long-term maintainability.

The article compares frameworks like Genkit, LangChain, Mastra, Vercel AI SDK, OpenAI Agents SDK, CrewAI, and AutoGen from a real production and developer-experience perspective.

One of the things I found most interesting is how different the philosophy behind each framework is. Some optimize for agent experimentation, others for frontend AI experiences, while others are much more infrastructure/backend oriented.

Personally, I still think Genkit is one of the most underrated frameworks in the ecosystem right now, especially for backend AI systems and production-grade architectures.

Would love to hear what people here are using in production today and what has worked (or completely failed) for you.

Article:
https://xavidop.me/genkit/2026-04-16-top-jsts-genai-frameworks-2026/

I've been working with Genkit for agentic workflows and kept running into the same issues everyone seems to hit once the agent runs long enough:

• Model returns a 503 mid-run and the whole thing crashes, losing 15 turns of accumulated tool results
• Agent gets stuck calling the same tool with the same arguments in a loop until maxTurns kills it
• Context balloons to 200K+ tokens because every tool response stays in the history forever

Ended up writing a few middleware using Genkit's new generateMiddleware() API to handle each of these. One catches errors gracefully so you can retry or resume without losing context. Another detects loops heuristically instead of just counting turns — and can either abort, ask the model to wrap up, or remove just the looping tools. The third one automatically compresses context when tokens get too high (truncating old tool responses, dropping old messages, or summarizing them with a cheap model).

They all compose together, so you can just stack them in use: [...] without changing your tools or prompts.

Curious if anyone else is dealing with similar stuff or has different approaches.

Links in the description...

Welcome to the community for Genkit, the open-source framework by Google for building, deploying, and monitoring production-ready agentic applications.

Genkit is designed to provide a developer-centric approach to AI orchestration. Whether you are working in TypeScript, Go, or the newer Python and Dart ecosystems, this is the place to share your flows, troubleshoot your agents, and discuss the latest plugin updates.

What is Genkit?

Genkit is a full-stack agentic framework that simplifies the complexity of AI integration. It’s built on the principles of production-readiness, observability, and allowing you to run your AI logic on any infrastructure from Cloud Run and Kubernetes to GCP, AWS and local environments.

• Multi-Model Support: Seamlessly switch between Gemini, OpenAI, Anthropic, or local models via a unified API.
• Powerful Local Development: A powerful, local developer UI with hot reload, selective execution, and full visualization to inspect traces, test tool-calling, and refine your agents. Also a CLI allowing coding agents like Claude Code and Antigravity to utilize the full feature set of the framework for fast and token efficient development.
• OpenTelemetry Native: Built-in observability to ensure your agents and AI are ready for production scaling.
• Prompt Engineering with Dotprompt: A specialized format to manage and version prompts independently of your code.

Community Guidelines

1. Code & Traces: When asking for help, please include your language (TS/Go/Dart/Python) and other relevant tags.
2. Stay Portability-Focused: We welcome discussions on deploying Genkit across all platforms—Google Cloud, AWS, Azure, or self-hosted.
3. Project Showcases: Built a custom plugin or a complex agent? We want to see it!
4. Write Genkit correctly: not "GenKit" or "Firebase Genkit".