Developers are buzzing about JetBrains’ new Koog AI framework, an open-source tool that lets you build cross-platform AI agents in pure Kotlin. The repo topped GitHub’s trending list today as coders share demos and fork the project.
What is Koog?
The Koog AI framework is a brand-new open-source project released by JetBrains, the company best known for IntelliJ IDEA, PyCharm, and other developer tools. Unlike most AI frameworks, which are built in Python, Koog allows developers to create AI agents entirely in idiomatic Kotlin.
According to its README, Koog supports agents that can interact with external tools, handle multi-step workflows, and engage in natural conversation with users. It integrates with the Model Context Protocol (MCP), provides vector-embedding capabilities for semantic search, and allows developers to build custom tools for real-world integrations.
In short, Koog brings the agentic AI boom into the Kotlin ecosystem — something developers have been asking for as Python has dominated the space.
Key features driving the buzz
The excitement around the Koog AI framework is fueled by a combination of technical capabilities and developer-friendly design.
Pure Kotlin implementation – No need for Python. Agents run seamlessly on JVM, JavaScript, WebAssembly, and even iOS.
Embeddings & retrieval – Built-in support for vector embeddings makes semantic search and knowledge retrieval straightforward.
Custom tools – Developers can build tools that let agents interact with APIs, databases, or external systems.
Pre-built components – Includes ready-made utilities for conversation history compression, streaming responses, and persistent memory.
Graph-based workflows – Offers a visual and structured way to design complex agent behaviors.
Multiplatform by default – Agents can run on Android, iOS, desktop, and web environments without extra setup.
One GitHub commenter summed it up: “This is the Flutter moment for AI agents in Kotlin.”
Another developer noted that JetBrains’ documentation is unusually polished for an alpha release. The quick-start demo shows how easy it is to set up an agent with just a few lines of Kotlin code: add your OpenAI API key, instantiate an agent, and start asking questions.
Why GitHub can’t stop talking about it
Within hours of launch, Koog surged to the top of GitHub’s trending repositories list. The project quickly earned hundreds of stars, forks, and discussion threads as developers rushed to test it.
On X (formerly Twitter), Kotlin enthusiasts shared screenshots of Koog agents running simultaneously on Android and iOS, demonstrating its multiplatform power. On Reddit, threads titled “Finally, Kotlin gets an AI framework!” and “Goodbye Python, hello Koog” gained traction.
This virality isn’t surprising. For years, Kotlin developers have relied on Python-based frameworks like LangChain or llama_index, often stitching together awkward integrations. The Koog AI framework finally gives them a first-class solution without leaving the Kotlin ecosystem.
And of course, JetBrains’ reputation adds fuel to the hype. The company has a track record of building robust tools beloved by developers, and Koog’s release signals a serious commitment to the AI agent space.
Potential use cases for Koog
The flexibility of the Koog AI framework opens the door for multiple real-world applications:
Mobile AI assistants – Kotlin is widely used in Android development. Koog makes it possible to embed AI agents directly into mobile apps.
Cross-platform chatbots – Build conversational agents that work across Android, iOS, and web clients with shared logic. This is the same trend driving the popularity of character-based companions like Airi AI Waifu Companion, which shows how personalized AI agents can capture both entertainment and emotional use cases.
Developer tools – JetBrains could integrate Koog into IDEs, enabling AI-assisted coding or documentation helpers.
Knowledge retrieval apps – Using embeddings, Koog agents can act as semantic search engines across large text or document databases.
Enterprise automation – With custom tools, agents can connect to internal APIs, CRMs, and knowledge bases, automating workflows.
Challenges and the road ahead
Despite the buzz, Koog is not without challenges:
Limited LLM providers – Currently, Koog only supports Google, OpenAI, Anthropic, OpenRouter, and Ollama. More integrations are needed for broader adoption.
Learning curve for beginners – While the framework has examples, new developers may struggle with embeddings, concurrency, and workflow design.
Competition from Python – Frameworks like LangChain and llama_index already have massive ecosystems and community support. Koog will need to grow quickly.
Ecosystem maturity – As an alpha release, APIs may change frequently. Long-term stability will depend on community feedback.
Licensing – Released under Apache 2.0, Koog is permissive but requires attribution and careful compliance in commercial settings.
Still, the momentum is undeniable. With GitHub stars climbing and YouTube tutorials already appearing, the Koog AI framework seems poised to become one of the most talked-about open-source releases of 2025.
