By ByteBot
AI agents are the hottest trend in developer tools, but nearly every framework forces you into Python. If you’re a Kotlin developer, you’ve been left out—until now. JetBrains released Koog, an open-source framework for building enterprise-ready AI agents in type-safe Kotlin that deploys to JVM, Android, iOS, and WebAssembly from a single codebase. You can build a functional AI agent in under 50 lines of Kotlin code.
What Is Koog? Type-Safe AI Agents for Kotlin Developers
Koog is JetBrains’ official framework for building AI agents with type-safe Kotlin. Unlike Python frameworks like LangChain and AutoGen, Koog gives you compile-time type safety, multiplatform deployment, and enterprise features like fault tolerance and state persistence built-in. Moreover, the latest version, 0.6.0, was released December 22, 2025—literally yesterday—adding improved observability and token optimization.
The framework supports seven LLM providers out of the box: OpenAI, Anthropic, Google, DeepSeek, Ollama, OpenRouter, and AWS Bedrock. Furthermore, you can switch providers mid-conversation without losing context. JetBrains uses Koog internally for their AI products, which means it’s battle-tested in production, not a prototype framework. With 3.6k GitHub stars and an Apache 2.0 license, it’s gaining traction fast.
Type safety isn’t just academic. When you’re building production AI systems that execute code or interact with databases, compile-time checks prevent runtime disasters. In contrast, Python’s dynamic typing makes prototyping fast but production scary. Consequently, Koog catches configuration errors before deployment.
Building Your First Koog Agent in Under 50 Lines
A minimal Koog agent requires just three components: a prompt executor (LLM connection), a system prompt (instructions), and an LLM model. The framework handles the execution loop, tool calling, and state management automatically. Here’s the installation and basic setup:
// build.gradle.kts
dependencies {
implementation("ai.koog:koog-agents:0.6.0")
}// Minimal working agent
val agent = AIAgent(
promptExecutor = simpleOpenAIExecutor(apiKey),
systemPrompt = "You are a helpful assistant...",
llmModel = OpenAIModels.Chat.GPT4o
)
val result = agent.run("Hello! How can you help me?")That’s it. Under 50 lines from zero to working agent. Additionally, the type-safe DSL ensures you can’t pass invalid configurations—your IDE catches mistakes immediately. No boilerplate, no magic strings, just clean Kotlin.
The framework abstracts away complexity: prompt engineering, conversation history, tool orchestration, retry logic. You define what the agent can do, Koog handles execution. For developers who live in IntelliJ and prefer strongly-typed languages, this beats wrestling with Python virtual environments and runtime type errors.
Custom Tools: Where Your Agent’s Power Lives
Tools are how agents interact with the world. Every Koog tool needs five components: a name (snake_case convention), a description (explains to the LLM what the tool does), an Args class (type-safe parameters), a Result class (structured response), and an execute() method (core logic).
JetBrains’ official tutorial demonstrates building an ExecuteShellCommandTool—agents that can run commands to verify code changes. The tool includes safety mechanisms: command confirmation (user reviews before execution), timeout handling (preserve partial output even on failures), and configurable handlers for development versus production modes. Notably, adding shell execution improved agent performance from 50% to 56% success on SWE-bench Verified tasks—a 6% lift from one well-designed tool.
val agent = AIAgent(
promptExecutor = executor,
toolRegistry = ToolRegistry {
tool(ListDirectoryTool())
tool(ReadFileTool())
tool(EditFileTool())
},
systemPrompt = "You are a code modifier...",
maxIterations = 100
)Tool design requires balance. However, too many tools overwhelm the agent’s decision-making—each capability adds cognitive load. Too few tools prevent task completion. JetBrains found three tools (list, read, edit) hit the sweet spot for code modification agents. Therefore, start minimal and add tools only when agents consistently fail specific tasks.
Koog’s type system shines here. The Args and Result classes enforce contracts: tools receive validated parameters and return structured data the LLM can parse reliably. In contrast, Python frameworks rely on documentation and hope—Koog enforces correctness at compile-time.
Multiplatform Deployment: Write Once, Run Everywhere
Python frameworks can’t deploy to mobile natively. In contrast, Koog agents compile to JVM, JavaScript, WebAssembly, Android, and iOS targets using Kotlin Multiplatform. Write your agent logic once, deploy it everywhere from Spring Boot microservices to on-device mobile apps.
This multiplatform capability unlocks use cases Python can’t touch. For example, deploy AI agents to Android apps for offline-capable, privacy-first features—no cloud APIs required, zero runtime costs. Build iOS agents with Kotlin Multiplatform Mobile, sharing business logic across platforms. Moreover, run agents in WebAssembly for browser-based tools. Integrate with Spring Boot or Ktor for backend services.
The single codebase advantage compounds over time. Fix a bug once, it’s fixed everywhere. Add a feature once, all platforms benefit. Maintain one test suite, not five. Consequently, for teams building cross-platform products, this eliminates Python infrastructure entirely—no Flask servers, no FastAPI backends, just Kotlin.
When to Choose Koog vs LangChain
Koog isn’t for everyone. Choose it when you need type safety, multiplatform deployment, or JVM integration. However, stick with LangChain if you need the richest ecosystem or are prototyping rapidly in Python.
Choose Koog when you’re a Kotlin or JVM shop with existing codebases, you need type safety for enterprise production systems, you want multiplatform deployment (especially mobile), you use Spring Boot or Ktor for backend services, or you value JetBrains tooling and IntelliJ integration.
Choose LangChain when you need the largest ecosystem with maximum third-party integrations, you’re prototyping rapidly and Python’s dynamism helps, your team is Python-first, or you need data science workflows with pandas and numpy.
The honest trade-offs: Koog has a smaller community (3.6k stars vs LangChain’s 100k+), it’s newer (released May 2025), and it’s JVM-centric. In contrast, LangChain has a massive ecosystem but no native mobile deployment and Python-only constraints. Both are enterprise-ready and production-proven.
Other alternatives serve specific niches. For instance, LangGraph excels at complex stateful workflows. CrewAI specializes in multi-agent collaboration. Microsoft’s Semantic Kernel targets .NET shops. LlamaIndex focuses on document retrieval. Google’s ADK integrates deeply with Google Cloud. Match your framework to your stack, team, and requirements—wrong choice wastes months.
Koog fills a gap: type-safe, multiplatform AI agents for JVM developers who don’t want to rewrite their stack in Python. If that’s you, try it on GitHub. The tutorial series walks through building code modification agents, custom tools, and observability setups. Start with the minimal agent example—50 lines proves the concept works.
