
koog
Koog is the official Kotlin framework for building predictable, fault-tolerant and enterprise-ready AI agents across all platforms – from backend services to Android and iOS, JVM, and even in-browser environments. Koog is based on our AI products expertise and provides proven solutions for complex LLM and AI problems
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Koog is a Kotlin-based framework for building and running AI agents entirely in idiomatic Kotlin. It allows users to create agents that interact with tools, handle complex workflows, and communicate with users. Key features include pure Kotlin implementation, MCP integration, embedding capabilities, custom tool creation, ready-to-use components, intelligent history compression, powerful streaming API, persistent agent memory, comprehensive tracing, flexible graph workflows, modular feature system, scalable architecture, and multiplatform support.
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Koog is a Kotlin-based framework designed to build and run AI agents entirely in idiomatic Kotlin. It lets you create agents that can interact with tools, handle complex workflows, and communicate with users.
Key features of Koog include:
- Multiplatform development: Deploy agents across JVM, JS, WasmJS, Android, and iOS targets using Kotlin Multiplatform.
- Reliability and fault-tolerance: Handle failures with built-in retries and restore the agent state at specific points during execution with the agent persistence feature.
- Intelligent history compression: Optimize token usage while maintaining context in long-running conversations using advanced built-in history compression techniques.
- Enterprise-ready integrations: Utilize integration with popular JVM frameworks such as Spring Boot and Ktor to embed Koog into your applications.
- Observability with OpenTelemetry exporters: Monitor and debug applications with built-in support for popular observability providers (W&B Weave, Langfuse).
- LLM switching and seamless history adaptation: Switch to a different LLM at any point without losing the existing conversation history, or reroute between multiple LLM providers.
- Integration with JVM and Kotlin applications: Build AI agents with an idiomatic, type-safe Kotlin DSL designed specifically for JVM and Kotlin developers.
- Model Context Protocol integration: Use Model Context Protocol (MCP) tools in AI agents.
- Knowledge retrieval and memory: Retain and retrieve knowledge across conversations using vector embeddings, ranked document storage, and shared agent memory.
- Powerful Streaming API: Process responses in real-time with streaming support and parallel tool calls.
- Modular feature system: Customize agent capabilities through a composable architecture.
- Flexible graph workflows: Design complex agent behaviors using intuitive graph-based workflows.
- Custom tool creation: Enhance your agents with tools that access external systems and APIs.
- Comprehensive tracing: Debug and monitor agent execution with detailed, configurable tracing.
The LLM providers and platforms whose LLMs you can use to power your agent capabilities:
- OpenAI
- Anthropic
- DeepSeek
- OpenRouter
- Ollama
- Bedrock
To help you get started with AI agents, here is a quick example:
fun main() = runBlocking {
// Before you run the example, assign a corresponding API key as an environment variable.
val apiKey = System.getenv("OPENAI_API_KEY") // or Anthropic, Google, OpenRouter, etc.
val agent = AIAgent(
executor = simpleOpenAIExecutor(apiKey), // or Anthropic, Google, OpenRouter, etc.
systemPrompt = "You are a helpful assistant. Answer user questions concisely.",
llmModel = OpenAIModels.Chat.GPT4o
)
val result = agent.run("Hello! How can you help me?")
println(result)
}
Currently, the framework supports the JVM, JS, WasmJS and iOS targets.
On JVM, JDK 17 or higher is required to use the framework.
Please check the libs.versions.toml to know more about the Koog dependencies.
-
Add dependencies to the
build.gradle.kts
file:dependencies { implementation("ai.koog:koog-agents:0.4.1") }
-
Make sure that you have
mavenCentral()
in the list of repositories.
-
Add dependencies to the
build.gradle
file:dependencies { implementation 'ai.koog:koog-agents:0.4.1' }
-
Make sure that you have
mavenCentral()
in the list of repositories.
-
Add dependencies to the
pom.xml
file:<dependency> <groupId>ai.koog</groupId> <artifactId>koog-agents-jvm</artifactId> <version>0.4.1</version> </dependency>
-
Make sure that you have
mavenCentral
in the list of repositories.
Read the Contributing Guidelines.
This project and the corresponding community are governed by the JetBrains Open Source and Community Code of Conduct. Please make sure you read it.
Koog is licensed under the Apache 2.0 License.
Please feel free to ask any questions in our official Slack channel (link) and to use Koog official YouTrack project for filing feature requests and bug reports.
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