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Key features

Key features of Koog include:

  • Idiomatic Kotlin and Java support: Choose between a type-safe Kotlin DSL or a dedicated, fluent Java builder API. The Java API is designed to feel natural to Java teams, using standard thread pool executors instead of exposing coroutines.
  • 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.
  • Multiplatform development: For agents written in Kotlin, deploy agents across JVM, JS, WasmJS, Android, and iOS targets using Kotlin Multiplatform.
  • 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, RAG, 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.