Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges.
The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context.
The project covers a broad range of capabilities, including multimodal data processing, OpenTelemetry-based observability, and schema-driven structured output enforcement. It provides comprehensive tool integration for browser automation and filesystem management, along with conversation history compression and state-checkpoint persistence.
The library is designed for JVM framework integration and supports multiplatform agent deployment.
