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AI Resources
Koog
Koog is a JetBrains framework for building AI agents in Kotlin and Java, with tools, graph workflows, memory, retrieval, tracing, and integrations for JVM application stacks.
The repository and docs describe Koog as a Kotlin-based agent framework with Kotlin and Java APIs, Kotlin Multiplatform targets, multiple LLM providers, MCP and Agent Client Protocol support, RAG, long-term memory, agent persistence, OpenTelemetry, Spring Boot, Ktor, streaming, and example projects. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A Kotlin and Java agent framework
Koog sits in the developer framework layer for AI agents, with public materials aimed at building agent behavior directly into JVM, Kotlin Multiplatform, and application-backend projects.
Why it stands out
Agent patterns for JVM teams
The source materials cover practical agent-building pieces that JVM developers may want to compare: tools, prompts, graph strategies, planner agents, memory, persistence, retrieval, streaming, tracing, and framework integrations.
Availability
Repository, docs, API reference, and packages
Readers can inspect the GitHub repository, Koog documentation, API reference, examples, Gradle and Maven setup snippets, and docs for agent, tool, memory, protocol, and observability features.
Why it matters
What makes it useful
Agent frameworks are not only Python or TypeScript projects. JetBrains gives readers a Kotlin/JVM route to inspect for tools, graph workflows, memory, RAG, MCP, protocol support, tracing, Spring Boot, Ktor, and multiplatform application targets.
What to know
Where it fits
Koog belongs in the agent-framework layer rather than the model or end-user assistant layer. It is most relevant to readers comparing developer frameworks for tools, agent strategies, LLM providers, protocol integrations, memory, RAG, and operational visibility.
Notable points
What stands out
The README and docs list Kotlin and Java APIs, Kotlin Multiplatform targets, OpenAI, Anthropic, Google, DeepSeek, OpenRouter, Ollama, and Bedrock providers, MCP tools, Agent Client Protocol, A2A protocol docs, vector embeddings, RAG, long-term memory, agent persistence, structured output, streaming, OpenTelemetry, Spring Boot, and Ktor.
Before using
What to review
Whether the target project is actually Kotlin, Java, JVM, Android, iOS, JS, or WasmJS enough to benefit from Koog's stack choice.
Which LLM providers, API keys, local runtimes, tools, retrieval stores, and memory providers would be connected to an agent.
How persistence, tracing, OpenTelemetry exporters, protocol integrations, and workflow state behave in the intended application environment.
Current requirements in the official docs, including JDK, Kotlin, Gradle, Maven, and package-version notes.
Reader fit
Who may find it relevant
Developers comparing Kotlin or Java frameworks for AI agents.
Teams building agent features inside JVM services, Spring Boot apps, Ktor servers, or Kotlin Multiplatform projects.
Readers tracking MCP, A2A, ACP, memory, RAG, tracing, and structured agent workflows across different agent ecosystems.
Less relevant for readers who want a no-code agent builder, a model checkpoint, or a finished consumer assistant.
Editorial note
Why LifeHubber lists it
Koog is useful to list because it shows how the agent-framework space is spreading beyond Python and TypeScript into Kotlin, Java, JVM services, and multiplatform application development.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare the framework around the agent workflow.
Koog keeps agent orchestration in Kotlin and Java. Compare a Python framework with a different workflow model, or browse the wider framework map by language, controls, and intended job.
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