--- title: "koog vs moby" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jetbrains-koog-vs-moby-moby" tools: ["jetbrains-koog", "moby-moby"] --- # koog vs moby *GraphCanon updated Jul 15, 2026* ## Verdict Pick koog when koog is primarily Kotlin; moby is Go; pick moby when moby is primarily Go; koog is Kotlin. [koog](https://docs.koog.ai) reports 4.4k GitHub stars, 447 forks, and 162 open issues, last pushed Jul 6, 2026. [moby](https://mobyproject.org/) has 72k stars, 19k forks, and 3.8k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [koog's repository](https://github.com/JetBrains/koog) and [moby's repository](https://github.com/moby/moby). | | [koog](/tools/jetbrains-koog.md) | [moby](/tools/moby-moby.md) | | --- | --- | --- | | Tagline | Koog is a JVM (Java and 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-brow | The Moby Project - a collaborative project for the container ecosystem to assemble container-based systems | | Stars | 4,447 | 71,899 | | Forks | 447 | 19,126 | | Open issues | 162 | 3,821 | | Language | Kotlin | Go | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Inference & Serving, LLM Frameworks | Developer Tools, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [koog](/tools/jetbrains-koog.md) | [moby](/tools/moby-moby.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 9d | 1d | | Open issues (now) | 162 | 3.8k | | Full report | [trust report](/tools/jetbrains-koog/trust.md) | [trust report](/tools/moby-moby/trust.md) | ## Choose when ### Choose koog if… - koog is primarily Kotlin; moby is Go. - Tags unique to koog: agentframework, agentic-ai, agents, ai. - Also covers AI Agents. ### Choose moby if… - moby is primarily Go; koog is Kotlin. - Tags unique to moby: containers, docker, go, golang. - Also covers Developer Tools. - moby ships Docker support for self-hosted deployment. ## When NOT to use koog - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use moby - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between koog and moby? koog: Koog is a JVM (Java and 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-brow. moby: The Moby Project - a collaborative project for the container ecosystem to assemble container-based systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose koog over moby? Choose koog over moby when koog is primarily Kotlin; moby is Go; Tags unique to koog: agentframework, agentic-ai, agents, ai; Also covers AI Agents. ### When should I choose moby over koog? Choose moby over koog when moby is primarily Go; koog is Kotlin; Tags unique to moby: containers, docker, go, golang; Also covers Developer Tools; moby ships Docker support for self-hosted deployment. ### When should I avoid koog? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid moby? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is koog or moby more popular on GitHub? moby has more GitHub stars (71,899 vs 4,447). Stars measure visibility, not whether either tool fits your constraints. ### Are koog and moby open source? Yes - both are open-source projects on GitHub (koog: Apache-2.0, moby: Apache-2.0). ### Where can I find alternatives to koog or moby? GraphCanon lists graph-backed alternatives at [koog alternatives](/tools/jetbrains-koog/alternatives) and [moby alternatives](/tools/moby-moby/alternatives) ([koog markdown twin](/tools/jetbrains-koog/alternatives.md), [moby markdown twin](/tools/moby-moby/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/jetbrains-koog-vs-moby-moby.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, koog or moby? koog: Active. moby: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for koog and moby? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [koog trust report](/tools/jetbrains-koog/trust); [moby trust report](/tools/moby-moby/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jetbrains-koog`](/api/graphcanon/graph?tool=jetbrains-koog) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_