What is JetBrains Koog 1.0?

Koog 1.0 is JetBrains' open-source AI agent framework for Kotlin and Java, released May 27, 2026 at the KotlinConf 2026 keynote. It targets production-grade agent applications with a stable core API (one-year stability guarantee), multiplatform deployment (JVM, Android, iOS, JavaScript, WebAssembly), local Android AI via LiteRT, decoupled HTTP transport, OpenTelemetry observability across all targets, persistence and memory for long-running agents, and Anthropic prompt caching support. It integrates with OpenAI, Anthropic, Google, and DeepSeek models.

Is Koog better than LangGraph for production agents?

Depends on your language stack. Koog 1.0 is the most production-ready choice if your backend is JVM (Kotlin/Java) — type safety, multiplatform deployment, OpenTelemetry baked in, JetBrains' track record on tooling. LangGraph is the production standard for Python teams — broader ecosystem, deterministic execution graphs, LangSmith observability, more model integrations and battle-tested community patterns. For Android-native AI agents Koog is unique because it supports on-device LiteRT models out of the box.

Should I use Koog or CrewAI?

Different goals. CrewAI is excellent for rapid agent prototyping and role-based multi-agent demos — you describe roles (researcher, writer, critic), give them tools, and watch them collaborate. It's Python-first and demo-friendly. Koog is for production JVM backends that need agents shipped to multiple platforms with proper observability, persistence, and one-year API stability. If you're shipping an Android app with agents, use Koog. If you're prototyping a multi-agent workflow in Python, use CrewAI.

Does Koog support local AI on Android?

Yes. Koog 1.0 supports local AI on Android devices through new provider integrations including Google's LiteRT (formerly TensorFlow Lite) models. This is a meaningful differentiator — you can build agents that run partially or fully on-device, which is important for privacy-sensitive applications, offline use, and reducing API costs. Combined with Koog's multiplatform support (JVM, Android, iOS, JS, Wasm), it's currently the most production-ready agent framework for cross-platform mobile development in May 2026.

Quick Answer

JetBrains Koog 1.0 vs LangGraph vs CrewAI (May 2026)

Published: May 28, 2026

JetBrains Koog 1.0 vs LangGraph vs CrewAI (May 2026)

JetBrains released Koog 1.0 at the KotlinConf 2026 keynote on May 27, 2026. It’s an open-source AI agent framework for Kotlin and Java with a one-year stability guarantee, multiplatform deployment, and local Android AI. Here’s how it stacks up against LangGraph and CrewAI in the broader 2026 agent framework landscape.

Last verified: May 28, 2026.

TL;DR table

	Koog 1.0 (JetBrains)	LangGraph (LangChain)	CrewAI
Released	May 27, 2026 (1.0 GA)	Mature, iterating	Mature, iterating
Primary language	Kotlin + Java	Python (+ TypeScript)	Python
Target	Production JVM backends, multiplatform mobile	Production Python teams	Rapid prototyping, demos
Stability guarantee	1-year API stability for 1.0 core	Semver, breaking changes possible	Active development, no LTS
Multiplatform	JVM, Android, iOS, JS, Wasm	Python only	Python only
Local on-device AI	Yes (LiteRT on Android)	No	No
Observability	OpenTelemetry built-in	LangSmith (paid) + OTel	OTel-compatible
Best abstraction	Type-safe agent graphs	Deterministic state graphs	Role-based crews

What Koog 1.0 actually is

Koog was introduced at KotlinConf 2025 as JetBrains’ AI agent framework. Koog for Java shipped March 17, 2026. Koog 1.0 — the production-ready GA release — landed May 27, 2026.

Key 1.0 features:

Stable core API with 1-year stability guarantee — meaningful for production backends where breaking changes hurt
Multiplatform deployment: JVM, Android, iOS, JavaScript, WebAssembly
Local AI on Android via LiteRT (Google’s on-device runtime, formerly TensorFlow Lite)
Decoupled HTTP transport — drop into existing service mesh, no opinionated client
OpenTelemetry across all targets — including Kotlin Multiplatform
Persistence + memory for long-running agents (sessions that survive restarts)
Anthropic prompt caching baked in for latency + cost savings
Cleaner Java interop layer vs early Koog
Model providers: OpenAI, Anthropic, Google, DeepSeek

Source: JetBrains AI blog, May 27, 2026, GitHub releases.

What LangGraph is

LangGraph is the production standard for Python AI agent teams in 2026. It’s the deterministic execution-graph layer that sits on top of (or alongside) LangChain.

Key strengths:

Deterministic graph execution — agents as state graphs, not free-form loops
Human-in-the-loop support — pause, inject decisions, resume
LangSmith observability — best-in-class tracing for LLM apps (paid SaaS)
Huge community and example library
Hundreds of integrations via the LangChain ecosystem
Production-tested at meaningful scale (used by Replit, Klarna, AirBnB and many others)

Trade-off: heavy ecosystem. The LangChain abstraction stack has been criticized for over-engineering and rapid breaking changes. LangGraph itself is cleaner but inherits some of that complexity.

What CrewAI is

CrewAI is the rapid-prototyping multi-agent framework. The abstraction is role-based: you declare a “crew” of agents (Researcher, Writer, Critic, etc.), give each tools and goals, and let them collaborate.

Key strengths:

Fast time-to-demo — working multi-agent flow in tens of lines of Python
Role-based abstraction is intuitive for non-experts
Strong community for solo founders and small teams
Good for content workflows (research → write → critique → polish patterns)

Trade-off: less suited to production-grade deterministic workflows. The role-based collaboration is non-deterministic by design. For enterprise-critical paths, LangGraph or Koog are stronger.

Where each one wins

Koog 1.0 wins

JVM backends. If your service is Kotlin or Java, Koog is now the obvious choice. Type safety, JetBrains’ tooling lineage, OpenTelemetry, 1-year stability — production-grade.
Cross-platform mobile. Koog’s multiplatform support means one agent codebase runs on Android, iOS, web, and backend. Unique in May 2026.
On-device Android AI. LiteRT integration enables hybrid cloud + local agents. Important for privacy and offline use.
Enterprise Kotlin/Java shops. Banks, telcos, large legacy enterprises that standardized on JVM. Koog finally gives them a first-class agent framework that’s not Python.

LangGraph wins

Python-first teams. Default choice for ML engineers and Python-native backends.
Best observability via LangSmith.
Largest community + integration count. If you need to integrate with N tools, LangChain ecosystem has the most.
Production maturity. Used by serious companies at scale for 2+ years.
Human-in-the-loop workflows. First-class pause/inject/resume primitives.

CrewAI wins

Demos and prototypes. Spin up a working multi-agent flow in 30 minutes.
Education and learning. Easiest framework to teach.
Role-decomposition workflows. Content creation, research synthesis, multi-perspective reasoning.
Small team velocity. Solo founders shipping agent SaaS demos.

The broader 2026 agent framework landscape

The May 2026 agent framework market has consolidated into roughly seven serious choices:

Framework	Best for	Maturity
LangGraph	Production Python teams	Mature, dominant
Koog 1.0	Production JVM, multiplatform	GA May 27, 2026
Microsoft AutoGen 2.0	Enterprise async multi-agent	Mature (Microsoft)
OpenAI Agents SDK	OpenAI-stack production	Mature
Anthropic Agent SDK	Claude-stack production	Mature
Google ADK (Agent Development Kit)	Multimodal Gemini agents on Vertex	Mature (2026 GA)
CrewAI	Prototyping, demos	Mature, community-led
LlamaIndex Workflows	RAG-heavy workflows	Mature
Mastra	TypeScript engineering teams	Growing

Koog’s positioning: first production-grade Kotlin/Java framework with multiplatform reach. Before Koog, JVM teams either rolled their own (brittle), called LangChain via API (impedance mismatch), or shimmed Python (operational pain). Koog 1.0 fills that gap.

Decision tree

What's your stack?
├── Python backend → LangGraph (production) or CrewAI (prototype)
├── Kotlin/Java backend → Koog 1.0
├── TypeScript / Node → Mastra
├── Multiplatform mobile (Android + iOS) → Koog 1.0
├── Microsoft 365 ecosystem → AutoGen 2.0
├── OpenAI exclusively → OpenAI Agents SDK
├── Anthropic exclusively → Anthropic Agent SDK
├── Google Vertex multimodal → Google ADK
└── RAG-first → LlamaIndex Workflows

Why Koog 1.0 matters beyond JetBrains

Three structural reasons Koog 1.0 is more important than the average framework release:

JVM is huge in enterprise. Banks, insurance, telcos, government — JVM remains dominant. Until Koog 1.0, these teams had no first-class agent framework in their native language. That’s a lot of latent demand.
Multiplatform is a real moat. Kotlin Multiplatform is the closest thing to “write once, run everywhere” that has worked at scale. Koog inherits that, so a single agent definition can run on Android, iOS, web, and backend.
On-device AI is increasingly serious. LiteRT on Android, Apple Intelligence on iOS, and small frontier models (Gemma 4, Llama 5 small) make hybrid agents (some on-device, some cloud) viable. Koog is built for that pattern.

Verdict

You’re shipping Python agents in production: LangGraph.
You’re shipping Kotlin/Java agents in production: Koog 1.0 — now the obvious answer.
You’re prototyping a multi-agent demo: CrewAI.
You’re shipping cross-platform mobile with on-device AI: Koog 1.0 — unique fit in May 2026.
You’re already inside one vendor’s stack: that vendor’s SDK (OpenAI, Anthropic, Google ADK).

The big story for May 2026: Koog 1.0 closes the long-standing gap for JVM teams. That alone moves a meaningful chunk of enterprise AI agent workloads off “we’ll wait” and onto “we’re shipping.”

Sources: JetBrains AI blog (May 27, 2026), JetBrains Koog GitHub releases, KotlinConf 2026 keynote, LangChain LangGraph docs, CrewAI documentation, Microsoft AutoGen 2.0 announcement, Google ADK launch (Vertex AI 2026).