GOOGLE ADK VS GENKIT: WHICH GOOGLE AI FRAMEWORK?
Google ships two things that sound like the same thing. ADK is about agents and their orchestration; Genkit is about building AI features into an application.
ADK — the Agent Development Kit — is Google's framework for building and orchestrating agents, including multi-agent systems. Genkit is a broader framework for building AI features into an application, with strong instrumentation and a developer UI. They overlap in the middle, which is exactly where the confusion lives.
The distinction that actually helps#
Ask what the unit is. In ADK, the unit is an agent: something with tools that decides its own next step, and which may delegate to other agents. In Genkit, the unit is a flow: an instrumented, callable, deployable piece of AI logic that your application invokes like any other function.
So: if you are building a system of agents, ADK is aimed at you. If you are building an application that uses models and want observability, evaluation and deployment handled sensibly, Genkit is.
Genkit's real strength#
The developer experience around inspecting what happened. Being able to see a trace of a flow, replay it, and evaluate changes is worth more in the second month than any framework feature is in the first week — because the hard part of shipping AI is not building it, it is finding out why today's output is worse than last Tuesday's.
It is also the more natural fit for a TypeScript codebase, which narrows the field considerably on its own. See Vercel AI SDK vs LangChain for the neighbouring options.
ADK's real strength#
Taking multi-agent composition seriously as a first-class concern rather than a pattern you assemble yourself, with the orchestration and delegation primitives that implies. If you have concluded you genuinely need several agents — and that conclusion deserves scrutiny — it is a serious option.
The consideration neither page mentions#
Both are first-party. That is the shortest path to something working and the longest path away from that vendor afterwards, and the pull is stronger than it looks on day one: the integrated model access, the deployment story and the tracing are the good parts, and they are the parts most tied to the platform.
That is a legitimate trade — vendor alignment buys real velocity. It is only a mistake when made accidentally. The same caution applies to the OpenAI Agents SDK.