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Google ADK offers flexible pricing options. Visit their website for detailed pricing information and to request a quote.
View Pricing Details →Pricing sourced from Google ADK · Last verified March 2026
ADK is more opinionated and ships with a built-in evaluation framework, local debugging UI, and structured workflow agent types (sequential, parallel, loop) out of the box, whereas LangChain/LangGraph are more flexible and modular with a larger third-party ecosystem. ADK also provides four official language SDKs (Python, TypeScript, Go, Java) versus LangChain's Python and JavaScript. Choose ADK for structured multi-agent development with Google Cloud integration; choose LangChain/LangGraph for maximum flexibility and community ecosystem breadth.
Yes. ADK supports any LLM through LiteLLM integration, and the documentation explicitly lists Anthropic Claude, Ollama, vLLM, LiteRT-LM, and Apigee AI Gateway as supported model providers alongside Gemini and Gemma. However, some Gemini-optimized features like built-in Google Search grounding are only available when using Google models.
No. ADK runs entirely locally for development and includes a local web UI, CLI, and API server runtime. You can also deploy to any container platform including Cloud Run, GKE, or non-Google clouds via standard Docker containers. Google Cloud and Vertex AI Agent Engine are optional managed deployment targets, not requirements.
ADK ships in four official SDKs: Python (currently 2.0 Beta with workflow agents and agent teams), TypeScript 1.0 (newly released in 2026 — making ADK one of the few enterprise agent frameworks with first-party TypeScript support), Go, and Java. The Python 2.0 Beta introduces new workflow primitives, ambient agents, resumable runs, and cancelable execution.
ADK ships with a comprehensive evaluation framework that includes criteria-based scoring, user simulation (synthetic user interactions), environment simulation (mocked tools and external systems), and custom metrics. It also offers an optimization module for iterative improvement, allowing teams to systematically test and refine agent quality before production deployment.
AI builders and operators use Google ADK to streamline their workflow.
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