Google Agent Development Kit (ADK) vs OpenMetadata

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Google Agent Development Kit (ADK) Agents	OpenMetadata Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Open-source metadata platform that gives AI agents a semantic context graph over your data stack.
Category	Agents	Agents
Pricing	Free· Framework itself is free and open-source (Apache 2.0). Costs come from the underlying model provider (e.g. Gemini API / Vertex AI usage) and any hosting infrastructure (Cloud Run, GKE, Agent Engine).	Freemium· Free self-hosted OSS; Collate managed cloud has free tier + paid plans
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	—
Editorial score	—	7.4 / 10
Use cases	Multi-agent research assistantCustomer support triage agentRAG chatbot backed by Vertex AI SearchCode review and refactoring agentBigQuery natural-language analytics agentDocument processing pipelineVoice/streaming conversational agentInternal tool-use agent orchestrating APIsEvaluation and regression testing of LLM workflowsEnterprise workflow automation on Agent Engine	data-catalogagent-contextdata-lineagegovernancemcp-servermetadata-management
Pros	Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly Rich multi-agent primitives out of the box: SequentialAgent, ParallelAgent, LoopAgent, and hierarchical sub-agents Tight Google Cloud integration for deployment (Cloud Run, GKE, Agent Engine) plus native BigQuery/Vertex Search tools Evaluation harness with trajectory-level scoring is included, not a separate paid add-on First-class MCP (Model Context Protocol) client and server support	Fully open-source with a large, active contributor base Native MCP server exposes governed metadata to any agent 130+ connectors across databases, BI, pipelines and ML Semantic Context Graph grounds LLM answers in trusted definitions Managed Collate cloud available if you don't want to self-host
Cons	Documentation and examples lean heavily on Gemini + Google Cloud; non-Google paths work but feel like second-class citizens API surface is still evolving — breaking changes between minor versions have been common through 2025-2026 Multi-agent orchestration primitives are powerful but the graph/callback model has a real learning curve compared to a plain prompt loop Agent Engine deployment is convenient but locks you into GCP billing and quotas TypeScript/Kotlin support lags the Python SDK in features and community examples	Heavy to deploy compared to lightweight RAG tools Real value only emerges at meaningful data-asset scale Agent/MCP layer is newer than the catalog core Metadata model has a learning curve for new teams
Website	google.github.io	open-metadata.org

Pick Google Agent Development Kit (ADK) if

✅ Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product
✅ Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only
✅ Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously
✅ Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly

Pick OpenMetadata if

✅ Fully open-source with a large, active contributor base
✅ Native MCP server exposes governed metadata to any agent
✅ 130+ connectors across databases, BI, pipelines and ML
✅ Semantic Context Graph grounds LLM answers in trusted definitions

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