Google Agent Development Kit (ADK) vs Oh My OpenAgent

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

	Google Agent Development Kit (ADK) Agents	Oh My OpenAgent Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Open-source multi-agent orchestration framework that coordinates specialized AI coders across planning, execution, and verification.
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).	Free· Free, open-source (SUL-1.0); bring your own model API keys
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Multi-model (Claude Opus 4.7, Sonnet 4.6, GPT 5.5, Gemini 3.1 Pro)
Editorial score	—	7.1 / 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	multi-agent-codingautonomous-task-executioncode-planningparallel-agentscli-automation
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	Routes tasks to model best suited per category instead of one-size-fits-all Parallel Team Mode runs up to 8 agents at once Plan/execute/verify separation reduces single-agent drift Open source under SUL-1.0 with extensive hook system Session resumption via boulder.json keeps long runs recoverable
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	No hosted product — local CLI install with config overhead Requires paid API keys from multiple model providers to use fully Documentation and ecosystem still young compared to mainstream agents
Website	google.github.io	omo.dev

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 Oh My OpenAgent if

✅ Routes tasks to model best suited per category instead of one-size-fits-all
✅ Parallel Team Mode runs up to 8 agents at once
✅ Plan/execute/verify separation reduces single-agent drift
✅ Open source under SUL-1.0 with extensive hook system

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