Google Agent Development Kit (ADK) vs OpenSandbox

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

	Google Agent Development Kit (ADK) Agents	OpenSandbox Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Open-source sandbox infrastructure for running AI-generated code, agents, and browsers in isolated Docker or Kubernetes environments.
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· Open source (Apache 2.0); managed pricing not disclosed
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Model-agnostic
Editorial score	—	7.0 / 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	code-executionagent-sandboxingbrowser-automationremote-developmentrl-environments
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	Apache 2.0 licensed and self-hostable on Docker or Kubernetes First-class SDKs for Python, JS/TS, Java/Kotlin, Go, and C#/.NET Built for agent workloads: Claude Code, Gemini CLI, browser automation, RL Full lifecycle API: provision, pause/resume, renew, terminate, stream logs
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 public pricing or managed tier details on the landing page Younger and less battle-tested than E2B or Daytona Operational burden falls on you if you self-host on Kubernetes
Website	google.github.io	open-sandbox.ai

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 OpenSandbox if

✅ Apache 2.0 licensed and self-hostable on Docker or Kubernetes
✅ First-class SDKs for Python, JS/TS, Java/Kotlin, Go, and C#/.NET
✅ Built for agent workloads: Claude Code, Gemini CLI, browser automation, RL
✅ Full lifecycle API: provision, pause/resume, renew, terminate, stream logs

Compare a different pair →