Google Agent Development Kit (ADK) vs Stagehand

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

	Google Agent Development Kit (ADK) Agents	Stagehand Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	The SDK for browser agents
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· SDK is free/open-source (MIT). Runs locally against any LLM API key. Optional Browserbase cloud runtime: Free $0/mo, Developer $20/mo, Startup $99/mo, Scale custom.
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Model-agnostic (OpenAI GPT-4o, Anthropic Claude, Google Gemini via Vercel AI SDK)
Editorial score	—	—
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	AI web scrapingAutonomous browser agentsEnd-to-end UI testingStructured data extractionForm-filling automationCompetitor price monitoringRPA replacementQA regression suitesMulti-step research agentsBooking and checkout 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	Open-source (MIT) and built directly on Playwright, so you never lose escape-hatch access to the raw page, frame, and network APIs Natural-language act/extract/observe primitives eliminate most brittle selectors and dramatically reduce maintenance when target sites change Zod-schema extraction returns typed, validated data instead of loose JSON, which fits cleanly into typed backends Model-agnostic via the Vercel AI SDK — swap OpenAI, Anthropic, Gemini, or a self-hosted model without rewriting the agent Optional Browserbase cloud provides stealth browsers, residential proxies, session replay, and captcha handling out of the box TypeScript and Python SDKs with good docs, a growing example library, and an active Discord observe() lets you inspect proposed actions before executing, which makes debugging and human-in-the-loop review practical
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	Every act/extract call issues an LLM request, so token costs and latency add up fast on large crawls compared with hand-written Playwright Non-deterministic by nature — the same prompt can occasionally pick a different element, so critical flows still need assertions and retries Cloud features (stealth, proxies, captcha) require a paid Browserbase plan; running purely local means you handle those problems yourself Still a young project — API surface and best practices are evolving and breaking changes appear between minor versions Requires developer skills (Node/TS or Python) — no visual builder for non-technical automators
Website	google.github.io	stagehand.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 Stagehand if

✅ Open-source (MIT) and built directly on Playwright, so you never lose escape-hatch access to the raw page, frame, and network APIs
✅ Natural-language act/extract/observe primitives eliminate most brittle selectors and dramatically reduce maintenance when target sites change
✅ Zod-schema extraction returns typed, validated data instead of loose JSON, which fits cleanly into typed backends
✅ Model-agnostic via the Vercel AI SDK — swap OpenAI, Anthropic, Gemini, or a self-hosted model without rewriting the agent

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