Google Agent Development Kit (ADK) vs GPT Builder

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

	Google Agent Development Kit (ADK) Agents	GPT Builder Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	OpenAI's no-code editor for spinning up custom ChatGPT agents with instructions, knowledge files, and tool calls.
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).	Paid· Basic: $10 · Pro: $20 · Enterprise: Contact sales
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	OpenAI GPT-4o / GPT-5 (plan-dependent)
Editorial score	—	6.9 / 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	custom-chatbotsinternal-assistantsknowledge-base-botsworkflow-automationapi-actions
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	No code: build a working agent in minutes from a chat conversation Custom Actions call your own APIs via OpenAPI with OAuth or API-key auth Built-in browsing, code interpreter, image gen, and file knowledge GPT Store distribution and private org-only sharing on Team/Enterprise
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	Locked to OpenAI's models and the ChatGPT runtime Limited control over memory, tool routing, and evals vs Assistants API or AgentKit Requires a paid ChatGPT subscription for both builder and end users Knowledge file retrieval is a black box with no tuning knobs
Website	google.github.io	chatgpt.com

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 GPT Builder if

✅ No code: build a working agent in minutes from a chat conversation
✅ Custom Actions call your own APIs via OpenAPI with OAuth or API-key auth
✅ Built-in browsing, code interpreter, image gen, and file knowledge
✅ GPT Store distribution and private org-only sharing on Team/Enterprise

Compare a different pair →