Google Agent Development Kit (ADK) vs GPT Pilot

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

	Google Agent Development Kit (ADK) Agents	GPT Pilot Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Open-source multi-agent 'AI developer' that builds apps step by step with human checkpoints
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 (MIT). Users supply their own LLM API keys (OpenAI, Anthropic, Groq, Azure, or OpenRouter), so real cost depends on token usage on the chosen provider.
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Model-agnostic via OpenAI-compatible API (OpenAI GPT-4/4o, Anthropic Claude, Groq-hosted Llama, Azure OpenAI, OpenRouter)
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	greenfield full-stack app scaffoldingmulti-agent coding researchAI developer workflow prototypingself-hosted code generation with your own API keysteaching the phased agent patternCLI-driven project bootstrappingVS Code AI pair-programming experiments
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	Multi-agent pipeline (Architect, Tech Lead, Developer, Reviewer, Debugger) that mirrors a real dev team rather than a single 'write code' prompt Human-in-the-loop checkpoints between tasks, so you can steer the build instead of babysitting a runaway autonomous loop Model-agnostic via OpenAI-compatible endpoints — works with OpenAI, Anthropic, Groq, Azure, and OpenRouter Ships as both a VS Code extension and a standalone CLI, with SQLite/Postgres state so long projects can be paused and resumed Fully open source (MIT), self-hostable, and one of the most-studied reference implementations of the phased-agent pattern
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	Repository is no longer actively maintained — bug fixes, model updates, and new provider support have stalled Maintainers disclosed malicious code in the codebase from August 2025 through June 2026; anyone who ran it in that window must rotate API keys and audit outbound traffic Token costs on large projects add up quickly because the multi-agent loop re-reads context repeatedly across phases Generated code quality is heavily dependent on the underlying model; weaker models produce brittle scaffolding that needs substantial rework Not a hosted product — you install, configure, and supply your own keys, which is friction for non-developers
Website	google.github.io	github.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 Pilot if

✅ Multi-agent pipeline (Architect, Tech Lead, Developer, Reviewer, Debugger) that mirrors a real dev team rather than a single 'write code' prompt
✅ Human-in-the-loop checkpoints between tasks, so you can steer the build instead of babysitting a runaway autonomous loop
✅ Model-agnostic via OpenAI-compatible endpoints — works with OpenAI, Anthropic, Groq, Azure, and OpenRouter
✅ Ships as both a VS Code extension and a standalone CLI, with SQLite/Postgres state so long projects can be paused and resumed

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