Google Agent Development Kit (ADK) vs gpt-engineer

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

	Google Agent Development Kit (ADK) Agents	gpt-engineer Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Describe software in natural language, watch an AI agent write, run, and improve it.
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 and open source under MIT license. Users pay only for the underlying LLM API calls (OpenAI, Anthropic, Azure OpenAI) or run local models at zero token cost.
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	OpenAI GPT (default), Anthropic Claude, Azure OpenAI, and open-weights models like WizardCoder via configuration
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 script and prototype generationSmall single-file utility creation from a specIterative code improvement via improve modeCoding-agent research and benchmarking (APPS, MBPP)Teaching example for LLM agent loopsScriptable code generation in CI pipelinesLocal-model code generation with self-hosted LLMs
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	Fully open source (MIT) with a small, readable codebase that's easy to fork or study. Model-agnostic: swap OpenAI, Azure OpenAI, Anthropic, or local open-weights models. Zero platform cost — you pay only for tokens, or nothing at all with local models. Simple CLI-first workflow (`gpte <dir>`) that scripts and CI can call. Built-in improve mode for iterating on existing code, not just greenfield generation. Preprompt customization lets you retune agent behavior without patching source. Ships with benchmarking against APPS and MBPP for coding-agent research.
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 was archived in April 2026 — no active maintenance, bug fixes, or new model support. Best suited to small greenfield projects; struggles on large multi-file codebases. No IDE integration — lives entirely in a terminal with a text prompt file. Requires bring-your-own API keys and manual configuration for non-OpenAI models. Python 3.10-3.12 only; older environments are unsupported. Newer agents (Aider, Cursor, Claude Code, Cline) have overtaken it on both quality and DX.
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-engineer if

✅ Fully open source (MIT) with a small, readable codebase that's easy to fork or study.
✅ Model-agnostic: swap OpenAI, Azure OpenAI, Anthropic, or local open-weights models.
✅ Zero platform cost — you pay only for tokens, or nothing at all with local models.
✅ Simple CLI-first workflow (`gpte <dir>`) that scripts and CI can call.

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