Google Agent Development Kit (ADK) vs GPT Researcher

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

	Google Agent Development Kit (ADK) Agents	GPT Researcher Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Open-source autonomous deep-research agent with cited long-form reports
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 (MIT License). Costs come only from your chosen LLM provider (OpenAI, Anthropic, Google, etc.) and retriever (Tavily, Bing, SerpAPI, DuckDuckGo is free).
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Model-agnostic (default GPT-4o; supports Anthropic Claude, Google Gemini, Groq, Ollama, and any LiteLLM-compatible provider)
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	Autonomous deep research reportsMarket research briefsCompetitive analysisLiterature reviewsDue-diligence memosGrounded context for downstream RAGMulti-source news synthesisResearch agent inside larger LLM pipelines
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 no SaaS lock-in — self-host anywhere Model- and retriever-agnostic; swap OpenAI, Anthropic, Gemini, Ollama, Tavily, Bing, DuckDuckGo, etc. Produces long-form reports with inline citations and a source list, not just raw snippets Parallel sub-agent architecture makes multi-source research meaningfully faster than sequential prompting Ships as Python library, FastAPI service, and Next.js UI — easy to embed or run standalone Ranked #1 on CMU's DeepResearchGym (May 2025), ahead of Perplexity and OpenAI Deep Research Exports to Markdown, PDF, DOCX, and JSON out of the box
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	Self-hosted only — no managed cloud, you handle deployment, keys, and quotas Token and retriever API costs add up on deep-research runs; a single long report can consume tens of thousands of tokens Quality is bounded by the LLM and retriever you choose; cheap combos produce shallow reports No built-in access control, team accounts, or audit log — you build governance yourself Latency is measured in minutes for deep research, not seconds — unsuitable for interactive chat
Website	google.github.io	gptr.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 GPT Researcher if

✅ Fully open-source (MIT) with no SaaS lock-in — self-host anywhere
✅ Model- and retriever-agnostic; swap OpenAI, Anthropic, Gemini, Ollama, Tavily, Bing, DuckDuckGo, etc.
✅ Produces long-form reports with inline citations and a source list, not just raw snippets
✅ Parallel sub-agent architecture makes multi-source research meaningfully faster than sequential prompting

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