Google Agent Development Kit (ADK) vs Open WebUI

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

	Google Agent Development Kit (ADK) Agents	Open WebUI Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Self-hosted, extensible AI chat platform that runs on your infrastructure
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· Free (self-hosted, MIT-style community license via pip/Docker) / Enterprise: custom pricing for SSO, RBAC, audit logs, air-gapped deployment, data-residency guarantees
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Backend-agnostic: any Ollama, llama.cpp, vLLM, or OpenAI-compatible API (OpenAI GPT, Anthropic Claude, Llama 3.x, Qwen, Mistral, Gemma, etc.)
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	Self-hosted ChatGPT alternative for a teamPrivate RAG chatbot over internal documentsUnified gateway across multiple LLM providersAir-gapped LLM chat for regulated industriesOllama front end for local Llama or Qwen modelsCustom Python tools and function callingPrompt library and workspace sharingVoice and vision chat on local modelsModel access control and usage auditingHome-lab AI assistant on a single GPU box
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	Genuinely self-hostable in minutes with Docker or pip; no account or callback required Backend-agnostic: talks to Ollama, llama.cpp, vLLM, OpenAI, Anthropic, Groq, OpenRouter and any OpenAI-compatible API through one UI Built-in RAG over uploaded docs, web search, image generation, voice, and function calling Python pipelines and tools framework lets you extend the app without forking it Multi-user with roles, workspaces, model access controls, and audit-friendly logs Very active community with a marketplace of shared prompts, models, tools, and functions Enterprise tier adds SSO, RBAC, and air-gapped deployment for regulated environments
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	You are responsible for hosting, updates, GPU/model provisioning, and backups Feature velocity is high, so breaking changes and rough edges appear between releases RAG and evaluation features are competent but not as deep as purpose-built tools like LangChain, LlamaIndex, or Ragas Enterprise pricing is not published and requires a sales conversation Multi-tenant performance depends entirely on the model backend you wire in; the UI cannot fix a slow local model
Website	google.github.io	openwebui.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 Open WebUI if

✅ Genuinely self-hostable in minutes with Docker or pip; no account or callback required
✅ Backend-agnostic: talks to Ollama, llama.cpp, vLLM, OpenAI, Anthropic, Groq, OpenRouter and any OpenAI-compatible API through one UI
✅ Built-in RAG over uploaded docs, web search, image generation, voice, and function calling
✅ Python pipelines and tools framework lets you extend the app without forking it

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