Dify vs Google Agent Development Kit (ADK)

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

	Dify Agents	Google Agent Development Kit (ADK) Agents
Tagline	Open-source LLMOps platform for building agentic workflows, RAG pipelines, and AI applications	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Freemium· Professional: $590 · Team: $1590 · Sandbox: Free · Enterprise: Custom · Community: Free	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).
Model	Model-agnostic: OpenAI (GPT-4o, GPT-4.1), Anthropic Claude, Google Gemini, Mistral, Cohere, Ollama, and any OpenAI-compatible endpoint	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	—	—
Use cases	RAG chatbot over internal documentsCustomer support automationMulti-step agent workflowsInternal copilots and assistantsDocument Q&A and summarisationPrompt orchestration and A/B testingEmbeddable chat widgets on marketing sitesMCP tool publishing for Claude and other clientsKnowledge base search APIsWorkflow automation with LLM decision nodes	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
Pros	Genuinely open-source (Apache-2.0-derivative) with a mature self-hosted Docker/Kubernetes deployment path Visual workflow builder covers branching, tool use, agents, and human-in-the-loop without dropping to code Model-agnostic: swap OpenAI, Anthropic, Gemini, Mistral, Ollama, or any OpenAI-compatible endpoint per node Built-in RAG pipeline handles chunking, embeddings, reranking, and multiple vector stores out of the box Publishes any app as a hosted UI, embed, REST API, or MCP tool with logs and analytics attached Large plugin/marketplace ecosystem and one of the most active LLM-framework communities on GitHub Cloud tier has a real free sandbox so you can prototype before choosing self-host vs SaaS	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
Cons	Self-hosting the full stack (API, worker, sandbox, vector DB, Redis, Postgres) is heavier than it looks and version upgrades occasionally break workflows Cloud message-credit limits are tight on the Professional tier and overages push teams to Team or self-host quickly Workflow debugging is weaker than code-first frameworks: complex agent loops can be hard to trace step-by-step Commercial use of the Community Edition has licence caveats (multi-tenant SaaS resale, logo/branding) that need reading before shipping Evaluation and offline testing tooling is thinner than dedicated eval platforms like Langfuse or Braintrust	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
Website	dify.ai	google.github.io

Pick Dify if

✅ Genuinely open-source (Apache-2.0-derivative) with a mature self-hosted Docker/Kubernetes deployment path
✅ Visual workflow builder covers branching, tool use, agents, and human-in-the-loop without dropping to code
✅ Model-agnostic: swap OpenAI, Anthropic, Gemini, Mistral, Ollama, or any OpenAI-compatible endpoint per node
✅ Built-in RAG pipeline handles chunking, embeddings, reranking, and multiple vector stores out of the box

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

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