Google Agent Development Kit (ADK) vs Langflow

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

	Google Agent Development Kit (ADK) Agents	Langflow Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Open-source visual builder for LangChain-style AI agents and RAG pipelines.
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· Open-source free; hosted free tier + paid enterprise via DataStax
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Multi-model
Editorial score	—	8.1 / 10
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	agent-prototypingrag-pipelinesworkflow-automationllm-orchestrationchatbot-development
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	Open source and self-hostable with a permissive license Visual graph maps cleanly to LangChain concepts developers already know Broad LLM and vector-DB coverage out of the box Every node is editable Python, not a locked black box Flows deploy as APIs without extra glue code
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	Fast-moving codebase; version upgrades can break existing flows Visual metaphor still assumes LangChain-level familiarity Hosted tier is tied to DataStax's Astra ecosystem
Website	google.github.io	www.langflow.org

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 Langflow if

✅ Open source and self-hostable with a permissive license
✅ Visual graph maps cleanly to LangChain concepts developers already know
✅ Broad LLM and vector-DB coverage out of the box
✅ Every node is editable Python, not a locked black box

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