Google Agent Development Kit (ADK)

✓ Editorially verified

Google's open-source framework for building, evaluating, and deploying production AI agents

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).AgentsGemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM

Visit website →

Best for

Developers and platform teams building production multi-agent systems on Google Cloud who want an opinionated open-source framework with real deployment, evaluation, and observability tooling.

Skip if

Solo hobbyists who just want a single prompt-loop chatbot, teams fully committed to a non-Google stack that already have LangGraph/CrewAI in production, or anyone who needs a no-code visual builder.

Google's Agent Development Kit (ADK) is an open-source framework for building, debugging, evaluating, and deploying AI agents at production scale. Originally released alongside the Gemini agent stack, it has grown into a multi-language toolkit — official SDKs cover Python, Java, and Go, with community/experimental support for TypeScript and Kotlin — that lets a developer describe an agent as code (tools, prompts, sub-agents, state) and then run it locally, in a hosted dev UI, on Cloud Run, on Kubernetes, or on Google's managed Agent Engine. The design point is production plumbing that hand-rolled LangChain-style scripts tend to skimp on: structured session/state management, a graph-based workflow model that mixes deterministic control flow with LLM reasoning, first-class multi-agent orchestration (sequential, parallel, hierarchical, loop patterns), streaming, tracing, evaluation harnesses, and safety/guardrail hooks. ADK is model-agnostic despite the Google branding — Gemini is the default via Vertex or the Gemini API, but LiteLLM adapters unlock Anthropic Claude, OpenAI, local Ollama models, and most other providers. Tools can be Python/Go/Java functions, MCP servers, OpenAPI specs, LangChain tools, or Google Cloud services (BigQuery, Vertex AI Search, Apigee). Typical workflows: a solo developer prototypes a single agent in `adk web`, iterates against the built-in evaluator with a golden-set of trajectories, then wraps that agent as a sub-agent inside a larger multi-agent system deployed to Agent Engine with tracing piped into Cloud Trace. It is a natural fit for teams already on Google Cloud who want an opinionated, batteries-included alternative to LangGraph, CrewAI, or writing bespoke orchestration on top of the raw Gemini SDK.

Editor's take

ADK is Google's most credible answer to LangGraph — and unlike a lot of Google developer launches it feels like it was built by people who actually deploy agents. If you're already on Vertex/Gemini, it is now the default choice; if you're not, the multi-language SDKs and clean MCP support still make it worth a serious look, provided you can stomach the API churn.

— The AI Tool Bible editorial team

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

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

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