Google ADK vs Claude Agent SDK: How They Compare for Building Agents

If you’re picking an agent framework in 2026, the field has narrowed. The serious options are OpenAI’s Agents SDK, Anthropic’s Claude Agent SDK, and Google’s Agent Development Kit (ADK). We covered the first two before. This post takes on the comparison people keep asking about: Google ADK versus Claude Agent SDK.

Both are first-party kits from frontier-model labs. Both shipped within months of each other. Both want to be the default way you build agents on their stack. The differences are real, and they show up early.

Google ADK in Brief

ADK is Google’s open-source framework for building, evaluating, and deploying agents. It’s Python-first with a TypeScript port and a Java SDK in beta. The framework leans hard on a few opinions.

Code-first agent definitions. You define agents, tools, and workflows in regular Python. ADK doesn’t ship a DSL or a graph editor. An agent is a class with a model, instructions, and a list of tools. That’s it.

Workflows are explicit. ADK ships SequentialAgent, ParallelAgent, LoopAgent, and a generic LlmAgent that lets the model pick the next step. The framework leans on you to compose these primitives rather than hiding orchestration behind a router.

Hosted deployment via Vertex AI Agent Engine. Once your agent works locally, you deploy it to Google Cloud with a few commands. Vertex handles state, scaling, and the observability story. You can run ADK anywhere, but the deployment path is the differentiator.

Native A2A support. ADK speaks the agent-to-agent protocol out of the box. Two ADK agents can delegate work to each other across services without you writing the wire format.

Model-flexible. ADK works with Gemini natively, but also Claude, GPT, and open-source models via LiteLLM. The framework is Google’s, the model choice isn’t locked.

Claude Agent SDK in Brief

The Claude Agent SDK is Anthropic’s framework, available in Python and TypeScript. It optimizes for a different set of priorities.

Tool-use first. Claude models have strong native tool-calling, and the SDK is built around that strength. You define tools with JSON schemas, the model decides when to call them, and the SDK loops until the work is done. There’s much less ceremony than ADK.

MCP-native. First-class support for the model context protocol. Your agent can connect to any MCP server and discover tools at runtime. This is the standout feature: capability expansion without code changes.

Skills. Reusable agent capabilities packaged as markdown plus optional scripts. A skill is a thing the agent learns to do well, with instructions and resources colocated. The SDK loads skills on demand, which keeps the context window clean.

Compaction and hooks. Built-in mechanisms for managing long-running sessions: automatic context compaction, lifecycle hooks for custom behavior, session resumption.

Claude-coupled. The SDK is optimized for Claude models. You can run it against other providers in principle, but the design assumptions track Claude’s capabilities (extended thinking, computer use, native MCP).

Where They Differ

Orchestration model. ADK gives you explicit workflow primitives (Sequential, Parallel, Loop) and expects you to compose them. The Claude Agent SDK leans on the model to decide what to do next, with tools and skills as the units of capability. ADK feels closer to a graph framework like LangGraph; the Claude SDK feels closer to a tool-rich REPL for the model.

Tool discovery. ADK tools are Python (or TypeScript) functions registered with the agent. The Claude Agent SDK can register tools the same way but also discovers tools dynamically through MCP. If you want your agent’s capabilities to grow without redeploying, MCP discovery is the play.

Cross-agent communication. ADK builds in A2A. The Claude SDK doesn’t, yet. If you’re building a system where agents owned by different teams need to delegate work to each other, ADK has a head start.

Deployment. ADK has a paved path to Vertex AI Agent Engine. The Claude SDK leaves deployment to you, which is more flexible but more work. If you’re already on GCP, ADK saves you a week.

Model lock-in. ADK is genuinely model-flexible. The Claude SDK is honest about being Claude-first. Both let you use other models, but only one is designed around that flexibility from the start.

Where They Agree

Both frameworks have settled on the same core loop: model gets a goal, picks tools, executes, observes results, repeats. Both support streaming. Both support structured outputs. Both ship reasonable defaults for retries, timeouts, and error handling.

For a single-agent system with a handful of tools, you can build the same thing in either SDK and the code will look more alike than different.

How to Choose

Pick Google ADK when:

  • You’re deploying on GCP and want Vertex AI Agent Engine.
  • You need explicit workflow control (sequential, parallel, loops as first-class concepts).
  • You’re building multi-agent systems where A2A communication is core.
  • You want model flexibility from day one (Gemini today, Claude tomorrow, both at once).

Pick Claude Agent SDK when:

  • Tool use is the core of your agent’s value and you want the strongest native tool-calling.
  • You want MCP-based tool discovery (connect once, get many tools).
  • You’re optimizing for Claude’s strengths (extended thinking, computer use, skills).
  • You don’t want to be opinionated about workflow shape, you want the model to figure it out.

Use both when: You’re building a platform that supports multiple model providers, or you have one team on ADK and another on the Claude SDK. They’re not mutually exclusive. An ADK agent can call MCP tools. A Claude agent can sit behind an A2A endpoint.

Where AgentPatch Fits

AgentPatch is a tool source, not a framework. It works with both.

If you’re on the Claude Agent SDK, you connect AgentPatch as an MCP server and your agent picks up dozens of tools through a single endpoint. Web search, email, image generation, scrape, financial data, the rest of the catalog.

If you’re on ADK, you call AgentPatch either through the same MCP integration (ADK supports MCP servers) or directly through the REST API. Wrap each AgentPatch tool as an ADK tool function and the agent calls it like any other Python function.

The point of a tool marketplace is that the framework choice and the tool choice are independent. You shouldn’t have to switch SDKs to get a new capability, and you shouldn’t have to write your own integration to use a tool that already exists.

Wrapping Up

Google ADK and the Claude Agent SDK both work. ADK wins on workflow control, cross-agent communication, and a paved deployment path. The Claude Agent SDK wins on tool-use depth, MCP integration, and model-specific features like skills and extended thinking. Pick the one whose strengths match your project. For tools that work with either, browse the AgentPatch catalog at agentpatch.ai.

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