Claude Agent SDK vs Google ADK: Which to Use in 2026
Google ADK 2.0 ships an explicit graph routing engine and the widest language support of any agent SDK. The Claude Agent SDK gives your agent direct access to a computer: Bash, filesystem, web, and any MCP tool in a single configuration dict. These are different machines built for different problems, and picking the wrong one adds friction from day one.
Reach for the Claude Agent SDK when your agent needs to read files, run commands, or wire up MCP tools with minimal glue code. Reach for Google ADK when you are building a multi-agent pipeline with deterministic routing, your team works in Java or Go, or you are deploying inside Google Cloud with Vertex AI.
Key takeaways
- Claude Agent SDK: direct OS/computer access, 10 built-in tools, MCP in one line, hooks, sessions; Python and TypeScript
- Google ADK: explicit graph routing (SequentialAgent, ParallelAgent, LoopAgent), Python/TS/Java/Go, GCP-native, A2A protocol
- Claude SDK wins on first-agent speed and MCP integration; ADK wins on deterministic multi-agent routing and language breadth
- Both run on Anthropic, Vertex AI, Bedrock, and Azure; neither forces you onto one cloud
TL;DR: two SDKs, two control planes
| Dimension | Claude Agent SDK | Google ADK |
|---|---|---|
| Language support | Python (3.10+), TypeScript | Python, TypeScript, Java, Go |
| OS/system access | Native (Bash, Read, Write, Edit, Glob, Grep) | None built-in |
| Orchestration | Subagents via AgentDefinition + Agent tool | SequentialAgent, ParallelAgent, LoopAgent, graph routing |
| MCP integration | mcp_servers dict, one block | Supported via toolsets, more setup |
| Cost controls | max_budget_usd parameter | No native budget cap |
| Sessions | Resume by session_id | State restoration ("context rewind") |
| Hooks | PreToolUse, PostToolUse, Stop, SessionStart, etc. | No native hook system |
| A2A protocol | No | Yes |
| Cloud portability | Anthropic API, Bedrock, Vertex AI, Azure Foundry | Google Cloud native; multi-cloud in principle |
| Install friction | Low (API key + pip install) | Medium (gcloud auth + folder structure) |
Both SDKs run on the same underlying model infrastructure, but they solve different orchestration problems. (Source: Claude Agent SDK docs)
What the Claude Agent SDK is built for
The Claude Agent SDK is the same engine that runs Claude Code, exposed as a Python or TypeScript library. Anthropic describes it as: "Build production AI agents with Claude Code as a library." The SDK ships ten named built-in tools: Read, Write, Edit, Bash, Monitor, Glob, Grep, WebSearch, WebFetch, and AskUserQuestion. An agent can read a codebase, run its tests, edit files, and search the web without any custom tool implementation. (Source: Claude Agent SDK docs)
The control model is intentionally open-ended. You pass a prompt and a set of allowed_tools, and the agent loop decides how to use them. This works well for open-ended tasks ("find and fix the bug in auth.py") where the exact path through tools is hard to predefine. When you need the agent to follow a strict sequence of operations, you wire that through subagents or explicit prompting rather than a routing graph.
MCP integration is one line. Any MCP-compatible server connects via:
options=ClaudeAgentOptions(
mcp_servers={"playwright": {"command": "npx", "args": ["@playwright/mcp@latest"]}}
)
The SDK also exposes a hooks system (PreToolUse, PostToolUse, and more) for audit logging, validation, or approval flows, plus session resume so an agent can continue a prior session with full context by passing resume=session_id. (Source: Claude Agent SDK docs)
What Google ADK is built for
Google ADK takes the opposite position on orchestration. Rather than letting the agent choose its own path, ADK gives you explicit routing primitives: LlmAgent for individual reasoning steps, SequentialAgent for ordered pipelines, ParallelAgent for concurrent branches, and LoopAgent for iteration. ADK 2.0 adds a graph-based workflow layer that makes routing decisions visible in code and auditable at runtime. (Source: Composio comparison)
This matters for enterprise-grade systems where you need a trace of exactly which agent ran which step. ADK also ships native support for the Agent2Agent (A2A) protocol, which lets ADK agents communicate with agents built on other platforms. And with four languages supported (Python, TypeScript, Java, Go), ADK is the only SDK in this comparison that lets a Java or Go backend team contribute to an agent codebase without a language-boundary abstraction.
The tradeoff is setup friction. ADK requires a specific folder structure (agent.py plus __init__.py at minimum), Google Cloud authentication, and more upfront configuration. Harsh, writing for Composio after testing all three frameworks head-to-head in April 2026, found that ADK "requires a lot of manual plumbing and security" and documented a looping tool call that ran up a $5 bill before hitting a rate limit. (Source: Composio comparison)
Orchestration: subagents vs graph routing
Claude SDK orchestration works through delegation. Your main agent spawns subagents by including Agent in allowed_tools and defining named agents with AgentDefinition. The main agent decides when to invoke a subagent based on context, not a hardcoded route:
agents={
"code-reviewer": AgentDefinition(
description="Expert code reviewer.",
prompt="Analyze code quality and suggest improvements.",
tools=["Read", "Glob", "Grep"],
)
}
This handles open-ended delegation well. It is less predictable for workflows where step order is a hard requirement, because the main agent chooses the path based on context rather than a defined graph. (Source: Claude Agent SDK docs)
ADK's routing is the inverse: you define the graph, the agents execute within it. A SequentialAgent runs its children in order every time. A ParallelAgent fans out. A LoopAgent iterates until a condition is met. For document processing pipelines, multi-step API workflows, or any task where "step A must always complete before step B" is a hard requirement, the graph model is safer and easier to test. (Source: Composio comparison)
If the routing decision requires judgment about what to do next, use Claude SDK subagents. If the routing is deterministic and needs to be auditable, use ADK's orchestration primitives.
MCP integration and built-in tooling
The Claude Agent SDK has no gap between install and MCP tools available. Any stdio MCP server connects via the mcp_servers dict with a command and args. Hundreds of pre-built MCP servers (Playwright, Postgres, GitHub, Slack) are available via npm or pip and connect identically. ADK supports MCP as well, but the integration path involves more configuration: defining tool schemas, handling session context, and managing connection state explicitly. (Source: Claude Agent SDK docs)
For teams already deep in the Google ecosystem, this overhead is offset by native Vertex AI integration and built-in ADK toolsets for Google services. For everyone else, the Claude SDK's MCP ergonomics are faster to ship.
First-agent setup: install to running
Operator note (first-hand): Testing first-agent setup from a clean environment on both SDKs. Claude Agent SDK required three steps: pip install claude-agent-sdk, export ANTHROPIC_API_KEY=your-key, and a 12-line Python script using query(). First result returned in under 30 seconds. Google ADK required pip install google-adk, gcloud auth application-default login, creating an agent folder with agent.py and __init__.py, and running adk web to launch the dev server. End-to-end to a first working agent: approximately 10 minutes, and that assumes gcloud is already installed. For prototyping or CI/CD automation, the Claude SDK's lower friction matters.
For team workflows that will eventually run in Google Cloud, the ADK's tighter Vertex AI integration pays back the setup cost over time. (Source: Claude Agent SDK docs)
Language support and cloud portability
Both SDKs support Python and TypeScript as first-class languages. ADK additionally supports Java and Go, which is a meaningful differentiator for teams with existing JVM or Go backends. If your agent pipeline will be called from a Java service, ADK is the only option without a language-boundary abstraction.
The Claude Agent SDK runs on four backends without code changes: Anthropic's API, Amazon Bedrock (CLAUDE_CODE_USE_BEDROCK=1), Google Vertex AI (CLAUDE_CODE_USE_VERTEX=1), and Microsoft Azure AI Foundry (CLAUDE_CODE_USE_FOUNDRY=1). This means you can prototype on the Anthropic API and deploy on Bedrock or Vertex in the same codebase. ADK is designed with Google Cloud first but is not strictly cloud-locked. (Source: Claude Agent SDK docs)
Which should you pick
| Use case | Recommendation |
|---|---|
| Agent needs Bash, files, or OS commands | Claude Agent SDK |
| Connecting MCP servers quickly | Claude Agent SDK |
| Deterministic multi-step pipeline routing | Google ADK |
| Java or Go backend team | Google ADK |
| Deploying in Google Cloud with Vertex AI | Google ADK |
| CI/CD automation or prototyping | Claude Agent SDK |
| Cross-platform A2A agent communication | Google ADK |
| Budget caps with max_budget_usd | Claude Agent SDK |
| Audit logging every tool call | Claude Agent SDK (hooks) |
If you are building your first agent and want to move fast, the Claude Agent SDK reaches a working agent in under five minutes and handles most tool-use scenarios out of the box. If you are building a production multi-agent system on Google Cloud with strict routing requirements and a Java or Go team, Google ADK is the better fit. (Source: Claude Agent SDK docs)
FAQ
What is the difference between Google SDK and ADK?
Google has two separate things: a general-purpose Google API client library, and the Agent Development Kit (ADK). ADK is specifically a multi-agent orchestration framework with routing primitives like SequentialAgent and ParallelAgent, plus A2A protocol support for cross-platform agent communication.
Can I use the Claude Agent SDK with Google Cloud?
Yes. Set CLAUDE_CODE_USE_VERTEX=1 and configure Google Cloud credentials. The SDK routes requests through Vertex AI while keeping the same Python or TypeScript API. Claude models are available on Vertex AI.
Does Google ADK support Claude models?
ADK supports multiple model backends. You can configure it to call Claude models via the Anthropic API or Vertex AI, though ADK is designed primarily around Gemini models and the Google ecosystem.
Which SDK is easier to set up for a first project?
The Claude Agent SDK requires one pip install and one environment variable. Google ADK requires Google Cloud authentication and a specific project structure before the first agent runs. For a solo developer or small team, the Claude SDK is faster to get started.
Does the Claude Agent SDK work on Amazon Bedrock?
Yes. Set CLAUDE_CODE_USE_BEDROCK=1 and configure standard AWS credentials. No other code changes are needed. (Source: Claude Agent SDK docs)
Related coverage
- Claude Agent SDK vs Claude Code: Same Engine, Different Use Case
- Claude Agent SDK vs OpenAI Agents SDK: Which Framework for Your Projects?
- AI agent framework status 2026: maintained, deprecated, archived
References
- Claude Agent SDK docs - https://code.claude.com/docs/en/agent-sdk/overview
- Composio: Claude Agents SDK vs OpenAI vs Google ADK - https://composio.dev/content/claude-agents-sdk-vs-openai-agents-sdk-vs-google-adk
- Google ADK Developer's Guide - https://developers.googleblog.com/developers-guide-to-building-adk-agents-with-skills/
