Claude Agent SDK vs OpenAI Agents SDK vs Google ADK: Which One Should You Build On?

Three production grade AI agent SDKs launched or reached stability in 2025. Claude Agent SDK v0.1.48, OpenAI Agents SDK v0.10.2, and Google ADK v1.26.0 are all past the preview stage and in active production use. The question now isn't whether to build on an SDK; it's which one fits your stack.

As of March 2026, six production grade agent frameworks are in active competition, and the three SDKs covered here represent the major lab-backed options (Langfuse, 2026). This comparison covers architecture, MCP support, model lock-in, state management, deployment flexibility, and the decision criteria that actually matter for production systems.

Why this comparison matters right now#

The choice of SDK shapes more than your initial build. It shapes which models you're effectively locked into, how your agents connect to external tools, what your on-premise deployment story looks like, and how you'll debug things when they break in production. Migrating later costs significantly more than getting this right upfront.

None of the three SDKs is universally superior. Each has genuine strengths and real limitations. The goal here is accurate signal for your actual decision, not a winner's podium.

The three SDKs at a glance#

Claude Agent SDK: the production agent runtime from Anthropic#

The Claude Agent SDK grew out of Claude Code, Anthropic's own coding agent. That lineage matters: its built-in tool catalog (Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch, AskUserQuestion, Agent) reflects actual production use, not a toy demo. The SDK renamed from Claude Code SDK to Claude Agent SDK in 2025 to reflect scope beyond coding workflows (Anthropic, 2025).

Its orchestration model is based on tool-use chains: Claude reasons through a task, calls tools to act on it, evaluates results, and continues autonomously. Subagents are spawned via the Agent tool; a main agent delegates focused subtasks to specialized subagents that report back. The most important differentiator for production use is its permission architecture: tools are blocked by default, lifecycle hooks let you intercept every tool call, and both feed into auditability without patching the agent's core logic.

OpenAI Agents SDK: the simplest path to multi-agent systems#

The OpenAI Agents SDK, released in March 2025, is the easiest of the three to get moving with. Its core mental model is handoffs: a triage agent receives input, then transfers control along with full conversation context to a specialist agent. Each agent is defined by instructions, a model reference, a tool list, and a list of agents it can hand off to.

Guardrails run in parallel with execution rather than sequentially, so safety checks don't add latency in the happy path. Tracing is on by default. State is ephemeral by default, which is fine for stateless workflows and a problem for anything longer-running.

Google ADK: the enterprise framework for the Google Cloud stack#

Google's Agent Development Kit (Python v1.0 stable, April 2025) is the most structured of the three. Agents are organized in a hierarchical tree with explicit workflow types: sequential, parallel, and loop. The framework integrates tightly with Vertex AI, and Model Garden gives access to 200+ models across providers, which is ADK's clearest advantage over the other two.

ADK's state management uses a blackboard pattern, an explicit State object that agents read from and write to as shared context. The A2A (Agent2Agent) protocol is ADK's answer to cross-framework communication: an open standard for agent-to-agent calls across different frameworks and vendors. A2A v0.3 added gRPC support and improved stability (Google Cloud Blog, 2025).

The honest cost: ADK is verbose. You define a lot upfront. If you're not already in the Google Cloud ecosystem, the learning curve is steeper than the other two.

Architecture: how each SDK orchestrates agents#

Claude Agent SDK: tool-use chain with subagents and lifecycle hooks#

Claude reasons through a task, calls tools to act on it, evaluates results, and continues. When a task benefits from delegation, subagents are spawned via the Agent tool. Sessions persist via session IDs and can be resumed or forked, which matters for long-running workflows or ones that branch.

The permission architecture is where this SDK actually earns its production credentials. Every tool is blocked by default and must be explicitly allowed via allowedTools. Lifecycle hooks at PreToolUse and PostToolUse give you an intervention point before and after every tool call: log it, block it, or transform the inputs without touching the agent's core logic. This is the kind of thing compliance teams want and most frameworks don't give you cleanly.

OpenAI Agents SDK: explicit handoffs between specialized agents#

Each agent has a defined scope. When it determines that a different agent is better suited for the current task, it explicitly hands off control with context. The result is multi-agent systems that are easy to trace: you can follow exactly which agent handled each step and why.

The limitation is rigidity. Handoff-based architectures work well when task boundaries are clear and stable. When workflows are less predictable, or when an agent needs to decide dynamically how to delegate, the model requires more redesign.

Google ADK: hierarchical agent trees with explicit workflow control#

ADK's explicit workflow types, sequential (step A then step B), parallel (A and B simultaneously), loop (repeat until condition), give you direct control over execution patterns. You're not relying on the model to reason about sequencing; you're specifying it. For complex structured pipelines, this is a genuine advantage.

The trade-off is verbosity. ADK requires significantly more upfront definition of workflow structure than Claude Agent SDK or OpenAI Agents SDK. Whether that's a feature or a cost depends on how much you trust the model to reason about sequencing on its own.

Feature comparison matrix#

MCP support: which SDK treats tool connectivity as a first-class concern#

MCP is now supported across all three SDKs (ClickHouse, 2025), but the depth of integration differs significantly.

Claude Agent SDK treats MCP as infrastructure. You can run in-process MCP servers (pure Python, no separate process), attach remote MCP servers over HTTP, or configure standard MCP servers from the growing ecosystem. Every MCP-compatible tool server plugs in without custom integration code. Over 10,000 published MCP servers and 97 million monthly SDK downloads as of December 2025 (Anthropic / Linux Foundation, December 2025). The ecosystem exists and is actively growing.

OpenAI Agents SDK added MCP support, but it's not the foundational integration layer. The SDK's tool model predates MCP, and MCP connectivity sits as an additional layer rather than the primary mechanism. It works; it's just not where the SDK was designed from.

Google ADK supports tool connectivity via its own tool ecosystem and increasingly via A2A for agent-to-agent calls. MCP is not the primary pattern. A2A is ADK's answer to cross-system connectivity.

If MCP compatibility matters for your stack, because you're building on the growing ecosystem or because you want tool servers to be portable across frameworks, Claude Agent SDK has the deepest native implementation today.

Model support and vendor lock-in#

Claude Agent SDK is built for Claude models accessed through the Anthropic API, Amazon Bedrock, Google Vertex AI, or Azure AI Foundry. Multi-cloud inference routing is supported. Non-Claude models are not supported; the SDK is architected around Claude's tool-use capabilities specifically.

OpenAI Agents SDK is optimized for GPT-4o and OpenAI models. There are documented paths to use non-OpenAI models, but the default experience, tracing, and most examples assume the OpenAI API. Production teams using it are overwhelmingly on OpenAI or Azure OpenAI models.

Google ADK is the most model-flexible through Model Garden: 200+ models across providers, including Gemini variants, open-weight models (Llama, Mistral), and specialized models. If provider flexibility is a core requirement, ADK is the clearest option.

The honest framing: all three SDKs are aligned with their creator's model ecosystem. Picking an SDK is partly a bet on which model provider you want to be dependent on.

State management and session persistence#

Claude Agent SDK persists sessions via session IDs. Sessions can be resumed (pick up where you left off) or forked (branch from a point for parallel exploration). This matters for workflows where a single task spans hours or requires human input at intermediate steps.

OpenAI Agents SDK state is ephemeral by default. Each run starts fresh. For workflows that need memory across runs, teams add an external memory layer, typically a vector database or key-value store read at the start of each run. The architecture works; it just adds infrastructure that the SDK doesn't provide.

Google ADK uses a State object (blackboard pattern): an explicit shared context that agents read from and write to during a workflow. The State is structured and typed, which makes it easier to reason about information flow between agents. Pluggable backends allow State to be persisted externally when needed.

Deployment flexibility: cloud, self hosted, and hybrid#

Claude Agent SDK is the most flexible for on-premise deployment. In-process MCP servers, local tool execution, and explicit control over what the agent can touch. Authentication routes through Bedrock, Vertex AI, or Azure AI Foundry for inference if you're not hitting Anthropic directly. For teams with data residency requirements or cloud provider commitments, the architecture accommodates this without workarounds.

OpenAI Agents SDK is primarily cloud-dependent: inference runs via the OpenAI API (or Azure OpenAI), and data leaves your environment unless you're on Azure with appropriate agreements. For regulated environments where data residency is a hard requirement, this is a real constraint.

Google ADK supports Docker / Cloud Run / GKE deployment and is designed for Vertex AI. That's cloud infrastructure, specifically Google Cloud infrastructure, which is fine if you're already there. True air-gapped local inference requires combining ADK with a self-hosted model server (Ollama or similar), which is possible but adds configuration work.

Observability, tracing, and production guardrails#

Claude Agent SDK tracing flows through the Claude Code toolchain. Lifecycle hooks at PreToolUse, PostToolUse, Stop, SessionStart, and SessionEnd give you structured intervention points: log, block, or transform at every step. This is flexible for teams building their own observability layer. It requires more integration work if you want a dashboard out of the box.

OpenAI Agents SDK has the most accessible observability out of the three. Tracing is on by default, capturing all LLM generations, tool calls, handoffs, and guardrail events in a searchable Traces dashboard. If you want production visibility without an observability buildout, this is the shortest path. Guardrails run in parallel with execution; they don't add latency on the happy path.

Google ADK routes observability through Vertex AI and Cloud Observability. Natural if you're already in GCP. An added infrastructure dependency if you're not.

When to use each SDK#

Choose Claude Agent SDK when...#

• Your team is building on Claude models and wants the native runtime that powers Claude Code
• MCP-native tool connectivity matters: you need to connect to databases, APIs, and internal systems using the MCP ecosystem
• You need fine-grained permission control over which tools an agent can use, particularly in compliance-sensitive workflows
• You're building developer tooling, code agents, or agentic workflows that interact with filesystems and terminals
• On-premise deployment is a hard requirement
• You need session persistence with resume and fork capabilities

Choose OpenAI Agents SDK when...#

• You're already in the OpenAI or Azure OpenAI ecosystem and want minimal friction
• Your architecture maps to specialist agents that hand off to each other: customer support routing, multi-step pipelines with clear stage transitions
• You want to prototype fast; the SDK's primitives are the most accessible of the three
• Built-in tracing via the Traces dashboard covers your observability needs without extra setup

Choose Google ADK when...#

• Your infrastructure is on Google Cloud and you're targeting Vertex AI
• You need cross-framework agent communication: A2A protocol lets ADK agents communicate with LangGraph, CrewAI, and other frameworks without custom bridging code
• Your agent workflows are complex, structured pipelines that benefit from explicit sequential/parallel/loop control
• Model diversity matters: Model Garden's 200+ models gives flexibility when mixing providers or needing specialized models
• Multimodal capabilities (bidirectional audio/video streaming) are part of your agent's interface

What none of these SDKs solve for you#

Picking an SDK is the easier part of building production agents. The harder work is the same regardless of which framework you use: mapping the actual workflow, defining what success looks like for each agent, handling partial execution and failures gracefully, writing evals that catch regressions before production, and building observability that tells you why an agent did what it did.

These SDKs provide primitives. Good primitives, but primitives. The systems that hold up are built by engineers who understand the operations being automated and can design agents that fail safely when something unexpected happens.

If you're deciding which SDK to standardize on, or you're ready to build and want support across the full stack, architecture, SDK selection, MCP server builds, agent design, deployment, our audit page is where to start. We build production agentic systems and have worked with all three frameworks in practice.

Frequently Asked Questions#

Can the Claude Agent SDK use models other than Claude?

No. The SDK is built specifically for Claude models accessed through the Anthropic API, Amazon Bedrock, Google Vertex AI, or Azure AI Foundry. If you need model-agnostic flexibility at the inference layer, Google ADK via Model Garden or a framework like LangGraph is a better fit.

Does the OpenAI Agents SDK work with non-OpenAI models?

There are documented paths, but the SDK is optimized for OpenAI models and its tooling (tracing, guardrails) is designed around the OpenAI API. Most production teams using it are running on GPT-4o or a variant.

Which SDK has the best self hosted deployment support?

Claude Agent SDK. In-process MCP servers, local tool execution, and routing through Bedrock or Vertex for inference if needed. Google ADK supports Docker and GKE deployment, but that's still cloud infrastructure. For true air-gapped inference with any of these SDKs, you'd combine the SDK with a self-hosted model server.

What is the A2A protocol and why does it matter for Google ADK?

Agent2Agent (A2A) is Google's open protocol for cross-framework agent communication. It lets an ADK agent call an agent built on LangGraph, CrewAI, or another framework, and vice versa, without custom bridging code. A2A v0.3 (2025) added gRPC support and improved stability. This matters in enterprise environments where different teams build agents on different frameworks but need them to talk to each other.

Is MCP only for the Claude Agent SDK?

No. MCP is an open standard supported across all three. Claude Agent SDK treats it as a first-class integration layer; OpenAI Agents SDK supports it as an add-on; Google ADK integrates with it but treats A2A as the primary cross-system pattern. Tools you build as MCP servers are portable across the ecosystem, which is the point of building on a standard.

Which SDK is best for a team just starting to build agents?

OpenAI Agents SDK. Clean handoff semantics, tracing on by default, and a conceptual model that new teams can reason about quickly. Claude Agent SDK is nearly as approachable if your team already works with Claude. Google ADK has more upfront learning, though the Python v1.0 stable release has made it considerably more accessible than earlier versions.