How to Choose the Right AI Agent SDK

Step 1: Identify Your Primary Programming Language

Your team's primary language immediately narrows the field. If your team works primarily in Python, all three vendor SDKs (Claude, OpenAI, Google ADK) are available with full feature support. Python teams should evaluate based on the remaining criteria. If your team works primarily in TypeScript or JavaScript, your options are Vercel AI SDK (TypeScript-native, provider-agnostic) and Claude Agent SDK (TypeScript support, Claude-only). OpenAI has partial TypeScript support. Google ADK is unavailable.

If your team uses both Python and TypeScript, Claude is the only SDK with first-class support in both. Alternatively, you can use different SDKs for different parts of your system (for example, Vercel for the web frontend and OpenAI or ADK for backend processing), though this adds integration complexity.

Step 2: Define Your Model Strategy

If you need multi-provider flexibility (using different models for different tasks, or keeping the option to switch providers), Vercel AI SDK is the only option that provides this through a unified API. You can also achieve multi-provider support by using multiple SDKs or building your own abstraction layer, but this is more work.

If you are committed to a single model provider, use that provider's SDK. Claude Agent SDK for Anthropic models, OpenAI Agents SDK for OpenAI models, and Google ADK for Gemini models. The provider's own SDK always has the deepest integration with its models, including access to provider-specific features like prompt caching optimizations, model-native harness (OpenAI), and session persistence (Claude).

If you have not yet chosen a model provider, evaluate the models first and let the model choice guide the SDK choice. The SDK experience is heavily influenced by the underlying model's capabilities. Try the same task across Claude, GPT, and Gemini to see which model fits your use case best, then select the corresponding SDK.

Step 3: Assess Your Agent Complexity

For single-agent applications (one agent with tools performing tasks), any of the four SDKs works well. Choose based on other criteria. For multi-agent systems with simple delegation (one agent passes work to another), OpenAI's handoffs or Claude's swarm patterns are sufficient.

For complex multi-agent systems with structured workflows, parallel execution, retry logic, and formal task delegation, Google ADK is the clear leader. Its graph-based workflow engine and Task API provide the most sophisticated orchestration capabilities. No other SDK matches ADK for enterprise-grade multi-agent coordination.

Be honest about your actual complexity needs. Many projects that seem to need multi-agent systems can be implemented effectively with a single well-configured agent. Starting with a single agent and adding complexity only when needed is usually more productive than designing a multi-agent system from the start.

Step 4: Evaluate Tool Requirements

If your agent needs file operations, code editing, and shell command execution out of the box, Claude Agent SDK provides these built-in. Getting the same capabilities with other SDKs requires either writing custom tools or connecting MCP servers.

If your agent needs to interact with Google Cloud services (BigQuery, Cloud Storage, Vertex AI), Google ADK provides native integrations. If your agent needs to interact with a wide variety of external services, MCP support across all four SDKs gives you access to the same ecosystem of 500+ tool servers.

If your agent needs secure code execution in an isolated environment, OpenAI's sandbox execution provides this capability built-in. Achieving the same with other SDKs requires configuring Docker containers or similar isolation mechanisms.

Step 5: Consider Deployment and Infrastructure

If your organization uses Google Cloud, ADK provides the smoothest deployment path with automatic scaling, authentication, and observability. If you deploy web applications on Vercel, the Vercel AI SDK integrates seamlessly with Vercel's hosting platform. Both Claude and OpenAI SDKs are infrastructure-agnostic, deploying on any platform that runs Python or Node.js.

For on-premises requirements, all SDKs can run on-premises, but the model API calls still go to the respective cloud providers. Claude offers a dedicated on-premises deployment option for session storage. Google ADK can be run entirely on-premises with self-hosted Gemini models on Vertex AI.

Step 6: Estimate Budget and Scale

For cost-sensitive projects, Google's Gemini Flash ($0.50/$3 per million tokens) is the most affordable option, accessible through either ADK or the Vercel AI SDK. For projects where quality is paramount, Claude Opus or GPT-5.5 provide the highest capability at the highest cost.

All providers offer prompt caching (90% discount on repeated input tokens) and Claude and OpenAI offer batch APIs (50% off for async workloads). Factor these optimizations into your cost estimates, as they can reduce agent costs by 60 to 80% compared to naive usage.

For teams just starting with agent development, the free tiers and credits offered by each provider are sufficient for prototyping. Claude's subscription plans now include Agent SDK credits. OpenAI and Google both offer free tier access for evaluation.