AI Agent SDK Comparison: Features and Pricing

Tool Calling and Built-in Tools

Claude Agent SDK ships with the most comprehensive set of built-in tools: file reading, file writing, code editing, shell command execution, web browsing, and codebase search. These tools require zero configuration and work immediately after installation. This makes it the fastest SDK to go from installation to a working agent that can perform real tasks.

OpenAI Agents SDK provides tool calling infrastructure but fewer built-in tools. The April 2026 sandbox execution feature added built-in code execution and file operations within isolated environments. Tool definitions use OpenAI's function calling format, which is the most widely supported standard in the ecosystem.

Google ADK provides tool calling with deep Google Cloud service integrations (BigQuery, Cloud Storage, Vertex AI) and supports custom Python function tools. Its tool system integrates with the graph-based workflow engine, allowing tools to participate in retry logic and conditional execution flows.

Vercel AI SDK requires all tools to be defined by the developer or connected via MCP servers. There are no built-in tools for file operations or code execution. Tools are defined using Zod schemas for type-safe validation, which provides excellent TypeScript developer experience but means more setup work for common operations.

MCP Integration Depth

Claude Agent SDK has the deepest MCP integration, treating it as the primary tool extension mechanism. Single-line configuration, automatic transport negotiation, full support for MCP resources, prompts, and sampling. This is expected given that Anthropic created the MCP standard.

Vercel AI SDK 6 added full MCP support that works identically across all 25+ providers. The provider-agnostic nature means MCP tools behave the same regardless of the underlying model, which is unique among the SDKs.

OpenAI Agents SDK supports MCP with multiple transport options. Integration is functional but less deeply embedded than in Claude's SDK. MCP was added after the SDK's initial release rather than being part of its foundational architecture.

Google ADK added MCP support in early 2026. The integration works alongside ADK's native tool system and Google Cloud integrations. MCP tools can participate in graph-based workflows, but the MCP integration is the newest among the four SDKs.

Multi-Agent Capabilities

Google ADK provides the most sophisticated multi-agent support with native multi-agent architectures, a Task API for structured delegation, graph-based workflow coordination, and both deterministic and dynamic routing. This is the clear leader for complex multi-agent systems.

Claude Agent SDK supports multi-agent patterns through swarm coordination. Agents can be specialized for different tasks and orchestrated as a team. Less structured than ADK but more flexible for open-ended task delegation.

OpenAI Agents SDK uses handoffs for agent-to-agent delegation. Effective for linear delegation chains but less suited for parallel coordination. The upcoming subagents feature will add more structured parent-child patterns.

Vercel AI SDK treats multi-agent coordination as a composition concern. You create multiple agent instances and coordinate them in application code. No built-in orchestration primitives, but full control over how agents interact.

Language Support

Claude Agent SDK: Python and TypeScript (both first-class). The only SDK with strong support in both major languages. Python is more mature, TypeScript is catching up rapidly.

OpenAI Agents SDK: Python-first. TypeScript available for core features but lags behind Python for newer capabilities like sandbox execution and the model-native harness.

Google ADK: Python only. No TypeScript SDK available or announced. This is the most significant limitation for web-focused development teams.

Vercel AI SDK: TypeScript only. The best TypeScript developer experience of any SDK, but Python developers are completely excluded.

Model Provider Flexibility

Vercel AI SDK: 25+ providers through a unified API. The clear leader for model flexibility and vendor independence. Switching providers requires a single configuration change.

Google ADK: Primarily Gemini-focused but supports other providers. Best performance and deepest integration with Gemini models. Using non-Google models is possible but may involve trade-offs in feature availability.

Claude Agent SDK: Claude models only. No support for other providers. The deepest Claude integration but the strictest vendor lock-in.

OpenAI Agents SDK: OpenAI models only. Similar vendor lock-in to Claude's SDK but with access to a broader range of OpenAI models including voice models via GPT-Realtime-2.

Deployment and Infrastructure

Google ADK: The most comprehensive deployment infrastructure. Native Google Cloud deployment with managed scaling, authentication, observability, and security. Can also run on any Kubernetes cluster or as standalone processes.

Claude Agent SDK: Cloud deployment through Anthropic's API with session storage on their servers. On-premises option available for data sovereignty requirements. Hook system enables custom deployment monitoring.

OpenAI Agents SDK: Cloud deployment through OpenAI's API. Sandbox execution environments are managed by OpenAI. Tracing integrates with OpenAI's dashboard and evaluation tools.

Vercel AI SDK: Deploys wherever Node.js runs. Natural integration with Vercel's hosting platform but works on any Node.js hosting environment including AWS Lambda, Google Cloud Functions, and self-hosted servers.

Pricing Structure

All four SDKs are free to use (open source or free tier). Costs come from model API usage. Claude models range from / (Haiku) to /5 (Opus) per million tokens. OpenAI ranges from .75/4 (Codex) to /0 (GPT-5.5). Google offers the lowest entry point at /bin/bash.50/ (Gemini Flash) and mid-range at /2 (Gemini Pro). Vercel's SDK is free, with costs depending on which provider you choose.

For cost-sensitive projects, Google's Gemini Flash models through either ADK or the Vercel AI SDK offer the lowest token costs. For projects where model quality is the priority and budget is flexible, Claude Opus through the Claude Agent SDK or GPT-5.5 through the OpenAI SDK provide the highest capability at the highest cost.

Prompt caching is available on all three proprietary platforms and reduces repeated input costs by up to 90%. The Batch API (available on Claude and OpenAI) provides an additional 50% discount for latency-tolerant workloads. These optimizations can significantly reduce agent costs since agent loops involve substantial token overhead from repeated context.

Observability and Debugging

OpenAI Agents SDK includes built-in tracing that captures every execution step, integrates with OpenAI's dashboard, and feeds into their evaluation and fine-tuning pipeline. This creates a powerful feedback loop where production agent behavior can improve the underlying model over time. Traces are exportable to third-party observability platforms for teams that need centralized monitoring.

Google ADK provides the most comprehensive observability through Google Cloud Trace integration. When deployed on Google Cloud, agents get distributed tracing across the entire multi-agent team, with latency breakdowns by node in the workflow graph. The evaluation framework also supports automated testing against defined test cases, which is valuable for compliance-oriented enterprises.

Claude Agent SDK offers observability through its 18 lifecycle hooks, which let developers capture and log every significant event in the agent's execution. While less structured than OpenAI's tracing or Google's Cloud Trace, the hook system is extremely flexible and can be integrated with any logging or monitoring platform the team already uses.

Vercel AI SDK provides event streams and DevTools for debugging agent behavior during development. In production, observability relies on the SDK's event system and integration with standard Node.js monitoring tools. The DevTools provide a visual interface for inspecting tool calls, model responses, and streaming behavior during development.

Best Use Cases

Claude Agent SDK is best for code-focused agents, rapid prototyping with rich built-in tools, and projects that need deep MCP ecosystem integration. It is the fastest path from zero to a working agent.

OpenAI Agents SDK is best for teams wanting maximum architectural flexibility, voice agent applications, and projects that need sandbox execution for secure code running.

Google ADK is best for complex enterprise multi-agent systems, teams already on Google Cloud, and projects requiring structured workflow orchestration with strong governance.

Vercel AI SDK is best for TypeScript web applications, projects requiring multi-provider flexibility, and teams building AI features into existing web products with streaming UI needs.