AI Agent SDKs: Claude, OpenAI, Google Compared

What Are AI Agent SDKs?

An AI agent SDK is a software development kit that abstracts away the complexity of building autonomous AI systems. Rather than manually managing prompt chains, tool execution loops, context windows, and error recovery, an SDK handles these concerns through a structured API. The developer defines the agent's capabilities, tools, and constraints, and the SDK manages the execution loop that lets the agent reason about tasks, call tools, interpret results, and decide next steps.

Traditional LLM API calls are stateless and single-turn. You send a prompt, you get a response. Agent SDKs transform this into a persistent, multi-turn execution environment where the model can take actions in the real world. The agent reads files, executes code, queries databases, calls external APIs, and iterates on its approach based on the results it observes. This shift from passive text generation to active task completion is what separates agent development from conventional LLM application development.

The core primitives that most agent SDKs share include tool definitions (structured descriptions of available actions), an orchestration loop (the cycle of reasoning, acting, and observing), context management (handling conversation history and memory within token limits), and guardrails (safety constraints that prevent harmful or unintended actions). How each SDK implements these primitives, and what additional capabilities it layers on top, defines its character and suitability for different use cases.

Why AI Agent SDKs Matter in 2026

The agent SDK landscape has matured rapidly since late 2024, when most developers were still building agent systems from scratch using raw API calls and custom orchestration logic. By mid-2026, using a raw API to build an agent is roughly equivalent to writing a web application without a framework. You can do it, but you will spend most of your engineering effort solving problems that have already been solved well.

Several converging trends have made SDKs essential rather than optional. First, the complexity of agent systems has grown substantially. Modern agents coordinate multiple models, manage persistent sessions across thousands of turns, execute tools in sandboxed environments, and handle failures gracefully. Building this infrastructure from scratch for every project is not practical. Second, the emergence of the Model Context Protocol (MCP) as an industry standard means that agents built on SDKs with MCP support can instantly connect to hundreds of pre-built tool servers covering everything from GitHub to Slack to databases to file systems. Third, enterprise adoption demands observability, security, and reliability features that are prohibitively expensive to build independently but come built into mature SDKs.

The market has consolidated around four primary offerings, each backed by a major platform with distinct strategic incentives. Understanding these incentives is important because they shape the SDK's design decisions, pricing model, and long-term roadmap.

The Major Players at a Glance

Before diving into individual SDK details, here is a high-level comparison of the four leading options. Each serves a different primary audience and optimizes for different trade-offs between simplicity, power, and flexibility.

Anthropic's Claude Agent SDK provides the same agent loop and tool infrastructure that powers Claude Code. It is tightly integrated with Claude models and offers the deepest MCP support of any SDK. OpenAI's Agents SDK takes a primitives-first approach with agents, handoffs, and guardrails as its core abstractions, and now includes sandbox execution environments for secure code execution. Google's Agent Development Kit (ADK) is an open-source, code-first framework that emphasizes multi-agent architectures and enterprise deployment through Google Cloud. Vercel's AI SDK is provider-agnostic, working across 25+ model providers, and optimizes for TypeScript-first web application development with streaming UI integration.

Claude Agent SDK (Anthropic)

The Claude Agent SDK, originally released as the Claude Code SDK and renamed in March 2026, provides programmatic access to the same agent capabilities that power Claude Code. This means developers get production-tested infrastructure for file operations, command execution, web browsing, and code editing out of the box rather than building these integrations themselves.

The SDK's defining feature is its deep MCP integration. With a single configuration line, agents can connect to any MCP server, giving them access to Playwright for browser automation, GitHub for repository operations, Slack for messaging, and hundreds of other services published by the community. As of mid-2026, MCP has become the de facto standard for connecting LLMs to external tools, and Claude's SDK treats it as a first-class citizen rather than a bolt-on feature.

Session persistence is another standout capability. The SDK captures a session ID from the first query and allows subsequent queries to resume with full context, including files read, commands executed, and conversation history. When the context window fills up, the SDK automatically compacts the conversation by summarizing older turns to free space, allowing agents to work on long-running tasks that span hundreds or thousands of interactions.

Lifecycle control through 18 hook events lets developers intercept nearly every point of agent execution. This enables custom logging, approval workflows, cost tracking, and safety interventions without modifying the core agent logic. The SDK also supports multi-agent coordination, where specialized agents for research, writing, review, or other tasks can be orchestrated as a team.

The primary trade-off is vendor lock-in. The Claude Agent SDK works exclusively with Claude models. If your strategy requires model portability or you need to use non-Anthropic models for certain tasks, you will need to either use a different SDK or build a wrapper layer.

OpenAI Agents SDK

OpenAI's Agents SDK is built around three core primitives: agents (LLMs equipped with instructions and tools), handoffs (mechanisms for agents to delegate to other agents), and guardrails (validation layers for agent inputs and outputs). This minimal set of abstractions is intentional. OpenAI's philosophy is to provide just enough structure to be useful without imposing opinions about how agents should be architected.

The April 2026 update introduced two significant capabilities. Sandbox execution allows agents to operate in controlled, isolated environments where they can inspect files, run commands, and edit code without risking the host system's integrity. The model-native harness handles tool dispatch, multi-step execution, and state persistence at the model level rather than in application code, reducing the amount of orchestration logic developers need to write.

Upcoming features include subagents (additional agents that operate under a primary agent for specific tasks) and a code mode for both Python and TypeScript that enables agents to write and execute code as part of their workflow. The SDK also includes built-in tracing for visualizing, debugging, and monitoring workflows, with integration into OpenAI's evaluation, fine-tuning, and distillation pipeline.

Voice agent support through GPT-Realtime-2 is a unique differentiator. Developers can build agents that communicate through speech with automatic interruption detection, context management, and guardrails, a capability that no other major SDK offers natively.

The SDK supports MCP integration, allowing agents to connect to standard MCP servers. However, MCP support in the OpenAI SDK is newer and less deeply integrated compared to Claude's implementation. The SDK primarily targets Python developers, with TypeScript support for the newer features arriving later.

Google Agent Development Kit (ADK)

Google's ADK takes the most ambitious approach to multi-agent systems among the four SDKs. It natively supports multi-agent architectures where developers can compose specialized teams of agents that collaborate, delegate tasks, and share context. The framework offers both predictable pipeline orchestration (using workflow agents) and adaptive dynamic routing where agents coordinate autonomously based on the task requirements.

ADK 2.0 introduced a graph-based workflow runtime that serves as a deterministic execution engine for agentic applications. This engine supports routing, fan-out and fan-in patterns, loops, retry logic, state management, dynamic nodes, human-in-the-loop approvals, and nested workflows. For teams building complex enterprise processes, this graph-based approach provides the predictability and debuggability that pure LLM orchestration often lacks.

The Task API provides structured agent-to-agent delegation with support for multi-turn task mode, single-turn controlled output, mixed delegation patterns, and task agents that function as workflow nodes. Context management is automatic, with the framework filtering irrelevant events, summarizing older conversational turns, lazy-loading artifacts, and tracking token usage across the agent team.

Deployment to Google Cloud is streamlined. When deploying via Agent Runtime, Cloud Run, or GKE, agents inherit managed infrastructure, built-in authentication, Cloud Trace observability, and enterprise-grade security. This makes ADK particularly attractive for organizations already invested in the Google Cloud ecosystem.

ADK is open source and available on GitHub, with both Python and documentation repositories actively maintained. The framework is model-flexible in principle, though its deepest integrations and best performance come from pairing it with Gemini models.

Vercel AI SDK

Vercel's AI SDK occupies a unique position in the market as the only major option that is truly provider-agnostic. It works with over 25 AI providers, including OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, xAI Grok, and Mistral, through a unified API. This makes it the natural choice for teams that want to avoid vendor lock-in or need to use different models for different tasks within the same application.

AI SDK 6, released in 2026, introduced the Agent abstraction as a first-class concept. Developers define an agent once with its model, instructions, and tools, then reuse it across the entire application. Agents integrate with the full AI SDK ecosystem, providing type-safe UI streaming, structured outputs, and seamless framework support for Next.js, Nuxt, SvelteKit, and other modern web frameworks.

Human-in-the-loop approval is built in with a single needsApproval flag on any tool, requiring no custom code. This is important for production agents that perform consequential actions like deleting files, processing payments, or modifying production data. The SDK also supports multi-step autonomous loops with stop conditions and tool sequencing, giving developers fine-grained control over how far an agent can go without human oversight.

Full MCP support means agents built with the Vercel AI SDK can connect to the same ecosystem of tool servers available to Claude and OpenAI agents. The SDK is TypeScript-first, which makes it the strongest option for JavaScript and TypeScript developers building web applications. However, it does not offer a Python SDK, which limits its appeal for data science and backend-heavy use cases.

The trade-off with Vercel's SDK is that it provides less built-in infrastructure for code execution, file operations, and sandboxing compared to the Claude and OpenAI SDKs. These capabilities need to be implemented through tools or MCP servers rather than coming out of the box.

Architecture and Design Philosophy

Each SDK reflects a distinct philosophy about how agents should be built, and understanding these philosophies helps predict how each SDK will evolve over time.

The Claude Agent SDK follows a batteries-included philosophy. It provides built-in tools for file reading, code editing, command execution, and web browsing because Anthropic's position is that the most common agent tasks should work immediately without any setup. The SDK's architecture mirrors Claude Code's internal design, which means it has been battle-tested across millions of real-world coding sessions before being released as a public SDK.

OpenAI's Agents SDK follows a primitives-first philosophy. The three core abstractions (agents, handoffs, guardrails) are intentionally minimal. OpenAI provides the building blocks and expects developers to compose them into whatever architecture their application requires. This gives maximum flexibility at the cost of requiring more initial setup work.

Google's ADK follows an enterprise orchestration philosophy. The graph-based workflow engine, structured task delegation, and native multi-agent support reflect Google's experience building large-scale distributed systems. ADK is designed for teams building complex, mission-critical agent systems where predictability and observability matter more than rapid prototyping.

Vercel's AI SDK follows a web-native philosophy. Everything is designed around the assumption that agents will be embedded in web applications, streaming results to users in real time. Type safety, UI integration, and framework compatibility are prioritized because Vercel's user base consists primarily of web application developers.

Tool Calling and MCP Support

Tool calling is the mechanism that transforms a language model from a text generator into an agent. The model decides when to call a tool, what arguments to pass, and how to interpret the results. Each SDK implements this differently.

Claude's SDK provides the deepest MCP integration, treating MCP servers as first-class tool providers that can be added with a single configuration line. The SDK handles MCP transport negotiation, tool discovery, and result parsing automatically. It also includes built-in tools for file operations, code editing, and command execution that don't require any MCP setup.

OpenAI's SDK supports MCP through the Agents Python SDK, which understands multiple MCP transports. Developers can reuse existing MCP servers or build custom ones to expose filesystem, HTTP, or connector-backed tools. Tool definitions use OpenAI's function calling format, which has been the most widely adopted standard since its introduction in 2023.

Google's ADK provides a rich tool ecosystem with built-in integrations for Google Cloud services, third-party applications, and custom code. MCP support was added following the industry's convergence on the protocol in early 2026. ADK's tool system is tightly integrated with its workflow engine, allowing tools to participate in graph-based execution flows with retry logic and error handling.

Vercel's AI SDK 6 added full MCP support alongside its existing tool system. Tools are defined using the tool function with a description, input schema (using Zod for type validation), and an execute function. The unified provider API means the same tool definitions work regardless of which model provider is being used.

Multi-Agent Capabilities

As agent systems grow more complex, the ability to coordinate multiple specialized agents becomes critical. A single agent handling research, analysis, writing, and review often produces worse results than a team of agents where each focuses on its strength.

Google's ADK leads in this area with native multi-agent architectures built into the framework from the beginning. The Task API provides structured delegation patterns, and the workflow runtime supports complex coordination patterns like fan-out (sending tasks to multiple agents simultaneously) and fan-in (aggregating results from multiple agents). This makes ADK the strongest choice for building systems where multiple agents need to collaborate on complex tasks.

Claude's SDK supports multi-agent coordination through swarm patterns where specialized agents for different tasks can be orchestrated as a team. The SDK's session persistence and context management handle the complexity of maintaining state across multiple agents, though the coordination patterns are less structured than ADK's workflow-based approach.

OpenAI's SDK handles multi-agent patterns through the handoff mechanism, where one agent can delegate to another agent for a specific task. The upcoming subagents feature will formalize the parent-child agent relationship. Handoffs are simple and effective for linear delegation chains but less suited for complex parallel coordination patterns.

Vercel's SDK supports multi-step agents with autonomous loop control but treats multi-agent systems as a composition concern rather than a built-in feature. Developers can create multiple agent instances and coordinate them in application code, but the SDK does not provide dedicated multi-agent orchestration primitives.

Language Support and Ecosystem

Language support determines which developer communities can adopt each SDK and how well it integrates with existing codebases.

Claude's Agent SDK supports both Python and TypeScript, covering the two largest communities in agent development. The Python SDK is more mature, reflecting Anthropic's origins in the research community, but the TypeScript SDK has been catching up rapidly and provides the same core capabilities.

OpenAI's Agents SDK launched in Python first, and the newer features (sandbox execution, model-native harness) are Python-only initially with TypeScript support planned. The Python ecosystem around OpenAI is the largest of any AI SDK, with extensive community libraries, tutorials, and integration examples.

Google's ADK is primarily a Python framework, available on PyPI as google-adk. The documentation and examples focus on Python usage. TypeScript support is not currently available, which limits ADK's adoption among web developers.

Vercel's AI SDK is TypeScript-only, which gives it the best developer experience for JavaScript and TypeScript projects but excludes the Python community entirely. The SDK's type safety, streaming support, and framework integrations are built specifically for the TypeScript ecosystem.

For teams using Python, all four SDKs are available (though Vercel's is not). For TypeScript teams, Claude and Vercel offer the strongest support. For teams that need both languages, Claude is the only SDK that provides first-class support in both Python and TypeScript.

Pricing and Token Economics

Agent systems consume significantly more tokens than single-turn applications because the orchestration loop, tool calls, and context management all add to the token count. Understanding the pricing models is essential for estimating production costs.

Claude's pricing as of May 2026 ranges from $1/$5 per million tokens (input/output) for Haiku 4.5, through $3/$15 for Sonnet 4.6, up to $5/$25 for Opus 4.7. Prompt caching reduces input costs by up to 90% for repeated context, and the Batch API provides a 50% discount on both input and output tokens. Starting June 15, 2026, Claude subscription plans include a monthly Agent SDK credit, and Agent SDK usage no longer counts toward plan limits.

OpenAI's flagship GPT-5.5 costs $5/$30 per million tokens, making it the most expensive option for output-heavy workloads. The Codex model at $1.75/$14 provides a more economical option for coding tasks. Prompt caching is available and provides similar savings to Claude's implementation.

Google's Gemini 3.1 Pro costs $2/$12 per million tokens in standard mode, with costs doubling beyond 200K tokens. Gemini 3 Flash at $0.50/$3 offers the most affordable mid-tier option among all providers. For budget-conscious projects, Flash models can handle many agent tasks at a fraction of the cost of flagship models.

Vercel's AI SDK is free and open source, with costs determined entirely by the model provider you choose. This gives maximum flexibility in cost optimization since you can switch between providers or use different models for different tasks based on cost-performance trade-offs.

Choosing the Right SDK for Your Project

The right choice depends on your specific requirements across several dimensions. If you are building code-focused agents and want the fastest path to production, Claude's Agent SDK provides the most complete out-of-box experience with built-in tools for file operations, code editing, and command execution. If you need model flexibility and are building web applications in TypeScript, Vercel's AI SDK lets you use any model provider through a unified API. If you are building complex enterprise agent systems with structured workflows, Google's ADK provides the most sophisticated orchestration and deployment infrastructure. If you want minimal abstractions with maximum control and are building on OpenAI models, the OpenAI Agents SDK gives you the primitives to build exactly what you need.

Consider your team's primary language. Python teams have the most options and should evaluate based on feature requirements. TypeScript teams should prioritize Claude or Vercel. Consider your cloud infrastructure. Google Cloud users get significant deployment advantages with ADK. Consider your model strategy. If you need multi-provider support, Vercel is the clear winner. If you are committed to a single provider, the provider's own SDK will always have the deepest integration.

The guides below cover each SDK, comparison dimension, and practical build tutorial in detail.

SDK Guides and Reviews

Comparisons and Analysis

Developer Guides

Build Tutorials