What AI Agent Framework is Best? Comparing OpenAI AgentSDK, Vercel AI SDK, and Claude Agent SDK

When building AI-powered applications in 2025, choosing the right agent framework can make or break your project. But what AI agent framework is best for your specific use case? In this comprehensive guide, we'll compare three leading solutions: OpenAI's AgentSDK, Vercel AI SDK, and Claude Agent SDK, helping you make an informed decision for your next AI project.

What Are AI Agent Frameworks?

AI agent frameworks provide developers with tools to build applications that leverage large language models (LLMs) to perform complex tasks autonomously. These frameworks handle the heavy lifting of API interactions, prompt management, tool calling, and state management, allowing developers to focus on building features rather than infrastructure.

OpenAI AgentSDK: The Pioneer's Approach

Overview

OpenAI's AgentSDK (also known as the Assistants API) provides a high-level abstraction for building AI agents with persistent threads, built-in retrieval, and code interpretation capabilities.

Key Features

• Persistent Threads: Conversations are stored on OpenAI's servers
• Built-in Tools: Code Interpreter, File Search, and Function Calling
• Stateful by Default: No need to manage conversation history manually
• Multi-model Support: Works with GPT-4, GPT-4 Turbo, and other OpenAI models

Code Example

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

// Create an assistant
const assistant = await openai.beta.assistants.create({
  name: "Data Analyst",
  instructions: "You are a helpful data analyst assistant.",
  tools: [{ type: "code_interpreter" }, { type: "file_search" }],
  model: "gpt-4-turbo"
});

// Create a thread
const thread = await openai.beta.threads.create();

// Add a message
await openai.beta.threads.messages.create(thread.id, {
  role: "user",
  content: "Analyze the sales data and show trends"
});

// Run the assistant
const run = await openai.beta.threads.runs.create(thread.id, {
  assistant_id: assistant.id
});

// Poll for completion
let runStatus = await openai.beta.threads.runs.retrieve(thread.id, run.id);
while (runStatus.status !== 'completed') {
  await new Promise(resolve => setTimeout(resolve, 1000));
  runStatus = await openai.beta.threads.runs.retrieve(thread.id, run.id);
}

// Get messages
const messages = await openai.beta.threads.messages.list(thread.id);

Pros

• Easy to Get Started: Minimal setup required for basic agents
• Managed Infrastructure: OpenAI handles thread storage and state management
• Built-in Capabilities: Code Interpreter and File Search work out of the box
• Robust Function Calling: Excellent support for tool use and API integrations
• Enterprise-Ready: Strong security and compliance features

Cons

• Vendor Lock-in: Tightly coupled to OpenAI's ecosystem
• Cost Considerations: Thread storage and built-in tools add to API costs
• Limited Flexibility: Less control over low-level implementation details
• Polling Required: Run status checking requires manual polling logic
• OpenAI Models Only: Cannot use models from other providers

Vercel AI SDK: The Full-Stack Framework

Overview

Vercel AI SDK is a comprehensive toolkit designed for building AI-powered applications with React, Next.js, and other JavaScript frameworks. It focuses on streaming responses and seamless frontend integration.

Key Features

• Framework Agnostic: Works with React, Vue, Svelte, and vanilla JavaScript
• Multi-Provider Support: OpenAI, Anthropic, Google, Mistral, and more
• Streaming First: Built-in support for real-time streaming responses
• React Hooks: useChat, useCompletion, and useAssistant for easy integration
• Edge Runtime Compatible: Optimized for serverless and edge deployments

Code Example

// app/api/chat/route.ts
import { openai } from '@ai-sdk/openai';
import { streamText } from 'ai';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = await streamText({
    model: openai('gpt-4-turbo'),
    messages,
    tools: {
      getWeather: {
        description: 'Get the current weather for a location',
        parameters: z.object({
          location: z.string().describe('The city name'),
        }),
        execute: async ({ location }) => {
          // Call weather API
          return { temperature: 72, condition: 'sunny' };
        },
      },
    },
  });

  return result.toDataStreamResponse();
}

// Client component
'use client';
import { useChat } from 'ai/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
    <div>
      {messages.map(m => (
        <div key={m.id}>
          <strong>{m.role}:</strong> {m.content}
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit">Send</button>
      </form>
    </div>
  );
}

Pros

• Excellent DX: Best-in-class developer experience for web applications
• Provider Flexibility: Easy to switch between different LLM providers
• Streaming Performance: Optimized for real-time user experiences
• Framework Integration: Seamless integration with React and Next.js
• Active Development: Rapid updates and community support
• Type Safety: Full TypeScript support throughout

Cons

• Web-Focused: Primarily designed for web applications, less ideal for backend services
• Learning Curve: Requires understanding of React patterns and hooks
• State Management: You handle conversation state on the client side
• Less Opinionated: More flexibility means more decisions to make
• Documentation Gaps: Rapidly evolving API can lead to outdated examples

Claude Agent SDK: The Anthropic Approach

Overview

While Anthropic doesn't have a dedicated "Agent SDK" per se, they provide robust tools through their API and Claude Code for building agentic applications. The focus is on powerful prompt engineering, tool use, and the Messages API.

Key Features

• Extended Context: Up to 200K tokens for complex reasoning tasks
• Advanced Tool Use: Sophisticated function calling capabilities
• Constitutional AI: Built-in safety and helpfulness
• Claude Code: Command-line tool for agentic coding workflows
• Streaming Support: Real-time response streaming

Code Example

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const tools = [
  {
    name: "get_stock_price",
    description: "Retrieves the current stock price for a given ticker symbol",
    input_schema: {
      type: "object",
      properties: {
        ticker: {
          type: "string",
          description: "The stock ticker symbol (e.g., AAPL for Apple)"
        }
      },
      required: ["ticker"]
    }
  }
];

async function runAgent(userMessage: string) {
  const messages = [{ role: "user", content: userMessage }];

  while (true) {
    const response = await anthropic.messages.create({
      model: "claude-sonnet-4-5-20250929",
      max_tokens: 4096,
      tools: tools,
      messages: messages
    });

    // Check if Claude wants to use a tool
    if (response.stop_reason === "tool_use") {
      const toolUse = response.content.find(block => block.type === "tool_use");

      // Execute the tool
      let toolResult;
      if (toolUse.name === "get_stock_price") {
        toolResult = await getStockPrice(toolUse.input.ticker);
      }

      // Add tool result to conversation
      messages.push({ role: "assistant", content: response.content });
      messages.push({
        role: "user",
        content: [{
          type: "tool_result",
          tool_use_id: toolUse.id,
          content: JSON.stringify(toolResult)
        }]
      });
    } else {
      // Final answer received
      return response.content[0].text;
    }
  }
}

const answer = await runAgent("What's the current price of Apple stock?");

Pros

• Powerful Reasoning: Claude excels at complex, multi-step reasoning tasks
• Long Context: Industry-leading context window for comprehensive tasks
• Safety Features: Strong constitutional AI guardrails built-in
• Quality Outputs: Consistently high-quality, well-structured responses
• Claude Code Integration: Excellent for development workflows
• Flexible API: Clean, straightforward API design

Cons

• No Official SDK: Lacks a dedicated agent framework like OpenAI's
• More Boilerplate: Requires more manual implementation for agent loops
• Single Provider: Only works with Claude models
• Newer Ecosystem: Smaller community and fewer third-party integrations
• Cost: Can be more expensive for high-volume applications

What AI Agent Framework is Best? The Verdict

The answer to "what AI agent framework is best" depends entirely on your use case:

Choose OpenAI AgentSDK If:

• You need a quick, production-ready agent solution
• You want managed infrastructure and state handling
• You require built-in code interpretation or file search
• You're building primarily with OpenAI models
• You prioritize ease of implementation over flexibility

Choose Vercel AI SDK If:

• You're building a web application with React or Next.js
• You need multi-provider support and flexibility
• Streaming UX is critical to your application
• You want excellent TypeScript support and DX
• You're deploying to edge or serverless environments

Choose Claude/Anthropic API If:

• You need the most powerful reasoning capabilities
• Your tasks require long context windows
• Safety and constitutional AI are priorities
• You're building complex, multi-step workflows
• You want maximum control over the agent loop

Performance and Cost Comparison

Conclusion: Building the Best AI Agents in 2025

When determining what AI agent framework is best, consider your specific requirements:

• For rapid prototyping: OpenAI AgentSDK wins with minimal setup
• For web applications: Vercel AI SDK provides unmatched DX
• For complex reasoning: Claude delivers superior intelligence

The best approach for many teams is to start with one framework and remain open to switching as your needs evolve. All three options are production-ready and backed by strong teams, so you can't go wrong with any of them.

Remember that the "best" framework is the one that helps you ship quickly while meeting your specific technical requirements. Start building, iterate based on user feedback, and don't be afraid to evaluate alternatives as your application matures.