Keywords AI

Semantic Kernel vs Vercel AI SDK

Compare Semantic Kernel and Vercel AI SDK side by side. Both are tools in the Agent Frameworks category.

Quick Comparison

	Semantic Kernel	Vercel AI SDK
Category	Agent Frameworks	Agent Frameworks
Pricing	Open Source	Open Source
Best For	Enterprise .NET developers building AI applications on Microsoft infrastructure	TypeScript and React developers building AI-powered web applications
Website	learn.microsoft.com	sdk.vercel.ai
Key Features	Enterprise AI orchestration by Microsoft C# and Python SDKs Plugin architecture for tools Memory and planning capabilities Azure and Microsoft 365 integration	TypeScript-first AI SDK Streaming-optimized for web apps React hooks for AI UI components Multi-provider support Edge runtime compatible
Use Cases	Enterprise AI on Microsoft stack Copilot-style applications Integrating AI into .NET applications Microsoft 365 automation Enterprise workflow orchestration	Next.js AI-powered applications Streaming chat interfaces AI features in React applications Serverless AI endpoints Full-stack TypeScript AI apps

When to Choose Semantic Kernel vs Vercel AI SDK

Choose Semantic Kernel if you need

Enterprise AI on Microsoft stack
Copilot-style applications
Integrating AI into .NET applications

Pricing: Open Source

Choose Vercel AI SDK if you need

Next.js AI-powered applications
Streaming chat interfaces
AI features in React applications

Pricing: Open Source

How to Choose a Agent Frameworks Tool

Key criteria to evaluate when comparing Agent Frameworks solutions:

Programming languagePython, TypeScript, or both — must match your team skills and existing codebase.

Architecture patternSingle-agent, multi-agent, or graph-based orchestration depending on task complexity.

Tool ecosystemBuilt-in tools and ease of creating custom tools for your specific needs.

ObservabilityBuilt-in tracing, debugging, and monitoring for understanding agent behavior.

Production readinessError handling, retries, streaming, and deployment options for production use.

About Semantic Kernel

Semantic Kernel is Microsoft's enterprise SDK for integrating AI into applications. It provides planners for multi-step task execution, plugin architectures for tool use, memory systems, and connectors for all major LLM providers. Available in C#, Python, and Java, Semantic Kernel is designed for enterprise .NET shops building AI-powered features into existing applications.

View Semantic Kernel profile →Visit website

About Vercel AI SDK

The Vercel AI SDK is a TypeScript toolkit for building AI-powered web applications with React, Next.js, and other frameworks. It provides streaming UI components, structured generation, tool calling, and multi-step agent workflows. The SDK supports all major LLM providers through a unified interface and is the most popular choice for frontend developers building AI features into web applications.

View Vercel AI SDK profile →Visit website

What is Agent Frameworks?

Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.

Browse all Agent Frameworks tools →

Frequently Asked Questions

What is an AI agent framework?▾

An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.

Do I need a framework or can I build agents with raw API calls?▾

For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.

Which agent framework should I choose?▾

LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.