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What are the differences between Anthropic Agent SDK, OpenAI Agents SDK, and Vercel AI SDK?

Cowrie
Cowrie
Dev @ Bswen

I needed an agent SDK for my project. Three hours later, I had three different codebases, three different mental models, and no clear answer to which one I should use.

My requirements were straightforward: build an agent system that could use tools, maybe coordinate multiple specialized agents, and definitely work with different LLM providers depending on cost and performance needs.

Anthropic, OpenAI, and Vercel all claimed their SDK was the solution. Each approached the problem from a different angle. Here’s what I found.

The Starting Problem

I was building a content management system that needed AI agents to:

  1. Fetch web content and extract structured data
  2. Plan content based on research
  3. Generate articles from plans
  4. Publish to multiple platforms
Content Pipeline:
┌─────────┐     ┌─────────┐     ┌─────────┐     ┌─────────┐
│ Fetch   │────▶│ Plan    │────▶│ Create  │────▶│ Publish │
│ Agent   │     │ Agent   │     │ Agent   │     │ Agent   │
└─────────┘     └─────────┘     └─────────┘     └─────────┘
     │               │               │               │
     └──▶ Tools      └──▶ Context7   └──▶ LLM        └──▶ APIs
         (MCP)           (MCP)          (Claude/GPT)

Each agent needed different capabilities. The fetch agent needed MCP tool discovery. The plan agent needed context retrieval. The create agent needed strong LLM reasoning. The publish agent needed API integrations.

Anthropic Agent SDK: Tool Use Champion

I started with Anthropic’s Agent SDK because my fetch agent needed MCP (Model Context Protocol) integration.

The first thing that struck me: MCP is a first-class citizen.

Anthropic Agent SDK Architecture:
┌─────────────────────────────────────────┐
│              Agent Loop                 │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐ │
│  │ Think   │──│ Tool    │──│ Execute │ │
│  │         │  │ Call    │  │         │ │
│  └─────────┘  └─────────┘  └─────────┘ │
│       │            │            │      │
│       └──▶ Extended Thinking            │
│       └──▶ Runtime Tool Discovery       │
│       └──▶ Hooks System                 │
└─────────────────────────────────────────┘
          │
          ▼
    ┌─────────────┐
    │ MCP Server  │
    │ (Dynamic)   │
    └─────────────┘

Runtime Tool Discovery

This was the game-changer. Instead of hardcoding tool definitions, the SDK discovers tools from MCP servers at runtime.

Traditional SDK:
1. Define tool schema in code
2. Register tool with agent
3. Agent can only use predefined tools


Anthropic SDK with MCP:
1. Connect to MCP server
2. SDK discovers available tools
3. Agent dynamically learns what's available
4. Tools can change without code changes

I connected my local MCP server providing web fetch capabilities. The SDK automatically discovered the tools without me writing any schema definitions.

Extended Thinking

When my plan agent needed complex reasoning, extended thinking mode showed me the actual thought process:

Extended Thinking Example:
User: "Plan a blog post about agent SDKs"


[Thinking Block - Visible]
Analyzing the request...
- User wants comparison content
- Need to cover multiple SDKs
- Should include practical examples
- Target audience: developers


Planning structure:
1. Introduction with problem statement
2. Individual SDK analysis
3. Decision framework
4. Recommendations


[Response]
Here's my plan for the blog post...

This transparency helped debugging. When the agent made unexpected decisions, I could trace back through the thinking blocks.

Hooks System

The hooks system let me inject behavior at key points:

Hook Points:
PreToolUse  ──▶ Before tool execution
PostToolUse ──▶ After tool execution
Stop        ──▶ When agent finishes

I used PreToolUse hooks to validate parameters before tool calls. PostToolUse hooks to log results. This observability was crucial for debugging complex agent workflows.

Where Anthropic SDK excels:

  • MCP integration (runtime tool discovery)
  • Extended thinking for debugging reasoning
  • Hooks for observability and control
  • Computer use for UI interaction

Where Anthropic SDK requires consideration:

  • Claude-optimized (less flexibility with other models)
  • Python + TypeScript only
  • Multi-agent coordination is manual
  • Learning curve for MCP concepts

OpenAI Agents SDK: Multi-Agent Orchestrator

Next I tried OpenAI’s Agents SDK. The pitch was clear: production-ready multi-agent systems with built-in safety features.

OpenAI Agents SDK Architecture:
┌─────────────────────────────────────────┐
│           Orchestration Layer           │
│  ┌─────────────────────────────────┐   │
│  │         Handoffs                │   │
│  │  Agent A ──▶ Agent B ──▶ Agent C│   │
│  └─────────────────────────────────┘   │
│                                         │
│  ┌─────────────┐  ┌─────────────┐      │
│  │  Guardrails │  │   Tracing   │      │
│  │  (Safety)   │  │ (Debugging) │      │
│  └─────────────┘  └─────────────┘      │
└─────────────────────────────────────────┘

Handoffs

The handoff mechanism was elegant. Each agent could transfer control to another specialized agent:

Handoff Flow:
User Query: "Fetch this URL and plan content based on it"


Router Agent receives query
    │
    ├──▶ Classification: URL fetch needed
    │
    ├──▶ Handoff to FetchAgent
    │         │
    │         └──▶ Tool: fetch_url
    │         │
    │         └──▶ Handoff back to Router
    │
    ├──▶ Classification: Planning needed
    │
    ├──▶ Handoff to PlanAgent
    │         │
    │         └──▶ Tool: context7_query
    │         │
    │         └──▶ Handoff back to Router
    │
    └──▶ Response to user

The SDK tracked which agent was active, passed context seamlessly, and handled transitions cleanly.

Guardrails

This feature surprised me. Guardrails are input/output validators that prevent agents from going off track:

Guardrail Examples:
Input Guardrail:
  - Validate user query format
  - Reject malformed requests
  - Sanitize inputs


Output Guardrail:
  - Check response length
  - Verify required fields present
  - Flag potentially harmful content

When my create agent started generating extremely long responses, an output guardrail caught it and truncated to reasonable length.

Tracing

Built-in tracing meant I could visualize the entire agent interaction:

Trace Visualization:
Span 1: RouterAgent.receive_query
  Span 2: RouterAgent.classify_intent
    Span 3: FetchAgent.handoff
      Span 4: FetchAgent.tool_call: fetch_url
    Span 5: PlanAgent.handoff
      Span 6: PlanAgent.tool_call: context7_query
  Span 7: RouterAgent.final_response

This trace showed me exactly where time was spent and which agents were involved.

Where OpenAI SDK excels:

  • Handoffs for specialist routing
  • Guardrails for production safety
  • Tracing for debugging
  • Python-first design
  • Realtime Agents for voice/audio

Where OpenAI SDK requires consideration:

  • Python only (no TypeScript)
  • GPT-optimized (works best with OpenAI models)
  • No MCP integration
  • Tools must be manually defined

Vercel AI SDK: Multi-Provider Flexibility

Finally, I tested Vercel’s AI SDK. The appeal was obvious: one SDK, multiple providers.

Vercel AI SDK Architecture:
┌─────────────────────────────────────────┐
│         Unified Provider Interface      │
│                                         │
│  ┌─────────┐ ┌─────────┐ ┌─────────┐   │
│  │ OpenAI  │ │ Anthropic│ │ Gemini  │   │
│  │ Provider│ │ Provider │ │ Provider│   │
│  └────┬────┘ └────┬────┘ └────┬────┘   │
│       │           │           │        │
│       └───┬───────┴───────┬───┘        │
│           │               │            │
│           ▼               ▼            │
│     ┌─────────────────────────┐       │
│     │   useChat / useCompletion│       │
│     │   (Streaming-first)     │       │
│     └─────────────────────────┘       │
└─────────────────────────────────────────┘

Unified Provider API

One function, multiple backends:

// Same call, different provider
const result = await generateText({
  model: openai('gpt-4'),      // or anthropic('claude-3')
  prompt: 'Plan a blog post',
});


// Switch providers by changing one line
const result = await generateText({
  model: anthropic('claude-3-sonnet'),
  prompt: 'Plan a blog post',
});

This abstraction meant I could prototype with GPT-4, then switch to Claude for production without rewriting code.

Streaming-First Design

Web applications benefit from streaming. Vercel SDK made this trivial:

Traditional API:
User request ──▶ Server ──▶ LLM ──▶ Full response ──▶ User
(Wait 10-30 seconds for complete response)


Vercel Streaming:
User request ──▶ Server ──▶ LLM ──▶ Stream chunks ──▶ User
(Response appears immediately, grows progressively)

The useChat and useCompletion hooks handled streaming automatically. My UI showed response progress instead of a loading spinner.

Framework-Agnostic

The SDK works with React, Vue, Svelte, or vanilla JS:

Integration Options:
┌─────────────┐
│ React/Next  │──▶ useChat, useCompletion hooks
├─────────────┤
│ Vue/Nuxt    │──▶ Vue equivalents
├─────────────┤
│ Svelte      │──▶ Svelte equivalents
├─────────────┤
│ Vanilla JS  │──▶ Core functions
└─────────────┘

This flexibility meant I wasn’t locked into a specific framework.

Where Vercel SDK excels:

  • Multi-provider abstraction
  • Streaming-first for web UX
  • TypeScript/JavaScript native
  • Framework-agnostic
  • Rapid prototyping

Where Vercel SDK requires consideration:

  • Multi-agent coordination is manual (you build it)
  • No MCP integration
  • Tools must be defined per-provider
  • Less depth for agent-specific features

The Decision Framework

After testing all three, I built a comparison matrix:

┌──────────────┬─────────────┬─────────────┬─────────────┐
│              │ Anthropic   │ OpenAI      │ Vercel      │
├──────────────┼─────────────┼─────────────┼─────────────┤
│ Language     │ Python+TS   │ Python only │ TS/JS only  │
│ Model Focus  │ Claude      │ GPT         │ Multi       │
│ MCP Support  │ Excellent   │ None        │ None        │
│ Multi-Agent  │ Manual      │ Excellent   │ Manual      │
│ Tool Define  │ Dynamic     │ Manual      │ Manual      │
│ Streaming    │ Basic       │ Basic       │ Excellent   │
│ Guardrails   │ Hooks       │ Built-in    │ Manual      │
│ Tracing      │ Hooks       │ Built-in    │ Manual      │
│ Production   │ Ready       │ Ready       │ Ready       │
└──────────────┴─────────────┴─────────────┴─────────────┘

When to Choose Anthropic Agent SDK

Pick Anthropic when:

  1. MCP is central to your architecture - You need runtime tool discovery
  2. Extended thinking matters - Debugging complex reasoning chains
  3. Computer use is needed - Agent interacts with UI/Desktop
  4. Hooks for observability - Fine-grained control over agent execution

My fetch agent with MCP tool discovery fit this perfectly.

When to Choose OpenAI Agents SDK

Pick OpenAI when:

  1. Multi-agent with routing - Specialists hand off to specialists
  2. Production guardrails - Input/output safety is built-in
  3. Tracing is critical - Need to debug multi-agent flows
  4. Voice/audio agents - Realtime Agents for conversational AI
  5. Python team - Your stack is Python-only

My router agent coordinating fetch/plan/create agents fit this model.

When to Choose Vercel AI SDK

Pick Vercel when:

  1. Multi-provider needs - Cost/performance optimization across models
  2. Web application - Streaming UX is critical
  3. TypeScript team - JS/TS is your stack
  4. Rapid prototyping - Need to move fast
  5. Framework flexibility - Not locked into React

My web UI needing streaming responses and provider flexibility fit here.

What I Actually Built

For my content management system, I ended up with a hybrid approach:

Final Architecture:
┌─────────────────────────────────────────────────────┐
│              Vercel AI SDK (Web Layer)              │
│  ┌─────────────────────────────────────────────┐   │
│  │  useChat hook (streaming to frontend)       │   │
│  └─────────────────────────────────────────────┘   │
│                       │                             │
│                       ▼                             │
│  ┌─────────────────────────────────────────────┐   │
│  │        OpenAI Agents SDK (Backend)          │   │
│  │  RouterAgent ──▶ Handoffs to specialists    │   │
│  │  Guardrails for input/output validation     │   │
│  └─────────────────────────────────────────────┘   │
│                       │                             │
│                       ▼                             │
│  ┌─────────────────────────────────────────────┐   │
│  │      Anthropic SDK (Specialist Agents)      │   │
│  │  FetchAgent: MCP tool discovery             │   │
│  │  PlanAgent: Extended thinking for reasoning │   │
│  └─────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────┘

Vercel handled the web interface with streaming. OpenAI coordinated the agents with handoffs and guardrails. Anthropic powered the specialist agents needing MCP and deep reasoning.

This combination wasn’t my initial plan. I assumed one SDK would solve everything. Reality required matching each SDK’s strengths to specific agent needs.

Lessons Learned

  1. No universal winner. Each SDK optimized for different problems.

  2. MCP vs manual tools. Anthropic’s dynamic discovery vs OpenAI/Vercel’s explicit definitions. If your tools change frequently, MCP matters.

  3. Multi-agent maturity. OpenAI’s handoffs and guardrails are production-ready. Anthropic and Vercel require manual orchestration.

  4. Provider flexibility vs depth. Vercel’s abstraction sacrifices agent-specific features for provider portability.

  5. Language alignment. Match SDK to your team’s stack. Python team + Vercel SDK creates friction. TypeScript team + OpenAI SDK requires Python backend.

  6. Start with the hardest problem. My MCP requirement made Anthropic SDK the first choice. The multi-agent coordination made OpenAI SDK necessary. The web streaming made Vercel SDK essential.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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