Vercel AI SDK 6 or Claude Agent SDK: The Decision Is Who Runs the Loop

Vercel AI SDK 6 is a provider-agnostic TypeScript library released December 22, 2025 that introduced ToolLoopAgent — a multi-step agent loop that runs entirely in your own infrastructure, across any supported LLM provider. [1] Anthropic's Agent Capabilities API, launched May 22, 2025 and now bundled as part of the Claude Agent SDK (rebranded from Claude Code SDK on April 28, 2026), ships three server tools — sandboxed code execution at $0.05/container-hour after 50 free daily hours, hosted web search, and an MCP Connector — that execute on Anthropic's managed compute, not yours. [2] By mid-2026 both are the dominant frameworks for production agent work, and the choice between them requires exactly one architectural decision.

Key facts

AI SDK 6 launched December 22, 2025 with ToolLoopAgent, full MCP OAuth support, DevTools middleware, and over 20 million monthly downloads across startups to Fortune 500 companies. [1]
Anthropic's Agent Capabilities API shipped May 22, 2025 with three server-side tools: code_execution, web_search_20260209, and a hosted MCP Connector that requires no client-side transport code. [2]
code_execution grants 50 free container-hours per organization per day; additional usage costs $0.05/container-hour. [2]
ToolLoopAgent handles up to 20 steps by default and exposes a needsApproval callback for human-in-the-loop approval gates before sensitive tool calls. [1]
Extended prompt caching (1-hour TTL) in the Claude Agent SDK cuts cost by up to 90% and latency by up to 85% for long-context agent loops. [2]
AI SDK 6 DevTools (@ai-sdk/devtools) capture every tool call, token count, and timing locally — no third-party observability service required to get started. [1]

What each SDK actually ships

Vercel AI SDK 6 centers ToolLoopAgent as its agent primitive. [1] The class wraps generateText in a multi-step cycle: call the model, execute any returned tool invocations, inject results back as context, repeat until end_turn or maxSteps. You provide the model string and your tools; the SDK owns the loop. Model selection is a plain string — 'anthropic/claude-sonnet-4-6', 'openai/gpt-4o', or any provider string routed via the Vercel AI Gateway. [5] Swapping models requires changing one string, not one SDK. The abstraction is deployment-neutral: it runs inside a Next.js API route, a plain Node.js script, or a Fluid Compute function without modification.

Claude Agent SDK (Anthropic) takes the inverse approach for its first-party tools. [2] Add { type: 'code_execution_20250522' } or { type: 'web_search_20260209' } to your tools array — when Claude calls them, Anthropic's infrastructure executes the operation. You write no fetch() calls, spin up no containers, and configure no sandbox security policies. The constraint is symmetric with the convenience: you're scoped to the tools Anthropic provides, priced at Anthropic's rates, on Anthropic's SLA.

One clarification that most comparisons miss: for client-defined tools — custom functions you author yourself — both SDKs behave identically. Claude returns a tool_use block, your code executes the function, you return a tool_result. The vendor-managed distinction applies only to Anthropic's first-party server tools. If your agent uses zero server tools, the ergonomic difference between the two SDKs narrows to personal preference.

The non-obvious angle

Most coverage frames this as a syntax comparison: ToolLoopAgent saves boilerplate or Anthropic's SDK feels more native to Claude. Those are real but minor. The actual tradeoff is architectural.

AI SDK 6 is a glossary/agent-harness — it abstracts the loop control flow, not the compute. You still own and operate every tool's execution environment. Anthropic's server tools are managed compute subscriptions: you don't operate the sandbox; you pay for its output. These aren't two implementations of the same idea. They sit at different layers of the stack, and confusing them leads to the wrong choice for the wrong reasons.

This distinction has two concrete downstream consequences. Portability: AI SDK 6 lets you swap providers by changing one string; your tool logic, loop configuration, and observability pipeline are untouched. With Anthropic server tools, code_execution has no equivalent surface in other providers' APIs — switching models means rebuilding that capability from scratch. Cost modeling: AI SDK adds zero execution overhead (you pay model tokens plus your own tool infrastructure). Anthropic's managed tools add per-use charges on top of model cost. At high volume — agents doing frequent code execution — $0.05/container-hour can exceed the cost of a self-managed Firecracker sandbox. Run the math for your workload before assuming fewer lines of code means lower bills.

Side-by-side: 5-tool agent

The same agent — weather lookup, web search, calculator, file read, and code execution — implemented in both SDKs:

```typescript // ── Vercel AI SDK 6 — ToolLoopAgent (~14 lines of agent logic) ───────────── import { ToolLoopAgent } from 'ai'; import { weatherTool, searchTool, calcTool, fileTool, codeTool } from './tools';

const agent = new ToolLoopAgent({ model: 'anthropic/claude-sonnet-4-6', instructions: 'You are a research assistant.', tools: { weatherTool, searchTool, calcTool, fileTool, codeTool }, maxSteps: 10, });

const result = await agent.generate({ prompt: 'Research São Paulo climate, run a conversion, save a summary.', }); ```

```typescript // ── Anthropic SDK — explicit loop (~26 lines; code_execution is a server tool) ─ import Anthropic from '@anthropic-ai/sdk'; import { weatherTool, searchTool, calcTool, fileTool } from './tools';

const client = new Anthropic(); const tools = [ weatherTool, searchTool, calcTool, fileTool, { type: 'code_execution_20250522', name: 'code_execution' }, // runs on Anthropic infra ];

async function runAgent(prompt: string) { const messages: Anthropic.MessageParam[] = [{ role: 'user', content: prompt }]; while (true) { const resp = await client.messages.create({ model: 'claude-sonnet-4-6', tools, messages, max_tokens: 4096, }); if (resp.stop_reason === 'end_turn') return resp; messages.push({ role: 'assistant', content: resp.content }); const results = await Promise.all( resp.content.filter(b => b.type === 'tool_use').map(b => executeTool(b)) ); messages.push({ role: 'user', content: results }); } } ```

| Dimension | AI SDK 6 ToolLoopAgent | Anthropic SDK (explicit loop) | |---|---|---| | Agent logic lines | ~14 | ~26 | | `code_execution` infra | You operate it | Anthropic operates it | | Token cost | Model tokens only | Model tokens + container-hours | | Observability | DevTools (local, no signup) | Inline usage in response; bring your own | | Model portability | Any provider string | Claude-only for server tools |

AI SDK's loop is shorter because ToolLoopAgent owns the cycle. The Anthropic version is explicit about every step — valuable when you need fine-grained control over retry logic, partial failures, or multi-agent routing. Notice that code_execution in the Anthropic version requires zero implementation: you declare the type, Anthropic's sandboxed Python runtime handles it entirely. [2]

▶ Interactive prompt cell (full demo on lesson pages)

When to pick each

Pick Vercel AI SDK 6 + ToolLoopAgent when: - Model portability is a requirement — swapping from Claude to GPT-4o must not touch agent logic - Your tools are all custom functions (web search and code execution can be self-managed) - You want local DevTools observability before committing to a full monitoring stack - Your deployment is in Vercel's ecosystem and you want end-to-end TypeScript type safety via InferAgentUIMessage

Pick Claude Agent SDK server tools when: - Sandboxed Python code execution is required and you don't want to operate container infrastructure - Hosted web search is first-class and you'd rather pay Anthropic than integrate SerpAPI or Brave Search - You're building Claude-only and the hosted MCP Connector's architecture fits your server topology [3] - Vendor-managed SLAs are acceptable or actively preferable for your compliance posture

For a deeper treatment of when Managed Agents compose well with the Files API and extended caching, see chapter production-agents-claude-agent-sdk-mcp-connector/02-managed-agents-when-to-use.

✓ Knowledge check (interactive on lesson pages)

What to do next

Start with the SDK that matches your execution model, not the one with the shorter syntax example. If you need model portability or explicit control over every loop step, ToolLoopAgent is the right primitive. If you need sandboxed code execution or hosted web search and you're building Claude-first, Anthropic's server tools eliminate that entire operational surface.

For how MCP connectors from both SDKs interact with the 2026 transport spec and auth changes, see blog/mcp-2026-roadmap-explained. For a hands-on build covering both patterns — MCP connectors, extended caching, multi-step agent observability in production — our course Production Agents with Claude Agent SDK + MCP Connector walks you from hello-world to a deployed, monitored agent.