---
title: Reactive Agents vs Vercel AI SDK
description: >-
  How Reactive Agents and the Vercel AI SDK compare — and how they complement
  each other. SDK toolkit vs agent harness: an honest, sourced breakdown.
sidebar:
  order: 23
---

If you're choosing between **Reactive Agents** and the **Vercel AI SDK**, the most useful thing to know up front is that they sit at *different altitudes*. The Vercel AI SDK is a lower-level **TypeScript toolkit** — a unified provider interface plus best-in-class UI streaming primitives. Reactive Agents is a higher-level **agent harness** that runs on top of that kind of foundation: a deterministic execution engine, reasoning strategies, memory, guardrails, durability, and governance.

They are frequently **complementary, not strictly either/or**. Plenty of teams use the AI SDK for its UI hooks and provider abstraction, and reach for a harness when their agent loop grows beyond a simple tool-calling loop. This page tries to be fair about where each shines.

> The Vercel AI SDK is excellent and extremely popular for exactly what it's designed to do — a unified provider API and the best UI streaming primitives in the TypeScript ecosystem (`useChat`, `streamText`, `generateObject`, `tool`, and now agent/loop primitives like `ToolLoopAgent` and `stopWhen`). Nothing here is "RA beats the AI SDK." It's about which layer you need.

## At a glance

| Capability | Reactive Agents | Vercel AI SDK |
|---|---|---|
| Positioning | Higher-level agent harness | Lower-level SDK / TypeScript toolkit |
| Unified provider API | ✅ 8 providers + LiteLLM (40+) | ✅ 25+ providers |
| Local model support | ✅ Ollama first-class, 4B → frontier same code | ✅ via Ollama community/compatible providers |
| UI streaming primitives | SSE + `@reactive-agents/{react,vue,svelte}` adapters | ✅ first-party `useChat` / `useCompletion` (React, Vue, Svelte, Angular) |
| Text + structured output | ✅ `.withOutputSchema(zod)` → `result.object` | ✅ `generateObject` / `streamObject` |
| Tool calling | ✅ + MCP-native | ✅ `tool()` + MCP support |
| Agent loop / multi-step | ✅ 12-phase deterministic engine | ✅ `ToolLoopAgent`, `stopWhen`, `prepareStep` |
| Reasoning strategies | ✅ 6 (ReAct, Reflexion, Plan-Execute, Tree-of-Thought, Adaptive, Code-Action) | — [^1] |
| Memory (multi-layer) | ✅ 4-layer (working/semantic/episodic/procedural) | — [^1] |
| Guardrails | ✅ built-in | — [^1] |
| Cost routing + budgets | ✅ | — [^1] |
| Durable execution + crash-resume | ✅ | — [^1] |
| Human-in-the-loop approvals | ✅ `.withApprovalPolicy` | ✅ tool execution approval (AI SDK 6) |
| Multi-agent (A2A) | ✅ | — [^1] |
| Observability | ✅ OpenTelemetry + Cortex studio | ✅ telemetry / observability |
| Runtime | Bun + Node 22.5+ | Node, edge, browser, Expo |
| License | MIT | Apache-2.0 |

[^1]: No first-party equivalent found as of 2026; corrections welcome via PR. The AI SDK evolves quickly — these are app-layer concerns it intentionally leaves to you or to a higher-level framework, not gaps.

## Different altitudes, not rivals

The cleanest way to think about it:

- **The Vercel AI SDK gives you primitives.** `generateText` / `streamText` for model calls, `tool()` for function definitions, `generateObject` / `streamObject` for schema-constrained output, `useChat` for UI, and — as of AI SDK 5/6 — agent loop primitives (`ToolLoopAgent`, `stopWhen`, `prepareStep`) that run a tool-calling loop for you. You assemble these into whatever shape your app needs.

- **Reactive Agents gives you a harness.** It owns the agent loop end-to-end: a deterministic 12-phase execution engine, pluggable reasoning strategies, memory, guardrails, cost governance, durability, and observability — exposed through a fluent builder so you configure behavior instead of wiring it.

These layers stack cleanly. A very common pattern: **use the AI SDK's `useChat` and SSE rendering on the front end, and a harness for the agent loop on the back end.** Reactive Agents emits SSE (`AgentStream.toSSE()`) and ships `@reactive-agents/react` / `vue` / `svelte` adapters precisely so it can feed UIs — including ones built with AI-SDK-style streaming patterns.

## Where Reactive Agents adds structure

If your "agent" is one model call plus a short tool loop, the AI SDK's primitives are likely all you need. The harness layer earns its keep when the loop grows up:

- **Deterministic 12-phase execution engine** — every run flows through the same observable phases, with hooks at each boundary, so behavior is inspectable and reproducible rather than ad hoc.
- **Six reasoning strategies** — ReAct, Reflexion, Plan-Execute, Tree-of-Thought, Adaptive, and Code-Action (experimental) — selectable per agent instead of hand-rolled.
- **Four-layer memory** — working, semantic, episodic, and procedural memory as a first-class subsystem.
- **Guardrails** — input/output validation and policy enforcement built into the loop.
- **Cost routing + budgets** — route to cheaper models and enforce spend ceilings.
- **Durable execution + crash-resume** — runs survive process restarts and pick up where they left off.
- **Human-in-the-loop approvals** — `.withApprovalPolicy` pauses runs awaiting a decision, persisted durably.
- **Multi-agent (A2A)** — agents delegate to other agents.
- **OpenTelemetry observability + Cortex studio** — traces and a studio for inspecting runs.

All of this is built on **Effect-TS**, so boundaries are schema-validated, errors are tagged, and the type system catches misconfiguration at compile time.

## Side-by-side: a minimal agent

**Reactive Agents**

```typescript
import { ReactiveAgents } from "reactive-agents";
import { z } from "zod";

const agent = ReactiveAgents.create()
  .withProvider("anthropic")
  .withModel("claude-sonnet-4-6")
  .withTool({
    name: "weather",
    description: "Get weather in a location (Fahrenheit)",
    inputSchema: z.object({ location: z.string() }),
    handler: async ({ location }) => ({ location, tempF: 68 }),
  })
  .build();

const result = await agent.run("What is the weather in San Francisco?");
console.log(result.output);
```

**Vercel AI SDK** (agent loop primitive, AI SDK 5/6)

```typescript
import { ToolLoopAgent, tool } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
import { z } from "zod";

const weatherAgent = new ToolLoopAgent({
  model: anthropic("claude-sonnet-4-6"),
  tools: {
    weather: tool({
      description: "Get weather in a location (Fahrenheit)",
      inputSchema: z.object({ location: z.string() }),
      execute: async ({ location }) => ({ location, tempF: 68 }),
    }),
  },
  // stopWhen: stepCountIs(20) by default
});

const result = await weatherAgent.generate({
  prompt: "What is the weather in San Francisco?",
});
console.log(result.text);
```

Both are clean. The difference is what's implied: the AI SDK example gives you a tool-calling loop and stops there — you add memory, retries, governance, and persistence yourself. The Reactive Agents example is already inside a harness, so reaching for memory, a different reasoning strategy, budgets, or durability is another builder method rather than new plumbing.

## When the Vercel AI SDK is enough

Reach for the AI SDK directly — and skip the harness — when:

- You primarily need **provider abstraction + UI streaming**, and your agent logic is a simple tool loop.
- You're building a **chat or generative UI on Next.js** (or React/Vue/Svelte/Angular) and want first-party hooks like `useChat`.
- You want the **lightest possible dependency** and full manual control over the loop.
- Structured output via `generateObject` / `streamObject` plus a few tools covers your use case.
- You'd rather assemble primitives yourself than adopt opinions about memory, strategies, or durability.

It's a fantastic foundation, and for a huge class of apps it's the right and complete answer.

## When to reach for Reactive Agents

Move up to the harness when:

- You need **durable, multi-step agents** that survive restarts and resume mid-run.
- You want **selectable reasoning strategies** (Reflexion, Plan-Execute, Tree-of-Thought) instead of hand-rolling them.
- You need **governance**: guardrails, cost routing, spend budgets, and HITL approvals as built-ins.
- You want **first-class observability** (OpenTelemetry traces + Cortex studio) without instrumenting by hand.
- You care about **local-model parity** — the same code running on a 4B local model and a frontier model.
- You're building **multi-agent** systems where agents delegate to one another.
- You value **Effect-TS type safety** — schema-validated boundaries and tagged errors across the whole loop.

And remember: choosing Reactive Agents for the loop doesn't mean dropping the AI SDK. Keep its UI hooks on the front end and let the harness own the back-end orchestration.

---

Ready to try it? Start with the [Quickstart](https://docs.reactiveagents.dev/guides/quickstart/), or see how the SSE + framework adapters plug into a UI in the [Web Integration guide](https://docs.reactiveagents.dev/guides/web-integration/).
