---
title: "Vercel AI SDK"
icon: "triangle"
iconType: "solid"
description: "Add persistent user memory and reasoning to any Vercel AI SDK app with Honcho"
sidebarTitle: "Vercel AI SDK"
---

Integrate Honcho with the Vercel AI SDK to build AI apps that remember users across sessions. The [Vercel AI SDK](https://sdk.vercel.ai) is an open-source TypeScript toolkit for building AI-powered apps with a unified API across providers. This guide shows you how to wrap any `generateText` or `streamText` call with Honcho's memory middleware and reasoning tools.

<Note>
The full package source and examples are available on [GitHub](https://github.com/plastic-labs/vercel-ai-sdk-package).
</Note>

## What We're Building

We'll wire Honcho into a Vercel AI SDK app so the model receives context from past conversations and can query what it knows about the user mid-generation. Here's how the pieces fit together:

- **Vercel AI SDK** handles model calls and streaming
- **Honcho** stores messages and retrieves user context before each generation
- **Your model provider** can be Anthropic, OpenAI, Google, etc.

The key benefit: you don't manually manage conversation history across sessions. Honcho handles persistence and context injection — the model always has a rich picture of who it's talking to. (New to Honcho's primitives? See [peers and sessions](/v3/documentation/core-concepts/architecture).)

## Setup

Install the package:

<CodeGroup>
```bash npm
npm install @honcho-ai/vercel-ai-sdk
```

```bash pnpm
pnpm add @honcho-ai/vercel-ai-sdk
```

```bash yarn
yarn add @honcho-ai/vercel-ai-sdk
```

```bash bun
bun add @honcho-ai/vercel-ai-sdk
```
</CodeGroup>

Get your API key at [app.honcho.dev](https://app.honcho.dev).

```bash
HONCHO_API_KEY=your-api-key
HONCHO_WORKSPACE_ID=your-workspace-id
```

## Use the Skill

The package ships a Skill that can walk an agent through wiring Honcho into your Vercel AI SDK app automatically — it greps for your `generateText` / `streamText` call sites, asks where `userId` / `sessionId` come from, and applies the integration in place.

```bash
npx skills add plastic-labs/vercel-ai-sdk
```

Then invoke `/honcho-vercel-ai-sdk`.

<Accordion title="Alternative: manual symlink from npm package">
If you've already installed `@honcho-ai/vercel-ai-sdk` via npm, you can symlink the skill directly. Example shown is for Claude Code:

```bash
mkdir -p ~/.claude/skills/honcho-vercel-ai-sdk
ln -sf "$(pwd)/node_modules/@honcho-ai/vercel-ai-sdk/skills/honcho-vercel-ai-sdk/SKILL.md" \
       ~/.claude/skills/honcho-vercel-ai-sdk/SKILL.md
```

Restart the session, then invoke `/honcho-vercel-ai-sdk`.
</Accordion>

## Create a Provider Instance

`createHoncho()` is the entry point. It reads your API key and workspace from environment variables and returns a provider object with `middleware()`, `tools()`, and `send()`.

```typescript
import { createHoncho } from '@honcho-ai/vercel-ai-sdk';

const honcho = createHoncho();
```

You can set a stable `defaultAssistantId` on the provider to identify the AI peer across all calls:

```typescript
const honcho = createHoncho({
  defaultAssistantId: 'my-assistant',
});
```

## Add Middleware

`honcho.middleware()` is compatible with `wrapLanguageModel`. Two things happen on each call:

1. **Before generation** — Honcho fetches the user's representation, peer card, session summary, and recent messages and injects them into the system prompt
2. **After generation** — the user message and assistant response are stored back in Honcho with correct peer attribution

```typescript
import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
import { wrapLanguageModel, generateText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';

const honcho = createHoncho();

const model = wrapLanguageModel({
  model: anthropic('claude-sonnet-4-6'),
  middleware: honcho.middleware({
    userId: 'user-abc',
    sessionId: 'session-123',
  }),
});

const { text } = await generateText({
  model,
  prompt: 'What should I focus on today?',
});
```

Pass `userId` and `sessionId` per request — no session handles to construct. Both default to lazily generated IDs if omitted, which is fine for local scripts but not for multi-user server traffic.

## Add Tools

`honcho.tools()` gives the model six tools it can call mid-generation to query or update what it knows about the user:

| Tool | What it does |
| --- | --- |
| `honcho_chat` | Dialectic reasoning — ask natural-language questions about the user; answers synthesized from full interaction history |
| `honcho_context` | Short summary of recent context within the session |
| `honcho_search` | Semantic search over stored conversation messages |
| `honcho_search_conclusions` | Query derived conclusions: personality traits, preferences, behavioral patterns |
| `honcho_get_representation` | Full synthesized profile of the user |
| `honcho_save_conclusion` | Persist an observation about the user for future sessions |

Pass the same `userId` and `sessionId` to `honcho.tools()` so tool calls bind to the same peers as the middleware:

```typescript
import { generateText, stepCountIs } from 'ai';

const { text } = await generateText({
  model,
  tools: honcho.tools({
    userId: 'user-abc',
    sessionId: 'session-123',
  }),
  stopWhen: stepCountIs(3),
  prompt: 'Based on our conversations, what do I care about most?',
});
```

## Complete Example

Here's a full working example combining middleware and tools.

Want a runnable end-to-end version? See the [Full Script](#full-script).

```typescript
import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
import { wrapLanguageModel, generateText, stepCountIs } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';

const honcho = createHoncho({
  defaultAssistantId: 'assistant',
});

const userId = 'user-abc';
const sessionId = 'session-123';

const model = wrapLanguageModel({
  model: anthropic('claude-sonnet-4-6'),
  middleware: honcho.middleware({ userId, sessionId }),
});

const { text } = await generateText({
  model,
  tools: honcho.tools({ userId, sessionId }),
  stopWhen: stepCountIs(3),
  prompt: 'What should we work on today?',
});

console.log(text);
```

## Streaming

`streamText` works the same way — middleware handles persistence after the stream completes:

```typescript
import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
import { wrapLanguageModel, streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

const honcho = createHoncho();

const userId = 'user-abc';
const sessionId = 'session-456';

const model = wrapLanguageModel({
  model: openai('gpt-4o'),
  middleware: honcho.middleware({ userId, sessionId }),
});

const result = streamText({
  model,
  tools: honcho.tools({ userId, sessionId }),
  prompt: 'What should we work on today?',
});

for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}
```

## Using with `messages`

If your app already manages conversation history and passes a `messages` array directly, set `injectHistory: false` to prevent Honcho from prepending duplicate history:

```typescript
honcho.middleware({
  userId,
  sessionId,
  injectHistory: false, // don't prepend history — we're passing messages directly
})
```

Honcho still injects the user's representation and peer card into the system prompt, and still persists messages after generation. With `injectHistory: false` you must pass a `messages` array — without either `messages` or `prompt`, the Vercel AI SDK throws `Invalid prompt: prompt or messages must be defined`.

## Verifying the Integration

### 1. Isolate Honcho's Contribution

Let's confirm the memory is actually coming from Honcho and not your app's existing conversation history.

Two ways to check: 1) through a developer method 2) through the UI.

**Token delta (developer check).** On a session with a few prior turns, run the same prompt twice — once with `injectHistory: false` and once without.

Compare `result.usage.inputTokens`:

```typescript
const baseline = await generateText({
  model: wrapLanguageModel({
    model: anthropic('claude-sonnet-4-6'),
    middleware: honcho.middleware({ userId, sessionId, injectHistory: false }),
  }),
  prompt: 'What do you know about my preferences?',
});

const injected = await generateText({
  model: wrapLanguageModel({
    model: anthropic('claude-sonnet-4-6'),
    middleware: honcho.middleware({ userId, sessionId }),
  }),
  prompt: 'What do you know about my preferences?',
});

console.log(injected.usage.inputTokens - baseline.usage.inputTokens);
```

A positive delta is Honcho's representation, peer card, and session summary being injected into the system prompt. Expect ~0 on a fresh peer — the deriver runs asynchronously after messages persist, so injected context only populates after a few prior turns.

**Dashboard (UI check).** Open [app.honcho.dev/explore](https://app.honcho.dev/explore), select your workspace, and confirm your peer and session appear under the Peers and Sessions tables.

With Honcho's contribution isolated, the rest of this section shows what the integration feels like in practice.

### 2. First turn

Send any message. The model responds normally — nothing is stored yet. Context injection returns empty on the first turn.

### 3. Build memory across turns

Have a multi-turn conversation and share something about yourself:

```text
I prefer concise answers and I mostly work in TypeScript.
```

After a few turns, ask:

```text
What do you know about my preferences?
```

If the model references TypeScript and concise answers without being told again in this session, memory is working.

### 4. Cross-session recall

Start a new session (new `sessionId`) with the same `userId`. Ask:

```text
Call your honcho_search tool with the query 'TypeScript' and quote the exact verbatim message that contained TypeScript. Do not paraphrase.
```

If the search returns a message from the prior session word-for-word, peer-scoped retrieval is crossing session boundaries. `honcho_search` queries the user's messages across all their sessions and doesn't depend on the deriver, so it works regardless of how short the prior session was.

To confirm the tool actually fired, inspect `result.steps[i].toolCalls`:

```typescript
const toolFires = result.steps?.flatMap((step, i) =>
  (step.toolCalls ?? []).map((tc) => ({ step: i, tool: tc.toolName, input: tc.input }))
) ?? [];
console.log(toolFires);
// [{ step: 0, tool: "honcho_search", input: { query: "TypeScript", limit: 10 } }]
```

When the model takes more than one turn (call a tool, see the result, then answer), the top-level `result.toolCalls` is empty — check inside each `step`.

## Full Script

<Accordion title="honcho_vercel_chat.ts">
```typescript
/**
 * Multi-turn chat with Honcho memory + Vercel AI SDK.
 *
 * Prerequisites:
 * 1. Install dependencies:
 *    npm install @honcho-ai/vercel-ai-sdk ai @ai-sdk/anthropic dotenv
 * 2. Set environment variables in `.env`:
 *    HONCHO_API_KEY=your-honcho-api-key
 *    HONCHO_WORKSPACE_ID=your-workspace-id
 *    ANTHROPIC_API_KEY=your-anthropic-api-key
 * 3. Run with: npx tsx honcho_vercel_chat.ts
 *
 * Pass a stable userId from your auth system and a sessionId for the conversation
 * thread; Honcho handles persistence and context injection on every turn.
 */

import 'dotenv/config';
import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
import { wrapLanguageModel, generateText, stepCountIs } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';
import * as readline from 'node:readline/promises';
import { stdin as input, stdout as output } from 'node:process';

const honcho = createHoncho({
  defaultAssistantId: 'assistant',
});

const userId = process.env.USER_ID ?? 'demo-user';
const sessionId = process.env.SESSION_ID ?? `session-${Date.now()}`;

const model = wrapLanguageModel({
  model: anthropic('claude-sonnet-4-6'),
  middleware: honcho.middleware({ userId, sessionId }),
});

async function chat(prompt: string): Promise<string> {
  const { text } = await generateText({
    model,
    tools: honcho.tools({ userId, sessionId }),
    stopWhen: stepCountIs(3),
    prompt,
  });
  return text;
}

async function main() {
  const rl = readline.createInterface({ input, output });
  console.log(`Honcho session: ${sessionId} (user: ${userId})`);
  console.log('Type a message, or "exit" to quit.\n');

  while (true) {
    const userMessage = (await rl.question('you > ')).trim();
    if (!userMessage || userMessage === 'exit') break;
    const reply = await chat(userMessage);
    console.log(`bot > ${reply}\n`);
  }

  rl.close();
}

main().catch((err) => {
  console.error(err);
  process.exit(1);
});
```
</Accordion>

## Next Steps

<CardGroup cols={2}>
  <Card title="Github Repository" icon="github" href="https://github.com/plastic-labs/vercel-ai-sdk-package">
    Source, tests, and full API reference for @honcho-ai/vercel-ai-sdk.
  </Card>

  <Card title="Honcho Architecture" icon="sitemap" href="/v3/documentation/core-concepts/architecture">
    Learn about peers, sessions, and dialectic reasoning.
  </Card>

  <Card title="Self-Hosting Guide" icon="server" href="/v3/contributing/self-hosting">
    Run Honcho locally with your Vercel AI SDK app.
  </Card>

  <Card title="Vercel AI SDK Docs" icon="book" href="https://sdk.vercel.ai">
    wrapLanguageModel, middleware, and tool use reference.
  </Card>
</CardGroup>