> ## Documentation Index
> Fetch the complete documentation index at: https://larkup.de/documentation/llms.txt
> Use this file to discover all available pages before exploring further.

# Vercel AI SDK

> Integrate Larkup with the Vercel AI SDK.

Larkup integrates seamlessly with the [Vercel AI SDK](https://sdk.vercel.ai/). You can connect using our official TypeScript SDK to build an AI agent tool, or connect directly to the OpenAI compatible endpoint generated by the Larkup server.

## Using the Official SDK (Tool Integration)

You can use the official `@larkup/sdk` to expose your knowledge base as a tool for the Vercel AI SDK. This enables the LLM to search your documents dynamically for current answers that reflect the available context.

```typescript theme={null}
import { tool } from 'ai';
import { z } from 'zod';
import { LarkupClient } from '@larkup/sdk';

// The client automatically picks up LARKUP_API_URL and LARKUP_API_KEY
const ragClient = new LarkupClient();

export const ragTool = tool({
  description: 'Search the knowledge base for relevant context.',
  parameters: z.object({
    query: z.string().describe('The search query'),
  }),
  execute: async ({ query }) => {
    // Retrieve the top 5 most relevant chunks
    const results = await ragClient.query(query, 5);

    // Concatenate the chunks into a single text block for the LLM
    return results.hits.map((hit) => hit.text).join('\n\n');
  },
});
```

## Connecting via OpenAI Compatible Endpoint

Because the Larkup server automatically exposes an OpenAI compatible API, you can use the `@ai-sdk/openai` package to connect directly, treating the RAG server as an LLM provider.

```typescript theme={null}
import { createOpenAI } from '@ai-sdk/openai';
import { generateText } from 'ai';

const larkup = createOpenAI({
  baseURL: 'http://localhost:8080/v1',
  apiKey: 'not-needed-for-local', // Replace with actual key if deployed
});

const { text } = await generateText({
  model: larkup('rag-model'), // The model name doesn't matter for local RAG
  prompt: 'What is Larkup?',
});

console.log(text);
```

## Using a local Agent Server as a remote tool

Start the Agent profile in **Settings → Larkup Server**. The card shows its endpoint,
Scalar reference at `/reference`, and machine-readable OpenAPI document at
`/openapi.json`.

Use the Agent client as an AI SDK tool executor. This calls the running agent
over HTTP, so the parent agent can be a separate application or model.

```typescript theme={null}
import { tool } from 'ai';
import { z } from 'zod';
import { createLarkupAgentToolExecutor } from '@larkup/sdk';

export const specialistAgent = tool({
  description: 'Ask the locally running Larkup specialist agent.',
  inputSchema: z.object({
    message: z.string().describe('Question to send to the specialist'),
  }),
  execute: createLarkupAgentToolExecutor({
    baseUrl: 'http://localhost:8083',
    // apiKey: process.env.LARKUP_AGENT_API_KEY,
    // joinCode: process.env.LARKUP_AGENT_JOIN_CODE,
  }),
});
```

The same operation is available directly at `POST /chat` and the explicit
`POST /agent/chat` alias. Both accept `{"messages":[...]}` and return an AI SDK
UI-message stream. For an OpenAI-compatible model provider, use
`http://localhost:8080/v1` with `createOpenAI`.