How to Use the LibreChat MCP in Vercel AI SDK

Q: Can Vercel AI SDK trigger LibreChat tools automatically?

Yes, you pass the tool definitions into your streamText call. The SDK handles the execution of chatcompletions or openresponses based on user input.

Q: How do I handle LibreChat auth tokens in Vercel AI SDK?

Use the authProvider in your client config to securely pass your credentials. This keeps your private session keys away from the client bundle.

Stream LibreChat responses directly into your Vercel AI SDK frontends for real-time data updates.

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Connect LibreChat MCP to Vercel AI SDK

Create your Vinkius account to connect LibreChat to Vercel AI SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup LibreChat with Vercel AI SDK

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Live LibreChat data streaming

Pipe your LibreChat agent output straight into your React or Next.js UI components. You use `chat_completions` to push text directly to the screen. Your users won't stare at loading spinners anymore. The data streams as it arrives from the server, making your application feel fast and responsive.

Manage agents with Vercel AI SDK

Access your entire model library by calling `list_models` within your edge functions. This lets your front-end logic switch between agents on the fly. It handles the heavy lifting of backend discovery. You just pick the agent and start the stream.

Secure auth for LibreChat sessions

Handle user sessions safely by running `login` through your SDK's auth provider. It keeps your tokens tucked away from the client-side code. This keeps your LibreChat connection locked down. No credentials hit the browser, just your authenticated session state.

Setup guide

Set up LibreChat MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all LibreChat tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent LibreChat transactions",
});

console.log(text);
await mcpClient.close();

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LibreChat. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about LibreChat MCP in Vercel AI SDK

You install the MCP client package and point it at your server URL. Use the `createMCPClient` function to establish the connection and fetch your tools.

Yes, you pass the tool definitions into your `streamText` call. The SDK handles the execution of `chat_completions` or `open_responses` based on user input.

It does. You need to ensure your transport layer is set to HTTP, which works perfectly within the edge runtime constraints.

The data flows directly through the MCP pipe to your UI. We don't store your chat history or tokens on our servers; you own the transport path.

Use the `authProvider` in your client config to securely pass your credentials. This keeps your private session keys away from the client bundle.

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