How to integrate Braintrust MCP with Vercel AI SDK v6

This guide walks you through connecting Braintrust to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Braintrust agent that can compare failed evaluations across recent experiments, create dataset from production failure logs, summarize latency spikes in braintrust traces through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Braintrust account through Composio's Braintrust MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Braintrust is an AI engineering platform for prompts, datasets, experiments, evaluations, observability, and production AI apps. Use it to ship better AI systems with faster debugging, clearer evals, and tighter feedback loops.

17 Tools

Introduction

This guide walks you through connecting Braintrust to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Braintrust agent that can compare failed evaluations across recent experiments, create dataset from production failure logs, summarize latency spikes in braintrust traces through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Braintrust account through Composio's Braintrust MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Braintrust with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Braintrust integration
  • Using Composio's Tool Router to dynamically load and access Braintrust tools
  • Creating an MCP client connection using HTTP transport
  • Building an interactive CLI chat interface with conversation history management
  • Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

  • streamText: Core function for streaming responses with real-time tool support
  • MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
  • Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
  • OpenAI Provider: Native integration with OpenAI models

What is the Braintrust MCP server, and what's possible with it?

The Braintrust MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Braintrust account. It provides structured and secure access to your projects, prompts, datasets, experiments, evaluations, and observability data, so your agent can create resources, manage evaluation data, inspect events, query results, and update configurations on your behalf.

  • Project and resource management: Have your agent create, list, update, or delete Braintrust projects and manage the experiments, datasets, and prompts within them.
  • Dataset creation and maintenance: Let the agent create datasets, add batches of examples, revise dataset details, and summarize their contents and metrics.
  • Experiment tracking and evaluation: Direct your agent to create experiments, add result events, update experiment details, and retrieve aggregate evaluation summaries.
  • Prompt configuration: Instruct your agent to create versioned prompts and update their metadata or configuration without running them.
  • Observability and data analysis: Have your agent fetch recent project, experiment, or dataset events and run read-only queries over Braintrust data to investigate results.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install required dependencies

bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

  • @ai-sdk/openai: Vercel AI SDK's OpenAI provider
  • @ai-sdk/mcp: MCP client for Vercel AI SDK
  • @composio/core: Composio SDK for tool integration
  • ai: Core Vercel AI SDK
  • dotenv: Environment variable management
4

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

  • OPENAI_API_KEY: Your OpenAI API key for GPT model access
  • COMPOSIO_API_KEY: Your Composio API key for tool access
  • COMPOSIO_USER_ID: A unique identifier for the user session
5

Import required modules and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});
What's happening:
  • We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
  • The dotenv/config import automatically loads environment variables
  • The MCP client import enables connection to Composio's tool server
6

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["braintrust"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Braintrust tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned mcp object contains the URL and authentication headers needed to connect to the MCP server
  • This session provides access to all Braintrust-related tools through the MCP protocol
7

Connect to MCP server and retrieve tools

typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
What's happening:
  • We're creating an MCP client that connects to our Composio Tool Router session via HTTP
  • The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
  • The type: "http" is important - Composio requires HTTP transport
  • tools() retrieves all available Braintrust tools that the agent can use
8

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to braintrust, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
What's happening:
  • We initialize an empty messages array to maintain conversation history
  • A readline interface is created to accept user input from the command line
  • Instructions are displayed to guide the user on how to interact with the agent
9

Handle user input and stream responses with real-time tool feedback

typescript
rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

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

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • We use streamText instead of generateText to stream responses in real-time
  • toolChoice: "auto" allows the model to decide when to use Braintrust tools
  • stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
  • onStepFinish callback displays which tools are being used in real-time
  • We iterate through the text stream to create a typewriter effect as the agent responds
  • The complete response is added to conversation history to maintain context
  • Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Braintrust and Vercel AI SDK:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["braintrust"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to braintrust, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

    if (!trimmedInput) {
      rl.prompt();
      return;
    }

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

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

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a Braintrust agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

  • Real-time streaming responses for a better user experience with typewriter effect
  • Live tool execution feedback showing which tools are being used as the agent works
  • Dynamic tool loading through Composio's Tool Router with secure authentication
  • Multi-step tool execution with configurable step limits (up to 10 steps)
  • Comprehensive error handling for robust agent execution
  • Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

Every Braintrust action and event your agent gets out of the box.

Create Dataset

Create a named dataset under a Braintrust project, or return the existing dataset unchanged when its name already exists in that project.

Create Experiment

Create an experiment under a Braintrust project.

Create Project

Create a Braintrust project, or return the existing project unchanged when its name already exists.

Create Prompt

Upsert a versioned prompt configuration under a Braintrust project without invoking it.

Delete Project

Soft-delete a Braintrust project and return its deletion state.

Delete Experiment, Dataset, or Prompt

Delete one Braintrust experiment, dataset, or prompt by UUID for explicit lifecycle cleanup.

Fetch Events

Fetch one newest-to-oldest page of project log, experiment, or dataset events.

Get Braintrust Resource

Get one project, experiment, dataset, prompt, or function by UUID.

Insert Dataset Events

Upsert a batch of examples into an existing Braintrust dataset.

Insert Experiment Events

Upsert a batch of result events into an existing Braintrust experiment.

List Braintrust Resources

List and filter one page of projects, experiments, datasets, prompts, or functions visible to the connected Braintrust API key.

Query Braintrust Data

Run one bounded synchronous read-only BTQL query over Braintrust project logs, an experiment, or a dataset.

Summarize Experiment or Dataset

Return metadata and optional aggregate summaries for one Braintrust experiment or dataset.

Update Dataset

Update the name, description, tags, or metadata of a Braintrust dataset.

Update Experiment

Update selected metadata and linkage fields on a Braintrust experiment.

Update Project

Update selected fields of a Braintrust project.

Update Prompt

Update a Braintrust prompt's metadata or configuration without invoking it.

FRAMEWORKS

How to build Braintrust MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Braintrust MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Braintrust MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Braintrust MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Braintrust MCP with Claude Cowork

Codex logo

Codex

Use Braintrust MCP with Codex

OpenClaw logo

OpenClaw

Use Braintrust MCP with OpenClaw

Hermes logo

Hermes

Use Braintrust MCP with Hermes

CLI logo

CLI

Use Braintrust MCP with CLI

Google ADK logo

Google ADK

Use Braintrust MCP with Google ADK

LangChain logo

LangChain

Use Braintrust MCP with LangChain

Mastra AI logo

Mastra AI

Use Braintrust MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Braintrust MCP with LlamaIndex

CrewAI logo

CrewAI

Use Braintrust MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Braintrust MCP server, the agents and LLMs can only access a fixed set of Braintrust tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Braintrust and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Vercel AI SDK v6 fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Braintrust tools.

Yes, absolutely. You can configure which Braintrust scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Braintrust data and credentials are handled as safely as possible.

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