# How to integrate Langbase MCP with Mastra AI

```json
{
  "title": "How to integrate Langbase MCP with Mastra AI",
  "toolkit": "Langbase",
  "toolkit_slug": "langbase",
  "framework": "Mastra AI",
  "framework_slug": "mastra-ai",
  "url": "https://composio.dev/toolkits/langbase/framework/mastra-ai",
  "markdown_url": "https://composio.dev/toolkits/langbase/framework/mastra-ai.md",
  "updated_at": "2026-05-12T10:17:11.733Z"
}
```

## Introduction

This guide walks you through connecting Langbase to Mastra AI using the Composio tool router. By the end, you'll have a working Langbase agent that can chunk a long document for semantic search, list all conversation threads for this agent, create a new memory for session data through natural language commands.
This guide will help you understand how to give your Mastra AI agent real control over a Langbase account through Composio's Langbase MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Langbase with

- [OpenAI Agents SDK](https://composio.dev/toolkits/langbase/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/langbase/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/langbase/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/langbase/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/langbase/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/langbase/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/langbase/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/langbase/framework/cli)
- [Google ADK](https://composio.dev/toolkits/langbase/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/langbase/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/langbase/framework/ai-sdk)
- [LlamaIndex](https://composio.dev/toolkits/langbase/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/langbase/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Set up your environment so Mastra, OpenAI, and Composio work together
- Create a Tool Router session in Composio that exposes Langbase tools
- Connect Mastra's MCP client to the Composio generated MCP URL
- Fetch Langbase tool definitions and attach them as a toolset
- Build a Mastra agent that can reason, call tools, and return structured results
- Run an interactive CLI where you can chat with your Langbase agent

## What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.
Key features include:
- MCP Client: Built-in support for Model Context Protocol servers
- Toolsets: Organize tools into logical groups
- Step Callbacks: Monitor and debug agent execution
- OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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

The Langbase MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Langbase account. It provides structured and secure access to your Langbase projects, letting your agent split content, manage memories, orchestrate threads, and build complex AI data flows on your behalf.
- Content chunking and processing: Automatically split large documents or text into manageable chunks for downstream processing and efficient AI handling.
- Memory management: Create, list, or delete memory objects so your agent can organize and retrieve contextual information as needed.
- Conversation thread orchestration: Start new conversation threads, fetch thread details, or list messages—making it easy for your agent to manage dialogue history and context.
- Pipe creation and listing: Let your agent create new processing pipes or retrieve all existing pipes, enabling seamless orchestration of data and AI workflows.
- Document and data retrieval: List all documents stored in a memory, giving your agent access to relevant information and knowledge for any task.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `LANGBASE_APPEND_THREAD_MESSAGES` | Append Thread Messages | Tool to add new messages to an existing conversation thread. Use when continuing a chat session or adding context to a thread. |
| `LANGBASE_CHECK_HEALTH` | Check API Health | Tool to check the health status of the Langbase API service. Use when you need to verify API availability. |
| `LANGBASE_CHUNKER_SPLIT_CONTENT` | Split Content into Chunks | Tool to split content into smaller chunks. Use when processing large text segments to fit downstream limits. |
| `LANGBASE_CREATE_THREAD` | Create Thread | Tool to create a new conversation thread. Use when starting a fresh chat session or grouping messages into a distinct thread. |
| `LANGBASE_DELETE_THREAD` | Delete Thread | Tool to delete a thread that is no longer needed to manage conversation history. Use when you need to permanently remove a thread by its ID. |
| `LANGBASE_DELETE_THREAD_MESSAGE` | Delete Thread Message | Tool to delete a specific message from a conversation thread. Use when you need to remove a message from a thread by its ID. |
| `LANGBASE_DOCUMENT_LIST` | List Documents in Memory | Tool to list documents in a specific memory. Use when you need to fetch document metadata (and optionally vectors) from a memory after confirming its name. Supports pagination via limit and startAfter parameters. |
| `LANGBASE_GET_PIPE` | Get Pipe Details | Tool to retrieve details of a specific pipe by owner and name. Use when you need to fetch configuration and settings of a particular pipe. |
| `LANGBASE_GET_THREAD` | Get Thread Details | Tool to retrieve details of a specific conversation thread. Use when you need the full thread details by its ID after confirming its existence. |
| `LANGBASE_LIST_MODELS` | List Available Models | Tool to get available AI models supported by Langbase. Use to discover text and image generation models from various providers. |
| `LANGBASE_LIST_THREAD_MESSAGES` | List Thread Messages | Tool to list all messages in a conversation thread. Use after obtaining the thread ID to fetch its messages. |
| `LANGBASE_LIST_TRACES` | List Execution Traces | Tool to get execution traces for debugging and monitoring pipe runs. Use when you need to retrieve trace logs for a specific primitive. |
| `LANGBASE_MEMORY_CREATE` | Create Memory | Tool to create a new memory. Use when storing a new memory record in Langbase after confirming memory details. |
| `LANGBASE_MEMORY_DELETE` | Delete Memory | Tool to delete a specific memory. Use when you need to permanently remove a stored memory by its name. |
| `LANGBASE_MEMORY_LIST` | List Memories | Tool to list all memory objects. Use when you need to fetch stored memories for context retrieval. |
| `LANGBASE_PIPE_CREATE` | Create a new pipe | Tool to create a new pipe. Use after configuring pipe parameters. Returns an array of pipe objects, each including API key and URL. |
| `LANGBASE_PIPE_LIST` | List all pipes | Tool to list all pipes. Use after authentication to retrieve the complete list of pipes. Returns an array of pipe objects; callers must handle list iteration. |
| `LANGBASE_UPDATE_PIPE` | Update an existing pipe | Tool to update an existing pipe's configuration on Langbase. Use when modifying model settings, parameters, prompts, tools, or memory. The pipe must already exist. |
| `LANGBASE_UPDATE_THREAD` | Update Thread Metadata | Tool to update an existing thread's metadata. Use when you need to modify metadata fields for managing and organizing conversation threads. |
| `LANGBASE_UPDATE_THREAD_MESSAGE` | Update Thread Message | Tool to update an existing message in a conversation thread. Use when you need to modify the content or metadata of a specific message. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Langbase MCP server is an implementation of the Model Context Protocol that connects your AI agent to Langbase. It provides structured and secure access so your agent can perform Langbase operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Node.js 18 or higher
- A Composio account with an active API key
- An OpenAI API key
- Basic familiarity with TypeScript

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key.
- You need credits or a connected billing setup to use the models.
- Store the key somewhere safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.
- This key lets your Mastra agent talk to Composio and reach Langbase through MCP.

### 2. Install dependencies

Install the required packages.
What's happening:
- @composio/core is the Composio SDK for creating MCP sessions
- @mastra/core provides the Agent class
- @mastra/mcp is Mastra's MCP client
- @ai-sdk/openai is the model wrapper for OpenAI
- dotenv loads environment variables from .env
```bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio
- COMPOSIO_USER_ID tells Composio which user this session belongs to
- OPENAI_API_KEY lets the Mastra agent call OpenAI models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import libraries and validate environment

What's happening:
- dotenv/config auto loads your .env so process.env.* is available
- openai gives you a Mastra compatible model wrapper
- Agent is the Mastra agent that will call tools and produce answers
- MCPClient connects Mastra to your Composio MCP server
- Composio is used to create a Tool Router session
```typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

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

if (!openaiAPIKey) 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 as string,
});
```

### 5. Create a Tool Router session for Langbase

What's happening:
- create spins up a short-lived MCP HTTP endpoint for this user
- The toolkits array contains "langbase" for Langbase access
- session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
```typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["langbase"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Langbase MCP URL:", composioMCPUrl);
```

### 6. Configure Mastra MCP client and fetch tools

What's happening:
- MCPClient takes an id for this client and a list of MCP servers
- The headers property includes the x-api-key for authentication
- getTools fetches the tool definitions exposed by the Langbase toolkit
```typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
```

### 7. Create the Mastra agent

What's happening:
- Agent is the core Mastra agent
- name is just an identifier for logging and debugging
- instructions guide the agent to use tools instead of only answering in natural language
- model uses openai("gpt-5") to configure the underlying LLM
```typescript
const agent = new Agent({
    name: "langbase-mastra-agent",
    instructions: "You are an AI agent with Langbase tools via Composio.",
    model: "openai/gpt-5",
  });
```

### 8. Set up interactive chat interface

What's happening:
- messages keeps the full conversation history in Mastra's expected format
- agent.generate runs the agent with conversation history and Langbase toolsets
- maxSteps limits how many tool calls the agent can take in a single run
- onStepFinish is a hook that prints intermediate steps for debugging
```typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\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({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        langbase: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

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

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

## Complete Code

```typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

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

if (!openaiAPIKey) 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 as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["langbase"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      langbase: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "langbase-mastra-agent",
    instructions: "You are an AI agent with Langbase tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

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

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { langbase: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();
```

## Conclusion

You've built a Mastra AI agent that can interact with Langbase through Composio's Tool Router.
You can extend this further by:
- Adding other toolkits like Gmail, Slack, or GitHub
- Building a web-based chat interface around this agent
- Using multiple MCP endpoints to enable cross-app workflows

## How to build Langbase MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/langbase/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/langbase/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/langbase/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/langbase/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/langbase/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/langbase/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/langbase/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/langbase/framework/cli)
- [Google ADK](https://composio.dev/toolkits/langbase/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/langbase/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/langbase/framework/ai-sdk)
- [LlamaIndex](https://composio.dev/toolkits/langbase/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/langbase/framework/crew-ai)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Langbase MCP?

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

### Can I use Tool Router MCP with Mastra AI?

Yes, you can. Mastra AI 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 Langbase tools.

### Can I manage the permissions and scopes for Langbase while using Tool Router?

Yes, absolutely. You can configure which Langbase 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.

### How safe is my data with Composio Tool Router?

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 Langbase data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)