# How to integrate Landbot MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Landbot MCP with OpenAI Agents SDK",
  "toolkit": "Landbot",
  "toolkit_slug": "landbot",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/landbot/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/landbot/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:17:09.571Z"
}
```

## Introduction

This guide walks you through connecting Landbot to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Landbot agent that can list all active bots in your account, find customer details by phone number, show all whatsapp message templates through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Landbot account through Composio's Landbot MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Landbot with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Landbot
- Configure an AI agent that can use Landbot as a tool
- Run a live chat session where you can ask the agent to perform Landbot operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

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

The Landbot MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Landbot account. It provides structured and secure access to your Landbot bots, agents, channels, customers, and WhatsApp templates, so your agent can perform actions like listing bots, retrieving customer details, managing agents, and more on your behalf.
- Bot management and discovery: Instantly list all your Landbot bots or remove unused ones, making it easy to oversee and streamline your chatbot fleet.
- Customer insights and lookup: Retrieve customer records or pull up detailed profiles by phone number, letting your agent surface valuable user data for support or engagement.
- Agent roster access: List all agents in your Landbot account, so your AI can help with team coordination or assign conversations based on up-to-date agent info.
- Channel integration overview: Get a full inventory of all messaging channels connected to your Landbot account, including WhatsApp, to ensure your bots are reaching the right audiences.
- WhatsApp template management: Fetch and review all available WhatsApp message templates, making it easy for your agent to suggest or automate template-driven outreach.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `LANDBOT_DELETE_BOT` | Delete Bot | Tool to delete a specific bot from your account. Use when you need to remove an unused or test bot after confirming the bot ID. |
| `LANDBOT_GET_BRAND` | Get Brand | Tool to retrieve your brand data including contact information and settings. Use when you need to access brand profile details, configuration, or contact information. |
| `LANDBOT_LIST_AGENTS` | List Agents | Tool to retrieve a list of agents in your Landbot account. Use after authenticating your account to enumerate all agents and their details. |
| `LANDBOT_LIST_BOTS` | List Bots | Tool to list all bots in your Landbot account. Use after authenticating to discover your configured bots. |
| `LANDBOT_LIST_CHANNELS` | List Channels | Tool to list all channels integrated with your account. Use after authenticating your account to enumerate available messaging channels and metadata. |
| `LANDBOT_LIST_CUSTOMERS` | List Customers | Tool to list customers who have interacted with your bot. Use when you need to retrieve customer records with optional filters (channel_id, opt_in, search) and pagination. |
| `LANDBOT_LIST_WHATSAPP_TEMPLATES` | List WhatsApp Templates | Tool to list all WhatsApp message templates available for the account. Use after obtaining your WhatsApp channel ID to fetch template IDs and parameter counts. |
| `LANDBOT_REPLACE_AGENT` | Replace Agent | Tool to replace all data for a specific agent (full update). Use when you need to update agent information like name or password. |
| `LANDBOT_REPLACE_BRAND` | Replace Brand | Tool to replace or update brand data with a full update (PUT operation). Use when you need to change company branding information in your Landbot account. |
| `LANDBOT_SEND_MESSAGE` | Send Message | Tool to send a plain text outbound message to a Landbot customer. Use when you need to reply to or continue a support chat with a known customer_id. |
| `LANDBOT_SET_AGENT_STATUS` | Set Agent Status | Tool to change your agent status to online, offline, or busy. Use when you need to update your availability status in Landbot. |
| `LANDBOT_UPDATE_AGENT` | Update Agent | Tool to update an agent's information in your Landbot account. Use when you need to modify agent details such as name, email, or password. This performs a partial update. |
| `LANDBOT_UPDATE_BRAND` | Update Brand | Tool to partially update your brand data in Landbot. Use when you need to modify brand information such as name, phone, address, city, zipcode, or country. |

## Supported Triggers

None listed.

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

The Landbot MCP server is an implementation of the Model Context Protocol that connects your AI agent to Landbot. It provides structured and secure access so your agent can perform Landbot 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:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Landbot project
- Some knowledge of Python or 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'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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Landbot.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only landbot.
- The router checks the user's Landbot connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Landbot.
- This approach keeps things lightweight and lets the agent request Landbot tools only when needed during the conversation.
```python
# Create a Landbot Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["landbot"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Landbot
const session = await composio.create(userId as string, {
  toolkits: ['landbot'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Landbot. "
        "Help users perform Landbot operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Landbot. Help users perform Landbot operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

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

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["landbot"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Landbot. "
        "Help users perform Landbot operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['landbot'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Landbot. Help users perform Landbot operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

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

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

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

## Conclusion

This was a starter code for integrating Landbot MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Landbot.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Landbot MCP Agent with another framework

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

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- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
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## Frequently Asked Questions

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

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

### Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Landbot tools.

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

Yes, absolutely. You can configure which Landbot 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 Landbot data and credentials are handled as safely as possible.

---
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