# How to integrate Sendbird ai chabot MCP with Pydantic AI

```json
{
  "title": "How to integrate Sendbird ai chabot MCP with Pydantic AI",
  "toolkit": "Sendbird ai chabot",
  "toolkit_slug": "sendbird_ai_chabot",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/sendbird_ai_chabot/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/sendbird_ai_chabot/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:25:14.572Z"
}
```

## Introduction

This guide walks you through connecting Sendbird ai chabot to Pydantic AI using the Composio tool router. By the end, you'll have a working Sendbird ai chabot agent that can list all group channels for support, create a new chatbot for onboarding, update bot nickname to match rebranding through natural language commands.
This guide will help you understand how to give your Pydantic AI agent real control over a Sendbird ai chabot account through Composio's Sendbird ai chabot MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Sendbird ai chabot with

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

## TL;DR

Here's what you'll learn:
- How to set up your Composio API key and User ID
- How to create a Composio Tool Router session for Sendbird ai chabot
- How to attach an MCP Server to a Pydantic AI agent
- How to stream responses and maintain chat history
- How to build a simple REPL-style chat interface to test your Sendbird ai chabot workflows

## What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.
Key features include:
- Type Safety: Built on Pydantic for automatic data validation
- MCP Support: Native support for Model Context Protocol servers
- Streaming: Built-in support for streaming responses
- Async First: Designed for async/await patterns

## What is the Sendbird ai chabot MCP server, and what's possible with it?

The Sendbird ai chabot MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Sendbird ai chabot account. It provides structured and secure access to your Sendbird bots and channels, so your agent can create bots, manage webhooks, update bot profiles, and fetch group channel details seamlessly on your behalf.
- Bot creation and management: Easily instruct your agent to create new AI or default bots, update their profiles, or fetch detailed bot information as needed.
- Automated webhook management: Let your agent register, update, or remove webhook URLs for bots, ensuring seamless event-driven integrations and real-time notifications.
- Group channel discovery: Ask your agent to list available group channels in your Sendbird application, complete with filtering and pagination support for targeted results.
- Bot information retrieval: Have your agent fetch comprehensive details about any bot by its user ID, helping you monitor and audit bot activity at a glance.
- Bot privacy and feature updates: Direct your agent to toggle privacy settings or adjust read-receipt and webhook configurations, keeping your bots up to date with business needs.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SENDBIRD_AI_CHABOT_CREATE_BOT` | Create Bot | Tool to create a new bot. Use when you need to add an AI or default bot to your Sendbird app. |
| `SENDBIRD_AI_CHABOT_GET_BOT` | Get Bot | Tool to retrieve information on a specific bot by its user ID. Use when you need to fetch bot details before performing subsequent operations. |
| `SENDBIRD_AI_CHABOT_LIST_BOTS` | List Bots | Tool to list all bots in the Sendbird application. Use when you need to fetch bot details with optional filters and pagination. |
| `SENDBIRD_AI_CHABOT_LIST_GROUP_CHANNELS` | List Group Channels | Tool to list group channels. Use when you need to fetch available group channels with filters and pagination. |
| `SENDBIRD_AI_CHABOT_UNREGISTER_BOT_WEBHOOK` | Unregister Bot Webhook | Tool to unregister the webhook URL for a bot. Use when you need to remove webhook configuration for a specific bot. |
| `SENDBIRD_AI_CHABOT_UPDATE_BOT` | Update Bot | Tool to update information on an existing bot. Use when you need to change a bot's user ID, nickname, profile image URL, or toggle read-receipt or privacy settings after creation. Run after confirming the bot ID. |

## Supported Triggers

None listed.

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

The Sendbird ai chabot MCP server is an implementation of the Model Context Protocol that connects your AI agent to Sendbird ai chabot. It provides structured and secure access so your agent can perform Sendbird ai chabot 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:
- Python 3.9 or higher
- A Composio account with an active API key
- Basic familiarity with Python and async programming

### 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).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Sendbird ai chabot
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your agent to Composio's API
- USER_ID associates your session with your account for secure tool access
- OPENAI_API_KEY to access OpenAI LLMs
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

What's happening:
- We load environment variables and import required modules
- Composio manages connections to Sendbird ai chabot
- MCPServerStreamableHTTP connects to the Sendbird ai chabot MCP server endpoint
- Agent from Pydantic AI lets you define and run the AI assistant
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
```

### 5. Create a Tool Router Session

What's happening:
- We're creating a Tool Router session that gives your agent access to Sendbird ai chabot tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned session.mcp.url is the MCP server URL that your agent will use
```python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Sendbird ai chabot
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["sendbird_ai_chabot"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

What's happening:
- The MCP client connects to the Sendbird ai chabot endpoint
- The agent uses GPT-5 to interpret user commands and perform Sendbird ai chabot operations
- The instructions field defines the agent's role and behavior
```python
# Attach the MCP server to a Pydantic AI Agent
sendbird_ai_chabot_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[sendbird_ai_chabot_mcp],
    instructions=(
        "You are a Sendbird ai chabot assistant. Use Sendbird ai chabot tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
```

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Sendbird ai chabot API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Sendbird ai chabot.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
```

### 8. Run the application

What's happening:
- The asyncio loop launches the agent and keeps it running until you exit
```python
if __name__ == "__main__":
    asyncio.run(main())
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Sendbird ai chabot
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["sendbird_ai_chabot"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    sendbird_ai_chabot_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[sendbird_ai_chabot_mcp],
        instructions=(
            "You are a Sendbird ai chabot assistant. Use Sendbird ai chabot tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Sendbird ai chabot.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())
```

## Conclusion

You've built a Pydantic AI agent that can interact with Sendbird ai chabot through Composio's Tool Router. With this setup, your agent can perform real Sendbird ai chabot actions through natural language.
You can extend this further by:
- Adding other toolkits like Gmail, HubSpot, or Salesforce
- Building a web-based chat interface around this agent
- Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Sendbird ai chabot for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## How to build Sendbird ai chabot MCP Agent with another framework

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

## Related Toolkits

- [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools.
- [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations.
- [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates.
- [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates.
- [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations.
- [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code.
- [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding.
- [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls.
- [Callingly](https://composio.dev/toolkits/callingly) - Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources.
- [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates.
- [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly.
- [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time.
- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [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.
- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
- [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support.
- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
- [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback.
- [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Sendbird ai chabot MCP?

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

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

Yes, you can. Pydantic 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 Sendbird ai chabot tools.

### Can I manage the permissions and scopes for Sendbird ai chabot while using Tool Router?

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

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