# How to integrate Sendbird ai chabot MCP with CrewAI

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

## Introduction

This guide walks you through connecting Sendbird ai chabot to CrewAI 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 CrewAI 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)

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Sendbird ai chabot connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Sendbird ai chabot
- Build a conversational loop where your agent can execute Sendbird ai chabot operations

## What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.
Key features include:
- Agent Roles: Define specialized agents with specific goals and backstories
- Task Management: Create tasks with clear descriptions and expected outputs
- Crew Orchestration: Combine agents and tasks into collaborative workflows
- MCP Integration: Connect to external tools through Model Context Protocol

## 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 and API key
- A Sendbird ai chabot connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

### 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

**What's happening:**
- composio connects your agent to Sendbird ai chabot via MCP
- crewai provides Agent, Task, Crew, and LLM primitives
- crewai-tools[mcp] includes MCP helpers
- python-dotenv loads environment variables from .env
```bash
pip install composio crewai crewai-tools[mcp] python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates with Composio
- USER_ID scopes the session to your account
- OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**What's happening:**
- CrewAI classes define agents and tasks, and run the workflow
- MCPServerHTTP connects the agent to an MCP endpoint
- Composio will give you a short lived Sendbird ai chabot MCP URL
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
```

### 5. Create a Composio Tool Router session for Sendbird ai chabot

**What's happening:**
- You create a Sendbird ai chabot only session through Composio
- Composio returns an MCP HTTP URL that exposes Sendbird ai chabot tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["sendbird_ai_chabot"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
```

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["sendbird_ai_chabot"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")
```

## Conclusion

You now have a CrewAI agent connected to Sendbird ai chabot through Composio's Tool Router. The agent can perform Sendbird ai chabot operations through natural language commands.
Next steps:
- Add role-specific instructions to customize agent behavior
- Plug in more toolkits for multi-app workflows
- Chain tasks for complex multi-step operations

## 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)

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## 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 CrewAI?

Yes, you can. CrewAI 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.

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