How to integrate Sendbird ai chabot MCP with CrewAI

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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, fetch bot details by user id 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.

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 & Triggers

Tools
Create BotTool to create a new bot.
Get BotTool to retrieve information on a specific bot by its user id.
List BotsTool to list all bots in the sendbird application.
List Group ChannelsTool to list group channels.
Unregister Bot WebhookTool to unregister the webhook url for a bot.
Update BotTool to update information on an existing bot.
Update Bot WebhookTool to update the webhook url of an existing bot.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

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

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.

Install dependencies

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

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

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

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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

Create a Composio Tool Router session for Sendbird ai chabot

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["sendbird_ai_chabot"],
)
url = session.mcp.url
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

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Sendbird ai chabot Assistant",
    goal="Help users interact with Sendbird ai chabot through natural language commands",
    backstory=(
        "You are an expert assistant with access to Sendbird ai chabot tools. "
        "You can perform various Sendbird ai chabot operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Sendbird ai chabot MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Sendbird ai chabot operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Sendbird ai chabot related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_sendbird_ai_chabot_agent.py

Complete Code

Here's the complete code to get you started with Sendbird ai chabot and CrewAI:

python
# file: crewai_sendbird_ai_chabot_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Sendbird ai chabot session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["sendbird_ai_chabot"],
    )
    url = session.mcp.url

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

    # Create Sendbird ai chabot assistant agent
    toolkit_agent = Agent(
        role="Sendbird ai chabot Assistant",
        goal="Help users interact with Sendbird ai chabot through natural language commands",
        backstory=(
            "You are an expert assistant with access to Sendbird ai chabot tools. "
            "You can perform various Sendbird ai chabot operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Sendbird ai chabot operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Sendbird ai chabot related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

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

OpenAI Agents SDK

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Claude Agent SDK

Claude Agent SDK

Use Sendbird ai chabot MCP with Claude Agent SDK

Google ADK

Google ADK

Use Sendbird ai chabot MCP with Google ADK

LangChain

LangChain

Use Sendbird ai chabot MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Sendbird ai chabot MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Sendbird ai chabot MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Sendbird ai chabot MCP with LlamaIndex

FAQ

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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