How to integrate Apipie ai MCP with CrewAI

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Introduction

This guide walks you through connecting Apipie ai to CrewAI using the Composio tool router. By the end, you'll have a working Apipie ai agent that can show all available ai models for text tasks, delete unused vectors from my collection, list country restrictions for gpt-4 models, analyze last week's api usage and costs through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Apipie ai account through Composio's Apipie ai 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 Apipie ai connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Apipie ai
  • Build a conversational loop where your agent can execute Apipie ai 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 Apipie ai MCP server, and what's possible with it?

The Apipie ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apipie ai account. It provides structured and secure access to a wide range of AI models and vector operations, so your agent can perform actions like listing models, checking restrictions, managing vectors, and analyzing usage history on your behalf.

  • Model discovery and selection: Instantly fetch a comprehensive list of available AI models, filter by type or provider, and stay up-to-date with the latest model options.
  • Model restriction checks: Retrieve country-specific deployment restrictions, helping you ensure compliance and make informed choices before launching models in new regions.
  • Vector data management: Effortlessly delete entire vector collections or specific vectors, enabling your agent to keep your data storage clean and up to date.
  • Usage analytics and auditing: Access historical API query logs—see latency, token usage, costs, and source IPs—so you can monitor performance, manage expenses, and audit activity anytime.

Supported Tools & Triggers

Tools
Delete VectorsTool to delete an entire vector collection or specific vectors.
Fetch model restrictionsTool to retrieve a list of country restrictions for models.
Get query historyTool to retrieve historic api usage logs including latency, token counts, costs, and source ip.
List AI ModelsTool to fetch a list of available ai models.

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 Apipie ai 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 Apipie ai 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 Apipie ai MCP URL

Create a Composio Tool Router session for Apipie ai

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["apipie_ai"],
)
url = session.mcp.url
What's happening:
  • You create a Apipie ai only session through Composio
  • Composio returns an MCP HTTP URL that exposes Apipie ai 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="Apipie ai Assistant",
    goal="Help users interact with Apipie ai through natural language commands",
    backstory=(
        "You are an expert assistant with access to Apipie ai tools. "
        "You can perform various Apipie ai 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 Apipie ai 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 Apipie ai 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 Apipie ai 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_apipie_ai_agent.py

Complete Code

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

python
# file: crewai_apipie_ai_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 Apipie ai session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["apipie_ai"],
    )
    url = session.mcp.url

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

    # Create Apipie ai assistant agent
    toolkit_agent = Agent(
        role="Apipie ai Assistant",
        goal="Help users interact with Apipie ai through natural language commands",
        backstory=(
            "You are an expert assistant with access to Apipie ai tools. "
            "You can perform various Apipie ai 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 Apipie ai 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 Apipie ai 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 Apipie ai through Composio's Tool Router. The agent can perform Apipie ai 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 Apipie ai MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Apipie ai MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Apipie ai MCP with Claude Agent SDK

Google ADK

Google ADK

Use Apipie ai MCP with Google ADK

LangChain

LangChain

Use Apipie ai MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Apipie ai MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Apipie ai MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Apipie ai MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Apipie ai MCP?

With a standalone Apipie ai MCP server, the agents and LLMs can only access a fixed set of Apipie ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Apipie ai 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 Apipie ai tools.

Can I manage the permissions and scopes for Apipie ai while using Tool Router?

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

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