# How to integrate Pilvio MCP with CrewAI

```json
{
  "title": "How to integrate Pilvio MCP with CrewAI",
  "toolkit": "Pilvio",
  "toolkit_slug": "pilvio",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/pilvio/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/pilvio/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:22:00.265Z"
}
```

## Introduction

This guide walks you through connecting Pilvio to CrewAI using the Composio tool router. By the end, you'll have a working Pilvio agent that can start a new virtual machine with ubuntu, list all active virtual machines in your account, resize an existing vm to a larger instance through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Pilvio account through Composio's Pilvio MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Pilvio with

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

## TL;DR

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

The Pilvio MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pilvio account. It provides structured and secure access to your cloud resources, so your agent can perform actions like provisioning virtual machines, managing object storage, automating infrastructure, and monitoring your resources on your behalf.
- Dynamic virtual machine management: Instantly create, start, stop, or delete virtual machines so your cloud infrastructure adapts to your needs on demand.
- Automated object storage operations: Let your agent upload, download, list, or delete files and buckets for seamless data management in your object storage.
- Infrastructure status monitoring: Query resource states and usage metrics so your agent keeps you informed about cloud health and performance.
- Resource provisioning and automation: Enable your agent to quickly provision new resources, allocate storage, or adjust configurations—without manual intervention.
- Security and access control: Manage API keys, permissions, and access policies to keep your cloud environment secure and compliant.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PILVIO_CHECK_ACTIVE_CAMPAIGNS` | Check Active Campaigns | Tool to check for active campaigns. Use when you need to verify whether any campaigns are currently active. |
| `PILVIO_GET_USER_INFO` | Get User Info | Tool to retrieve information about the authenticated user. Use when you need to fetch the current user's profile after authentication. |
| `PILVIO_LIST_BILLING_ACCOUNTS` | List billing accounts | Tool to list billing accounts. Use when you need to fetch all billing accounts for management or reporting. Use after authenticating your Pilvio API key. |
| `PILVIO_LIST_CREDIT_CARDS` | List Credit Cards | Tool to list credit cards attached to a billing account. Use after confirming the billing account ID. Example: "Retrieve cards for billing_account_id 'ba_1234567890abcdef'." |
| `PILVIO_LIST_INVOICES` | List Invoices | Tool to retrieve a list of invoices. Use after authentication to fetch and filter invoices for a billing account. |
| `PILVIO_LIST_LOCATIONS` | List Pilvio data center locations | Tool to retrieve the list of available data center locations. Use after authentication to see where you can deploy resources. |
| `PILVIO_LIST_VM_RESOURCE_POOLS` | List VM Resource Pools | Tool to retrieve the list of available VM resource pools. Use after authentication to determine where you can provision VMs. |
| `PILVIO_LIST_VMS` | List Virtual Machines | Tool to retrieve a list of all virtual machines. Use after authenticating to fetch your VMs. |
| `PILVIO_LIST_VM_SNAPSHOTS` | List VM Snapshots | Tool to list snapshots (replicas) of a specific VM. Use this to get all point-in-time snapshots or backups for a virtual machine. |
| `PILVIO_UPDATE_USER_PROFILE` | Update User Profile | Tool to update the authenticated user's profile. Use after obtaining a valid API key when you need to modify profile details (e.g., change email or name). |

## Supported Triggers

None listed.

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

The Pilvio MCP server is an implementation of the Model Context Protocol that connects your AI agent to Pilvio. It provides structured and secure access so your agent can perform Pilvio 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 Pilvio 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 Pilvio 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 Pilvio 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 Pilvio

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

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=["pilvio"],
)
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 Pilvio through Composio's Tool Router. The agent can perform Pilvio 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 Pilvio MCP Agent with another framework

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

## Related Toolkits

- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
- [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses.
- [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure.
- [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure.
- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
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- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.

## Frequently Asked Questions

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

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

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

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

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