How to integrate Flutterwave MCP with Pydantic AI

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Introduction

This guide walks you through connecting Flutterwave to Pydantic AI using the Composio tool router. By the end, you'll have a working Flutterwave agent that can create a payment link for a new order, generate virtual account numbers for customers, fetch details of a specific subaccount, disable an existing payment link after use through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Flutterwave account through Composio's Flutterwave 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Flutterwave
  • 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 Flutterwave 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 Flutterwave MCP server, and what's possible with it?

The Flutterwave MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Flutterwave account. It provides structured and secure access to your payment infrastructure, so your agent can perform actions like creating payment links, managing beneficiaries, setting up virtual accounts, and handling subaccounts on your behalf.

  • Instant payment link creation: Let your agent generate hosted payment URLs for one-time or recurring transactions, making it easy to collect payments from customers.
  • Beneficiary management: Add, fetch, or remove transfer beneficiaries directly through your agent, streamlining the process of managing who receives your payouts.
  • Virtual account generation: Automatically create single or bulk virtual bank accounts for customers, enabling seamless and trackable bank transfers.
  • Subaccount setup and retrieval: Have your agent create, configure, or fetch subaccounts to manage split payments and disbursements for complex business needs.
  • Payment link control: Disable active payment links when necessary to prevent further transactions, ensuring you stay in control of your payment flows.

Supported Tools & Triggers

Tools
Create BeneficiaryTool to create a new transfer beneficiary.
Create Bulk Virtual Account NumbersTool to create multiple virtual account numbers.
Create Payment LinkTool to create a hosted payment link.
Create Payment PlanTool to create a new payment plan.
Create SubaccountTool to create a new subaccount.
Create Virtual AccountTool to create a new virtual account number.
Delete BeneficiaryTool to delete a beneficiary by id.
Disable Payment LinkTool to disable a flutterwave payment link.
Fetch BeneficiaryTool to retrieve details of a specific beneficiary by id.
Fetch SubaccountTool to retrieve details of a specific subaccount by id.
Generate Transaction ReferenceTool to generate a unique transaction reference.
Get All SubscriptionsTool to retrieve all subscriptions, including cancelled ones.
Retrieve all transactionsTool to retrieve a list of all transactions with optional filters.
Get All Wallet BalancesTool to retrieve all wallet balances across currencies.
Get Balances per CurrencyTool to retrieve wallet balance for a specific currency.
Get Bill CategoriesTool to retrieve available bill categories.
Get Multiple Refund TransactionsTool to retrieve multiple refund transactions with optional filters.
Get Payment PlansTool to retrieve a list of all payment plans.
Get TransactionTool to retrieve details of a specific transaction by id.
Get Transaction FeeTool to retrieve the fee for a specific transaction.
Get Transfer FeeTool to retrieve the fee for initiating a transfer.
Initiate Mobile Money TanzaniaTool to initiate a mobile money payment in tanzania.
List All BeneficiariesTool to list all saved beneficiaries.
View Transaction TimelineTool to retrieve the event timeline for a transaction.

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 with an active API key
  • Basic familiarity with Python and async programming

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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Flutterwave
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

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

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

Import dependencies

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()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Flutterwave
  • MCPServerStreamableHTTP connects to the Flutterwave MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

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 Flutterwave
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["flutterwave"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Flutterwave 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

Initialize the Pydantic AI Agent

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

Build the chat interface

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 Flutterwave.\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()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Flutterwave API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

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

Complete Code

Here's the complete code to get you started with Flutterwave and Pydantic AI:

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 Flutterwave
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["flutterwave"],
    )
    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
    flutterwave_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[flutterwave_mcp],
        instructions=(
            "You are a Flutterwave assistant. Use Flutterwave 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 Flutterwave.\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 Flutterwave through Composio's Tool Router. With this setup, your agent can perform real Flutterwave 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 + Flutterwave 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 Flutterwave MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Flutterwave MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Flutterwave MCP with Claude Agent SDK

Google ADK

Google ADK

Use Flutterwave MCP with Google ADK

LangChain

LangChain

Use Flutterwave MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Flutterwave MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Flutterwave MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Flutterwave MCP with LlamaIndex

CrewAI

CrewAI

Use Flutterwave MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Flutterwave MCP?

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

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

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

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