How to integrate CheckFlow MCP with Pydantic AI

This guide walks you through connecting CheckFlow to Pydantic AI using the Composio tool router. By the end, you'll have a working CheckFlow agent that can find your checklists, inspect task details, review analytics, browse templates, and create tags through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a CheckFlow account through Composio's CheckFlow MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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CheckFlow is a workflow and checklist platform for repeatable processes, tasks, templates, teams, data sets, and automation. Use it to standardize operations, track process execution, and keep teams aligned.

10 Tools

Introduction

This guide walks you through connecting CheckFlow to Pydantic AI using the Composio tool router. By the end, you'll have a working CheckFlow agent that can find your checklists, inspect task details, review analytics, browse templates, and create tags through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a CheckFlow account through Composio's CheckFlow MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate CheckFlow with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 CheckFlow
  • 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 CheckFlow 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 CheckFlow MCP server, and what's possible with it?

The CheckFlow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your CheckFlow account. It provides structured and secure access to your checklists, templates, tasks, team directory, tags, analytics, and webhook subscriptions, so your agent can find checklists, inspect task details, review activity, browse team resources, and manage tags on your behalf.

  • Checklist discovery and review: Have your agent find checklists by template, name, tag, or status, then review their tasks and controls.
  • Task detail retrieval: Direct your agent to inspect a checklist task, including its controls, comments, and assignees.
  • Template and team lookup: Let the agent list active templates and their tasks, or search active team members and groups by name.
  • Analytics and subscription visibility: Instruct your agent to review checklist and task activity for a date range, or list webhook subscriptions by source and event type.
  • Tag organization: Have your agent list available team tags or create a tag without duplicating an existing tag with the same name.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step09 STEPS
1

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
2

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.
3

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 CheckFlow
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

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
5

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 CheckFlow
  • MCPServerStreamableHTTP connects to the CheckFlow MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

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 CheckFlow
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["checkflow"],
    )
    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 CheckFlow 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
checkflow_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[checkflow_mcp],
    instructions=(
        "You are a CheckFlow assistant. Use CheckFlow tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the CheckFlow endpoint
  • The agent uses GPT-5 to interpret user commands and perform CheckFlow operations
  • The instructions field defines the agent's role and behavior
8

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 CheckFlow.\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
  • CheckFlow API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

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 CheckFlow 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 CheckFlow
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["checkflow"],
    )
    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
    checkflow_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[checkflow_mcp],
        instructions=(
            "You are a CheckFlow assistant. Use CheckFlow 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 CheckFlow.\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 CheckFlow through Composio's Tool Router. With this setup, your agent can perform real CheckFlow 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 + CheckFlow for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every CheckFlow action and event your agent gets out of the box.

Create Tag

Create a team tag by name, or return the existing same-name tag without creating a duplicate.

Find Checklists

Find checklist summaries for a template, optionally filtered by name, tag, or lifecycle status.

Get Analytics

Return checklist and task activity analytics for a date range.

Get Task Details

Get one checklist task with its controls, comments, and assignees.

Get Team Directory

Return active team members and groups, including each group's membership, optionally filtered by name.

List Checklist Details

Return one page of full checklist instances, including tasks and controls, with a cursor for the next page.

List Tags

List the tags available to the connected CheckFlow team.

List Templates

List active checklist templates and their keys for creating or finding checklists.

List Template Tasks

List the task keys and names defined by one checklist template.

List Webhook Subscriptions

List webhook subscriptions, optionally filtered by source and event type.

FRAMEWORKS

How to build CheckFlow MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use CheckFlow MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use CheckFlow MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use CheckFlow MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use CheckFlow MCP with Claude Cowork

Codex logo

Codex

Use CheckFlow MCP with Codex

OpenClaw logo

OpenClaw

Use CheckFlow MCP with OpenClaw

Hermes logo

Hermes

Use CheckFlow MCP with Hermes

CLI logo

CLI

Use CheckFlow MCP with CLI

Google ADK logo

Google ADK

Use CheckFlow MCP with Google ADK

LangChain logo

LangChain

Use CheckFlow MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use CheckFlow MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use CheckFlow MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use CheckFlow MCP with LlamaIndex

CrewAI logo

CrewAI

Use CheckFlow MCP with CrewAI

FAQ

Frequently asked questions

With a standalone CheckFlow MCP server, the agents and LLMs can only access a fixed set of CheckFlow tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from CheckFlow and many other apps based on the task at hand, all through a single MCP endpoint.

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 CheckFlow tools.

Yes, absolutely. You can configure which CheckFlow 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.

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 CheckFlow data and credentials are handled as safely as possible.

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