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How to integrate Timelinesai MCP with Pydantic AI

Connect Pydantic AI to Timelinesai MCP. Get the last 10 messages from sales chat, list all unread whatsapp chats assigned to me, and more using natural language, with authentication handled for you.

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TimelinesAI is a platform for managing and automating WhatsApp communications across teams. It streamlines workflows by integrating WhatsApp with CRMs and productivity tools.

13 Tools1 Triggers

Introduction

This guide walks you through connecting Timelinesai to Pydantic AI using the Composio tool router. By the end, you'll have a working Timelinesai agent that can send WhatsApp messages, read chats and message history, manage uploaded files, list WhatsApp accounts, and set up webhooks through natural language commands.

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

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

Also integrate Timelinesai with

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

The Timelinesai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Timelinesai account. It provides structured and secure access to your WhatsApp communications, so your agent can retrieve chat messages, manage files, automate webhook workflows, and keep your team’s communication organized—all on your behalf.

  • WhatsApp chat management: Fetch recent or historical messages from specific chats, or list all active and unread chats to help you stay on top of conversations.
  • Automated webhook integration: Set up, review, or delete webhook subscriptions to automate notifications and keep your workflows synced across tools.
  • File and attachment handling: List uploaded files, retrieve file details or secure download links, and delete files when they’re no longer needed.
  • WhatsApp account verification: Quickly list and verify all WhatsApp accounts connected to your workspace for streamlined onboarding and troubleshooting.
  • Workspace insight and cleanup: Get a comprehensive view of all webhooks or uploaded files, making workspace management and housekeeping a breeze.

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 Timelinesai
  • 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 Timelinesai
  • MCPServerStreamableHTTP connects to the Timelinesai 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 Timelinesai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["timelinesai"],
    )
    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 Timelinesai 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
timelinesai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[timelinesai_mcp],
    instructions=(
        "You are a Timelinesai assistant. Use Timelinesai tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Timelinesai endpoint
  • The agent uses GPT-5 to interpret user commands and perform Timelinesai 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 Timelinesai.\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
  • Timelinesai 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 Timelinesai 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 Timelinesai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["timelinesai"],
    )
    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
    timelinesai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[timelinesai_mcp],
        instructions=(
            "You are a Timelinesai assistant. Use Timelinesai 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 Timelinesai.\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 Timelinesai through Composio's Tool Router. With this setup, your agent can perform real Timelinesai 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 + Timelinesai 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 & TRIGGERS

Supported Tools and Triggers

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

Delete File

Tool to delete an uploaded file by its UID.

Delete Webhook

Tool to delete a webhook subscription by its ID.

Get Chat Messages

Tool to get messages from a specific chat in TimelinesAI.

Get Chats

List or search chats with optional filters and pagination.

Get File Details

Tool to retrieve metadata and temporary download URL for an uploaded file.

Get Webhook

Retrieves detailed information about a specific webhook subscription by its ID.

Get Webhooks

Retrieves all webhook subscriptions configured for the workspace.

Get WhatsApp Accounts

Tool to list all WhatsApp accounts connected to the workspace.

List Uploaded Files

Tool to list files uploaded in your TimelinesAI workspace.

Send WhatsApp Message to Number

Send a WhatsApp text message to a phone number via TimelinesAI using a connected WhatsApp account as the sender.

Create Webhook Subscription

Create a webhook subscription for an event and callback URL.

Update Webhook

Update an existing webhook subscription by ID, supplying its URL and event type and optionally enabling or disabling it.

Send Message to Chat

Send a WhatsApp message to an existing chat in TimelinesAI.

FRAMEWORKS

How to build Timelinesai MCP Agent with another framework

Claude Code logo

Claude Code

Use Timelinesai MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Timelinesai MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Timelinesai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Timelinesai MCP with Claude Agent SDK

Codex logo

Codex

Use Timelinesai MCP with Codex

OpenClaw logo

OpenClaw

Use Timelinesai MCP with OpenClaw

Hermes logo

Hermes

Use Timelinesai MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Timelinesai MCP with Atomic Agent

Google ADK logo

Google ADK

Use Timelinesai MCP with Google ADK

LangChain logo

LangChain

Use Timelinesai MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Timelinesai MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Timelinesai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Timelinesai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Timelinesai MCP with CrewAI

AutoGen logo

AutoGen

Use Timelinesai MCP with AutoGen

FAQ

Frequently asked questions

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

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

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