LangChain iMessage Integration — Add iMessage to LangChain Agents

How to Connect LangChain + Sendblue

Example: Python Tool Wrapper

import requests
from langchain.tools import tool

@tool
def send_imessage(phone_number: str, message: str) -> str:
    """Send an iMessage to a phone number via Sendblue.
    Use E.164 format for the phone number (e.g. +15551234567)."""
    response = requests.post(
        "https://api.sendblue.co/api/send-message",
        headers={
            "sb-api-key-id": SENDBLUE_API_KEY,
            "sb-api-secret-key": SENDBLUE_API_SECRET,
            "Content-Type": "application/json",
        },
        json={"number": phone_number, "content": message},
    )
    return response.json()

Key Features

Why LangChain + iMessage?

LangChain is the most popular framework for building LLM-powered agents. By adding Sendblue as a tool, you give those agents the ability to communicate through iMessage — the highest-engagement messaging channel available in the US.

There is no native LangChain integration for Sendblue. Instead, you wrap Sendblue's REST API as a custom tool. This is the standard LangChain pattern for external APIs, and it takes about 15 lines of code (see example above).

Once connected, your agents can send appointment reminders, order confirmations, follow-up messages, and support responses — all over iMessage. Combine it with retrieval tools, database lookups, and other APIs to build complete customer communication workflows.

For production deployments, pair the LangChain agent with Sendblue's webhook system to handle incoming replies. When a customer texts back, the webhook fires, your application passes the message to the agent, and the agent responds — creating an autonomous conversation loop. This works alongside Sendblue's native CRM integrations for HubSpot, Salesforce, and GoHighLevel.