# How to integrate Timelinesai MCP with LangChain

```json
{
  "title": "How to integrate Timelinesai MCP with LangChain",
  "toolkit": "Timelinesai",
  "toolkit_slug": "timelinesai",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/timelinesai/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/timelinesai/framework/langchain.md",
  "updated_at": "2026-05-12T10:28:34.037Z"
}
```

## Introduction

This guide walks you through connecting Timelinesai to LangChain using the Composio tool router. By the end, you'll have a working Timelinesai agent that can get the last 10 messages from sales chat, list all unread whatsapp chats assigned to me, create webhook for new incoming messages through natural language commands.
This guide will help you understand how to give your LangChain 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

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Timelinesai project to Composio
- Create a Tool Router MCP session for Timelinesai
- Initialize an MCP client and retrieve Timelinesai tools
- Build a LangChain agent that can interact with Timelinesai
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TIMELINESAI_DELETE_FILE` | Delete File | Tool to delete an uploaded file by its UID. Use after confirming the file is no longer needed. |
| `TIMELINESAI_DELETE_WEBHOOK` | Delete Webhook | Tool to delete a webhook subscription by its ID. Use when you need to remove an existing webhook after confirming the webhook ID. Example: "Delete the webhook with ID '9f6a8c3d-56b7-4a1e-8f2e-abcdef123456'." |
| `TIMELINESAI_GET_CHAT_MESSAGES` | Get Chat Messages | Tool to get messages from a specific chat in TimelinesAI. Use when you need to retrieve message history or recent messages from a chat. Example: "Get the last 20 messages from chat 'chat_123abc'." |
| `TIMELINESAI_GET_CHATS` | Get Chats | Tool to get full or filtered list of all chats. Use when you need to browse or search chats with optional filters. Example: "List all unread chats assigned to me." |
| `TIMELINESAI_GET_FILE_DETAILS` | Get File Details | Tool to retrieve metadata and temporary download URL for an uploaded file. Use after uploading a file or when needing its URL. |
| `TIMELINESAI_GET_WEBHOOK` | Get Webhook | Retrieves detailed information about a specific webhook subscription by its ID. Use this action to: - Check webhook configuration (URL, event type, enabled status) - Monitor webhook health (error counter) - Verify webhook existence before updating or deleting Prerequisites: You must have a valid webhook ID. Use the Get Webhooks action to list all available webhooks first. |
| `TIMELINESAI_GET_WEBHOOKS` | Get Webhooks | Retrieves all webhook subscriptions configured for the workspace. Webhooks notify external systems about events (e.g., 'message:new', 'chat:new') in real-time. Use this to view existing webhook configurations, check their status, or retrieve webhook IDs for updates/deletion. Supports optional pagination via limit and offset parameters. |
| `TIMELINESAI_GET_WHATSAPP_ACCOUNTS` | Get WhatsApp Accounts | Tool to list all WhatsApp accounts connected to the workspace. Use after configuring WhatsApp integration to verify accounts. |
| `TIMELINESAI_LIST_UPLOADED_FILES` | List Uploaded Files | Tool to list files uploaded in your TimelinesAI workspace. Use when you need to retrieve all uploaded files. |
| `TIMELINESAI_POST_MESSAGE` | Send WhatsApp Message to Number | Tool to send a WhatsApp text message to a phone number via TimelinesAI. Use this to send messages to any recipient phone number using one of your connected WhatsApp accounts as the sender. The message will be delivered immediately if the recipient number is reachable on WhatsApp. Example: Send 'Hello, how can I help you today?' from +15105566777 to +14151231234. |
| `TIMELINESAI_POST_WEBHOOK` | Create Webhook Subscription | Tool to create a new webhook subscription. Use when you have the event type and callback URL ready. |
| `TIMELINESAI_PUT_WEBHOOK` | Update Webhook | Tool to update an existing webhook subscription. Use after confirming the webhook ID when you need to change the URL, subscribed event types, or enable/disable a webhook. |
| `TIMELINESAI_SEND_MESSAGE` | Send Message to Chat | Send a WhatsApp message to an existing chat in TimelinesAI. Use this action when you have a chat ID from the Get Chats action and want to send a message to that conversation. For sending messages to new phone numbers (not existing chats), use the 'Send WhatsApp Message to Number' action instead. Example: "Send message 'Hello world' to chat with ID 'chat_123abc'." |

## Supported Triggers

| Trigger slug | Name | Description |
|---|---|---|
| `TIMELINESAI_NEW_MESSAGE_RECEIVED` | New WhatsApp Message Received | Polling trigger that monitors for new messages received in TimelinesAI WhatsApp chats. |

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

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

No description provided.

### 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

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Timelinesai tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

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
- This approach allows the agent to dynamically load and use Timelinesai tools as needed
```python
# Create Tool Router session for Timelinesai
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['timelinesai']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['timelinesai']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "timelinesai-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "timelinesai-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Timelinesai related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Timelinesai related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['timelinesai']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "timelinesai-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Timelinesai related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['timelinesai']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "timelinesai-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Timelinesai related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Timelinesai through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Timelinesai MCP Agent with another framework

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

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## Frequently Asked Questions

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

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.

### Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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.

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

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.

### 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 Timelinesai data and credentials are handled as safely as possible.

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