# How to integrate Zixflow MCP with LangChain

```json
{
  "title": "How to integrate Zixflow MCP with LangChain",
  "toolkit": "Zixflow",
  "toolkit_slug": "zixflow",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/zixflow/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/zixflow/framework/langchain.md",
  "updated_at": "2026-03-29T06:56:25.114Z"
}
```

## Introduction

This guide walks you through connecting Zixflow to LangChain using the Composio tool router. By the end, you'll have a working Zixflow agent that can send a bulk sms campaign to all new leads, add a follow-up reminder for every hot lead, list conversations from whatsapp with unread responses through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Zixflow account through Composio's Zixflow MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Zixflow with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Zixflow project to Composio
- Create a Tool Router MCP session for Zixflow
- Initialize an MCP client and retrieve Zixflow tools
- Build a LangChain agent that can interact with Zixflow
- 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 Zixflow MCP server, and what's possible with it?

The Zixflow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zixflow account. It provides structured and secure access so your agent can perform Zixflow operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ZIXFLOW_CREATE_ATTRIBUTE` | Create Attribute | Tool to create a custom attribute for a Zixflow collection or list. Use when you need to add new fields to track specific data types (text, number, email, etc.). Attributes define the structure of your data. Common use cases: adding custom fields for contacts, creating status trackers, adding reference fields between collections. |
| `ZIXFLOW_GET_CAMPAIGN_REPORT_WHATSAPP` | Get WhatsApp Campaign Report | Tool to retrieve WhatsApp campaign message report. Use when you need to check the delivery status and details of a WhatsApp message sent via campaign. |
| `ZIXFLOW_GET_EMAIL_REPORT` | Get Email Report | Tool to retrieve email message delivery report from Zixflow. Use when you need to check the delivery status of a sent campaign email. The report includes delivery status (SENT, OPENED, CLICKED, BOUNCE, COMPLAINT, UNSUBSCRIBED), recipient information, and timestamps. Requires the message ID obtained when sending the campaign. |
| `ZIXFLOW_GET_SMS_REPORT` | Get SMS Report | Tool to retrieve SMS message delivery report from Zixflow. Use when you need to check the delivery status, destination, and other details of a sent SMS campaign message. |
| `ZIXFLOW_GET_WHATSAPP_TEMPLATE_VARIABLES` | Get WhatsApp Template Variables | Tool to retrieve template variable details for a WhatsApp template. Use when you need to understand what variables a specific WhatsApp template expects before sending a message. |
| `ZIXFLOW_LIST_ATTRIBUTE_OPTIONS` | List Attribute Options | Tool to retrieve the list of options for select/multiselect attributes. Use when you need to get available options for a specific attribute in a collection or list. |
| `ZIXFLOW_LIST_ATTRIBUTE_STATUS_OPTIONS` | List Attribute Status Options | Tool to retrieve the list of options for status attributes in Zixflow. Use when you need to fetch available status configurations for a specific attribute within a collection or list. |

## Supported Triggers

None listed.

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

The Zixflow MCP server is an implementation of the Model Context Protocol that connects your AI agent to Zixflow. It provides structured and secure access so your agent can perform Zixflow 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 Zixflow 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 Zixflow 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 Zixflow tools as needed
```python
# Create Tool Router session for Zixflow
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['zixflow']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "zixflow-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({
    "zixflow-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 Zixflow 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 Zixflow 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=['zixflow']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "zixflow-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 Zixflow 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: ['zixflow']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "zixflow-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 Zixflow 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 Zixflow 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 Zixflow MCP Agent with another framework

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

## Related Toolkits

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- [Hubspot](https://composio.dev/toolkits/hubspot) - HubSpot is an all-in-one marketing, sales, and customer service platform. It lets teams nurture leads, automate outreach, and track every customer interaction in one place.
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- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing.
- [Apollo](https://composio.dev/toolkits/apollo) - Apollo is a CRM and lead generation platform that helps businesses discover contacts and manage sales pipelines. Use it to streamline customer outreach and track your deals from one place.
- [Attio](https://composio.dev/toolkits/attio) - Attio is a customizable CRM and workspace for managing your team's relationships and workflows. It helps teams organize contacts, automate tasks, and collaborate more efficiently.
- [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages.
- [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections.
- [Acculynx](https://composio.dev/toolkits/acculynx) - AccuLynx is a cloud-based roofing business management software for contractors. It streamlines project tracking, lead management, and document sharing.
- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.

## Frequently Asked Questions

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

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

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

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

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