# How to integrate Tiktok MCP with LangChain

```json
{
  "title": "How to integrate Tiktok MCP with LangChain",
  "toolkit": "Tiktok",
  "toolkit_slug": "tiktok",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/tiktok/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/tiktok/framework/langchain.md",
  "updated_at": "2026-05-12T10:28:29.420Z"
}
```

## Introduction

This guide walks you through connecting Tiktok to LangChain using the Composio tool router. By the end, you'll have a working Tiktok agent that can upload a new video from your library, list your most recent tiktok videos, fetch your latest tiktok follower stats through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Tiktok account through Composio's Tiktok MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Tiktok with

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

## TL;DR

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

The Tiktok MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Tiktok account. It provides structured and secure access to your Tiktok profile and content, so your agent can fetch user analytics, manage your videos, post new content, and monitor publishing status—all on your behalf.
- Automated video uploads and publishing: Let your agent upload single or multiple videos, then finalize and publish them to your Tiktok account seamlessly.
- Profile insights and analytics: Fetch comprehensive user information and performance stats, giving you quick access to follower counts, engagement metrics, and more.
- Content management: List all your videos or those of a specified creator, making it easy to organize, review, or reference your posted content.
- Photo posting automation: Enable your agent to create and post photos directly through the Tiktok content posting API, streamlining your visual content workflow.
- Real-time publish status monitoring: Check the current status of your video uploads or publishing process, so you’re always up to date on which content is live or pending.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TIKTOK_FETCH_PUBLISH_STATUS` | Fetch publish status | Check the processing status of a TikTok video or photo post using its publish_id. Use this action to poll the status of content after initiating an upload or post. The API returns detailed information about processing stages (upload, download, moderation) and any errors that occurred. Non-terminal statuses mean processing is still pending — never re-initiate TIKTOK_PUBLISH_VIDEO for the same publish_id. Use exponential backoff when polling (e.g., 5s→10s→20s) to avoid the 30 requests/minute per access token rate limit. |
| `TIKTOK_GET_ACTION_CATEGORIES` | Get action categories | Tool to retrieve available action categories from TikTok Marketing API. Use when you need to get the list of conversion event categories for creating or managing TikTok ad campaigns with conversion tracking. |
| `TIKTOK_GET_TERM` | Get terms | Tool to retrieve terms from TikTok Business API. Use when you need to fetch advertiser or agency terms for a specific advertiser ID. |
| `TIKTOK_GET_USER_STATS` | Get user stats | Fetches TikTok user information and statistics for the authenticated user. Retrieves user stats (follower_count, following_count, likes_count, video_count) and can optionally fetch profile fields (display_name, username, bio_description, etc.) and basic info (open_id, union_id, avatar URLs). Returns only the fields requested in the fields parameter. Only works for the authenticated account; cannot fetch arbitrary public profiles. Stats may be delayed and not reflect the most recent activity. |
| `TIKTOK_LIST_GMV_MAX_OCCUPIED_CUSTOM_SHOP_ADS` | List GMV Max occupied custom shop ads | Tool to get GMV Max occupied custom shop ads list for a TikTok advertiser. Use this action when you need to retrieve information about which custom shop ads are currently occupied for GMV Max campaigns. This is part of the TikTok Business API and requires appropriate advertiser access. |
| `TIKTOK_LIST_VIDEOS` | List videos | Lists videos for the authenticated user (or specified creator). Does not provide a global TikTok-wide feed. |
| `TIKTOK_POST_PHOTO` | Post photo | Create a photo post (1-35 images) on TikTok via Content Posting API. Supports two modes: - MEDIA_UPLOAD: Uploads photos to user's inbox for review/editing before posting - DIRECT_POST: Immediately posts photos to user's TikTok account IMPORTANT: Photo URLs must be from your TikTok-verified domain. Unverified domains will return 403 Forbidden. Unaudited apps can only post with privacy='SELF_ONLY'. Rate limit: 6 requests per minute per user access token. Reference: https://developers.tiktok.com/doc/content-posting-api-reference-photo-post |
| `TIKTOK_PUBLISH_VIDEO` | Publish video | Publishes a video to TikTok by pulling it from a public URL. TikTok downloads the video from the provided URL and publishes it directly to the creator's profile. Publishing is asynchronous — after calling this action, poll TIKTOK_FETCH_PUBLISH_STATUS with the returned publish_id to check completion. For uploading video files instead of URLs, use TIKTOK_UPLOAD_VIDEO. |
| `TIKTOK_UPLOAD_VIDEO` | Upload video | Uploads a video to TikTok via the Content Posting API (init + single-part upload). This action initializes an upload session to obtain a presigned upload URL, then uploads the entire file with a single PUT request. Use a subsequent action to publish the post. Ensure the video file is fully generated and available before calling this action. |
| `TIKTOK_UPLOAD_VIDEOS` | Upload videos (batch) | Uploads multiple videos to TikTok concurrently (init + single-part upload per file). |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

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- [Dotsimple](https://composio.dev/toolkits/dotsimple) - Dotsimple is a social media management platform for planning, creating, and publishing content. It helps teams boost their reach with AI-powered content generation and actionable analytics.
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- [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable.
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- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
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- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.
- [Slack](https://composio.dev/toolkits/slack) - Slack is a channel-based messaging platform for teams and organizations. It helps people collaborate in real time, share files, and connect all their tools in one place.
- [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [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.
- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [Gong](https://composio.dev/toolkits/gong) - Gong is a platform for video meetings, call recording, and team collaboration. It helps teams capture conversations, analyze calls, and turn insights into action.

## Frequently Asked Questions

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

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

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

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

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