# How to integrate Tiktok MCP with Autogen

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

## Introduction

This guide walks you through connecting Tiktok to AutoGen 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 AutoGen 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)
- [LangChain](https://composio.dev/toolkits/tiktok/framework/langchain)
- [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
- Install the required dependencies for Autogen and Composio
- Initialize Composio and create a Tool Router session for Tiktok
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Tiktok tools
- Run a live chat loop where you ask the agent to perform Tiktok operations

## What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.
Key features include:
- Multi-Agent Systems: Build collaborative agent workflows
- MCP Workbench: Native support for Model Context Protocol tools
- Streaming HTTP: Connect to external services through streamable HTTP
- AssistantAgent: Pre-built agent class for tool-using assistants

## 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 agents and assistants directly to Tiktok. Instead of manually wiring Tiktok APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
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

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Tiktok account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 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

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Tiktok via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

Create a .env file in your project folder.
What's happening:
- COMPOSIO_API_KEY is required to talk to Composio
- OPENAI_API_KEY is used by Autogen's OpenAI client
- USER_ID is how Composio identifies which user's Tiktok connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Tiktok tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Tiktok session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["tiktok"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.
What's happening:
- url points to the Tool Router MCP endpoint from Composio
- timeout is the HTTP timeout for requests
- sse_read_timeout controls how long to wait when streaming responses
- terminate_on_close=True cleans up the MCP server process when the workbench is closed
```python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Tiktok tools from the workbench
```python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Tiktok assistant agent with MCP tools
    agent = AssistantAgent(
        name="tiktok_assistant",
        description="An AI assistant that helps with Tiktok operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Tiktok tools to call via MCP
- agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
- Typing exit, quit, or bye ends the loop
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Tiktok related question or task to the agent.\n")

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Tiktok session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["tiktok"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Tiktok assistant agent with MCP tools
        agent = AssistantAgent(
            name="tiktok_assistant",
            description="An AI assistant that helps with Tiktok operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

## Conclusion

You now have an Autogen assistant wired into Tiktok through Composio's Tool Router and MCP. From here you can:
- Add more toolkits to the toolkits list, for example notion or hubspot
- Refine the agent description to point it at specific workflows
- Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Tiktok, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## 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)
- [LangChain](https://composio.dev/toolkits/tiktok/framework/langchain)
- [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

- [Twitter](https://composio.dev/toolkits/twitter) - Twitter is a social media platform for sharing real-time updates, conversations, and news. Stay connected, informed, and engaged with communities worldwide.
- [Instagram](https://composio.dev/toolkits/instagram) - Instagram is a social platform for sharing photos, videos, and stories with your audience. It helps brands and creators engage, grow, and analyze their online presence.
- [Ayrshare](https://composio.dev/toolkits/ayrshare) - Ayrshare is a Social Media API for managing, automating, and analyzing posts across multiple platforms. It helps you streamline social media workflows and centralize analytics.
- [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.
- [Strava](https://composio.dev/toolkits/strava) - Strava is a social fitness network and app for cyclists and runners. It's perfect for tracking workouts, sharing progress, and joining active communities.
- [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.
- [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options.
- [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.
- [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management.
- [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 Autogen?

Yes, you can. Autogen 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.

---
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