Hugging Face with AutoGen

How to integrate Hugging Face MCP with Autogen

Introduction TL;DR What is AutoGen What is the Hugging Face MCP server Supported Tools & Triggers Creating MCP Server - Stand-alone vs Tool Router Step-by-step Guide Complete Code Conclusion How to build Hugging Face MCP Agent with another framework Explore Other Toolkits FAQ

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Introduction

This guide walks you through connecting Hugging Face to AutoGen using the Composio tool router. By the end, you'll have a working Hugging Face agent that can run text summarization on uploaded document, list all my hugging face model repositories, deploy a new model to spaces through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Hugging Face account through Composio's Hugging Face MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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 Hugging Face
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call Hugging Face tools
Run a live chat loop where you ask the agent to perform Hugging Face 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 Hugging Face MCP server, and what's possible with it?

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

Supported Tools & Triggers

Tools

Change Discussion StatusTool to change the status of a Hugging Face repository discussion.

Check Dataset ValidityTool to check whether a specific dataset is valid on Hugging Face Hub.

Check Models Upload MethodTool to check if files should be uploaded through the Large File mechanism or directly.

Check Spaces Upload MethodTool to check if files should be uploaded through the Large File mechanism or directly to Hugging Face Spaces.

Claim Paper AuthorshipTool to claim authorship of a paper on Hugging Face.

Request Repository AccessTool to request access to a gated repository on Hugging Face Hub.

Create CollectionTool to create a new collection on Hugging Face.

Create Datasets BranchTool to create a new branch in a Hugging Face dataset repository.

Create Datasets CommitTool to create a commit in a Hugging Face dataset repository.

Check Dataset File Upload MethodTool to check if files should be uploaded via Large File Storage (LFS) or directly to a Hugging Face dataset repository.

Create Datasets TagTool to create a tag on a Hugging Face dataset repository.

Create DiscussionTool to create a new discussion on a Hugging Face repository (model, dataset, or Space).

Create Discussion CommentTool to create a new comment on a Hugging Face repository discussion.

Pin discussionTool to pin or unpin a discussion on a Hugging Face repository (model, dataset, or Space).

Create models branchTool to create a new branch in a Hugging Face model repository.

Create Models CommitTool to create a commit to a Hugging Face model repository.

Create Models TagTool to create a tag on a Hugging Face model repository.

Create Paper CommentTool to create a new comment on a Hugging Face paper.

Create Papers Comment ReplyTool to create a reply to a comment on a Hugging Face paper.

Create Papers IndexTool to index a paper from arXiv by its ID on Hugging Face.

Create RepositoryTool to create a new repository (model, dataset, or Space) on Hugging Face Hub.

Create spaces branchTool to create a new branch in a Hugging Face space repository.

Create Spaces CommitTool to create a commit in a Hugging Face Space repository.

Create or update Space secretTool to create or update a secret in a Hugging Face Space.

Create Spaces TagTool to create a tag on a Hugging Face space repository.

Create or update Space variableTool to create or update a variable in a Hugging Face Space.

Create SQL Console EmbedTool to create a SQL Console embed for querying datasets on Hugging Face.

Create WebhookTool to create a webhook on Hugging Face that triggers on repository or discussion events.

Delete dataset branchTool to delete a branch from a Hugging Face dataset repository.

Delete Dataset TagTool to delete a tag from a Hugging Face dataset.

Delete discussionTool to delete a discussion from a Hugging Face repository.

Delete network CIDR listTool to delete a network CIDR list entry from Hugging Face Inference Endpoints.

Delete notificationsTool to delete notifications from Hugging Face.

Delete Settings WebhookTool to delete a webhook from Hugging Face settings.

Delete space branchTool to delete a branch from a Hugging Face space repository.

Delete space secretTool to delete a secret from a Hugging Face space.

Delete Spaces TagTool to delete a tag from a Hugging Face space.

Delete space variableTool to delete a variable from a Hugging Face space.

Filter dataset rowsTool to filter rows in a Hugging Face dataset split based on SQL-like query conditions.

Generate Chat CompletionTool to generate a response given a list of messages in a conversational context.

Generate Text EmbeddingsTool to convert text into vector embeddings for feature extraction, semantic search, and similarity tasks.

Get Daily PapersTool to retrieve daily papers from Hugging Face.

Get Dataset Croissant MetadataTool to get Croissant metadata about a Hugging Face dataset.

Get Dataset First RowsTool to get the first 100 rows of a dataset split along with column data types and features.

Get Dataset InfoTool to get general information about a dataset including description, citation, homepage, license, and features (column schemas).

Get Dataset Repository InfoTool to retrieve detailed information about a Hugging Face dataset repository.

Get Dataset RowsTool to retrieve a slice of rows from a Hugging Face dataset split at any given location (offset).

Get Datasets CompareTool to get a comparison (diff) between two revisions of a Hugging Face dataset.

Get Dataset SizeTool to get the size of a Hugging Face dataset including number of rows and size in bytes.

Get Datasets JWTTool to generate a JWT token for accessing a Hugging Face dataset repository.

Get Datasets LeaderboardTool to retrieve evaluation results ranked by score for a dataset's leaderboard.

Get Dataset Notebook URLTool to get a Jupyter notebook URL from a Hugging Face dataset repository.

Get Datasets ResolveTool to resolve and download a file from a Hugging Face dataset repository.

Get Dataset Security ScanTool to retrieve the security scan status of a Hugging Face dataset repository.

Get Dataset Tags by TypeTool to retrieve all possible tags used for datasets on Hugging Face, grouped by tag type.

Get Dataset StatisticsTool to get comprehensive statistics about a dataset split including column statistics and data distribution information.

Get dataset repository sizeTool to get the total size of a Hugging Face dataset repository at a specific revision and path.

Get Datasets XET Read TokenTool to get a read short-lived access token for XET from Hugging Face datasets.

Get Discussion DetailsTool to get detailed information about a specific discussion or pull request on Hugging Face Hub.

Get Discussion DetailsTool to retrieve discussion details from a Hugging Face repository.

Get Available Job HardwareTool to retrieve available hardware configurations for Hugging Face Jobs with their specifications and pricing.

Get Model InformationTool to retrieve detailed information about a Hugging Face model repository.

Get Models CompareTool to compare two revisions of a Hugging Face model repository.

Get Models JWTTool to generate a JWT token for accessing a Hugging Face model repository.

Get Models NotebookTool to retrieve a Jupyter notebook URL from a Hugging Face model repository.

Get Model Security Scan StatusTool to retrieve the security scan status of a Hugging Face model repository.

Get model repository sizeTool to get the total size of a Hugging Face model repository at a specific revision and path.

Get Model XET Read TokenTool to retrieve a short-lived XET read access token for a Hugging Face model repository.

Get Model Tags By TypeTool to retrieve all possible tags used for Hugging Face models, grouped by tag type.

Get Organization AvatarTool to retrieve the avatar URL for a Hugging Face organization.

Get Organization MembersTool to retrieve a list of members for a Hugging Face organization.

Get Organization Social HandlesTool to retrieve an organization's social media handles from Hugging Face.

Get ResolveTool to resolve a file in a Hugging Face repository.

Get Resolve Cache DatasetsTool to resolve a file from cache in a Hugging Face dataset repository.

Get Resolve Cache ModelsTool to resolve and retrieve files from the Hugging Face model cache.

Get Resolve Cache SpacesTool to resolve and retrieve a file from Hugging Face Spaces cache.

Get Billing UsageTool to retrieve user billing usage for a given period from Hugging Face.

Get Jobs UsageTool to retrieve Jobs usage and billing information for the current subscription period from Hugging Face.

Get Live Billing UsageTool to retrieve live billing usage stream from Hugging Face.

Get Billing Usage V2Tool to retrieve user billing usage for a custom date range from Hugging Face.

Get MCP SettingsTool to retrieve MCP (Model Context Protocol) tools configuration for the authenticated user.

Get Settings WebhookTool to retrieve a specific webhook configuration from Hugging Face settings.

Get Space InfoTool to retrieve detailed information about a Hugging Face Space repository.

Get Spaces CompareTool to compare two revisions of a Hugging Face Space repository.

Get Spaces EventsTool to stream status updates for a Hugging Face Space using SSE protocol.

Get Spaces JWTTool to generate a JWT token for accessing a Hugging Face space repository.

Get Space MetricsTool to get live metrics for a specific Space in a streaming fashion, with SSE protocol, such as current Zero-GPU usage.

Get Spaces NotebookTool to retrieve a Jupyter notebook URL from a Hugging Face space repository.

Get Spaces ResolveTool to resolve and retrieve a file from a Hugging Face Space repository.

Get Space Security Scan StatusTool to retrieve the security scan status of a Hugging Face space repository.

Get space repository sizeTool to get the total size of a Hugging Face space repository at a specific revision and path.

Get Space XET Read TokenTool to retrieve a short-lived XET read access token for a Hugging Face Space repository.

Get Spaces XET Write TokenTool to retrieve a short-lived XET write access token for a Hugging Face space repository.

Get Trending RepositoriesTool to retrieve trending repositories from Hugging Face.

Get User AvatarTool to retrieve the avatar URL for a Hugging Face user.

Get User OverviewTool to retrieve a comprehensive overview of a Hugging Face user's profile.

Get User Social HandlesTool to retrieve a user's social media handles from Hugging Face.

Get Authenticated User InfoTool to get information about the authenticated Hugging Face user including username, email, organizations, and token details.

Handle Dataset User Access RequestTool to handle a user's access request to a gated Hugging Face dataset.

List CollectionsTool to list collections on the Hugging Face Hub.

List Dataset Parquet FilesTool to get the list of Parquet files for a dataset.

List Dataset Paths InfoTool to list detailed information about specific paths in a Hugging Face dataset repository.

List DatasetsTool to list datasets on the Hugging Face Hub.

List Dataset CommitsTool to list commits from a Hugging Face dataset repository.

List Dataset SplitsTool to get the list of subsets and splits of a dataset.

List Dataset ReferencesTool to list all references (branches, tags, converts, pull requests) in a Hugging Face dataset repository.

List datasets treeTool to list the content of a Hugging Face dataset repository tree with pagination support.

List Dataset Access RequestsTool to list access requests for a gated Hugging Face dataset repository.

List DiscussionsTool to list discussions for a Hugging Face repository.

List Repository DiscussionsTool to list discussions and pull requests for a Hugging Face repository.

List Available DocumentationTool to retrieve the list of available documentation from Hugging Face.

List Inference EndpointsTool to list Hugging Face Inference Endpoints for a specific user or organization.

List models on Hugging Face HubTool to list models on the Hugging Face Hub with filtering options.

List Model CommitsTool to list commits from a Hugging Face model repository.

List Model Paths InfoTool to list detailed information about specific paths in a Hugging Face model repository.

List Model ReferencesTool to list all references (branches, tags, converts, and optionally pull requests) in a Hugging Face model repository.

List model repository treeTool to list the contents of a Hugging Face model repository tree at a specific revision and path, with pagination support.

List NotificationsTool to list notifications for the authenticated Hugging Face user.

List Repository CommitsTool to list commits for a Hugging Face repository.

List repository filesTool to get the file tree of a Hugging Face repository with pagination support.

List WebhooksTool to list all webhooks configured in Hugging Face settings.

List Spaces on Hugging Face HubTool to list Spaces on the Hugging Face Hub with filtering options.

List Space CommitsTool to list commits from a Hugging Face Space repository.

List Available Space HardwareTool to retrieve available hardware configurations for Hugging Face Spaces with their specifications and pricing.

List Space LFS FilesTool to list LFS (Large File Storage) files from a Hugging Face Space repository.

List Space Paths InfoTool to list detailed information about specific paths in a Hugging Face space repository.

List Space ReferencesTool to list all references (branches, tags, converts, pull requests) in a Hugging Face space repository.

List spaces treeTool to list the content of a Hugging Face space repository tree with pagination support.

List Cloud Provider VendorsTool to list available cloud provider vendors for Hugging Face Inference Endpoints.

Search datasetTool to search text in a dataset split on Hugging Face.

Search DocumentationTool to search Hugging Face documentation across all products and libraries.

Search PapersTool to perform hybrid semantic/full-text search on papers in Hugging Face.

Squash Dataset CommitsTool to squash all commits in a dataset ref into a single commit with the given message.

Squash Spaces CommitsTool to squash all commits in a space ref into a single commit with the given message.

Update Dataset SettingsTool to update settings for a Hugging Face dataset repository.

Update Discussion TitleTool to change the title of an existing discussion on a Hugging Face repository (model, dataset, or Space).

Update Model Repository SettingsTool to update settings for a Hugging Face model repository.

Update Notification SettingsTool to update notification settings for the authenticated Hugging Face user.

Update Watch SettingsTool to update watch settings for your Hugging Face account.

Update WebhookTool to update an existing webhook in Hugging Face settings.

Update Spaces Repository SettingsTool to update settings for a Hugging Face Spaces repository.

Update SQL Console EmbedTool to update an existing SQL console embed for a Hugging Face dataset.

Enable or Disable WebhookTool to enable or disable a webhook on Hugging Face.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A Hugging Face account you can connect to Composio
Some basic familiarity with Autogen and Python async

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard 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.
Navigate to your API settings and generate a new API key.
Store this key securely as you'll need it for authentication.

Install dependencies

bash

pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

composio connects your agent to Hugging Face via MCP
autogen-agentchat provides the AssistantAgent class
autogen-ext-openai provides the OpenAI model client
autogen-ext-tools provides MCP workbench support

Set up environment variables

bash

COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

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 Hugging Face connections to use

Import dependencies and create Tool Router session

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 Hugging Face session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["hugging_face"]
    )
    url = session.mcp.url

What's happening:

load_dotenv() reads your .env file
Composio(api_key=...) initializes the SDK
create(...) creates a Tool Router session that exposes Hugging Face tools
session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

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")}
)

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

Create the model client and agent

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 Hugging Face assistant agent with MCP tools
    agent = AssistantAgent(
        name="hugging_face_assistant",
        description="An AI assistant that helps with Hugging Face operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

OpenAIChatCompletionClient wraps the OpenAI model for Autogen
McpWorkbench connects the agent to the MCP tools
AssistantAgent is configured with the Hugging Face tools from the workbench

Run the interactive chat loop

python

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Hugging Face 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")

What's happening:

The script prompts you in a loop with You:
Autogen passes your input to the model, which decides which Hugging Face 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

Complete Code

Here's the complete code to get you started with Hugging Face and AutoGen:

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 Hugging Face session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["hugging_face"]
    )
    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 Hugging Face assistant agent with MCP tools
        agent = AssistantAgent(
            name="hugging_face_assistant",
            description="An AI assistant that helps with Hugging Face 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 Hugging Face 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 Hugging Face 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 Hugging Face, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Hugging Face MCP Agent with another framework

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Use Hugging Face MCP with OpenAI Agents SDK

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Use Hugging Face MCP with Vercel AI SDK

Mastra AI

Use Hugging Face MCP with Mastra AI

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Use Hugging Face MCP with LlamaIndex

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Use Hugging Face MCP with CrewAI

Claude Code

Use Hugging Face MCP with Claude Code

Codex

Use Hugging Face MCP with Codex

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FAQ

What are the differences in Tool Router MCP and Hugging Face MCP?

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

Can I manage the permissions and scopes for Hugging Face while using Tool Router?

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

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Harsha GaddipatiCo-founder, Slashy

Karan skipped his own birthday party to fix our critical issue. It was 10 pm and he diverted his Waymo to help us instead. This really sets the bar, shows you the commitment you need to have when users rely on your software.

Abhi AryaCo-founder, Opennote

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it has immensely helped in keeping new users on the platform.

Nirman DaveCEO, Zams

We chose Composio over Pipedream because it delivered depth where it mattered. It supported niche tools and tricky edge cases that other platforms simply ignored. Giving us confidence to scale without compromising.

Ryan YuFounder, Extra Thursday

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down in the complexities of managing agent auth would have been a death sentence for Extra Thursday.

Tomisin JenrolaFounder & CEO, SwarmZero

Before partnering with Composio, adding tool integrations was a slow, resource-intensive process. Each integration could take weeks or months of engineering time, and maintaining them meant constantly keeping up with API changes.

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

How to integrate Hugging Face MCP with Autogen

Table of Contents

Connect Hugging Face without Auth hassles

Introduction

TL;DR

What is AutoGen?

What is the Hugging Face MCP server, and what's possible with it?

Supported Tools & Triggers

What is the Composio tool router, and how does it fit here?

What is Tool Router?

How the Tool Router works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

How to build Hugging Face MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Claude Code

Codex

Explore Other Toolkits

Ai ml api

Aivoov

All images ai

FAQ

What are the differences in Tool Router MCP and Hugging Face MCP?

Can I use Tool Router MCP with Autogen?

Can I manage the permissions and scopes for Hugging Face while using Tool Router?

How safe is my data with Composio Tool Router?

Used by agents from

Never worry about agent reliability