Can my AI run inference on Hugging Face models?

Yes. Use `run_inference`, `run_text_generation`, `run_text_classification`, or `run_summarization` to send input to any hosted model and get results instantly.

How do I find the best model for a task?

Use `list_models_by_task` with a pipeline tag like 'text-generation' or 'image-classification'. Results are sorted by downloads so the most popular appear first.

Can I browse datasets and Spaces?

Yes. `list_datasets` and `list_spaces` let you search by keyword, and `get_dataset` / `get_space` return full metadata.

INSTANT PLUG AND PLAY

Hugging Face MCP Server for OpenAI Agents SDKGive OpenAI Agents SDK instant access to 15 tools to Check Hf Status, Get Account, Get Dataset, and more

Name: Hugging Face
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 15 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Hugging Face through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Use with Your AI Agent

Ask AI about this App Connector for OpenAI Agents SDK

Open in ChatGPT Open in Claude Open in Perplexity

The Hugging Face app connector for OpenAI Agents SDK is a standout in the Loved By Devs category — giving your AI agent 15 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Hugging Face Assistant",
            instructions=(
                "You help users interact with Hugging Face. "
                "You have access to 15 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Hugging Face"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Hugging Face MCP Server

Connect your Hugging Face account to any AI agent and interact with the Hub through natural conversation.

The OpenAI Agents SDK auto-discovers all 15 tools from Hugging Face through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Hugging Face, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Model Discovery — Search models by keyword, author, or pipeline task
Dataset Exploration — Browse and inspect dataset schemas and metadata
Spaces — Search and view interactive ML demo applications
Collections — List curated groups of models, datasets, and Spaces
Inference — Run any hosted model: text generation, classification, summarization
Account — View your profile, orgs, and token scopes
Health Check — Verify API connectivity

The Hugging Face MCP Server exposes 15 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 15 Hugging Face tools available for OpenAI Agents SDK

When OpenAI Agents SDK connects to Hugging Face through Vinkius, your AI agent gets direct access to every tool listed below — spanning machine-learning, model-discovery, datasets, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

check_hf_status

Verify API connectivity

get_account

Get account info

get_dataset

Get dataset details

get_model

Get model details

get_space

Get Space details

list_collections

List curated collections

list_datasets

Search datasets

list_models

Search models on Hugging Face Hub

list_models_by_author

List models by author

list_models_by_task

) sorted by downloads. List models by task

list_spaces

Search Spaces

run_inference

Run model inference

run_summarization

Summarize text

run_text_classification

Classify text

run_text_generation

Generate text with a model

Connect Hugging Face to OpenAI Agents SDK via MCP

Follow these steps to wire Hugging Face into OpenAI Agents SDK. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 15 tools from Hugging Face

Why Use OpenAI Agents SDK with the Hugging Face MCP Server

OpenAI Agents SDK provides unique advantages when paired with Hugging Face through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Hugging Face + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Hugging Face MCP Server delivers measurable value.

Automated workflows: build agents that query Hugging Face, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Hugging Face, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Hugging Face tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Hugging Face to resolve tickets, look up records, and update statuses without human intervention

Example Prompts for Hugging Face in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Hugging Face immediately.

"Find the top text generation models."

"Generate text with mistralai/Mistral-7B: 'Explain quantum computing in simple terms'."

"Search datasets about sentiment analysis."

Troubleshooting Hugging Face MCP Server with OpenAI Agents SDK

Common issues when connecting Hugging Face to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Hugging Face + OpenAI Agents SDK FAQ

Common questions about integrating Hugging Face MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.

Can I use multiple MCP servers in one agent?

Yes. Pass a list of MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.

Does the SDK support streaming responses?

Yes. The SDK supports SSE and Streamable HTTP transports, both of which work natively with Vinkius.