What can an AI agent do with Hugging Face?

A Hugging Face AI agent can search and browse the model hub, run inference against hosted models, retrieve dataset and model metadata, and list Spaces. It can automate model evaluation, surface recommendations, and integrate open-source AI into scheduled workflows. Write actions like running inference require your approval.

Which models can my agent run inference on?

Your agent can call any model available through the Hugging Face Inference API, including text generation, summarization, translation, classification, and embedding models. Model availability depends on your Hugging Face account tier.

Is inference output stored anywhere?

Inference results are processed during task execution and saved only as part of your run output. Raw inputs and outputs are not stored separately beyond your run history.

How is a Hugging Face AI agent different from running the Inference API directly?

Calling the API directly requires writing code for every request. A Hugging Face AI agent reasons about which model to use, chains multiple calls together, compares outputs, and delivers a summary - all on a schedule or triggered by another workflow, without any manual scripting.

Developer tools

Hugging Face, run by an agent.

An AI agent that runs inference, manages models, and automates ML workflows on Hugging Face.

Connect Hugging Face Book a demo

What needs your approval

Hugging Face never changes anything without you.

Anything that creates, edits or deletes in Hugging Face waits for your approval. Approve a recurring task once and it runs unattended after that.

Connecting Hugging Face

About 5 minutes

Auth: API key
Setup: About 5 minutes

Where to get access

Create a User Access Token in the Hugging Face settings. Use a token with read access (or write if you need to push models).

huggingface.co

Example tasks

Real work people hand to an agent that uses Hugging Face.

run weekly inference benchmarks across candidate models and post results to Slack
search the hub for the best open-source model for a new task and summarize the top options
monitor a model repository for new versions and notify the team when updates are released
pull dataset metadata and generate a data card summary for internal documentation