Changelog

You can now upload traces from your agents (Claude Code, Codex, Pi), Hermes Agent, and Factory Droid directly to Hugging Face Datasets. The Hub auto-detects trace formats and tags your dataset as Traces, with a dedicated viewer for browsing sessions, turns, tool calls, and model responses.

No preprocessing needed, just upload the JSONL files from your local session directories as-is:

For Hermes Agent, you can export your sessions with the following command:

hermes sessions export session.jsonl --session-id <session_id>

Useful for sharing debugging workflows, benchmarking agent behavior across models, or building training data from real coding sessions.

You can now mount HF Buckets as persistent storage volumes directly in your Spaces. In Space settings, the new "Storage Buckets" section lets you create or select a bucket, set the mount path and access mode. You can also attach a bucket when creating a new Space.

Useful for caching model weights, storing user uploads, or sharing files between Spaces under the same organization.

You can now attach any Storage Bucket, model or dataset from the Hub as a local filesystem. It allows you to attach remote storage that is 100x bigger than your local machine's disk. This is also perfect for Agentic storage! Read-write for Storage Buckets, read-only for models and datasets.

Get started by installing hf-mount from the GitHub repo: https://github.com/huggingface/hf-mount

You can now set your Spaces to protected, making them private on Hugging Face while still keeping their URL publicly accessible.

This is useful for deploying production-ready demos or internal tools without exposing model weights, prompts, or proprietary logic.

Combined with custom domains you can also host your website on Hugging Face.

When AI agents such as Cursor or Claude Code fetch a Hugging Face Papers page, Markdown versions are served automatically, saving tokens and improving efficiency — e.g. huggingface.co/papers/2601.15621.md.

A new hugging-face-paper-pages skill for AI agents lets agents search papers by title, author, or semantic similarity, read their content, and discover linked models, datasets, and Spaces on the Hub.