---
pretty_name: Agent Usage on the Hugging Face Hub
tags:
  - analytics
  - agents
configs:
  - config_name: monthly
    default: true
    data_files: data/monthly/*.parquet
  - config_name: daily
    data_files: data/daily/*.parquet
---

# Agent Usage on the Hugging Face Hub

**Coding agents are real users of the Hugging Face Hub.** Claude Code, Codex, Cursor, and a growing list of harnesses are searching for models, building and pushing datasets, training models on [Jobs](https://huggingface.co/docs/hub/jobs), spinning up Spaces — tens of millions of requests so far ([hf CLI for agents](https://huggingface.co/blog/hf-cli-for-agents)). Now there's public data on which ones.

Requests made through the `huggingface_hub` library (including the `hf` CLI) carry an [`agent/<name>` User-Agent token](https://huggingface.co/docs/hub/agents-overview) identifying the harness. This dataset publishes **each harness's share of that agent-attributed traffic**, month by month and day by day, updated by a scheduled [HF Job](https://huggingface.co/docs/hub/jobs).

![Current leaderboard](assets/leaderboard.png)

_Named harnesses ranked by share of requests, data through **2026-06** · updated 2026-07-02. The **Dataset Viewer** at the top of this page lets you browse, sort, and filter both tables — no code needed._

## What you can see

- **Who's calling the Hub** — the monthly leaderboard of named harnesses, and how it shifts as new tools launch and register.
- **Usage styles** — compare request share with user share. An agent with 30% of requests but 8% of users is a small crowd running heavy automated pipelines; the reverse means many users, each doing a little.
- **Day-by-day detail** — the `daily` config picks up what monthly numbers smooth over: launch spikes, growth curves, weekday-vs-weekend patterns.

## Get your harness on the board

If you build a harness, register it to make sure your agent isn't missed — unregistered tools are counted only as `unknown`.

Attribution is automatic: `huggingface_hub` detects registered harnesses from environment variables and reports them in the User-Agent. To register, follow [Register your agent harness](https://huggingface.co/docs/hub/agents-overview#register-your-agent-harness) — a Pull Request adding your tool to [`agent-harnesses.ts`](https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/agent-harnesses.ts). No release is needed on either side: installed clients refresh the registry within a day, and your harness appears from the next monthly snapshot.

Only traffic through the Python `huggingface_hub` library (including the `hf` CLI) is attributed; direct HTTP calls to the Hub API are not counted. To confirm detection works, run inside your harness:

```bash
python -c "from huggingface_hub.utils import build_hf_headers; print(build_hf_headers()['user-agent'])"
# should contain agent/<your-id>
```

## Columns

| column          | description                                                                                                  |
| --------------- | ------------------------------------------------------------------------------------------------------------ |
| `month` / `day` | period the share is computed over                                                                            |
| `agent`         | harness name from the `agent/<name>` token; `unknown` = token present but no registered name                 |
| `pct_requests`  | harness's share of agent-attributed `huggingface_hub` requests in the period (0–100; sums to 100 per period) |
| `pct_users`     | same, for distinct authenticated users — someone using two harnesses counts once for each                    |

## Loading programmatically

```python
from datasets import load_dataset

monthly = load_dataset("huggingface/agent-usage", "monthly", split="train")
```

```sql
-- DuckDB: full monthly history in one query
SELECT month, agent, pct_requests
FROM 'hf://datasets/huggingface/agent-usage/data/monthly/*.parquet'
WHERE agent != 'unknown'
ORDER BY month, pct_requests DESC;
```

```python
import polars as pl

daily = pl.scan_parquet("hf://datasets/huggingface/agent-usage/data/daily/*.parquet")
```

New months append as new parquet files, so these queries always return the full history unchanged.

## Reading the data

- **This measures Hub usage, not overall agent popularity.** A widely used tool that rarely touches the Hugging Face Hub will rank low here.
- **Shares are zero-sum.** A falling share doesn't mean falling usage — total agent traffic is growing, so a harness can double its requests while its share shrinks.
- **Start month-over-month comparisons from May 2026.** The `agent/` token rolled out April 3 and harnesses added detection at different times, so April reflects the rollout, not relative usage.
- **Smooth daily shares** with a 7-day rolling mean — weekends and small denominators make single days noisy.
- **Attribution is self-declared** (a User-Agent token set by the client library) and covers Python-library traffic only.

_Built by [`build_local.py`](./build_local.py) (bundled in this repo) on a scheduled HF Job — only relative shares are published._