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agent-usage / README.md
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davanstrien HF Staff
Initial release: agent-usage shares through 2026-06
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metadata 
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, spinning up Spaces — tens of millions of requests so far (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 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.

Current leaderboard

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 — a Pull Request adding your tool to 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:

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 

from datasets import load_dataset

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

-- 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;

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 (bundled in this repo) on a scheduled HF Job — only relative shares are published.