README.md

Agent traces on the Hub — analysis (Oct 2026)

What models, harnesses, tasks and session sizes show up in the Hub datasets tagged agent-traces (505 datasets).

data/

file	one row per	notes
datasets.jsonl	dataset (505)	harness, files, bytes, dates, downloads, license, models named in the card, copy_of
sessions.jsonl	session (12,323 after content dedup)	harness, models, providers, user msgs, tool calls (+ per-tool counts), tool errors, tokens, cost (pi), compactions, aborts, wall/active seconds, category
prompts.jsonl	unique user message (32,670)	dataset, format, turn index, text (≤ 3k chars), number of copies
prompt_labels.jsonl	labelled message (3,185; ≤ 40 per dataset)	intent, domain, follow-up kind, human/automated, scope — labelled by GLM-5.3-Flash
people_datasets.json	—	datasets counted as people working with an agent
icml_repro_sessions.jsonl, icml_repro_harness.json	ICML 2026 reproduction-challenge logbooks	harness recorded per logbook (Trackio format)

category in sessions.jsonl: real (people: multi-turn, not templated), synthetic-dump (one generated prompt per session), benchmark/harness (eval sweeps), paper-repro-bot (ICML challenge runs).

Coverage: pi datasets read in full; Claude Code / Codex ≤ 40 files per dataset. Copies and re-uploads collapsed by session content. Active time ignores gaps > 30 min.

charts/

Sticker-style charts + 9_infographic.png. thread.txt is the accompanying Twitter thread.

scripts/

inventory.py (dataset list) → extract_prompts.py (user messages) → sessions.py (per-session stats) → agg_sessions.py (dedup, categories) → label_asks.py (LLM labels) → make_charts.py.