agentpulse/old-code

AgentPulse — Data and Code Release

NeurIPS 2026 Datasets & Benchmarks Track companion artifact.

This bundle contains all data, code, and reproduction scripts for the AgentPulse evaluation framework described in the paper AgentPulse: A Continuous Multi-Signal Framework for Evaluating AI Agents in Deployment.

License: CC BY 4.0 (see LICENSE).

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Contents

AgentPulse-Data-v6/
├── README.md                  ← this file
├── LICENSE                    ← CC BY 4.0
├── croissant.json             ← Croissant metadata (JSON-LD)
├── data/
│   ├── csv/                   ← 18 CSV exports of every relevant DB table
│   └── sqlite/                ← (optional) full SQLite database, if present
├── code/
│   ├── collectors/            ← 18 signal-collector implementations
│   ├── scoring/               ← NLP pipeline + factor composite + data quality
│   ├── pipeline/              ← scheduler / orchestration
│   ├── config.py              ← global pipeline configuration
│   ├── main.py                ← entrypoint (`python main.py --serve`, etc.)
│   └── requirements.txt       ← Python dependencies
├── paper/
│   ├── neurips_agentpulse.tex ← LaTeX source
│   ├── neurips_agentpulse.pdf ← compiled paper
│   ├── neurips_2026.sty       ← NeurIPS 2026 style file
│   ├── checklist_.tex         ← NeurIPS Paper Checklist
│   ├── regen_figures.py       ← regenerates all 5 paper figures from the data
│   └── fig*.{png,pdf}         ← compiled figures
└── docs/
    └── schema.md              ← per-table column documentation

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Data tables (data/csv/)

File	Rows	Description
agent_scores.csv	13,504	Composite + per-factor scores per agent per category, time-stamped. The headline scoring output.
agent_signals_raw.csv	3,283	Raw per-signal observations per agent (pre-aggregation).
agent_benchmark_signals.csv	1,563	Published benchmark scores (SWE-bench, GAIA, WebArena, HumanEval+, τ-bench).
agent_github_history.csv	62	GitHub stars / contributors / commits time-series.
agent_pypi_history.csv	1,440	PyPI / npm download history.
sentiment_scores.csv	60,639	NLP-scored texts (VADER + TextBlob + FinBERT + DistilBERT-SST2 ensemble).
data_quality.csv	38,965	Composite quality score per text (uniqueness × bot × credibility × specificity).
models.csv	148	Agent + model registry with vendor / category metadata.
bluesky_signals.csv	35,326	Per-platform raw text + engagement.
reddit_signals.csv	2,413	""
hn_signals.csv	21,205	""
stackoverflow_signals.csv	571	""
github_signals.csv	23,288	""
github_discussions_signals.csv	2,090	""
devto_signals.csv	596	""
mastodon_signals.csv	1,160	""
v2ex_signals.csv	1,794	""
devtools_signals.csv	89	VS Code / IDE marketplace install counts.

All CSVs are UTF-8 encoded with a header row. Timestamps are ISO-8601 in UTC. See docs/schema.md for full column documentation.

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Reproducing paper results

cd code/
pip install -r requirements.txt

# Reproduce all figures (writes into ../paper/)
python ../paper/regen_figures.py

# Re-derive composite scores from raw signals
python -m scoring.agent_scoring_v2 --rebuild

# Re-run the full pipeline end-to-end (continuous mode)
python main.py

The cross-factor predictive validity result (Table 3, ρₛ=0.52, p<0.01, n=35) is computed in code/scoring/agent_scoring_v2.py and can be re-derived directly from data/csv/agent_scores.csv and external adoption proxies.

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Provenance

All signals are collected from public APIs with documented rate-limits under each API's terms of service. Specifically:

• GitHub: REST + GraphQL APIs (60 req/hr unauth, 5,000 req/hr authed)
• PyPI: BigQuery public dataset
• VS Code Marketplace: Public extension API
• Bluesky / Mastodon: AT-Protocol / ActivityPub public firehose
• Reddit / Hacker News / Stack Overflow / Lemmy / Lobsters / Dev.to / V2EX: Documented public APIs
• Benchmark scores: Scraped from the official leaderboard pages cited in the paper (SWE-bench, GAIA, WebArena, HumanEval+, τ-bench)

No private data, no scraped paywalled content, no PII beyond public authorship metadata is included. The release contains no individual user records — only aggregated per-text metadata required for sentiment scoring (post id, author handle if public, engagement counts).

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Citation

@inproceedings{agentpulse2026,
  title = {AgentPulse: A Continuous Multi-Signal Framework for Evaluating
           AI Agents in Deployment},
  author = {Anonymous},
  booktitle = {NeurIPS Datasets and Benchmarks Track},
  year = {2026}
}

Contact

Per the NeurIPS double-blind review policy, author contact is withheld during review. Issues with the artifact may be reported via the submission system.