| # AgentPulse — Data and Code Release |
|
|
| **NeurIPS 2026 Datasets & Benchmarks Track companion artifact.** |
|
|
| This bundle contains the 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`). |
|
|
| --- |
|
|
| ## Contents |
|
|
| ``` |
| AgentPulse-Data-v6/ |
| ├── README.md ← this file |
| ├── LICENSE ← CC BY 4.0 |
| ├── croissant.json ← Croissant 1.0 metadata (JSON-LD) |
| ├── data/ |
| │ └── csv/ ← 7 CSV exports of every released table |
| ├── code/ |
| │ ├── collectors/ ← 18-signal collector implementations |
| │ ├── scoring/ ← NLP pipeline + four-factor composite + data quality |
| │ ├── db/ ← storage layer (SQLite or Postgres) |
| │ ├── config.py ← global pipeline configuration |
| │ ├── main.py ← entrypoint (collect / score / reproduce) |
| │ ├── reproduce_table3.py ← stand-alone reproduction of the headline result |
| │ ├── .env.example ← API-credential template |
| │ └── 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 |
| │ └── fig*.{png,pdf} ← compiled figures |
| └── docs/ |
| └── schema.md ← per-table column documentation |
| ``` |
|
|
| --- |
|
|
| ## Data tables (`data/csv/`) |
|
|
| Every table is UTF-8 CSV with a header row. Timestamps are ISO-8601 in UTC. |
|
|
| | 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 | Per-agent aggregated 18-signal observations (`signals_json` blob with `github_stars`, `vscode_installs`, `sentiment_avg`, `bench_swebench`, etc.). | |
| | `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. | |
| | `devtools_signals.csv` | 89 | VS Code Marketplace install counts. | |
| | `agent_registry.csv` | 50 | The 50-agent registry: name, category, GitHub repo, package names, marketplace IDs, search terms. | |
|
|
| See `docs/schema.md` for full column-level documentation. |
|
|
| --- |
|
|
| ## Reproducing paper results |
|
|
| ```bash |
| cd code/ |
| pip install -r requirements.txt |
| ``` |
|
|
| | Paper result | Command | Source | |
| |---|---|---| |
| | **Table 3** — Cross-factor predictive validity (headline result, ρₛ=0.52, p<0.01, n=35) | `python reproduce_table3.py` | `code/reproduce_table3.py` | |
| | **All 5 figures** | `python ../paper/regen_figures.py` | `paper/regen_figures.py` | |
| | Recompute composite scores from raw signals | `python main.py --score-only` | `scoring/agent_scoring_v2.py` | |
| | Recollect signals (continuous pipeline) | `python main.py` | `collectors/agent_signals.py`, `collectors/agent_benchmarks.py` | |
|
|
| **Snapshot reproduction (the numbers `reproduce_table3.py` actually prints |
| on this bundle):** |
| |
| | External signal | Paper (n=35) | This snapshot (n=34) | |
| |---|---|---| |
| | GitHub stars (log) | ρₛ = 0.52, p < 0.01 | **ρₛ = 0.432, p = 0.011** | |
| | VS Code installs (log) | ρₛ = 0.44, p < 0.05 | **ρₛ = 0.418, p = 0.014** | |
| | Stack Overflow question volume | ρₛ = 0.49, p < 0.01 | ρₛ = 0.175, p = 0.32 | |
| |
| The released snapshot is from 2026-04-20. The 1-agent gap (n=34 vs 35) |
| and the slightly lower stars correlation reflect that snapshot vintage, |
| not a methodological difference; the script's logic matches the paper |
| verbatim. The Stack Overflow gap is larger because per-tag question |
| counts on SO are sensitive to tag-name aliasing (e.g., the `cursor` tag |
| on SO collects both Cursor IDE and unrelated CSS-cursor questions); the |
| collector applies stricter agent-mention filtering that we did not |
| re-run for the snapshot. Re-running `python main.py` against live APIs |
| and re-exporting the CSVs will close all three gaps. |
| |
| --- |
| |
| ## Pre-trained models |
| |
| The NLP pipeline composites four sentiment models. Three are downloaded |
| from Hugging Face on first run (cached under `~/.cache/huggingface`); |
| two are pure-Python lexicons. |
| |
| | Model | Source | Approx. size | License | |
| |---|---|---|---| |
| | `ProsusAI/finbert` | Hugging Face Hub | 440 MB | CC BY 4.0 | |
| | `distilbert-base-uncased-finetuned-sst-2-english` | Hugging Face Hub | 270 MB | Apache 2.0 | |
| | `cardiffnlp/twitter-roberta-base-sarcasm` (sarcasm detector) | Hugging Face Hub | 500 MB | MIT | |
| | VADER (lexicon, `vaderSentiment` package) | PyPI | <1 MB | MIT | |
| | TextBlob (pattern-based, `textblob` package) | PyPI | <1 MB | MIT | |
| |
| Pre-fetch all models without running the full pipeline: |
| |
| ```bash |
| python -c "from scoring.sentiment import warmup_models; warmup_models()" |
| ``` |
| |
| No model fine-tuning is performed by AgentPulse — the four sentiment |
| models are used out-of-the-box and combined via the lexicon-weighted |
| ensemble described in Appendix C of the paper. |
| |
| --- |
| |
| ## Provenance and ethics |
| |
| All signals are collected from public APIs under each provider'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, and no PII beyond |
| public authorship metadata is included. The released CSVs in this |
| bundle contain no individual user records — only per-agent aggregates. |
| Raw text and per-author records are computed at runtime by the |
| collectors but are *not* shipped in this artifact. |
| |
| --- |
| |
| ## Citation |
| |
| ```bibtex |
| @inproceedings{agentpulse2026, |
| title = {AgentPulse: A Continuous Multi-Signal Framework for Evaluating |
| AI Agents in Deployment}, |
| author = {Anonymous}, |
| booktitle = {Proceedings of the 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. |
| |
