MaintainerPulse

temporal prediction of dependency maintenance risk

web3 ethereum/web3.py ● elevated

47%
no release in the next 12 months
54%
new issues will go unanswered (30 days)
100%
maintainer activity collapse within 12 months
62%
chance of a release within 12 months (survival model)

Model risk over time

calibrated P(no release next 12mo) at each historical monthly snapshot
0%50%100%2018-012020-072022-122025-06high risk

Repository activity, last 36 months

pushes issues PyPI release
025502023-112024-092025-072026-05

What drives this score

TreeSHAP contributions; raises risk / lowers risk
releases, last 12mo-vulns published, 12mo-days since last release-events, all time+

Survival curve

P(still no release) m months ahead, discrete-time hazard model
0%50%100%036912

What if? — poke the model

drag a signal and the deployed models rescore this package live (implied signals move together — two years without a release also zeroes "releases, last 12mo"). The needle tracks the activity-collapse model, a logistic regression: monotone by construction, so it responds smoothly to counterfactuals where the tree models step.

maintainer activity collapse, 12mo

In plain words

Maintained alternatives

similar packages with low predicted risk; ranked by summary similarity blended with shared-dependents overlap, validated against known migrations
packagewhat it issimilarity shared dependentsits 12mo risk
shiny A web development framework for Python. 0.69 0 6%
ollama The official Python client for Ollama. 0.67 0 29%
together The official Python library for the together API 0.66 0 0%
authlib The ultimate Python library in building OAuth and OpenID Connect servers and clients. 0.64 0 4%
panel The powerful data exploration & web app framework for Python. 0.62 0 2%

Signals at the latest snapshot

days since last release 62 releases, last 12mo 7
people pushing, last 12mo 2 bus factor (top pusher share) 82%
issues opened, last 12mo 3 30-day response rate 0%
stars accumulated 5523 open vulns without fix 0

Probabilities are isotonic-calibrated on a held-out validation year and evaluated on future snapshots the models never saw (PR-AUC 0.894, precision@50 = 1.00 on the headline target).