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Agent infrastructure

microsoft/autogen

A programming framework for agentic AI

PythonsteadyLive GitHub sample
View JSON View on GitHub

Current momentum

59capped sample

Stars

61.5K

+66 since sync

Commits

1

in period

Active contributors

1

+100%

Issues closed

2

2333h avg. resolution

Recorded trajectory

Movement you can verify over time.

Loading daily momentum history.

Behind the number

A score you can inspect.

Each signal contributes points to the total. These are capped GitHub samples, not complete activity counts or proof of adoption.

Development activity40.0 / 40 pts

1 commits · 20 opened PRs · 2 closed issues in 7 days

24 + 22 × ln(1 + weekly weighted activity). Activity = commits + 2 × opened PRs + closed issues. Signal bounded to 0–100, then weighted 40%.

Contributor breadth14.1 / 25 pts

1 sampled contributors in 7 days · 100% contributor-growth input

24 + 25 × ln(1 + contributors × √(7 / days)) + 0.15 × growth. Growth uses the 30-day windows; capped samples use a neutral 0 input. Signal bounded to 0–100, then weighted 25%.

Issue resolution0.9 / 20 pts

2333 hours resolution input

105 − 9 × log₂(1 + hours). Uses sampled closed issues, not first-response time. With no resolved issues, the model uses 168 hours. Signal bounded to 0–100, then weighted 20%.

Release cadence4.5 / 15 pts

15 days cadence input

105 − 5 × cadence days. With fewer than two published releases, the model uses time since the last push as a proxy. Signal bounded to 0–100, then weighted 15%.

Weighted total · rounded once59 / 100

What changed in the recorded sample?

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9.3K forks

Downstream adoption

1.1K open issues

Community demand

15 day cadence

Shipping consistency