tensorflow/tensorflow
Rank #26·Top 100·All-time stars·Cached ranking snapshot·
An Open Source Machine Learning Framework for Everyone
First read
Strong enough to justify a deeper source review
tensorflow/tensorflow looks visible enough to matter, active enough to trust for a next pass, and adopted enough to merit checking maintainers, releases, and real integration cost in the source repository.
Visible
197K public stars in the current GitStar snapshot.
Long-term anchor
Active
Last commit Aug 15, 2026.
Fresh activity
Adopted
pypi gives the strongest production-style signal.
8.2M/week
Confidence
Both package adoption and momentum are available.
High-confidence read
Snapshot facts
- 197K stars
- 76K forks
- Last commit Aug 15, 2026
- pypi 8.2M/week
Compare lens
pytorch/pytorch and keras-team/keras are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.
Signal trail
Trajectory
Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.
Daily momentum
+9
Weekly momentum
+90
Monthly momentum
Recent momentum is not captured
Last commit
Aug 15, 2026
Package reality
GitStar found pypi:tensorflow. Package traffic can help separate visible repositories from dependencies that are quietly used in real workflows.
Hype vs Reality
Balanced
Package mapping
pypi · tensorflow
Cross-links
Standalone repo read
Validation note
GitStar can summarize public signals for tensorflow/tensorflow, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.
Package signal
Install & Package Signals
GitStar uses the strongest linked package signal below when it reads Hype vs Reality for this repository.
Balanced
Recent attention and package usage are moving in the same direction.
PyPI package
tensorflow
8.2M downloads/week
pip install tensorflow
🌙Nightshift Coder
🛡️ Editorial Context
GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.
Why this rank
This repository stands out because it combines 9 daily momentum and package adoption.
9 daily momentumPackage adoptionFresh updateHigh lifetime stars
📈 Momentum & Adoption Signals
Approximate star trajectory
Reconstructed from current stars and cached daily/weekly/monthly deltas.
Now: 197K
Daily momentum
+9
Weekly momentum
+90
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars197K
Weekly package downloads8.2M
🧭 Hype vs Reality
BalancedRecent attention and package usage are moving in the same direction.
This usually means the project has both mindshare and a measurable production footprint, which makes it a stronger validation candidate.
Status
Balanced
Package footprint
PyPI · 8.2M/week
Method note
Balanced means GitStar sees both meaningful package adoption and enough current star velocity or scale to avoid reading the repo as purely niche. Compare it against other C++ repos before treating stars as a moat.
Related alternative
pytorch/pytorch
Shares the learning category footprint with tensorflow/tensorflow, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
keras-team/keras
Shares the learning category footprint with tensorflow/tensorflow, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
explosion/spaCy
Shares the learning category footprint with tensorflow/tensorflow, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
lutzroeder/netron
Shares the learning category footprint with tensorflow/tensorflow, so the comparison is closer to a same-problem decision than a same-language coincidence.
Research links
Cross-links
Compare against related repos
GitStar picked pytorch + keras as the closest next comparison from the related repository set.
Repo utility
Embed Badge
Preview:
Markdown
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<a href="https://gitstar.space/repo/tensorflow/tensorflow"><img src="https://gitstar.space/api/badge/tensorflow/tensorflow" alt="GitStar"></a>Styles:
Wider nearby ecosystem
Python
102.4K starspytorch/pytorch
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64.2K starskeras-team/keras
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33.8K starsexplosion/spaCy
💫 Industrial-strength Natural Language Processing (NLP) in Python
JavaScript
33.4K starslutzroeder/netron
Visualizer for neural network, deep learning and machine learning models
Python
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C++
23.7K starsTencent/ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform
Next step after the validation read
Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
About This Page
This page provides a quick overview of tensorflow/tensorflow based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.
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