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Data sourced from GitHub API

  1. Home
  2. Repositories
  3. apache
  4. airflow
PythonVisible projectFresh activityNo linked package signalPartial snapshot

apache/airflow

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Compare closest alternativesOpen GitHub

First read

Visible and active, but adoption proof is still thinner

apache/airflow has enough public attention and recent movement to stay on the shortlist, but package usage is still partial, so the next step should be source and ecosystem validation rather than a quick yes.

Visible

45.8K public stars in the current GitStar snapshot.

Recognizable in the ecosystem

Active

Last commit Jun 14, 2026.

Fresh activity

Adopted

Treat stars as discovery context until a linked package appears.

No linked package mapping

Confidence

One or more key signals are partial, so GitStar keeps the interpretation conservative.

Partial snapshot

Snapshot facts

  • 45.8K stars
  • 17.2K forks
  • Last commit Jun 14, 2026
  • Package usage not mapped yet

Compare lens

apache/superset and langgenius/dify 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
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Jun 14, 2026

Package reality

No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.

GitStar expects a package signal here, but no npm or PyPI package is linked to this repository yet.

Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read

Validation note

GitStar can summarize public signals for apache/airflow, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

airflowapacheapache-airflowautomationdagdata-engineeringdata-integrationdata-orchestratordata-pipelinesdata-scienceeltetlmachine-learningmlopsorchestrationpythonschedulerworkflowworkflow-engineworkflow-orchestration
🌙Nightshift Coder🐙Polyglot
🛡️ 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 fresh update and stable visibility.

Fresh updateStable visibility
📈 Momentum & Adoption Signals
Approximate star trajectory

Reconstructed from current stars and cached daily/weekly/monthly deltas.

Now: 45.8K
30d ago7d ago1d agoNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars45.8K
🧭 Hype vs Reality
Insufficient Package Data

GitStar can see repository momentum, but it does not have a reliable linked package signal yet.

Treat stars and recent movement as discovery context only until npm or PyPI usage is available.

Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other Python repos before treating stars as a moat.
📐 Read the Hype vs Reality heuristic
Related alternative

apache/superset

Shares the data category footprint with apache/airflow, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

langgenius/dify

Overlaps on topics such as automation and orchestration, which makes it a stronger alternative than a random popularity neighbor.

CompareOpen detail
Related alternative

pytorch/pytorch

Shares the learning category footprint with apache/airflow, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

huggingface/transformers

Shares the learning category footprint with apache/airflow, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Research links

Cross-links

Compare against related repos

GitStar picked superset + dify as the closest next comparison from the related repository set.

Open compare presetCompare with supersetCompare with dify
Repo utility

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GitHub Repositoryapache/airflowExport Ranking DataDownload CSV

Wider nearby ecosystem

Compare with supersetCompare with dify
TypeScript
72.3K stars
apache/superset

Apache Superset is a Data Visualization and Data Exploration Platform

TypeScript
134.8K stars
langgenius/dify

Production-ready platform for agentic workflow development.

Python
98.6K stars
pytorch/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Python
158.5K stars
huggingface/transformers

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Python
103.8K stars
Shubhamsaboo/awesome-llm-apps

Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

Python
107.2K stars
Comfy-Org/ComfyUI

The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.

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.
Compare the closest alternativesView the organizationRead the heuristic

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

About This Page

This page provides a quick overview of apache/airflow 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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