No known security advisories.
The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.
flaml has 736K monthly downloads and 4K GitHub stars. Version 2.6.0 supports Python 3.10+, first published in 2020.
It is actively maintained under a permissive license, last released in Apr 2026.
It is built on top of numpy. It is used by 44 tracked packages, including nannyml, functime and composio-autogen.
GitHub topics include automl, hyperparam and automated-machine-learning.
Package Insights
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
100
Popularity
78
Adoption
58
Community
80
Provenance
0
How the maintenance status was determined
Last commit
3 days ago
Monthly downloads
735,807
•
Active
Activity within 30 days
Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
735,807
Stars
4,369
Stars per 1k downloads
5.94
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
•
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
Unknown
Fewer than 1,000 monthly downloads, or no star data
How download momentum is calculated
Week daily avg
21,471
Month daily avg
24,527
Momentum
-12.5%
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100Python version support tiers
requires_python
>=3.10
Cutting Edge
Minimum supported version is 3.12 or newer
•
Modern
Minimum supported version is 3.9, 3.10, or 3.11
Legacy
Supports 3.8 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
MIT
•
Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
69 days ago
Total releases
105
Releases/year
18.8
•
Rapid
Last release within 365 days and > 12 releases/year
Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
10,000
Monthly downloads
735,807
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
•
Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
87
Issue Burden
35.9
per 1k stars
Discussions
21.7
per 1k stars
Fork Ratio
1792.1%
contrib / forks
Stale Release
66d
PR Velocity
Quiet
Weekly Downloads
GitHub Stars
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.
Downloads by OS
Linux
15.5K
Windows
412
macOS
169
Python Versions
3.12
40.0%
6.1K
3.10
25.8%
3.9K
3.11
22.3%
3.4K
3.9
7.7%
1.2K
3.7
4.2%
630
Top Countries
1.
US
13.0K
US
2.
FI
725
FI
3.
DE
533
DE
4.
JP
455
JP
5.
NL
381
NL
6.
IN
230
IN
7.
SG
174
SG
8.
HK
107
HK
9.
IE
85
IE
10.
GB
77
GB
Dependencies
1
- numpy >=1.17
88 optional dependencies
- azureml-mlflow[azureml]
- catboost[test]
- catboost[benchmark]
- catboost[catboost]
- coverage[test]
- datasets[nlp]
- datasets[test]
- datasets[hf]
- dill[test]
- evaluate[test]
- hcrystalball[test]
- hcrystalball[ts-forecast]
- hcrystalball[forecast]
- holidays[ts-forecast]
- holidays[forecast]
- ipykernel[test]
- joblib[spark]
- joblib[test]
- joblibspark[test]
- joblibspark[spark]
- joblibspark[synapse]
- jupyter[notebook]
- jupyter[test]
- lightgbm[test]
- lightgbm[automl]
- mlflow-skinny[test]
- nbconvert[test]
- nbformat[test]
- nltk[hf]
- nltk[nlp]
- nltk[test]
- nni[nni]
- numpy[test]
- openml[test]
- optuna[blendsearch]
- optuna[synapse]
- optuna[test]
- packaging[autozero]
- packaging[blendsearch]
- packaging[test]
- pandas[spark]
- pandas[automl]
- pandas[autozero]
- pandas[benchmark]
- pandas[test]
- pre-commit[test]
- prophet[forecast]
- prophet[ts-forecast]
- psutil[benchmark]
- psutil[test]
- pyspark[spark]
- pyspark[synapse]
- pytest[test]
- pytest-rerunfailures[test]
- pytorch-forecasting[forecast]
- pytorch-forecasting[test]
- pytorch-lightning[forecast]
- pytorch-lightning[test]
- ray[ray]
- requests[test]
- rgf-python[test]
- rouge-score[hf]
- rouge-score[nlp]
- rouge-score[test]
- scikit-learn[automl]
- scikit-learn[autozero]
- scikit-learn[test]
- scikit-learn[vw]
- scipy[automl]
- scipy[test]
- seqeval[hf]
- seqeval[nlp]
- seqeval[test]
- statsmodels[forecast]
- statsmodels[test]
- statsmodels[ts-forecast]
- tensorboardx[forecast]
- tensorboardx[test]
- thop[test]
- torch[test]
- torchvision[test]
- transformers[hf]
- transformers[nlp]
- transformers[test]
- vowpalwabbit[vw]
- xgboost[automl]
- xgboost[benchmark]
- xgboost[test]
Used By
44
- nannyml
- functime
- composio-autogen
- fudstop
- domino-code-assist
- tencent-wedata-auto-ml
- bisheng-pyautogen
- rascpy
- tencent-wedata3-auto-ml
- changtianml
- webull-options
- algorin-cli
- caml
- bayes-xw
- tabnetics
- seed-autogen
- geochemistrypi
- gpbench
- bambu-qsar
- polars-features
- causal-forecast
- ez-automl-lite
- mktdata
- autogen-agentchat-um
- ta-automl
- tencent-wedata-auto-ml-dev
- noventis
- wedata-automl
- palma
- tencent-wedata3-auto-ml-dev
- bisheng-pyautogen-lc1
- pyautogenmagic
- geom-x
- autogen-fargate-executor
- tianhe-pyautogen
- centricube-pyautogen
- wedata3-automl
- pymads
- dynamicdml
- seed-pyautogen
- dpro
- guinsoft-pyautogen
- neo-agent-cli
- climl
GitHub Topics
Tags from the package's GitHub repository describing what it does.
Automl
Hyperparam
Automated Machine Learning
Machine Learning
Data Science
Python
Jupyter Notebook
Hyperparameter Optimization
Random Forest
Scikit Learn
Deep Learning
Natural Language Processing
Natural Language Generation
Timeseries Forecasting
Classification
Regression
Tuning
Finetuning
Tabular Data
Package Information
License
MIT
First Release
December 4, 2020
Last Release
2 months ago
Last Commit
3 days ago
Latest Version
2.6.0
Total Releases
105
Contributors
10,000
Open PRs
23
Archived
No
Python Versions
3
3.10
3.11
3.12
3.13
Wheels
py3
Tracking Since
January 6, 2026
Data Updated
July 5, 2026
Data Freshness
Fresh