Lightning-AI/LitLogger
A minimal Python logger that tracks everything you try when building AI - metrics, prompts, models, etc, so you can see what changed and why.
Building AI models involves many changes to code, data, and prompts, making it hard to track what caused improvements or regressions. This tool automatically records inputs, outputs, metrics, prompts, and files for each model run, allowing you to easily compare results and understand what changed. It's designed for AI developers and researchers who need to manage and reproduce their machine learning experiments.
Use this if you are developing AI models and need to systematically log and compare inputs, outputs, and performance metrics across different experiment runs without extensive setup.
Not ideal if you are looking for a general-purpose logging solution for applications that do not involve AI model development or experimentation.
Stars
24
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 19, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/mlops/Lightning-AI/LitLogger"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
mlflow/mlflow
The open source AI engineering platform. MLflow enables teams of all sizes to debug, evaluate,...
kitops-ml/kitops
An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets,...
aws-samples/mlops-e2e
MLOps End-to-End Example using Amazon SageMaker Pipeline, AWS CodePipeline and AWS CDK
tensorchord/envd
🏕️ Reproducible development environment for humans and agents
techiescamp/mlops-for-devops
MLOps for DevOps Engineers - A hands-on, project-based guide to Machine Learning Operations