Guild AI
by Community
Experiment tracking, ML developer tools
OSS
Guild AI
Added 1 June 2026
Overview
Guild AI is an open-source experiment tracking tool for machine learning. It logs metrics, hyperparameters, and outputs from Python scripts, enabling developers to compare runs and reproduce results via a command-line interface and Python API.
Best for
Best for
Individual developers or small teams seeking a simple, local experiment tracker without heavy infrastructure.
Use cases
- Track hyperparameter sweeps and log results for comparison
- Reproduce previous ML experiments with saved configurations
- Integrate experiment logging into existing Python training scripts
Notes
Guild AI is an open-source experiment tracking tool for machine learning. It logs metrics, hyperparameters, and outputs from Python scripts, enabling developers to compare runs and reproduce results via a command-line interface and Python API.
899 stars on GitHub. Last updated 2025-04-29. Licensed Apache-2.0.
Use cases
- Track hyperparameter sweeps and log results for comparison
- Reproduce previous ML experiments with saved configurations
- Integrate experiment logging into existing Python training scripts
Pros
- Lightweight and easy to set up in local environments
- Works seamlessly with standard Python ML code without vendor lock-in
- Free and open-source with no server required for basic use
Cons
- Smaller community and fewer integrations than established tools like MLflow
- Limited built-in visualization and dashboard capabilities
- Development activity appears slower than competing open-source projects
Indexed from awesome-llmops and enriched against its public facts.
Pros
- Lightweight and easy to set up in local environments
- Works seamlessly with standard Python ML code without vendor lock-in
- Free and open-source with no server required for basic use
Cons
- Smaller community and fewer integrations than established tools like MLflow
- Limited built-in visualization and dashboard capabilities
- Development activity appears slower than competing open-source projects
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