Ludwig

Overview

Ludwig is a low-code Python framework for building custom large language models, neural networks, and other AI models. It provides a declarative configuration system that reduces the amount of code needed to train, evaluate, and deploy models.

Best for

Best for
Data scientists and ML engineers who want to quickly build and deploy AI models without extensive coding

Use cases

• Rapidly prototype and train custom LLMs with minimal coding
• Build and fine-tune neural networks for tabular data or text
• Deploy trained AI models into production with predefined pipelines

Notes

Ludwig is a low-code Python framework for building custom large language models, neural networks, and other AI models. It provides a declarative configuration system that reduces the amount of code needed to train, evaluate, and deploy models.

11,707 stars on GitHub. Last updated 2026-05-29. Licensed Apache-2.0.

Use cases

• Rapidly prototype and train custom LLMs with minimal coding
• Build and fine-tune neural networks for tabular data or text
• Deploy trained AI models into production with predefined pipelines

Pros

• Low-code design accelerates experimentation and reduces boilerplate
• Wide compatibility with multiple model architectures and data types
• Strong open-source community with over 11,700 GitHub stars

Cons

• Limited flexibility for highly customized or non-standard model architectures
• Python dependency restricts usage outside of Python-based stacks
• Performance tuning may still require deep understanding of underlying models

Indexed from awesome-generative-ai and enriched against its public facts.