DataEval/dingo

Overview

Dingo is an open-source Python library for evaluating the quality of AI data, models, and applications. It provides a structured framework to run automated tests and benchmarks across different stages of the AI development pipeline.

Best for

Best for
Developers building AI pipelines who need a unified evaluation tool for data, models, and applications.

Use cases

• Assess dataset quality before training a model
• Benchmark model performance on custom evaluation tasks
• Validate application outputs against expected quality standards

Notes

Dingo is an open-source Python library for evaluating the quality of AI data, models, and applications. It provides a structured framework to run automated tests and benchmarks across different stages of the AI development pipeline.

706 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Assess dataset quality before training a model
• Benchmark model performance on custom evaluation tasks
• Validate application outputs against expected quality standards

Pros

• Covers data, model, and application evaluation in one tool
• Open-source with a growing community (706 stars)
• Python-native, easy to integrate into existing workflows

Cons

• Limited documentation and examples for advanced use cases
• Smaller community compared to more established evaluation libraries
• May lack support for some specialized evaluation metrics

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.