Contradish is a toolkit designed to detect and address contradictions in answers generated by AI models before deployment. It targets scenarios where large language models provide inconsistent or conflicting responses to similar questions, particularly when emotional framing or context alters the model's output. The tool aims to catch these failures early, preventing them from reaching end users.
The platform offers a command-line interface, a Python API, and a GitHub Action for integration into development workflows. Contradish enables benchmarking of AI models by running extensive tests across multiple domains and identifying cases where the same model session yields opposing answers. For example, it can surface situations where a model gives different dosage advice for medication depending on how the question is framed emotionally. The tool quantifies such inconsistencies using a 'strain' metric and flags critical failures, supporting analysis across domains like medication, legal, and mental health advice. It also provides detailed failure reports, identifies root causes of contradictions, and suggests remedies such as prompt modifications or fine-tuning with specific training pairs.
Contradish is intended for teams and practitioners developing or deploying AI models, especially those concerned with reliability and safety in production environments. Its integration with GitHub Actions allows automated checks on pull requests, blocking merges when critical failures are detected according to configurable thresholds. The tool supports benchmarking across multiple models and domains, and maintains a leaderboard to track performance metrics.
Installation is available via pip, and the toolkit is accessible through the CLI, Python API, and as a GitHub Action.
contradish is a LLM eval & observability product. It focuses on identifying and resolving inconsistent or contradictory answers from large language models in production. It is built as an open-source project for AI researchers and ML engineers. contradish is open source under the MIT license. The product ships for the web and the command line, and it can be self-hosted.
contradish first shipped in 2026. Development happens publicly on GitHub with 42 commits in the last 90 days. Key capabilities include contradiction detection, LLM benchmarking, and prompt rewriting. It exposes integrations via a public API.
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