Best explainable AI tools for tracing LLM decisions in 2026

Explainability in AI systems varies between model-level and application-level, especially in Large Language Model (LLM) applications, where understanding the entire AI application's behavior is crucial. Application-level explainability involves tracing the execution path, encompassing factors like context retrieval, tool selection, and memory, to identify errors, with tools like Arize Phoenix and Braintrust facilitating this through trace-based evaluations. In contrast, model-level explainability for tabular and vision models focuses on feature attribution methods like SHAP and LIME, which estimate how inputs influence predictions. Token-level attribution in LLMs falls short as it measures influence within a single model call, missing errors originating in earlier application steps. Braintrust emerges as a powerful tool for LLM explainability, linking trace steps with evaluation scores to create a feedback loop for ongoing improvement, while Arize Phoenix offers an open-source alternative. For classical machine learning models, feature attribution remains central, with tools like SHAP, LIME, Fiddler AI, and Captum offering various capabilities for different model types and deployment needs. The choice of explainability tool depends on the specific requirements of understanding either feature contributions or application traces.

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