ReasonLens
Understand, compare, and debug AI application traces
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ReasonLens - Understand, compare, and debug AI application traces

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ReasonLens is an open-source AI observability platform that helps developers understand how AI applications reach their answers. Visualize LLM traces, RAG retrieval evidence, claims, and execution paths. Compare runs, inspect evidence changes, evaluate quality gates, and detect potential regressions in latency, cost, and provenance. Built for developers building reliable AI applications, with self-hosting and team workspaces in mind.

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Hi Product Hunt! I built ReasonLens around a problem I kept coming back to: when an AI application produces a different answer, how do you understand what changed? Looking at the final response isn't enough. The retrieval results may have changed, a tool may have failed, evidence may have disappeared, or the execution may simply have become slower and more expensive. ReasonLens lets you trace AI execution, inspect evidence and claim relationships, compare runs, and investigate potential quality regressions. It supports chat, RAG, and agent workflows, with comparisons designed to distinguish observable quality signals from structural differences. I'm sharing it to get feedback from developers building real AI applications. **I'd especially like to know:** What's the hardest part of debugging your LLM or RAG application today? Try it: GitHub: Feedback and feature requests are welcome!