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ReasonLens
Understand, compare, and debug AI application traces
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.
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: https://reasonlens.vercel.app/
GitHub: https://github.com/talusannni/Re...
Feedback and feature requests are welcome!
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About ReasonLens on Product Hunt
“Understand, compare, and debug AI application traces”
ReasonLens was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #153 on the daily leaderboard. 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.
ReasonLens was featured in Analytics (174.5k followers), Developer Tools (523.1k followers), Artificial Intelligence (483.8k followers) and GitHub (41.5k followers) on Product Hunt. Together, these topics include over 284.5k products, making this a competitive space to launch in.
Who hunted ReasonLens?
ReasonLens was hunted by Shashi Kiran. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how ReasonLens stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Shashi Kiran