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agent-lens
Run the scientific method on your LLM agent
Most LLM agent debugging is vibes-based: edit a prompt, eyeball the output, repeat. agent-lens makes it structuralPause a running agent at any LLM call. State a hypothesis. Fork with edited messages. Get a structural diff — latency, tokens, cost — with verdict: improved/regressed.No other tool has pause, fork, or run diff. Local-first, SQLite, zero infra. pip install agentlens-tracer
Hey PH! I'm Raju, maker of agent-lens.
The frustration that started this: every prompt change is a hypothesis test, but the only tool we have is eyeballing two outputs in two terminal windows. No record of why you made each change. No structural way to know if it actually helped. agent-lens adds the missing layer: pause your agent at any LLM call, write down your hypothesis, fork with the edit, and get a diff with exact numbers. The note travels with the run forever — future you can read your reasoning, not just see the final code.
Zero infra. SQLite on your machine. No API keys needed to start.
Happy to answer anything — what's your biggest pain point debugging LLM agents?
About agent-lens on Product Hunt
“Run the scientific method on your LLM agent”
agent-lens was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #114 on the daily leaderboard. Most LLM agent debugging is vibes-based: edit a prompt, eyeball the output, repeat. agent-lens makes it structuralPause a running agent at any LLM call. State a hypothesis. Fork with edited messages. Get a structural diff — latency, tokens, cost — with verdict: improved/regressed.No other tool has pause, fork, or run diff. Local-first, SQLite, zero infra. pip install agentlens-tracer
On the analytics side, agent-lens competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how agent-lens performed against the three products that launched closest to it on the same day.
Who hunted agent-lens?
agent-lens was hunted by Raju Shanigarapu. 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.
For a complete overview of agent-lens including community comment highlights and product details, visit the product overview.
Raju Shanigarapu