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Documentation Index

Fetch the complete documentation index at: https://docs.nolma.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Nolma Lens

Lens turns user behavior into actionable recommendations. It answers: are users actually happy with what your agents produce?

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How it works

User interacts with AI output
        |
Your code calls nolma.signal()
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Lens tracks acceptance rates,
edit distances, retry rates
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After 100+ signals: Claude generates
evidence-backed recommendations
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Dashboard shows: "Switch to gpt-4o-mini,
save $43/mo, low quality risk"

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What Lens measures

MetricDescription
Acceptance rate% of outputs used without changes
Edit rate% of outputs the user modified
Edit distanceHow many characters users change
Retry rate% of times user regenerated
Abandon rate% of times user left without using
Downgrade score0-100 readiness to use cheaper model

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Downgrade readiness score

Calculated weekly per agent:
ComponentWeight
Acceptance rate40%
Stability (low retry rate)25%
Focus (low abandon rate)20%
Consistency (low edit rate)15%
  • Score >= 75 → Shadow mode recommended
  • Score >= 90 → Safe to switch models

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Getting started

Add 2 lines to your agent code: 
# After user acts on the output:
await nolma.signal_async(session_id, "accepted")
That’s all. Lens starts working after 100 signals per agent.