I built a one-line observability decorator for Python AI agents

mamoor123 — Sun, 05 Apr 2026 12:22:02 +0000

The problem

Your AI agents break and you find out when users complain. No logs, no metrics, no idea what went wrong.

The fix

A @observe decorator that tracks every call to local SQLite. No cloud, no API key, no signup.

How it works

from agentdna import observe

@observe
def transcribe(audio):
    return whisper.transcribe(audio)

@observe
def summarize(text):
    return llm.summarize(text)
markdown
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## The problem

Your AI agents break and you find out when users complain. No logs, no metrics, no idea what went wrong.

## The fix

A `@observe` decorator that tracks every call to local SQLite. No cloud, no API key, no signup.

## How it works

python
from agentdna import observe

@observe
def transcribe(audio):
return whisper.transcribe(audio)

@observe
def summarize(text):
return llm.summarize(text)
That's it. Every call is now logged with latency, success/failure, and error types.

View your stats
$ agentdna stats

📌 transcribe
✅ Healthy 42 calls ██████████ 97% avg 1250ms

📌 summarize
⚠️ Degraded 38 calls █████████░ 92% avg 3400ms
❌ 3 failures: Timeout(2), RateLimit(1)

Health thresholds:

✅ Healthy — 95%+ success rate
⚠️ Degraded — 80-95%
🔴 Unhealthy — below 80%

Features

Sync and async support
Latency percentiles (p50, p95, p99)
Error type breakdown
SQLite persistence — survives restarts
Zero dependencies beyond stdlib
Export to JSON or CSV

Install
pip install agentdna-sdk

Open source

Apache 2.0 —
GitHub

Built this because I was tired of debugging agents blind. Would love feedback!

DEV Community: mamoor123

I built a one-line observability decorator for Python AI agents

The problem

The fix

How it works