Debugging AI agents is broken.
When your agent fails, you currently have to:
Re-run the entire workflow
Burn API credits again
Wait for slow operations to repeat
Hope the failure reproduces
I built Flight Recorder to fix this.
The idea: Record execution like a black box flight recorder. When something fails, replay from the exact failure point. Cache what worked.
Example:
You have a 5-step agent workflow:
Search database ✅ (1 second)
Call GPT-4 ✅ ($0.01, 10 seconds)
Validate result ❌ (crashes here)
Send email
Log to database
Traditional debugging:
Fix the bug → re-run steps 1-5 → waste time + money
With Flight Recorder:
Fix the bug → flight-recorder replay last → steps 1-2 cached, jump to step 3 → done in 2 seconds
It’s open source:
pip install flight-recorder
GitHub: https://github.com/whitepaper27/Flight-Recorder Works with any agent framework (LangChain, CrewAI, custom). Curious what others think - is debugging becoming a bottleneck for agent development? submitted by /u/coolsoftcoin
Originally posted by u/coolsoftcoin on r/ArtificialInteligence