Musa Molla

GraphBit- The black-box recorder for AI agents

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Here’s the hidden truth:

Most AI systems fail not when they break, but when you can’t see why.

Logs are scattered. Failures look random. Debugging feels like chasing ghosts.

When 100s of agents run in production, that’s a nightmare.

We built GraphBit to change that.

It’s not just an agentic AI framework- it’s a black-box recorder for your AI systems.

What that means:

  • Every agent action is logged + classified → instant root cause analysis

  • Circuit breakers + retries fire with full context → no silent failures

  • Deterministic execution → reproducible runs (no “it just worked yesterday”)

  • Hybrid data layer → queries + memory are versioned and auditable

And yes, it’s ultra-efficient (sub-percent CPU, micro-MB memory), because visibility without stability is useless.

Why it matters:

Enterprises don’t adopt AI because they don’t trust it.

Trust = transparency.

GraphBit gives you both.

👉 GitHub: https://github.com/InfinitiBit/graphbit

Question for you:

What’s the hardest bug or failure you’ve ever chased in an AI system and how long did it take to trace it?

— Musa

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Indiana Joshi

I'll never forget losing two weeks chasing an invisible race condition in multi-agent scheduling. Logs scattered everywhere. Only reproducibility could've shown the exact path. GraphBit feels like the tool I desperately needed.

Musa Molla

@indiana_joshi Race conditions are the sneakiest killers. Deterministic execution in GraphBit makes those paths reproducible, so no more scattered log hunts. Code’s open: https://github.com/InfinitiBit/graphbit

Abdul Rehman

I once spent 3 days chasing a phantom bug in an agent workflow… only to realize it was a retry loop firing silently.

Musa Molla

@abod_rehman Phantom retry loops, we’ve been there. GraphBit’s circuit breakers + full-context logs are built to stop exactly that. Repo: https://github.com/InfinitiBit/graphbit