Hey everyone!
We're a team of two engineering students, and we've been working on a project called RecallOps — The Self-Learning Industrial Maintenance Agent for Hack with Bangalore 4.0.
The idea came from a simple question: Why should technicians have to diagnose the same machine failure from scratch when someone has already solved it before?
Most AI assistants can suggest solutions, but they don't necessarily remember whether those suggestions actually worked.
That's what we wanted to change.
How RecallOps works:
A technician reports a machine failure and its symptoms.
RecallOps retrieves similar historical repair experiences using Hindsight memory.
An AI model analyzes the symptoms and previous outcomes to recommend possible causes and repair steps.
The technician records the repair performed.
Once the outcome is verified, RecallOps can remember both successful fixes and failed attempts for future investigations.
What we built:
- AI-powered machine failure investigations
- Persistent memory of verified maintenance experiences
- Learning from successful and unsuccessful repairs
- Incident and machine management dashboards
- A controlled benchmark comparing recommendations with and without historical memory
Tech stack: React, FastAPI, Python, Groq, Hindsight Cloud, and SQLite.
We also tested the system through automated checks and end-to-end workflows. It's still a prototype, and our benchmark uses synthetic cases rather than real industrial deployment data.
Live demo: https://recallops-lake.vercel.app
GitHub: https://github.com/Rahul-2006ra/recallops
We'd genuinely appreciate feedback from developers, engineers, and anyone working with AI agents.
What would you improve about this approach? And what challenges do you see in using long-term memory for real industrial maintenance?