... fallback. But in AI? We still deploy and pray. The model hallucinates we shrug. The agent crashes we retry. The data drifts we patch and move on. Somewhere along the way, fragile but flashy became normal. When we built GraphBit , we borrowed from aviation, not automation. Deterministic execution Redundant state systems Circuit breakers instead of blind retries It s not about making AI smarter, it s about making it airworthy . So here s the question: If AI systems ... ... pass a flight test before production, what would your checklist include? - Musa Try Graphbit

... used to think we were building smarter agents. But really, we were building fragile ones. Clever, but brittle. That s when we realized something: AI doesn t just need better prompts. It needs stronger systems. So we built GraphBit , an open-source framework that treats orchestration like infrastructure. Rust for precision. Python for flexibility. Reliability as a design principle, not an afterthought. When your agents can recover, restart, and self-heal, you stop firefighting and start scaling ... ... lost a weekend debugging a phantom AI failure, you ll get why we built this. Try it here- https://github.com/InfinitiBit/g... And tell me, What s the hardest silent failure you ve seen in production AI? - Musa Co-founder, GraphBit

... that wall again and again while building agent workflows until we stopped trying to patch it and started asking a different question: What if AI systems behaved more like operating systems? Predictable. Observable. Deterministic. That question turned into GraphBit, an open-source framework that treats orchestration like real infrastructure. Built with Rust for execution. Wrapped in Python for developers. Designed for scale, not demos. It s fast, fault-tolerant, and production-ready. We ve just open-sourced it here ... ... github.com/InfinitiBit/graphbit If you re building AI agents or workflows that need to actually survive in production, I d love for you to try it or break it. Your feedback will shape what we build next. - Musa Founder, GraphBit

... same- great demos, endless chaos in production. Retries, timeouts, context loss, concurrency bugs and a dashboard that says running when everything s actually on fire. So instead of patching it again, we built something that stops breaking. GraphBit- an open-source, Rust-powered agentic AI framework that treats workflows like infrastructure, not improvisation. Rust for execution (fast, safe, lock-free) Python API for accessibility Observability, retries, timeouts, and circuit breakers baked in Multi-LLM orchestration that actually holds under load ... ... /InfinitiBit/graphbit If you ve ever watched your working demo collapse at scale, you ll understand why we built this. Would love your feedback what s the one thing that s broken your AI workflows the most? -Musa Co-founder, GraphBit

... users show up, everything breaks- concurrency, state, context, you name it. So while others chased milliseconds, we started asking a different question: What if we could make AI predictable instead of just fast? That question became the foundation for GraphBit, a framework built to treat AI workflows like infrastructure, not experiments. Rust for execution. Python for accessibility. Deterministic recovery baked right into the runtime. The result? Workflows that don t panic under load, they adapt. Because the future ... ... just about power. It s about trust. So here s a question for you: What s one failure you think AI teams have started accepting as normal but shouldn t? -Musa  Founder, GraphBit

... Launching on Product Hunt has always felt like a rite of passage for builders and on September 14, our team finally took that leap with GraphBit, our agentic AI framework built with Rust and Python. What made this special? It was our first-ever Product Hunt launch. No legacy audience. No prior community presence. Just a small, focused team and one week to prepare. Halfway through, we thought we may need a hunter to build our credibility as we were ... ... completely new in PH. Within two days, we found an incredible Hunter who believed in what we were building and advised us for going live: @byalexai The Challenge Our top competitor

... Good Peoples, Musa Here. We believe GraphBit is unbreakable enough to handle real-world workloads. But don t take our word for it. We re throwing down a challenge try to break GraphBit

... background- compute burn, idle tokens, redundant calls, or that temporary caching fix that quietly eats your budget. We ve seen it again and again: AI projects don t collapse because of complexity They collapse because of inefficiency. While building GraphBit, we kept asking Can we make agents faster, cheaper, and lighter without cutting corners on reliability? That question led us down the path of Rust, concurrency, and smarter orchestration. But I m curious What s the biggest invisible inefficiency ... ... musa_molla): @yash_patidar_  That s a real challenge, Yash. Cost creep during testing can be brutal. We ve seen similar issues across AI teams, which is why we re building fine-grained monitoring and caching layers into GraphBit

... 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 ... ... 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

... here. We re entering a new era not just of models, but of agents. But there s a missing piece nobody talks about: the infrastructure layer that keeps them alive, efficient, and accountable. That s why we re building GraphBit- not just as a framework, but as an open foundation for agentic systems. Instead of closed clouds or black-box APIs, GraphBit

... Most frameworks shine in demos. But when you put them under real workloads they burn CPU, leak memory, crash agents, and cost huge amount of dollar in electricity. We built GraphBit to fix that. Why it s different: Rust core lock-free concurrency, dependency-aware scheduling Efficiency CPU usage 0.000 0.352%, memory footprint <0.12 MB Resilience circuit breakers, error classification, adaptive retries Data layer hybrid search, versioned memory, real-time ingest Green AI ~14 more efficient billions in electricity ... ... savings + millions of tons of CO avoid The bigger picture IDC forecasts 1.3B AI agents by 2028. At today s efficiency that s 34 TWh/year 7.3B in energy bills ~8M tons of CO . GraphBit

... prototype works beautifully, the demo looks magical and then everything breaks when real users hit it. Sometimes it s context loss. Sometimes it s API chaos. Sometimes it s that mysterious it works on my machine moment. While building GraphBit, we saw the same pattern again and again, great ideas collapsing under real-world load. So I m curious to hear from you: What s the #1 failure point you ve hit when scaling an AI workflow to production ... ... maybe we can make production AI a little less painful together. Musa Comment from Musa Molla(@musa_molla): @whereflightteam  Haha, that s a real one, we ve all had that who wrote this? moment. That s exactly why GraphBit

... Hello good people, Musa here- When we built GraphBit, one thing became clear: the hardest part isn t building AI demos, it s keeping agents alive in production. That means solving: CPU spikes and wasted cycles Memory leaks that creep in at scale Retries spiraling out of control Data freshness and reliability under load GraphBit

... Every developer who s tried to scale AI knows this pain: Agents crash randomly Logs are scattered everywhere Retries spin forever Cloud bills explode You end up firefighting instead of building. We built GraphBit to stop that. What it does Rust-powered core agents run stable + predictable Sub-percent CPU usage + tiny memory footprint Circuit breakers + adaptive retries no infinite loops Built-in data layer hybrid search + versioned memory In short: GraphBit

... Product Hunt! I m Musa, Founder & one of the first engineers behind @GraphBit We re all in on agentic AI at scale, but we ve seen firsthand that most frameworks collapse when they leave the demo stage. CPU waste, flaky retries, memory leaks- they kill production adoption. That s why we built GraphBit

... agents don t fail because of prompts. They fail because of data. Most frameworks obsess over orchestration but production agents collapse when their data plane can t keep up. That s the problem we set out to solve with GraphBit. Why a new data layer matters RAG pipelines break without fresh, reliable data. Vector DBs alone can t handle hybrid recall or scale gracefully. Enterprise teams need auditability + efficiency, not black-box memory. What GraphBit

... scale Fast and efficient Observable when things break Easy to use without fighting the API The truth is most frameworks today feel great in a demo but don t hold up in production. That s why we re building @GraphBit - a developer-first, enterprise-grade Agentic AI framework. Built with Rust performance + Python simplicity, GraphBit

... Product Hunters, I m Musa, CEO of @GraphBit . We re building an enterprise-grade AI workflow framework that helps teams deploy AI agents reliably, at scale, and at speed. Some challenges we re tackling: AI workflows that break under pressure Orchestrating multiple LLMs without chaos Maintaining observability in complex pipelines Ensuring scalability and reliability with serialization & atomic references I d love to hear from you: what s been the hardest part of running AI workflows in production ... ... opening early access soon- your feedback will help shape GraphBit

... Product Hunt Community! I m Musa Molla, founder & CEO at InfinitiBit GmbH , based in Munich. We re the team behind @GraphBit , a production-grade AI workflow framework that helps enterprises build, operate, and scale agentic AI systems safely and efficiently. Our team includes engineers with experience at OpenAI and Google, and we re passionate about bridging AI, automation, and real-world business processes. We re excited to be part of this community and looking forward to: Engaging with other ...

... Everyone, I m Musa, founder of @GraphBit , an LLM framework for building, monitoring, and scaling AI workflows in enterprises. We d love to share ideas, learn from your experience, and discuss AI workflow challenges: AI agents for repetitive tasks Multi-LLM orchestration Observability & production reliability If you re experimenting with AI workflows, what s been the hardest part for your team ...

... everyone! We re getting ready to launch, a framework to build AI workflows that actually work in production no more toy demos that break on step three. Follow here: https://www.producthunt.com/prod... GraphBit gives you: Multi-step, tool-using workflow orchestration Multi-LLM support with a clean Python API Production-ready primitives: state, retries, timeouts, guards RAG-ready building blocks It s built for AI engineers, product teams, and devs shipping real workflows. I d love to hear from ... ... your biggest challenge with multi-step AI workflows , and which feature matters most - observability, evals, or sandboxed tool-use? Musa Comment from Musa Molla(@musa_molla): @debra_salt  Love to hear that, that s exactly why we built GraphBit