GraphBit
Rust-core, Python-first Agentic AI framework
5.0•21 reviews•1.5K followers
Rust-core, Python-first Agentic AI framework
5.0•21 reviews•1.5K followers
GraphBit is a high-performance AI agent framework with a Rust core and seamless Python bindings. It combines Rust’s speed and reliability with Python’s simplicity, empowering developers to build intelligent, enterprise-grade agents with ease.
GraphBit
Hey Product Hunt! 👋 Musa here, Founder of @GraphBit
I built GraphBit because I was tired of the same developer pain:
Juggling slow, brittle frameworks that crash under load
Choosing between Python’s simplicity or Rust’s speed- never both
Losing control of observability and scaling in enterprise builds
GraphBit solves that.
Rust under the hood for blazing speed, safety, and async concurrency
Python bindings for a dev-friendly, easy-to-learn interface
Enterprise-first features: real-time observability, crash resilience, multi-LLM orchestration
Our vision? Make building scalable, production-ready AI agents feel as natural as microservices- secure, performant, and developer-first.
🙏 I’d love to hear: What’s your biggest pain when building AI agents? Happy to get feedback, mid-launch or post-launch.
Thanks for being here, excited to build together!
— Musa
@musa_molla 🔥 Congrats on launching GraphBit! Love how you’ve combined Rust’s performance with Python’s accessibility — that’s a killer combo for AI agent frameworks. The enterprise-first angle (observability + resilience) really stands out since most tools ignore those until it’s too late. Curious — how do you see GraphBit handling complex workflows with multiple LLMs at scale? 🚀
GraphBit
@monir_zaman4 Thanks, Exactly- resilience has to come first. For multi-LLM, GraphBit’s lock-free scheduling lets you run models in parallel, so scaling complex workflows stays predictable
GraphBit
@monir_zaman4 Appreciate your question! To add, one of the things we obsessed over in GraphBit was making multi-LLM workflows not just scalable but stable. The lock-free scheduler + async Rust core means even as complexity grows, execution stays predictable without hidden bottlenecks. That’s where GraphBit really sets itself apart.
@musa_molla Really interesting launch! From a people standpoint, I see GraphBit easing one of the biggest organizational pain points: when teams scale, you often end up with developers split between different tech stacks and struggling with bottlenecks. A framework that combines Rust’s performance with Python’s accessibility could help companies onboard talent faster, reduce skill gaps, and keep dev teams more collaborative.
GraphBit
@md_tanzir_hossain Appreciate that, You’re spot on, part of our vision is making it easier for teams to scale without fragmenting across stacks. Rust gives us performance, Python keeps it accessible, and together it helps devs collaborate without the usual bottlenecks
GraphBit
@md_tanzir_hossainAbsolutely, that balance between performance and accessibility is exactly what we designed for. Teams shouldn’t have to choose between speed and collaboration, and GraphBit makes sure they get both.
🔥 This looks really promising, @musa_molla !
The balance between Rust performance and Python accessibility is exactly what a lot of AI teams are struggling with right now. I’m especially curious about the multi-LLM orchestration and how it handles real-world scaling challenges.
My pain point: most frameworks start strong in prototyping but collapse when moving into production workloads. If GraphBit can truly bridge that gap, it’s a game-changer. 🚀
Looking forward to testing it out!
GraphBit
@shoaib_hossain37 Thanks a lot, Shoaib. You nailed the core problem, too many frameworks stay stuck in “demo mode.” GraphBit was built with production workloads first, so multi-LLM orchestration, retries, and concurrency are baked into the execution layer. Excited for you to test it out
GraphBit
@shoaib_hossain37 Exactly, bridging the gap from prototype to production is where we wanted GraphBit to stand out. By making reliability and multi-LLM scaling native to the framework, teams don’t have to rebuild everything once they go beyond demos.
PicWish
@musa_molla Congrats on the launch Musa! Love how you’ve combined Rust’s speed with Python’s simplicity.
what’s been the most exciting use case you’ve seen so far with GraphBit?
GraphBit
@mohsinproduct Thanks so much, One exciting one: a team used GraphBit to run a code-analyzer agent that reviews PRs with multi-LLM orchestration- parallel checks, no crashes, and way faster than their old setup. Seeing it cut review cycles from hours to minutes has been a real highlight.
GraphBit
@mohsinproduct That PR review use case really shows what we’re aiming for, turning complex, multi-LLM workflows into something stable and production-ready. Watching teams save hours while gaining reliability has been one of the most exciting validations for us.
Sprinto
@musa_molla congrats on the launch!
GraphBit
@tuneerprod Thanks a lot, Appreciate the support!
GIL bypass is the hardest architectural call in this space and GraphBit choosing Rust for the core instead of asyncio workarounds is the right split. Most Python agent frameworks I've stress-tested hit a wall past a few dozen concurrent agents. Native circuit breakers and retry logic save a painful post-outage bolting-on cycle too.
Agnes AI
Wow.... Rust-powered speed with Python simplicity? That combo is honestly genius—I’ve always hated picking between them for AI agents. Is multi-LLM orchestration as smooth as it sounds?
GraphBit
@cruise_chen Thanks a lot. We felt the same, no one should have to choose between speed and simplicity. And yes, multi-LLM orchestration is smooth: GraphBit’s parallel, lock-free scheduling lets you mix models without bottlenecks
GraphBit
@cruise_chen Exactly, that blend of Rust + Python is at the heart of GraphBit. With lock-free scheduling, multi-LLM orchestration stays smooth even under heavy load, so teams can focus on building instead of firefighting bottlenecks.
GraphBit
@cruise_chen Oh Yes! We made it easy as it sounds.
Repo: https://github.com/InfinitiBit/graphbit
Docs: https://docs.graphbit.ai
Huge congrats on the launch! Love the Rust core with Python bindings,best of both worlds. Is there a quickstart to spin up a simple agent from Python in a few minutes?
GraphBit
@thanh_th_o_le Thanks a lot. Exactly, Rust speed + Python simplicity was the goal! Yes, we have a Python quickstart in the GitHub repo, you can spin up a simple agent in just a few lines. Here’s the link: https://github.com/InfinitiBit/graphbit
GraphBit
@thanh_th_o_le Appreciate the support! The quickstart makes it super easy, a few lines in Python, and you’re running an agent powered by Rust under the hood. Excited for you to try it out!
GraphBit
@thanh_th_o_le yes, checkout from : https://docs.graphbit.ai/ to learn more.
Jinna.ai
Congrats on the launch! As I understand, it lets me build AI workflows using python/rust. Does it have any capability when it comes to JavaScript?
GraphBit
@nikitaeverywhere Yes, the JavaScript (js) is in beta version currently in development, after final testing it also will be available ready to use
GraphBit
@nikitaeverywhere Great question! While Python & Rust are our primary focus, we’re actively working on expanding into JS so teams can plug GraphBit into more ecosystems without friction. Stay tuned!
Congratulations on launching GraphBit! Wishing all the best.
I will be testing GraphBit soon and may keep using it. Best of luck.
GraphBit
@shakauthossain Thanks a lot. Excited for you to test it out can’t wait to hear your feedback once you’ve tried GraphBit
GraphBit
@shakauthossain
Really appreciate it! When you fire it up, I’m curious which use case you’ll try first. Your notes will directly shape our roadmap.
GraphBit
@shakauthossain Thanks for the support! Looking forward to your first impressions, real-world feedback is what helps us push GraphBit forward.
GraphBit
@shakauthossain
Repo: https://github.com/InfinitiBit/graphbit
Docs: https://docs.graphbit.ai
Looks great, @musa_molla Musa! Love the Rust + Python combo. How does @GraphBit handle scaling when lots of AI agents run together?
GraphBit
@lina_huchok Thanks, With Rust + lock-free scheduling, GraphBit scales smoothly even when many agents run in parallel
GraphBit
@lina_huchok GraphBit scales multi-agent workloads by combining a Rust async core with a tunable execution engine and production-grade safeguards. Concretely, it offers:
Parallel, graph-based execution with fine-grained concurrency controls. You can run fan-out/fan-in branches in parallel and cap concurrency globally and per node type (e.g., `agent`, `http_request`, `transform`). Prebuilt “high-throughput” executors maximize parallelism out of the box.
Configurable async runtime (Tokio) at the Rust layer. Adjust worker threads, blocking pools, and stack sizes for the runtime, then initialize once for the process.
Multiple executor profiles. Choose between standard, high-throughput, and low-latency (lightweight) executors depending on your scaling target.
Protective reliability layer under load. Built-in circuit breakers, retries with backoff/jitter, fail-fast vs. continue modes, and per-node timeouts prevent cascades when many agents are active.
Resource efficiency for dense concurrency. The core uses connection pooling per LLM provider and keeps a small baseline memory footprint (docs note < \~50 MB base), helping you pack more agents per host.
Observability for horizontal scale. First-class metrics, health checks, and performance tracking let you watch throughput/latency and autoscale safely.