GraphBit

Name: GraphBit
Rating: 5.0 (21 reviews)

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

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Databases and backend frameworks

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AI Infrastructure Tools

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LLM Developer Tools

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.

Free

Launch tags:Open Source•Developer Tools•Artificial Intelligence

Launch Team / Built With

Intercom — Startups get 90% off Intercom + 1 year of Fin AI Agent free

Startups get 90% off Intercom + 1 year of Fin AI Agent free

Promoted

GraphBit

Maker

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

And yes! Our core architecture is patent pending ⚡

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

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7mo ago

@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? 🚀

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6mo ago

GraphBit

Maker

@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

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6mo ago

GraphBit

Maker

@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.

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6mo ago

@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.

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6mo ago

GraphBit

Maker

@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

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6mo ago

GraphBit

Maker

@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.

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6mo ago

🔥 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!

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6mo ago

GraphBit

Maker

@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

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6mo ago

GraphBit

Maker

@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.

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6mo ago

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?

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6mo ago

GraphBit

Maker

@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.

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6mo ago

GraphBit

Maker

@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.

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6mo ago

Sprinto

@musa_molla congrats on the launch!

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6mo ago

GraphBit

Maker

@tuneerprod Thanks a lot, Appreciate the support!

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6mo ago

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.

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22d ago

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?

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6mo ago

GraphBit

Maker

@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

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6mo ago

GraphBit

Maker

@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.

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6mo ago

GraphBit

Maker

@cruise_chen Oh Yes! We made it easy as it sounds.
Repo: https://github.com/InfinitiBit/graphbit

Docs: https://docs.graphbit.ai

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6mo ago

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?

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6mo ago

GraphBit

Maker

@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

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6mo ago

GraphBit

Maker

@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!

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6mo ago

GraphBit

Maker

@thanh_th_o_le yes, checkout from : https://docs.graphbit.ai/ to learn more.

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6mo ago

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?

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6mo ago

GraphBit

Maker

@nikitaeverywhere Yes, the JavaScript (js) is in beta version currently in development, after final testing it also will be available ready to use

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6mo ago

GraphBit

Maker

@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!

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6mo ago

Congratulations on launching GraphBit! Wishing all the best.
I will be testing GraphBit soon and may keep using it. Best of luck.

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6mo ago

GraphBit

Maker

@shakauthossain Thanks a lot. Excited for you to test it out can’t wait to hear your feedback once you’ve tried GraphBit

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6mo ago

GraphBit

Maker

@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.

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6mo ago

GraphBit

Maker

@shakauthossain Thanks for the support! Looking forward to your first impressions, real-world feedback is what helps us push GraphBit forward.

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6mo ago

GraphBit

Maker

@shakauthossain
Repo: https://github.com/InfinitiBit/graphbit

Docs: https://docs.graphbit.ai

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6mo ago

Looks great, @musa_molla Musa! Love the Rust + Python combo. How does @GraphBit handle scaling when lots of AI agents run together?

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6mo ago

GraphBit

Maker

@lina_huchok Thanks, With Rust + lock-free scheduling, GraphBit scales smoothly even when many agents run in parallel

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6mo ago

GraphBit

Maker

@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.

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6mo ago

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Forum Threads

p/graphbit

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3mo ago

Featured

The most underrated trend in AI is how humans are redesigning their work with AI, not around it.

Teams aren t just adding AI into existing workflows.
They re reshaping the workflows themselves with the AI agent in the loop.

Steps get removed.
Objectives become clearer.
Old constraints disappear.
Processes reorganize around what the system can now do natively.

The real productivity gain isn t automation.
It s rethinking the architecture entirely.

Curious to hear from this crowd:
What s one workflow you rebuilt because AI made the old version irrelevant?

p/graphbit

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5mo ago

Featured

What’s the biggest hidden cost you’ve faced when running AI in production?

It s easy to measure latency or accuracy.

But the real costs often hide in the background- compute burn, idle tokens, redundant calls, or that temporary caching fix that quietly eats your budget.

p/graphbit

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11d ago