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.
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.
Here s something uncomfortable I ve learned building AI agent systems:
AI rarely fails at the step we re watching.
It fails somewhere quieter a retry that hides a timeout, a queue that grows by every hour, a memory leak that only matters at scale, a slow drift that looks like variation until it s too late.
Most teams measure accuracy. Some measure latency.
Most reviews praise GraphBit’s speed, stability, and production readiness, highlighting smooth concurrency, clear docs, and a clean Python API over a resilient Rust core. Makers of LangChain and CrewAI users note GraphBit holds up better at scale, with stronger observability, retries, and multi-LLM orchestration. The maker of
emphasizes real-world reliability, enterprise features, and patent-pending execution. A minority flag suspicious review patterns, but hands-on users report efficient performance even on modest hardware and a notably frictionless setup.
What I like about GraphBit compared to LangChain or CrewAI is the underlying architecture. It’s modular, built for scale, and doesn’t hide the complexity - you still get control. The patent-pending design feels like a forward step toward making agent orchestration as robust as microservices.
I’ve used LangChain and CrewAI quite a bit, and while they’re great for prototyping, concurrency often becomes a bottleneck at scale. GraphBit’s Rust core really shines here - async execution feels smoother and safer without the random crashes I’ve run into elsewhere.
Just dove into the GraphBit repo, awesome engineering! The Rust core wrapped in a lean Python API delivers compiled-speed with Python ergonomics, and the design squeezes performance by minimizing allocations and avoiding extra memory overhead. 🥳
What's great
fast performance (2)high performance (13)memory efficiency (3)clean API (5)Rust core (13)Python bindings (14)
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