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

Visit website

Databases and backend frameworks

•

AI Infrastructure Tools

•

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.

Have a question about GraphBit? Ask it here and get a real answer.

Wispr Flow: Dictation That Works Everywhere — Stop typing. Start speaking. 4x faster.

Stop typing. Start speaking. 4x faster.

Promoted

Do you use GraphBit?

Forum Threads

p/graphbit

•

2mo 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

•

4mo 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

•

3mo ago

Featured

The most dangerous failure in AI is the one you don’t measure

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.

View all

Maker Comment

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

Report

6mo ago

See discussion

Forum Threads

p/graphbit

•

2mo 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

•

4mo 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

•

3mo ago

Featured

The most dangerous failure in AI is the one you don’t measure

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.

View all

Maker Comment

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

Report

6mo ago

See discussion