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.
Love the Rust-Python hybrid approach - finally getting performance AND developer experience without compromise!
As someone who's battled production bottlenecks, your real-time observability features sound like a game-changer. My biggest pain? Context switching between toolchains just to optimize performance.
Quick question: How does multi-LLM orchestration work - are you abstracting provider differences or giving granular control?
Congrats on the launch! 🎉
GraphBit blends Rust’s speed and reliability with Python’s simplicity giving devs a high-performance framework for building intelligent, enterprise-grade AI agents. Seamless bindings, serious power, and no trade-offs.
Congrats on the launch! The Rust core with Python simplicity hits a great balance. Real‑time observability and crash resilience sound perfect for production agents—excited to see where this goes.
🔥 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.
Since Rust is at the core, it might be valuable to offer guides for developers who are purely Python users but curious to learn more about how the Rust layer works. That could create a bridge for more developers to adopt and understand the deeper optimizations.
Curious about performance: do you have benchmarks on latency/throughput compared to pure‑Python agent frameworks? The Rust core seems ideal for low‑latency tool calls.
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?
Congrats on the launch — the emphasis on crash resilience and lock-free scheduling is compelling 👍. I'm curious about deployment patterns: can GraphBit run agents across hybrid environments (on-prem + cloud) with consistent scheduling and failover? If so, how do you handle secure model access and secret management in those mixed setups? Would love to see examples or deployment guides.
I would love to see more integration options with existing AI infrastructure tools. Things like plug-and-play connectors for vector databases, observability tools, or deployment platforms could make it much easier to adopt without having to stitch everything together manually.
Great Product. We are currently using Agno in production but will explore this and maybe switch to this in coming times. Kudos to the team!