We built @GraphBit because most agent frameworks felt great for demos, but started breaking down in production. Either they consumed too many resources, lacked reliability across multi-step workflows, or made observability a nightmare.
GraphBit flips that script:
Rust core blazing speed, async concurrency, and safety
Python interface developer-friendly, simple to learn
Why did you choose Rust under the hood and Python on top for GraphBit? What business and technical benefits did it deliver, and what trade-offs came with it? If you were starting today, would you make the same choice?
GraphBit s CPU-efficient design is a really smart approach making agentic AI more accessible without heavy GPU costs. I m curious though, how does it handle scaling when moving from smaller prototypes to production workloads? Are there built-in optimizations for parallel tasks or would that require additional infrastructure?
I ve tried a few agent frameworks but keep hitting reliability walls in multi-step workflows. Makers of GraphBit: what specific reliability features matter most in production?
Hey Hunters! We re gearing up to launch GraphBit an agentic AI framework with a Rust-powered core and a Python-simple API for building dependable, multi-step AI workflows.
Hey PH! We ve been heads-down on GraphBit, a framework to build reliable AI agents. Rust performance under the hood, Python-simple on the surface.
What s different: typed, declarative workflows, built-in retries/circuit breakers, execution tracing, multi-LLM support, and resource controls for prod workloads. More in the docs: docs.graphbit.ai.
Would love your take on:
Top 1 2 things that break when you move agents from demo production
Must-have batteries included for RAG pipelines
Telemetry you rely on when agents call tools / other services
