AI agents don t fail because of prompts. They fail because of data.

Most frameworks obsess over orchestration but production agents collapse when their data plane can t keep up.

That s the problem we set out to solve with GraphBit.

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

AI is powerful, but it s also power-hungry

Every headline about LLMs seems to highlight the same problem: soaring compute costs and rising carbon footprints. For enterprises in Europe especially, this is becoming a boardroom and regulatory concern.

Here s what we ve learned while benchmarking frameworks:

We ve talked a lot about the pain points in building multi-step AI workflows but what about the parts you actually enjoy?

Is it designing the workflow?

Watching the agents collaborate? O