14x faster, zero crashes: our benchmark results

Most frameworks look magical in a demo. But when you put them under real workloads- things start to break: context loss, runaway retries, and crashes halfway through.

We’ve been building a new execution model for agentic AI, designed from the ground up for speed and reliability (and now patent-pending).

Here’s what our latest benchmarks show:

⚡ 14x faster execution under concurrent workloads

🛡 Zero crashes on long-running multi-agent tasks

🔀 Stable memory & context even in 50+ step workflows

📊 Built-in tracing that makes debugging practical

Instead of patching issues with retries and hacks, we rethought the runtime architecture- Rust at the core for performance, Python at the edge for developer simplicity.

We’ll share the full benchmark report at launch- @GraphBit on Product Hunt

Curious to hear from this community:

When you evaluate an AI framework, what do you care about most- speed, reliability, or observability?

— Musa