The Agentic AI Framework that makes data as strong as the code

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.

Why a new data layer matters

• RAG pipelines break without fresh, reliable data.

• Vector DBs alone can’t handle hybrid recall or scale gracefully.

• Enterprise teams need auditability + efficiency, not black-box memory.

What GraphBit’s Data Layer does differently

• Unified Retrieval → One interface across pgvector, Qdrant, Weaviate, Elastic. No lock-in.

• Hybrid Search → Dense + sparse + graph edges for accuracy & fewer hallucinations.

• Versioned Memory → Short-term, long-term & time-scoped recall with audit logs.

• Targeted Caching → Hot responses + tool I/O caching to cut infra spend.

• Real-time Ingest → CDC streams keep data fresh within seconds.

• Privacy & Policy → Row-level tenancy + field-level encryption baked in.

• Observability → Drift monitors, query traces, and auto-reindex alerts.

It’s not “just RAG.” It’s a database strategy for agentic AI: portable, efficient, and production-grade.

We’ve paired this with our Rust-powered execution core (lock-free scheduling + atomic counters) so GraphBit doesn’t just orchestrate- it scales, predictably.

Would love your feedback:

What’s the biggest data pain you’ve faced in scaling AI agents?

👉 Try it here: @GraphBit

— Musa 🚀