The agentic AI framework with a real data layer

Hey Product Hunt, Musa here.

AI agents don’t fail on prompts- they fail on data: stale indexes, missing context, black-box memory, and expensive retries. We built GraphBit’s Data Layer so production agents stay fast, factual, and affordable.

What’s inside (TL;DR):

• Unified Retrieval → One API over pgvector, Qdrant, Weaviate, Elastic (swap backends, keep your code).

• Hybrid Search → Dense + sparse + lightweight knowledge graph edges = higher precision, fewer hallucinations.

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

• Targeted Caching → LLM response + tool I/O memoization (TTLs, freshness policies) to cut infra spend.

• Real-time Ingest → CDC/streams keep vectors/metadata fresh in seconds, not hours.

• Deterministic Writes → Idempotent commits, conflict-free merges, exact-once effects.

• Privacy & Policy → Tenancy, namespace isolation, field-level encryption, redact-on-export.

• Observability → Query traces (latency/tokens/hit source), embedding drift monitors, auto-reindex alerts.

Why this matters: it’s not “yet another RAG.”

It’s a data plane engineered for agent workloads — portable, observable, and cost-disciplined.

Paired with our Rust core (lock-free concurrency, dependency-aware wakeups), GraphBit keeps retrieval predictable under load, so agents scale instead of crumble.

Ask to the community:

What’s your biggest pain with agent memory or retrieval today (freshness, cost, audit, multi-db portability)?

Try it / Launch link: https://www.producthunt.com/p/graphbit

— Musa