Introducing Engramma Cloud: Memory That Thinks
Announcing Engramma Cloud — unlimited patterns, causal reasoning, safety regimes, and 43 premium capabilities. Same API, no migration needed.
From Local to Production
Engramma started as a research project: can we build a memory engine for AI that goes beyond retrieval? One that composes, generalizes, and adapts?
The local version proved it works. Multi-head attention enables native composition. Energy-based memory provides soft generalization. Confidence routing learns which pathway to use.
[!WARNING] But local has limits: 1,000 patterns, RAM-only, no persistence, and equal-weight composition only.
Today we're opening the private beta for Engramma Cloud — the same API, no limits, plus 43 premium capabilities built on our research.
What Cloud Unlocks
Causal Reasoning (Phase 6)
Your memory can now discover causal relationships inherently in the data:
graph = mem.get_causal_graph()
effect = mem.predict_causal_effect(cause_key=a, effect_key=b)
Safety Regimes (Phase 8)
Three operating modes with automatic anomaly detection. If your memory detects out-of-distribution inputs, it blocks risky operations before they contaminate your agent's reasoning.
Text Interface (Phase 10)
Store and query with natural language — no external embedding model needed:
mem.store_text("The user prefers Python over JavaScript")
results = mem.query_text("what language does the user prefer?")
Full Explainability
Understand exactly why a result was returned down to the very last tensor:
explanation = mem.explain(query)
# Returns: pathway, confidence breakdown, attention maps, regime state
Same API. Infinite Scale.
The switch to cloud is literally one line of code:
# Before (Local)
mem = EngrammaMemory(dim=256, backend="local")
# After (Cloud)
mem = EngrammaMemory(dim=256, backend="cloud", api_key="nx_live_...")
[!TIP] No code changes. No data migration. Your existing
store(),query(),compose(), andretrieve()calls work identically — they just scale infinitely.
Join the Beta
We're onboarding teams in batches. If you're building AI agents that need real memory — not just retrieval — join the waitlist.
Limited spots available. Priority access is given to teams with active production use cases.