Engram
Memory is strange.
AI memory systems have an accuracy crisis. Recent benchmarks show answer accuracies below 56%, with hallucination and omission rates remaining high. Most systems use LLM extraction on every message, compounding errors until the original truth is lost. RAG is not the answer when your memory layer is the problem.
Engram takes a different approach: memory you can trust.
What it does
Engram is a memory system for AI applications that preserves ground truth and tracks confidence:
- Store first, derive later — Raw conversations stored verbatim. LLM extraction happens in background where errors can be caught.
- Track confidence — Every fact carries provenance:
verbatim(highest),extracted(high),inferred(variable). - Verify on retrieval — Applications filter by confidence. High-stakes queries use only trusted facts.
- Enable recovery — Derived facts trace to sources. Errors can be corrected by re-deriving.
Memory types
Six memory types, each with different confidence and decay characteristics:
- Working — Current conversation context, volatile and in-memory
- Episodic — Immutable ground truth, verbatim storage
- Factual — Pattern-extracted facts (emails, dates, names)
- Semantic — LLM-inferred knowledge with variable confidence
- Procedural — Behavioral patterns and preferences
- Negation — What is explicitly NOT true
The philosophy
Ground truth is sacred. Every derived memory points back to source episodes. If extraction makes a mistake, re-derive from the original. Forgetting is a feature—memories decay over time, keeping the store relevant.
Status
Engram is in pre-alpha. Built on Pydantic AI with durable execution (DBOS/Temporal) and Qdrant for vector storage.