Tools: Pawz Engram biologically-inspired memory architecture for persistent AI agents

What is Engram? ## Why build this? ## The Engram loop (the part we care about) ## Architecture ## 1) Three tiers, three time scales ## 2) Hybrid retrieval (BM25 + vectors + graph) with fusion ## 3) Retrieval intelligence (a.k.a. don’t retrieve blindly) ## 4) Measured forgetting + safe rollback ## 5) Security by default ## What’s next ## Read the full whitepaper Most “agent memory” today is basically: Project Engram is our attempt to treat memory like a cognitive system instead of a dumping ground. It’s a three-tier architecture inspired by how humans handle information: Engram is implemented in OpenPawz, an open-source Tauri v2 desktop AI platform. Everything runs local-first. Read the full whitepaper: ENGRAM.md (Project Engram) Flat memory stores tend to fail the same way: Engram’s core bet is simple: Intelligent memory is not more memory — it’s better memory, injected at the right time and the right amount. Engram is built around a reinforcing loop: Tier 0: Sensory Buffer Tier 1: Working Memory Tier 2: Long-Term Memory Graph Engram fuses multiple signals rather than betting on one: Then it merges rankings with Reciprocal Rank Fusion (RRF) and can apply MMR for diversity. So weak results get corrected or rejected instead of injected as noise. Forgetting is first-class: That means storage stays lean without silently losing what matters. Engram encrypts sensitive fields before they hit disk: A few “high-leverage” additions we are actively working toward: If any of this resonates, the full architecture, modules, schema, and research mapping. And if you want to contribute, issues + PRs are welcome. Star the repo if you want to track progress. 🙏 Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to ? It will become hidden in your post, but will still be visible via the comment's permalink. as well , this person and/or - Sensory Buffer (Tier 0): short-lived, raw input for a single turn - Working Memory (Tier 1): what the agent is “currently aware of” under a strict token budget - Long-Term Memory Graph (Tier 2): persistent episodic + semantic + procedural memory with typed edges - everything competes equally (no prioritization) - nothing fades (stale facts stick around forever) - “facts”, “events”, and “how-to” get mixed into one blob pile - retrieval runs even when it shouldn’t (latency + context pollution) - security is often an afterthought - Gate: decide if memory retrieval is needed at all (skip trivial queries) - Retrieve: hybrid search (BM25 + vectors + graph signals) - Cap: budget-first context assembly (no overflow, no dilution) - Skill: store “how to do things” as procedural memory that compounds - Evaluate: track quality (NDCG, precision@k, latency) - Forget: measured decay + fusion + rollback if quality drops - FIFO ring buffer for this turn’s raw inputs (messages, tool outputs, recalled items) - drained into the prompt and then discarded - priority-evicted slots with a hard token budget - snapshots persist across agent switching - Episodic: what happened (sessions, outcomes, task results) - Semantic: what is true (subject–predicate–object triples) - Procedural: how to do things (step-by-step skills with success/failure tracking) - memories connect via typed edges (RelatedTo, Contradicts, Supports, FollowedBy, etc.) - BM25 for exactness and keyword reliability - Vector similarity when embeddings are available (optional) - Graph spreading activation to pull adjacent context - a Retrieval Gate: Skip / Retrieve / DeepRetrieve / Refuse / Defer - a Quality Gate (CRAG-style tiers): Correct / Ambiguous / Incorrect - decay follows a dual-layer model (fast-fade short memory vs slow-fade long memory) - near-duplicates are merged (“fusion”) - garbage collection is transactional: if retrieval quality drops beyond a threshold, Engram rolls back - automatic PII detection - AES-256-GCM field-level encryption - local-first storage design (no cloud vector DB dependency) - sensory buffer + working memory caches - graph store + typed edges - hybrid search + reranking - consolidation + fusion + decay + rollback - encryption + redaction defenses - observability (metrics + tracing) - proposition-level storage (atomic facts) - a stronger vector index backend (HNSW) - community / GraphRAG summaries for “global” queries - skill verification + compositional skills - evaluation harnesses (dilution testing + regression gates)