Persistent Memory System
A 15-signal hybrid retrieval engine with five memory types, consensus-based trust scoring, and cross-session continuity. Your AI does not start over every conversation. It picks up exactly where it left off — with every decision, every preference, every lesson learned still intact.
Memory Architecture
Modeled on how human cognition actually stores and retrieves knowledge — not just a key-value store with a timestamp.
Active processing buffer. What the system is thinking about right now. Fast access, limited capacity, high relevance.
Knowledge and facts. What the system knows about your domain, your preferences, your architecture.
Session records. What happened, when, in what context. The narrative thread that connects conversations over time.
Techniques and methods. How to do things. The learned approaches that get refined through use and feedback.
Self-knowledge. What the system knows about its own performance, biases, and reliability across different domains.
Engine Capabilities
Not a database with a search index. A living memory system that learns, heals, forgets, and develops intuition.
FTS5 full-text search, TF-IDF scoring, neural embeddings, BM25 ranking, vector similarity, and 10 more signals blended into a single retrieval engine. Not keyword matching. Genuine understanding of what you need right now.
Physics-based trust scoring across memory entries. Confidence scores, contradiction detection, and confidence decay over time. The system does not just remember — it knows how much to trust each memory.
A PageRank-inspired crawler that continuously audits, repairs, and optimizes the memory graph. Broken links get repaired. Orphaned entries get reconnected. The system maintains its own integrity.
Specialized AI agents with persistent identity and private memory. Each persona has its own mind.db for independent thinking, plus shared organizational memory for collaboration. They remember who they are across sessions.
Memories that get retrieved together strengthen their connections. Over time, the system develops something that functions like intuition — surfacing related knowledge before you ask for it.
Memories unused for 90+ days with confidence scores below 0.8 get archived. Not deleted — archived. The system keeps its working memory clean while preserving everything in cold storage.
Each cognitive core owns its domain data. URI addressing (core://{core_id}/{memory_type}/{item_key}) enables precise cross-core retrieval without central bottlenecks.
Your AI remembers everything — preferences, decisions, context, lessons learned. No re-briefing. No starting over. Every conversation compounds on every previous one.
When new information conflicts with existing memory, the system flags it. Contradictions are not silently overwritten — they are surfaced, compared, and resolved with confidence scoring.
Under the Hood
Each agent has a private mind.db for independent thinking. Finished research and verified knowledge gets published to shared organizational memory. Think of it as the difference between a personal notebook and a team wiki.
Every memory item has a unique address: core://core_id/type/key. This enables precise cross-core retrieval, distributed ownership, and clean API boundaries between cognitive domains.
Confidence scores decay over time like radioactive half-lives. Recently verified information scores higher. Contradicted information scores lower. The system models epistemic uncertainty, not just recency.
The 14-phase crawler treats memory like a web. High-connectivity nodes (frequently referenced memories) get higher trust. Orphaned nodes get re-linked or archived. The graph stays healthy automatically.
Landscape
| System | Approach | vs. Enovari Memory |
|---|---|---|
| Mem0 | Key-value memory store. Simple, fast, limited structure. | Structured memory with basic graph support — no multi-signal retrieval, no self-healing, no memory types |
| Zep | Session memory with summarization. Good for conversation history. | No self-healing crawler. No consensus engine. No multi-agent private memory. |
| Letta (formerly MemGPT) | Virtual context management. Clever paging of context windows. | No multi-agent OS. No persona system. No distributed memory architecture. |
| LangChain Memory | Conversation buffer with optional vector store. Framework-level abstraction. | Single retrieval signal. No confidence scoring. No contradiction detection. No active forgetting. |
Pricing
Persistent memory for individual developers.
For organizations with advanced requirements.
Every conversation makes the next one sharper. Every decision is remembered. Every lesson is retained. Stop re-explaining yourself to your tools.
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