Neuro-Biological Memory for AI
A purpose-built neural database that gives your AI systems the ability to remember, learn, and reason—the way biological memory actually works.
Traditional databases store data. ngramDB stores memories—with the same biological mechanisms that make human expertise possible.
Store and retrieve exact records. No learning, no connection discovery, no knowledge that emerges over time.
Model relationships explicitly. But connections are static—they don't strengthen with use or fade without reinforcement.
Memories that learn from use. Connections strengthen through co-activation. Knowledge consolidates and abstracts automatically.
ngramDB implements the same memory mechanisms found in biological neural systems, adapted for reliable, scalable production use.
Instead of searching, ngramDB activates. A query triggers energy that propagates across weighted connections, naturally surfacing related memories—including non-obvious connections no explicit search would find.
Like expert recall: "I've seen this pattern before" → related knowledge activates automatically → insight emerges.
"Neurons that fire together, wire together." When memories are activated together, their connections automatically strengthen. Unused connections naturally weaken. Your knowledge graph evolves with real usage.
No manual curation needed—the system learns which associations matter from actual retrieval patterns.
Like sleep consolidation in the brain, ngramDB periodically clusters related memories, merges redundant patterns into prototypes, extracts higher-order abstractions, and archives irrelevant details.
Store 1,000 raw observations → automatically distilled into actionable patterns and strategic insights, with full provenance.
Memory weights decay naturally over time. Frequently reinforced knowledge stays strong. Outdated patterns fade from relevance without manual cleanup. Memories are never lost—just deprioritized.
Self-cleaning memory that keeps what matters and gracefully ages what doesn't—no deletion, no data loss.
Raw observations automatically progress through multiple levels of abstraction: from individual data points to clustered prototypes to detected correlations to behavioral tendencies to strategic insights.
From "50 individual bug reports" to "this system has a recurring pattern of X"—extracted automatically.
Storing the same knowledge twice doesn't create duplicates—it reinforces the existing memory. Frequently observed patterns grow stronger automatically, mirroring how repetition strengthens biological memory.
Storage stays lean while frequently-seen patterns gain prominence in retrieval results.
From a single instance to a horizontally sharded cluster—ngramDB scales with your memory requirements.
Consistent-hash sharding distributes memories across nodes with minimal data movement during scaling. Add capacity without downtime or full rebalancing.
Built-in replication with configurable consistency guarantees—from eventual consistency for throughput to strong consistency for critical memory operations.
Vectorized operations deliver sub-millisecond memory encoding and comparison. Activation queries complete in under 100ms even across thousands of interconnected memories.
Write-ahead logging and atomic snapshots ensure memory durability. Fast startup with health-check readiness in seconds, not minutes.
<10ms
Memory Storage
<100ms
Activation Query
<5ms
Edge Reinforcement
<15s
Cold Start
Anywhere AI needs to remember, learn, and improve over time.
Give AI agents persistent memory that learns from every interaction. Successful approaches strengthen, failed approaches fade. Agents get smarter over time, not just bigger.
Compress thousands of raw observations into actionable patterns and strategic insights. Automatic abstraction with full provenance— always trace an insight back to its sources.
Discover hidden correlations and non-obvious connections across your data. Spreading activation finds patterns that no explicit query would surface.
Build game characters and virtual agents with believable memory. Personality evolution, emotional recall, and memory consolidation tied to narrative events.
Track successes and failures with automatic reinforcement. Anti-pattern detection, root cause analysis, and trend discovery—all emerging naturally from stored memories.
Move beyond fixed context windows. Give your conversational AI long-term memory that recalls relevant past interactions, learns preferences, and builds expertise over time.
| Capability | Traditional DB | Graph DB | Vector DB | ngramDB |
|---|---|---|---|---|
| Learns from usage | No | No | No | Yes — Hebbian learning |
| Auto-consolidation | No | No | No | Yes — periodic |
| Graceful forgetting | Manual delete | Manual delete | Manual delete | Automatic decay |
| Pattern discovery | Explicit queries | Traversal | Similarity search | Spreading activation |
| Knowledge abstraction | Application logic | Application logic | Application logic | Built-in multi-level |
| Connection evolution | Static | Static | N/A | Dynamic — evolves with use |
Async-first Python client with straightforward integration into any AI pipeline or application.
Built for modern async applications with full type annotations
Configurable scoring across memory strength, recency, and relevance
Powerful query predicates for precise memory retrieval
Built-in feedback mechanism drives Hebbian learning from real usage
Hierarchical document ingestion with automatic semantic clustering
Namespace memories by persona, tenant, or domain for multi-context use
Docker-native with health checks, graceful shutdown, and resource limits
Built-in telemetry for consolidation cycles, deduplication rates, edge evolution, and memory health
ngramDB is available for enterprise integration. Contact us to discuss your use case, request a demo, or explore licensing options.
Contact Enterprise Salesenterprise@engramforge.com
ngramDB is currently in beta. Features, APIs, and performance characteristics described on this page reflect current development targets and may change prior to general availability.