The Neural Encyclopedia for AI Agents
Hierarchical RAG with Hebbian Learning.
+118% context preservation vs standard RAG.
"Every AI conversation starts from zero"
Context learned...
❌ Forgotten
Start from zero...
❌ Isolated
No memory...
❌ Repeated
Standard RAG = Flat vectors, no learning, no hierarchy.
Humans have encyclopedias. AIs have... nothing shared.
3-Level Cognitive Architecture + Hebbian Learning
Long-term vision • User profiles • Architectural decisions
Semantic patterns • Inter-concept relations • Spreading activation
Atomic facts • Session details • 1024D embeddings (bge-m3)
Inspired by neuroscience: "Neurons that fire together, wire together"
Δw = α × relevance × feedback
α = 0.05 (learning rate)
Synaptic weights strengthen with use
Multiple AI agents sharing the same cognitive substrate. One learns → all benefit.
Claude
GPT
Llama
Try HRAG right now - no signup required
Basic text search across the hierarchical knowledge base.
See the difference! Same query, two engines: flat RAG vs hierarchical HRAG.
Visualize the 3-level neural hierarchy with spreading activation between neurons.
Interactive Hebbian learning: search, select a neuron, provide feedback, watch weights change!
Results will appear here...
HRAG vs Standard RAG
Where HRAG shines
Multiple AI agents sharing memory. Claude, GPT, and local models learning from each other.
Vectorized codebases with semantic search. Debug smarter, understand faster.
Organizational memory that learns and improves. Knowledge management at scale.
Start free, scale as you grow
AGPL-3.0
Commercial License
Custom Agreement
Building the next generation of AI memory systems.
We're seeking research partnerships.
We'd love to hear from you