Hippo Fabric replaces vector search with a biological cognitive memory engine — weighted concepts, Hebbian edges, spreading activation. Memory that actually learns from use.
The problem
Two years building AI systems led us to one root cause — semantic search was never designed to think. It finds similar text. It doesn't understand context, build associations, or learn.
Semantic similarity returns near-matches that are confidently wrong. The bigger your knowledge base, the worse it gets — not better.
vector_search.find_similar()Vector search finds similar vectors — not related concepts. "Budget" and "Q4 forecast" live far apart in embedding space but are deeply connected in meaning.
no graph traversalEmbeddings are computed once and never change. They can't learn from use, strengthen with repetition, or adapt from correction. Memory that can't grow isn't memory.
embeddings.frozen = TrueEvery session starts from zero. User preferences, corrections, and learned rules vanish the moment the conversation ends. Your agent forgets you every single time.
session.memory = NoneLive playground
No embeddings, no cosine similarity. Hippo Fabric activates one concept and propagates through weighted graph edges to surface everything genuinely related.
Try a different query:
API surface
ingest() · think() · sleep(). The entire Hippo Fabric API fits on a postcard.
ingest()
Concepts and relationships stored as weighted nodes and edges. Every new piece of knowledge joins the graph and auto-links to what's already there.
think()
Activate one concept — spreading activation traverses links by weight. The right context surfaces because it's connected, not because it's a cosine match.
sleep()
The brain replays traces, strengthens useful edges, and crystallises schemas. Your agent gets measurably sharper every night.
Sleep consolidation
While your users sleep, Hippo Fabric replays the day's activation traces, strengthens the edges that actually mattered, prunes noise, and crystallises recurring patterns into stable schemas. No other production memory does this.
Performance
Evaluated against the ICLR 2025 gold standard for long-term memory in conversational AI.
State of the Art Comparison
Category Breakdown (Strict)
0.46s
Inference Speed
vs 2–5s competitors
$0.00
API Cost for Retrieval
Free forever
85%
Token Cost Reduction
22,000 → 3,300 tokens
"90.6% accuracy in multi-session reasoning — more than 50% better than ChatGPT, at 10× faster inference speed and zero API cost."
LongMemEval · ICLR 2025 gold standard
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