Memory · Associative
Finish a thought from a fragment.
Modern Hopfield networks give your agent content-addressable memory: recall a full pattern from a partial cue, with up to 30% noise tolerance and exponential capacity scaling.
Capabilities
What it does.
Every capability is production-grade. No flags, no betas — these ship the day you adopt Minds.
Modern Hopfield (2016)
Continuous-state formulation, exponential capacity, energy-based retrieval.
Configurable β (inverse temperature)
Tune the sharpness of recall — soft averaging vs. hard winner-take-all.
30% noise tolerance
Corrupt cues still converge to the correct pattern.
Key + metadata addressing
Store rich patterns; address by partial key or by content similarity.
Multi-iteration retrieval
One-shot for fast recall, multi-step for ambiguous cues.
Capacity telemetry
Live utilization, retrieval confidence, attractor stability.
API
A few lines is all it takes.
The SDK reflects the conceptual model directly. No glue code, no orchestrator, no learning curve beyond the data shape.
exampletypescript
// Store a pattern.
await minds.memory.associative.store({
key: "auth-flow-pattern",
pattern: [/* embedding or symbolic vector */],
metadata: { module: "auth", verified: true },
});
// Retrieve from a partial cue.
const result = await minds.memory.associative.recall({
cue: partialEmbedding, // 70% complete is fine
beta: 8.0,
maxIterations: 3,
});Specs
What it costs, what it guarantees.
Performance
- Capacity (continuous)
- exponential in dim
- Noise tolerance
- up to 30%
- Retrieval latency
- <2ms for 100K patterns
- Iterations
- 1–8 (auto-stop on convergence)
Guarantees
- Durability
- fsync per WAL commit
- Isolation
- MVCC · snapshot
- Audit
- BLAKE3 Merkle chain
- Encryption
- AES-256-GCM per-namespace
- Concurrency
- 100K+ ops/sec
Build it on Minds.
Start with the SDK. Ship in an afternoon. Run anywhere — Akasha Cloud, on-prem, or air-gapped.