The core idea is simple — fetch only the context your AI actually needs from memory, as fast as possible, at the lowest cost possible, so it can give better answers.
Every time your AI starts a new session, it starts from zero. Current solutions either scan everything (expensive and slow) or return irrelevant chunks (inaccurate).
Most memory systems are flat RAG pipelines — they embed a query, find similar text, dump it in the prompt, and hope for the best. There's no intelligence in the retrieval. No concept of what's important. No hierarchy.
CaSVeM treats memory like a CPU cache hierarchy — layered, intelligent, and fast. The right context reaches your AI at near-zero cost, every time.
| Feature | CaSVeM | Mem0 | HydraDB | Supermemory | Zep |
|---|---|---|---|---|---|
| Fully local deployment | ✓ Always | ✗ | ✗ | ✗ | Partial |
| Zero per-query cost | ✓ ~$0 | ✗ API cost | ✗ $249/mo+ | ✗ Token-based | ✗ API cost |
| LLM as cache controller | ✓ | ✗ | ✗ | ✗ | ✗ |
| Learned cache index | ✓ Stage 4.5 | ✗ | ✗ | ✗ | ✗ |
| Hierarchical cache layers | ✓ L1–L5 | ✗ Flat | ✗ Flat | ✗ Flat | ✗ Flat |
| Query-aware synthesis | ✓ | ✗ Raw chunks | ✗ Raw chunks | ✗ Raw chunks | ✗ Raw chunks |
| Typed memory chains | ✓ Causal/temporal | ✗ | Partial | Partial | Graph only |
| Open source | ✓ Apache 2.0 | ✓ | ✗ | Partial | Partial |
| Privacy preserving | ✓ Never leaves device | ✗ Their servers | ✗ Cloud | ✗ Cloudflare | ✗ Cloud |
A neural classifier trained on your access patterns predicts which memory slots to search before vector search runs. 90% smaller search space. Gets smarter with every query.
Novel — first in categoryA local model decides retrieval depth dynamically. No algorithm. No fixed rules. The controller understands your query and decides whether L2 is enough or L4 is needed.
Intelligent retrievalTyped directed graphs for causal, temporal, and departmental retrieval. Decision chains, finance chains, entity chains — traverse history in one hop, not dozens of searches.
Causal + temporalRuns entirely on your hardware. No cloud API. No data sent anywhere. No per-query cost after setup. Works offline. Air-gapped enterprise deployments supported.
Privacy preservingEvery memory line has a composite score: importance × recency × access frequency × uniqueness. Hot memories promote up. Cold memories demote down. Automatically.
Auto-managedThe output is not raw chunks. A local LLM synthesises a tailored memory block written specifically for each query. Every prompt gets a different memory. Every answer gets better context.
Not raw chunks