Long-running autonomous agents
Multi-day workflows that wake up, recall what they did yesterday, and resume — without dumping the entire conversation back into context every turn.
Solutions · For builders
The memory layer for autonomous agents.
Today's agents are amnesiac. They restart from zero every conversation, lose everything you taught them last week, and burn tokens re-reading the same transcript. Hypersave is the cognitive memory layer that fixes that — five sectors, brain-inspired decay, sub-200ms hybrid retrieval.
LoCoMo benchmark
86%
agent-memory standard
p95 latency
<200ms
hybrid retrieval
Token savings
92%
vs. full transcript
Fact accuracy
94%
cited answers
What you can build
Agents that compound, instead of starting over.
Per-user agent isolation
One agent codebase, millions of separate cognitive sectors. Memory isolation is enforced at the storage layer, not bolted on with prompt prefixes.
Multi-agent teams with shared memory
A research agent learns; a writing agent reads. Shared episodic memory across agent boundaries with RRF hybrid retrieval.
Token-efficient context
92% token savings vs. dumping the full transcript. Synthesised answers, not chunks. The agent reads what it needs and skips what it doesn't.
In code
Three lines to give an agent a mind.
The Hypersave SDK is the contract. remember() writes into the cognitive sectors. recall() fuses graph, vector and keyword search across the user's history and returns a synthesised answer with source citations — not chunks.
SDKs
TS · Py
Sectors
5
cognitive types
agent.tsts
import { Hypersave } from "@hypersave/sdk";
const memory = new Hypersave({ apiKey: process.env.HYPERSAVE_KEY });
// After every turn — write the salient bits.
await memory.remember({
userId: "alice",
text: "Alice prefers responses in markdown. Working on a Q3 board deck.",
sector: "semantic",
});
// Before the next turn — recall the synthesised answer.
const { answer, citations, confidence } = await memory.recall({
userId: "alice",
query: "what format does Alice want, and what is she working on?",
});
// answer → "Alice wants markdown. She is preparing a Q3 board deck."
// citations[0] → { sector: "semantic", capturedAt: "2026-04-22T14:11:09Z", ... }
// confidence → 0.94Synthesised answer + citations + confidence. Not a wall of chunks.
Built on
One stack. The same research powers Khyaa, Nuro Chat and your agents.
Hypersave
Cognitive memory layerFive sectors (reflective, semantic, procedural, episodic, emotional). Ebbinghaus decay. Knowledge graph + vector + keyword + RRF fusion. SOC 2 Type II. TS + Python SDKs.
Read the docs →AVALON-2B
Self-RAG runtime (optional)Sub-3B Self-Reflective RAG model. Apache 2.0. The agent itself can decide when to consult memory using a five-token reflection vocabulary. 40 tok/s on Apple M3.
See the model →We replaced a 4,000-token rolling-buffer hack with three Hypersave calls. Our agents stopped repeating themselves in week one. Bills dropped, evals went up.
Get started
Ship a memory-aware agent this afternoon.
Free tier on platform.hypersave.io. SOC 2 Type II for production. Self-host available for regulated workloads — see sovereign AI.