Vector Memory · Agentic Memory for Autonomous Agents

Memory that
thinks
not retrieves.

00 architecture

Raw input → AUDN curation → persistent graph.

INPUT_LAYER

Raw Input

Conversation turns, tool outputs, observations any unstructured agent context fed in as text.

AUDN_LAYER

AUDN Curation

Every memory evaluated: ADD, UPDATE, DELETE, or NO_OP if already known. Zero duplicates.

MAGMA_LAYER

MAGMA Graph

Persisted across 4 graph types in SQLite. Survives all session resets. REM compresses while idle.

Memory that thinks, not retrieves.
02 the problem

Standard RAG is amnesia with extra steps.

WITHOUT VEKTOR // SESSION AMNESIA
SESSION_001stored in context
SESSION_002✗ MEMORY WIPED
SESSION_003✗ starting over, again
WITH VEKTOR // ASSOCIATIVE GRAPH
SESSION_001→ stored · AUDN: ADD
SESSION_002→ graph +3 nodes +7 edges
SESSION_003→ 247 nodes · 7,180 edges
03 magma · live retrieval

Memory recalls in real time

Spec-decoding retrieval bi-encoder shortlist re-ranked by cross-encoder. Ranked, scored, graph-aware.

0.97
user prefers TypeScript over JavaScript2m ago
0.91
meeting with Sarah Friday 3pm14m ago
0.88
project: data pipeline · Python1h ago
0.74
active: 4,100 edges · 22,496 total3h ago
0.61
dreams: 11 REM last run 04:121d ago
04 audn · autonomous curation

Memory that curates itself

Every new input is evaluated: ADD, UPDATE, DELETE, or NO_OP. Zero drift. Zero bloat.

ADDstack: Node.js + Python
UPDATErecall latency → ~28ms avg
DELETEdeprecated endpoint noted
NO_OPprefers IBM Plex Mono already stored
Graph accuracy
0%
Drift rate
0.00%
Bloat pruned
0 KB
Token cost saved
$0.000
05 magma graph

Four layers. One mind.

Semantic
Temporal
Causal
Entity
~28ms graph-aware recall across 22,496 live edges
Temporal layer decays unused edges automatically no manual cleanup
Every save is an ADD / UPDATE / DELETE the graph edits itself in real time
One SQLite file. Portable graph db, zero servers to run
06 rem compression
1:1
fragment compression ratio
50 raw fragments → 1 core insight · 98% noise removed · gets smarter while idle
07 live recall

Retrieval that's intelligent, not just similar.

"What are my coding preferences?"
◐ searching MAGMA graph 22,496 edges ~28ms
User prefers TypeScript over JavaScript. Works mainly in the data pipeline project. Prefers concise, minimal explanations.
0.97 confidencesemantic + entity2m ago
08 magma graph types

Four layers. One mind.

LAYER_01

Semantic

Similarity between memories. Finds related concepts across your full context history.

LAYER_02

Causal

Cause → effect relationships. Understands why things happened, not just what.

LAYER_03

Temporal

Before → after sequences. Tracks how knowledge evolves and decays over time.

LAYER_04

Entity

Named entity co-occurrence. Connects people, projects, and events automatically.

09 install

Drop into any Node.js agent in minutes.

QUICKSTARTjavascript
// 1. Install
// npm install vektor-slipstream

import { createMemory } from 'vektor-slipstream';

// 2. Initialise
const memory = await createMemory({
  provider: 'gemini',
  apiKey:   process.env.GEMINI_API_KEY,
  agentId:  'my-agent',
  dbPath:   './my-agent.db',
});

// 3. Remember
await memory.remember("User prefers TypeScript");

// 4. Recall
const ctx = await memory.recall("coding preferences");
01

No external services

Pure SQLite. No cloud dependency. Your memory graph never leaves your server.

02

Model agnostic

Claude, Gemini, Groq, Mistral, OpenAI, Ollama, OpenRouter switch with one config change.

03

AUDN keeps it clean

Automatic curation prevents contradictions and duplicates. No manual management.

04

REM Cycle

Compresses 50 fragments into 3 core insights while your agent is idle.

Everything runs on your machine.

50+ MCP tools, one install, zero cloud.

Verified by benchmark.
0%
Adjusted accuracy
LongMemEval benchmark
Long-context memory recall
0ms
Avg. recall latency
12× faster than cloud
Local SQLite · zero network hop
0%
Graph accuracy
Drift rate near zero
MAGMA graph engine
MAGMA Graph
4-layer memory architecture
Semantic
Causal
Temporal
Entity
BM25 + vector RRF dual-recall
0/31
Causal inference tests
All passing
G-formula · MSM · IV · RCA
Judge GPT-4o-mini
Build Slipstream v1.8.0
Metric Adjusted accuracy
// LOCAL-FIRST
Zero cloud.
Zero data leakage.
// PEER-REVIEWED
Built on published
memory research
// COMMERCIAL
Production licence.
Email support.
// OPEN SOURCE
Vex & Vek-Sync
on GitHub
// INTEGRATIONS
LangChain · Claude
OpenAI · Mistral
Two paradigms. One winner.

VEKTOR is an active memory layer it evolves, curates, and reasons. Never passive.

PASSIVE STORE

The File Cabinet

Standard RAG vector stores

  • Stores vectors. Returns nearest neighbors. That's it.
  • No understanding of relationships between memories
  • Grows forever no curation, no decay, no prioritization
  • Requires you to engineer retrieval logic from scratch
  • Cloud dependency, monthly billing, data leaves your server
  • Retrieves the past. Cannot reason about the present.
MENTAL MODEL A drawer full of notes. You ask, it searches. Nothing more.
VS
ACTIVE MEMORY LAYER

The State Machine

VEKTOR Memory

  • MAGMA graph maps relationships: semantic, causal, temporal, entity
  • Memories evolve importance scores decay, conflicts resolve
  • Auto-curates: duplicate collapse, contradiction detection, pruning
  • Retrieval is intelligent: returns what's relevant now, not just similar
  • Local-first SQLite. $9/month. Your data, your server.
  • Knows what the agent learned, forgot, and should prioritize next.
MENTAL MODEL A mind that thinks about what it knows and gets smarter over time.
Skeptical devs ask: "Why not just use a vector store with a wrapper?" Because a vector store wrapper gives your agent a search bar, not a memory. VEKTOR installs once, runs locally, and uses the LLM provider you already pay for no cloud, no per-call fees.
Works with every stack.

The GUI, CLI and 50+ MCP-DXT tools are all inclusive across 24 integrated connectors.

who’s behind it
Privacy-first, by design
Built by a 30-year IT, storage & data networks veteran
PET privacy-enhancing technology, our customers’ privacy is a focus
Security & compliance docs are public, not behind a form
no risk to try
No lock in, no embedding charges
Cancel anytime, one click
Your memories are your SQLite file keep and migrate them, always, no lock-in, ever
Every question answered up front, no sales call needed
verified & transparent
Every claim has a receipt
Benchmark results published LongMemEval, peer-reviewed
SOC-style disclosure available on request
Open-source core, five Apache 2.0 tools, audit it yourself

Two bills. Consolidated.

VEKTOR: one price, forever. Runs on your machine. Zero embedding fees. No second bill, ever.
CLOUD MEMORY API
Bill 1 Monthly subscription
Bill 2 Embedding fee per operation
Bill 3 Egress & storage at scale
Your data lives on their servers.
ONGOING COST → GROWS WITH USAGE
VEKTOR LOCAL-FIRST
$9/month cancel any time.
Zero embedding fees uses your provider
Zero egress SQLite stays on your machine
Your graph. Your server. Your rules.
FLAT COST → ZERO ONGOING
NOTE Embedding costs vary by provider, typically $5–$40/month at modest volume on top of any memory subscription actual cost depends on your provider, model, and call frequency. VEKTOR doesn't eliminate LLM provider costs, only the memory subscription and embedding overhead stacked on top. For zero LLM and embedding costs, run Ollama with a local model that fits your GPU.

Own your stack.
Own your data. Forever.

No cloud. No embedding bill. No data handshake. VEKTOR runs on your machine, under your control, private backed by five Apache 2.0 CLI tools.

Open Source Ecosystem

Five tools. Apache 2.0. Built to pair with VEKTOR.

Free, open-source companions each one solves a piece of the memory-infrastructure puzzle so VEKTOR doesn’t have to.

VexVector DB migration move embeddings between Pinecone, Qdrant, Chroma, and SQLite without hand-rolled scripts.
Vek‑SyncMCP config sync keeps your MCP server configs consistent across every machine and client you run Claude on.
ViaUniversal AI integration one adapter layer that speaks to Claude, GPT, Gemini, and local models interchangeably.
ProvenanceCryptographic proof-of-authorship signs and timestamps agent output so you can prove what your AI actually wrote.
VörwatchVPS anomaly detection watches your server for unusual behaviour and flags it before it becomes an incident.
EXPLORE OPEN SOURCE TOOLS
Full Product Ecosystem

One price. Everything included. Own it forever.

No cloud, no add-ons, no upsell ladder every core system VEKTOR ships with, bundled into one flat subscription.

Zero‑knowledge architectureYour data never leaves your machine VEKTOR can’t see it, we can’t see it, nobody sees it but you.
Self‑organising MAGMA graphFour-layer memory graph that prunes, decays, and reorganises itself automatically as your agent works.
Spec‑decoding retrievalBi-encoder shortlist re-ranked by cross-encoder ranked, scored, graph-aware recall in ~28ms.
Sovereign identity & Cloak vaultCredential vault and identity management that stays local no third party ever holds your keys.
Slipstream SDKOne npm install, $9/month, cancel any time no infra to stand up, no ops to run.