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Vector Memory & Agentic AI Memory for Autonomous Agents

Memory that
thinks
not retrieves.

// why vektor

Local-first vector memory with a self-organising 4-layer graph. Spec-decoding retrieval. Zettelkasten-linked edges.

✓Owns your data — zero egress, zero API dependency
✓~28ms recall vs 340ms cloud — 12× faster in production
✓One-time licence — no monthly embedding bill ever
✓Remembers why things connect, not just what they are
✓81% LongMemEval (vs 62% baseline) — peer-reviewed benchmark
~28ms
avg recall
<50ms
p95 latency
$0
embedding cost
100%
local · zero egress
Cloud memory vendors average 200–800ms recall · VEKTOR: ~28ms
$9/month · Cancel any time. Built on peer-reviewed research
// VEKTOR
Get Vektor
own your memories forever
→
// DOCS
Read
the Docs
→
// OSS
Vektor
GitHub
↗
// APP
VEKTOR Notes
Google Play
↗
See it in action
Everything runs on your machine.

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

Benchmark · LongMemEval
Built to perform. 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 v1.7.7
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
THE PROBLEM
The Problem
Standard RAG is amnesia with extra steps.
WITHOUT VEKTOR // SESSION AMNESIA
SESSION_001
SESSION_002
✗ MEMORY WIPED — context lost
SESSION_003
✗ MEMORY WIPED — starting over again
SESSION_N
✗ Agent has no idea who you are
WITH VEKTOR // ASSOCIATIVE GRAPH
SESSION_001
→ STORED · AUDN: ADD
SESSION_002
→ GRAPH UPDATED +3 NODES +7 EDGES
SESSION_003
→ GRAPH: 247 NODES · 7,180 EDGES
SESSION_N
→ COMPLETE ASSOCIATIVE MEMORY INTACT
// 01 · recall speed
~28ms
average recall latency
Instant memory retrieval
Local SQLite lookup — no API roundtrip, no cloud latency. Your agent gets context in ~28ms avg, under 50ms p95.
live · cloud vendors avg 200–800ms
// 02 · graph growth
247
nodes · 7,180 edges · growing
Self-organising MAGMA graph
Semantic · Causal · Temporal · Entity. Every remember() call wires new edges. The graph builds itself while your agent works.
live · AUDN curation · zero duplicates
// 03 · rem compression
50:1
fragment compression ratio
Gets smarter while idle
7-phase REM dream cycle runs while your agent sleeps. 50 raw fragments → 1 core insight. 98% noise removed. Signal preserved.
async · never blocks your agent
THE ARCHITECTURE
Architecture
Raw input → AUDN curation → persistent graph.
INPUT_LAYER

Raw Input

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

CONVERSATIONTOOL_OUTPUTOBSERVATION
→
AUDN_LAYER

AUDN Curation

Every memory is evaluated: ADD new info, UPDATE existing, DELETE contradictions, or NO_OP if already known. Zero duplicates.

ADDUPDATEDELETENO_OP
→
MAGMA_LAYER

MAGMA Graph

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

SEMANTICCAUSALTEMPORALENTITY
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.

The Core Difference
Two paradigms. One winner.

Most vector stores are passive. They store what you put in and return what you ask for. VEKTOR is an active memory layer — it evolves, curates, and reasons about what your agent should remember.

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.
THE CORE
Core Systems
Built different. By design.
MAGMA · Live Retrieval
Memory recalls in real time
Spec-decoding retrieval — bi-encoder shortlist re-ranked by cross-encoder. Two-stage precision. Ranked, scored, graph-aware.
0.97
user prefers TypeScript over JavaScript
2m ago
0.91
meeting with Sarah — Friday 3pm
14m ago
0.88
project: data pipeline · Python
1h ago
0.74
active: 4,100 edges: 22,496
3h ago
0.61
dreams: 11 — REM last run 04:12
1d ago
REM Compression
Gets smarter while idle
7-phase dream cycle. 50 raw fragments → 1 core insight. 98% noise removed. Signal preserved.
Before REM — 50 raw fragments
After REM — core signal retained
50:1
COMPRESSION
RATIO
SELFORG · Zettelkasten Engine
Graph that wires itself
On every remember() call, a background agent extracts keywords, finds related memories, classifies the edge type — SUPPORTS, EXTENDS, CONTRASTS, PREREQUISITE — and writes a Zettelkasten context note linking it to everything connected. Async. Never blocks your agent.
SUPPORTS EXTENDS CONTRASTS PREREQUISITE
MEMORY_GRAPH // LIVE
ROOT SEMANTIC CAUSAL TEMPORAL ENTITY MEM 001–084 MEM 085–147 MEM 148–215 MEM 216–343 SEMANTIC 340 CAUSAL 190 TEMPORAL 215 ENTITY 127
AUDN · Autonomous curation
Memory that curates itself
Every new input is evaluated: ADD new info, UPDATE contradictions, DELETE stale facts, or NO_OP if already known. Zero drift. Zero bloat. Graph stays clean automatically.
0
added
0
updated
0
deleted
0
no-op
graph accuracy
99.1%
drift rate
0.00%
memory deviation/cycle
bloat pruned
0 KB
stale data removed
token cost saved
$0.000
vs naive full-context
zero drift · zero bloat
0 ops processed
THE ECOSYSTEM
Integrations
Works with every stack.

LangChain

Drop-in memory layer for LangChain agents.
recall() returns context, remember() stores.
v1 + v2 adapters included.

OpenAI Agents SDK

Persistent memory for OpenAI agent loops.
Recalled context injected into system prompt.
GPT-4o and o-series models supported.

Claude MCP Server

Full MCP module — vektor_recall, vektor_store,
cloak_ssh_exec, cloak_fetch tools.
Connect Claude Desktop in minutes.

Gemini / Groq / Ollama / OpenRouter

Provider-agnostic single config switch.
Key pooling for Gemini — multiple API keys,
waterfall rotation, zero rate-limit downtime.

Mistral MCP

vektor_memoire HTTP tool for Le Chat
and Mistral API agents. Local bridge on
localhost:3847. French-first sovereign memory.

CLOAK

50+ tool MCP layer for Claude Desktop.
Stealth browser, credential vault, CAPTCHA solving,
behaviour injection. Zero cloud. One install.
Integration
LATEST RELEASE
10 New Agentic Skills & Faraday Hardening

Ten new skills built for agentic workflows (token conservation, agent delegation, task orchestration, PR prep, slop detection, and more), a hardened Faraday security gate with an independent integrity watchdog and tamper-evident audit log, and JOT interface fixes across flashcards, collab styling, and the synthesis panel scrollbar.

10 skills  ·  watchdog  ·  audit-log  ·  JOT fixes
Effort Parameter Support

Per-session effort control for Claude models, real memory search for the Desk agent, and a refreshed model catalog across Claude, OpenRouter, and Groq.

effort  ·  desk-search  ·  model-catalog
TUI Menu — Featured Top Section

Menu leads with gold starred section. ★ jot and ★ graph open notes desk and memory graph straight from terminal.

★ activate  ·  ★ chat  ·  ★ jot  ·  ★ graph
Chat — /prompt Command

New /prompt command shows your current system prompt, or /prompt <text> to override it on the fly. Wired into tab autocomplete and the /help table.

/prompt
New CLI Commands

vektor graph (alias vektor dashboard) starts the graph server and opens it in your browser. vektor jot stores a quick idea straight to memory at importance 4 — no need to drop into chat first.

vektor graph  ·  vektor dashboard  ·  vektor jot
Causal Inference Engine

Your agent now knows why memories are connected — not just what. Four-phase causal engine: G-Formula, MSM/IPW, IV Bounds, and Root Cause Analysis. Traces agent failures backwards through the causal chain and predicts the fix.

G-Formula  ·  MSM/IPW  ·  IV Bounds  ·  RCA
DeepFlow v2 — Deterministic Research

8-step deterministic pipeline replaces the old unbounded loop: DECOMPOSE → VAULT-FIRST → SWEEP → LOCI → COMMIT → ADVERSARIAL → SYNTHESISE → CRITIC+PATCH. Every run is auditable, reproducible, and hallucination-resistant.

deep:true  ·  adversarial_search  ·  loci_rank  ·  patch
JOT — Write With Your Memory

Two-pass whitepaper generation via Groq LLaMA with APA7 citation infrastructure. Your notes surface relevant memories as you write. Ghost-text autocomplete, briefing scheduler, post-generation citation scanner.

Notes RAG  ·  Two-pass  ·  APA7  ·  Briefing
JOT — Notes & Writing

Integrated notes layer with TAG pill, notes RAG, and two-pass article generation via Groq LLaMA. APA7 citation infrastructure, post-generation citation scanner, ghost-text autocomplete, and briefing scheduler. Notes live alongside memories in local SQLite — never leaves your machine.

Notes  ·  RAG  ·  Synthesis  ·  Citations  ·  Briefing
MAGMA Causal Graph

Four-layer associative memory graph: semantic, causal, temporal, and entity. Memories connect to each other across all four dimensions simultaneously. The graph server visualises live relationships as you work.

vektor graph  ·  vektor dashboard
FadeMem & REM Consolidation

Importance-weighted memory decay with REM cycle consolidation. Low-signal memories fade over time. High-signal memories strengthen. Pinned memories are permanent. The result is a memory store that stays focused on what actually matters.

memory.pin(id)  ·  memory.briefing()  ·  rem cycle
Full changelog →
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 — AUDN decides ADD/UPDATE/DELETE
await memory.remember("User prefers TypeScript");

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

// 5. Traverse the graph
const g = await memory.graph("TypeScript", { hops: 2 });

// 6. What changed in 7 days?
const d = await memory.delta("architecture", 7);
01

No external services

Pure SQLite. No cloud dependency, no API keys for memory. Your memory graph never leaves your server. LLM providers process queries per their own privacy policies.

02

Model agnostic

Claude, Gemini, Groq, Mistral, OpenAI, Ollama, OpenRouter. Switch provider with one config change. Key pooling for Gemini — waterfall rotation across multiple keys.

03

AUDN keeps it clean

Automatic curation loop prevents contradictions and duplicates. The graph stays consistent without any manual management.

04

REM Cycle

Background process compresses 50 fragments into 3 core insights. Runs while your agent is idle. Run via vektor rem from the CLI.

Built on Research
Implementation original. Concepts peer-reviewed.
ARXIV // 2601.03236

MAGMA: A Multi-Graph-based Agentic Memory Architecture for AI Agents

The graph type system underpinning VEKTOR's four memory layers — semantic, causal, temporal, and entity.

GRAPH ARCHITECTURE ARXIV // 2601.02163

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning

Self-organizing memory architecture for structured long-horizon reasoning. Informs VEKTOR's REM cycle approach to memory consolidation and lifecycle management across extended agent sessions.

MEMORY LIFECYCLE ARXIV // 2504.19413

Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Scalable long-term memory via dynamic extraction, consolidation, and graph-based retrieval. Informs VEKTOR's AUDN curation loop and REM compression cycle. Mem0 benchmarks show 90%+ token cost reduction — consistent with VEKTOR's synthesis approach.

MEMORY COMPRESSION LETTA.COM

Letta / MemGPT: LLM as Operating System

The memory-as-OS paradigm that inspired VEKTOR's approach to agent context management and recall.

AGENT OS
READ FULL RESEARCH BREAKDOWN →
// THE REAL COST OF AI MEMORY

Two bills.
Or one price. Forever.

Cloud memory APIs charge twice: a subscription for the service, and an embedding API fee on every single store and recall operation. Those embedding calls add up fast — at production agent volume they often exceed the subscription itself. VEKTOR runs on your machine and routes through the LLM provider you already pay for. No second bill. No hidden meter.

// 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 and model. At modest agent volume — hundreds of daily memory operations — embedding API charges typically run $5–$40/month on top of any memory subscription. This estimate is illustrative; your actual cost depends on your provider, model, and call frequency. VEKTOR does not eliminate your LLM provider costs — it eliminates the memory subscription and the dedicated embedding overhead on top of it.
// OPEN SOURCE ECOSYSTEM

Vex, Vek-Sync, Via & Provenance

Four Apache 2.0 CLI tools that pair with VEKTOR — vector DB migration, MCP config sync, universal AI integration, and cryptographic proof-of-authorship.

EXPLORE OPEN SOURCE TOOLS →
// FULL PRODUCT — EVERYTHING INCLUDED

One price.
Own your data forever.

No cloud. No embedding bill. No data handshake.
VEKTOR runs on your machine, under your control, permanently.

Zero-knowledge architecture Self-organising MAGMA graph Spec-decoding retrieval Sovereign identity & Cloak vault Slipstream SDK — npm install $9/month · cancel any time
GET VEKTOR — $9/mo → FULL SPECS →

Your memory graph is a portable SQLite file — no lock-in, ever.