VEKTOR MEMORY - Slipstream

Vector memory for agentic AI. Local-first. Privacy Enhanced Technology. Data Sovereignty.

Documentation · Install · Quick Start · MCP Tools · Pricing

VEKTOR fixes the architecture. Not the prompt.

The problems are architectural, not instructional. You cannot prompt your way out of a stateless architecture.

Session starts        ──►  Reconstruct context from logs   ──►  10,000–30,000 tokens burned
                                                                 before a single line of work runs

Cron job fires        ──►  Agent has no memory of last run  ──►  Repeats completed work
                                                                 Loops. Bills stack up.

Add more guardrails   ──►  Longer prompts                   ──►  More tokens
                      ──►  More complexity                  ──►  More failure surface
                      ──►  More maintenance                 ──►  Less time saved

The control paradox: the more control you try to add through prompts, the more expensive and fragile the system becomes. You end up spending more time fixing the automation than the automation saves.

Related MCP server: aura-memory

The Solution Stack

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   DXT       drag-and-drop install · 50+ tools registered    │
│             automatically · no JSON editing                 |  
│                                                             │
│   MCP       stateless on-demand tool invocation             │
│             no persistent process between runs              │
│             agent wakes, works, terminates cleanly          │
│                                                             │
│   Skill     ~150 tokens of scoped context injected          │
│   Files     only when relevant · unloaded when done         │
│             90% less context overhead per session           │
│                                                             │
│   VEKTOR ◄─ persistent memory graph · BM25 + vector RRF     │
│             recall · self-organising intelligence layer     │
│             state that actually survives between sessions   │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Install

npm install -g vektor-slipstream
vektor setup

Or drag vektor-slipstream.dxt directly into Claude Desktop. All 50+ tools register automatically. No JSON editing. No path configuration.

Quick Start

const { createMemory } = require('vektor-slipstream');

const memory = await createMemory({
  agentId: 'my-agent',
  licenceKey: process.env.VEKTOR_LICENCE_KEY,
});

// Store a memory
await memory.remember('User prefers TypeScript. Deployed to prod on Friday.');

// Recall by semantic similarity -- sub-28ms, fully local
const results = await memory.recall('deployment preferences', 5);
// → [{ content, score, id, timestamp }]

// Traverse the associative memory graph
const graph = await memory.graph('TypeScript', { hops: 2 });

// What changed in the last 7 days?
const delta = await memory.delta('project decisions', 7);

// Morning briefing from recent memories
const brief = await memory.briefing();

Before vs After

Session Flow

Task triggered (cron / webhook / user action)
        │
        ▼
Skill File injected based on task context          ~150 tokens
        │
        ▼
vektor_recall_rrf called                           ~800 tokens
Top-10 semantically relevant memories returned
        │
        ▼
Agent classifies situation from memory history
        │
   ┌────┴─────────────────────┐
   ▼                          ▼
familiar pattern          novel / previously failed
proceed autonomously       surface for human review
   │                          │
   └────────────┬─────────────┘
                ▼
Execute task via MCP tools
        │
        ▼
Result stored via vektor_store
Memory graph updated with outcome
        │
        ▼
Session ends · SQLite persists everything
        │
        ▼
Next invocation: same startup cost · full outcome history available

Total context overhead for a routine task: under 2,000 tokens. The same task with a monolithic system prompt and history reconstruction: 15,000–50,000 tokens, with no retention of outcome.

Performance

**LoCoMo benchmark results

CLI Chat -- Persistent Memory Across Every Session

npx vektor chat                                        # auto-detects Ollama
npx vektor chat --provider claude                      # Anthropic Claude
npx vektor chat --provider groq --model llama-3.3-70b-versatile
npx vektor chat --provider openai
npx vektor chat --provider gemini

In-chat commands:

One-liner commands:

# Store facts
npx vektor remember "I prefer TypeScript over JavaScript"
npx vektor remember "deadline is Friday" --importance 5
cat meeting-notes.txt | npx vektor remember

# Query
npx vektor ask "what stack am I using?"
npx vektor ask "what did we decide about the database?"

# Autonomous agent
npx vektor agent "summarise everything I know about project Alpha"
npx vektor agent "research AI memory tools" --steps 15 --provider groq

Claude Desktop Extension (DXT)

Install the .dxt extension for zero-config persistent memory in every Claude Desktop session.

Install: drag vektor-slipstream.dxt onto the Claude Desktop Extensions page.

Once installed, Claude automatically:

• Recalls relevant context at session start

• Stores facts and decisions during conversation

• Summarises and consolidates at session end

All 50+ tools available. No configuration beyond your licence key.

Download: vektormemory.com/docs/dxt

MCP Tools -- 50+

Memory

Cloak -- Stealth Browser, SSH, Fetch

Identity + Behaviour (Anti-Bot Bypass)

CAPTCHA

Compression

Multimodal

Agent

All CLI Commands

npx vektor setup       # First-run wizard -- licence, hardware, integrations
npx vektor activate    # Activate licence key on this machine
npx vektor test        # Test memory engine with progress bar
npx vektor status      # System health check
npx vektor mcp         # Start Claude Desktop MCP server
npx vektor rem         # Run REM dream cycle (memory consolidation)
npx vektor chat        # Persistent memory chat (all LLMs)
npx vektor remember    # Store a fact
npx vektor ask         # Query memory + LLM answer
npx vektor agent       # Autonomous goal executor
npx vektor help        # All commands

Claude Code Setup

Add to .claude/settings.json in your project:

{
  "mcpServers": {
    "vektor": {
      "command": "node",
      "args": ["/path/to/node_modules/vektor-slipstream/index.js"],
      "env": {
        "VEKTOR_LICENCE_KEY": "your-licence-key",
        "CLOAK_PROJECT_PATH": "/path/to/your/project"
      }
    }
  }
}

What's Included

Memory Core (MAGMA)

• 4-layer associative graph -- semantic, causal, temporal, entity edges

• bge-small-en-v1.5 bi-encoder + ms-marco cross-encoder reranker

• BM25 + stemmed BM25 + RRF fusion -- keyword + semantic dual-channel recall

• Persistent entity index -- guaranteed named-entity retrieval

• Foresight extraction -- future-tense statements stored with temporal metadata

• ADD-only contradiction detection -- full history preserved, no silent overwrites

• REM dream cycle -- up to 50:1 memory compression

• Sub-28ms recall -- local SQLite, no network required

• Local ONNX embeddings -- $0 embedding cost, no API key required

Intelligence Layer (runs automatically, no config)

Integrations

• Claude Desktop -- DXT extension, 50+ tools, auto-memory on every session

• Claude Code -- MCP server, all 50+ tools

• CLI -- chat, remember, ask, agent commands

• LangChain -- v1 + v2 adapter included

• OpenAI Agents SDK -- drop-in integration

• Groq · Gemini · Ollama · NVIDIA NIM -- provider agnostic

Hardware Auto-Detection

Zero config. VEKTOR detects and uses the best available accelerator:

• NVIDIA CUDA -- GPU acceleration

• Apple Silicon -- CoreML

• CPU -- optimised fallback, works everywhere

Environment Variables

Research Foundation

Built on peer-reviewed research:

• MAGMA (arxiv:2601.03236) -- Multi-Graph Agentic Memory Architecture

• EverMemOS (arxiv:2601.02163) -- Self-Organizing Memory OS

• HippoRAG (arxiv:2405.14831) -- Neurobiologically Inspired Long-Term Memory (NeurIPS 2024)

• Mem0 (arxiv:2504.19413) -- Production-Ready Agent Memory

• LoCoMo Benchmark (arxiv:2402.17753) -- Long-Context Conversational Memory

Pricing

Purchase at vektormemory.com

What's New in the Changelog:

v1.7.8 PREVIEW 16 Jul 2026 — Catch-up Brief Deterministic Grounding · Reasoning-Model Tool-Call Fix · Floating Desk Toolbar · Cross-Theme Colour Consistency Catch-up Brief — Deterministic Memory Grounding The catch-up brief previously left it up to whichever model was selected to decide whether to search memory before answering — strong tool-callers mostly stayed grounded, but weaker/local models frequently skipped retrieval and padded the answer with plausible-sounding invention. Retrieval now runs server-side first, always, via a fixed set of memory queries covering focus/decisions/open-questions/recent-notes, merged into one context block. The model receives a strict section template plus an explicit instruction to only state what's in that context, writing “Nothing new this week” for empty sections instead of inventing content. Output is now consistent across providers, including smaller local models, and is inherently self-updating since it re-queries live memory on every request.

Bug Fix — Reasoning-Model Tool Calls (Luna, Terra, Sol & o-series) The new gpt-5.6 family models added in v1.7.7 (Luna, Terra, Sol) plus other o-series/gpt-5-family models failed every DESK tool-calling request with Function tools with reasoning_effort are not supported. Simply omitting reasoning_effort wasn’t enough — these models still apply their own default server-side, which conflicts with function tools on /v1/chat/completions. Fixed by explicitly sending reasoning_effort: 'none' whenever a reasoning-family model is in play, since this code path always sends tools.

Desk Toolbar — Floating Frosted Panel The bottom input bar (formatting row, model picker, THINK/COLLAB/JOT) is now a floating translucent panel with backdrop blur and rounded corners on all sides, inset from the window edge, instead of a flat opaque bar flush to the bottom.

Cross-Theme Colour Consistency Fixed the silver theme’s background layering, where the card surface colour was identical to the page background (no visible depth) and the next step jumped straight to a harshly dark hover state. Standardised the quick-action toolbar, send buttons, and sidebar navigation highlighting to draw from the same theme accent variables instead of one-off hardcoded colours, and gave graph “Semantic” nodes a fixed, theme-independent colour so they stay visible against every theme instead of fading to near-white.

PREVIEW — Install: npm install -g ./vektor-slipstream-1.7.8-preview.tgz Upgrade from v1.7.7 or v1.7.6 at any time. Download →

v1.7.7 PREVIEW 12 Jul 2026 — Sentinel Proactive Memory Injection · Faraday Independent Watchdog · Tamper-Evident Audit Log · Supersession Fix · Early Access Sentinel — Selective Proactive Memory Injection New module wired into both DESK and JOT chat paths. Adds a proactive push on top of VEKTOR’s existing pull-based recall: on each turn, before the LLM call, checks whether a stored memory is relevant enough to the current message to surface unprompted. A live task-completion aid — does not affect LongMemEval/LoCoMo-style benchmark scores, which are pull-based QA. Three layers: a core gate with per-agent adaptive threshold and cooldown, integrity gating that re-verifies the candidate against the DB right before injection (follows the supersession chain to its active tip, rejects expired rows), and an opt-in self-questioning pass that judges relevance with one extra LLM call. Counterfactual threshold calibration available via CLI (node vektor-sentinel.js calibrate|feedback|stats).

Model Catalog Refresh Gemini updated to Gemini 3.5 Flash. xAI updated to Grok 4.5. OpenAI additions Luna, Terra, and Sol are now live.

Bug Fixes — Supersession & Data Integrity Fixed the root cause of supersession never firing in production: three stacked score-scale bugs (Layer 6 reranking, RRF fusion, cross-encoder reranking) each overwrote the recall score with a progressively less-comparable value before the dedup threshold check ever saw it. The true raw cosine similarity is now captured at the point it’s computed and threaded through every later stage. Also fixed a NULL id bug affecting every fresh memory on Windows (an explicit id is now generated before insert, rather than relying on SQLite’s unrelated internal rowid), and fixed supersession silently reporting success even when the LLM-verified conflict-resolution gate had actually declined the write.

Faraday — Independent Integrity Watchdog & Tamper-Evident Audit Log New standalone watchdog process runs continuously via OS task scheduling, independent of any active Faraday MCP session, watching AI-assistant MCP configuration files across seven clients plus Faraday’s own core enforcement files. Every gate event now also carries a chained hash linking it to the previous event, so altering, deleting, or reordering a historical entry breaks every hash after it, detectably.

Other Fixes Faraday corpus auto-update now actually extracts the verified archive. Faraday status display no longer shows a corrupted glyph for session state. JOT flashcards copy button and collab-panel CSS fixed. Packaging cleaned up further (missing files added to the npm allowlist), and a long-standing display-glyph corruption pattern was cleared from 16 additional files across the SDK.

PREVIEW — Install: npm install -g ./vektor-slipstream-1.7.7-preview.tgz Upgrade from v1.7.6 at any time. Download →

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