Private Beta — 500+ devs on waitlist

Your AI Has
Amnesia

Every API call starts from zero. You're paying to re-teach the same context, over and over. That ends today.

$1
spent on memory
=
$2-3
saved on inference
Calculate Your Savings See How It Works

Works with OpenAI, Anthropic, Google, and 100+ models

the entire integration 
// Before: AI forgets everything
const client = new OpenAI({
  baseURL: "https://api.openai.com/v1"
});

// After: AI remembers everything
const client = new OpenAI({
  baseURL: "https://api.memoryrouter.ai/v1"
});

// That's it. Same code. Now with memory.
50-70%
Token Reduction
<50ms
Memory Retrieval
100+
Models Supported
∞
Memory Contexts

💰 Savings Calculator

How Much Are You Wasting?

Drag the slider. Watch your money come back.

$100 $5,000/mo $50,000
❌ Without MemoryRouter
Monthly inference $5,000
Wasted on re-context ~$2,500
$5,000/mo
✓ With MemoryRouter
Reduced inference $2,000
Memory cost $450
$2,450/mo
You save
$2,550/mo
51% reduction in AI costs
That's $30,600 back in your pocket per year

The Problem

The Hidden Tax on Every AI Call

You're not just paying for AI. You're paying for AI to re-learn what it already knew.

🔄

Groundhog Day Prompts

Every session, you re-explain user preferences, project context, conversation history. Again. And again.

📦

Bloated Context Windows

Stuffing 50k+ tokens into every request because the alternative is an AI that doesn't know anything.

💸

Token Inflation

50-70% of your tokens are redundant. You're paying for the same information over and over.

✗

Standard AI Integration

// Support chat - EVERY message:
messages: [
system: "Customer context... (2000 tokens)",
system: "Product catalog... (5000 tokens)",
system: "Past tickets... (3000 tokens)",
user: "What's my order status?"
]
10,000+ tokens per simple question
✓

With MemoryRouter

// Support chat - EVERY message:
messages: [
user: "What's my order status?"
]
// Memory auto-injects: customer context,
// relevant orders, past conversations
Only send what's new. Memory handles the rest.

Use Cases

Memory Changes Everything

Real products. Real savings. Real results.

🎧

Customer Support Bots

AI that actually knows your customers.

Token savings
73%
Before: Every message
"Load customer profile... past 50 tickets... product history... account status..."
After: Just the message
"Why was my refund delayed?"
Memory: Customer context, refund history, account status auto-injected
  • ✓ Remembers customer preferences
  • ✓ Knows past interactions across channels
  • ✓ No more "As I mentioned before..."
📈

Sales Intelligence

AI that remembers every deal detail.

Prep time saved
90%
How reps use it
"Brief me on the Acme Corp deal before my call"
→ AI recalls all past emails, objections, stakeholders, pricing discussions, competitor mentions, and decision timeline — instantly.
  • ✓ Full deal context, always available
  • ✓ Remembers past objections & responses
  • ✓ Tracks relationship history over months
🏥

Healthcare Assistants

Patient context that persists.

Context accuracy
100%
Continuity of care
Patient returns 3 months later. AI immediately knows:
• Previous symptoms discussed
• Medications mentioned
• Allergies noted
• Preferred communication style
  • ✓ Per-patient memory isolation
  • ✓ Longitudinal context tracking
  • ✓ HIPAA-ready architecture
📚

Docs & Knowledge Base

AI that learns what teams ask about.

Query accuracy
+40%
Smart context building
AI remembers which docs users reference most, common follow-up questions, and successful answer patterns.
"How do I set up OAuth?" → AI knows you're using Node.js, already tried the basic guide, need enterprise SSO
  • ✓ Learns from every interaction
  • ✓ Per-user context awareness
  • ✓ No re-explaining your setup
🤖

Personal AI Companions

AI that actually knows you.

Engagement
3x
True personalization
Week 1: Learning your communication style
Month 1: Knows your goals, preferences, habits
Month 6: Feels like talking to an old friend
  • ✓ Remembers conversations across months
  • ✓ Learns communication preferences
  • ✓ Builds genuine rapport over time
⚖️

Legal Assistants

Case context that sticks.

Research time
-60%
Per-case memory
AI remembers every document reviewed, argument developed, precedent cited, and strategy discussed — across weeks of case prep.
  • ✓ Full case history at query time
  • ✓ Tracks evolving legal strategy
  • ✓ Matter-level memory isolation

How It Works

Three Steps. Zero Complexity.

No vector database. No embedding pipeline. No ops burden.

1

Add Your API Keys

Bring your OpenAI, Anthropic, or OpenRouter keys. You pay providers directly — we never touch your inference spend.

2

Create Memory Keys

Each MemoryRouter key is a memory context. Create one per user, per project, per conversation — unlimited.

3

Memory Just Works

Every call builds memory. Every response uses it. Your AI gets smarter automatically. No extra code.

Powered by KRONOS — 3D Context Engine
Your App
Same SDK
MemoryRouter
KRONOS Engine
<50ms
AI Provider
+ memories
KRONOS analyzes context across 3 dimensions: Semantic (meaning), Temporal (time), Spatial (structure)

Integration

Your Code. Now With Memory.

Drop-in compatible with every OpenAI SDK.

Python 
# pip install openai
from openai import OpenAI

# Memory key = isolated context
client = OpenAI(
    base_url="https://api.memoryrouter.ai/v1",
    api_key="mr-user-123-key"
)

# That's it. AI now remembers this user.
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "..."}]
)
TypeScript 
// npm install openai
import OpenAI from 'openai';

// Each key = separate memory context
const client = new OpenAI({
  baseURL: 'https://api.memoryrouter.ai/v1',
  apiKey: 'mr-conversation-456'
});

// Same API. Memory handled automatically.
const response = await client.chat.completions.create({
  model: 'claude-3-5-sonnet-20241022',
  messages: [{ role: 'user', content: '...' }]
});
Multi-Tenant Pattern — One memory per user 
// SaaS pattern: each user gets isolated memory
function getClientForUser(userId: string) {
  return new OpenAI({
    baseURL: 'https://api.memoryrouter.ai/v1',
    apiKey: userMemoryKeys[userId]  // Per-user memory isolation
  });
}

// User A: "I prefer dark mode and brief responses"
// User B: "I like detailed explanations with examples"
// Each gets a personalized AI - memories never leak between users

Pricing

Memory That Pays for Itself

The math is simple: spend a little, save a lot.

Simple Pricing
$1 per 1M memory tokens
2-3x ROI
guaranteed return
  • ✓ Unlimited memory contexts
  • ✓ 90-day retention included
  • ✓ All 100+ models supported
  • ✓ Sub-50ms retrieval
  • ✓ Ephemeral key auto-cleanup
  • ✓ No inference markup — ever
How billing works
You bring your own API keys and pay providers directly for inference at their prices. We only charge for memory tokens — the storage and retrieval that makes your AI smarter. No markup on inference. No hidden fees. Ever.
Join the Beta — Free Tier at Launch

FAQ

Questions? Answered.

How does memory actually save me money? ▼
Without memory, you stuff context into every API call — user preferences, conversation history, project details. That's often 50-70% of your tokens. With MemoryRouter, relevant context is automatically retrieved and injected. You send less, get the same (or better) results. The $1/1M tokens you spend on memory saves $2-3/1M on inference.
What's KRONOS? How is it different from RAG? ▼
KRONOS is our proprietary 3D context engine that analyzes memory across three dimensions: Semantic (meaning and relationships), Temporal (when things happened and in what sequence), and Spatial (structure and hierarchy). Unlike basic RAG that just does similarity search, KRONOS understands context holistically — retrieving not just "similar" memories, but the right memories for your specific query.
Do you markup inference costs? ▼
Never. You bring your own API keys (OpenAI, Anthropic, OpenRouter, etc.) and pay providers directly at their published rates. We only charge for memory tokens. This keeps our incentives aligned: we make money when we save you money.
How does memory isolation work? ▼
Each MemoryRouter API key represents an isolated memory context. User A's memories never touch User B's memories. Create one key per user, per conversation, per project — whatever granularity makes sense for your app. Memories are encrypted at rest and in transit.
What happens to unused memory keys? ▼
Ephemeral keys that are never used are never persisted — no bloat, no cost. Active memories have a 90-day retention by default. You can extend retention for specific contexts or delete memories programmatically.
Which models are supported? ▼
All of them. MemoryRouter is OpenRouter-compatible, which means 100+ models work out of the box: GPT-4o, GPT-4 Turbo, Claude 3.5 Sonnet, Claude 3 Opus, Gemini Pro, Llama 3, Mistral, and many more. If it works with the OpenAI SDK, it works with MemoryRouter.
How fast is memory retrieval? ▼
Sub-50ms. KRONOS is optimized for real-time retrieval. In practice, memory lookup adds negligible latency to your API calls — usually less than the variance in provider response times.
Can I control what gets remembered? ▼
Yes. You can mark specific messages as "do not remember," delete specific memories, or wipe an entire context. We also provide analytics so you can see what's being stored and retrieved.
🚀

Stop Paying for AI Amnesia

Join 500+ developers in the private beta. Free tier at launch.

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