automem.ai

Deploy once, remember everywhere. Works with every AI.

The only universal memory layer for AI assistants. One-click deployment, cross-device sync, works with Claude, Cursor, and any MCP-compatible LLM. Your AI's memory follows you everywhere for just $5/month.

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Latest News

Stay updated with the latest AutoMem Cloud features

October 2025

AutoMem Cloud Now Features Optional SSE MCP

Automem cloud now includes an optional Server-Sent Events (SSE) sidecar that enables real-time memory integration with conversational AI platforms that don't natively support MCP servers.

This update allows AutoMem to work seamlessly with:

  • ElevenLabs Conversational AI - Real-time voice agents with sub-100ms latency, now with memory capabilities
  • ChatGPT - Use memories in your ChatGPT conversations (requires developer mode)
  • Claude Web & Mobile - No longer limited to Claude Desktop, Automem works with iOS/Android apps and web interface

The SSE sidecar acts as a lightweight bridge, streaming memory updates and retrievals in real-time while maintaining AutoMem's signature performance (20-50ms queries). This means conversational AI agents can now access contextual memory without waiting for full response cycles.

Technical Details:

  • Low-latency memory streaming via SSE protocol
  • Compatible with WebSocket and HTTP streaming APIs
  • Automatic fallback for platforms without native MCP support
  • Runs great on Railway, or anywhere you can run a Docker container

Why AutoMem?

🌐

Universal MCP Compatibility

Works with ANY MCP-compatible LLM. Claude Desktop, Cursor, ChatGPT, Claude Web & Mobile, ElevenLabs Conversational AI - even future AI tools automatically work. Not vendor-locked.

🚀

One-Click Deploy

Railway deployment with a single command. Docker containerized. Once deployed, accessible from ALL devices and services. $5/month.

🧠

Graph-Based Memory

Powered by FalkorDB + Qdrant. Creates rich semantic relationships between memories. Knowledge graphs with 11 relationship types, not just vector search.

See It In Action

Watch how AutoJack uses AutoMem to recall your preferences and context, making every interaction feel natural and personalized.

AutoJack
Online

👋 Hi! I'm AutoJack. How can I help you today?

Hey! I'm starting a new web project.

Awesome! Let me check what you usually work with...

Thinking...

Let me recall their development preferences

recall_memory
query: "web development stack"
tags: ["coding", "preferences"]
Preference
Prefers React with TypeScript for web apps
⭐⭐⭐
Pattern
Always uses Tailwind CSS for styling
⭐⭐⭐

Perfect! I remember you like React + TypeScript with Tailwind CSS. Want me to set that up?

Yes! And use Vite this time.

Great choice! Do you want your usual folder structure too?

Yes please!
Thinking...

Vite is new - I should store this preference

store_memory
content: "Prefers Vite as build tool"
type: "Preference"
importance: 0.9
associate_memories
type: "RELATES_TO"
Vite ↔ React + TypeScript

✅ Perfect! Setting up React + TypeScript + Vite + Tailwind

💾 Remembered your Vite preference for next time!
Perfect, thanks! 🙌

You're all set! I'll remember these preferences for your future projects. Happy coding! 🚀

Session Summary
✅ Retrieved 2 existing memories
💾 Stored 1 new preference
🔗 Created 1 memory association

The Only Memory Solution That...

✅

Works with any MCP LLM

Not locked to Claude or Cursor. Any MCP-enabled AI instantly works.

✅

Deploys to cloud in one click

No complex setup. Railway deployment takes seconds.

✅

Syncs across all devices

Same memory on desktop, laptop, mobile. True cross-device AI.

✅

Costs only $5/month

Railway hosting + databases. Unlimited memories, massive value.

Built for Performance

Lightning Fast

  • ⚡ Single queries in 20-50ms
  • ⚡ Complex graph traversals under 200ms
  • ⚡ Hybrid semantic + keyword search
  • ⚡ Automatic consolidation & importance decay

Memory Types

Decision Pattern Insight Preference Style Habit Context 
# Store with importance scoring
POST /memory
{
  "content": "Prefers TypeScript with strict mode",
  "type": "Preference",
  "tags": ["coding", "typescript"],
  "importance": 0.9,
  "metadata": {
    "rationale": "Type safety prevents bugs"
  }
}

# Hybrid search: semantic + tags + time
GET /recall?query="what language"&tags=coding

# Associate memories (knowledge graph)
POST /associate
{
  "memory1_id": "abc123",
  "memory2_id": "def456",
  "type": "RELATES_TO",
  "strength": 0.85
}

# All queries return in ~25ms

Ready to Give Your AI Perfect Memory?

Join developers building truly intelligent AI agents with AutoMem

Quick Start Guide Deploy on Railway