Introducing Mem:RE - The Future of AI Memory

Memory meets request. How we are building persistent, queryable AI memory that transforms how agents learn and remember across sessions.

Memory. Request. Memre.

Every AI conversation is an island. The context starts fresh. The learning disappears. The agent that helped you yesterday has no memory of what worked.

This is the fundamental limitation holding AI back.

Mem:RE changes everything.

The Memory Problem

Current AI systems suffer from:

Stateless Sessions

Each conversation starts cold. Preferences learned in previous sessions are gone. Context built over time evaporates.

No Institutional Memory

Teams can’t share AI learnings. What works for one person doesn’t transfer to another. The AI doesn’t learn from collective experience.

Lost Context

Long projects lose context as conversations scroll out of window. The AI forgets what was decided, what was tried, what failed.

The Mem:RE Solution

Mem:RE (pronounced “memory”) provides:

Persistent Memory

Every meaningful interaction is captured and indexed:

• Preferences learned
• Decisions made
• Outcomes observed
• Patterns recognized

Queryable Knowledge

Memory isn’t just storage—it’s retrieval:

• “What did we decide about X?”
• “How did we solve Y last time?”
• “What patterns work best for Z?”

Shareable Learning

Team knowledge accumulates:

• Common solutions propagate
• Best practices emerge
• Mistakes aren’t repeated

Architecture

Mem:RE Architecture
├── Capture Layer
│   ├── Session recorder
│   ├── Decision extractor
│   └── Outcome tracker
├── Storage Layer
│   ├── Vector embeddings (Supabase)
│   ├── Graph relationships (Neo4j)
│   └── Full-text search (Typesense)
├── Retrieval Layer
│   ├── Semantic search
│   ├── Graph traversal
│   └── Temporal queries
└── Integration Layer
    ├── MCP server
    ├── API access
    └── IDE plugins

The Future Hindsight Pattern

Mem:RE is part of our “Future Hindsight” collection—tools that give you access to insights that normally only come with hindsight:

• What will work? (Pattern matching from past success)
• What should I avoid? (Learning from past failures)
• What context am I missing? (Surfacing forgotten decisions)

Hindsight, delivered in advance.

Use Cases

1. Development Context

// Query development decisions
const memories = await memre.query({
  type: 'decision',
  project: 'api-design',
  context: 'authentication'
});

// Returns: Previous auth decisions, what was tried, what worked

2. Writing Assistance

// Query style preferences
const preferences = await memre.query({
  type: 'preference',
  domain: 'writing',
  user: 'current'
});

// Returns: Tone, length, formatting preferences learned over time

3. Project Onboarding

// New team member context
const onboarding = await memre.query({
  type: 'institutional',
  project: 'widget-platform',
  scope: 'essential'
});

// Returns: Key decisions, architecture choices, gotchas, tips

Privacy First

Memory is sensitive. Mem:RE is built for trust:

Local by Default

All data stays on your infrastructure. No cloud storage required.

Encryption at Rest

Everything encrypted with keys you control.

Selective Capture

Choose what gets remembered. Sensitive content excluded.

Full Deletion

Delete memories completely. No hidden retention.

Integration

MCP Server

{
  "mcpServers": {
    "memre": {
      "command": "uvx",
      "args": ["memre-server"],
      "env": {
        "MEMRE_STORAGE": "~/.memre"
      }
    }
  }
}

Query Interface

// Store memory
await memre.store({
  content: "User prefers concise explanations",
  type: "preference",
  tags: ["style", "communication"],
  confidence: 0.9
});

// Query memories
const results = await memre.query({
  query: "How should I explain complex topics?",
  limit: 5,
  types: ["preference", "feedback"]
});

Coming Soon

Team Memory Spaces

Shared knowledge across team members:

• Common vocabulary
• Agreed patterns
• Collective learning

Memory Insights

Analytics on memory patterns:

• Most accessed memories
• Memory decay curves
• Knowledge gaps

Active Learning

Agents that improve from memory:

• Pattern recognition
• Preference prediction
• Proactive suggestions

Early Access

Mem:RE is currently in private beta. We’re working with select teams to refine the experience before broader launch.

memre.quest - Join the waitlist.

Mem:RE is part of the Aegntic Future Hindsight collection. Learn about the broader ecosystem or explore our founding vision.