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Ruly Altamirano
Ruly Altamirano

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LuminoraCore v1.1: Your AI Memory That Actually Travels 🧠✨

#tooling #ai #showdev #opensource

TL;DR: I built an open-source framework that gives AI persistent memory across platforms. Your ChatGPT conversations can inform Claude, your Claude chats can continue in Gemini. True data portability, privacy-first.

🎬 See It In Action (2 minutes)

Watch how memory persists when switching between ChatGPT, Claude, and other LLMs

🤔 The Problem I Was Trying to Solve

We've all been there:

  1. ChatGPT forgets your conversation from yesterday
  2. Claude doesn't know what you discussed with ChatGPT
  3. Gemini starts fresh every time
  4. Your data is locked to each platform
  5. Export options? LOL, good luck

I was frustrated. I wanted my AI to remember me, know my preferences, and travel with me across platforms.

So I built LuminoraCore.

✨ What Makes It Different

Not Just Another Chatbot Framework

LuminoraCore isn't about building yet another chatbot. It's about creating portable AI identities that persist across:

  • 🔄 Any LLM provider (OpenAI, Anthropic, DeepSeek, Groq, Mistral...)
  • 💾 Any storage backend (SQLite, PostgreSQL, MongoDB, DynamoDB...)
  • 🚀 Any application (your own apps, integrations, plugins...)

The Three Pillars

1. Persistent Memory 🧠 

from luminoracore import LuminoraCoreClient

client = LuminoraCoreClient()

# First conversation
response = await client.send_message_with_memory(
    session_id="user_123",
    message="I'm Carlos, I love Python and basketball"
)
# AI: "Nice to meet you, Carlos! Python and basketball, great combo!"

# Next day, different conversation
response = await client.send_message_with_memory(
    session_id="user_123",
    message="What do you know about me?"
)
# AI: "You're Carlos, you love Python and basketball!"
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How it works:

  • Automatically extracts facts from conversations (9 categories)
  • Classifies importance (0-10 scale)
  • Creates episodic memories for significant events
  • Compiles relevant context for each response

2. Affinity System 💖

The AI's relationship with you evolves naturally:

Stranger (0-10 points)    → Formal, polite
    ↓
Acquaintance (11-30)      → Friendly, helpful  
    ↓
Friend (31-60)            → Warm, personal
    ↓
Confidant (61+)           → Very personal, empathetic
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Real example from my own usage:

  • Day 1 (Stranger): "Hello. How may I assist you today?"
  • Week 2 (Acquaintance): "Hey! How's your Python project going?"
  • Month 2 (Friend): "Carlos! Ready to talk about that basketball game?"
  • Month 6 (Confidant): "I remember you mentioned family stress last week. How are you holding up?"

The tone, depth, and personalization adapt automatically based on your relationship.

3. Provider Agnostic 🔄

Switch LLM providers mid-conversation without losing context:

# Start with ChatGPT
client = LuminoraCoreClient(provider="openai")
response = await client.send_message_with_memory(...)

# Switch to Claude (memory persists!)
client = LuminoraCoreClient(provider="anthropic")
response = await client.send_message_with_memory(...)
# Claude knows everything ChatGPT learned

# Try DeepSeek (20x cheaper!)
client = LuminoraCoreClient(provider="deepseek")
response = await client.send_message_with_memory(...)
# Still remembers everything
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Supported providers:

  • OpenAI (GPT-4, GPT-3.5)
  • Anthropic (Claude 3.5, Claude 3)
  • DeepSeek
  • Groq
  • Mistral
  • Google (Gemini)
  • Local models via Ollama

🏗️ Architecture Deep Dive

How Memory Actually Works 

User: "I'm learning React"
    ↓
┌─────────────────────┐
│  Fact Extraction    │ ← LLM analyzes message
│  (9 categories)     │
└──────────┬──────────┘
           │
           ├─> Category: "interests"
           ├─> Key: "programming_framework"
           ├─> Value: "React"
           ├─> Importance: 0.7
           ↓
┌─────────────────────┐
│  Update Affinity    │ ← Points +3 (shared interest)
│  (0-100 scale)      │
└──────────┬──────────┘
           │
           ├─> Total: 15 points
           ├─> Level: Acquaintance
           ↓
┌─────────────────────┐
│ Select Personality  │ ← Choose based on affinity
│ (4 hierarchical)    │
└──────────┬──────────┘
           │
           ├─> Use: "friendly_assistant"
           ↓
┌─────────────────────┐
│ Compile Context     │ ← Gather relevant facts
│ (Dynamic selection) │
└──────────┬──────────┘
           │
           ├─> Include: React interest, other programming facts
           ↓
┌─────────────────────┐
│ Generate Response   │ ← LLM with full context
│ (Provider-agnostic) │
└──────────┬──────────┘
           │
           ↓
Response: "That's awesome! React is great. 
           Since you're into Python too, 
           have you tried Django + React?"
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Fact Categories (9 Types)

  1. basics: Name, age, location, occupation
  2. preferences: Likes, dislikes, favorites
  3. interests: Hobbies, topics of interest
  4. family_friends: Relationships, social connections
  5. work: Career, projects, professional info
  6. health: Wellness, fitness, medical (sensitive)
  7. experiences: Past events, travel, life moments
  8. beliefs: Values, opinions, worldview
  9. goals: Aspirations, plans, ambitions

Episodic Memory (7 Types)

  • conversation: Significant discussions
  • achievement: Accomplishments, milestones
  • challenge: Obstacles, difficulties
  • decision: Important choices made
  • insight: Realizations, learnings
  • social: Interactions with others
  • milestone: Life events

🚀 Getting Started (5 Minutes)

Installation 

pip install luminoracore
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Minimal Example 

import asyncio
from luminoracore import LuminoraCoreClient

async def main():
    # Initialize client
    client = LuminoraCoreClient(
        provider="openai",  # or anthropic, deepseek, etc.
        api_key="your-api-key"
    )

    # Create session
    session_id = "user_carlos_session1"

    # First message
    response = await client.send_message_with_memory(
        session_id=session_id,
        message="Hi! I'm Carlos, I'm a software engineer from Madrid"
    )
    print(response['response'])
    print(f"Facts learned: {response['new_facts_count']}")

    # Second message (will remember Carlos)
    response = await client.send_message_with_memory(
        session_id=session_id,
        message="What do you know about me?"
    )
    print(response['response'])
    print(f"Total facts: {response['memory_facts_count']}")

    # Export all memory
    export = await client.export_session(session_id)
    print(export)  # Full JSON with all memories

asyncio.run(main())
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Output:

Response: "Hello Carlos! Nice to meet you..."
Facts learned: 3

Response: "You're Carlos, a software engineer from Madrid!"
Total facts: 3

{
  "learned_facts": [
    {"category": "basics", "key": "name", "value": "Carlos"},
    {"category": "basics", "key": "occupation", "value": "software engineer"},
    {"category": "basics", "key": "location", "value": "Madrid"}
  ],
  "affinity": {"points": 5, "level": "stranger"}
}
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💡 Real-World Use Cases

1. Personal AI Assistant 

# Remembers your preferences, schedule, habits
assistant = LuminoraCoreClient(provider="anthropic")

await assistant.send_message_with_memory(
    session_id="my_assistant",
    message="Schedule my morning workout"
)
# AI knows: You prefer 7am, you do HIIT, 30 minutes
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2. Customer Support Bot 

# Remembers customer history, past issues, preferences
support = LuminoraCoreClient(provider="deepseek")  # Cheaper!

await support.send_message_with_memory(
    session_id=f"customer_{customer_id}",
    message="My order hasn't arrived"
)
# AI knows: Order #12345, placed 3 days ago, shipped to Madrid
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3. Learning Tutor 

# Adapts to student's level, tracks progress
tutor = LuminoraCoreClient(provider="openai")

await tutor.send_message_with_memory(
    session_id=f"student_{student_id}",
    message="I don't understand recursion"
)
# AI knows: Student struggles with CS concepts, prefers visual examples
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4. Mental Health Companion 

# Builds trust over time, remembers emotional patterns
companion = LuminoraCoreClient(provider="anthropic")

await companion.send_message_with_memory(
    session_id="user_therapy",
    message="I'm feeling anxious today"
)
# AI knows: User has anxiety triggers, past coping strategies
# Relationship: Confidant → very empathetic, personal response
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🎨 Advanced Features

PersonaBlend™: Mix Personalities 

# 70% friendly + 30% professional
blended = client.blend_personalities(
    base_personality="friendly_assistant",
    blend_with="professional_consultant",
    ratio=0.7
)

response = await client.send_message_with_memory(
    session_id=session_id,
    message="Help me write a proposal",
    personality=blended
)
# Gets: Warm tone + professional structure
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Multi-Storage Support 

# Development: SQLite (local, fast)
client = LuminoraCoreClient(
    provider="openai",
    storage_backend="sqlite",
    db_path="./memory.db"
)

# Production: PostgreSQL (robust, scalable)
client = LuminoraCoreClient(
    provider="anthropic",
    storage_backend="postgresql",
    connection_string="postgresql://..."
)

# Cloud: DynamoDB (managed, serverless)
client = LuminoraCoreClient(
    provider="deepseek",
    storage_backend="dynamodb",
    table_name="luminora_memories"
)
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Export & Migrate 

# Export from ChatGPT setup
export = await client_gpt.export_session("user_123")

# Import to Claude setup
client_claude = LuminoraCoreClient(provider="anthropic")
await client_claude.import_session("user_123", export)

# All memories preserved!
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🎯 Roadmap

v1.2 (Q1 2025): Semantic Search 

# Instead of exact key matching
results = await client.semantic_search(
    user_id="carlos",
    query="what sports does carlos like",  # Natural language!
    top_k=5
)
# Returns: basketball, running, cycling...
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v1.3 (Q2 2025): Knowledge Graphs 

# Connect related facts
graph = await client.get_knowledge_graph(user_id="carlos")
# Carlos → knows → John → plays → basketball
#       ↓
#     likes → basketball
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v1.4 (Q3 2025): Memory Compression 

# Problem: 6 months = 92K tokens = $2,775 per request!
# Solution: Tiered compression (recent=full, old=compressed)
# Result: 20K tokens = $600 per request (78% savings)
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v2.0 (2026): API SaaS

  • Managed API service
  • Multi-tenant
  • Integrations (LangChain, N8N, etc.)
  • Browser extension marketplace

🤝 Contributing

LuminoraCore is MIT licensed and community-driven.

Ways to contribute:

  1. 🐛 Report bugs
  2. 💡 Suggest features
  3. 📝 Improve documentation
  4. 🧪 Add test cases
  5. 🔧 Submit PRs

Priority areas:

  • Semantic search implementation
  • Additional LLM providers
  • Storage backend optimizations
  • Real-world use case examples

📚 Resources

Code & Docs:

🎬 Try It Now 

# Install
pip install luminoracore

# Clone examples
git clone https://github.com/luminoracore/luminoracore
cd luminoracore/examples

# Run simple chat
python simple_chat.py
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🙏 Why I Built This

I'm tired of:

  • 😤 AI platforms that forget me
  • 😤 Vendor lock-in
  • 😤 Opaque costs
  • 😤 Lost conversation history

I wanted:

  • ✅ My data, my control
  • ✅ Privacy-first (self-hosted option)
  • ✅ Portable (works anywhere)
  • ✅ Transparent (open source)

If you feel the same, give it a try ⭐

💬 Let's Talk

What would you build with persistent AI memory?

  • Personal assistant?
  • Customer service bot?
  • Learning tutor?
  • Something else?

Drop a comment below! I'd love to hear your ideas and help you build it.

P.S. If you found this useful, consider:

Building in public. Let's make AI memory better together. 🚀

Updated: November 2025 | Version: 1.1 | Author: @rulyaltamirano

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