🧠 Mem4AI: A LLM Friendly memory management library.

Mem4AI enhances AI assistants and agents with an intelligent memory layer, enabling personalized and context-aware AI interactions. Perfect for building smarter chatbots, AI assistants, and autonomous systems that remember and learn.

✨ Core Features

• 🔀 Multi-Level Memory: User, Session, and AI Agent memory retention
• 🎯 Adaptive Personalization: Continuous improvement based on interactions
• 🚀 Fast Local Storage: Efficient on-disk storage with quick access
• 🔍 Advanced Semantic Search: Find relevant memories quickly
• 🏷️ Flexible Metadata: Tag and filter memories easily
• 🔧 Customizable Strategies: Adapt embedding and storage to your needs
• 💾 Persistent Storage: Memories that last across sessions

🚀 Quick Start

Install Mem4AI:

pip install Mem4AI

Here's a simple example of how to use Mem4AI:

from mem4ai import Memtor

# Initialize Memtor
memtor = Memtor()

# Add a memory
memory_id = memtor.add_memory(
    "The user prefers dark mode in all applications",
    metadata={"preference": "ui", "mode": "dark"},
    user_id="alice"
)

# Search for memories
results = memtor.search_memories("user interface preferences", user_id="alice")
print(results[0].content)  # Output: The user prefers dark mode in all applications

# Update a memory
memtor.update_memory(
    memory_id,
    "The user prefers dark mode, except for document editing",
    metadata={"preference": "ui", "mode": "dark", "exception": "document_editing"}
)

# Delete a memory
memtor.delete_memory(memory_id)

🛠️ Use Cases

• 🤖 Chatbots with long-term memory
• 📊 Personalized recommendation systems
• 🧠 Knowledge management systems
• 🎯 Context-aware AI assistants

📚 Documentation

Coming soon!

🤝 Contributing

We welcome contributions! Check out our contribution guidelines for more information.

📄 License

Mem4AI is released under the Apache 2.0 license. See LICENSE for more information.

Built with ❤️ by Unclecode. (https://x.com/unclecode)

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