MemoriLabs/Memori v1.0.0

Memori AI v1.0.0

on GitHub

[1.0.0] - 2025-08-03

🎉 Production-Ready Memory Layer for AI Agents

Complete professional-grade memory system with modular architecture, comprehensive error handling, and configuration management.

✨ Core Features

• Universal LLM Integration: Works with ANY LLM library (LiteLLM, OpenAI, Anthropic)
• Pydantic-based Intelligence: Structured memory processing with validation
• Automatic Context Injection: Relevant memories automatically added to conversations
• Multiple Memory Types: Short-term, long-term, rules, and entity relationships
• Advanced Search: Full-text search with semantic ranking

🏗️ Architecture

• Modular Design: Separated concerns with clear component boundaries
• SQL Query Centralization: Dedicated query modules for maintainability
• Configuration Management: Pydantic-based settings with auto-loading
• Comprehensive Error Handling: Context-aware exceptions with sanitized logging
• Production Logging: Structured logging with multiple output targets

🗄️ Database Support

• Multi-Database: SQLite, PostgreSQL, MySQL connectors
• Query Optimization: Indexed searches and connection pooling
• Schema Management: Version-controlled migrations and templates
• Full-Text Search: FTS5 support for advanced text search

🔧 Developer Experience

• Type Safety: Full Pydantic validation throughout
• Simple API: One-line enablement with memori.enable()
• Flexible Configuration: File, environment, or programmatic setup
• Rich Examples: Basic usage, personal assistant, advanced config

📊 Memory Processing

• Entity Extraction: People, technologies, projects, skills
• Smart Categorization: Facts, preferences, skills, rules, context
• Importance Scoring: Multi-dimensional relevance assessment
• Relationship Mapping: Entity interconnections and memory graphs

🔌 Integrations

• LiteLLM Native: Uses official callback system (recommended)
• OpenAI/Anthropic: Clean wrapper classes for direct usage
• Tool Support: Memory search tools for function calling

🛡️ Security & Reliability

• Input Sanitization: Protection against injection attacks
• Error Context: Detailed error information without exposing secrets
• Graceful Degradation: Continues operation when components fail
• Resource Management: Automatic cleanup and connection pooling

📁 Project Structure

memoriai/
├── core/              # Main memory interface and database
├── config/            # Configuration management system
├── agents/            # Pydantic-based memory processing
├── database/          # Multi-database support and queries
├── integrations/      # LLM provider integrations
├── utils/             # Helpers, validation, logging
└── tools/             # Memory search and retrieval tools

🎯 Philosophy Alignment

• Second-memory for LLM work: Never repeat context again
• Flexible database connections: Production-ready adapters
• Simple, reliable architecture: Just works out of the box
• Conscious context injection: Intelligent memory retrieval

⚡ Quick Start

from memoriai import Memori

memori = Memori(
    database_connect="sqlite:///my_memory.db",
    conscious_ingest=True,
    openai_api_key="sk-..."
)
memori.enable()  # Start recording all LLM conversations

# Use any LLM library - context automatically injected!
from litellm import completion
response = completion(model="gpt-4", messages=[...])

📚 Documentation

• Clean, focused README aligned with project vision
• Essential examples without complexity bloat
• Configuration guides for development and production
• Architecture documentation for contributors

🗂️ Archive Management

• Moved outdated files to archive/ folder
• Updated .gitignore to exclude archive from version control
• Preserved development history while cleaning main structure

💡 Breaking Changes from Pre-1.0

• Moved from enum-driven to Pydantic-based processing
• Simplified API surface with focus on enable()/disable()
• Restructured package layout for better modularity
• Enhanced configuration system replaces simple parameters