Memori is a SQL-native memory layer for LLMs, AI agents, and multi-agent systems that plugs into your existing software and infrastructure. It's LLM, datastore, and framework agnostic, seamlessly integrating into your architecture.
One-Minute Overview#
Memori is a memory layer designed for large language models (LLMs), AI agents, and multi-agent systems that persists AI interactions in SQL databases. It's ideal for AI application developers needing long-term memory capabilities, especially for enterprise AI systems. Its core value is adding memory capabilities to AI systems without requiring architectural changes.
Core Value: Provides seamless memory integration for AI systems with support for multiple LLMs and data stores.
Getting Started#
Installation Difficulty: Low - Single pip install with simple configuration
pip install memori
Is this suitable for my needs?
- ✅ AI agents requiring long-term memory: Memori persists user preferences and conversation history
- ✅ Multi-agent collaboration systems: Supports three-level memory management for entities, processes, and sessions
- ❌ Simple one-time AI applications: May be overly complex without long-term memory requirements
- ❌ Lightweight applications without database environments: Memori requires SQL database support
Core Capabilities#
1. Three-Level Memory Management - Precise AI Memory#
- Supports memory at entity (users), process (AI agents), and session (current interactions) levels Actual Value: AI can remember user preferences and history, providing personalized experiences
2. Enhanced Memory Features - Rich Context Enhancement#
- Automatically extracts attributes, events, facts, relationships, and more Actual Value: AI not only remembers content but understands semantic relationships for more accurate responses
3. Multi-Database Support - Flexible Data Storage#
- Supports SQLite, PostgreSQL, MySQL, MongoDB, and other databases Actual Value: Use your team's familiar database without additional learning costs
4. LLM-Agnostic Design - Broad Compatibility#
- Supports OpenAI, Anthropic, Gemini, and other major LLMs Actual Value: Can switch LLM providers without rewriting the memory system
5. Zero-Latency Processing - High Performance Experience#
- Asynchronous processing of enhancement functions without affecting AI interaction response speed Actual Value: Unnoticeable memory processing for users while maintaining responsive AI interactions
Technology Stack & Integration#
Development Language: Python (3.8+) Key Dependencies: OpenAI SDK, database drivers (PEP 249 compliant), SQLAlchemy (optional), Django ORM (optional) Integration Method: Library
Ecosystem & Extensions#
- Plugins/Extensions: Supports expansion through adapter/driver architecture, community can contribute new database integrations
- Integration Capabilities: Integrates with frameworks like Agno and LangChain, supports Django and SQLAlchemy
Maintenance Status#
- Development Activity: Actively maintained, with recent v3 release including significant performance improvements
- Recent Updates: Recent updates include advanced augmentation, vectorized memories, and semantic search
- Community Response: Active Discord community, regularly responding to issues and accepting contributions
Commercial & License#
License: Apache 2.0
- ✅ Commercial: Commercial use allowed
- ✅ Modification: Modifications and distribution allowed
- ⚠️ Restrictions: Must include original license and copyright notices
Documentation & Learning Resources#
- Documentation Quality: Comprehensive
- Official Documentation: https://memorilabs.ai/docs
- Example Code: Provides complete examples and cookbook