Mem0 system

1. Multi-Level Memory Architecture:
   • Mem0 implements a three-tiered memory approach:
     • User memory (long-term preferences and facts)
     • Session memory (current conversation context)
     • AI agent memory (operational context)

# Example operations from documentation

memory.add("User prefers email responses.", user_id="client123")
graph_query = memory.search("manager", user_id="user123", context_type="graph")
relationships = memory.search("colleague", user_id="user123", context_type="graph")
semantic_search = memory.search("AI trends", user_id="user999", context_type="vector")

Hybrid Database Approach:

Mem0 uses a combination of: Graph database for relationship tracking Vector database for semantic similarity Traditional database for metadata storage

Mem0's hybrid datastore is a sophisticated foundation that balances the need for fast fact retrieval (KV), deep contextual understanding (Vector), and nuanced relationship mapping (Graph)

Integration with AI Frameworks:

Look at how Mem0 integrates with platforms like CrewAI:

from crewai.memory import LongTermMemory
# Example of integrating Mem0 for enhanced user memory

Links:

https://docs.mem0.ai/quickstart

https://github.com/EthicalML/awesome-production-machine-learning

https://microsoft.github.io/autogen/0.2/ >→→ more

https://microsoft.github.io/autogen/0.2/docs/ecosystem. →→→Research more!

https://dev.to/yigit-konur/mem0-the-comprehensive-guide-to-building-ai-with-persistent-memory-fbm