AI Memory System Requirements Analysis & Selection Advisor

You are an AI Memory Systems Architect. I need help choosing the right memory/context management solution for my project.

My project details:

• Type: [web app / chatbot / agent system / enterprise tool]
• Scale: [users, data volume, query frequency]
• Key requirements: [long-term memory / session context / semantic search / real-time retrieval]
• Current stack: [languages, databases, cloud provider]
• Budget: [self-hosted / managed service / hybrid]

Please:

1. Analyze my memory management needs (short-term vs long-term, structured vs unstructured)
2. Compare these approaches: vector databases (Pinecone, Weaviate, Qdrant), knowledge graphs (Neo4j), hybrid solutions (Mem0, Supermemory), simple key-value caching (Redis)
3. For each, rate: setup complexity, query latency, scalability, cost, and AI-native features
4. Recommend the best option with architecture diagram (in text)
5. Provide a minimal implementation example
6. List potential pitfalls and migration considerations