Use cases hub · 3 applications
AI Memory Use Cases: Where It Matters
AI memory matters wherever agents interact repeatedly — customer support, coding agents and personal assistants each need different memory types and frameworks.
Every use case needs
Applications
Where agent memory matters
Mapping
Memory types per use case
| Use case | Primary memory types | Common frameworks |
|---|---|---|
| Customer support | Episodic + semantic (user prefs) | Engram, Mem0, Zep |
| Coding agents | Procedural + semantic (codebase facts) | Engram, LangMem, Letta |
| Personal assistants | Long-term + episodic (user history) | Engram, Mem0, Letta |
All use cases share the write → retrieve loop, evaluation and conflict handling. → How AI memory works
FAQ
Frequently asked questions
Which use case should I start with?
Customer support and personal assistants are the most common production entry points — both need episodic + semantic memory. Coding agents add procedural memory requirements. Pick the guide closest to your product.
What memory does a support bot need?
Episodic memory for ticket/thread history and semantic memory for stable user preferences and account facts. Zep helps when facts change over time. See customer support.
What memory do coding agents need?
Semantic memory for codebase facts, procedural memory for learned workflows, and episodic memory for past debugging sessions. See coding agents.
What memory do personal assistants need?
Long-term and episodic user memory — preferences, routines, past requests. Engram, Mem0 and Letta are common choices. See personal assistants.
How do I integrate CRM data into agent memory?
Sync CRM fields into semantic memory on user login — CRM is a write source, not the memory store. See user memory personalization.
Which framework fits each use case?
Engram for Weaviate-native stacks; Mem0 for personalization POCs; Zep for temporal facts; Letta for deep conversations; LangMem for LangGraph coding agents. See best tools.