Why LLM Memory Isn't Enough for Your AI App (And What to Use Instead)
Most AI Apps Still Forget
If you’ve built an AI-powered product—whether it’s a chatbot, assistant, or internal tool—you’ve probably run into a frustrating issue:
It forgets.
Maybe your app can handle a few turns of conversation, but once the session ends, the context disappears. Even worse, there’s no easy way to recall information across users, projects, or workspaces.
So you search:
Does GPT have memory?
What about LangChain’s memory module?
Can I just use a vector database?
These are good instincts. But unfortunately, they’re not enough.
Why Built-In LLM Memory Falls Short
Most LLMs (like GPT-5 or Claude) do have “memory” of some kind—but it’s often tied to a single user in a specific interface, like ChatGPT. That memory isn’t accessible through the API, can’t be scoped by project, and doesn’t support structured queries or deletion for compliance.
Here’s how it compares:
| Feature | Built-in LLM Memory | What Real Apps Need |
|---|---|---|
| User-level scope | ✅ Yes | ✅ Yes |
| Project/workspace scope | ❌ No | ✅ Yes |
| API access | ❌ No | ✅ Yes |
| Audit trails & TTL | ❌ No | ✅ Yes |
| Works across agents & tools | ❌ No | ✅ Yes |
For teams building real products - not just demos - this becomes a major limitation.
Vector DBs and RAG Aren’t a Silver Bullet Either
A lot of devs try a DIY approach:
- Embed data, store it in Pinecone or Weaviate, and run similarity searches when needed.
This works - for a while. But as your app grows, problems surface:
- No memory scoping by user or session
- No expiration policies or deletion tracking
- No observability or audit logs
- Complex pipelines just to “remember something”
You end up maintaining infrastructure you never planned for. And still, the user experience feels disconnected.
What You Actually Need: External, Scoped Memory
Real-world apps need a memory system that’s structured, accessible, and designed for product-level integration - not just LLM-level recall.
That means:
- Memory scoped per user, project, or team
- Read and write access via simple APIs
- Optional time limits, exportability, and auditability
- Semantic recall that fits into prompts or agents easily
- No vendor lock-in or dependency on a specific LLM or framework
Introducing Recallio
Recallio.ai is designed to fill this gap.
It’s a memory layer you can drop into any AI-powered app—without rebuilding your infrastructure or committing to a specific agent framework. It’s model-agnostic, privacy-aware, and built for developers who want their apps to remember like humans do.
Some early use cases:
- An AI sales tool that recalls each lead’s history and feedback
- A knowledge assistant that keeps personal data scoped per user
- A tutoring app that adapts over time based on learning patterns
- A customer support agent that remembers previous conversations—even across sessions
