Why does memory not follow you between AI tools?

Most AI chat tools (ChatGPT, Claude, Gemini) now remember you, but each keeps a shallow memory locked to its own product. Switch tools and the context stays behind, so without a portable memory layer every new product starts from zero.

Is platform-native memory (ChatGPT Memory, Claude Projects) enough?

For light personal use it can be. For developers and power users it usually is not — capacity is small (~1,400 words on ChatGPT), it is locked to one platform, and there is no API to integrate it into your own apps. An independent memory layer like Alma works across every model and tool.

How is persistent memory different from RAG?

RAG retrieves chunks from a static document corpus per query. Persistent memory grows over time from your conversations: it captures preferences, decisions and patterns and re-injects them automatically. RAG is for searching documents; memory is for remembering you.

Where do my memories live and can I export them?

With Alma, memories are stored in your own account. You can export everything (JSON, PDF, DOCX, XLSX), edit any memory, or delete your account at any time. GDPR-compliant by design.

Why AI Needs Persistent Memory (And How to Add It)

2026-01-15 · Alma Team

Each AI tool keeps its own shallow memory of you, and none of them share it. Persistent memory fixes this with a separate layer that survives across sessions, models and platforms — your AI rebuilds context automatically instead of asking you to repeat yourself. Alma adds it via MCP, SDK or REST API.

Every AI platform now remembers you, but each memory is shallow and walled off in its own product. Context window ends, memory gone. ChatGPT stores ~1,400 words total. Claude projects are better but locked to one platform. Alma stores memories independently with hybrid search, scoring by relevance, importance, and recency.

Read the full article · Alma vs ChatGPT Memory · See plans

See plans