Persistent memory for LLMs: what it's for and how to choose one
Persistent memory keeps, from one conversation to the next, what an AI has learned while working with you: your decisions, your preferences, the state of your projects. It lives outside the conversation and is fed back to the model at the right moment.
Context and memory: two different things
Context is what the model has in front of it during a conversation. Memory is what remains once the conversation is over. A model starts from scratch with every new conversation: it is the platform, or a dedicated tool, that gives it a memory.
What a good memory should be able to do
| Ability | What it means |
|---|---|
| Retain | Keep facts, preferences and instructions |
| Recall | Bring back the right information at the right moment, without being asked |
| Update | Replace a piece of information when it changes |
| Flag a contradiction | Notice that two pieces of information cannot both be true |
| Know what it does not know | Say that information is missing instead of filling the gap |
BEAM, a benchmark presented at ICLR 2026, scores ten abilities of this kind.
Three families of solutions
- Memory built into your platform (Claude, ChatGPT, Gemini, Le Chat, Copilot): included, and specific to that platform.
- Memory shared across several AIs (Mycelora, MemoryPlugin, Basic Memory): a single memory that each of your AIs reads and writes.
- Memory layer for developers (Mem0, Zep, Letta): to be integrated into an application or agent you build.
The details are in our comparison.
How Mycelora does it
- Every memory has a type: current rule, pitfall, rejected idea, reference point, status.
- Every memory belongs to a space, meaning a project, with a project brief kept up to date.
- A replaced or rejected memory is no longer recalled. Its history stays available. A model handles replacement, within a single project. It can miss cases, so every memory stays editable.
- A contradiction with a current decision is flagged in the conversation.
- The same memory serves Claude, ChatGPT, Mistral Vibe and Perplexity, through a standard connector (MCP) and a plugin. With the plugin on Claude or on the ChatGPT desktop app, and a paid plan, recall is automatic on every message; elsewhere, the memory is consulted on request.
- Data is hosted in Paris, and AI processing runs in France and Germany. Every memory can be read, corrected, deleted and exported.
On BEAM, over very long conversations (one million tokens), Mycelora scores 84.5% on contradiction detection and 80.7% on tracking updates. The overall score is 74.5%. See the method, answers and weak spots.
Six questions to help you choose
- Do you work with a single AI, or with several?
- Do you use existing AIs, or are you building your own agent?
- What happens to a piece of information when it changes?
- Can you read, correct and delete every memory?
- Where is your data hosted, and can you export it?
- Does the vendor publish its measurements and its method?
Frequently asked questions
Does an LLM have a memory?
The model itself starts from scratch with every conversation. Memory comes from the platform around it, or from a dedicated tool such as Mycelora.
What is the difference between persistent memory and RAG?
RAG retrieves passages from documents. Persistent memory retains what was said and decided over the course of the work, and keeps it up to date.
What is MCP?
MCP (Model Context Protocol) is a standard that links an AI to outside tools. It is what lets several AIs read and write the same memory.
Does persistent memory replace my platform's memory?
It complements it. Your platform's memory stays active. The shared memory brings what needs to follow you from one AI to another.