LLMs lack persistent memory across sessions
🧭 workproduct problemSingle report ●●●●●
The developer reports that current LLM systems are stateless, meaning they reset and forget all previous interactions once a chat session ends, which prevents the development of a continuous cognitive thread.
What people do today
- Vector databases
- RAG pipelines
- Prompt stuffing with conversation history
🔧 Fix this
Ideas a builder, maker or community group could start on:
- A middleware runtime that maintains a persistent memory graph between user sessions.
- An automated reflection service that summarizes and updates world state after each interaction.
- A standardized API for LLM agents to store and retrieve long-term episodic memory.
Real voices
“Everything resets. You ask something, the system answers, the chat ends, and the whole thing forgets everything that happened.”
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