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LLM apps with long-term memory: mem0 vs Supermemory

A new LogRocket write-up compares two open-source “memory” layers—mem0 and Supermemory—for making LLM apps stateful across sessions. It focuses on where chat history and standard RAG fall short, and what purpose-built memory systems add.

Published today, LogRocket compares mem0 and Supermemory as open-source options for giving LLM-powered apps durable, user-specific memory across conversations.

  • The piece recaps how most apps maintain context by sending an ordered message history (system/user/assistant) on every request, which gets costly and brittle as threads grow.
  • It separates retrieval (RAG) from memory, using an Oct 2025 → Jan 2026 preference timeline to show how naive semantic search can surface an old “favorite dessert” while missing later constraints like diabetes and dairy-free requirements.
  • mem0’s approach centers on explicit “memory items” managed through APIs, with the application deciding when to write and when to query—aimed at inspectable, debuggable behavior.
  • mem0 also supports automatic categorization (plus custom categories), optional LLM-based inference to store structured signals, and graph-style relationships to fetch related items together.
  • The article notes a mem0 MCP integration that exposes add/search memory operations as callable tools, but that design assumes an MCP-compatible runtime.