Scholar Feed

Agent / long-term memory

Mem0

Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Heavily superseded#1 of 63 most-superseded · first seen Apr 28, 2025

Heavily superseded — a standard baseline that newer methods routinely beat

3 papers critique it · 15 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites Mem0 as a baseline.

While graph databases provide structured organization for memory systems, their reliance on predefined schemas and relationships fundamentally limits their adaptability.
— A-MEM: Agentic Memory for LLM Agents
“LLMs cannot use memory tools effectively and using such tools increases redundancy in planning and overall tool use.”
— MEMTRACK: Evaluating Long-Term Memory and State Tracking in Multi-Platform Dynamic Agent Environments
“they operate in an ``open loop'' without feedback on whether the constructed memories benefit downstream tasks”
— MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards

Beaten on benchmarks

Head-to-head results where a newer method reports beating Mem0. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.