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State of Memory in Agent Harness
A Mem0 survey of agent memory systems, including Claude Code and Devin, revealed widespread issues like bounded storage and 57-71% cross-user contamination.
What: Mem0 conducted a survey across several AI agent memory implementations, including Claude Code, Codex, Copilot, OpenClaw, Hermes, Bedrock AgentCore, Windsurf, and Devin, identifying common failures such as limited local storage, reliance on keyword retrieval, harness-level scoping, poor staleness handling, and significant cross-user contamination rates ranging from 57% to 71%.
Why it matters: This survey highlights that fundamental memory challenges persist across various prominent AI agent systems, suggesting that despite rapid advancements, core infrastructure for agent persistence and context management is still immature and prone to critical failures like data leakage.
Original article
Mem0 surveyed memory implementations across Claude Code, Codex, Copilot, OpenClaw, Hermes, Bedrock AgentCore, Windsurf, and Devin, and found the same boundary failures everywhere: bounded local storage, mostly keyword retrieval, harness scoping, weak staleness handling, and 57-71% cross-user contamination rates.
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