Memory benchmarks that judge answers rather than actions reward fluency while hiding real shipping trade-offs; DolphinBench's three-axis scoring makes cost-versus-latency comparisons visible when choosing between memory backends. Builders picking a memory layer for production agents can use it to weigh accuracy against infrastructure spend and response time. It sits alongside existing public boards for coding and long-context retrieval rather than replacing them.
#DolphinBench
Memory benchmarks have mostly measured recall on quiz-style questions, ignoring the cost and latency trade-offs that decide what teams actually ship. DolphinBench scores downstream actions after long histories, so results better reflect real agent behaviour. Builders choosing a memory layer for production agents can use it to compare systems on all three axes at once.
Mem0 把 GPT-5.6-Luna、MiniMax M3、Claude Sonnet 5 扔进 Hermes 和 Claude Code 两套框架跑分了,排行榜在 dolphinbench.ai,可以直接看哪个模型换框架后掉分最少。
A dedicated memory benchmark from Mem0 gives teams a standard way to compare long-term memory layers across agents and chat systems instead of relying on vendor self-reports. Builders choosing a memory backend can check the leaderboard and submit their own configurations via the hosted run page. The open dataset and code make results reproducible for anyone evaluating agent stacks.
做智能体记忆的可以看这份新基准:500k token 历史里找信息完成任务,还得报成本和延迟。