菜单
Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines. Use when the user wants proof MemMesh is better, is evaluating a migration, or asks "how does this compare to mem0".
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ThinkfleetAI/memmesh --skill benchmark命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Surface emergent behavior patterns MemMesh has mined from a subject's history — recurring habits nobody predefined, each with prevalence, stability, and the evidence behind it. Use when the user asks "what patterns do you see", "what are this user's habits", or wants the patterns that drive predictions.
Load relevant MemMesh context before starting work — searches memory and, for a specific subject, assembles a token-budgeted bundle (profile + behavior patterns + forward predictions + top memories) in one call. Use when beginning a task, switching context, or when project history / past decisions / a subject's profile would help.
Query MemMesh's bi-temporal knowledge graph — multi-hop reasoning across entities, point-in-time "what did we believe on date X", and anticipatory retrieval via spreading activation. Use for questions no single stored fact answers, or to see how knowledge about an entity changed over time.
基于 SOC 职业分类
正在显示 SKILL.md
| name | benchmark |
| description | Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines. Use when the user wants proof MemMesh is better, is evaluating a migration, or asks "how does this compare to mem0". |
Put numbers on the comparison. MemMesh ships a real benchmark harness that runs the public LOCOMO dataset end-to-end against competing systems.
Systems: thinkfleet (MemMesh) vs full_context vs naive_rag, and — with keys
— Mem0 / Zep. Metrics: answer accuracy (rubric-scored), tokens consumed, latency,
and cost per conversation.
The harness lives in the engine repo at crates/eval/competitive/:
cd crates/eval/competitive
python bench.py --systems thinkfleet,mem0,full_context --dataset locomo
# results land in results/
(Set the competitors' API keys via env for a head-to-head; without them you still get MemMesh vs full-context vs naive-RAG.)
MemMesh's positioning is calibration over raw accuracy — "80% means 80%" and honest abstention beat a slightly higher accuracy with overconfident wrong answers. So report the full picture:
Don't cherry-pick a single accuracy number. If a competitor wins on one axis, say so, and show where MemMesh's calibration/cost advantage pays off.
Prove the win on the cheap tiers (LOCOMO, BEAM-100K) before spending on BEAM-1M/10M — a single 10M-token conversation is expensive. Escalate tiers only once the cheaper tier shows a clear, defensible lead.