星火 SparkCN

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创始人 / Maker 评论

优先展示 Product Hunt 上对理解条目有帮助的人类文本。

Topics

Open SourceDeveloper ToolsArtificial Intelligence

The memory layer design is genuinely artistic. Instead of treating memory as compressed chat history, @Memori turns agent execution traces into reusable state: what tools were called, what worked, what failed, what decisions were made, and what patterns should carry forward. That is a much stronger memory primitive for agents. Real agent context lives in the execution path, not just in the conversation around it. Kudos to @_gordee @adam_b_struck @memorilabs and the team! 15K GitHub stars and rising — well deserved!

评论作者信息不可用Maker 身份6 votes查看原评论

痛点分析发布于 2026/05/28

痛点为 AI 基于上游原始证据的初步提炼；未包含额外中国市场检索。

痛点

开发者在构建有状态AI Agent时，现有记忆方案依赖压缩对话历史，导致因果上下文丢失。用户需要Agent跨会话记住执行路径、工具调用结果、决策逻辑等结构化信息，但当前方法（如手动维护Claude.md文件）只能记录对话摘要，无法捕获Agent实际执行过程中的“为什么”。这造成Agent在后续会话中重复错误、遗漏关键上下文，需要人工干预修复，增加了调试和协作成本。同时，全量上下文推理导致token消耗高（基准测试显示可节省95%以上推理成本），但现有方案缺乏高效的结构化记忆存储与检索机制。

Discussion

精选 Product Hunt 讨论

保留原始讨论语境，用来交叉验证上游条目的真实反馈。

See how Memori works: https://memorilabs.ai/benchmark/#demo Select a task and watch both agents in real time: https://memorilabs.ai/agent-trace/#demo While most customer facing AI agents are limited by short-term memory constraints, Memori brings the long-term persistent memory and unlike traditional memory systems that rely primarily on long-form natural language conversation history, Memori enables agents to automatically create structured, long-term memory directly from the agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic. - Structured, persistent memory for AI agents — Memori replaces flat markdown memory files with a structured knowledge graph that captures facts, decisions, outcomes, and patterns across every session — without bloating the prompt. - Grounded in what agents actually do, not just what they say — Memori captures tool calls, execution traces, and real-time agent decisions alongside conversation, giving agents a fuller picture of prior task execution. - Agent-controlled intelligent recall — Agents decide when and what to retrieve, scoped precisely by project, session, entity, or time range — eliminating irrelevant context and cross-project noise. - Automatic memory building, zero latency impact — Memory is structured and updated asynchronously after each interaction, so it never slows the agent's response. - Smarter daily briefs — Memori generates structured daily briefings built from execution traces and structured memory — covering priorities, risks, active goals, open loops, and known failure patterns — far beyond a simple conversation recap. - Built for multi-user, multi-project environments — Memory is fully scoped and isolated by project, process, session, and entity, preventing data bleed across users and contexts. - Production-ready observability — Full visibility into memory creation, recall activity, retrieval performance, and quota usage via Memori Cloud.

评论作者信息不可用筛选：高信号评论8 votes查看原评论

The trace-based memory model is architecturally clever. Capturing tool calls, decisions, and outcomes from execution rather than compressing conversation history preserves the why behind agent behavior, not just the what. We've hit this ceiling building stateful agent workflows; chat summaries lose causal context fast. How do you handle storage and retrieval at scale when agent runs produce deeply nested execution graphs?

评论作者信息不可用筛选：高信号评论1 votes查看原评论

Interesting one @memorilabs @adam_b_struck @_gordee ! Upvoted :) What's the memory an agent gets from the trace, that would be useful for another session? Like I have set up a rule in Claude.md asking the agent to keep adding new learnings (during the session) into a project level .md file. How would your tool be different from that?

评论作者信息不可用筛选：高信号评论1 votes查看原评论

The "memory from execution trace, not just conversation" framing is exactly the right primitive for agents that actually do things vs just talk. Quick question: when a tool call returns a large blob (say a 10MB SQL result), how does Memori decide what to keep in long-term memory vs discard? Curious about the pruning side.

评论作者信息不可用筛选：高信号评论1 votes查看原评论

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      "body": "<p>See how Memori works: <a href=\"https://memorilabs.ai/benchmark/#demo\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">https://memorilabs.ai/benchmark/#demo</a></p><p>Select a task and watch both agents in real time: <a href=\"https://memorilabs.ai/agent-trace/#demo\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">https://memorilabs.ai/agent-trace/#demo</a></p><p></p><p>While most customer facing AI agents are limited by short-term memory constraints, Memori brings the long-term persistent memory and unlike traditional memory systems that rely primarily on long-form natural language conversation history, Memori enables agents to automatically create structured, long-term memory directly from the agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic. <br><br>- Structured, persistent memory for AI agents — Memori replaces flat markdown memory files with a structured knowledge graph that captures facts, decisions, outcomes, and patterns across every session — without bloating the prompt.<br><br>- Grounded in what agents actually do, not just what they say — Memori captures tool calls, execution traces, and real-time agent decisions alongside conversation, giving agents a fuller picture of prior task execution.<br><br>- Agent-controlled intelligent recall — Agents decide when and what to retrieve, scoped precisely by project, session, entity, or time range — eliminating irrelevant context and cross-project noise.<br><br>- Automatic memory building, zero latency impact — Memory is structured and updated asynchronously after each interaction, so it never slows the agent's response.<br><br>- Smarter daily briefs — Memori generates structured daily briefings built from execution traces and structured memory — covering priorities, risks, active goals, open loops, and known failure patterns — far beyond a simple conversation recap.<br><br>- Built for multi-user, multi-project environments — Memory is fully scoped and isolated by project, process, session, and entity, preventing data bleed across users and contexts.<br><br>- Production-ready observability — Full visibility into memory creation, recall activity, retrieval performance, and quota usage via Memori Cloud.</p>",
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      "body": "<p>The trace-based memory model is architecturally clever. Capturing tool calls, decisions, and outcomes from execution rather than compressing conversation history preserves the why behind agent behavior, not just the what. We've hit this ceiling building stateful agent workflows; chat summaries lose causal context fast. How do you handle storage and retrieval at scale when agent runs produce deeply nested execution graphs?</p>",
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      "body": "<p>Interesting one <a href=\"https://www.producthunt.com/@memorilabs\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@memorilabs</a> <a href=\"https://www.producthunt.com/@adam_b_struck\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@adam_b_struck</a> <a href=\"https://www.producthunt.com/@_gordee\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">@_gordee</a> ! Upvoted :)</p><p>What's the memory an agent gets from the trace, that would be useful for another session?</p><p>Like I have set up a rule in Claude.md asking the agent to keep adding new learnings (during the session) into a project level .md file. How would your tool be different from that?</p>",
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      "body": "<p>The \"memory from execution trace, not just conversation\" framing is exactly the right primitive for agents that actually do things vs just talk. Quick question: when a tool call returns a large blob (say a 10MB SQL result), how does Memori decide what to keep in long-term memory vs discard? Curious about the pruning side.</p>",
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