问HN：Mem0存储记忆，但不学习用户模式
我们是一家YC W23的公司，专注于为工程实验室构建人工智能代理。我们的客户需要重复进行类似的分析，而代理在每次会话中都像一张白纸一样处理。
我们考察了Mem0、Letta/MemGPT以及其他类似的记忆解决方案。它们解决的是不同的问题：存储对话中的事实——“用户偏好Python”，“用户是素食者”。这是一种带有语义搜索的键值记忆，虽然有用，但并不是我们所需要的。
我们需要的是一种能够从用户行为中隐式学习模式的系统。当客户连续三次将阈值从85%修正为80%时，代理应该能够在下次会话中自动识别这一点。当一个团队总是使用更严格的过滤条件重新运行时，系统也应该能够捕捉到这一模式。
因此，我们围绕一个简单的想法构建了一个内部API：用户的修正是最有价值的数据。我们不是简单地摄取聊天信息并希望大型语言模型（LLM）能提取出有用的信息，而是捕捉结构化事件——代理生成了什么，用户更改了什么，他们接受了什么。后台作业定期运行LLM，以提取模式并为每个用户/团队/组织构建一个带有置信度加权的偏好档案。
在每次会话之前，代理会获取该档案，并随着时间的推移变得更加智能。
我看到的差距是：
Mem0 = 记忆存储 + 检索。无法学习模式。
Letta = 自我编辑代理记忆。更接近，但没有从行为中隐式学习。
缺失的是一个偏好学习层，它观察用户如何与代理互动，并构建一个不断演变的模型。就像一个用于代理个性化的推荐引擎。
我为我们的领域构建了这个系统，但这种方法是领域无关的。我很好奇其他人在他们的代理中是否也遇到了同样的瓶颈。乐意详细分享架构、提示和置信度评分的方法。
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We're a YC W23 company building AI agents for engineering labs - our customers run similar analyses repeatedly, and the agent treated every session like a blank slate.<p>We looked at Mem0, Letta/MemGPT, and similar memory solutions. They all solve a different problem: storing facts from conversations — "user prefers Python," "user is vegetarian." That's key-value memory with semantic search. Useful, but not what we needed.<p>What we needed was something that learns user patterns implicitly from behavior over time. When a customer corrects a threshold from 85% to 80% three sessions in a row, the agent should just know that next time. When a team always re-runs with stricter filters, the system should pick up on that pattern.
So we built an internal API around a simple idea: user corrections are the highest-signal data. Instead of ingesting chat messages and hoping an LLM extracts something, we capture structured events — what the agent produced, what the user changed, what they accepted. A background job periodically runs an LLM pass to extract patterns and builds a confidence-weighted preference profile per user/team/org.<p>Before each session, the agent fetches the profile and gets smarter over time.
The gap as I see it:<p>Mem0 = memory storage + retrieval. Doesn't learn patterns.<p>Letta = self-editing agent memory. Closer, but no implicit learning from behavior.<p>Missing = a preference learning layer that watches how users interact with agents and builds an evolving model. Like a rec engine for agent personalization.<p>I built this for our domain but the approach is domain-agnostic. Curious if others are hitting the same wall with their agents. Happy to share the architecture, prompts, and confidence scoring approach in detail.