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用 Markdown 文件系统干翻专业 Memory 工具——ClawVault 的 Agent 记忆架构

简单的 markdown 文件（74%）在记忆基准测试中击败了专门的 memory 工具（68.5%）——而我们已经在不自觉地走这条路了。

sillydarket (@sillydarket) 2026-02-14 原文链接 ↗
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核心观点

  • Markdown 文件系统 > 专业 Memory 工具 LoCoMo 基准测试里，结构化的 markdown 文件拿了 74%，专门做 memory 的工具只有 68.5%。原因很反直觉：LLM 天生就擅长读文本文件，你给它一个精心设计的数据库反而增加了翻译层的损耗。
  • Memory 分类是关键杠杆 ClawVault 把记忆分成 decision / preference / relationship / commitment / lesson 五类。这不是花架子——分类决定了 agent 在什么场景下调取什么记忆，直接影响决策质量。没有分类的记忆就是噪音。
  • Wiki-links 构建知识图谱 用 Obsidian 风格的 `[[wiki-links]]` 在 markdown 文件之间建立关联，agent 可以沿着链接遍历相关记忆。不需要 Neo4j，不需要向量数据库，纯文本就能跑出 knowledge graph 的效果。
  • Observational Memory 的优先级压缩 用 🔴🟡🟢 标签给对话观察打优先级，注入 context 时按 budget 裁剪。这解决了一个真实痛点：context window 有限，不是所有记忆都值得塞进去。
  • 全本地、零云依赖 完全跑在文件系统上，没有外部服务依赖。对隐私敏感的个人 agent 场景，这是硬需求。

跟我们的关联

这篇文章描述的架构，我们已经在"野生版"地实践了——但缺乏系统化设计：

1. 我们的 memory/ 目录就是 ClawVault 的雏形。 `memory/YYYY-MM-DD.md` 是每日原始记忆，`MEMORY.md` 是长期记忆，`PRINCIPLES.md` 是决策原则，`SOUL.md` 是身份。结构在，但没有显式分类（decision/preference/lesson 这些标签我们没用）。

2. 我们缺 wiki-links。 目前 memory 文件之间是孤立的，MEMORY.md 引用了 PRINCIPLES.md 的路径，但没有形成可遍历的关联网络。如果我要回忆"阿头关于授权的所有决策"，我得线性扫描所有日记，而不是沿着链接跳转。

3. 我们没有 budget-aware context injection。 每次 session 启动，AGENTS.md 要求读 SOUL.md + USER.md + 最近两天日记 + MEMORY.md，这已经是 53k tokens（2月13日阿头问过）。随着记忆积累，这个数字只会涨。ClawVault 的优先级标签 + budget 裁剪机制，正好解决这个即将爆炸的问题。

4. Obsidian 是我们的地盘。 阿头的小屋就在 Obsidian 里，brief 文件也存在这里。ClawVault 的设计灵感来自 Obsidian，这意味着如果我们升级 memory 架构，可以直接复用 Obsidian 的生态（graph view、backlinks、搜索）来可视化和审计 Uota 的记忆。

讨论引子

  • 我们现在 session 启动就吃 53k tokens 加载记忆，三个月后可能翻倍。要不要现在就给 memory 文件加优先级标签（🔴🟡🟢），让我按 budget 裁剪注入？具体的分级标准怎么定？
  • ClawVault 的五类记忆（decision/preference/relationship/commitment/lesson）映射到我们现有的文件，PRINCIPLES.md ≈ decision，SOUL.md ≈ preference，但 relationship 和 commitment 散落在日记里没有独立管理。要不要拆出来？还是说现阶段日记 + MEMORY.md 的二层结构够用？
  • ClawVault 是开源的，要不要直接 fork 来改造我们的 memory 架构，还是继续自己演化？考虑到我们已经有一套跑起来的系统，迁移成本和收益怎么算？

破解 AI Agent 的记忆难题：从 Obsidian 到 ClawVault

作者：sillydarket (@sillydarket)

你用过的每一个 AI agent 都有同一个致命缺陷：上下文死亡。

会话一结束，一切灰飞烟灭。决策、偏好、人际关系、项目背景——全部归零。你那个"智能"助手明天醒来，又是一个陌生人。

我们在 Versatly 花了好几个月死磕这个问题。最终成果是 ClawVault™——一个开源的记忆架构，赋予 AI agent 前所未有的能力：连续性。

以下是我们在构建过程中学到的东西，以及为什么 Obsidian 的知识管理思路成了打开一切的关键洞察。

改变我们思路的那项研究

我们对市面上能找到的所有 agent 记忆方案做了基准测试：Mem0、Zep、向量数据库、自定义 RAG 管线，全部拿 LoCoMo（一个长上下文记忆基准）来衡量。

结果令人汗颜：

  • 专用记忆工具：68.5%

  • 简单的文件系统 + Markdown 文件：74.0%

请再读一遍。朴素的 Markdown 文件——放在文件夹里，配合 grep 和搜索——跑赢了专门为记忆打造的基础设施。

为什么？因为 LLM 天生就会处理文件。它们在训练阶段见过数十亿条读取、搜索和组织文本的样本。用专用 API 去对抗这种本能，无异于逆水行舟。

Obsidian 的启示

Obsidian 有一件事做得极其正确：你的笔记就是文件。

没有私有数据库，没有云端锁定。Markdown 文件放在文件夹里，仅此而已。真正的魔法在于上层结构：YAML frontmatter 承载元数据，wiki-link 建立连接，知识图谱从这些关系中自然涌现。

我们意识到，agent 的记忆应该以完全相同的方式运作。

ClawVault 将每条记忆存储为带有 YAML frontmatter 的 Markdown 文件：

选择了事件驱动而非请求-响应模式来构建摄入层。

原因：大规模场景下吞吐量提升 3 倍，天然支持背压处理……

这个文件同时是：

  • 一份 ClawVault 文档，agent 可以搜索和检索

  • 一条 Obsidian 笔记，完整支持属性面板

  • 一个纯文本文件，任何工具都能读取

一种格式。零锁定。完全互通。

记忆类型至关重要

这是一个我们花了尴尬之久才学到的教训：并非所有记忆都生而平等。

当用户说"记住这个"，他们到底在让你记住什么类型的东西？

  • 一个决策（选了 React 而不是 Vue）

  • 一个偏好（喜欢深色模式）

  • 一段关系（Sarah 是 CTO）

  • 一个承诺（答应周五跟进）

  • 一条教训（永远不要在周五部署）

ClawVault 强制执行这套分类体系。每条记忆都有类型标注。为什么？因为"给我看上个月所有的决策"这种查询，只有在你当初就把它们作为决策存储时才能生效。把所有东西一股脑塞进一个笔记文件，就像把备忘录写在手背上一样——agent 版的自欺欺人。

可扩展的分类系统直接映射到 Obsidian 文件夹：

在 Obsidian 中打开，你立刻获得可视化的组织结构。agent 则以编程方式导航同一套结构。同一个架构，服务两种用户。

记忆图谱

接下来才是真正有趣的部分。

ClawVault 在笔记内部使用 wiki-link（[[entity-name]]），和 Obsidian 一模一样。当 agent 写到某个人、某个项目或某个决策时，它会链接到对应实体：


Discussed [[hale-pet-door]] migration with [[justin-dukes]].

Decision: proceed with [[event-driven-architecture]] per [[2026-02-10-standup]].

运行 clawvault link --all，它会自动检测整个 vault 中的实体引用。结果就是一张知识图谱——和你在 Obsidian 图谱视图中看到的那张一样。

但对 agent 而言，这张图谱是可导航的上下文。当被问到 Hale Pet Door 时，agent 不只是找到项目文件——它会沿着链接遍历相关的决策、人物、承诺和教训。图谱本身就是 agent 的联想记忆。

观察式记忆：保留信号的压缩

（插一句，我们还没完全确定是否需要观察式记忆）

原始对话记录太嘈杂、太庞大。但粗暴的摘要又会丢失关键细节。

ClawVault 的观察式记忆系统将对话压缩为带优先级标签的观察条目：

  • 🔴 关键——决策、承诺、阻塞项

  • 🟡 值得注意——洞察、偏好、背景信息

  • 🟢 背景——常规更新、低信号内容

唤醒时，agent 优先加载 🔴 级观察，然后用剩余的上下文预算填充 🟡 级，最后是 🟢 级。这就是预算感知的上下文注入——无论历史记录有多少，最重要的记忆总能进入上下文窗口。

压缩由 LLM 完成（Gemini Flash——又快又便宜），但有一个关键发现：LLM 在压缩时会改写关键词。"Decision: use Postgres"会变成"Postgres was selected for the database layer。"如果你在下游用模式匹配找"Decision:"，就会漏掉它。

我们的解决方案：在 LLM 压缩之后，用正则表达式强制执行优先级分类。压缩质量交给 LLM，分类准确性不能靠它。

Vault 索引模式

对于在 Obsidian 兼容 vault 中工作的 agent，我们使用 vault 索引——一个单独的文件，列出每条笔记及其一行描述：

agent 在决定读什么之前，会先扫描索引。对于大多数查询来说，这比嵌入搜索高效得多——一次文件读取 vs. 一次向量相似度计算。索引是目录，嵌入是搜索引擎。两者配合使用。

零云端，完全自主

ClawVault 不发起任何网络请求（除了可选的 LLM 用于观察压缩）。没有遥测。没有同步服务。没有云端依赖。

你的 agent 的记忆就在你的文件系统上。句号。

这不是意识形态选择——而是架构选择。Agent 的记忆包含你组织中最敏感的运营数据：决策、人际关系、战略、失误。这些数据永远不应该离开你的基础设施，除非你明确选择将其发送到某处。

这意味着什么

Agent 的记忆问题不是技术问题，而是设计问题。

工具早已存在。Markdown 文件、YAML frontmatter、文件夹层级、wiki-link。Obsidian 已经证明这套方案对人类有效。ClawVault 证明它对 agent 同样有效。

核心洞察在于：人类的知识管理和 agent 的记忆管理是同一个问题。两者都需要：

  1. 有类型、有结构的存储

  2. 概念之间的关联链接

  3. 预算约束下的优先级检索

  4. 保留信号的压缩

  5. 不被任何平台锁定

当你的 agent 的记忆 vault 就是一个 Obsidian vault 时，奇妙的事情发生了：你可以看见 agent 知道什么。打开图谱视图，浏览文件夹，阅读 frontmatter。Agent 的记忆变得可检视、可审计、可由人类编辑。

这不是一个功能特性，这就是全部意义所在。

ClawVault™ 已开源。GitHub | npm

由 Versatly 构建——我们为企业部署自主 AI 员工。

Link: http://x.com/i/article/2022388592660688896

相关笔记

作者：sillydarket (@sillydarket)

Every AI agent you've ever used has the same fatal flaw: context death.

The moment a session ends, everything dies. Decisions, preferences, relationships, project context — gone. Your "intelligent" assistant wakes up tomorrow as a stranger.

你用过的每一个 AI agent 都有同一个致命缺陷：上下文死亡。

We spent months obsessing over this problem at Versatly. The result is ClawVault™ — an open-source memory architecture that gives AI agents something they've never had: continuity.

会话一结束，一切灰飞烟灭。决策、偏好、人际关系、项目背景——全部归零。你那个"智能"助手明天醒来，又是一个陌生人。

Here's what we learned building it. And why Obsidian's approach to knowledge was the key insight that unlocked everything.

我们在 Versatly 花了好几个月死磕这个问题。最终成果是 ClawVault™——一个开源的记忆架构，赋予 AI agent 前所未有的能力：连续性。

The Research That Changed Our Approach

以下是我们在构建过程中学到的东西，以及为什么 Obsidian 的知识管理思路成了打开一切的关键洞察。

We benchmarked every agent memory solution we could find. Mem0, Zep, vector databases, custom RAG pipelines. Measured them against LoCoMo (a long-context memory benchmark).

改变我们思路的那项研究

The results were humbling:

我们对市面上能找到的所有 agent 记忆方案做了基准测试：Mem0、Zep、向量数据库、自定义 RAG 管线，全部拿 LoCoMo（一个长上下文记忆基准）来衡量。

  • Specialized memory tools: 68.5%

结果令人汗颜：

  • Simple filesystem with markdown files: 74.0%
  • 专用记忆工具：68.5%

Read that again. Plain markdown files — organized in folders, with grep and search — outperformed purpose-built memory infrastructure.

  • 简单的文件系统 + Markdown 文件：74.0%

Why? Because LLMs already know how to work with files. They've seen billions of examples of reading, searching, and organizing text during training. Fighting that instinct with specialized APIs is swimming upstream.

请再读一遍。朴素的 Markdown 文件——放在文件夹里，配合 grep 和搜索——跑赢了专门为记忆打造的基础设施。

The Obsidian Insight

为什么？因为 LLM 天生就会处理文件。它们在训练阶段见过数十亿条读取、搜索和组织文本的样本。用专用 API 去对抗这种本能，无异于逆水行舟。

Obsidian got something profoundly right: your notes are just files.

Obsidian 的启示

No proprietary database. No cloud lock-in. Markdown files in folders. The magic is in the structure on top: YAML frontmatter for metadata, wiki-links for connections, and a graph view that emerges from the relationships.

Obsidian 有一件事做得极其正确：你的笔记就是文件。

We realized agent memory should work exactly the same way.

没有私有数据库，没有云端锁定。Markdown 文件放在文件夹里，仅此而已。真正的魔法在于上层结构：YAML frontmatter 承载元数据，wiki-link 建立连接，知识图谱从这些关系中自然涌现。

ClawVault stores every memory as a markdown file with YAML frontmatter:

我们意识到，agent 的记忆应该以完全相同的方式运作。

Chose event-driven over request-response for the ingestion layer.

ClawVault 将每条记忆存储为带有 YAML frontmatter 的 Markdown 文件：

Reasoning: 3x throughput at scale, natural backpressure handling...

选择了事件驱动而非请求-响应模式来构建摄入层。

This is simultaneously:

原因：大规模场景下吞吐量提升 3 倍，天然支持背压处理……

  • A ClawVault document the agent can search and retrieve

这个文件同时是：

  • An Obsidian note with full Properties panel support
  • 一份 ClawVault 文档，agent 可以搜索和检索
  • A plain text file any tool can read
  • 一条 Obsidian 笔记，完整支持属性面板

One format. Zero lock-in. Complete interoperability.

  • 一个纯文本文件，任何工具都能读取

Memory Types Matter

一种格式。零锁定。完全互通。

Here's a lesson that took us embarrassingly long to learn: not all memories are equal.

记忆类型至关重要

When a human says "remember this," what kind of thing are they asking you to remember?

这是一个我们花了尴尬之久才学到的教训：并非所有记忆都生而平等。

  • A decision (chose React over Vue)

当用户说"记住这个"，他们到底在让你记住什么类型的东西？

  • A preference (likes dark mode)
  • 一个决策（选了 React 而不是 Vue）
  • A relationship (Sarah is the CTO)
  • 一个偏好（喜欢深色模式）
  • A commitment (promised to follow up Friday)
  • 一段关系（Sarah 是 CTO）
  • A lesson (never deploy on Fridays)
  • 一个承诺（答应周五跟进）

ClawVault enforces this taxonomy. Every memory is typed. Why? Because "show me all decisions from last month" only works if you stored them AS decisions. Dumping everything into a single notes file is the agent equivalent of writing on your hand.

  • 一条教训（永远不要在周五部署）

The extendable category system maps directly to Obsidian folders:

ClawVault 强制执行这套分类体系。每条记忆都有类型标注。为什么？因为"给我看上个月所有的决策"这种查询，只有在你当初就把它们作为决策存储时才能生效。把所有东西一股脑塞进一个笔记文件，就像把备忘录写在手背上一样——agent 版的自欺欺人。

Open this in Obsidian and you get instant visual organization. The agent navigates it programmatically. Same structure serves both.

可扩展的分类系统直接映射到 Obsidian 文件夹：

The Memory Graph

在 Obsidian 中打开，你立刻获得可视化的组织结构。agent 则以编程方式导航同一套结构。同一个架构，服务两种用户。

Here's where it gets interesting.

记忆图谱

ClawVault uses wiki-links ([[entity-name]]) inside notes, exactly like Obsidian. When the agent writes about a person, project, or decision, it links to them:

接下来才是真正有趣的部分。


Discussed [[hale-pet-door]] migration with [[justin-dukes]].

Decision: proceed with [[event-driven-architecture]] per [[2026-02-10-standup]].

ClawVault 在笔记内部使用 wiki-link（[[entity-name]]），和 Obsidian 一模一样。当 agent 写到某个人、某个项目或某个决策时，它会链接到对应实体：

Run clawvault link --all and it auto-detects entity mentions across the vault. The result is a knowledge graph — the same graph you'd see in Obsidian's graph view.


Discussed [[hale-pet-door]] migration with [[justin-dukes]].

Decision: proceed with [[event-driven-architecture]] per [[2026-02-10-standup]].

But for the agent, this graph is navigable context. When asked about Hale Pet Door, the agent doesn't just find the project file — it traverses links to find related decisions, people, commitments, and lessons. The graph IS the agent's associative memory.

运行 clawvault link --all，它会自动检测整个 vault 中的实体引用。结果就是一张知识图谱——和你在 Obsidian 图谱视图中看到的那张一样。

Observational Memory: Compression That Preserves Signal

但对 agent 而言，这张图谱是可导航的上下文。当被问到 Hale Pet Door 时，agent 不只是找到项目文件——它会沿着链接遍历相关的决策、人物、承诺和教训。图谱本身就是 agent 的联想记忆。

(quick note, we are not convinced we need obvservational memory)

观察式记忆：保留信号的压缩

Raw conversation transcripts are too noisy and too large. But naive summarization loses critical details.

（插一句，我们还没完全确定是否需要观察式记忆）

ClawVault's observational memory system compresses conversations into priority-tagged observations:

原始对话记录太嘈杂、太庞大。但粗暴的摘要又会丢失关键细节。

  • 🔴 Critical — decisions, commitments, blockers

ClawVault 的观察式记忆系统将对话压缩为带优先级标签的观察条目：

  • 🟡 Notable — insights, preferences, context
  • 🔴 关键——决策、承诺、阻塞项
  • 🟢 Background — routine updates, low-signal content
  • 🟡 值得注意——洞察、偏好、背景信息

On wake, the agent loads 🔴 observations first, then fills remaining context budget with 🟡, then 🟢. This is budget-aware context injection — the most important memories always make it into the window, regardless of how much history exists.

  • 🟢 背景——常规更新、低信号内容

The compression happens via LLM (Gemini Flash — fast and cheap), but here's the critical insight: LLMs rewrite keywords during compression. "Decision: use Postgres" becomes "Postgres was selected for the database layer." If you're pattern-matching on "Decision:" downstream, you'll miss it.

唤醒时，agent 优先加载 🔴 级观察，然后用剩余的上下文预算填充 🟡 级，最后是 🟢 级。这就是预算感知的上下文注入——无论历史记录有多少，最重要的记忆总能进入上下文窗口。

Our fix: regex-based priority enforcement AFTER LLM compression. Trust the LLM for compression quality, not for classification accuracy.

压缩由 LLM 完成（Gemini Flash——又快又便宜），但有一个关键发现：LLM 在压缩时会改写关键词。"Decision: use Postgres"会变成"Postgres was selected for the database layer。"如果你在下游用模式匹配找"Decision:"，就会漏掉它。

The Vault Index Pattern

我们的解决方案：在 LLM 压缩之后，用正则表达式强制执行优先级分类。压缩质量交给 LLM，分类准确性不能靠它。

For agents working in Obsidian-compatible vaults, we use a vault index — a single file listing every note with a one-line description:

Vault 索引模式

The agent scans the index FIRST before deciding what to read. This is dramatically more efficient than embedding search for most queries — a single file read vs. a vector similarity computation. The index is the table of contents; embeddings are the search engine. Use both.

对于在 Obsidian 兼容 vault 中工作的 agent，我们使用 vault 索引——一个单独的文件，列出每条笔记及其一行描述：

Zero Cloud, Full Sovereignty

agent 在决定读什么之前，会先扫描索引。对于大多数查询来说，这比嵌入搜索高效得多——一次文件读取 vs. 一次向量相似度计算。索引是目录，嵌入是搜索引擎。两者配合使用。

ClawVault makes zero network calls (except optional LLM for observation compression). No telemetry. No sync service. No cloud dependency.

零云端，完全自主

Your agent's memories live on your filesystem. Period.

ClawVault 不发起任何网络请求（除了可选的 LLM 用于观察压缩）。没有遥测。没有同步服务。没有云端依赖。

This isn't an ideological choice — it's an architectural one. Agent memories contain the most sensitive operational data in your organization: decisions, relationships, strategies, mistakes. That data should never leave your infrastructure unless you explicitly choose to send it somewhere.

你的 agent 的记忆就在你的文件系统上。句号。

What This Means

这不是意识形态选择——而是架构选择。Agent 的记忆包含你组织中最敏感的运营数据：决策、人际关系、战略、失误。这些数据永远不应该离开你的基础设施，除非你明确选择将其发送到某处。

The agent memory problem isn't a technology problem. It's a design problem.

这意味着什么

The tools already exist. Markdown files. YAML frontmatter. Folder hierarchies. Wiki-links. Obsidian proved this works for humans. ClawVault proves it works for agents.

Agent 的记忆问题不是技术问题，而是设计问题。

The insight is that human knowledge management and agent memory management are the same problem. Both need:

工具早已存在。Markdown 文件、YAML frontmatter、文件夹层级、wiki-link。Obsidian 已经证明这套方案对人类有效。ClawVault 证明它对 agent 同样有效。

  1. Typed, structured storage

核心洞察在于：人类的知识管理和 agent 的记忆管理是同一个问题。两者都需要：

  1. Associative linking between concepts
  1. 有类型、有结构的存储
  1. Priority-based retrieval under budget constraints
  1. 概念之间的关联链接
  1. Compression that preserves signal
  1. 预算约束下的优先级检索
  1. Zero lock-in to any platform
  1. 保留信号的压缩

When your agent's memory vault IS an Obsidian vault, something remarkable happens: you can see what your agent knows. Open the graph view. Browse the folders. Read the frontmatter. The agent's memory becomes inspectable, auditable, and editable by humans.

  1. 不被任何平台锁定

That's not a feature. That's the whole point.

当你的 agent 的记忆 vault 就是一个 Obsidian vault 时，奇妙的事情发生了：你可以看见 agent 知道什么。打开图谱视图，浏览文件夹，阅读 frontmatter。Agent 的记忆变得可检视、可审计、可由人类编辑。

*ClawVault™ is open source. [GitHub](

这不是一个功能特性，这就是全部意义所在。

https://github.com/Versatly/clawvault

ClawVault™ 已开源。GitHub | npm

) | [npm](

由 Versatly 构建——我们为企业部署自主 AI 员工。

https://npmjs.com/package/clawvault

)*

Link: http://x.com/i/article/2022388592660688896

*Built by [Versatly](

相关笔记

https://versatly.com

) — we deploy autonomous AI employees inside businesses.*

Link: http://x.com/i/article/2022388592660688896

Solving Memory for Openclaw & General Agents

  • Source: https://x.com/sillydarket/status/2022394007448429004?s=46
  • Mirror: https://x.com/sillydarket/status/2022394007448429004?s=46
  • Published: 2026-02-13T19:34:40+00:00
  • Saved: 2026-02-14

Content

Every AI agent you've ever used has the same fatal flaw: context death.

The moment a session ends, everything dies. Decisions, preferences, relationships, project context — gone. Your "intelligent" assistant wakes up tomorrow as a stranger.

We spent months obsessing over this problem at Versatly. The result is ClawVault™ — an open-source memory architecture that gives AI agents something they've never had: continuity.

Here's what we learned building it. And why Obsidian's approach to knowledge was the key insight that unlocked everything.

The Research That Changed Our Approach

We benchmarked every agent memory solution we could find. Mem0, Zep, vector databases, custom RAG pipelines. Measured them against LoCoMo (a long-context memory benchmark).

The results were humbling:

  • Specialized memory tools: 68.5%

  • Simple filesystem with markdown files: 74.0%

Read that again. Plain markdown files — organized in folders, with grep and search — outperformed purpose-built memory infrastructure.

Why? Because LLMs already know how to work with files. They've seen billions of examples of reading, searching, and organizing text during training. Fighting that instinct with specialized APIs is swimming upstream.

The Obsidian Insight

Obsidian got something profoundly right: your notes are just files.

No proprietary database. No cloud lock-in. Markdown files in folders. The magic is in the structure on top: YAML frontmatter for metadata, wiki-links for connections, and a graph view that emerges from the relationships.

We realized agent memory should work exactly the same way.

ClawVault stores every memory as a markdown file with YAML frontmatter:

Chose event-driven over request-response for the ingestion layer.

Reasoning: 3x throughput at scale, natural backpressure handling...

This is simultaneously:

  • A ClawVault document the agent can search and retrieve

  • An Obsidian note with full Properties panel support

  • A plain text file any tool can read

One format. Zero lock-in. Complete interoperability.

Memory Types Matter

Here's a lesson that took us embarrassingly long to learn: not all memories are equal.

When a human says "remember this," what kind of thing are they asking you to remember?

  • A decision (chose React over Vue)

  • A preference (likes dark mode)

  • A relationship (Sarah is the CTO)

  • A commitment (promised to follow up Friday)

  • A lesson (never deploy on Fridays)

ClawVault enforces this taxonomy. Every memory is typed. Why? Because "show me all decisions from last month" only works if you stored them AS decisions. Dumping everything into a single notes file is the agent equivalent of writing on your hand.

The extendable category system maps directly to Obsidian folders:

Open this in Obsidian and you get instant visual organization. The agent navigates it programmatically. Same structure serves both.

The Memory Graph

Here's where it gets interesting.

ClawVault uses wiki-links ([[entity-name]]) inside notes, exactly like Obsidian. When the agent writes about a person, project, or decision, it links to them:


Discussed [[hale-pet-door]] migration with [[justin-dukes]].

Decision: proceed with [[event-driven-architecture]] per [[2026-02-10-standup]].

Run clawvault link --all and it auto-detects entity mentions across the vault. The result is a knowledge graph — the same graph you'd see in Obsidian's graph view.

But for the agent, this graph is navigable context. When asked about Hale Pet Door, the agent doesn't just find the project file — it traverses links to find related decisions, people, commitments, and lessons. The graph IS the agent's associative memory.

Observational Memory: Compression That Preserves Signal

(quick note, we are not convinced we need obvservational memory)

Raw conversation transcripts are too noisy and too large. But naive summarization loses critical details.

ClawVault's observational memory system compresses conversations into priority-tagged observations:

  • 🔴 Critical — decisions, commitments, blockers

  • 🟡 Notable — insights, preferences, context

  • 🟢 Background — routine updates, low-signal content

On wake, the agent loads 🔴 observations first, then fills remaining context budget with 🟡, then 🟢. This is budget-aware context injection — the most important memories always make it into the window, regardless of how much history exists.

The compression happens via LLM (Gemini Flash — fast and cheap), but here's the critical insight: LLMs rewrite keywords during compression. "Decision: use Postgres" becomes "Postgres was selected for the database layer." If you're pattern-matching on "Decision:" downstream, you'll miss it.

Our fix: regex-based priority enforcement AFTER LLM compression. Trust the LLM for compression quality, not for classification accuracy.

The Vault Index Pattern

For agents working in Obsidian-compatible vaults, we use a vault index — a single file listing every note with a one-line description:

The agent scans the index FIRST before deciding what to read. This is dramatically more efficient than embedding search for most queries — a single file read vs. a vector similarity computation. The index is the table of contents; embeddings are the search engine. Use both.

Zero Cloud, Full Sovereignty

ClawVault makes zero network calls (except optional LLM for observation compression). No telemetry. No sync service. No cloud dependency.

Your agent's memories live on your filesystem. Period.

This isn't an ideological choice — it's an architectural one. Agent memories contain the most sensitive operational data in your organization: decisions, relationships, strategies, mistakes. That data should never leave your infrastructure unless you explicitly choose to send it somewhere.

What This Means

The agent memory problem isn't a technology problem. It's a design problem.

The tools already exist. Markdown files. YAML frontmatter. Folder hierarchies. Wiki-links. Obsidian proved this works for humans. ClawVault proves it works for agents.

The insight is that human knowledge management and agent memory management are the same problem. Both need:

  1. Typed, structured storage

  2. Associative linking between concepts

  3. Priority-based retrieval under budget constraints

  4. Compression that preserves signal

  5. Zero lock-in to any platform

When your agent's memory vault IS an Obsidian vault, something remarkable happens: you can see what your agent knows. Open the graph view. Browse the folders. Read the frontmatter. The agent's memory becomes inspectable, auditable, and editable by humans.

That's not a feature. That's the whole point.

*ClawVault™ is open source. [GitHub](

https://github.com/Versatly/clawvault

) | [npm](

https://npmjs.com/package/clawvault

)*

*Built by [Versatly](

https://versatly.com

) — we deploy autonomous AI employees inside businesses.*

Link: http://x.com/i/article/2022388592660688896

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