Memory Fabric

Name: Memory Fabric
Author: elViRafa

作者：elViRafa版本 1.4.5i★ –i在你的电脑上运行

注册名称io.github.elViRafa/memory-fabric

安装

概览

作者提供的说明（英文原文）

File-first, local-first MCP memory for AI coding assistants: Markdown + YAML, no vector DB.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "memory-fabric": {
      "command": "uvx",
      "args": [
        "memory-fabric"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add memory-fabric -- uvx memory-fabric

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory-fabric": {
      "command": "uvx",
      "args": [
        "memory-fabric"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "memory-fabric": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "memory-fabric"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 elViRafa 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7251 个服务器的 0%
发布到注册中心的时间比此分类中其他 7251 个服务器的 89% 更新
基础检查结果等于或优于此分类中其他 7251 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Memory Fabric 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.4.5）于4天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Memory Fabric

File-first, local-first memory layer for MCP-compatible AI coding assistants.

Memory Fabric gives AI tools like Claude Code, Cursor, and GitHub Copilot a consistent, project-aware context layer without locking you into one model, editor, cloud provider, or operating system.

Memory is stored as human-readable Markdown with YAML frontmatter. No vector database. No cloud account. No embeddings required.

Features

MCP-native: exposes memory tools through the standard Model Context Protocol
File-first: Markdown files are the source of truth, inspectable and commit-ready
Local-first: core reads and writes work offline
Store-first: facts live in memory-store/, one per file; root maps (architecture.md, decisions.md, ...) are generated views rebuilt by Dreaming, never hand-written
Captures itself: every git commit is recorded as episodic memory automatically — no agent cooperation required — via an opt-in post-commit hook
Git-native merge: an optional custom merge driver lets two branches' memory merge as cleanly as their code, instead of conflicting on a shared timestamp line
Self-verifying: memories can cite the file/line/commit they depend on; ai-memory verify flags citations that rotted
Learns from failure: write_failure_memory_tool deduplicates repeat occurrences of the same error into one growing record instead of scattering near-duplicates
Secret-safe: API keys and credentials are redacted before writing
Token-budget aware: assembles context within limits; never slices files mid-document
Quality eval: scores memory usefulness and Dreaming before/after results locally
Unicode-safe: works with any human language
Graceful degradation: works without rg, git hooks, or Dreaming configured

Status

v1.4.5 — field diary pack/analyze and live posture pinning. [Live on PyPI](https://pypi.org/project/memory-fabric/). Core CLI and MCP tools work end-to-end. See ROADMAP.md for what shipped, what's in progress, and what's next.

Demo

The CLI and self-capture flow: initialize a project, write a memory as a plain Markdown file in your repo, and watch a git commit record itself as episodic memory with no agent cooperation. The cross-tool moment — an agent writes memory in one tool and a different tool reads it back — is storyboarded in DEMO.md for the full video.

Privacy — no telemetry

Memory Fabric never sends data anywhere. There is no account, no cloud, no analytics, no phone-home. The core read and write paths make no network calls at all; the only optional network requests are a PyPI version-drift check and an LLM-provider preflight, both of which you can turn off with --offline. Your memory is plain Markdown in your own git history — it never leaves your machine unless you push it.

An optional, off-by-default field diary can record operational counts on your machine after you run ai-memory diary approve. That is not the session journal, and it is not telemetry: nothing is transmitted. MEMORY_FABRIC_DIARY=0 is a kill switch. ai-memory diary pack builds a shareable folder under the app data field-packs/ directory (Windows: %APPDATA%/memory-fabric/field-packs/); you copy that folder yourself. ai-memory diary analyze scores product signals from a pack or the local diary. This is a deliberate guarantee, not a default we might change.

Installation

From PyPI (recommended)

Requires Python ≥ 3.11.

pipx install memory-fabric          # CLI only

pipx install "memory-fabric[mcp]" # CLI + MCP server

Or with plain pip (inside a virtual environment):

pip install memory-fabric          # CLI only

pip install "memory-fabric[mcp]" # CLI + MCP server

Zero-install one-off run (requires uv):

uvx --from "memory-fabric[mcp]" memory-fabric-mcp   # starts the MCP server on stdio

From GitHub (latest, pre-release)

# CLI only

pipx install "git+https://github.com/elViRafa/agentic-memory.git"

CLI + MCP server

pipx install "memory-fabric[mcp] @ git+https://github.com/elViRafa/agentic-memory.git"

Or with plain pip (inside a virtual environment):

pip install "git+https://github.com/elViRafa/agentic-memory.git"          # CLI only

pip install "memory-fabric[mcp] @ git+https://github.com/elViRafa/agentic-memory.git" # + MCP

Upgrading

Because Memory Fabric is in active development, we recommend upgrading regularly:

Upgrade the Package:

   # If installed via pipx:

pipx upgrade memory-fabric

If installed in a virtual environment via pip:

pip install --upgrade "memory-fabric[mcp]"

If installed from GitHub (pre-release):

pipx install --force "memory-fabric[mcp] @ git+https://github.com/elViRafa/agentic-memory.git"

Refresh the MCP client config (uvx installs): If your client config launches the server via uvx (the default written by older ai-memory install runs), uv serves the build it cached the first time and never re-resolves an unpinned spec — restarting the client is not enough and can leave the server several releases behind without any signal. After upgrading, either:

   # Re-write the client config (now pins the exact installed version, or points

at the local memory-fabric-mcp binary when one sits next to the CLI):

ai-memory install --client --project

...or drop the stale cached build so uvx re-resolves:

uv cache clean memory-fabric

   `ai-memory doctor` warns when the local version drifts from the latest on PyPI or

when a different ai-memory installation shadows this one on PATH.

Restart MCP Clients: After upgrading, restart your IDE (Cursor, VS Code) or assistant process (Claude Code) to ensure the client reloads the updated memory-fabric-mcp server.

Refresh Local Projects (Optional): If you have projects initialized with older versions of Memory Fabric, navigate to the project directory and run:

   ai-memory init --install-hooks

This will safely refresh starter templates and local git hook integration to the latest format without overwriting your existing memory markdown files.

Windows terminals: the CLI emits UTF-8 (memory content legitimately contains

em-dashes and bullets). Windows Terminal and PowerShell 7 render it out of the box;

on legacy consoles (PowerShell 5.1 with an OEM code page) set chcp 65001 or

PYTHONUTF8=1 if you see replacement characters.

Or clone and install in editable mode for local development:

git clone https://github.com/elViRafa/agentic-memory.git

cd agentic-memory pip install -e . # CLI only pip install -e ".[mcp]" # CLI + MCP server

Quick Start

1. Initialize a project

ai-memory init

Creates .ai-memory/ in the current directory with starter sections and a .gitignore.

2. Check health

ai-memory doctor

3. Evaluate memory quality

ai-memory eval

ai-memory eval --json

eval scores whether memories are useful for coding assistants. It checks section coverage, starter-template content, summary quality, metadata, retrieval readiness, and likely secrets.

If .ai-memory/ exists, reports are saved under ignored local files:

.ai-memory/evals/latest.json

.ai-memory/evals/latest.md .ai-memory/evals/ -memory.json .ai-memory/evals/ -memory.md

If .ai-memory/ does not exist yet, eval prints a pre-init report only and creates no files.

4. Query memory

ai-memory query "authentication"

5. Run maintenance (Dreaming)

ai-memory dream --mode light

ai-memory dream --mode deep

Dreaming creates a snapshot before maintenance. You can evaluate whether a Dreaming run improved memory quality:

ai-memory eval --dream latest

ai-memory dream --mode light --eval

Dream eval compares the pre-dream snapshot to current memory and reports score delta, changed files, improvements, and regressions.

MCP Server — Install

One command per client. Safe to re-run: merges with your existing config, never overwrites it, and backs up the original file if it can't be parsed.

One-click install

These call uvx --from "memory-fabric[mcp]" memory-fabric-mcp under the hood — same canonical invocation as ai-memory install, just without the CLI step. Requires uv.

Client	Command
Claude Code	`ai-memory install --client claude-code`
Claude Desktop	`ai-memory install --client claude-desktop`
VS Code	`ai-memory install --client vscode`
Cursor	`ai-memory install --client cursor`
Windsurf	`ai-memory install --client windsurf`
Codex	`ai-memory install --client codex`
Antigravity	`ai-memory install --client antigravity`
Gemini CLI	`ai-memory install --client gemini-cli`
Cline	`ai-memory install --client cline`
All detected clients	`ai-memory install --client all`

Add --project to write project-scoped config instead of the global/user config (where the client supports it). Add --dry-run to preview the change as a unified diff without writing anything. Add --uninstall to remove only the memory-fabric entry, leaving everything else in the file untouched.

Available MCP Tools

Tool	Description
`initialize_memory_fabric_tool`	Create `.ai-memory/` scaffolding in a project
`read_combined_context_tool`	Load Tier 0 directives + prioritized memory within token budget
`read_section_tool`	Read a single memory section by name
`keyword_search_tool`	Search memory with ripgrep or Python fallback
`write_local_memory_tool`	Update steering sections (`framework-rules`, `ubiquitous-language`) only. Writing facts or root maps is rejected (store-first) — root maps are generated from `memory-store/`; use `write_memory_store_tool`
`propose_memory_patch_tool`	Preview proposed memory changes without applying
`dream_tool`	Run memory maintenance / consolidation (--mode light	deep)
`prepare_dream_payload_tool`	Prepare candidate snapshot and return consolidation prompt for client-driven dreaming
`apply_dream_results_tool`	Apply LLM consolidated JSON output to candidate snapshot and save changes
`evaluate_memory_fabric_tool`	Evaluate local memory quality
`evaluate_dream_quality_tool`	Evaluate a Dreaming run against a snapshot
`write_memory_store_tool`	Write a memory file to a semantic store path (e.g. `architecture/decisions/auth`); accepts an optional `evidence` citation list

只显示了开头部分；其余内容请在仓库中阅读

Memory Fabric

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Memory Fabric

Features

Status

Demo

Privacy — no telemetry

Installation

From PyPI (recommended)

From GitHub (latest, pre-release)

CLI + MCP server

Upgrading

If installed in a virtual environment via pip:

If installed from GitHub (pre-release):

at the local memory-fabric-mcp binary when one sits next to the CLI):

...or drop the stale cached build so uvx re-resolves:

Quick Start

1. Initialize a project

2. Check health

3. Evaluate memory quality

4. Query memory

5. Run maintenance (Dreaming)

MCP Server — Install

One-click install

Available MCP Tools