Memory Fabric

Name: Memory Fabric
Author: elViRafa

by elViRafaversion 1.4.5i★ –iRuns on your computer

Registry nameio.github.elViRafa/memory-fabric

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account elViRafa, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7251 servers in this category
Published to the registry more recently than 90% of the other 7251 servers in this category
Basic checks equal to or better than 100% of the other 7251 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Memory Fabric safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.4.5) reached the registry 4 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

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

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

Memory Fabric

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

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