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Memfs Search

Organization
letta-ai
memfs-search

Semantic search over agent memory files. Use when you need to find conceptually related memory blocks, discover forgotten reference files, check what you already know before creating new memory, or search beyond exact keyword matching. Currently supports QMD (local, no API keys).

Overview

Publisherletta-ai
Repositoryskills
Skill namememfs-search
Stars
144
Forks
25
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by letta-ai on GitHub. Read the source before you install it.

Installation

Install the Memfs Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/letta-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/tools/memfs-search .claude/skills/memfs-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memfs Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Memfs Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Memfs Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

MemFS Search

Semantic search over your memory filesystem. Useful when Grep isn't enough — finding conceptually related blocks, discovering forgotten reference files, or answering "what do I know about X" across all memory.

Setup

First time only. Run the setup script to create the index and generate embeddings:

bash
bash <SKILL_DIR>/scripts/memfs-search.sh setup

This creates a QMD collection over $MEMORY_DIR, adds context annotations, and embeds all .md files. First run downloads ~2GB of local GGUF models to ~/.cache/qmd/models/.

For installation, embedding model options, and troubleshooting: references/qmd-setup.md.

Searching

Three tiers. Pick based on what you know about your query:

You have...UseCommandSpeed
An exact term or phrasekeywordsearch~0.3s
A vague concept ("what do I know about X")semanticvsearch~2s cold, <1s warm
No idea, need the best resultshybridquery~3s cold, <1s warm
bash
S="bash <SKILL_DIR>/scripts/memfs-search.sh"

# Keyword — fast, use first
$S search "lettabot architecture"

# Semantic — conceptual, use when keyword misses
$S vsearch "how does the user feel about code reviews"

# Hybrid — best quality, uses keyword + vectors + reranking
$S query "projects cameron is working on"

Always start with keyword search. Only escalate when it misses. Hybrid is 10x slower than keyword.

Output Formats

All commands accept output flags forwarded to QMD:

bash
$S search "topic" --json       # structured (for processing)
$S search "topic" --files      # file paths only (pipe into Read)
$S search "topic" --full       # full document, not snippet
$S search "topic" -n 15        # more results (default: 5)

--json returns an array of objects with file, score, snippet, and context fields.

Retrieval

Fetch a specific file or batch of files without searching:

bash
# Single file
qmd get "system/human/identity.md" -c memory --full

# Batch by glob
qmd multi-get "reference/projects/*" -c memory

When to Search Proactively

Don't wait to be asked. Search memory when:

  1. Before creating a new memory file — check if the topic already exists. $S search "topic" --files tells you instantly.
  2. User asks "do you know about X" — search before saying no. Reference files you haven't loaded recently might have it.
  3. During /init or memory reorg — verify coverage. Search for key concepts and confirm they're stored somewhere.
  4. Debugging "I told you about this" — the user thinks you should know something. Search memory before falling back to message history.

Maintenance

After bulk memory changes (e.g. after /init, reorganization, creating many files):

bash
bash <SKILL_DIR>/scripts/memfs-search.sh reindex

Check index health:

bash
bash <SKILL_DIR>/scripts/memfs-search.sh status

When NOT to Use

  • Exact string matching — use Grep.
  • Finding files by name/pattern — use Glob.
  • Reading a file you already know the path to — use Read.
  • Searching message history — use the searching-messages skill.
  • The query is a single word that would match literally — keyword Grep is faster.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Memfs Search AI skill do?

Semantic search over agent memory files. Use when you need to find conceptually related memory blocks, discover forgotten reference files, check what you already know before creating new memory, or search beyond exact keyword matching. Currently supports QMD (local, no API keys).

Why use Memfs Search on TypingMind?

Because you install it once and use it with any model. Memfs Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Memfs Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/letta-ai/skills/tree/main/tools/memfs-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Memfs Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Memfs Search?

As many as you like. As long as a model supports skills, you can use Memfs Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Memfs Search AI skill free?

It is published on GitHub by letta-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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