The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the MyAITwin listing page.
A personal RAG database and semantic search engine you build and control from inside your AI chat. Store your knowledge, voice, and skills as you work. Retrieve them in seconds, source always cited. Your AI then creates output that is recognisably you, in every conversation.
Live at https://myaitwin.lutolearn.com. Free during early access.
MyAITwin is two things at once.
The toolbox. A production-grade RAG database with semantic search that you shape from chat. You define the structure, the types, the tags. It is yours, it is visible, and you are the architect of it.
The twin. The layer on top that greets you, guides you, assesses what you store, and creates output that sounds like you. It knows the difference between what you know (your knowledge) and how you say things (your skills), and it uses both.
Three steps. Under two minutes.
/create.Or use the canonical OAuth-authenticated endpoint:
https://myaitwin.lutolearn.com/mcpRequires a client with MCP capability. Currently Claude Pro, Claude Team, and ChatGPT Pro.
Storing knowledge
| Tool | What it does |
|---|---|
add_knowledge | Store a typed, tagged knowledge item |
add_voice_note | Store a voice note transcript with automatic extraction |
add_document | Store a long document with automatic chunking |
add_from_url | Fetch and store a web page |
add_reference_record | Store a creation event linking knowledge and skills used |
Retrieving knowledge
| Tool | What it does |
|---|---|
search_twin | Semantic search across all knowledge |
search_for_creation | Dual search returning skills and knowledge separately |
get_by_type | Retrieve all items of a specific type |
get_by_tag | Retrieve all items with a specific tag |
list_recent | List recently added items |
Understanding your twin
| Tool | What it does |
|---|---|
get_schema | Overview of your types and how many items you have |
get_sources | List all source documents |
find_patterns | Surface recurring patterns across your knowledge |
synthesise | Synthesise across multiple knowledge items on a topic |
Managing your twin
| Tool | What it does |
|---|---|
get_welcome | Session initialisation and system prompt |
update_knowledge | Update an existing item |
add_schema_type | Define a new knowledge type |
update_schema_type | Update an existing type definition |
delete_knowledge | Delete an item (destructive) |
All tools are annotated with title, readOnlyHint, and destructiveHint
per the MCP spec. Of the 19: 10 read-only, 8 write (non-destructive), 1
destructive (delete_knowledge).
RAG is Retrieval-Augmented Generation. It is the architecture that lets AI answer using your specific knowledge rather than its training data alone.
Two layers:
When you search, both layers work together and return results ranked by relevance. Every result is cited with source and date, and tagged with provenance: personal (your own thinking), organisational (from your organisation), or external (from someone else).
The architectural insight worth getting right:
Knowledge is what you know. Facts, decisions, transcripts, observations.
Skills are how you express things. Your LinkedIn voice. Your email style. Your proposal structure.
Exceptional output needs both. Take a meeting transcript and ask for a follow-up email. The twin needs the transcript and your email skill to produce something that is accurate and unmistakably yours. Neither alone is enough.
/create.
Deletion is immediate and irreversible.Privacy policy: https://myaitwin.lutolearn.com/privacy Security contact: security@lutolearning.com Privacy contact: privacy@lutolearning.com
com.lutolearn/myaitwinMIT. See LICENSE.
MyAITwin MCP by Luto.