> For the complete documentation index, see [llms.txt](https://neutron-4.gitbook.io/neutron-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://neutron-4.gitbook.io/neutron-docs/getting-started/seeds-the-foundation-of-myneutron.md).

# Seeds: The Foundation of myNeutron

Everything in myNeutron starts with a **Seed.**

> **A Seed is a Saved Piece of Knowledge.**

It captures what you save, upload, or sync and turns it into structured context your AI can use.

Each Seed acts like a memory cell for your personal AI brain.\
Together, they form your permanent, searchable memory inside myNeutron.

***

#### A Seed Could Be

* A webpage you saved
* A paragraph you highlighted
* A PDF, note, or image you uploaded
* An email or file synced from Gmail or Drive

***

#### Each Seed Contains

* **Original Content:** Text, media, or data you added
* **Metadata:** Title, source, date captured, type, and tags
* **AI Embeddings:** Semantic representation for deep search and recall
* **Context Links:** Relationships between similar or related Seeds

Your Seeds form a living, connected memory graph.\
myNeutron and your connected AIs can query, learn from, and summarize this graph.

***

#### Why Seeds Matter

Traditional notes and files stay static.\
Seeds are portable, searchable, and ready for AI use.\
They can be accessed by any AI tool through myNeutron’s **Model Context Protocol (MCP).**

| Without myNeutron             | With myNeutron                         |
| ----------------------------- | -------------------------------------- |
| Notes stay trapped in one app | Knowledge is portable across tools     |
| Search is keyword-based       | Search understands meaning and context |
| Files and emails are separate | All unified as Seeds in one workspace  |


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# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://neutron-4.gitbook.io/neutron-docs/getting-started/seeds-the-foundation-of-myneutron.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.