> ## Documentation Index
> Fetch the complete documentation index at: https://docs.linksee.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Memory Layers

> The 6-layer structure that separates Linksee from flat memory stores

Most agent memory systems store everything in a flat list. Linksee Memory organizes memories into **6 cognitive layers**, each with different retention and retrieval behavior.

## The 6 layers

```mermaid theme={null}
graph TB
    G[goal] --> C[context]
    C --> E[emotion]
    E --> I[implementation]
    I --> CA[caveat]
    CA --> L[learning]

    style G fill:#059669,color:#fff
    style CA fill:#dc2626,color:#fff
    style L fill:#6366f1,color:#fff
```

<AccordionGroup>
  <Accordion title="goal" icon="bullseye">
    **WHY this work exists.** The target outcome. Persists across sessions so the agent doesn't drift.

    * Never auto-forgotten (risk = 0)
    * Set at session start or when the user states a new objective
    * Example: *"Ship v1.0 by end of Q2 with cross-agent recall and token-saving"*
  </Accordion>

  <Accordion title="context" icon="clock">
    **WHY THIS, NOW.** Situational timing, background info, reasons for current priorities.

    * Normal decay rate
    * Consolidated after going cold
    * Example: *"Vercel had a security incident in April — rotating all API keys across projects"*
  </Accordion>

  <Accordion title="emotion" icon="face-smile">
    **User tone and sentiment.** Frustration, excitement, urgency expressed during work.

    * Normal decay rate
    * Helps agents calibrate tone in future sessions
    * Example: *"User frustrated with freee API pagination — 3 sessions debugging"*
  </Accordion>

  <Accordion title="implementation" icon="code">
    **HOW it was done.** What worked, what failed, technical details of execution.

    * Normal decay rate
    * Most common layer for day-to-day memories
    * Example: *"Switched from REST to GraphQL for freee sync — 3x faster batch queries"*
  </Accordion>

  <Accordion title="caveat" icon="triangle-exclamation">
    **PAIN lessons.** "Never X when Y." The protected pile of things you don't want to relearn.

    * **Always protected** — never auto-forgotten, never consolidated
    * Start with a verb: "Never", "Always", "Watch out"
    * Example: *"Never use pgbouncer session mode with Supabase — prepared statement conflicts"*
  </Accordion>

  <Accordion title="learning" icon="graduation-cap">
    **GROWTH.** Decisions made, insights gained, patterns recognized.

    * Normal decay rate but typically higher importance
    * Target layer for consolidation output
    * Example: *"freee webhook reliability is \~95% — always implement polling fallback"*
  </Accordion>
</AccordionGroup>

## Layer aliases

You don't need to remember exact layer names. Common aliases are automatically resolved:

| You say                                       | Stored as        |
| --------------------------------------------- | ---------------- |
| `why`, `intent`, `targets`                    | `goal`           |
| `background`, `reason`, `situation`, `timing` | `context`        |
| `tone`, `feelings`, `mood`                    | `emotion`        |
| `impl`, `how`, `tried`, `success`, `failure`  | `implementation` |
| `warning`, `pain`, `pitfall`, `dont`, `rule`  | `caveat`         |
| `decision`, `insight`, `growth`, `learned`    | `learning`       |

## Retention behavior

| Layer            | Auto-forget | Consolidation target | Protection |
| ---------------- | ----------- | -------------------- | ---------- |
| `goal`           | Never       | No                   | Implicit   |
| `context`        | Normal      | Yes → `learning`     | No         |
| `emotion`        | Normal      | Yes → `learning`     | No         |
| `implementation` | Normal      | Yes → `learning`     | No         |
| `caveat`         | Never       | No                   | Always     |
| `learning`       | Normal      | No (already target)  | No         |

## Why layers matter

Without layers, `recall("Supabase")` returns a wall of undifferentiated text. With layers, the agent can:

* Start with `goal` to understand direction
* Check `caveat` before making changes
* Skim `implementation` for prior approaches
* Use `learning` for distilled wisdom

This is the difference between "I read my notes" and "I understand my history."