> ## 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.

# Forgetting Curve

> Ebbinghaus-inspired memory lifecycle — heat bands, decay, and protection

Not all memories should live forever. Linksee Memory uses an **Ebbinghaus-inspired forgetting curve** to naturally decay unimportant memories while permanently preserving critical ones.

## Heat bands

Every memory has a **heat score** (0-100) that decays over time since last access:

| Band     | Heat range | Meaning                              |
| -------- | ---------- | ------------------------------------ |
| `hot`    | 70-100     | Recently accessed, actively relevant |
| `warm`   | 40-69      | Accessed within days, still relevant |
| `cold`   | 10-39      | Not accessed recently, fading        |
| `frozen` | 0-9        | Very old, candidate for forgetting   |

Heat is computed using a decay function inspired by the Ebbinghaus forgetting curve — rapid initial decay that slows over time.

## Forgetting risk

When `forget` or `consolidate` runs, each memory gets a **forgetting risk** score:

```
risk = (1 - heat/100) × (1 - importance) × daysSinceAccess × (1 + daysSinceAccess/30) × altitudeMultiplier
```

### Risk thresholds

| Risk   | Action                                       |
| ------ | -------------------------------------------- |
| \< 50  | **Keep** — still valuable                    |
| 50-200 | **Compress** — merge into a learning summary |
| > 200  | **Drop** — safe to delete                    |

## What's always protected

Some memories **never decay**, regardless of heat or age:

| Category                   | Why                                  |
| -------------------------- | ------------------------------------ |
| Caveat layer               | Pain lessons must never be relearned |
| Goal layer                 | Direction must persist               |
| Pinned (importance >= 0.9) | Explicitly marked as critical        |
| Mission altitude           | Foundational purpose never fades     |

## Accessing refreshes heat

Every `recall` that returns a memory **bumps its heat** back up. This means:

* Frequently used memories stay hot naturally
* Important but rarely accessed memories can be pinned (`importance >= 0.9`) to prevent decay
* Truly forgotten memories fade gracefully

<Tip>
  Use `recall` with `mark_accessed: false` for preview queries that shouldn't affect heat scores.
</Tip>

## Consolidation lifecycle

```mermaid theme={null}
graph LR
    H[Hot memory] -->|time passes| W[Warm]
    W -->|more time| C[Cold]
    C -->|consolidate| L[Learning summary]
    C -->|forget sweep| D[Deleted]
    L -->|protected| L
```

Cold memories are candidates for `consolidate`, which clusters them by (entity, layer) and produces a single learning-layer summary. The originals are deleted, but the essential knowledge is preserved in compressed form.