# Memory Decay

Older memories drift in relevance at different speeds. A user’s coffee order matters every morning; a one-off project name from last quarter rarely matters again. Memory Decay makes that intuition explicit at search time: every time a memory is returned in a search it gets a small reinforcement, and memories that haven’t been touched in a while have their ranking score gently dampened. It is **a soft ranking bias, never a filter.** Decay never zeroes a candidate out: at worst it scales its score by `0.3×`. Anything that would have surfaced without decay can still surface with decay on, just with a different ranking among similarly-scored results.

**Use Memory Decay when…**

- Search results are crowded with old facts the user no longer cares about.
- You want recently-used memories to drift to the top automatically: without writing custom scoring logic.
- You want this preference applied per project so cohorts can be compared side-by-side.

Memory Decay is **opt-in per project** and **off by default**. Search behavior is bit-identical to today until you turn it on. The toggle applies to v3 search only.

## How it works

Every memory carries a small piece of bookkeeping: when was it last retrieved, and how often. Memory Decay turns that history into a _scaling factor_ in the range `0.3×` to `1.5×` and multiplies it into the ranking score at search time.

| Memory state | Scaling factor | Ranking effect |
| --- | --- | --- |
| Just accessed | ≈ **1.5×** | Strong boost |
| Touched today | 1.2 – 1.4× | Mild boost |
| Idle for a few days | 0.6 – 1.0× | Mild dampening |
| Idle for weeks | 0.4 – 0.6× | Stronger dampening |
| Idle for many months / years | ≈ **0.3×** | Floor: never lower |

The bounds matter: `0.3` is the floor and `1.5` is the ceiling, so decay can meaningfully reorder candidates without ever dominating the underlying relevance score. At search time the pipeline:

1. Widens the candidate pool (`top_k × 3`, with a floor of 50) so reordering has room.
2. Multiplies each candidate’s score by its scaling factor.
3. Sorts on the unclamped product so the full `0.3×–1.5×` range can rearrange candidates.
4. Returns the public `score` clamped to `[0, 1]` so the API contract is preserved.
5. Truncates to the `top_k` you requested.
6. Records a fire-and-forget reinforcement against each returned memory: its access history grows by one, capped at the most recent 20 touches.

Memories created before decay was enabled don’t yet have an access history. They use a sensible fallback: their `updated_at` is treated as a single past touch, so the same scale above applies based on how stale that update is: a recently-updated legacy memory enters near the neutral band, a long-stale one sits closer to the floor. Once surfaced in a search after decay is on, they accumulate access history naturally and behave like any other memory.

## Configure access

- Set `MEM0_API_KEY` in your environment, or pass it to the SDK constructor.
- Initialize the client with the organization and project you want to scope to.

The toggle lives on the project. You enable decay by patching the project’s `decay` field; everything else: your `add` calls, your `search` calls, your application code: stays exactly the same.

## Enable decay for a project

### 1. Turn the flag on

The toggle is exposed on the standard project-update endpoint, the same place where `multilingual` and `custom_categories` live.

Python

```python
client.project.update(decay=True)
```

JavaScript

```javascript
await client.project.update({ decay: true });
```

cURL

```bash
curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
  -H "Authorization: Token $MEM0_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"decay": true}'
```

Response

```
{ "message": "Updated decay" }
```

### 2. Confirm the state

`decay` is returned on every project read. To fetch only this field, use `?fields=decay`.

Python

```python
response = client.project.get(fields=["decay"])
print(response["decay"])
```

JavaScript

```javascript
const response = await client.project.get({ fields: ["decay"] });
console.log(response.decay);
```

cURL

```bash
curl "https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/?fields=decay" \
  -H "Authorization: Token $MEM0_API_KEY"
```

Response

```
{ "decay": true }
```

### 3. Turn it back off

The toggle is fully reversible. Setting it to `false` immediately restores the pre-decay ranking; nothing about your stored memories is modified or lost.

Python

```python
client.project.update(decay=False)
```

JavaScript

```javascript
await client.project.update({ decay: false });
```

cURL

```bash
curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
  -H "Authorization: Token $MEM0_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"decay": false}'
```

The toggle is idempotent. Re-applying the same value is a no-op, and access history accumulated while decay was on is preserved if you flip it back on later.

## What changes when decay is on

- **Search ranking reorders.** A relevant memory you reinforced an hour ago will tend to outrank an equally-relevant memory that was last touched a month ago.
- **The candidate pool over-fetches** to give the scaling factor room to reorder. You still get exactly the `top_k` you requested, but the items returned can come from a deeper slice of the pre-decay ranking than before.
- **The public `score` field stays in `[0, 1]`.** Even when the internal product exceeds 1, the field returned to the client is clamped, so existing assertions and downstream UI logic continue to work.

## What stays the same

- **Public API shape**: every endpoint accepts the same parameters and returns the same fields. You don’t touch your client code.
- **Threshold semantics on the request side**: your `threshold` is still applied during candidate selection.
- **Memory creation and storage**: every new memory still lands the same way. Decay is a search-time concern.
- **Per-memory data**: categories, metadata, timestamps, embeddings: untouched.

Because the scaling factor is applied _after_ the threshold filter has already run, an item that passed the request `threshold` can come back with a public `score` slightly below it (a stale candidate dampened by `0.3×`). This is intentional: decay is a soft bias, not a filter. If you require a hard `score >= threshold` invariant on the response, filter client-side after the call.

## Lifecycle of a memory under decay

| Stage | Scaling factor | Effect |
| --- | --- | --- |
| Just added | ≈ 1.5× | Strong boost: fresh facts surface easily. |
| Reinforced on a recent search | 1.2 – 1.5× | Sustains its boost for the next several searches. |
| Idle for a few days | 0.6 – 1.0× | Falls back into the neutral band. |
| Idle for weeks | 0.4 – 0.6× | Mild dampening: can still surface for strong matches. |
| Pre-decay legacy memory (no access history) | 0.3 – 1.0× | Falls back to `updated_at`: recently-updated entries land near 1.0×, long-stale entries approach the 0.3× floor. |

The reinforcement is bounded: each memory tracks at most the last 20 access timestamps, so the boost stays well-behaved no matter how many times a memory is retrieved.

## FAQ

**Will decay ever drop a result that would otherwise surface?**  
No. The floor is `0.3×`: the scaling factor can dampen a score, never zero it. Threshold filtering happens _before_ decay, so any candidate that cleared the threshold is in the pool decay reorders.

**Why is the public score sometimes below my requested threshold?**  
The threshold is applied to the candidate pool pre-decay; the scaling factor then reshapes scores in the `0.3×–1.5×` band. A stale-but-relevant candidate can come back with a final score slightly under your threshold by design: the candidate stays visible but visibly dampened. Filter client-side if you need a hard floor on the response.

**Does decay change how I add memories?**  
No. The `client.add(...)` path is unchanged. Decay is a search-time ranking adjustment.

**What if I had memories before turning decay on?**  
They use a fallback: the memory’s `updated_at` is treated as a single historical touch, so the same scaling applies based on how stale that update is: a recently-updated legacy memory enters near the neutral band (~1.0×), a long-stale one closer to the floor (~0.3×). Once retrieved they accumulate access history and behave like any other memory.

**Can I tune how aggressively decay scales scores?**  
Not in this version. The current scaling is calibrated to be conservative: wide enough to meaningfully reorder candidates, narrow enough to never dominate the underlying relevance score. Per-project tuning is on the roadmap.

**Can I see the scaling factor per result?**  
Internal scoring details are persisted on the search Event for support and debugging. They aren’t exposed in the public response by design: the response surface stays a single `score` field.

**Does decay interact with reranking?**  
Yes: they layer cleanly. The reranker produces a richer relevance score; decay then biases that score by reinforcement history before final truncation to `top_k`.

## What’s next

This release is deliberately the simplest version of decay we could ship: every memory contributes to ranking through its access history alone, so the signal can be evaluated in isolation. On the roadmap:

- **Category-aware weighting.** A fact tagged `health` will be able to carry more weight than a passing observation tagged `misc`, so important categories don’t get dampened the same way as noise.
- **Auto-tuning per project.** Project-scoped automatic adjustment of how aggressively decay scales scores, based on observed access patterns: replacing the fixed scaling band with one that fits your workload.

Both extensions are forward-compatible: no migration on your side will be needed when they ship.