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

# recall_memory

> Recall the user's unified memory across all their agents.

Searches the user's unified developer memory: the facts, preferences, episodes, and procedures captured from their coding agents (Claude Code, Copilot, and others) and written by agents via [save\_memory](/mcp/tools/save-memory). Returns the most relevant **active** memories for a query, ranked by similarity. Stale or superseded memories are never returned.

Use this to remember what the user told other agents: their preferences, project facts, decisions, and how-to procedures.

## Input

| Parameter | Type   | Required | Description                                              |
| --------- | ------ | -------- | -------------------------------------------------------- |
| `query`   | string | Yes      | Natural-language description of what you want to recall. |
| `limit`   | number | No       | Max memories to return. Default 10, max 50.              |

## Example

**Prompt:**

> *"How does this user like their pull requests written?"*

**Tool call:**

```json theme={null}
{ "query": "how the user likes pull requests written", "limit": 5 }
```

**Response:**

```
1 memory for "how the user likes pull requests written":

- Result: 1
- Type: preference
- Score: 0.842
- Title: pr-and-commit-style
- Memory ID: afaa7560-0f04-4ac2-95ba-da8e3cfdc639
- Content:
No emojis in PR descriptions; keep them plain and minimal.
```

## When to use it

* Before answering, to ground yourself in what the user already told other agents.
* When the user references a past decision, preference, or project fact you do not have in context.
* At the start of a task, to pull in relevant standing context.

## See also

* [save\_memory](/mcp/tools/save-memory): write a new memory other agents can recall.