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/export

Exports all project memories to a portable Markdown file for backup or migration. Use when backing up memories, migrating to another project, sharing memory state with teammates, or archiving before cleanup.

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mem0
63k32 skills1 MCP
Install
$ npx -y skills add mem0ai/mem0 --skill export --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/export

Context preview

The summary Claude sees to decide when to auto-load this skill.

Exports all project memories to a portable Markdown file for backup or migration. Use when backing up memories, migrating to another project, sharing memory state with teammates, or archiving before cleanup.

SKILL.md

export.SKILL.md
name: export
description: Exports all project memories to a portable Markdown file for backup or migration. Use when backing up memories, migrating to another project, sharing memory state with teammates, or archiving before cleanup.

Mem0 Export

Export all memories for the current project to a portable Markdown file.

Execution

Step 1: Resolve identity

Determine the active identity:

  • `user_id` from `MEM0_USER_ID` env var, else `$USER`, else `"default"`
  • `project_id` (used as `app_id`) from `MEM0_PROJECT_ID` env var, or via the project resolver

Step 2: Fetch all memories

Call `get_memories` with:

  • `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`
  • `page_size=200`

If the response is paginated (i.e. the result contains a `next` cursor or the count equals `page_size`), continue fetching pages until all memories are retrieved.

Step 3: Format each memory as a YAML-frontmatter block

For each memory record, produce a block in this exact format:

---
id: <memory.id>
created_at: <memory.created_at>
type: <memory.metadata.type or "">
confidence: <memory.metadata.confidence or "">
branch: <memory.metadata.branch or "">
files: <memory.metadata.files joined with ", " or "">
categories: <memory.categories joined with ", " or "">
---
<memory.memory or memory content string>

Notes:

  • The `---` delimiters must be on their own lines with no extra whitespace.
  • `files` and `categories` are written as comma-separated values on a single line.
  • Leave a blank line after the content before the next `---` (for readability).
  • If a field is missing or null, write an empty string (not "null").

Step 4: Write the export file

Determine the output filename:

mem0-export-<project_id>-<YYYY-MM-DD>.md

Where `<YYYY-MM-DD>` is today's date in UTC.

Write all formatted blocks to this file using the Write tool (or equivalent). The file is written to the current working directory.

Step 5: Print summary

Exported <N> memories to <filename>

Where `<N>` is the total number of memory blocks written.

Error Handling

  • If `get_memories` returns an error or zero memories, print:
  No memories found for project <project_id>. Nothing exported.
  • If the write fails, report the error to the user.
Read more
Ships withmem0

Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions.

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Python
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Apache-2.0
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2d ago
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Repo: mem0ai/mem0

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