[docs] Essential cookbook added and overall revamp to the structure of cookbooks (#3668)
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Parth Sharma
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---
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title: Export Stored Memories
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description: "Retrieve, review, and migrate user memories with structured exports."
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---
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Mem0 is a dynamic memory store that gives you full control over your data. Along with storing memories, it gives you the ability to retrieve, export, and migrate your data whenever you need.
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This cookbook shows you how to retrieve and export your data for inspection, migration, or compliance.
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---
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## Setup
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```python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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```
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<Note>
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Your API key needs export permissions to download memory data. Check your project settings on the [dashboard](https://app.mem0.ai) if export operations fail with authentication errors.
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</Note>
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Let's add some sample memories to work with:
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```python
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# Dev's work history
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client.add(
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"Dev works at TechCorp as a senior engineer",
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user_id="dev",
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metadata={"type": "professional"}
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)
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# Arjun's preferences
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client.add(
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"Arjun prefers morning meetings and async communication",
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user_id="arjun",
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metadata={"type": "preference"}
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)
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# Carl's project notes
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client.add(
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"Carl is leading the API redesign project, targeting Q2 launch",
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user_id="carl",
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metadata={"type": "project"}
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)
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```
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---
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## Getting All Memories
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Use `get_all()` with filters to retrieve everything for a specific user:
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```python
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dev_memories = client.get_all(
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filters={"user_id": "dev"},
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page_size=50
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)
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print(f"Total memories: {dev_memories['count']}")
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print(f"First memory: {dev_memories['results'][0]['memory']}")
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```
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**Output:**
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```
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Total memories: 1
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First memory: Dev works at TechCorp as a senior engineer
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```
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<Info>
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**Expected output:** `get_all()` retrieved Dev's complete memory record. This method returns everything matching your filters—no semantic search, no ranking, just raw retrieval. Perfect for exports and audits.
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</Info>
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You can filter by metadata to get specific types:
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```python
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carl_projects = client.get_all(
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filters={
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"AND": [
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{"user_id": "carl"},
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{"metadata": {"type": "project"}}
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]
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}
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)
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for memory in carl_projects['results']:
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print(memory['memory'])
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```
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**Output:**
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```
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Carl is leading the API redesign project, targeting Q2 launch
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```
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---
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## Searching Memories
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When you need semantic search instead of retrieving everything, use `search()`:
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```python
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results = client.search(
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query="What does Dev do for work?",
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filters={"user_id": "dev"},
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top_k=5
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)
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for result in results['results']:
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print(f"{result['memory']} (score: {result['score']:.2f})")
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```
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**Output:**
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```
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Dev works at TechCorp as a senior engineer (score: 0.89)
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```
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Search works across all memory fields and ranks by relevance. Use it when you have a specific question, use `get_all()` when you need everything.
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---
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## Exporting to Structured Format
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For migrations or compliance, you can export memories into a structured schema using Pydantic-style JSON schemas.
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### Step 1: Define the schema
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```python
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professional_profile_schema = {
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"properties": {
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"full_name": {
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"type": "string",
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"description": "The person's full name"
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},
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"current_role": {
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"type": "string",
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"description": "Current job title or role"
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},
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"company": {
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"type": "string",
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"description": "Current employer"
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}
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},
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"title": "ProfessionalProfile",
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"type": "object"
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}
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```
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### Step 2: Create export job
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```python
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export_job = client.create_memory_export(
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schema=professional_profile_schema,
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filters={"user_id": "dev"}
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)
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print(f"Export ID: {export_job['id']}")
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print(f"Status: {export_job['status']}")
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```
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**Output:**
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```
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Export ID: exp_abc123
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Status: processing
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```
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<Info>
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**Export initiated:** Status is "processing". Large exports may take a few seconds. Poll with `get_memory_export()` until status changes to "completed" before downloading data.
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</Info>
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### Step 3: Download the export
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```python
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# Get by ID
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export_data = client.get_memory_export(
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memory_export_id=export_job['id']
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)
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print(export_data['data'])
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```
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**Output:**
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```json
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{
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"full_name": "Dev",
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"current_role": "senior engineer",
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"company": "TechCorp"
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}
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```
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You can also retrieve exports by filters:
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```python
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# Get latest export matching filters
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export_by_filters = client.get_memory_export(
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filters={"user_id": "dev"}
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)
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print(export_by_filters['data'])
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```
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---
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## Adding Export Instructions
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Guide how Mem0 resolves conflicts or formats the export:
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```python
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export_with_instructions = client.create_memory_export(
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schema=professional_profile_schema,
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filters={"user_id": "arjun"},
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export_instructions="""
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1. Use the most recent information if there are conflicts
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2. Only include confirmed facts, not speculation
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3. Return null for missing fields rather than guessing
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"""
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)
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```
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<Tip>
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Always check export status before downloading. Call `get_memory_export()` in a loop with a short delay until `status == "completed"`. Attempting to download while still processing returns incomplete data.
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</Tip>
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---
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## Platform Export
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You can also export memories directly from the Mem0 platform UI:
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1. Navigate to **Memory Exports** in your project dashboard
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2. Click **Create Export**
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3. Select your filters and schema
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4. Download the completed export as JSON
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This is useful for one-off exports or manual data reviews.
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<Warning>
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Exported data expires after 7 days. Download and store exports locally if you need long-term archives. After expiration, you'll need to recreate the export job.
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</Warning>
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---
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## What You Built
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A complete memory export system with multiple retrieval methods:
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- **Bulk retrieval (get_all)** - Fetch all memories matching filters for comprehensive audits
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- **Semantic search** - Query-based lookups with relevance scoring
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- **Structured exports** - Pydantic-schema exports for migrations and compliance
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- **Export instructions** - Guide conflict resolution and data formatting
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- **Platform UI exports** - One-off manual downloads via dashboard
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This covers data portability, GDPR compliance, system migrations, and manual reviews.
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---
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## Summary
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Use **`get_all()`** for bulk retrieval, **`search()`** for specific questions, and **`create_memory_export()`** for structured data exports with custom schemas. Remember exports expire after 7 days—download them locally for long-term archives.
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<CardGroup cols={2}>
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<Card title="Expire Short-Term Data" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
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Keep exports lean by clearing session context before you archive it.
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</Card>
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<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
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Ensure only verified insights make it into your export pipeline.
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</Card>
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</CardGroup>
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