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POST
/
v1
/
exports
Python
# To use the Python SDK, install the package:
# pip install mem0ai

from mem0 import MemoryClient

client = MemoryClient(api_key="your_api_key")

json_schema = {pydantic_json_schema}
filters = {
    "AND": [
        {"user_id": "alex"}
    ]
}

response = client.create_memory_export(
    schema=json_schema,
    filters=filters
)
print(response)
{
  "message": "Memory export request received. The export will be ready in a few seconds.",
  "id": "550e8400-e29b-41d4-a716-446655440000"
}

Documentation Index

Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt

Use this file to discover all available pages before exploring further.

Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you’re exporting a large number of memories. You can tailor the export by applying various filters (e.g., user_id, agent_id, run_id, or session_id) and by modifying the Pydantic schema to ensure the final data matches your exact needs.

Authorizations

​
Authorization
string
header
required

API key authentication. Prefix your Mem0 API key with 'Token '. Example: 'Token your_api_key'

Body

application/json
​
schema
object
required

Schema definition for the export

​
filters
object

Filters to apply while exporting memories. Available fields are: user_id, agent_id, app_id, run_id.

​
org_id
string

Filter exports by organization ID.

​
project_id
string

Filter exports by project ID.

Response

Export created successfully.

​
message
string
required
Example:

"Memory export request received. The export will be ready in a few seconds."

​
id
string<uuid>
required
Example:

"550e8400-e29b-41d4-a716-446655440000"