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POST
/
api
/
v1
/
users
/
buffer
/
{user_id}
/
{buffer_type}
Python
# To use the Python SDK, install the package:
# pip install memobase

from memobase import MemoBaseClient

client = MemoBaseClient(project_url='PROJECT_URL', api_key='PROJECT_TOKEN')

u = client.get_user(uid)
u.flush()
u.flush(sync=True) # wait for the buffer to be processed
{
  "data": [
    {
      "event_id": "<string>",
      "add_profiles": [
        "<string>"
      ],
      "update_profiles": [
        "<string>"
      ],
      "delete_profiles": [
        "<string>"
      ]
    }
  ],
  "errno": 0,
  "errmsg": ""
}

Documentation Index

Fetch the complete documentation index at: https://docs.memobase.io/llms.txt

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

Flush the memory buffer for a specific user and buffer type. This endpoint ensures all pending memory operations are processed and committed to long-term storage.

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

​
user_id
required

The ID of the user

​
buffer_type
enum<string>
required

The type of buffer to flush

Available options:
chat,
summary,
doc,
image,
code,
transcript

Query Parameters

​
wait_process
boolean
default:false

Whether to wait for the buffer to be processed

Response

Successful Response

​
data
ChatModalResponse · object[] | null

Response containing chat modal data

​
errno
enum<integer>
default:0

Error code, 0 means success

Available options:
0,
400,
401,
403,
404,
405,
409,
422,
500,
501,
502,
503,
504,
520
​
errmsg
string
default:""

Error message, empty when success