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litellm - 💡(How to fix) Fix [Bug]: Periodic price data reload falling off [1 comments, 1 participants]

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BerriAI/litellm#24930•Fetched 2026-04-08 02:23:38
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Check for existing issues

  • I have searched the existing issues and checked that my issue is not a duplicate.

What happened?

After navigating to Models + Endpoints → Price Data Reload in the Admin UI and using "Set Up Periodic Reload" to configure a 6-hour interval, the dashboard subsequently shows "No periodic reload scheduled" when revisited. No changes were made to the environment or database between setting the reload and checking back.

Expected behavior

The periodic price data reload should remain active and continue firing every 6 hours indefinitely. The dashboard should reflect the configured interval persistently.

Actual behavior

After setting up the periodic reload via the UI, returning to the Price Data Reload page shows the reload schedule has been cleared — "No periodic reload scheduled" — despite no configuration changes being made. Version litellm == v1.81.9

Additional context

  • No changes were made to the environment, config, or database between step 3 and step 4
  • This appears to be either a persistence issue (the schedule is not being saved to the DB) or a display issue (the scheduler is running but the UI is not reading its state correctly)
  • Including proxy logs between setup and re-check would help confirm whether the background task was ever registered

Steps to Reproduce

  1. Log into the Admin UI with an admin account
  2. Navigate to Models + Endpoints → Price Data Reload
  3. Click "Set Up Periodic Reload" and enter a 6-hour interval
  4. Navigate away and return to the Price Data Reload page
  5. Observe: the scheduled reload is gone — "No periodic reload scheduled" is displayed (this might take a while to fall off)

Relevant log output

What part of LiteLLM is this about?

UI Dashboard

What LiteLLM version are you on ?

v1.81.9

Twitter / LinkedIn details

No response

extent analysis

TL;DR

Check the database to see if the periodic reload schedule is being saved and verify the UI's ability to read its state correctly.

Guidance

  • Investigate the database to confirm whether the scheduled reload is being persisted after setup, focusing on any potential issues with data storage or retrieval.
  • Review the UI code responsible for displaying the periodic reload schedule to ensure it correctly reads the schedule from the database or relevant data storage.
  • Include proxy logs between setup and re-check to determine if the background task was registered, which can help differentiate between a persistence issue and a display issue.
  • Test the periodic reload functionality with different intervals to see if the issue is specific to the 6-hour interval or a more general problem.

Example

No specific code snippet can be provided without more details on the database schema or the UI code, but a general approach might involve checking the database for a record of the scheduled reload after it's been set up:

# Hypothetical example, actual implementation depends on the database and UI code
def check_scheduled_reload():
    # Query the database for the scheduled reload
    scheduled_reload = db.query(ScheduledReload).first()
    if scheduled_reload:
        print("Scheduled reload found in database")
    else:
        print("Scheduled reload not found in database")

Notes

The actual fix depends on identifying whether the issue lies in the persistence of the scheduled reload or the UI's display of it. Further investigation into the database and UI code is necessary.

Recommendation

Apply workaround: Temporarily, manually verify and potentially reconfigure the periodic reload schedule each time it appears to be lost, while investigating the root cause of the persistence or display issue.

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FAQ

Expected behavior

The periodic price data reload should remain active and continue firing every 6 hours indefinitely. The dashboard should reflect the configured interval persistently.

#response parsing#generation error#database connection#vector store#embedding generation

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