supermemoryai/supermemory · warning

Some background memory tasks did not complete on exit

Error message

Some background memory tasks did not complete on exit

What it means

Logged in the async context manager's __aexit__ when waiting for pending background memory tasks exceeds the hardcoded 5-second timeout. Remaining tasks are cancelled, so unsaved memories from those tasks are dropped. It signals the fire-and-forget pipeline could not drain in time during cleanup.

Source

Thrown at packages/openai-sdk-python/src/supermemory_openai/middleware.py:589

        cancelled_count = 0
        for task in self._background_tasks:
            if not task.done():
                task.cancel()
                cancelled_count += 1

        if cancelled_count > 0:
            self._logger.debug(f"Cancelled {cancelled_count} pending background tasks")

    async def __aenter__(self):
        """Async context manager entry."""
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        """Async context manager exit - wait for background tasks."""
        try:
            await self.wait_for_background_tasks(timeout=5.0)
        except asyncio.TimeoutError:
            self._logger.warn("Some background memory tasks did not complete on exit")

    def __enter__(self):
        """Sync context manager entry."""
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        """Sync context manager exit - attempt to wait for background tasks."""
        if self._background_tasks:
            try:
                # Try to wait for background tasks in sync context
                asyncio.run(self.wait_for_background_tasks(timeout=5.0))
            except RuntimeError as e:
                if "cannot be called from a running event loop" in str(e):
                    # In async context, just cancel the tasks
                    self._logger.warn(
                        "Cannot wait for background tasks in sync context from async environment. "
                        "Use async context manager or call wait_for_background_tasks() manually."
                    )

View on GitHub (pinned to d436792e77)

Solutions

1. Before exiting the context, await client.wait_for_background_tasks(timeout=30) with a larger timeout to drain tasks
2. Lower the number of background saves per session or batch them
3. Investigate slow Supermemory API responses (network, auth tenant throttling) and add retries to the underlying HTTP client
4. Consider disabling background mode so saves are awaited inline and failures surface immediately

Example fix

# before
async with client:
    await do_work(client)  # exit gives tasks only 5s

# after
async with client:
    await do_work(client)
    await client.wait_for_background_tasks(timeout=60.0)

if client._background_tasks:
    await client.wait_for_background_tasks(timeout=60.0)  # before exiting 'async with'

When it happens

Trigger: Using 'async with wrapped_client:' and exiting while background memory saves (embeddings, HTTP ingestion calls) are still running longer than 5s; slow network, cold-start latency, or many queued saves at exit.

Common situations: Serverless/Lambda handlers wrapping the client in an async context manager per request, batch scripts that finish quickly but leave slow ingestion behind, environments with throttled Supermemory API access.

Related errors

• Background tasks did not complete within {timeout}s timeout
• Cannot save memory in sync client from async context
• Cannot wait for background tasks in sync context from async
• Supermemory API request failed: ${error}
• Supermemory API request failed: ${error}

AI-assisted analysis of supermemoryai/supermemory@d436792e77 (2026-08-28). Data as JSON: /api/errors/74f0631bba8508cf. Report an issue: GitHub.