langflow-ai/langflow · error · HTTPException
Failed to persist knowledge base '{kb_name}' with backend '{
Error message
Failed to persist knowledge base '{kb_name}' with backend '{backend_type_value}'. Please retry. What it means
Error "Failed to persist knowledge base '{kb_name}' with backend '{backend_type_value}'. Please retry." thrown in langflow-ai/langflow.
Source
Thrown at src/backend/base/langflow/api/v1/knowledge_bases.py:849
}
await knowledge_base_service.create_record(
user_id=current_user.id,
name=kb_name,
model_selection=persisted_selection,
column_config=column_config_dicts or [],
backend_type=backend_type_value,
backend_config=backend_config_value,
record_id=kb_id,
)
except Exception as exc:
await logger.aerror(
"KB DB persist failed for backend %s (kb=%s): %s — rolling back",
backend_type_value,
kb_name,
exc,
)
KBStorageHelper.delete_storage(kb_path, kb_name)
raise HTTPException(
status_code=500,
detail=(
f"Failed to persist knowledge base '{kb_name}' with backend '{backend_type_value}'. Please retry."
),
) from exc
return KnowledgeBaseInfo(
id=str(kb_id),
dir_name=kb_name,
name=kb_name.replace("_", " "),
embedding_provider=request.embedding_provider,
embedding_model=request.embedding_model,
size=0,
words=0,
characters=0,
chunks=0,
avg_chunk_size=0.0,
status="empty",View on GitHub (pinned to 976ec789d2)
Solutions
- Retry the create request; if it persists, verify the configured vector-store backend is reachable.
When it happens
Trigger: Occurs when persisting a new knowledge base to the configured vector backend fails; a retry may succeed.
Common situations: See trigger scenarios.
AI-assisted analysis of langflow-ai/langflow@976ec789d2 (2026-08-14).
Data as JSON: /api/errors/c1739065b8039b6d.
Report an issue: GitHub.