Great! Your version of the “topology reconstruction plan” is already very close to a stable, maintainable design, logically self-consistent, and indeed solves the “missing closed loop” problem you initially mentioned. Let me add a couple of points on implementation details and potential pitfalls to help you go straight to a POC.
1. Your 4-point plan – A point-by-point confirmation
- Create a Global Variable at the Start
Absolutely correct. I suggest you use a Code / variable assignment node near the “Start” to initialize these:
{
"workflow_status": "Success", // Default to success
"error_messages": [], // Collect error messages uniformly
"important_flags": {} // Optional, record which types of steps failed
}
If using Dify’s “Variables” capability, you can also use multiple independent variables, depending on your preference.
- Handle Error Branches for Critical Nodes
The key points for this step are:
- Error branches should not terminate directly, but instead:
- Update the global status (
workflow_status to "Failure" or "PartialSuccess")
- Record the error description for the current node (e.g., node name, error code)
- Jump to a unified “Continue Execution” node (this can be a regular transit node, specifically responsible for rejoining the main flow)
In a Code node, you can write something similar to this (pseudocode):
workflow_status = "Failure" # or "PartialSuccess"
error_messages.append({
"node": "fetch_user_profile",
"error": node_error_message
})
As long as all critical error branches can eventually converge to one or a few “continue” nodes, your future maintenance costs will be manageable.
- Merge HTTP Webhook Endpoints
Merging both success/failure webhooks into a single, unified HTTP node is the correct direction:
- The request body should uniformly include:
status: {{ workflow_status }}errors: {{ error_messages }}- And business data (e.g., results, partial results, trace id, etc.)
This way, the Supabase side only needs one endpoint to:
- Distinguish between success/failure/partial success based on the
status field
- Decide whether to display detailed errors or provide more user prompts based on
errors
- Remove Hardcoded
status: "failed"
This point is also crucial. You are now entrusting the status calculation logic to be determined by the “actual progression of the overall workflow” rather than making a final decision in a local error branch:
- In some scenarios, you might need:
- Some nodes fail but are degradable =>
"PartialSuccess"
- Some non-critical nodes fail without affecting the overall outcome => still
"Success"
- These strategies can be centrally written in:
- A “status aggregation” Code node
- Or simply set flags in each error branch, and the last Code node calculates the final
workflow_status based on these flags
For example, the last Code node can do this (pseudocode):
if any(fatal_errors):
workflow_status = "Failure"
elif any(non_fatal_errors):
workflow_status = "PartialSuccess"
else:
workflow_status = "Success"
Then send this result to Supabase.
2. When actually implementing, you can also pay attention to two small optimizations
2.1 Record a stable run identifier
It is recommended to write Dify’s run ID into Supabase as well:
- The field can be named
workflow_run_id or trace_id
- Benefits:
- When troubleshooting issues in frontend / monitoring tools, you can use this ID to directly navigate back to Dify’s run records
- In the future, if you integrate third-party tracing (Langfuse / LangSmith / Opik, etc.), you can also link them using the same ID
2.2 Retain a fallback strategy for “no response” scenarios
Your current solution works well for calling back to Supabase when the “workflow executes normally to completion.”
In a real environment, you might also encounter a few cases where:
- The entire workflow fails / is rate-limited before the entry point
- An orchestration bug prevents it from reaching the final webhook node
For such extreme cases, you can implement an additional fallback on the Supabase side:
- When creating a new record:
status = 'pending'
- When Dify callbacks successfully: update to
success / failure / partial
- Scheduled task (or database trigger + timeout detection):
- If a record remains in
pending status for more than X minutes without being updated => mark as timeout or unknown, and the frontend displays a “retry later” prompt
This can prevent the problem of the “frontend getting stuck in loading forever due to extreme errors.”
3. How you can validate this solution next
I suggest you first create a Minimum Viable Product (MVP):
- Keep only 2–3 critical nodes, intentionally creating an error in one (e.g., by consistently requesting a 404 HTTP node).
- Verify 3 scenarios:
- All nodes succeed => Supabase receives
status = "Success"
- A critical node fails but the workflow continues => Supabase receives
status = "Failure" / "PartialSuccess"
- Check if the
error_messages content in the Supabase table helps you quickly pinpoint the issue.
If you encounter any of the following during your testing:
- Error branches not reaching the unified node
- Global variables not updating successfully on certain paths
- Issues with Supabase table structure / trigger design
You can post screenshots of the simplified workflow + Supabase table structure, and I can help you review it again to see if there are further areas for simplification or automation.
In summary, your approach to this adjustment is very correct:
Using “status aggregation + unified webhook + Supabase status field” to compensate for the “system-level workflow status callback” that Dify currently doesn’t provide is already a relatively elegant solution from an integration perspective.
