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ITI Knowledge System
- Chapter 1: Introduction
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Requirements
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Technical Document
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- Chapter 22: Safety & Guardrails
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Chapter 10: n8n — Debugging & Operations
Chapter 10: n8n — Debugging & Operations
Last Updated: 2026-03
## 10.1 Debugging Approach
When an n8n workflow fails, always start with the Executions panel before looking at logs or infrastructure. Most workflow failures are caused by:
1. Expression errors (wrong field name or path)
2. Credential issues (expired or missing API key)
3. External API errors (rate limit, endpoint change, invalid payload)
4. Timeout (operation exceeded the default 30-second window)
5. Data type mismatch (node received the wrong shape of data)
10.2 Using the Executions Panel
- Open
http://localhost:5678. - Click Executions in the left sidebar.
- Filter by Status: Error to see only failed runs.
- Click any failed execution to open it.
- The canvas shows each node colored by status:
- Green = success
- Red = error
- Gray = not reached
- Click the red node to see the error message and the input data it received.
This view is the primary debugging tool. Use it first.
10.3 Common Expression Errors
“Cannot read properties of undefined (reading ‘fieldName’)”
The field does not exist at the path you referenced. The data shape from the previous node is different than expected.
Diagnosis:
- Click the node before the failing one.
- Look at its output data in the execution view.
- Find the actual path to the field you need.
Fix:
- Update the expression to match the actual path.
- Add an If or Code node to handle cases where the field might be missing.
“Expression is invalid”
Syntax error in the expression.
Common mistakes:
// Wrong: missing closing brace
{{ $json.name }
// Wrong: invalid JavaScript in expression
{{ $json.items.forEach(i => i.id) }} // Array methods don't return values
// Correct
{{ $json.items.map(i => i.id).join(", ") }}
Accessing nested data
// If the response is: {"data": {"user": {"name": "Alice"}}}
{{ $json.data.user.name }} // Correct
// If the response is: {"data": [{"name": "Alice"}]}
{{ $json.data[0].name }} // Correct (first array item)
10.4 Webhook Troubleshooting
Webhook returns 404
The workflow is not active. Workflows must be published (active) for their webhooks to respond.
# Check if the workflow is active via API
curl -s "http://localhost:5678/api/v1/workflows?active=true" \
-H "X-N8N-API-KEY: <your-api-key>"
Re-activate through the UI: open the workflow > toggle the Active switch.
Webhook returns 500
The workflow triggered but failed during execution. Check the Executions panel for the error details.
Webhook does not trigger
- Verify the URL is correct (copy from the Webhook node settings in n8n UI).
- Verify the HTTP method matches (
POSTvsGET). - Check that
Content-Type: application/jsonis set if sending a JSON body. - Check n8n container logs for incoming request receipt:
docker compose logs iti-n8n --since 5m | grep -i "webhook"
Test a webhook manually
curl -X POST http://localhost:5678/webhook/your-webhook-path \
-H "Content-Type: application/json" \
-d '{"test": "payload", "key": "value"}'
10.5 Timeout Issues
The default n8n workflow execution timeout is controlled by EXECUTIONS_TIMEOUT in .env. The default is often 3600 seconds (1 hour), but individual node operations may time out sooner.
HTTP Request node timeouts
The HTTP Request node has a default 30-second timeout per request. For long-running Claude API calls (especially with large context), increase the timeout:
In the HTTP Request node settings: Options > Timeout → set to 120000 (120 seconds, in milliseconds).
AI Agent node timeouts
The LangChain AI Agent node defaults to 60 seconds per LLM call. For complex multi-tool agent calls:
Set Options > Max Execution Time to a higher value.
Detecting timeout-caused failures
In the execution view, a node that times out shows:
- Error message containing “timeout” or “ETIMEDOUT”
- The node started but has no completion time
10.6 Execution Data Management
n8n stores every execution’s input and output data in PostgreSQL. This grows over time.
Check execution database size
docker exec iti-postgres psql -U postgres -d n8n -c \
"SELECT pg_size_pretty(pg_database_size('n8n'));"
Configure data pruning
In n8n UI: Settings > Executions:
- Set Prune data older than to
30days. - Enable Save successful executions for audit purposes (optional; increases DB size).
Manually prune old executions
Via the n8n API:
# Delete executions older than 30 days
curl -X DELETE "http://localhost:5678/api/v1/executions" \
-H "X-N8N-API-KEY: <your-api-key>" \
-G --data-urlencode "lastId=<last-execution-id-to-keep>"
10.7 Monitoring Active Workflows
List all active workflows
curl -s "http://localhost:5678/api/v1/workflows?active=true" \
-H "X-N8N-API-KEY: <your-api-key>" | python3 -m json.tool
Check if a specific workflow is active
curl -s "http://localhost:5678/api/v1/workflows/<workflow-id>" \
-H "X-N8N-API-KEY: <your-api-key>" | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['active'])"
Error monitoring workflow
ITI maintains an Error Monitor workflow (ID: yL5Ks9bxfoE2BMj5) that subscribes to n8n’s error trigger and can send alerts. Verify it is active:
curl -s "http://localhost:5678/api/v1/workflows/yL5Ks9bxfoE2BMj5" \
-H "X-N8N-API-KEY: <your-api-key>" | python3 -c "import sys,json; d=json.load(sys.stdin); print('Active:', d['active'])"
10.8 n8n Container Logs
For issues that are not visible in the Executions panel (startup errors, credential decryption failures, memory issues):
# Last 100 lines of n8n logs
docker compose logs iti-n8n --tail 100
# Follow live
docker compose logs -f iti-n8n
# Filter for errors only
docker compose logs iti-n8n --since 1h | grep -i "error\|exception\|fail"
Common log messages to look for:
| Log Message | Meaning |
|---|---|
"Error triggering webhook" |
Webhook fired but workflow failed |
"Workflow activation failed" |
Workflow could not be started (check node configuration) |
"Credential not found" |
A node references a deleted or renamed credential |
"Migration" messages |
DB migration in progress — wait for completion |
Previous: Chapter 9 — n8n Workflow Development | Next: Chapter 11 — Dify Knowledge Bases & RAG
