What's the best way to handle error logging in Lindy agents?

I agree with your assessment about the built-in feed. In my own tests for a budget reporting workflow, it wasn't enough for tracing which line item in a spreadsheet caused a validation failure.

For your second approach, the pattern user1154 mentioned is the one I've settled on. One caveat I found is that you need to be careful not to log sensitive data like payroll figures directly. I usually hash the employee ID and log a reference to the batch file instead of the raw values.

Regarding webhooks, I haven't used that method yet. Could you compare the effort of setting up webhook alerts versus the custom logging in Code steps for someone already using a tool like Datadog? I'm trying to decide which direction to go for audit compliance.

Relying on the built-in activity feed for payroll sync is a compliance risk waiting to happen. It's useless for forensics. You need to know which specific employee record or calculation blew up in a batch of 500.

Your structured logging in Code steps is the only viable path. The pattern is straightforward: wrap your logic in a try-catch, serialize the relevant context (hashed employee ID, batch job ID, step name) into a JSON object, and POST it to your external log aggregator *before* you re-throw the error. This lets Lindy's native failure handling still work while you capture the detail.

The webhook alert payload from Lindy is too minimal for your needs - it's basically just the agent name and a timestamp. For audit trails, you must push the structured data out yourself. The effort to set up a webhook endpoint is trivial compared to the work of enriching those webhooks with the actual failure context, which you'd have to do anyway. Skip the middleman and log directly from the Code step to your monitoring stack.

That's not a dumb question at all - it's a valid architectural consideration. I've evaluated that exact approach.

Treating the agent as a black box and implementing a wrapper introduces its own set of failure modes you now have to manage. You're responsible for the reliability of your logging wrapper's execution environment, its error handling, and its data persistence. If your wrapper fails silently, you've lost visibility into both the wrapper and the agent, which is worse than the original problem.

The hybrid approach others described, logging within Code steps before throwing the error, keeps the agent's native failure handling active while giving you control over the detailed payload. It does create more work initially, but it's contained work - each step's logging is independent, so a failure in one doesn't compromise logging for others.

Thank you for sharing that pattern, especially the detail about creating a dedicated log step. That approach of explicitly capturing state at the end of major sections makes a lot of sense for building a reliable timeline.

I do have a follow-up question about using Google Sheets as the log destination. For payroll audits, we often need to retain logs for several years and demonstrate they haven't been altered. How do you manage access controls and immutability with Sheets, compared to using a more formal logging service? I'm weighing the simplicity against long-term compliance needs.

You're right to question the built-in feed for something like payroll. It's a great high-level status board, but it won't save you during an audit. You can't add custom fields to it, so capturing a specific employee ID or a bad data point at failure just isn't possible there.

For your third point on webhooks, the payload is unfortunately quite limited - usually just agent name, ID, status, and a timestamp. It's perfect for triggering a PagerDuty alert, but it won't include any of your operational data. That means you'd still need to fetch the agent run details via API from your monitoring system to get any context, which adds more steps.

The custom logging in a Code step is the way to go for control. A pattern I like is setting up a "log context" object at the start of a critical step with all the safe, non-sensitive variables (like a batch ID, record count, source system), then appending to it if things go wrong before sending it off. That gives you a full snapshot, not just the error.

Exactly. That limited webhook payload turns what should be a simple alert into a multi-step investigation every single time. I've seen teams burn hours correlating timestamps across systems just to find the failed record.

Your pattern with a log context object is spot-on for creating a useful timeline. One thing I'd add is to consider logging that context object *before* the step even attempts its main logic, not just when things go wrong. If the step crashes on initialization, you still have a breadcrumb showing what it was trying to do, which is often the hardest failure to debug.

You're correct that the built-in activity feed isn't configurable for custom variable states, so that path is a dead end for compliance. The webhook payload is similarly constrained - it's a basic JSON envelope with agent metadata, not the operational data you need.

For structured logging within Code steps, the prevailing pattern is effective, but it has a critical design flaw for audit trails: it's only triggered on error. In a payroll context, you must also log successful operations for a complete ledger. I modify the pattern to log key context (hashed ID, batch reference, action type) at the start of a transaction and again on completion with a status field. This creates an immutable sequence in your external system, whether that's a Google Sheet append or a POST to a logging service, which is far more defensible during an audit than an error-only log.

The real effort comparison isn't between webhooks and custom logging, but between building this once as a reusable utility function in your Code steps versus managing fragmented, inconsistent logs later.

You've hit on the crucial requirement of logging successes, not just errors. A complete audit trail is a series of state transitions, not a collection of failures.

That "start and end" logging pattern is excellent, but to make it truly robust, I'd suggest adding a correlation ID that's generated at the very beginning of the agent run and threaded through every step's log context. Without that, tracing a single employee's data through a multi-step agent across hundreds of batch entries becomes a nightmare of timestamp matching.

Have you thought about the trade-off in log volume? For a high-throughput payroll agent, logging every transaction start and end can get expensive. We sometimes implement a sampling flag in the context object, so only a percentage of successful ops get full logging, while errors are always captured.

> If you miss a variable in a new agent, you have no log.

This is the part that gets really painful in practice. We tried that manual mapping approach for a month and it was a source of constant, subtle bugs. Someone would add a new field like `overtime_factor` and forget to add it to the logging payload. The failure would log, but we couldn't see *why*.

One trick that helped us was creating a small, shared utility function in our Code steps. At the start, you define a 'log template' object with all your critical fields, even if some are null initially. Then you just pass that object around. It's still manual, but it centralizes the schema in one spot per step, making it harder to miss.