Logging and debugging automations
Good logging and debugging for OpenClaw automations use structured logs, error context, and optional tracing so US users can find why a task failed and fix it fast. Send key events to SingleAnalytics for a single view of automation health.
When an automation fails, you need to know what happened and where. Logging and debugging practices: structured logs, rich error context, and correlation IDs: make that possible. OpenClaw and your skills can emit consistent, queryable logs and events so US users can troubleshoot without guessing. This post covers logging and debugging automations for OpenClaw.
Why logging and debugging matter
Reproducibility.
Logs should contain enough context (task name, inputs, step, timestamp) to reproduce the run. When a user reports “my daily digest didn’t run,” you find the run, see the error, and fix.
Root cause.
Errors should include the underlying exception or API response, not just “something failed.” Stack traces and request/response snippets (with secrets redacted) speed up debugging.
Trends.
Aggregated logs and events show patterns: “this step fails every Tuesday” or “failures spiked after we added the new integration.” US teams use this to prioritize fixes and tune retries.
What to log
Per run.
Run ID (or task_id), task type, start time, and optionally end time and status. So you can filter “all runs of task X” and see duration and outcome.
Per step.
Step name, start/end, and outcome. For failures: error type, message, and optional stack or response. Redact secrets (tokens, passwords); keep enough to debug (e.g., “HTTP 429” or “invalid JSON at key X”).
Inputs and outputs (carefully).
Log input parameters (e.g., “query: X, limit: 10”) and, for small outputs, a summary (e.g., “returned 5 results”). Avoid logging full PII or large payloads. When in doubt, log structure and size, not content.
Context.
User or tenant ID if multi-user; channel (e.g., WhatsApp) if relevant. So you can trace “this user’s run failed” and see their history.
Structured format
JSON lines.
One JSON object per line: {"ts":"...","level":"error","task_id":"...","step":"fetch","error":"..."}. Easy to parse and query with standard tools (jq, Elasticsearch, etc.). US teams often ship these to a log aggregator.
Levels.
Use levels: debug, info, warn, error. In production, set minimum level to info (or warn) so debug logs don’t flood. Keep error for failures and warn for retries or degraded behavior.
Correlation.
Carry a request_id or run_id through the whole pipeline. Attach it to every log line and event so you can trace one run across services or steps. OpenClaw or your skill can generate and propagate the ID.
Debugging workflow
1. Reproduce.
Use logs to find the failing run: filter by task type and time. Confirm inputs and environment (e.g., API version).
2. Locate.
Find the step and the exact error. Read the error message and any stack or response. Check if the same step failed in other runs (pattern) or only this one (flaky or bad input).
3. Fix.
Fix code, config, or input. Add a test or safeguard so the failure doesn’t recur. Optionally add more logging for that step so next time you have even better context.
4. Verify.
Re-run the task or wait for the next run. Check logs and events to confirm success. Use SingleAnalytics to see task success rate before and after, so debugging is validated by data.
Sending events to analytics
Emit high-level events (task_started, step_completed, task_failed) to your analytics platform in addition to logs. SingleAnalytics gives you a single view: which automations run, which fail, and how often. You can correlate with product and revenue events so US teams see the full impact of automation health.
Summary
Logging and debugging for OpenClaw automations mean structured logs (JSON, levels, correlation ID), rich error context, and a clear debugging workflow. US users get faster root cause and trend visibility. Send key events to SingleAnalytics so automation health is in one place and your fixes are measurable.