Error Log Analysis: Automatic Bug Triage
Your agent monitors error logs in real time, clusters related errors, ranks them by severity and frequency, and creates GitHub issues for the most critical ones.
What You Will Get
After this walkthrough, your OpenClaw agent will continuously monitor your application's error logs and turn raw log data into prioritized, actionable bug reports. Instead of scrolling through thousands of log lines, you get a curated list of issues ranked by frequency, severity, and user impact.
The agent clusters related errors together, so 500 instances of the same NullPointerException do not show up as 500 separate issues. It identifies the root cause pattern, counts occurrences, tracks when the error first appeared, and determines which users or endpoints are affected.
When an error meets your severity threshold, the agent automatically creates a GitHub issue with all the context: error message, stack trace, frequency, affected endpoints, and suggested investigation steps. Your team gets actionable tickets instead of noisy alerts.
How to Set It Up
Configure log monitoring and automatic triage
Install the Error Analysis Skill
Go to Skills and install the error-log-analysis skill. This skill provides log ingestion, error pattern recognition, clustering algorithms, and integration with GitHub issues for automated ticket creation.
Connect Your Log Source
Configure the agent to read from your log source. It supports webhook-based ingestion, direct log file monitoring, and integration with logging platforms. Set up the connection in your agent's Connections tab and specify the log format (JSON, plain text, or structured logging format).
Define Error Severity Levels
Map your log levels to the agent's severity system. Configure which log levels (error, fatal, warning) the agent should monitor and which it should ignore. Set custom severity rules based on error patterns, such as any error containing payment or auth is automatically flagged as critical.
Configure Error Clustering
Set up the clustering parameters. The agent groups errors by stack trace similarity, error message patterns, and originating code location. Adjust the similarity threshold if the agent is grouping too aggressively (merging distinct errors) or too loosely (creating duplicate clusters).
Set Up Alert Thresholds
Define when the agent should alert you versus silently tracking. A common configuration: new error patterns trigger an immediate alert, errors exceeding 100 occurrences per hour trigger a critical alert, and all other errors are included in the daily digest. Customize these thresholds based on your application's normal error volume.
Enable Automatic Issue Creation
Configure the agent to create GitHub issues for errors that meet specific criteria. Set rules like: create an issue for any new critical error pattern, create an issue when an error cluster exceeds 1,000 occurrences, and create an issue for any error affecting more than 5% of users. The agent formats the issue with all relevant context.
Review the Initial Analysis
Run the first log analysis pass and review the results. The agent will present all error clusters, their frequencies, severity rankings, and any issues it would create. Use this initial pass to tune your thresholds, adjust clustering sensitivity, and verify that the automated issues are well-formatted and actionable.
Tips and Best Practices
Start with Critical Errors Only
Begin by monitoring only error and fatal level logs. Once you are comfortable with the analysis quality, expand to include warnings. This prevents information overload during the initial setup phase.
Create Noise Filters
Some errors are expected and harmless, like connection timeouts during deployments. Add these patterns to a suppression list so the agent ignores them. This keeps your alert stream focused on genuine issues.
Review Cluster Quality Weekly
Check the error clusters periodically to ensure the agent is grouping errors correctly. Merge clusters that represent the same root cause and split clusters that conflate different issues. This feedback improves clustering accuracy over time.
Link Errors to Deployments
Connect your deployment events so the agent can correlate new error patterns with recent deploys. When a new error appears right after a deployment, the agent flags the deploy as the likely cause.
Frequently Asked Questions
Related Pages
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