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Conversation logs provide detailed visibility into every interaction with your AI application. Use them to debug specific issues, understand user behavior patterns, and collect feedback for continuous improvement.

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What Gets Logged

All User Interactions Every conversation through your web app or API is logged with complete input/output history, timing data, and system metadata. User Feedback Thumbs up/down ratings and user comments are captured alongside the conversations they reference. System Context Model used, token consumption, response times, and any errors or warnings during processing. Exclusions: Debugging sessions and prompt testing are not included in logs.

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Use the Logs Console

Access logs from your application’s navigation menu. The interface shows:
  • Conversation Timeline: Chronological list of user interactions
  • Message Details: Full conversation context with AI responses
  • Performance Data: Response times and token usage per interaction
  • User Feedback: Ratings and comments from users and team members

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Debug with Logs

Failed Interactions Quickly identify conversations where the AI provided poor responses, failed to understand user intent, or encountered errors. Performance Issues Spot slow responses, high token usage, or system errors that affect user experience. User Journey Analysis Follow individual users through multiple conversations to understand usage patterns and pain points.

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Feedback Collection

User Ratings Users can provide thumbs up/down feedback on AI responses. Track satisfaction trends over time. Team Annotations Team members can add internal notes and improved responses directly in the log interface. Feedback Analysis Identify common complaint patterns, successful interaction types, and areas needing improvement.

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Log Retention

Ensure your application complies with local data privacy regulations. Publish a privacy policy and obtain user consent where required.
Logs are retained indefinitely by default. To cap database growth, enable scheduled cleanup and set a retention window through the workflow log cleanup environment variables. See Workflow Log Cleanup for the full set of variables. Consider configuring shorter retention periods for applications handling sensitive data, or anonymize logs where appropriate.

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Improve Applications with Logs

Pattern Recognition Look for recurring user questions that your application handles poorly. These indicate opportunities for prompt improvements or knowledge base updates. Response Quality Use feedback patterns to identify which types of responses work well and which need refinement. Performance Optimization Track response times and token usage to identify inefficient prompts or model configurations. Content Gaps Spot topics or question types where your application consistently struggles, indicating areas for knowledge base expansion.

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Privacy Considerations

Logs contain complete user conversations and may include sensitive information. Implement appropriate access controls and ensure compliance with applicable data protection regulations.
Last modified on June 10, 2026