Langfuse Data Handling
Contents
Overview
Manage the Langfuse data lifecycle including export (JSON/CSV), retention policies, GDPR compliance, anonymization, and audit trails.
Prerequisites
- Langfuse account with data access
- Understanding of data retention requirements
- Compliance framework knowledge (GDPR, SOC2, etc.)
Instructions
Step 1: Export Trace Data
Paginate through traces with date range filters. Export as JSON or CSV with optional input/output inclusion.
Step 2: Implement Retention Policy
Define retention periods per data type (traces: 90d, generations: 30d, scores: 1y). Auto-identify expired data.
Step 3: Handle GDPR Requests
Implement data access (export user traces) and data deletion (mark for removal) endpoints for data subject requests.
Step 4: Anonymize Data for Analytics
Hash user IDs, redact inputs/outputs, and strip PII metadata fields before sharing data externally.
Step 5: Create Audit Trail
Log all data access and export events with tamper-evident hashing. Support time-range and actor-based queries.
See detailed implementation for advanced patterns.
Output
- Data export functionality (JSON/CSV)
- Retention policy enforcement
- GDPR compliance handlers
- Data anonymization pipeline
- Audit trail logging
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Export timeout | Too much data | Use pagination with smaller pages |
| Missing user data | Wrong userId format | Verify identifier format |
| Deletion failed | No DB access | Contact Langfuse support (cloud) |
| Audit gaps | Async logging | Use sync logging for critical events |
Examples
Data Categories
| Category | Retention | Sensitivity |
|---|---|---|
| Traces | 90 days | Medium |
| Generations | 30-90 days | High |
| Scores | 1 year | Low |
| Sessions | 90 days | High |