Langfuse Data Handling

Contents

• Overview

• Prerequisites

• Instructions

• Output

• Error Handling

• Examples

• Resources

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

Resources

• Langfuse Privacy Policy

• GDPR Guidelines

• Langfuse Data Protection