monitoring

Configure LibreChat monitoring with logs, metrics, Langfuse tracing, and token tracking.

2|Updated Mar 23, 2026

Search Tags:#monitoring #logging #metrics #observability #langfuse #librechat

Ready to run

npx skills add https://github.com/bethanychamberlain/claude-skills-librechat --skill monitoring-bethanychamberlain

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: monitoring
Source: https://github.com/bethanychamberlain/claude-skills-librechat/tree/main/librechat-ops/skills/monitoring
Command: npx skills add https://github.com/bethanychamberlain/claude-skills-librechat --skill monitoring-bethanychamberlain

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enable end-to-end observability for LibreChat deployments by configuring logs, metrics, Langfuse tracing, and token-cost tracking to detect performance issues and usage anomalies.

Core Features & Use Cases

Configure logging and log rotation across environments.
Integrate Langfuse for end-to-end traceability of LLM calls.
Track token usage and cost via the LibreChat YAML and transactions records.
Analyze server logs and performance metrics to diagnose issues in production.

Quick Start

Load the reference docs for logging and Langfuse setup, update your LibreChat configuration to enable monitoring, and restart the services.

Frequently Asked Questions about monitoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set up monitoring and logging for a LibreChat production deployment?▼

To set up monitoring for LibreChat, you must load the on-demand reference docs for logging and Langfuse, update your librechat.yaml configuration, modify your .env file, and restart the services to enable log rotation and end-to-end observability.

What is Langfuse tracing and how does it work with LibreChat?▼

Langfuse tracing provides end-to-end traceability of LLM calls within LibreChat. It integrates via librechat.yaml and .env changes, allowing you to audit usage and detect performance issues across your deployment by visualizing token usage and request flows.

How do I track token usage and cost in LibreChat?▼

You can track token usage and cost in LibreChat by configuring the librechat.yaml file and analyzing transaction records. This configuration enables accurate cost auditing and helps detect usage anomalies across your LLM deployments.

Can I use this monitoring approach to diagnose performance issues in production?▼

Yes, this monitoring setup is specifically applicable to production deployments for troubleshooting performance. By analyzing server logs, metrics, and Langfuse traces together, you can detect anomalies and diagnose issues across your LibreChat environment.

What configuration files do I need to modify to enable observability in LibreChat?▼

You need to modify the librechat.yaml and .env files to enable observability in LibreChat. After applying the exact configuration changes for logging and Langfuse, you must execute restart commands to apply the monitoring settings.