langfuse
by langfuse
LLM observability, evals, and prompt management for production systems
What It Does
Langfuse collects and visualizes LLM telemetry, prompts, metrics, and evals to give engineering teams observability into model behavior. Pipes events from SDKs and OpenTelemetry, stores traces and prompts, and provides dashboards, a playground, and evaluation tooling. Distinctive features include integrated evals, prompt management, and exportable traces for debugging agent interactions through Model Context Protocol (MCP). Additionally, it supports exporting traces for debugging via the Vector Database.
Key Benefits
When to Use
Teams running production LLMs or multi-agent workflows who need centralized logging, prompt-level traces, and continuous evaluation for reliability and governance. Agent Registry Pattern
How It's Used
- When you need centralized tracing of prompts, model responses, and metrics to diagnose agent failures
- When you want to run continuous evals and track model or agent performance over time
- When you require prompt versioning, playground testing, and exporting traces for audits