How does Logfire handle sensitive data?

The skill includes patterns for configuring scrubbing options to mask API keys, tokens, and other PII before they are sent to your observability platform.

Does it support LLM monitoring?

Absolutely. It features specific instrumentation for Pydantic AI, OpenAI, and Anthropic to provide detailed traces of agent logic and model interactions.

Is it compatible with OpenTelemetry?

Yes, Logfire is built on top of OpenTelemetry, ensuring your observability data follows industry standards and is portable across different backends.

What is the main benefit of using the Logfire skill?

It provides a standardized way to implement high-quality observability, making it easy to track performance, debug errors, and monitor LLM calls with minimal boilerplate.

Can I use this with frameworks other than FastAPI?

Yes, it supports a wide range of integrations including Django, Flask, SQLAlchemy, Celery, and various HTTP clients like HTTPX and Requests.

Logfire Observability

Name: Logfire Observability
Author: jiatastic

byjiatastic

•

分析与监控

Implements structured logging, tracing, and metrics for Python applications using Pydantic Logfire and OpenTelemetry.

The Logfire skill provides comprehensive patterns for integrating Pydantic Logfire into Python backends, enabling deep observability with minimal effort. It simplifies the setup of structured logging, distributed tracing, and custom metrics across a wide range of frameworks including FastAPI, SQLAlchemy, and HTTPX. By leveraging OpenTelemetry standards, it allows developers to capture high-fidelity traces, monitor LLM interactions via Pydantic AI, and ensure production readiness with advanced features like automated sensitive data scrubbing and trace sampling.

主要功能

013 GitHub stars

02Specialized LLM observability for Pydantic AI, OpenAI, and Anthropic

03Advanced data security through configurable PII scrubbing and sampling

04Seamless instrumentation for FastAPI, SQLAlchemy, and common HTTP clients

05Built-in support for custom metrics including counters, gauges, and histograms

06Structured logging with automatic variable extraction from Python f-strings

使用场景

01Debugging distributed microservices by tracing requests across API boundaries

02Monitoring and evaluating LLM agent performance and token usage in real-time

03Implementing production-grade observability with automated PII masking

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jiatastic/open-python-skills logfire

For use in Claude.ai and ChatGPT

主要功能

013 GitHub stars

02Specialized LLM observability for Pydantic AI, OpenAI, and Anthropic

03Advanced data security through configurable PII scrubbing and sampling

04Seamless instrumentation for FastAPI, SQLAlchemy, and common HTTP clients

05Built-in support for custom metrics including counters, gauges, and histograms

06Structured logging with automatic variable extraction from Python f-strings

使用场景

01Debugging distributed microservices by tracing requests across API boundaries

02Monitoring and evaluating LLM agent performance and token usage in real-time

03Implementing production-grade observability with automated PII masking

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jiatastic/open-python-skills logfire

For use in Claude.ai and ChatGPT