Instrument with Logfire

When to Use This Skill

Invoke this skill when:

• User asks to "add logfire", "add observability", "add tracing", or "add monitoring"
• User wants to instrument an app with structured logging or tracing (Python, JS/TS, or Rust)
• User mentions Logfire in any context
• User asks to "add logging" or "see what my app is doing"
• User wants to monitor AI/LLM calls (PydanticAI, OpenAI, Anthropic)
• User asks to add observability to an AI agent or LLM pipeline

How Logfire Works

Logfire is an observability platform built on OpenTelemetry. It captures traces, logs, and metrics from applications. Logfire has native SDKs for Python, JavaScript/TypeScript, and Rust, plus support for any language via OpenTelemetry.

The reason this skill exists is that Claude tends to get a few things subtly wrong with Logfire - especially the ordering of configure() vs instrument_*() calls, the structured logging syntax, and which extras to install. These matter because a misconfigured setup silently drops traces.

Step 1: Detect Language and Frameworks

Identify the project language and instrumentable libraries:

• Python: Read pyproject.toml or requirements.txt. Common instrumentable libraries: FastAPI, httpx, asyncpg, SQLAlchemy, psycopg, Redis, Celery, Django, Flask, requests, PydanticAI.
• JavaScript/TypeScript: Read package.json. Common frameworks: Express, Next.js, Fastify. Also check for Cloudflare Workers or Deno.
• Rust: Read Cargo.toml.

Then follow the language-specific steps below.

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Python

Install with Extras

Install logfire with extras matching the detected frameworks. Each instrumented library needs its corresponding extra - without it, the instrument_*() call will fail at runtime with a missing dependency error.

uv add 'logfire[fastapi,httpx,asyncpg]'