logfire

Q: How do I install logfire?

Run npx skills add https://github.com/jiatastic/open-python-skills --skill logfire in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/logfire/ (personal) or .claude/skills/logfire/ (project).

by jiatasticDevOps & Cloud

Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.

Installs: 78
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/jiatastic/open-python-skills --skill logfire

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads logfire automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Logfire

Structured observability for Python using Pydantic Logfire - fast setup, powerful features, OpenTelemetry-compatible.

Quick Start

uv pip install logfire

import logfire

logfire.configure(service_name="my-api", service_version="1.0.0")
logfire.info("Application started")

Core Patterns

1. Service Configuration

Always set service metadata at startup:

import logfire

logfire.configure(
    service_name="backend",
    service_version="1.0.0",
    environment="production",
    console=False,           # Disable console output in production
    send_to_logfire=True,    # Send to Logfire platform
)

2. Framework Instrumentation

Instrument frameworks before creating clients/apps:

import logfire
from fastapi import FastAPI

# Configure FIRST
logfire.configure(service_name="backend")

# Then instrument
logfire.instrument_fastapi()
logfire.instrument_httpx()
logfire.instrument_sqlalchemy()

# Then create app
app = FastAPI()

3. Log Levels and Structured Logging

# All log levels (trace → fatal)
logfire.trace("Detailed trace", step=1)
logfire.debug("Debug context", variable=locals())
logfire.info("User action", action="login", success=True)
logfire.notice("Important event", event_type="milestone")
logfire.warn("Potential issue", threshold_exceeded=True)
logfire.error("Operation failed", error_code=500)
logfire.fatal("Critical failure", component="database")

# Python 3.11+ f-string magic (auto-extracts variables)
user_id = 123
status = "active"
logfire.info(f"User {user_id} status: {status}")
# Equivalent to: logfire.info("User {user_id}...", user_id=user_id, status=status)

# Exception logging with automatic traceback
try:
    risky_operation()
except Exception:
    logfire.exception("Operation failed", context="extra_info")

4. Manual Spans

# Spans for tracing operations
with logfire.span("Process order {order_id}", order_id="ORD-123"):
    logfire.info("Validating cart")
    # ... processing logic
    logfire.info("Order complete")

# Dynamic span attributes
with logfire.span("Database query") as span:
    results = execute_query()
    span.set_attribute("result_count", len(results))
    span.message = f"Query returned {len(results)} results"

5. Custom Metrics

# Counter - monotonically increasing
request_counter = logfire.metric_counter("http.requests", unit="1")
request_counter.add(1, {"endpoint": "/api/users", "method": "GET"})

# Gauge - current value
temperature = logfire.metric_gauge("temperature", unit="°C")
temperature.set(23.5)

# Histogram - distribution of values
latency = logfire.metric_histogram("request.duration", unit="ms")
latency.record(45.2, {"endpoint": "/api/data"})

6. LLM Observability

import logfire
from pydantic_ai import Agent

logfire.configure()
logfire.instrument_pydantic_ai()  # Traces all agent interactions

agent = Agent("openai:gpt-4o", system_prompt="You are helpful.")
result = agent.run_sync("Hello!")

7. Suppress Noisy Instrumentation

# Suppress entire scope (e.g., noisy library)
logfire.suppress_scopes("google.cloud.bigquery.opentelemetry_tracing")

# Suppress specific code block
with logfire.suppress_instrumentation():
    client.get("https://internal-healthcheck.local")  # Not traced

8. Sensitive Data Scrubbing

import logfire

# Add custom patterns to scrub
logfire.configure(
    scrubbing=logfire.ScrubbingOptions(
        extra_patterns=["api_key", "secret", "token"]
    )
)

# Custom callback for fine-grained control
def scrubbing_callback(match: logfire.ScrubMatch):
    if match.path == ("attributes", "safe_field"):
        return match.value  # Don't scrub this field
    return None  # Use default scrubbing

logfire.configure(
    scrubbing=logfire.ScrubbingOptions(callback=scrubbing_callback)
)

9. Sampling for High-Traffic Services

import logfire

# Sample 50% of traces
logfire.configure(sampling=logfire.SamplingOptions(head=0.5))

# Disable metrics to reduce volume
logfire.configure(metrics=False)

10. Testing

import logfire
from logfire.testing import CaptureLogfire

def test_user_creation(capfire: CaptureLogfire):
    create_user("Alice", "alice@example.com")
    
    spans = capfire.exporter.exported_spans
    assert len(spans) >= 1
    assert spans[0].attributes["user_name"] == "Alice"
    
    capfire.exporter.clear()  # Clean up for next test

Available Integrations

Category	Integration	Method
Web	FastAPI	`logfire.instrument_fastapi(app)`
	Starlette	`logfire.instrument_starlette(app)`
	Django	`logfire.instrument_django()`
	Flask	`logfire.instrument_flask(app)`
	AIOHTTP Server	`logfire.instrument_aiohttp_server()`
	ASGI	`logfire.instrument_asgi(app)`
	WSGI	`logfire.instrument_wsgi(app)`
HTTP	HTTPX	`logfire.instrument_httpx()`
	Requests	`logfire.instrument_requests()`
	AIOHTTP Client	`logfire.instrument_aiohttp_client()`
Database	SQLAlchemy	`logfire.instrument_sqlalchemy(engine)`
	Asyncpg	`logfire.instrument_asyncpg()`
	Psycopg	`logfire.instrument_psycopg()`
	Redis	`logfire.instrument_redis()`
	PyMongo	`logfire.instrument_pymongo()`
LLM	Pydantic AI	`logfire.instrument_pydantic_ai()`
	OpenAI	`logfire.instrument_openai()`
	Anthropic	`logfire.instrument_anthropic()`
	MCP	`logfire.instrument_mcp()`
Tasks	Celery	`logfire.instrument_celery()`
	AWS Lambda	`logfire.instrument_aws_lambda()`
Logging	Standard logging	`logfire.instrument_logging()`
	Structlog	`logfire.instrument_structlog()`
	Loguru	`logfire.instrument_loguru()`
	Print	`logfire.instrument_print()`
Other	Pydantic	`logfire.instrument_pydantic()`
	System Metrics	`logfire.instrument_system_metrics()`

Common Pitfalls

Issue	Symptom	Fix
Missing service name	Spans hard to find in UI	Set `service_name` in `configure()`
Late instrumentation	No spans captured	Call `configure()` before creating clients
High-cardinality attrs	Storage explosion	Use IDs, not full payloads as attributes
Console noise	Logs pollute stdout	Set `console=False` in production

References

Configuration Options - All configure() parameters
Integrations Guide - Framework-specific setup
Metrics Guide - Counter, gauge, histogram, system metrics
Advanced Patterns - Sampling, scrubbing, suppression, testing
Pitfalls & Troubleshooting - Common issues and solutions
Official Docs

FAQ

What does the logfire skill do?

How do I install logfire?

Run npx skills add https://github.com/jiatastic/open-python-skills --skill logfire in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/logfire/ (personal) or .claude/skills/logfire/ (project).

Which AI agents can use logfire?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is logfire safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains logfire?

It is published by jiatastic in jiatastic/open-python-skills. It has 78 recorded installs.

More from jiatastic/open-python-skills

View all →

python-backend

jiatastic/open-python-skills

Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring AI-generated Python code (deslopification), (6) Designing API patterns, or (7) Optimizing backend performance.

Backend & APIs2.2K installs