logfire-4-32-1

name	logfire-4-32-1
description	AI observability platform built on OpenTelemetry by the Pydantic team. Native SDKs for Python, JavaScript/TypeScript, and Rust. Use when instrumenting applications with distributed tracing, metrics, and logs; configuring auto-instrumentation for FastAPI, OpenAI, LangChain, and databases; querying trace data via SQL; or integrating with the Logfire cloud platform.

Pydantic Logfire 4.32.1

Overview

From the team behind Pydantic Validation, Pydantic Logfire is an observability platform built on OpenTelemetry with native SDKs for Python, JavaScript/TypeScript, and Rust — plus support for any language via standard OpenTelemetry exporters. It unifies traces, metrics, and structured logs into a single platform with SQL-based querying powered by Apache DataFusion.

Logfire is AI-native: it provides purpose-built features for LLM applications including conversation panels, token tracking, cost monitoring, tool call inspection, streaming support, and multi-turn conversation tracing. Unlike AI-only observability tools, Logfire traces your entire application stack so you can debug whether a problem is in the AI layer or the backend.

Key differentiators:

• SQL-based analysis — query all observability data using PostgreSQL-compatible SQL
• MCP server — LLMs can directly query production telemetry via the Model Context Protocol
• Deep Python integration — rich display of Python objects, event-loop telemetry, profiling, auto-tracing
• Pydantic integration — automatic validation analytics and structured model display
• No lock-in — built on OpenTelemetry; data export to any OTel-compatible backend

When to Use

• Instrumenting Python, JavaScript/TypeScript, or Rust applications with distributed tracing
• Adding auto-tracing to web frameworks (FastAPI, Django, Flask, Starlette, AIOHTTP)
• Monitoring LLM/AI application calls (OpenAI, Anthropic, LangChain, LlamaIndex, Pydantic AI)
• Setting up metrics collection (counters, histograms, gauges, callbacks)
• Querying observability data with SQL or via the MCP server
• Configuring sampling strategies (head, tail, combined)
• Scrubbing sensitive data from logs and spans
• Deploying self-hosted Logfire on Kubernetes
• Integrating alternative OTel clients (Go, Java, .NET, etc.)

Core Concepts

Logfire is built on four key observability concepts:

Span — The atomic unit of telemetry data. A span has a start and end time (thus a duration), can carry structured attributes, and nests within other spans. Think of spans as logs with extra functionality.

Trace — A tree structure of spans showing the path of any request through your application. Spans are ordered and nested, like a stack trace showing all services touched.

Metric — Calculated values collected at regular intervals (request latency, CPU load, queue length). Aggregated over time for charting trends, SLOs, and alerts.

Log — A timestamped text record with no duration. Structured logs (recommended) carry attributes as JSON.

Installation / Setup

Python

logfire auth                          # authenticate (stores credentials in ~/.logfire/default.toml)
logfire projects use <project-name>   # select project

import logfire

logfire.configure()                   # initialize once before logging
logfire.info('Hello, {name}!', name='world')

JavaScript/TypeScript

import * as logfire from '@pydantic/logfire-node'

logfire.configure({
  token: 'your-write-token',
  serviceName: 'my-service',
})

logfire.info('Hello from Node.js', { key: 'value' }, { tags: ['example'] })

Rust

Use the logfire crate from crates.io. Configuration follows standard OTel patterns with Logfire-specific defaults.

CLI Commands

• logfire auth — authenticate with browser login
• logfire clean [--logs] — clean generated files
• logfire inspect — identify missing OTel instrumentation packages
• logfire projects list — list accessible projects
• logfire projects use <name> — select active project
• logfire projects new <name> — create a new project
• logfire --region eu auth or --region us auth — specify data region

Advanced Topics

Concepts Deep Dive: Spans, traces, metrics, logs with examples → Concepts

Manual Tracing: Spans, attributes, messages, f-strings, exceptions, log levels → Manual Tracing

Auto-Tracing: Automatic function-level tracing with module filtering and duration thresholds → Auto-Tracing

Integrations: Web frameworks, databases, HTTP clients, LLMs, task queues, logging libraries → Integrations

AI Observability: LLM panels, token tracking, cost monitoring, tool call inspection, evaluations → AI Observability

Metrics: Counters, histograms, up-down counters, gauges, callback metrics → Metrics

Configuration: Programmatic, environment variables, pyproject.toml, multiple configs → Configuration

Sampling: Head sampling, tail sampling by level/duration, combined strategies → Sampling

Distributed Tracing: Context propagation, thread/pool executors, cross-service traces → Distributed Tracing

SQL Querying: Records table schema, columns, JSON operators, time bucketing → SQL Reference

Scrubbing: Sensitive data redaction, custom patterns, callbacks, security tips → Scrubbing

Alternative Backends: Jaeger, OTel Collector, environment variable configuration → Alternative Backends

JavaScript SDK: Browser, Next.js, Cloudflare Workers, Express, Node.js, Deno → JavaScript SDK

MCP Server: Remote MCP for LLM access to telemetry data → MCP Server

Self-Hosted: Kubernetes deployment, Helm chart, system requirements → Self-Hosted