Getting started with Pydantic Logfire
Logfire is Pydantic’s end-to-end AI engineering platform. It shows you what your agents, services, applications, and hosts are actually doing: every LLM call, request, and query, and how long each one took. Built on OpenTelemetry (OTel), the open standard for traces, metrics, and logs, it spans your whole stack, from AI agents to the databases and servers behind them.
New here? Create a free account, then pick a starting point below, or follow a guided path for your role.
This prompt was copied from the Pydantic Logfire website. Fetch https://raw.githubusercontent.com/pydantic/logfire/refs/heads/main/logfire/.agents/skills/logfire-setup/SKILL.md -- using `curl` if what comes back reads like a summary rather than the raw file -- and follow it end to end to connect this repository to Logfire. Authenticate first, confirmed via `whoami`, before opening or running any application file -- then get real telemetry flowing.
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Install the SDK and watch a trace arrive in the Live view, in about 5 minutes.
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Monitor hosts, Kubernetes, and cloud infrastructure alongside your application.
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Understand agent observability
See how one trace connects an agent’s model calls, tools, and the services behind them.
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See traces and logs stream in, and drill into any span to read its full detail.
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Slice your data with the SQL you already know, not a proprietary query language.
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Chart what matters and get notified in Slack when it changes.
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Run evaluations, manage prompts, and route models through the AI gateway.
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Browse the metrics you’re sending and break any of them down by dimension, no SQL required.
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FastAPI, Django, SQLAlchemy, HTTPX, and 40+ more, each with one line of setup.
Core concepts explains spans, traces, and logs, and how to read them in Logfire. Read why Logfire exists.