Pydantic Logfire
Python observability with OpenTelemetry traces, metrics, logs, and SQL-queryable telemetry.
What does this agent do, and when should you use it?
Pydantic Logfire is an observability platform from the Pydantic team; this open-source repository contains the Python `logfire` SDK and its documentation. Developers can emit manual logs and spans or instrument supported Python packages such as FastAPI. The SDK is built around OpenTelemetry and supports traces, metrics, and logs, with export to any OTel-compatible backend. Logfire’s recording and display service—the UI and backend—is closed source, although enterprise licensing permits self-hosting. It is a Python observability product rather than an autonomous AI agent.
After pip install logfire, an application calls logfire.configure() to configure the SDK. It can emit telemetry with logfire.info() and logfire.debug(), and create manual traced scopes with with logfire.span(...). A FastAPI application can be instrumented through logfire.instrument_fastapi(app); the README also directs users to instrument database connectors and HTTP libraries and add a logging handler. The resulting traces, metrics, and logs can be recorded and displayed by the Logfire platform or exported to an OTel-compatible backend, while the platform supports querying data with standard SQL.
- A Python backend team investigating a slow request can wrap important operations in `logfire.span(...)` and inspect the resulting telemetry.
- A team running FastAPI with Pydantic `BaseModel` request data can add `logfire.instrument_fastapi(app)` to observe application behavior and validation-related data flow.
- An engineering team that needs traces, metrics, and logs can adopt a Python SDK built on OpenTelemetry.
- A team with an existing OTel-compatible backend can export data through the Logfire SDK without requiring the Logfire backend as its destination.
- A data or operations team that wants to query observability data with standard SQL or existing BI and database-querying tools can use Logfire’s SQL interface.
What are this agent's strengths and limitations?
- Built on OpenTelemetry, with documented support for traces, metrics, and logs plus export to any OTel-compatible backend.
- Offers direct Python APIs—`logfire.info()`, `logfire.debug()`, and `logfire.span(...)`—for incremental manual instrumentation.
- Includes `logfire.instrument_fastapi(app)`, and the README identifies further instrumentation for database connectors and HTTP libraries.
- Supports standard SQL queries, including use with existing BI tools and database-querying libraries.
- The Logfire UI and backend that record and display data are closed source and are not implemented in this repository.
- Self-hosting the Logfire platform requires an enterprise license.
- The supplied material does not specify authentication credentials, configuration parameters, data retention, or failure-handling behavior.
- The open-source repository provides the Python SDK and documentation, not a complete platform-server deployment package.
How do you install or deploy this agent?
In an environment with Python and pip, run:
pip install logfire
Then authenticate with:
logfire auth
The supplied material shows the authentication command but does not document the required account, token, or interactive credential flow.
How do you use this agent?
Minimal usage:
import logfire
logfire.configure()
logfire.info('Hello, {name}!', name='world')
Use with logfire.span('Asking the user their {question}', question='age'): around an operation to create a span. For FastAPI, create the FastAPI() app, call logfire.configure(), then call logfire.instrument_fastapi(app).