Pydantic¶

Logfire has a Pydantic plugin to instrument Pydantic models. The plugin provides logs and metrics about model validation.

To enable the plugin, do one of the following:

• Set the LOGFIRE_PYDANTIC_PLUGIN_RECORD environment variable to all.
• Set pydantic_plugin_record in pyproject.toml, e.g:

[tool.logfire]
pydantic_plugin_record = "all"

• Call logfire.instrument_pydantic with the desired configuration, e.g:

import logfire

logfire.instrument_pydantic()  # Defaults to record='all'

Note that if you only use the last option then only model classes defined and imported after calling logfire.instrument_pydantic will be instrumented.

Third party modules¶

By default, third party modules are not instrumented by the plugin to avoid noise. You can enable instrumentation for those using the include configuration.

logfire.instrument_pydantic(include={'openai'})

You can also disable instrumentation for your own modules using the exclude configuration.

logfire.instrument_pydantic(exclude={'app.api.v1'})

Model configuration¶

If you want more granular control over the plugin, you can use the plugin_settings class parameter in your Pydantic models.

from logfire.integrations.pydantic import PluginSettings
from pydantic import BaseModel


class Foo(BaseModel, plugin_settings=PluginSettings(logfire={'record': 'failure'})):
    ...

Record¶

The record argument is used to configure what to record. It can be one of the following values:

• all: Send traces and metrics for all events. This is default value for logfire.instrument_pydantic.
• failure: Send metrics for all validations and traces only for validation failures.
• metrics: Send only metrics.
• off: Disable instrumentation.

Tags¶

Tags are used to add additional information to the traces, and metrics. They can be included by adding the tags key in plugin_settings.

from pydantic import BaseModel


class Foo(
  BaseModel,
  plugin_settings={'logfire': {'record': 'all', 'tags': ('tag1', 'tag2')}}
):