Logfire

Logfire is the observability tool focused on developer experience.

Logfire

Logfire(
    *,
    config: LogfireConfig = GLOBAL_CONFIG,
    sample_rate: float | None = None,
    tags: Sequence[str] = (),
    console_log: bool = True,
    otel_scope: str = "logfire"
)

The main logfire class.

Source code in logfire/_internal/main.py

def __init__(
    self,
    *,
    config: LogfireConfig = GLOBAL_CONFIG,
    sample_rate: float | None = None,
    tags: Sequence[str] = (),
    console_log: bool = True,
    otel_scope: str = 'logfire',
) -> None:
    self._tags = tuple(tags)
    self._config = config
    self._sample_rate = sample_rate
    self._console_log = console_log
    self._otel_scope = otel_scope

trace

trace(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None

Log a trace message.

import logfire

logfire.configure()

logfire.trace('This is a trace log')

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False

Source code in logfire/_internal/main.py

def trace(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None:
    """Log a trace message.

    ```py
    import logfire

    logfire.configure()

    logfire.trace('This is a trace log')
    ```

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('trace', msg_template, attributes, tags=_tags, exc_info=_exc_info)

debug

debug(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None

Log a debug message.

import logfire

logfire.configure()

logfire.debug('This is a debug log')

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False

Source code in logfire/_internal/main.py

def debug(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None:
    """Log a debug message.

    ```py
    import logfire

    logfire.configure()

    logfire.debug('This is a debug log')
    ```

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('debug', msg_template, attributes, tags=_tags, exc_info=_exc_info)

info

info(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None

Log an info message.

import logfire

logfire.configure()

logfire.info('This is an info log')

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False

Source code in logfire/_internal/main.py

def info(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None:
    """Log an info message.

    ```py
    import logfire

    logfire.configure()

    logfire.info('This is an info log')
    ```

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('info', msg_template, attributes, tags=_tags, exc_info=_exc_info)

notice

notice(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None

Log a notice message.

import logfire

logfire.configure()

logfire.notice('This is a notice log')

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False

Source code in logfire/_internal/main.py

def notice(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None:
    """Log a notice message.

    ```py
    import logfire

    logfire.configure()

    logfire.notice('This is a notice log')
    ```

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('notice', msg_template, attributes, tags=_tags, exc_info=_exc_info)

warn

warn(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None

Log a warning message.

import logfire

logfire.configure()

logfire.warn('This is a warning log')

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False

Source code in logfire/_internal/main.py

def warn(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None:
    """Log a warning message.

    ```py
    import logfire

    logfire.configure()

    logfire.warn('This is a warning log')
    ```

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('warn', msg_template, attributes, tags=_tags, exc_info=_exc_info)

error

error(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None

Log an error message.

import logfire

logfire.configure()

logfire.error('This is an error log')

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False

Source code in logfire/_internal/main.py

def error(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None:
    """Log an error message.

    ```py
    import logfire

    logfire.configure()

    logfire.error('This is an error log')
    ```

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('error', msg_template, attributes, tags=_tags, exc_info=_exc_info)

fatal

fatal(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None

Log a fatal message.

import logfire

logfire.configure()

logfire.fatal('This is a fatal log')

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False

Source code in logfire/_internal/main.py

def fatal(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = False,
    **attributes: Any,
) -> None:
    """Log a fatal message.

    ```py
    import logfire

    logfire.configure()

    logfire.fatal('This is a fatal log')
    ```

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('fatal', msg_template, attributes, tags=_tags, exc_info=_exc_info)

exception

exception(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = True,
    **attributes: Any,
) -> None

The same as error but with _exc_info=True by default.

This means that a traceback will be logged for any currently handled exception.

Parameters:

Name	Type	Description	Default
msg_template	str	The message to log.	required
attributes	Any	The attributes to bind to the log.	{}
_tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
_exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message.	True

Source code in logfire/_internal/main.py

def exception(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _exc_info: ExcInfo = True,
    **attributes: Any,
) -> None:
    """The same as `error` but with `_exc_info=True` by default.

    This means that a traceback will be logged for any currently handled exception.

    Args:
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        _tags: An optional sequence of tags to include in the log.
        _exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.
    """
    if any(k.startswith('_') for k in attributes):  # pragma: no cover
        raise ValueError('Attribute keys cannot start with an underscore.')
    self.log('error', msg_template, attributes, tags=_tags, exc_info=_exc_info)

span

span(
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _span_name: str | None = None,
    _level: LevelName | None = None,
    **attributes: Any,
) -> LogfireSpan

Context manager for creating a span.

import logfire

logfire.configure()

with logfire.span('This is a span {a=}', a='data'):
    logfire.info('new log 1')

Parameters:

Name	Type	Description	Default
msg_template	str	The template for the span message.	required
_span_name	str | None	The span name. If not provided, the msg_template will be used.	None
_tags	Sequence[str] | None	An optional sequence of tags to include in the span.	None
_level	LevelName | None	An optional log level name.	None
attributes	Any	The arguments to include in the span and format the message template with. Attributes starting with an underscore are not allowed.	{}

Source code in logfire/_internal/main.py

def span(
    self,
    msg_template: str,
    /,
    *,
    _tags: Sequence[str] | None = None,
    _span_name: str | None = None,
    _level: LevelName | None = None,
    **attributes: Any,
) -> LogfireSpan:
    """Context manager for creating a span.

    ```py
    import logfire

    logfire.configure()

    with logfire.span('This is a span {a=}', a='data'):
        logfire.info('new log 1')
    ```

    Args:
        msg_template: The template for the span message.
        _span_name: The span name. If not provided, the `msg_template` will be used.
        _tags: An optional sequence of tags to include in the span.
        _level: An optional log level name.
        attributes: The arguments to include in the span and format the message template with.
            Attributes starting with an underscore are not allowed.
    """
    if any(k.startswith('_') for k in attributes):
        raise ValueError('Attribute keys cannot start with an underscore.')
    return self._span(
        msg_template,
        attributes,
        _tags=_tags,
        _span_name=_span_name,
        _level=_level,
    )

instrument

instrument(
    msg_template: LiteralString | None = None,
    *,
    span_name: str | None = None,
    extract_args: bool = True
) -> Callable[[Callable[P, R]], Callable[P, R]]

Decorator for instrumenting a function as a span.

import logfire

logfire.configure()


@logfire.instrument('This is a span {a=}')
def my_function(a: int):
    logfire.info('new log {a=}', a=a)

Note

• This decorator MUST be applied first, i.e. UNDER any other decorators.
• The source code of the function MUST be accessible.

Parameters:

Name	Type	Description	Default
msg_template	LiteralString | None	The template for the span message. If not provided, the module and function name will be used.	None
span_name	str | None	The span name. If not provided, the msg_template will be used.	None
extract_args	bool	Whether to extract arguments from the function signature and log them as span attributes.	True

Source code in logfire/_internal/main.py

def instrument(
    self,
    msg_template: LiteralString | None = None,
    *,
    span_name: str | None = None,
    extract_args: bool = True,
) -> Callable[[Callable[P, R]], Callable[P, R]]:
    """Decorator for instrumenting a function as a span.

    ```py
    import logfire

    logfire.configure()


    @logfire.instrument('This is a span {a=}')
    def my_function(a: int):
        logfire.info('new log {a=}', a=a)
    ```

    !!! note
        - This decorator MUST be applied first, i.e. UNDER any other decorators.
        - The source code of the function MUST be accessible.

    Args:
        msg_template: The template for the span message. If not provided, the module and function name will be used.
        span_name: The span name. If not provided, the `msg_template` will be used.
        extract_args: Whether to extract arguments from the function signature and log them as span attributes.
    """
    args = LogfireArgs(tuple(self._tags), self._sample_rate, msg_template, span_name, extract_args)
    return instrument(self, args)

log

log(
    level: LevelName | int,
    msg_template: str,
    attributes: dict[str, Any] | None = None,
    tags: Sequence[str] | None = None,
    exc_info: ExcInfo = False,
    console_log: bool | None = None,
) -> None

Log a message.

import logfire

logfire.configure()

logfire.log('info', 'This is a log {a}', {'a': 'Apple'})

Parameters:

Name	Type	Description	Default
level	LevelName | int	The level of the log.	required
msg_template	str	The message to log.	required
attributes	dict[str, Any] | None	The attributes to bind to the log.	None
tags	Sequence[str] | None	An optional sequence of tags to include in the log.	None
exc_info	ExcInfo	Set to an exception or a tuple as returned by sys.exc_info() to record a traceback with the log message. Set to True to use the currently handled exception.	False
console_log	bool | None	Whether to log to the console, defaults to True.	None

Source code in logfire/_internal/main.py

def log(
    self,
    level: LevelName | int,
    msg_template: str,
    attributes: dict[str, Any] | None = None,
    tags: Sequence[str] | None = None,
    exc_info: ExcInfo = False,
    console_log: bool | None = None,
) -> None:
    """Log a message.

    ```py
    import logfire

    logfire.configure()

    logfire.log('info', 'This is a log {a}', {'a': 'Apple'})
    ```

    Args:
        level: The level of the log.
        msg_template: The message to log.
        attributes: The attributes to bind to the log.
        tags: An optional sequence of tags to include in the log.
        exc_info: Set to an exception or a tuple as returned by [`sys.exc_info()`][sys.exc_info]
            to record a traceback with the log message.

            Set to `True` to use the currently handled exception.
        console_log: Whether to log to the console, defaults to `True`.
    """
    with handle_internal_errors():
        stack_info = get_user_stack_info()

        attributes = attributes or {}
        merged_attributes = {**stack_info, **attributes}
        if (msg := attributes.pop(ATTRIBUTES_MESSAGE_KEY, None)) is None:
            fstring_frame = None
            if self._config.inspect_arguments:
                fstring_frame = inspect.currentframe()
                if fstring_frame.f_back.f_code.co_filename == Logfire.log.__code__.co_filename:  # type: ignore
                    # fstring_frame.f_back should be the user's frame.
                    # The user called logfire.info or a similar method rather than calling logfire.log directly.
                    fstring_frame = fstring_frame.f_back  # type: ignore

            msg, extra_attrs, msg_template = logfire_format_with_magic(
                msg_template,
                merged_attributes,
                self._config.scrubber,
                fstring_frame=fstring_frame,
            )
            if extra_attrs:
                merged_attributes.update(extra_attrs)
                # Only do this if extra_attrs is not empty since the copy of `attributes` might be expensive.
                # We update both because attributes_json_schema_properties looks at `attributes`.
                attributes = {**attributes, **extra_attrs}
        else:
            # The message has already been filled in, presumably by a logging integration.
            # Make sure it's a string.
            msg = merged_attributes[ATTRIBUTES_MESSAGE_KEY] = str(msg)
            msg_template = str(msg_template)

        otlp_attributes = user_attributes(merged_attributes)
        otlp_attributes = {
            ATTRIBUTES_SPAN_TYPE_KEY: 'log',
            **log_level_attributes(level),
            ATTRIBUTES_MESSAGE_TEMPLATE_KEY: msg_template,
            ATTRIBUTES_MESSAGE_KEY: msg,
            **otlp_attributes,
        }
        if json_schema_properties := attributes_json_schema_properties(attributes):
            otlp_attributes[ATTRIBUTES_JSON_SCHEMA_KEY] = attributes_json_schema(json_schema_properties)

        tags = self._tags + tuple(tags or ())
        if tags:
            otlp_attributes[ATTRIBUTES_TAGS_KEY] = uniquify_sequence(tags)

        sample_rate = (
            self._sample_rate
            if self._sample_rate is not None
            else otlp_attributes.pop(ATTRIBUTES_SAMPLE_RATE_KEY, None)
        )
        if sample_rate is not None and sample_rate != 1:  # pragma: no cover
            otlp_attributes[ATTRIBUTES_SAMPLE_RATE_KEY] = sample_rate

        if not (self._console_log if console_log is None else console_log):
            otlp_attributes[DISABLE_CONSOLE_KEY] = True
        start_time = self._config.ns_timestamp_generator()

        span = self._logs_tracer.start_span(
            msg_template,
            attributes=otlp_attributes,
            start_time=start_time,
        )

        if exc_info:
            if exc_info is True:
                exc_info = sys.exc_info()
            if isinstance(exc_info, tuple):
                exc_info = exc_info[1]
            if isinstance(exc_info, BaseException):
                _record_exception(span, exc_info)
                if otlp_attributes[ATTRIBUTES_LOG_LEVEL_NUM_KEY] >= LEVEL_NUMBERS['error']:  # type: ignore
                    # Set the status description to the exception message.
                    # OTEL only lets us set the description when the status code is ERROR,
                    # which we only want to do when the log level is error.
                    _set_exception_status(span, exc_info)
            elif exc_info is not None:  # pragma: no cover
                raise TypeError(f'Invalid type for exc_info: {exc_info.__class__.__name__}')

        span.end(start_time)

with_tags

with_tags(*tags: str) -> Logfire

A new Logfire instance which always uses the given tags.

import logfire

logfire.configure()

local_logfire = logfire.with_tags('tag1')
local_logfire.info('a log message', _tags=['tag2'])

# This is equivalent to:
logfire.info('a log message', _tags=['tag1', 'tag2'])

Parameters:

Name	Type	Description	Default
tags	str	The tags to add.	()

Returns:

Type	Description
Logfire	A new Logfire instance with the tags added to any existing tags.

Source code in logfire/_internal/main.py

def with_tags(self, *tags: str) -> Logfire:
    """A new Logfire instance which always uses the given tags.

    ```py
    import logfire

    logfire.configure()

    local_logfire = logfire.with_tags('tag1')
    local_logfire.info('a log message', _tags=['tag2'])

    # This is equivalent to:
    logfire.info('a log message', _tags=['tag1', 'tag2'])
    ```

    Args:
        tags: The tags to add.

    Returns:
        A new Logfire instance with the `tags` added to any existing tags.
    """
    return self.with_settings(tags=tags)

with_settings

with_settings(
    *,
    tags: Sequence[str] = (),
    stack_offset: int | None = None,
    console_log: bool | None = None,
    custom_scope_suffix: str | None = None
) -> Logfire

A new Logfire instance which uses the given settings.

Parameters:

Name	Type	Description	Default
tags	Sequence[str]	Sequence of tags to include in the log.	()
stack_offset	int | None	The stack level offset to use when collecting stack info, also affects the warning which message formatting might emit, defaults to 0 which means the stack info will be collected from the position where logfire.log was called.	None
console_log	bool | None	Whether to log to the console, defaults to True.	None
custom_scope_suffix	str | None	A custom suffix to append to logfire. e.g. logfire.loguru. It should only be used when instrumenting another library with Logfire, such as structlog or loguru. See the instrumenting_module_name parameter on TracerProvider.get_tracer for more info.	None

Returns:

Type	Description
Logfire	A new Logfire instance with the given settings applied.

Source code in logfire/_internal/main.py

def with_settings(
    self,
    *,
    tags: Sequence[str] = (),
    stack_offset: int | None = None,
    console_log: bool | None = None,
    custom_scope_suffix: str | None = None,
) -> Logfire:
    """A new Logfire instance which uses the given settings.

    Args:
        tags: Sequence of tags to include in the log.
        stack_offset: The stack level offset to use when collecting stack info, also affects the warning which
            message formatting might emit, defaults to `0` which means the stack info will be collected from the
            position where [`logfire.log`][logfire.Logfire.log] was called.
        console_log: Whether to log to the console, defaults to `True`.
        custom_scope_suffix: A custom suffix to append to `logfire.` e.g. `logfire.loguru`.

            It should only be used when instrumenting another library with Logfire, such as structlog or loguru.

            See the `instrumenting_module_name` parameter on
            [TracerProvider.get_tracer][opentelemetry.sdk.trace.TracerProvider.get_tracer] for more info.

    Returns:
        A new Logfire instance with the given settings applied.
    """
    # TODO add sample_rate once it's more stable
    return Logfire(
        config=self._config,
        tags=self._tags + tuple(tags),
        sample_rate=self._sample_rate,
        console_log=self._console_log if console_log is None else console_log,
        otel_scope=self._otel_scope if custom_scope_suffix is None else f'logfire.{custom_scope_suffix}',
    )

force_flush

force_flush(timeout_millis: int = 3000) -> bool

Force flush all spans and metrics.

Parameters:

Name	Type	Description	Default
timeout_millis	int	The timeout in milliseconds.	3000

Returns:

Type	Description
bool	Whether the flush of spans was successful.

Source code in logfire/_internal/main.py

def force_flush(self, timeout_millis: int = 3_000) -> bool:  # pragma: no cover
    """Force flush all spans and metrics.

    Args:
        timeout_millis: The timeout in milliseconds.

    Returns:
        Whether the flush of spans was successful.
    """
    return self._config.force_flush(timeout_millis)

log_slow_async_callbacks

log_slow_async_callbacks(
    slow_duration: float = 0.1,
) -> ContextManager[None]

Log a warning whenever a function running in the asyncio event loop blocks for too long.

This works by patching the asyncio.events.Handle._run method.

Parameters:

Name	Type	Description	Default
slow_duration	float	the threshold in seconds for when a callback is considered slow.	0.1

Returns:

Type	Description
ContextManager[None]	A context manager that will revert the patch when exited. This context manager doesn't take into account threads or other concurrency. Calling this method will immediately apply the patch without waiting for the context manager to be opened, i.e. it's not necessary to use this as a context manager.

Source code in logfire/_internal/main.py

def log_slow_async_callbacks(self, slow_duration: float = 0.1) -> ContextManager[None]:
    """Log a warning whenever a function running in the asyncio event loop blocks for too long.

    This works by patching the `asyncio.events.Handle._run` method.

    Args:
        slow_duration: the threshold in seconds for when a callback is considered slow.

    Returns:
        A context manager that will revert the patch when exited.
            This context manager doesn't take into account threads or other concurrency.
            Calling this method will immediately apply the patch
            without waiting for the context manager to be opened,
            i.e. it's not necessary to use this as a context manager.
    """
    return async_.log_slow_callbacks(self, slow_duration)

install_auto_tracing

install_auto_tracing(
    modules: (
        Sequence[str] | Callable[[AutoTraceModule], bool]
    ),
    *,
    check_imported_modules: Literal[
        "error", "warn", "ignore"
    ] = "error",
    min_duration: float = 0
) -> None

Install automatic tracing.

This will trace all non-generator function calls in the modules specified by the modules argument. It's equivalent to wrapping the body of every function in matching modules in with logfire.span(...):.

Note

This function MUST be called before any of the modules to be traced are imported.

Generator functions will not be traced for reasons explained here.

This works by inserting a new meta path finder into sys.meta_path, so inserting another finder before it may prevent it from working.

It relies on being able to retrieve the source code via at least one other existing finder in the meta path, so it may not work if standard finders are not present or if the source code is not available. A modified version of the source code is then compiled and executed in place of the original module.

Parameters:

Name	Type	Description	Default
modules	Sequence[str] | Callable[[AutoTraceModule], bool]	List of module names to trace, or a function which returns True for modules that should be traced. If a list is provided, any submodules within a given module will also be traced.	required
check_imported_modules	Literal['error', 'warn', 'ignore']	If this is 'error' (the default), then an exception will be raised if any of the modules in sys.modules (i.e. modules that have already been imported) match the modules to trace. Set to 'warn' to issue a warning instead, or 'ignore' to skip the check.	'error'
min_duration	float	An optional minimum duration in seconds for which a function must run before it's traced. The default is 0, which means all functions are traced from the beginning. Otherwise, the first time(s) each function is called, it will be timed but not traced. Only after the function has run for at least min_duration will it be traced in subsequent calls.	0

Source code in logfire/_internal/main.py

def install_auto_tracing(
    self,
    modules: Sequence[str] | Callable[[AutoTraceModule], bool],
    *,
    check_imported_modules: Literal['error', 'warn', 'ignore'] = 'error',
    min_duration: float = 0,
) -> None:
    """Install automatic tracing.

    This will trace all non-generator function calls in the modules specified by the modules argument.
    It's equivalent to wrapping the body of every function in matching modules in `with logfire.span(...):`.

    !!! note
        This function MUST be called before any of the modules to be traced are imported.

        Generator functions will not be traced for reasons explained [here](https://logfire.pydantic.dev/docs/guides/advanced/generators/).

    This works by inserting a new meta path finder into `sys.meta_path`, so inserting another finder before it
    may prevent it from working.

    It relies on being able to retrieve the source code via at least one other existing finder in the meta path,
    so it may not work if standard finders are not present or if the source code is not available.
    A modified version of the source code is then compiled and executed in place of the original module.

    Args:
        modules: List of module names to trace, or a function which returns True for modules that should be traced.
            If a list is provided, any submodules within a given module will also be traced.
        check_imported_modules: If this is `'error'` (the default), then an exception will be raised if any of the
            modules in `sys.modules` (i.e. modules that have already been imported) match the modules to trace.
            Set to `'warn'` to issue a warning instead, or `'ignore'` to skip the check.
        min_duration: An optional minimum duration in seconds for which a function must run before it's traced.
            The default is `0`, which means all functions are traced from the beginning.
            Otherwise, the first time(s) each function is called, it will be timed but not traced.
            Only after the function has run for at least `min_duration` will it be traced in subsequent calls.
    """
    install_auto_tracing(self, modules, check_imported_modules=check_imported_modules, min_duration=min_duration)

instrument_fastapi

instrument_fastapi(
    app: FastAPI,
    *,
    capture_headers: bool = False,
    request_attributes_mapper: (
        Callable[
            [Request | WebSocket, dict[str, Any]],
            dict[str, Any] | None,
        ]
        | None
    ) = None,
    use_opentelemetry_instrumentation: bool = True,
    excluded_urls: str | Iterable[str] | None = None,
    record_send_receive: bool = False,
    **opentelemetry_kwargs: Any
) -> ContextManager[None]

Instrument a FastAPI app so that spans and logs are automatically created for each request.

Parameters:

Name	Type	Description	Default
app	FastAPI	The FastAPI app to instrument.	required
capture_headers	bool	Set to True to capture all request and response headers.	False
request_attributes_mapper	Callable[[Request | WebSocket, dict[str, Any]], dict[str, Any] | None] | None	A function that takes a Request or WebSocket and a dictionary of attributes and returns a new dictionary of attributes. The input dictionary will contain: values: A dictionary mapping argument names of the endpoint function to parsed and validated values. errors: A list of validation errors for any invalid inputs. The returned dictionary will be used as the attributes for a log message. If None is returned, no log message will be created. You can use this to e.g. only log validation errors, or nothing at all. You can also add custom attributes. The default implementation will return the input dictionary unchanged. The function mustn't modify the contents of values or errors.	None
excluded_urls	str | Iterable[str] | None	A string of comma-separated regexes which will exclude a request from tracing if the full URL matches any of the regexes. This applies to both the Logfire and OpenTelemetry instrumentation. If not provided, the environment variables OTEL_PYTHON_FASTAPI_EXCLUDED_URLS and OTEL_PYTHON_EXCLUDED_URLS will be checked.	None
use_opentelemetry_instrumentation	bool	If True (the default) then FastAPIInstrumentor will also instrument the app. See OpenTelemetry FastAPI Instrumentation.	True
record_send_receive	bool	Set to True to allow the OpenTelemetry ASGI to create send/receive spans. These are disabled by default to reduce overhead and the number of spans created, since many can be created for a single request, and they are not often useful. If enabled, they will be set to debug level, meaning they will usually still be hidden in the UI.	False
opentelemetry_kwargs	Any	Additional keyword arguments to pass to the OpenTelemetry FastAPI instrumentation.	{}

Returns:

Type	Description
ContextManager[None]	A context manager that will revert the instrumentation when exited. This context manager doesn't take into account threads or other concurrency. Calling this method will immediately apply the instrumentation without waiting for the context manager to be opened, i.e. it's not necessary to use this as a context manager.

Source code in logfire/_internal/main.py

def instrument_fastapi(
    self,
    app: FastAPI,
    *,
    capture_headers: bool = False,
    request_attributes_mapper: Callable[
        [
            Request | WebSocket,
            dict[str, Any],
        ],
        dict[str, Any] | None,
    ]
    | None = None,
    use_opentelemetry_instrumentation: bool = True,
    excluded_urls: str | Iterable[str] | None = None,
    record_send_receive: bool = False,
    **opentelemetry_kwargs: Any,
) -> ContextManager[None]:
    """Instrument a FastAPI app so that spans and logs are automatically created for each request.

    Args:
        app: The FastAPI app to instrument.
        capture_headers: Set to `True` to capture all request and response headers.
        request_attributes_mapper: A function that takes a [`Request`][fastapi.Request] or [`WebSocket`][fastapi.WebSocket]
            and a dictionary of attributes and returns a new dictionary of attributes.
            The input dictionary will contain:

            - `values`: A dictionary mapping argument names of the endpoint function to parsed and validated values.
            - `errors`: A list of validation errors for any invalid inputs.

            The returned dictionary will be used as the attributes for a log message.
            If `None` is returned, no log message will be created.

            You can use this to e.g. only log validation errors, or nothing at all.
            You can also add custom attributes.

            The default implementation will return the input dictionary unchanged.
            The function mustn't modify the contents of `values` or `errors`.
        excluded_urls: A string of comma-separated regexes which will exclude a request from tracing if the full URL
            matches any of the regexes. This applies to both the Logfire and OpenTelemetry instrumentation.
            If not provided, the environment variables
            `OTEL_PYTHON_FASTAPI_EXCLUDED_URLS` and `OTEL_PYTHON_EXCLUDED_URLS` will be checked.
        use_opentelemetry_instrumentation: If True (the default) then
            [`FastAPIInstrumentor`][opentelemetry.instrumentation.fastapi.FastAPIInstrumentor]
            will also instrument the app.

            See [OpenTelemetry FastAPI Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/fastapi/fastapi.html).
        record_send_receive: Set to True to allow the OpenTelemetry ASGI to create send/receive spans.
            These are disabled by default to reduce overhead and the number of spans created,
            since many can be created for a single request, and they are not often useful.
            If enabled, they will be set to debug level, meaning they will usually still be hidden in the UI.
        opentelemetry_kwargs: Additional keyword arguments to pass to the OpenTelemetry FastAPI instrumentation.

    Returns:
        A context manager that will revert the instrumentation when exited.
            This context manager doesn't take into account threads or other concurrency.
            Calling this method will immediately apply the instrumentation
            without waiting for the context manager to be opened,
            i.e. it's not necessary to use this as a context manager.
    """
    from .integrations.fastapi import instrument_fastapi

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_fastapi(
        self,
        app,
        capture_headers=capture_headers,
        request_attributes_mapper=request_attributes_mapper,
        excluded_urls=excluded_urls,
        use_opentelemetry_instrumentation=use_opentelemetry_instrumentation,
        record_send_receive=record_send_receive,
        **opentelemetry_kwargs,
    )

instrument_openai

instrument_openai(
    openai_client: (
        OpenAI
        | AsyncOpenAI
        | type[OpenAI]
        | type[AsyncOpenAI]
        | None
    ) = None,
    *,
    suppress_other_instrumentation: bool = True
) -> ContextManager[None]

Instrument an OpenAI client so that spans are automatically created for each request.

The following methods are instrumented for both the sync and the async clients:

• client.chat.completions.create — with and without stream=True
• client.completions.create — with and without stream=True
• client.embeddings.create
• client.images.generate

When stream=True a second span is created to instrument the streamed response.

Example usage:

import logfire
import openai

client = openai.OpenAI()
logfire.configure()
logfire.instrument_openai(client)

response = client.chat.completions.create(
    model='gpt-4',
    messages=[
        {'role': 'system', 'content': 'You are a helpful assistant.'},
        {'role': 'user', 'content': 'What is four plus five?'},
    ],
)
print('answer:', response.choices[0].message.content)

Parameters:

Name	Type	Description	Default
openai_client	OpenAI | AsyncOpenAI | type[OpenAI] | type[AsyncOpenAI] | None	The OpenAI client or class to instrument: None (the default) to instrument both the openai.OpenAI and openai.AsyncOpenAI classes. The openai.OpenAI class or a subclass The openai.AsyncOpenAI class or a subclass An instance of openai.OpenAI An instance of openai.AsyncOpenAI	None
suppress_other_instrumentation	bool	If True, suppress any other OTEL instrumentation that may be otherwise enabled. In reality, this means the HTTPX instrumentation, which could otherwise be called since OpenAI uses HTTPX to make HTTP requests.	True

Returns:

Type	Description
ContextManager[None]	A context manager that will revert the instrumentation when exited. Use of this context manager is optional.

Source code in logfire/_internal/main.py

def instrument_openai(
    self,
    openai_client: openai.OpenAI
    | openai.AsyncOpenAI
    | type[openai.OpenAI]
    | type[openai.AsyncOpenAI]
    | None = None,
    *,
    suppress_other_instrumentation: bool = True,
) -> ContextManager[None]:
    """Instrument an OpenAI client so that spans are automatically created for each request.

    The following methods are instrumented for both the sync and the async clients:

    - [`client.chat.completions.create`](https://platform.openai.com/docs/guides/text-generation/chat-completions-api) — with and without `stream=True`
    - [`client.completions.create`](https://platform.openai.com/docs/guides/text-generation/completions-api) — with and without `stream=True`
    - [`client.embeddings.create`](https://platform.openai.com/docs/guides/embeddings/how-to-get-embeddings)
    - [`client.images.generate`](https://platform.openai.com/docs/guides/images/generations)

    When `stream=True` a second span is created to instrument the streamed response.

    Example usage:

    ```python
    import logfire
    import openai

    client = openai.OpenAI()
    logfire.configure()
    logfire.instrument_openai(client)

    response = client.chat.completions.create(
        model='gpt-4',
        messages=[
            {'role': 'system', 'content': 'You are a helpful assistant.'},
            {'role': 'user', 'content': 'What is four plus five?'},
        ],
    )
    print('answer:', response.choices[0].message.content)
    ```

    Args:
        openai_client: The OpenAI client or class to instrument:

            - `None` (the default) to instrument both the `openai.OpenAI` and `openai.AsyncOpenAI` classes.
            - The `openai.OpenAI` class or a subclass
            - The `openai.AsyncOpenAI` class or a subclass
            - An instance of `openai.OpenAI`
            - An instance of `openai.AsyncOpenAI`

        suppress_other_instrumentation: If True, suppress any other OTEL instrumentation that may be otherwise
            enabled. In reality, this means the HTTPX instrumentation, which could otherwise be called since
            OpenAI uses HTTPX to make HTTP requests.

    Returns:
        A context manager that will revert the instrumentation when exited.
            Use of this context manager is optional.
    """
    import openai

    from .integrations.llm_providers.llm_provider import instrument_llm_provider
    from .integrations.llm_providers.openai import get_endpoint_config, is_async_client, on_response

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_llm_provider(
        self,
        openai_client or (openai.OpenAI, openai.AsyncOpenAI),
        suppress_other_instrumentation,
        'OpenAI',
        get_endpoint_config,
        on_response,
        is_async_client,
    )

instrument_anthropic

instrument_anthropic(
    anthropic_client: (
        Anthropic
        | AsyncAnthropic
        | type[Anthropic]
        | type[AsyncAnthropic]
        | None
    ) = None,
    *,
    suppress_other_instrumentation: bool = True
) -> ContextManager[None]

Instrument an Anthropic client so that spans are automatically created for each request.

The following methods are instrumented for both the sync and the async clients:

• client.messages.create
• client.messages.stream
• client.beta.tools.messages.create

When stream=True a second span is created to instrument the streamed response.

Example usage:

import logfire
import anthropic

client = anthropic.Anthropic()
logfire.configure()
logfire.instrument_anthropic(client)

response = client.messages.create(
    model='claude-3-haiku-20240307',
    system='You are a helpful assistant.',
    messages=[
        {'role': 'user', 'content': 'What is four plus five?'},
    ],
)
print('answer:', response.content[0].text)

Parameters:

Name	Type	Description	Default
anthropic_client	Anthropic | AsyncAnthropic | type[Anthropic] | type[AsyncAnthropic] | None	The Anthropic client or class to instrument: None (the default) to instrument both the anthropic.Anthropic and anthropic.AsyncAnthropic classes. The anthropic.Anthropic class or a subclass The anthropic.AsyncAnthropic class or a subclass An instance of anthropic.Anthropic An instance of anthropic.AsyncAnthropic	None
suppress_other_instrumentation	bool	If True, suppress any other OTEL instrumentation that may be otherwise enabled. In reality, this means the HTTPX instrumentation, which could otherwise be called since OpenAI uses HTTPX to make HTTP requests.	True

Returns:

Type	Description
ContextManager[None]	A context manager that will revert the instrumentation when exited. Use of this context manager is optional.

Source code in logfire/_internal/main.py

def instrument_anthropic(
    self,
    anthropic_client: anthropic.Anthropic
    | anthropic.AsyncAnthropic
    | type[anthropic.Anthropic]
    | type[anthropic.AsyncAnthropic]
    | None = None,
    *,
    suppress_other_instrumentation: bool = True,
) -> ContextManager[None]:
    """Instrument an Anthropic client so that spans are automatically created for each request.

    The following methods are instrumented for both the sync and the async clients:

    - [`client.messages.create`](https://docs.anthropic.com/en/api/messages)
    - [`client.messages.stream`](https://docs.anthropic.com/en/api/messages-streaming)
    - [`client.beta.tools.messages.create`](https://docs.anthropic.com/en/docs/tool-use)

    When `stream=True` a second span is created to instrument the streamed response.

    Example usage:

    ```python
    import logfire
    import anthropic

    client = anthropic.Anthropic()
    logfire.configure()
    logfire.instrument_anthropic(client)

    response = client.messages.create(
        model='claude-3-haiku-20240307',
        system='You are a helpful assistant.',
        messages=[
            {'role': 'user', 'content': 'What is four plus five?'},
        ],
    )
    print('answer:', response.content[0].text)
    ```

    Args:
        anthropic_client: The Anthropic client or class to instrument:

            - `None` (the default) to instrument both the
                `anthropic.Anthropic` and `anthropic.AsyncAnthropic` classes.
            - The `anthropic.Anthropic` class or a subclass
            - The `anthropic.AsyncAnthropic` class or a subclass
            - An instance of `anthropic.Anthropic`
            - An instance of `anthropic.AsyncAnthropic`

        suppress_other_instrumentation: If True, suppress any other OTEL instrumentation that may be otherwise
            enabled. In reality, this means the HTTPX instrumentation, which could otherwise be called since
            OpenAI uses HTTPX to make HTTP requests.

    Returns:
        A context manager that will revert the instrumentation when exited.
            Use of this context manager is optional.
    """
    import anthropic

    from .integrations.llm_providers.anthropic import get_endpoint_config, is_async_client, on_response
    from .integrations.llm_providers.llm_provider import instrument_llm_provider

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_llm_provider(
        self,
        anthropic_client or (anthropic.Anthropic, anthropic.AsyncAnthropic),
        suppress_other_instrumentation,
        'Anthropic',
        get_endpoint_config,
        on_response,
        is_async_client,
    )

instrument_asyncpg

instrument_asyncpg(
    **kwargs: Unpack[AsyncPGInstrumentKwargs],
) -> None

Instrument the asyncpg module so that spans are automatically created for each query.

Source code in logfire/_internal/main.py

def instrument_asyncpg(self, **kwargs: Unpack[AsyncPGInstrumentKwargs]) -> None:
    """Instrument the `asyncpg` module so that spans are automatically created for each query."""
    from .integrations.asyncpg import instrument_asyncpg

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_asyncpg(**kwargs)

instrument_httpx

instrument_httpx(
    **kwargs: Unpack[HTTPXInstrumentKwargs],
) -> None

Instrument the httpx module so that spans are automatically created for each request.

Uses the OpenTelemetry HTTPX Instrumentation library, specifically HTTPXClientInstrumentor().instrument(), to which it passes **kwargs.

Source code in logfire/_internal/main.py

def instrument_httpx(self, **kwargs: Unpack[HTTPXInstrumentKwargs]) -> None:
    """Instrument the `httpx` module so that spans are automatically created for each request.

    Uses the
    [OpenTelemetry HTTPX Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/httpx/httpx.html)
    library, specifically `HTTPXClientInstrumentor().instrument()`, to which it passes `**kwargs`.
    """
    from .integrations.httpx import instrument_httpx

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_httpx(**kwargs)

instrument_celery

instrument_celery(
    **kwargs: Unpack[CeleryInstrumentKwargs],
) -> None

Instrument celery so that spans are automatically created for each task.

Uses the OpenTelemetry Celery Instrumentation library.

Source code in logfire/_internal/main.py

def instrument_celery(self, **kwargs: Unpack[CeleryInstrumentKwargs]) -> None:
    """Instrument `celery` so that spans are automatically created for each task.

    Uses the
    [OpenTelemetry Celery Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/celery/celery.html)
    library.
    """
    from .integrations.celery import instrument_celery

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_celery(**kwargs)

instrument_django

instrument_django(
    capture_headers: bool = False,
    is_sql_commentor_enabled: bool | None = None,
    request_hook: (
        Callable[[Span, HttpRequest], None] | None
    ) = None,
    response_hook: (
        Callable[[Span, HttpRequest, HttpResponse], None]
        | None
    ) = None,
    excluded_urls: str | None = None,
    **kwargs: Any,
) -> None

Instrument django so that spans are automatically created for each web request.

Uses the OpenTelemetry Django Instrumentation library.

Parameters:

Name	Type	Description	Default
capture_headers	bool	Set to True to capture all request and response headers.	False
is_sql_commentor_enabled	bool | None	Adds comments to SQL queries performed by Django, so that database logs have additional context. This does NOT create spans/logs for the queries themselves. For that you need to instrument the database driver, e.g. with logfire.instrument_psycopg(). To configure the SQL Commentor, see the OpenTelemetry documentation for the values that need to be added to settings.py.	None
request_hook	Callable[[Span, HttpRequest], None] | None	A function called right after a span is created for a request. The function should accept two arguments: the span and the Django Request object.	None
response_hook	Callable[[Span, HttpRequest, HttpResponse], None] | None	A function called right before a span is finished for the response. The function should accept three arguments: the span, the Django Request object, and the Django Response object.	None
excluded_urls	str | None	A string containing a comma-delimited list of regexes used to exclude URLs from tracking.	None
**kwargs	Any	Additional keyword arguments to pass to the OpenTelemetry instrument method, for future compatibility.	{}

Source code in logfire/_internal/main.py

def instrument_django(
    self,
    capture_headers: bool = False,
    is_sql_commentor_enabled: bool | None = None,
    request_hook: Callable[[Span, HttpRequest], None] | None = None,
    response_hook: Callable[[Span, HttpRequest, HttpResponse], None] | None = None,
    excluded_urls: str | None = None,
    **kwargs: Any,
) -> None:
    """Instrument `django` so that spans are automatically created for each web request.

    Uses the
    [OpenTelemetry Django Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/django/django.html)
    library.

    Args:
        capture_headers: Set to `True` to capture all request and response headers.
        is_sql_commentor_enabled: Adds comments to SQL queries performed by Django,
            so that database logs have additional context.

            This does NOT create spans/logs for the queries themselves.
            For that you need to instrument the database driver, e.g. with `logfire.instrument_psycopg()`.

            To configure the SQL Commentor, see the OpenTelemetry documentation for the
            values that need to be added to `settings.py`.

        request_hook: A function called right after a span is created for a request.
            The function should accept two arguments: the span and the Django `Request` object.

        response_hook: A function called right before a span is finished for the response.
            The function should accept three arguments:
            the span, the Django `Request` object, and the Django `Response` object.

        excluded_urls: A string containing a comma-delimited list of regexes used to exclude URLs from tracking.

        **kwargs: Additional keyword arguments to pass to the OpenTelemetry `instrument` method,
            for future compatibility.

    """
    from .integrations.django import instrument_django

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_django(
        capture_headers=capture_headers,
        is_sql_commentor_enabled=is_sql_commentor_enabled,
        request_hook=request_hook,
        response_hook=response_hook,
        excluded_urls=excluded_urls,
        **kwargs,
    )

instrument_requests

instrument_requests(
    excluded_urls: str | None = None, **kwargs: Any
) -> None

Instrument the requests module so that spans are automatically created for each request.

Parameters:

Name	Type	Description	Default
excluded_urls	str | None	A string containing a comma-delimited list of regexes used to exclude URLs from tracking	None
**kwargs	Any	Additional keyword arguments to pass to the OpenTelemetry instrument methods, particularly request_hook and response_hook.	{}

Source code in logfire/_internal/main.py

def instrument_requests(self, excluded_urls: str | None = None, **kwargs: Any) -> None:
    """Instrument the `requests` module so that spans are automatically created for each request.

    Args:
        excluded_urls: A string containing a comma-delimited list of regexes used to exclude URLs from tracking
        **kwargs: Additional keyword arguments to pass to the OpenTelemetry `instrument` methods,
            particularly `request_hook` and `response_hook`.
    """
    from .integrations.requests import instrument_requests

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_requests(excluded_urls=excluded_urls, **kwargs)

instrument_psycopg

instrument_psycopg(
    conn_or_module: Any = None,
    **kwargs: Unpack[PsycopgInstrumentKwargs],
) -> None

Instrument a psycopg connection or module so that spans are automatically created for each query.

Uses the OpenTelemetry instrumentation libraries for psycopg and psycopg2.

Parameters:

Name	Type	Description	Default
conn_or_module	Any	Can be: The psycopg (version 3) or psycopg2 module. The string 'psycopg' or 'psycopg2' to instrument the module. None (the default) to instrument whichever module(s) are installed. A psycopg or psycopg2 connection.	None
**kwargs	Unpack[PsycopgInstrumentKwargs]	Additional keyword arguments to pass to the OpenTelemetry instrument methods, particularly enable_commenter and commenter_options.	{}

Source code in logfire/_internal/main.py

def instrument_psycopg(self, conn_or_module: Any = None, **kwargs: Unpack[PsycopgInstrumentKwargs]) -> None:
    """Instrument a `psycopg` connection or module so that spans are automatically created for each query.

    Uses the OpenTelemetry instrumentation libraries for
    [`psycopg`](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/psycopg/psycopg.html)
    and
    [`psycopg2`](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/psycopg2/psycopg2.html).

    Args:
        conn_or_module: Can be:

            - The `psycopg` (version 3) or `psycopg2` module.
            - The string `'psycopg'` or `'psycopg2'` to instrument the module.
            - `None` (the default) to instrument whichever module(s) are installed.
            - A `psycopg` or `psycopg2` connection.

        **kwargs: Additional keyword arguments to pass to the OpenTelemetry `instrument` methods,
            particularly `enable_commenter` and `commenter_options`.
    """
    from .integrations.psycopg import instrument_psycopg

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_psycopg(conn_or_module, **kwargs)

instrument_flask

instrument_flask(
    app: Flask,
    *,
    capture_headers: bool = False,
    **kwargs: Unpack[FlaskInstrumentKwargs]
) -> None

Instrument app so that spans are automatically created for each request.

Set capture_headers to True to capture all request and response headers.

Uses the OpenTelemetry Flask Instrumentation library, specifically FlaskInstrumentor().instrument_app(), to which it passes **kwargs.

Source code in logfire/_internal/main.py

def instrument_flask(
    self, app: Flask, *, capture_headers: bool = False, **kwargs: Unpack[FlaskInstrumentKwargs]
) -> None:
    """Instrument `app` so that spans are automatically created for each request.

    Set `capture_headers` to `True` to capture all request and response headers.

    Uses the
    [OpenTelemetry Flask Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/flask/flask.html)
    library, specifically `FlaskInstrumentor().instrument_app()`, to which it passes `**kwargs`.
    """
    from .integrations.flask import instrument_flask

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_flask(app, capture_headers=capture_headers, **kwargs)

instrument_starlette

instrument_starlette(
    app: Starlette,
    *,
    capture_headers: bool = False,
    record_send_receive: bool = False,
    **kwargs: Unpack[StarletteInstrumentKwargs]
) -> None

Instrument app so that spans are automatically created for each request.

Set capture_headers to True to capture all request and response headers.

Set record_send_receive to True to allow the OpenTelemetry ASGI to create send/receive spans. These are disabled by default to reduce overhead and the number of spans created, since many can be created for a single request, and they are not often useful. If enabled, they will be set to debug level, meaning they will usually still be hidden in the UI.

Uses the OpenTelemetry Starlette Instrumentation library, specifically StarletteInstrumentor.instrument_app(), to which it passes **kwargs.

Source code in logfire/_internal/main.py

def instrument_starlette(
    self,
    app: Starlette,
    *,
    capture_headers: bool = False,
    record_send_receive: bool = False,
    **kwargs: Unpack[StarletteInstrumentKwargs],
) -> None:
    """Instrument `app` so that spans are automatically created for each request.

    Set `capture_headers` to `True` to capture all request and response headers.

    Set `record_send_receive` to `True` to allow the OpenTelemetry ASGI to create send/receive spans.
    These are disabled by default to reduce overhead and the number of spans created,
    since many can be created for a single request, and they are not often useful.
    If enabled, they will be set to debug level, meaning they will usually still be hidden in the UI.

    Uses the
    [OpenTelemetry Starlette Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/starlette/starlette.html)
    library, specifically `StarletteInstrumentor.instrument_app()`, to which it passes `**kwargs`.
    """
    from .integrations.starlette import instrument_starlette

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_starlette(
        self,
        app,
        record_send_receive=record_send_receive,
        capture_headers=capture_headers,
        **kwargs,
    )

instrument_aiohttp_client

instrument_aiohttp_client(**kwargs: Any) -> None

Instrument the aiohttp module so that spans are automatically created for each client request.

Uses the OpenTelemetry aiohttp client Instrumentation library, specifically AioHttpClientInstrumentor().instrument(), to which it passes **kwargs.

Source code in logfire/_internal/main.py

def instrument_aiohttp_client(self, **kwargs: Any) -> None:
    """Instrument the `aiohttp` module so that spans are automatically created for each client request.

    Uses the
    [OpenTelemetry aiohttp client Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/aiohttp_client/aiohttp_client.html)
    library, specifically `AioHttpClientInstrumentor().instrument()`, to which it passes `**kwargs`.
    """
    from .integrations.aiohttp_client import instrument_aiohttp_client

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_aiohttp_client(**kwargs)

instrument_sqlalchemy

instrument_sqlalchemy(
    **kwargs: Unpack[SQLAlchemyInstrumentKwargs],
) -> None

Instrument the sqlalchemy module so that spans are automatically created for each query.

Uses the OpenTelemetry SQLAlchemy Instrumentation library, specifically SQLAlchemyInstrumentor().instrument(), to which it passes **kwargs.

Source code in logfire/_internal/main.py

def instrument_sqlalchemy(self, **kwargs: Unpack[SQLAlchemyInstrumentKwargs]) -> None:
    """Instrument the `sqlalchemy` module so that spans are automatically created for each query.

    Uses the
    [OpenTelemetry SQLAlchemy Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/sqlalchemy/sqlalchemy.html)
    library, specifically `SQLAlchemyInstrumentor().instrument()`, to which it passes `**kwargs`.
    """
    from .integrations.sqlalchemy import instrument_sqlalchemy

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_sqlalchemy(**kwargs)

instrument_pymongo

instrument_pymongo(
    **kwargs: Unpack[PymongoInstrumentKwargs],
) -> None

Instrument the pymongo module so that spans are automatically created for each operation.

Uses the OpenTelemetry pymongo Instrumentation library, specifically PymongoInstrumentor().instrument(), to which it passes **kwargs.

Source code in logfire/_internal/main.py

def instrument_pymongo(self, **kwargs: Unpack[PymongoInstrumentKwargs]) -> None:
    """Instrument the `pymongo` module so that spans are automatically created for each operation.

    Uses the
    [OpenTelemetry pymongo Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/pymongo/pymongo.html)
        library, specifically `PymongoInstrumentor().instrument()`, to which it passes `**kwargs`.
    """
    from .integrations.pymongo import instrument_pymongo

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_pymongo(**kwargs)

instrument_redis

instrument_redis(
    capture_statement: bool = False,
    **kwargs: Unpack[RedisInstrumentKwargs],
) -> None

Instrument the redis module so that spans are automatically created for each operation.

Uses the OpenTelemetry Redis Instrumentation library, specifically RedisInstrumentor().instrument(), to which it passes **kwargs.

Parameters:

Name	Type	Description	Default
capture_statement	bool	Set to True to capture the statement in the span attributes.	False
kwargs	Unpack[RedisInstrumentKwargs]	Additional keyword arguments to pass to the OpenTelemetry instrument methods.	{}

Source code in logfire/_internal/main.py

def instrument_redis(self, capture_statement: bool = False, **kwargs: Unpack[RedisInstrumentKwargs]) -> None:
    """Instrument the `redis` module so that spans are automatically created for each operation.

    Uses the
    [OpenTelemetry Redis Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/redis/redis.html)
    library, specifically `RedisInstrumentor().instrument()`, to which it passes `**kwargs`.

    Args:
        capture_statement: Set to `True` to capture the statement in the span attributes.
        kwargs: Additional keyword arguments to pass to the OpenTelemetry `instrument` methods.
    """
    from .integrations.redis import instrument_redis

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_redis(capture_statement=capture_statement, **kwargs)

instrument_mysql

instrument_mysql(
    conn: MySQLConnection = None,
    **kwargs: Unpack[MySQLInstrumentKwargs],
) -> MySQLConnection

Instrument the mysql module or a specific MySQL connection so that spans are automatically created for each operation.

Uses the OpenTelemetry MySQL Instrumentation library.

Parameters:

Name	Type	Description	Default
conn	MySQLConnection	The mysql connection to instrument, or None to instrument all connections.	None
**kwargs	Unpack[MySQLInstrumentKwargs]	Additional keyword arguments to pass to the OpenTelemetry instrument methods.	{}

Returns:

Type	Description
MySQLConnection	If a connection is provided, returns the instrumented connection. If no connection is provided, returns None.

Source code in logfire/_internal/main.py

def instrument_mysql(
    self,
    conn: MySQLConnection = None,
    **kwargs: Unpack[MySQLInstrumentKwargs],
) -> MySQLConnection:
    """Instrument the `mysql` module or a specific MySQL connection so that spans are automatically created for each operation.

    Uses the
    [OpenTelemetry MySQL Instrumentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/mysql/mysql.html)
    library.

    Args:
        conn: The `mysql` connection to instrument, or `None` to instrument all connections.
        **kwargs: Additional keyword arguments to pass to the OpenTelemetry `instrument` methods.

    Returns:
        If a connection is provided, returns the instrumented connection. If no connection is provided, returns None.

    """
    from .integrations.mysql import instrument_mysql

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_mysql(conn, **kwargs)

instrument_system_metrics

instrument_system_metrics(
    config: Config | None = None, base: Base = "basic"
) -> None

Collect system metrics.

See the guide for more information.

Parameters:

Name	Type	Description	Default
config	Config | None	A dictionary where the keys are metric names and the values are optional further configuration for that metric.	None
base	Base	A string indicating the base config dictionary which config will be merged with, or None for an empty base config.	'basic'

Source code in logfire/_internal/main.py

def instrument_system_metrics(
    self, config: SystemMetricsConfig | None = None, base: SystemMetricsBase = 'basic'
) -> None:
    """Collect system metrics.

    See [the guide](https://logfire.pydantic.dev/docs/integrations/system_metrics/) for more information.

    Args:
        config: A dictionary where the keys are metric names
            and the values are optional further configuration for that metric.
        base: A string indicating the base config dictionary which `config` will be merged with,
            or `None` for an empty base config.
    """
    from .integrations.system_metrics import instrument_system_metrics

    self._warn_if_not_initialized_for_instrumentation()
    return instrument_system_metrics(self, config, base)

metric_counter

metric_counter(
    name: str, *, unit: str = "", description: str = ""
) -> Counter

Create a counter metric.

A counter is a cumulative metric that represents a single numerical value that only ever goes up.

import logfire

logfire.configure()
counter = logfire.metric_counter('exceptions', unit='1', description='Number of exceptions caught')

try:
    raise Exception('oops')
except Exception:
    counter.add(1)

See the Opentelemetry documentation about counters.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
unit	str	The unit of the metric.	''
description	str	The description of the metric.	''

Returns:

Type	Description
Counter	The counter metric.

Source code in logfire/_internal/main.py

def metric_counter(self, name: str, *, unit: str = '', description: str = '') -> Counter:
    """Create a counter metric.

    A counter is a cumulative metric that represents a single numerical value that only ever goes up.

    ```py
    import logfire

    logfire.configure()
    counter = logfire.metric_counter('exceptions', unit='1', description='Number of exceptions caught')

    try:
        raise Exception('oops')
    except Exception:
        counter.add(1)
    ```

    See the [Opentelemetry documentation](https://opentelemetry.io/docs/specs/otel/metrics/api/#counter) about
    counters.

    Args:
        name: The name of the metric.
        unit: The unit of the metric.
        description: The description of the metric.

    Returns:
        The counter metric.
    """
    return self._config.meter.create_counter(name, unit, description)

metric_histogram

metric_histogram(
    name: str, *, unit: str = "", description: str = ""
) -> Histogram

Create a histogram metric.

A histogram is a metric that samples observations (usually things like request durations or response sizes).

import logfire

logfire.configure()
histogram = logfire.metric_histogram('bank.amount_transferred', unit='$', description='Amount transferred')


def transfer(amount: int):
    histogram.record(amount)

See the Opentelemetry documentation about

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
unit	str	The unit of the metric.	''
description	str	The description of the metric.	''

Returns:

Type	Description
Histogram	The histogram metric.

Source code in logfire/_internal/main.py

def metric_histogram(self, name: str, *, unit: str = '', description: str = '') -> Histogram:
    """Create a histogram metric.

    A histogram is a metric that samples observations (usually things like request durations or response sizes).

    ```py
    import logfire

    logfire.configure()
    histogram = logfire.metric_histogram('bank.amount_transferred', unit='$', description='Amount transferred')


    def transfer(amount: int):
        histogram.record(amount)
    ```

    See the [Opentelemetry documentation](https://opentelemetry.io/docs/specs/otel/metrics/api/#histogram) about

    Args:
        name: The name of the metric.
        unit: The unit of the metric.
        description: The description of the metric.

    Returns:
        The histogram metric.
    """
    return self._config.meter.create_histogram(name, unit, description)

metric_gauge

metric_gauge(
    name: str, *, unit: str = "", description: str = ""
) -> _Gauge

Create a gauge metric.

Gauge is a synchronous instrument which can be used to record non-additive measurements.

import logfire

logfire.configure()
gauge = logfire.metric_gauge('system.cpu_usage', unit='%', description='CPU usage')


def update_cpu_usage(cpu_percent):
    gauge.set(cpu_percent)

See the Opentelemetry documentation about gauges.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
unit	str	The unit of the metric.	''
description	str	The description of the metric.	''

Returns:

Type	Description
_Gauge	The gauge metric.

Source code in logfire/_internal/main.py

def metric_gauge(self, name: str, *, unit: str = '', description: str = '') -> Gauge:
    """Create a gauge metric.

    Gauge is a synchronous instrument which can be used to record non-additive measurements.

    ```py
    import logfire

    logfire.configure()
    gauge = logfire.metric_gauge('system.cpu_usage', unit='%', description='CPU usage')


    def update_cpu_usage(cpu_percent):
        gauge.set(cpu_percent)
    ```

    See the [Opentelemetry documentation](https://opentelemetry.io/docs/specs/otel/metrics/api/#gauge) about gauges.

    Args:
        name: The name of the metric.
        unit: The unit of the metric.
        description: The description of the metric.

    Returns:
        The gauge metric.
    """
    return self._config.meter.create_gauge(name, unit, description)

metric_up_down_counter

metric_up_down_counter(
    name: str, *, unit: str = "", description: str = ""
) -> UpDownCounter

Create an up-down counter metric.

An up-down counter is a cumulative metric that represents a single numerical value that can be adjusted up or down.

import logfire

logfire.configure()
up_down_counter = logfire.metric_up_down_counter('users.logged_in', unit='1', description='Users logged in')


def on_login(user):
    up_down_counter.add(1)


def on_logout(user):
    up_down_counter.add(-1)

See the Opentelemetry documentation about up-down counters.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
unit	str	The unit of the metric.	''
description	str	The description of the metric.	''

Returns:

Type	Description
UpDownCounter	The up-down counter metric.

Source code in logfire/_internal/main.py

def metric_up_down_counter(self, name: str, *, unit: str = '', description: str = '') -> UpDownCounter:
    """Create an up-down counter metric.

    An up-down counter is a cumulative metric that represents a single numerical value that can be adjusted up or
    down.

    ```py
    import logfire

    logfire.configure()
    up_down_counter = logfire.metric_up_down_counter('users.logged_in', unit='1', description='Users logged in')


    def on_login(user):
        up_down_counter.add(1)


    def on_logout(user):
        up_down_counter.add(-1)
    ```

    See the [Opentelemetry documentation](https://opentelemetry.io/docs/specs/otel/metrics/api/#updowncounter) about
    up-down counters.

    Args:
        name: The name of the metric.
        unit: The unit of the metric.
        description: The description of the metric.

    Returns:
        The up-down counter metric.
    """
    return self._config.meter.create_up_down_counter(name, unit, description)

metric_counter_callback

metric_counter_callback(
    name: str,
    *,
    callbacks: Sequence[CallbackT],
    unit: str = "",
    description: str = ""
) -> None

Create a counter metric that uses a callback to collect observations.

The counter metric is a cumulative metric that represents a single numerical value that only ever goes up.

import logfire
import psutil
from opentelemetry.metrics import CallbackOptions, Observation

logfire.configure()


def cpu_usage_callback(options: CallbackOptions):
    cpu_percents = psutil.cpu_percent(percpu=True)

    for i, cpu_percent in enumerate(cpu_percents):
        yield Observation(cpu_percent, {'cpu': i})


cpu_usage_counter = logfire.metric_counter_callback(
    'system.cpu.usage',
    callbacks=[cpu_usage_callback],
    unit='%',
    description='CPU usage',
)

See the Opentelemetry documentation about asynchronous counter.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
callbacks	Sequence[CallbackT]	A sequence of callbacks that return an iterable of Observation.	required
unit	str	The unit of the metric.	''
description	str	The description of the metric.	''

Source code in logfire/_internal/main.py

def metric_counter_callback(
    self,
    name: str,
    *,
    callbacks: Sequence[CallbackT],
    unit: str = '',
    description: str = '',
) -> None:
    """Create a counter metric that uses a callback to collect observations.

    The counter metric is a cumulative metric that represents a single numerical value that only ever goes up.

    ```py
    import logfire
    import psutil
    from opentelemetry.metrics import CallbackOptions, Observation

    logfire.configure()


    def cpu_usage_callback(options: CallbackOptions):
        cpu_percents = psutil.cpu_percent(percpu=True)

        for i, cpu_percent in enumerate(cpu_percents):
            yield Observation(cpu_percent, {'cpu': i})


    cpu_usage_counter = logfire.metric_counter_callback(
        'system.cpu.usage',
        callbacks=[cpu_usage_callback],
        unit='%',
        description='CPU usage',
    )
    ```

    See the [Opentelemetry documentation](https://opentelemetry.io/docs/specs/otel/metrics/api/#asynchronous-counter)
    about asynchronous counter.

    Args:
        name: The name of the metric.
        callbacks: A sequence of callbacks that return an iterable of
            [Observation](https://opentelemetry-python.readthedocs.io/en/latest/api/metrics.html#opentelemetry.metrics.Observation).
        unit: The unit of the metric.
        description: The description of the metric.
    """
    self._config.meter.create_observable_counter(name, callbacks, unit, description)

metric_gauge_callback

metric_gauge_callback(
    name: str,
    callbacks: Sequence[CallbackT],
    *,
    unit: str = "",
    description: str = ""
) -> None

Create a gauge metric that uses a callback to collect observations.

The gauge metric is a metric that represents a single numerical value that can arbitrarily go up and down.

import threading

import logfire
from opentelemetry.metrics import CallbackOptions, Observation

logfire.configure()


def thread_count_callback(options: CallbackOptions):
    yield Observation(threading.active_count())


logfire.metric_gauge_callback(
    'system.thread_count',
    callbacks=[thread_count_callback],
    unit='1',
    description='Number of threads',
)

See the Opentelemetry documentation about asynchronous gauge.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
callbacks	Sequence[CallbackT]	A sequence of callbacks that return an iterable of Observation.	required
unit	str	The unit of the metric.	''
description	str	The description of the metric.	''

Source code in logfire/_internal/main.py

def metric_gauge_callback(
    self, name: str, callbacks: Sequence[CallbackT], *, unit: str = '', description: str = ''
) -> None:
    """Create a gauge metric that uses a callback to collect observations.

    The gauge metric is a metric that represents a single numerical value that can arbitrarily go up and down.

    ```py
    import threading

    import logfire
    from opentelemetry.metrics import CallbackOptions, Observation

    logfire.configure()


    def thread_count_callback(options: CallbackOptions):
        yield Observation(threading.active_count())


    logfire.metric_gauge_callback(
        'system.thread_count',
        callbacks=[thread_count_callback],
        unit='1',
        description='Number of threads',
    )
    ```

    See the [Opentelemetry documentation](https://opentelemetry.io/docs/specs/otel/metrics/api/#asynchronous-gauge)
    about asynchronous gauge.

    Args:
        name: The name of the metric.
        callbacks: A sequence of callbacks that return an iterable of
            [Observation](https://opentelemetry-python.readthedocs.io/en/latest/api/metrics.html#opentelemetry.metrics.Observation).
        unit: The unit of the metric.
        description: The description of the metric.
    """
    self._config.meter.create_observable_gauge(name, callbacks, unit, description)

metric_up_down_counter_callback

metric_up_down_counter_callback(
    name: str,
    callbacks: Sequence[CallbackT],
    *,
    unit: str = "",
    description: str = ""
) -> None

Create an up-down counter metric that uses a callback to collect observations.

The up-down counter is a cumulative metric that represents a single numerical value that can be adjusted up or down.

import logfire
from opentelemetry.metrics import CallbackOptions, Observation

logfire.configure()

items = []


def inventory_callback(options: CallbackOptions):
    yield Observation(len(items))


logfire.metric_up_down_counter_callback(
    name='store.inventory',
    description='Number of items in the inventory',
    callbacks=[inventory_callback],
)

See the Opentelemetry documentation about asynchronous up-down counters.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
callbacks	Sequence[CallbackT]	A sequence of callbacks that return an iterable of Observation.	required
unit	str	The unit of the metric.	''
description	str	The description of the metric.	''

Source code in logfire/_internal/main.py

def metric_up_down_counter_callback(
    self, name: str, callbacks: Sequence[CallbackT], *, unit: str = '', description: str = ''
) -> None:
    """Create an up-down counter metric that uses a callback to collect observations.

    The up-down counter is a cumulative metric that represents a single numerical value that can be adjusted up or
    down.

    ```py
    import logfire
    from opentelemetry.metrics import CallbackOptions, Observation

    logfire.configure()

    items = []


    def inventory_callback(options: CallbackOptions):
        yield Observation(len(items))


    logfire.metric_up_down_counter_callback(
        name='store.inventory',
        description='Number of items in the inventory',
        callbacks=[inventory_callback],
    )
    ```

    See the [Opentelemetry documentation](https://opentelemetry.io/docs/specs/otel/metrics/api/#asynchronous-updowncounter)
    about asynchronous up-down counters.

    Args:
        name: The name of the metric.
        callbacks: A sequence of callbacks that return an iterable of
            [Observation](https://opentelemetry-python.readthedocs.io/en/latest/api/metrics.html#opentelemetry.metrics.Observation).
        unit: The unit of the metric.
        description: The description of the metric.
    """
    self._config.meter.create_observable_up_down_counter(name, callbacks, unit, description)

shutdown

shutdown(
    timeout_millis: int = 30000, flush: bool = True
) -> bool

Shut down all tracers and meters.

This will clean up any resources used by the tracers and meters and flush any remaining spans and metrics.

Parameters:

Name	Type	Description	Default
timeout_millis	int	The timeout in milliseconds.	30000
flush	bool	Whether to flush remaining spans and metrics before shutting down.	True

Returns:

Type	Description
bool	False if the timeout was reached before the shutdown was completed, True otherwise.

Source code in logfire/_internal/main.py

def shutdown(self, timeout_millis: int = 30_000, flush: bool = True) -> bool:  # pragma: no cover
    """Shut down all tracers and meters.

    This will clean up any resources used by the tracers and meters and flush any remaining spans and metrics.

    Args:
        timeout_millis: The timeout in milliseconds.
        flush: Whether to flush remaining spans and metrics before shutting down.

    Returns:
        `False` if the timeout was reached before the shutdown was completed, `True` otherwise.
    """
    start = time()
    if flush:  # pragma: no branch
        self._tracer_provider.force_flush(timeout_millis)
    remaining = max(0, timeout_millis - (time() - start))
    if not remaining:  # pragma: no cover
        return False
    self._tracer_provider.shutdown()

    remaining = max(0, timeout_millis - (time() - start))
    if not remaining:  # pragma: no cover
        return False
    if flush:  # pragma: no branch
        self._meter_provider.force_flush(remaining)
    remaining = max(0, timeout_millis - (time() - start))
    if not remaining:  # pragma: no cover
        return False
    self._meter_provider.shutdown(remaining)
    return (start - time()) < timeout_millis

Logfire is the observability tool focused on developer experience.

METRICS_PREFERRED_TEMPORALITY module-attribute

METRICS_PREFERRED_TEMPORALITY = {
    Counter: DELTA,
    UpDownCounter: CUMULATIVE,
    Histogram: DELTA,
    ObservableCounter: DELTA,
    ObservableUpDownCounter: CUMULATIVE,
    ObservableGauge: CUMULATIVE,
}

This should be passed as the preferred_temporality argument of metric readers and exporters.

LevelName module-attribute

LevelName = Literal[
    "trace",
    "debug",
    "info",
    "notice",
    "warn",
    "warning",
    "error",
    "fatal",
]

Level names for records.

SamplingOptions dataclass

SamplingOptions(
    head: float | Sampler = 1.0,
    tail: (
        Callable[[TailSamplingSpanInfo], float] | None
    ) = None,
)

Options for logfire.configure(sampling=...).

See the sampling guide.

head class-attribute instance-attribute

head: float | Sampler = 1.0

Head sampling options.

If it's a float, it should be a number between 0.0 and 1.0. This is the probability that an entire trace will randomly included.

Alternatively you can pass a custom OpenTelemetry Sampler.

tail class-attribute instance-attribute

tail: Callable[[TailSamplingSpanInfo], float] | None = None

An optional tail sampling callback which will be called for every span.

It should return a number between 0.0 and 1.0, the probability that the entire trace will be included. Use SamplingOptions.level_or_duration for a common use case.

Every span in a trace will be stored in memory until either the trace is included by tail sampling or it's completed and discarded, so large traces may consume a lot of memory.

level_or_duration classmethod

level_or_duration(
    *,
    head: float | Sampler = 1.0,
    level_threshold: LevelName | None = "notice",
    duration_threshold: float | None = 5.0,
    background_rate: float = 0.0
) -> Self

Returns a SamplingOptions instance that tail samples traces based on their log level and duration.

If a trace has at least one span/log that has a log level greater than or equal to level_threshold, or if the duration of the whole trace is greater than duration_threshold seconds, then the whole trace will be included. Otherwise, the probability is background_rate.

The head parameter is the same as in the SamplingOptions constructor.

Source code in logfire/sampling/_tail_sampling.py

@classmethod
def level_or_duration(
    cls,
    *,
    head: float | Sampler = 1.0,
    level_threshold: LevelName | None = 'notice',
    duration_threshold: float | None = 5.0,
    background_rate: float = 0.0,
) -> Self:
    """Returns a `SamplingOptions` instance that tail samples traces based on their log level and duration.

    If a trace has at least one span/log that has a log level greater than or equal to `level_threshold`,
    or if the duration of the whole trace is greater than `duration_threshold` seconds,
    then the whole trace will be included.
    Otherwise, the probability is `background_rate`.

    The `head` parameter is the same as in the `SamplingOptions` constructor.
    """
    head_sample_rate = head if isinstance(head, (float, int)) else 1.0

    if not (0.0 <= background_rate <= head_sample_rate <= 1.0):
        raise ValueError('Invalid sampling rates, must be 0.0 <= background_rate <= head <= 1.0')

    def get_tail_sample_rate(span_info: TailSamplingSpanInfo) -> float:
        if duration_threshold is not None and span_info.duration > duration_threshold:
            return 1.0

        if level_threshold is not None and span_info.level >= level_threshold:
            return 1.0

        return background_rate

    return cls(head=head, tail=get_tail_sample_rate)

AutoTraceModule dataclass

AutoTraceModule(name: str, filename: str | None)

Information about a module being imported that should maybe be traced automatically.

This object will be passed to a function that should return True if the module should be traced. In particular it'll be passed to a function that's passed to install_auto_tracing as the modules argument.

name instance-attribute

name: str

Fully qualified absolute name of the module being imported.

filename instance-attribute

filename: str | None

Filename of the module being imported.

parts_start_with

parts_start_with(prefix: str | Sequence[str]) -> bool

Return True if the module name starts with any of the given prefixes, using dots as boundaries.

For example, if the module name is foo.bar.spam, then parts_start_with('foo') will return True, but parts_start_with('bar') or parts_start_with('foo_bar') will return False. In other words, this will match the module itself or any submodules.

If a prefix contains any characters other than letters, numbers, and dots, then it will be treated as a regular expression.

Source code in logfire/_internal/auto_trace/types.py

def parts_start_with(self, prefix: str | Sequence[str]) -> bool:
    """Return True if the module name starts with any of the given prefixes, using dots as boundaries.

    For example, if the module name is `foo.bar.spam`, then `parts_start_with('foo')` will return True,
    but `parts_start_with('bar')` or `parts_start_with('foo_bar')` will return False.
    In other words, this will match the module itself or any submodules.

    If a prefix contains any characters other than letters, numbers, and dots,
    then it will be treated as a regular expression.
    """
    if isinstance(prefix, str):
        prefix = (prefix,)
    pattern = '|'.join([get_module_pattern(p) for p in prefix])
    return bool(re.match(pattern, self.name))

ConsoleOptions dataclass

ConsoleOptions(
    colors: ConsoleColorsValues = "auto",
    span_style: Literal[
        "simple", "indented", "show-parents"
    ] = "show-parents",
    include_timestamps: bool = True,
    verbose: bool = False,
    min_log_level: LevelName = "info",
)

Options for controlling console output.

span_style class-attribute instance-attribute

span_style: Literal[
    "simple", "indented", "show-parents"
] = "show-parents"

How spans are shown in the console.

include_timestamps class-attribute instance-attribute

include_timestamps: bool = True

Whether to include timestamps in the console output.

verbose class-attribute instance-attribute

verbose: bool = False

Whether to show verbose output.

It includes the filename, log level, and line number.

min_log_level class-attribute instance-attribute

min_log_level: LevelName = 'info'

The minimum log level to show in the console.

PydanticPlugin dataclass

PydanticPlugin(
    record: PydanticPluginRecordValues = "off",
    include: set[str] = set(),
    exclude: set[str] = set(),
)

Options for the Pydantic plugin.

record class-attribute instance-attribute

record: PydanticPluginRecordValues = 'off'

The record mode for the Pydantic plugin.

It can be one of the following values:

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

include class-attribute instance-attribute

include: set[str] = field(default_factory=set)

By default, third party modules are not instrumented. This option allows you to include specific modules.

exclude class-attribute instance-attribute

exclude: set[str] = field(default_factory=set)

Exclude specific modules from instrumentation.

LogfireSpan

LogfireSpan(
    span_name: str,
    otlp_attributes: dict[str, AttributeValue],
    tracer: Tracer,
    json_schema_properties: JsonSchemaProperties,
)

Bases: ReadableSpan

Source code in logfire/_internal/main.py

def __init__(
    self,
    span_name: str,
    otlp_attributes: dict[str, otel_types.AttributeValue],
    tracer: Tracer,
    json_schema_properties: JsonSchemaProperties,
) -> None:
    self._span_name = span_name
    self._otlp_attributes = otlp_attributes
    self._tracer = tracer
    self._json_schema_properties = json_schema_properties

    self._added_attributes = False
    self._end_on_exit: bool | None = None
    self._token: None | object = None
    self._span: None | trace_api.Span = None
    self.end_on_exit = True

end

end() -> None

Sets the current time as the span's end time.

The span's end time is the wall time at which the operation finished.

Only the first call to this method is recorded, further calls are ignored so you can call this within the span's context manager to end it before the context manager exits.

Source code in logfire/_internal/main.py

def end(self) -> None:
    """Sets the current time as the span's end time.

    The span's end time is the wall time at which the operation finished.

    Only the first call to this method is recorded, further calls are ignored so you
    can call this within the span's context manager to end it before the context manager
    exits.
    """
    if self._span is None:  # pragma: no cover
        raise RuntimeError('Span has not been started')
    if self._span.is_recording():
        with handle_internal_errors():
            if self._added_attributes:
                self._span.set_attribute(
                    ATTRIBUTES_JSON_SCHEMA_KEY, attributes_json_schema(self._json_schema_properties)
                )

            self._span.end()

set_attribute

set_attribute(key: str, value: Any) -> None

Sets an attribute on the span.

Parameters:

Name	Type	Description	Default
key	str	The key of the attribute.	required
value	Any	The value of the attribute.	required

Source code in logfire/_internal/main.py

@handle_internal_errors()
def set_attribute(self, key: str, value: Any) -> None:
    """Sets an attribute on the span.

    Args:
        key: The key of the attribute.
        value: The value of the attribute.
    """
    self._added_attributes = True
    self._json_schema_properties[key] = create_json_schema(value, set())
    key, otel_value = set_user_attribute(self._otlp_attributes, key, value)
    if self._span is not None:  # pragma: no branch
        self._span.set_attribute(key, otel_value)

set_attributes

set_attributes(
    attributes: dict[str, AttributeValue]
) -> None

Sets the given attributes on the span.

Source code in logfire/_internal/main.py

def set_attributes(self, attributes: dict[str, otel_types.AttributeValue]) -> None:
    """Sets the given attributes on the span."""
    for key, value in attributes.items():
        self.set_attribute(key, value)

record_exception

record_exception(
    exception: BaseException,
    attributes: Attributes = None,
    timestamp: int | None = None,
    escaped: bool = False,
) -> None

Records an exception as a span event.

Delegates to the OpenTelemetry SDK Span.record_exception method.

Source code in logfire/_internal/main.py

def record_exception(
    self,
    exception: BaseException,
    attributes: otel_types.Attributes = None,
    timestamp: int | None = None,
    escaped: bool = False,
) -> None:  # pragma: no cover
    """Records an exception as a span event.

    Delegates to the OpenTelemetry SDK `Span.record_exception` method.
    """
    if self._span is None:
        raise RuntimeError('Span has not been started')

    # Check if the span has been sampled out first, since _record_exception is somewhat expensive.
    if not self._span.is_recording():
        return

    _record_exception(
        self._span,
        exception,
        attributes=attributes,
        timestamp=timestamp,
        escaped=escaped,
    )

set_level

set_level(level: LevelName | int)

Set the log level of this span.

Source code in logfire/_internal/main.py

@handle_internal_errors()
def set_level(self, level: LevelName | int):
    """Set the log level of this span."""
    attributes = log_level_attributes(level)
    if self._span is None:
        self._otlp_attributes.update(attributes)
    else:
        self._span.set_attributes(attributes)

ScrubbingOptions dataclass

ScrubbingOptions(
    callback: ScrubCallback | None = None,
    extra_patterns: Sequence[str] | None = None,
)

Options for redacting sensitive data.

callback class-attribute instance-attribute

callback: ScrubCallback | None = None

A function that is called for each match found by the scrubber. If it returns None, the value is redacted. Otherwise, the returned value replaces the matched value. The function accepts a single argument of type logfire.ScrubMatch.

extra_patterns class-attribute instance-attribute

extra_patterns: Sequence[str] | None = None

A sequence of regular expressions to detect sensitive data that should be redacted. For example, the default includes 'password', 'secret', and 'api[._ -]?key'. The specified patterns are combined with the default patterns.

ScrubMatch dataclass

ScrubMatch(
    path: JsonPath, value: Any, pattern_match: Match[str]
)

An object passed to a ScrubbingOptions.callback function.

path instance-attribute

path: JsonPath

The path to the value in the span being considered for redaction, e.g. ('attributes', 'password').

value instance-attribute

value: Any

The value in the span being considered for redaction, e.g. 'my_password'.

pattern_match instance-attribute

pattern_match: Match[str]

The regex match object indicating why the value is being redacted. Use pattern_match.group(0) to get the matched string.

LogfireLoggingHandler

LogfireLoggingHandler(
    level: int | str = NOTSET,
    fallback: Handler = StreamHandler(),
    logfire_instance: Logfire | None = None,
)

Bases: Handler

A logging handler that sends logs to Logfire.

Source code in logfire/integrations/logging.py

def __init__(
    self,
    level: int | str = NOTSET,
    fallback: LoggingHandler = StreamHandler(),
    logfire_instance: Logfire | None = None,
) -> None:
    super().__init__(level=level)
    self.fallback = fallback
    self.logfire_instance = (logfire_instance or logfire.DEFAULT_LOGFIRE_INSTANCE).with_settings(
        custom_scope_suffix=self.custom_scope_suffix
    )

emit

emit(record: LogRecord) -> None

Send the log to Logfire.

Parameters:

Name	Type	Description	Default
record	LogRecord	The log record to send.	required

Source code in logfire/integrations/logging.py

def emit(self, record: LogRecord) -> None:
    """Send the log to Logfire.

    Args:
        record: The log record to send.
    """
    if is_instrumentation_suppressed():
        self.fallback.handle(record)
        return

    attributes = self.fill_attributes(record)

    self.logfire_instance.log(
        msg_template=attributes.pop(ATTRIBUTES_MESSAGE_TEMPLATE_KEY, record.msg),
        level=LOGGING_TO_OTEL_LEVEL_NUMBERS.get(record.levelno, record.levelno),
        attributes=attributes,
        exc_info=record.exc_info,
    )

fill_attributes

fill_attributes(record: LogRecord) -> dict[str, Any]

Fill the attributes to send to Logfire.

This method can be overridden to add more attributes.

Parameters:

Name	Type	Description	Default
record	LogRecord	The log record.	required

Returns:

Type	Description
dict[str, Any]	The attributes for the log record.

Source code in logfire/integrations/logging.py

def fill_attributes(self, record: LogRecord) -> dict[str, Any]:
    """Fill the attributes to send to Logfire.

    This method can be overridden to add more attributes.

    Args:
        record: The log record.

    Returns:
        The attributes for the log record.
    """
    attributes = {k: v for k, v in record.__dict__.items() if k not in RESERVED_ATTRS}
    attributes['code.filepath'] = record.pathname
    attributes['code.lineno'] = record.lineno
    attributes['code.function'] = record.funcName

    attributes[ATTRIBUTES_MESSAGE_KEY], args = _format_message(record)
    attributes.update(args)

    return attributes

StructlogProcessor

StructlogProcessor(
    *,
    console_log: bool = False,
    logfire_instance: Logfire | None = None
)

Logfire processor for structlog.

Source code in logfire/integrations/structlog.py

def __init__(
    self,
    *,
    console_log: bool = False,
    logfire_instance: Logfire | None = None,
) -> None:
    self.console_log = console_log
    self.logfire_instance = (logfire_instance or logfire.DEFAULT_LOGFIRE_INSTANCE).with_settings(
        custom_scope_suffix='structlog'
    )

__call__

__call__(
    logger: WrappedLogger, name: str, event_dict: EventDict
) -> EventDict

A middleware to process structlog event, and send it to Logfire.

Source code in logfire/integrations/structlog.py

def __call__(self, logger: WrappedLogger, name: str, event_dict: EventDict) -> EventDict:
    """A middleware to process structlog event, and send it to **Logfire**."""
    attributes = {k: v for k, v in event_dict.items() if k not in RESERVED_ATTRS}
    level = event_dict.get('level', 'info').lower()
    # NOTE: An event can be `None` in structlog. We may want to create a default msg in those cases.
    attributes[ATTRIBUTES_MESSAGE_KEY] = message = event_dict.get('event') or 'structlog event'
    self.logfire_instance.log(
        level=level,  # type: ignore
        msg_template=message,
        attributes=attributes,
        console_log=self.console_log,
    )
    return event_dict

no_auto_trace

no_auto_trace(x: T) -> T

Decorator to prevent a function/class from being traced by logfire.install_auto_tracing.

This is useful for small functions that are called very frequently and would generate too much noise.

The decorator is detected at import time. Only @no_auto_trace or @logfire.no_auto_trace are supported. Renaming/aliasing either the function or module won't work. Neither will calling this indirectly via another function.

Any decorated function, or any function defined anywhere inside a decorated function/class, will be completely ignored by logfire.install_auto_tracing.

This decorator simply returns the argument unchanged, so there is zero runtime overhead.

Source code in logfire/_internal/auto_trace/rewrite_ast.py

def no_auto_trace(x: T) -> T:
    """Decorator to prevent a function/class from being traced by `logfire.install_auto_tracing`.

    This is useful for small functions that are called very frequently and would generate too much noise.

    The decorator is detected at import time.
    Only `@no_auto_trace` or `@logfire.no_auto_trace` are supported.
    Renaming/aliasing either the function or module won't work.
    Neither will calling this indirectly via another function.

    Any decorated function, or any function defined anywhere inside a decorated function/class,
    will be completely ignored by `logfire.install_auto_tracing`.

    This decorator simply returns the argument unchanged, so there is zero runtime overhead.
    """
    return x  # pragma: no cover

configure

configure(
    *,
    send_to_logfire: (
        bool | Literal["if-token-present"] | None
    ) = None,
    token: str | None = None,
    service_name: str | None = None,
    service_version: str | None = None,
    console: ConsoleOptions | Literal[False] | None = None,
    show_summary: bool | None = None,
    config_dir: Path | str | None = None,
    data_dir: Path | str | None = None,
    base_url: str | None = None,
    id_generator: IdGenerator | None = None,
    ns_timestamp_generator: Callable[[], int] | None = None,
    additional_span_processors: (
        Sequence[SpanProcessor] | None
    ) = None,
    additional_metric_readers: (
        Sequence[MetricReader] | None
    ) = None,
    pydantic_plugin: PydanticPlugin | None = None,
    fast_shutdown: bool = False,
    scrubbing: (
        ScrubbingOptions | Literal[False] | None
    ) = None,
    inspect_arguments: bool | None = None,
    sampling: SamplingOptions | None = None,
    **deprecated_kwargs: Unpack[DeprecatedKwargs]
) -> None

Configure the logfire SDK.

Parameters:

Name	Type	Description	Default
send_to_logfire	bool | Literal['if-token-present'] | None	Whether to send logs to logfire.dev. Defaults to the LOGFIRE_SEND_TO_LOGFIRE environment variable if set, otherwise defaults to True. If if-token-present is provided, logs will only be sent if a token is present.	None
token	str | None	The project token. Defaults to the LOGFIRE_TOKEN environment variable.	None
service_name	str | None	Name of this service. Defaults to the LOGFIRE_SERVICE_NAME environment variable.	None
service_version	str | None	Version of this service. Defaults to the LOGFIRE_SERVICE_VERSION environment variable, or the current git commit hash if available.	None
console	ConsoleOptions | Literal[False] | None	Whether to control terminal output. If None uses the LOGFIRE_CONSOLE_* environment variables, otherwise defaults to ConsoleOption(colors='auto', indent_spans=True, include_timestamps=True, verbose=False). If False disables console output. It can also be disabled by setting LOGFIRE_CONSOLE environment variable to false.	None
show_summary	bool | None	When to print a summary of the Logfire setup including a link to the dashboard. If None uses the LOGFIRE_SHOW_SUMMARY environment variable, otherwise defaults to True.	None
config_dir	Path | str | None	Directory that contains the pyproject.toml file for this project. If None uses the LOGFIRE_CONFIG_DIR environment variable, otherwise defaults to the current working directory.	None
data_dir	Path | str | None	Directory to store credentials, and logs. If None uses the LOGFIRE_CREDENTIALS_DIR environment variable, otherwise defaults to '.logfire'.	None
base_url	str | None	Root URL for the Logfire API. If None uses the LOGFIRE_BASE_URL environment variable, otherwise defaults to https://logfire-api.pydantic.dev.	None
id_generator	IdGenerator | None	Generator for span IDs. Defaults to RandomIdGenerator() from the OpenTelemetry SDK.	None
ns_timestamp_generator	Callable[[], int] | None	Generator for nanosecond timestamps. Defaults to time.time_ns from the Python standard library.	None
additional_span_processors	Sequence[SpanProcessor] | None	Span processors to use in addition to the default processor which exports spans to Logfire's API.	None
additional_metric_readers	Sequence[MetricReader] | None	Sequence of metric readers to be used in addition to the default reader which exports metrics to Logfire's API.	None
pydantic_plugin	PydanticPlugin | None	Configuration for the Pydantic plugin. If None uses the LOGFIRE_PYDANTIC_PLUGIN_* environment variables, otherwise defaults to PydanticPlugin(record='off').	None
fast_shutdown	bool	Whether to shut down exporters and providers quickly, mostly used for tests. Defaults to False.	False
scrubbing	ScrubbingOptions | Literal[False] | None	Options for scrubbing sensitive data. Set to False to disable.	None
inspect_arguments	bool | None	Whether to enable f-string magic. If None uses the LOGFIRE_INSPECT_ARGUMENTS environment variable. Defaults to True if and only if the Python version is at least 3.11.	None
sampling	SamplingOptions | None	Sampling options. See the sampling guide.	None

Source code in logfire/_internal/config.py

def configure(  # noqa: D417
    *,
    send_to_logfire: bool | Literal['if-token-present'] | None = None,
    token: str | None = None,
    service_name: str | None = None,
    service_version: str | None = None,
    console: ConsoleOptions | Literal[False] | None = None,
    show_summary: bool | None = None,
    config_dir: Path | str | None = None,
    data_dir: Path | str | None = None,
    base_url: str | None = None,
    id_generator: IdGenerator | None = None,
    ns_timestamp_generator: Callable[[], int] | None = None,
    additional_span_processors: Sequence[SpanProcessor] | None = None,
    additional_metric_readers: Sequence[MetricReader] | None = None,
    pydantic_plugin: PydanticPlugin | None = None,
    fast_shutdown: bool = False,
    scrubbing: ScrubbingOptions | Literal[False] | None = None,
    inspect_arguments: bool | None = None,
    sampling: SamplingOptions | None = None,
    **deprecated_kwargs: Unpack[DeprecatedKwargs],
) -> None:
    """Configure the logfire SDK.

    Args:
        send_to_logfire: Whether to send logs to logfire.dev. Defaults to the `LOGFIRE_SEND_TO_LOGFIRE` environment
            variable if set, otherwise defaults to `True`. If `if-token-present` is provided, logs will only be sent if
            a token is present.
        token: The project token. Defaults to the `LOGFIRE_TOKEN` environment variable.
        service_name: Name of this service. Defaults to the `LOGFIRE_SERVICE_NAME` environment variable.
        service_version: Version of this service. Defaults to the `LOGFIRE_SERVICE_VERSION` environment variable, or the
            current git commit hash if available.
        console: Whether to control terminal output. If `None` uses the `LOGFIRE_CONSOLE_*` environment variables,
            otherwise defaults to `ConsoleOption(colors='auto', indent_spans=True, include_timestamps=True, verbose=False)`.
            If `False` disables console output. It can also be disabled by setting `LOGFIRE_CONSOLE` environment variable to `false`.
        show_summary: When to print a summary of the Logfire setup including a link to the dashboard. If `None` uses the `LOGFIRE_SHOW_SUMMARY` environment variable, otherwise
            defaults to `True`.
        config_dir: Directory that contains the `pyproject.toml` file for this project. If `None` uses the
            `LOGFIRE_CONFIG_DIR` environment variable, otherwise defaults to the current working directory.
        data_dir: Directory to store credentials, and logs. If `None` uses the `LOGFIRE_CREDENTIALS_DIR` environment variable, otherwise defaults to `'.logfire'`.
        base_url: Root URL for the Logfire API. If `None` uses the `LOGFIRE_BASE_URL` environment variable, otherwise defaults to https://logfire-api.pydantic.dev.
        id_generator: Generator for span IDs. Defaults to `RandomIdGenerator()` from the OpenTelemetry SDK.
        ns_timestamp_generator: Generator for nanosecond timestamps. Defaults to [`time.time_ns`][time.time_ns] from the
            Python standard library.
        additional_span_processors: Span processors to use in addition to the default processor which exports spans to Logfire's API.
        additional_metric_readers: Sequence of metric readers to be used in addition to the default reader
            which exports metrics to Logfire's API.
        pydantic_plugin: Configuration for the Pydantic plugin. If `None` uses the `LOGFIRE_PYDANTIC_PLUGIN_*` environment
            variables, otherwise defaults to `PydanticPlugin(record='off')`.
        fast_shutdown: Whether to shut down exporters and providers quickly, mostly used for tests. Defaults to `False`.
        scrubbing: Options for scrubbing sensitive data. Set to `False` to disable.
        inspect_arguments: Whether to enable
            [f-string magic](https://logfire.pydantic.dev/docs/guides/onboarding_checklist/add_manual_tracing/#f-strings).
            If `None` uses the `LOGFIRE_INSPECT_ARGUMENTS` environment variable.
            Defaults to `True` if and only if the Python version is at least 3.11.
        sampling: Sampling options. See the [sampling guide](https://logfire.pydantic.dev/docs/guides/advanced/sampling/).
    """
    processors = deprecated_kwargs.pop('processors', None)  # type: ignore
    if processors is not None:  # pragma: no cover
        raise ValueError(
            'The `processors` argument has been replaced by `additional_span_processors`. '
            'Set `send_to_logfire=False` to disable the default processor.'
        )

    metric_readers = deprecated_kwargs.pop('metric_readers', None)  # type: ignore
    if metric_readers is not None:  # pragma: no cover
        raise ValueError(
            'The `metric_readers` argument has been replaced by `additional_metric_readers`. '
            'Set `send_to_logfire=False` to disable the default metric reader.'
        )

    collect_system_metrics = deprecated_kwargs.pop('collect_system_metrics', None)  # type: ignore
    if collect_system_metrics is False:
        raise ValueError(
            'The `collect_system_metrics` argument has been removed. '
            'System metrics are no longer collected by default.'
        )

    if collect_system_metrics is not None:
        raise ValueError(
            'The `collect_system_metrics` argument has been removed. '
            'Use `logfire.instrument_system_metrics()` instead.'
        )

    scrubbing_callback = deprecated_kwargs.pop('scrubbing_callback', None)  # type: ignore
    scrubbing_patterns = deprecated_kwargs.pop('scrubbing_patterns', None)  # type: ignore
    if scrubbing_callback or scrubbing_patterns:
        if scrubbing is not None:
            raise ValueError(
                'Cannot specify `scrubbing` and `scrubbing_callback` or `scrubbing_patterns` at the same time. '
                'Use only `scrubbing`.'
            )
        warnings.warn(
            'The `scrubbing_callback` and `scrubbing_patterns` arguments are deprecated. '
            'Use `scrubbing=logfire.ScrubbingOptions(callback=..., extra_patterns=[...])` instead.',
            DeprecationWarning,
        )
        scrubbing = ScrubbingOptions(callback=scrubbing_callback, extra_patterns=scrubbing_patterns)  # type: ignore

    project_name = deprecated_kwargs.pop('project_name', None)  # type: ignore
    if project_name is not None:
        warnings.warn(
            'The `project_name` argument is deprecated and not needed.',
            DeprecationWarning,
        )

    trace_sample_rate: float | None = deprecated_kwargs.pop('trace_sample_rate', None)  # type: ignore
    if trace_sample_rate is not None:
        if sampling:
            raise ValueError(
                'Cannot specify both `trace_sample_rate` and `sampling`. '
                'Use `sampling.head` instead of `trace_sample_rate`.'
            )
        else:
            sampling = SamplingOptions(head=trace_sample_rate)
            warnings.warn(
                'The `trace_sample_rate` argument is deprecated. '
                'Use `sampling=logfire.SamplingOptions(head=...)` instead.',
            )

    if deprecated_kwargs:
        raise TypeError(f'configure() got unexpected keyword arguments: {", ".join(deprecated_kwargs)}')

    GLOBAL_CONFIG.configure(
        base_url=base_url,
        send_to_logfire=send_to_logfire,
        token=token,
        service_name=service_name,
        service_version=service_version,
        console=console,
        show_summary=show_summary,
        config_dir=Path(config_dir) if config_dir else None,
        data_dir=Path(data_dir) if data_dir else None,
        id_generator=id_generator,
        ns_timestamp_generator=ns_timestamp_generator,
        additional_span_processors=additional_span_processors,
        additional_metric_readers=additional_metric_readers,
        pydantic_plugin=pydantic_plugin,
        fast_shutdown=fast_shutdown,
        scrubbing=scrubbing,
        inspect_arguments=inspect_arguments,
        sampling=sampling,
    )

load_spans_from_file

load_spans_from_file(
    file_path: str | Path | IO[bytes] | None,
) -> Iterator[ExportTraceServiceRequest]

Load a backup file.

Parameters:

Name	Type	Description	Default
file_path	str | Path | IO[bytes] | None	The path to the backup file.	required

Raises:

Type	Description
ValueError	If the file is not a valid backup file.

Returns:

Type	Description
Iterator[ExportTraceServiceRequest]	An iterator over each ExportTraceServiceRequest message in the backup file.

Source code in logfire/_internal/exporters/file.py

def load_file(file_path: str | Path | IO[bytes] | None) -> Iterator[ExportTraceServiceRequest]:
    """Load a backup file.

    Args:
        file_path: The path to the backup file.

    Raises:
        ValueError: If the file is not a valid backup file.

    Returns:
        An iterator over each `ExportTraceServiceRequest` message in the backup file.
    """
    if file_path is None:  # pragma: no cover
        file_path = Path(DEFAULT_FALLBACK_FILE_NAME)
    elif isinstance(file_path, str):  # pragma: no cover
        file_path = Path(file_path)
    with file_path.open('rb') if isinstance(file_path, Path) else file_path as f:
        parser = FileParser()
        while True:
            data = f.read(parser.get_suggested_read_size())
            if not data:
                parser.finish()
                return
            yield from parser.push(data)

suppress_instrumentation

suppress_instrumentation()

Context manager to suppress all logs/spans generated by logfire or OpenTelemetry.

Source code in logfire/_internal/utils.py

@contextmanager
def suppress_instrumentation():
    """Context manager to suppress all logs/spans generated by logfire or OpenTelemetry."""
    new_context = context.get_current()
    for key in SUPPRESS_INSTRUMENTATION_CONTEXT_KEYS:
        new_context = context.set_value(key, True, new_context)
    token = context.attach(new_context)
    try:
        yield
    finally:
        context.detach(token)