Magentic

Third-party integrations

Third-party integrations are not officially supported by Logfire.

They are maintained by the community and may not be as reliable as the integrations developed by Logfire.

Magentic is a lightweight library for working with structured output from LLMs, built around standard python type annotations and Pydantic. It integrates with Logfire to provide observability into prompt-templating, retries, tool/function call execution, and other features.

Magentic instrumentation requires no additional setup beyond configuring Logfire itself. You might also want to enable the OpenAI and/or Anthropic integrations.

from typing import Annotated

import logfire
from magentic import chatprompt, OpenaiChatModel, SystemMessage, UserMessage
from pydantic import BaseModel, Field
from pydantic.functional_validators import AfterValidator

logfire.configure()
logfire.instrument_openai()


def assert_upper(value: str) -> str:
    if not value.isupper():
        raise ValueError('Value must be upper case')
    return value


class Superhero(BaseModel):
    name: Annotated[str, AfterValidator(assert_upper)]
    powers: list[str]
    city: Annotated[str, Field(examples=["New York, NY"])]


@chatprompt(
    SystemMessage('You are professor A, in charge of the A-people.'),
    UserMessage('Create a new superhero named {name}.'),
    model=OpenaiChatModel("gpt-4o"),
    max_retries=3,
)
def make_superhero(name: str) -> Superhero: ...


hero = make_superhero("The Bark Night")
print(hero)

This creates the following in Logfire:

• A span for the call to make_superhero showing the input arguments
• A span showing that retries have been enabled for this query
• A warning for each retry that was needed in order to generate a valid output
• The chat messages to/from the LLM, including tool calls and invalid outputs that required retrying

Magentic chatprompt-function call span and conversation

To learn more about Magentic, check out magentic.dev.