Logfire¶

Logfire, a new tool from Pydantic, is built on OpenTelemetry. Since Pydantic powers many of Mirascope's features, it's appropriate for us to ensure seamless integration with them.

You can install the necessary packages directly or using the logfire extras flag:

pip install "mirascope[logfire]"

You can then use the with_logfire decorator to automatically log calls:

ShorthandMessagesString TemplateBaseMessageParam

OpenAIAnthropicMistralGeminiGroqCohereLiteLLMAzure AIVertex AIBedrock

import logfire
from mirascope.core import openai
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@openai.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book."


print(recommend_book("fantasy"))

import logfire
from mirascope.core import anthropic
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import mistral
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@mistral.call("mistral-large-latest", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import gemini
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@gemini.call("gemini-1.5-flash", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import groq
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import cohere
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@cohere.call("command-r-plus", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import litellm
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@litellm.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import azure
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@azure.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import vertex
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@vertex.call("gemini-1.5-flash", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"


print(recommend_book("fantasy"))

import logfire
from mirascope.core import bedrock
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book."


print(recommend_book("fantasy"))

OpenAIAnthropicMistralGeminiGroqCohereLiteLLMAzure AIVertex AIBedrock

import logfire
from mirascope.core import Messages, openai
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@openai.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book.")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, anthropic
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, mistral
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@mistral.call("mistral-large-latest", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, gemini
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@gemini.call("gemini-1.5-flash", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, groq
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, cohere
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@cohere.call("command-r-plus", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, litellm
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@litellm.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, azure
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@azure.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, vertex
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@vertex.call("gemini-1.5-flash", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book")


print(recommend_book("fantasy"))

import logfire
from mirascope.core import Messages, bedrock
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def recommend_book(genre: str) -> Messages.Type:
    return Messages.User(f"Recommend a {genre} book.")


print(recommend_book("fantasy"))

OpenAIAnthropicMistralGeminiGroqCohereLiteLLMAzure AIVertex AIBedrock

import logfire
from mirascope.core import openai, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@openai.call("gpt-4o-mini", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import anthropic, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import mistral, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@mistral.call("mistral-large-latest", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import gemini, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@gemini.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import groq, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@groq.call("llama-3.1-70b-versatile", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import cohere, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@cohere.call("command-r-plus", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import litellm, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@litellm.call("gpt-4o-mini", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import azure, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@azure.call("gpt-4o-mini", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import prompt_template, vertex
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@vertex.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

import logfire
from mirascope.core import bedrock, prompt_template
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
@prompt_template("Recommend a {genre} book")
def recommend_book(genre: str): ...


print(recommend_book("fantasy"))

OpenAIAnthropicMistralGeminiGroqCohereLiteLLMAzure AIVertex AIBedrock

import logfire
from mirascope.core import BaseMessageParam, openai
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@openai.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book.")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, anthropic
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, mistral
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@mistral.call("mistral-large-latest", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, gemini
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@gemini.call("gemini-1.5-flash", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, groq
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, cohere
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@cohere.call("command-r-plus", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, litellm
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@litellm.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, azure
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@azure.call("gpt-4o-mini", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, vertex
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@vertex.call("gemini-1.5-flash", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book")]


print(recommend_book("fantasy"))

import logfire
from mirascope.core import BaseMessageParam, bedrock
from mirascope.integrations.logfire import with_logfire
from pydantic import BaseModel

logfire.configure()


class Book(BaseModel):
    title: str
    author: str


@with_logfire()
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def recommend_book(genre: str) -> list[BaseMessageParam]:
    return [BaseMessageParam(role="user", content=f"Recommend a {genre} book.")]


print(recommend_book("fantasy"))

This will give you:

A span called recommend_book that captures items like the prompt template, templating properties and fields, and input/output attributes
Human-readable display of the conversation with the agent
Details of the response, including the number of tokens used

Example log

logfire-call

Handling streams

When logging streams, the span will not be logged until the stream has been exhausted. This is a function of how streaming works.

You will also need to set certain call_params for usage to be tracked for certain providers (such as OpenAI).