Integrations

Pydantic Logfire supports first-class integration with many popular Python packages using a single logfire.instrument_<package>() function call. Each of these should be called exactly once after logfire.configure().

For example, to instrument FastAPI and HTTPX, you would do:

from fastapi import FastAPI

import logfire

app = FastAPI()

logfire.configure()
logfire.instrument_fastapi(app)
logfire.instrument_httpx()

# ... your application code here ...

If a package you are using is not listed in this documentation, please let us know on our Slack!

Documented Integrations

Logfire has documented integrations with many technologies, including:

• LLM Clients and AI Frameworks: Pydantic AI, OpenAI, Anthropic, LangChain, LlamaIndex, Mirascope, LiteLLM, Magentic
• Web Frameworks: FastAPI, Django, Flask, Starlette, AIOHTTP, ASGI, WSGI
• Database Clients: Psycopg, SQLAlchemy, Asyncpg, PyMongo, MySQL, SQLite3, Redis, BigQuery
• HTTP Clients: HTTPX, Requests, AIOHTTP
• Task Queues and Schedulers: Airflow, FastStream, Celery
• Logging Libraries: Standard Library Logging, Loguru, Structlog
• Testing: Pytest
• and more, such as Stripe, AWS Lambda, and system metrics.

The below table lists these integrations and any corresponding logfire.instrument_<package>() calls:

Package	Type	Logfire Instrument Call / Notes
Pydantic Validation	Data Validation	logfire.instrument_pydantic()
Pydantic AI	AI	logfire.instrument_pydantic_ai()
AIOHTTP	HTTP Client	logfire.instrument_aiohttp_client(), logfire.instrument_aiohttp_server()
Airflow	Task Scheduler	N/A (built in, config needed)
Anthropic	AI	logfire.instrument_anthropic()
ASGI	Web Framework Interface	logfire.instrument_asgi()
AWS Lambda	Cloud Function	logfire.instrument_aws_lambda()
Asyncpg	Database	logfire.instrument_asyncpg()
BigQuery	Database	N/A (built in, no config needed)
Celery	Task Queue	logfire.instrument_celery()
Django	Web Framework	logfire.instrument_django()
FastAPI	Web Framework	logfire.instrument_fastapi()
FastStream	Task Queue	N/A (built in, config needed)
Flask	Web Framework	logfire.instrument_flask()
HTTPX	HTTP Client	logfire.instrument_httpx()
LangChain	AI Framework	N/A (built-in OpenTelemetry support)
LlamaIndex	AI Framework	N/A (requires LlamaIndex OpenTelemetry package)
LiteLLM	AI Gateway	N/A (requires LiteLLM callback setup)
Loguru	Logging	See documentation
Magentic	AI Framework	N/A (built-in Logfire support)
Mirascope	AI Framework	N/A (use mirascope @with_logfire decorator)
MySQL	Database	logfire.instrument_mysql()
OpenAI	AI	logfire.instrument_openai()
Psycopg	Database	logfire.instrument_psycopg()
Pytest	Testing	N/A (built-in plugin, use pytest --logfire)
PyMongo	Database	logfire.instrument_pymongo()
Redis	Database	logfire.instrument_redis()
Requests	HTTP Client	logfire.instrument_requests()
SQLAlchemy	Database	logfire.instrument_sqlalchemy()
SQLite3	Database	logfire.instrument_sqlite3()
Standard Library Logging	Logging	See documentation
Starlette	Web Framework	logfire.instrument_starlette()
Stripe	Payment Gateway	N/A (requires other instrumentations)
Structlog	Logging	See documentation
System Metrics	System Metrics	logfire.instrument_system_metrics()
WSGI	Web Framework Interface	logfire.instrument_wsgi()

If you are using Logfire with a web application, we also recommend reviewing our Web Frameworks documentation.

OpenTelemetry Integrations

Since Logfire is OpenTelemetry compatible, it can be used with any OpenTelemetry instrumentation package. You can find the list of all OpenTelemetry instrumentation packages here.

Many of the integrations documented in the previous section are based upon the OpenTelemetry instrumentation packages with first-class support built into Logfire.

Creating Custom Integrations

If you are a maintainer of a package and would like to create an integration for Logfire, you can do it! 😄

We’ve created a shim package called logfire-api, which can be used to integrate your package with Logfire.

The idea of logfire-api is that it doesn’t have any dependencies. It’s a very small package that matches the API of Logfire. We created it so that you can create an integration for Logfire without having to install Logfire itself.

You can use logfire-api as a lightweight dependency of your own package. If logfire is installed, then logfire-api will use it. If not, it will use a no-op implementation. This way users of your package can decide whether or not they want to install logfire, and you don’t need to check whether or not it’s installed.

Here’s how you can use logfire-api:

import logfire_api as logfire

logfire.info('Hello, Logfire!')

Note

You generally don’t want to call logfire_api.configure(), it’s up to your users to call logfire.configure() if they want to use the integration.

All the Logfire API methods are available in logfire-api.