Pydantic

🚀 Introducing PyNest v0.3.2: Faster, Smarter, and More Reliable Python Microservices! We’re thrilled to announce a new release of PyNest that takes your microservices development experience to the next level. Here’s what’s new in v0.3.2: - Transition to Poetry Say goodbye to the complexity of setup.py and requirements.txt—we’ve fully embraced Poetry! Enjoy streamlined dependency management and a consistent environment across teams. Simplify versioning and publishing workflows with Poetry’s all-in-one solution. - Upgrade to Pydantic v2 - Get ready for blazing-fast validation! Up to 50x faster validation compared to Pydantic v1, making it perfect for data-heavy applications. Cleaner, more intuitive APIs to define and manage your models. New features to reduce boilerplate and simplify complex data handling. - Why It Matters: This release focuses on improving your workflow and runtime performance. With Poetry, you get a robust, modern toolchain that’s designed to eliminate common pain points in Python packaging and dependencies. With Pydantic v2, you unlock massively improved validation speeds, giving your applications a significant performance boost, especially when working with large datasets or high-frequency API calls. Together, these changes make PyNest faster, easier to use, and more powerful than ever. 🚀 Curious about the details? Dive into the release notes and experience PyNest v0.3.2 today! Your microservices deserve it. 💪

Big PydanticAI release, includes: * Anthropic support * Mistral AI support * big refactor to message history Overall this release includes 40 PRs from 16 different people! 🤘

It's early, but I'm amazed by how PydanticAI downloads are already beginning to rip. 🦓

I'm shocked by how easy it is to run PydanticAI with a model running locally. Here we have Ollama running llama3.2 locally, being used by Pydantic AI extracting structured data from an input.

PydanticAI is here! An Agent Framework designed for production, from the team who created and maintain Pydantic. As some of you will know, I've been working on this for some time, can't wait to see what people build with it. https://ai.pydantic.dev

I’m incredibly grateful to the PyCon Hong Kong organizers, Vitaliy Bozhkov, Artur Padalianchyk, Sanchit Balchandani, Shivam C., SreeRamaChandra Dasari and EPAM Systems for their continued support in making my talk on Pydantic Logfire at PyCon Hong Kong possible. Though it was a short conference, it was incredibly well-managed, with organizers present every step of the way. From engaging coding games to exciting stalls, the experience was seamless and full of fun surprises! It was great networking with Judy Wong, Alva Tse ☁, Cristiano Pierandrei, Sau Yee C., Tarun R Jain, Scotty Kwok, Calvin T., Ka Ki Ting and Adrian Tam — such inspiring conversations! 💬 A special mention to Adrian Tam’s talk on High Throughput Python. The feedback from my talk has been overwhelmingly positive. Looking forward to staying connected with this amazing community! #PyConHK #PythonObservability #PydanticLogfire #Networking #PythonPerformance #HighThroughputPython #EPAM

Pydantic Logfire feature: span links demo'd by Marcelo Trylesinski. Span links are an alternative to nested spans that let you link between spans like an HTML link. They're useful when observing jobs and RPC, where it doesn't make sense for the job execution span to occur within the span that triggered it.

logfire.info now takes you to the docs for logfire.info. This makes me happy.