Read the show’s notes in full
Chapters
* 00:00:00 Introductions
* 00:00:24 Origins of Pydantic
* 00:05:28 Pydantic's AI moment
* 00:08:05 Why build a new agents framework?
* 00:10:17 Overview of Pydantic AI
* 00:12:33 Becoming a believer in graphs
* 00:24:02 God Model vs Compound AI Systems
* 00:28:13 Why not build an LLM gateway?
* 00:31:39 Programmatic testing vs live evals
* 00:35:51 Using OpenTelemetry for AI traces
* 00:43:19 Why they don't use Clickhouse
* 00:48:34 Competing in the observability space
* 00:50:41 Licensing decisions for Pydantic and LogFire
* 00:51:48 Building Pydantic.run
* 00:55:24 Marimo and the future of Jupyter notebooks
* 00:57:44 London's AI scene
Show Notes
* Sam Colvin
* Pydantic
* Pydantic AI
* Logfire
* Pydantic.run
* Zod
* E2B
* Arize
* Langsmith
* Marimo
* Prefect
* GLA (Google Generative Language API)
* OpenTelemetry
* Jason Liu
* Sebastian Ramirez
* Bogomil Balkansky
* Hood Chatham
* Jeremy Howard
* Andrew Lamb
Transcript
Alessio [00:00:03]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.
Swyx [00:00:12]: Good morning. And today we're very excited to have Sam Colvin join us from Pydantic AI. Welcome. Sam, I heard that Pydantic is all we need. Is that true?
Samuel [00:00:24]: I would say you might need Pydantic AI and Logfire as well, but it gets you a long way, that's for sure.
Swyx [00:00:29]: Pydantic almost basically needs no introduction. It's almost 300 million downloads in December. And obviously, in the previous podcasts and discussions we've had with Jason Liu, he's been a big fan and promoter of Pydantic and AI.
Samuel [00:00:45]: Yeah, it's weird because obviously I didn't create Pydantic originally for uses in AI, it predates LLMs. But it's like we've been lucky that it's been picked up by that community and used so widely.
Swyx [00:00:58]: Actually, maybe we'll hear it. Right from you, what is Pydantic and maybe a little bit of the origin story?
Samuel [00:01:04]: The best name for it, which is not quite right, is a validation library. And we get some tension around that name because it doesn't just do validation, it will do coercion by default. We now have strict mode, so you can disable that coercion. But by default, if you say you want an integer field and you get in a string of 1, 2, 3, it will convert it to 123 and a bunch of other sensible conversions. And as you can imagine, the semantics around it. Exactly when you convert and when you don't, it's complicated, but because of that, it's more than just validation. Back in 2017, when I first started it, the different thing it was doing was using type hints to define your schema. That was controversial at the time. It was genuinely disapproved of by some people. I think the success of Pydantic and libraries like FastAPI that build on top of it means that today that's no longer controversial in Python. And indeed, lots of other people have copied that route, but yeah, it's a data validation library. It uses type hints for the for the most part and obviously does all the other stuff you want, like serialization on top of that. But yeah, that's the core.
Alessio [00:02:06]: Do you have any fun stories on how JSON schemas ended up being kind of like the structure output standard for LLMs? And were you involved in any of these discussions? Because I know OpenAI was, you know, one of the early adopters. So did they reach out to you? Was there kind of like a structure output console in open source that people were talking about or was it just a random?
Samuel [00:02:26]: No, very much not. So I originally. Didn't implement JSON schema inside Pydantic and then Sebastian, Sebastian Ramirez, FastAPI came along and like the first I ever heard of him was over a weekend. I got like 50 emails from him or 50 like emails as he was committing to Pydantic, adding JSON schema long pre version one. So the reason it was added was for OpenAPI, which is obviously closely akin to JSON schema. And then, yeah, I don't know why it was JSON that got picked up and used by OpenAI. It was obviously very convenient for us. That's because it meant that not only can you do the validation, but because Pydantic will generate you the JSON schema, it will it kind of can be one source of source of truth for structured outputs and tools.
Swyx [00:03:09]: Before we dive in further on the on the AI side of things, something I'm mildly curious about, obviously, there's Zod in JavaScript land. Every now and then there is a new sort of in vogue validation library that that takes over for quite a few years and then maybe like some something else comes along. Is Pydantic? Is it done like the core Pydantic?
Samuel [00:03:30]: I've just come off a call where we were redesigning some of the internal bits. There will be a v3 at some point, which will not break people's code half as much as v2 as in v2 was the was the massive rewrite into Rust, but also fixing all the stuff that was broken back from like version zero point something that we didn't fix in v1 because it was a side project. We have plans to move some of the basically store the data in Rust types after validation. Not completely. So we're still working to design the Pythonic version of it, in order for it to be able to convert into Python types. So then if you were doing like validation and then serialization, you would never have to go via a Python type we reckon that can give us somewhere between three and five times another three to five times speed up. That's probably the biggest thing. Also, like changing how easy it is to basically extend Pydantic and define how particular types, like for example, NumPy arrays are validated and serialized. But there's also stuff going on. And for example, Jitter, the JSON library in Rust that does the JSON parsing, has SIMD implementation at the moment only for AMD64. So we can add that. We need to go and add SIMD for other instruction sets. So there's a bunch more we can do on performance. I don't think we're going to go and revolutionize Pydantic, but it's going to continue to get faster, continue, hopefully, to allow people to do more advanced things. We might add a binary format like CBOR for serialization for when you'll just want to put the data into a database and probably load it again from Pydantic. So there are some things that will come along, but for the most part, it should just get faster and cleaner.
Alessio [00:05:04]: From a focus perspective, I guess, as a founder too, how did you think about the AI interest rising? And then how do you kind of prioritize, okay, this is worth going into more, and we'll talk about Pydantic AI and all of that. What was maybe your early experience with LLAMP, and when did you figure out, okay, this is something we should take seriously and focus more resources on it?
Samuel [00:05:28]: I
