Samuel Colvin

Pydantic’s AI leverage comes from owning Python’s typed-data boundary, not from having been designed for LLMs. Colvin notes nearly 300 million December downloads, while saying one unnamed foundation-model company cut time to first token by 20% after moving from Pydantic v1 to v2. “The AI has kind of come to us,” but that accidental positioning now gives Pydantic unusual distribution across model SDKs and structured-output tooling.

Pydantic AI is betting that production engineering will distinguish durable agent frameworks from opportunistic wrappers. Colvin criticizes the ecosystem’s weak type checking, coverage, linting and documentation discipline, while acknowledging that early projects such as LangChain were inventing the category. Pydantic AI accepts harder generics in exchange for production safety: every documentation example and printed result runs in tests because these practices should be “no-brainers.”

Type-safe graphs changed Colvin’s mind about orchestration by turning sprawling control flow into inspectable Python. Nodes return the next typed node, allowing Pydantic to infer valid edges and generate a Mermaid diagram without string-based graph definitions. The standard agent implementation is now itself a graph, although durable state storage and distributed execution remain planned rather than finished.

Agent frameworks are compensating for model weakness, so their long-term value may fall as models improve. Alessio raises the “bitter lesson”: developers can hand-build reflection and routing graphs, only for a larger trained model to absorb the workflow. Colvin agrees that “the more intelligent they are, the less we need to tell them,” comparing today’s constrained agents with poorly trained call-center staff who need scripts.

Logfire’s thesis is that AI observability cannot remain separate from application observability. Colvin argues that traces containing model prompts will carry an “order of magnitude” more sensitive information than traditional telemetry, making self-hosting important for some customers. Logfire therefore targets general-purpose observability with first-class AI support, while OpenTelemetry semantic conventions could standardize model-call data across frameworks.

The infrastructure bet underneath Logfire is technically differentiated but commercially expensive. The team moved from ClickHouse to Timescale and finally DataFusion, favoring Rust extensibility, user-friendly SQL and an open-source architecture over faster time to market. Colvin concedes the choice “definitely slowed us down,” but it avoids surrendering margin to a hosted database vendor and lets Pydantic implement missing kernels itself.

Management is protecting runway while using open source and browser demos to improve conversion. Pydantic and Pydantic AI remain MIT-licensed, while Logfire is explicitly closed-source and for-profit; Colvin wants more revenue before hiring despite the team effectively running “three to four startups at the same time.” The browser-based pydantic.run aims to remove setup friction by letting prospects execute tested examples; a model proxy is planned, with possible Logfire integration.

🔗 Original source & video: Agent Engineering with Pydantic + Graphs — with Samuel Colvin, CEO of Pydantic Logfire