Pydantic

Pydantic is a set of tools for teams that build AI applications in production and need evidence, not guesswork. It brings agent tracing, evaluation, and cost control into one workflow so you can debug wrong answers, reproduce failures, and improve what actually gets deployed.

It’s a fit for engineering teams building AI agents or production AI services with Python or TypeScript who want better visibility across model calls, APIs, and backend systems.

Key capabilities you can expect:

• End-to-end tracing with Logfire: keeps a single execution path together across the agent, model calls, APIs, and database so you can see where things went wrong.
• Evaluation from real traces (Pydantic Evals): turns production failures into eval cases, so your tests match the issues you actually see.
• Type-safe agent building (Pydantic AI): helps you build agents using Pydantic-focused, production-minded patterns in Python or TypeScript.
• Cost tracking and spend control (AI Gateway): tracks LLM costs and helps you catch cost spikes before they run away.

What stands out is the focus on production evidence and OpenTelemetry-based tracing. Logfire is built on OpenTelemetry with SDKs for Python and JavaScript/TypeScript (and Rust), plus it can work with any OpenTelemetry-instrumented stack. That means you can trace through databases and infrastructure that sit underneath each request without reworking your whole system to fit a vendor.