Pydantic

Founded by Samuel Colvin in 2018

Build reliable AI agents, trace multi-step workflows, and track model costs in production

Pricing model

Free

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Pydantic to build type-safe AI applications and monitor their behavior in production environments. It provides libraries to create agents in Python or TypeScript, enforce data validation, and connect models to external databases and APIs. With its observability features, you can trace requests across every layer of your application, inspect tool execution, monitor token usage, and manage cloud spending. You can also turn production failures into automated evaluation test cases to systematically verify bug fixes and keep your agents running smoothly.

What Pydantic does

Inspect distributed application traces and database queriesMonitor real-time token spend across LLM providersRun automated evaluation test suites on AI agentsValidate runtime data types and schemas in Python applicationsSet up OpenTelemetry instrumentation for backend servicesAnalyze production error logs and failed agent executionsDebug code execution directly from code editors using MCP serversTrace AI agent decisions and tool calls across full execution pathsConvert production agent failures directly into evaluation test casesTrack model token usage and API spending in real timeInstrument backend services using standard OpenTelemetry protocolsEnforce runtime type checking and data validation in PythonInspect database queries, API calls, and model outputs in a single trace viewConnect with development frameworks like FastAPI, Django, and Next.js