Pydantic architecture
Python data validation driven by type hints, with a compiled Rust core doing the actual work.
What is Pydantic?
Pydantic validates data using ordinary Python type hints. You declare a model as a class, and it derives validation, serialization, and JSON Schema from the annotations — with the heavy lifting done by a compiled Rust core rather than by Python.
That two-language split is the architectural story. Which behaviour lives in Python and which in Rust? What crosses the binding layer, and how often? When you define a model, how much work happens once at class-creation time versus on every validation call? For anyone tuning Pydantic's performance or extending it, that boundary is the thing to understand.
The map on this page is JigsawML's read of the Pydantic repository. It separates the Python surface from the Rust core and the bindings between them, and lets you open any area to see the code.
Key components in the Pydantic architecture
JigsawML identified and labelled these components automatically by analysing the Pydantic repository. Open the interactive map to expand any of them and read the underlying code.
Pydantic Public
The models, fields, and decorators users import and subclass.
Internal Schema
The Python layer turning annotations into a schema the core can run.
Compatibility Plugins
Shims for older versions and integrations with surrounding tools.
Rust Validation
The compiled validators that check incoming data against a schema.
Rust Serialization
The compiled path turning validated models back into output formats.
Rust Input
Input handling and data interchange on the Rust side of the boundary.
Python Bindings
The binding layer letting Python call into the compiled core.
Pydantic Test
The Python test suite covering models and public behaviour.
Rust Test
Tests exercising the compiled core directly.
Documentation
The documentation sources published as the Pydantic docs.
CI Release
Build, benchmark, and release automation across both languages.
External Services
Registries and services used when building and publishing the package.
Explore the Pydantic architecture map
The map is live and interactive - pan, zoom, drill into a component, and ask questions about how Pydantic fits together. No login required.
Open the Pydantic mapHow this map was generated
JigsawML ingested the public repository, resolved its dependencies, and grouped the code into architectural components using AI agents and a knowledge graph. Nothing here was drawn by hand. The same process runs against private repositories and cloud accounts, and re-runs on every commit so the map never goes stale.
Map your own codebaseOther open-source architectures
- Axios architecturePromise-based HTTP client for the browser and node.js
- Flask architectureThe Python micro framework for building web applications
- Hono architectureAn ultrafast web framework built on Web Standards
- Instro architectureAn open-source Python library for scripting and automating test equipment
- Nanoclaw architectureA personal AI agent which runs securely in containers
- Nemoclaw architectureA reference stack for running OpenClaw in OpenShell
- OpenClaw architectureA self-hosted gateway that connects chat apps to AI coding agents
- OpenHands architectureA self-hosted developer control center for coding agents and automations
- Pyramid architectureThe Start Small, Finish Big, Stay Finished Framework
- Zod architectureA TypeScript-first validation library
