pydantic-avro
Converting pydantic classes to avro schemas
Decision gist · record as of 2026-08-14
Yes. Low install friction, active maintenance, no known vulnerabilities, and a permissive MIT license make it a safe choice. Install it if you need bidirectional conversion between pydantic and Avro—either to export pydantic models for Avro systems or to generate pydantic classes from Avro schemas.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a single runtime dependency on pydantic.
- Actively maintained with recent commits; last release 189 days ago with an active repository.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects without licensing concerns.
last release 2026-02-06 (189 days) · last repo commit 2026-07-30 · 85 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,401,151 downloads/mo, #3,080 on PyPI
Alternatives
Verify before relying
pip install pydantic-avro
from pydantic_avro.base import AvroBase
class TestModel(AvroBase):
key1: str
key2: int
schema_dict = TestModel.avro_schema()
print(schema_dict)- Whether the package handles complex nested pydantic models and all Avro logical types beyond the examples shown
- Performance characteristics when converting large or deeply nested schemas
- Compatibility guarantees with different pydantic versions beyond the implicit dependency
What it is and what it does
pydantic-avro bridges pydantic data models and Apache Avro schemas. It lets you define a pydantic class that inherits from AvroBase, then export it as an Avro schema dictionary for serialization or integration with Avro-based systems. It also works in reverse: you can provide an Avro schema file and generate a pydantic class from it via a command-line tool. The library supports explicit Avro type specification through pydantic Field annotations, so you can control how Python types map to Avro types when the default inference isn't what you need.
The package is lightweight, depending only on pydantic itself. It's useful in data pipelines, message brokers, and systems that need both pydantic's validation and Avro's schema portability. The CLI tool makes it easy to generate boilerplate code from existing Avro schemas without manual translation.
Use it for
- Generate Avro schemas from pydantic models for use in Kafka producers or other Avro-based message systems
- Convert existing Avro schema files into pydantic classes to maintain type safety in Python applications
- Explicitly map Python datetime fields to Avro timestamp types or other non-default Avro representations
- Automate schema generation in data pipelines where both validation and schema registry integration are needed
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Low install friction, active maintenance, no known vulnerabilities, and a permissive MIT license make it a safe choice. Install it if you need bidirectional conversion between pydantic and Avro—either to export pydantic models for Avro systems or to generate pydantic classes from Avro schemas.
Install
pydantic-avro on PyPI
Before you install
Low install friction with a single runtime dependency on pydantic. Actively maintained with recent commits; last release 189 days ago with an active repository.
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most projects without licensing concerns.
Quickstart
pip install pydantic-avro
from pydantic_avro.base import AvroBase
class TestModel(AvroBase):
key1: str
key2: int
schema_dict = TestModel.avro_schema()
print(schema_dict)Verify before relying
- Whether the package handles complex nested pydantic models and all Avro logical types beyond the examples shown
- Performance characteristics when converting large or deeply nested schemas
- Compatibility guarantees with different pydantic versions beyond the implicit dependency
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.8.1 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepydantic |
| Maintenance | Actively maintained 189 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,401,151 / month, #3,080 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.9 |
Evidence: pydantic_avro-0.10.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “pydantic to avro schema conversion”
- pydantic-avroConverts between pydantic class definitions and Apache Avro schemas…
- dataclasses-avroschemaGenerates Avro schemas from Python dataclasses, Pydantic models, and…
- py-avro-schemaGenerates Apache Avro schemas from Python dataclasses and Pydantic…
Give your agent the search over MCP, or paste the wish link into any chat.
More Application Frameworks packages
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Textual is a Python framework for building cross-platform user interfaces that run in the terminal or web browser using a modern, component-based API.
Install it if you're developing CLI tools, dashboards, or interactive terminal applications.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Build and connect to Model Context Protocol servers that expose tools, resources, and prompts to LLM applications over stdio, HTTP, or SSE transports.
Install it if you need to build or connect to servers.
Werkzeug is a WSGI utility library providing request/response objects, URL routing, an interactive debugger, HTTP utilities, and a development server for building web applications.
See also py-avro-schema · pydantic-spark · dataclasses-avroschema · avro-gen · avro-gen3 · avro-python3 · avro-validator · jsonschema-pydantic · dydantic · avro