pkonfig 2.2.0

Pythonic agile application configuration helpers

Configs for humans

PKonfig

Pragmatic, type-safe configuration for Python apps. PKonfig turns scattered env vars and config files into one clean, validated object with predictable precedence and great DX.

Why PKonfig

• Avoid config spaghetti: declare your config as code, not as ad‑hoc parsing scattered around the app.
• Fail fast: catch missing or invalid values at startup, not at 2 a.m. in production.
• Predictable precedence: layer env vars, .env, YAML/JSON/TOML/INI in the order you choose.
• Type-safe by default: fields convert and validate values; your IDE autocompletion just works.
• Fast and lightweight: minimal dependencies, small surface area, no magic.
• Framework-agnostic: works equally well in CLIs, services, scripts, and jobs.

What problems it solves

• “Where does this setting come from?” → Single source of truth with clear precedence.
• “Why did prod behave differently?” → Explicit, validated defaults and fail-fast checks.
• “Why is this a string not an int?” → Built-in casting and validation for common types.

Key features

• Typed, validated configuration objects
• Multiple sources (env vars, .env, YAML, JSON, TOML, INI) with flexible precedence
• Pydantic Settings integration through PKonfigBaseSettings
• Minimal dependencies, fail-fast checks, and great IDE autocompletion
• Extensible API with high performance
• List values parsing with validation

Quick start

from pkonfig.config import Config
from pkonfig.storage.env import Env
from pkonfig.storage.yaml_ import Yaml

class App(Config):
    host: str = "127.0.0.1"
    port: int = 8000
    debug = False

# Highest precedence first
cfg = App(
    Env(prefix="APP"),
    Yaml("config.yaml", missing_ok=True),
)

print(cfg.host, cfg.port, cfg.debug)
# Env example: APP_PORT=9000 python app.py → 9000 overrides file/defaults

Install

pip install pkonfig
# extras for file formats
pip install pkonfig[yaml]
pip install pkonfig[toml]
pip install pkonfig[pydantic]

pydantic-settings integration is available on Python 3.10+.

Pydantic Settings integration

from pydantic import BaseModel

from pkonfig import DictStorage
from pkonfig.pydantic_settings import PKonfigBaseSettings, pkonfig_settings_config


class Database(BaseModel):
    host: str
    port: int


class Settings(PKonfigBaseSettings):
    model_config = pkonfig_settings_config(
        DictStorage(database={"host": "db.internal", "port": "5432"}),
    )

    database: Database


settings = Settings()
assert settings.database.port == 5432

Documentation

• https://ngladkikh.github.io/pkonfig/
• For AI Agents and LLMs: https://ngladkikh.github.io/pkonfig/agents.html

Links

• PyPI: https://pypi.org/project/pkonfig/
• Source: https://github.com/ngladkikh/pkonfig
• License: MIT