pydantic-settings
Configuration management library for Python using Pydantic models. pydantic-settings loads configuration from environment variables, .env files, AWS Secrets Manager, Azure Key Vault, and other sources — automatically parsing and validating values with Pydantic's type system. Define a Settings class once and get type-safe configuration with validation, documentation, and source priority out of the box. The standard for 12-factor Python applications.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
SecretStr type prevents secret values from appearing in logs/repr. .env files should be in .gitignore — pydantic-settings doesn't enforce this. AWS/Azure integration uses cloud provider auth.
⚡ Reliability
Best When
You're already using Pydantic and need 12-factor app configuration — pydantic-settings' BaseSettings provides type-validated, documented configuration with zero boilerplate.
Avoid When
You don't use Pydantic and don't want to add it as a dependency — python-decouple or dynaconf are lighter alternatives.
Use Cases
- • Load and validate agent service configuration from environment variables with automatic type coercion (DATABASE_URL, PORT, REDIS_URL)
- • Manage agent configuration across environments (dev/staging/prod) with .env file support and environment variable overrides
- • Validate required settings at startup — catch missing environment variables with descriptive errors before agents fail mid-execution
- • Load secrets from AWS Secrets Manager or Azure Key Vault into Pydantic settings models for agent secure configuration
- • Document agent configuration requirements as self-describing Pydantic models with field descriptions and validators
Not For
- • Non-Pydantic stacks — use dynaconf or python-decouple for configuration management without Pydantic dependency
- • Applications with very complex configuration hierarchy — Hydra or OmegaConf are better for deeply nested, composable config
- • Configuration that changes at runtime — pydantic-settings loads once at startup; use a dynamic config service for runtime-changing config
Interface
Authentication
Library — no external auth. Optional: AWS/Azure integrations for Secrets Manager use cloud provider auth.
Pricing
MIT-licensed open source, part of the Pydantic ecosystem by Samuel Colvin.
Agent Metadata
Known Gotchas
- ⚠ Environment variable names are case-insensitive by default on most systems but BaseSettings matches uppercase by default — set env_prefix or case_sensitive=True explicitly for deterministic behavior
- ⚠ Nested settings require model_config = ConfigDict(env_nested_delimiter='__') — without the delimiter, nested Pydantic models in BaseSettings are not populated from env vars
- ⚠ Secret fields (SecretStr type) are redacted in __repr__ and logs — accessing the actual value requires .get_secret_value() which is easy to forget in agent debugging
- ⚠ pydantic-settings v2 dropped Settings class in favor of BaseSettings with model_config — v1 to v2 migration requires updating all class Config inner classes to model_config
- ⚠ Loading from multiple .env files requires explicit env_file parameter with tuple of paths — later files override earlier files; ordering matters for environment-specific overrides
- ⚠ Field aliases don't affect environment variable name lookup by default — use validation_alias or set env_names explicitly for fields where the env var name differs from the field name
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Scores are editorial opinions as of 2026-03-07.