Configuration

ConfigDict is Pydantic's ConfigDict with four extra keys. Because it subclasses the original, every standard Pydantic setting keeps working next to the JWT ones:

from pydantic_jwt import ConfigDict, JWTModel


class AccessToken(JWTModel):
    model_config = ConfigDict(
        # JWT keys
        algorithm="HS256",
        encoding_key=SECRET,
        decoding_key=SECRET,
        require_keys=True,
        # ordinary Pydantic keys
        extra="forbid",
        frozen=True,
        populate_by_name=True,
    )

    sub: str

The keys

Key	Type	Default	Meaning
algorithm	str	—	Algorithm used to sign and verify, e.g. "HS256". Passed straight to PyJWT.
encoding_key	str \| None	—	Key used by generate() and str().
decoding_key	str \| None	—	Key used to verify incoming tokens.
require_keys	bool	True	Whether a missing key is an error. False accepts tokens without verifying the signature.

ConfigDict is total=False, so any subset is valid — including none of them, for a model that only ever receives keys per call.

All four are plain TypedDict keys, which means a typo like algorythm= is caught by mypy rather than silently ignored at runtime.

Symmetric keys

For the HMAC family (HS256, HS384, HS512) the same secret signs and verifies:

import os

SECRET = os.environ["JWT_SECRET"]


class AccessToken(JWTModel):
    model_config = ConfigDict(algorithm="HS256", encoding_key=SECRET, decoding_key=SECRET)

    sub: str

Use at least 32 bytes of entropy — secrets.token_hex(32) — or PyJWT warns with InsecureKeyLengthWarning.

Asymmetric algorithms

RS*, ES*, PS* and EdDSA need PyJWT's cryptography backend (pip install "pyjwt[crypto]"). The keys are PEM strings: the private key signs, the public key verifies.

The issuing service holds both, or just the private key:

class AccessToken(JWTModel):
    model_config = ConfigDict(
        algorithm="RS256",
        encoding_key=PRIVATE_KEY_PEM,
        decoding_key=PUBLIC_KEY_PEM,
    )

    sub: str
    exp: Exp = after(minutes=15)

A consuming service that only ever reads tokens configures the public key and nothing else. It cannot mint tokens, and generate() on it raises jwt_missing_key — which is exactly the desired failure:

class IncomingToken(JWTModel):
    model_config = ConfigDict(algorithm="RS256", decoding_key=PUBLIC_KEY_PEM)

    sub: str
    exp: Exp

Per-call keys

Keys do not have to live in model_config. Both directions accept them per call, which covers key rotation and multi-tenant setups where the key depends on the request.

Signing — generate():

raw = token.generate(encoding_key=current_key, algorithm="HS256")

Verifying — from_token():

token = AccessToken.from_token(raw, decoding_key=current_key, algorithm="HS256")

Verifying through model_validate() — the same values, passed in the validation context:

token = AccessToken.model_validate(
    raw,
    context={"decoding_key": current_key, "algorithm": "HS256", "require_keys": True},
)

The context form is the one to use when the token is a field of a larger model, since there is no from_token() call to pass arguments to. Only str values for decoding_key/algorithm and a bool for require_keys are read; anything else in the context is ignored by the signature check (but still reaches your own validators, and validate_claims is read by the claim validators).

Rotating a signing key

Sign with the newest key, accept any key still in the rotation window:

from pydantic import ValidationError
from pydantic_core import PydanticCustomError

KEYS = {"2024-06": OLD_SECRET, "2024-09": NEW_SECRET}
CURRENT = "2024-09"


def issue(sub: str) -> str:
    return AccessToken(sub=sub).generate(encoding_key=KEYS[CURRENT], algorithm="HS256")


def read(raw: str) -> AccessToken:
    for key in KEYS.values():
        try:
            return AccessToken.from_token(raw, decoding_key=key, algorithm="HS256")
        except (ValidationError, PydanticCustomError):
            continue
    raise ValueError("no configured key verifies this token")

Once every token signed with OLD_SECRET has expired, drop it from KEYS.

Retry only on signature failures

The loop above retries on any error, so an expired token is tried against every key before failing. To keep the diagnostics sharp, catch jwt_invalid_signature specifically — see Validation and errors.

require_keys

When a model has no decoding_key/algorithm and none is supplied per call, require_keys decides what happens:

• True (the default) — verification is impossible, so reading the token fails with jwt_missing_key.
• False — the signature is not checked. The claims are still validated, a warning is logged to the pydantic_jwt.base logger, and the model is returned.

class UnverifiedToken(JWTModel):
    model_config = ConfigDict(require_keys=False)

    sub: str
    exp: Exp


UnverifiedToken.from_token(anything_at_all)
# WARNING pydantic_jwt.base: JWT token approved without signature verification

This is for tests, local development and one-off inspection scripts. In an application it turns your auth layer into a formality — anybody can forge a token. See Security notes.

Note that require_keys only matters when a key is missing. With a key configured, the signature is always checked, whatever require_keys says.

Reading configuration at runtime

model_config is a plain dict, so the effective values are readable — handy in tests and health checks:

AccessToken.model_config["algorithm"]  #> 'HS256'
AccessToken.model_config.get("require_keys", True)  #> True

API reference

Full definition: ConfigDict.