python-saga-orchestrator 0.1.4

Lightweight embedded saga orchestrator for asyncio Python services

python-saga-orchestrator

Lightweight embedded saga orchestration for asyncio Python services.

The library implements the Saga pattern for long-running business processes that:

• span multiple steps,
• call external systems,
• need retry and compensation,
• must survive worker crashes and process restarts.

Unlike external workflow platforms, this library runs inside your service and stores saga state in your application's database through SQLAlchemy.

What it provides

• typed step definitions with Pydantic models
• saga construction with SagaBuilder and StepRef
• persisted saga state through SagaStateMixin
• runtime execution through SagaOrchestrator and SagaEngine
• retry, timeout, recovery, and compensation
• async queue-style steps through StepAwaitEvent and notify(...)
• administrative operations through SagaAdmin
• PostgreSQL-first reliability using SELECT ... FOR UPDATE

Installation

Requirements:

• Python 3.12+
• PostgreSQL for production-grade execution semantics

Install from PyPI:

pip install python-saga-orchestrator

Or with uv:

uv pip install python-saga-orchestrator

Install from the repository source:

pip install .

For local development:

pip install '.[dev]'

Core concepts

BaseStep

Each saga step is a class with:

• execute(inp) -> out
• optional compensate(inp, out) -> None

Steps are regular Python objects. In practice they are created once at application startup and reused.

SagaBuilder

SagaBuilder creates an immutable SagaDefinition.
Each added step includes:

• the step object
• input_map
• optional timeout
• retry policy
• optional dependency on a previous step via StepRef

SagaStateMixin

Your SQLAlchemy model inherits SagaStateMixin to store:

• current status
• current step index
• execution token
• context
• step history
• deadline
• retry counter

SagaOrchestrator

SagaOrchestrator is the public runtime API used by application code:

• register(...)
• start(...)
• notify(...)
• run_due(...)
• get_snapshot(...)

SagaAdmin

SagaAdmin exposes operational controls:

• get_saga(...)
• retry_step(...)
• skip_step(...)
• compensate_step(...)
• abort(...)

Quick start

from datetime import timedelta

from pydantic import BaseModel
from sqlalchemy.ext.asyncio import async_sessionmaker
from sqlalchemy.orm import DeclarativeBase

from saga_orchestrator import (
    BaseStep,
    ExponentialRetry,
    SagaAdmin,
    SagaBuilder,
    SagaOrchestrator,
    SagaStateMixin,
)


class Base(DeclarativeBase):
    pass


class OrderSagaState(Base, SagaStateMixin):
    __tablename__ = "order_saga_state"


class ReserveInput(BaseModel):
    order_id: str


class ReserveOutput(BaseModel):
    reservation_id: str


class ChargeInput(BaseModel):
    reservation_id: str


class ChargeOutput(BaseModel):
    payment_id: str


class ReserveInventoryStep(BaseStep[ReserveInput, ReserveOutput]):
    async def execute(self, inp: ReserveInput) -> ReserveOutput:
        return ReserveOutput(reservation_id=f"res-{inp.order_id}")

    async def compensate(self, inp: ReserveInput, out: ReserveOutput) -> None:
        return None


class ChargePaymentStep(BaseStep[ChargeInput, ChargeOutput]):
    async def execute(self, inp: ChargeInput) -> ChargeOutput:
        return ChargeOutput(payment_id=f"pay-{inp.reservation_id}")


def build_order_saga():
    builder = SagaBuilder()

    reserve_ref = builder.add_step(
        step=ReserveInventoryStep(),
        input_map=lambda ctx: ReserveInput(order_id=ctx.initial_data["order_id"]),
    )

    builder.add_step(
        step=ChargePaymentStep(),
        depends_on=reserve_ref,
        input_map=lambda out: ChargeInput(reservation_id=out.reservation_id),
        retry_policy=ExponentialRetry(
            max_attempts=3,
            base_delay=timedelta(seconds=5),
        ),
    )

    return builder.build()


def setup_saga(
        session_maker: async_sessionmaker,
) -> tuple[SagaOrchestrator[OrderSagaState], SagaAdmin[OrderSagaState]]:
    orchestrator = SagaOrchestrator[OrderSagaState](
        model_class=OrderSagaState,
        session_maker=session_maker,
    )
    orchestrator.register("create_order_v1", build_order_saga())

    admin = SagaAdmin[OrderSagaState](engine=orchestrator.engine)
    return orchestrator, admin

Start a saga:

orchestrator, admin = setup_saga(session_maker)

saga_id = await orchestrator.start(
    saga_name="create_order_v1",
    initial_data={"order_id": "order-123"},
    aggregation_id="order-123",
)

Recovery model

The library persists enough state to recover work after failures:

• RUNNING sagas with expired execution leases can be reclaimed
• SUSPENDED sagas with expired retry deadlines can be resumed
• COMPENSATING sagas can continue rollback after a crash

The recovery entry point is:

await orchestrator.run_due(limit=100)

In production this should be called by a background worker or scheduled job.

Notifications and external events

Use notify(...) when a suspended saga should resume because of an external signal:

accepted = await orchestrator.notify(
    saga_id=saga_id,
    token=current_token,
    event={"approved": True},
)

Configure explicit event expectations through a public API:

token = await orchestrator.await_event(
    saga_id=saga_id,
    event=AwaitingEvent(
        event_type="model.approved",
        correlation_id="corr-123",
    ),
)

The event payload is stored in saga context and can be used by root-step input_map functions through InputContext.

For distributed consumers, use transactional inbox ingestion first, then process inbox rows:

stored = await orchestrator.ingest_event(
    aggregation_id="order-123",
    event={
        "event_id": "evt-123",
        "event_type": "payment.completed",
        "correlation_id": "corr-123",
        "payload": {"payment_id": "pay-1"},
    },
)

if stored:
    await orchestrator.run_inbox_due(limit=100)

Administrative operations

Get the full persisted state:

snapshot = await admin.get_saga(saga_id)
print(snapshot.status)
print(snapshot.step_history)

Retry the current failed step:

await admin.retry_step(saga_id)

Skip the current suspended step:

await admin.skip_step(
    saga_id,
    mock_output={"payment_id": "manual-payment"},
)

Start compensation manually:

await admin.compensate_step(saga_id)

Abort the saga:

await admin.abort(saga_id)

Persistence expectations

The library is PostgreSQL-first.

Important implementation details:

• state transitions are performed inside database transactions
• mutating reads use SELECT ... FOR UPDATE
• JSON state is stored in context and step_history
• step_execution_token is used to reject stale events and stale step completions

SQLite may be sufficient for local experiments, but PostgreSQL should be used for integration testing and production use.

Example workflow

A runnable end-to-end example is available in:

• examples/llm_deploy.py
• examples/retry_recovery.py
• examples/compensation_flow.py
• examples/admin_skip.py
• examples/http_and_queue.py

These examples demonstrate:

• basic model deployment
• retry and recovery through run_due()
• compensation after failure
• admin-driven step skipping

Running tests

Run unit tests:

pytest -q tests/unit

Run PostgreSQL integration tests:

export TEST_DATABASE_URL='postgresql+asyncpg://postgres:postgres@localhost:5432/saga_test_db'
pytest -q tests/integration

The integration fixture creates an isolated schema per test, so it does not require a dedicated empty database schema.

Current limitations

• the implementation is optimized for sequential saga execution, not parallel DAG execution
• PostgreSQL is the intended reliability target
• tracing integration is not implemented yet

License

MIT. See LICENSE.