sayiir 0.1.0a1

Durable workflow engine with Rust core and Python bindings — checkpointing, fork/join, distributed workers

Sayiir

Durable workflows for Python, powered by a Rust runtime.

Write plain Python functions. Sayiir makes them durable — automatic checkpointing, crash recovery, and parallel execution with zero infrastructure.

from sayiir import task, Flow, run_workflow

@task
def fetch_user(user_id: int) -> dict:
    return {"id": user_id, "name": "Alice"}

@task
def send_email(user: dict) -> str:
    return f"Sent welcome to {user['name']}"

workflow = Flow("welcome").then(fetch_user).then(send_email).build()
result = run_workflow(workflow, 42)
# "Sent welcome to Alice"

No DSL. No YAML. No determinism constraints. No infrastructure to deploy.

Why Sayiir?

• No replay, no determinism rules — Unlike Temporal, Restate, and other replay-based engines, Sayiir checkpoints after each task and resumes from the last checkpoint. Your tasks can call any API, use any library, read the clock, generate random values. No restrictions.
• A library, not a platform — pip install sayiir and write workflows. No server cluster, no database requirement, no separate services.
• Rust core — All orchestration, checkpointing, and execution runs in Rust via PyO3. You write Python; Rust handles the hard parts.
• Pydantic integration — Automatic input validation and output serialization for BaseModel types.
• Type-safe — Full type stubs (.pyi) and PEP 561 py.typed marker. Works with mypy and pyright.

Installation

pip install sayiir

From source (development):

git clone https://github.com/sayiir/sayiir.git
cd sayiir/sayiir-python
pip install -e ".[dev]"

Requires a Rust toolchain (rustup) for building from source.

Quickstart

Sequential workflow

from sayiir import task, Flow, run_workflow

@task
def double(x: int) -> int:
    return x * 2

@task
def add_ten(x: int) -> int:
    return x + 10

workflow = Flow("math").then(double).then(add_ten).build()
result = run_workflow(workflow, 5)
# 20  (5 * 2 = 10, 10 + 10 = 20)

Durable workflow (survives crashes)

from sayiir import task, Flow, run_durable_workflow

@task(timeout_secs=30)
def process_order(order_id: int) -> dict:
    return {"order_id": order_id, "status": "processed"}

@task
def send_confirmation(order: dict) -> str:
    return f"Confirmed order {order['order_id']}"

workflow = Flow("order").then(process_order).then(send_confirmation).build()

# Checkpoints after each task — resumes from last checkpoint on crash
status = run_durable_workflow(workflow, "order-123", 42)
print(status.output)           # "Confirmed order 42"
print(status.is_completed())   # True

Parallel execution (fork/join)

from sayiir import task, Flow, run_workflow

@task
def validate_payment(order: dict) -> dict:
    return {"payment": "valid"}

@task
def check_inventory(order: dict) -> dict:
    return {"stock": "available"}

@task
def finalize(results: dict) -> str:
    return f"Order complete: {results}"

workflow = (
    Flow("checkout")
    .fork()
        .branch(validate_payment)
        .branch(check_inventory)
    .join(finalize)
    .build()
)
result = run_workflow(workflow, {"order_id": 1})

Multi-step branches

workflow = (
    Flow("pipeline")
    .fork()
        .branch(fetch_data, transform, validate)  # 3-step branch
        .branch(fetch_metadata)                    # 1-step branch
    .join(merge_results)
    .build()
)

Pydantic integration

from pydantic import BaseModel
from sayiir import task, Flow, run_workflow

class OrderInput(BaseModel):
    order_id: int
    amount: float

class OrderResult(BaseModel):
    status: str
    message: str

@task
def process(order: OrderInput) -> OrderResult:
    return OrderResult(status="ok", message=f"Processed ${order.amount}")

workflow = Flow("typed").then(process).build()
result = run_workflow(workflow, {"order_id": 1, "amount": 99.99})
# Automatic validation on input, serialization on output

Task metadata

@task(
    name="Process Payment",
    timeout_secs=60,
    tags=["payments", "critical"],
    description="Charges the customer's payment method",
)
def process_payment(order: dict) -> dict:
    ...

API Reference

Decorators

• @task — Mark a function as a workflow task. Optional params: name, timeout_secs, tags, description.

Flow Builder

• Flow(name) — Create a new workflow builder.
• .then(task_fn) — Append a task to the workflow.
• .fork() — Start parallel branches. Returns a ForkBuilder.
• .branch(task_fn, ...) — Add a branch (one or more chained tasks).
• .join(task_fn) — Merge parallel branches. Join function receives dict[str, value].
• .build() — Finalize and return a Workflow.

Execution

• run_workflow(workflow, input) — Execute a workflow in-memory. Returns the final output.
• run_durable_workflow(workflow, instance_id, input, backend=None) — Execute with checkpointing. Returns a WorkflowStatus.

WorkflowStatus

• .output — The final output value (if completed).
• .status — "completed", "failed", "cancelled", or "in_progress".
• .is_completed() / .is_failed() / .is_cancelled() — Status checks.
• .error — Error message (if failed).
• .reason / .cancelled_by — Cancellation details.

Backends

• InMemoryBackend() — In-memory storage for development and testing (default).

Architecture

Your Python code          Sayiir (Rust)              Storage
┌──────────────┐    ┌─────────────────────┐    ┌──────────────┐
│  @task       │───>│  Orchestration      │───>│  Checkpoint  │
│  functions   │    │  Checkpointing      │    │  after each  │
│              │<───│  Crash recovery     │<───│  task        │
└──────────────┘    │  Fork/join          │    └──────────────┘
                    │  Serialization      │
                    └─────────────────────┘

Python provides task implementations. Rust handles everything else: building the execution graph, running tasks in order, checkpointing results, recovering from crashes, and managing parallel branches.

The project follows hexagonal architecture — the core domain has zero infrastructure dependencies, all dependencies flow inward, and every integration point (storage, serialization, execution) is a swappable trait-based adapter.

Requirements

• Python 3.10+
• Optional: pydantic >= 2.0 for automatic model validation

License

MIT

Links

• GitHub
• Discord
• Roadmap