sayiir 1.0.0

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 separate services. Optional PostgreSQL for production persistence.
• 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

Inline lambdas — zero boilerplate

from sayiir import Flow, run_workflow

workflow = (
    Flow("pipeline")
    .then(lambda x: x * 2)
    .then(lambda x: x + 1)
    .then(lambda x: str(x))
    .build()
)
result = run_workflow(workflow, 5)
# "11"  (5 * 2 = 10, 10 + 1 = 11, str(11))

No decorators, no registration — just pass any callable. Use @task when you need metadata (retries, timeouts, tags) or explicit naming.

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="30s")
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

PostgreSQL persistence

from sayiir import task, Flow, PostgresBackend, run_durable_workflow

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

workflow = Flow("persistent").then(process).build()

# Auto-runs migrations on first connect
backend = PostgresBackend("postgresql://localhost/sayiir")
status = run_durable_workflow(workflow, "run-001", 21, backend=backend)

Retry policy

from sayiir import task, RetryPolicy

# Int shorthand (1s initial delay, 2x backoff)
@task(retries=3)
def flaky_call(url: str) -> dict:
    return requests.get(url).json()

# Full control
@task(retries=RetryPolicy(max_retries=3, initial_delay_secs=0.5, backoff_multiplier=2.0))
def precise_retry(url: str) -> dict:
    return requests.get(url).json()

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()
)

Loops

Repeat a task until it signals completion with LoopResult.done().

from sayiir import task, Flow, LoopResult, run_workflow

@task
def refine(draft: str) -> dict:
    improved = improve(draft)
    if is_good_enough(improved):
        return LoopResult.done(improved).to_dict()
    return LoopResult.again(improved).to_dict()

workflow = (
    Flow("iterative")
    .then(initial_draft)
    .loop(refine, max_iterations=5)
    .then(publish)
    .build()
)
result = run_workflow(workflow, "rough draft")

The body task returns LoopResult.again(value) to continue iterating or LoopResult.done(value) to exit. When max_iterations is reached, the default behavior is to fail; pass on_max="exit_with_last" to exit with the last value instead.

Task execution context

Access workflow and task metadata from within a running task using get_task_context().

from sayiir import task, get_task_context

@task(timeout="30s", tags=["io"])
def fetch_data(url: str) -> dict:
    ctx = get_task_context()
    if ctx is not None:
        print(f"Running task {ctx.task_id} in workflow {ctx.workflow_id}")
        print(f"Instance: {ctx.instance_id}")
        print(f"Timeout: {ctx.metadata.timeout_secs}s")
        print(f"Tags: {ctx.metadata.tags}")
        print(f"Workflow metadata: {ctx.workflow_metadata}")
    return do_fetch(url)

get_task_context() returns a TaskExecutionContext with workflow_id, instance_id, task_id, metadata (timeout, retries, tags, version, etc.), and workflow_metadata (the dict passed via Flow("name", metadata={...})), or None if called outside of a task execution.

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

Conditional branching

from sayiir import task, Flow, run_workflow

@task
def classify(ticket: dict) -> str:
    return "billing" if ticket["type"] == "invoice" else "tech"

@task
def handle_billing(ticket: dict) -> str:
    return f"Billing handled: {ticket['id']}"

@task
def handle_tech(ticket: dict) -> str:
    return f"Tech resolved: {ticket['id']}"

@task
def fallback(ticket: dict) -> str:
    return f"Routed to general: {ticket['id']}"

workflow = (
    Flow("support-router")
    .route(classify, keys=["billing", "tech"])
        .branch("billing", handle_billing)
        .branch("tech", handle_tech)
        .default_branch(fallback)
    .done()
    .build()
)
result = run_workflow(workflow, {"id": 1, "type": "invoice"})
# {"branch": "billing", "result": "Billing handled: 1"}

The key function returns a string routing key. The matching branch runs; if no match and no default, the workflow fails. The output is a BranchEnvelope with branch (the key) and result (the branch output).

Task metadata

@task(
    "Process Payment",
    timeout="60s",
    retries=3,
    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. Accepts a positional name string: @task("name"). Optional params: name, timeout (duration string or seconds), retries (int shorthand or RetryPolicy), tags, description.

Flow Builder

• Flow(name) — Create a new workflow builder.
• .then(task_fn, *, name=None) — Append a task to the workflow. Accepts @task-decorated functions, plain functions, or lambdas. Use name to set an explicit task ID.
• .loop(task_fn, *, max_iterations=10, on_max="fail", name=None) — Add a loop. Body returns LoopResult.again(value) or LoopResult.done(value).
• .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].
• .delay(name, duration) — Add a durable delay ("30s", "5m", "1h", seconds, or timedelta).
• .wait_for_signal(signal_name, *, timeout=None) — Wait for an external signal.
• .route(key_fn, *, keys=["a", "b"]) — Start conditional branching. Returns a BranchBuilder.
• BranchBuilder.branch(key, *tasks) — Add a named branch for a routing key.
• BranchBuilder.default_branch(*tasks) — Set the fallback branch for unmatched keys.
• BranchBuilder.done() — Finish branching and return to the Flow builder.
• .build() — Finalize and return a Workflow.

Task Context

• get_task_context() — Returns a TaskExecutionContext with workflow_id, instance_id, task_id, metadata, and workflow_metadata, or None outside of task execution.

Execution

• run_workflow(workflow, input, *, instance_id=None, backend=None) — Execute a workflow. Without instance_id, runs in-memory. With instance_id and backend, runs with full checkpointing (raises WorkflowError if the workflow doesn't complete). Returns the final output.
• run_durable_workflow(workflow, instance_id, input, backend=None) — Execute with checkpointing. Returns a WorkflowStatus.
• resume_workflow(workflow, instance_id, backend) — Resume a workflow from its last checkpoint.
• cancel_workflow(instance_id, backend, reason=None, cancelled_by=None) — Cancel a running workflow.
• pause_workflow(instance_id, backend, reason=None, paused_by=None) — Pause a running workflow.
• unpause_workflow(instance_id, backend) — Unpause a paused workflow.
• send_signal(instance_id, signal_name, payload, backend) — Send an external signal.

WorkflowStatus

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

Retry

• RetryPolicy(max_retries=2, initial_delay_secs=1.0, backoff_multiplier=2.0) — Exponential backoff retry policy for tasks.

Loop Control

• LoopResult.again(value) — Continue iterating with a new value.
• LoopResult.done(value) — Exit the loop with a final value.
• OnMax.FAIL / OnMax.EXIT_WITH_LAST — Policy when max iterations is reached.

Backends

• InMemoryBackend() — In-memory storage for development and testing (default).
• PostgresBackend(url) — PostgreSQL persistence. Auto-runs migrations on first connect.

WorkflowClient (distributed)

• WorkflowClient(backend, *, conflict_policy=None) — Client for submitting and controlling workflow instances without executing tasks. Used with Worker for the distributed model.
• .submit(workflow, instance_id, input) — Submit a workflow for execution. Returns a WorkflowStatus.
• .cancel(instance_id, *, reason=None, cancelled_by=None) — Cancel a workflow instance.
• .pause(instance_id, *, reason=None, paused_by=None) — Pause a workflow instance.
• .unpause(instance_id) — Unpause a paused workflow.
• .send_signal(instance_id, signal_name, payload) — Send an external signal.
• .status(instance_id) — Get the current status. Returns a WorkflowStatus.

Architecture

graph LR
    A["Your Python code<br/><b>@task</b> functions"] -->|input| B["Sayiir · Rust<br/>Orchestration<br/>Checkpointing<br/>Crash recovery<br/>Fork/join/branch<br/>Loops &amp; routing<br/>Serialization"]
    B -->|checkpoint<br/>after each task| C["Storage"]
    C -->|resume| B
    B -->|output| A

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

• Documentation
• Examples
• GitHub
• Discord
• Roadmap

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