casty

A minimalist, type-safe actor framework for Python 3.12+ with built-in distributed clustering.

Write an actor as a plain async def, send it typed messages, and run the same code in one process or on a cluster that places each actor on a node, replicates its state to a quorum and moves it as nodes come and go.

• Typed end to end. pyright checks every message, state and answer against the actor's annotation, and casty serializes them from it.
• Virtual actors. Nothing to spawn, supervise or stop: an actor is activated by its first message and deactivated when it goes idle.
• Replicated state. A write returns once a quorum of replicas confirms it, with the number of replicas and the write level chosen per actor type.
• No coordinator. Consistent hashing places keys, gossip keeps the membership, and keys move with their state when nodes join, leave or crash.
• Durable when needed. A type can also keep its state in a store, such as the SQLite one included.
• Schedules and collections. Timers that outlive the node running them, and replicated counters, registers, dicts, sets, multimaps, queues, semaphores, locks and barriers.
• Rust runtime. One compiled extension, no Python dependencies, free-threaded 3.14t included.

Contents

• Installation
• The actor model
• State
• Clustered actors
• Ask
• Awaiting coroutines and blocking I/O
• Schedulers
• Collections
• Failures
• Types
• Guarantees and limits
• Development
• License

Installation

uv add casty        # or: pip install casty

casty needs Python 3.12 or later and has no Python dependencies. Wheels cover every version with the GIL (cp312-abi3) and free-threaded 3.14t (cp314-cp314t); on a platform without one, the install builds from source and needs Rust 1.90 or later. examples has one runnable program per topic.

The actor model

An actor is a unit of computation that keeps its own state and talks to others only through messages. A few rules define it:

• Actors. An actor has an address, a mailbox and a behavior. In casty the behavior is an async def decorated with @actor, the actor type, and the address is that type plus a key, a string you choose, such as a user id. (account, "acc-1") and (account, "acc-2") are two actors running the same code, each with its own state and its own mailbox.
• Messages. Actors do not call each other or share memory. They send messages, immutable values queued in the mailbox of the receiver, and sending does not wait for the receiver.
• One message at a time. An actor handles its messages one by one, in the order they arrive. Nothing else runs on it meanwhile, so it needs no locks.
• Private state. Only the actor reads and writes its state; everything else reaches that state through messages to it.
• Location transparency. A reference to an actor names it, not where it runs. A message sent through it reaches the actor in this process or on any node of a cluster, and the code is the same.
• Virtual actors. casty never creates or destroys an actor. Every (type, key) can be addressed at any time: its first message activates it on the node that owns its key, and it is deactivated when it goes idle, with its state kept in between.
• Failures restart from state. When a body raises, casty restarts it from the last saved state, after a backoff. Local variables do not survive a restart, so what must survive goes in the state.

In code:

import asyncio
from dataclasses import dataclass

from casty import ActorSystem, Context, actor


@dataclass(frozen=True)
class Greet:
    name: str


@actor
async def greeter(ctx: Context[None, Greet]) -> None:
    async for msg in ctx.inbox:
        print(f"hello, {msg.name}, from {ctx.key}")


async def main() -> None:
    async with ActorSystem() as system:
        ana = system.ref(greeter, "ana")
        ana.tell(Greet("world"))  # hello, world, from ana
        ana.tell(Greet("again"))  # hello, again, from ana
        system.ref(greeter, "bia").tell(Greet("world"))  # hello, world, from bia
        await asyncio.sleep(1)  # tell does not wait: leaving the block stops the system and drops what is queued


asyncio.run(main())

• Context[None, Greet] declares the state of greeter, none so far, and the messages it takes, each a frozen dataclass. pyright checks every tell against it, and casty serializes every message, even within one process.
• system.ref(greeter, "ana") is a Ref, the address of an actor. A ref is a value: a message or a state can carry one.
• tell queues the message and returns. Delivery is at most once, and the messages one node sends to one key arrive in the order they were sent.
• The body takes the next message when it reads ctx.inbox again. The inbox ends after idle_after without messages (one minute by default), the body returns, and the actor is deactivated.

State

ctx.state holds the state of the actor, of the type S of Context[S, M]:

• ctx.state.value is the last saved state.
• await ctx.state.set(new) replaces it, and await ctx.state.update(change) saves what change makes of it and returns the result. Both return once the replicas confirm the write, and value changes only then.
• await ctx.state.delete() deletes it, and the key starts from its initial state again.

@actor(initial=0)
async def greeter(ctx: Context[int, Greet]) -> None:
    async for msg in ctx.inbox:
        count = await ctx.state.update(lambda count: count + 1)
        print(f"hello, {msg.name}! greeting #{count} from {ctx.key}")

A key nothing has written starts from the initial given to system.ref(actor, key, initial=...), otherwise from the initial of @actor, otherwise from None when the state type allows it. The state is serialized like the messages, so it is immutable: frozen dataclasses, tuples, Mappings and frozensets (see Types).

ctx.become(other, state) hands the key to another actor type that takes the same messages, so each state of a state machine can be a function of its own; the key and its refs stay the same (examples/01-state-machine).

The state lives in memory on its replicas. A type declared durable= is also kept by the store of the system, which every node reaches, so its keys survive the loss of every replica and a restart of the whole cluster:

from casty.sqlite import SQLiteStore


@actor(initial=0, durable="write")  # or a timedelta: saved at most that long after each write
async def account(ctx: Context[int, AccountMsg]) -> None: ...


async with SQLiteStore("accounts.db") as store, ActorSystem(cluster=cluster, store=store) as system: ...

A store is any object with async load, save and drop (casty.Store). SQLiteStore serves the nodes of one machine; src/casty/sqlite.py is the shape of one over a database that several machines reach. examples/11-durable-state stops a cluster and reads its keys back on new nodes.

Clustered actors

To run on a cluster, give the system a Cluster: the address it listens on and the nodes it joins through. The actors do not change.

from casty import Cluster

cluster = Cluster(bind="0.0.0.0:7400", advertise="10.0.0.5:7400", seeds=("10.0.0.4:7400",))
async with ActorSystem(cluster=cluster) as system:
    system.ref(greeter, "ana").tell(Greet("world"))  # printed on the node that runs ana

• Each key is placed on a node by consistent hashing, and every message for it is routed there, from whichever node sends it. There is no coordinator: every node computes the placement from its own view of the members, which gossip keeps up to date.
• A node that meets an actor type it does not know imports it by module:qualname. All nodes of a cluster run the same code, with actor types at module scope.
• Leaving async with normally is an orderly exit: the node finishes the messages in progress and hands its keys to the next replicas. A crash is detected by heartbeat, and its keys are activated elsewhere from their replicas.

Each key is kept by several nodes, as many as the actor type declares:

@actor(initial=Ledger(), replicas=3, write="majority")  # the defaults
async def ledger(ctx: Context[Ledger, LedgerMsg]) -> None: ...

A Client sends messages to a cluster without joining it. It hosts no actors and takes no part in membership, which fits web handlers, scripts and batch jobs; it can be answered, but not told.

async with Client(seeds=("10.0.0.4:7400",)) as client:
    client.ref(greeter, "ana").tell(Greet("client"))

@actor(pinned=True) runs each key on the node its ref names, system.ref(agent, "agent", at=member), with one copy, for an agent per node. Cluster also takes tls (mutual TLS against a CA), compression (zstd, lz4 or zlib) and limits (sizes of messages and frames, the same on every node). examples/06-distribution runs a cluster on one machine and shows keys moving as nodes join and leave.

Ask

tell does not wait for an answer. A message that has one is an Askable[R], R being the type of its answer; it carries a reply_to: Ref[R], and the actor answers by telling it:

@dataclass(frozen=True)
class Count(Askable[int]):
    pass


@actor(initial=0)
async def greeter(ctx: Context[int, Greet | Count]) -> None:
    async for msg in ctx.inbox:
        match msg:
            case Greet(name):
                count = await ctx.state.update(lambda count: count + 1)
                print(f"hello, {name}! greeting #{count} from {ctx.key}")
            case Count():
                msg.reply_to.tell(ctx.state.value)

ref.ask(msg) sends the message with a reply_to of its own and waits for what is told to it. pyright reads the type of the answer from the message:

async with ActorSystem() as system:
    ana = system.ref(greeter, "ana")
    ana.tell(Greet("world"))
    print(await ana.ask(Count()))  # 1

reply_to is keyword-only, so case Count() matches without it. ask raises TimeoutError after the ask_timeout of the type or of the system (10 seconds by default), and asyncio.timeout sets a shorter deadline.

ref.ask is how code outside the actors reads from them. A body that awaits it holds up its key until the answer arrives, and a cycle of such asks, a key asking itself or two keys asking each other, raises ReentrancyError at once. Between actors, ctx.ask(target, msg, mapper) gets the answer without holding up the key: it sends msg and returns, and the answer comes back to the actor as one more message, the one mapper makes of it.

@actor(initial=0)
async def teller(ctx: Context[int, Transfer | Withdrawn]) -> None:
    async for msg in ctx.inbox:
        match msg:
            case Transfer(source, amount):
                source_account = ctx.system.ref(account, source)
                ctx.ask(source_account, Withdraw(amount), lambda ok, transfer=msg: Withdrawn(transfer, ok))
            case Withdrawn(transfer, ok):
                ...

The actor goes on with its mailbox meanwhile, so what it needs when the answer arrives goes in the message: a lambda reads the variables of the loop when it runs, and transfer=msg binds the message in hand. failed= turns what the ask raised, such as a TimeoutError, into a message too.

Awaiting coroutines and blocking I/O

A body takes its next message when it reads ctx.inbox again, so what it waits for in between decides what waits with it. An await holds up the key: its messages queue until the coroutine returns, while the other keys of the node go on. A call that blocks, such as requests.get, time.sleep or a long computation, holds up the event loop, and with it every key of the node.

• Await the writes of the key: state.set, update and delete, become and schedule. The next message sees what they wrote.
• Pipe every other coroutine with ctx.to_self(work, mapper), or with ctx.ask for an ask: a call to another service, a gather of several asks. The body goes on with its mailbox, and the result comes back as a message.
• Run blocking calls in a thread with asyncio.to_thread, piped like any other coroutine. A computation in Python holds the GIL on a thread as well, so it goes to a ProcessPoolExecutor, or to a thread on free-threaded 3.14t.

@actor
async def profiles(ctx: Context[Profile | None, Refresh | Fetched | Resized]) -> None:
    async for msg in ctx.inbox:
        match msg:
            case Refresh():
                ctx.to_self(crm.fetch(ctx.key), Fetched)  # an async client: piped as it is
            case Fetched(profile):
                await ctx.state.set(profile)  # a write of the key: awaited
                ctx.to_self(asyncio.to_thread(resize, profile.avatar), Resized)  # a blocking call: piped from a thread
            case Resized(avatar):
                ...

A body does not have to wait for messages at all: it can read a stream or run a TaskGroup, and ctx.merge(source) interleaves its mailbox with any async iterable (examples/03-streams, examples/08-consumers).

Schedulers

ctx.schedule(name, delay, interval, message) tells the actor message after delay and then every interval, or once when interval is None. The schedule is saved with the state and sent only by the node where the actor is active, so a key with a schedule is a singleton of the cluster that goes on when its node dies:

@dataclass(frozen=True)
class Check:
    pass


@actor(initial=date(2026, 1, 1))  # the last day with a report
async def nightly(ctx: Context[date, Check]) -> None:
    await ctx.schedule("check", timedelta(0), timedelta(hours=1), Check())
    async for _ in ctx.inbox:
        today = datetime.now(UTC).date()
        if ctx.state.value < today:
            await ctx.state.set(today)
            await build_report(today)


system.ref(nightly, "report")  # creates the key and starts the body, once for the whole cluster

• Scheduling under a name in use replaces that schedule, so the body above schedules on every activation without adding a second one. ctx.schedules maps each name to its schedule, which cancel() stops.
• A key with schedules does not go idle. A schedule goes off only while its key is active: a key that is not wakes on a message or a ref, never on the clock.
• Times follow the wall clock, and a time missed by more than one interval is skipped. A change of owner can lose a time or send it twice, so the body checks its state, as nightly does with the last day built.
• Saving the day before building the report builds it at most once per day; building it first builds it at least once.

Collections

Named, replicated data structures built on actors, over an ActorSystem or a Client.

from casty import Collections
from casty.collections import MISSING

collections = Collections(system)

visits = collections.counter("visits")
await visits.add(5)

prices = collections.dict("prices", key=str, value=int)
await prices.put("book", 30)
await prices.get("pen") is MISSING  # True

async with collections.lock("report", ttl=60) as lease:
    lease.token  # fencing token, increasing with each acquisition

All take replicas (default 3). Data collections also take write (default "majority"); semaphores, locks and barriers always use "majority".

• An absent entry is MISSING, distinct from a stored None.
• The first operation fixes the configuration of a name, and other settings or types raise ConfigurationError. Keep one Collections per system.
• Mutating calls return after the state is saved. A call that failed or timed out may have committed, and is not retried.
• scan() reads a Dict, Set or MultiMap of any size one segment at a time; it is not a snapshot. size and clear are not atomic either.
• Semaphore and Lock grant a Lease with a TTL and a fencing token. Expiry uses the wall clocks of the nodes, which must agree, and a lease can expire under a slow holder, so the protected resource must reject tokens older than the newest it has seen.
• acquire and Barrier.wait wait indefinitely; bound them with asyncio.timeout.
• The collections are not durable.

Failures

For tell, the same situations drop the message and report a MessageDropped to the observer of the system, which by default logs it. ActorSystem(observer=...) takes any callable of a casty.Event, and system.stats() reads what the node counts.

Types

State, messages and answers are always serialized, even within one process. Types are checked when @actor runs, and an unsupported one raises SchemaError naming the field: state: Cart.items: list[int] is not supported; use tuple[int, ...].

• Supported: None, bool, int (64 bits), float, str, bytes, Literal, timezone-aware datetime and time, date, timedelta, Decimal, UUID, enums, paths, tuple, frozenset, Mapping, frozen dataclasses (generic ones too), unions, type aliases and Ref[T].
• Refused: list, dict, set, mutable dataclasses, plain classes, Any, object and Callable. Mutating the state in place would change it without replicating it; use tuple, Mapping and frozenset.
• Any other type travels as bytes through Annotated[T, Opaque(encode, decode)], which casty does not read.

Dataclasses are encoded by field name, so two versions of the code can share a cluster: a missing field takes its default, an unknown field is ignored, and a missing field without a default raises SchemaError. Moving or renaming an actor body renames its type, and the keys saved under the old name are no longer reached.

Guarantees and limits

• Delivery is at most once. Messages from one node to one key arrive in order while the owner of the key does not change.
• A confirmed write of the state is not lost while fewer replicas fail than the write level tolerates. A failed write may reappear, and Unavailable means the operation may or may not have happened.
• Two nodes may briefly run the same key during a change of owner, but with "majority" or "all" only one of them can save.
• Side effects in a body can repeat after a restart or a change of owner.
• Reads are not linearizable: the handover of a key relies on timing, not consensus, so a process paused long enough can answer from a stale state.
• State lives in memory unless its type is durable: losing every replica of a key of any other type loses the key. A write of a durable="write" type that returned survives a restart of the whole cluster.
• Leases and schedules follow the wall clocks of the nodes, which must agree.
• A key of a pinned type has one copy and is unavailable while its node is down.

Development

uv sync                              # builds the extension and installs the dev tools
uv sync --reinstall-package casty    # rebuilds the extension after a change to the Rust code
make check                           # what CI checks: ruff, rustfmt, clippy, pyright, both suites and the docs
make help                            # the other targets: the suite on 3.14t, the chaos run, benchmarks, wheels

The Python suite runs real systems over TCP on loopback, with crashes and partitions made by TCP proxies. CASTY_CHAOS=1 uv run pytest tests/chaos -s runs the chaos suite.

License

MIT, see LICENSE.