Transports

Transports connect module streams across process boundaries and/or networks.

Module: a running component (e.g., camera, mapping, nav).
Stream: a unidirectional flow of messages owned by a module (one broadcaster → many receivers).
Topic: the name/identifier used by a transport or pubsub backend.
Message: payload carried on a stream (often dimos.msgs.*, but can be bytes / images / pointclouds / etc.).

Each edge in the graph is a transported stream (potentially different protocols). Each node is a module:

What the transport layer guarantees (and what it doesn’t)

Modules don’t know or care how data moves. They just:

emit messages (broadcast)
subscribe to messages (receive)

A transport is responsible for the mechanics of delivery (IPC, sockets, Redis, ROS 2, etc.).

Important: delivery semantics depend on the backend:

Some are best-effort (e.g., UDP multicast / LCM): loss can happen.
Some can be reliable (e.g., TCP-backed, Redis, some DDS configs) but may add latency/backpressure.

So: treat the API as uniform, but pick a backend whose semantics match the task.

Benchmarks

Quick view on performance of our pubsub backends:

python -m pytest -svm tool -k "not bytes" dimos/protocol/pubsub/benchmark/test_benchmark.py

Abstraction layers

Pikchr

color = white
fill = none
linewid = 0.5in
boxwid = 1.0in
boxht = 0.4in

# Boxes with labels
B: box "Blueprints" rad 10px
arrow
M: box "Modules" rad 5px
arrow
T: box "Transports" rad 5px
arrow
P: box "PubSub" rad 5px

# Descriptions below
text "robot configs" at B.s + (0.1, -0.2in)
text "camera, nav" at M.s + (0, -0.2in)
text "LCM, SHM, ROS" at T.s + (0, -0.2in)
text "pub/sub API" at P.s + (0, -0.2in)

We’ll go through these layers top-down.

Using transports with blueprints

See Blueprints for the blueprint API.

From unitree/go2/blueprints/smart/unitree_go2.py.

Example: rebind a few streams from the default LCMTransport to ROSTransport (defined at transport.py) so you can visualize in rviz2.

nav = autoconnect(
    basic,
    voxel_mapper(voxel_size=0.1),
    cost_mapper(),
    replanning_a_star_planner(),
    wavefront_frontier_explorer(),
).global_config(n_workers=6, robot_model="unitree_go2")

ros = nav.transports(
    {
        ("lidar", PointCloud2): ROSTransport("lidar", PointCloud2),
        ("global_map", PointCloud2): ROSTransport("global_map", PointCloud2),
        ("odom", PoseStamped): ROSTransport("odom", PoseStamped),
        ("color_image", Image): ROSTransport("color_image", Image),
    }
)

Using transports with modules

Each stream on a module can use a different transport. Set .transport on the stream before starting modules.

The runnable example below uses a tiny synthetic image publisher instead of CameraModule so it works without a webcam and in CI; the wiring is the same as with a real camera.

import time

import numpy as np
import reactivex as rx

from dimos.core.core import rpc
from dimos.core.coordination.module_coordinator import ModuleCoordinator
from dimos.core.module import Module, ModuleConfig
from dimos.core.stream import In, Out
from dimos.core.transport import LCMTransport
from dimos.msgs.sensor_msgs.Image import Image, ImageFormat


class TickerCameraConfig(ModuleConfig):
    frequency_hz: float = 2.0


class TickerCameraModule(Module):
    """Publish synthetic frames so this example runs without a webcam."""

    config: TickerCameraConfig
    color_image: Out[Image]

    @rpc
    def start(self) -> None:
        super().start()

        def emit(_: int) -> None:
            img = Image.from_numpy(
                np.zeros((480, 640, 3), dtype=np.uint8),
                format=ImageFormat.RGB,
                frame_id="synthetic",
            )
            self.color_image.publish(img)

        period = 1.0 / max(self.config.frequency_hz, 0.1)
        self.register_disposable(rx.interval(period).subscribe(emit))


class ImageListener(Module):
    image: In[Image]

    async def handle_image(self, img: Image) -> None:
        print(f"Received: {img.shape}")


if __name__ == "__main__":
    # Start local cluster and deploy modules to separate processes
    dimos = ModuleCoordinator()
    dimos.start()

    camera = dimos.deploy(TickerCameraModule, frequency_hz=2.0)
    listener = dimos.deploy(ImageListener)

    # Choose a transport for the stream (example: LCM typed channel)
    camera.color_image.transport = LCMTransport("/camera/rgb", Image)

    # Connect listener input to camera output
    listener.image.connect(camera.color_image)

    dimos.start_all_modules()

    time.sleep(2)
    dimos.stop()

02:57:31.428 [inf][ation/worker_manager_python.py] Worker pool started. n_workers=2
02:57:31.761 [inf][/coordination/python_worker.py] Deployed module. module=TickerCameraModule module_id=0 worker_id=0
02:57:31.768 [inf][/coordination/python_worker.py] Deployed module. module=ImageListener module_id=1 worker_id=1
02:57:33.778 [inf][dination/module_coordinator.py] Stopping module... module=ImageListener
02:57:33.793 [inf][dination/module_coordinator.py] Module stopped. module=ImageListener
02:57:33.793 [inf][dination/module_coordinator.py] Stopping module... module=TickerCameraModule
02:57:33.802 [inf][dination/module_coordinator.py] Module stopped. module=TickerCameraModule
02:57:33.802 [inf][ation/worker_manager_python.py] Shutting down all workers...
Received: (480, 640, 3)
Received: (480, 640, 3)
Received: (480, 640, 3)
02:57:33.803 [inf][/coordination/python_worker.py] Worker stopping module... module=ImageListener module_id=1 worker_id=1
02:57:33.803 [inf][/coordination/python_worker.py] Worker module stopped. module=ImageListener module_id=1 worker_id=1
02:57:33.861 [inf][/coordination/python_worker.py] Worker stopping module... module=TickerCameraModule module_id=0 worker_id=0
02:57:33.862 [inf][/coordination/python_worker.py] Worker module stopped. module=TickerCameraModule module_id=0 worker_id=0
02:57:33.892 [inf][ation/worker_manager_python.py] All workers shut down

See Modules for more on module architecture.

Inspecting LCM traffic (CLI)

lcmspy shows topic frequency/bandwidth stats:

dimos topic echo /topic listens on typed channels like /topic#pkg.Msg and decodes automatically:

Listening on /camera/rgb (inferring from typed LCM channels like '/camera/rgb#pkg.Msg')... (Ctrl+C to stop)
Image(shape=(480, 640, 3), format=RGB, dtype=uint8, dev=cpu, ts=2026-01-24 20:28:59)

Implementing a transport

At the stream layer, a transport is implemented by subclassing Transport (see core/stream.py) and implementing:

broadcast(...)
subscribe(...)

Your Transport.__init__ args can be anything meaningful for your backend:

(ip, port)
a shared-memory segment name
a filesystem path
a Redis channel

Encoding is an implementation detail, but we encourage using LCM-compatible message types when possible.

Encoding helpers

Many of our message types provide lcm_encode / lcm_decode for compact, language-agnostic binary encoding (often faster than pickle). For details, see LCM.

PubSub transports

Even though transport can be anything (TCP connection, unix socket) for now all our transport backends implement the PubSub interface.

publish(topic, message)
subscribe(topic, callback) -> unsubscribe

from dimos.protocol.pubsub.spec import PubSub
import inspect

print(inspect.getsource(PubSub.publish))
print(inspect.getsource(PubSub.subscribe))

    @abstractmethod
    def publish(self, topic: TopicT, message: MsgT) -> None:
        """Publish a message to a topic."""
        ...

    @abstractmethod
    def subscribe(
        self, topic: TopicT, callback: Callable[[MsgT, TopicT], None]
    ) -> Callable[[], None]:
        """Subscribe to a topic with a callback. returns unsubscribe function"""
        ...

Topic/message types are flexible: bytes, JSON, or our ROS-compatible LCM types. We also have pickle-based transports for arbitrary Python objects.

LCM (UDP multicast)

LCM is UDP multicast. It’s very fast on a robot LAN, but it’s best-effort (packets can drop). For local emission it autoconfigures system in a way in which it's more robust and faster then other more common protocols like ROS, DDS

from dimos.msgs.geometry_msgs.Vector3 import Vector3
from dimos.protocol.pubsub.impl.lcmpubsub import LCM, Topic

lcm = LCM()
lcm.start()

received = []
topic = Topic("/robot/velocity", Vector3)

lcm.subscribe(topic, lambda msg, t: received.append(msg))
lcm.publish(topic, Vector3(1.0, 0.0, 0.5))

import time
time.sleep(0.1)

print(f"Received velocity: x={received[0].x}, y={received[0].y}, z={received[0].z}")
lcm.stop()

Received velocity: x=1.0, y=0.0, z=0.5

Shared memory (IPC)

Shared memory is highest performance, but only works on the same machine.

from dimos.protocol.pubsub.impl.shmpubsub import PickleSharedMemory

shm = PickleSharedMemory(prefer="cpu")
shm.start()

received = []
shm.subscribe("test/topic", lambda msg, topic: received.append(msg))
shm.publish("test/topic", {"data": [1, 2, 3]})

import time
time.sleep(0.1)

print(f"Received: {received}")
shm.stop()

Received: [{'data': [1, 2, 3]}]

DDS Transport

For network communication, DDS uses the Data Distribution Service (DDS) protocol:

from dataclasses import dataclass
from cyclonedds.idl import IdlStruct

from dimos.protocol.pubsub.impl.ddspubsub import DDS, Topic

@dataclass
class SensorReading(IdlStruct):
    value: float

dds = DDS()
dds.start()

received = []
sensor_topic = Topic(name="sensors/temperature", data_type=SensorReading)

dds.subscribe(sensor_topic, lambda msg, t: received.append(msg))
dds.publish(sensor_topic, SensorReading(value=22.5))

import time
time.sleep(0.1)

print(f"Received: {received}")
dds.stop()

Received: [SensorReading(value=22.5)]

A minimal transport: `Memory`

The simplest toy backend is Memory (single process). Start from there when implementing a new pubsub backend.

from dimos.protocol.pubsub.impl.memory import Memory

bus = Memory()
received = []

unsubscribe = bus.subscribe("sensor/data", lambda msg, topic: received.append(msg))

bus.publish("sensor/data", {"temperature": 22.5})
bus.publish("sensor/data", {"temperature": 23.0})

print(f"Received {len(received)} messages:")
for msg in received:
    print(f"  {msg}")

unsubscribe()

Received 2 messages:
  {'temperature': 22.5}
  {'temperature': 23.0}

See pubsub/impl/memory.py for the complete source.

Encode/decode mixins

Transports often need to serialize messages before sending and deserialize after receiving.

PubSubEncoderMixin at pubsub/encoders.py provides a clean way to add encoding/decoding to any pubsub implementation.

Available mixins

Mixin	Encoding	Use case
`PickleEncoderMixin`	Python pickle	Any Python object, Python-only
`LCMEncoderMixin`	LCM binary	Cross-language (C/C++/Python/Go/…)
`JpegEncoderMixin`	JPEG compressed	Image data, reduces bandwidth

LCMEncoderMixin is especially useful: you can use LCM message definitions with any transport (not just UDP multicast). See LCM for details.

Creating a custom mixin

import json

from dimos.protocol.pubsub.encoders import PubSubEncoderMixin


class JsonEncoderMixin(PubSubEncoderMixin[str, dict, bytes]):
    def encode(self, msg: dict, topic: str) -> bytes:
        return json.dumps(msg).encode("utf-8")

    def decode(self, msg: bytes, topic: str) -> dict:
        return json.loads(msg.decode("utf-8"))

Combine with a pubsub implementation via multiple inheritance:

from dimos.protocol.pubsub.impl.memory import Memory


class MyJsonPubSub(JsonEncoderMixin, Memory):
    pass

Swap serialization by changing the mixin:

from dimos.protocol.pubsub.encoders import PickleEncoderMixin
from dimos.protocol.pubsub.impl.memory import Memory


class MyPicklePubSub(PickleEncoderMixin, Memory):
    pass

Testing and benchmarks

Spec tests

See pubsub/test_spec.py for the grid tests your new backend should pass.

Benchmarks

Add your backend to benchmarks to compare in context:

python -m pytest -svm tool -k "not bytes" dimos/protocol/pubsub/benchmark/test_benchmark.py

Available transports

Transport	Use case	Cross-process	Network	Notes
`Memory`	Testing only, single process	No	No	Minimal reference impl
`SharedMemory`	Multi-process on same machine	Yes	No	Highest throughput (IPC)
`LCM`	Robot LAN broadcast (UDP multicast)	Yes	Yes	Best-effort; can drop packets on LAN
`Redis`	Network pubsub via Redis server	Yes	Yes	Central broker; adds hop
`ROS`	ROS 2 topic communication	Yes	Yes	Integrates with RViz/ROS tools
`DDS`	Cyclone DDS without ROS (WIP)	Yes	Yes	WIP

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Transports

What the transport layer guarantees (and what it doesn’t)

Benchmarks

Abstraction layers

Using transports with blueprints

Using transports with modules

Inspecting LCM traffic (CLI)

Implementing a transport

Encoding helpers

PubSub transports

LCM (UDP multicast)

Shared memory (IPC)

DDS Transport

A minimal transport: `Memory`

Encode/decode mixins

Available mixins

Creating a custom mixin

Testing and benchmarks

Spec tests

Benchmarks

Available transports

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Transports

What the transport layer guarantees (and what it doesn’t)

Benchmarks

Abstraction layers

Using transports with blueprints

Using transports with modules

Inspecting LCM traffic (CLI)

Implementing a transport

Encoding helpers

PubSub transports

LCM (UDP multicast)

Shared memory (IPC)

DDS Transport

A minimal transport: Memory

Encode/decode mixins

Available mixins

Creating a custom mixin

Testing and benchmarks

Spec tests

Benchmarks

Available transports

A minimal transport: `Memory`