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msghandler - Cross-Platform Bi-Directional Data Bridge

A high-performance, bi-directional data bridge for Julia, JavaScript, Python, and MicroPython applications using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.

License: MIT NATS

Table of Contents

Quick Data Format Reference

All platforms use the same input format for smartpack():

Format: [(dataname, data, type), ...]

Element Type Description
dataname String Name for the payload (e.g., "message", "config.json")
data Any The actual data (type depends on the type parameter below)
type String Payload type identifier

Supported Payload Types

Type Julia Data JavaScript Data Python Data Description
"text" String string str Plain text
"dictionary" Dict Object dict JSON object
"arrowtable" DataFrame ❌ pandas.DataFrame Arrow IPC table (Desktop only)
"jsontable" DataFrame Array<Object> list[dict] JSON table
"image" Vector{UInt8} Uint8Array bytes Image data
"audio" Vector{UInt8} Uint8Array bytes Audio data
"video" Vector{UInt8} Uint8Array bytes Video data
"binary" Vector{UInt8} Uint8Array bytes Binary data

Examples

Sending multiple payloads:

# Julia
data = [
    ("message", "Hello!", "text"),
    ("config", Dict("key" => "value"), "dictionary"),
    ("file", file_bytes, "binary")
]
env, json_str = msghandler.smartpack("subject", data, options...)

# Python
data = [
    ("message", "Hello!", "text"),
    ("config", {"key": "value"}, "dictionary"),
    ("file", file_bytes, "binary")
]
env, json_str = await smartpack("subject", data, **options)

// JavaScript
const data = [
    ["message", "Hello!", "text"],
    ["config", {key: "value"}, "dictionary"],
    ["file", fileBytes, "binary"]
];
const [env, json_str] = await smartpack("subject", data, options);

Important Notes:

  • Always wrap payloads in a list [], even for single payloads
  • Type must be one of the supported types above
  • Large payloads (≥500KB) automatically use link transport
  • Browser only supports: "text", "dictionary", "jsontable", "image", "audio", "video", "binary" (No Arrow IPC due to browser incompatibility)

Overview

msghandler enables seamless communication across multiple platforms through NATS, with intelligent transport selection based on payload size:

Transport Payload Size Method
Direct < 500KB Sent directly via NATS (Base64 encoded)
Link ≥ 500KB Uploaded to HTTP file server, URL sent via NATS

Use Cases

  • Chat Applications: Text, images, audio, video in a single message
  • File Transfer: Efficient transfer of large files using claim-check pattern
  • IoT/Embedded: Sensor data, telemetry, and analytics pipelines (MicroPython)
  • Cross-Platform Communication: Interoperability between Julia, JavaScript, Python, and MicroPython systems

Cross-Platform Support

Platform Implementation Features
Julia src/msghandler.jl Full feature set, Arrow IPC, multiple dispatch
JavaScript (Node.js) src/msghandler_ssr.js Node.js, async/await, Arrow IPC
JavaScript (Browser) src/msghandler_csr.js Browser, WebSocket NATS, async/await, JSON table only
Python src/msghandler.py Desktop Python, asyncio, type hints, Arrow IPC
MicroPython src/msghandler_mpy.py Memory-constrained, synchronous API

Platform Comparison

Feature Julia JavaScript JavaScript (Browser) Python MicroPython
Multiple Dispatch ✅ Native ❌ ❌ ❌ ❌
Async/Await ❌ ✅ Native ✅ Native ✅ Native ⚠️ (uasyncio)
Type Safety ✅ Strong ⚠️ (TypeScript) ⚠️ (TypeScript) ✅ (Type hints) ❌
Arrow IPC ✅ Native ✅ Native ❌ (Browser incompatible) ✅ Native ❌
JSON Table ✅ ✅ ✅ (Only table type) ✅ ⚠️ (Limited)
Direct Transport ✅ ✅ ✅ ✅ ✅
Link Transport ✅ ✅ ✅ ✅ ⚠️ (Limited)
Handler Functions ✅ ✅ ✅ ✅ ✅
Cross-Platform API ✅ ✅ ✅ ✅ ✅
WebSocket NATS ❌ ❌ ✅ ❌ ❌

Features

  • ✅ Cross-platform messaging for Julia, JavaScript, Python, and MicroPython applications
  • ✅ Bi-directional messaging with request-reply patterns
  • ✅ Multi-payload support - send multiple payloads with different types in one message
  • ✅ Automatic transport selection - direct vs link based on payload size
  • ✅ Claim-Check pattern for payloads ≥ 500KB
  • ✅ Apache Arrow IPC support for tabular data (Desktop: Julia/Python/Node.js)
  • ✅ JSON Table support for tabular data (All platforms including Browser)
  • ✅ Exponential backoff for reliable file server downloads
  • ✅ Correlation ID tracking for message tracing
  • ✅ Reply-to support for request-response patterns
  • ✅ Handler function abstraction - pluggable file server implementations (Plik, AWS S3, custom)

Quick Start

Prerequisites

  1. NATS Server - Install and run a NATS server:

    docker run -p 4222:4222 nats:latest

  2. HTTP File Server (optional, for large payloads) - Install and run a file server:

    # Using Plik
docker run -p 8080:8080 -v /tmp/fileserver:/var/lib/plik -e PLIK_ADMIN_PASSWORD=admin plik/plik

# OR using simple Python HTTP server
mkdir -p /tmp/fileserver
python3 -m http.server 8080 --directory /tmp/fileserver

Send Your First Message

Julia

using msghandler, NATS

# Data format: [(dataname, data, type), ...]
# Each tuple contains:
#   1. dataname: String name for the payload (e.g., "message")
#   2. data: The actual data (String, bytes, DataFrame, etc.)
#   3. type: String type indicator (e.g., "text", "binary", "dictionary")
#      Supported types: "text", "dictionary", "arrowtable", "jsontable", 
#                       "image", "audio", "video", "binary"

payload_1 = ("test_message", "Hello World", "text")

file_path_large_image = "./test/large_image.png"
file_data_large_image = read(file_path_large_image)
filename_large_image = basename(file_path_large_image)
payload_2 = (filename_large_image, file_data_large_image, "binary")

payloads = [payload_1, payload_2]  # List of tuples
envelope, envelope_json_str = msghandler.smartpack("test.topic", 
                                                  payloads; 
                                                  broker_url="nats.yiem.cc",
                                                  fileserver_url="http://192.168.88.104:8080")
conn = NATS.connect("nats.yiem.cc")  # Create NATS connection
NATS.publish(conn, "test.topic", envelope_json_str)  # Publish message to NATS
NATS.drain(conn)

JavaScript (Node.js)

import msghandler from './src/msghandler_ssr.js';

const data = [["message", "Hello World", "text"]];
const [env, env_json_str] = await msghandler.smartpack(
    "/chat/room1",
    data,
    { broker_url: "nats://localhost:4222" }
);
console.log("Message sent!");

JavaScript (Browser)

import msghandler from './src/msghandler_csr.js';

const data = [["message", "Hello World", "text"]];
const [env, env_json_str] = await msghandler.smartpack(
    "/chat/room1",
    data,
    { broker_url: "ws://localhost:4222" }
);
console.log("Message sent!");

Python

from msghandler import smartpack

# Data format: [(dataname, data, type), ...]
# Each tuple contains:
#   1. dataname: String name for the payload (e.g., "message")
#   2. data: The actual data (str, bytes, dict, list, etc.)
#   3. type: String type indicator (e.g., "text", "binary", "dictionary")
#      Supported types: "text", "dictionary", "jsontable", "image", "audio", "video", "binary"

data = [("message", "Hello World", "text")]
env, env_json_str = await smartpack(
    "/chat/room1",
    data,
    broker_url="nats://localhost:4222"
)
print("Message sent!")

MicroPython

from msghandler import smartpack

data = [("message", "Hello World", "text")]
env, env_json_str = smartpack(
    "/chat/room1",
    data,
    broker_url="nats://localhost:4222",
    size_threshold=100000
)
print("Message sent!")

Receive Your First Message

Julia

using msghandler, NATS

conn = NATS.connect("nats.yiem.cc")
NATS.subscribe(conn, "test.topic") do msg
    println("Received message on $(msg.subject)")
    envelope_json_str = String(msg.payload)
    
    envelope = msghandler.smartunpack(
        envelope_json_str;
        max_retries = 5,
        base_delay = 100,
        max_delay = 5000
    )
    println(envelope.payloads[1])
end

API Reference

Unified API Standard

All platforms use the same input/output format for payloads.

Input Format for smartpack

Format: [(dataname1, data1, type1), (dataname2, data2, type2), ...]

Each tuple contains 3 elements:

  1. dataname (String) - Name for the payload (e.g., "message", "config.json", "avatar.png")
  2. data (Any) - The actual data to send (type depends on type):
    • text → String
    • dictionary → Dict/Object
    • arrowtable → DataFrame/Arrow.Table
    • jsontable → List of dicts/Vector{Dict}
    • image/audio/video/binary → bytes/Uint8Array/Vector{UInt8}
  3. type (String) - Payload type (required):
    • "text" - Plain text string
    • "dictionary" - JSON-serializable dictionary
    • "arrowtable" - Apache Arrow IPC table (Desktop only)
    • "jsontable" - JSON table (all platforms)
    • "image" - Image data (PNG, JPG)
    • "audio" - Audio data (WAV, MP3)
    • "video" - Video data (MP4, AVI)
    • "binary" - Generic binary data

Important:

  • Always wrap payloads in a list, even for single payloads
  • Type must be one of the supported types above
  • Large payloads (≥500KB by default) automatically use link transport

Example:

# Julia
data = [
    ("message", "Hello World", "text"),
    ("config", Dict("key" => "value"), "dictionary"),
    ("file", file_bytes, "binary")
]

# Python
data = [
    ("message", "Hello World", "text"),
    ("config", {"key": "value"}, "dictionary"),
    ("file", file_bytes, "binary")
]

// JavaScript
const data = [
    ["message", "Hello World", "text"],
    ["config", {key: "value"}, "dictionary"],
    ["file", fileBytes, "binary"]
];

Common Payload Examples

Use Case Data Format Type
Simple text message ("message", "Hello World", "text") "text"
Configuration JSON ("config", Dict("key" => "value"), "dictionary") "dictionary"
Table as Arrow ("data", dataframe, "arrowtable") "arrowtable"
Table as JSON ("data", [{"col": 1}], "jsontable") "jsontable"
Image file ("avatar.png", read("file.png"), "image") "image"
Audio file ("audio.mp3", audio_bytes, "audio") "audio"
Video file ("video.mp4", video_bytes, "video") "video"
Binary file ("data.bin", file_bytes, "binary") "binary"

Output Format for smartunpack

{
  "correlation_id": "...",
  "msg_id": "...",
  "timestamp": "...",
  "send_to": "...",
  "msg_purpose": "...",
  "sender_name": "...",
  "sender_id": "...",
  "receiver_name": "...",
  "receiver_id": "...",
  "reply_to": "...",
  "reply_to_msg_id": "...",
  "broker_url": "...",
  "metadata": {...},
  "payloads": [(dataname1, data1, type1), (dataname2, data2, type2), ...]
}

smartpack

Sends data either directly via NATS or via a fileserver URL, depending on payload size.

Julia

using msghandler

env, env_json_str = msghandler.smartpack(
    subject::String,
    data::AbstractArray{Tuple{String, Any, String}};
    broker_url::String = "nats://localhost:4222",
    fileserver_url = "http://localhost:8080",
    fileserver_upload_handler::Function = plik_oneshot_upload,
    size_threshold::Int = 500_000,
    correlation_id::String = string(uuid4()),
    msg_purpose::String = "chat",
    sender_name::String = "msghandler",
    receiver_name::String = "",
    receiver_id::String = "",
    reply_to::String = "",
    reply_to_msg_id::String = "",
    is_publish::Bool = true,
    NATS_connection::Union{NATS.Connection, Nothing} = nothing,
    msg_id::String = string(uuid4()),
    sender_id::String = string(uuid4())
)
# Returns: ::Tuple{msg_envelope_v1, String}

JavaScript (Node.js)

import msghandler from './src/msghandler_ssr.js';

const [env, env_json_str] = await msghandler.smartpack(
    subject,
    data,  // Array of [dataname, data, type] tuples
    {
        broker_url: 'nats://localhost:4222',
        fileserver_url: 'http://localhost:8080',
        fileserver_upload_handler: msghandler.plikOneshotUpload,
        size_threshold: 500_000,
        correlation_id: uuidv4(),
        msg_purpose: 'chat',
        sender_name: 'msghandler',
        receiver_name: '',
        receiver_id: '',
        reply_to: '',
        reply_to_msg_id: '',
        is_publish: true,
        nats_connection: null,
        msg_id: uuidv4(),
        sender_id: uuidv4()
    }
);
// Returns: Promise<[env, env_json_str]>

JavaScript (Browser)

import msghandler from './src/msghandler_csr.js';

const [env, env_json_str] = await msghandler.smartpack(
    subject,
    data,
    {
        broker_url: 'ws://localhost:4222',
        fileserver_url: 'http://localhost:8080',
        fileserver_upload_handler: msghandler.plikOneshotUpload,
        size_threshold: 500_000,
        correlation_id: uuidv4(),
        msg_purpose: 'chat',
        sender_name: 'msghandler',
        receiver_name: '',
        receiver_id: '',
        reply_to: '',
        reply_to_msg_id: '',
        is_publish: true,
        nats_connection: null,
        msg_id: uuidv4(),
        sender_id: uuidv4()
    }
);
// Returns: Promise<[env, env_json_str]>

Python

from msghandler import msghandler

env, env_json_str = await msghandler.smartpack(
    subject: str,
    data: List[Tuple[str, Any, str]],
    broker_url: str = "nats://localhost:4222",
    fileserver_url: str = "http://localhost:8080",
    fileserver_upload_handler: Callable = plik_oneshot_upload,
    size_threshold: int = 500_000,
    correlation_id: str = None,
    msg_purpose: str = "chat",
    sender_name: str = "msghandler",
    receiver_name: str = "",
    receiver_id: str = "",
    reply_to: str = "",
    reply_to_msg_id: str = "",
    is_publish: bool = True,
    nats_connection: Any = None,
    msg_id: str = None,
    sender_id: str = None
)
# Returns: Tuple[Dict, str]

MicroPython

from msghandler import smartpack

# Limited to direct transport (< 100KB threshold)
env, env_json_str = smartpack(
    "/device/config",
    [("config", config, "dictionary")],
    broker_url="nats://localhost:4222",
    size_threshold=100000
)
print("Message sent!")

smartunpack

Receives and processes messages from NATS, handling both direct and link transport.

Julia

using msghandler

env = msghandler.smartunpack(
    msg::NATS.Msg;
    fileserver_download_handler::Function = _fetch_with_backoff,
    max_retries::Int = 5,
    base_delay::Int = 100,
    max_delay::Int = 5000
)
# Returns: ::JSON.Object{String, Any}

JavaScript (Node.js)

import msghandler from './src/msghandler_ssr.js';

const env = await msghandler.smartunpack(
    msg,
    {
        fileserver_download_handler: msghandler.fetchWithBackoff,
        max_retries: 5,
        base_delay: 100,
        max_delay: 5000
    }
);
// Returns: Promise<env_object>

JavaScript (Browser)

import msghandler from './src/msghandler_csr.js';

const env = await msghandler.smartunpack(
    msg,
    {
        fileserver_download_handler: msghandler.fetchWithBackoff,
        max_retries: 5,
        base_delay: 100,
        max_delay: 5000
    }
);
// Returns: Promise<env_object>

Python

from msghandler import msghandler

env = await msghandler.smartunpack(
    msg,
    fileserver_download_handler=fetch_with_backoff,
    max_retries=5,
    base_delay=100,
    max_delay=5000
)
# Returns: Dict with "payloads" key

MicroPython

from msghandler import smartunpack

env = smartunpack(
    msg,
    fileserver_download_handler=_sync_fileserver_download,
    max_retries=3,
    base_delay=100,
    max_delay=1000
)
print("Received payloads:", env["payloads"])

Cross-Platform Examples

Example 1: Chat with Mixed Content

Send text, image, and large file in one message.

Julia

using msghandler

data = [
    ("message_text", "Hello!", "text"),
    ("user_avatar", image_data, "image"),
    ("large_document", large_file_data, "binary")
]

env, env_json_str = smartpack("/chat/room1", data; fileserver_url="http://localhost:8080")

JavaScript (Node.js)

import msghandler from './src/msghandler_ssr.js';

const data = [
    ["message_text", "Hello!", "text"],
    ["user_avatar", imageData, "image"],
    ["large_document", largeFileData, "binary"]
];

const [env, env_json_str] = await msghandler.smartpack(
    "/chat/room1",
    data,
    { fileserver_url: 'http://localhost:8080' }
);

JavaScript (Browser)

import msghandler from './src/msghandler_csr.js';

const data = [
    ["message_text", "Hello!", "text"],
    ["user_avatar", image_data, "image"]
];

const [env, env_json_str] = await msghandler.smartpack(
    "/chat/room1",
    data,
    { broker_url: 'ws://localhost:4222' }
);
console.log("Message sent!");

Python

import asyncio
from msghandler import smartpack

async def main():
    image_data = open("user_avatar.png", "rb").read()
    large_file_data = open("large_document.pdf", "rb").read()

    data = [
        ("message_text", "Hello!", "text"),
        ("user_avatar", image_data, "image"),
        ("large_document", large_file_data, "binary")
    ]

    env, env_json_str = await smartpack(
        "/chat/room1",
        data,
        fileserver_url="http://localhost:8080"
    )
    print("Message sent!")

if __name__ == "__main__":
    asyncio.run(main())

MicroPython

from msghandler import smartpack

# Note: MicroPython only supports direct transport (< 100KB threshold)
# Large files must be uploaded via file server first
data = [
    ("message_text", "Hello!", "text"),
    ("user_avatar", image_bytes, "image")
]

env, env_json_str = smartpack(
    "/chat/room1",
    data,
    broker_url="nats://localhost:4222",
    size_threshold=100000
)
print("Message sent!")

Example 2: Dictionary Exchange

Send configuration data between platforms.

Julia

using msghandler

config = Dict(
    "wifi_ssid" => "MyNetwork",
    "wifi_password" => "password123",
    "update_interval" => 60
)

data = [("config", config, "dictionary")]
env, env_json_str = smartpack("/device/config", data)

JavaScript (Node.js)

import msghandler from './src/msghandler_ssr.js';

const config = {
    wifi_ssid: "MyNetwork",
    wifi_password: "password123",
    update_interval: 60
};

const [env, env_json_str] = await msghandler.smartpack(
    "/device/config",
    [["config", config, "dictionary"]]
);
console.log("Message sent!");

Python

import asyncio
from msghandler import smartpack

async def main():
    config = {
        "wifi_ssid": "MyNetwork",
        "wifi_password": "password123",
        "update_interval": 60
    }

    data = [("config", config, "dictionary")]
    env, env_json_str = await smartpack("/device/config", data)
    print("Message sent!")

if __name__ == "__main__":
    asyncio.run(main())

MicroPython

from msghandler import smartpack

config = {
    "wifi_ssid": "MyNetwork",
    "wifi_password": "password123",
    "update_interval": 60
}

data = [("config", config, "dictionary")]
env, env_json_str = smartpack(
    "/device/config",
    data,
    broker_url="nats://localhost:4222",
    size_threshold=100000
)
print("Message sent!")

Example 3: Table Data (Arrow IPC)

Send tabular data using Apache Arrow IPC format.

Julia

using msghandler
using DataFrames

df = DataFrame(
    id = [1, 2, 3],
    name = ["Alice", "Bob", "Charlie"],
    score = [95, 88, 92]
)

data = [("students", df, "arrowtable")]
env, env_json_str = smartpack("/data/analysis", data)

JavaScript (Node.js)

import msghandler from './src/msghandler_ssr.js';

const df = [
    { id: 1, name: "Alice", score: 95 },
    { id: 2, name: "Bob", score: 88 },
    { id: 3, name: "Charlie", score: 92 }
];

const [env, env_json_str] = await msghandler.smartpack(
    "/data/analysis",
    [["students", df, "arrowtable"]]
);

Python

from msghandler import msghandler
import pandas as pd

df = pd.DataFrame({
    "id": [1, 2, 3],
    "name": ["Alice", "Bob", "Charlie"],
    "score": [95, 88, 92]
})

data = [("students", df, "arrowtable")]
env, env_json_str = await msghandler.smartpack("/data/analysis", data)

JavaScript (Browser)

import msghandler from './src/msghandler_csr.js';

const df = [
    { id: 1, name: "Alice", score: 95 },
    { id: 2, name: "Bob", score: 88 },
    { id: 3, name: "Charlie", score: 92 }
];

const [env, env_json_str] = await msghandler.smartpack(
    "/data/analysis",
    [["students", df, "jsontable"]],
    { broker_url: 'ws://localhost:4222' }
);
console.log("Message sent!");

MicroPython

from msghandler import smartpack

# Note: MicroPython only supports direct transport (< 100KB threshold)
# MicroPython doesn't support Arrow IPC, only jsontable
df = [
    {"id": 1, "name": "Alice", "score": 95},
    {"id": 2, "name": "Bob", "score": 88},
    {"id": 3, "name": "Charlie", "score": 92}
]

data = [("students", df, "jsontable")]
env, env_json_str = smartpack(
    "/data/analysis",
    data,
    broker_url="nats://localhost:4222",
    size_threshold=100000
)
print("Message sent!")

Example 4: Request-Response Pattern

Bi-directional communication with reply-to support.

Julia

using msghandler, NATS

# Requester
env, env_json_str = msghandler.smartpack(
    "/device/command",
    [("command", Dict("action" => "read_sensor"), "dictionary")];
    broker_url="nats://localhost:4222",
    reply_to="/device/response"
)

conn = NATS.connect("nats://localhost:4222")
NATS.publish(conn, "/device/command", env_json_str)
NATS.drain(conn)

# Receiver (in separate application)
conn = NATS.connect("nats://localhost:4222")
NATS.subscribe(conn, "/device/command") do msg
    env = msghandler.smartunpack(msg)
    println("Received command: ", env["payloads"])
    
    result = Dict("value" => 42)
    response_env, response_json = msghandler.smartpack(
        "/device/response",
        [("result", result, "dictionary")],
        reply_to="/device/command",
        reply_to_msg_id=env["msg_id"]
    )
    
    NATS.publish(conn, "/device/response", response_json)
    NATS.drain(conn)
end

JavaScript (Node.js)

import msghandler from './src/msghandler_ssr.js';
import { connect } from 'nats';

// Requester
const [env, env_json_str] = await msghandler.smartpack(
    "/device/command",
    [["command", { action: "read_sensor" }, "dictionary"]],
    { broker_url: 'nats://localhost:4222', reply_to: '/device/response' }
);
const nc = await connect({ port: 4222 });
nc.publish("/device/command", env_json_str);
await nc.flush();

// Receiver (in separate application)
const nc = await connect({ port: 4222 });
const sub = nc.subscribe("/device/command");
for await (const msg of sub) {
    const env = await msghandler.smartunpack(msg);
    console.log("Received command:", env.payloads);
    
    const response_env, response_json = await msghandler.smartpack(
        "/device/response",
        [["result", { value: 42 }, "dictionary"]],
        { reply_to: '/device/command', reply_to_msg_id: env.msg_id }
    );
    
    nc.publish("/device/response", response_json);
    await nc.flush();
}

Python

import asyncio
from msghandler import smartpack

async def main():
    # Requester
    env, env_json_str = await smartpack(
        "/device/command",
        [("command", {"action": "read_sensor"}, "dictionary")],
        broker_url="nats://localhost:4222",
        reply_to="/device/response"
    )
    print("Request sent!")
    
    # Receiver (in separate application)
    # await nats_consumer.next()
    # env = await smartunpack(msg)
    # Process request and send response
    # response_env, response_json = await smartpack(
    #     "/device/response",
    #     [("result", {"value": 42}, "dictionary")],
    #     reply_to="/device/command",
    #     reply_to_msg_id=env["msg_id"]
    # )

if __name__ == "__main__":
    asyncio.run(main())

JavaScript (Browser)

import msghandler from './src/msghandler_csr.js';

// Requester
const [env, env_json_str] = await msghandler.smartpack(
    "/device/command",
    [["command", { action: "read_sensor" }, "dictionary"]],
    { broker_url: 'ws://localhost:4222', reply_to: '/device/response' }
);
console.log("Request sent!");

// Receiver (in separate application)
// const msg = await natsConsumer.next();
// const env = await msghandler.smartunpack(msg);
// console.log("Received command:", env.payloads);
// const response_env, response_json = await msghandler.smartpack(
//     "/device/response",
//     [["result", { value: 42 }, "dictionary"]],
//     { reply_to: '/device/command', reply_to_msg_id: env.msg_id }
// );

MicroPython

from msghandler import smartpack

# Requester
env, env_json_str = smartpack(
    "/device/command",
    [("command", {"action": "read_sensor"}, "dictionary")],
    broker_url="nats://localhost:4222",
    reply_to="/device/response",
    size_threshold=100000
)
print("Request sent!")

Testing

Test File Organization

Platform Sender Tests Receiver Tests
Julia test/test_julia_*_sender.jl test/test_julia_*_receiver.jl
JavaScript test/test_js_*_sender.js test/test_js_*_receiver.js
Python test/test_py_*_sender.py test/test_py_*_receiver.py

Run Tests

Julia

# Text message exchange
julia test/test_julia_text_sender.jl
julia test/test_julia_text_receiver.jl

# Dictionary exchange
julia test/test_julia_dict_sender.jl
julia test/test_julia_dict_receiver.jl

# File transfer
julia test/test_julia_file_sender.jl
julia test/test_julia_file_receiver.jl

# Mixed payload types
julia test/test_julia_mix_payloads_sender.jl
julia test/test_julia_mix_payloads_receiver.jl

# Table exchange
julia test/test_julia_table_sender.jl
julia test/test_julia_table_receiver.jl

JavaScript (Node.js)

# Text message exchange
node test/test_js_text_sender.js
node test/test_js_text_receiver.js

# Dictionary exchange
node test/test_js_dictionary_sender.js
node test/test_js_dictionary_receiver.js

# Binary transfer
node test/test_js_binary_sender.js
node test/test_js_binary_receiver.js

# Table exchange
node test/test_js_table_sender.js
node test/test_js_table_receiver.js

Python

# Text message exchange
python3 test/test_py_text_sender.py
python3 test/test_py_text_receiver.py

# Dictionary exchange
python3 test/test_py_dictionary_sender.py
python3 test/test_py_dictionary_receiver.py

# Binary transfer
python3 test/test_py_binary_sender.py
python3 test/test_py_binary_receiver.py

# Table exchange
python3 test/test_py_table_sender.py
python3 test/test_py_table_receiver.py

Browser Deployment

Using with Node.js Build Tools

The browser implementation (src/msghandler_csr.js) can be bundled for production deployment using modern JavaScript build tools.

Prerequisites

# Install the browser-compatible NATS client
npm install nats.ws

Vite (Recommended)

npm create vite@latest my-app -- --template vanilla
cd my-app
npm install nats.ws

In vite.config.js:

import { defineConfig } from 'vite';
export default defineConfig({
  resolve: {
    alias: {
      'nats.ws': 'nats.ws/dist/esm/browser.js'
    }
  }
});

Build command:

npm run build  # Outputs to dist/ folder

Webpack

npm install webpack webpack-cli --save-dev
npm install nats.ws

In webpack.config.js:

module.exports = {
  entry: './src/index.js',
  output: {
    filename: 'bundle.js',
    path: __dirname + '/dist'
  },
  resolve: {
    alias: {
      'nats.ws': 'nats.ws/dist/esm/browser.js'
    }
  }
};

Build command:

npx webpack

esbuild (Simple & Fast)

npm install esbuild nats.ws --save-dev

Create build.js:

import esbuild from 'esbuild';

esbuild.buildSync({
  entryPoints: ['src/msghandler_csr.js'],
  bundle: true,
  outfile: 'dist/msghandler-csr-bundle.js',
  format: 'esm',
  platform: 'browser',
  target: 'es2020'
});

Build command:

node build.js

Using in Your HTML

<!DOCTYPE html>
<html>
<head>
  <title>My App</title>
</head>
<body>
  <script type="module" src="dist/msghandler-csr-bundle.js"></script>
  <script type="module">
    import msghandlerCSR from './dist/msghandler-csr-bundle.js';
    
    // Use the library
    const [env, envJson] = await msghandlerCSR.smartpack(
      "/chat/user/v1/message",
      [["msg", "Hello", "text"]],
      { broker_url: "wss://nats.example.com" }
    );
  </script>
</body>
</html>

Documentation

For detailed architecture and implementation information, see:

License

MIT License

Copyright (c) 2026 msghandler Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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