pynode-flow 0.2.0

Python based Node-RED-like visual workflow system

PyNode - Visual Workflow System

A Node-RED-like visual workflow editor with a Python backend. Create workflows by connecting Python nodes that process and route messages.

https://github.com/user-attachments/assets/0b53085a-2cc6-4c26-bd43-e0de1e0716a2

Features

• Visual Node Editor: Drag-and-drop interface for creating workflows
• Python Backend: All nodes are Python classes that can be easily extended
• Fully Extensible: Third-party nodes can be added without modifying core code
• UI Components: Nodes can define interactive controls (buttons, toggles, displays) in their cards
• Node-RED Compatible Messages: Message structure with payload and topic fields
• Built-in Nodes:
  • InjectNode: Generate messages with configurable payloads
  • FunctionNode: Execute custom Python code on messages
  • DebugNode: Display messages in the debug panel
  • ChangeNode: Modify message properties
  • SwitchNode: Route messages based on conditions
  • DelayNode: Delay message delivery
  • GateNode: Control message flow with real-time toggle
  • RateProbeNode: Monitor message throughput
  • QueueLengthProbeNode: Monitor queue lengths
  • CounterNode: Count messages
  • MQTTNode: MQTT communication
  • MessageWriterNode: Save message data
  • VideoWriterNode: Save video output
  • Vision Nodes: Camera input, YOLO detection, tracking, image processing, display, and file output
• REST API: Complete API for programmatic workflow management
• Export/Import: Save and load workflows as JSON
• Dynamic Properties: Node properties and UI components defined in node classes

Quick Start

Install from PyPI (recommended for users)

PyNode is published on PyPI as pynode-flow (the import package stays pynode):

# Core install
pip install pynode-flow

# ...or everything PyPI-installable (all optional nodes)
pip install "pynode-flow[full]"

# Run it
pynode

See INSTALL.md for the full list of extras (vision, mqtt, camera, inference, vlm, upload, discovery, full) and how to install per-node dependencies with pynode-install-nodes.

Install from source (for development)

Clone the repository:

git clone https://github.com/olkham/pynode.git
cd pynode

Option 1: Automated Setup (Recommended)

The setup scripts will create a virtual environment, detect CUDA if available, install PyTorch with appropriate GPU support, and install all dependencies.

Windows:

# Use Python from PATH
setup.bat

# Or specify Python path
setup.bat "C:\Python312\python.exe"

Linux/Mac:

chmod +x setup.sh
./setup.sh

The scripts will:

• Create a virtual environment in appenv/
• Detect CUDA version and install matching PyTorch build
• Install CPU-only PyTorch if CUDA is not detected
• Install all required dependencies
• Optionally install node-specific dependencies

Activate the environment:

• Windows: appenv\Scripts\activate.bat
• Linux/Mac: source appenv/bin/activate

Option 2: Manual Installation

If you prefer manual installation or have specific requirements:

# Core install (extras optional — see INSTALL.md)
pip install -e .

# With optional extras: specific node groups...
pip install -e ".[vision,mqtt]"

# ...or everything PyPI-installable
pip install -e ".[full]"

pip install only pulls the dependencies and extras declared in pyproject.toml; it does not run each node's requirements.txt. The [full] extra covers every PyPI-installable node. For the few nodes that need a vendor SDK (e.g. Omron's stapipy), run pynode-install-nodes after installing.

Run the Server

pynode
# or
python -m pynode

Open Your Browser

Navigate to http://localhost:5000

Data Directory

PyNode persists workflows under <data dir>/workflows/ (workflow.json plus timestamped backups in _backups/). The data directory is resolved in this order:

1. pynode --data-dir <path> CLI flag,
2. PYNODE_DATA_DIR environment variable,
3. the source checkout root when running from a git clone / editable install (i.e. pyproject.toml sits next to the pynode package — this keeps the familiar workflows/ folder in the repo),
4. ~/.pynode otherwise (e.g. a regular pip install).

The resolved location is logged at startup (Workflow data directory: ...).

Securing PyNode

PyNode executes arbitrary Python by design (e.g. FunctionNode runs whatever code is in the workflow), so anyone who can reach the API can run code on the host. Authentication is the trust boundary — secure the server before exposing it beyond your own machine:

• API key: start with pynode --api-key <secret> (or set the PYNODE_API_KEY env var). All /api/ requests then require the key via the X-API-Key header or an api_key query parameter; the web UI prompts for it on first load and remembers it in the browser. Unset/empty = no authentication (the default).
• CORS: restrict allowed browser origins with pynode --cors-origins http://localhost:5000,https://myhost (or the PYNODE_CORS_ORIGINS env var). Default is * (all origins).
• Bind locally: when you don't need network access, run pynode --host 127.0.0.1 so the server is only reachable from the local machine.

Docker Setup

PyNode can be run in a Docker container with GPU support (CUDA 12.6).

Running with Docker Compose

For mDNS service discovery to work correctly inside Docker, set the HOST_IP environment variable to your host machine's IP address.

# Set the host IP address
export HOST_IP=$(hostname -I | awk '{print $1}')

# Start the container
docker compose up -d

The container will:

• Use NVIDIA CUDA 12.6 runtime (requires nvidia-docker)
• Install PyTorch with CUDA 12.6 support
• Install all dependencies including node-specific packages
• Expose port 5000 for web interface
• Support mDNS broadcasting with the correct host IP

Why set HOST_IP?
When using the mDNS Broadcast Node inside Docker, it needs to advertise the host machine's IP address rather than the container's internal IP so other devices on your network can discover and connect to the service.

Access the application:

• Web UI: http://localhost:5000
• From other devices: http://<your-host-ip>:5000

GPU Access:
The Docker setup requires NVIDIA Container Toolkit to be installed on the host system.

For more details, see DOCKER.md.

Extending PyNode

PyNode is designed to be easily extended with custom nodes:

• Creating Custom Nodes - Complete guide to creating your own nodes
• UI Components Guide - Add interactive controls to your nodes
• Extensibility Overview - Architecture and design principles

Project Structure

pynode/                     # Project root
├── pynode/                 # Main package
│   ├── __init__.py
│   ├── __main__.py         # Entry point for 'python -m pynode'
│   ├── _version.py         # Version (generated by setuptools_scm at build)
│   ├── main.py             # CLI application
│   ├── server.py           # Flask REST API with SSE support
│   ├── workflow_engine.py  # Workflow management
│   ├── models/             # ML model storage
│   ├── nodes/              # Node implementations (each in its own folder)
│   │   ├── __init__.py
│   │   ├── base_node.py    # BaseNode class
│   │   ├── InjectNode/     # Generate messages
│   │   ├── FunctionNode/   # Custom Python code
│   │   ├── DebugNode/      # Debug output
│   │   ├── ChangeNode/     # Modify messages
│   │   ├── SwitchNode/     # Route based on conditions
│   │   ├── DelayNode/      # Delay messages
│   │   ├── GateNode/       # Control message flow
│   │   ├── RateProbeNode/  # Monitor throughput
│   │   ├── QueueLengthProbeNode/  # Monitor queue lengths
│   │   ├── CounterNode/    # Count messages
│   │   ├── CameraNode/     # Camera input
│   │   ├── UltralyticsNode/ # YOLO detection
│   │   ├── ImageViewerNode/ # Display images
│   │   ├── ImageWriterNode/ # Save images
│   │   ├── TrackerNode/    # Object tracking
│   │   ├── MQTTNode/       # MQTT communication
│   │   ├── MessageWriterNode/ # Save message data
│   │   ├── VideoWriterNode/   # Save video
│   │   ├── OpenCV/         # OpenCV operations
│   │   └── ...             # 30+ other node types
│   └── static/             # Web UI
│       ├── index.html
│       ├── style.css
│       ├── js/             # JavaScript modules
│       │   ├── nodes.js
│       │   ├── events.js
│       │   ├── connections.js
│       │   ├── debug.js
│       │   └── ...
│       └── images/         # UI assets
├── examples/               # Example workflows and tutorials
│   ├── camera_workflow.py
│   ├── camera_yolo_workflow.py
│   └── README.md
├── readmes/                # Extended documentation
├── models/                 # ML models (YOLO, etc.)
├── docs/                   # Documentation files
│   ├── CUSTOM_NODES.md     # Guide to creating custom nodes
│   ├── UI_COMPONENTS.md    # Guide to node UI components
│   └── EXTENSIBILITY.md    # Extensibility overview
├── _backup/                # Workflow backups
├── setup.py                # Package installation
├── setup.bat / setup.sh    # Setup scripts
├── requirements.txt        # Convenience installer (deps live in pyproject.toml)
├── pyproject.toml          # Package metadata, dependencies and build config
├── INSTALL.md              # Installation guide
├── DOCKER.md               # Docker setup
├── docker-compose.yml      # Docker compose config
├── Dockerfile              # Docker build (CUDA)
├── Dockerfile.cpu          # Docker build (CPU only)
├── README.md
└── workflow.json           # Current workflow

Creating Custom Nodes

PyNode is fully extensible. All node information (visual properties, property schemas, and behavior) is contained within the node class itself. The main application has no hardcoded knowledge of specific node types.

For a complete guide, see docs/CUSTOM_NODES.md

Here is a simple example:

from pynode.nodes.base_node import BaseNode

class MyCustomNode(BaseNode):
    """Example custom node."""

    category = 'custom'
    color = '#FFA07A'
    border_color = '#FF7F50'
    text_color = '#000000'

    properties = [
        {
            'name': 'multiplier',
            'label': 'Multiplier',
            'type': 'text'
        }
    ]

    def __init__(self, node_id=None, name="custom"):
        super().__init__(node_id, name)
        self.configure({
            'multiplier': 2
        })

    def on_input(self, msg, input_index=0):
        payload = msg.get('payload')
        multiplier = float(self.config.get('multiplier', 2))
        new_payload = payload * multiplier

        new_msg = self.create_message(
            payload=new_payload,
            topic=msg.get('topic', '')
        )
        self.send(new_msg)

Register the node with the workflow engine used by your application:

from pynode.workflow_engine import WorkflowEngine
from my_custom_node import MyCustomNode

engine = WorkflowEngine()
engine.register_node_type(MyCustomNode)

Message Structure

Messages follow the Node-RED format:

{
    'payload': 'any data type',
    'topic': 'string',
    '_msgid': 'unique-id',
    # ... any additional properties
}

API Endpoints

Nodes

• GET /api/nodes - List all nodes
• POST /api/nodes - Create a node
• GET /api/nodes/<id> - Get node details
• PUT /api/nodes/<id> - Update node
• DELETE /api/nodes/<id> - Delete node
• POST /api/nodes/<id>/<action> - Trigger node action

Connections

• POST /api/connections - Create connection
• DELETE /api/connections - Delete connection

Workflow

• GET /api/workflow - Export workflow
• POST /api/workflow - Import workflow
• POST /api/workflow/start - Start workflow
• POST /api/workflow/stop - Stop workflow
• GET /api/workflow/stats - Get statistics

Debug

• GET /api/nodes/<id>/debug - Get debug messages
• DELETE /api/nodes/<id>/debug - Clear debug messages

Example Programmatic Usage

from pynode.workflow_engine import WorkflowEngine
from pynode.nodes import InjectNode, FunctionNode, DebugNode

engine = WorkflowEngine()
engine.register_node_type(InjectNode)
engine.register_node_type(FunctionNode)
engine.register_node_type(DebugNode)

inject = engine.create_node('InjectNode', name='source')
inject.configure({'payload': 10, 'payloadType': 'num'})

func = engine.create_node('FunctionNode', name='multiply')
func.configure({'func': 'msg["payload"] = msg["payload"] * 2\nreturn msg'})

debug = engine.create_node('DebugNode', name='output')

engine.connect_nodes(inject.id, func.id)
engine.connect_nodes(func.id, debug.id)

engine.start()
engine.trigger_inject_node(inject.id)

messages = engine.get_debug_messages(debug.id)
print(messages)

Web UI Usage

1. Add Nodes: Drag nodes from the palette onto the canvas
2. Connect Nodes: Click an output port and drag to an input port
3. Configure Nodes: Click a node to show its properties panel
4. Test Workflow:
   • Click Start to activate the workflow
   • Use the Inject button on inject nodes to send messages
   • View output in the debug panel at the bottom
5. Save/Load: Use Export/Import buttons to save workflows

Extending the System

Adding New Node Types

1. Create a new Python class in pynode/nodes/
2. Inherit from BaseNode
3. Override on_input() for message processing
4. Define properties for UI configuration
5. Create requirements.txt in the node's directory if needed
6. Reload the server to detect the new node

Custom Message Processing

Nodes can:

• Modify message payload
• Add or remove message properties
• Send to multiple outputs
• Send multiple messages
• Filter messages
• Store state between messages

Advanced Features

• Background Processing: Use threading for long-running operations
• External APIs: Make HTTP requests from function nodes
• Database Integration: Store and retrieve data from databases
• File I/O: Read and write files in custom nodes
• Scheduling: Implement timed node execution

Development TODOs

Ongoing

• ⬜ Centralize more strings / constants
• ⬜ Test all nodes
• ✅ Add multiple workspaces / canvases

Planned Nodes

• ⬜ OCR (PaddlePaddle) Node
• ✅ Qwen VLM Node
• ⬜ SAM3 Node
• ✅ REST Endpoint Node
• ✅ Webhook Node
• ⬜ UDP/TCP Node

Example Flow Documentation Needed

• ⬜ Bird seed level monitor
• ⬜ Capture data and send to Roboflow / Geti
• ⬜ Track objects time in zone
• ⬜ Live VLMs
• ⬜ ANPR (Detect, Crop, OCR, MQTT)

Node-Specific

• ✅ YOLO: Add custom model support
• ✅ YOLO: Add custom target HW string
• ⬜ Roboflow: RF-DETR
• ⬜ Roboflow: Upload images
• ⬜ DeepSort: Add option to use a different feature extractor model

License

MIT License - Feel free to use and modify.

Contributing

Contributions are welcome. Add new node types, improve the UI, or enhance the engine.