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What You’ll Learn

By the end of this guide, you’ll understand:
  • ✅ How to create and structure Base objects
  • ✅ The difference between direct attributes and the properties dictionary
  • ✅ How to access and discover object properties
  • ✅ How to work with lists and collections

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Prerequisites

Before starting this guide, you should:
This guide focuses on working with objects - not sending/receiving them. For send/receive operations, see the Quickstart.

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How do I create a Speckle object?

You need to create custom data structures or add metadata to geometry for your Speckle project. Use the Base class - it’s the foundation of all Speckle objects and supports dynamic properties: 
from specklepy.objects import Base

# Create a base object
obj = Base()

# Add direct attributes
obj.name = "My Wall"
obj.elementId = "wall-001"
obj.height = 3.5
obj.isLoadBearing = True

# Add nested structure
obj.elements = []  # Will hold child objects

print(f"Created: {obj.name}")
Base objects are Python classes that accept any property name dynamically (no predefined schema needed), serialize automatically for Speckle, support nesting (objects within objects), and work with Python’s native attribute access. When to create Base objects:
  • Building custom data structures (surveys, analysis results, schedules)
  • Grouping related objects together
  • Adding application-specific metadata
Don’t use reserved names: Avoid property names starting with _ or matching Base methods like id, speckle_type, get_member_names(). These have special meanings.
# ❌ Bad - overwrites internal property
obj.id = "custom-123"  # This sets the Speckle hash ID

# ✅ Good - use your own naming
obj.elementId = "custom-123"

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How do I add metadata: direct attributes vs. properties dictionary?

You need to attach metadata to objects, but you’re unsure whether to use direct attributes (obj.name) or the properties dictionary (obj.properties["name"]). Use direct attributes for structure and organization: 
from specklepy.objects import Base

# Direct attributes define YOUR data structure
building = Base()
building.name = "Building A"
building.address = "123 Main St"
building.levels = []  # Your hierarchy
building.elements = []  # Your organization

# Add a level
level = Base()
level.name = "Level 1"
level.elevation = 0.0
level.elements = []  # Objects on this level
building.levels.append(level)
Use the properties dictionary for queryable metadata: 
from specklepy.objects import Base

# Properties dict for metadata that other apps should find
wall = Base()
wall.name = "W-101"

# Standard BIM metadata
wall.properties = {
    "category": "Walls",
    "material": "Concrete",
    "thickness": 200,
    "fireRating": "2 hour",
    "isLoadBearing": True,
    "cost": 1500.00
}

# Access safely with defaults
material = wall.properties.get("material", "Unknown")
thickness = wall.properties.get("thickness", 0)
Direct attributes (obj.name) are part of your object’s structure, provide Python-level property access, and are good for relationships and hierarchies like obj.levels, obj.elements, obj.children. Properties dictionary (obj.properties) is a standardized metadata container, follows BIM connector conventions, makes data searchable across applications, and commonly uses keys like category, family, type, material.
Which to use? Both are valid Speckle patterns! The properties dictionary is a convention that makes metadata more discoverable. If Revit sends a wall, its category will be in properties["category"] - following this pattern makes your data easier to work with across platforms.
Combining both approaches: 
from specklepy.objects import Base
from specklepy.objects.geometry import Point, Mesh

# Create a beam with both structure and metadata
beam = Base()

# Direct attributes - your structure
beam.name = "B-205"
beam.startPoint = Point(x=0, y=0, z=3.5)
beam.endPoint = Point(x=5, y=0, z=3.5)
beam.displayValue = mesh  # Geometry reference

# Properties dict - queryable metadata
beam.properties = {
    "category": "Structural Framing",
    "family": "Steel Beam",
    "type": "W12x26",
    "material": "Steel",
    "length": 5.0,
    "weight": 130.0
}

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How do I access object properties?

You have a Speckle object and need to read its properties, but you don’t know what properties it has or how to access them safely. For direct attributes: 
# Direct access (if you know the property exists)
name = obj.name
height = obj.height

# Safe access with hasattr()
if hasattr(obj, "name"):
    print(f"Name: {obj.name}")
else:
    print("No name property")

# Safe access with getattr() and default
name = getattr(obj, "name", "Unnamed")
height = getattr(obj, "height", 0.0)
For properties dictionary: 
# Direct access (might raise KeyError)
category = obj.properties["category"]  # KeyError if missing!

# ✅ Safe access with .get() and default
category = obj.properties.get("category", "Unknown")
material = obj.properties.get("material", "Not specified")

# Check if key exists
if "category" in obj.properties:
    print(f"Category: {obj.properties['category']}")
Discovering all properties - use get_member_names() to discover what properties an object has: 
from specklepy.objects import Base

obj = Base()
obj.name = "Widget"
obj.value = 42
obj.tags = ["important", "new"]

# Get all property names
property_names = obj.get_member_names()
print(property_names)
# ['name', 'value', 'tags', 'id', 'speckle_type', ...]

# Loop through all properties
for prop_name in obj.get_member_names():
    value = getattr(obj, prop_name, None)
    print(f"{prop_name}: {value}")
get_member_names() returns all property names on the object, automatically filtering out:
  • Private members (starting with _)
  • Methods and functions
  • Class attributes
This means you get a clean list of just the data properties. No need to check if not name.startswith("_") - it’s already filtered!
# get_member_names() already excludes private members
for name in obj.get_member_names():
    value = getattr(obj, name, None)  # Clean, no filtering needed
Safe property access pattern: 
def safely_read_object(obj):
    """Read all properties from an object safely."""

    # Read known direct attributes with defaults
    name = getattr(obj, "name", "Unnamed")

    # Read properties dictionary safely
    properties = {}
    if hasattr(obj, "properties") and obj.properties:
        category = obj.properties.get("category", "Unknown")
        material = obj.properties.get("material", "Not specified")
        properties = {
            "category": category,
            "material": material
        }

    return {
        "name": name,
        "properties": properties
    }

# Use it
info = safely_read_object(wall)
print(f"Wall: {info['name']}, Category: {info['properties']['category']}")
Don’t assume properties exist! Speckle objects can come from different sources with different schemas. Always use safe access patterns:
# ❌ Bad - will crash if property doesn't exist
category = obj.properties["category"]
name = obj.name

# ✅ Good - handles missing properties gracefully
category = obj.properties.get("category", "Unknown")
name = getattr(obj, "name", "Unnamed")

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How do I work with lists and collections?

You need to work with multiple objects - filtering them, counting them, or processing them in bulk. Looping through lists: 
from specklepy.objects import Base

# Create a collection of objects
building = Base()
building.elements = []

for i in range(5):
    element = Base()
    element.name = f"Element-{i}"
    element.properties = {"category": "Walls" if i % 2 == 0 else "Floors"}
    building.elements.append(element)

# Loop through elements
for element in building.elements:
    name = getattr(element, "name", "unnamed")
    category = element.properties.get("category", "Unknown")
    print(f"{name}: {category}")
Filtering lists: 
# Filter with list comprehension
walls = [
    obj for obj in building.elements
    if obj.properties.get("category") == "Walls"
]

print(f"Found {len(walls)} walls")

# Filter with multiple criteria
load_bearing_walls = [
    obj for obj in building.elements
    if obj.properties.get("category") == "Walls"
    and obj.properties.get("isLoadBearing", False)
]
Counting and aggregating: 
# Count by category
from collections import Counter

categories = Counter(
    obj.properties.get("category", "Unknown")
    for obj in building.elements
)

print(categories)
# Counter({'Walls': 3, 'Floors': 2})

# Sum numeric properties
total_area = sum(
    obj.properties.get("area", 0.0)
    for obj in building.elements
)

print(f"Total area: {total_area}")
Working with nested collections - many Speckle objects use elements arrays for hierarchical structures:
Terminology Note: Examples here use terms like “building”, “level”, “room” to illustrate hierarchical concepts. Real BIM data from connectors (Revit, Rhino, etc.) uses proxy structures rather than direct nesting - for example, Revit levels are LevelProxy objects that reference elements by ID. See Proxification and BIM Data Patterns for actual BIM structures.
from specklepy.objects import Base

# Create hierarchical structure (illustrative example)
building = Base()
building.name = "Building A"
building.elements = []

# Add levels
for level_num in range(3):
    level = Base()
    level.name = f"Level {level_num}"
    level.properties = {"category": "Levels", "elevation": level_num * 3.5}
    level.elements = []  # Objects on this level

    # Add rooms to this level
    for room_num in range(4):
        room = Base()
        room.name = f"Room {level_num}{room_num}"
        room.properties = {"category": "Rooms", "area": 25.0}
        level.elements.append(room)

    building.elements.append(level)

# Process hierarchy
for level in building.elements:
    level_name = getattr(level, "name", "unnamed")
    room_count = len(level.elements) if hasattr(level, "elements") else 0
    print(f"{level_name}: {room_count} rooms")
Common collection patterns: 
# Check if object has elements
if hasattr(obj, "elements") and obj.elements:
    print(f"Object has {len(obj.elements)} elements")
    for element in obj.elements:
        process(element)

# Safe iteration (handles missing elements)
elements = getattr(obj, "elements", [])
for element in elements:
    process(element)

# Find first match
def find_first(objects, category):
    """Find first object with matching category."""
    for obj in objects:
        if obj.properties.get("category") == category:
            return obj
    return None

wall = find_first(building.elements, "Walls")
Check if elements exists before looping:
# ❌ Bad - crashes if no elements property
for element in obj.elements:
    process(element)

# ✅ Good - safe check first
if hasattr(obj, "elements"):
    for element in obj.elements:
        process(element)

# ✅ Also good - use getattr with default
for element in getattr(obj, "elements", []):
    process(element)

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Practical Examples

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Example 1: Create a Survey Point Collection

from specklepy.objects import Base
from specklepy.objects.geometry import Point

# Create survey collection
survey = Base()
survey.name = "Site Survey 2024-01"
survey.date = "2024-01-15"
survey.surveyor = "Jane Smith"
survey.points = []

# Add survey points
coordinates = [
    (0, 0, 100.5),
    (10, 0, 100.8),
    (10, 10, 101.2),
    (0, 10, 100.9)
]

for i, (x, y, z) in enumerate(coordinates):
    point = Point(x=x, y=y, z=z)
    point.units = "m"

    # Add metadata as properties
    point.properties = {
        "pointId": f"SP-{i+1:03d}",
        "accuracy": 0.01,
        "method": "GPS"
    }

    survey.points.append(point)

print(f"Survey '{survey.name}' has {len(survey.points)} points")

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Example 2: Filter and Summarize Objects

from collections import Counter

def filter_by_property(objects, key, value):
    """Filter objects by property key-value pair."""
    return [
        obj for obj in objects
        if obj.properties.get(key) == value
    ]

def summarize_objects(objects):
    """Create summary statistics for objects."""

    if not objects:
        return "No objects to summarize"

    # Count by category
    categories = Counter(
        obj.properties.get("category", "Unknown")
        for obj in objects
    )

    # Calculate total area (if present)
    total_area = sum(
        obj.properties.get("area", 0.0)
        for obj in objects
    )

    return {
        "count": len(objects),
        "categories": dict(categories),
        "total_area": total_area
    }

# Use the functions
elements = building.elements
walls = filter_by_property(elements, "category", "Walls")
summary = summarize_objects(walls)

print(f"Found {summary['count']} walls")
print(f"Categories: {summary['categories']}")
print(f"Total area: {summary['total_area']} m²")

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Example 3: Build a Hierarchy

from specklepy.objects import Base

def create_project_hierarchy():
    """Create a multi-level project hierarchy."""

    # Project root
    project = Base()
    project.name = "Office Building"
    project.elements = []

    # Building
    building = Base()
    building.name = "Building A"
    building.properties = {"type": "Office", "floors": 3}
    building.elements = []

    # Add levels
    for floor in range(3):
        level = Base()
        level.name = f"Level {floor + 1}"
        level.properties = {
            "category": "Levels",
            "elevation": floor * 3.5,
            "area": 1000.0
        }
        level.elements = []

        # Add spaces
        for space in range(5):
            room = Base()
            room.name = f"Room {floor + 1}.{space + 1}"
            room.properties = {
                "category": "Rooms",
                "area": 25.0,
                "occupancy": 4
            }
            level.elements.append(room)

        building.elements.append(level)

    project.elements.append(building)
    return project

# Create and inspect
project = create_project_hierarchy()
building = project.elements[0]

for level in building.elements:
    level_name = level.name
    room_count = len(level.elements)
    print(f"{level_name}: {room_count} rooms")

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Learn More

Core Concepts: Guides: API Reference:

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Next Steps

Now that you understand how to work with Speckle objects, you’re ready to:
  1. Learn traversal - Navigate nested object structures recursively
  2. Extract data - Filter and find specific objects in complex hierarchies
  3. Work with geometry - Extract and manipulate displayValue meshes from BIM objects
Continue to Intermediate: Finding and Extracting Data →