CodeExecute VsPyRevit

RevitDevTool vs pyRevit

Two tools, two different audiences.

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Target Audiences

pyRevit: End-User Automation Platform

Who it's for:

• End users creating Revit automation tools
• Building ribbon interfaces for teams
• Creating reusable buttons and commands
• Focus on pure automation tasks

Strengths:

• Mature ribbon UI framework
• Extensive community scripts library
• Easy button creation and deployment
• Large user base and documentation

RevitDevTool: Developer & Research Platform

Who it's for:

• Developers building computational design tools
• Researchers experimenting with Python algorithms
• AI/ML integration with Revit
• Data scientists analyzing building data
• Prototyping and rapid development

Strengths:

• Modern Python 3.13 with full ecosystem
• VSCode debugger integration
• Automatic dependency management
• Research-friendly (pandas, numpy, scikit-learn)
• Computational design workflows

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Key Philosophical Differences

Aspect	pyRevit	RevitDevTool
Primary Goal	Ribbon automation for end users	Developer tools & research
UI Focus	Ribbon buttons, menus, toolbars	Code execution & debugging
Target User	End users clicking buttons	Developers writing code
Workflow	Deploy → Click → Run	Write → Debug → Iterate
Distribution	Share buttons with teams	Share scripts/libraries

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Python Runtime Comparison

pyRevit: Multiple Python Options

pyRevit offers 3 Python runtime choices, each with trade-offs:

1. IronPython 2.7 (Default)

• ✅ Direct .NET integration
• ✅ No dependency management needed
• ❌ Python 2.7 (obsolete since 2010)
• ❌ No modern packages (pandas, numpy)
• ❌ Limited ecosystem

2. IronPython 3.4

• ✅ Newer Python syntax
• ❌ Still incompatible with CPython packages
• ❌ No dependency resolver
• ❌ Small ecosystem

3. CPython 3.12 (Experimental)

• ✅ Modern Python with full ecosystem
• ❌ No automatic dependency resolver
• ❌ Module reload bugs (state pollution)
• ❌ Requires manual environment setup

RevitDevTool: CPython 3.13

Single, optimized choice:

• ✅ Latest Python 3.13
• ✅ Full CPython ecosystem (pandas, numpy, scikit-learn, etc.)
• ✅ Automatic dependency resolution (UV + PEP 723)
• ✅ Clean module reload (no state pollution)
• ✅ VSCode debugger integration
• ❌ Not designed for end-user ribbon deployment

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IronPython vs CPython: Quick Overview

IronPython strengths:

• Direct .NET interface implementation
• No marshaling overhead for .NET objects
• Simpler for pure Revit API automation

IronPython weaknesses:

• Obsolete Python versions (2.7, 3.4)
• Incompatible with modern Python packages
• Limited ecosystem (no pandas, numpy, scikit-learn)

CPython strengths:

• Modern Python (3.13)
• Full ecosystem access
• Better for data science, AI/ML, research
• Standard Python tooling and debugging

CPython weaknesses:

• Requires PythonNet for .NET interop
• Slight marshaling overhead

Verdict: Use IronPython for simple Revit automation. Use CPython for research, data analysis, and modern development.

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Dependency Management

pyRevit

IronPython (2.7/3.4):

• No dependencies needed (Revit API only)
• Can't use pandas, numpy, scikit-learn

CPython (3.12):

• Manual pip install required
• User manages virtual environments
• No automatic conflict resolution
• Pre-install before running scripts

RevitDevTool

CPython (3.13) with PEP 723:

# /// script
# dependencies = ["pandas==1.5.3", "numpy>=1.24"]
# ///

import pandas as pd  # Auto-installed on first run

• Inline dependency declarations
• Automatic installation via UV resolver
• Per-script isolation
• Zero manual setup

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Debugging Experience

pyRevit

Traditional debugging:

# Option 1: pdb debugger
import pdb; pdb.set_trace()

# Option 2: Print debugging
print("Debug:", walls)

• Command-line pdb interface
• No IDE integration
• Manual stepping through code

RevitDevTool

Modern VSCode debugging:

1. Set breakpoints in VSCode (click line margin)
2. Press F5 → Attach to Revit
3. Execute script → Pauses at breakpoints
4. Inspect variables, step through code
5. Use Debug Console for expressions

Feature	RevitDevTool	pyRevit
VSCode debugger	✅ Full support	❌ Not available
Breakpoints	✅ Visual in IDE	❌ No IDE integration
Variable inspection	✅ VSCode panel	⚠️ pdb commands
Step debugging	✅ F10, F11	⚠️ pdb only
Conditional breakpoints	✅ Native	❌ Manual

See: Python Debugging for complete guide

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Use Cases Comparison

pyRevit Excels At:

✅ End-user automation:

• Creating ribbon buttons for teams
• Deploying automation tools to non-programmers
• Building reusable command libraries
• Simple Revit API automation

✅ Community sharing:

• Large script library
• Established patterns
• Easy deployment

RevitDevTool Excels At:

✅ Developer workflows:

• Rapid prototyping with modern Python
• Debugging complex algorithms
• Iterative development

✅ Computational design:

• Generative design algorithms
• Optimization problems
• Custom geometry generation

✅ Data science & research:

• Building data analysis with pandas
• Machine learning integration
• Statistical analysis of BIM data
• Export to data science formats

✅ AI/ML integration:

• Using modern Python AI libraries
• LLM-assisted development (Copilot works well)
• Computer vision with OpenCV
• Deep learning with PyTorch/TensorFlow

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Real-World Scenarios

Scenario 1: Simple Automation (Both Work)

Task: Create button to count walls

pyRevit approach:

# button.pushbutton/script.py
from pyrevit import revit
from Autodesk.Revit import DB

doc = revit.doc
walls = DB.FilteredElementCollector(doc).OfClass(DB.Wall).ToElements()
print(f"Found {len(walls)} walls")

RevitDevTool approach:

# count_walls.script.py
# /// script
# dependencies = []
# ///

from Autodesk.Revit import DB

doc = __revit__.ActiveUIDocument.Document
walls = DB.FilteredElementCollector(doc).OfClass(DB.Wall).ToElements()
print(f"Found {len(walls)} walls")

Verdict: pyRevit better (ribbon button for end users)

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Scenario 2: Data Analysis (RevitDevTool Better)

Task: Analyze wall data with pandas, create visualizations

pyRevit:

# Can't use pandas in IronPython 2.7 ❌
# Must use manual data structures
# Limited analysis capabilities

RevitDevTool:

# /// script
# dependencies = ["pandas==1.5.3", "plotly==5.18.0"]
# ///

import pandas as pd
import plotly.express as px
from Autodesk.Revit import DB

doc = __revit__.ActiveUIDocument.Document
walls = DB.FilteredElementCollector(doc).OfClass(DB.Wall).ToElements()

# Use pandas for analysis
data = [{"Name": w.Name, "Level": w.LevelName, "Area": w.Area} 
        for w in walls]
df = pd.DataFrame(data)

# Statistical analysis
summary = df.groupby("Level").agg({"Area": ["sum", "mean", "count"]})
print(summary)

# Create visualization
fig = px.bar(df, x="Level", y="Area", title="Wall Areas by Level")
fig.show()  # Opens in browser or WebView2

Verdict: RevitDevTool better (modern data science stack)

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Scenario 3: Machine Learning (RevitDevTool Only)

Task: Predict element categories using ML

pyRevit:

# scikit-learn not available in IronPython ❌

RevitDevTool:

# /// script
# dependencies = ["scikit-learn==1.3.0", "pandas==1.5.3"]
# ///

from sklearn.ensemble import RandomForestClassifier
import pandas as pd
from Autodesk.Revit import DB

# Collect training data from Revit
doc = __revit__.ActiveUIDocument.Document
elements = DB.FilteredElementCollector(doc).WhereElementIsNotElementType().ToElements()

# Extract features
data = extract_features(elements)
df = pd.DataFrame(data)

# Train model
model = RandomForestClassifier()
model.fit(df[features], df["category"])

# Predict on new elements
predictions = model.predict(new_data)

Verdict: RevitDevTool only option

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When to Use What

Use pyRevit if:

• ✅ Building automation tools for end users
• ✅ Need ribbon buttons and menus
• ✅ Deploying to non-programmers
• ✅ Simple Revit API automation
• ✅ Maintaining existing pyRevit scripts
• ✅ Sharing with large pyRevit community

Use RevitDevTool if:

• ✅ Developer/researcher workflow
• ✅ Need modern Python ecosystem (pandas, numpy, scikit-learn)
• ✅ VSCode debugging required
• ✅ Computational design projects
• ✅ Data science & analytics
• ✅ AI/ML integration
• ✅ Rapid prototyping and iteration
• ✅ Research and experimentation

Use Both if:

• ✅ pyRevit for end-user deployment
• ✅ RevitDevTool for development and research
• ✅ Different tools for different workflows

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Migration Guide

From pyRevit to RevitDevTool

Minimal code changes:

# pyRevit
from pyrevit import revit
doc = revit.doc

# RevitDevTool
doc = __revit__.ActiveUIDocument.Document

File naming:

pyrevit_script.py       → script_name.script.py
requirements.txt        → # /// script ... dependencies = [...] ///

What stays the same:

• Revit API usage (DB.Wall, FilteredElementCollector, etc.)
• Script logic and structure
• External package imports (if using CPython)

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Summary

Aspect	pyRevit	RevitDevTool
Target User	End users	Developers & researchers
Primary Use	Automation buttons	Code development
Python	IronPython 2.7 (default)	CPython 3.13
Packages	Limited (Revit API only)	Full ecosystem
Debugging	pdb (command-line)	VSCode (full IDE)
Dependencies	Manual pip	Automatic (PEP 723)
Best For	Ribbon automation	Research & development

Both are excellent tools for different purposes.

• pyRevit: Best automation platform for end users
• RevitDevTool: Best development platform for researchers and developers

Choose based on your audience and workflow.

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See Also

• Python Ecosystems - All Python options for Revit
• Python Debugging - VSCode debugger guide
• Python Execution - How Python scripts work
• Examples - Real-world examples

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External Resources:

• pyRevit GitHub
• pyRevit Documentation