textual-capture 0.1.0

Sequenced screen capture for Textual TUI applications. Enables AI assistants to review and test TUIs.

textual-capture

Sequenced screenshot capture for Textual TUI applications

textual-capture lets you define reproducible sequences of interactions (key presses, clicks, delays) in your Textual apps and automatically capture multiple SVG + text snapshots at key moments.

Perfect for:

• 🤖 LLM-driven TUI review and testing (primary use case!)
• Creating consistent documentation screenshots
• Building demos and tutorials
• Generating "before/after" visuals for READMEs
• Visual regression prep (pair with snapshot testing)

Unlike single-shot tools, textual-capture supports multi-step sequences defined in clean, readable TOML files.

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Installation

pip install textual-capture

Or with PDM:

pdm add textual-capture

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Quick Example

Create a file called demo_sequence.toml next to your app:

app_module = "my_app"
app_class = "MyTextualApp"
screen_width = 90
screen_height = 40

[[step]]
type = "press"
key = "tab,tab,enter"

[[step]]
type = "delay"
seconds = 2.0

[[step]]
type = "capture"
output = "running_state"

[[step]]
type = "press"
key = "q"

Then run:

textual-capture demo_sequence.toml           # Default: quiet mode
textual-capture demo_sequence.toml --verbose # Show all actions
textual-capture demo_sequence.toml --quiet   # Errors only

This will:

• Launch your app in test mode
• Press Tab twice, then Enter
• Wait 2 seconds
• Save running_state.svg and running_state.txt
• Press q to quit cleanly

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🤖 LLM-Driven TUI Review

Primary Use Case: Enable AI assistants like Claude Code to review and test your TUI applications by capturing screenshots at different states.

When working with an LLM on a Textual app, the LLM can:

• Generate TOML configurations on the fly to capture specific UI states
• Automatically verify UI layout changes after code modifications
• Review button placement, labels, and visual hierarchy from text output
• Test interaction sequences without manual intervention

Example workflow:

User: "I just added a new settings dialog. Can you check if it looks good?"
LLM:  Creates llm_review.toml → Runs textual-capture → Analyzes .txt output →
      Reports: "Settings dialog opens correctly, but Cancel button is off-screen..."

See examples/llm_review.toml for a template AI assistants can adapt.

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Why textual-capture?

Feature	textual-dev screenshot	pytest-textual-snapshot	textual-capture
Single capture	Yes	Yes	Yes
Multi-step interaction sequences	No	No	Yes
Click buttons by label	No	No	Yes
Delays between actions	No	No	Yes
Multiple timed captures	No	No	Yes
Human-readable config (TOML)	No	No	Yes
Works with any Textual app	Yes	Yes	Yes

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Configuration Options

In your .toml file:

# Required fields
app_module = "path.to.module"      # Module containing your Textual app
app_class = "MyApp"                # Textual App class name

# Optional configuration
screen_width = 100                 # Terminal width (default: 80)
screen_height = 40                 # Terminal height (default: 40)
initial_delay = 1.0                # Wait before first action (default: 1.0)
scroll_to_top = true               # Press "home" at start (default: true)
module_path = "path/to/modules"    # Add to sys.path for imports (optional)

# Action steps
[[step]]
type = "press"                     # Press keyboard keys
key = "tab,down,enter"             # Comma-separated keys

[[step]]
type = "click"                     # Click a button
label = "Run Selected"             # Button text (spaces removed for ID)

[[step]]
type = "delay"                     # Pause for timing
seconds = 1.5                      # Seconds to wait

[[step]]
type = "capture"                   # Take screenshot
output = "my_state"                # Optional: custom name (saves my_state.svg + .txt)
                                   # If omitted: auto-generates capture_001.svg, capture_002.svg, etc.

Auto-sequencing: Omit the output field in capture actions to automatically generate sequential filenames (capture_001, capture_002, etc.). Mix and match named and auto-sequenced captures as needed!

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Contributing

Contributions are welcome! See CONTRIBUTING.md (coming soon).

Issues and feature requests: https://github.com/eyecantell/textual-capture/issues

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License

MIT © 2025 Paul Neumann