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📝 Utility Options

📋 Options

Option Description
--debug Show debug messages during code generation
--generate-prompt Generate a prompt for consulting LLMs about CLI options
--help Show help message and exit
--list-deprecations List registered deprecations and scheduled breaking changes
--list-experimental List registered experimental features
--no-color Disable colorized output
--profile Use a named profile from pyproject.toml
--version Show program version and exit

--debug

Show debug messages during code generation.

Enables verbose debug output to help troubleshoot issues with schema parsing or code generation. Requires the debug extra to be installed.

Usage

datamodel-codegen --input schema.json --debug # (1)!
  1. --debug - the option documented here

Requires extra dependency

The debug feature requires the debug extra:

pip install 'datamodel-code-generator[debug]'

--generate-prompt

Generate a prompt for consulting LLMs about CLI options.

Outputs a formatted prompt containing your current options, all available options by category, and full help text. Pipe to CLI LLM tools or copy to clipboard for web-based LLM chats.

See also: LLM Integration for detailed usage examples

Usage

datamodel-codegen --generate-prompt # (1)!
datamodel-codegen --generate-prompt "How do I generate strict types?" # (2)!
  1. --generate-prompt - generate prompt without a question
  2. Include a specific question in the prompt
Quick Examples

Pipe to CLI tools: 

datamodel-codegen --generate-prompt | claude -p    # Claude Code
datamodel-codegen --generate-prompt | codex exec   # OpenAI Codex

Copy to clipboard: 

datamodel-codegen --generate-prompt | pbcopy      # macOS
datamodel-codegen --generate-prompt | xclip -selection clipboard  # Linux
datamodel-codegen --generate-prompt | clip.exe    # WSL2

--help

Show help message and exit.

Displays all available command-line options with their descriptions and default values.

Aliases: -h

Usage

datamodel-codegen --help # (1)!
  1. --help - the option documented here
Output 
usage: datamodel-codegen [-h] [--input INPUT] [--url URL] ...

Generate Python data models from schema files.

options:
  -h, --help            show this help message and exit
  --input INPUT         Input file path (default: stdin)
  ...

--list-deprecations

List registered deprecations and scheduled breaking changes, then exit.

The option reads from the central deprecation registry used by runtime warnings, generated documentation, and release-note snippets.

datamodel-codegen --list-deprecations
datamodel-codegen --list-deprecations json
datamodel-codegen --list-deprecations markdown

--list-experimental

List registered experimental features, then exit.

The optional format argument can be table, json, or markdown. The default is table.

The option reads from the central experimental feature registry used by generated documentation and release-note snippets.

datamodel-codegen --list-experimental
datamodel-codegen --list-experimental json
datamodel-codegen --list-experimental markdown

--no-color

Disable colorized output.

By default, datamodel-codegen uses colored output for better readability. Use this option to disable colors, which is useful for CI/CD pipelines or when redirecting output to files.

Usage

datamodel-codegen --input schema.json --no-color # (1)!
  1. --no-color - the option documented here

Environment variable

You can also disable colors by setting the NO_COLOR environment variable:

NO_COLOR=1 datamodel-codegen --input schema.json

--profile

Use a named profile from pyproject.toml configuration.

Profiles allow you to define multiple named configurations in your pyproject.toml file. Each profile can override the default settings with its own set of options.

Related: pyproject.toml Configuration

Usage

datamodel-codegen --input schema.json --profile strict # (1)!
  1. --profile - the option documented here
Configuration (pyproject.toml) 
[tool.datamodel-codegen]
# Default configuration
output-model-type = "pydantic_v2.BaseModel"

[tool.datamodel-codegen.profiles.strict]
# Strict profile with additional options
strict-types = ["str", "int", "float", "bool"]
strict-nullable = true

[tool.datamodel-codegen.profiles.dataclass]
# Dataclass profile
output-model-type = "dataclasses.dataclass"

Use profiles:

# Use the strict profile
datamodel-codegen --input schema.json --profile strict

# Use the dataclass profile
datamodel-codegen --input schema.json --profile dataclass

--version

Show program version and exit.

Displays the installed version of datamodel-code-generator.

Usage

datamodel-codegen --version # (1)!
  1. --version - the option documented here
Output 
datamodel-codegen version: 0.x.x