NestedText: A Human Friendly Data Format
Authors: Ken & Kale Kundert
Version: 3.1.0
Released: 2021-07-23
Please post all questions, suggestions, and bug reports to: Github.

NestedText is a file format for holding structured data that is to be entered, edited, or viewed by people. It allows data to be organized into a nested collection of dictionaries, lists, and strings without the need for quoting or escaping. In this way it is similar to JSON, YAML and TOML, but without the complexity and risk of YAML and without the syntactic clutter of JSON and TOML. NestedText is both simple and natural. Only a small number of concepts and rules must be kept in mind when creating it. It is easily created, modified, or viewed with a text editor and easily understood and used by both programmers and non-programmers.

NestedText is convenient for configuration files, address books, account information and the like. Here is an example of a file that contains a few addresses:

# Contact information for our officers

    name: Katheryn McDaniel
        > 138 Almond Street
        > Topeka, Kansas 20697
        cell: 1-210-555-5297
        home: 1-210-555-8470
            # Katheryn prefers that we always call her on her cell phone.
    additional roles:
        - board member

vice president:
    name: Margaret Hodge
        > 2586 Marigold Lane
        > Topeka, Kansas 20682
    phone: 1-470-555-0398
    additional roles:
        - new membership task force
        - accounting task force

        name: Fumiko Purvis
            > 3636 Buffalo Ave
            > Topeka, Kansas 20692
        phone: 1-268-555-0280
        additional roles:
            - accounting task force
        name: Merrill Eldridge
            # Fumiko's term is ending at the end of the year.
            # She will be replaced by Merrill.
        phone: 1-268-555-3602

The format holds dictionaries (ordered collections of name/value pairs), lists (ordered collections of values) and strings (text) organized hierarchically to any depth. Indentation is used to indicate the hierarchy of the data, and a simple natural syntax is used to distinguish the types of data in such a manner that needs no quoting or escaping. Specifically, lines that begin with a word (or words) followed by a colon are dictionary items, lines that begin with a dash are list items, lines that begin with a greater-than sign are part of a multiline string, and lines that begin with a hash are comments and are ignored. Dictionaries and lists can be nested arbitrarily.

NestedText is somewhat unique in that the leaf values are always strings. Of course the values start off as strings in the input file, but alternatives like YAML or TOML aggressively convert those values into the underlying data types such as integers, floats, and Booleans. For example, a value like 2.10 would be converted to a floating point number. But making the decision to do so based purely on the form of the value, not the context in which it is found, can lead to misinterpretations. For example, assume that this value is the software version number two point ten. By converting it to a floating point number it becomes two point one, which is wrong. There are many possible versions of this basic issue. But there is also the inverse problem; values that should be converted to particular data types but are not recognized. For example, a value of $2.00 should be converted to a real number but would remain a string instead. There are simply too many values types for a general purpose solution that is only looking at the values themselves to be able to interpret all of them. For example, 12/10/09 is likely a date, but is it in MM/DD/YY, YY/MM/DD or DD/MM/YY form? The fact is, the value alone is often insufficient to reliably determine how to convert values into internal data types. NestedText avoids these problems by leaving the values in their original form and allowing the decision to be made by the end application where more context is available to help guide the conversions. If a price is expected for a value, then $2.00 would be checked and converted accordingly. Similarly, local conventions along with the fact that a date is expected for a particular value allows 12/10/09 to be correctly validated and converted. This process of validation and conversion is referred to as applying a schema to the data. There are packages such as Pydantic and Voluptuous available that make this process easy and reliable.


This package contains a Python reference implementation of NestedText and a test suite. Implementation in many languages is required for NestedText to catch on widely. If you like the format, please consider contributing additional implementations.