Gabriella439/grace

A prompt engineering functional programming language

Source code at GitHub https://trygrace.dev/

{
  "createdAt": "2020-10-22T06:22:49Z",
  "defaultBranch": "main",
  "description": "A prompt engineering functional programming language",
  "fullName": "Gabriella439/grace",
  "homepage": "https://trygrace.dev/",
  "language": "Haskell",
  "name": "grace",
  "pushedAt": "2025-11-27T05:21:13Z",
  "stargazersCount": 472,
  "topics": [
    "ai",
    "functional-programming",
    "interpreter",
    "llm",
    "programming-language",
    "prompt-engineering"
  ],
  "updatedAt": "2025-11-27T05:21:16Z",
  "url": "https://github.com/Gabriella439/grace"
}

Grace

Grace (short for Fall-from-Grace) is a domain-specific programming language for prompting models. In particular, Grace is well-suited for building and auto-generating elaborate prompt chains

You can use Grace in your browser without installing anything by visiting trygrace.dev. That website includes an interactive tutorial and is the recommended way to both learn and get started with using Grace.

Features

Integrated language support for models

You don’t need to install any dependencies or import anything to get started. Everything you need is built directly into the language.

The language provides a built-in prompt function for prompting a model:

>>> let key = ./openai.key : Key

>>> prompt{ key, text: "Generate a list of names" }
"
Here are 40 varied first names (mixed genders and cultures):

- Aiden
- Sofia
- Mateo
…
- Mabel
- Imani
- Zane

Want names filtered by gender, culture, style (modern/vintage), or as full names/surnames?"

… and you can structure the output by giving a type annotation:

>>> prompt{ key, text: "Generate a list of names" } : List Text
[ "Ava Thompson"
, "Liam Patel"
, "Sophia Martinez"
…
, "Jackson Rivera"
, "Zoe Wilson"
, "Aiden Park"
]

If the type is sufficiently self-explanatory, you can even omit the prompt:

>>> prompt{ key } : List { name: Text }
[ { "name": "Alice" }
, { "name": "Bob" }
, { "name": "Charlie" }
, { "name": "Diana" }
, { "name": "Evan" }
]

In fact, you can omit the type, too, if the type can be inferred from use:

>>> for { name } of prompt{ key } in "Hello, ${name}!"
[ "Hello, Alice!"
, "Hello, Bob!"
, "Hello, Carol!"
, "Hello, Dave!"
, "Hello, Eve!"
]

JSON schemas inferred from use

That last example works even without a prompt, schema, or type because Grace’s type checker reasons backwards from how the output is used to infer the correct JSON schem, like this:
- type checker infers that the name variable must be Text
  
  … because the name variable is interpolated into “Hello, ${name}!”
- the type checker infers that the prompt function must generate a List
  
  … because the program loops over the output using a for … of loop.
- the type checker infers each element of the List has type { name: Text }
  
  … because the for … of loop destructures each element using { name }
- therefore the prompt function outputs a value of type List{ name: Text }
… which you can read as “a List of records, each of which has a name field containing Text”.

The interpreter then converts that Grace type into the following matching JSON schema to constrain the model’s output:
```
{
  "type": "array",
  "items": {
    "type": "object",
    "properties": {
      "name": {
        "type": "string"
      }
    },
    "required": ["name"],
    "additionalProperties": false
  }
}
```
Finally, the model infers from that JSON schema alone (without any additional prompt) that it should generate a JSON-encoded list of names.

Code generation

You can prefix the prompt keyword with import to ask the model to generate a Grace expression of any type. For example:

>>> import prompt{ key, text: "increment" }
\n -> n + 1

You can use an explicit type annotation to guide the generated code:

>>> import prompt{ key, text: "increment" } : { input: Natural } -> { output: Natural }
\{ input } -> { "output": input + 1 }

… and if the type is informative enough then you can omit the prompt:

>>> import prompt{ key } : { "Job Description": Text } -> { "Is Finance?": Bool }
let key = 🔒

in  \{ "Job Description" } ->
      prompt
        { "key":
            key
        , "text":
            "
            Determine whether the following job description is for a finance role.
            Return a JSON object with a single boolean field \"Is Finance?\": true if it is a finance role, otherwise false.
            Answer only valid JSON, nothing else.

            Job description:
            ${.'Job Description'}
            "
        , "model":
            null
        , "search":
            null
        , "effort":
            null
        }
        : { "Is Finance?": Bool }

Notice in that last example how the model can generate code which itself prompts an model. Neat!

Inferred types also guide the code generation process, too!

>>> let upper = import prompt{ key, text: "uppercase" } in "Hello, ${upper "gabby"}!"
"Hello, GABBY!"

There the model infers that the type of the upper function needs to be Text -> Text (a function whose input is Text and whose output is Text) and generates an function matching that type which uppercases Text.

Command line

This Haskell package builds a grace executable with the following command-line API:

$ grace --help
Usage: grace COMMAND
  Command-line utility for the Grace language

Available options:
  -h,--help                Show this help text

Available commands:
  interpret                Interpret a Grace file
  text                     Render a Grace text literal
  format                   Format Grace code
  builtins                 List all built-in functions and their types
  repl                     Enter a REPL for Grace

You can use the interpret subcommand for interpreting a single file:

for { name } of prompt{ key: ./openai-key.txt }

in  "Hello, ${name}!"

$ grace interpret ./greet.ffg

[ "Hello, Alice!", "Hello, Bob!", "Hello, Charlie!" ]

… and you can specify - to process standard input instead of a file, like this:

$ grace interpret - <<< '2 + 2'

You can also use the repl subcommand for interactive usage:

$ grace repl

>>> let key = ./openai-key.txt
>>> prompt{ key } : List { "First Name": Text, "Last Name": Text }
[ { "First Name": "John", "Last Name": "Doe" }
, { "First Name": "Jane", "Last Name": "Smith" }
, { "First Name": "Alice", "Last Name": "Johnson" }
, { "First Name": "Michael", "Last Name": "Brown" }
, { "First Name": "Emily", "Last Name": "Davis" }
]

Name

Like all of my programming language projects, Grace is named after a character from PlaneScape: Torment, specifically Fall-from-Grace.

The name of this programming language conflicts with another programming language, so use the longer name, “Fall-from-Grace”, to disambiguate when it’s not clear from the context.