GitHub - jargnar/orangensaft: A new age post-AI programming language

Orangensaft is an experimental, new age, post-AI programming language. This is a hobby project. It's a toy language. Do not use it. It's only for me.

Ok. This is like a mini python where prompts are deeply part of the language. You can fully intermix deterministic scripting with probabilistic LLM calls. LLM calls are a fundamental part of the language runtime.

This is a valid code:

// example.saft

people = ["alice", "bob", "charlie", "mr. karabalabaloofal"]

longest_name: string = $
  who has the longest name in {people}
$

assert longest_name == people[3] // yup true

When anything is enclosed between $ .. $, then the language runtime:

• calls an LLM
• enforces the declared type
• stores the result into the specified variable

Below is the same code, but written normally and deterministically:

// old.saft

people = ["alice", "bob", "charlie", "mr. karabalabaloofal"]

longest_name = people[0]
for p in people:
  if len(p) > len(longest_name):
    longest_name = p

print(longest_name)

You can choose what level of probabilistic or deterministic code you write. WCGW?.

To run a saft program, clone the repo and run it like this:

% cargo run -- run examples/11_simple_array_op_2.saft \
  --provider openrouter \
  --api-key-env OPENROUTER_API_KEY \
  --model openai/gpt-4o-mini \
  --temperature 0 \
  --max-tool-rounds 8 \
  --max-tool-calls 32

A few more examples

verbs = ["build", "test", "ship"]

upper: [string] = $
    convert each item in {verbs} to uppercase
$

assert upper[0] == "BUILD"
assert upper[1] == "TEST"
assert upper[2] == "SHIP"

or even sth like this

people = ["alice", "bob", "charlie"]

f greet(x, y):
    ret x + " says hi to " + y

z: string = $
    hey it seems among {people}, bob
    wants to talk to alice
    can you {greet} them
$

assert z == "bob says hi to alice"

This is true func calling at runtime.

You can also load CSVs with Polars-backed dataframes:

df = read("examples/data/team_stats.csv")

winner = $
    which column from {df} has highest average
$

When a dataframe is interpolated in a prompt ({df}), runtime injects a structured JSON context block instead of raw full-table dumps. That block includes:

• shape (rows, columns)
• column names + dtypes
• sample_rows (bounded sample)
• numeric_profile (mean, min, max per numeric column, bounded)
• truncation metadata so models know context was summarized

This keeps prompts token-efficient while still giving the model enough tabular signal for questions like "highest average column". For exact numeric answers, deterministic stdlib functions (mean, sum, etc.) are still available.

See all other examples in the examples folder.

Tiny stdlib

Current builtin functions:

• upper(string) -> string
• print(any) -> nil
• len(string|list|tuple|object|dataframe) -> int
• type(any) -> string
• read(path: string) -> dataframe (CSV)
• shape(df: dataframe) -> (int, int) (rows, columns)
• columns(df: dataframe) -> [string]
• head(df: dataframe) -> [object] (first 5 rows)
• select(df: dataframe, cols: [string]) -> dataframe
• mean(df: dataframe, column: string) -> float
• sum(df: dataframe, column: string) -> float
• min(df: dataframe, column: string) -> float
• max(df: dataframe, column: string) -> float

Build notes

You can also use shorthand (no run subcommand):

% cargo run -- examples/11_simple_array_op_2.saft --provider openrouter

You can auto-format in-memory before running/checking:

% cargo run -- run examples/14_polars_agentic_scouting_report.saft --autofmt
% cargo run -- check examples/14_polars_agentic_scouting_report.saft --autofmt

And format files directly:

% cargo run -- fmt examples/14_polars_agentic_scouting_report.saft --check
% cargo run -- fmt examples/14_polars_agentic_scouting_report.saft --write

Note: formatter output is AST-based and can rewrite layout aggressively.

If you want plain orangensaft ... commands:

Then set defaults once in your shell profile:

export ORANGENSAFT_PROVIDER=openrouter
export ORANGENSAFT_API_KEY_ENV=OPENROUTER_API_KEY
export ORANGENSAFT_MODEL=openai/gpt-4o-mini
export ORANGENSAFT_TEMPERATURE=0
export ORANGENSAFT_MAX_TOOL_ROUNDS=8
export ORANGENSAFT_MAX_TOOL_CALLS=32

After that, this works:

% orangensaft examples/11_simple_array_op_2.saft

AI Agent entrypoint

For AI-assisted maintenance and development in this repo:

• AGENTS.md is the canonical, complete agent guide.