Arabian Sand Boa: Python interpreter with frontier intelligence conditional eval

Run a Python file where string if conditions are decided by an LLM.

arabian_sand_boa

is a single, dependency-free script (standard library only). It reads your target file, rewrites every if

/elif

at the AST level, and runs it. When a condition resolves to a non-empty string, the string is treated as a natural-language clause and sent to an LLM along with the variables currently in scope; the model's True

/False

answer decides which branch runs. Every other condition resolves the normal way via bool()

.

./arabian_sand_boa <file.py> [args...]          # it's executable

--debug

(or -d

) prints, to stderr, the exact prompt sent to the LLM and the raw reply for each clause it evaluates.

The target file runs with __name__ == "__main__"

, so its main block executes.

The LLM endpoint is configured entirely through environment variables, read lazily — a target that only uses ordinary (non-string) conditions never needs them.

The endpoint is expected to speak the OpenAI-style chat-completions protocol (POST

a JSON body with model

and messages

, get back choices[0].message.content

).

Copy .example.env

to .env

, fill in your values, and load it:

cp .example.env .env
set -a; source .env; set +a

python3 arabian_sand_boa example.py

.env

is gitignored; .example.env

is the tracked template. If any required variable is missing when an LLM call is needed, the run fails with a clear error naming the missing variable(s).

Rewrite. Anast.NodeTransformer

replaces eachif <test>:

withif __if_hook__(<test>, "<source text of test>"):

.Decide.__if_hook__

evaluates<test>

. If it's a non-empty string, the hook gathers the caller's non-dunder local variables and asks the LLM whether the clause holds. Otherwise it returnsbool(<test>)

and no API call is made.Parse. The reply is lower-cased and stripped:true

→ take the branch,false

→ skip it, anything else → take the branch (cautious default).

Your local variables leave the machine. For every string condition, the in-scope (non-dunder) local variables are serialized and sent to the configured LLM endpoint. Do not run this over sensitive data.- An LLM call fires for every executed string condition, so loops with string clauses are slow and chatty against the endpoint. - Decisions are only as reliable as the model and the wording of your clause, and may vary between runs.

See example.py

for a runnable showcase mixing natural-language clauses (drinking age, admin privileges, account health) with an ordinary boolean condition.