Kora

Pre-alpha: Kora is an experimental language. It is not ready for production workloads, and it has not yet earned independent security review or adoption evidence.

Kora is a small language for policy-safe, replayable AI workflows over sensitive documents. Its narrow goal is to make extraction and review jobs for claims, invoices, compliance, and internal operations easier to inspect and harder to run outside policy.

It is useful only if you need several of these guarantees together:

• declared input and output shapes checked before execution;
• explicit outcomes for model uncertainty, budget exhaustion, and outages;
• information-flow labels and named declassification sinks;
• record/replay tests with no live model call;
• opt-in durable execution for effects and human approval;
• one shared budget across parallel work.

If you only need to call a model, use Python or TypeScript. They have mature ecosystems and far lower adoption cost.

A real Kora workflow

type Employee:
    name: str
    classified salary: int

type Assessment:
    band: str
    rationale: str

def review(employee: Employee) -> str:
    declassify employee as approved for local_model:
        result: Assessment = analyze(
            approved,
            "assess salary band and explain the result"
        )

    match result:
        case Ok(assessment):
            return f"{employee.name}: {assessment.band}"
        case Uncertain(reason):
            return f"human review: {reason}"
        case Exhausted(meter):
            return f"budget exhausted: {meter}"
        case Failed(why):
            return f"provider failed: {why}"

def main():
    print(review(Employee("Maya", 142000)))

The declassification is valid only when the project policy names the same sink:

[models]
default = { name = "qwen2.5:7b", endpoint = "http://localhost:11434", api = "ollama" }

[sinks]
local_model = { allow = ["classified"] }

Then check and replay the committed examples:

kora check examples/03_salary_review.ko
kora run --replay examples/03_salary_review.ko
kora test examples/07_tests.ko

kora run checks the entry file before starting effects. kora check currently checks local declared types, calls, constructors, field access, control-flow placement, and direct classified flow into analyze. Unknown values from dynamic integrations stay runtime-checked. Sink policy is enforced again at runtime.

What is real today

• A parser, static checker, interpreter, CLI, language server, debugger, and VS Code extension.
• Typed model results validated against Kora declarations.
• Ok, Uncertain, Exhausted, and Failed outcomes.
• Scoped token, call, step, time, and context budgets.
• Classified values, named sinks, runtime enforcement, and audit output.
• Record and replay cassettes for deterministic model tests.
• parallel for with isolated worker values and shared budget accounting.
• Durable journals for model calls, tools, writes, reads, output, and ask_human when --durable is requested.
• Standard modules for JSON, CSV, files, HTTP, SQL, environment, time, regex, notes, PDF, YAML, and XML.
• Source packages with lockfiles, checksums, capability grants, and no install scripts or in-process native libraries.

What is not proven yet

• No production users or published design-partner case study.
• No independent security audit.
• No stable language or package compatibility promise.
• A tree-walking interpreter, not a high-performance general-purpose runtime.
• A small ecosystem with fewer integrations than established frameworks.
• Durable execution is opt-in, not automatic.
• Static checking is conservative, not whole-program verification across Python, MCP, helpers, or network responses.

These are release gates, not footnotes. See the roadmap for the validation plan.

Install

curl -fsSL https://raw.githubusercontent.com/ImAbhishekTomar/kora-lang/main/scripts/install.sh | sh

Or use a package manager:

brew install ImAbhishekTomar/tap/kora
cargo install kora-cli
npm install -g @abhishektomar/kora-cli

Prebuilt archives for Linux, macOS, and Windows are on the releases page.

Start with evidence

The repository includes runnable programs and recorded model responses:

kora run examples/00_basics.ko
kora run --replay examples/01_expense_check.ko
kora run --durable examples/19_durable_pipeline.ko

The first two require no API key. The durable example performs real local file effects, so run it in a disposable working directory when evaluating it.

Documentation

Resource	Purpose
Start here	Install and run the first checked program
Language reference	Syntax and semantics
Standard library	Built-in modules and safety behavior
CLI reference	Commands, config, replay, and durability
Decisions	Design choices and accepted trade-offs
Comparison	When Kora is and is not a sensible choice
Examples	Runnable workflows and tests

Contributing

Read AGENTS.md before changing the language. A construct is not finished until its compiler, runtime, editor, docs, examples, and tests agree.

cargo fmt --all
cargo clippy --all-targets --all-features
cargo test
cargo build && python3 scripts/check_docs.py --kora ./target/debug/kora

License

Apache-2.0