nekova-lang 1.11.0

NEKOVA - The AI-Native Programming Language by SYNEKCOT Tech

NEKOVA Programming Language

The AI-Native Programming Language by SYNEKCOT Tech

"The first programming language where AI is syntax, not a library."

Install · Features · Examples · CLI Reference · Roadmap · 📖 Full Documentation

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What is NEKOVA?

NEKOVA means "Connected Forge" — from Latin nectere (to connect) and kova (to forge). Built by SYNEKCOT Tech in Nigeria, for the world.

NEKOVA is an AI-native programming language where think is syntax. AI isn't a library you import — it's part of the language itself. In one file you can write web routes, query a database, call an AI model, run sandboxed code, and define a class — with no boilerplate.

# AI is just syntax
think "What should I build today?" as text

# Speak and listen — built in
speak "Hello, world!"
let command = listen "Say a command"

# Schedule tasks
every 5 s:
    think "Check for new emails" as text

# Run untrusted code safely
sandbox strict:
    let result = 1 + 1
    show result

show sandbox_result["safe"]

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Why NEKOVA?

"Because every other language makes you import AI as a library, and I believe if AI is the future of how we build software, it should be a keyword — not an afterthought." — Emmanuel King Christopher, Founder of SYNEKCOT Tech and Sole Author of NEKOVA

NEKOVA was born in Nigeria to prove that world-class programming languages can come from anywhere. 1,513 tests. 26 phases shipped. One language.

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Installation

Option 1 — pip (recommended)

pip install nekova-lang

Add your AI key to a .env file in your project:

# You only need ONE key — NEKOVA auto-detects it
GEMINI_API_KEY=your_key_here        # Free — aistudio.google.com
ANTHROPIC_API_KEY=your_key_here     # console.anthropic.com
OPENAI_API_KEY=your_key_here        # platform.openai.com

Run your first program:

nekova hello.nk

Option 2 — Clone from GitHub

git clone https://github.com/kinghenesey/NEKOVA.git
cd NEKOVA
pip install -e .

VS Code Extension

Search "NEKOVA" in the VS Code Extension Marketplace, or install directly:

ext install SYNEKCOTTech.nekova

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Features

Core Language

# Variables
let name = "Emmanuel"
let age  = 21

# Tasks (functions) with type hints
task add(a: int, b: int) -> int:
    return a + b

# Default parameters
task greet(name, greeting="Hello"):
    show greeting + " " + name

# Varargs
task total(*nums):
    return sum(nums)

# Generators
task count(n: int):
    let i = 0
    while i < n:
        yield i
        let i = i + 1

for x in count(5):
    show x

Classes and Objects

class Animal:
    name: str
    init(name: str):
        self.name = name
    func speak():
        return self.name + " says hello"

class Dog extends Animal:
    func fetch():
        return self.name + " fetches!"

let d = new Dog("Rex")
show d.speak()
show d.fetch()

Decorators

task log(fn):
    task wrapper(x):
        show "calling with " + str(x)
        return fn(x)
    return wrapper

@log
task double(n):
    return n * 2

show double(21)   # → calling with 21 \n 42

Error Types

error NetworkError:
    message str
    code    int = 500

try:
    raise NetworkError("timeout", 408)
catch e:
    show e["message"]   # → timeout
    show e["code"]      # → 408

Language Completeness II (Phase 24)

# Tuple-style destructuring — same semantics as the bracket form
let pair = (1, 2)
let (a, b) = pair
show a   # → 1
show b   # → 2

# "Multiple return values" for free
let (quotient, remainder) = divmod(10, 3)
show quotient    # → 3
show remainder   # → 1

# Rest capture works in either form
let (first, ...rest) = [1, 2, 3, 4]
show first   # → 1
show rest    # → [2, 3, 4]

# Named/keyword arguments — mixed positional+keyword, gap-filling
# with declared defaults
task greet(name, greeting = "Hello"):
    show greeting + ", " + name
greet(name="Sam", greeting="Hi")   # → Hi, Sam

# const — immutable once set; reassigning raises
const MAX_RETRIES = 5
show MAX_RETRIES   # → 5

# Spread syntax — lists and dicts, mixed spread+literal items
let combined = [...[1, 2], ...[3, 4]]
show combined       # → [1, 2, 3, 4]

let defaults  = {"color": "blue", "size": "M"}
let overrides = {"size": "L"}
show {...defaults, ...overrides}   # → {color: blue, size: L}

# Optional chaining — short-circuits to null instead of raising
let user = {"email": "[e-mail]"}
show user?.email        # → [e-mail]

let nothing = null
show nothing?.email     # → null   (no error)

# Enums — each member is its own name as a string
enum Status: PENDING, ACTIVE, DONE
show Status.ACTIVE                  # → ACTIVE
show Status.ACTIVE == "ACTIVE"      # → true

# Set type — disambiguated from a dict literal at parse time
let a = {1, 2, 3}
let b = {2, 3, 4}
show set_union(a, b)          # → {1, 2, 3, 4}
show set_intersection(a, b)   # → {2, 3}
show set_difference(a, b)     # → {1}

AI — Built In

# Single line AI calls
think "Summarise this in 3 words" as text
think "Extract the names" as list
think "Is this positive?" as bool
think "Parse this data" as json

# Remember context across calls
remember "user" as "Emmanuel"
let name = recall "user"

# Streaming
stream think "Write a short story about Lagos" as text

AI-Native Differentiators II (Phase 25)

# think "..." as <ShapeName> — builds an implicit schema from the
# shape's own fields; the response comes back type-coerced and
# tagged with the shape's name
shape User:
    name str
    age  int

let u = think "extract from: Ada, 30" as User
show u["name"]         # → mock_name (a real API key returns "Ada")
show u["age"]          # → 42 (a real API key returns 30)
show u["__shape__"]    # → User

# Cost/token tracking — raises if the estimated tokens exceed budget
let result = think "summarize this" with budget: 500
show ai_usage()    # → {calls: 2, tokens: 33} (running total so far)

# Explicit model selection
let selected = think "analyse this" using "claude-sonnet"

# converse: blocks — multi-turn dialogue with automatic context.
# Every think/listen inside automatically carries prior turns.
let topic = "pricing"
converse:
    think f"ask a clarifying question about {topic}"
    listen
    think "respond based on what they said"

# imagine "..." as file — cached on disk under
# .nekova_cache/imagine/, so repeated calls during a dev loop don't
# regenerate (or re-bill) the same image
let image = imagine "a futuristic Lagos skyline at sunset" as file
show image

Prompt Blocks (Phase 21)

Named, composable, reusable prompts — call them like any other function:

prompt summarize(text, style="professional", max_sentences=3):
    """
    Summarize the following in a {style} tone.
    Use at most {max_sentences} sentences.
    Text: {text}
    """

let summary = think summarize(article, style="casual")

prompt is intentionally not a reserved word — it's only treated as a definition when it looks like one (prompt name(...):), so existing code using prompt as a plain variable keeps working unchanged.

Retry and Fallback (Phase 21)

First-class resilience for AI and network calls, with configurable backoff:

retry 3 times with exponential backoff:
    let result = think "analyse this" as json
fallback:
    let result = {error: "unavailable"}

Observability, Mock Testing, and Pipes (Phase 22)

# Tag and trace a block of execution
observe "pipeline run" with tags {user: user_id}:
    let summary = think summarize(document)

# Deterministic AI output in tests — no real API call, no ambiguity
# about whether a response is real or mocked
test "classifier":
    mock think as "sports"
    expect classify(text) == "sports"

# Pipe operator — chain transformations left to right
let result = data |> parse() |> filter() |> sort() |> take(10)

Speak, Listen, Imagine

# Text-to-speech
speak "Your report is ready"

# Speech-to-text
let answer = listen "What city are you in?"

# AI image generation
let img = imagine "a futuristic Lagos skyline at sunset" as url
show img

Scheduled Execution

# Run every 10 seconds, 5 times
every 10 s 5 times:
    show "checking..."

# Run forever in background
every 1 m:
    think "Any breaking news?" as text

Built-in Test Runner

task add(a, b):
    return a + b

test "addition":
    expect add(1, 2) == 3
    expect add(0, 0) == 0
    expect add(-1, 1) == 0

test "strings":
    expect len("hello") == 5
    expect "hello"[0] == "h"

# Snapshot testing (Phase 26) — for AI outputs where writing out
# the exact expected value by hand isn't practical. First run saves
# a baseline; later runs compare against it and fail on drift.
test "AI summary shape":
    let summary = think "Summarize this in one word: excellent!" as text
    expect_snapshot(summary, "one_word_summary")

Data Shapes

shape User:
    name  str
    age   int
    email str = "unknown"

let u = User("Emmanuel", 21)
show u["name"]      # → Emmanuel
show u["__shape__"] # → User

Sandbox — Safe Execution

# Run untrusted code in isolation
sandbox strict:
    let x = 10 * 10
    show x              # prints 100

show sandbox_result["safe"]      # → true
show sandbox_result["duration"]  # → 0.001

# Programmatic sandbox API
let result = sandbox_run("show 42")
show result["output"]  # → 42
show result["safe"]    # → true

Standard Library in NEKOVA

# Math — written in NEKOVA
use math
show pi                     # → 3.141592653589793
show clamp(15, 0, 10)       # → 10
show factorial(10)          # → 3628800
show lerp(0, 100, 0.5)      # → 50.0

# String — written in NEKOVA
use string
show repeat("ha", 3)        # → hahaha
show pad_left("5", 4)       # → "   5"
show is_palindrome("racecar") # → true

# File — written in NEKOVA
use file
write("data.txt", "hello")
let content = read("data.txt")
show line_count("data.txt")

# Date — written in NEKOVA
use date
show today()                # → 2026-06-30
show day_of_week(today())   # → Tuesday
show add_days(today(), 7)   # → 2026-07-07

Pattern Matching

let status = 404

match status:
    when 200: show "OK"
    when 404: show "Not Found"
    when 500: show "Server Error"

Web Routes

route GET "/":
    think "Write a welcome message" as text

route POST "/api/chat":
    let msg = request["body"]["message"]
    think msg as text

serve port: 8080

Generators and Lazy Sequences

task fibonacci():
    let a = 0
    let b = 1
    while true:
        yield a
        let temp = b
        let b = a + b
        let a = temp

let count = 0
for n in fibonacci():
    show n
    let count = count + 1
    if count == 10:
        break

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Developer Experience (Phase 26)

A real Language Server Protocol implementation — nekova lsp — backs the VS Code extension (and any other LSP-aware editor): live inline errors as you type, hover docs that resolve to a task/class's actual signature and docstring, and autocomplete for keywords, builtins, and everything declared in the open file, including type-aware method suggestions right after obj..

# Preview formatting changes without writing them
nekova fmt app.nk --diff

# Name the exact internal check that raised an error
nekova run app.nk --why

# A committed, reproducible snapshot of resolved dependency versions
nekova lock
nekova lock --check       # detect drift, e.g. in CI

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Self-Hosting Blockers (Phase 19b)

All five blockers for writing NEKOVA in NEKOVA are now fixed:

# 1. Dict subscript assignment
let tokens = {}
let tokens["IF"] = "keyword"
show tokens["IF"]   # → keyword

# 2. Hex literals
let mask = 0xFF
let color = 0xDEADBEEF

# 3. Scientific notation
let avogadro = 6.022e23
let epsilon  = 1e-9

# 4. Underscore separators
let million = 1_000_000
let pi_approx = 3.141_592

# 5. Range arms in match
let c = "k"
match c:
    when "a".."z": show "lowercase"
    when "A".."Z": show "uppercase"
    when "0".."9": show "digit"

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CLI Reference

# Run a file
nekova run app.nk

# Run in sandbox mode
nekova run app.nk --sandbox
nekova run app.nk --sandbox --sandbox-mode relaxed

# Watch for changes
nekova run app.nk --watch

# Start the REPL
nekova repl

# Format code
nekova fmt app.nk
nekova fmt app.nk --diff       # preview changes without writing them

# Check for errors
nekova check app.nk

# Create a new project
nekova new myproject
nekova new myproject --template web
nekova new myproject --template ai
nekova new myproject --template fullstack
nekova new                      # interactive wizard — prompts for
                                 # name, template, author, description

# Dependency lockfile
nekova lock                     # (re)generate nekova.lock
nekova lock --check             # verify it's in sync (for CI)

# Explain which internal check raised an error
nekova run app.nk --why

# Accept new AI-output snapshots as the baseline
nekova run app.nk --update-snapshots

# Language server (used by editor integrations, not typically run by hand)
nekova lsp

# Package management
nekova install requests
nekova uninstall requests
nekova search "http client"

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Language Reference

📖 The full language reference — every keyword, every construct, with runnable examples — lives at kinghenesey.github.io/NEKOVA. The tables below are a quick-glance summary, not the complete picture.

Keywords

Category	Keywords
Control flow	if else elif while for in return break continue match when yield
Declarations	task let const enum use import class object error shape
Exception	try catch finally raise assert pass
AI	think remember recall forget imagine speak listen converse (Phase 25)
Resilience	retry fallback (Phase 21)
Scheduling	every
Testing	test expect mock (Phase 22)
Observability	observe (Phase 22)
Watching	watch
Sandbox	sandbox strict relaxed
OOP	init self new extends func
Async	async await stream
Logic	and or not is in not in is not

prompt (Phase 21) is intentionally not a reserved word — it's recognized contextually only when it looks like a definition (prompt name(...):), so existing code using prompt as an ordinary variable name keeps working. using and budget (Phase 25, inside a think clause) work the same way — soft keywords, matched by value only where they're expected, plain identifiers everywhere else.

Operators

Operator	Description
+ - * /	Arithmetic
//	Floor division
% **	Modulo, power
== != < > <= >=	Comparison
in not in	Membership
is is not	Identity
and or not	Logic
@	Decorator
->	Return type hint
|>	Pipe (Phase 22) — data |> parse() |> sort()
?.	Optional chaining (Phase 24) — user?.email
...	Spread (Phase 24) — [...a, ...b] — and rest capture in destructuring
x if c else y	Ternary

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Project Structure

NEKOVA/
├── nekova/              ← Core package: lexer, parser, interpreter, AI runtime, stdlib (.nk + .py)
├── nekova-vscode/        ← VS Code extension source (published on the marketplace)
├── myproject/            ← Example / scaffold project generated by `nekova new`
├── tests/                ← Test suite (1,513 tests across 26 phases)
├── main.py                ← Entry point
├── runner.py              ← Pipeline orchestrator
├── nekova_cli.py          ← pip CLI entry point
├── repl.py                ← Interactive shell
├── debugger.py            ← Debugger
├── formatter.py           ← `nekova fmt`
├── pyproject.toml         ← Package metadata
└── website.html           ← Project landing page

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Roadmap

Phase	Status	Description
1–14	✅	Core language, AI, classes, web, packages
15	✅	Stability — in/not in, //, range(), slicing, builtins
16	✅	Standout features — speak, listen, every, test/expect, imagine, shape, watch
17	✅	Power user layer — generators, decorators, error types, typed tasks, class keyword
18	✅	Standard library in NEKOVA — math.nk, string.nk, file.nk, date.nk
19	✅	NEKOVA Sandbox — isolated execution, resource limits, violation tracking
19b	✅	Security fixes — 38 bugs fixed, self-hosting blockers cleared
20	✅	Self-hosting begins — NEKOVA lexer written in NEKOVA (nekova/stdlib/nk/lexer.nk), verified token-for-token identical to the Python reference lexer, including on its own source
21	✅	prompt blocks, retry/fallback with backoff
22	✅	observe telemetry, mock think in tests, |> pipe operator
23a	✅	Correctness & Trust Part 1 — accurate recursion errors, labeled mock AI responses, type-mismatch errors, near-miss variable suggestions, semver policy
23b	✅	Correctness & Trust Part 2 — indentation-depth errors, full builtin-exception audit
24	✅	Language completeness II — destructuring, keyword arguments, const, spread, optional chaining, enums, Set type
24b	✅	Documentation website — 28 pages at kinghenesey.github.io/NEKOVA
25	✅	AI-native differentiators II — cost/token tracking, think as <Shape>, converse: blocks, explicit model selection, imagine as file caching, sandbox prompt-injection guard
26	✅	Developer experience — Language Server Protocol (real inline errors, hover docs, autocomplete), nekova fmt --diff, multi-error parser recovery, interactive nekova new wizard, nekova.lock, --why, expect_snapshot(...) snapshot testing
26b	🔜	Next — Education layer: nekova learn, nekova explain, classroom mode
27	🔜	NEKOVA parser in NEKOVA — v2.0 milestone
31	🎯	Full self-hosting — interpreter in NEKOVA — v3.0

Long term: NEKOVA Game Engine, WASM compilation.

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License

NEKOVA is licensed under the Business Source License 1.1: free to use, modify, and build on for personal projects, learning, and commercial products under $1M/year in revenue. The license converts automatically to Apache 2.0 four years after each release. See LICENSE for full terms, or the Licensing FAQ for a plain-English explanation.

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Built By

Emmanuel King Christopher — Founder, SYNEKCOT Tech, Nigeria. Built from scratch in Python 3.11, starting October 2025.

"Because every other language makes you import AI as a library, and I believe if AI is the future of how we build software, it should be a keyword — not an afterthought."

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Star ⭐ this repo if NEKOVA inspired you!

github.com/kinghenesey/NEKOVA · PyPI · Built by SYNEKCOT Tech 🇳🇬