Mapanare — The AI-Native Programming Language

Mapanare is the first AI-native compiled programming language where agents, signals, streams, and tensors are first-class language primitives — not libraries bolted on after the fact. It compiles to native binaries via LLVM and WebAssembly for up to 63x faster execution. The language specification is frozen at v1.0 Final. Self-hosted compiler (8,288+ lines of .mn), 4,465+ tests, no Python required at runtime.

Key Features

• Agents — Concurrent actors with typed channels, lifecycle management, supervision, and backpressure. Native LLVM backend uses OS threads with lock-free ring buffers.
• Signals — Reactive state with automatic dependency tracking and computed values.
• Streams — Async pipelines with the pipe operator. Adjacent map/filter operations fuse automatically.
• Tensors — N-dimensional arrays with compile-time shape validation and matrix multiplication.
• GPU Compute — CUDA and Vulkan via dlopen. @gpu, @cuda, @vulkan annotations for automatic dispatch. Built-in tensor kernels.
• WebAssembly — Compile to WAT/WASM for browser and server-side execution. WASI preview 1 support.
• AI Stdlib — LLM drivers, embedding providers, RAG pipelines as native stdlib modules.
• Data Engine — Dato: tables, aggregations, joins, null handling, reshape, CSV/JSON I/O.
• Database Drivers — SQLite, PostgreSQL, Redis, KV, connection pooling, schema migrations.
• Web & Security — Web crawler, vulnerability scanner, HTTP fuzzer, HTTP server toolkit.
• Native Compilation — Compile to native binaries via LLVM or WebAssembly. Python transpiler deprecated.

Get Started

Install Mapanare with a single command: curl -fsSL https://mapanare.dev/install | bash on Linux/macOS, or irm https://mapanare.dev/install.ps1 | iex on Windows PowerShell. You can also install via pip: pip install mapanare.

Quick Example

agent Classifier {
    input text: String
    output label: String

    fn handle(text: String) -> String {
        return "positive"
    }
}

fn main() {
    let cls = spawn Classifier()
    cls.text <- "Mapanare is fast"
    let result = sync cls.label
    print(result)
}

Documentation

• Getting Started
• Variables & Mutability
• Functions & Lambdas
• Control Flow
• Structs & Enums
• Type System
• Error Handling
• Operators
• Modules & Imports
• Traits
• Decorators
• Agents
• Signals
• Streams
• Tensors
• Pipes
• CLI Reference
• Standard Library

Performance

Mapanare's LLVM backend delivers significant performance gains: Fibonacci (n=35) runs 26.5x faster than Python, stream pipelines are 62.8x faster, and matrix multiplication is 22.9x faster.

For AI & LLMs

Full machine-readable documentation is available at llms.txt (summary) and llms-full.txt (complete reference with all code examples).