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 Python for ecosystem access or to native binaries via LLVM for up to 63x faster execution.

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.
• Traits — Generic abstractions with trait bounds. Builtin traits: Display, Eq, Ord, Hash.
• Modules — File-based module resolution with pub visibility and circular import detection.
• Type System — Static typing with inference, generics, Option, Result, and error propagation.
• Dual Compilation — Transpile to Python or compile to native binaries via LLVM.

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).