Magpie eliminates ambiguity so LLMs can write perfect code on the first try. Native speed, instant feedback, zero guesswork.
CA: 0x07d8e2810040A5d175c7beD4C84296406dD30B07
Get the CLI running on your machine in seconds.
git clone https://github.com/magpie-lang/magpie.git
cd magpie
cargo build -p magpie_cliMost languages are optimized for human typing. Magpie is optimized for AI generation.
Every operation is self-documenting. AI never has to guess what a `+` sign means or infer hidden types. If it writes it, it's intentional.
There is exactly one way to do things. Fewer syntactic choices means fewer decision points, leading to dramatically fewer errors from AI agents.
The compiler runs in milliseconds, giving AI agents immediate structural and type-checking feedback after every generation.
Comparing Magpie with Rust and TypeScript on the same program.
Milliseconds to compile. Faster means a tighter AI feedback loop.
Time to run the compiled program natively.
Maximum memory footprint during execution.
Lower ratio means higher predictability for LLMs.
Why LLMs write Magpie better than they write Rust or TypeScript.
// Is this addition or string concat?
// Does it overflow? Panic? Coerce types?
let sum = a + b;
// Implicit branches across multiple forms
if cond { return x; }
match cond { true => x, false => y }
// Implicit memory management
// Invisible lifetime rules or opaque GC cycles
let b = &name;
let s = Arc::new(value);; Explicit types, named operands, explicit overflow rules
%sum: i64 = i.add { lhs=%a, rhs=%b }
; Exactly one way to branch (cbr/br)
cbr %cond bb_true bb_false
; Ownership transitions are explicit operations
%b: borrow Str = borrow.shared { v=%name }
%s: shared T = share { v=%value }Fewer choices = fewer LLM decision points = fewer errors. Magpie uses ~2.3× more tokens per operation, but eliminates the hidden rules that cause AI retries and borrow checker failures.
Common questions about Magpie and its LLM-first design.
Magpie compiles to native machine code via LLVM, just like Rust or C++. It provides the execution speed of a systems language with a sub-200ms compilation cycle.
Magpie uses a highly structured, explicit SSA (Static Single Assignment) syntax. While unconventional for humans, it is dramatically easier for AI agents to write without errors.
Magpie uses a mix of ARC (Automatic Reference Counting) for deterministic heap management and Rust-like explicit ownership rules (borrow, mutborrow, share) to guarantee safety without a garbage collector.
Dynamic languages have hidden semantics and ambiguous patterns that cause LLMs to make subtle mistakes. Magpie forces the AI to be explicit, resulting in higher first-try success rates for complex code generation.