Mojo
Python-superset systems language for AI: Python's usability with C-level performance.
About
Mojo is a new systems programming language created by Swift/LLVM architect Chris Lattner at Modular, designed as a Python superset that compiles through MLIR to match C/C++ and Rust-level performance while keeping Pythonic syntax and Python library interoperability. It is aimed squarely at AI workloads: memory-safe systems programming, GPU kernel authoring, and heterogeneous accelerators from one codebase.
In August 2026 Modular released Mojo 1.0 — compiler, standard library, and tooling fully open source under Apache 2.0 with LLVM exceptions, completing a staged opening that began with the standard library (2024) and MAX kernels (2025). 1.0 guarantees backward compatibility, adds a reliable LSP server, lambda syntax, and memory-safety diagnostics. It is the language core of the MAX platform and Modular Cloud.
What it can do
- Python-superset syntax with C/Rust-level performance and memory safety
- Compile via MLIR to CPUs, GPUs, TPUs, and custom accelerators
- Write GPU kernels in a general-purpose language, not CUDA dialects
- Apache 2.0 open-source compiler and standard library (1.0, Aug 2026)
Setup
Install the Modular platform (mojo CLI included); VS Code extension provides the LSP.
Prerequisites
Python familiarity; target accelerator only for GPU kernels.
Use cases
Works with
Trust score — 9/10
Scored 9/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 73; Trending score 86/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.