We’re excited to share the latest Mojo 
roadmap, outlining our priorities for the first half of 2025. This update is organized into three focus areas: compiler, libraries, and tooling.
Have questions or feedback?
Drop a comment on this thread and join us at our next community meeting on May 12 at 10 AM PT. We’ll address forum questions and take live ones during the meeting.
Overview
Our teams are focused on expanding platform capabilities and increasing developer productivity. Highlights for the first half of 2025 include:
- Adding more type system features and improving compiler performance/error messages
- Open sourcing remaining components of Mojo standard library and MAX AI Kernels (done!
) - Simplifying tensor types and improving GPU kernel programming
- Enhancing Python → Mojo interoperability
- Improving LSP, documentation, and developer tooling
Our top priority this year is making Mojo the best language for GPU programming. That means continuous investment in the language, its libraries, and the surrounding tooling ecosystem.
Compiler Roadmap
Our current focus is on boosting productivity for in-house developers. To date, they’ve authored nearly 500,000 lines of Mojo code—over 30% of Modular’s codebase—and that number keeps growing!
What’s in that code?
The AI space evolves rapidly. New algorithms and levels of abstraction must support an expanding array of GPU hardware targets. This creates a perfect storm of complexity, with developers pushing against every dimension of the Cartesian product of algorithms × data types × hardware targets.
Developer needs:
- Add language features to help developers manage complexity through concise, highly parameterized code.
- Reduce development overhead by making the iterative development process faster and smoother.
Key compiler initiatives
1. Main focus: add more expressivity to the Mojo type system
- Add trait unions:
alias CopyableAndMovable = Copyable & Movable - Add parametric aliases:
alias Scalar[DT: DType] = SIMD[1, DT] - Default methods implementation in traits:
# Support all comparison operators by implementing just one method
trait Comparable:
fn compare(s: Self, other: Self) -> Int
...
fn __eq__(s: Self, other: Self): return compare(s, other) == 0
fn __ne__(s: Self, other: Self): return compare(s, other) != 0
- New
requireskeyword for adding constraints to structs and functions
struct SIMD[dtype: DType, size: Int]
requires size.is_power_of_two(), "simd width must be power of 2"
requires dtype is not DType.invalid, "simd type cannot be DType.invalid"
2. Improve error messages
When compilation fails, the Mojo compiler prints detailed diagnostic messages. These messages have sometimes included compiler implementation details, such as MLIR representation and type system internals. This forces users to spend extra time parsing messages to find the relevant information.
We plan to make compiler errors more concise and helpful by:
- Shortening error messages from the
constrained()function and improving their source location accuracy - Investigating implementing stack traces for runtime errors and exceptions
3. Speed up the Mojo compiler
It has always been our goal to make Mojo lighting fast. 
In the past, our focus was on the LLVM and code generation phase. Now that the code generation pass runs in parallel [details], we want to focus on the front-end, to speed up the parser, package importer, and elaborator.
We will continue improving the quality of generated code while expanding support for additional hardware targets.
Libraries Roadmap
1. Open sourcing the remaining Mojo standard library modules and the MAX AI Kernels
We recently open sourced the final components of the Mojo Standard Library, along with the MAX AI Kernels, which contains over 200k lines of Mojo code! 
This is a major milestone that unlocks more of Mojo’s capabilities for community exploration, contribution, and extension.
We aren’t yet accepting contributions to the MAX AI Kernels, but will share an update soon on when that will change. We are accepting contributions to the recently open sourced standard library modules today! Be sure to check out the standard library contribution guidelines if you’re planning to submit a pull request.
2. Core standard library evolution
We continue to invest in foundational types and APIs. For example, String recently got a major overhaul and now includes Small String Optimization for better performance. We welcome and appreciate your contributions as we evolve the core standard library together. 
3. Simplifying tensor types
We’re streamlining the tensor type system to make it easier to understand and work with. This includes:
- Deprecating
max.Tensorin favor of a cleaner type hierarchy. - Removing
NDBufferfrom the kernel library APIs. - Standardizing on
ManagedTensorSlicefor host-side operations andLayoutTensorfor device-side (GPU) operations.
These changes aim to reduce friction and make tensor-based programming more intuitive.
4. Python → Mojo interoperability
One of our most exciting initiatives: enabling progressive adoption of Mojo in existing Python applications. This work lets you integrate Mojo where it matters most—unlocking performance without needing to rewrite everything.
We’re actively collaborating with other internal teams on real-world use cases like model serving, by writing “manual bindings” — something akin to nanobind or pybind11 for calling into Mojo functions from Python. Relatedly, we also plan to release enhancements to the Mojo compiler for compiling Mojo to dynamic libraries.
5. B200 support + kernel intrinsics
We’re bringing up support for NVIDIA Blackwell GPUs, along with foundational intrinsics needed to write performant kernels for operations like matrix multiplication and attention.
6. LayoutTensor enhancements & GPU kernel ergonomics
We’re evolving the LayoutTensor APIs and refining the kernel library to make GPU kernel programming more ergonomic and composable. Expect improvements that make it easier to express high-performance code with less boilerplate.
Tooling Roadmap
LSP Improvements
-
Reducing parsed code
The language server needs only the signatures of code dependencies, not their implementation details. We can dramatically reduce its workload by parsing just the signatures, since function bodies are irrelevant for our purposes. When computing completions, we can avoid parsing most of the document—we only need to fully parse the function body where the cursor is located. -
Progress Reporting
Even with extensive optimization, larger documents may still exceed our performance targets. To address this, we’ll add a progress indicator to show users that their input is being processed. Instead of waiting to collect all results, we’ll stream them to the client as they become available. Our target response times are under 200ms for code editing operations and under 2 seconds for non-editing operations like symbol definition requests. -
General bug fixes to improve stability and performance while reducing community-reported crashes.
Mojo REPL
We will invest in fixing critical Mojo REPL bugs.
Doc tooling
Improving public documentation on docs.modular.com:
- Fixing DocString section handling to properly publish sections that contain blank lines or mismatched indentation.
- Enhancing type names in documentation by shortening built-in Standard Library types with long namespaces.
- Adding associated aliases to trait documentation for better completeness.