Releases: flagos-ai/KernelGen
Release list
KernelGen V2.1.0 release
KernelGen v2.1.0 Release
This is FlagOS KernelGen version 2.1.0
Overview
KernelGen is an AI-powered automatic Triton kernel development platform built on the FlagOS ecosystem. It provides a fully automated workflow for operator (kernel) generation, optimization, testing, and deployment across diverse hardware platforms.
With the 2.1 release, KernelGen further expands its hardware ecosystem and introduces experimental support for Triton Language Extensions (TLE) across the full development workflow — including Web, MCP, and Skills integration.
Experience it from: https://kernelgen.flagos.io
MCP Service (ModelScope): https://www.modelscope.cn/mcp/servers/flagos-ai/FlagOS_KernelGen
Core Updates in v2.1
1. New Hardware Backend Support — Sunrise
KernelGen now officially supports the Sunrise AI accelerator platform, expanding hardware coverage from 6 supported chips to 7.
The Sunrise backend is fully integrated into the existing KernelGen ecosystem and supports the complete feature set already available on other hardware platforms, including:
- Web Platform support
- MCP automated workflows
- Skills integration
- Kernel generation & optimization
- Auto testing & benchmarking
- Multi-hardware adaptation workflows
This enables developers targeting Sunrise hardware to use the same AI-native Triton development experience already available across the FlagOS ecosystem.
2. Experimental Triton Language Extensions (TLE) Support
KernelGen v2.1 introduces experimental support for Triton Language Extensions (TLE) across:
- Web Platform
- MCP workflows
- Skills-based development
TLE support is currently available as a laboratory / experimental feature and will continue evolving in future releases.
TLE documentation:
https://github.com/flagos-ai/FlagTree/wiki/TLE
What is TLE?
TLE (Triton Language Extensions) is an extension architecture for Triton designed to address limitations in:
- Hardware adaptation
- Memory hierarchy abstraction
- Tile programming
- Parallelism abstraction
- Distributed kernel programming
As modern AI accelerators evolve rapidly, traditional Triton workflows increasingly face challenges in portability, fine-grained optimization, and heterogeneous hardware adaptation.
TLE extends Triton across three abstraction layers:
| Layer | Description |
|---|---|
| TLE-Lite | Lightweight Triton-compatible extensions with minimal code changes |
| TLE-Struct | Architecture-clustered abstractions for deeper performance tuning |
| TLE-Raw | Hardware-native programming interfaces for maximum performance |
Key goals include:
- Better portability across diverse AI chips
- Improved tile-level programming abstractions
- Flexible memory hierarchy optimization
- Enhanced performance tuning capabilities
- Future distributed programming support
KernelGen v2.1 now enables developers to experiment with TLE-powered kernel generation and optimization directly through AI-native workflows.
Core Features
Fully Automated Workflow
End-to-end kernel lifecycle automation with MCP + AI agents.
Multi-Backend Support
Broad compatibility across AI frameworks and hardware platforms.
AI-Native Development Experience
Deep integration with IDEs, agents, and developer workflows.
Standardized Verification
Automatic correctness and performance validation.
Deep Ecosystem Integration
Seamless collaboration with:
- FlagGems
- FlagTree
- FlagOS infrastructure
Hardware Platform Coverage
KernelGen v2.1 now supports 7 hardware platforms, including newly added Sunrise support.
Supported capabilities across platforms include:
- Triton kernel generation
- Auto optimization
- Auto tuning
- Testing & benchmarking
- Web workflows
- MCP integration
- Skills integration
- TLE experimental workflows
AI Skills Integration
KernelGen continues providing unified AI skill integration through:
- Claude Code
- VS Code (with Copilot)
- OpenClaw
- Other MCP-compatible agents
Skills repository:
https://github.com/flagos-ai/skills
Available workflows include:
- Kernel generation
- Code adaptation
- Testing & benchmarking
- Auto PR integration
- TLE experimental workflows
Web Platform Enhancements
The KernelGen web platform continues providing:
- Kernel history tracking
- Enhanced workflow visibility
- Generation & optimization management
- TLE-enabled experimental workflows
- Multi-backend kernel management
Access:
https://kernelgen.flagos.io
Quick Start (v2.1)
Step 1: Get Bearer Token
Get your Bearer Token from the MCP Service section on:
Step 2: Install Skill
npx skills add flagos-ai/skills --skill kernelgen -a claude-codeStep 3: Run
/kernelgen-flagos relu_plus_expExperimental Feature Notice
TLE Experimental Status
Current TLE support in KernelGen is still in an experimental laboratory stage.
Users may encounter:
- Unstable optimization results
- Inconsistent performance across hardware
- Incomplete backend feature coverage
- Ongoing API and workflow changes
The TLE ecosystem will continue evolving with future KernelGen and FlagTree updates.
Known Issues
- Some complex kernels may require multiple optimization iterations for optimal performance
- Auto-tuning may incur longer execution time on certain hardware platforms
- MCP workflows may depend on external agent/IDE integration stability
- Experimental TLE workflows may produce unstable or non-deterministic optimization results
Future Roadmap
- Broader hardware backend support
- Enhanced TLE capabilities
- Improved TLE compiler/runtime integration
- More intelligent AI-driven optimization
- Expanded distributed programming support
- Deeper AI agent integration
- Improved debugging & profiling tools
Contact Information
Powered by FlagOS — Building the unified open-source system software stack for diverse AI chips.
KernelGen V2.0.0 release
KernelGen v2.0.0
This is FlagOS KernelGen version 2.0.0
Overview
KernelGen is an AI-powered automatic Triton kernel development platform built on the FlagOS ecosystem. It provides a fully automated workflow for operator (kernel) generation, optimization, testing, and deployment across diverse hardware platforms.
With the 2.0 release, KernelGen evolves into a complete AI-native kernel engineering system, introducing MCP-based automation, IDE-integrated skills, enhanced web capabilities, and advanced Triton language extensions.
Experience it from: https://kernelgen.flagos.io
MCP Service (ModelScope): https://www.modelscope.cn/mcp/servers/flagos-ai/FlagOS_KernelGen
Core Features (Enhanced)
- Fully Automated Workflow
End-to-end kernel lifecycle automation with MCP + AI agents - Multi-Backend Support
Broad compatibility across AI frameworks and hardware platforms - AI-Native Development Experience
Deep integration with IDEs, agents, and developer workflows - Standardized Verification
Automatic correctness and performance validation - Deep Ecosystem Integration
Seamless collaboration with:- FlagGems
- FlagTree
- FlagOS infrastructure
Core Capabilities Comparison
KernelGen 2.0 transforms Triton kernel development from a fixed pipeline into a fully AI-native, agent-driven system — enabling automatic generation, optimization, and integration across hardware and repositories.
Kernel Development & Optimization
| Feature | Version 1.0 | Version 2.0 |
|---|---|---|
| Workflow Type | Fixed step (Linear pipeline) | Agentic (Iterative & Adaptive) |
| Error Handling | Manual debugging | Automatic error fixing (log-driven) |
| Optimization | Basic performance test | Auto-tuning + AI-driven optimization |
| Testing | Basic correctness & performance tests | Fully automated test generation (correctness + benchmark) |
| Kernel Lifecycle Management | Partial | Full lifecycle (generate → optimize → test → integrate) |
Hardware & Performance Capabilities
| Feature | Version 1.0 | Version 2.0 |
|---|---|---|
| Multi-Hardware Adaptation | Supported | Intelligent auto-adaptation & specialization |
Developer Experience
| Feature | Version 1.0 | Version 2.0 |
|---|---|---|
| Interface | Web Browser only | Web + IDE + CLI (MCP) |
| Development Entry | Web UI only | Natural language + CLI + AI agents |
| IDE / Agent Integration | Not supported | Claude Code / VS Code / OpenClaw / MCP agents |
| User Productivity | Assisted development | Fully automated development |
Integration & Ecosystem
| Feature | Version 1.0 | Version 2.0 |
|---|---|---|
| Repository Integration | Manual download & integration | Automatic PR generation via Skills |
| Web Platform Features | Basic UI | Operator history tracking + enhanced UX |
| Ecosystem Integration | FlagOS basic integration | Deep integration with FlagGems / FlagTree / Skills |
| Target Users | Triton developers | Triton developers + AI-native developers |
Major Updates in v2.0
MCP-Powered Kernel Automation
KernelGen now introduces the MCP (Model Context Protocol) Server, enabling fully automated kernel development workflows through AI agents.
Key Capabilities
-
Automatic Operator Generation
Generate Triton kernels directly from natural language descriptions -
Automatic Error Fixing
Fix kernel code based on compilation and test logs -
Automatic Performance Optimization
Optimize kernels using runtime feedback and performance logs -
Auto Tuning & Specialization
Automatically tune and specialize kernels for target hardware -
Multi-Hardware Adaptation
Seamless support for diverse hardware platforms (including domestic AI chips) -
Automated Testing Pipeline
Auto-generated correctness test cases
Auto-generated performance benchmarks (vs CUDA) -
One-Click Workflow
End-to-end kernel development (generation → optimization → testing) in a single pipeline
AI Skills Integration (IDE & Agent Native)
KernelGen 2.0 introduces a unified AI skill: kernelgen-flagos, enabling deep integration with modern AI coding environments:
- Claude Code
- VS Code (with Copilot)
- OpenClaw
- Other MCP-compatible agents
Highlights
- One-command kernel generation
/kernelgen-flagos relu
-
Automatic repository detection
- FlagGems → specialized workflow
- vLLM → specialized workflow
- Generic Triton repo → adaptive workflow
-
Built-in workflows
- Kernel generation
- Code adaptation
- Testing & benchmarking
- Auto PR integration
-
Unified Skill Architecture
- kernelgen-general
- kernelgen-for-flaggems
- kernelgen-for-vllm
- kernelgen-submit-feedback
Skills repository: https://github.com/flagos-ai/skills
Web Platform Enhancements
-
Operator History Tracking
Users can now view and manage previously generated operators directly from the web interface -
Improved usability and workflow transparency
Better visibility into generation, testing, and optimization processes
Access: https://kernelgen.flagos.io/
Quick Start (v2.0)
Step 1: Get Bearer Token
Get your Bearer Token from the MCP Service section on:
https://kernelgen.flagos.io/
Step 2: Install Skill
npx skills add flagos-ai/skills --skill kernelgen -a claude-code
Step 3: Run
/kernelgen-flagos relu_plus_exp
Known Issues
- Some complex operators may require multiple optimization iterations for optimal performance
- Auto-tuning may incur longer execution time on certain hardware platforms
- MCP workflows may depend on external agent/IDE integration stability
Future Roadmap
- Broader hardware backend support
- Enhanced MCP intelligence
- Deeper AI agent integration
- Improved debugging tools
Contact Information
Powered by FlagOS — Building the unified open-source system software stack for diverse AI chips.
KernelGen V1.0.0 release
This is FlagOS KernelGen version 1.0.0
Overview
KernelGen is an AI-powered automatic Triton kernel development tool built on the FlagOS ecosystem. It offers a fully automated workflow for operator (kernel) development, tuning, testing, and deployment, significantly boosting efficiency and speeding up multi-hardware adaptation.
Experience it from: https://kernelgen.flagos.io
Initial Release
- Basic Workflow : Supports single, fixed - step code generation including GroundTruth → TritonKernel → Correctness Test → Performance Test.
- User Registration : Supports self - service registration and application, with platform approval required for trial use.
- Web Interface : Online access at https://kernelgen.flagos.io/.
- Core Features :
- Fully Automated Workflow : Automatically generates, tests, and optimizes complete AI operator sets.
- Multi - Backend Support : Seamlessly supports multiple AI libraries and chips with automatic adaptation and debugging.
- Easy - to - Use : Browser - based interface requiring no setup or prior experience.
- Standardized Verification : Automatically generates test cases to ensure operator correctness.
- Deep Ecosystem Integration : Collaborates with FlagGems and FlagTree to accelerate operator library development.
Known Issues
- Some complex operators may require multiple iterations to achieve optimal performance.
- Performance test timeouts may occur for certain operators.
- Limited support for edge computing platforms (work in progress).
Future Roadmap
- Integration with more AI frameworks and hardware platforms
- Enhanced operator optimization capabilities
Contact Information
For questions or feedback, please contact the FlagOS team at contact@flagos.io or record issues in this repository.
Powered by FlagOS - Building the unified open - source system software stack for diverse AI chips.