Releases: flagos-ai/KernelGen
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.