AKO4X: Accelerate GPU Kernel Tuning with Multi‑Agent Campaign Loops
From the repo · “Agentic Kernel Optimization — advanced & eXtensible: a closed-loop, campaign-based multi-agent system for optimizing GPU kernels (benchmark-swappable; default flashinfer-bench).”
AKO4X optimizes GPU kernels using a closed-loop multi-agent system, allowing startup founders to improve the performance of their GPU-based applications
- •When you need to optimize GPU kernels for deep learning or other compute-intensive workloads
- •When you want to automate the optimization process using a closed-loop system
- •When you need to benchmark and compare the performance of different GPU kernels
python run.py --bench flashinfer-bench --kernel your_kernel_name --agents 4 --epochs 10
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Expect 20–40 minutes in a terminal — or let your AI agent drive it.
mkdir -p ~/.claude/skills/ako4x && curl -fsSL https://workflowstacks.com/api/skills/ako4x/claude-skill -o ~/.claude/skills/ako4x/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
AKO4X: Accelerate GPU Kernel Tuning with Multi‑Agent Campaign Loops is a large Python project (~46k lines across 72 code files). It is a full software project: use it through its install path rather than reading it end to end. Last commit a month ago, MIT license, no tests found.
- 1README.mdStart here — what it does and how to install it
- 2CLAUDE.mdThe instructions the AI actually follows
- 3pyproject.tomlDependencies and the commands it exposes
- 4ako4x/__init__.pyInside ako4x/ — the main logic begins here
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Expect 20–40 minutes in a terminal — or let your AI agent drive it.
mkdir -p ~/.claude/skills/ako4x && curl -fsSL https://workflowstacks.com/api/skills/ako4x/claude-skill -o ~/.claude/skills/ako4x/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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