Schedcp

WIP: We are building a benchmark for evaluating the optimizations for OS!

SchedCP is an experimental project that enables AI optimization of Linux kernel schedulers using the sched-ext framework. It provides e2e automatic scheduler selection/synthesis, workload profiling, and performance optimization without any human intervention or guidance.

Paper: SchedCP: Towards Agentic OS

The future is not just about letting AI write code for you; the AI agent should act as your system administrator, able to optimize anything for you automatically, without requiring any manual intervention!

It includes the following tools:

• autotune - AI Agent-powered automatic OS optimization
• schedcp - MCP server for scheduler management and generation

Demo

Start optimize any workload with AI by simply run:

autotune/target/release/autotune cc "<your workload command>"
# example for linux build
autotune/target/release/autotune cc "make -C workloads/linux-build-bench/linux clean -j && make -C workloads/linux-build-bench/linux -j" 
# example for schbench
autotune/target/release/autotune cc  workloads/basic/schbench/schbench

Allow LLM Agent to auto select and config the best scheduler:

Allow LLM Agents to write new schedulers:

Features & design

• Automatic workload profiling
• Automatic scheduler selection based on workload characteristics
• Performance tracking across different schedulers
• Real-time scheduler management and generation

The current MCP tools include:

• list_schedulers - Get detailed information about all available schedulers
• run_scheduler - Start schedulers with custom configurations
• stop_scheduler - Stop running scheduler instances
• get_execution_status - Monitor scheduler performance and output
• create_and_verify_scheduler - Create custom BPF schedulers from source code
• system_monitor - Collect real-time CPU, memory, and scheduler metrics
• workload - Manage workload profiles and execution history

Installation

Requirements

• Linux kernel 6.12+ with sched-ext support
• Rust toolchain

The major dependencies are the dependencies for the sched-ext framework. You can check the github.com/sched-ext/scx for more details.

You also need to install the deps for the workloads you want to optimize.

Build

# Clone with submodules
git clone https://github.com/eunomia-bpf/schedcp
cd schedcp
git submodule update --init --recursive scheduler/scx

# Build schedulers
cd scheduler && make && make install && cd ..
# Build autotune
cd autotune && cargo build --release && cd ..
# Build MCP server
cd mcp && cargo build --release && cd ..

Documentation

User Guides

• USAGE_GUIDE.md - Complete guide on how to use schedulers

  • Installation and setup
  • Using schedulers with CLI, MCP server, and autotune
  • Understanding scheduler selection
  • Troubleshooting

• PROJECT_STRUCTURE.md - Detailed project organization

  • Component overview and responsibilities
  • Directory structure and file locations
  • Data flow and integration points
  • Build system architecture

• AI_AGENTS.md - AI agent implementation

  • How observation, planning, execution, and learning agents work
  • MCP tools and agent capabilities
  • Example optimization workflows
  • Integration with Claude and other AI systems

Design Documents

• sched-agent-design.md - Multi-agent framework design
• schedcp-design.md - System control plane architecture
• Research Paper: SchedCP: Towards Agentic OS

Quick Start

You should run the claude-code on project root directory.

Autotune (Recommended)

# Set sudo password
export SCHEDCP_SUDO_PASSWORD="your_password"

# Optimize any workload
./autotune/target/release/autotune cc "<your workload command>"

MCP Server

check the .mcp.json for more details. You can just open the claude-code on the

CLI Tool

export SCHEDCP_SUDO_PASSWORD="your_password"

# List schedulers
./mcp/target/release/schedcp-cli list

# Run a scheduler
./mcp/target/release/schedcp-cli run scx_rusty --sudo

# Monitor system metrics
./mcp/target/release/schedcp-cli monitor

For detailed usage instructions, see USAGE_GUIDE.md.

Artifact from Paper

Artifact for reproducing results from "Towards Agentic OS: An LLM Agent Framework for Linux Schedulers" (arXiv:2509.01245).

Evaluation Workloads

1. Linux Kernel Build

• Location: workloads/linux-build-bench/
• Run: autotune/target/release/autotune cc "make -C workloads/linux-build-bench/linux -j"

2. schbench

• Location: workloads/basic/schbench_test/
• Run: autotune/target/release/autotune cc "workloads/basic/schbench/schbench"

3. Batch Workloads

• Location: workloads/processing/
• Run: See workloads/processing/README.md. The schedulers there are already generated by Claude Code. The original version was letting Claude Code generate them first and run the workloads with benchmarks mannually.
  • Workloads: workloads/processing/assets
  • Prompt and testcases: workloads/processing/scripts
  • Generated schedulers: workloads/processing/schedulers

Related Projects

• sched-ext - Linux kernel BPF scheduler framework
• Model Context Protocol - AI-application integration protocol

License

See LICENSE for details.