AI SummaryMCP Server that enables AI agents to analyze and auto-optimize Linux kernel schedulers using eBPF, helping systems engineers improve performance through intelligent workload profiling and optimization strategies.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to set up the "AI Agents in SchedCP" agent in my project. Please run this command in my terminal: # Add AGENTS.md to your project root curl --retry 3 --retry-delay 2 --retry-all-errors -o AGENTS.md "https://raw.githubusercontent.com/eunomia-bpf/schedcp/master/document/AI_AGENTS.md" Then explain what the agent does and how to invoke it.
Description
MCP Server for Linux Scheduler Management and Auto optimization
AI Agents in SchedCP
This document explains how AI agents are implemented in SchedCP and how they work together to optimize Linux kernel schedulers. While the conceptual design describes four specialized agents, the implementation uses a flexible approach that achieves the same goals.
Table of Contents
• Conceptual Design vs Implementation • Agent Roles and Implementation • How Agents Work • MCP Tools and Agent Capabilities • AI Integration Points • Example Agent Workflows
Conceptual Design (from Research Paper)
The research paper "Towards Agentic OS" (document/sched-agent-design.md) describes a multi-agent framework with four specialized agents: • Observation & Analysis Agent: Workload profiling and system sensing • Planning Agent: Strategy selection and optimization planning • Execution Agent: Code synthesis and safe deployment • Learning Agent: Performance feedback and knowledge curation
Actual Implementation
SchedCP implements these agent capabilities through: • AI Assistant (Claude/LLM): Provides reasoning and decision-making • MCP Server Tools: Expose system capabilities to AI • Autotune Orchestrator: Coordinates end-to-end workflows • Storage System: Maintains persistent knowledge Rather than having separate code files for each agent, the agent behaviors emerge from: • The AI's natural language understanding and reasoning • The tools available through the MCP protocol • The prompts and context provided by autotune • The structured data in schedulers.json and workload profiles This design is more flexible and maintainable while achieving the same functionality.
Discussion
Health Signals
My Fox Den
Community Rating
Sign in to rate this booster