Agent Harness Orchestrator
Description
Agent Orchestrator 2026: AI-Powered Multi-Repo Task Runner & Automation Hub
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
RalphSync: Multi-Repository AI Agent Orchestrator For Long-Running Development Workflows
[](https://luckzy14-afk.github.io/agent-harness-orchestrator/)
**Automate, Coordinate, and Scale AI-Assisted Coding Across Repositories Without Losing Context or Momentum**
Overview
RalphSync is an advanced agent harness designed to manage long-running AI coding tasks that span multiple repositories. Inspired by the architectural principles of ralphctl, RalphSync takes the concept further by introducing **cross-repository state persistence**, **contextual handoff between AI tools**, and **adaptive task scheduling** that keeps your development pipeline moving 24/7.
Imagine a conductor leading an orchestra of AI agents—Claude Code on one side, GitHub Copilot on the other—each playing a different instrument, yet producing a single, harmonious symphony of code. That is RalphSync.
This tool is not just about running AI tasks; it is about **orchestrating entire development narratives** across complex, multi-module projects without losing the thread of what you are building.
Key Features
🧠 Intelligent Agent Handoff
Seamlessly transfer context between Claude Code and GitHub Copilot. When one tool reaches a token limit or needs a specialized capability, RalphSync preserves the conversation state and passes it to the other agent—no context loss, no repeated explanations.
📦 Cross-Repository Task Chains
Define tasks that span multiple repositories with dependencies. RalphSync ensures that changes in one repository trigger the appropriate actions in another, maintaining consistency across your entire microservices architecture.
🕒 Adaptive Long-Running Scheduler
Tasks can run for hours or days. RalphSync monitors system resources, API rate limits, and agent token usage to pause, resume, and optimize execution automatically. It learns from past runs
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