Hi everyone,
I wanted to share a multi-agent orchestration project I’ve been developing and stress-testing on accelerated compute. With the growing focus on local, high-performance agentic workflows, I built Qwen Orchestrator—an extension designed exclusively for the Qwen Code CLI that transforms a single terminal assistant into a coordinated team of 22 specialized agents.
⚙️ Hardware Setup & Test Cases (sparkrun)
To handle the heavy parallel inference required by multiple agents operating simultaneously, I deployed the orchestrator on a cluster of 2 Gigabyte AI TOP Atom nodes.
For the test cases, I utilized sparkrun to efficiently distribute the workload across the cluster while running the Qwen 3 Coder Next model. This distributed setup handles the heavy lifting perfectly, allowing the Commander agent to constantly stream context updates to background workers (like the Frontend and Backend agents) doing parallel code generation without bottlenecking.
🛡️ Solving the Long-Context Loop Problem
One of the biggest bottlenecks I encountered during these high-throughput tests was LLM degradation in massive context windows. When orchestrating 22 agents, the context gets huge fast, and I noticed the model would occasionally fall into random, infinite reasoning loops—wasting valuable GPU cycles.
To fix this, I implemented an “Anti-Loop” Monitor Agent. It acts as a continuous background watchdog that analyzes the reasoning stream. If it detects redundant logic loops or stalled execution in the long context, it automatically interrupts and redirects the agent, saving compute time and keeping the cluster utilization optimal.
🚀 Core Architecture Highlights
Beyond the hardware scaling, here is what the orchestrator actually does:
- True Parallel Execution: The Planner breaks down tasks, and sparkrun helps distribute the concurrent generation from the Frontend, Backend, and QA agents.
- Strict “No Lazy Code” Policy: I wrote an anti-pattern skill that outright forbids agents from outputting placeholders (e.g.,
// TODO: implement later). The Tech Lead agent forces complete CRUD generation. - Persistent Knowledge Graph: Uses an MCP (Model Context Protocol) memory server to store architectural decisions across sessions, reducing the need to re-feed context to the GPUs on every new prompt.
- Zero Vendor Lock-in: It’s built entirely for the CLI and local models.
🔗 Links & Setup
If anyone here is experimenting with multi-agent workflows on their DGX or custom clusters, I’d love to hear your thoughts or see how it performs on your setups.
- Project Repo (Instructions & Install): https://github.com/Omar-Obando/qwen-orchestrator
- Base CLI Requirement: https://github.com/QwenLM/qwen-code
Any feedback on optimizing the prompt routing for multi-node setups is highly welcome!