DeepSeek Open-Sources dsh-superagent, a Router-Worker Coding Agent

On October 31, the DeepSeek Harness team published a design essay, Exploring Super-agent Design: Towards a More Autonomous Agent System, alongside an MIT-licensed research prototype called dsh-superagent-prototype. The idea is not a new foundation model. It is orchestration around existing model-powered tools, built for one GitHub repository. A persistent Router session handles incoming conversations and dispatch decisions, but does not do the work itself. Each accepted work item gets its own Worker session and directory, so tasks run in parallel without sharing context. The prototype listens to GitHub events, and optionally to Feishu messages, and returns results in the original conversation, often as a pull request. A local web panel lists only the decisions that need a human. The stated

“dsh-superagent is a proactive coding agent for one GitHub repository, packaged as a bundle for DeepSeek Harness . It listens to GitHub and, optionally, Feishu. When someone allowed asks the agent for work, it opens a work item, works on it in its own agent session and directory, and answers in the original conversation, for example with a pull request. A web panel shows what needs a person.” submitted by /u/Beneficial-Ad-8127 [link] [comments]

DeepSeek published a prototype of what it calls a Super-agent: one session routes incoming requests, separate worker sessions do the coding. The interesting part is the ablation, not the architecture diagram. In a simulation of roughly 300 messages and 20 work requests per run, five runs per configuration, the router-and-worker setup finished at 12.4 min median and 15.7 min p95. The single-session baseline with a neutral prompt sat at 20.4 and 29.5 min, so about a 47% cut at the tail. Caveat: that baseline never once called its subagent tool across the five runs. A delegation-first prompt brought it to 13.8 min median, with p95 unchanged at 29.0. The cost side is the part I would want before adopting the pattern: 23.2M cache-read tokens per run against 6.1M. Cache reads are not total spend

DeepSeek's new open-source Super-Agent prototype helps coding agents receive GitHub tasks, continue assignments, and handle follow-up activity. # ArtificialIntelligence # DeepSeek # GitHub # AICodingAgents # TechiesJournal # PrasadKukkala https://www. techiesjournal.com/deepseek-op en-source-super-agent/10040/