
HyperWorker
Project management harness for AI agents
What it does
Your AI agent starts strong...then drifts, loses context, and skips steps. HyperWorker stops that. Six mechanisms in markdown and YAML keep long-running projects on track: Lock (no drift), Atomicity (no "mostly finished"), Dependency (no skipped steps), Memory (session survival), Precedence (rule conflicts), Verification (evidence-based completion). Point your agent at HARNESS.md and it scaffolds everything. Built in Cowork. Works with any LLM that reads files. Open source, MIT licensed.
Does the same job
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HarnessRouterJul 2026 · harnessrouter.ai · ▲238Bring the world's best AI agents into your app, with one API
HarnessRouter Community Edition21d ago · harnessrouter.ai · ▲392Open-source unified interface for agent harnesses

Deep Work PlanJun 2026 · deepworkplan.com · ▲114Models matter. Context matters more. Give your agent a plan.
- 2C20+ Claude Code agents coordinating on real work (open source)Feb 2026 · github.com · ▲53
Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…

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