Red Queen: A Deterministic Orchestrator That Runs Claude Code as a Worker Pool
After burning $40 of tokens on AutoGPT-style experiments that produced nothing, Red Queen's author built a deterministic orchestrator that treats Claude Code as a worker pool. The key insight: routing decisions that LLMs make are just switch statements — so they moved those decisions to a state machine.
How it works
Red Queen runs a pipeline defined as a state graph, not a prompt. Each phase (spec, code, review, test) is a separate Claude Code subprocess with a focused, isolated prompt. No shared context bloat, no mega-prompt. The state machine decides which phase to run based on explicit transitions — not LLM heuristics.
Pipeline
- Jira ticket → spec → human approves → code → auto review → auto test → human approves → merged PR
Human gates are built into the state graph, not bolted on. You can't accidentally skip approval and deploy to prod.
Key features
- Zero tokens spent on scheduling — the orchestrator is purely deterministic, written in code, not prompts.
- Self-hosted — BYO Claude Code. MIT licensed.
- Isolated skills — every skill runs with a focused prompt. No shared context.
Who it's for
Developers who've hit the limits of autonomous agents and want a safer, more predictable way to integrate LLMs into CI/CD pipelines without sacrificing control.
Get started
The repo is at github.com/odyth/red-queen. Named after the AI from Resident Evil.
📖 Read the full source: r/ClaudeAI
👀 See Also
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ATLAS: Open-Source Test-Time Compute Pipeline for Qwen3-14B Achieves Frontier-Level Coding Performance
A college student has developed ATLAS, an open-source test-time compute pipeline built around Qwen3-14B that achieves 74.6% pass@1 on LiveCodeBench v5 problems at ~$0.004 per task in electricity costs. The system is slow for complex problems but offers comparable performance to frontier models like GPT-5 (84.6%) and Claude 4.5 Sonnet (71.4%).