Athanor：自维持的代理工作流编排器，用对抗规划重塑AI协作

Athanor is a Claude Code plugin that innovatively solves traditional AI tool pain points (context bloat, single model limitations) through thin leader architecture, dual-model adversarial planning, and self-learning mechanisms. It aims to build a self-sustaining, evolving intelligent workflow system.

Named after the alchemical '贤者之炉' (self-sustaining furnace), Athanor's core goal is to create a workflow system that grows smarter with use. Traditional AI tools face two main issues: 1) Main session context膨胀 leading to performance decay; 2) Plan quality limited by single model boundaries. Athanor addresses both via innovative architecture and planning mechanisms.

The thin leader (main session) never executes work directly; it only parses user input, distributes tasks to sub-agents, collects result briefs, and presents outputs. This keeps its context lean. Athanor defines 7 specialized agents:

代理名称	使用模型	核心职责
researcher	sonnet	头脑风暴研究+魔鬼代言人视角
analyst	sonnet	快速并行分析
planner	opus	实施方案规划
critic	opus	方案综合与评审
executor	sonnet	代码执行与验证循环
learner	sonnet	会话学习提取
cleaner	haiku	记忆衰减与清理

Athanor's innovative adversarial planning流程: Input → Planner A (standard plan) → Reviewer B评审; Input → Planner B (reverse plan) → Reviewer A评审; Critic synthesizes both to form final plan. This 'red-blue对抗' mechanism improves plan quality by balancing innovation and rigor, overcoming single model limitations.

Athanor's workflow has 5 stages: /athanor:discuss (decision brainstorm), /athanor:analyze (parallel analysis), /athanor:plan (adversarial planning), /athanor:work (task execution). Planning stages do not modify files; only work stage does. Execution modes:

• Serial (Solo): One task at a time, clean context (for dependent tasks).
• Parallel (Team): Group tasks into waves based on dependencies; same wave runs in parallel, with 'discovery relay' between waves.

Post /athanor:work session: Learner agent extracts structured lessons; Cleaner agent applies memory decay (time + access count). Memory uses double layers:

• Permanent layer: Architecture decisions, key patterns (never deleted).
• Work layer: Task-specific details (auto-cleaned). This allows Athanor to learn user habits and project specifics over time.

File Organization: All communication uses .athanor/sessions/{id}/ markdown files (e.g., research-a.md, plan.md, lessons/). Configuration: Customizable via athanor.json (e.g., codex.enabled, work.defaultMode, memory.decayDays). Use Cases: Complex multi-step tasks, long-term project maintenance, high-quality code review, team collaboration.

Athanor represents an important attempt to evolve AI tools into self-sustaining systems. Its core value lies in optimizing AI collaboration via architecture design (not just model power). It solves context bloat, breaks single model limits, and enables continuous evolution. It's a promising paradigm for developers seeking high-quality AI assistance.