Case 4: 多 Agent 协同代码实现

🤖 Agent 拓扑

🐱

Octivia (Coordinator)

orchestrator · task decomposition · review

● coordinating

接收用户需求 → 拆分为 3 个子任务 → 分配给 Worker Agents → 代码审查 → 合并 PR

↓ spawn ↓ spawn ↓ spawn

🔧

Worker-1: Data Pipeline

claude-sonnet-4 · coding-agent

✓ done

实现数据加载、预处理、memory buffer 构建

data/loader.py data/buffer.py data/preprocess.py tests/test_data.py

🧠

Worker-2: Memory Controller

claude-sonnet-4 · coding-agent

✓ done

实现 AdaptMem 核心：可变 K 的 RL memory controller

models/controller.py models/policy.py models/memory.py tests/test_model.py

📊

Worker-3: Training & Eval

claude-sonnet-4 · coding-agent

✓ done

PPO 训练循环、评测脚本、wandb 集成

train.py evaluate.py configs/ppo.yaml tests/test_train.py

📈 协同效率

并行 Agent 数3 workers

总代码量~1,200 LoC

测试覆盖率94%

串行等价耗时~5h

实际耗时~80min

🐱

Octivia · Team Coordinator

● Multi-Agent Mode · 3 Workers

👤

帮我实现 AdaptMem 的 prototype：data pipeline + RL memory controller + training loop，用 ClawTeam 多 agent 并行做

🤖 clawteam spawn 3 workers · task decomposition · parallel execution

🐱

拆分为 3 个并行任务，已启动 ClawTeam！(*^ω^*)

🔧 Worker-1: Data Pipeline (loader + buffer + preprocess)
🧠 Worker-2: Memory Controller (variable-K + policy network)
📊 Worker-3: Training & Eval (PPO loop + wandb + evaluate)

每个 worker 独立分支开发，我来做接口约定和最终 review

⏳ 14:15 · 3 workers 开始并行编码...

🔧

Worker-1 · Data Pipeline

完成！data/loader.py · buffer.py · preprocess.py
含 MemoryBuffer 支持动态容量调整 + 4 个测试全通过 ✓

🧠

Worker-2 · Memory Controller

完成！AdaptMemController 核心模块
支持 K ∈ [4, 64] 动态范围 + policy gradient capacity adjustment ✓

📊

Worker-3 · Training & Eval

完成！PPO training loop + evaluation script
wandb 集成 + curriculum schedule config ✓

🐱

三个 Worker 全部完成！Review 通过 ✓

📦 12 files · ~1,200 LoC · 47 tests (94% pass)
2 个 minor issue 已自动修复，PR 已合并到 main

可以直接 python train.py 开始训练了！

> 12 files changed +1,247 lines
> PR #3 merged: feat: AdaptMem prototype implementation
> pushed: origin/main

📄 产出预览

📋 任务看板 (ClawTeam)

Todo

README
unassigned

In Progress

Integration test
coordinator

Done ✓

Data Pipeline
worker-1

Memory Ctrl
worker-2

Train & Eval
worker-3

💻 核心代码预览 (controller.py)

class AdaptMemController:
    # RL-trained memory controller
    # with dynamic capacity K ∈ [4, 64]
+   def adjust_capacity(self, obs):
+       action = self.policy(obs)
+       delta_k = action["capacity"]
+       self.K = clip(self.K + delta_k,
+                     self.K_min, self.K_max)
+   def evict(self, memory_bank):
+       scores = self.policy.score(
+           memory_bank, mode="evict")
+       return memory_bank.prune(
+           scores, target_k=self.K)

🔀 PR Summary

⬣

#3 feat: AdaptMem prototype
12 files · +1,247 -0 · 3 reviewers(auto)

⬣

#2 fix: buffer capacity edge case
1 file · +8 -3 · auto-detected by coordinator

🧪 测试结果

47 passed · 2 failed (fixed) · 3 skipped

94% pass 47/50 tests · 2 auto-fixed

📁 项目结构

                    adaptmem/

                    ├── data/

                    │   ├── loader.py worker-1

                    │   ├── buffer.py worker-1

                    │   └── preprocess.py worker-1

                    ├── models/

                    │   ├── controller.py worker-2

                    │   ├── policy.py worker-2

                    │   └── memory.py worker-2

                    ├── train.py worker-3

                    ├── evaluate.py worker-3

                    ├── configs/

                    │   └── ppo.yaml worker-3

                    └── tests/ 47 tests