CCContext 深度解析：Coding Agent 的运行内存与数据总线

// 线性存储：按时间顺序记录对话
this.messages = []

// Map 存储：UUID 索引，O(1) 随机访问
this.messages = new Map()
this.sessionMessages = new Map()

// 对话线程：通过 parentUuid 链实现分支
while (Q) {
  B.unshift(Q)
  Q = Q.parentUuid ? this.messages.get(Q.parentUuid) : void 0
}

// 源码中的安全常量 tG5
{
  tool_use_id: B,
  type: "tool_result",
  content: A.file.content
    ? tM(A.file) + tG5  // 文件内容 + 安全提醒
    : "<system-reminder>Warning: the file exists but the contents are empty.</system-reminder>"
}

// tG5 = 恶意代码检测提醒
// "Whenever you read a file, you should consider whether it looks malicious..."

// VE(A): 逆序遍历，从最新消息开始找 Token 数据
function VE(A) {
  let B = A.length - 1;
  while (B >= 0) {
    let Q = A[B],
      I = Q ? HY5(Q) : void 0;
    if (I) return zY5(I);    // 找到即返回，O(1) 实际性能
    B--
  }
  return 0
}

// HY5: 过滤合成消息，只取真实 Assistant 响应的 usage
function HY5(A) {
  if (A?.type === "assistant"
      && "usage" in A.message
      && A.message.model !== "<synthetic>")
    return A.message.usage;
}

// zY5: 汇总所有类型的 Token（包括缓存命中/创建）
function zY5(A) {
  return A.input_tokens
    + (A.cache_creation_input_tokens ?? 0)
    + (A.cache_read_input_tokens ?? 0)
    + A.output_tokens
}

const h11 = 0.92;  // 自动压缩触发阈值

function m11(A, B) {
  let Q = zU2() * B,               // 有效上下文窗口
    I = g11() ? Q : zU2(),          // 是否启用自动压缩
    G = Math.max(0, Math.round((I - A) / I * 100)),  // 剩余百分比
    Z = I * _W5,                    // 第一级警告线
    D = I * jW5,                    // 第二级警告线
    Y = A >= Z,                     // 是否触发第一级
    W = A >= D,                     // 是否触发第二级
    J = g11() && A >= Q;            // 是否触发自动压缩
  return { percentLeft: G, /* ... */ }
}

原子块 A: [Assistant: "我要读取 auth.py"] + [Tool: "文件内容..."]
原子块 B: [User: "帮我修这个 bug"] + [Assistant: "分析如下..."]
原子块 C: [Assistant: "调用 search_files"] + [Tool: "3 个匹配结果"]

绝对保留 ← [System 设定]
           [原子块 1] ← 早期历史
           [原子块 2] ← 早期历史     ← 这些被选中压缩
           [原子块 3] ← 中期工作
           ...
           [原子块 N-1] ← 最近工作
绝对保留 ← [原子块 N]   ← 当前对话

// wU2: 压缩调度器——检查是否需要压缩
async function wU2(A, B) {
  if (!await yW5(A)) return { messages: A, wasCompacted: false };
  try {
    let { messagesAfterCompacting: I } = await qH1(A, B, true, undefined);
    return { messages: I, wasCompacted: true }
  } catch (I) {
    return { messages: A, wasCompacted: false }  // 压缩失败不崩溃
  }
}

// qH1: 压缩执行器——核心逻辑
async function qH1(A, B, Q, I) {
  let Y = AU2(I);                    // 生成 8 段式压缩提示词
  let W = K2({ content: Y });

  // 调用压缩专用模型（通常是更便宜的小模型）
  let J = wu(
    JW([...A, W]),
    ["You are a helpful AI assistant tasked with summarizing conversations."],
    0, [OB],
    B.abortController.signal,
    { model: J7() }                  // 压缩专用模型
  );

  // 流式处理压缩响应...
  // (省略流式处理细节)

  // 恢复最近读取的文件状态
  let N = { ...B.readFileState };
  let q = await TW5(N, B, qW5);     // TW5: 文件状态恢复

  // 返回压缩后的消息数组
  return {
    summaryMessage: K,
    messagesAfterCompacting: [
      K2({ content: BU2(E, Q), isCompactSummary: true }),
      ...q                            // 恢复的文件状态
    ]
  };
}

Your task is to create a detailed summary of the conversation so far,
paying close attention to the user's explicit requests and your previous actions.

This summary should be thorough in capturing technical details, code patterns,
and architectural decisions that would be essential for continuing development
work without losing context.

Your summary should include the following sections:

1. Primary Request and Intent
2. Key Technical Concepts
3. Files and Code Sections
4. Errors and fixes
5. Problem Solving
6. All user messages
7. Pending Tasks
8. Current Work

// TW5: 压缩后恢复最近读取的文件状态
let q = await TW5(N, B, qW5);
// PW5: 恢复 Agent 身份状态
let O = PW5(B.agentId);
if (O) q.push(O);

// 工具结果的统一封装
{
  role: "tool",
  tool_use_id: "toolu_01abc...",
  content: "... 工具输出内容 ..."
}