Context Compact：三层压缩换无限会话

KEEP_RECENT = 3   # 保留最近 3 个 tool_result

def micro_compact(messages: list) -> list:
    """把旧的 tool_result 替换为简短占位符"""
    # 收集所有 tool_result
    tool_results = []
    for i, msg in enumerate(messages):
        if msg["role"] == "user" and isinstance(msg.get("content"), list):
            for j, part in enumerate(msg["content"]):
                if isinstance(part, dict) and part.get("type") == "tool_result":
                    tool_results.append((i, j, part))

    if len(tool_results) <= KEEP_RECENT:
        return messages

    # 把早期的大结果替换为占位符
    for i, j, part in tool_results[:-KEEP_RECENT]:
        if len(str(part.get("content", ""))) > 100:
            # 从对应的 tool_use block 里找工具名
            tool_name = _find_tool_name(messages, part.get("tool_use_id", ""))
            part["content"] = f"[Previous: used {tool_name}]"

    return messages

def _find_tool_name(messages: list, tool_use_id: str) -> str:
    for msg in messages:
        if msg["role"] == "assistant":
            for block in (msg.get("content") or []):
                if hasattr(block, "id") and block.id == tool_use_id:
                    return block.name
    return "tool"

import json, time
from pathlib import Path

TRANSCRIPT_DIR = Path(".transcripts")
TRANSCRIPT_DIR.mkdir(exist_ok=True)

def full_compact(messages: list) -> list:
    """Fork Sonnet 生成结构化摘要"""
    # 1. 保存完整记录
    path = TRANSCRIPT_DIR / f"transcript_{int(time.time())}.jsonl"
    with open(path, "w") as f:
        for msg in messages:
            f.write(json.dumps(msg, default=str) + "\n")
    print(f"[compact] Saved transcript to {path}")

    # 2. LLM 生成摘要
    response = client.messages.create(
        model=MODEL,
        messages=[{
            "role": "user",
            "content": (
                "以下是一段 Agent 的对话记录。"
                "请生成一份结构化摘要，包含：\n"
                "1. 用户请求  2. 技术概念  3. 文件和代码\n"
                "4. 错误和修复  5. 待完成任务  6. 当前工作\n\n"
                "先在 <analysis> 中梳理思路，再在 <summary> 中给出最终摘要。\n\n"
                + json.dumps(messages, default=str)[:80000]
            )
        }],
        max_tokens=2000,
    )
    summary = response.content[0].text

    # 3. 用摘要替换整个 messages
    return [
        {"role": "user", "content": f"[对话摘要]\n\n{summary}"},
        {"role": "assistant", "content": "明白，我会在此基础上继续。"},
    ]

THRESHOLD = 50_000    # token 阈值
MAX_FAILURES = 3      # 熔断器

_consecutive_failures = 0

def auto_compact_if_needed(messages: list) -> list:
    """阈值触发自动压缩，带熔断器"""
    global _consecutive_failures

    if _consecutive_failures >= MAX_FAILURES:
        return messages

    if estimate_tokens(messages) < THRESHOLD:
        return messages

    try:
        result = full_compact(messages)
        _consecutive_failures = 0
        return result
    except Exception:
        _consecutive_failures += 1
        return messages

# 手动压缩工具
TOOL_HANDLERS["compact"] = lambda **kw: _manual_compact()
_compact_requested = False

def _manual_compact():
    global _compact_requested
    _compact_requested = True
    return "Compacting conversation..."

def agent_loop(messages: list):
    while True:
        # Layer 1：静默微压缩（每轮）
        micro_compact(messages)

        # Layer 3：超出阈值自动压缩（内部调用 Layer 2）
        messages[:] = auto_compact_if_needed(messages)

        response = client.messages.create(
            model=MODEL, system=SYSTEM,
            messages=messages, tools=TOOLS, max_tokens=8000,
        )
        messages.append({"role": "assistant", "content": response.content})

        if response.stop_reason != "tool_use":
            break

        results = []
        for block in response.content:
            if block.type == "tool_use":
                output = TOOL_HANDLERS.get(block.name, lambda **kw: "Unknown tool")(**block.input)
                results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": str(output)[:50000]
                })
        messages.append({"role": "user", "content": results})

        # 手动压缩标志
        global _compact_requested
        if _compact_requested:
            messages[:] = full_compact(messages)
            _compact_requested = False