如何给 Agent 的 System Prompt 做一次彻底的“去耦手术”？（Agent架构实操十）

SYSTEM = (
    f"You are a coding agent at {WORKDIR}. "
    "Use tools to solve tasks. Act, don't explain. "
    "Before starting any multi-step task, use todo_write. "
    "Skills are available via list_skills and load_skill. "
    "Relevant memories are injected below when available. "
    # ... 更多段落
)

PROMPT_SECTIONS = {
    "identity": "You are a coding agent. Act, don't explain.",
    "tools": "Available tools: bash, read_file, write_file.",
    "workspace": f"Working directory: {WORKDIR}",
    "memory": "Relevant memories are injected below when available.",
}

def assemble_system_prompt(context: dict) -> str:
    sections = []

    # 始终加载基础信息
    sections.append(PROMPT_SECTIONS["identity"])
    sections.append(PROMPT_SECTIONS["tools"])
    sections.append(PROMPT_SECTIONS["workspace"])

    # 按需加载 — 基于真实状态
    memories = context.get("memories", "")
    if memories:
        sections.append(f"Relevant memories:\n{memories}")

    return "\n\n".join(sections)

_last_context_key = None
_last_prompt = None

def get_system_prompt(context: dict) -> str:
    global _last_context_key, _last_prompt
    key = json.dumps(context, sort_keys=True, ensure_ascii=False, default=str)
    if key == _last_context_key and _last_prompt:
        return _last_prompt
    _last_context_key = key
    _last_prompt = assemble_system_prompt(context)
    return _last_prompt

• 稳定性：Python 3.3+ 的 hash() 具有进程级随机化，且不支持 dict/list 类型，而 Context 正是嵌套结构。
• 一致性：json.dumps 配合 sort_keys=True 确保相同内容的字典无论键序如何均产生唯一序列化结果。

update_context：基于真实状态推导

def update_context(context: dict, messages: list) -> dict:
    memories = ""
    if MEMORY_INDEX.exists():
        content = MEMORY_INDEX.read_text().strip()
        if content:
            memories = content
    return {
        "enabled_tools": list(TOOL_HANDLERS.keys()),
        "workspace": str(WORKDIR),
        "memories": memories,
    }

def agent_loop(messages: list, context: dict):
    system = get_system_prompt(context)
    while True:
        response = client.chat.completions.create(
            model=MODEL,
            messages=[{"role": "system", "content": system}] + list(messages),
            tools=TOOLS,
            max_tokens=8000
        )
        messages.append(response.choices[0].message)
        if not response.choices[0].message.tool_calls:
            return

        results = []
        for tool_call in response.choices[0].message.tool_calls:
            print(f"\033[36m> {tool_call.function.name}\033[0m")
            handler = TOOL_HANDLERS.get(tool_call.function.name)
            output = (
                handler(**json.loads(tool_call.function.arguments))
                if handler
                elsef"Unknown: {tool_call.function.name}"
            )
            print(str(output)[:200])
            results.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": str(output)
            })
        messages.append({"role": "user", "content": results})

        # 每轮工具执行后重新评估上下文
        context = update_context(context, messages)
        system = get_system_prompt(context)