處理停止原因

當您向 Messages API 發出請求時，Claude 的回應包含一個 stop_reason 欄位，該欄位指示模型停止生成回應的原因。理解這些值對於構建能夠適當處理不同回應類型的強大應用程式至關重要。

有關 API 回應中 stop_reason 的詳細信息，請參閱 Messages API 參考。

什麼是 stop_reason？

stop_reason 欄位是每個成功的 Messages API 回應的一部分。與指示請求處理失敗的錯誤不同，stop_reason 告訴您 Claude 為什麼成功完成了其回應生成。

Example response

{
  "id": "msg_01234",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Here's the answer to your question..."
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 100,
    "output_tokens": 50
  }
}

停止原因值

end_turn

最常見的停止原因。表示 Claude 自然完成了其回應。

if response.stop_reason == "end_turn":
    # Process the complete response
    print(response.content[0].text)

使用 end_turn 的空回應

有時 Claude 會返回空回應（恰好 2-3 個令牌，沒有內容），其中 stop_reason: "end_turn"。這通常發生在 Claude 認為助手回合已完成時，特別是在工具結果之後。

常見原因：

在工具結果之後立即添加文本塊（Claude 學會期望用戶始終在工具結果之後插入文本，因此它結束回合以遵循該模式）
發送 Claude 的已完成回應而不添加任何內容（Claude 已經決定完成，因此它將保持完成）

如何防止空回應：

# INCORRECT: Adding text immediately after tool_result
messages = [
    {"role": "user", "content": "Calculate the sum of 1234 and 5678"},
    {"role": "assistant", "content": [
        {
            "type": "tool_use",
            "id": "toolu_123",
            "name": "calculator",
            "input": {"operation": "add", "a": 1234, "b": 5678}
        }
    ]},
    {"role": "user", "content": [
        {
            "type": "tool_result",
            "tool_use_id": "toolu_123",
            "content": "6912"
        },
        {
            "type": "text",
            "text": "Here's the result"  # Don't add text after tool_result
        }
    ]}
]

# CORRECT: Send tool results directly without additional text
messages = [
    {"role": "user", "content": "Calculate the sum of 1234 and 5678"},
    {"role": "assistant", "content": [
        {
            "type": "tool_use",
            "id": "toolu_123",
            "name": "calculator",
            "input": {"operation": "add", "a": 1234, "b": 5678}
        }
    ]},
    {"role": "user", "content": [
        {
            "type": "tool_result",
            "tool_use_id": "toolu_123",
            "content": "6912"
        }
    ]}  # Just the tool_result, no additional text
]

# If you still get empty responses after fixing the above:
def handle_empty_response(client, messages):
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=messages
    )

    # Check if response is empty
    if (response.stop_reason == "end_turn" and
        not response.content):

        # INCORRECT: Don't just retry with the empty response
        # This won't work because Claude already decided it's done

        # CORRECT: Add a continuation prompt in a NEW user message
        messages.append({"role": "user", "content": "Please continue"})

        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=1024,
            messages=messages
        )

    return response

最佳實踐：

永遠不要在工具結果之後立即添加文本塊 - 這會教導 Claude 在每次工具使用後期望用戶輸入
不要在沒有修改的情況下重試空回應 - 簡單地發送空回應返回不會有幫助
使用延續提示作為最後的手段 - 僅當上述修復無法解決問題時

max_tokens

Claude 停止是因為達到了您在請求中指定的 max_tokens 限制。

# Request with limited tokens
response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=10,
    messages=[{"role": "user", "content": "Explain quantum physics"}]
)

if response.stop_reason == "max_tokens":
    # Response was truncated
    print("Response was cut off at token limit")
    # Consider making another request to continue

stop_sequence

Claude 遇到了您的自訂停止序列之一。

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    stop_sequences=["END", "STOP"],
    messages=[{"role": "user", "content": "Generate text until you say END"}]
)

if response.stop_reason == "stop_sequence":
    print(f"Stopped at sequence: {response.stop_sequence}")

tool_use

Claude 正在調用工具並期望您執行它。

對於大多數工具使用實現，我們建議使用 tool runner，它會自動處理工具執行、結果格式化和對話管理。

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    tools=[weather_tool],
    messages=[{"role": "user", "content": "What's the weather?"}]
)

if response.stop_reason == "tool_use":
    # Extract and execute the tool
    for content in response.content:
        if content.type == "tool_use":
            result = execute_tool(content.name, content.input)
            # Return result to Claude for final response

pause_turn

與網路搜尋等伺服器工具一起使用，當 Claude 需要暫停長時間運行的操作時。

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    tools=[{"type": "web_search_20250305", "name": "web_search"}],
    messages=[{"role": "user", "content": "Search for latest AI news"}]
)

if response.stop_reason == "pause_turn":
    # Continue the conversation
    messages = [
        {"role": "user", "content": original_query},
        {"role": "assistant", "content": response.content}
    ]
    continuation = client.messages.create(
        model="claude-sonnet-4-5",
        messages=messages,
        tools=[{"type": "web_search_20250305", "name": "web_search"}]
    )

refusal

Claude 由於安全考慮而拒絕生成回應。

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "[Unsafe request]"}]
)

if response.stop_reason == "refusal":
    # Claude declined to respond
    print("Claude was unable to process this request")
    # Consider rephrasing or modifying the request

如果在使用 Claude Sonnet 4.5 或 Opus 4.1 時經常遇到 refusal 停止原因，您可以嘗試更新 API 呼叫以使用 Sonnet 4（claude-sonnet-4-20250514），它具有不同的使用限制。了解更多關於理解 Sonnet 4.5 的 API 安全過濾器。

要了解有關 Claude Sonnet 4.5 的 API 安全過濾器觸發的拒絕的更多信息，請參閱理解 Sonnet 4.5 的 API 安全過濾器。

model_context_window_exceeded

Claude 停止是因為達到了模型的上下文窗口限制。這允許您請求最大可能的令牌，而無需知道確切的輸入大小。

# Request with maximum tokens to get as much as possible
response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=64000,  # Model's maximum output tokens
    messages=[{"role": "user", "content": "Large input that uses most of context window..."}]
)

if response.stop_reason == "model_context_window_exceeded":
    # Response hit context window limit before max_tokens
    print("Response reached model's context window limit")
    # The response is still valid but was limited by context window

此停止原因在 Sonnet 4.5 和更新的模型中默認可用。對於較早的模型，使用測試版標頭 model-context-window-exceeded-2025-08-26 來啟用此行為。

處理停止原因的最佳實踐

1. 始終檢查 stop_reason

養成在回應處理邏輯中檢查 stop_reason 的習慣：

def handle_response(response):
    if response.stop_reason == "tool_use":
        return handle_tool_use(response)
    elif response.stop_reason == "max_tokens":
        return handle_truncation(response)
    elif response.stop_reason == "model_context_window_exceeded":
        return handle_context_limit(response)
    elif response.stop_reason == "pause_turn":
        return handle_pause(response)
    elif response.stop_reason == "refusal":
        return handle_refusal(response)
    else:
        # Handle end_turn and other cases
        return response.content[0].text

2. 優雅地處理截斷的回應

當回應因令牌限制或上下文窗口而被截斷時：

def handle_truncated_response(response):
    if response.stop_reason in ["max_tokens", "model_context_window_exceeded"]:
        # Option 1: Warn the user about the specific limit
        if response.stop_reason == "max_tokens":
            message = "[Response truncated due to max_tokens limit]"
        else:
            message = "[Response truncated due to context window limit]"
        return f"{response.content[0].text}\n\n{message}"

        # Option 2: Continue generation
        messages = [
            {"role": "user", "content": original_prompt},
            {"role": "assistant", "content": response.content[0].text}
        ]
        continuation = client.messages.create(
            model="claude-sonnet-4-5",
            max_tokens=1024,
            messages=messages + [{"role": "user", "content": "Please continue"}]
        )
        return response.content[0].text + continuation.content[0].text

3. 為 pause_turn 實現重試邏輯

對於可能暫停的伺服器工具：

def handle_paused_conversation(initial_response, max_retries=3):
    response = initial_response
    messages = [{"role": "user", "content": original_query}]
    
    for attempt in range(max_retries):
        if response.stop_reason != "pause_turn":
            break
            
        messages.append({"role": "assistant", "content": response.content})
        response = client.messages.create(
            model="claude-sonnet-4-5",
            messages=messages,
            tools=original_tools
        )
    
    return response

停止原因與錯誤

區分 stop_reason 值和實際錯誤很重要：

停止原因（成功回應）

回應正文的一部分
指示生成正常停止的原因
回應包含有效內容

錯誤（失敗的請求）

HTTP 狀態碼 4xx 或 5xx
指示請求處理失敗
回應包含錯誤詳細信息

try:
    response = client.messages.create(...)
    
    # Handle successful response with stop_reason
    if response.stop_reason == "max_tokens":
        print("Response was truncated")
    
except anthropic.APIError as e:
    # Handle actual errors
    if e.status_code == 429:
        print("Rate limit exceeded")
    elif e.status_code == 500:
        print("Server error")

串流考慮

使用串流時，stop_reason 是：

在初始 message_start 事件中為 null
在 message_delta 事件中提供
在任何其他事件中不提供

with client.messages.stream(...) as stream:
    for event in stream:
        if event.type == "message_delta":
            stop_reason = event.delta.stop_reason
            if stop_reason:
                print(f"Stream ended with: {stop_reason}")

常見模式

處理工具使用工作流程

使用 tool runner 更簡單：下面的示例顯示手動工具處理。對於大多數用例，tool runner 會自動處理工具執行，代碼少得多。

def complete_tool_workflow(client, user_query, tools):
    messages = [{"role": "user", "content": user_query}]

    while True:
        response = client.messages.create(
            model="claude-sonnet-4-5",
            messages=messages,
            tools=tools
        )

        if response.stop_reason == "tool_use":
            # Execute tools and continue
            tool_results = execute_tools(response.content)
            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": tool_results})
        else:
            # Final response
            return response

確保完整回應

def get_complete_response(client, prompt, max_attempts=3):
    messages = [{"role": "user", "content": prompt}]
    full_response = ""

    for _ in range(max_attempts):
        response = client.messages.create(
            model="claude-sonnet-4-5",
            messages=messages,
            max_tokens=4096
        )

        full_response += response.content[0].text

        if response.stop_reason != "max_tokens":
            break

        # Continue from where it left off
        messages = [
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": full_response},
            {"role": "user", "content": "Please continue from where you left off."}
        ]

    return full_response

在不知道輸入大小的情況下獲取最大令牌

使用 model_context_window_exceeded 停止原因，您可以請求最大可能的令牌，而無需計算輸入大小：

def get_max_possible_tokens(client, prompt):
    """
    Get as many tokens as possible within the model's context window
    without needing to calculate input token count
    """
    response = client.messages.create(
        model="claude-sonnet-4-5",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=64000  # Set to model's maximum output tokens
    )

    if response.stop_reason == "model_context_window_exceeded":
        # Got the maximum possible tokens given input size
        print(f"Generated {response.usage.output_tokens} tokens (context limit reached)")
    elif response.stop_reason == "max_tokens":
        # Got exactly the requested tokens
        print(f"Generated {response.usage.output_tokens} tokens (max_tokens reached)")
    else:
        # Natural completion
        print(f"Generated {response.usage.output_tokens} tokens (natural completion)")

    return response.content[0].text

通過適當處理 stop_reason 值，您可以構建更強大的應用程式，優雅地處理不同的回應場景並提供更好的用戶體驗。