openai · seratch · Jul 19, 2026 · Jul 19, 2026
diff --git a/docs/agents.md b/docs/agents.md
@@ -328,7 +328,7 @@ Supplying a list of tools doesn't always mean the LLM will use a tool. You can f
 When you are using OpenAI Responses tool search, named tool choices are more limited: you cannot target bare namespace names or deferred-only tools with `tool_choice`, and `tool_choice="tool_search"` does not target [`ToolSearchTool`][agents.tool.ToolSearchTool]. In those cases, prefer `auto` or `required`. See [Hosted tool search](tools.md#hosted-tool-search) for the Responses-specific constraints.
 
 ```python
-from agents import Agent, Runner, function_tool, ModelSettings
+from agents import Agent, function_tool, ModelSettings
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -351,7 +351,7 @@ The `tool_use_behavior` parameter in the `Agent` configuration controls how tool
 - `"stop_on_first_tool"`: The output of the first tool call is used as the final response, without further LLM processing.
 
 ```python
-from agents import Agent, Runner, function_tool, ModelSettings
+from agents import Agent, function_tool
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -369,7 +369,7 @@ agent = Agent(
 - `StopAtTools(stop_at_tool_names=[...])`: Stops if any specified tool is called, using its output as the final response.
 
 ```python
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 from agents.agent import StopAtTools
 
 @function_tool
@@ -393,7 +393,7 @@ agent = Agent(
 - `ToolsToFinalOutputFunction`: A custom function that processes tool results and decides whether to stop or continue with the LLM.
 
 ```python
-from agents import Agent, Runner, function_tool, FunctionToolResult, RunContextWrapper
+from agents import Agent, function_tool, FunctionToolResult, RunContextWrapper
 from agents.agent import ToolsToFinalOutputResult
 from typing import List, Any
 

diff --git a/docs/context.md b/docs/context.md
@@ -97,7 +97,7 @@ For this, you can use the [`ToolContext`][agents.tool_context.ToolContext] class
 ```python
 from typing import Annotated
 from pydantic import BaseModel, Field
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 from agents.tool_context import ToolContext
 
 class WeatherContext(BaseModel):

diff --git a/docs/human_in_the_loop.md b/docs/human_in_the_loop.md
@@ -13,7 +13,7 @@ This page focuses on the manual approval flow via `interruptions`. If your app c
 Set `needs_approval` to `True` to always require approval or provide an async function that decides per call. The callable receives the run context, parsed tool parameters, and the tool call ID.
 
 ```python
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 
 
 @function_tool(needs_approval=True)

diff --git a/docs/ja/agents.md b/docs/ja/agents.md
@@ -49,7 +49,7 @@ SDK は、OpenAI モデルに対してデフォルトで Responses API を使用
 | `reset_tool_choice` | いいえ | ツール使用のループを回避するため、ツール呼び出し後に `tool_choice` をリセットします（デフォルト: `True`）。[ツール使用の強制](#forcing-tool-use)を参照してください。 |
 
 ```python
-from agents import Agent, ModelSettings, function_tool
+from agents import Agent, function_tool
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -332,7 +332,7 @@ robot_agent = pirate_agent.clone(
 OpenAI Responses のツール検索を使用する場合、名前付きツール選択肢にはより多くの制限があります。`tool_choice` では、単独の名前空間名や遅延専用ツールを指定できず、`tool_choice="tool_search"` で [`ToolSearchTool`][agents.tool.ToolSearchTool] を指定することもできません。このような場合は、`auto` または `required` を使用してください。Responses 固有の制約については、[ホステッドツール検索](tools.md#hosted-tool-search)を参照してください。
 
 ```python
-from agents import Agent, Runner, function_tool, ModelSettings
+from agents import Agent, function_tool, ModelSettings
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -355,7 +355,7 @@ agent = Agent(
 - `"stop_on_first_tool"`: 最初のツール呼び出しの出力を、LLM による追加処理を行わずに最終応答として使用します。
 
 ```python
-from agents import Agent, Runner, function_tool, ModelSettings
+from agents import Agent, function_tool
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -373,7 +373,7 @@ agent = Agent(
 - `StopAtTools(stop_at_tool_names=[...])`: 指定したツールのいずれかが呼び出された場合、その出力を最終応答として使用して停止します。
 
 ```python
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 from agents.agent import StopAtTools
 
 @function_tool
@@ -397,7 +397,7 @@ agent = Agent(
 - `ToolsToFinalOutputFunction`: ツールの実行結果を処理し、停止するか LLM で処理を続行するかを決定するカスタム関数です。
 
 ```python
-from agents import Agent, Runner, function_tool, FunctionToolResult, RunContextWrapper
+from agents import Agent, function_tool, FunctionToolResult, RunContextWrapper
 from agents.agent import ToolsToFinalOutputResult
 from typing import List, Any
 

diff --git a/docs/ja/context.md b/docs/ja/context.md
@@ -101,7 +101,7 @@ if __name__ == "__main__":
 ```python
 from typing import Annotated
 from pydantic import BaseModel, Field
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 from agents.tool_context import ToolContext
 
 class WeatherContext(BaseModel):

diff --git a/docs/ja/human_in_the_loop.md b/docs/ja/human_in_the_loop.md
@@ -17,7 +17,7 @@ search:
 常に承認を要求するには `needs_approval` を `True` に設定するか、呼び出しごとに判定する async 関数を指定します。この呼び出し可能オブジェクトは、実行コンテキスト、解析済みのツールパラメーター、ツール呼び出し ID を受け取ります。
 
 ```python
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 
 
 @function_tool(needs_approval=True)

diff --git a/docs/ja/models/index.md b/docs/ja/models/index.md
@@ -353,9 +353,10 @@ agent= Agent(name="Helping Agent", instructions="You are a Helping Agent", model
     SDK は [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] と [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] の両方の形式をサポートしていますが、2 つの形式ではサポートされる機能とツールが異なるため、各ワークフローで単一のモデル形式を使用することを推奨します。ワークフローでモデル形式を組み合わせる必要がある場合は、使用するすべての機能が両方で利用できることを確認してください。
 
 ```python
-from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
 import asyncio
 
+from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
+
 spanish_agent = Agent(
     name="Spanish agent",
     instructions="You only speak Spanish.",
@@ -381,6 +382,10 @@ triage_agent = Agent(
 async def main():
     result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
     print(result.final_output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 1.  OpenAI モデルの名前を直接設定します。

diff --git a/docs/ja/sessions/advanced_sqlite_session.md b/docs/ja/sessions/advanced_sqlite_session.md
@@ -68,7 +68,7 @@ session = AdvancedSQLiteSession(
 # With persistent storage
 session = AdvancedSQLiteSession(
     session_id="user_123",
-    db_path="path/to/conversations.db",
+    db_path="conversations.db",
     create_tables=True
 )
 

diff --git a/docs/ja/tools.md b/docs/ja/tools.md
@@ -466,7 +466,7 @@ def score_b(score: Annotated[int, Field(..., ge=0, le=100, description="Score fr
 
 ```python
 import asyncio
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 
 
 @function_tool(timeout=2.0)
@@ -549,9 +549,10 @@ def get_user_profile(user_id: str) -> str:
 ワークフローによっては、制御をハンドオフする代わりに、中央のエージェントで特化型エージェントのネットワークをオーケストレーションしたい場合があります。これは、エージェントをツールとしてモデル化することで実現できます。
 
 ```python
-from agents import Agent, Runner
 import asyncio
 
+from agents import Agent, Runner
+
 spanish_agent = Agent(
     name="Spanish agent",
     instructions="You translate the user's message to Spanish",
@@ -583,6 +584,10 @@ orchestrator_agent = Agent(
 async def main():
     result = await Runner.run(orchestrator_agent, input="Say 'Hello, how are you?' in Spanish.")
     print(result.final_output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 ### ツールエージェントのカスタマイズ

diff --git a/docs/ja/voice/quickstart.md b/docs/ja/voice/quickstart.md
@@ -51,7 +51,6 @@ graph LR
 まず、いくつかのエージェントをセットアップします。この SDK でエージェントを構築した経験があれば、馴染みのある作業でしょう。ここでは、2 つのエージェント、1 つのハンドオフ、1 つのツールを用意します。
 
 ```python
-import asyncio
 import random
 
 from agents import (
@@ -136,7 +135,6 @@ import sounddevice as sd
 from agents import (
     Agent,
     function_tool,
-    set_tracing_disabled,
 )
 from agents.voice import (
     AudioInput,

diff --git a/docs/ko/agents.md b/docs/ko/agents.md
@@ -49,7 +49,7 @@ SDK는 OpenAI 모델에 기본적으로 Responses API를 사용하지만, 여기
 | `reset_tool_choice` | 아니요 | 도구 사용 루프를 방지하기 위해 도구 호출 후 `tool_choice`를 재설정합니다(기본값: `True`). [도구 사용 강제](#forcing-tool-use)를 참조하세요. |
 
 ```python
-from agents import Agent, ModelSettings, function_tool
+from agents import Agent, function_tool
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -332,7 +332,7 @@ robot_agent = pirate_agent.clone(
 OpenAI Responses 도구 검색을 사용할 때는 이름이 지정된 도구 선택에 더 많은 제한이 있습니다. `tool_choice`로 단독 네임스페이스 이름이나 지연 전용 도구를 지정할 수 없으며, `tool_choice="tool_search"`는 [`ToolSearchTool`][agents.tool.ToolSearchTool]을 대상으로 하지 않습니다. 이러한 경우에는 `auto` 또는 `required`를 사용하는 것이 좋습니다. Responses 전용 제약 조건은 [호스티드 툴 검색](tools.md#hosted-tool-search)을 참조하세요.
 
 ```python
-from agents import Agent, Runner, function_tool, ModelSettings
+from agents import Agent, function_tool, ModelSettings
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -355,7 +355,7 @@ agent = Agent(
 - `"stop_on_first_tool"`: 추가적인 LLM 처리 없이 첫 번째 도구 호출의 출력을 최종 응답으로 사용합니다.
 
 ```python
-from agents import Agent, Runner, function_tool, ModelSettings
+from agents import Agent, function_tool
 
 @function_tool
 def get_weather(city: str) -> str:
@@ -373,7 +373,7 @@ agent = Agent(
 - `StopAtTools(stop_at_tool_names=[...])`: 지정된 도구 중 하나가 호출되면 해당 출력을 최종 응답으로 사용하고 중지합니다.
 
 ```python
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 from agents.agent import StopAtTools
 
 @function_tool
@@ -397,7 +397,7 @@ agent = Agent(
 - `ToolsToFinalOutputFunction`: 도구 결과를 처리하고 LLM을 중지할지 계속 실행할지 결정하는 사용자 지정 함수입니다.
 
 ```python
-from agents import Agent, Runner, function_tool, FunctionToolResult, RunContextWrapper
+from agents import Agent, function_tool, FunctionToolResult, RunContextWrapper
 from agents.agent import ToolsToFinalOutputResult
 from typing import List, Any
 

diff --git a/docs/ko/context.md b/docs/ko/context.md
@@ -101,7 +101,7 @@ if __name__ == "__main__":
 ```python
 from typing import Annotated
 from pydantic import BaseModel, Field
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 from agents.tool_context import ToolContext
 
 class WeatherContext(BaseModel):

diff --git a/docs/ko/human_in_the_loop.md b/docs/ko/human_in_the_loop.md
@@ -17,7 +17,7 @@ search:
 항상 승인을 요구하려면 `needs_approval`을 `True`로 설정하거나, 호출마다 결정하는 비동기 함수를 제공합니다. 호출 가능한 함수는 실행 컨텍스트, 파싱된 도구 매개변수, 도구 호출 ID를 받습니다.
 
 ```python
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 
 
 @function_tool(needs_approval=True)

diff --git a/docs/ko/models/index.md b/docs/ko/models/index.md
@@ -353,9 +353,10 @@ agent= Agent(name="Helping Agent", instructions="You are a Helping Agent", model
     SDK는 [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel]과 [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] 형식을 모두 지원하지만, 두 형식이 서로 다른 기능과 도구 집합을 지원하므로 각 워크플로에서는 하나의 모델 형식만 사용하는 것이 좋습니다. 워크플로에서 모델 형식을 혼합해야 한다면 사용하는 모든 기능을 양쪽 모두에서 사용할 수 있는지 확인하세요.
 
 ```python
-from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
 import asyncio
 
+from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
+
 spanish_agent = Agent(
     name="Spanish agent",
     instructions="You only speak Spanish.",
@@ -381,6 +382,10 @@ triage_agent = Agent(
 async def main():
     result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
     print(result.final_output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 1.  OpenAI 모델의 이름을 직접 설정합니다.

diff --git a/docs/ko/sessions/advanced_sqlite_session.md b/docs/ko/sessions/advanced_sqlite_session.md
@@ -68,7 +68,7 @@ session = AdvancedSQLiteSession(
 # With persistent storage
 session = AdvancedSQLiteSession(
     session_id="user_123",
-    db_path="path/to/conversations.db",
+    db_path="conversations.db",
     create_tables=True
 )
 

diff --git a/docs/ko/tools.md b/docs/ko/tools.md
@@ -466,7 +466,7 @@ def score_b(score: Annotated[int, Field(..., ge=0, le=100, description="Score fr
 
 ```python
 import asyncio
-from agents import Agent, Runner, function_tool
+from agents import Agent, function_tool
 
 
 @function_tool(timeout=2.0)
@@ -549,9 +549,10 @@ def get_user_profile(user_id: str) -> str:
 일부 워크플로에서는 제어를 핸드오프하는 대신 중앙 에이전트가 전문 에이전트 네트워크를 오케스트레이션하도록 할 수 있습니다. 에이전트를 도구로 모델링하여 이를 구현할 수 있습니다.
 
 ```python
-from agents import Agent, Runner
 import asyncio
 
+from agents import Agent, Runner
+
 spanish_agent = Agent(
     name="Spanish agent",
     instructions="You translate the user's message to Spanish",
@@ -583,6 +584,10 @@ orchestrator_agent = Agent(
 async def main():
     result = await Runner.run(orchestrator_agent, input="Say 'Hello, how are you?' in Spanish.")
     print(result.final_output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 ### 도구 에이전트 사용자 지정

diff --git a/docs/ko/voice/quickstart.md b/docs/ko/voice/quickstart.md
@@ -51,7 +51,6 @@ graph LR
 먼저 몇 가지 에이전트를 설정하겠습니다. 이 SDK로 에이전트를 만들어 본 적이 있다면 익숙할 것입니다. 몇 개의 에이전트와 하나의 핸드오프, 하나의 도구를 사용합니다.
 
 ```python
-import asyncio
 import random
 
 from agents import (
@@ -136,7 +135,6 @@ import sounddevice as sd
 from agents import (
     Agent,
     function_tool,
-    set_tracing_disabled,
 )
 from agents.voice import (
     AudioInput,

diff --git a/docs/models/index.md b/docs/models/index.md
@@ -349,9 +349,10 @@ Within a single workflow, you may want to use different models for each agent. F
     While our SDK supports both the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] and the [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] shapes, we recommend using a single model shape for each workflow because the two shapes support a different set of features and tools. If your workflow requires mixing and matching model shapes, make sure that all the features you're using are available on both.
 
 ```python
-from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
 import asyncio
 
+from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
+
 spanish_agent = Agent(
     name="Spanish agent",
     instructions="You only speak Spanish.",
@@ -377,6 +378,10 @@ triage_agent = Agent(
 async def main():
     result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
     print(result.final_output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 1.  Sets the name of an OpenAI model directly.

diff --git a/docs/sessions/advanced_sqlite_session.md b/docs/sessions/advanced_sqlite_session.md
@@ -64,7 +64,7 @@ session = AdvancedSQLiteSession(
 # With persistent storage
 session = AdvancedSQLiteSession(
     session_id="user_123",
-    db_path="path/to/conversations.db",
+    db_path="conversations.db",
     create_tables=True
 )