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Hard code model for sheel tool samples
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@@ -18,6 +18,9 @@ This sample demonstrates implementing a local shell tool using get_shell_tool(fu
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that wraps Python's subprocess module. Unlike the hosted shell tool (get_shell_tool()),
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local shell execution runs commands on YOUR machine, not in a remote container.
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Currently not all models support the shell tool. Refer to the OpenAI documentation for the
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list of supported models: https://developers.openai.com/api/docs/models/
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SECURITY NOTE: This example executes real commands on your local machine.
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Only enable this when you trust the agent's actions. Consider implementing
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allowlists, sandboxing, or approval workflows for production use.
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@@ -53,7 +56,10 @@ async def main() -> None:
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print("=== OpenAI Agent with Local Shell Tool Example ===")
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print("NOTE: Commands will execute on your local machine.\n")
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client = OpenAIChatClient()
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# Currently not all models support the shell tool. Refer to the OpenAI
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# documentation for the list of supported models:
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# https://developers.openai.com/api/docs/models/
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client = OpenAIChatClient(model="gpt-5.4-nano")
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local_shell_tool = client.get_shell_tool(
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func=run_bash,
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)
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@@ -17,6 +17,9 @@ for executing shell commands in a managed container environment hosted by OpenAI
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The shell tool allows the model to run commands like listing files, running scripts,
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or performing system operations within a secure, sandboxed container.
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Currently not all models support the shell tool. Refer to the OpenAI documentation
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for the list of supported models: https://developers.openai.com/api/docs/models/
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"""
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@@ -24,7 +27,10 @@ async def main() -> None:
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"""Example showing how to use the shell tool with OpenAI Chat."""
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print("=== OpenAI Chat Client Agent with Shell Tool Example ===")
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client = OpenAIChatClient()
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# Currently not all models support the shell tool. Refer to the OpenAI
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# documentation for the list of supported models:
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# https://developers.openai.com/api/docs/models/
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client = OpenAIChatClient(model="gpt-5.4-nano")
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# Create a hosted shell tool with the default auto container environment
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shell_tool = client.get_shell_tool()
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