chore: import upstream snapshot with attribution

python/samples/02-agents/tools/local_code_interpreter/README.md

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+# Hyperlight local code interpreter
+
+Demonstrates the standalone [Hyperlight](https://github.com/hyperlight-dev/hyperlight)
+`HyperlightExecuteCodeTool` — a sandboxed local code interpreter that the agent
+can invoke directly. Two patterns are shown:
+
+| File | Pattern |
+|------|---------|
+| [`local_code_interpreter.py`](local_code_interpreter.py) | **Standalone tool** — `HyperlightExecuteCodeTool` is added to the agent tool list and self-describes its sandbox tools, so no extra agent instructions are needed. Best for quick prototyping. |
+| [`local_code_interpreter_manual_wiring.py`](local_code_interpreter_manual_wiring.py) | **Manual static wiring** — sandbox tools and CodeAct instructions are built once and passed to the `Agent` constructor alongside a direct-only tool (`send_email`). Best when the tool set is fixed for the agent's lifetime. |
+
+For the recommended provider-driven pattern (with dynamic tool / capability
+management), see
+[`../../context_providers/code_act/`](../../context_providers/code_act/).
+
+## Installation
+
+```bash
+pip install agent-framework agent-framework-hyperlight --pre
+```
+
+> The Hyperlight Wasm backend is currently published only for `linux/x86_64` and
+> `win32/AMD64` with Python `<3.14`. On other platforms `execute_code` will fail
+> at runtime when it tries to create the sandbox.
+
+## Prerequisites
+
+- An Azure AI Foundry project endpoint (`FOUNDRY_PROJECT_ENDPOINT`)
+- A deployed model (`FOUNDRY_MODEL`)
+- Azure CLI authenticated (`az login`)
+
+## Run
+
+```bash
+python local_code_interpreter.py
+python local_code_interpreter_manual_wiring.py
+```

python/samples/02-agents/tools/local_code_interpreter/local_code_interpreter.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+from __future__ import annotations
+
+import asyncio
+import os
+from typing import Annotated, Any, Literal
+
+from agent_framework import Agent, tool
+from agent_framework.foundry import FoundryChatClient
+from agent_framework.hyperlight import HyperlightExecuteCodeTool
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+"""This sample demonstrates the standalone Hyperlight execute_code tool.
+
+The sample adds `HyperlightExecuteCodeTool` directly to the agent. The tool's
+own description advertises `call_tool(...)`, the registered sandbox tools, and
+the current capability configuration, so no extra CodeAct-specific agent
+instructions are required.
+"""
+
+load_dotenv()
+
+
+@tool(approval_mode="never_require")
+def compute(
+    operation: Annotated[
+        Literal["add", "subtract", "multiply", "divide"],
+        "Math operation: add, subtract, multiply, or divide.",
+    ],
+    a: Annotated[float, "First numeric operand."],
+    b: Annotated[float, "Second numeric operand."],
+) -> float:
+    """Perform a math operation used by sandboxed code."""
+    operations = {
+        "add": a + b,
+        "subtract": a - b,
+        "multiply": a * b,
+        "divide": a / b if b else float("inf"),
+    }
+    return operations[operation]
+
+
+@tool(approval_mode="never_require")
+def fetch_data(
+    table: Annotated[str, "Name of the simulated table to query."],
+) -> list[dict[str, Any]]:
+    """Fetch simulated records from a named table."""
+    data: dict[str, list[dict[str, Any]]] = {
+        "users": [
+            {"id": 1, "name": "Alice", "role": "admin"},
+            {"id": 2, "name": "Bob", "role": "user"},
+            {"id": 3, "name": "Charlie", "role": "admin"},
+        ],
+        "products": [
+            {"id": 101, "name": "Widget", "price": 9.99},
+            {"id": 102, "name": "Gadget", "price": 19.99},
+        ],
+    }
+    return data.get(table, [])
+
+
+async def main() -> None:
+    """Run the standalone execute_code sample."""
+    # 1. Create the packaged execute_code tool and register sandbox tools on it.
+    execute_code = HyperlightExecuteCodeTool(
+        tools=[compute, fetch_data],
+        approval_mode="never_require",
+    )
+
+    # 2. Create the client and the agent.
+    agent = Agent(
+        client=FoundryChatClient(
+            project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+            model=os.environ["FOUNDRY_MODEL"],
+            credential=AzureCliCredential(),
+        ),
+        name="HyperlightExecuteCodeToolAgent",
+        instructions="You are a helpful assistant.",
+        tools=execute_code,
+    )
+
+    # 3. Run one request through the direct-tool surface.
+    print("=" * 60)
+    print("Hyperlight execute_code tool sample")
+    print("=" * 60)
+    query = (
+        "Fetch all users, find admins, multiply 6*7, and print the users, admins, "
+        "and multiplication result. Use one execute_code call."
+    )
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Agent: {result.text}")
+
+
+"""
+Sample output (shape only):
+
+============================================================
+Hyperlight execute_code tool sample
+============================================================
+User: Fetch all users, find admins, multiply 6*7, ...
+Agent: ...
+"""
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/02-agents/tools/local_code_interpreter/local_code_interpreter_manual_wiring.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+from __future__ import annotations
+
+import asyncio
+import os
+from typing import Annotated, Any, Literal
+
+from agent_framework import Agent, tool
+from agent_framework.foundry import FoundryChatClient
+from agent_framework.hyperlight import HyperlightExecuteCodeTool
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+"""This sample demonstrates manual static wiring of CodeAct without a provider.
+
+Instead of using `HyperlightCodeActProvider` with `context_providers=`, this
+sample creates a `HyperlightExecuteCodeTool` directly, extracts its CodeAct
+instructions once, and passes both to the `Agent` constructor at build time.
+
+This avoids the per-run provider lifecycle (`before_run` / `after_run`) and is
+well-suited when the tool registry, file mounts, and network allow-list are
+fixed for the agent's lifetime. The tradeoff is that dynamic tool or capability
+changes between runs are not supported — any mutations to the tool would not
+update the agent's instructions automatically.
+"""
+
+load_dotenv()
+
+
+@tool(approval_mode="never_require")
+def compute(
+    operation: Annotated[
+        Literal["add", "subtract", "multiply", "divide"],
+        "Math operation: add, subtract, multiply, or divide.",
+    ],
+    a: Annotated[float, "First numeric operand."],
+    b: Annotated[float, "Second numeric operand."],
+) -> float:
+    """Perform a math operation used by sandboxed code."""
+    operations = {
+        "add": a + b,
+        "subtract": a - b,
+        "multiply": a * b,
+        "divide": a / b if b else float("inf"),
+    }
+    return operations[operation]
+
+
+@tool(approval_mode="never_require")
+def fetch_data(
+    table: Annotated[str, "Name of the simulated table to query."],
+) -> list[dict[str, Any]]:
+    """Fetch simulated records from a named table."""
+    data: dict[str, list[dict[str, Any]]] = {
+        "users": [
+            {"id": 1, "name": "Alice", "role": "admin"},
+            {"id": 2, "name": "Bob", "role": "user"},
+            {"id": 3, "name": "Charlie", "role": "admin"},
+        ],
+        "products": [
+            {"id": 101, "name": "Widget", "price": 9.99},
+            {"id": 102, "name": "Gadget", "price": 19.99},
+        ],
+    }
+    return data.get(table, [])
+
+
+@tool(approval_mode="never_require")
+def send_email(
+    to: Annotated[str, "Recipient email address."],
+    subject: Annotated[str, "Email subject line."],
+    body: Annotated[str, "Email body text."],
+) -> str:
+    """Simulate sending an email (direct-only tool, not available inside the sandbox)."""
+    return f"Email sent to {to}: {subject}"
+
+
+async def main() -> None:
+    """Run the manual static-wiring sample."""
+    # 1. Create the execute_code tool and register sandbox tools on it.
+    execute_code = HyperlightExecuteCodeTool(
+        tools=[compute, fetch_data],
+        approval_mode="never_require",
+    )
+
+    # 2. Build CodeAct instructions once. Setting tools_visible_to_model=False
+    #    tells the instructions builder that sandbox tools are not in the agent's
+    #    direct tool list, so the model must use call_tool(...) inside execute_code.
+    codeact_instructions = execute_code.build_instructions(tools_visible_to_model=False)
+
+    # 3. Create the client and the agent with everything wired at construction time.
+    #    - send_email is a direct-only tool (not available inside the sandbox).
+    #    - execute_code carries sandbox tools (compute, fetch_data) via call_tool.
+    agent = Agent(
+        client=FoundryChatClient(
+            project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+            model=os.environ["FOUNDRY_MODEL"],
+            credential=AzureCliCredential(),
+        ),
+        name="ManualWiringAgent",
+        instructions=f"You are a helpful assistant.\n\n{codeact_instructions}",
+        tools=[send_email, execute_code],
+    )
+
+    # 4. Run a request that exercises both the sandbox and the direct tool.
+    print("=" * 60)
+    print("Manual static-wiring CodeAct sample")
+    print("=" * 60)
+    query = (
+        "Fetch all users, find admins, multiply 6*7, and print the users, admins, "
+        "and multiplication result. Use one execute_code call. "
+        "Then send an email to admin@example.com summarising the results."
+    )
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Agent: {result.text}")
+
+
+"""
+Sample output (shape only):
+
+============================================================
+Manual static-wiring CodeAct sample
+============================================================
+User: Fetch all users, find admins, multiply 6*7, ...
+Agent: ...
+"""
+
+
+if __name__ == "__main__":
+    asyncio.run(main())