chore: import upstream snapshot with attribution

python/packages/gemini/samples/README.md

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+# Google Gemini Examples
+
+This folder contains examples demonstrating how to use Google Gemini models with the Agent Framework.
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| [`gemini_basic.py`](gemini_basic.py) | Basic agent with a weather tool, demonstrating both streaming and non-streaming responses. |
+| [`gemini_advanced.py`](gemini_advanced.py) | Extended thinking via `ThinkingConfig` for reasoning-heavy questions (Gemini 2.5+). |
+| [`gemini_with_google_search.py`](gemini_with_google_search.py) | Google Search grounding for up-to-date answers. |
+| [`gemini_with_google_maps.py`](gemini_with_google_maps.py) | Google Maps grounding for location and mapping information. |
+| [`gemini_with_code_execution.py`](gemini_with_code_execution.py) | Built-in code execution tool for computing precise answers in a sandboxed environment. |
+
+## Environment Variables
+
+- `GOOGLE_MODEL` or `GEMINI_MODEL`: The Gemini model to use (for example,
+  `gemini-2.5-flash-lite` or `gemini-2.5-pro`)
+- For Gemini Developer API: `GEMINI_API_KEY` or `GOOGLE_API_KEY`
+- For Vertex AI: `GOOGLE_GENAI_USE_VERTEXAI=true`, `GOOGLE_CLOUD_PROJECT`, and `GOOGLE_CLOUD_LOCATION`

python/packages/gemini/samples/__init__.py

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+# Copyright (c) Microsoft. All rights reserved.

python/packages/gemini/samples/gemini_advanced.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Shows how to enable extended thinking with ThinkingConfig.
+
+Allows the model to reason through complex problems before responding.
+
+Requires ``GOOGLE_MODEL`` or ``GEMINI_MODEL`` and either Gemini Developer API credentials
+(``GEMINI_API_KEY`` or ``GOOGLE_API_KEY``) or Vertex AI settings
+(``GOOGLE_GENAI_USE_VERTEXAI``, ``GOOGLE_CLOUD_PROJECT``, and ``GOOGLE_CLOUD_LOCATION``).
+"""
+
+import asyncio
+
+from agent_framework import Agent
+from dotenv import load_dotenv
+
+from agent_framework_gemini import GeminiChatClient, GeminiChatOptions, ThinkingConfig
+
+load_dotenv()
+
+
+async def main() -> None:
+    """Example of extended thinking with a Python version comparison question."""
+    print("=== Extended thinking ===")
+
+    # 1. Configure Gemini extended thinking for a reasoning-heavy request.
+    options: GeminiChatOptions = {
+        "thinking_config": ThinkingConfig(thinking_budget=2048),
+    }
+
+    # 2. Create the agent with the Gemini chat client and default thinking options.
+    agent = Agent(
+        client=GeminiChatClient(),
+        name="PythonAgent",
+        instructions="You are a helpful Python expert.",
+        default_options=options,
+    )
+
+    # 3. Stream the answer so you can see the final response as it arrives.
+    query = "What new language features were introduced in Python between 3.10 and 3.14?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run(query, stream=True):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+=== Extended thinking ===
+User: What new language features were introduced in Python between 3.10 and 3.14?
+Agent: Python 3.11 introduced exception groups and TaskGroup.
+Python 3.12 added PEP 695 type parameter syntax.
+Python 3.13-3.14 continued improving typing, performance, and developer ergonomics.
+"""

python/packages/gemini/samples/gemini_basic.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Shows how to use GeminiChatClient with an agent and a custom tool.
+
+Covers both non-streaming and streaming responses.
+
+Requires ``GOOGLE_MODEL`` or ``GEMINI_MODEL`` and either Gemini Developer API credentials
+(``GEMINI_API_KEY`` or ``GOOGLE_API_KEY``) or Vertex AI settings
+(``GOOGLE_GENAI_USE_VERTEXAI``, ``GOOGLE_CLOUD_PROJECT``, and ``GOOGLE_CLOUD_LOCATION``).
+"""
+
+import asyncio
+from random import randint
+from typing import Annotated
+
+from agent_framework import Agent, tool
+from dotenv import load_dotenv
+
+from agent_framework_gemini import GeminiChatClient
+
+load_dotenv()
+
+
+# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production
+@tool(approval_mode="never_require")
+def get_weather(
+    location: Annotated[str, "The location to get the weather for."],
+) -> str:
+    """Get the weather for a given location."""
+    conditions = ["sunny", "cloudy", "rainy", "stormy"]
+    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
+
+
+async def non_streaming_example() -> None:
+    """Runs the agent and waits for the complete response before printing it."""
+    print("=== Non-streaming ===")
+
+    # 1. Create the agent with the Gemini chat client and local weather tool.
+    agent = Agent(
+        client=GeminiChatClient(),
+        name="WeatherAgent",
+        instructions="You are a helpful weather agent.",
+        tools=[get_weather],
+    )
+
+    # 2. Ask the agent for a single weather lookup and print the final response.
+    query = "What's the weather like in Karlsruhe, Germany?"
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Result: {result}\n")
+
+
+async def streaming_example() -> None:
+    """Runs the agent and prints each chunk as it is received."""
+    print("=== Streaming ===")
+
+    # 1. Create the same agent configuration for a streaming tool-call example.
+    agent = Agent(
+        client=GeminiChatClient(),
+        name="WeatherAgent",
+        instructions="You are a helpful weather agent.",
+        tools=[get_weather],
+    )
+
+    # 2. Ask a multi-location question and stream the model output as it arrives.
+    query = "What's the weather like in Portland and in Paris?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run(query, stream=True):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print("\n")
+
+
+async def main() -> None:
+    """Run non-streaming and streaming examples."""
+    await non_streaming_example()
+    await streaming_example()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+=== Non-streaming ===
+User: What's the weather like in Karlsruhe, Germany?
+Result: The weather in Karlsruhe, Germany is currently sunny with a high of 16°C.
+
+=== Streaming ===
+User: What's the weather like in Portland and in Paris?
+Agent: In Portland, it is currently rainy with a high of 11°C. In Paris, it is cloudy with a high of 27°C.
+"""

python/packages/gemini/samples/gemini_with_code_execution.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Shows how to enable Gemini's built-in code execution tool.
+
+Allows the model to write and run code in a sandboxed environment to answer questions.
+
+Requires ``GOOGLE_MODEL`` or ``GEMINI_MODEL`` and either Gemini Developer API credentials
+(``GEMINI_API_KEY`` or ``GOOGLE_API_KEY``) or Vertex AI settings
+(``GOOGLE_GENAI_USE_VERTEXAI``, ``GOOGLE_CLOUD_PROJECT``, and ``GOOGLE_CLOUD_LOCATION``).
+"""
+
+import asyncio
+
+from agent_framework import Agent
+from dotenv import load_dotenv
+
+from agent_framework_gemini import GeminiChatClient
+
+load_dotenv()
+
+
+async def main() -> None:
+    """Run the code execution example."""
+    print("=== Code execution ===")
+
+    # 1. Create the agent with Gemini and the built-in code execution tool.
+    agent = Agent(
+        client=GeminiChatClient(),
+        name="CodeAgent",
+        instructions="You are a helpful assistant. Use code execution to compute precise answers.",
+        tools=[GeminiChatClient.get_code_interpreter_tool()],
+    )
+
+    # 2. Ask for a computed answer and stream the generated code and final result.
+    query = "What are the first 20 prime numbers? Compute them in code."
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run(query, stream=True):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+=== Code execution ===
+User: What are the first 20 prime numbers? Compute them in code.
+Agent: The first 20 prime numbers are 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, and 71.
+"""

python/packages/gemini/samples/gemini_with_google_maps.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Shows how to enable Google Maps grounding.
+
+Allows Gemini to retrieve location and mapping information before responding.
+
+Requires ``GOOGLE_MODEL`` or ``GEMINI_MODEL`` and either Gemini Developer API credentials
+(``GEMINI_API_KEY`` or ``GOOGLE_API_KEY``) or Vertex AI settings
+(``GOOGLE_GENAI_USE_VERTEXAI``, ``GOOGLE_CLOUD_PROJECT``, and ``GOOGLE_CLOUD_LOCATION``).
+"""
+
+import asyncio
+
+from agent_framework import Agent
+from dotenv import load_dotenv
+
+from agent_framework_gemini import GeminiChatClient
+
+load_dotenv()
+
+
+async def main() -> None:
+    """Run the Google Maps grounding example."""
+    print("=== Google Maps grounding ===")
+
+    # 1. Create the agent with Gemini and the built-in Google Maps grounding tool.
+    agent = Agent(
+        client=GeminiChatClient(),
+        name="MapsAgent",
+        instructions="You are a helpful travel assistant. Use Google Maps to provide accurate location information.",
+        tools=[GeminiChatClient.get_maps_grounding_tool()],
+    )
+
+    # 2. Ask a location-aware question and stream the grounded answer.
+    query = "What are some highly rated restaurants in the city center of Karlsruhe, Germany?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run(query, stream=True):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+=== Google Maps grounding ===
+User: What are some highly rated restaurants in the city center of Karlsruhe, Germany?
+Agent: Here are several highly rated restaurants near Karlsruhe city center,
+along with their cuisine styles and approximate walking distance.
+"""

python/packages/gemini/samples/gemini_with_google_search.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Shows how to enable Google Search grounding.
+
+Allows Gemini to retrieve up-to-date information from the web before responding.
+
+Requires ``GOOGLE_MODEL`` or ``GEMINI_MODEL`` and either Gemini Developer API credentials
+(``GEMINI_API_KEY`` or ``GOOGLE_API_KEY``) or Vertex AI settings
+(``GOOGLE_GENAI_USE_VERTEXAI``, ``GOOGLE_CLOUD_PROJECT``, and ``GOOGLE_CLOUD_LOCATION``).
+"""
+
+import asyncio
+
+from agent_framework import Agent
+from dotenv import load_dotenv
+
+from agent_framework_gemini import GeminiChatClient
+
+load_dotenv()
+
+
+async def main() -> None:
+    """Run the Google Search grounding example."""
+    print("=== Google Search grounding ===")
+
+    # 1. Create the agent with Gemini and the built-in Google Search grounding tool.
+    agent = Agent(
+        client=GeminiChatClient(),
+        name="SearchAgent",
+        instructions="You are a helpful assistant. Use Google Search to provide accurate, up-to-date answers.",
+        tools=[GeminiChatClient.get_web_search_tool()],
+    )
+
+    # 2. Ask a current-events style question and stream the grounded answer.
+    query = "What is the latest stable release of the .NET SDK?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run(query, stream=True):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+=== Google Search grounding ===
+User: What is the latest stable release of the .NET SDK?
+Agent: As of April 14, 2026, the latest stable release of the .NET SDK is .NET 10.0 (SDK 10.0.201).
+"""