chore: import upstream snapshot with attribution

examples/integration-google-adk/agent.py

@@ -0,0 +1,138 @@
+"""Google ADK agents used by the Promptfoo integration example."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from google.adk.agents import Agent, SequentialAgent
+from google.adk.apps import App
+from google.adk.plugins import BasePlugin
+from google.adk.tools import ToolContext
+from google.genai import types
+
+APP_NAME = "promptfoo_adk_demo"
+
+WEATHER_REPORTS = {
+    "london": "London is cloudy with light drizzle and 14 C temperatures.",
+    "new york": "New York is sunny with a light breeze and 22 C temperatures.",
+    "tokyo": "Tokyo is clear with mild humidity and 24 C temperatures.",
+}
+
+
+def _normalize_city(city: str) -> str:
+    return city.strip().casefold()
+
+
+def before_agent_callback(callback_context) -> None:
+    """Track how often ADK enters the main conversational agent."""
+
+    callback_context.state["callback_invocations"] = (
+        int(callback_context.state.get("callback_invocations", 0)) + 1
+    )
+
+
+def get_weather(city: str, tool_context: ToolContext) -> dict[str, Any]:
+    """Return sample weather data and remember the most recent city."""
+
+    normalized_city = _normalize_city(city)
+    report = WEATHER_REPORTS.get(
+        normalized_city,
+        f"No sample weather is stored for {city}.",
+    )
+    tool_context.state["last_city"] = city
+    return {
+        "city": city,
+        "status": "success" if normalized_city in WEATHER_REPORTS else "not_found",
+        "report": report,
+    }
+
+
+async def save_trip_note(
+    city: str,
+    summary: str,
+    tool_context: ToolContext,
+) -> dict[str, Any]:
+    """Save a short trip note as an ADK artifact."""
+
+    filename = f"{_normalize_city(city).replace(' ', '-')}-trip-note.md"
+    artifact = types.Part.from_bytes(
+        data=f"# Trip note for {city}\n\n{summary}\n".encode("utf-8"),
+        mime_type="text/markdown",
+    )
+    version = await tool_context.save_artifact(filename=filename, artifact=artifact)
+    tool_context.state["last_saved_artifact"] = filename
+    return {
+        "city": city,
+        "filename": filename,
+        "version": version,
+    }
+
+
+class AuditPlugin(BasePlugin):
+    """Record runner lifecycle callbacks so the provider can expose them."""
+
+    def __init__(self) -> None:
+        super().__init__(name="audit_plugin")
+        self.events: list[str] = []
+
+    async def before_run_callback(self, *, invocation_context) -> None:
+        self.events.append(f"before_run:{invocation_context.session.id}")
+
+    async def after_run_callback(self, *, invocation_context) -> None:
+        self.events.append(f"after_run:{invocation_context.session.id}")
+
+
+def build_conversational_app(model: str) -> tuple[App, AuditPlugin]:
+    """Build the conversational ADK app used by the main eval."""
+
+    audit_plugin = AuditPlugin()
+    root_agent = Agent(
+        name="weather_agent",
+        model=model,
+        description="Answers weather questions and saves trip notes.",
+        instruction=(
+            "You are a concise travel weather assistant. "
+            "When the user asks about weather, call get_weather. "
+            "When the user asks to save a note, call save_trip_note using the "
+            "city already discussed and a brief summary of the weather. "
+            "When the user asks what city was discussed earlier, answer from the "
+            "conversation and current session state."
+        ),
+        tools=[get_weather, save_trip_note],
+        before_agent_callback=before_agent_callback,
+    )
+    return (
+        App(name=APP_NAME, root_agent=root_agent, plugins=[audit_plugin]),
+        audit_plugin,
+    )
+
+
+def build_workflow_app(model: str) -> App:
+    """Build a small SequentialAgent workflow for the workflow eval."""
+
+    weather_lookup_agent = Agent(
+        name="weather_lookup_agent",
+        model=model,
+        description="Looks up weather for the requested city.",
+        instruction=(
+            "Call get_weather for the city in the user's request, then return a "
+            "single-sentence weather summary."
+        ),
+        tools=[get_weather],
+        output_key="weather_snapshot",
+    )
+    briefing_agent = Agent(
+        name="briefing_agent",
+        model=model,
+        description="Turns the weather snapshot into a compact travel brief.",
+        instruction=(
+            "Use the weather snapshot from state to write a one-sentence trip "
+            "brief that includes the city name and a packing suggestion."
+        ),
+    )
+    workflow = SequentialAgent(
+        name="trip_planning_workflow",
+        description="Looks up weather, then writes a travel brief.",
+        sub_agents=[weather_lookup_agent, briefing_agent],
+    )
+    return App(name=APP_NAME, root_agent=workflow)