chore: import upstream snapshot with attribution

examples/integrations/strands-python/agent/src/a2ui/schemas/flight_schema.json

@@ -0,0 +1,37 @@
+[
+  {
+    "id": "root",
+    "component": "Row",
+    "children": {
+      "componentId": "flight-card",
+      "path": "/flights"
+    },
+    "gap": 16
+  },
+  {
+    "id": "flight-card",
+    "component": "FlightCard",
+    "airline": { "path": "airline" },
+    "airlineLogo": { "path": "airlineLogo" },
+    "flightNumber": { "path": "flightNumber" },
+    "origin": { "path": "origin" },
+    "destination": { "path": "destination" },
+    "date": { "path": "date" },
+    "departureTime": { "path": "departureTime" },
+    "arrivalTime": { "path": "arrivalTime" },
+    "duration": { "path": "duration" },
+    "status": { "path": "status" },
+    "price": { "path": "price" },
+    "action": {
+      "event": {
+        "name": "book_flight",
+        "context": {
+          "flightNumber": { "path": "flightNumber" },
+          "origin": { "path": "origin" },
+          "destination": { "path": "destination" },
+          "price": { "path": "price" }
+        }
+      }
+    }
+  }
+]

examples/integrations/strands-python/agent/src/a2ui_dynamic_schema.py

@@ -0,0 +1,64 @@
+"""Dynamic A2UI tool: LLM-generated UI from conversation context.
+
+A secondary LLM (langchain_openai) generates v0.9 A2UI components via a
+structured tool call. The generate_a2ui tool wraps the output as
+a2ui_operations, which the middleware/runtime detects and renders
+automatically. Identical surface to the canonical demo's tool —
+implementation differs only by the secondary-LLM wiring.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any, Dict, List
+
+from copilotkit import a2ui
+from langchain_core.messages import SystemMessage
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+from strands import tool
+
+CATALOG_ID = "copilotkit://app-dashboard-catalog"
+
+
+class _A2UIRenderArgs(BaseModel):
+    surfaceId: str = "dynamic-surface"
+    catalogId: str = CATALOG_ID
+    components: List[Dict[str, Any]] = Field(default_factory=list)
+    data: Dict[str, Any] = Field(default_factory=dict)
+
+
+@tool
+def generate_a2ui(user_intent: str) -> str:
+    """Generate dynamic A2UI components based on the conversation.
+
+    A secondary LLM designs the UI schema and data. The result is returned
+    as an a2ui_operations container for the runtime to detect and render.
+    """
+    model = ChatOpenAI(model="gpt-4.1")
+    model_with_tool = model.bind_tools(
+        [_A2UIRenderArgs.model_json_schema()],
+        tool_choice={
+            "type": "function",
+            "function": {"name": "_A2UIRenderArgs"},
+        },
+    )
+    try:
+        response = model_with_tool.invoke([SystemMessage(content=user_intent)])
+    except Exception as exc:  # surface LLM/network failures
+        return json.dumps({"error": f"dynamic-a2ui LLM call failed: {exc}"})
+
+    if not response.tool_calls:
+        return json.dumps({"error": "LLM did not emit dynamic A2UI arguments"})
+
+    args = response.tool_calls[0]["args"]
+    parsed = _A2UIRenderArgs.model_validate(args)
+
+    ops = [
+        a2ui.create_surface(parsed.surfaceId, catalog_id=parsed.catalogId),
+        a2ui.update_components(parsed.surfaceId, parsed.components),
+    ]
+    if parsed.data:
+        ops.append(a2ui.update_data_model(parsed.surfaceId, parsed.data))
+
+    return a2ui.render(operations=ops)

examples/integrations/strands-python/agent/src/a2ui_fixed_schema.py

@@ -0,0 +1,61 @@
+"""Fixed-schema A2UI tool: flight search results.
+
+Schema is loaded from a JSON file. Only the data changes per invocation.
+"""
+
+from pathlib import Path
+from typing import List
+
+from copilotkit import a2ui
+from pydantic import BaseModel
+from strands import tool
+
+CATALOG_ID = "copilotkit://app-dashboard-catalog"
+SURFACE_ID = "flight-search-results"
+FLIGHT_SCHEMA = a2ui.load_schema(
+    Path(__file__).parent / "a2ui" / "schemas" / "flight_schema.json"
+)
+
+
+class Flight(BaseModel):
+    id: str
+    airline: str
+    airlineLogo: str
+    flightNumber: str
+    origin: str
+    destination: str
+    date: str
+    departureTime: str
+    arrivalTime: str
+    duration: str
+    status: str
+    statusIcon: str
+    price: str
+
+
+@tool
+def search_flights(flights: List[Flight]) -> str:
+    """Search for flights and display the results as rich cards.
+
+    Return exactly 2 flights. Each flight must have: id, airline (e.g.
+    "United Airlines"), airlineLogo (Google favicon API URL like
+    "https://www.google.com/s2/favicons?domain=united.com&sz=128"),
+    flightNumber, origin, destination, date (e.g. "Tue, Mar 18" — use
+    near-future dates), departureTime, arrivalTime, duration (e.g.
+    "4h 25m"), status (e.g. "On Time" or "Delayed"), statusIcon (colored
+    dot URL: https://placehold.co/12/22c55e/22c55e.png for On Time,
+    https://placehold.co/12/eab308/eab308.png for Delayed,
+    https://placehold.co/12/ef4444/ef4444.png for Cancelled), and price
+    (e.g. "$289").
+    """
+    # Strands @tool passes plain dicts to the function body, so ``flights``
+    # is a list of dicts here. Validate to enforce the schema, then dump
+    # for a2ui rendering.
+    flights_payload = [Flight.model_validate(f).model_dump() for f in flights]
+    return a2ui.render(
+        operations=[
+            a2ui.create_surface(SURFACE_ID, catalog_id=CATALOG_ID),
+            a2ui.update_components(SURFACE_ID, FLIGHT_SCHEMA),
+            a2ui.update_data_model(SURFACE_ID, {"flights": flights_payload}),
+        ],
+    )

examples/integrations/strands-python/agent/src/db.csv

@@ -0,0 +1,41 @@
+date,category,subcategory,amount,type,notes
+2026-01-05,Revenue,Enterprise Subscriptions,28000,income,3 new enterprise customers (Acme Corp, TechFlow, DataViz Inc)
+2026-01-05,Revenue,Pro Tier Upgrades,18000,income,24 users upgraded from free to pro
+2026-01-08,Revenue,API Usage Overages,9500,income,High API usage from top 5 customers
+2026-01-10,Expenses,Engineering Salaries,42000,expense,7 engineers + 2 contractors
+2026-01-10,Expenses,Product Team,18000,expense,PM and 2 designers
+2026-01-12,Expenses,AWS Infrastructure,8200,expense,Increased compute for new AI features
+2026-01-15,Expenses,Marketing - Paid Ads,12000,expense,Google Ads and LinkedIn campaigns
+2026-01-18,Revenue,Consulting Services,14500,income,Custom integration for Acme Corp
+2026-01-20,Expenses,Customer Success,15000,expense,3 CSMs + support tools (Intercom)
+2026-01-22,Expenses,AI Model Costs,4200,expense,OpenAI API usage for product features
+2026-01-25,Revenue,Marketplace Sales,12800,income,Template and plugin sales
+2026-01-28,Expenses,Office & Equipment,3500,expense,New laptops and coworking spaces
+2026-02-03,Revenue,Enterprise Subscriptions,31000,income,2 new customers + expansion from TechFlow
+2026-02-03,Revenue,Pro Tier Upgrades,22500,income,31 upgrades + reduced churn
+2026-02-05,Revenue,API Usage Overages,11800,income,DataViz Inc heavy API usage spike
+2026-02-07,Expenses,Engineering Salaries,42000,expense,Same headcount as January
+2026-02-07,Expenses,Product Team,18000,expense,No changes to product team
+2026-02-10,Expenses,AWS Infrastructure,9500,expense,Traffic spike from viral social post
+2026-02-12,Expenses,Marketing - Paid Ads,15000,expense,Increased ad spend for Q1 push
+2026-02-14,Revenue,Consulting Services,18000,income,2 custom projects (TechFlow + new client)
+2026-02-18,Expenses,Customer Success,16500,expense,Hired 1 additional CSM
+2026-02-20,Expenses,AI Model Costs,5800,expense,Increased usage from new AI features launch
+2026-02-22,Revenue,Marketplace Sales,14200,income,Top template hit featured list
+2026-02-25,Expenses,Conference & Travel,4500,expense,Team attended SaaS Conference 2026
+2026-02-27,Revenue,Partnership Revenue,11500,income,Referral fees from integration partners
+2026-03-02,Revenue,Enterprise Subscriptions,35000,income,Major win: Fortune 500 customer signed
+2026-03-02,Revenue,Pro Tier Upgrades,26000,income,42 upgrades - best month yet
+2026-03-05,Revenue,API Usage Overages,13200,income,Consistent high usage across top tier
+2026-03-08,Expenses,Engineering Salaries,48000,expense,Hired 1 senior engineer for AI team
+2026-03-08,Expenses,Product Team,21000,expense,Promoted designer to senior level
+2026-03-10,Expenses,AWS Infrastructure,11000,expense,Scaled infrastructure for enterprise client
+2026-03-12,Expenses,Marketing - Paid Ads,18000,expense,Doubled down on successful campaigns
+2026-03-14,Revenue,Consulting Services,21500,income,Fortune 500 onboarding + 2 other projects
+2026-03-16,Expenses,Customer Success,19500,expense,Hired dedicated enterprise CSM
+2026-03-18,Expenses,AI Model Costs,7200,expense,Fortune 500 client heavy AI usage
+2026-03-20,Revenue,Marketplace Sales,15800,income,3 new templates in top 10
+2026-03-22,Expenses,Sales & BD,12000,expense,Hired first sales rep for enterprise
+2026-03-24,Revenue,Partnership Revenue,14200,income,New integration partnerships launched
+2026-03-26,Expenses,Security & Compliance,6500,expense,SOC 2 audit and security tools
+2026-03-28,Revenue,Training & Workshops,10200,income,Conducted 2 customer training sessions

examples/integrations/strands-python/agent/src/query.py

@@ -0,0 +1,20 @@
+import csv
+import json
+from pathlib import Path
+
+from strands import tool
+
+# Read data at module load time to avoid file I/O issues in sandboxed
+# tool execution environments.
+_csv_path = Path(__file__).parent / "db.csv"
+with open(_csv_path) as _f:
+    _cached_data = list(csv.DictReader(_f))
+
+
+@tool
+def query_data(query: str) -> str:
+    """Query the database with a natural-language query.
+
+    Always call this before rendering a chart so the UI has data to plot.
+    """
+    return json.dumps(_cached_data)

examples/integrations/strands-python/agent/src/todos.py

@@ -0,0 +1,42 @@
+from uuid import uuid4
+
+from pydantic import BaseModel
+from strands import tool
+
+
+class Todo(BaseModel):
+    id: str = ""
+    title: str
+    description: str
+    emoji: str
+    status: str = "pending"  # "pending" | "completed"
+
+
+@tool
+def manage_todos(todos: list[Todo]) -> str:
+    """Manage the current todos.
+
+    IMPORTANT: Always pass the full list, not just new items. Each todo
+    should have a title, description, emoji, and status (pending/completed).
+    """
+    # Strands @tool passes ``model_dump()`` output, so list elements arrive
+    # as plain dicts. Rehydrate before accessing fields.
+    todos = [Todo.model_validate(t) for t in todos]
+    for todo in todos:
+        if not todo.id:
+            todo.id = str(uuid4())
+    return "Successfully updated todos"
+
+
+@tool
+def get_todos() -> str:
+    """Get the current todos.
+
+    The current list is injected into the prompt by the state context
+    builder, so this tool just acknowledges that and tells the model to
+    read it from there.
+    """
+    return "See the current todos list already provided in the conversation context."
+
+
+todo_tools = [manage_todos, get_todos]