chore: import upstream snapshot with attribution

showcase/integrations/langgraph-python/tools/__init__.py

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+"""Barrel exports for all shared showcase tool implementations."""
+
+from .types import (
+    SalesStage,
+    SalesTodo,
+    Flight,
+    WeatherResult,
+)
+from .get_weather import get_weather_impl
+from .query_data import query_data_impl
+from .sales_todos import (
+    INITIAL_TODOS,
+    manage_sales_todos_impl,
+    get_sales_todos_impl,
+)
+from .search_flights import search_flights_impl
+from .generate_a2ui import (
+    DESIGN_A2UI_SURFACE_TOOL_SCHEMA,
+    generate_a2ui_impl,
+    build_a2ui_operations_from_tool_call,
+)
+from .schedule_meeting import schedule_meeting_impl
+
+__all__ = [
+    # Types
+    "SalesStage",
+    "SalesTodo",
+    "Flight",
+    "WeatherResult",
+    # Weather
+    "get_weather_impl",
+    # Query data
+    "query_data_impl",
+    # Sales todos
+    "INITIAL_TODOS",
+    "manage_sales_todos_impl",
+    "get_sales_todos_impl",
+    # Flight search (fixed-schema A2UI)
+    "search_flights_impl",
+    # Dynamic A2UI
+    "DESIGN_A2UI_SURFACE_TOOL_SCHEMA",
+    "generate_a2ui_impl",
+    "build_a2ui_operations_from_tool_call",
+    # Schedule meeting (HITL)
+    "schedule_meeting_impl",
+]

showcase/integrations/langgraph-python/tools/generate_a2ui.py

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+"""Dynamic A2UI tool: LLM-generated UI from conversation context.
+
+This module provides the data preparation for a secondary LLM call that
+generates v0.9 A2UI components. The actual LLM call is made by the
+framework-specific wrapper (LangGraph, CrewAI, etc.) since each framework
+has its own way of invoking LLMs.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Optional
+
+_logger = logging.getLogger(__name__)
+
+CUSTOM_CATALOG_ID = "copilotkit://app-dashboard-catalog"
+
+# The _design_a2ui_surface tool schema that the secondary LLM is bound to.
+DESIGN_A2UI_SURFACE_TOOL_SCHEMA = {
+    "name": "_design_a2ui_surface",
+    "description": (
+        "Render a dynamic A2UI v0.9 surface.\n\n"
+        "Args:\n"
+        "    surfaceId: Unique surface identifier.\n"
+        '    catalogId: The catalog ID (use "copilotkit://app-dashboard-catalog").\n'
+        "    components: A2UI v0.9 component array (flat format). "
+        'The root component must have id "root".\n'
+        "    data: Optional initial data model for the surface."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "surfaceId": {
+                "type": "string",
+                "description": "Unique surface identifier.",
+            },
+            "catalogId": {"type": "string", "description": "The catalog ID."},
+            "components": {
+                "type": "array",
+                "items": {"type": "object"},
+                "description": "A2UI v0.9 component array (flat format).",
+            },
+            "data": {
+                "type": "object",
+                "description": "Optional initial data model for the surface.",
+            },
+        },
+        "required": ["surfaceId", "catalogId", "components"],
+    },
+}
+
+
+def generate_a2ui_impl(
+    messages: list[dict[str, Any]],
+    context_entries: Optional[list[dict[str, Any]]] = None,
+) -> dict[str, Any]:
+    """Prepare inputs for a secondary LLM call that generates A2UI components.
+
+    Returns a dict with:
+      - system_prompt: The system prompt for the secondary LLM (built from context)
+      - tool_schema: The _design_a2ui_surface tool schema to bind to the LLM
+      - tool_choice: The tool name to force
+      - messages: The conversation messages to pass through
+      - catalog_id: The default catalog ID
+
+    The framework wrapper should:
+      1. Make an LLM call with these inputs
+      2. Extract the tool call args (surfaceId, catalogId, components, data)
+      3. Build a2ui_operations from the args and return them
+    """
+    context_text = ""
+    if context_entries:
+        context_text = "\n\n".join(
+            entry.get("value", "")
+            for entry in context_entries
+            if isinstance(entry, dict) and entry.get("value")
+        )
+
+    return {
+        "system_prompt": context_text,
+        "tool_schema": DESIGN_A2UI_SURFACE_TOOL_SCHEMA,
+        "tool_choice": "_design_a2ui_surface",
+        "messages": messages,
+        "catalog_id": CUSTOM_CATALOG_ID,
+    }
+
+
+def build_a2ui_operations_from_tool_call(args: dict[str, Any]) -> dict[str, Any]:
+    """Build a2ui_operations dict from the secondary LLM's tool call args.
+
+    Call this after the framework wrapper extracts the tool call arguments.
+    """
+    surface_id = args.get("surfaceId", "dynamic-surface")
+    catalog_id = args.get("catalogId", CUSTOM_CATALOG_ID)
+    components = args.get("components", [])
+    if not components:
+        _logger.warning(
+            "build_a2ui_operations_from_tool_call received empty components list"
+        )
+    data = args.get("data")
+
+    ops: list[dict[str, Any]] = [
+        {
+            "version": "v0.9",
+            "createSurface": {"surfaceId": surface_id, "catalogId": catalog_id},
+        },
+        {
+            "version": "v0.9",
+            "updateComponents": {"surfaceId": surface_id, "components": components},
+        },
+    ]
+    if data:
+        ops.append(
+            {
+                "version": "v0.9",
+                "updateDataModel": {
+                    "surfaceId": surface_id,
+                    "path": "/",
+                    "value": data,
+                },
+            }
+        )
+
+    return {"a2ui_operations": ops}

showcase/integrations/langgraph-python/tools/get_weather.py

@@ -0,0 +1,40 @@
+"""Mock weather data tool implementation."""
+
+import random
+from .types import WeatherResult
+
+_CONDITIONS = [
+    "Sunny",
+    "Partly Cloudy",
+    "Cloudy",
+    "Overcast",
+    "Light Rain",
+    "Heavy Rain",
+    "Thunderstorm",
+    "Snow",
+    "Foggy",
+    "Windy",
+]
+
+
+def get_weather_impl(city: str) -> WeatherResult:
+    """Return mock weather data for the given city.
+
+    Uses a seeded random based on the city name so repeated calls
+    for the same city return consistent results within a session.
+    """
+    rng = random.Random(city.lower())
+    temperature = rng.randint(20, 95)
+    humidity = rng.randint(30, 90)
+    wind_speed = rng.randint(2, 30)
+    feels_like = temperature + rng.randint(-5, 5)
+    conditions = rng.choice(_CONDITIONS)
+
+    return WeatherResult(
+        city=city,
+        temperature=temperature,
+        humidity=humidity,
+        wind_speed=wind_speed,
+        feels_like=feels_like,
+        conditions=conditions,
+    )

showcase/integrations/langgraph-python/tools/query_data.py

@@ -0,0 +1,60 @@
+"""Query data tool implementation — reads db.csv at module load time."""
+
+from __future__ import annotations
+
+import csv
+import logging
+from pathlib import Path
+from typing import Any
+
+_logger = logging.getLogger(__name__)
+
+_csv_path = Path(__file__).resolve().parent.parent / "data" / "db.csv"
+
+_MOCK_DATA = [
+    {
+        "date": "2026-01-05",
+        "category": "Revenue",
+        "subcategory": "Enterprise Subscriptions",
+        "amount": "28000",
+        "type": "income",
+        "notes": "3 new enterprise customers",
+    },
+    {
+        "date": "2026-01-10",
+        "category": "Expenses",
+        "subcategory": "Engineering Salaries",
+        "amount": "42000",
+        "type": "expense",
+        "notes": "7 engineers + 2 contractors",
+    },
+    {
+        "date": "2026-02-03",
+        "category": "Revenue",
+        "subcategory": "Pro Tier Upgrades",
+        "amount": "22500",
+        "type": "income",
+        "notes": "31 upgrades + reduced churn",
+    },
+]
+
+try:
+    with open(_csv_path) as _f:
+        _cached_data: list[dict[str, Any]] = list(csv.DictReader(_f))
+    if not _cached_data:
+        _logger.warning("CSV at %s is empty, falling back to mock data", _csv_path)
+        _cached_data = _MOCK_DATA
+except (FileNotFoundError, OSError) as exc:
+    _logger.warning(
+        "Could not load CSV at %s (%s), falling back to mock data", _csv_path, exc
+    )
+    _cached_data = _MOCK_DATA
+
+
+def query_data_impl(query: str) -> list[dict[str, Any]]:
+    """Query the database. Takes natural language.
+
+    Always call before showing a chart or graph. Returns the full
+    dataset as a list of dicts (rows from the CSV).
+    """
+    return _cached_data

showcase/integrations/langgraph-python/tools/sales_todos.py

@@ -0,0 +1,63 @@
+"""Sales todos tool implementation."""
+
+from __future__ import annotations
+
+import uuid
+from typing import Optional
+
+from .types import SalesTodo
+
+INITIAL_TODOS: list[SalesTodo] = [
+    SalesTodo(
+        id="st-001",
+        title="Follow up with Acme Corp on enterprise proposal",
+        stage="proposal",
+        value=85000,
+        dueDate="2026-04-15",
+        assignee="Sarah Chen",
+        completed=False,
+    ),
+    SalesTodo(
+        id="st-002",
+        title="Qualify lead from TechFlow demo request",
+        stage="prospect",
+        value=42000,
+        dueDate="2026-04-18",
+        assignee="Mike Johnson",
+        completed=False,
+    ),
+    SalesTodo(
+        id="st-003",
+        title="Send contract to DataViz Inc for final review",
+        stage="negotiation",
+        value=120000,
+        dueDate="2026-04-20",
+        assignee="Sarah Chen",
+        completed=False,
+    ),
+]
+
+
+def manage_sales_todos_impl(todos: list[dict]) -> list[SalesTodo]:
+    """Assign UUIDs to any todos missing an ID, then return the updated list."""
+    result: list[SalesTodo] = []
+    for todo in todos:
+        result.append(
+            SalesTodo(
+                id=todo.get("id") or str(uuid.uuid4()),
+                title=todo.get("title", ""),
+                stage=todo.get("stage", "prospect"),
+                value=todo.get("value", 0),
+                dueDate=todo.get("dueDate", ""),
+                assignee=todo.get("assignee", ""),
+                completed=todo.get("completed", False),
+            )
+        )
+    return result
+
+
+def get_sales_todos_impl(current_todos: Optional[list[dict]] = None) -> list[SalesTodo]:
+    """Return current todos or initial defaults if none provided."""
+    if current_todos is not None:
+        return manage_sales_todos_impl(current_todos)
+    return list(INITIAL_TODOS)

showcase/integrations/langgraph-python/tools/schedule_meeting.py

@@ -0,0 +1,31 @@
+"""Schedule meeting tool implementation.
+
+The HITL gating happens on the frontend via useHumanInTheLoop.
+This tool just returns a pending approval status for the framework
+wrapper to surface.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def schedule_meeting_impl(
+    reason: str,
+    duration_minutes: int = 30,
+) -> dict[str, Any]:
+    """Schedule a meeting (requires human approval).
+
+    Returns a pending_approval status. The actual gating is done by the
+    frontend's useHumanInTheLoop hook — the agent pauses until the user
+    approves or rejects.
+    """
+    return {
+        "status": "pending_approval",
+        "reason": reason,
+        "duration_minutes": duration_minutes,
+        "message": (
+            f"Meeting request: {reason} ({duration_minutes} min). "
+            "Awaiting human approval."
+        ),
+    }

showcase/integrations/langgraph-python/tools/search_flights.py

@@ -0,0 +1,122 @@
+"""Fixed-schema A2UI tool: flight search results.
+
+Packages flight data with an A2UI schema for rendering. The schema is loaded
+from the shared frontend package's flight_schema.json.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+from .types import Flight
+
+_logger = logging.getLogger(__name__)
+
+CATALOG_ID = "copilotkit://app-dashboard-catalog"
+SURFACE_ID = "flight-search-results"
+
+# Resolve the flight schema from the shared frontend package.
+# Walk up from this file to showcase/shared/, then into frontend/src/a2ui/.
+_SHARED_DIR = Path(__file__).resolve().parent.parent.parent  # showcase/shared/
+_SCHEMA_CANDIDATES = [
+    _SHARED_DIR / "frontend" / "src" / "a2ui" / "flight-schema.json",
+    _SHARED_DIR / "frontend" / "src" / "a2ui" / "flight_schema.json",
+]
+
+_flight_schema: list[dict[str, Any]] | None = None
+for _candidate in _SCHEMA_CANDIDATES:
+    if _candidate.exists():
+        with open(_candidate) as _f:
+            _flight_schema = json.load(_f)
+        _logger.info("Loaded flight schema from shared frontend: %s", _candidate)
+        break
+
+# Fallback: use the schema from the examples directory if present
+if _flight_schema is None:
+    try:
+        _fallback = Path(__file__).resolve().parents[4] / (
+            "examples/integrations/langgraph-python/apps/agent/src/a2ui/schemas/flight_schema.json"
+        )
+        if _fallback.exists():
+            with open(_fallback) as _f:
+                _flight_schema = json.load(_f)
+            _logger.info("Loaded flight schema from examples fallback: %s", _fallback)
+    except IndexError:
+        # In Docker the file path is too shallow for parents[4]; skip this fallback.
+        pass
+
+# Last resort: inline minimal schema
+if _flight_schema is None:
+    _logger.warning("No flight schema file found, using inline minimal schema")
+    _flight_schema = [
+        {
+            "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"},
+                    },
+                }
+            },
+        },
+    ]
+
+
+def search_flights_impl(flights: list[Flight]) -> dict[str, Any]:
+    """Package flight data with A2UI schema for rendering.
+
+    Returns a dict with a2ui_operations that the middleware detects in the
+    TOOL_CALL_RESULT and renders automatically.
+
+    Each flight should have: airline, airlineLogo, flightNumber, origin,
+    destination, date, departureTime, arrivalTime, duration, status,
+    statusColor, price, currency.
+    """
+    return {
+        "a2ui_operations": [
+            {
+                "version": "v0.9",
+                "createSurface": {"surfaceId": SURFACE_ID, "catalogId": CATALOG_ID},
+            },
+            {
+                "version": "v0.9",
+                "updateComponents": {
+                    "surfaceId": SURFACE_ID,
+                    "components": _flight_schema,
+                },
+            },
+            {
+                "version": "v0.9",
+                "updateDataModel": {
+                    "surfaceId": SURFACE_ID,
+                    "path": "/",
+                    "value": {"flights": flights},
+                },
+            },
+        ]
+    }

showcase/integrations/langgraph-python/tools/types.py

@@ -0,0 +1,47 @@
+"""Shared type definitions for showcase tools."""
+
+from typing import TypedDict, Literal
+
+SalesStage = Literal[
+    "prospect",
+    "qualified",
+    "proposal",
+    "negotiation",
+    "closed-won",
+    "closed-lost",
+]
+
+
+class SalesTodo(TypedDict):
+    id: str
+    title: str
+    stage: SalesStage
+    value: int
+    dueDate: str
+    assignee: str
+    completed: bool
+
+
+class Flight(TypedDict):
+    airline: str
+    airlineLogo: str
+    flightNumber: str
+    origin: str
+    destination: str
+    date: str
+    departureTime: str
+    arrivalTime: str
+    duration: str
+    status: str
+    statusColor: str
+    price: str
+    currency: str
+
+
+class WeatherResult(TypedDict):
+    city: str
+    temperature: int
+    humidity: int
+    wind_speed: int
+    feels_like: int
+    conditions: str