Fully Headless UI

"""Claude Agent SDK (Python) -- sales assistant with weather, HITL, and generative UI.Implements the AG-UI protocol directly using the Anthropic Python SDK.All demo routes share this single agent instance served by agent_server.py."""from __future__ import annotationsimport jsonimport osimport randomimport tracebackfrom collections.abc import AsyncIteratorfrom textwrap import dedentfrom typing import Anyimport anthropicfrom ag_ui.core import (    EventType,    Message,    RunAgentInput,    RunFinishedEvent,    RunStartedEvent,    StateSnapshotEvent,    TextMessageContentEvent,    TextMessageEndEvent,    TextMessageStartEvent,    ToolCallArgsEvent,    ToolCallEndEvent,    ToolCallResultEvent,    ToolCallStartEvent,)from ag_ui.encoder import EventEncoderfrom dotenv import load_dotenvfrom fastapi import FastAPI, Requestfrom fastapi.middleware.cors import CORSMiddlewarefrom fastapi.responses import StreamingResponsefrom pydantic import BaseModelfrom starlette.middleware.base import BaseHTTPMiddlewarefrom starlette.responses import JSONResponsefrom agents.claude_agent_sdk_adapter import (    normalize_claude_model,    run_with_claude_agent_sdk,    should_use_claude_agent_sdk,)from agents._anthropic_message_safety import sanitize_unresolved_tool_uses# Serve /health via middleware so it short-circuits BEFORE route resolution.# Any later catch-all mount at "/" (whether added here or by a downstream# adapter) would shadow a plain `@app.get("/health")` decorator. Middleware# runs above routing so the health endpoint stays reachable regardless.class HealthMiddleware(BaseHTTPMiddleware):    async def dispatch(self, request, call_next):        if request.url.path == "/health" and request.method == "GET":            return JSONResponse({"status": "ok"})        return await call_next(request)load_dotenv()DEFAULT_ANTHROPIC_MODEL = "claude-sonnet-4.6"# Import shared tool implementations (via tools symlink -> ../../shared/python/tools)from tools import (    get_weather_impl,    query_data_impl,    manage_sales_todos_impl,    get_sales_todos_impl,    schedule_meeting_impl,    search_flights_impl,    build_a2ui_operations_from_tool_call,    RENDER_A2UI_TOOL_SCHEMA,)from tools.types import Flight# ============# Tool schemas# ============TOOLS: list[dict[str, Any]] = [    {        "name": "get_weather",        "description": (            "Get current weather for a location. "            "Use this to render the frontend weather card."        ),        "input_schema": {            "type": "object",            "properties": {                "location": {                    "type": "string",                    "description": "The city or region to get weather for.",                },            },            "required": ["location"],        },    },    {        "name": "query_data",        "description": (            "Query the financial database for chart data. "            "Always call before showing a chart or graph."        ),        "input_schema": {            "type": "object",            "properties": {                "query": {                    "type": "string",                    "description": "Natural language query for financial data.",                },            },            "required": ["query"],        },    },    {        "name": "manage_sales_todos",        "description": (            "Replace the entire list of sales todos with the provided values. "            "Always include every todo you want to keep."        ),        "input_schema": {            "type": "object",            "properties": {                "todos": {                    "type": "array",                    "items": {                        "type": "object",                        "properties": {                            "id": {"type": "string"},                            "title": {"type": "string"},                            "stage": {                                "type": "string",                                "enum": [                                    "prospect",                                    "qualified",                                    "proposal",                                    "negotiation",                                    "closed-won",                                    "closed-lost",                                ],                            },                            "value": {"type": "number"},                            "dueDate": {"type": "string"},                            "assignee": {"type": "string"},                            "completed": {"type": "boolean"},                        },                        "required": [                            "title",                            "stage",                            "value",                            "dueDate",                            "assignee",                            "completed",                        ],                    },                    "description": "The complete list of sales todos.",                },            },            "required": ["todos"],        },    },    {        "name": "get_sales_todos",        "description": "Get the current sales pipeline todos.",        "input_schema": {            "type": "object",            "properties": {},        },    },    {        "name": "schedule_meeting",        "description": (            "Schedule a meeting with the user. Requires human approval. "            "Call this when the user wants to schedule or book a meeting."        ),        "input_schema": {            "type": "object",            "properties": {                "reason": {                    "type": "string",                    "description": "Reason for the meeting.",                },            },            "required": ["reason"],        },    },    {        "name": "generate_task_steps",        "description": (            "Propose a list of steps for the user to review and approve. "            "Used for human-in-the-loop task planning. "            "Always call this tool when the user asks you to plan something."        ),        "input_schema": {            "type": "object",            "properties": {                "steps": {                    "type": "array",                    "items": {                        "type": "object",                        "properties": {                            "description": {"type": "string"},                            "status": {                                "type": "string",                                "enum": ["enabled", "disabled", "executing"],                            },                        },                        "required": ["description", "status"],                    },                    "description": "The ordered list of steps for the user to review.",                }            },            "required": ["steps"],        },    },    {        "name": "change_background",        "description": (            "Change the background color or gradient of the chat UI. "            "ONLY call this when the user explicitly asks to change the background."        ),        "input_schema": {            "type": "object",            "properties": {                "background": {                    "type": "string",                    "description": "CSS background value. Prefer gradients.",                }            },            "required": ["background"],        },    },    {        "name": "search_flights",        "description": (            "Search for flights and display the results as rich A2UI cards. "            "Return exactly 2 flights. Each flight must have: airline, airlineLogo, "            "flightNumber, origin, destination, date, departureTime, arrivalTime, "            "duration, status, statusColor, price, currency. "            "For airlineLogo use: https://www.google.com/s2/favicons?domain={airline_domain}&sz=128"        ),        "input_schema": {            "type": "object",            "properties": {                "flights": {                    "type": "array",                    "items": {                        "type": "object",                        "properties": {                            "airline": {"type": "string"},                            "airlineLogo": {"type": "string"},                            "flightNumber": {"type": "string"},                            "origin": {"type": "string"},                            "destination": {"type": "string"},                            "date": {"type": "string"},                            "departureTime": {"type": "string"},                            "arrivalTime": {"type": "string"},                            "duration": {"type": "string"},                            "status": {"type": "string"},                            "statusColor": {"type": "string"},                            "price": {"type": "string"},                            "currency": {"type": "string"},                        },                    },                    "description": "List of flight objects to display.",                },            },            "required": ["flights"],        },    },    {        "name": "generate_a2ui",        "description": (            "Generate dynamic A2UI components based on the conversation. "            "A secondary LLM designs the UI schema and data."        ),        "input_schema": {            "type": "object",            "properties": {                "context": {                    "type": "string",                    "description": "Conversation context to generate UI for.",                },            },            "required": ["context"],        },    },]MANAGE_TODOS_TOOL_SCHEMA: dict[str, Any] = {    "name": "manage_todos",    "description": (        "Replace the beautiful-chat task manager todo list. Always include every "        "todo that should remain visible."    ),    "input_schema": {        "type": "object",        "properties": {            "todos": {                "type": "array",                "description": "The complete task-manager todo list.",                "items": {                    "type": "object",                    "properties": {                        "id": {"type": "string"},                        "title": {"type": "string"},                        "description": {"type": "string"},                        "emoji": {"type": "string"},                        "status": {                            "type": "string",                            "enum": ["pending", "completed"],                        },                    },                    "required": ["title", "description", "emoji", "status"],                },            },        },        "required": ["todos"],    },}GET_TODOS_TOOL_SCHEMA: dict[str, Any] = {    "name": "get_todos",    "description": "Get the current beautiful-chat task manager todo list.",    "input_schema": {        "type": "object",        "properties": {},    },}BEAUTIFUL_CHAT_TOOLS = [    *TOOLS,    MANAGE_TODOS_TOOL_SCHEMA,    GET_TODOS_TOOL_SCHEMA,]# Dedicated demo tool sets. These demos register render-only frontend# surfaces, so their executable tools must stay backend-owned.HEADLESS_GET_WEATHER_TOOL_SCHEMA = TOOLS[0]HEADLESS_GET_STOCK_PRICE_TOOL_SCHEMA: dict[str, Any] = {    "name": "get_stock_price",    "description": (        "Get a mock current price for a stock ticker. Returns ticker, "        "price_usd, and change_pct."    ),    "input_schema": {        "type": "object",        "properties": {            "ticker": {                "type": "string",                "description": "Stock ticker symbol, e.g. AAPL.",            },        },        "required": ["ticker"],    },}SEARCH_FLIGHTS_SIMPLE_TOOL_SCHEMA: dict[str, Any] = {    "name": "search_flights",    "description": (        "Search for mock flights between two airports. Returns origin, "        "destination, and a list of flights."    ),    "input_schema": {        "type": "object",        "properties": {            "origin": {"type": "string", "description": "Origin airport code."},            "destination": {                "type": "string",                "description": "Destination airport code.",            },        },        "required": ["origin", "destination"],    },}ROLL_D20_TOOL_SCHEMA: dict[str, Any] = {    "name": "roll_d20",    "description": (        "Roll a 20-sided die. Accepts an optional value for deterministic demos."    ),    "input_schema": {        "type": "object",        "properties": {            "value": {                "type": "number",                "description": "Optional fixed result.",            },        },    },}SET_STEPS_TOOL_SCHEMA: dict[str, Any] = {    "name": "set_steps",    "description": (        "Publish the current plan and step statuses. The provided list replaces "        "the previous state."    ),    "input_schema": {        "type": "object",        "properties": {            "steps": {                "type": "array",                "items": {                    "type": "object",                    "properties": {                        "id": {"type": "string"},                        "title": {"type": "string"},                        "status": {                            "type": "string",                            "enum": ["pending", "in_progress", "completed"],                        },                    },                    "required": ["id", "title", "status"],                },            },        },        "required": ["steps"],    },}WRITE_DOCUMENT_TOOL_SCHEMA: dict[str, Any] = {    "name": "write_document",    "description": (        "Write a document into shared agent state. Use for poems, emails, "        "summaries, explainers, and other drafted text."    ),    "input_schema": {        "type": "object",        "properties": {            "document": {                "type": "string",                "description": "The full document text to render in shared state.",            },        },        "required": ["document"],    },}SHARED_STATE_STREAMING_TOOLS = [WRITE_DOCUMENT_TOOL_SCHEMA]SHARED_STATE_STREAMING_SYSTEM_PROMPT = dedent("""    You are a collaborative writing assistant. Whenever the user asks you to    write, draft, or revise text, call `write_document` with the full content    in the `document` argument. Do not paste the document into the chat message    directly; the UI renders shared state.""").strip()def _decode_partial_json_string(raw: str) -> str | None:    """Decode the largest safe prefix of a streamed JSON string literal body."""    while raw.endswith("\\"):        raw = raw[:-1]    unicode_start = raw.rfind("\\u")    if unicode_start != -1:        hex_digits = raw[unicode_start + 2 :]        if len(hex_digits) < 4 or any(            c not in "0123456789abcdefABCDEF" for c in hex_digits        ):            raw = raw[:unicode_start]    try:        return json.loads(f'"{raw}"')    except json.JSONDecodeError:        return Nonedef _partial_json_string_property(source: str, key: str) -> str | None:    key_literal = json.dumps(key)    key_pos = source.find(key_literal)    if key_pos < 0:        return None    colon_pos = source.find(":", key_pos + len(key_literal))    if colon_pos < 0:        return None    value_start = colon_pos + 1    while value_start < len(source) and source[value_start].isspace():        value_start += 1    if value_start >= len(source) or source[value_start] != '"':        return None    raw_chars: list[str] = []    escaped = False    for char in source[value_start + 1 :]:        if escaped:            raw_chars.append("\\" + char)            escaped = False            continue        if char == "\\":            escaped = True            continue        if char == '"':            break        raw_chars.append(char)    if escaped:        raw_chars.append("\\")    return _decode_partial_json_string("".join(raw_chars))HEADLESS_COMPLETE_TOOLS = [    HEADLESS_GET_WEATHER_TOOL_SCHEMA,    HEADLESS_GET_STOCK_PRICE_TOOL_SCHEMA,    {        "name": "get_revenue_chart",        "description": (            "Return a mock six-month revenue trend chart. Use this when the "            "user asks for revenue, sales, or trend charts."        ),        "input_schema": {"type": "object", "properties": {}},    },]TOOL_RENDERING_TOOLS = [    HEADLESS_GET_WEATHER_TOOL_SCHEMA,    HEADLESS_GET_STOCK_PRICE_TOOL_SCHEMA,    SEARCH_FLIGHTS_SIMPLE_TOOL_SCHEMA,    ROLL_D20_TOOL_SCHEMA,]GEN_UI_AGENT_TOOLS = [SET_STEPS_TOOL_SCHEMA]HEADLESS_COMPLETE_SYSTEM_PROMPT = dedent("""    You are a helpful, concise assistant wired into a headless chat surface.    Routing rules:    - If the user asks about weather, call `get_weather`.    - If the user asks about a stock or ticker, call `get_stock_price`.    - If the user asks for a revenue, sales, or trend chart, call      `get_revenue_chart`.    - If the user asks you to highlight, flag, or mark a note, call the      frontend `highlight_note` tool with text and a color.    - Otherwise, reply in plain text.    After a tool returns, write one short sentence summarizing the result.    Never fabricate data a tool could provide.""").strip()TOOL_RENDERING_SYSTEM_PROMPT = dedent("""    You are a helpful, concise assistant in a demo that renders every tool    call as a branded card. Pick the right backend tool for each user question.    Routing rules:    - Weather questions: call `get_weather`.    - Flight searches: call `search_flights` with origin and destination codes.    - Stock/ticker questions: call `get_stock_price`.    - A d20 roll: call `roll_d20`. If the user asks for several rolls, call it      once per roll.    - "Chain a few tools": call get_weather, search_flights, and roll_d20.    After the tools return, write one short sentence summarizing the results.    Never fabricate data a tool could provide.""").strip()GEN_UI_AGENT_SYSTEM_PROMPT = dedent("""    You are an agentic planner. For each user request, follow this exact    sequence:    1. Plan exactly 3 concrete steps and call `set_steps` once with all three       steps at status "pending".    2. Move step 1 to "in_progress", then "completed", calling `set_steps`       after each transition.    3. Move step 2 to "in_progress", then "completed", calling `set_steps`       after each transition.    4. Move step 3 to "in_progress", then "completed", calling `set_steps`       after each transition.    5. Send one final conversational assistant message summarizing the plan.    Never call set_steps in parallel. Always pass the full step list.""").strip()SYSTEM_PROMPT = dedent("""    You are a helpful sales assistant that manages a sales pipeline, discusses weather,    queries financial data, schedules meetings, and helps with planning.    Sales pipeline management:    - The current list of sales todos is provided in the conversation context.    - When you add, remove, or update todos, call `manage_sales_todos` with the FULL list.    - CRITICAL: When asked to "add" a todo, include ALL existing todos + the new one.    - When asked to "remove" a todo, include everything EXCEPT the removed one.    Tool usage:    - `get_weather`: only call when the user explicitly asks about weather.    - `query_data`: call when the user asks about financial data, charts, or graphs.    - `manage_sales_todos`: call to update the sales pipeline.    - `get_sales_todos`: call to retrieve current sales pipeline.    - `schedule_meeting`: call when the user wants to schedule a meeting.    - `generate_task_steps`: call when the user asks you to plan something step-by-step.      Wait for approval/rejection before continuing with the plan.    - `change_background`: only call when user explicitly asks to change the background.    - `search_flights`: call when the user asks about flights. Generate 2 realistic flights.    - `generate_a2ui`: call when the user asks for a dashboard or dynamic UI.    After executing tools, provide a brief summary of what changed.    Keep responses concise and friendly.""").strip()BEAUTIFUL_CHAT_SYSTEM_PROMPT = dedent("""    You are a helpful CopilotKit demo assistant. Use tools to render rich UI    instead of describing UI in prose.    Routing rules:    - Charts: call `query_data` first when the user asks for financial data,      then use the frontend chart tool requested by the user.    - Flights: call `search_flights` with exactly two complete flight objects      so the A2UI flight cards can render.    - Dashboards: call `query_data`, then `generate_a2ui`.    - Todos: call `enableAppMode` first, then `manage_todos` with the full      todo list.    - Meetings and theme changes are frontend tools; call the matching      frontend tool when requested.    After tools complete, summarize the result in one short sentence.""").strip()# ===========# AG-UI runner# ===========class AgentState(BaseModel):    todos: list[dict] = []    steps: list[dict] = []    document: str = ""def _coerce_beautiful_chat_todos(value: Any) -> list[dict[str, Any]]:    if not isinstance(value, list):        return []    todos: list[dict[str, Any]] = []    for raw_todo in value:        if not isinstance(raw_todo, dict):            continue        todos.append(            {                "id": str(raw_todo.get("id") or f"todo-{random.randint(1000, 9999)}"),                "title": str(raw_todo.get("title") or ""),                "description": str(raw_todo.get("description") or ""),                "emoji": str(raw_todo.get("emoji") or "*"),                "status": (                    "completed" if raw_todo.get("status") == "completed" else "pending"                ),            }        )    return todosdef _get_stock_price_impl(ticker: str) -> dict[str, Any]:    return {        "ticker": ticker.upper(),        "price_usd": 189.42,        "change_pct": 1.27,    }def _search_flights_by_route_impl(origin: str, destination: str) -> dict[str, Any]:    return {        "origin": origin,        "destination": destination,        "flights": [            {                "airline": "United",                "flight": "UA231",                "depart": "08:15",                "arrive": "16:45",                "price_usd": 348,            },            {                "airline": "Delta",                "flight": "DL412",                "depart": "11:20",                "arrive": "19:50",                "price_usd": 312,            },            {                "airline": "JetBlue",                "flight": "B6722",                "depart": "17:05",                "arrive": "01:35",                "price_usd": 289,            },        ],    }def _execute_tool(    name: str,    tool_input: dict[str, Any],    state: AgentState,    conversation_messages: list[dict[str, Any]] | None = None,) -> tuple[str, AgentState | None]:    """Execute backend tools and return (result_text, new_state_or_None)."""    if name == "get_weather":        return json.dumps(get_weather_impl(tool_input["location"])), None    if name == "query_data":        return json.dumps(query_data_impl(tool_input["query"])), None    if name == "manage_todos":        state.todos = _coerce_beautiful_chat_todos(tool_input.get("todos"))        return json.dumps({"status": "updated", "count": len(state.todos)}), state    if name == "get_todos":        return json.dumps(_coerce_beautiful_chat_todos(state.todos)), None    if name == "manage_sales_todos":        result = manage_sales_todos_impl(tool_input["todos"])        state.todos = [dict(t) for t in result]        return json.dumps({"status": "updated", "count": len(result)}), state    if name == "get_sales_todos":        return json.dumps(            get_sales_todos_impl(state.todos if state.todos else None)        ), None    if name == "schedule_meeting":        return json.dumps(schedule_meeting_impl(tool_input["reason"])), None    if name == "generate_task_steps":        # Frontend HITL tool -- backend just acknowledges; UI handles the interaction        steps = tool_input.get("steps", [])        return f"Presented {len(steps)} steps for review.", None    if name == "change_background":        # Frontend tool -- backend just acknowledges        return f"Background change requested: {tool_input.get('background', '')}", None    if name == "search_flights":        if "flights" in tool_input:            flights_data = tool_input.get("flights", [])            typed_flights = [Flight(**f) for f in flights_data]            result = search_flights_impl(typed_flights)            return json.dumps(result), None        return json.dumps(            _search_flights_by_route_impl(                str(tool_input.get("origin", "")),                str(tool_input.get("destination", "")),            )        ), None    if name == "get_stock_price":        return json.dumps(            _get_stock_price_impl(str(tool_input.get("ticker", "")))        ), None    if name == "get_revenue_chart":        return json.dumps(            {                "title": "Revenue trend",                "subtitle": "Last six months, USD thousands",                "data": [                    {"label": "Jan", "value": 42},                    {"label": "Feb", "value": 48},                    {"label": "Mar", "value": 53},                    {"label": "Apr", "value": 57},                    {"label": "May", "value": 63},                    {"label": "Jun", "value": 71},                ],            }        ), None    if name == "roll_d20":        value = tool_input.get("value")        return json.dumps(            {                "value": int(value)                if isinstance(value, (int, float))                else random.randint(1, 20)            }        ), None    if name == "set_steps":        steps = tool_input.get("steps", [])        state.steps = [dict(step) for step in steps if isinstance(step, dict)]        return json.dumps({"status": "updated", "count": len(state.steps)}), state    if name == "write_document":        document = str(tool_input.get("document", ""))        state.document = document        return json.dumps({"status": "updated", "length": len(document)}), state    if name == "generate_a2ui":        context = tool_input.get("context", "")        client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY", ""))        render_tool_schema = {            "name": RENDER_A2UI_TOOL_SCHEMA["name"],            "description": RENDER_A2UI_TOOL_SCHEMA["description"],            "input_schema": RENDER_A2UI_TOOL_SCHEMA["parameters"],        }        llm_messages: list[dict[str, Any]] = []        # Pass conversation messages to the secondary LLM for context        if conversation_messages:            llm_messages.extend(                sanitize_unresolved_tool_uses(                    conversation_messages,                )            )        else:            llm_messages.append(                {                    "role": "user",                    "content": "Generate a dynamic A2UI dashboard based on the conversation.",                }            )        response = client.messages.create(            model=normalize_claude_model(                os.getenv("ANTHROPIC_MODEL", DEFAULT_ANTHROPIC_MODEL)            ),            max_tokens=4096,            system=context or "Generate a useful dashboard UI.",            messages=llm_messages,            tools=[render_tool_schema],            tool_choice={"type": "tool", "name": "render_a2ui"},        )        for block in response.content:            if (                getattr(block, "type", None) == "tool_use"                and block.name == "render_a2ui"            ):                a2ui_result = build_a2ui_operations_from_tool_call(dict(block.input))                return json.dumps(a2ui_result), None        return json.dumps({"error": "LLM did not call render_a2ui"}), None    return f"Unknown tool: {name}", Nonedef _build_frontend_tools(input_data: RunAgentInput) -> list[dict[str, Any]]:    """Extract frontend-defined tools from the AG-UI request.    The CopilotKit runtime forwards frontend tool definitions (registered    via ``useFrontendTool``, ``useHumanInTheLoop``, etc.) in    ``input_data.tools``. We convert them to the Anthropic ``tools``    schema so the LLM can call them. The runtime intercepts the resulting    tool-call events and routes them to the frontend for resolution.    """    out: list[dict[str, Any]] = []    for t in input_data.tools or []:        name = getattr(t, "name", None) or (            t.get("name") if isinstance(t, dict) else None        )        description = getattr(t, "description", None) or (            t.get("description", "") if isinstance(t, dict) else ""        )        parameters = getattr(t, "parameters", None) or (            t.get("parameters", {}) if isinstance(t, dict) else {}        )        if not name:            continue        out.append(            {                "name": name,                "description": description or "",                "input_schema": parameters or {"type": "object", "properties": {}},            }        )    return outasync def run_agent(    input_data: RunAgentInput,    *,    system_prompt_override: str | None = None,    disable_tools: bool = False,    preprocess_user_parts: Any = None,    tools_override: list[dict[str, Any]] | None = None,    frontend_tool_names_allowlist: set[str] | None = None,    latest_user_message_only: bool = False,) -> AsyncIterator[str]:    """Run the Claude agent and yield AG-UI SSE events.    Keyword arguments let dedicated demo endpoints reuse this streaming    loop with targeted overrides:    - ``system_prompt_override`` — replace the shared ``SYSTEM_PROMPT``      (e.g. BYOC demos emit a JSON envelope, so the sales-assistant      prompt is irrelevant).    - ``disable_tools`` — run the model with no tool schemas. Useful for      BYOC / pure-text demos where tool calls would derail the output.    - ``preprocess_user_parts`` — a ``callable(part) -> part`` applied to      each content part of every user message before they are sent to      Claude. Used by the multimodal demo to convert AG-UI      ``image``/``document`` parts into Claude's Messages API shape      (``{"type": "image", "source": {...}}``) and to flatten PDFs to      text via ``pypdf``.    """    encoder = EventEncoder()    client = anthropic.AsyncAnthropic(api_key=os.getenv("ANTHROPIC_API_KEY", ""))    # Extract state    state = AgentState()    if input_data.state and isinstance(input_data.state, dict):        state = AgentState(**input_data.state)    # Convert AG-UI messages to Anthropic format. When a preprocessor is    # supplied we preserve the structured content list (image blocks,    # document text, etc.) — otherwise we collapse to a flat string for    # the text-only happy path used by most demos.    #    # AG-UI delivers three message roles:    #   - "user"      → plain user text    #   - "assistant" → assistant text + optional tool_use blocks    #   - "tool"      → tool result from a resolved frontend tool    #    # Anthropic's Messages API represents tool results as a "user" role    # message with content blocks of type "tool_result". We must convert    # AG-UI "tool" messages into that shape so the LLM sees the resolved    # result and aimock's ``hasToolResult`` matcher fires correctly.    messages: list[dict[str, Any]] = []    for msg in input_data.messages or []:        role = msg.role.value if hasattr(msg.role, "value") else str(msg.role)        # Handle tool result messages from AG-UI (resolved frontend tools).        # Convert to Anthropic's format: role="user" with tool_result blocks.        if role == "tool":            tool_call_id = getattr(msg, "tool_call_id", None) or (                getattr(msg, "toolCallId", None)            )            raw_content = getattr(msg, "content", None)            result_text = ""            if isinstance(raw_content, str):                result_text = raw_content            elif isinstance(raw_content, list):                parts = []                for part in raw_content:                    if hasattr(part, "text"):                        parts.append(part.text)                    elif isinstance(part, dict) and "text" in part:                        parts.append(part["text"])                parts_text = "".join(parts)                if parts_text:                    result_text = parts_text                else:                    result_text = json.dumps(raw_content)            if tool_call_id:                # Anthropic expects the assistant message containing the                # tool_use to precede this tool_result message. The runtime                # ensures message ordering, so we just need to emit the                # tool_result in the right shape.                messages.append(                    {                        "role": "user",                        "content": [                            {                                "type": "tool_result",                                "tool_use_id": tool_call_id,                                "content": result_text,                            }                        ],                    }                )            continue        if role not in ("user", "assistant"):            continue        raw_content = getattr(msg, "content", None)        if (            preprocess_user_parts is not None            and role == "user"            and isinstance(raw_content, list)        ):            converted_parts: list[Any] = []            for part in raw_content:                # AG-UI emits pydantic models; normalise to a plain dict                # before handing to the converter so the demo-specific                # code can rely on ``.get(...)`` semantics.                if hasattr(part, "model_dump"):                    part_dict = part.model_dump()                elif isinstance(part, dict):                    part_dict = part                else:                    part_dict = part                converted = preprocess_user_parts(part_dict)                if converted is not None:                    converted_parts.append(converted)            if converted_parts:                messages.append({"role": role, "content": converted_parts})            continue        # For assistant messages, check if there are tool calls (AG-UI's        # AssistantMessage stores them in `tool_calls`, not in `content`).        # Anthropic requires tool_use blocks in the assistant content so        # the subsequent tool_result can pair with them.        if role == "assistant":            msg_tool_calls = getattr(msg, "tool_calls", None)            text_content = ""            if isinstance(raw_content, str):                text_content = raw_content            elif isinstance(raw_content, list):                for part in raw_content:                    if hasattr(part, "text"):                        text_content += part.text                    elif isinstance(part, dict) and "text" in part:                        text_content += part["text"]            if msg_tool_calls:                content_blocks: list[dict[str, Any]] = []                if text_content:                    content_blocks.append({"type": "text", "text": text_content})                for tc in msg_tool_calls:                    # AG-UI ToolCall: {id, function: {name, arguments}}                    tc_id = getattr(tc, "id", None) or (                        tc.get("id") if isinstance(tc, dict) else None                    )                    func = getattr(tc, "function", None) or (                        tc.get("function") if isinstance(tc, dict) else None                    )                    if func:                        tc_name = getattr(func, "name", None) or (                            func.get("name") if isinstance(func, dict) else "unknown"                        )                        tc_args_str = getattr(func, "arguments", None) or (                            func.get("arguments", "{}")                            if isinstance(func, dict)                            else "{}"                        )                    else:                        tc_name = "unknown"                        tc_args_str = "{}"                    try:                        tc_args = (                            json.loads(tc_args_str)                            if isinstance(tc_args_str, str)                            else tc_args_str                        )                    except json.JSONDecodeError:                        tc_args = {}                    content_blocks.append(                        {                            "type": "tool_use",                            "id": tc_id or "unknown",                            "name": tc_name,                            "input": tc_args,                        }                    )                messages.append({"role": "assistant", "content": content_blocks})                continue            elif text_content:                messages.append({"role": "assistant", "content": text_content})                continue            # Fall through to the generic handler if nothing matched        content = ""        if isinstance(raw_content, str):            content = raw_content        elif isinstance(raw_content, list):            parts = []            for part in raw_content:                if hasattr(part, "text"):                    parts.append(part.text)                elif isinstance(part, dict) and "text" in part:                    parts.append(part["text"])            content = "".join(parts)        if content:            messages.append({"role": role, "content": content})    sdk_input_data = input_data    if latest_user_message_only:        latest_user_message = next(            (m for m in reversed(messages) if m.get("role") == "user"),            None,        )        messages = [latest_user_message] if latest_user_message else []        latest_input_message = next(            (                m                for m in reversed(input_data.messages or [])                if (m.role.value if hasattr(m.role, "value") else str(m.role)) == "user"            ),            None,        )        sdk_messages = [latest_input_message] if latest_input_message else []        if hasattr(input_data, "model_copy"):            sdk_input_data = input_data.model_copy(update={"messages": sdk_messages})        else:  # pragma: no cover - compatibility with older pydantic models            sdk_input_data = input_data.copy(update={"messages": sdk_messages})    # Inject sales pipeline state into system prompt if state exists    if system_prompt_override is not None:        system = system_prompt_override    else:        system = SYSTEM_PROMPT        if state.todos:            todos_json = json.dumps(state.todos, indent=2)            system = f"{SYSTEM_PROMPT}\n\nCurrent sales pipeline:\n{todos_json}"    context_entries = getattr(input_data, "context", None) or []    if context_entries:        context_lines: list[str] = []        for entry in context_entries:            if isinstance(entry, dict):                description = entry.get("description")                value = entry.get("value")            else:                description = getattr(entry, "description", None)                value = getattr(entry, "value", None)            if description:                context_lines.append(f"{description}: {value}")        if context_lines:            system = f"{system}\n\nContext:\n" + "\n".join(context_lines)    sdk_backend_tools = (        []        if disable_tools        else (tools_override if tools_override is not None else TOOLS)    )    sdk_frontend_tools = [] if disable_tools else _build_frontend_tools(input_data)    if frontend_tool_names_allowlist is not None:        sdk_frontend_tools = [            t for t in sdk_frontend_tools if t["name"] in frontend_tool_names_allowlist        ]    sdk_frontend_tool_names = {t["name"] for t in sdk_frontend_tools}    if should_use_claude_agent_sdk(        input_data=input_data,        backend_tools=sdk_backend_tools,        frontend_tool_names=sdk_frontend_tool_names,        preprocess_user_parts=preprocess_user_parts,    ):        async for chunk in run_with_claude_agent_sdk(            sdk_input_data,            system_prompt=system,            tools=sdk_backend_tools,            state=state,            model=os.getenv("ANTHROPIC_MODEL", DEFAULT_ANTHROPIC_MODEL),            execute_tool=_execute_tool,        ):            yield chunk        return    thread_id = input_data.thread_id or "default"    run_id = input_data.run_id or "run-1"    yield encoder.encode(        RunStartedEvent(type=EventType.RUN_STARTED, thread_id=thread_id, run_id=run_id)    )    # Agentic loop -- keep calling Claude until no more tool calls    while True:        response_text = ""        tool_calls: list[dict[str, Any]] = []        msg_id = f"msg-{run_id}-{len(messages)}"        yield encoder.encode(            TextMessageStartEvent(                type=EventType.TEXT_MESSAGE_START,                message_id=msg_id,                role="assistant",            )        )        # Build the combined tools list: backend TOOLS + any frontend-        # defined tools forwarded by the CopilotKit runtime in        # input_data.tools. Frontend tools (registered via useFrontendTool,        # useHumanInTheLoop, etc.) are included so the LLM can call them;        # the runtime intercepts the resulting events and routes them to        # the frontend for resolution. Backend tools are executed locally.        backend_tools = tools_override if tools_override is not None else TOOLS        backend_tool_names = {t["name"] for t in backend_tools}        frontend_tools = _build_frontend_tools(input_data)        if frontend_tool_names_allowlist is not None:            frontend_tools = [                t for t in frontend_tools if t["name"] in frontend_tool_names_allowlist            ]        # Merge: backend tools first, then frontend tools that don't        # shadow a backend tool (frontend wins when names collide, because        # the frontend registration means the runtime should intercept).        frontend_tool_names = {t["name"] for t in frontend_tools}        combined_tools: list[dict[str, Any]] = []        for t in backend_tools:            if t["name"] not in frontend_tool_names:                combined_tools.append(t)        combined_tools.extend(frontend_tools)        stream_kwargs: dict[str, Any] = {            "model": normalize_claude_model(                os.getenv("ANTHROPIC_MODEL", DEFAULT_ANTHROPIC_MODEL)            ),            "max_tokens": 4096,            "system": system,            "messages": messages,        }        if not disable_tools:            stream_kwargs["tools"] = combined_tools  # type: ignore[assignment]        try:            async with client.messages.stream(**stream_kwargs) as stream:                current_tool_id: str | None = None                current_tool_name: str | None = None                current_tool_args = ""                last_streamed_document = state.document                async for event in stream:                    etype = type(event).__name__                    if etype == "RawContentBlockStartEvent":                        block = event.content_block  # type: ignore[attr-defined]                        if block.type == "text":                            pass  # streaming text chunks follow                        elif block.type == "tool_use":                            current_tool_id = block.id                            current_tool_name = block.name                            current_tool_args = ""                            yield encoder.encode(                                ToolCallStartEvent(                                    type=EventType.TOOL_CALL_START,                                    tool_call_id=current_tool_id,                                    tool_call_name=current_tool_name,                                    parent_message_id=msg_id,                                )                            )                    elif etype == "RawContentBlockDeltaEvent":                        delta = event.delta  # type: ignore[attr-defined]                        if delta.type == "text_delta":                            response_text += delta.text                            yield encoder.encode(                                TextMessageContentEvent(                                    type=EventType.TEXT_MESSAGE_CONTENT,                                    message_id=msg_id,                                    delta=delta.text,                                )                            )                        elif delta.type == "input_json_delta":                            current_tool_args += delta.partial_json                            yield encoder.encode(                                ToolCallArgsEvent(                                    type=EventType.TOOL_CALL_ARGS,                                    tool_call_id=current_tool_id or "",                                    delta=delta.partial_json,                                )                            )                            if current_tool_name == "write_document":                                streamed_document = _partial_json_string_property(                                    current_tool_args,                                    "document",                                )                                if (                                    streamed_document is not None                                    and streamed_document != last_streamed_document                                ):                                    state.document = streamed_document                                    last_streamed_document = streamed_document                                    yield encoder.encode(                                        StateSnapshotEvent(                                            type=EventType.STATE_SNAPSHOT,                                            snapshot=state.model_dump(),                                        )                                    )                    elif etype in (                        "RawContentBlockStopEvent",                        "ParsedContentBlockStopEvent",                    ):                        if current_tool_id and current_tool_name:                            yield encoder.encode(                                ToolCallEndEvent(                                    type=EventType.TOOL_CALL_END,                                    tool_call_id=current_tool_id,                                )                            )                            try:                                parsed_args = (                                    json.loads(current_tool_args)                                    if current_tool_args                                    else {}                                )                            except json.JSONDecodeError:                                parsed_args = {}                            tool_calls.append(                                {                                    "id": current_tool_id,                                    "name": current_tool_name,                                    "input": parsed_args,                                }                            )                            current_tool_id = None                            current_tool_name = None                            current_tool_args = ""        except Exception:            # Surface the error as visible text in the chat so D5            # probes see a non-empty assistant response instead of a            # silent broken SSE stream. Full traceback is logged            # server-side by FastAPI's exception handler.            err_text = f"Agent error: {traceback.format_exc()}"            yield encoder.encode(                TextMessageContentEvent(                    type=EventType.TEXT_MESSAGE_CONTENT,                    message_id=msg_id,                    delta=err_text,                )            )        yield encoder.encode(            TextMessageEndEvent(                type=EventType.TEXT_MESSAGE_END,                message_id=msg_id,            )        )        # No tool calls -- we're done        if not tool_calls:            break        # Separate tool calls into backend (locally executed) and frontend        # (deferred to the CopilotKit runtime / frontend for resolution).        # A tool whose name was registered on the frontend (present in        # frontend_tool_names) is a frontend tool even if the backend also        # defines it — the frontend registration takes precedence because        # hooks like useHumanInTheLoop rely on intercepting the tool call.        # Add assistant turn with tool calls to message history        assistant_content: list[dict[str, Any]] = []        if response_text:            assistant_content.append({"type": "text", "text": response_text})        for tc in tool_calls:            assistant_content.append(                {                    "type": "tool_use",                    "id": tc["id"],                    "name": tc["name"],                    "input": tc["input"],                }            )        messages.append({"role": "assistant", "content": assistant_content})        # Execute backend tools and build tool-result turn. Frontend tools are        # intentionally left unresolved here so the CopilotKit runtime can        # intercept them and re-invoke the agent after the browser handler runs.        tool_results: list[dict[str, Any]] = []        has_frontend_tool = False        for tc in tool_calls:            if tc["name"] in frontend_tool_names:                has_frontend_tool = True                continue            result_text, new_state = _execute_tool(                tc["name"], tc["input"], state, conversation_messages=messages            )            if new_state is not None:                state = new_state                yield encoder.encode(                    StateSnapshotEvent(                        type=EventType.STATE_SNAPSHOT,                        snapshot=state.model_dump(),                    )                )            yield encoder.encode(                ToolCallResultEvent(                    type=EventType.TOOL_CALL_RESULT,                    tool_call_id=tc["id"],                    message_id=f"{msg_id}-tool-result-{tc['id']}",                    content=result_text,                )            )            tool_results.append(                {                    "type": "tool_result",                    "tool_use_id": tc["id"],                    "content": result_text,                }            )        if tool_results:            messages.append({"role": "user", "content": tool_results})        if has_frontend_tool:            # At least one tool call targets a frontend tool. Break the            # agentic loop after any backend siblings have been resolved; the            # runtime owns the frontend continuation from here.            break    yield encoder.encode(        RunFinishedEvent(            type=EventType.RUN_FINISHED, thread_id=thread_id, run_id=run_id        )    )def create_app() -> FastAPI:    """Create the FastAPI app with AG-UI endpoint."""    # Local import to avoid a top-level ``agents._header_forwarding``    # dependency in this module (kept agnostic so unit tests that import    # individual handlers don't need the starlette middleware shape).    from agents._header_forwarding import HeaderForwardingHTTPMiddleware    app = FastAPI(title="Claude Agent SDK (Python) Agent Server")    app.add_middleware(HealthMiddleware)    # Capture inbound CopilotKit ``x-*`` headers (e.g. ``x-aimock-context``)    # into a per-request ContextVar so any outbound LLM/provider httpx call    # made inside the request scope copies them onto its outbound request.    # Paired with ``install_global_httpx_hook`` at the top of agent_server.py.    app.add_middleware(HeaderForwardingHTTPMiddleware)    app.add_middleware(        CORSMiddleware,        allow_origins=["*"],        allow_methods=["*"],        allow_headers=["*"],    )    @app.post("/")    async def run_agent_endpoint(request: Request) -> StreamingResponse:        body = await request.json()        input_data = RunAgentInput(**body)        async def event_stream() -> AsyncIterator[str]:            async for chunk in run_agent(input_data):                yield chunk        return StreamingResponse(            event_stream(),            media_type="text/event-stream",            headers={                "Cache-Control": "no-cache",                "X-Accel-Buffering": "no",            },        )    return app