Reasoning

"use client";// Reasoning — Custom//// Pairs with `reasoning-default` so users can compare default vs custom// reasoning rendering side by side. Both demos share the same backend// (the `_thinking_chat` ADK agent built via `build_thinking_chat_agent`// — see `src/agents/registry.py:144-145` where both `reasoning-default`// and `reasoning-custom` resolve to `AgentSpec(_thinking_chat)`) and// the same runtime URL (/api/copilotkit). This cell overrides the// `reasoningMessage` slot on the `messageView` slot with// `ReasoningBlock` — a tagged amber banner that emphasizes the agent's// thinking chain.//// Reasoning is a first-class message type in v2: see// packages/react-core/src/v2/components/chat/CopilotChatMessageView.tsx,// which discriminates messages by `message.role === "reasoning"` and// renders them via the `reasoningMessage` slot (default component:// `CopilotChatReasoningMessage`). The slot override below is the public,// stable way to customize that output.import type { CopilotChatReasoningMessage } from "@copilotkit/react-core/v2";import { CopilotKit, CopilotChat } from "@copilotkit/react-core/v2";import { ReasoningBlock } from "./reasoning-block";import { useReasoningCustomSuggestions } from "./suggestions";const AGENT_ID = "reasoning-custom";export default function ReasoningCustomDemo() {  return (    <CopilotKit runtimeUrl="/api/copilotkit" agent={AGENT_ID}>      <div className="flex justify-center items-center h-screen w-full">        <div className="h-full w-full max-w-4xl">          <Chat />        </div>      </div>    </CopilotKit>  );}function Chat() {  useReasoningCustomSuggestions();  return (    <CopilotChat      agentId={AGENT_ID}      className="h-full rounded-2xl"      messageView={{        reasoningMessage:          ReasoningBlock as unknown as typeof CopilotChatReasoningMessage,      }}    />  );}

"""Shared LlmAgent factories used across multiple demos.`build_simple_chat_agent` produces a plain Gemini chat agent with no backendtools — appropriate for any demo whose only customisation is on the frontend(prebuilt-sidebar, prebuilt-popup, chat-slots, chat-customization-css,headless-simple, headless-complete, voice, frontend-tools, agentic-chat).`build_thinking_chat_agent` uses Gemini 2.5 Flash with the thinking_configexposed so reasoning is streamed back as `thought` parts; the v2 React corerenders these via CopilotChatReasoningMessage.`get_model` returns a `Gemini` instance configured with the aimock proxyendpoint when `GOOGLE_GEMINI_BASE_URL` is set, or the default model stringotherwise. All agent modules should call `get_model()` instead ofhard-coding `"gemini-2.5-flash"` so Railway deployments route throughaimock.`stop_on_terminal_text` is the canonical after_model_callback shared by everyregistered LlmAgent. Gemini 2.5-flash does not naturally end its agenticloop after a successful tool call — it keeps re-issuing the same tool. Thecallback inspects each non-partial model response and, when it containstext with no pending function_call, sets `_invocation_context.end_invocation= True` so ADK terminates the loop. Without this guard every backend orfrontend tool in this package fires infinitely."""from __future__ import annotationsimport loggingimport osfrom typing import Optional, Unionfrom google.adk.agents import LlmAgentfrom google.adk.agents.callback_context import CallbackContextfrom google.adk.models.google_llm import Geminifrom google.adk.models.llm_response import LlmResponsefrom google.genai import typesfrom ag_ui_adk import AGUIToolsetlogger = logging.getLogger(__name__)DEFAULT_MODEL = "gemini-2.5-flash"def stop_on_terminal_text(    callback_context: CallbackContext, llm_response: LlmResponse) -> Optional[LlmResponse]:    """Terminate the ADK agentic loop on a final text-only model turn.    Lifted from the (orphaned) `simple_after_model_modifier` in    `agents/main.py`, with the SalesPipelineAgent name-gate removed so it    applies to every registered agent. Guards:    1. Skip partial streaming events — never end on a mid-stream chunk       (belt-and-suspenders with `ADK_DISABLE_PROGRESSIVE_SSE_STREAMING=1`       in `entrypoint.sh`).    2. Only terminate when the final non-partial response contains TEXT       and NO pending function_call — mixed text+function_call responses       (a known Gemini 2.5-flash quirk) must NOT terminate.    3. `_invocation_context` is an ADK private attribute; if it disappears       in a future ADK release, log-and-degrade rather than crash the       callback (which would stall the request).    Without this guard, Gemini calls the same tool indefinitely after a    successful tool result because no native termination condition fires.    """    content = llm_response.content    if not content or not content.parts:        if llm_response.error_message:            logger.warning(                "stop_on_terminal_text: Gemini returned error_message for agent=%s: %s",                callback_context.agent_name,                llm_response.error_message,            )        return None    if getattr(llm_response, "partial", False):        return None    # Under thinking mode (`include_thoughts=True`), Gemini emits a turn    # as TWO separate non-partial chunks:    #   1. text-only chunk: thought + reply text, `finish_reason=None`    #   2. function_call-only chunk: `finish_reason=FUNCTION_CALL`    # The callback fires on both. Without the finish_reason guard below,    # chunk 1's text-without-function-call shape causes premature    # termination — the function call in chunk 2 still streams but the    # agentic loop is already marked `end_invocation=True`, so the    # post-tool-result re-invocation that would chain to the next tool    # never happens (tool-rendering-reasoning-chain AAPL→MSFT regression).    # Only terminate when Gemini signals the turn is genuinely done with    # `finish_reason=STOP` (no further chunks coming). FUNCTION_CALL and    # None mean "more chunks are inbound" — defer.    finish_reason = getattr(llm_response, "finish_reason", None)    finish_reason_name = (        getattr(finish_reason, "name", None) if finish_reason is not None else None    )    if finish_reason_name != "STOP" and finish_reason != "STOP":        return None    has_text = any(getattr(part, "text", None) for part in content.parts)    has_function_call = any(        getattr(part, "function_call", None) for part in content.parts    )    if content.role != "model" or not has_text or has_function_call:        return None    invocation_context = getattr(callback_context, "_invocation_context", None)    if invocation_context is None:        logger.debug(            "stop_on_terminal_text: callback_context has no "            "_invocation_context attribute; skipping end_invocation."        )        return None    try:        invocation_context.end_invocation = True    except AttributeError:        logger.debug(            "stop_on_terminal_text: _invocation_context lacks "            "end_invocation; ADK private-API shape may have drifted."        )    return Nonedef get_model(model: str = DEFAULT_MODEL) -> Union[str, Gemini]:    """Return a model suitable for LlmAgent's `model=` parameter.    When `GOOGLE_GEMINI_BASE_URL` is set (Railway aimock proxy), returns a    `Gemini` instance with its `base_url` pointed at the proxy. Otherwise    returns the plain model string so the ADK resolves the default endpoint.    """    base_url = os.environ.get("GOOGLE_GEMINI_BASE_URL")    if base_url:        return Gemini(model=model, base_url=base_url)    return modeldef build_simple_chat_agent(    *,    name: str,    instruction: str,    model: str = DEFAULT_MODEL,) -> LlmAgent:    return LlmAgent(        name=name,        model=get_model(model),        instruction=instruction,        tools=[AGUIToolset()],        after_model_callback=stop_on_terminal_text,    )def build_thinking_chat_agent(    *,    name: str,    instruction: str,    model: str = DEFAULT_MODEL,) -> LlmAgent:    """LlmAgent with Gemini thinking enabled.    `include_thoughts=True` makes Gemini emit `thought=True` parts alongside    final answer parts; ADK forwards these through ag-ui as reasoning chunks    so v2's CopilotChatReasoningMessage / useRenderReasoning can show them.    `thinking_budget=-1` lets the model decide how much to think.    """    return LlmAgent(        name=name,        model=get_model(model),        instruction=instruction,        tools=[AGUIToolset()],        generate_content_config=types.GenerateContentConfig(            thinking_config=types.ThinkingConfig(                include_thoughts=True,                thinking_budget=-1,            ),        ),        after_model_callback=stop_on_terminal_text,    )