Voice

/** * LangGraph TypeScript agent — CopilotKit showcase integration * * Defines a graph with a chat node and all showcase tools, * wired to CopilotKit via the sdk-js LangGraph adapter so frontend actions * and shared state flow seamlessly. */import { z } from "zod";import { RunnableConfig } from "@langchain/core/runnables";import { tool } from "@langchain/core/tools";import { ToolNode } from "@langchain/langgraph/prebuilt";import { AIMessage, SystemMessage } from "@langchain/core/messages";import {  MemorySaver,  START,  StateGraph,  Annotation,} from "@langchain/langgraph";import { ChatOpenAI } from "@langchain/openai";import {  convertActionsToDynamicStructuredTools,  CopilotKitStateAnnotation,} from "@copilotkit/sdk-js/langgraph";import {  getWeatherImpl,  queryDataImpl,  manageSalesTodosImpl,  getSalesTodosImpl,  scheduleMeetingImpl,  searchFlightsImpl,  generateA2uiImpl,  buildA2uiOperationsFromToolCall,} from "../../shared-tools";// ---------------------------------------------------------------------------// 1. Agent state — extends CopilotKit state with a proverbs list// ---------------------------------------------------------------------------const AgentStateAnnotation = Annotation.Root({  ...CopilotKitStateAnnotation.spec,  proverbs: Annotation<string[]>,});export type AgentState = typeof AgentStateAnnotation.State;// ---------------------------------------------------------------------------// 2. Tools — shared implementations wrapped for LangChain// ---------------------------------------------------------------------------const getWeather = tool(  async ({ location }) => JSON.stringify(getWeatherImpl(location)),  {    name: "get_weather",    description: "Get current weather for a location",    schema: z.object({      location: z.string().describe("City name"),    }),  },);const queryData = tool(  async ({ query }) => JSON.stringify(queryDataImpl(query)),  {    name: "query_data",    description: "Query financial database for chart data",    schema: z.object({      query: z.string().describe("Natural language query"),    }),  },);const manageSalesTodos = tool(  async ({ todos }) => JSON.stringify(manageSalesTodosImpl(todos)),  {    name: "manage_sales_todos",    description: "Create or update the sales todo list",    schema: z.object({      todos: z        .array(          z.object({            id: z.string().optional(),            title: z.string(),            stage: z.string().optional(),            value: z.number().optional(),            dueDate: z.string().optional(),            assignee: z.string().optional(),            completed: z.boolean().optional(),          }),        )        .describe("Array of sales todo items"),    }),  },);const getSalesTodos = tool(  async ({ currentTodos }) => JSON.stringify(getSalesTodosImpl(currentTodos)),  {    name: "get_sales_todos",    description: "Get the current sales todo list",    schema: z.object({      currentTodos: z        .array(          z.object({            id: z.string().optional(),            title: z.string().optional(),            stage: z.string().optional(),            value: z.number().optional(),            dueDate: z.string().optional(),            assignee: z.string().optional(),            completed: z.boolean().optional(),          }),        )        .optional()        .nullable()        .describe("Current todos if any"),    }),  },);const scheduleMeeting = tool(  async ({ reason, durationMinutes }) =>    JSON.stringify(scheduleMeetingImpl(reason, durationMinutes)),  {    name: "schedule_meeting",    description: "Schedule a meeting (requires user approval via HITL)",    schema: z.object({      reason: z.string().describe("Reason for the meeting"),      durationMinutes: z.number().optional().describe("Duration in minutes"),    }),  },);const searchFlights = tool(  async ({ flights }) => JSON.stringify(searchFlightsImpl(flights)),  {    name: "search_flights",    description: "Search for available flights",    schema: z.object({      flights: z        .array(          z.object({            airline: z.string(),            airlineLogo: z.string().optional(),            flightNumber: z.string(),            origin: z.string(),            destination: z.string(),            date: z.string(),            departureTime: z.string(),            arrivalTime: z.string(),            duration: z.string(),            status: z.string(),            statusColor: z.string().optional(),            price: z.string(),            currency: z.string().optional(),          }),        )        .describe("Array of flight results"),    }),  },);const generateA2ui = tool(  async ({ messages, contextEntries }) => {    const prep = generateA2uiImpl({ messages, contextEntries });    const secondaryModel = new ChatOpenAI({ temperature: 0, model: "gpt-4.1" });    const renderTool = tool(async () => "rendered", {      name: "render_a2ui",      description: "Render a dynamic A2UI v0.9 surface.",      schema: z.object({        surfaceId: z.string().describe("Unique surface identifier."),        catalogId: z.string().describe("The catalog ID."),        components: z          .array(z.record(z.unknown()))          .describe("A2UI v0.9 component array."),        data: z          .record(z.unknown())          .optional()          .describe("Optional initial data model."),      }),    });    const modelWithTool = secondaryModel.bindTools!([renderTool], {      tool_choice: { type: "function", function: { name: "render_a2ui" } },    });    const response = await modelWithTool.invoke([      new SystemMessage({ content: prep.systemPrompt }),      ...prep.messages.map((m) => m as any),    ]);    const aiMsg = response as AIMessage;    if (!aiMsg.tool_calls?.length) {      return JSON.stringify({ error: "LLM did not call render_a2ui" });    }    const args = aiMsg.tool_calls[0].args as Record<string, unknown>;    return JSON.stringify(buildA2uiOperationsFromToolCall(args));  },  {    name: "generate_a2ui",    description: "Generate dynamic A2UI surface components",    schema: z.object({      messages: z.array(z.record(z.unknown())).describe("Chat messages"),      contextEntries: z        .array(z.record(z.unknown()))        .optional()        .describe("Context entries"),    }),  },);const tools = [  getWeather,  queryData,  manageSalesTodos,  getSalesTodos,  scheduleMeeting,  searchFlights,  generateA2ui,];// ---------------------------------------------------------------------------// 3. Chat node — binds backend + frontend tools, invokes the model// ---------------------------------------------------------------------------async function chatNode(state: AgentState, config: RunnableConfig) {  const model = new ChatOpenAI({ temperature: 0, model: "gpt-4o" });  const modelWithTools = model.bindTools!([    ...convertActionsToDynamicStructuredTools(state.copilotkit?.actions ?? []),    ...tools,  ]);  const systemMessage = new SystemMessage({    content: `You are a helpful assistant. The current proverbs are ${JSON.stringify(state.proverbs)}.`,  });  const response = await modelWithTools.invoke(    [systemMessage, ...state.messages],    config,  );  return { messages: response };}// ---------------------------------------------------------------------------// 4. Routing — send tool calls to tool_node unless they're CopilotKit actions// ---------------------------------------------------------------------------function shouldContinue({ messages, copilotkit }: AgentState) {  const lastMessage = messages[messages.length - 1] as AIMessage;  if (lastMessage.tool_calls?.length) {    const actions = copilotkit?.actions;    const toolCallName = lastMessage.tool_calls![0].name;    if (!actions || actions.every((action) => action.name !== toolCallName)) {      return "tool_node";    }  }  return "__end__";}// ---------------------------------------------------------------------------// 5. Compile the graph// ---------------------------------------------------------------------------const workflow = new StateGraph(AgentStateAnnotation)  .addNode("chat_node", chatNode)  .addNode("tool_node", new ToolNode(tools))  .addEdge(START, "chat_node")  .addEdge("tool_node", "chat_node")  .addConditionalEdges("chat_node", shouldContinue as any);const memory = new MemorySaver();export const graph = workflow.compile({  checkpointer: memory,});