better-chatbot/
├── src/
│   ├── app/                    # Next.js App Router + API routes
│   │   ├── api/[resource]/     # RESTful API organized by domain
│   │   ├── (auth)/             # Auth route group
│   │   ├── (chat)/             # Chat UI route group
│   │   └── store/              # Zustand stores
│   ├── components/             # UI components by domain
│   │   ├── layouts/
│   │   ├── agent/
│   │   ├── chat/
│   │   └── export/
│   ├── lib/                    # Core logic and utilities
│   │   ├── action-utils.ts     # Server action validators (CRITICAL)
│   │   ├── ai/                 # AI integration (models, tools, MCP, speech)
│   │   ├── db/                 # Database (Drizzle ORM + repositories)
│   │   ├── validations/        # Zod schemas
│   │   └── [domain]/           # Domain-specific helpers
│   ├── hooks/                  # Custom React hooks
│   │   ├── queries/            # Data fetching hooks
│   │   └── use-*.ts
│   └── types/                  # TypeScript types by domain
├── tests/                      # E2E tests (Playwright)
├── docs/                       # Setup guides and tips
├── docker/                     # Docker configs
└── drizzle/                    # Database migrations

/api/[resource]/route.ts         → GET/POST collection endpoints
/api/[resource]/[id]/route.ts    → GET/PUT/DELETE item endpoints
/api/[resource]/actions.ts       → Server actions (mutations)

export async function POST(request: Request) {
  try {
    // 1. Parse and validate request body with Zod
    const json = await request.json();
    const parsed = zodSchema.parse(json);

    // 2. Check authentication
    const session = await getSession();
    if (!session?.user.id) return new Response("Unauthorized", { status: 401 });

    // 3. Check authorization (ownership/permissions)
    if (resource.userId !== session.user.id) return new Response("Forbidden", { status: 403 });

    // 4. Load/compose dependencies (tools, context, etc.)
    const tools = await loadMcpTools({ mentions, allowedMcpServers });

    // 5. Execute with streaming if applicable
    const stream = createUIMessageStream({ execute: async ({ writer }) => { ... } });

    // 6. Return response
    return createUIMessageStreamResponse({ stream });
  } catch (error) {
    logger.error(error);
    return Response.json({ message: error.message }, { status: 500 });
  }
}

// Shared utility function
export const loadMcpTools = (opt?) =>
  safe(() => mcpClientsManager.tools())
    .map((tools) => {
      if (opt?.mentions?.length) {
        return filterMCPToolsByMentions(tools, opt.mentions);
      }
      return filterMCPToolsByAllowedMCPServers(tools, opt?.allowedMcpServers);
    })
    .orElse({} as Record<string, VercelAIMcpTool>);

// Used in multiple routes
// - /api/chat/route.ts
// - /api/chat/temporary/route.ts
// - /api/workflow/[id]/execute/route.ts

// Returns empty object on failure, chat continues
const MCP_TOOLS = await safe()
  .map(errorIf(() => !isToolCallAllowed && "Not allowed"))
  .map(() => loadMcpTools({ mentions, allowedMcpServers }))
  .orElse({});  // Graceful fallback

// In route handler
const stream = createUIMessageStream({
  execute: async ({ writer }) => {
    // Stream intermediate results
    writer.write({ type: "text", content: "Processing..." });

    // Execute with streaming
    const result = await streamText({
      model,
      messages,
      tools,
      onChunk: (chunk) => writer.write({ type: "text-delta", delta: chunk })
    });

    return { output: result };
  }
});

return createUIMessageStreamResponse({ stream });

Tier 1: MCP Tools (External)
  ↓ Can be used in
Tier 2: Workflow Tools (User-Created)
  ↓ Can be used in
Tier 3: Default Tools (Built-In)

// mcp-manager.ts - Singleton for all MCP clients
export const mcpClientsManager = globalThis.__mcpClientsManager__;

// API:
mcpClientsManager.init()              // Initialize configured servers
mcpClientsManager.getClients()        // Get connected clients
mcpClientsManager.tools()             // Get all tools as Vercel AI SDK tools
mcpClientsManager.toolCall(serverId, toolName, args)  // Execute tool

// MCP tools are tagged with metadata for filtering
type VercelAIMcpTool = Tool & {
  _mcpServerId: string;
  _originToolName: string;
  _toolName: string; // Transformed for AI SDK
};

// Branded type for runtime checking
VercelAIMcpToolTag.create(tool)

enum NodeKind {
  Input = "input",      // Entry point
  LLM = "llm",          // AI reasoning
  Tool = "tool",        // Call MCP/default tools
  Http = "http",        // HTTP requests
  Template = "template",// Text processing
  Condition = "condition", // Branching logic
  Output = "output",    // Exit point
}

// Workflows stream intermediate results
executor.subscribe((e) => {
  if (e.eventType == "NODE_START") {
    dataStream.write({
      type: "tool-output-available",
      toolCallId,
      output: { status: "running", node: e.nodeId }
    });
  }
  if (e.eventType == "NODE_END") {
    dataStream.write({
      type: "tool-output-available",
      toolCallId,
      output: { status: "complete", result: e.result }
    });
  }
});

export const APP_DEFAULT_TOOL_KIT = {
  [AppDefaultToolkit.Visualization]: {
    CreatePieChart, CreateBarChart, CreateLineChart,
    CreateTable, CreateTimeline
  },
  [AppDefaultToolkit.WebSearch]: {
    WebSearch, WebContent
  },
  [AppDefaultToolkit.Http]: {
    Http
  },
  [AppDefaultToolkit.Code]: {
    JavascriptExecution, PythonExecution
  },
};

// Execution returns "Success", rendering happens client-side
export const createTableTool = createTool({
  description: "Create an interactive table...",
  inputSchema: z.object({
    title: z.string(),
    columns: z.array(...),
    data: z.array(...)
  }),
  execute: async () => "Success"
});

// Client-side rendering in components/tool-invocation/
export function InteractiveTable({ part }) {
  const args = part.input;
  return <DataTable columns={args.columns} data={args.data} />;
}

1. Request → /api/chat/route.ts
2. Parse mentions (@tool, @workflow, @agent)
3. Load tools based on mentions/permissions:
   - loadMcpTools() → filters by mentions or allowedMcpServers
   - loadWorkFlowTools() → converts workflows to tools
   - loadAppDefaultTools() → filters default toolkits
4. Merge all tools into single Record<string, Tool>
5. Handle toolChoice mode:
   - "manual" → LLM proposes, user confirms
   - "auto" → full execution
   - "none" → no tools loaded
6. Pass tools to streamText()
7. Stream results back

components/
├── ui/              → shadcn/ui primitives
├── layouts/         → App structure
├── agent/           → Agent-specific
├── workflow/        → Workflow editor
├── tool-invocation/ → Tool result rendering
└── *.tsx            → Shared components

// message.tsx exports multiple related components
export function PreviewMessage({ message }) { ... }
export function ErrorMessage({ error }) { ... }

// message-parts.tsx handles polymorphic content
export function MessageParts({ parts }) {
  return parts.map(part => {
    if (isToolUIPart(part)) return <ToolInvocation part={part} />;
    if (part.type === 'text') return <Markdown text={part.text} />;
    // ... other types
  });
}

export default function ChatBot({ threadId, initialMessages }) {
  // 1. State management (Zustand)
  const [model, toolChoice] = appStore(useShallow(state => [...]));

  // 2. Vercel AI SDK hook
  const { messages, append, status } = useChat({
    id: threadId,
    initialMessages,
    body: { chatModel: model, toolChoice },
  });

  // 3. Render orchestration
  return (
    <>
      <ChatGreeting />
      <MessageList messages={messages} />
      <PromptInput onSubmit={append} />
    </>
  );
}

// Server-side (lib/ai/tools/create-table.ts)
execute: async (params) => "Success"

// Client-side (components/tool-invocation/interactive-table.tsx)
export function InteractiveTable({ part }) {
  const { columns, data } = part.input;
  return <DataTable columns={columns} data={data} />;
}

db/
├── repository.ts          → Single import point
├── pg/
│   ├── db.pg.ts          → Drizzle connection
│   ├── schema.pg.ts      → Table definitions
│   └── repositories/     → Feature queries
└── migrations/           → Drizzle migrations

// 1. Define interface in src/types/[domain].ts
export type ChatRepository = {
  insertThread(thread: Omit<ChatThread, "createdAt">): Promise<ChatThread>;
  selectThread(id: string): Promise<ChatThread | null>;
  selectThreadDetails(id: string): Promise<ThreadDetails | null>;
};

// 2. Implement in src/lib/db/pg/repositories/[domain]-repository.pg.ts
export const pgChatRepository: ChatRepository = {
  selectThreadDetails: async (id: string) => {
    const [thread] = await db
      .select()
      .from(ChatThreadTable)
      .leftJoin(UserTable, eq(ChatThreadTable.userId, UserTable.id))
      .where(eq(ChatThreadTable.id, id));

    if (!thread) return null;

    const messages = await pgChatRepository.selectMessagesByThreadId(id);

    return {
      id: thread.chat_thread.id,
      title: thread.chat_thread.title,
      userId: thread.chat_thread.userId,
      createdAt: thread.chat_thread.createdAt,
      userPreferences: thread.user?.preferences,
      messages,
    };
  },
};

// 3. Export from src/lib/db/repository.ts
export const chatRepository = pgChatRepository;

export const ChatThreadTable = pgTable("chat_thread", {
  id: uuid("id").primaryKey(),
  userId: uuid("user_id").references(() => UserTable.id),
}, (table) => ({
  userIdIdx: index("chat_thread_user_id_idx").on(table.userId),
}));

// Load minimal data
selectThread(id) → { id, title, userId, createdAt }

// Load full data when needed
selectThreadDetails(id) → { ...thread, messages, userPreferences }

// Get threads with last message timestamp
const threadsWithActivity = await db
  .select({
    threadId: ChatThreadTable.id,
    lastMessageAt: sql<string>`MAX(${ChatMessageTable.createdAt})`,
  })
  .from(ChatThreadTable)
  .leftJoin(ChatMessageTable, eq(ChatThreadTable.id, ChatMessageTable.threadId))
  .groupBy(ChatThreadTable.id)
  .orderBy(desc(sql`last_message_at`));

undefined

---

Base Layer: Chat + LLM
    ↓
Tool Layer: Default + MCP
    ↓
Composition Layer: Workflows (tools as nodes)
    ↓
Personalization Layer: Agents (workflows + prompts)

const tools = await safe(() => loadMcpTools())
  .orElse({});  // Returns default on failure

// Zod defines runtime validation AND TypeScript types
const schema = z.object({ name: z.string() });
type SchemaType = z.infer<typeof schema>;

// Discriminated unions for polymorphic data
type WorkflowNodeData =
  | { kind: "input"; ... }
  | { kind: "llm"; ... }
  | { kind: "tool"; ... };

// Brand types for runtime checking
VercelAIMcpToolTag.isMaybe(tool)

1. Define types (src/types/[domain].ts)
2. Create Zod schema (lib/validations/[domain].ts)
3. Add DB table + repository (lib/db/pg/)
4. Create API route (app/api/[resource]/route.ts)
5. Create UI component (components/[domain]/)
6. Create data hook (hooks/queries/use-[resource].ts)
7. Add E2E test (tests/[feature].spec.ts)
8. Run: pnpm check && pnpm test:e2e

1. Implement tool (lib/ai/tools/[category]/[name].ts)
2. Add to toolkit (lib/ai/tools/tool-kit.ts)
3. Create rendering component (components/tool-invocation/[name].tsx)
4. Add to tool invocation switch (components/tool-invocation/index.tsx)
5. Test with @toolname mention in chat

1. Add NodeKind enum (lib/ai/workflow/workflow.interface.ts)
2. Define node data type (same file)
3. Add executor (lib/ai/workflow/executor/node-executor.ts)
4. Add validator (lib/ai/workflow/validator/node-validate.ts)
5. Create UI config (components/workflow/node-config/[name]-node.tsx)
6. Test in workflow builder

@tool("web-search") find recent AI papers
@tool("create-table") show sales data
@tool("python-execution") calculate fibonacci

@mcp("github") create an issue in my repo
@mcp("playwright") navigate to google.com
@mcp("slack:send-message") post update to #general

@workflow("customer-onboarding") process new signup
@workflow("data-pipeline") transform and analyze CSV

User: @tool("web-search") find AI news, then @tool("create-table") summarize
→ LLM searches → formats results → creates table → returns message

User: @mcp("github") create issue
→ LLM proposes: create_issue(repo="...", title="...", body="...")
→ User reviews and clicks "Execute"
→ Tool runs → result shown

Preset: "Quick Chat"
- Model: GPT-4o-mini (fast)
- Tools: None
- Use for: Rapid Q&A

Preset: "Research Assistant"
- Model: Claude Sonnet 4.5
- Tools: @tool("web-search"), @mcp("wikipedia")
- Use for: Deep research

Preset: "Code Review"
- Model: GPT-5
- Tools: @mcp("github"), @tool("python-execution")
- Use for: Reviewing PRs with tests

1. Start chat (no @mentions) → Default tools available
2. Ask: "Search for news about AI"
3. LLM automatically uses @tool("web-search")
4. User sees: Search results → Formatted answer
5. Learns: Tools work automatically in Auto mode

1. Create workflow in Workflow Builder:
   Input → Web Search → LLM Summary → Output
2. Save as "research-workflow"
3. In chat: "@workflow('research-workflow') AI trends 2025"
4. Sees: Live progress per node
5. Gets: Formatted research report

1. Create agent "DevOps Assistant"
2. Agent instructions include: @mcp("github"), @workflow("deploy-pipeline")
3. Select agent from dropdown
4. Chat: "Deploy latest commit to staging"
5. Agent: Uses GitHub MCP → triggers deploy workflow → monitors → reports

// 1. Define in lib/ai/tools/[category]/[tool-name].ts
import { tool as createTool } from "ai";
import { z } from "zod";

export const myNewTool = createTool({
  description: "Clear description for LLM to understand when to use this",
  inputSchema: z.object({
    param: z.string().describe("What this parameter does"),
  }),
  execute: async (params) => {
    // For visualization tools: return "Success"
    // For data tools: return actual data
    return "Success";
  },
});

// 2. Add to lib/ai/tools/tool-kit.ts
import { DefaultToolName } from "./index";
import { myNewTool } from "./[category]/[tool-name]";

export enum DefaultToolName {
  // ... existing
  MyNewTool = "my_new_tool",
}

export const APP_DEFAULT_TOOL_KIT = {
  [AppDefaultToolkit.MyCategory]: {
    [DefaultToolName.MyNewTool]: myNewTool,
  },
};

// 3. Create rendering in components/tool-invocation/my-tool-invocation.tsx
export function MyToolInvocation({ part }: { part: ToolUIPart }) {
  const args = part.input as z.infer<typeof myNewTool.inputSchema>;
  return <div>{/* Render based on args */}</div>;
}

// 4. Add to components/tool-invocation/index.tsx switch
if (toolName === DefaultToolName.MyTool) {
  return <MyToolInvocation part={part} />;
}

// src/app/api/[resource]/route.ts
import { getSession } from "auth/server";
import { [resource]Repository } from "lib/db/repository";
import { z } from "zod";

const querySchema = z.object({
  limit: z.coerce.number().default(10),
});

export async function GET(request: Request) {
  // 1. Auth check
  const session = await getSession();
  if (!session?.user.id) {
    return new Response("Unauthorized", { status: 401 });
  }

  // 2. Parse & validate
  try {
    const url = new URL(request.url);
    const params = querySchema.parse(Object.fromEntries(url.searchParams));

    // 3. Use repository
    const data = await [resource]Repository.selectByUserId(
      session.user.id,
      params.limit
    );

    return Response.json(data);
  } catch (error) {
    if (error instanceof z.ZodError) {
      return Response.json(
        { error: "Invalid params", details: error.message },
        { status: 400 }
      );
    }
    console.error("Failed:", error);
    return new Response("Internal Server Error", { status: 500 });
  }
}

export async function POST(request: Request) {
  const session = await getSession();
  if (!session?.user.id) {
    return new Response("Unauthorized", { status: 401 });
  }

  try {
    const body = await request.json();
    const data = createSchema.parse(body);

    const item = await [resource]Repository.insert({
      ...data,
      userId: session.user.id,
    });

    return Response.json(item);
  } catch (error) {
    if (error instanceof z.ZodError) {
      return Response.json({ error: "Invalid input" }, { status: 400 });
    }
    return Response.json({ error: "Internal error" }, { status: 500 });
  }
}