better-chatbot Contribution & Standards Skill

Status: Production Ready Last Updated: 2025-11-04 (v2.1.0 - Added extension points + UX patterns) Dependencies: None (references better-chatbot project) Latest Versions: Next.js 15.3.2, Vercel AI SDK 5.0.82, Better Auth 1.3.34, Drizzle ORM 0.41.0

Overview

better-chatbot is an open-source AI chatbot platform for individuals and teams, built with Next.js 15 and Vercel AI SDK v5. It combines multi-model AI support (OpenAI, Anthropic, Google, xAI, Ollama, OpenRouter) with advanced features like MCP (Model Context Protocol) tool integration, visual workflow builder, realtime voice assistant, and team collaboration.

This skill teaches Claude the project-specific conventions and patterns used in better-chatbot to ensure contributions follow established standards and avoid common pitfalls.

Project Architecture

Directory Structure

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 Architecture & Design Patterns

Route Structure Philosophy

Convention: RESTful resources with Next.js App Router conventions

/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)

Standard Route Handler Pattern

Location: src/app/api/

Template structure:

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 Business Logic Pattern

Key Insight: Extract complex orchestration logic into shared utilities

Example: src/app/api/chat/shared.chat.ts

This file demonstrates how to handle:

• Tool loading (loadMcpTools, loadWorkFlowTools, loadAppDefaultTools)
• Filtering and composition (filterMCPToolsByMentions, excludeToolExecution)
• System prompt building (mergeSystemPrompt)
• Manual tool execution handling

Pattern:

// 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

Why: DRY principle, single source of truth, consistent behavior

Defensive Programming with safe()

Library: ts-safe for functional error handling

Philosophy: Never crash the chat - degrade features gracefully

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

Streaming-First Architecture

Pattern: Use Vercel AI SDK streaming utilities

// 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 });

Why: Live feedback, better UX, handles long-running operations

Tool System Deep Dive

Three-Tier Tool Architecture

Design Goal: Balance extensibility (MCP), composability (workflows), and batteries-included (default tools)

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)

Tier 1: MCP Tools (External Integrations)

Location: src/lib/ai/mcp/

Philosophy: Model Context Protocol servers become first-class tools

Manager Pattern:

// 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

Why Global Singleton?

• Next.js dev hot-reloading → reconnecting MCP servers on every change is expensive
• Persists across HMR updates
• Production: only one instance needed

Tool Wrapping:

// 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)

Tier 2: Workflow Tools (Visual Composition)

Location: src/lib/ai/workflow/

Philosophy: Visual workflows become callable tools via @workflow_name

Node Types:

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
}

Execution with Streaming:

// 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",