代理工具包

// Import the helper from the ai-sdk path
import { createClerkToolkit } from '@clerk/agent-toolkit/ai-sdk';
import { openai } from '@ai-sdk/openai';
import { streamText } from 'ai';
import { auth } from '@clerk/nextjs/server';
import { systemPrompt } from '@/lib/ai/prompts';

export const maxDuration = 30;

export async function POST(req: Request) {
  const { messages } = await req.json();
  // Optional - get the auth context from the request
  const authContext = await auth.protect();

  // Instantiate a new Clerk toolkit
  // Optional - scope the toolkit to this session
  const toolkit = await createClerkToolkit({ authContext });

  const result = streamText({
    model: openai('gpt-4o'),
    messages,
    // Optional - inject session claims into the system prompt
    system: toolkit.injectSessionClaims(systemPrompt),
    tools: {
      // Provide the tools you want to use
      ...toolkit.users(),
      ...toolkit.organizations(),
    },
  });

  return result.toDataStreamResponse();
}

// Import the helper from the langchain path
import { createClerkToolkit } from '@clerk/agent-toolkit/langchain';
import { ChatOpenAI } from '@langchain/openai';
import { auth } from '@clerk/nextjs/server';
import { HumanMessage, SystemMessage } from '@langchain/core/messages';
import { LangChainAdapter } from 'ai';
import { systemPrompt } from '@/lib/ai/prompts';

export const maxDuration = 30;

export async function POST(req: Request) {
  const { prompt } = await req.json();
  // Optional - get the auth context from the request
  const authContext = await auth.protect();

  // Instantiate a new Clerk toolkit
  // Optional - scope the toolkit to a specific user
  const toolkit = await createClerkToolkit({ authContext });

  const model = new ChatOpenAI({ model: 'gpt-4o', temperature: 0 });

  // Bind the tools you want to use to the model
  const modelWithTools = model.bindTools(toolkit.users());

  const messages = [new SystemMessage(toolkit.injectSessionClaims(systemPrompt)), new HumanMessage(prompt)];
  const aiMessage = await modelWithTools.invoke(messages);
  messages.push(aiMessage);

  for (const toolCall of aiMessage.tool_calls || []) {
    // Call the selected tool
    const selectedTool = toolkit.toolMap()[toolCall.name];
    const toolMessage = await selectedTool.invoke(toolCall);
    messages.push(toolMessage);
  }

  // To simplify the setup, this example uses the ai-sdk langchain adapter
  // to stream the results back to the /langchain page.
  // For more details, see: https://sdk.vercel.ai/providers/adapters/langchain
  const stream = await modelWithTools.stream(messages);
  return LangChainAdapter.toDataStreamResponse(stream);
}

// Provide the Clerk secret key as an environment variable
CLERK_SECRET_KEY=sk_123 npx -y @clerk/agent-toolkit -p local-mcp

// Alternatively, you can pass the secret key as an argument
npx -y @clerk/agent-toolkit -p local-mcp --secret-key sk_123

// This example assumes the CLERK_SECRET_KEY environment variable is set

// Use all tools
npx -y @clerk/agent-toolkit -p local-mcp
npx -y @clerk/agent-toolkit -p local-mcp --tools="*"

// Use only a specific tool category
npx -y @clerk/agent-toolkit -p local-mcp --tools users
npx -y @clerk/agent-toolkit -p local-mcp --tools "users.*"

// Use multiple tool categories
npx -y @clerk/agent-toolkit -p local-mcp --tools users organizations

// Use specific tools
npx -y @clerk/agent-toolkit -p local-mcp --tools users.getUserCount organizations.getOrganization

{
  "mcpServers": {
    "clerk": {
      "command": "npx",
      "args": ["-y", "@clerk/agent-toolkit", "-p=local-mcp", "--tools=users", "--secret-key=sk_123"]
    }
  }
}

import { createClerkToolkit } from '@clerk/agent-toolkit/ai-sdk';
import { createClerkClient } from '@clerk/backend';

export async function POST(req: Request) {
  // Create a new Clerk client
  const clerkClient = createClerkClient({ secretKey: 'sk_' });

  // Instantiate a new Clerk toolkit with the custom client
  const toolkit = await createClerkToolkit({ clerkClient });

  // Use the toolkit as usual
  const result = streamText({
    model: openai('gpt-4o'),
    messages,
    tools: toolkit.users(),
  });
}