@clerk/agent-toolkit

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Important

Agent behavior is typically non-deterministic. Ensure you thoroughly test your integration and evaluate your application's performance. Additionally, consider scoping this toolkit's tools to specific users to limit resource access.

If your app's code path is predetermined, it's always preferable to call APIs directly instead of using agents and tool calling.

This SDK is recommended for testing purposes only unless you are confident in the agent's behavior and have implemented necessary security measures such as guardrails and best practices.

Table of Contents

• Table of Contents
• Getting Started
• API Reference
  • Import Paths
  • Methods
    • Initialization & generic helpers
    • Available tools
    • Langchain-specific methods
    • MCP Specific Methods
• Prerequisites
• Example Repository
• Using Vercel's AI SDK
• Using Langchain
• Model Context Protocol (MCP Server)
  • Running a local MCP server
  • Usage with Claude Desktop
• Advanced Usage
  • Using a Custom clerkClient
• Support
• Contributing
• License

Getting Started

Use this SDK to integrate Clerk into your agentic workflows. The Clerk Agent Toolkit enables popular agent frameworks, including Vercel's AI SDK and LangChain, to integrate with Clerk using tools (also known as function calling).

This package exposes a subset of Clerk's functionality to agent frameworks, allowing you to build powerful agentic systems capable of managing users, user data, organizations, and more.

API Reference

Import Paths

The Clerk Agent Toolkit package provides two main import paths:

• @clerk/agent-toolkit/ai-sdk: Helpers for integrating with Vercel's AI SDK.
• @clerk/agent-toolkit/langchain: Helpers for integrating with Langchain.
• @clerk/agent-toolkit/modelcontextprotocol: Low level helpers for integrating with the Model Context Protocol (MCP).

The toolkit offers the same tools and core APIs across frameworks, but their public interfaces may vary slightly to align with each framework's design:

Methods

Initialization & generic helpers

• createClerkToolkit(options): Instantiates a new Clerk toolkit.
• toolkit.injectSessionClaims(systemPrompt): Injects session claims (userId, sessionId, orgId, etc.) into the system prompt, making them accessible to the AI model.

Available tools

Currently, are only exposing a subset of Clerk Backend API functionality as tools. We plan to expand this list as we receive feedback from the community. You are welcome to open an issue or reach out to us on Discord to request additional tools.

• toolkit.users(): Provides tools for managing users. Details.
• toolkit.organizations(): Provides tools for managing organizations. Details.
• toolkit.invitations(): Provides tools for managing invitations. Details.
• toolkit.allTools(): Returns all available tools.

Langchain-specific methods

• toolkit.toolMap(): Returns an object mapping available tools, useful for calling tools by name.

MCP Specific Methods

• createClerkMcpServer(): Instantiates a new Clerk MCP server. For more details, see

For more details on each tool, refer to the framework-specific directories or the Clerk Backend API documentation.

Prerequisites

• ai-sdk: "^3.4.7 || ^4.0.0", or langchain: "^0.3.6"
• An existing Clerk application. Create your account for free.
• An API key for an AI model compatible with Langchain

Example Repository

• Clerk AI SDK Example

Using Vercel's AI SDK

1. Install the Clerk Agent Toolkit package:

   npm install @clerk/agent-toolkit

2. Set the Clerk secret key as an environment variable in your project. Ensure you also configure any required LLM model keys.

   CLERK_SECRET_KEY=sk_
   

3. Import the helper from the /ai-sdk path, instantiate a new Clerk toolkit, and use it in your agent function:

// 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();
}

Using Langchain

1. Install the Clerk Agent Toolkit package:

   npm install @clerk/agent-toolkit

2. Set the Clerk secret key as an environment variable:

   CLERK_SECRET_KEY=sk_

3. Import the helper from the /langchain path, instantiate a new Clerk toolkit, and use it in your agent function:

// 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);
}

Model Context Protocol (MCP Server)

The @clerk/agent-toolkit/modelcontextprotocol import path provides a low-level helper for integrating with the Model Context Protocol (MCP). This is considered an advanced use case, as most users will be interested in running a local Clerk MCP server directly instead.

Running a local MCP server

To run the Clerk MCP server locally using npx, run the following command:

// 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

By default, the MCP server will use all available Clerk tools as described in the Available tools: section. To limit the tools available to the server, use the --tools (-t) flag:

// 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

Use the --help flag to view additional server options.

Usage with Claude Desktop

Add the following to your claude_desktop_config.json file to use the local MCP server:

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

For more information, please refer to the Claude Desktop documentation.

Advanced Usage

Using a Custom clerkClient

If you need to set the Clerk secret key dynamically or use different Clerk instances, pass a custom clerkClient. Install @clerk/backend into your project and call the createClerkClient function:

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(),
  });
}

Support

You can get in touch with us in any of the following ways:

• Join our official community Discord server
• On our support page

Contributing

We're open to all community contributions! If you'd like to contribute in any way, please read our contribution guidelines and code of conduct.

License

This project is licensed under the MIT license.

See LICENSE for more information.