How to Use the Lindy (Autonomous AI Employees) MCP in Mastra AI

Q: Can I use Mastra AI to handle Lindy (Autonomous AI Employees) failures?

Yes. Monitor getrun statuses within your workflow. If a run returns an error, use your existing Mastra error handling to trigger a notification or a retry.

Q: How are triggers managed for Lindy (Autonomous AI Employees) in Mastra AI?

The listtriggers tool provides a clear breakdown of how your agents are scheduled. You can use this to sync your Mastra agent schedules with your existing Lindy setups.

Q: What is the best way to debug agent reasoning?

Use getrunlogs to pull the exact reasoning chain. This is the most direct way to see how the agent interpreted your instructions.

Build complex, multi-step workflows in Mastra AI using autonomous Lindy agents. Handle failures and retries with direct API control.

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Connect Lindy (Autonomous AI Employees) MCP to Mastra AI

Create your Vinkius account to connect Lindy (Autonomous AI Employees) to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Lindy (Autonomous AI Employees) with Mastra AI

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Orchestrate agent workflows with Mastra AI

Use `list_triggers` to see exactly how your agents wake up. Whether it's a cron job or an API call, you can manage the entry point for your entire autonomous pipeline. Implement conditional logic by chaining `list_runs` with your own Mastra workflows. You can evaluate the success of a run and decide whether to restart or escalate based on the output.

Deep dive into reasoning with run logs

Pull raw output from `get_run_logs` to debug why a specific agent step failed. This lets you build better error handling and retry logic into your Mastra agents. Use `get_lindy` to verify which prompts and tools are active for a specific agent. This ensures your workflow framework has the right context before you trigger a new task.

Manage organizational boundaries

Call `list_workspaces` to ensure your agents are operating in the correct environment. This is critical for keeping your production and test workflows isolated. List all available Lindies with `list_lindies` and dynamically assign them to different Mastra branches. You get granular control over which agent performs which part of your multi-step process.

Setup guide

Set up Lindy (Autonomous AI Employees) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Lindy (Autonomous AI Employees) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "lindy-autonomous-ai-employees-mcp-client",
  servers: {
    "lindy-autonomous-ai-employees-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Lindy (Autonomous AI Employees) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Lindy (Autonomous AI Employees) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Lindy (Autonomous AI Employees) transactions"
);
console.log(result.text);

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Common questions about Lindy (Autonomous AI Employees) MCP in Mastra AI

Register the Lindy MCP server in your Mastra config. From there, you can spread the available tools into your agent definitions to start calling `trigger_lindy`.

Yes. Monitor `get_run` statuses within your workflow. If a run returns an error, use your existing Mastra error handling to trigger a notification or a retry.

The `list_triggers` tool provides a clear breakdown of how your agents are scheduled. You can use this to sync your Mastra agent schedules with your existing Lindy setups.

All authentication is handled via the endpoint token you provide. The server doesn't store your keys; it uses them to verify your identity with the workspace.

Use `get_run_logs` to pull the exact reasoning chain. This is the most direct way to see how the agent interpreted your instructions.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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