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

Q: Can I have multiple Lindy (Autonomous AI Employees) instances in one CrewAI crew?

Absolutely. You can list all available agents with listlindies and assign different ones to different agents within your crew based on their specific toolsets.

Q: How do I coordinate Lindy (Autonomous AI Employees) actions with CrewAI agents?

Use the output of listruns to inform your CrewAI agents of current activity. This allows for sequential execution where one agent acts based on the status of another.

Q: How do I handle task completion status?

Query getrun for the current status. It will tell you if the task is running, waiting, or finished, allowing your agents to proceed accordingly.

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Connect Lindy (Autonomous AI Employees) MCP to CrewAI

Create your Vinkius account to connect Lindy (Autonomous AI Employees) to CrewAI 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 CrewAI

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Hierarchical agent management with CrewAI

Assign specific Lindy tasks to individual agents in your crew using `trigger_lindy`. You can have one agent act as a monitor while another handles the actual execution. Review the entire execution graph with `list_runs`. This lets your moderator agents see which tasks are active and which have completed, allowing for better coordination.

Monitor autonomous operations

Use `get_run` to check if a task is waiting on human input or external data. Your CrewAI agents can then decide whether to prompt a user or move to the next step. Dump reasoning logs with `get_run_logs` to audit what your agents are doing. This transparency is key for maintaining control in a multi-agent setup.

Manage workspace integrations

Use `list_integrations` to verify that your agents have access to the necessary tools. This ensures your CrewAI crew is properly equipped before they start a project. Cancel stuck runs with `cancel_run` if your agents detect a loop. This prevents your autonomous team from wasting tokens or hitting API rate limits unnecessarily.

Setup guide

Set up Lindy (Autonomous AI Employees) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Lindy (Autonomous AI Employees) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Lindy (Autonomous AI Employees) Analyst",
    goal="Access and analyze Lindy (Autonomous AI Employees) data via MCP.",
    backstory="Expert analyst with direct Lindy (Autonomous AI Employees) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Lindy (Autonomous AI Employees) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Lindy (Autonomous AI Employees) Analyst",
        goal="Access and analyze Lindy (Autonomous AI Employees) data via MCP.",
        backstory="Expert analyst with direct Lindy (Autonomous AI Employees) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Lindy (Autonomous AI Employees) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lindy. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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

Pass the Lindy MCP server URL to your agent configuration. Your agents will then be able to call tools like `list_lindies` and `trigger_lindy` as part of their assigned roles.

Absolutely. You can list all available agents with `list_lindies` and assign different ones to different agents within your crew based on their specific toolsets.

Use the output of `list_runs` to inform your CrewAI agents of current activity. This allows for sequential execution where one agent acts based on the status of another.

Yes. Access is restricted to the workspace you explicitly authenticate. The server acts only as a pass-through for the tools you invoke.

Query `get_run` for the current status. It will tell you if the task is running, waiting, or finished, allowing your agents to proceed accordingly.

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