How to Use the Trigger.dev MCP in LangChain

Q: Can I use Trigger.dev with LangChain to check schedules?

Yeah, you can list all cron jobs using listschedules. This helps your agent figure out what automated tasks are waiting to fire in the background.

Q: If I'm building a multi-step LangChain application, how do I trigger a task?

You call triggertask directly. Your agent sends the payload, and the workflow starts. The result of that tool call can then feed into the next step in your chain.

Q: What data does Trigger.dev expose to LangChain?

It exposes operational data like project names (listprojects), environment details, and task run logs. This is all critical context for building robust automation chains.

Manage background jobs and workflows with LangChain.

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Connect Trigger.dev MCP to LangChain

Create your Vinkius account to connect Trigger.dev to LangChain 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 Trigger.dev with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Monitoring Task Runs via MCP Server

The `get_run` tool lets your agent check specific task details. You get the status, inputs, and outputs for any job run directly, without opening the Trigger.dev dashboard. Need to see everything? Use `list_runs` to pull a comprehensive list of all historical tasks that have run across your projects.

Controlling Background Jobs

Want to stop something running? The `cancel_run` tool lets your agent kill an active task immediately. It's useful for testing or when a job goes rogue. Or, if you need the job to start again, `replay_run` takes a completed task and runs it as if nothing happened.

Discovering Available Workflows

Before running anything, your agent needs context. Call `list_projects` to see every project you manage. This shows the top-level containers for all your workflows. Also check out `list_environments` when building complex chains; it shows which deployment stages are available in Trigger.dev.

Setup guide

Set up Trigger.dev MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Trigger.dev tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "triggerdev-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Trigger.dev transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Trigger.dev. 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 Trigger.dev MCP in LangChain

Your agent calls the MCP tools, and LangChain handles the rest. It treats every tool call—like `trigger_task` or `get_run`—as a specific step in your reasoning chain.

Yeah, you can list all cron jobs using `list_schedules`. This helps your agent figure out what automated tasks are waiting to fire in the background.

You call `trigger_task` directly. Your agent sends the payload, and the workflow starts. The result of that tool call can then feed into the next step in your chain.

It exposes operational data like project names (`list_projects`), environment details, and task run logs. This is all critical context for building robust automation chains.

The MCP Server handles authentication via a single endpoint token. Your AI client uses this secure method to access all underlying data types, such as task run status and environment names.

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