How do I get my Trigger.dev API key?

Log in to your Trigger.dev dashboard at **cloud.trigger.dev**. Open your project, then find the **API Keys** section in the project settings. Copy your **Secret API Key** (it starts with `tr_dev_` for development or `tr_prod_` for production). Paste it into the configuration field below. For Personal Access Tokens (used for admin operations), go to **Profile → Personal Access Tokens** tab instead.

Can my AI agent tell me why a background job failed in production?

Yes. Ask your agent to list failed runs and it returns the task name, error message, stack trace, execution duration, and retry count for each failure. You can then drill into a specific run to see the exact input payload and which step failed — cutting your debugging time from minutes to seconds.

What if I'm on-call and need to check job health at 2 AM?

Just ask your AI agent 'Are there any failed jobs in production?' and get an instant triage report — number of failures, which tasks are affected, when they started failing, and whether retries resolved them. No need to open a browser, log in, or navigate dashboards in the middle of the night.

Does it support multiple environments like dev, staging, and production?

Yes. Each API key is scoped to a specific environment (dev or prod), just like in Trigger.dev itself. You can configure separate integrations for each environment, or switch between them by updating the API key — giving you full control over which environment your AI agent queries.

Trigger.dev MCP Server for LangChain 8 tools — connect in under 2 minutes

Name: Trigger.dev
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Trigger.dev through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "triggerdev": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Trigger.dev, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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High SecurityEnterprise-grade

IAMAccess control

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Trigger.dev MCP Server

Connect Trigger.dev to your AI agent and manage your background job infrastructure conversationally.

LangChain's ecosystem of 500+ components combines seamlessly with Trigger.dev through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Job Monitoring — List active, completed, and failed task runs with execution times, statuses, and error details.
Project Overview — Query projects, environments, and their associated job definitions.
Run Inspection — Drill into individual runs to view payloads, outputs, logs, and retry history.
Environment Management — Switch between dev, staging, and production environments to inspect runs across your deployment pipeline.

The Trigger.dev MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Trigger.dev to LangChain via MCP

Follow these steps to integrate the Trigger.dev MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 8 tools from Trigger.dev via MCP

Why Use LangChain with the Trigger.dev MCP Server

LangChain provides unique advantages when paired with Trigger.dev through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Trigger.dev MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Trigger.dev queries for multi-turn workflows

Trigger.dev + LangChain Use Cases

Practical scenarios where LangChain combined with the Trigger.dev MCP Server delivers measurable value.

RAG with live data: combine Trigger.dev tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Trigger.dev, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Trigger.dev tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Trigger.dev tool call, measure latency, and optimize your agent's performance

Trigger.dev MCP Tools for LangChain (8)

These 8 tools become available when you connect Trigger.dev to LangChain via MCP:

cancel_run

Cancel a running task

get_run

Get run details

list_environments

List deployment environments

list_projects

List all projects

list_runs

List task runs

list_schedules

List cron schedules

replay_run

Replay a completed task

trigger_task

Trigger a background task

Example Prompts for Trigger.dev in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Trigger.dev immediately.

"Are there any failed background jobs in production?"

"Show me the details of the last 'process-webhook' run."

"How many jobs ran successfully today?"

Troubleshooting Trigger.dev MCP Server with LangChain

Common issues when connecting Trigger.dev to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Trigger.dev + LangChain FAQ

Common questions about integrating Trigger.dev MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.