How to Use the Transloadit MCP in LangChain

Q: Can I track historical file processing assemblies with Transloadit and LangChain?

Yes. You can list all past jobs using listassemblies to see the history. The agent then calls getassembly for specific status updates or results.

Q: Does the MCP Server support billing queries when integrating with Transloadit and LangChain?

Absolutely. You can call getbill to retrieve specific billing information for any given month, making financial data part of your agent's reasoning.

Q: What is the best way to start a new job using Transloadit and LangChain?

Start by having your agent use createassembly. This initiates the file processing workflow, giving you an ID that you'll use later with getassembly to track progress.

Build multi-step reasoning pipelines connecting Transloadit to your agents using LangChain.

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Create your Vinkius account to connect Transloadit 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 Transloadit with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Discover and manage processing assets.

You use `list_assemblies` and `list_templates` to map out the available resources. This lets your agent determine what file processing assemblies are running or which reusable templates exist before taking action. LangChain allows you to chain these discovery calls into a single decision flow. The output of listing templates can immediately feed into calling `get_template` for deep inspection.

Initiate and control file processing jobs.

Your agent decides it needs new data processed, so it first uses `create_assembly`. Later, if the job fails or needs adjustment, the agent can call `cancel_assembly` to stop the run immediately. It even has the option to rerun a finished job using `replay_assembly`. This sequence of actions—create, monitor, potentially cancel, and replay—forms a complete, observable reasoning path within your multi-step chain.

Check status and retrieve core account data.

Before starting anything complex, the agent calls `get_me` to verify connectivity and confirm the current account context. To check on a job's progress, it runs `get_assembly`, pulling back both status details and final results. Observing these tool inputs and outputs through LangSmith tracing gives you full visibility into how your MCP Server integration is functioning.

Setup guide

Set up Transloadit 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 Transloadit 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({
    "transloadit-alternative-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 Transloadit transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Transloadit MCP in LangChain

Transloadit's tools become discrete steps in your chain. The agent decides the order and inputs for each tool call, ensuring a multi-step process flows correctly.

Yes. You can list all past jobs using `list_assemblies` to see the history. The agent then calls `get_assembly` for specific status updates or results.

Absolutely. You can call `get_bill` to retrieve specific billing information for any given month, making financial data part of your agent's reasoning.

The server handles file processing assemblies, templates, and billing information. Your AI client manages these various types of structured data through specific tool calls.

Start by having your agent use `create_assembly`. This initiates the file processing workflow, giving you an ID that you'll use later with `get_assembly` to track progress.

Use it with your favorite AI tools

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

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