How to Use the Transloadit MCP in LangChain

Q: How do I check cost usage with Transloadit and LangChain?

You instruct your agent to call getbillingusage and provide the YYYY-MM format for the month you want. The returned data lets you see exactly what file processing costs were incurred.

Q: What if I need to re-run a failed process using Transloadit and LangChain?

First, your agent uses listassemblies to find the ID of the assembly that needs reprocessing. Then, it executes replayassembly on that specific job ID to kick off the run again.

Q: Can I define new media processing logic with Transloadit and LangChain?

Yes. You let your agent use createprocessingtemplate, passing it a steps JSON defining the desired pipeline. This defines the reusable logic that all future assemblies will follow.

Run complex media pipelines with your AI client using LangChain.

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Connect Transloadit MCP to LangChain

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

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Claude

Perplexity

Automating Multi-Step Media Pipelines

Your agent builds entire media processing workflows. It uses the output of one tool—say, checking `get_assembly_details` for a status code—as the input argument for the next step, like calling `cancel_assembly`. This allows your AI client to reason through complex tasks without manual intervention. It's perfect for sequential logic. The agent determines if it needs to check recent runs by calling `list_assemblies`, and then uses that list data directly in a subsequent function call to proceed, creating robust chains.

Managing Processing Templates

Need to build reusable media processes? Your agent handles the full lifecycle. It calls `create_processing_template` with a specific steps JSON when you define a new format. Later, it can list and inspect existing ones using `list_templates`, ensuring everything is configured before deployment. The chain logic extends to maintenance: If a template is obsolete, your agent knows how to call `delete_template`. You're building an operational toolset, not just running single commands.

Financial and Operational Visibility

The system gives you visibility into usage. Your agent can retrieve cost data by calling `get_billing_usage`, passing the month in YYYY-MM format to see exactly what's being charged for file processing. This output is crucial context. It also tracks operational history. If a job fails, your agent can use `list_assemblies` or `get_assembly_details` to figure out *why* it failed and then execute a fix, like calling `replay_assembly`, closing the loop on troubleshooting.

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-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

You instruct your agent to call `get_billing_usage` and provide the YYYY-MM format for the month you want. The returned data lets you see exactly what file processing costs were incurred.

First, your agent uses `list_assemblies` to find the ID of the assembly that needs reprocessing. Then, it executes `replay_assembly` on that specific job ID to kick off the run again.

Yes. You let your agent use `create_processing_template`, passing it a steps JSON defining the desired pipeline. This defines the reusable logic that all future assemblies will follow.

Yes, it manages media processing pipelines which include tasks like encoding videos and resizing images. You simply define these steps in a JSON template for the assembly to execute.

The server handles file processing usage/costs, assembly status/results, and media processing templates. These are all structured pipeline metadata that your agent reads and writes.

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