How to Use the Balena MCP in LangChain

Build complex IoT automation chains by connecting Balena tools directly to your LangChain agents.

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…and any MCP-compatible client

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Connect Balena MCP to LangChain

Create your Vinkius account to connect Balena 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 Balena with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Balena fleet data in LangChain

Feed your agent device state data using `list_devices` and `list_fleets`. You define the logic flow, and the agent pulls the exact state it needs to make the next call. Everything happens in sequence. The output from one tool becomes the input for the next, letting you build multi-step workflows that manage your edge infrastructure without manual intervention.

Manage edge configuration with LangChain

Update your deployment settings by firing `create_device_env_var` or `create_device_tag` when your agent detects a drift in requirements. These actions integrate into your existing LangGraph pipelines. You get full visibility into every tool call, letting you debug your infrastructure logic through standard tracing.

Deploy OS images via LangChain

Trigger `get_os_download_url` and `list_os_versions` to automate your build pipeline. Your agent checks available hardware targets before initiating a new release. This ensures your deployment code stays synced with the latest available firmware. You stop guessing which versions are compatible with your specific device types.

Setup guide

Set up Balena 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 Balena 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({
    "balena-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 Balena 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 Balena. 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.

Why Choose Vinkius

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Common questions about Balena MCP in LangChain

Install the necessary MCP adapters and point your configuration to the Vinkius endpoint. You initialize the client, fetch the toolset, and inject them into your agent's executor.

Yes, you use the client session to maintain context. This lets the agent remember previous device queries when executing long-running control loops.

The server uses isolated memory sandboxes to process your credentials. Only the specific device configuration data you request enters the agent's context window.

Absolutely, the adapter handles multiple endpoints simultaneously. You can bridge your edge infrastructure data with other databases in a single execution chain.

The server manages all authentication internally. You only provide one endpoint token, keeping your sensitive keys out of your application code.

Use it with your favorite AI tools

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

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