How to Use the Daytona (Dev Workspaces) MCP in LangChain

Q: Can LangChain orchestrate multiple Daytona (Dev Workspaces) instances?

Yes. Your agent can call listsandboxes to track active environments and manage several instances concurrently within a single chain.

Chain Daytona (Dev Workspaces) actions directly into your LangChain workflows for automated infrastructure control.

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Connect Daytona (Dev Workspaces) MCP to LangChain

Create your Vinkius account to connect Daytona (Dev Workspaces) 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 Daytona (Dev Workspaces) with LangChain

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Automate LangChain environment lifecycles

Use `create_sandbox` and `start_sandbox` to spin up fresh dev environments as part of your agentic chains. You can build logic that provisions resources only when specific tasks hit your pipeline. Once the work finishes, call `stop_sandbox` or `archive_sandbox` to clean up. This keeps your resource usage tight and ensures every chain run starts from a known state.

Dynamic resource management in chains

Adjust your compute power on the fly using `resize_sandbox` within a LangChain graph. If your agent detects a heavy compilation task, it can trigger a resize before running the build. This MCP Server lets you bridge the gap between abstract reasoning and raw machine power. Your agents now handle their own hardware requirements without manual intervention.

Snapshot and recover infrastructure

Capture the state of a running workspace with `create_snapshot` to checkpoint your progress. This is useful for long-running processes where you need a quick way to save work. If something breaks, `recover_sandbox` pulls your environment back from an error state. You spend less time debugging infrastructure and more time refining your agent logic.

Setup guide

Set up Daytona (Dev Workspaces) 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 Daytona (Dev Workspaces) 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({
    "daytona-dev-workspaces-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 Daytona (Dev Workspaces) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Daytona (Dev Workspaces) MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. You then pass the tools to your agent definition to grant it direct control over your sandboxes.

Yes. Your agent can call `list_sandboxes` to track active environments and manage several instances concurrently within a single chain.

It does. The tools provide a stable interface for managing remote environments, making it suitable for automated dev workflows that run outside your local terminal.

Vinkius manages your authentication tokens securely. You provide the endpoint and the server handles the handshake so your credentials never touch your local code.

Your source code stays inside your sandboxes. The tools only pass metadata like IDs and status codes between the agent and the environment.

Use it with your favorite AI tools

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

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