How to Use the LibreChat MCP in LangChain

Q: How do I authenticate LangChain with my LibreChat instance?

Run the login tool inside your LangChain setup to fetch your session tokens. Once authenticated, the adapter handles passing these credentials to subsequent chatcompletions calls.

Q: Can a LangChain agent dynamically switch between LibreChat models?

Yes. Your LangChain agent can call listmodels to check what's available, then pass the selected model ID directly to the chatcompletions tool in the next step of the chain.

Q: How does LangSmith tracing work with these tools?

Every tool call, from running login to executing chatcompletions, is tracked automatically as a run in LangSmith. You can inspect the inputs, outputs, and latency of each step in your LangChain pipeline.

Build multi-step LangChain pipelines that talk directly to your LibreChat agents and run completions on the fly.

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Connect LibreChat MCP to LangChain

Create your Vinkius account to connect LibreChat 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 LibreChat with LangChain

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LangChain chains meet the LibreChat MCP Server

Connect your LangChain chains directly to your self-hosted LibreChat setups. By feeding the output of `list_models` straight into your LLMChain, your agent dynamically selects the right LibreChat model for the job without hardcoded parameters. You don't need to manually pass session states between LangChain steps anymore. Run the `login` tool first, grab your tokens, and let LangChain pass that authenticated state downstream to execute authenticated `chat_completions` on your LibreChat backend.

Chained agent execution

Stop writing custom wrapper code for your custom LibreChat agents. This MCP Server lets your LangChain agent call `open_responses` to grab raw text generations, then immediately pipe that text into a secondary validation chain. You get full visibility over every step in LangSmith. Every time your LangChain pipeline triggers `chat_completions` on your LibreChat instance, you see the exact token count, latency, and agent payload.

Dynamic model routing

Let your LangChain DAGs decide which LibreChat model to use based on real-time availability. The agent calls `list_models` to see what is currently configured on your server, then routes the next prompt accordingly. If a model goes offline, the system catches the error. It then picks an alternative from the list and runs `chat_completions` to keep your LangChain pipeline moving without manual intervention.

Setup guide

Set up LibreChat 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 LibreChat 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({
    "librechat-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 LibreChat 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 LibreChat. 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.

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Common questions about LibreChat MCP in LangChain

Run the `login` tool inside your LangChain setup to fetch your session tokens. Once authenticated, the adapter handles passing these credentials to subsequent `chat_completions` calls.

Yes. Your LangChain agent can call `list_models` to check what's available, then pass the selected model ID directly to the `chat_completions` tool in the next step of the chain.

Standard wrappers don't give you direct access to your custom LibreChat agents. This server exposes tools like `open_responses` so your LangChain pipeline can interact with your specific self-hosted configurations.

Every tool call, from running `login` to executing `chat_completions`, is tracked automatically as a run in LangSmith. You can inspect the inputs, outputs, and latency of each step in your LangChain pipeline.

Your LibreChat authentication tokens and prompt data stay strictly within your local environment and the Vinkius sandbox. We never store your session tokens on external servers, keeping your private instance traffic isolated.

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