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Gmelius MCP Server for LangChainGive LangChain instant access to 9 tools to Check Gmelius Status, Create Gmelius Card, Get Gmelius Board, and more

Built by Vinkius GDPR 9 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Gmelius through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Ask AI about this App Connector for LangChain

The Gmelius app connector for LangChain is a standout in the Productivity category — giving your AI agent 9 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "gmelius": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Gmelius, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())
Gmelius
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Gmelius MCP Server

Connect your Gmelius account to any AI agent and take full control of your team's collaborative workspace and high-fidelity shared inbox orchestration through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Gmelius through native MCP adapters. Connect 9 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

  • Conversation Portfolio Orchestration — List all collaborative email threads, retrieve detailed high-fidelity history, and monitor ticket status programmatically
  • Kanban Pipeline Intelligence — Query team project boards, retrieve detailed technical metadata, and stay on top of workflow progress in real-time
  • Card & Task Orchestration — Programmatically generate new task cards or email items on specific boards directly through your agent for perfectly coordinated delivery
  • Sequence Monitoring — Access configured automated high-fidelity email sequences and monitor their status directly through your agent for outreach optimization
  • Template Discovery — Access your complete directory of high-fidelity shared email templates and inboxes to choose the right context for every interaction
  • Operational Monitoring — Verify account-level API connectivity and monitor collaborative volume directly through your agent for perfectly coordinated service scaling

The Gmelius MCP Server exposes 9 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 9 Gmelius tools available for LangChain

When LangChain connects to Gmelius through Vinkius, your AI agent gets direct access to every tool listed below — spanning shared-inbox, email-delegation, kanban-boards, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

check_gmelius_status

Check API Status

create_gmelius_card

Add a new card to a board

get_gmelius_board

Get details for a specific board

get_gmelius_conversation

Get details for a specific conversation

list_gmelius_board_cards

List cards on a Kanban board

list_gmelius_boards

List collaborative Kanban boards

list_gmelius_conversations

List Gmelius shared conversations

list_gmelius_sequences

List email sequences

list_gmelius_templates

List shared email templates

Connect Gmelius to LangChain via MCP

Follow these steps to wire Gmelius into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save the code and run python agent.py
04

Explore tools

The agent discovers 9 tools from Gmelius via MCP

Why Use LangChain with the Gmelius MCP Server

LangChain provides unique advantages when paired with Gmelius through the Model Context Protocol.

01

The largest ecosystem of integrations, chains, and agents. combine Gmelius MCP tools with 500+ LangChain components

02

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

03

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

04

Memory and conversation persistence let agents maintain context across Gmelius queries for multi-turn workflows

Gmelius + LangChain Use Cases

Practical scenarios where LangChain combined with the Gmelius MCP Server delivers measurable value.

01

RAG with live data: combine Gmelius tool results with vector store retrievals for answers grounded in both real-time and historical data

02

Autonomous research agents: LangChain agents query Gmelius, synthesize findings, and generate comprehensive research reports

03

Multi-tool orchestration: chain Gmelius tools with web scrapers, databases, and calculators in a single agent run

04

Production monitoring: use LangSmith to trace every Gmelius tool call, measure latency, and optimize your agent's performance

Example Prompts for Gmelius in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Gmelius immediately.

01

"List all active Kanban boards and show their status."

02

"Show the last 5 conversations assigned to me."

03

"Check the available email templates for the 'Sales' team."

Troubleshooting Gmelius MCP Server with LangChain

Common issues when connecting Gmelius to LangChain through the Vinkius, and how to resolve them.

01

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Gmelius + LangChain FAQ

Common questions about integrating Gmelius MCP Server with LangChain.

01

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.
02

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.
03

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.