Trengo MCP Server for LangChainGive LangChain instant access to 12 tools to Create Ticket, Create Webhook, Get Account Profile, and more
LangChain is the leading Python framework for composable LLM applications. Connect Trengo 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 Trengo app connector for LangChain is a standout in the Communication Messaging category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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({
"trengo": {
"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 Trengo, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 Trengo MCP Server
Connect your Trengo omnichannel inbox to any AI agent and simplify how you manage customer conversations, team collaboration, and support tickets through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Trengo through native MCP adapters. Connect 12 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
- Unified Inbox Management — List all tickets and conversations across WhatsApp, Email, and Chat in one place.
- Ticket Control — Create new support tickets, update statuses (OPEN, CLOSED, ASSIGNED), and manage assignments via AI.
- Omichannel Messaging — Send messages to customers or add internal team notes to any conversation.
- Contact & Channel Directory — List your customer database and verify all configured communication channels.
- Team Coordination — Query team member lists to understand availability and workload.
- Event Monitoring — List and create webhooks to track conversation events in real-time.
The Trengo MCP Server exposes 12 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 12 Trengo tools available for LangChain
When LangChain connects to Trengo through Vinkius, your AI agent gets direct access to every tool listed below — spanning omnichannel-inbox, helpdesk-ticketing, shared-inbox, 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.
Create a new ticket
Create a new webhook
Get current user profile
Get ticket details
). List communication channels
List all contacts
List ticket messages
List team users
List all support tickets
List configured webhooks
Send a message
Update ticket status
Connect Trengo to LangChain via MCP
Follow these steps to wire Trengo into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Trengo MCP Server
LangChain provides unique advantages when paired with Trengo through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Trengo MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Trengo queries for multi-turn workflows
Trengo + LangChain Use Cases
Practical scenarios where LangChain combined with the Trengo MCP Server delivers measurable value.
RAG with live data: combine Trengo tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Trengo, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Trengo tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Trengo tool call, measure latency, and optimize your agent's performance
Example Prompts for Trengo in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Trengo immediately.
"List all currently open support tickets."
"Show me the last 3 messages for ticket #88231."
"Close ticket #10293 as 'CLOSED' and add a note 'Resolved via AI'."
Troubleshooting Trengo MCP Server with LangChain
Common issues when connecting Trengo to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersTrengo + LangChain FAQ
Common questions about integrating Trengo MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.