Chatwoot MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Chatwoot through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
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({
"chatwoot": {
"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 Chatwoot, 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 Chatwoot MCP Server
Connect your Chatwoot account to any AI agent and take full control of your customer support and engagement through natural conversation. Streamline how you manage chats across Web, WhatsApp, Facebook, and more.
LangChain's ecosystem of 500+ components combines seamlessly with Chatwoot through native MCP adapters. Connect 8 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 Oversight — List and retrieve details for all active and resolved conversations natively
- Live Replying — Send messages to customers in active chat sessions flawlessly
- Contact Management — List and retrieve detailed customer contact information and history securely
- Inbox Intelligence — Monitor all configured inboxes, including Web widgets and social integrations flawlessly
- Agent Tracking — List all support agents and manage team availability in real-time
- Message History — Access complete chat histories to understand customer context directly within your workspace
The Chatwoot MCP Server exposes 8 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.
How to Connect Chatwoot to LangChain via MCP
Follow these steps to integrate the Chatwoot MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from Chatwoot via MCP
Why Use LangChain with the Chatwoot MCP Server
LangChain provides unique advantages when paired with Chatwoot through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Chatwoot 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 Chatwoot queries for multi-turn workflows
Chatwoot + LangChain Use Cases
Practical scenarios where LangChain combined with the Chatwoot MCP Server delivers measurable value.
RAG with live data: combine Chatwoot tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Chatwoot, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Chatwoot tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Chatwoot tool call, measure latency, and optimize your agent's performance
Chatwoot MCP Tools for LangChain (8)
These 8 tools become available when you connect Chatwoot to LangChain via MCP:
get_chat_history
Retrieve the message history for a specific conversation
get_contact_details
Get detailed information for a specific customer contact
get_conversation_details
Get detailed information for a specific conversation
list_chatwoot_contacts
List all customer contacts
list_chatwoot_inboxes
List all configured inboxes (Web, WhatsApp, etc)
list_support_agents
List all support agents in the account
list_woot_conversations
List all conversations in the account
send_chat_message
Send a message to a customer in a specific conversation
Example Prompts for Chatwoot in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Chatwoot immediately.
"List all active conversations in Chatwoot."
"What did the customer in conversation ID 555 say last?"
"Reply to conversation 555: 'I'll look into this right now for you, Sarah.'"
Troubleshooting Chatwoot MCP Server with LangChain
Common issues when connecting Chatwoot to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersChatwoot + LangChain FAQ
Common questions about integrating Chatwoot 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.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Chatwoot with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Chatwoot to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.