Retell AI MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Retell AI 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 MCP SERVER
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({
"retell-ai": {
"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 Retell AI, 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 Retell AI MCP Server
Connect your conversational assistant directly to Retell AI, a powerful platform for building voice-driven conversational agents. Empower your AI to orchestrate, analyze, and automate phone calls or web-based voice interactions seamlessly via simple text commands. From provisioning intelligent voice agents to placing outbound calls to customers, this integration brings the full telecommunication stack directly to your chat interface.
LangChain's ecosystem of 500+ components combines seamlessly with Retell AI through native MCP adapters. Connect 10 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
- Automate Phone Calls — Command your assistant to initiate outbound voice interactions on your behalf (
create_phone_call) or register active sessions for web browser integration (register_web_call). - Build and Manage Voice Agents — Dynamically orchestrate AI agent personalities (
create_agent,update_agent) and configure their underlying conversational brain (create_llm) with specific system instructions and models. - Analyze Telemetry — Keep track of your infrastructure by querying historical call logs (
list_calls), investigating specific conversations for transcripts and sentiment analysis (get_call_details), surveying available text-to-speech voices (list_voices), and reviewing provisioned communication lines (list_phone_numbersandlist_agents).
The Retell AI MCP Server exposes 10 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 Retell AI to LangChain via MCP
Follow these steps to integrate the Retell AI 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 10 tools from Retell AI via MCP
Why Use LangChain with the Retell AI MCP Server
LangChain provides unique advantages when paired with Retell AI through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Retell AI 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 Retell AI queries for multi-turn workflows
Retell AI + LangChain Use Cases
Practical scenarios where LangChain combined with the Retell AI MCP Server delivers measurable value.
RAG with live data: combine Retell AI tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Retell AI, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Retell AI tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Retell AI tool call, measure latency, and optimize your agent's performance
Retell AI MCP Tools for LangChain (10)
These 10 tools become available when you connect Retell AI to LangChain via MCP:
create_agent
Creates a new AI voice agent
create_llm
Configures a Retell-hosted LLM
create_phone_call
Provide a JSON payload with "from_number" and "to_number". Initiates an outbound phone call
get_call_details
Retrieves details for a specific call
list_agents
Lists all configured AI voice agents
list_calls
Lists all historical and active calls
list_phone_numbers
Lists all phone numbers associated with the account
list_voices
Lists all available text-to-speech voices
register_web_call
Registers a new web-based call
update_agent
Updates an existing AI voice agent
Example Prompts for Retell AI in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Retell AI immediately.
"Can you show me the transcripts for call ID `c_f3a123`?"
"List all available agents I can use."
"We are testing out new numbers. Please use 'from_number' `+18005551234` and dial `+14085551234` assigning my 'agent_555'."
Troubleshooting Retell AI MCP Server with LangChain
Common issues when connecting Retell AI to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersRetell AI + LangChain FAQ
Common questions about integrating Retell AI 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 Retell AI 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 Retell AI to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.