Does Retell handle the telephone lines, or do I need my own Twilio/carrier?

Retell AI is designed to integrate seamlessly into telephony workflows but generally focuses on the conversational agent side. They provide features to list and attach numbers (`list_phone_numbers`), but you often purchase those numbers or link a custom SIP trunk through Retell accounts. You can run `list_phone_numbers` to see your currently available direct lines.

How do I create an outbound call programmatically with my assistant?

Simply ask your assistant to "Call X using my Y agent." Behind the scenes, the AI relies on the `create_phone_call` tool, transmitting a JSON payload supplying the `from_number` (a Retell registered phone number), `to_number` (destination), and an `override_agent_id` parameter to assign your voice agent correctly.

Can the AI initiate voice calls in a web browser instead of over the phone?

Yes. Use the `register_web_call` tool to create a browser-based voice session. This returns a connection URL and access token that your frontend application can use to start a real-time WebRTC conversation with a Retell voice agent, without requiring any phone line.

Retell AI MCP Server for AutoGen 10 tools — connect in under 2 minutes

Name: Retell AI
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Retell AI as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="retell_ai_agent",
            tools=tools,
            system_message=(
                "You help users with Retell AI. "
                "10 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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 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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Retell AI tools. Connect 10 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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_numbers and list_agents).

The Retell AI MCP Server exposes 10 tools through the Vinkius. Connect it to AutoGen 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 AutoGen via MCP

Follow these steps to integrate the Retell AI MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 10 tools from Retell AI automatically

Why Use AutoGen with the Retell AI MCP Server

AutoGen provides unique advantages when paired with Retell AI through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Retell AI tools to solve complex tasks

Role-based architecture lets you assign Retell AI tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Retell AI tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Retell AI tool responses in an isolated environment

Retell AI + AutoGen Use Cases

Practical scenarios where AutoGen combined with the Retell AI MCP Server delivers measurable value.

Collaborative analysis: one agent queries Retell AI while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Retell AI, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Retell AI data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Retell AI responses in a sandboxed execution environment

Retell AI MCP Tools for AutoGen (10)

These 10 tools become available when you connect Retell AI to AutoGen 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 AutoGen

Ready-to-use prompts you can give your AutoGen 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 AutoGen

Common issues when connecting Retell AI to AutoGen through the Vinkius, and how to resolve them.

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Retell AI + AutoGen FAQ

Common questions about integrating Retell AI MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Retell AI tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.