How to integrate Retellai MCP with LlamaIndex

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Introduction

This guide walks you through connecting Retellai to LlamaIndex using the Composio tool router. By the end, you'll have a working Retellai agent that can list all phone numbers linked to your account, retrieve call details for a specific agent this week, buy a new phone number with area code 415 through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a Retellai account through Composio's Retellai MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Retellai with

TL;DR

Here's what you'll learn:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Retellai
  • Connect LlamaIndex to the Retellai MCP server
  • Build a Retellai-powered agent using LlamaIndex
  • Interact with Retellai through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

What is the Retellai MCP server, and what's possible with it?

The Retellai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Retellai account. It provides structured and secure access to your call records, phone numbers, and conversation transcripts, so your agent can perform actions like retrieving call details, managing phone numbers, initiating outbound calls, and analyzing voice data on your behalf.

  • Retrieve and analyze call records: Your agent can fetch detailed call logs, filter by agent or time, and surface insights from past conversations.
  • Initiate outbound and web-based calls: Easily direct your agent to start new phone or web calls between specific numbers or agents, supporting various business workflows.
  • Manage phone numbers and assignments: Buy, update, or delete phone numbers, and bind them to agents for streamlined inbound and outbound call handling.
  • Access and review call transcripts and details: Let your agent drill down into specific calls, pulling transcripts and metadata for compliance, training, or analytics.
  • Explore and configure voice settings: Fetch detailed information about available voice options, including provider, accent, gender, and preview audio for customization of call experiences.

Supported Tools & Triggers

Tools
Add community voiceAdd a community voice from ElevenLabs to your Retell voice library.
Add sources to knowledge baseTool to add sources (documents, URLs, text) to an existing knowledge base in Retell AI.
Buy a new phone number bind agentsThis endpoint allows purchasing a new phone number with a specified area code and binding it to designated agents for inbound and outbound calls.
Create Voice AI AgentCreate a new voice AI agent with specified configuration.
Create a new outbound phone callInitiate an outbound call by POST to '/v2/create-phone-call'.
Create a new web callThe /v2/create-web-call endpoint creates a web call with a unique agent ID, returning call details like type, token, call ID, and status in JSON format, with a 201 response.
Create Batch TestTool to create a batch test job that runs multiple test cases against an agent.
Create a new chat sessionTool to create a new chat session with a chat agent.
Create a new chat agentCreate a new chat agent with specified configuration.
Create chat completionTool to create a chat completion for an existing chat session, generating the agent's response to a user message.
Create conversation flowCreate a new Conversation Flow that can be attached to an agent for response generation.
Create conversation flow componentCreates a new shared conversation flow component at POST '/create-conversation-flow-component'.
Create a new knowledge baseTool to create a new knowledge base in Retell AI with texts, files, and URLs.
Create Retell LLM Response EngineCreate a new Retell LLM Response Engine that can be attached to an agent.
Create Test Case DefinitionTool to create a test case definition for agent QA testing in Retell AI.
Delete agentDeletes an existing agent by its unique identifier.
Delete callDelete a specific call and its associated data by call ID.
Delete chat agentDelete an existing chat agent by its unique identifier.
Delete conversation flowDelete a conversation flow and all its versions.
Delete conversation flow componentDelete a shared conversation flow component.
Delete knowledge baseDelete an existing knowledge base by its unique identifier.
Delete knowledge base sourceDelete an existing source from a knowledge base.
Delete phone numberTool to delete an existing phone number from Retell AI.
Delete Retell LLMDelete an existing Retell LLM Response Engine by its unique identifier.
Delete test case definitionDelete a test case definition by its unique identifier.
End chatTool to end an active chat session.
Retrieve details of a specific agentRetrieve details of a specific agent by its unique identifier.
Get agent versionsTool to retrieve all versions of a specific agent.
Get batch testRetrieve details and results of a specific batch test job.
Get chat detailsTool to retrieve details of a specific chat session by chat ID.
Retrieve details of a specific chat agentRetrieve details of a specific chat agent by its unique identifier.
Get all versions of a chat agentRetrieve all versions of a specific chat agent by its unique identifier.
Get concurrencyRetrieves the current concurrency and concurrency limits for the organization.
Get Conversation FlowRetrieve details of a specific Conversation Flow by its ID.
Get conversation flow componentRetrieves a shared conversation flow component by its unique identifier.
Get knowledge baseRetrieve details of a specific knowledge base by its unique identifier.
Retrieve details of a specific Retell LLMRetrieve details of a specific Retell LLM Response Engine by its unique identifier.
Import phone numberTool to import a phone number from custom telephony and bind agents to it.
List agentsRetrieves a list of all agents associated with the account.
List all chatsTool to retrieve a list of all chats associated with the account.
List all phone numbersRetrieves a list of all phone numbers associated with the account.
List batch testsTool to list batch test jobs for a response engine.
List chat agentsTool to retrieve a list of all chat agents associated with the account.
List conversation flow componentsRetrieves a list of all shared conversation flow components.
List conversation flowsTool to list all conversation flows that can be attached to an agent.
List knowledge basesTool to retrieve all knowledge bases associated with the account.
List Retell LLMsTool to list all Retell LLM Response Engines that can be attached to an agent.
List test case definitionsTool to list test case definitions for a response engine (Retell LLM or Conversation Flow).
List test runsTool to list all test case jobs (test runs) for a batch test job.
List voicesList all voices available to the user.
Publish agentPublishes the latest version of the agent and creates a new draft agent with a newer version.
Publish chat agentPublishes the latest version of the chat agent and creates a new draft chat agent with a newer version.
Register phone callRegister a phone call for custom telephony integration with Retell AI.
Retrieve call detailsTool to retrieve call details with filtering options.
Retrieve call details by idRetrieve call details by ID for web/phone calls, including type, agent ID, status, timestamps, and web access token; covering responses from success to server errors.
Retrieve phone number detailsTool to retrieve details of a specific phone number from Retell AI.
Retrieve details of a specific voiceTool to retrieve details of a specific voice by its voice_id.
Search community voiceSearch for community voices from voice providers.
Update agentUpdate an existing agent's latest draft version.
Update callUpdate an active call's parameters such as metadata, dynamic variables, or data storage settings.
Update chat agentUpdate an existing chat agent configuration.
Update chat metadataTool to update metadata and sensitive data storage settings for an existing chat.
Update conversation flowUpdate an existing conversation flow configuration.
Update conversation flow componentUpdate an existing shared conversation flow component by its ID.
Update phone number configurationUpdate agent bound to a purchased phone number.
Update Retell LLM Response EngineUpdate an existing Retell LLM Response Engine by its unique identifier.
Update test case definitionUpdate a test case definition for agent testing.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Retellai account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Retellai

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Retellai access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called retellai_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["retellai"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Retellai actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Retellai actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, retellai)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Retellai tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Retellai database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Retellai

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Retellai, then start asking questions.

Complete Code

Here's the complete code to get you started with Retellai and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["retellai"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Retellai actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Retellai actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Retellai to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Retellai tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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FAQ

What are the differences in Tool Router MCP and Retellai MCP?

With a standalone Retellai MCP server, the agents and LLMs can only access a fixed set of Retellai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Retellai and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Retellai tools.

Can I manage the permissions and scopes for Retellai while using Tool Router?

Yes, absolutely. You can configure which Retellai scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Retellai data and credentials are handled as safely as possible.

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