# How to integrate Reply io MCP with LlamaIndex

```json
{
  "title": "How to integrate Reply io MCP with LlamaIndex",
  "toolkit": "Reply io",
  "toolkit_slug": "reply_io",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/reply_io/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/reply_io/framework/llama-index.md",
  "updated_at": "2026-05-12T10:23:56.407Z"
}
```

## Introduction

This guide walks you through connecting Reply io to LlamaIndex using the Composio tool router. By the end, you'll have a working Reply io agent that can list all active campaigns this week, show contacts added to sales lists, delete a campaign by campaign id through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Reply io account through Composio's Reply io MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Reply io with

- [OpenAI Agents SDK](https://composio.dev/toolkits/reply_io/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/reply_io/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/reply_io/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/reply_io/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/reply_io/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/reply_io/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/reply_io/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/reply_io/framework/cli)
- [Google ADK](https://composio.dev/toolkits/reply_io/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/reply_io/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/reply_io/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/reply_io/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/reply_io/framework/crew-ai)

## 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 Reply io
- Connect LlamaIndex to the Reply io MCP server
- Build a Reply io-powered agent using LlamaIndex
- Interact with Reply io 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 Reply io MCP server, and what's possible with it?

The Reply io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Reply io account. It provides structured and secure access to your sales engagement platform, so your agent can manage campaigns, handle contacts, organize sequences, and automate routine sales operations on your behalf.
- Campaign and sequence management: Effortlessly list, browse, and delete campaigns or sequences to keep your outreach organized and up to date.
- Contact and list organization: Let your agent fetch, review, and organize your Reply io contacts and contact lists for targeted sales actions.
- Email account administration: Retrieve all connected email accounts or remove outdated ones, making sure your sales tools stay streamlined.
- User and access control: Easily remove users or generate unique identifiers for tasks, maintaining security and clarity in your team’s workflow.
- Automated data retrieval: Quickly pull up paginated lists of campaigns, sequences, email accounts, or contact lists to inform your sales strategies and next steps.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `REPLY_IO_ADD_CONTACT_TO_SEQUENCE` | Add Contact to Sequence | Move an existing contact to a sequence in Reply.io. Use this action to enroll contacts in email sequences for automated outreach campaigns. This action allows you to specify where in the sequence to start, whether to remove the contact from their current sequence, and when to begin processing. |
| `REPLY_IO_ARCHIVE_SEQUENCE` | Archive Sequence | Tool to archive a sequence. Use when you need to archive an existing sequence without permanently deleting it. |
| `REPLY_IO_CLEAR_CONTACT_STATUS` | Clear Contact Status | Tool to clear statuses from contacts. Use when you need to remove specific or all clearable statuses from contacts. |
| `REPLY_IO_CONNECT_EXCHANGE_ACCOUNT` | Connect Exchange Account via OAuth | Tool to initiate OAuth connection for an Exchange email account. Use when you need to connect an Exchange account to Reply.io via OAuth flow. Returns the Microsoft OAuth consent page URL where the user should be redirected. |
| `REPLY_IO_CONNECT_GMAIL_ACCOUNT` | Connect Gmail Account | Tool to initiate Gmail account connection via OAuth. Returns the OAuth authorization URL where the user should be redirected to grant permissions. |
| `REPLY_IO_CREATE_CONTACT` | Create Contact | Tool to create a new contact in Reply.io. Use when adding contacts to your outreach database. |
| `REPLY_IO_CREATE_SEQUENCE_STEP` | Create Sequence Step | Tool to add a new step to an existing sequence. Use when you need to build or modify sequence workflows with Email, Call, Task, SMS, WhatsApp, LinkedIn, Condition, or Zapier steps. |
| `REPLY_IO_DELETE_CONTACT` | Delete Contact | Tool to delete a contact. Use after confirming the contact exists to remove it permanently. |
| `REPLY_IO_DELETE_EMAIL_ACCOUNT` | Delete Email Account | Tool to delete a specific email account. Use when you need to remove an existing email account identified by its ID. |
| `REPLY_IO_DELETE_SCHEDULE` | Delete Schedule | Tool to delete a schedule. Use after confirming the schedule exists to remove it permanently. |
| `REPLY_IO_DELETE_SEQUENCE` | Delete Sequence | Tool to delete a sequence. Use after confirming the sequence exists to remove it permanently. |
| `REPLY_IO_DELETE_USER` | Delete User | Tool to delete a user. Use after confirming the user exists to remove them permanently. |
| `REPLY_IO_GENERATE_ULID` | Generate ULID | Generate ULID |
| `REPLY_IO_GET_CONTACT_BY_ID` | Get Contact by ID | Tool to retrieve a contact by ID. Use when you have a contact ID and need detailed contact information. |
| `REPLY_IO_GET_CONTACT_STATUS` | Get Contact Status | Tool to get contact status. Use when you need to retrieve all current statuses for a contact. |
| `REPLY_IO_GET_CURRENT_USER` | Get Current User | Tool to get the current authenticated user's ID. Use when you need to verify API key validity or identify the current user. |
| `REPLY_IO_GET_DISCONNECTED_EMAIL_ACCOUNTS` | Reply.io Get Disconnected Email Accounts | Tool to list email accounts that are currently disconnected due to authentication or connection errors. Use when you need to identify and troubleshoot problematic email accounts. |
| `REPLY_IO_GET_SEQUENCE_BY_ID` | Get Sequence By ID | Tool to retrieve detailed information about a sequence by its ID. Use when you need to get comprehensive sequence details including settings, email accounts, and workflow steps. |
| `REPLY_IO_GET_SEQUENCE_CONTACTS_EXTENDED` | Get Sequence Contacts Extended | Tool to retrieve all contacts enrolled in a sequence with additional details. Use when you need to see contact engagement status, current step, or completion timestamps within a sequence. |
| `REPLY_IO_GET_SEQUENCE_STEP_BY_ID` | Get Sequence Step by ID | Tool to retrieve details of a specific sequence step. Use when you need to inspect step configuration including type, delays, execution mode, and type-specific settings. |
| `REPLY_IO_LIST_CONTACTS_BASIC` | List Contacts Basic | Tool to list contacts. Use when verifying API access and gathering contact IDs. |
| `REPLY_IO_LIST_EMAIL_ACCOUNTS` | Reply.io List Email Accounts | Tool to list all email accounts. Use when you need to retrieve email accounts page by page. |
| `REPLY_IO_LIST_LISTS` | Reply.io List Lists | Tool to list all contact lists. Use when you need to retrieve all available lists in your Reply.io account. |
| `REPLY_IO_LIST_SEQUENCES` | List Sequences | Tool to retrieve a paginated list of sequences. Use when you need to browse sequences with optional filtering by name. |
| `REPLY_IO_LIST_SEQUENCE_STEPS` | List Sequence Steps | Tool to retrieve all steps in a sequence. Use when you need to get the complete list of steps configured for a specific sequence. |
| `REPLY_IO_PAUSE_SEQUENCE` | Pause Sequence | Tool to pause a running sequence. Use when you need to temporarily stop a sequence from sending emails or executing steps. |
| `REPLY_IO_REMOVE_CONTACT_FROM_SEQUENCE` | Remove Contact From Sequence | Tool to remove a contact from a sequence. Use when you need to stop a contact from receiving further steps in a specific sequence. |
| `REPLY_IO_REMOVE_CONTACTS_FROM_SEQUENCE` | Bulk Remove Contacts from Sequence | Tool to bulk remove multiple contacts from a sequence at once. Use when you need to remove several contacts from a sequence efficiently in a single operation. |
| `REPLY_IO_SEARCH_CONTACTS` | Search Contacts by Email | Tool to search contacts by email. Use when you need to find existing contact IDs for update tests. |
| `REPLY_IO_SET_CONTACT_STATUS` | Set Contact Status | Tool to set the status of one or more contacts. Use when you need to update contact statuses in bulk. |
| `REPLY_IO_START_SEQUENCE` | Start Sequence | Tool to start a sequence. Use when you need to activate a sequence that is in New or Paused status. |
| `REPLY_IO_UPDATE_CONTACT` | Update Contact | Tool to update an existing contact's information. Use when you need to modify contact details. |
| `REPLY_IO_UPDATE_EMAIL_ACCOUNT` | Update Email Account | Tool to update an existing email account with custom SMTP/IMAP settings. Use when you need to modify email account configuration such as sender name, signature, server settings, or daily limits. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Reply io MCP server is an implementation of the Model Context Protocol that connects your AI agent to Reply io. It provides structured and secure access so your agent can perform Reply io operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. 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 Reply io account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Reply io

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

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 Reply io access
```bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id
```

### 4. Import modules

No description provided.
```python
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()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
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")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

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, reply io)
- 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 Reply io tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
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=["reply_io"],
    )

    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 Reply io actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Reply io actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)
```

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["reply_io"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Reply io actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
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}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

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 Reply io
```python
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!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Reply io, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
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=["reply_io"],
    )

    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 Reply io actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Reply io 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!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["reply_io"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Reply io actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Reply io to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Reply io 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.

## How to build Reply io MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/reply_io/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/reply_io/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/reply_io/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/reply_io/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/reply_io/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/reply_io/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/reply_io/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/reply_io/framework/cli)
- [Google ADK](https://composio.dev/toolkits/reply_io/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/reply_io/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/reply_io/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/reply_io/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/reply_io/framework/crew-ai)

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- [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly.
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- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
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- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Reply io MCP?

With a standalone Reply io MCP server, the agents and LLMs can only access a fixed set of Reply io tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Reply io 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 Reply io tools.

### Can I manage the permissions and scopes for Reply io while using Tool Router?

Yes, absolutely. You can configure which Reply io 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 Reply io data and credentials are handled as safely as possible.

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