# How to integrate Bolna MCP with LlamaIndex

```json
{
  "title": "How to integrate Bolna MCP with LlamaIndex",
  "toolkit": "Bolna",
  "toolkit_slug": "bolna",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/bolna/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/bolna/framework/llama-index.md",
  "updated_at": "2026-05-12T10:03:27.760Z"
}
```

## Introduction

This guide walks you through connecting Bolna to LlamaIndex using the Composio tool router. By the end, you'll have a working Bolna agent that can list all voice agents available to me, initiate a call using your sales agent, get status of recent agent executions through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Bolna account through Composio's Bolna MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Bolna with

- [OpenAI Agents SDK](https://composio.dev/toolkits/bolna/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/bolna/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/bolna/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/bolna/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/bolna/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/bolna/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/bolna/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/bolna/framework/cli)
- [Google ADK](https://composio.dev/toolkits/bolna/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/bolna/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/bolna/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/bolna/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/bolna/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 Bolna
- Connect LlamaIndex to the Bolna MCP server
- Build a Bolna-powered agent using LlamaIndex
- Interact with Bolna 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 Bolna MCP server, and what's possible with it?

The Bolna MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bolna account. It provides structured and secure access to your Bolna voice agent platform, so your agent can perform actions like listing agents, making phone calls, managing executions, and retrieving analytics on your behalf.
- Automated voice call initiation: Let your AI agent instantly initiate phone calls using your Bolna conversational agents, streamlining outreach and support tasks.
- Agent and phone number management: Effortlessly fetch and list all your Bolna agents or phone numbers, making it easy to review and organize your voice assets.
- Real-time execution monitoring: Retrieve detailed information about specific call executions or monitor all executions for a given agent to track performance and outcomes.
- Batch processing for agents: List and manage batch operations associated with your agents, supporting bulk workflows and campaign management.
- Agent cleanup and maintenance: Quickly delete agents or batches that are no longer needed, keeping your Bolna environment organized and up to date.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BOLNA_ADD_PROVIDER` | Add Provider to Bolna | Tool to add a new telephony or voice service provider to your Bolna account. Use when you need to configure API keys for providers like Twilio, Deepgram, or ElevenLabs before creating agents. |
| `BOLNA_COPY_AGENT` | Copy Bolna Agent | Tool to create a duplicate copy of an existing Bolna voice AI agent. Use when you need to replicate an agent's complete configuration (tasks, prompts, LLM settings, etc.) with a new name. |
| `BOLNA_CREATE_AGENT` | Create Bolna Voice AI Agent (v2) | Tool to create a new Bolna Voice AI agent using the v2 API. Use when you need to set up a new conversational agent from scratch with custom LLM, synthesizer, transcriber, and task configurations. This fills the gap for end-to-end agent setup in workflows starting from an empty account state. |
| `BOLNA_CREATE_BATCH` | Create Bolna Batch | Tool to create a new outbound calling batch by uploading a CSV of contacts to obtain a batch_id. Use when initiating a batch campaign; follow by calling BOLNA_SCHEDULE_BATCH_BY_ID to schedule execution. |
| `BOLNA_CREATE_KNOWLEDGEBASE` | Create Bolna Knowledgebase | Tool to create a new knowledgebase for Voice AI agents to reference during conversations. Use when you need to provide agents with domain-specific knowledge from PDFs or web URLs. Note: Initially returns status 'processing'; poll or wait for status to become 'processed' before use. |
| `BOLNA_CREATE_TEMPLATE_AGENT` | Create Template Agent | Tool to create a new Bolna Voice AI agent from a template. Use when you want to quickly set up an agent using predefined templates instead of building from scratch. |
| `BOLNA_DELETE_AGENT_BY_ID` | Delete agent by id | Permanently delete a Voice AI agent and all associated data including batches, executions, and configurations |
| `BOLNA_DELETE_BATCH_BY_ID` | Delete batch by id | Permanently delete a batch campaign by its ID, removing it from the system. This operation cannot be undone. |
| `BOLNA_DELETE_KNOWLEDGEBASE` | Delete Knowledgebase | Tool to permanently delete a knowledgebase from your Bolna account. Use when you need to remove an existing RAG knowledgebase that is no longer needed. This operation cannot be undone. |
| `BOLNA_FETCH_ALL_BATCHES_BY_AGENT_ID` | Fetch all batches by agent id | Retrieve all batches associated with a specific Bolna Voice AI agent. Returns a comprehensive list of batches with details including batch status (scheduled, created, queued, executed), creation and scheduled times, contact counts, file names, and execution status breakdown. Use this to monitor batch campaigns, track their progress, and manage outbound calling operations for the agent. |
| `BOLNA_GET_ALL_AGENTS` | Get all agents | Retrieve all agents configured in your Bolna account Returns a comprehensive list of all voice agents with their configurations including: - Agent metadata (ID, name, type, status) - Task configurations (conversation settings, toolchains) - AI model settings (LLM, transcriber, synthesizer) - Webhook and phone number assignments - System prompts and guardrails This is useful for listing available agents, checking agent configurations, or finding specific agents by their properties. |
| `BOLNA_GET_EXECUTION_BY_ID` | Get execution by id | Retrieve detailed information about a specific phone call execution by its ID. Returns comprehensive execution data including conversation transcript, duration, costs (LLM, TTS, STT, network, platform), telephony details (phone numbers, recording URL, provider info), usage metrics (tokens, characters, duration), and extracted structured data. Use this to: - Analyze individual call performance and outcomes - Access conversation transcripts and recordings - Review cost breakdowns and resource usage - Monitor call status and error messages - Retrieve extracted structured data from conversations |
| `BOLNA_GET_KNOWLEDGEBASE` | Get knowledgebase by ID | Tool to retrieve details of a specific knowledgebase by its ID. Returns complete configuration including processing status, file information, vector ID, and embedding parameters (chunk size, similarity top k, overlapping). Use when you need to check if a knowledgebase has finished processing or to inspect its configuration before using it with an agent. |
| `BOLNA_GET_USER_INFO` | Get User Information | Tool to retrieve information about the authenticated user. Use when you need details like name, email, wallet balance, or concurrency limits for the current user. |
| `BOLNA_IMPORT_AGENT` | Import Bolna Agent | Tool to import an existing Bolna voice AI agent by its ID. Use when you need to copy or duplicate an agent configuration, create a new agent from a template, or migrate an agent from another environment. |
| `BOLNA_LIST_AGENTS_PAGINATED` | List agents (paginated) | Tool to retrieve a paginated list of all agents in your Bolna account. Use when you need to fetch agents with optional filtering by user_id or sub_account_id. |
| `BOLNA_LIST_KNOWLEDGEBASES` | List Knowledgebases | Tool to retrieve all knowledgebases from your Bolna account. Use when you need to view available RAG knowledgebases, check their processing status, or find specific knowledgebases by status. |
| `BOLNA_LIST_PHONE_NUMBERS` | List all phone numbers | Tool to list all phone numbers associated with your Bolna account. Use when you need to retrieve details of all phone numbers including provider, associated agent, pricing, and rental status. |
| `BOLNA_LIST_PROVIDERS` | List all providers | Retrieve all providers associated with your Bolna account Returns a list of all configured providers including: - Provider IDs (unique identifiers) - Provider names (e.g., API key types) - Masked provider values (secrets) - Creation timestamps (both absolute and human-readable) Use this when you need to view all configured API providers, check provider details, or verify provider setup in your Bolna account. |
| `BOLNA_LIST_VOICES` | List available voices | Tool to list all available voices that can be utilized for Voice AI agents. Use when you need to see which voices are available across different providers. |
| `BOLNA_MAKE_A_PHONE_CALL_FROM_AGENT` | Make an outbound phone call from agent | Initiate an outbound phone call using a configured Bolna Voice AI agent. The agent will call the specified recipient and engage in a conversation based on its configured prompt and capabilities. |
| `BOLNA_REMOVE_PROVIDER` | Remove Provider from Bolna Account | Tool to remove a provider from your Bolna account by its key name. Use when you need to delete a provider configuration that is no longer needed or needs to be replaced. |
| `BOLNA_RETRIEVE_AGENT_BY_ID` | Retrieve agent by id | Retrieve complete configuration and details for a specific Bolna voice AI agent by its ID. Returns comprehensive agent information including name, type, status, conversation tasks, LLM/synthesizer/transcriber settings, system prompts, webhook configuration, and timestamps. Use this to inspect agent setup before making calls or to verify agent configuration. |
| `BOLNA_RETRIEVE_AGENT_EXECUTION_DETAILS` | Retrieve agent execution details | Retrieve detailed information about a specific execution (call/conversation) by an agent, including transcript, costs, duration, status, and telephony data |
| `BOLNA_RETRIEVE_AGENT_EXECUTION_STATUS` | Retrieve agent execution status | Retrieve all executions for a specific agent with pagination and filtering support. Returns a paginated list of agent execution records including call status, cost breakdown, transcripts, and telephony data. |
| `BOLNA_RETRIEVE_BATCH_DETAILS_BY_ID` | Retrieve Batch Details by ID | Retrieve comprehensive details about a specific Bolna batch by its ID. Returns batch metadata including creation time, execution status, scheduled time, contact statistics, and call status breakdown. Use this to monitor batch progress or retrieve information about previously created batch calling campaigns. |
| `BOLNA_RETRIEVE_BATCH_EXECUTION_LIST` | Retrieve batch execution list | Retrieve all executions from a specific batch with pagination support. Returns detailed information about each call execution including conversation metrics, transcripts, costs, and resource usage breakdown (LLM tokens, synthesizer characters, etc.). Use this to monitor batch campaign results and analyze individual call outcomes. |
| `BOLNA_SCHEDULE_BATCH_BY_ID` | Schedule Batch by ID | Schedule a batch to execute at a specific time. After creating a batch with BOLNA_CREATE_BATCH, use this action to set when the batch calls should begin. The batch must exist and be in a schedulable state (e.g., 'created' or 'stopped'). |
| `BOLNA_SEARCH_PHONE_NUMBERS` | Search available phone numbers | Tool to search for available phone numbers that can be purchased for Bolna Voice agents. Use when you need to find purchasable phone numbers by country or prefix pattern before buying. |
| `BOLNA_SETUP_INBOUND_CALL_FOR_AGENT` | Setup inbound call for agent | Add agent for inbound calls |
| `BOLNA_STOP_AGENT_CALLS` | Stop Agent Calls | Tool to stop all queued or scheduled calls for a specific Voice AI agent. Use when you need to immediately halt all pending calls for an agent. |
| `BOLNA_STOP_BATCH_BY_ID` | Stop batch by id | Stop a running batch by its ID. This halts all queued calls in the batch. Any calls currently in the queue waiting to be executed will be cancelled and will not be processed. Use this when you need to immediately halt a batch campaign that's in progress. |
| `BOLNA_UPDATE_AGENT` | Update Bolna Voice AI Agent (v2) | Tool to update all settings and configuration of an existing Bolna Voice AI agent using the v2 API. Use when you need to modify an agent's full configuration including LLM settings, synthesizer, transcriber, tasks, prompts, or any other agent property. This performs a complete update (PUT operation). |

## Supported Triggers

None listed.

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

The Bolna MCP server is an implementation of the Model Context Protocol that connects your AI agent to Bolna. It provides structured and secure access so your agent can perform Bolna 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 Bolna account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Bolna

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 Bolna 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, bolna)
- 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 Bolna 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=["bolna"],
    )

    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 Bolna actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Bolna 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: ["bolna"],
    },
  );

  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 Bolna 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 Bolna
```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 Bolna, 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=["bolna"],
    )

    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 Bolna actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Bolna 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: ["bolna"],
    },
  );

  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 Bolna 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 Bolna to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Bolna 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 Bolna MCP Agent with another framework

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

## Related Toolkits

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- [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations.
- [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates.
- [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates.
- [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code.
- [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding.
- [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls.
- [Callingly](https://composio.dev/toolkits/callingly) - Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources.
- [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates.
- [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.
- [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time.
- [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.
- [Docsbot ai](https://composio.dev/toolkits/docsbot_ai) - Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality.
- [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.
- [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support.
- [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.
- [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback.
- [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Bolna MCP?

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

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

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

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)