# How to integrate Flowiseai MCP with LlamaIndex

```json
{
  "title": "How to integrate Flowiseai MCP with LlamaIndex",
  "toolkit": "Flowiseai",
  "toolkit_slug": "flowiseai",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/flowiseai/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/flowiseai/framework/llama-index.md",
  "updated_at": "2026-05-12T10:11:53.393Z"
}
```

## Introduction

This guide walks you through connecting Flowiseai to LlamaIndex using the Composio tool router. By the end, you'll have a working Flowiseai agent that can list all chatflows available in your account, clone an existing chatflow for testing, delete chat messages from a specific chatflow through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Flowiseai account through Composio's Flowiseai MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Flowiseai with

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

The Flowiseai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Flowiseai account. It provides structured and secure access to your FlowiseAI workspace, so your agent can manage chatflows, automate workflow creation, clone or delete existing flows, and handle tool configurations on your behalf.
- Automated chatflow creation and management: Instantly create new chatflows, fetch details of existing ones, or update and organize your LLM workflows programmatically.
- Seamless cloning and exporting of chatflows: Duplicate any chatflow with a single request or export them for backup, sharing, or versioning across projects.
- Easy clean-up and deletion: Direct your agent to delete chatflows, remove outdated tools, or erase chat messages to keep your workspace tidy and relevant.
- Tool and workflow introspection: Retrieve detailed metadata for specific tools or chatflows so your agent can make informed decisions about which components to use or modify.
- Effortless import and migration: Import chatflows from exported JSON files, making it simple to migrate or restore entire AI workflows with minimal manual effort.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FLOWISEAI_CLONE_CHATFLOW` | Clone Chatflow | Tool to clone an existing chatflow. Use when you need to duplicate a chatflow by its ID. |
| `FLOWISEAI_CREATE_CHATFLOW` | Create Chatflow | Creates a new chatflow in FlowiseAI. Chatflows are visual workflows that define AI agent behavior using nodes and edges. Use this to programmatically create single-agent (CHATFLOW) or multi-agent (MULTIAGENT) flows. Only 'name' is required; type defaults to 'CHATFLOW' and flowData defaults to an empty flow structure. |
| `FLOWISEAI_CREATE_DOCUMENT_STORE` | Create Document Store | Creates a new document store in FlowiseAI. Document stores are used to manage embeddings and vector data for AI applications. Use this to programmatically create storage for documents that will be embedded and searched. |
| `FLOWISEAI_CREATE_LEAD` | Create Lead | Tool to create a new lead in a chatflow. Use when you need to capture lead information from a chat session for follow-up or CRM integration. |
| `FLOWISEAI_CREATE_TOOL` | Create Tool | Tool to create a new FlowiseAI tool. Use when you need to create a custom tool with specific name, description, and color. Optionally provide icon URL, JSON schema, or JavaScript function code. |
| `FLOWISEAI_CREATE_VARIABLE` | Create Variable | Creates a new variable in FlowiseAI. Variables are used to store configuration values, API keys, and other data that can be referenced across chatflows. Use this to programmatically create string or number variables with optional values. |
| `FLOWISEAI_DELETE_CHATFLOW` | Delete Chatflow | Tool to delete a chatflow by its ID. Use after confirming the chatflow ID is correct. |
| `FLOWISEAI_DELETE_CHAT_MESSAGES` | Delete Chat Messages | Tool to delete chat messages for a specific chatflow. Use when you need to remove messages based on optional filters. Use after confirming the chatflow ID. |
| `FLOWISEAI_DELETE_DOCUMENT_STORE` | Delete Document Store | Tool to delete a specific document store by its ID. Use when you need to permanently remove a document store. This action is destructive and cannot be undone. |
| `FLOWISEAI_DELETE_TOOL_BY_ID` | Delete Tool By ID | Permanently deletes a FlowiseAI tool by its unique ID. This action is destructive and cannot be undone. Use FLOWISEAI_LIST_ALL_TOOLS first to verify the correct tool ID before deletion. |
| `FLOWISEAI_DELETE_VARIABLE` | Delete Variable | Tool to delete a variable by its unique ID. Use when you need to permanently remove a variable from FlowiseAI. |
| `FLOWISEAI_EDIT_DOCUMENT_STORE_FILE_CHUNK` | Edit Document Store File Chunk | Tool to update a specific chunk in a FlowiseAI document store. Use when you need to modify the content or metadata of an existing chunk. At least one of pageContent or metadata must be provided. |
| `FLOWISEAI_GET_ALL_CHATFLOWS` | Get All Chatflows | Retrieves all chatflows from the authenticated FlowiseAI account. Use this to list available chatflows, get their IDs for subsequent operations (like update, delete, export), or check chatflow deployment status. Returns an empty list if no chatflows exist. |
| `FLOWISEAI_GET_ALL_CHAT_MESSAGE_FEEDBACK` | Get All Chat Message Feedback | Tool to list all chat message feedbacks for a chatflow. Use when you need to view feedback given on messages in a specific chatflow. |
| `FLOWISEAI_GET_ALL_LEADS_FOR_CHATFLOW` | Get All Leads for Chatflow | Tool to retrieve all leads for a specific chatflow. Use when you need to see lead information collected from a chatflow's interactions. |
| `FLOWISEAI_GET_ALL_UPSERT_HISTORY` | Get All Upsert History | Tool to retrieve all upsert history records for a specific chatflow. Use when you need to view the history of upsert operations. |
| `FLOWISEAI_GET_ALL_VARIABLES` | Get All Variables | Tool to retrieve a list of all variables. Use when you need to list all variables available in the FlowiseAI workspace. Returns an empty list if no variables exist. |
| `FLOWISEAI_GET_DOCUMENT_STORE_BY_ID` | Get Document Store By ID | Tool to retrieve a document store by its ID. Use when you have a document store ID and need its full details including configuration and status. |
| `FLOWISEAI_GET_DOCUMENT_STORE_FILE_CHUNKS` | Get Document Store File Chunks | Tool to get chunks from a specific document loader. Use when you need to retrieve chunked content from a document in a FlowiseAI document store. |
| `FLOWISEAI_GET_SINGLE_CHATFLOW` | Get Single Chatflow | Tool to retrieve a chatflow by its ID. Use when you have a chatflow ID and need its full details. |
| `FLOWISEAI_GET_TOOL_BY_ID` | Get Tool By ID | Tool to retrieve a specific FlowiseAI tool by its ID. Use when you need detailed metadata of a tool before interacting with it. |
| `FLOWISEAI_LIST_ALL_TOOLS` | List All Tools | Tool to retrieve a list of all tools. Use when you need to list every tool available after authentication. |
| `FLOWISEAI_LIST_ASSISTANTS` | List Assistants | Tool to retrieve a list of all assistants. Use when you need to list every assistant available in the authenticated FlowiseAI account. |
| `FLOWISEAI_LIST_CHAT_MESSAGES` | List Chat Messages | Tool to list chat messages of a chatflow. Use after selecting a chatflow when you need to view its messages. |
| `FLOWISEAI_PING_SERVER` | Ping Server | Tool to ping the FlowiseAI server to verify it is running and accessible. Use this to perform a health check before executing other operations or to diagnose connectivity issues. |
| `FLOWISEAI_UPDATE_CHATFLOW_DETAILS` | Update Chatflow Details | Tool to update details of an existing chatflow. Use when you have confirmed the chatflow ID and want to modify its fields. |
| `FLOWISEAI_UPDATE_DOCUMENT_STORE` | Update Document Store | Tool to update a specific document store. Use when you need to modify properties of an existing document store. |
| `FLOWISEAI_UPDATE_TOOL_BY_ID` | Update Tool By ID | Updates a FlowiseAI tool's properties by its ID. Use this to modify tool name, description, color, icon, JSON schema, or JavaScript function code. Requires the tool's UUID from LIST_ALL_TOOLS or GET_TOOL_BY_ID. At least one field must be provided for update. |
| `FLOWISEAI_UPDATE_VARIABLE` | Update Variable | Tool to update a variable by its ID. Use when you need to modify a variable's name, value, or type. |

## Supported Triggers

None listed.

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

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

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

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 Flowiseai 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, flowiseai)
- 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 Flowiseai 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=["flowiseai"],
    )

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

  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 Flowiseai 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 Flowiseai
```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 Flowiseai, 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=["flowiseai"],
    )

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

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

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

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.

## Frequently Asked Questions

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

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

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

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

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