# How to integrate Ragie MCP with LlamaIndex ```json { "title": "How to integrate Ragie MCP with LlamaIndex", "toolkit": "Ragie", "toolkit_slug": "ragie", "framework": "LlamaIndex", "framework_slug": "llama-index", "url": "https://composio.dev/toolkits/ragie/framework/llama-index", "markdown_url": "https://composio.dev/toolkits/ragie/framework/llama-index.md", "updated_at": "2026-03-29T06:47:09.580Z" } ``` ## Introduction This guide walks you through connecting Ragie to LlamaIndex using the Composio tool router. By the end, you'll have a working Ragie agent that can ingest new product documentation into ragie, run a semantic search for project roadmap, summarize key findings from all q2 reports through natural language commands. This guide will help you understand how to give your LlamaIndex agent real control over a Ragie account through Composio's Ragie MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Ragie with - [OpenAI Agents SDK](https://composio.dev/toolkits/ragie/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ragie/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ragie/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ragie/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ragie/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ragie/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ragie/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ragie/framework/cli) - [Google ADK](https://composio.dev/toolkits/ragie/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ragie/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/ragie/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/ragie/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/ragie/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 Ragie - Connect LlamaIndex to the Ragie MCP server - Build a Ragie-powered agent using LlamaIndex - Interact with Ragie 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 Ragie MCP server, and what's possible with it? The Ragie MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ragie account. It provides structured and secure access so your agent can perform Ragie operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RAGIE_CREATE_DOCUMENT` | Create Document | Tool to upload and process a document file in Ragie. Use when you need to create a new document with support for various formats including text, images, and documents. The endpoint accepts multipart/form-data and returns a Document object with processing status and metadata. | | `RAGIE_CREATE_DOCUMENT_FROM_URL` | Create Document From URL | Tool to ingest a document from a publicly accessible URL. Use when you need to add documents to Ragie from external sources. The document undergoes processing steps (pending, partitioning, indexed, ready) before becoming available for retrieval. | | `RAGIE_CREATE_DOCUMENT_RAW` | Create Document Raw | Tool to ingest a document as raw text or JSON. Use when creating a new document from text or JSON data. The document goes through processing steps and becomes available for retrieval once in the ready state. | | `RAGIE_CREATE_INSTRUCTION` | Create Instruction | Tool to create a new instruction that applies natural language directives to documents as they're ingested or updated. Use when you need to define structured data extraction or analysis rules for documents in Ragie. | | `RAGIE_CREATE_OAUTH_REDIRECT_URL` | Create OAuth Redirect URL | Tool to create an OAuth redirect URL for initializing embedded connector OAuth flows. Use when you need to set up OAuth authentication for connectors like Google Drive, Notion, or HubSpot. | | `RAGIE_CREATE_PARTITION` | Create Partition | Tool to create a new partition for scoping documents and connections in Ragie. Use when you need to organize documents and set resource limits for different workspaces or tenants. | | `RAGIE_DELETE_DOCUMENT` | Delete Document | Tool to delete a document from Ragie. Use when you need to remove a document permanently from the system. Supports both synchronous and asynchronous deletion modes. | | `RAGIE_DELETE_INSTRUCTION` | Delete Instruction | Tool to delete an instruction and all associated entities. Use when you need to permanently remove an instruction (irreversible operation). Requires the instruction ID (UUID format). | | `RAGIE_DELETE_PARTITION` | Delete Partition | Tool to delete a partition and all associated data irreversibly. Use when you need to permanently remove a partition. Returns status 200 for synchronous deletion or 202 for asynchronous deletion. | | `RAGIE_GET_DOCUMENT` | Get Document | Tool to retrieve a specific document by its unique identifier. Use when you need to get document details, metadata, processing status, or check for errors. Returns comprehensive document information including chunk count, page count, and any processing errors. | | `RAGIE_GET_DOCUMENT_CHUNK` | Get Document Chunk | Tool to retrieve a specific document chunk by its document and chunk ID. Use when you need detailed information about a specific chunk within a document, including its content, metadata, position index, and optional modality data for audio/video chunks. | | `RAGIE_GET_DOCUMENT_CHUNK_CONTENT` | Get Document Chunk Content | Tool to retrieve document chunk content in requested format with streaming support for media. Use when you need to get the actual content of a specific chunk from a document. | | `RAGIE_GET_DOCUMENT_CHUNKS` | Get Document Chunks | Tool to retrieve document chunks with pagination support. Lists all document chunks sorted by index in ascending order (max 100 items per page). Documents created prior to 9/18/2024 that have not been updated since have chunks sorted by ID instead of index. | | `RAGIE_GET_DOCUMENT_CONTENT` | Get Document Content | Tool to retrieve the content of a document by its ID. Use when you need to access the full content of a specific document. The media_type parameter can be used to request content in different formats. | | `RAGIE_GET_DOCUMENT_SUMMARY` | Get Document Summary | Tool to retrieve an LLM-generated summary of a document by its ID. Use when you need to get a concise summary of a document's content. | | `RAGIE_GET_PARTITION` | Get Partition | Tool to retrieve a partition by ID with usage statistics and resource limits. Use when you need to get detailed information about a specific partition. | | `RAGIE_GET_RESPONSE` | Get Response | Tool to retrieve a response by its unique identifier. Use when you need to check the status or details of a previously created response. | | `RAGIE_LIST_CONNECTIONS` | List Connections | Tool to list all connections sorted by creation date descending with pagination support. Use when you need to retrieve connections, optionally filtered by metadata. | | `RAGIE_LIST_CONNECTION_SOURCE_TYPES` | List Connection Source Types | Tool to list available connection source types like 'google_drive' and 'notion' along with their metadata. Use when you need to discover what connector types are available in Ragie. | | `RAGIE_LIST_DOCUMENTS` | List Documents | Tool to list all documents sorted by creation date (descending) with pagination support. Use when you need to browse or retrieve document metadata. Supports filtering and pagination up to 100 items per page. | | `RAGIE_LIST_ENTITIES_BY_DOCUMENT` | List Entities By Document | Tool to retrieve all extracted entities from a specific document with pagination support. Use when you need to access structured data that has been extracted from a document by Ragie's entity extraction instructions. | | `RAGIE_LIST_ENTITIES_BY_INSTRUCTION` | List Entities by Instruction | Tool to retrieve entities generated by a specific instruction. Use when you need to fetch entities extracted from documents based on a specific instruction's processing. | | `RAGIE_LIST_INSTRUCTIONS` | List Instructions | Tool to retrieve all instruction records from the Ragie system. Use when you need to view all available instructions that define natural language prompts and entity schemas applied to documents. | | `RAGIE_LIST_PARTITIONS` | List Partitions | Tool to retrieve a paginated list of all partitions sorted by name in ascending order. Use when you need to list available partitions with their configurations and limits. | | `RAGIE_PATCH_DOCUMENT_METADATA` | Patch Document Metadata | Tool to update metadata for a specific document with partial update support. Use when you need to modify document metadata fields without replacing the entire metadata object. Supports both synchronous and asynchronous updates. | | `RAGIE_RETRIEVE_DOCUMENT_CHUNKS` | Retrieve Document Chunks | Tool to retrieve relevant document chunks based on a query. Use when you need to search and retrieve document content that matches a specific query, with optional filtering and reranking capabilities. | | `RAGIE_SET_PARTITION_LIMITS` | Set Partition Limits | Tool to set usage limits on partition pages and media. Use when you need to configure monthly or maximum limits for pages processed/hosted, video/audio processing, or media streaming/hosting for a specific partition. | | `RAGIE_UPDATE_DOCUMENT_FROM_URL` | Update Document From URL | Tool to update an existing document by fetching content from a publicly accessible URL. Use when you need to refresh or replace a document's content with data from a web URL. The document goes through processing steps before it is ready for retrieval. | | `RAGIE_UPDATE_DOCUMENT_RAW` | Update Document Raw | Tool to update a document's content from raw text or JSON data. Use when modifying existing document content. The document undergoes processing and becomes available for retrieval once it reaches the ready state. | | `RAGIE_UPDATE_INSTRUCTION` | Update Instruction | Tool to update an instruction's active status. Use when you need to activate or deactivate an existing instruction. | | `RAGIE_UPDATE_PARTITION` | Update Partition | Tool to update a partition's configuration including description, context-aware settings, and metadata schema. Use when you need to modify an existing partition's settings. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Ragie MCP server is an implementation of the Model Context Protocol that connects your AI agent to Ragie. It provides structured and secure access so your agent can perform Ragie 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 Ragie account and project - Basic familiarity with async Python/Typescript ### 1. Getting API Keys for OpenAI, Composio, and Ragie 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 Ragie 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, ragie) - 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 Ragie 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=["ragie"], ) 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 Ragie actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Ragie 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: ["ragie"], }, ); 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 Ragie 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) { 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 Ragie ```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 Ragie, 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=["ragie"], ) 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 Ragie actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Ragie 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: ["ragie"], }, ); 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 Ragie actions." , llm, tools, }); return agent; } async function chatLoop(agent: ReturnType) { 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 Ragie to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways: - Tool Router dynamically exposes Ragie 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 Ragie MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/ragie/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ragie/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ragie/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ragie/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ragie/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ragie/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ragie/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ragie/framework/cli) - [Google ADK](https://composio.dev/toolkits/ragie/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ragie/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/ragie/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/ragie/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/ragie/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 Ragie MCP? With a standalone Ragie MCP server, the agents and LLMs can only access a fixed set of Ragie tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Ragie 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 Ragie tools. ### Can I manage the permissions and scopes for Ragie while using Tool Router? Yes, absolutely. You can configure which Ragie 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 Ragie data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)