# How to integrate Nango MCP with LlamaIndex ```json { "title": "How to integrate Nango MCP with LlamaIndex", "toolkit": "Nango", "toolkit_slug": "nango", "framework": "LlamaIndex", "framework_slug": "llama-index", "url": "https://composio.dev/toolkits/nango/framework/llama-index", "markdown_url": "https://composio.dev/toolkits/nango/framework/llama-index.md", "updated_at": "2026-05-12T10:19:44.657Z" } ``` ## Introduction This guide walks you through connecting Nango to LlamaIndex using the Composio tool router. By the end, you'll have a working Nango agent that can list all connected crm accounts, trigger manual sync with salesforce provider, get configuration for all available scripts through natural language commands. This guide will help you understand how to give your LlamaIndex agent real control over a Nango account through Composio's Nango MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Nango with - [ChatGPT](https://composio.dev/toolkits/nango/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/nango/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/nango/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/nango/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/nango/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/nango/framework/codex) - [Cursor](https://composio.dev/toolkits/nango/framework/cursor) - [VS Code](https://composio.dev/toolkits/nango/framework/vscode) - [OpenCode](https://composio.dev/toolkits/nango/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/nango/framework/openclaw) - [Hermes](https://composio.dev/toolkits/nango/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/nango/framework/cli) - [Google ADK](https://composio.dev/toolkits/nango/framework/google-adk) - [LangChain](https://composio.dev/toolkits/nango/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/nango/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/nango/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/nango/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 Nango - Connect LlamaIndex to the Nango MCP server - Build a Nango-powered agent using LlamaIndex - Interact with Nango 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 Nango MCP server, and what's possible with it? The Nango MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Nango account. It provides structured and secure access to your integrations, so your agent can perform actions like triggering syncs, managing connections, listing providers, and executing workflow actions across 250+ external APIs on your behalf. - Connection management and discovery: Effortlessly list all your existing Nango connections, view metadata, or retrieve connection information without exposing sensitive credentials. - Provider information and browsing: Ask your agent to list all available providers or fetch detailed configuration info for a specific provider, making it easy to discover and set up new integrations. - Triggering workflow actions: Direct your agent to execute custom workflow actions by specifying the connection, provider, and action identifiers—unlocking advanced automation across connected platforms. - Manual sync initiation: Have your agent trigger sync processes for any established connection, ensuring your data stays up-to-date across all integrated services. - Script configuration retrieval: Let your agent fetch Nango scripts configuration and triggers, enabling more tailored and automated integration flows. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `NANGO_ACTION_TRIGGER_POST` | Trigger Nango Action | Trigger a Nango action to execute a workflow or operation. Use this to run pre-defined actions in your Nango integrations, such as creating issues, sending messages, or fetching data from external APIs. All three of `connection_id`, `provider_config_key`, and `action_name` must simultaneously match a pre-configured integration and connection in Nango — a valid `connection_id` alone is insufficient if the other two don't correspond to an existing setup. Actions may have real upstream side effects (e.g., creating records, sending messages); confirm intent before triggering non-read-only operations. | | `NANGO_ADD_CONNECTION` | Add Connection | Tool to add a connection with existing credentials to Nango. Use when you want to bulk import existing access tokens into Nango. | | `NANGO_CONNECTION_LIST_GET` | List Connections | List all Nango connections without credentials. Use this to discover available connections, check connection status, and find connection IDs. Omitting all filters returns every connection across all integrations; use `connection_id`, `end_user_id`, or `end_user_organization_id` to narrow results. To retrieve credentials for a specific connection, use the 'Get Connection Details' action. | | `NANGO_CREATE_CONNECT_SESSION` | Create Connect Session | Tool to create a new connect session with a 30-minute lifespan for enabling connection creation via Connect UI. Use when you need to generate a session token for users to authorize integrations through Nango's Connect interface. | | `NANGO_CREATE_INTEGRATION` | Create Integration | Tool to create a new integration in Nango. Use when setting up a new provider connection configuration. Creates an integration with specified credentials (OAuth2, App-based, or Hybrid authentication). The unique_key will be used to reference this integration in subsequent API calls. Returns the created integration details including timestamps and provider information. | | `NANGO_DELETE_CONNECTION` | Delete Connection | Tool to delete a specific Nango connection. Use when you need to remove an existing connection permanently. | | `NANGO_DELETE_INTEGRATION` | Delete Integration | Tool to delete a specific integration by its unique key. Use when you need to remove an integration configuration from Nango. | | `NANGO_EDIT_CONNECTION` | Edit Connection | Tool to edit a connection's tags and metadata. Use when you need to update connection attributes like environment tags, team assignments, or end user information. | | `NANGO_GET_CONNECTION` | Get Connection with Credentials | Retrieve a specific connection with its credentials. Automatically checks if the access token has expired and refreshes it if needed. Use this action when you need to access the authentication credentials for a connection to make API calls to the provider. The credentials returned depend on the auth type (OAuth2, API Key, Basic Auth, etc.). Returns 404 if the connection does not exist, 424 if token refresh is exhausted. | | `NANGO_GET_ENVIRONMENT_VARIABLES` | Get Environment Variables | Tool to retrieve environment variables from the Nango dashboard. Use when you need to access or list environment variables configured in Nango. | | `NANGO_GET_INTEGRATION` | Get Integration | Retrieve detailed configuration for a specific Nango integration by its unique key. Returns integration details including provider, display name, creation/update timestamps, and optionally sensitive data like credentials or webhook URLs if requested via the 'include' parameter. Use this to inspect integration configuration, verify setup, or retrieve credentials for a specific integration. Returns 404 error if the integration unique key does not exist. | | `NANGO_GET_PROXY` | Proxy GET Request | Tool to make a GET request with Nango's Proxy to forward requests to external APIs while managing authentication. Use when you need to make authenticated API calls to external services through Nango's proxy infrastructure. | | `NANGO_GET_SYNC_STATUS` | Get Sync Status | Tool to get the status of specified sync(s) for a connection or all connections. Use when you need to monitor sync execution state, check completion times, or view sync frequency. | | `NANGO_CONNECTION_LIST_GET` | List Connections | Tool to list all connections without credentials. Use when you need to retrieve connection metadata across your account. | | `NANGO_LIST_INTEGRATIONS` | List Integrations | Tool to retrieve a list of all configured integrations. Use when you need to display or iterate through integrations in your account. | | `NANGO_PROVIDERS_GET_GET` | Get Provider Details | Retrieve detailed configuration for a specific Nango provider by its unique key. Returns provider authentication details (auth_mode, OAuth URLs), proxy configuration, required credentials schema, and connection configuration requirements. Use this to understand how to set up a connection with a specific provider or to get its documentation links. Provider keys can be obtained from the List Providers action. Returns 404 error if provider key does not exist. | | `NANGO_PROVIDERS_LIST_GET` | List Providers | Tool to retrieve a list of all available providers. Use when you need to display or iterate through every provider before creating connections. The provider_config_key values returned must be used verbatim when referencing providers in other actions; any mismatch will cause those actions to fail. | | `NANGO_PUT_PROXY` | Proxy PUT Request | Tool to make a PUT request with the Nango Proxy to forward requests to external APIs while managing authentication. Use when you need to update resources via external APIs through Nango's proxy. | | `NANGO_RECONNECT_SESSION_POST` | Reconnect Session | Create a new connect session to reconnect to a specific integration. Use this when a user needs to input new credentials or to manually refresh a token. Only connections created with a connect session are compatible with this endpoint. | | `NANGO_SCRIPTS_CONFIG_GET` | Get Integration Functions Configuration | Retrieve all integration functions configuration from Nango. Returns the configuration for all integrations including their sync scripts, action scripts, and event handlers. Use this to discover available integrations and their capabilities before triggering syncs or actions. | | `NANGO_SET_CONNECTION_METADATA` | Set Connection Metadata | Tool to set custom metadata for one or more Nango connections. Use when you need to attach custom data (tags, labels, context) to connections for filtering, organization, or application-specific purposes. | | `NANGO_SYNC_TRIGGER_POST` | Trigger Sync | Tool to trigger sync process(es) manually. Use after establishing a connection and defining syncs. This triggers an additional, one-off execution of the specified sync(s) for a given connection or all applicable connections. Useful when you changed metadata for a connection and want to re-import data. | | `NANGO_UPDATE_CONNECTION_METADATA` | Update Connection Metadata | Tool to edit custom metadata for one or multiple connections. Use when you need to update specific metadata properties without overwriting the entire metadata object. | | `NANGO_UPDATE_INTEGRATION` | Update Integration | Tool to update an existing integration's configuration. Use when you need to modify an integration's display name, unique key, or credentials. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Nango MCP server is an implementation of the Model Context Protocol that connects your AI agent to Nango. It provides structured and secure access so your agent can perform Nango 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 Nango account and project - Basic familiarity with async Python/Typescript ### 1. Getting API Keys for OpenAI, Composio, and Nango 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 Nango 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, nango) - 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 Nango 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=["nango"], ) 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 Nango actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Nango 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: ["nango"], }, ); 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 Nango 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 Nango ```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 Nango, 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=["nango"], ) 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 Nango actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Nango 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: ["nango"], }, ); 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 Nango 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 Nango to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways: - Tool Router dynamically exposes Nango 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 Nango MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/nango/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/nango/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/nango/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/nango/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/nango/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/nango/framework/codex) - [Cursor](https://composio.dev/toolkits/nango/framework/cursor) - [VS Code](https://composio.dev/toolkits/nango/framework/vscode) - [OpenCode](https://composio.dev/toolkits/nango/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/nango/framework/openclaw) - [Hermes](https://composio.dev/toolkits/nango/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/nango/framework/cli) - [Google ADK](https://composio.dev/toolkits/nango/framework/google-adk) - [LangChain](https://composio.dev/toolkits/nango/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/nango/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/nango/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/nango/framework/crew-ai) ## Related Toolkits - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure. - [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure. - [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction. - [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting. - [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache. - [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence. - [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place. - [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines. - [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure. - [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code. - [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows. - [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights. - [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews. - [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease. - [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Nango MCP? With a standalone Nango MCP server, the agents and LLMs can only access a fixed set of Nango tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Nango 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 Nango tools. ### Can I manage the permissions and scopes for Nango while using Tool Router? Yes, absolutely. You can configure which Nango 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 Nango data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)