# How to integrate Appdrag MCP with LangChain ```json { "title": "How to integrate Appdrag MCP with LangChain", "toolkit": "Appdrag", "toolkit_slug": "appdrag", "framework": "LangChain", "framework_slug": "langchain", "url": "https://composio.dev/toolkits/appdrag/framework/langchain", "markdown_url": "https://composio.dev/toolkits/appdrag/framework/langchain.md", "updated_at": "2026-05-12T10:01:47.808Z" } ``` ## Introduction This guide walks you through connecting Appdrag to LangChain using the Composio tool router. By the end, you'll have a working Appdrag agent that can deploy new website from template, update api endpoint with new logic, list all active database tables through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Appdrag account through Composio's Appdrag MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Appdrag with - [OpenAI Agents SDK](https://composio.dev/toolkits/appdrag/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/appdrag/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/appdrag/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/appdrag/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/appdrag/framework/codex) - [OpenClaw](https://composio.dev/toolkits/appdrag/framework/openclaw) - [Hermes](https://composio.dev/toolkits/appdrag/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/appdrag/framework/cli) - [Google ADK](https://composio.dev/toolkits/appdrag/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/appdrag/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/appdrag/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/appdrag/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/appdrag/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get and set up your OpenAI and Composio API keys - Connect your Appdrag project to Composio - Create a Tool Router MCP session for Appdrag - Initialize an MCP client and retrieve Appdrag tools - Build a LangChain agent that can interact with Appdrag - Set up an interactive chat interface for testing ## What is LangChain? LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context. Key features include: - Agent Framework: Build agents that can use tools and make decisions - MCP Integration: Connect to external services through Model Context Protocol adapters - Memory Management: Maintain conversation history across interactions - Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers ## What is the Appdrag MCP server, and what's possible with it? The Appdrag MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Appdrag account. It provides structured and secure access to your web projects, APIs, and databases, so your agent can perform actions like managing cloud databases, deploying APIs, editing website content, and automating backend operations on your behalf. - Cloud database management: Enable your agent to create, query, update, and delete records in Appdrag's cloud databases, streamlining data-driven workflows. - API deployment and invocation: Let your agent publish new APIs, call existing ones, or automate API management tasks for rapid development and integration. - Website content editing: Allow your agent to update text, images, or dynamic content on your Appdrag-powered websites, making real-time site changes a breeze. - File and asset management: Have your agent upload, organize, or remove files and media assets from your Appdrag project without manual intervention. - Workflow automation and monitoring: Empower your agent to trigger backend scripts, monitor deployment status, or automate operations using Appdrag's serverless features. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `APPDRAG_EXECUTE_FUNCTION_DELETE_DEFAULT` | Execute Cloud Backend function via DELETE | Tool to execute a Cloud Backend API function via DELETE on the default environment. Use when you need to call a function with DELETE parameters and optional APIKey. | | `APPDRAG_EXECUTE_FUNCTION_DELETE_PREPROD` | Execute Preprod Function (DELETE) | Tool to execute the pre-production version of a Cloud Backend API function via DELETE. Use when you need to test or validate delete operations in the preprod environment before production deployment. | | `APPDRAG_EXECUTE_FUNCTION_GET_PROD` | Execute PROD API Function (GET) | Tool to execute a production Cloud Backend API function via GET. Includes robust URL handling and fallbacks to accommodate management base URLs. | | `APPDRAG_EXECUTE_FUNCTION_PATCH_DEV` | Execute Dev Function (PATCH) | Tool to execute the development version of a Cloud Backend API function via PATCH. Use after deploying or updating your function to the dev environment. | | `APPDRAG_EXECUTE_FUNCTION_POST_DEFAULT` | Execute Cloud Backend function via POST | Tool to execute a Cloud Backend API function via POST on the default environment. Use when you need to call a function with POST parameters and optional APIKey. | | `APPDRAG_EXECUTE_FUNCTION_POST_PREPROD` | Execute Function POST (Preprod Env) | Tool to execute a Cloud Backend API function via POST on the preprod environment. Use when you need to test a function in the preprod environment before releasing to production. Include apiKey if your function requires APIKey security. | | `APPDRAG_EXECUTE_FUNCTION_PUT_DEFAULT` | Execute Cloud Backend function via PUT (default) | Tool to execute a Cloud Backend API function via PUT on the default environment. Use when you need to call a function with PUT parameters and optional APIKey. | | `APPDRAG_EXECUTE_FUNCTION_PUT_PREPROD` | Execute Cloud Backend function via PUT (preprod) | Tool to execute a Cloud Backend API function via PUT on the preprod environment. Use when you need to call a function with PUT parameters and optional APIKey in preprod. | | `APPDRAG_VISUAL_SQL_DELETE` | Visual SQL Delete | Tool to delete rows via a Visual SQL Delete function. Use when you need to delete records from a Cloud DB table using a Visual SQL Delete function. | | `APPDRAG_VISUAL_SQL_SELECT` | Visual SQL SELECT | Tool to execute a Visual SELECT Cloud Backend function. Use when you need to read rows from a database table using a visual SQL function configured in AppDrag. | | `APPDRAG_VISUAL_SQL_UPDATE` | Visual SQL Update | Tool to execute a Visual SQL UPDATE via an AppDrag Visual UPDATE function. Use when you need to update database rows based on your Visual UPDATE mapping. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Appdrag MCP server is an implementation of the Model Context Protocol that connects your AI agent to Appdrag. It provides structured and secure access so your agent can perform Appdrag 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 No description provided. ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies No description provided. ```python pip install composio-langchain langchain-mcp-adapters langchain python-dotenv ``` ```typescript npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your requests to Composio's API - COMPOSIO_USER_ID identifies the user for session management - OPENAI_API_KEY enables access to OpenAI's language models ```bash COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_composio_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies No description provided. ```python from langchain_mcp_adapters.client import MultiServerMCPClient from langchain.agents import create_agent from dotenv import load_dotenv from composio import Composio import asyncio import os load_dotenv() ``` ```typescript import { Composio } from '@composio/core'; import { LangchainProvider } from '@composio/langchain'; import { MultiServerMCPClient } from "@langchain/mcp-adapters"; import { createAgent } from "langchain"; import * as readline from 'readline'; import 'dotenv/config'; dotenv.config(); ``` ### 5. Initialize Composio client What's happening: - We're loading the COMPOSIO_API_KEY from environment variables and validating it exists - Creating a Composio instance that will manage our connection to Appdrag tools - Validating that COMPOSIO_USER_ID is also set before proceeding ```python async def main(): composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) if not os.getenv("COMPOSIO_API_KEY"): raise ValueError("COMPOSIO_API_KEY is not set") if not os.getenv("COMPOSIO_USER_ID"): raise ValueError("COMPOSIO_USER_ID is not set") ``` ```typescript const composioApiKey = process.env.COMPOSIO_API_KEY; const userId = process.env.COMPOSIO_USER_ID; if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set'); if (!userId) throw new Error('COMPOSIO_USER_ID is not set'); async function main() { const composio = new Composio({ apiKey: composioApiKey as string, provider: new LangchainProvider() }); ``` ### 6. Create a Tool Router session What's happening: - We're creating a Tool Router session that gives your agent access to Appdrag tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use - This approach allows the agent to dynamically load and use Appdrag tools as needed ```python # Create Tool Router session for Appdrag session = composio.create( user_id=os.getenv("COMPOSIO_USER_ID"), toolkits=['appdrag'] ) url = session.mcp.url ``` ```typescript const session = await composio.create( userId as string, { toolkits: ['appdrag'] } ); const url = session.mcp.url; ``` ### 7. Configure the agent with the MCP URL No description provided. ```python client = MultiServerMCPClient({ "appdrag-agent": { "transport": "streamable_http", "url": session.mcp.url, "headers": { "x-api-key": os.getenv("COMPOSIO_API_KEY") } } }) tools = await client.get_tools() agent = create_agent("gpt-5", tools) ``` ```typescript const client = new MultiServerMCPClient({ "appdrag-agent": { transport: "http", url: url, headers: { "x-api-key": process.env.COMPOSIO_API_KEY } } }); const tools = await client.getTools(); const agent = createAgent({ model: "gpt-5", tools }); ``` ### 8. Set up interactive chat interface No description provided. ```python conversation_history = [] print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Appdrag related question or task to the agent.\n") while True: user_input = input("You: ").strip() if user_input.lower() in ['exit', 'quit', 'bye']: print("\nGoodbye!") break if not user_input: continue conversation_history.append({"role": "user", "content": user_input}) print("\nAgent is thinking...\n") response = await agent.ainvoke({"messages": conversation_history}) conversation_history = response['messages'] final_response = response['messages'][-1].content print(f"Agent: {final_response}\n") ``` ```typescript let conversationHistory: any[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log("Ask any Appdrag related question or task to the agent.\n"); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: 'You: ' }); rl.prompt(); rl.on('line', async (userInput: string) => { const trimmedInput = userInput.trim(); if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } conversationHistory.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); const response = await agent.invoke({ messages: conversationHistory }); conversationHistory = response.messages; const finalResponse = response.messages[response.messages.length - 1]?.content; console.log(`Agent: ${finalResponse}\n`); rl.prompt(); }); rl.on('close', () => { console.log('\n👋 Session ended.'); process.exit(0); }); ``` ### 9. Run the application No description provided. ```python if __name__ == "__main__": asyncio.run(main()) ``` ```typescript main().catch((err) => { console.error('Fatal error:', err); process.exit(1); }); ``` ## Complete Code ```python from langchain_mcp_adapters.client import MultiServerMCPClient from langchain.agents import create_agent from dotenv import load_dotenv from composio import Composio import asyncio import os load_dotenv() async def main(): composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) if not os.getenv("COMPOSIO_API_KEY"): raise ValueError("COMPOSIO_API_KEY is not set") if not os.getenv("COMPOSIO_USER_ID"): raise ValueError("COMPOSIO_USER_ID is not set") session = composio.create( user_id=os.getenv("COMPOSIO_USER_ID"), toolkits=['appdrag'] ) url = session.mcp.url client = MultiServerMCPClient({ "appdrag-agent": { "transport": "streamable_http", "url": url, "headers": { "x-api-key": os.getenv("COMPOSIO_API_KEY") } } }) tools = await client.get_tools() agent = create_agent("gpt-5", tools) conversation_history = [] print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Appdrag related question or task to the agent.\n") while True: user_input = input("You: ").strip() if user_input.lower() in ['exit', 'quit', 'bye']: print("\nGoodbye!") break if not user_input: continue conversation_history.append({"role": "user", "content": user_input}) print("\nAgent is thinking...\n") response = await agent.ainvoke({"messages": conversation_history}) conversation_history = response['messages'] final_response = response['messages'][-1].content print(f"Agent: {final_response}\n") if __name__ == "__main__": asyncio.run(main()) ``` ```typescript import { Composio } from '@composio/core'; import { LangchainProvider } from '@composio/langchain'; import { MultiServerMCPClient } from "@langchain/mcp-adapters"; import { createAgent } from "langchain"; import * as readline from 'readline'; import 'dotenv/config'; const composioApiKey = process.env.COMPOSIO_API_KEY; const userId = process.env.COMPOSIO_USER_ID; if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set'); if (!userId) throw new Error('COMPOSIO_USER_ID is not set'); async function main() { const composio = new Composio({ apiKey: composioApiKey as string, provider: new LangchainProvider() }); const session = await composio.create( userId as string, { toolkits: ['appdrag'] } ); const url = session.mcp.url; const client = new MultiServerMCPClient({ "appdrag-agent": { transport: "http", url: url, headers: { "x-api-key": process.env.COMPOSIO_API_KEY } } }); const tools = await client.getTools(); const agent = createAgent({ model: "gpt-5", tools }); let conversationHistory: any[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log("Ask any Appdrag related question or task to the agent.\n"); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: 'You: ' }); rl.prompt(); rl.on('line', async (userInput: string) => { const trimmedInput = userInput.trim(); if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } conversationHistory.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); const response = await agent.invoke({ messages: conversationHistory }); conversationHistory = response.messages; const finalResponse = response.messages[response.messages.length - 1]?.content; console.log(`Agent: ${finalResponse}\n`); rl.prompt(); }); rl.on('close', () => { console.log('\nSession ended.'); process.exit(0); }); } main().catch((err) => { console.error('Fatal error:', err); process.exit(1); }); ``` ## Conclusion You've successfully built a LangChain agent that can interact with Appdrag through Composio's Tool Router. Key features of this implementation: - Dynamic tool loading through Composio's Tool Router - Conversation history maintenance for context-aware responses - Async Python provides clean, efficient execution of agent workflows You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## How to build Appdrag MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/appdrag/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/appdrag/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/appdrag/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/appdrag/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/appdrag/framework/codex) - [OpenClaw](https://composio.dev/toolkits/appdrag/framework/openclaw) - [Hermes](https://composio.dev/toolkits/appdrag/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/appdrag/framework/cli) - [Google ADK](https://composio.dev/toolkits/appdrag/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/appdrag/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/appdrag/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/appdrag/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/appdrag/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. 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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. - [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Appdrag MCP? With a standalone Appdrag MCP server, the agents and LLMs can only access a fixed set of Appdrag tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Appdrag and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with LangChain? Yes, you can. LangChain 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 Appdrag tools. ### Can I manage the permissions and scopes for Appdrag while using Tool Router? Yes, absolutely. You can configure which Appdrag 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 Appdrag data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)