# How to integrate Cody MCP with LangChain ```json { "title": "How to integrate Cody MCP with LangChain", "toolkit": "Cody", "toolkit_slug": "cody", "framework": "LangChain", "framework_slug": "langchain", "url": "https://composio.dev/toolkits/cody/framework/langchain", "markdown_url": "https://composio.dev/toolkits/cody/framework/langchain.md", "updated_at": "2026-03-29T06:28:13.118Z" } ``` ## Introduction This guide walks you through connecting Cody to LangChain using the Composio tool router. By the end, you'll have a working Cody agent that can summarize key findings from q2 reports, find policy details on employee benefits, draft onboarding checklist for new hires through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Cody account through Composio's Cody MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Cody with - [OpenAI Agents SDK](https://composio.dev/toolkits/cody/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/cody/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/cody/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/cody/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/cody/framework/codex) - [OpenClaw](https://composio.dev/toolkits/cody/framework/openclaw) - [Hermes](https://composio.dev/toolkits/cody/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/cody/framework/cli) - [Google ADK](https://composio.dev/toolkits/cody/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/cody/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/cody/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/cody/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/cody/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get and set up your OpenAI and Composio API keys - Connect your Cody project to Composio - Create a Tool Router MCP session for Cody - Initialize an MCP client and retrieve Cody tools - Build a LangChain agent that can interact with Cody - 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 Cody MCP server, and what's possible with it? The Cody MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cody account. It provides structured and secure access so your agent can perform Cody operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CODY_CREATE_CONVERSATION` | Create Conversation | Tool to create a new conversation with a specified bot. Use when starting a new conversation thread with optional focus mode to limit bot's knowledge base to specific documents. | | `CODY_CREATE_DOCUMENT` | Create Document | Tool to create a new document with text or HTML content in Cody AI. Use when you need to add documents to Cody's knowledge base with up to 768 KB of content. | | `CODY_CREATE_DOCUMENT_FROM_FILE` | Create Document From File | Tool to create a document by uploading a file (up to 100 MB). Supports txt, md, rtf, pdf, ppt, pptx, pptm, doc, docx, docm formats. Use when you need to add file-based documents to Cody's knowledge base. The file is processed asynchronously. | | `CODY_CREATE_DOCUMENT_FROM_WEBPAGE` | Create Document from Webpage | Tool to create a document from a publicly accessible webpage URL. Use when you need to import content from a webpage into Cody AI. The webpage must be accessible without login. If request fails, ensure the URL is publicly accessible and not blocked by a firewall. | | `CODY_CREATE_FOLDER` | Create Folder | Tool to create a new folder in Cody AI for organizing content. Use when you need to create a folder to organize documents or conversations. | | `CODY_DELETE_CONVERSATION` | Delete Conversation | Tool to delete a conversation by its ID. Use when you need to permanently remove a conversation from the system. | | `CODY_DELETE_DOCUMENT` | Delete Document | Tool to delete a document by id. Use when removing a document that is no longer needed. | | `CODY_GET_CONVERSATION` | Get Conversation | Tool to fetch a conversation by its ID from Cody AI. Use when you need to retrieve details about a specific conversation. Supports optional includes parameter to filter response to list document IDs. | | `CODY_GET_DOCUMENT` | Get Document | Tool to retrieve a specific document by its identifier from Cody AI. Use when you need to get details about a particular document including its status, content URL, and metadata. | | `CODY_GET_FOLDER` | Get Folder | Tool to retrieve a specific folder by its identifier. Use when you need to get details about a folder. | | `CODY_GET_MESSAGE` | Get Message | Tool to fetch a specific message by its ID from Cody AI. Use when you need to retrieve details about a particular message, with optional includes for sources or usage metrics. | | `CODY_GET_UPLOAD_SIGNED_URL` | Get Upload Signed URL | Tool to get an AWS S3 signed upload URL for file uploads. Use when you need to obtain a signed URL to upload a file to Cody's storage. | | `CODY_LIST_BOTS` | List Bots | Tool to get all bots with optional keyword filtering. Use when you need to retrieve the list of available bots in a Cody account. | | `CODY_LIST_CONVERSATIONS` | List Conversations | Tool to get all conversations with optional filtering by bot, keyword, or includes. Use when you need to retrieve conversation history, filter by bot, search by name, or get document associations. | | `CODY_LIST_DOCUMENTS` | List Documents | Tool to retrieve all documents from Cody AI account with optional filtering. Use when you need to list documents by folder, conversation, or search by keyword. Returns document details including learning status and content URL. | | `CODY_LIST_FOLDERS` | List Folders | Tool to retrieve all folders with optional keyword filtering. Use when you need to list or search for folders in the account. | | `CODY_LIST_MESSAGES` | List Messages | Tool to retrieve a paginated list of messages from Cody, optionally filtered by conversation. Use when you need to list messages, with optional filtering by conversation_id and extra attributes (sources or usage). | | `CODY_SEND_MESSAGE` | Send Message | Tool to send a message to Cody AI and receive an AI-generated response. Use when you need to send a user message to a conversation and get the AI's reply. | | `CODY_SEND_MESSAGE_FOR_STREAM` | Send Message for Stream | Tool to send a message to Cody AI and receive a Server-Sent Events (SSE) stream URL for the AI response. Use when you need streaming responses instead of waiting for the complete message. The response contains a stream_url that can be used to connect to the SSE stream and receive the AI's response in real-time chunks. | | `CODY_UPDATE_CONVERSATION` | Update Conversation | Tool to update a conversation by its ID including name, bot_id, and document_ids. Use when you need to modify an existing conversation's properties. | | `CODY_UPDATE_FOLDER` | Update Folder | Tool to update a folder by its ID. Use when you need to modify an existing folder's name. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Cody MCP server is an implementation of the Model Context Protocol that connects your AI agent to Cody. It provides structured and secure access so your agent can perform Cody 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 Cody 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 Cody 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 Cody tools as needed ```python # Create Tool Router session for Cody session = composio.create( user_id=os.getenv("COMPOSIO_USER_ID"), toolkits=['cody'] ) url = session.mcp.url ``` ```typescript const session = await composio.create( userId as string, { toolkits: ['cody'] } ); const url = session.mcp.url; ``` ### 7. Configure the agent with the MCP URL No description provided. ```python client = MultiServerMCPClient({ "cody-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({ "cody-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 Cody 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 Cody 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=['cody'] ) url = session.mcp.url client = MultiServerMCPClient({ "cody-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 Cody 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: ['cody'] } ); const url = session.mcp.url; const client = new MultiServerMCPClient({ "cody-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 Cody 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 Cody 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 Cody MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/cody/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/cody/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/cody/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/cody/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/cody/framework/codex) - [OpenClaw](https://composio.dev/toolkits/cody/framework/openclaw) - [Hermes](https://composio.dev/toolkits/cody/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/cody/framework/cli) - [Google ADK](https://composio.dev/toolkits/cody/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/cody/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/cody/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/cody/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/cody/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [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. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [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. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [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. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Cody MCP? With a standalone Cody MCP server, the agents and LLMs can only access a fixed set of Cody tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Cody 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 Cody tools. ### Can I manage the permissions and scopes for Cody while using Tool Router? Yes, absolutely. You can configure which Cody 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 Cody data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)