# How to integrate Langbase MCP with LangChain ```json { "title": "How to integrate Langbase MCP with LangChain", "toolkit": "Langbase", "toolkit_slug": "langbase", "framework": "LangChain", "framework_slug": "langchain", "url": "https://composio.dev/toolkits/langbase/framework/langchain", "markdown_url": "https://composio.dev/toolkits/langbase/framework/langchain.md", "updated_at": "2026-05-12T10:17:11.733Z" } ``` ## Introduction This guide walks you through connecting Langbase to LangChain using the Composio tool router. By the end, you'll have a working Langbase agent that can chunk a long document for semantic search, list all conversation threads for this agent, create a new memory for session data through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Langbase account through Composio's Langbase MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Langbase with - [OpenAI Agents SDK](https://composio.dev/toolkits/langbase/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/langbase/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/langbase/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/langbase/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/langbase/framework/codex) - [OpenClaw](https://composio.dev/toolkits/langbase/framework/openclaw) - [Hermes](https://composio.dev/toolkits/langbase/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/langbase/framework/cli) - [Google ADK](https://composio.dev/toolkits/langbase/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/langbase/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/langbase/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/langbase/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/langbase/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get and set up your OpenAI and Composio API keys - Connect your Langbase project to Composio - Create a Tool Router MCP session for Langbase - Initialize an MCP client and retrieve Langbase tools - Build a LangChain agent that can interact with Langbase - 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 Langbase MCP server, and what's possible with it? The Langbase MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Langbase account. It provides structured and secure access to your Langbase projects, letting your agent split content, manage memories, orchestrate threads, and build complex AI data flows on your behalf. - Content chunking and processing: Automatically split large documents or text into manageable chunks for downstream processing and efficient AI handling. - Memory management: Create, list, or delete memory objects so your agent can organize and retrieve contextual information as needed. - Conversation thread orchestration: Start new conversation threads, fetch thread details, or list messages—making it easy for your agent to manage dialogue history and context. - Pipe creation and listing: Let your agent create new processing pipes or retrieve all existing pipes, enabling seamless orchestration of data and AI workflows. - Document and data retrieval: List all documents stored in a memory, giving your agent access to relevant information and knowledge for any task. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `LANGBASE_APPEND_THREAD_MESSAGES` | Append Thread Messages | Tool to add new messages to an existing conversation thread. Use when continuing a chat session or adding context to a thread. | | `LANGBASE_CHECK_HEALTH` | Check API Health | Tool to check the health status of the Langbase API service. Use when you need to verify API availability. | | `LANGBASE_CHUNKER_SPLIT_CONTENT` | Split Content into Chunks | Tool to split content into smaller chunks. Use when processing large text segments to fit downstream limits. | | `LANGBASE_CREATE_THREAD` | Create Thread | Tool to create a new conversation thread. Use when starting a fresh chat session or grouping messages into a distinct thread. | | `LANGBASE_DELETE_THREAD` | Delete Thread | Tool to delete a thread that is no longer needed to manage conversation history. Use when you need to permanently remove a thread by its ID. | | `LANGBASE_DELETE_THREAD_MESSAGE` | Delete Thread Message | Tool to delete a specific message from a conversation thread. Use when you need to remove a message from a thread by its ID. | | `LANGBASE_DOCUMENT_LIST` | List Documents in Memory | Tool to list documents in a specific memory. Use when you need to fetch document metadata (and optionally vectors) from a memory after confirming its name. Supports pagination via limit and startAfter parameters. | | `LANGBASE_GET_PIPE` | Get Pipe Details | Tool to retrieve details of a specific pipe by owner and name. Use when you need to fetch configuration and settings of a particular pipe. | | `LANGBASE_GET_THREAD` | Get Thread Details | Tool to retrieve details of a specific conversation thread. Use when you need the full thread details by its ID after confirming its existence. | | `LANGBASE_LIST_MODELS` | List Available Models | Tool to get available AI models supported by Langbase. Use to discover text and image generation models from various providers. | | `LANGBASE_LIST_THREAD_MESSAGES` | List Thread Messages | Tool to list all messages in a conversation thread. Use after obtaining the thread ID to fetch its messages. | | `LANGBASE_LIST_TRACES` | List Execution Traces | Tool to get execution traces for debugging and monitoring pipe runs. Use when you need to retrieve trace logs for a specific primitive. | | `LANGBASE_MEMORY_CREATE` | Create Memory | Tool to create a new memory. Use when storing a new memory record in Langbase after confirming memory details. | | `LANGBASE_MEMORY_DELETE` | Delete Memory | Tool to delete a specific memory. Use when you need to permanently remove a stored memory by its name. | | `LANGBASE_MEMORY_LIST` | List Memories | Tool to list all memory objects. Use when you need to fetch stored memories for context retrieval. | | `LANGBASE_PIPE_CREATE` | Create a new pipe | Tool to create a new pipe. Use after configuring pipe parameters. Returns an array of pipe objects, each including API key and URL. | | `LANGBASE_PIPE_LIST` | List all pipes | Tool to list all pipes. Use after authentication to retrieve the complete list of pipes. Returns an array of pipe objects; callers must handle list iteration. | | `LANGBASE_UPDATE_PIPE` | Update an existing pipe | Tool to update an existing pipe's configuration on Langbase. Use when modifying model settings, parameters, prompts, tools, or memory. The pipe must already exist. | | `LANGBASE_UPDATE_THREAD` | Update Thread Metadata | Tool to update an existing thread's metadata. Use when you need to modify metadata fields for managing and organizing conversation threads. | | `LANGBASE_UPDATE_THREAD_MESSAGE` | Update Thread Message | Tool to update an existing message in a conversation thread. Use when you need to modify the content or metadata of a specific message. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Langbase MCP server is an implementation of the Model Context Protocol that connects your AI agent to Langbase. It provides structured and secure access so your agent can perform Langbase 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 Langbase 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 Langbase 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 Langbase tools as needed ```python # Create Tool Router session for Langbase session = composio.create( user_id=os.getenv("COMPOSIO_USER_ID"), toolkits=['langbase'] ) url = session.mcp.url ``` ```typescript const session = await composio.create( userId as string, { toolkits: ['langbase'] } ); const url = session.mcp.url; ``` ### 7. Configure the agent with the MCP URL No description provided. ```python client = MultiServerMCPClient({ "langbase-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({ "langbase-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 Langbase 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 Langbase 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=['langbase'] ) url = session.mcp.url client = MultiServerMCPClient({ "langbase-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 Langbase 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: ['langbase'] } ); const url = session.mcp.url; const client = new MultiServerMCPClient({ "langbase-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 Langbase 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 Langbase 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 Langbase MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/langbase/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/langbase/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/langbase/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/langbase/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/langbase/framework/codex) - [OpenClaw](https://composio.dev/toolkits/langbase/framework/openclaw) - [Hermes](https://composio.dev/toolkits/langbase/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/langbase/framework/cli) - [Google ADK](https://composio.dev/toolkits/langbase/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/langbase/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/langbase/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/langbase/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/langbase/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 Langbase MCP? With a standalone Langbase MCP server, the agents and LLMs can only access a fixed set of Langbase tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Langbase 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 Langbase tools. ### Can I manage the permissions and scopes for Langbase while using Tool Router? Yes, absolutely. You can configure which Langbase 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 Langbase data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)