# How to integrate Replicate MCP with LangChain ```json { "title": "How to integrate Replicate MCP with LangChain", "toolkit": "Replicate", "toolkit_slug": "replicate", "framework": "LangChain", "framework_slug": "langchain", "url": "https://composio.dev/toolkits/replicate/framework/langchain", "markdown_url": "https://composio.dev/toolkits/replicate/framework/langchain.md", "updated_at": "2026-05-12T10:23:52.496Z" } ``` ## Introduction This guide walks you through connecting Replicate to LangChain using the Composio tool router. By the end, you'll have a working Replicate agent that can run stable diffusion to generate an image, list all your uploaded files on replicate, get readme documentation for a model through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Replicate account through Composio's Replicate MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Replicate with - [ChatGPT](https://composio.dev/toolkits/replicate/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/replicate/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/replicate/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/replicate/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/replicate/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/replicate/framework/codex) - [Cursor](https://composio.dev/toolkits/replicate/framework/cursor) - [VS Code](https://composio.dev/toolkits/replicate/framework/vscode) - [OpenCode](https://composio.dev/toolkits/replicate/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/replicate/framework/openclaw) - [Hermes](https://composio.dev/toolkits/replicate/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/replicate/framework/cli) - [Google ADK](https://composio.dev/toolkits/replicate/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/replicate/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/replicate/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/replicate/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/replicate/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get and set up your OpenAI and Composio API keys - Connect your Replicate project to Composio - Create a Tool Router MCP session for Replicate - Initialize an MCP client and retrieve Replicate tools - Build a LangChain agent that can interact with Replicate - 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 Replicate MCP server, and what's possible with it? The Replicate MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Replicate account. It provides structured and secure access to your Replicate resources, so your agent can perform actions like running AI model predictions, managing files, browsing model collections, and retrieving model documentation on your behalf. - Run and manage AI model predictions: Easily instruct your agent to create, monitor, and manage predictions on any deployed Replicate model using custom input parameters. - Browse and discover model collections: Ask your agent to fetch and list available model collections or retrieve example predictions to explore what’s possible on Replicate. - Upload and organize files: Let your agent upload new files, list all stored files, or inspect file details to streamline your model workflows. - Access model metadata and documentation: Retrieve full model details, schemas, and markdown README docs for any model to help you choose and utilize the right model for your tasks. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `REPLICATE_ACCOUNT_GET` | Get Account Information | Tool to get authenticated account information. Use when you need to retrieve details about the account associated with the API token. | | `REPLICATE_CANCEL_PREDICTION` | Cancel Prediction | Tool to cancel a prediction that is still running. Use when you need to stop an in-progress prediction to free up resources or halt execution. | | `REPLICATE_COLLECTIONS_GET` | Get model collection | Tool to get a specific collection of models by its slug. Use when you need detailed information about a collection and its models. | | `REPLICATE_COLLECTIONS_LIST` | List model collections | Tool to list all collections of models. Use when you need to retrieve available model collections. Collections are curated groupings of related models. Response includes only collection metadata (name, slug, description), not individual models within each collection; use REPLICATE_MODELS_GET for per-model details. Response may include a non-null `next` field indicating additional pages; follow it to enumerate all collections. | | `REPLICATE_CREATE_MODEL` | Create Model | Tool to create a new Replicate model with specified owner, name, visibility, and hardware. Use when you need to create a destination model before launching LoRA/fine-tune training. | | `REPLICATE_CREATE_PREDICTION` | Create Prediction | Tool to create a prediction for a Replicate Deployment. IMPORTANT: This action ONLY works with Replicate Deployments (persistent instances you create and manage), NOT public models. Deployments are created via REPLICATE_DEPLOYMENTS_CREATE. To run public models (e.g., 'meta/llama-2-70b-chat', 'stability-ai/sdxl'), use REPLICATE_MODELS_PREDICTIONS_CREATE instead. Use 'wait_for' to wait until the prediction completes. | | `REPLICATE_DEPLOYMENTS_CREATE` | Create Deployment | Tool to create a new deployment with specified model, version, hardware, and scaling parameters. Use when you need to deploy a model for production use with auto-scaling. | | `REPLICATE_DEPLOYMENTS_DELETE` | Delete Deployment | Tool to delete a deployment from your account. Use when you need to remove a deployment. Deployments must be offline and unused for at least 15 minutes before deletion. | | `REPLICATE_DEPLOYMENTS_GET` | Get Deployment Details | Tool to get deployment details by owner and name. Use when you need information about a specific deployment including its release configuration and hardware settings. | | `REPLICATE_DEPLOYMENTS_LIST` | List deployments | Tool to list all deployments associated with the account. Use when you need to retrieve deployment configurations and their latest releases. | | `REPLICATE_CREATE_FILE` | Create File | Tool to create or upload a file to Replicate. Use when you need to upload file content with optional metadata. | | `REPLICATE_FILES_DELETE` | Delete File | Tool to delete a file by its ID. Use when you need to remove a file from storage. Returns 204 No Content on success. | | `REPLICATE_FILES_GET` | Get File Details | Tool to get details of a file by its ID. Use when you need to inspect uploaded file information before further operations. Returned URLs may be short-lived; download or persist needed files promptly after retrieval. | | `REPLICATE_FILES_LIST` | List Files | Tool to retrieve a paginated list of uploaded files. Use to view all files created by the authenticated user or organization. Files are sorted with most recent first. Pagination is cursor-based: follow the next cursor until empty to retrieve all files. Limit requests to 1–2/second to avoid 429 Too Many Requests errors. Use to validate current file_ids before passing to prediction tools, as stale file_ids cause runtime errors. | | `REPLICATE_GET_PREDICTION` | Get Prediction | Tool to get the status and output of a prediction by its ID. Use when you need to check on a running prediction or retrieve the results of a completed prediction. | | `REPLICATE_HARDWARE_LIST` | List Available Hardware | Tool to list available hardware SKUs for models and deployments. Use when you need to see what hardware options are available on the Replicate platform. | | `REPLICATE_MODELS_EXAMPLES_LIST` | List model examples | Tool to list example predictions for a specific model. Use when you want to retrieve author-provided illustrative examples after identifying the model. Returned examples are minimal working payloads; cross-reference with REPLICATE_MODELS_README_GET before calling REPLICATE_CREATE_PREDICTION to satisfy strict input validation. | | `REPLICATE_MODELS_GET` | Get Model Details | Tool to get details of a specific model by owner and name. Consult the returned input schema before constructing any prediction request — each model defines its own required/optional fields (e.g., `prompt`, `aspect_ratio`, `version`); missing or unknown keys cause validation errors. Model schemas and available versions may change over time; recheck before production use. | | `REPLICATE_MODELS_LIST` | List Public Models | Tool to list public models with pagination and sorting. Use when you need to browse available models or find models sorted by creation date. | | `REPLICATE_MODELS_PREDICTIONS_CREATE` | Create Model Prediction | Tool to create a prediction using an official Replicate model. Use when you need to run inference with a specific model using its owner and name. Supports synchronous waiting (up to 60 seconds) and webhooks for async notifications. | | `REPLICATE_MODELS_README_GET` | Get Model README | Tool to get the README content for a model in Markdown format. Consult alongside REPLICATE_MODELS_EXAMPLES_LIST before calling REPLICATE_CREATE_PREDICTION — Replicate enforces strict JSON schemas on model inputs and returns 422 errors for incorrect keys or types. Use after retrieving model details when you want to view its documentation. | | `REPLICATE_MODELS_VERSIONS_GET` | Get Model Version | Tool to get a specific version of a model. Use when you need details about a particular model version including its schema and metadata. | | `REPLICATE_MODELS_VERSIONS_LIST` | List Model Versions | Tool to list all versions of a specific model. Use when you need to see all available versions of a model, sorted by newest first. | | `REPLICATE_CREATE_PREDICTION` | Create Prediction | Tool to create a prediction to run a model by version ID. Use when you have a specific model version identifier and need to run inference with provided inputs. Supports synchronous waiting and webhook notifications. | | `REPLICATE_PREDICTIONS_LIST` | List All Predictions | Tool to list all predictions for the authenticated user or organization with pagination. Use when you need to retrieve prediction history or filter predictions by creation date. | | `REPLICATE_SEARCH` | Search Models and Collections | Tool to search for models, collections, and docs using text queries (beta). Use when you need to find relevant models or collections based on keywords or descriptions. | | `REPLICATE_TRAININGS_CANCEL` | Cancel Training | Tool to cancel an ongoing training operation in Replicate. Use when you need to stop a training job that is in progress. | | `REPLICATE_TRAININGS_CREATE` | Create Training Job | Tool to create a training job for a specific model version. Use when you need to fine-tune a model with custom training data. Supports webhook notifications for training status updates. | | `REPLICATE_TRAININGS_LIST` | List Training Jobs | Tool to list all training jobs for the authenticated user or organization with pagination. Use when you need to retrieve training history or check the status of training jobs. | | `REPLICATE_UPDATE_MODELS` | Update Model Metadata | Tool to update metadata for a model including description, URLs, and README. Use when you need to modify a model's visibility, documentation, or associated links. | | `REPLICATE_WEBHOOKS_SECRET_GET` | Get Webhook Signing Secret | Tool to get the signing secret for the default webhook. Use when you need to retrieve the secret key used to verify webhook authenticity. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Replicate MCP server is an implementation of the Model Context Protocol that connects your AI agent to Replicate. It provides structured and secure access so your agent can perform Replicate 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 Replicate 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 Replicate 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 Replicate tools as needed ```python # Create Tool Router session for Replicate session = composio.create( user_id=os.getenv("COMPOSIO_USER_ID"), toolkits=['replicate'] ) url = session.mcp.url ``` ```typescript const session = await composio.create( userId as string, { toolkits: ['replicate'] } ); const url = session.mcp.url; ``` ### 7. Configure the agent with the MCP URL No description provided. ```python client = MultiServerMCPClient({ "replicate-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({ "replicate-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 Replicate 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 Replicate 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=['replicate'] ) url = session.mcp.url client = MultiServerMCPClient({ "replicate-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 Replicate 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: ['replicate'] } ); const url = session.mcp.url; const client = new MultiServerMCPClient({ "replicate-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 Replicate 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 Replicate 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 Replicate MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/replicate/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/replicate/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/replicate/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/replicate/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/replicate/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/replicate/framework/codex) - [Cursor](https://composio.dev/toolkits/replicate/framework/cursor) - [VS Code](https://composio.dev/toolkits/replicate/framework/vscode) - [OpenCode](https://composio.dev/toolkits/replicate/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/replicate/framework/openclaw) - [Hermes](https://composio.dev/toolkits/replicate/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/replicate/framework/cli) - [Google ADK](https://composio.dev/toolkits/replicate/framework/google-adk) - [Vercel AI SDK](https://composio.dev/toolkits/replicate/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/replicate/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/replicate/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/replicate/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 Replicate MCP? With a standalone Replicate MCP server, the agents and LLMs can only access a fixed set of Replicate tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Replicate 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 Replicate tools. ### Can I manage the permissions and scopes for Replicate while using Tool Router? Yes, absolutely. You can configure which Replicate 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 Replicate data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)