# How to integrate Re amaze MCP with CrewAI ```json { "title": "How to integrate Re amaze MCP with CrewAI", "toolkit": "Re amaze", "toolkit_slug": "re_amaze", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/re_amaze/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/re_amaze/framework/crew-ai.md", "updated_at": "2026-05-12T10:23:25.810Z" } ``` ## Introduction This guide walks you through connecting Re amaze to CrewAI using the Composio tool router. By the end, you'll have a working Re amaze agent that can list all tags used in recent reports, show most popular response templates for support, analyze tag trends from last week's conversations through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Re amaze account through Composio's Re amaze MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Re amaze with - [OpenAI Agents SDK](https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/re_amaze/framework/codex) - [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw) - [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/re_amaze/framework/cli) - [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk) - [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Re amaze connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Re amaze - Build a conversational loop where your agent can execute Re amaze operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## What is the Re amaze MCP server, and what's possible with it? The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Re:amaze account. It provides structured and secure access to your customer support environment, so your agent can retrieve report tags, access canned response templates, and streamline support workflows for your team. - Tag usage analytics: Instantly pull all tags used in reports so your agent can analyze conversation trends, monitor support topics, or generate insights about ticket categorization. - Effortless response template retrieval: Fetch your brand's pre-built response templates, making it easy for the agent to suggest or automate consistent replies across channels. - Streamlined reply suggestions: Quickly surface relevant canned responses for agents or bots to use, speeding up customer replies and ensuring brand consistency. - Support workflow automation: Leverage tags and templates to help your agent automate repetitive customer support tasks and standardize communication processes. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RE_AMAZE_GET_REPORTS_TAGS` | Get report tags | Tool to retrieve a list of tags used in reports. Use when analyzing tag usage metrics across conversations. | | `RE_AMAZE_GET_RESPONSE_TEMPLATES` | Get Response Templates | Tool to retrieve response templates for the brand. Use when you need canned responses to streamline replies. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agent to Re amaze. It provides structured and secure access so your agent can perform Re amaze operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before starting, make sure you have: - Python 3.9 or higher - A Composio account and API key - A Re amaze connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 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 **What's happening:** - composio connects your agent to Re amaze via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Re amaze MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Re amaze **What's happening:** - You create a Re amaze only session through Composio - Composio returns an MCP HTTP URL that exposes Re amaze tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["re_amaze"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["re_amaze"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Re amaze through Composio's Tool Router. The agent can perform Re amaze operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## How to build Re amaze MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/re_amaze/framework/codex) - [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw) - [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/re_amaze/framework/cli) - [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk) - [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index) ## Related Toolkits - [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. 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Use it to quickly find and verify professional email addresses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Re amaze MCP? With a standalone Re amaze MCP server, the agents and LLMs can only access a fixed set of Re amaze tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Re amaze and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with CrewAI? Yes, you can. CrewAI 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 Re amaze tools. ### Can I manage the permissions and scopes for Re amaze while using Tool Router? Yes, absolutely. You can configure which Re amaze 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 Re amaze data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)