# How to integrate Leverly MCP with CrewAI ```json { "title": "How to integrate Leverly MCP with CrewAI", "toolkit": "Leverly", "toolkit_slug": "leverly", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/leverly/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/leverly/framework/crew-ai.md", "updated_at": "2026-05-06T08:18:29.872Z" } ``` ## Introduction This guide walks you through connecting Leverly to CrewAI using the Composio tool router. By the end, you'll have a working Leverly agent that can show all current ingestion reattempts, stop reattempts for a specific lead, list reattempts history for lead 1234 through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Leverly account through Composio's Leverly MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Leverly with - [OpenAI Agents SDK](https://composio.dev/toolkits/leverly/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/leverly/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/leverly/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/leverly/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/leverly/framework/codex) - [OpenClaw](https://composio.dev/toolkits/leverly/framework/openclaw) - [Hermes](https://composio.dev/toolkits/leverly/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/leverly/framework/cli) - [Google ADK](https://composio.dev/toolkits/leverly/framework/google-adk) - [LangChain](https://composio.dev/toolkits/leverly/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/leverly/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/leverly/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/leverly/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Leverly connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Leverly - Build a conversational loop where your agent can execute Leverly 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 Leverly MCP server, and what's possible with it? The Leverly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Leverly account. It provides structured and secure access to your Leverly workflows, so your agent can perform actions like listing reattempt histories, stopping ongoing reattempts, and streamlining automation management on your behalf. - View reattempt history: Quickly ask your agent to retrieve a complete list of all ingestion reattempts for easy review and troubleshooting. - Stop ongoing reattempts: Direct your agent to halt any ongoing reattempts for specific leads by providing the relevant reattempt ID. - Monitor workflow automation: Let your agent inspect and keep tabs on the status of automated processes, ensuring nothing falls through the cracks. - Accelerate process interventions: Empower your agent to step in and manage exceptions or stuck workflows by stopping unnecessary retries as needed. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `LEVERLY_LIST_REATTEMPTS` | List Reattempts | Tool to list all reattempts in leverly. use when you need to inspect the history of ingestion reattempts before taking further action. | | `LEVERLY_STOP_REATTEMPTS` | Stop Leverly Reattempts | Tool to stop ongoing reattempts for a lead in leverly. use when you need to halt retries for a given reattempt id. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Leverly MCP server is an implementation of the Model Context Protocol that connects your AI agent to Leverly. It provides structured and secure access so your agent can perform Leverly 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 Leverly 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 Leverly 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 Leverly 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 Leverly **What's happening:** - You create a Leverly only session through Composio - Composio returns an MCP HTTP URL that exposes Leverly tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["leverly"]) 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=["leverly"], ) 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 Leverly through Composio's Tool Router. The agent can perform Leverly 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 Leverly MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/leverly/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/leverly/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/leverly/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/leverly/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/leverly/framework/codex) - [OpenClaw](https://composio.dev/toolkits/leverly/framework/openclaw) - [Hermes](https://composio.dev/toolkits/leverly/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/leverly/framework/cli) - [Google ADK](https://composio.dev/toolkits/leverly/framework/google-adk) - [LangChain](https://composio.dev/toolkits/leverly/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/leverly/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/leverly/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/leverly/framework/llama-index) ## Related Toolkits - [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. It helps you build flexible automations with complex conditions, schedules, and integrations. - [Basin](https://composio.dev/toolkits/basin) - Basin is a no-code form backend for quickly setting up reliable contact forms. It lets you collect and manage form submissions without writing any server-side code. - [Bouncer](https://composio.dev/toolkits/bouncer) - Bouncer is an email validation platform that verifies the authenticity of email addresses in real-time and batch. It helps boost deliverability and reduce bounce rates for your communications. - [Conveyor](https://composio.dev/toolkits/conveyor) - Conveyor is a platform that automates security reviews with a Trust Center and AI-driven questionnaire automation. It streamlines compliance and vendor security processes for faster, hassle-free reviews. - [Crowdin](https://composio.dev/toolkits/crowdin) - Crowdin is a localization management platform that streamlines translation workflows and collaboration. It helps teams centralize multilingual content, boost productivity, and automate translation processes. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Detrack](https://composio.dev/toolkits/detrack) - Detrack is a delivery management platform for real-time tracking and proof of delivery. It helps businesses automate notifications and keep customers updated every step of the way. - [Dnsfilter](https://composio.dev/toolkits/dnsfilter) - Dnsfilter is a cloud-based DNS security and content filtering solution. It helps organizations block online threats and manage safe internet access with ease. - [Faraday](https://composio.dev/toolkits/faraday) - Faraday lets you embed AI in workflows across your stack for smarter automation. It boosts your favorite tools with actionable intelligence and seamless integration. - [Feathery](https://composio.dev/toolkits/feathery) - Feathery is an AI-powered platform for building dynamic data intake forms with advanced logic. It helps teams automate complex workflows and collect structured data with ease. - [Fillout forms](https://composio.dev/toolkits/fillout_forms) - Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease. - [Formdesk](https://composio.dev/toolkits/formdesk) - Formdesk is an online form builder for creating and managing professional forms. It's perfect for collecting data, automating workflows, and integrating form submissions with your favorite services. - [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows. - [Graphhopper](https://composio.dev/toolkits/graphhopper) - GraphHopper is an enterprise-grade Directions API for routing, optimization, and geocoding across multiple vehicle types. It enables fast, reliable route planning and logistics automation for businesses. - [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale. - [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals. - [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination. - [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code. - [Ntfy](https://composio.dev/toolkits/ntfy) - Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams. - [Persona](https://composio.dev/toolkits/persona) - Persona offers identity infrastructure to automate user verification and compliance. It helps organizations securely verify users and reduce fraud risk. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Leverly MCP? With a standalone Leverly MCP server, the agents and LLMs can only access a fixed set of Leverly tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Leverly 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 Leverly tools. ### Can I manage the permissions and scopes for Leverly while using Tool Router? Yes, absolutely. You can configure which Leverly 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 Leverly data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)