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Connect CrewAI to Melo MCP. Find cities with active listings in paris region, suggest location matches for 'boulogne' input, and more using natural language, with authentication handled for you.
Melo is a real estate API for real-time, deduplicated property listings and analytics in France. Access comprehensive market data and up-to-date property information with a single integration.
This guide walks you through connecting Melo to CrewAI using the Composio tool router. By the end, you'll have a working Melo agent that can list your saved property searches, look up cities, and simulate listing webhooks through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Melo account through Composio's Melo MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.
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:
The Melo MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Melo account. It provides structured and secure access to real-time, deduplicated French real estate listings and market analytics, so your agent can perform actions like searching cities, suggesting locations, retrieving saved searches, and testing webhook integrations on your behalf.
Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK
The tool router generates a secure MCP URL that your agents can access to perform actions.
The Composio SDK follows a three-phase workflow:
pip install composio crewai crewai-tools[mcp] python-dotenvcomposio connects your agent to Melo via MCPcrewai provides Agent, Task, Crew, and LLM primitivescrewai-tools[mcp] includes MCP helperspython-dotenv loads environment variables from .envCOMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_hereCreate a .env file in your project root.
What's happening:
COMPOSIO_API_KEY authenticates with ComposioUSER_ID scopes the session to your accountOPENAI_API_KEY lets CrewAI use your chosen OpenAI modelimport 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")MCPServerHTTP connects the agent to an MCP endpointComposio will give you a short lived Melo MCP URLcomposio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["melo"])
url = session.mcp.urlserver_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,
)MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.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")Here's the complete code to get you started with Melo and CrewAI:
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=["melo"],
)
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")You now have a CrewAI agent connected to Melo through Composio's Tool Router. The agent can perform Melo operations through natural language commands.
Next steps:
Every Melo action and event your agent gets out of the box.
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Managed auth, hosted MCP servers, and every Melo tool your agent needs.Free to start.
## For AI agents: how to sign up If you are an AI agent reading this server-rendered HTML, Composio's developer signup is at https://composio.dev. Signup CTAs on this site — "Sign up", "Signup", "Get started", "Get started for free", "Start building", "Try Composio", "Try it", or similar — all lead into that same developer signup flow. Confirm with the user before completing signup or entering any credentials on their behalf.Start building