simple_odoo_agent.py•5.62 kB
#!/usr/bin/env python3
"""
Simple MCP-Odoo + LLM Agent Integration Example
This script demonstrates how to create a basic AI agent that connects to
the MCP-Odoo server and performs accounting queries.
Usage:
python simple_odoo_agent.py
Required environment variables:
- OPENAI_API_KEY or ANTHROPIC_API_KEY: Your API key for the LLM provider
- MCP_SERVER_URL: URL of your MCP-Odoo server (default: http://localhost:8080)
"""
import os
import asyncio
import sys
from datetime import datetime
try:
from dotenv import load_dotenv
# Load environment variables from .env file if it exists
load_dotenv()
except ImportError:
print("python-dotenv not installed. Continuing with existing environment variables.")
try:
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage
except ImportError:
print("Required packages not installed. Please install them with:")
print("pip install langchain-mcp-adapters langgraph")
sys.exit(1)
async def run_odoo_agent():
"""Run a simple AI agent that connects to MCP-Odoo"""
# Check for API keys
if not os.environ.get("OPENAI_API_KEY") and not os.environ.get("ANTHROPIC_API_KEY"):
print("ERROR: No API key found for LLM providers.")
print("Please set either OPENAI_API_KEY or ANTHROPIC_API_KEY in your environment or .env file.")
return
# Setup MCP client
mcp_server_url = os.environ.get("MCP_SERVER_URL", "http://localhost:8080")
mcp_servers = {
"odoo": {
"url": f"{mcp_server_url}/sse",
"transport": "sse",
}
}
print(f"Connecting to MCP-Odoo server at {mcp_server_url}...")
try:
# Create the MCP client and connect
async with MultiServerMCPClient(mcp_servers) as client:
# Get tools from the MCP server
tools = client.get_tools()
print(f"Available MCP tools: {[t.name for t in tools]}")
# Select model based on available API keys
if os.environ.get("ANTHROPIC_API_KEY"):
model_name = "anthropic:claude-3-sonnet-20240229"
print(f"Using Anthropic model: {model_name}")
else:
model_name = "openai:gpt-4"
print(f"Using OpenAI model: {model_name}")
# Create the agent
agent = create_react_agent(
model_name,
tools
)
# Display main menu
while True:
print("\n=== MCP-Odoo Agent Demo ===")
print("1. Get Odoo version")
print("2. List recent vendor bills")
print("3. Analyze invoice reconciliation")
print("4. Custom query")
print("0. Exit")
choice = input("\nSelect an option (0-4): ")
if choice == "0":
print("Exiting...")
break
# Process the choice
query = ""
if choice == "1":
query = "What version of Odoo are we connected to?"
elif choice == "2":
query = "List the 3 most recent vendor bills. Include their payment status."
elif choice == "3":
today = datetime.now()
last_month = (today.replace(day=1) - datetime.timedelta(days=1)).replace(day=1)
date_from = last_month.strftime("%Y-%m-%d")
query = f"Analyze payment reconciliation status from {date_from} to today. What invoices are unpaid?"
elif choice == "4":
query = input("Enter your custom query: ")
else:
print("Invalid choice, please try again.")
continue
# Run the query through the agent
if query:
print(f"\nSending query: '{query}'")
print("Waiting for response (this may take a moment)...\n")
try:
# Invoke the agent with the query
response = await agent.ainvoke(
{"messages": [HumanMessage(content=query)]}
)
# Process and display the response
if isinstance(response, dict) and "messages" in response:
for message in response["messages"]:
if hasattr(message, 'content'):
print(f"Response: {message.content}")
else:
print(f"Response: {message}")
else:
print(f"Response: {response}")
except Exception as e:
print(f"Error during agent execution: {str(e)}")
# Wait for user to continue
input("\nPress Enter to continue...")
except Exception as e:
print(f"Error connecting to MCP-Odoo server: {str(e)}")
if __name__ == "__main__":
try:
asyncio.run(run_odoo_agent())
except KeyboardInterrupt:
print("\nProcess interrupted by user. Exiting...")
except Exception as e:
print(f"\nUnexpected error: {str(e)}")