import os import sys import argparse from dotenv import load_dotenv from langchain_community.utilities import SQLDatabase from langchain_community.agent_toolkits import SQLDatabaseToolkit from deepagents import create_deep_agent from deepagents.backends import FilesystemBackend from langchain_anthropic import ChatAnthropic from rich.console import Console from rich.panel import Panel

Load environment variables

load_dotenv()

console = Console()

def create_sql_deep_agent(): """Create and return a text-to-SQL Deep Agent"""

# Get base directory base_dir = os.path.dirname(os.path.abspath(__file__))

# Connect to Chinook database db_path = os.path.join(base_dir, "chinook.db") db = SQLDatabase.from_uri( f"sqlite:///{db_path}", sample_rows_in_table_info=3 )

# Initialize Claude Sonnet 4.5 for toolkit initialization model = ChatAnthropic( model="claude-sonnet-4-5-20250929", temperature=0 )

# Create SQL toolkit and get tools toolkit = SQLDatabaseToolkit(db=db, llm=model) sql_tools = toolkit.get_tools()

# Create the Deep Agent with all parameters agent = create_deep_agent( model=model, # Claude Sonnet 4.5 with temperature=0 memory="./AGENTS.md"], # Agent identity and general instructions skills="./skills/"], # Specialized workflows (query-writing, schema-exploration) tools=sql_tools, # SQL database tools subagents=], # No subagents needed backend=FilesystemBackend(root_dir=base_dir) # Persistent file storage )

return agent

def main(): """Main entry point for the SQL Deep Agent CLI""" parser = argparse.ArgumentParser( description="Text-to-SQL Deep Agent powered by LangChain DeepAgents and Claude Sonnet 4.5", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: python agent.py "What are the top 5 best-selling artists?" python agent.py "Which employee generated the most revenue by country?" python agent.py "How many customers are from Canada?" """ ) parser.add_argument( "question", type=str, help="Natural language question to answer using the Chinook database" )

args = parser.parse_args()

# Display the question console.print(Panel( f"bold cyan]Question:/bold cyan] {args.question}", border_style="cyan" )) console.print()

# Create the agent console.print("dim]Creating SQL Deep Agent.../dim]") agent = create_sql_deep_agent()

# Invoke the agent console.print("dim]Processing query.../dim]\n")

try: result = agent.invoke({ "messages": {"role": "user", "content": args.question}] })

# Extract and display the final answer final_message = result"messages"]-1] answer = final_message.content if hasattr(final_message, 'content') else str(final_message)

console.print(Panel( f"bold green]Answer:/bold green]\n\n{answer}", border_style="green" ))

except Exception as e: console.print(Panel( f"bold red]Error:/bold red]\n\n{str(e)}", border_style="red" )) sys.exit(1)

if __name__ == "__main__": main()