mirror of
https://github.com/langchain-ai/deepagents.git
synced 2026-07-16 09:44:27 -04:00
Mason Daugherty
111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
import argparse
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import os
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import sys
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend
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from dotenv import load_dotenv
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from langchain_anthropic import ChatAnthropic
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from langchain_community.agent_toolkits import SQLDatabaseToolkit
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from langchain_community.utilities import SQLDatabase
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from rich.console import Console
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from rich.panel import Panel
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# Load environment variables
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load_dotenv()
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console = Console()
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def create_sql_deep_agent():
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"""Create and return a text-to-SQL Deep Agent"""
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# Get base directory
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base_dir = os.path.dirname(os.path.abspath(__file__))
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# Connect to Chinook database
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db_path = os.path.join(base_dir, "chinook.db")
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db = SQLDatabase.from_uri(f"sqlite:///{db_path}", sample_rows_in_table_info=3)
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# Initialize Claude Sonnet 4.5 for toolkit initialization
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model = ChatAnthropic(model="claude-sonnet-4-5-20250929", temperature=0)
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# Create SQL toolkit and get tools
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toolkit = SQLDatabaseToolkit(db=db, llm=model)
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sql_tools = toolkit.get_tools()
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# Create the Deep Agent with all parameters
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agent = create_deep_agent(
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model=model, # Claude Sonnet 4.5 with temperature=0
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memory=["./AGENTS.md"], # Agent identity and general instructions
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skills=[
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"./skills/"
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], # Specialized workflows (query-writing, schema-exploration)
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tools=sql_tools, # SQL database tools
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subagents=[], # No subagents needed
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backend=FilesystemBackend(root_dir=base_dir), # Persistent file storage
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)
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return agent
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def main():
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"""Main entry point for the SQL Deep Agent CLI"""
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parser = argparse.ArgumentParser(
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description="Text-to-SQL Deep Agent powered by LangChain Deep Agents and Claude Sonnet 4.5",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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python agent.py "What are the top 5 best-selling artists?"
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python agent.py "Which employee generated the most revenue by country?"
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python agent.py "How many customers are from Canada?"
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""",
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)
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parser.add_argument(
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"question",
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type=str,
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help="Natural language question to answer using the Chinook database",
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)
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args = parser.parse_args()
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# Display the question
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console.print(
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Panel(f"[bold cyan]Question:[/bold cyan] {args.question}", border_style="cyan")
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)
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console.print()
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# Create the agent
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console.print("[dim]Creating SQL Deep Agent...[/dim]")
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agent = create_sql_deep_agent()
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# Invoke the agent
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console.print("[dim]Processing query...[/dim]\n")
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try:
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result = agent.invoke(
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{"messages": [{"role": "user", "content": args.question}]}
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)
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# Extract and display the final answer
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final_message = result["messages"][-1]
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answer = (
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final_message.content
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if hasattr(final_message, "content")
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else str(final_message)
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)
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console.print(
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Panel(f"[bold green]Answer:[/bold green]\n\n{answer}", border_style="green")
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)
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except Exception as e:
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console.print(
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Panel(f"[bold red]Error:[/bold red]\n\n{str(e)}", border_style="red")
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)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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