agent

description	Imported skill agent from langchain
name	agent
signature	ac06846d24176b9dcb6f00521336d9bee0eaf54ebbdf473d877e368180c5ec58
source	/a0/tmp/skills_research/langchain/examples/text-to-sql-agent/agent.py

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