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import asyncio
import os
from importlib.metadata import version
from dotenv import load_dotenv
from langchain_core.tools import tool
from langchain_openai import AzureChatOpenAI
from langgraph.checkpoint.redis.aio import AsyncRedisSaver
from redis.asyncio import Redis
from azure.ai.agentserver.langgraph import from_langgraph
load_dotenv()
client = Redis(
host=os.getenv("CHECKPOINTER_REDIS_URL"),
port=os.getenv("CHECKPOINTER_REDIS_PORT"),
password=os.getenv("CHECKPOINTER_REDIS_KEY"),
ssl=True,
decode_responses=False, # RedisSaver expects bytes
)
deployment_name = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", "gpt-4o")
model = AzureChatOpenAI(model=deployment_name)
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
return len(word)
@tool
def calculator(expression: str) -> str:
"""Evaluates mathematical expression"""
try:
maths_result = eval(expression)
return str(maths_result)
except Exception as e:
return f"Error: {str(e)}"
tools = [get_word_length, calculator]
def create_agent(model, tools, checkpointer):
# for different langgraph versions
langgraph_version = version("langgraph")
if langgraph_version < "1.0.0":
from langgraph.prebuilt import create_react_agent
return create_react_agent(model, tools, checkpointer=checkpointer)
else:
from langchain.agents import create_agent
return create_agent(model, tools, checkpointer=checkpointer)
async def run_async():
# Pass the configured client to RedisSaver
# adapter uses astream/ainvoke so we need async checkpointer
saver = AsyncRedisSaver(redis_client=client)
await saver.asetup()
executor = create_agent(model, tools, checkpointer=saver)
# start server with async
await from_langgraph(executor).run_async()
if __name__ == "__main__":
# host the langgraph agent
asyncio.run(run_async())
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