LangGraph: Build Stateful AI Agents in Python

This folder contains the source code for LangGraph: Build Stateful AI Agents in Python

Setup

Create a new virtual environment, and run the following command to install LangGraph and the additional requirements for this project:

(venv) $ python -m pip install -r requirements.txt

You'll use langchain-openai to interact with OpenAI LLMs, but keep in mind you can use any LLM provider you like with LangGraph and LangChain. You'll use pydantic to validate the information your agent parses from emails.

Before moving forward, if you choose to use OpenAI, make sure you're signed up for an OpenAI account and you have a valid API key. You'll need to set the following environment variable before running any examples in this tutorial:

OPENAI_API_KEY=<YOUR-OPENAI-API-KEY>

Usage

Once your environment is set up, you can run the final graph agent on an example input with the following code:

from graphs.email_agent import email_agent_graph
from example_emails import EMAILS

escalation_criteria = """"There's an immediate risk of electrical,
water, or fire damage"""

message_with_criteria = f"""
The escalation criteria is: {escalation_criteria}

Here's the email:
{EMAILS[3]}
"""
message_3 = {"messages": [("human", message_with_criteria)]}
 
for chunk in email_agent_graph.stream(message_3, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

See the tutorial for all the details on what's going on here.