I’m working on a Python project that uses AI to chat with job candidates and analyze their behavior. Right now I have basic functions working but I want to add LangGraph to make the conversations smarter and more dynamic.
Here’s what my current code looks like:
import os
import secrets
import openai
from personality_types import PERSONALITY_TRAITS
# Store chat history in a file
def store_chat_history(chat_data):
file_id = secrets.token_hex(6) + '.txt'
with open(file_id, 'w') as f:
for message in chat_data:
if len(message) == 3: # Context, Query, Answer
f.write(f"Context: {message[0]}\nQuery: {message[1]}\nAnswer: {message[2]}\n\n")
else: # Query, Answer
f.write(f"Query: {message[0]}\nAnswer: {message[1]}\n\n")
# Get candidate info at start
def get_candidate_info():
client = openai.OpenAI()
intro_prompt = "Create a friendly professional greeting to ask for the candidate's full name. Keep it simple and welcoming."
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": intro_prompt}]
)
greeting = response.choices[0].message.content
user_name = input(f"\nAI: {greeting}\n\nYou: ").strip()
print(f"\nAI: Nice to meet you, {user_name}. Ready to start the interview?\n")
return user_name
# Handle user input with validation
def get_user_response(question):
while True:
answer = input(f"\nAI: {question}\n\nYou: ").strip()
if answer:
return answer
else:
print("AI: I didn't catch that. Could you try again?")
# Start the interview process
candidate_name = get_candidate_info()
I’ve tried using Crew AI before but it was too complicated for what I need. I heard LangGraph might be better for creating conversation flows that can branch based on the candidate’s answers. Has anyone successfully added LangGraph to a similar project? I’m looking for practical examples of how to set up the graph nodes and edges for interview scenarios.
Also wondering if there are simpler alternatives to LangGraph that might work better for a beginner. Any code examples or tutorials would be super helpful.
Looking at your existing code structure, you’re already handling the conversation flow manually which is exactly what LangGraph can streamline for you. I integrated LangGraph into a candidate screening system about six months ago and the biggest benefit was removing all the complex conditional logic I had scattered throughout my code. Instead of hardcoding question sequences, I created a graph where each node handled specific interview components like skills assessment, culture fit evaluation, and behavioral analysis. The routing between nodes happened automatically based on candidate responses and scores from previous interactions. Your current approach with storing chat history and getting user responses translates perfectly into LangGraph’s state management system. One thing that really helped me was starting with a simple three-node setup: introduction, main interview, and wrap-up. Each node can access the shared state containing candidate information and previous responses. The conditional edges let you branch to different question sets based on role requirements or detected personality traits from your existing PERSONALITY_TRAITS module. I found the documentation examples for chatbots to be the most relevant starting point. The transition from your current linear flow to a graph-based approach took me about a week to implement properly.
honestly langgraph might be overkill for what your doing right now. your existing code looks pretty solid already and you could just add some simple branching logic with dictionaries to map responses to next questions. ive seen people overcomplicate interview bots with fancy frameworks when basic state tracking would work fine. maybe try adding a simple conversation_state variable first before jumping into graphs?
I actually implemented LangGraph in a similar HR screening tool last year and found it quite straightforward once you understand the basic concepts. The key is thinking of your interview as a state machine where each node represents a different phase of questioning. For your use case, you’d create nodes for different interview stages like technical questions, behavioral assessment, and follow-up probes. The edges would route based on candidate responses or personality trait detection. What made it powerful for me was the ability to dynamically adjust question difficulty based on previous answers. The main advantage over your current linear approach is that LangGraph handles the conversation state automatically and lets you define complex routing logic without nested if-statements. You can also easily add memory persistence between sessions which sounds useful for your candidate tracking. I’d recommend starting with their basic tutorial and adapting it to your interview flow. The learning curve isn’t too steep if you already have the OpenAI integration working. Your existing code structure actually translates well to LangGraph nodes.
Had to deal with this exact situation when we rebuilt our technical screening pipeline. Your code is actually in a good spot to migrate to LangGraph because you already have the OpenAI integration and state management pieces.
The main thing I learned is that LangGraph shines when you need complex routing logic. In our case, we had different interview paths for junior vs senior candidates, and the bot needed to adapt questions based on confidence levels in previous answers.
Here’s how I’d approach your migration:
Create a simple graph with nodes for each interview phase. Your get_candidate_info function becomes the entry node. Then add nodes for technical screening, behavioral questions, and wrap up. The edges handle routing based on candidate performance scores.
The real power comes from the state persistence between nodes. You can pass candidate info, chat history, and personality analysis results through the entire flow without manually managing it like you’re doing now.
One gotcha I ran into was overengineering the graph structure initially. Start simple with maybe 4-5 nodes max and expand from there.
This tutorial covers the basics pretty well and shows practical examples:
Your existing personality analysis module will integrate nicely with LangGraph’s conditional routing. Way cleaner than the nested conditionals we had before.
Been working with LangGraph for interview automation since early this year and I think the transition makes sense for your project, especially given how you’re already handling conversation state. What worked well for me was treating each interview topic as a separate node rather than trying to model the entire conversation flow upfront. Your existing store_chat_history function actually maps perfectly to LangGraph’s state updates between nodes. The real advantage I found was in handling unexpected candidate responses - instead of trying to anticipate every possible answer with if-else logic, you can create fallback nodes that gracefully handle off-topic responses and redirect back to the main interview flow. Your personality analysis integration will be much cleaner too since you can evaluate traits at each node and use that data to influence routing decisions. I’d suggest keeping your current OpenAI setup and just wrapping the conversation logic in LangGraph nodes. The migration took me about three days for a similar system and the code became significantly more maintainable afterward.
