How to integrate LangGraph framework with existing AI chatbot system for interview automation

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1

I’m working on a Python project that uses AI to conduct interviews with job candidates. My current setup works but I want to make it better by adding LangGraph for more advanced conversation handling.

Here’s what my code looks like right now:

import os
import secrets
import openai
from personality_traits import BEHAVIORAL_TRAITS

# Save chat history to file
def store_chat_log(chat_data):
    file_id = secrets.token_hex(6) + '.txt'
    with open(file_id, 'w') as log_file:
        for message in chat_data:
            if len(message) == 3:  # Context, Query, Answer
                log_file.write(f"Context: {message[0]}\nQuery: {message[1]}\nAnswer: {message[2]}\n\n")
            else:  # Query, Answer
                log_file.write(f"Query: {message[0]}\nAnswer: {message[1]}\n\n")

# Get candidate information
def get_candidate_info():
    client = openai.OpenAI()
    greeting_prompt = "Create a professional greeting to ask for the candidate's full name. Keep it brief and welcoming."
    greeting_response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": greeting_prompt}]
    )
    greeting_text = greeting_response.choices[0].message.content
    user_name = input(f"\nAssistant: {greeting_text} ").strip()
    print(f"\nAssistant: Nice to meet you, {user_name}. We'll now begin the interview process.\n")
    return user_name

# Collect user input
def collect_response(question_text):
    while True:
        user_input = input(f"\nAssistant: {question_text}\n\nYou: ").strip()
        if user_input:
            return user_input
        else:
            print("Assistant: I didn't catch that. Could you please respond?")

# Start the interview process
candidate_name = get_candidate_info()

The system works fine for basic interviews but I want to add LangGraph to make the conversations flow better and handle complex decision trees. I’ve tried using CrewAI before but found it hard to set up properly.

I need help with:

  • Adding LangGraph to my existing code without breaking what already works
  • Making the AI agent smarter about choosing follow-up questions
  • Better ways to organize the conversation flow

Has anyone successfully integrated LangGraph with interview automation systems? What approach worked best for you?

2

I just dealt with this exact problem! The trick is using LangGraph as a conversation orchestrator, not replacing your interview logic entirely. I built a simple state machine where each node handles a different interview phase. My OpenAI integration stayed the same - I just wrapped it inside LangGraph nodes. Now the framework decides what question comes next while my existing code does the actual work. The conditional edges are where it gets interesting. They route conversations based on how candidates respond. Weak technical answer? Graph jumps to deeper technical questions. Strong performance? Moves to leadership scenarios. Each node gets the full conversation history and candidate profile, then generates targeted questions from that context. You can test this piece by piece - start with two or three connected nodes for basic routing, then build from there. Your logging and UI code doesn’t need to change at all.

3

I’ve been using LangGraph in production for interview systems, so here’s what I’ve learned. Don’t rebuild everything from scratch - start with gradual integration instead. Keep your existing logging and user interaction functions, but create a separate LangGraph workflow for conversation logic. Build a state graph that tracks interview progress, candidate responses, and picks the next questions based on context. What really helped me was defining clear states for different phases - intro, technical questions, behavioral assessment, and wrap-up. Each state handles its own logic for generating follow-ups based on previous responses. The biggest win? LangGraph maintains conversation context across multiple turns. Instead of treating each question independently, you can build on previous answers for more targeted follow-ups. Made our interviews feel way more natural and adaptive. Start by wrapping your existing OpenAI calls in LangGraph nodes, then gradually add more sophisticated routing logic. You won’t break what’s working while adding the advanced features you want.