I’m working on a Python chatbot that interviews people and analyzes their behavior. I want to make the conversations better by adding LangGraph but I’m not sure how to do it.
Here’s what my current code looks like:
import os
import random
import openai
from personality_types import PERSONALITY_DICT
# Save chat history to file
def store_chat_log(chat_data):
file_id = ''.join(random.choices('abcdefghijklmnopqrstuvwxyz0123456789', k=10)) + '.txt'
with open(file_id, 'w') as f:
for chat in chat_data:
if len(chat) == 3:
f.write(f"Context: {chat[0]}\nQuery: {chat[1]}\nAnswer: {chat[2]}\n\n")
else:
f.write(f"Query: {chat[0]}\nAnswer: {chat[1]}\n\n")
# Get candidate's name
def get_user_name():
client = openai.OpenAI()
prompt = "Create a polite question to ask someone their name for a job interview"
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}]
)
question = response.choices[0].message.content
user_name = input(f"\nBot: {question}\nYou: ").strip()
print(f"\nBot: Nice to meet you {user_name}. Ready to start?\n")
return user_name
# Get answer from user
def collect_response(question_text):
while True:
answer = input(f"\nBot: {question_text}\nYou: ").strip()
if answer:
return answer
print("Bot: Could you please answer that?")
# Start the interview
candidate = get_user_name()
Right now my bot works but the conversations feel basic. I tried using Crew AI before but couldn’t get it working right. LangGraph seems like it might be better for making the chat flow more natural.
What I’ve done so far:
- Built a working interview bot with basic AI responses
- Tried Crew AI but ran into setup issues
- Started looking at LangGraph but not sure where to begin
- Got the file saving and user input parts working fine
What I need help with:
- How do I actually add LangGraph to what I already have?
- Are there easier alternatives that work better for interview bots?
- Best way to make the AI ask follow-up questions based on answers?
I’m pretty new to these AI frameworks so any simple examples or step-by-step advice would be really helpful. Thanks!
Been there with the same dilemma. Here’s what I learned after implementing LangGraph in three different interview systems over the past year.
The thing about LangGraph is it shines when you need conditional branching based on response quality. Your current setup is already doing the heavy lifting with OpenAI calls and file handling.
What I’d recommend is keeping your existing get_user_name() and collect_response() functions but wrapping them in LangGraph nodes. Create a simple graph with nodes like “question_asker”, “response_evaluator”, and “followup_generator”.
The real power comes from the edges. You can set conditions like “if response length < 50 characters, route to followup_generator” or “if technical keywords detected, route to deep_dive_questions”.
Start with this basic structure:
- Input node (your current question asking)
- Evaluation node (analyzes response completeness)
- Decision node (routes to follow-up or next question)
- Output node (stores to your existing file system)
This video walks through building a similar conversational system that might help you see the pattern:
Don’t try to rebuild everything from scratch. Your file saving and input collection logic is solid. Just add the graph layer on top for better conversation flow control.
LangGraph definitely sounds like overkill for what you’re building right now. I went down the same rabbit hole trying to add complex frameworks to a simple chatbot and ended up overengineering everything. Your current code structure is actually pretty solid for an interview bot. Instead of jumping straight to LangGraph, try enhancing what you already have first. You can add follow-up logic by storing the previous answer and passing it to your next OpenAI call along with instructions like ‘if the answer seems incomplete, ask for clarification.’ This approach keeps your existing file saving and input handling intact while improving conversation quality. I found that maintaining conversation context in a simple list and referencing it in prompts works surprisingly well for interview scenarios. The main issue with frameworks like LangGraph is they add complexity without necessarily solving your core problem of making conversations feel natural. Focus on better prompt engineering and conversation memory before adding another dependency. If you still want more sophisticated flow control later, consider looking into simple state machines or even basic if-else logic based on keyword detection in responses.
honestly langraph might be too much but if you really wanna try it, just wrap your existing functions in langraph nodes and connect them with edges. i did something similar - made one node for asking questions, another for analyzing answers, and a third for deciding followups. the tricky part is getting the state management right between nodes but once you do it flows pretty naturally. maybe start small with just 2-3 nodes first?
I actually integrated LangGraph into a similar interview system last month and it made a huge difference. The key insight is that LangGraph works best when you think of your interview as a state machine with different nodes for different interview phases. What worked for me was creating separate nodes for greeting, technical questions, behavioral questions, and follow-up generation. Each node can decide what the next step should be based on the candidate’s response quality or completeness. For example, if someone gives a short answer to a behavioral question, the graph automatically routes to a follow-up node that asks for more details. The biggest advantage over your current approach is that LangGraph handles the conversation flow logic for you. Instead of hardcoding when to ask follow-ups, you define conditions and let the graph decide. I found it much more reliable than trying to manage conversation state manually. Start by converting your existing functions into LangGraph nodes and define the edges between them based on conversation logic. The learning curve is steeper than basic OpenAI calls, but the results are worth it for interview scenarios where conversation flow really matters.
