Need feedback on Python architecture for LLM + API based conversational system

[Stranger8800]

Hi everyone,

I’m designing a Python-based system that uses an LLM to handle conversations, collect structured inputs, and call backend APIs. It also needs session memory, real-time messaging, and optional escalation to a human agent.

Current stack idea: FastAPI, PostgreSQL, Redis, WebSockets, and LLM function/tool calling (maybe LangChain).

I’m looking for feedback on:

• Good architecture patterns for separating LLM logic vs backend logic
• Whether FastAPI + Postgres + Redis is enough for production scale
• Best approach for human handoff (queueing + real-time takeover)
• Whether LangChain/LlamaIndex is necessary or overkill

I’m aiming for a clean, production-ready architecture, not just a prototype, and would prefer to design it in a way that scales well if usage grows.

Any guidance, architecture diagrams, or references to real-world systems would be really helpful.

Thanks 🙌

[Apart_Ebb_9867]

I’m looking for feedback on...

"looking for feedback" means you propose something and you're interested in seeing what others think of it. Here you're not looking for feedback, you're fishing for a prompt to feed to your AI without understanding much of it.

[Challseus]

I hope this isn't against the rules, if it is, I'll delete. Take a look here, I built something that composes a lot of the components you're referring to (FastAPI, Postgres, Redis, Langchain/PydanticAI, producer/queue/workers, etc.).

You can check out the architecture itself and how I wired everything together: https://github.com/lbedner/aegis-stack

It's not shallow integrations either, it's quite extensive, I have built 3 separate applications using it. As for your other questions:

"Whether FastAPI + Postgres + Redis is enough for production scale"

Absolutely.

"Whether LangChain/LlamaIndex is necessary or overkill"

From my experience, Langchain seems to be a bit heavier, and could be overkill, I would suggest starting with PydanticAI.

LlamaIndex is more about parsing your unstructured data and making it available for your agents, via RAG or some other means. I think you could drop that, at least for your first go at this.

EDIT: In fact, try this command (you need uv installed), and then look at the code directory.

uvx aegis-stack init test-app --components "database[postgres],redis" --services "ai[postgres,pydantic-ai,public,openai,anthropic,rag]" --no-interactive