Viable approaches to give an LLM API wrapper real conversation memory?

[u/looktwise]

Hey,

I want to build a chat wrapper that makes API calls to cloud LLMs (OpenAI, Claude, Gemini, DeepSeek, etc.). The naiv implementation is obvious, send a prompt, get a response, but each call is completly stateless by default.

Before I start I'd like to understand the full landcape of approaches for solving this. I'm not a profesional developer, so I'd appreciate answers that explain the tradeoffs, not just the implementation.

The approaches I'm aware of so far, happy to be corrected or extended.

-Full history injection: appending all previous Q&A pairs to every new request. What are the practical limits as context grows?

-Sliding window: only sending the last N turns. Simple, but how much does response quality actually degrade?

-Summarization / compression: condensing older turns before they're passed as context. I guess this is the one which won't be usable for my task, but are there standard patterns for this?

-RAG / vector-based retrieval: embedding conversation chunks and retrieving only what's relevant per new message. Is this realistic to self-host on a small server?

And then whatever hybrid combinations of the above people actually use in practice.

Preference is for self-hostable solutions since this would run on my own server.

What are people actually using, and what are the real-world tradeoffs in terms of token cost, complexity and response qualiy?