ARTICLE AD BOX
I am working on an AI agent that interacts with users and performs multi‑step tasks. I’m using the OpenAI API (GPT‑4.1) to generate responses and guide the agent’s behavior.
My goal is to maintain conversation context and agent state across multiple turns so the agent can remember user preferences and previous decisions. However, I’m unsure how to store and manage this context effectively without sending the entire history to the API every time (which becomes expensive and slow).
What I’ve Tried
Appending conversation history to every request (works but grows too large)
Using a simple list of “important messages” and trimming older ones
Storing state in a local database and re‑sending only selected parts
Problem
When I trim the history too aggressively, the agent loses context (e.g., earlier user preferences). When I send full history, API latency and cost become problematic.
What I’m Looking For
Best practices for state and context management in AI agent development
Ways to summarize or compress context without losing important information
Examples of architectural patterns (e.g., memory modules, embeddings, vector stores) that work well with the OpenAI API
Expected Behavior
I expect the agent to:
Maintain context over long interactions
Avoid redundant or irrelevant history in API requests
Be efficient in both performance and cost
Thanks in advance for any suggestions or examples!
