Conversation History Management — Smart Multi-Turn Context

I will build a conversation history management system for your multi-turn AI application — implementing rolling summarisation of older turns, importance-weighted retention of specific turns, and a context assembly strategy that maintains conversational coherence within your token budget across arbitrarily long conversations. Multi-turn AI applications without conversation history management work well for short conversations and fail on long ones — either truncating history and losing conversational coherence, or consuming an ever-growing context window until cost and latency become unacceptable; a managed history system maintains coherence within a fixed budget across conversations of any length.

The system covers a rolling summarisation strategy for older turns, importance scoring for specific turn retention (questions, key facts, user preferences), context assembly with configurable token budget, coherence testing across long simulated conversations, and cost monitoring.