Faster Long-Conversation Context Compression
dee_hw · x · 2026-07-15
This forwarded discussion highlights the real-world engineering performance of long-conversation context compression.
- In long sessions, Relace's auto-compression takes just 5 seconds, whereas native GPT 5.6 Sol's compaction needs over 1 minute; the author notes they've sometimes waited up to 7 minutes.
- Their approach is to auto-compact during long conversations, keeping the agent's context shorter and reducing wait times.
- The cited text explains the technical motivation: compressing agent context after a cache miss can slash token costs by 50%+.
- By constraining the context to 128k–256k tokens, expenses shift from quadratic growth per turn to something closer to linear.
- The tradeoff is that standard self-summaries are too slow, so they trained a faster compact model claiming speeds of 50k tok/s, which actually costs less than a cache read.
Related event: Relace Compact Model Compresses Agent Context in 5 Seconds to Cut Costs(3 posts)→
More from coding & agent
- Design Is Becoming Game Dev: Build Your Own Tools With AI as the Executor — hewarsaber · 2026-10-27
- Dev turns DJI Mavic into local AI security drone built with Opus 5.5 — alexcovo_eth · 2026-10-27
- Microsoft Research: ForkPilot cuts long-horizon agent compute by up to 59% — EliasEskin · 2026-10-27
- Cursor launches Agent SDK: agents as code with Markdown prompts and TypeScript tools — soleio · 2026-10-27
- Vibecheck, the 'usable formal methods' hackathon, sells out two floors in SF — spikedoanz · 2026-10-27
- How do you verify AI agent outputs beyond unit tests and LLM-as-judge? — Practical_Green5775 · 2026-10-27