# How open-strix handles long-lived agent memory by rebuilding context

DevFeed: [How open-strix handles long-lived agent memory by rebuilding context](<https://devfeed.tech/articles/how-to-forget-33488.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2026/04/14/forgetting>)

Published: 2026-04-14T00:00:00Z

Content type: opinion

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [OpenClaw](<https://devfeed.tech/topics/openclaw.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [context](<https://devfeed.tech/topics/context.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [ChatGPT](<https://devfeed.tech/topics/chatgpt.md>), [Claude Code](<https://devfeed.tech/topics/claude-code.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [caching](<https://devfeed.tech/tags/caching.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [claude-code](<https://devfeed.tech/tags/claude-code.md>), [context](<https://devfeed.tech/tags/context.md>), [openclaw](<https://devfeed.tech/tags/openclaw.md>)

## AI overview

The article contrasts open-strix with open-claw and other agent systems. It argues that open-strix prioritizes remembering by rebuilding context with a sliding window, avoiding abrupt context compaction and relying less on sequential prompt caching.

## Source excerpt

Most agent frameworks optimize for recall. Open-strix optimizes for forgetting -- and that turns out to be the whole trick.