Building GPT-6 Astra's Memory Pattern From Scratch

​OpenAI just shipped one of the most interesting memory features in any frontier product: with Astra, Codex keeps notes across context windows instead of repeatedly compacting, and earlier windows stay searchable even when a detail never made it into the notes.

​Nobody outside OpenAI knows how they built it.

​But the pattern they describe is implementable, and in this session we build an open-source version of it, live, using MemoRizz.

​What we'll cover:

• ​Why compaction loses information, and how the Astra pattern fixes it: decoded into an architecture you can build

• ​Live build with MemoRizz: a note store with a write policy, plus searchable archives of previous context windows

• ​Retrieval logic: when the agent answers from notes and when it searches the archive

• ​Why this is hard at scale, and what it means that OpenAI ships it behind an experimental flag

​You leave with the full implementation in a GitHub repo, shared in the session.

​For engineers, Forward Deployed Engineers, AI architects and AI practitioners building agents that need to remember. Recorded; registrants get the replay link.

​Hosted by Richmond Alake, who coined the discipline of Memory Engineering and is on a mission to create 100 AI Memory Engineers.

​Richmond is the creator of the open-source MemoRizz library, Director of AI Developer Experience at Oracle, formerly AI/ML Developer Advocate at MongoDB, and the creator of two DeepLearning.AI courses on AI agents with Andrew Ng's team, and author of 100 Days of Agent Memory.