Codex Chronicle Screen Memory

Codex Chronicle Screen Memory

Codex Chronicle screen memory is the research-preview Codex memory layer that captures recent screen context, using screenshots/OCR-style “screen intelligence” to help Codex answer questions about what the user has been doing [src-058].

Key points

  • Roberts describes Codex memory as three layers: AGENTS.md or global instructions, learned conversation memory, and Chronicle screen history [src-058].
  • Chronicle captures the user’s recent screen state and can use that context to infer what the user is working on [src-058].
  • The source says Chronicle has a six-hour auto-delete window and must be explicitly enabled, with privacy and prompt-injection warnings [src-058].
  • The practical use case is asking Codex to reconstruct recent work, critique a screen or website the user viewed, or suggest how Codex can help with the current task [src-058].
  • This is a richer but more sensitive memory surface than ordinary chat memory, so it belongs in the same risk family as computer use, browser use, and high-permission automations [src-058].

Related entities

Related concepts

Source references

  • [src-058] Jack Roberts — “How to use Codex Better than 99% of People” (2026-05-06)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

Recommended next

Keep reading from this thread

From 494 indexed pages and articles.

  1. Wiki concept Jack Roberts A YouTube creator and startup builder whose Codex and Claude Code walkthroughs frame AI tools as practical work systems: Codex as Related by 058
  2. Wiki concept Multi-Brain Model Strategy The workflow of deliberately combining several AI tools or models inside one project, assigning each to the kind of Related by 058
  3. Insight Agentic Web Readiness A practical checklist for making websites understandable, navigable, and useful for AI agents and answer engines Readers have engaged with this next