self-improving-for-codex

You can automate Codex memory self-improvement by coordinating AGENTS.md with a persistent memories directory. This approach uses deterministic nightly maintenance scripts to sync bridges and refine memory files.

The memory lifecycle uses structured files like PROFILE.md, ACTIVE.md, and LEARNINGS.md. It applies audit logging and promotion rules to elevate stable learnings across cross-task scenarios.

You run the nightly memory pipeline using scripts like nightly_refine.py and generate_local_skill_index.py. These scripts perform bridge syncing, memory refinement, and weekly indexing automatically.

Yes, the memory loop promotes stable learnings into ACTIVE.md or PROFILE.md. It leverages memory_sync.py to maintain memory hygiene and ensure durable improvements across different tasks.

No, it operates without external dependencies. The self-improving memory loop relies entirely on internal scripts like run_night_memory_pipeline.py to perform deterministic maintenance and bridge syncing.

Codex loses context across sessions without a durable memory loop. A persistent memories directory with structured files and audit logging minimizes reliance on indirect primitives and ensures memory hygiene.