Why Codex workflows need a memory layer
In Codex-heavy workflows, tasks are often completed in discrete sessions. Without explicit project memory, each new session repeats discovery work and may miss prior assumptions.
What to record after each Codex task
Decision
Record the tradeoff if behavior, architecture, or design changed.
Work log
Capture what was finished and the files that changed.
Task
Add unresolved follow-ups instead of leaving them in chat only.
Summary
Refresh global project context for the next kickoff.
Codex-friendly end-of-task checklist
- Run
sidecar context --format markdownbefore starting. - Implement and verify the code change.
- Run
sidecar worklog recordwith outcomes and touched files. - Add
sidecar decision recordwhen significant choices were made. - Add
sidecar task addfor follow-up items. - Run
sidecar summary refresh.
Example Codex handoff
sidecar worklog record --done "Added retry middleware and circuit-breaker fallback" --files src/http/retry.ts,src/http/client.ts --by agent
sidecar decision record --title "Prefer middleware retries over call-site retries" --summary "Central policy gives consistent behavior and less duplicated logic" --by agent
sidecar task add "Add retry metrics panel in local UI" --priority medium --by agent
sidecar summary refresh Where this fits in the stack
Sidecar is a companion to Codex, AGENTS.md, and Git. It does not replace source control or prompt discipline. It gives Codex sessions a stable, repo-local place for context that should outlive one run. See command reference for full options.
FAQ
Does Sidecar make Codex remember everything automatically?
No. Sidecar provides structured context that Codex can read and update. Memory quality depends on using the record commands consistently.
Should I record a decision after every small code edit?
Not necessarily. Record decisions when behavior, architecture, or design tradeoffs changed in a way future contributors should understand.
Can humans use the same records created by Codex?
Yes. Sidecar records are intended for both humans and agents in the same repository workflow.