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codex/codex-rs/memories
T
efrazer-oai c8e5db16c9 feat: show enterprise monthly credit limits in status (#24812)
## Summary

Enterprise users can have an effective monthly credit limit, but Codex
`/status` currently drops that metadata from the account-usage response.

This change adds the optional `spend_control.individual_limit`
projection to the existing rate-limit snapshot flow. The backend client
reads the monthly limit, app-server exposes it as `individualLimit`, and
the TUI renders a `Monthly credit limit` row through the existing
progress-bar renderer.

When the backend does not return an effective monthly limit, existing
rate-limit behavior is unchanged.

## Existing backend state

The account-usage backend already returns the effective monthly limit
and current usage together:

```json
{
  "spend_control": {
    "reached": false,
    "individual_limit": {
      "limit": "25000",
      "used": "8000",
      "remaining": "17000",
      "used_percent": 32,
      "remaining_percent": 68,
      "reset_after_seconds": 86400,
      "reset_at": 1778137680
    }
  }
}
```

Before this change, Codex projected rolling `primary` and `secondary`
windows plus `credits`. It ignored `spend_control.individual_limit`, so
app-server clients and `/status` could not render the monthly cap.

The updated flow is:

```text
account usage backend
  -> backend-client reads spend_control.individual_limit
  -> existing rate-limit snapshot carries optional individual_limit
  -> app-server exposes optional individualLimit
  -> TUI renders Monthly credit limit
```

## App-server contract

`account/rateLimits/read` and sparse `account/rateLimits/updated`
notifications now include an additive nullable
`rateLimits.individualLimit` field:

```json
{
  "individualLimit": {
    "limit": "25000",
    "used": "8000",
    "remainingPercent": 68,
    "resetsAt": 1778137680
  }
}
```

In an `account/rateLimits/read` response, `null` means no monthly limit
is available. `account/rateLimits/updated` remains a sparse rolling
notification: clients merge available values into their most recent
`account/rateLimits/read` snapshot or refetch. Nullable account metadata
in a rolling notification does not clear a previously observed value.

## Design decisions

- Extend the existing rate-limit snapshot instead of introducing a
separate request or wire-level update protocol.
- Keep the Codex projection narrow: `/status` needs the effective limit,
current usage, remaining percentage, and reset timestamp.
- Render the monthly row through the existing progress-bar renderer,
with one optional detail line for `8,000 of 25,000 credits used`.
- Keep the backend response optional so existing accounts and older
usage states preserve their current behavior.
- Preserve cached monthly metadata when sparse rolling notifications
omit it. Live account-usage reads remain authoritative and can clear a
removed limit.

## Visual evidence

```text
 Monthly credit limit:   [██████████████░░░░░░] 68% left (resets 07:08 on 7 May)
                         8,000 of 25,000 credits used
```

Snapshot:
`codex-rs/tui/src/status/snapshots/codex_tui__status__tests__status_snapshot_includes_enterprise_monthly_credit_limit.snap`

## Testing

Tests: generated app-server schema verification, protocol tests,
backend-client tests, app-server integration coverage, TUI snapshot
coverage, formatting, and workspace lint cleanup.
c8e5db16c9 · 2026-06-01 21:25:42 -07:00
History
..

Memories

This directory owns reusable memory crates and the memory pipeline documentation.

Runtime orchestration for Phase 1 and Phase 2 still lives in codex-core under codex-rs/core/src/memories/.

Crates

  • codex-rs/memories/read (codex-memories-read) owns the read path: memory developer-instruction injection, memory citation parsing, and read-usage telemetry classification.
  • codex-rs/memories/write (codex-memories-write) owns the write path: Phase 1 and Phase 2 prompt rendering, filesystem artifact helpers, workspace diff helpers, and extension resource pruning.

Prompt Templates

Memory prompt templates live with the crate that uses them:

  • The undated template files are the canonical latest versions used at runtime:
    • read/templates/memories/read_path.md
    • write/templates/memories/stage_one_system.md
    • write/templates/memories/stage_one_input.md
    • write/templates/memories/consolidation.md
  • In codex, edit those undated template files in place.
  • The dated snapshot-copy workflow is used in the separate openai/project/agent_memory/write harness repo, not here.

When it runs

The pipeline is triggered when a root session starts, and only if:

  • the session is not ephemeral
  • the memory feature is enabled
  • the session is not a sub-agent session
  • the state DB is available

It runs asynchronously in the background and executes two phases in order: Phase 1, then Phase 2.

Phase 1: Rollout Extraction (per-thread)

Phase 1 finds recent eligible rollouts and extracts a structured memory from each one.

Eligible rollouts are selected from the state DB using startup claim rules. In practice this means the pipeline only considers rollouts that are:

  • from allowed interactive session sources
  • within the configured age window
  • idle long enough (to avoid summarizing still-active/fresh rollouts)
  • not already owned by another in-flight phase-1 worker
  • within startup scan/claim limits (bounded work per startup)

What it does:

  • claims a bounded set of rollout jobs from the state DB (startup claim)
  • filters rollout content down to memory-relevant response items
  • sends each rollout to a model (in parallel, with a concurrency cap)
  • expects structured output containing:
    • a detailed raw_memory
    • a compact rollout_summary
    • an optional rollout_slug
  • redacts secrets from the generated memory fields
  • stores successful outputs back into the state DB as stage-1 outputs

Concurrency / coordination:

  • Phase 1 runs multiple extraction jobs in parallel (with a fixed concurrency cap) so startup memory generation can process several rollouts at once.
  • Each job is leased/claimed in the state DB before processing, which prevents duplicate work across concurrent workers/startups.
  • Failed jobs are marked with retry backoff, so they are retried later instead of hot-looping.

Job outcomes:

  • succeeded (memory produced)
  • succeeded_no_output (valid run but nothing useful generated)
  • failed (with retry backoff/lease handling in DB)

Phase 1 is the stage that turns individual rollouts into DB-backed memory records.

Phase 2: Global Consolidation

Phase 2 consolidates the latest stage-1 outputs into the filesystem memory artifacts and then runs a dedicated consolidation agent.

What it does:

  • claims a single global phase-2 lock before touching the memories root (so only one consolidation inspects or mutates the workspace at a time)
  • loads a bounded set of stage-1 outputs from the state DB using phase-2 selection rules:
    • ignores memories whose last_usage falls outside the configured max_unused_days window
    • for memories with no last_usage, falls back to generated_at so fresh never-used memories can still be selected
    • ranks eligible memories by usage_count first, then by the most recent last_usage / generated_at
  • computes a completion watermark from the claimed watermark + newest input timestamps
  • syncs local memory artifacts under the memories root:
    • raw_memories.md (merged raw memories, stable ascending thread-id order)
    • rollout_summaries/ (one summary file per selected rollout)
  • keeps the memories root itself as a git-baseline directory, initialized under ~/.codex/memories/.git by codex-git-utils
  • prunes stale rollout summaries that are no longer selected
  • prunes memory extension resource files older than the extension retention window, so cleanup appears in the workspace diff
  • writes phase2_workspace_diff.md in the memories root with the git-style diff from the previous successful Phase 2 baseline to the current worktree
  • if the memory workspace has no changes after artifact sync/pruning, marks the job successful and exits

If the memory workspace has changes, it then:

  • spawns an internal consolidation sub-agent
  • builds the Phase 2 prompt with the path to the generated workspace diff
  • points the agent at phase2_workspace_diff.md for the detailed diff context
  • runs it with no approvals, no network, and local write access only
  • disables collab for that agent (to prevent recursive delegation)
  • watches the agent status and heartbeats the global job lease while it runs
  • resets the memory git baseline after the agent completes successfully; the generated diff file is removed before this reset so deleted content is not kept in the prompt artifact or unreachable git objects
  • marks the phase-2 job success/failure in the state DB when the agent finishes

Selection and workspace-diff behavior:

  • successful Phase 2 runs mark the exact stage-1 snapshots they consumed with selected_for_phase2 = 1 and persist the matching selected_for_phase2_source_updated_at
  • Phase 1 upserts preserve the previous selected_for_phase2 baseline until the next successful Phase 2 run rewrites it
  • Phase 2 loads only the current top-N selected stage-1 inputs, syncs rollout_summaries/ directly to that selection, renders raw_memories.md in stable ascending thread-id order to avoid usage-rank churn, then lets the git-style workspace diff surface additions, modifications, and deletions against the previous successful memory baseline
  • when the selected input set is empty, stale rollout_summaries/ files are removed and raw_memories.md is rewritten to the empty-input placeholder; consolidated outputs such as MEMORY.md, memory_summary.md, and skills/ are left for the agent to update

Watermark behavior:

  • The global phase-2 lock does not use DB watermarks as a dirty check; git workspace dirtiness decides whether an agent needs to run.
  • The global phase-2 job row still tracks an input watermark as bookkeeping for the latest DB input timestamp known when the job was claimed.
  • Phase 2 recomputes a new_watermark using the max of:
    • the claimed watermark
    • the newest source_updated_at timestamp in the stage-1 inputs it actually loaded
  • On success, Phase 2 stores that completion watermark in the DB.
  • This avoids moving the recorded completion watermark backwards, but does not decide whether Phase 2 has work.

In practice, this phase is responsible for refreshing the on-disk memory workspace and producing/updating the higher-level consolidated memory outputs.

Why it is split into two phases

  • Phase 1 scales across many rollouts and produces normalized per-rollout memory records.
  • Phase 2 serializes global consolidation so the shared memory artifacts are updated safely and consistently.