## Summary
Generated memory rows and their stage-one/stage-two job state currently
live in `state_5.sqlite` alongside thread metadata. That makes memory
cleanup and regeneration share the main state schema even though those
rows are memory-pipeline data and can be rebuilt independently from the
durable thread records.
This PR moves the memory-owned tables into a dedicated
`memories_1.sqlite` runtime database while keeping thread metadata in
`state_5.sqlite`.
## Changes
- Adds a separate memories DB runtime, migrator, path helpers, telemetry
kind, and Bazel compile data for `state/memory_migrations`.
- Introduces `MemoryStore` behind `StateRuntime::memories()` and moves
memory table/job operations onto that store.
- Drops the old memory tables from the state DB and recreates their
schema in `state/memory_migrations/0001_memories.sql`.
- Updates memory startup, citation usage tracking, rollout pollution
handling, `debug clear-memories`, and app-server `memory/reset` to
operate through the memories DB.
- Preserves cross-DB behavior by hydrating thread metadata from the
state DB when selecting visible memory outputs and checking stage-one
staleness.
## Verification
- Added/updated `codex-state` tests for deleted-thread memory visibility
and already-polluted phase-two enqueue behavior.
- Updated `debug clear-memories`, app-server `memory/reset`, and
memories startup tests to seed and assert memory rows through
`memories_1.sqlite`.
## Why
This PR make the `morpheus` agent (memory phase 2) use a git diff to
start it's consolidation. The workflow is the following:
1. The agent acquire a lock
2. If `.codex/memories` does not exist or is not a git root, initialize
everything (and make a first empty commit)
3. Update `raw_memories.md` and `rollout_summaries/` as before.
Basically we select max N phase 1 memories based on a given policy
4. We use git (`gix`) to get a diff between the current state of
`.codex/memories` and the last commit.
5. Dump the diff in `phase2_workspace_diff.md`
6. Spawn `morpheus` and point it to `phase2_workspace_diff.md`
7. Wait for `morpheus` to be done
8. Re-create a new `.git` and make one single commit on it. We do this
because we don't want to preserve history through `.git` and this is
cheap anyway
9. We release the lock
On top of this, we keep the retry policies etc etc
The goals of this new workflow are:
* Better support of any memory extensions such as `chronicle`
* Allow the user to manually edit memories and this will be considered
by the phase 2 agent
As a follow-up we will need to add support for user's edition while
`morpheus` is running
## What Changed
- Added memory workspace helpers that prepare the git baseline, compute
the diff, write `phase2_workspace_diff.md`, and reset the baseline after
successful consolidation.
- Updated Phase 2 to sync current inputs into `raw_memories.md` and
`rollout_summaries/`, prune old extension resources, skip clean
workspaces, and run the consolidation subagent only when the workspace
has changes.
- Tightened Phase 2 job ownership around long-running consolidation with
heartbeats and an ownership check before resetting the baseline.
- Simplified the prompt and state APIs so DB watermarks are bookkeeping,
while workspace dirtiness decides whether consolidation work exists.
- Updated the memory pipeline README and tests for workspace diffs,
extension-resource cleanup, pollution-driven forgetting, selection
ranking, and baseline persistence.
## Verification
- Added/updated coverage in `core/src/memories/tests.rs`,
`core/src/memories/workspace_tests.rs`, `state/src/runtime/memories.rs`,
and `core/tests/suite/memories.rs`.
---------
Co-authored-by: Codex <noreply@openai.com>
Adds the persisted goal foundation for the rest of the stack. This PR is
intentionally limited to feature flag and state-layer behavior;
app-server APIs, model tools, runtime continuation, and TUI UX are
layered in later PRs.
## Why
Goal mode needs durable thread-level state before clients or model tools
can safely build on it. The state layer needs to know whether a goal
exists, what objective it tracks, whether it is active, paused,
budget-limited, or complete, and how much time/token usage has already
been accounted.
## What changed
- Added the `goals` feature flag and generated config schema entry.
- Added the `thread_goals` state table and Rust model for persisted
thread goals.
- Added state runtime APIs for creating, replacing, updating, deleting,
and accounting goal usage.
- Added `goal_id`-based stale update protection so an old goal update
cannot overwrite a replacement.
- Kept this PR scoped to persistence and state runtime behavior, with no
app-server, model-facing, continuation, or TUI behavior yet.
## Verification
- Added state runtime coverage for goal creation, replacement, stale
update protection, status transitions, token-budget behavior, and usage
accounting.
To improve performance of UI loads from the app, add two main
improvements:
1. The `thread/list` api now gets a `sortDirection` request field and a
`backwardsCursor` to the response, which lets you paginate forwards and
backwards from a window. This lets you fetch the first few items to
display immediately while you paginate to fill in history, then can
paginate "backwards" on future loads to catch up with any changes since
the last UI load without a full reload of the entire data set.
2. Added a new `thread/turns/list` api which also has sortDirection and
backwardsCursor for the same behavior as `thread/list`, allowing you the
same small-fetch for immediate display followed by background fill-in
and resync catchup.
To allow the ability to have guaranteed-unique cursors, we make two
important updates:
* Add new updated_at_ms and created_at_ms columns that are in
millisecond precision
* Guarantee uniqueness -- if multiple items are inserted at the same
millisecond, bump the new one by one millisecond until it becomes unique
This lets us use single-number cursors for forwards and backwards paging
through resultsets and guarantee that the cursor is a fixed point to do
(timestamp > cursor) and get new items only.
This updated implementation is backwards-compatible since multiple
appservers can be running and won't handle the previous method well.
Add a representation of the agent graph. This is now used for:
* Cascade close agents (when I close a parent, it close the kids)
* Cascade resume (oposite)
Later, this will also be used for post-compaction stuffing of the
context
Direct fix for: https://github.com/openai/codex/issues/14458
## Summary
This PR makes SQLite rollout backfill resumable and repeatable instead
of one-shot-on-db-create.
## What changed
- Added a persisted backfill state table:
- state/migrations/0008_backfill_state.sql
- Tracks status (pending|running|complete), last_watermark, and
last_success_at.
- Added backfill state model/types in codex-state:
- BackfillState, BackfillStatus (state/src/model/backfill_state.rs)
- Added runtime APIs to manage backfill lifecycle/progress:
- get_backfill_state
- mark_backfill_running
- checkpoint_backfill
- mark_backfill_complete
- Updated core startup behavior:
- Backfill now runs whenever state is not Complete (not only when DB
file is newly created).
- Reworked backfill execution:
- Collect rollout files, derive deterministic watermark per path, sort,
resume from last_watermark.
- Process in batches (BACKFILL_BATCH_SIZE = 200), checkpoint after each
batch.
- Mark complete with last_success_at at the end.
## Why
Previous behavior could leave users permanently partially backfilled if
the process exited during initial async backfill. This change allows
safe continuation across restarts and avoids restarting from scratch.
- Schema: thread_id (PK, FK to threads.id with cascade delete),
trace_summary, memory_summary, updated_at.
- Migration: creates the table and an index on (updated_at DESC,
thread_id DESC) for efficient recent-first reads.
- Runtime API (DB-only):
- `get_thread_memory(thread_id)`: fetch one memory row.
- `upsert_thread_memory(thread_id, trace_summary, memory_summary)`:
insert/update by thread id and always advance updated_at.
- `get_last_n_thread_memories_for_cwd(cwd, n)`: join thread_memory with
threads and return newest n rows for an exact cwd match.
- Model layer: introduced ThreadMemory and row conversion types to keep
query decoding typed and consistent with existing state models.
jif-oaiandGitHub
Eric TrautandGitHub
David de RegtandGitHub
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