Three-tier batching refactor: replace alphabetical 4-at-a-time batching with
fixture-class-isolated tiers (0 opt-in, 1 unit/xdist, 2 mock_app, 3 live_gui
in one session, H headless, P performance).
Hybrid classification: auto-infer from filename + AST fixture scan; hand-curated
tests/test_categories.toml overrides for cross-cutting and ambiguous files.
Opt-in per-test order control via [[files.X.test_order]] sub-tables, gated on
a conftest-loaded pytest plugin (no-op without entries).
Priority order: B (process isolation) > A (subsystem diagnostic) > C (speed).
Lightweight, in-memory profiler for AppController init phases. Used by
the startup_speedup_20260606 track to measure where the time goes
during boot (config hydration, hook server start, subsystem init, etc.).
The profiler is exposed via /api/startup_profile (Phase 8 work) and
the Diagnostics panel so the user can see the exact per-phase cost.
Public API:
StartupProfiler() - create
.phase(name) - context manager
.snapshot() - {phases: {name: {start_ts, duration_ms}}, total_ms, count}
.reset() - clear recorded phases
.enable() / .disable() - toggle recording
Implementation:
- dataclass with list of _Phase(name, start_ts, end_ts)
- @contextmanager records wall-clock via time.perf_counter
- records duration even if the body raises (try/finally)
- snapshot is a copy, so consumers can't mutate the live state
TDD: 5 tests in tests/test_startup_profiler.py cover: basic
recording, total math, snapshot isolation, exception safety, empty
state.
Architectural shift driven by user clarification: lazy-loading on first
use causes user-perceptible lag when the user-triggered action (e.g.
provider switch) propagates to a controller method that triggers the
first import. The fix is to pre-import heavy modules on a bg thread
at startup and have functions access them via _require_warmed().
Old design (rejected):
- from google import genai inside _send_gemini (lazy on first call)
- First user action that triggers this pays the cost; UI feels laggy
New design (this commit):
- Top-level heavy imports REMOVED from main-thread-reachable files
- AppController.__init__ submits warmup jobs to _io_pool (4 threads,
named 'controller-io-N')
- Each warmup worker imports its module and updates a thread-safe
warmup_status dict
- Functions access modules via _require_warmed(name), which assumes
the module is in sys.modules (warmed at startup)
- When all jobs complete, _warmup_done_event is set and registered
on_warmup_complete callbacks fire
- GUI shows status indicator + toast when warmup completes
- Hook API exposes /api/warmup_status and /api/warmup_wait
- Tests can call controller.wait_for_warmup() before exercising
warmup-dependent functionality
Phase 2 now bundles job pool + warmup (T2.3+T2.4 add warmup tests +
implementation). Phases 3-5 do 'remove top-level imports' instead of
'lazy-load'. Phase 7 is the notification surface (Hook API + GUI).
Definition of Done includes warmup-completion criteria, the
'no function-body imports' check, and an end-to-end 'provider switch
is INSTANT' smoke test.
No code changes; this is a planning update only.
Track.get_executable_tickets (in models.py) called TrackDAG at
runtime, forcing a top-level import of src.dag_engine into models.py
and creating a 2-cycle that broke whichever module loaded second
(Ticket was not yet defined when models.py loaded first; TrackDAG
was not yet defined when dag_engine.py loaded first).
Fix: hoist the method out of the Track dataclass and into a free
function get_executable_tickets(track) in dag_engine.py. models.py
no longer needs TrackDAG at all, so the cycle is one-directional
(models -> dag_engine) and resolves cleanly in any import order.
Tests updated:
- tests/test_mma_models.py: import get_executable_tickets and call
it instead of track.get_executable_tickets() (4 call sites)
- tests/test_conductor_engine_v2.py: comment update
Verified both import orders resolve cleanly:
forward: import src.models; import src.dag_engine -> OK
reverse: import src.dag_engine; import src.models -> OK
34 tests pass (test_mma_models, test_dag_engine, test_execution_engine,
test_arch_boundary_phase3, test_track_state_schema).
Fulfills the existing backlog entry at conductor/tracks.md:152
(2026-06-05 root-cause analysis of live_gui wait_for_server timeouts).
Main Thread Purity Invariant: the main thread (entering immapp.run())
must never import a module heavier than imgui_bundle and the lean
gui_2 skeleton. Enforced by:
- static gate: scripts/audit_main_thread_imports.py (CI)
- runtime hook: tests/test_main_thread_purity.py (sys.addaudithook)
Threading constraint: no new threading.Thread(...) calls in src/.
All background work goes through AppController._io_pool
(ThreadPoolExecutor, max_workers=4, thread_name_prefix='controller-io').
9 phases, 57 tasks: audit+baseline, job pool, lazy-load SDKs, lazy-load
FastAPI, lazy-load feature-gated GUI, migrate ad-hoc threads, runtime
enforcement, hook API + diagnostics, verify+checkpoint.
Expected savings: ~2000-2400ms off main-thread import cost.
Target: import src.ai_client < 50ms (from ~1800ms), live_gui fixtures
no longer time out at wait_for_server(timeout=15).
When switching projects, the previous implementation ran the entire
save/load/refresh sequence on the main thread. With large project files
or slow disks, this caused the UI to freeze for several seconds.
Fix:
- _switch_project now returns immediately after setting flags; the
actual work runs in a daemon thread (_do_project_switch)
- New is_project_stale() property returns True while a switch is queued
or running; the GUI renders an amber/yellow tint overlay to signal
the controller state lags the user's last click
- AI ops are gated: _api_generate returns HTTP 409, _handle_generate_send
and _handle_md_only early-return with ai_status feedback, all when
is_project_stale() is true
- Queued switches (clicking project A then B in rapid succession) are
coalesced: B replaces A as the target; once A completes, B is
triggered automatically via the finally branch in _do_project_switch
- New state fields: _project_switch_in_progress, _project_switch_pending_path,
_project_switch_thread, _project_switch_lock
- AppController state class attributes use hasattr guard for _app to
keep the controller usable standalone in tests/headless mode
UX:
- Render loop keeps drawing during the switch
- User can still scroll, switch tabs, browse files
- Amber tint + popup explains what's happening and that AI ops are paused
- ai_status shows the target project name
Tests:
- _wait_for_switch helper added for the new async switch flow
- All 7 existing switch tests updated to call _wait_for_switch
- 2 new tests:
- test_switch_project_non_blocking: verifies _switch_project returns
in <0.2s and is_project_stale() is True during the switch
- test_api_generate_blocked_while_stale: verifies _api_generate
raises HTTPException(409) while a switch is in progress
All 33 related tests pass.
When switching projects, the previous project's context_files remained
visible in the Context Composition panel because the controller's
self.context_files list was not reloaded from the new project's TOML
files.paths entry.
Fix in _refresh_from_project:
- After loading self.files from the project TOML, populate
self.context_files with deep copies of those FileItem objects
- Reset self._app.ui_selected_context_files to match the new project's
auto_aggregate set
- Guard the _app access with hasattr so the controller is usable
standalone (in tests, headless mode, etc.) without an attached App
Test: 1 new test in tests/test_project_switch_persona_preset.py
- test_switch_project_resets_context_files: switches from project_a
(forth + gte_hello files) to project_b (gencpp timing files) and
asserts context_files contains ONLY project_b's files
Two fixes for the regression introduced in b92daef3 (and an additional
hardening for the persona->context_preset stale-reference class of bug):
1. Regression: persona_manager was missing on first project load.
_load_active_project creates preset_manager and tool_preset_manager
but did not create persona_manager, so the new
self.personas = self.persona_manager.load_all() line in
_refresh_from_project raised AttributeError on app startup before
the post-_load_active_project persona_manager creation could run.
Fix: create self.persona_manager in _load_active_project alongside
the other managers, so the manager is available when
_refresh_from_project runs.
2. Stale reference: persona's context_preset field pointed to a
preset (e.g. 'GTE') that no longer exists in the project, causing
load_context_preset to raise KeyError and crash the persona
selector panel (which triggered the cascading 'Missing End()' imgui
assertion).
Fix: wrap the load_context_preset call in render_persona_selector_panel
with try/except KeyError, surface the error in app.ai_status, and
clear app.ui_active_context_preset to keep the GUI state consistent.
Tests: 2 new tests in tests/test_project_switch_persona_preset.py
- test_load_active_project_creates_persona_manager (regression guard)
- test_load_context_preset_missing_raises_keyerror (verifies the
contract that load_context_preset raises for missing names; the
GUI layer is now responsible for catching the error)
When switching projects, the previous project's project-specific persona and
presets remained selected in the AI Settings panel because:
1. self.personas was not reloaded after switching project root
2. self.ui_active_persona / tool_preset / bias_profile / project_preset_name
were not validated against the newly-loaded personas/presets
Fix:
- Reload self.personas from self.persona_manager in _refresh_from_project
- Validate each active selection and reset to None/empty if it does not
exist in the newly-loaded manager dictionaries
- Push the active tool preset and bias profile to ai_client after the swap
- Initialize self.ui_active_bias_profile in class attribute block (was only
set later in __init__, causing AttributeError on direct attribute access)
Tests: 4 new tests in tests/test_project_switch_persona_preset.py verify
the reset behavior for persona, preset, tool preset, and global preset
preservation.
Previously, context (files, screenshots) was always sent with every message,
even on subsequent messages where the AI provider already had the context
from the first message via its history mechanism.
This change:
- Detects if the discussion has any AI responses already
- Only sends md_content (stable_md) on the first message
- Subsequent messages pass empty string for md_content to avoid redundant sending
- Context now properly goes in md_content parameter, not crammed into user_message
The fix is in _api_generate() in src/app_controller.py
ed