The previous refactor (commit 344a66fc) dropped the tool-call loop
in _send_minimax. The original function executed tool calls when the
response had tool_calls; the refactor was single-shot. This is a real
behavior regression (tools stop working) even though the existing
tests don't catch it.
Restore the tool loop:
- For each round (up to MAX_TOOL_ROUNDS + 2), call send_openai_compatible
with tools=_get_deepseek_tools() and tool_choice='auto'
- If response has tool_calls: dispatch each via
_execute_tool_calls_concurrently (handles both async context and
sync via run_coroutine_threadsafe / asyncio.run), append each
result to _minimax_history with role='tool' and tool_call_id
- If no tool_calls: return the response text (with thinking tags for
reasoning models)
- The lock is acquired/released per iteration to avoid holding it
during the API call (which can take seconds)
Preserved:
- 10-arg signature
- _minimax_history_lock (now acquired per iteration)
- _repair_minimax_history
- discussion_history handling
- System + context message wrapping
- Reasoning content extraction (response.raw_response.choices[0].message
.reasoning_details[0].get('text', ''))
- <thinking> tags wrap on the final response
Dropped (still):
- extra_body={reasoning_split: True} (not supported by send_openai_compatible;
would be a Phase 5 adapter addition if minimax-reasoner models need it)
New line count: 75 lines (vs 41 single-shot, vs 231 pre-refactor).
Net effect: 231 -> 75 = 68% reduction; tool loop preserved.
Verification: 38/38 tests pass (no regressions).
Phase 3 of qwen_llama_grok_integration_20260606 ships Grok and Llama
provider support. 16 of 18 state tasks done (t3.4 and t3.15 cancelled:
no credentials_template.toml exists; t3.6 and t3.17 completed in
Phase 1's initial registry population).
Modules shipped:
- src/ai_client.py: state globals (_grok_*, _llama_* including _llama_base_url
and _llama_api_key), _ensure_grok_client() (OpenAI SDK with base_url
https://api.x.ai/v1), _ensure_llama_client() (OpenAI SDK with
configurable base_url + api_key for Ollama/OpenRouter/custom backends),
_send_grok() and _send_llama() (both 10-param signature matching
_send_minimax, both call send_openai_compatible), _list_grok_models()
and _list_llama_models() (return from capability registry),
_get_llama_cost_tracking() (the local-LLM signal: returns False when
base_url is localhost/127.0.0.1), 2 new branches in list_models(),
Grok + Llama state reset in reset_session()
- src/models.py: 'grok' and 'llama' added to PROVIDERS (centralized;
gui_2.py and app_controller.py import from this list)
- src/cost_tracker.py: 11 new regex pricing entries (3 Grok + 8 Llama)
Tests shipped:
- tests/test_grok_provider.py (28 lines, 2 tests)
- tests/test_llama_provider.py (68 lines, 6 tests)
- Total new tests this phase: 8 (all passing)
- Cumulative: 38 tests in batch (qwen + grok + llama + minimax + caps +
openai_compat + cost + no_top_level_sdk_imports)
Architectural correction (Grok-consulted 2026-06-11):
- Spec section 3.1.1 added: 'best API per vendor' principle
- Spec section 4.3 reverted from 'Native REST API' to 'OpenAI-Compatible'
per Grok's own confirmation: 'the OpenAI-compatible endpoint is
fully compatible and clean with no meaningful unique native surface
lost'
- Follow-up track B renamed: 'Llama Native APIs' (Ollama native +
Meta Llama API), not 'Native Vendor APIs' (no Grok native refactor
needed)
- v2 matrix field expansion documented (per Grok's recommendation):
audio, video, grounding, computer_use, local, reasoning,
web_search, x_search, code_execution, file_search, mcp_support,
structured_output
Deviations from plan (consistent with Phase 1 and Phase 2):
- Test signatures use 10-arg (real _send_minimax shape), not 12-arg
- PROVIDERS change is at src/models.py:56 (centralized), not in
gui_2.py and app_controller.py (which import from models)
- t3.4 and t3.15 (credentials template) skipped: no template file
exists; the user maintains their own credentials.toml directly
Phase 4 (MiniMax refactor) is now unblocked. The refactor replaces
~250 lines of inline OpenAI-compatible send logic in _send_minimax
with a thin wrapper around the shared send_openai_compatible helper
(per the spec §5.2 target: ~50 lines).
Side concerns for Phase 3:
1. PROVIDERS: src/models.py:56 now includes 'grok' and 'llama' alongside
the 6 existing vendors. Centralized registry; gui_2.py and
app_controller.py import from here. State tasks t3.5 and t3.16
were scoped to gui_2.py/app_controller.py but the actual change
is at the centralized registry, per the project's single-source-of-
truth pattern (per src/models.py module docstring and the Phase 5
audit script audit_no_models_config_io.py which enforces that
PROVIDERS lives in models.py).
2. cost_tracker.py: added 11 regex pricing entries (3 Grok + 8 Llama):
Grok (per xAI public pricing):
- grok-2: 2.00 / 10.00
- grok-2-vision: 2.00 / 10.00
- grok-beta: 5.00 / 15.00
Llama (per Grok's consultation: pricing varies by backend; registry
entries represent the most common case):
- llama-3.1-8b-instant: 0.05 / 0.08 (Groq)
- llama-3.1-70b-versatile: 0.59 / 0.79 (Groq)
- llama-3.1-405b-reasoning: 3.00 / 3.00 (OpenRouter avg)
- llama-3.2-1b-preview: 0.04 / 0.04
- llama-3.2-3b-preview: 0.06 / 0.06
- llama-3.2-11b-vision-preview: 0.18 / 0.18
- llama-3.2-90b-vision-preview: 0.90 / 0.90
- llama-3.3-70b-specdec: 0.59 / 0.79 (Groq)
(all per 1M tokens, USD; matches the structure of existing entries;
note: 'llama-3.1', 'llama-3.2', 'llama-3.3' are regex patterns to
allow future model variants in the same family.)
Spot check:
- estimate_cost('grok-2', 1000, 500) = 0.007 (= 0.002 + 0.005)
- estimate_cost('llama-3.3-70b-specdec', 1000, 500) = 0.000985
3. SKIPPED t3.4 and t3.15 (credentials templates): no
credentials_template.toml exists in the project (Phase 2 established
this). The user maintains their own credentials.toml directly.
4. t3.6 and t3.17 (Grok/Llama models in capability registry) were
completed in Phase 1's initial population of 22 entries
(commit 6be04bc). Grok has 4 entries (1 wildcard + 3 models);
Llama has 9 entries (1 wildcard + 8 models). Grok-2-vision has
vision=True; Llama 3.2-11b/90b vision variants have vision=True.
Verification: 38/38 tests pass in batch.
Grok's own recommendation (consulted 2026-06-11):
'xAI (Grok) | xAI official OpenAI-compatible (https://api.x.ai/v1) |
Fully compatible and clean. Supports Grok-2 + Grok-2-Vision. No
meaningful unique native surface lost by using the compatible
endpoint.'
This REVERSES the earlier 'xAI native' correction. The OpenAI-
compatible approach for Grok is the canonical full-featured path;
the implementation in Phase 3 (OpenAI SDK with base_url=https://api.x.ai/v1
+ send_openai_compatible helper) is correct as-is.
Updates to the spec:
1. §3.1.1: replaced the 'use xAI native' decision with the confirmed
per-vendor table. Qwen=Native, Grok=OpenAI-Compatible (per Grok's
own confirmation), MiniMax=OpenAI-Compatible, DeepSeek=OpenAI-
Compatible, Ollama=OpenAI-Compatible-in-v1 (native in v2),
Meta Llama API=Native (new 4th backend, follow-up), Gemini=Native
(follow-up), Anthropic=Native (follow-up). Also added Grok's
recommended v2 matrix field expansion: audio, video, grounding,
computer_use, local, reasoning/extended_thinking, web_search,
x_search, code_execution, file_search, mcp_support, structured_output.
2. §4.3: reverted from 'Grok via xAI (Native REST API)' back to
'Grok via xAI (OpenAI-Compatible) - confirmed 2026-06-11'. The
implementation does NOT need a native refactor; the OpenAI SDK
at https://api.x.ai/v1 is the canonical approach. Removed the
earlier 'caching: true' entry from the registry (since the
OpenAI-compat shim doesn't expose prompt_cache_key) and the
'no persistent client' state struct (back to the OpenAI SDK
pattern).
3. §13.1.B: renamed from 'Native Vendor APIs' to 'Llama Native APIs
(Ollama native + Meta Llama API)' and removed the Grok native
refactor item (Grok says OpenAI-compat is fine). Kept the Ollama
native + Meta Llama API items + matrix expansion. Clarified that
Grok tests do NOT need rewriting; only Llama tests get 2 more
(native Ollama, Meta Llama API).
Net effect: the Phase 3 work that just shipped (Grok+Llama Green
using OpenAI-compat shim) is CORRECT as-is. The implementation
matches Grok's actual recommendation. No code rollback needed.
Three additions to the spec, per the user's architectural correction
in this session:
1. NEW section 3.1.1: 'Architectural principle: Use the best API per
vendor' — explains why the OpenAI-compatible shim loses vendor-
specific features (xAI: prompt_cache_key, reasoning_effort, server-
side tools, cost_in_usd_ticks; Ollama: think param, images array,
thinking field, structured outputs) and states the principle:
'use each vendor's native SDK or REST API when one exists, falling
back to OpenAI-compatible only when no native option exists.'
Also notes that the capability matrix IS the aggregate tracker;
future native features go into the matrix, and the GUI filters
based on it (no per-vendor UI branches).
2. UPDATED section 4.3 (Grok): 'Grok via xAI (Native REST API)' — was
'OpenAI-Compatible'. Now specifies two native endpoints
(/v1/chat/completions and /v1/responses), the native features that
matter, the updated capability registry (caching=true for Grok
via prompt_cache_key), and a 'Phase 3 placeholder behavior' note
that this track's Phase 3 ships the OpenAI-compatible Grok as a
placeholder. The native refactor is deferred to follow-up B.
3. UPDATED section 13.1: added follow-up track B 'Native Vendor APIs
(post-OpenAI-compatible-placeholder)' which documents:
- Grok → xAI native REST
- Llama (Ollama) → native /api/chat
- Llama (Meta Llama API) → new 4th backend (deferred pending
verification of Meta's API spec; llama.developer.meta.com/docs/overview
returned 400 on fetch this session)
- Capability matrix expansion (web_search, x_search, code_execution,
file_search, mcp_support, reasoning_effort, structured_output)
- Test rewrites (mock requests.post instead of chat.completions.create)
This is a docs-only commit; no code changes. The Phase 3 Green work
continues with the OpenAI-compatible approach as planned in the
existing Red tests (t3.3 Grok + t3.14 Llama), and the follow-up track
B handles the native refactor when prioritized.
8 failing tests in 2 new files for the upcoming Grok and Llama
provider implementations.
Grok (tests/test_grok_provider.py, 2 tests):
1. test_send_grok_uses_xai_endpoint: _send_grok calls _ensure_grok_client
and uses an xAI client (base_url https://api.x.ai/v1)
2. test_grok_2_vision_supports_image: structural check that the
capability registry has vision=True for grok-2-vision (already
populated in Phase 1, so this test passes in Red phase; it is a
regression guard for the registry, not an implementation test)
Llama (tests/test_llama_provider.py, 6 tests):
1. test_send_llama_ollama_backend: _send_llama with localhost:11434
(Ollama) base URL
2. test_send_llama_openrouter_backend: _send_llama with OpenRouter URL
3. test_send_llama_custom_url: _send_llama with custom URL
(escape hatch for self-hosted)
4. test_llama_model_discovery_unions_ollama_and_openrouter: _list_llama_models
returns the 8 models from the capability registry
5. test_llama_3_2_vision_vision_capability: structural check for
llama-3.2-11b-vision-preview (passes in Red phase)
6. test_llama_local_backend_cost_tracking_false_for_ollama: the local-LLM
signal -- when base_url is localhost, _get_llama_cost_tracking()
returns False. This is the first test that exercises the local LLM
support that the capability matrix was designed for.
Both _reset_grok_state and _reset_llama_state fixtures use hasattr() to
be no-ops when the state doesn't exist (Red phase).
Test signatures use the real 10-arg _send_minimax signature, NOT the
plan's 12-arg with enable_tools / rag_engine.
Red phase: 6/8 tests fail (4 AttributeError on missing _send_*,
2 ImportError on missing _list_*/_get_*). 2/8 pass (registry structural
checks).
Next: Green phase - implement _send_grok + _ensure_grok_client +
_send_llama + _ensure_llama_client + _list_llama_models +
_get_llama_cost_tracking in src/ai_client.py.
Phase 2 of qwen_llama_grok_integration_20260606 ships Qwen support via
the Alibaba Cloud DashScope native SDK. 10 of 11 state tasks done
(t2.7 cancelled: no credentials_template.toml exists in the project;
t2.9 was completed in Phase 1's initial registry population).
Modules shipped:
- src/qwen_adapter.py (31 lines): build_dashscope_tools() (OpenAI shape
-> DashScope shape), classify_dashscope_error() (5 exception classes
-> ProviderError kinds: auth/network/quota)
- src/ai_client.py: state globals (_qwen_client, _qwen_history,
_qwen_history_lock, _qwen_region), _ensure_qwen_client() (sets
dashscope.base_http_api_url based on region: china vs international),
_dashscope_call() + _dashscope_exception_from_response() +
_extract_dashscope_tool_calls(), _send_qwen() (10-param signature
matching _send_minimax), _list_qwen_models()
- src/models.py: 'qwen' added to PROVIDERS (centralized; gui_2.py and
app_controller.py import from this list)
- src/cost_tracker.py: 7 Qwen pricing entries (regex-matched,
USD per 1M tokens)
Tests shipped: tests/test_qwen_provider.py (55 lines, 5 tests, all passing)
Total new tests this phase: 5
Total tests in new modules: 30 (qwen + minimax + capabilities +
openai_compatible + cost_tracker + no_top_level_sdk_imports)
Verification:
- 30/30 tests pass in batch
- No regressions
- 4/4 audit scripts pass (audit_main_thread_imports, audit_weak_types,
check_test_toml_paths, audit_no_models_config_io)
DashScope alignment (post-cleanup):
- Uses dashscope.common.error.AuthenticationError (real class in
1.25.21) instead of the non-existent InvalidApiKey
- Removed the InvalidApiKey -> AuthenticationError monkey-patch
- TimeoutException -> network (not rate_limit)
- ServiceUnavailableError -> network (not quota)
- _ensure_qwen_client sets base_http_api_url per region (china vs
international) per the latest DashScope API spec
Deviations from the plan:
- Test signature adapted from 12-param (plan) to 10-param (matching
real _send_minimax) -- the plan's enable_tools / rag_engine params
don't exist on _send_minimax
- PROVIDERS change is at src/models.py:56 (centralized), not in
gui_2.py and app_controller.py (which import from models)
- t2.7 (credentials template) skipped: no template file exists;
the user maintains their own credentials.toml directly
Phase 3 (Grok + Llama) is now unblocked. Local LLM support lands
in Phase 3 via Llama's Ollama backend (default base_url
http://localhost:11434/v1).
Side concerns for Phase 2:
1. PROVIDERS: src/models.py:56 now includes 'qwen' alongside the existing
5 vendors. The other 4 references to PROVIDERS in src/gui_2.py and
src/app_controller.py import from this centralized list, so this
one edit propagates everywhere. State task t2.8 was scoped to
'gui_2.py and app_controller.py' but the actual change is at the
centralized registry, per the project's single-source-of-truth
pattern (per src/models.py module docstring and the Phase 5 audit
script audit_no_models_config_io.py which enforces that PROVIDERS
lives in models.py).
2. cost_tracker.py: added 7 regex pricing entries for the Qwen models
shipped in Phase 1's vendor_capabilities.py:
- qwen-turbo: 0.05 / 0.10
- qwen-plus: 0.40 / 1.20
- qwen-max: 2.00 / 6.00
- qwen-long: 0.07 / 0.28
- qwen-vl-plus: 0.21 / 0.63
- qwen-vl-max: 0.50 / 1.50
- qwen-audio: 0.10 / 0.30
(all per 1M tokens, USD; matches the structure of existing entries)
Spot check: estimate_cost('qwen-max', 1000, 500) = 0.005 (= 0.002 + 0.003)
3. SKIPPED t2.7 (credentials template): no credentials_template.toml
exists in the project. The only credentials file is the active
credentials.toml which the user maintains directly with their own
API keys. The plan's assumption of a template file does not match
the project's actual structure. Documented in the commit log
rather than modifying the user's actual credentials.toml with a
placeholder key (which would be inconsistent with the rest of
that file's pattern of real keys). When the user obtains a
DashScope API key, they can add a [qwen] section directly.
4. t2.9 (Qwen models in capability registry) was completed in Phase 1's
initial population of 22 entries (commit 6be04bc). The 8 qwen
entries (1 wildcard + 7 specific models) are in src/vendor_capabilities.py.
Verification: 30/30 tests pass in batch
(test_qwen_provider, test_minimax_provider, test_ai_client_no_top_level_sdk_imports,
test_vendor_capabilities, test_openai_compatible, test_cost_tracker)
5 failing tests in tests/test_qwen_provider.py that establish the
core behaviors of the new Qwen (DashScope) provider:
1. test_send_qwen_routes_to_dashscope: _send_qwen calls _ensure_qwen_client
and _dashscope_call, returns the text from the DashScope response
2. test_qwen_vision_vl_model_accepts_image: when file_items contains an
image, the messages passed to _dashscope_call include the image ref
3. test_qwen_tool_format_translation: build_dashscope_tools converts
OpenAI-shaped tool dicts to DashScope shape (name/description/parameters
flat structure, not wrapped in function:)
4. test_qwen_error_classification: classify_dashscope_error maps
dashscope.common.error.InvalidApiKey -> ProviderError(kind='auth',
provider='qwen')
5. test_list_qwen_models_returns_hardcoded_registry: _list_qwen_models
returns the 7 Qwen models registered in src/vendor_capabilities.py
The autouse _reset_qwen_state fixture uses hasattr() so it is a no-op
when _qwen_client / _qwen_history do not exist (yet); this keeps the
fixture working in the Red phase.
All 5 tests fail:
- Tests 1, 2: AttributeError: src.ai_client has no _ensure_qwen_client /
_send_qwen / _dashscope_call
- Tests 3, 4: ModuleNotFoundError: No module named src.qwen_adapter
- Test 5: ImportError: cannot import name _list_qwen_models
Test signature adapted to match the real _send_minimax signature at
src/ai_client.py:2143-2148 (10 params, no enable_tools / rag_engine)
rather than the plan's 12-param signature.
Next: Green phase - implement src/qwen_adapter.py + src/ai_client.py
state + _ensure_qwen_client + _send_qwen + _list_qwen_models.
Green phase: src/openai_compatible.py now exists and all 6 Red-phase
tests in tests/test_openai_compatible.py pass.
Implementation (144 lines, 1-space indent, no comments):
Data structures:
- NormalizedResponse: frozen dataclass with text, tool_calls,
usage_input_tokens, usage_output_tokens, usage_cache_read_tokens,
usage_cache_creation_tokens, raw_response
- OpenAICompatibleRequest: regular dataclass with messages, model,
temperature=0.0, top_p=1.0, max_tokens=8192, tools=None,
tool_choice='auto', stream=False, stream_callback=None
Algorithms:
- send_openai_compatible(client, request, *, capabilities) -> NormalizedResponse
Dispatches to _send_blocking or _send_streaming based on request.stream.
Catches openai.OpenAIError and re-raises as classified ProviderError.
- _send_blocking: extracts message text + tool_calls, converts tool_calls
to dicts via _to_dict_tool_call, reads usage.prompt_tokens /
usage.completion_tokens (with int() coercion for MagicMock test compat).
- _send_streaming: iterates chunks, accumulates text parts, aggregates
tool_calls by index, fires stream_callback per text delta, reads
chunk.usage for final token counts.
- _classify_openai_compatible_error: maps RateLimitError -> 'rate_limit',
AuthenticationError/PermissionDeniedError -> 'auth', APIConnectionError
-> 'network', APIStatusError with 402/429/401-403/500-504 -> 'balance'/
'rate_limit'/'auth'/'network', BadRequestError -> 'quota', fallback
'unknown'. All use provider='openai_compatible'.
Fixed plan's code smell: removed the 'MagicMock_noop' forward-reference
class (defined after first use) and replaced with the cleaner Pythonic
pattern 'int(getattr(usage, prompt_tokens, 0) or 0)'. Real OpenAI SDK
always sets usage on responses; the defensive fallback was noise.
Function-level import of ProviderError inside _classify_openai_compatible_error
avoids any circular import risk.
6 failing tests in tests/test_openai_compatible.py that establish the
core behaviors of the new send_openai_compatible() shared helper:
1. test_send_non_streaming_returns_normalized_response: blocking call
returns text, empty tool_calls, and correct usage token counts
2. test_send_streaming_aggregates_chunks: streaming call aggregates
deltas into final text and fires stream_callback per chunk
3. test_tool_call_detection_in_response: tool_calls from the response
are converted to dicts with id/type/function/arguments fields
4. test_vision_multimodal_message: messages with multimodal content
(text + image_url) are passed through unchanged to the client
5. test_error_classification_429_to_rate_limit: RateLimitError from
openai SDK is caught and re-raised as ProviderError(kind='rate_limit')
6. test_normalized_response_is_frozen_dataclass: NormalizedResponse is
a frozen dataclass (FrozenInstanceError on attribute assignment)
All 6 tests fail with ModuleNotFoundError: No module named
'src.openai_compatible' (confirmed via pytest). The implementation file
will be created in the next commit (Green phase).
ProviderError confirmed importable from src.ai_client (no stub needed).
Green phase: src/vendor_capabilities.py now exists and all 3 Red-phase
tests in tests/test_vendor_capabilities.py pass.
Implementation:
- VendorCapabilities frozen dataclass with 12 fields (vendor, model, vision,
tool_calling, caching, streaming, model_discovery, context_window,
cost_tracking, cost_input_per_mtok, cost_output_per_mtok, notes)
- Module-level _REGISTRY dict keyed by (vendor, model)
- register() inserts/overwrites entries
- get_capabilities() returns specific entry if present, else vendor '*'
default, else raises KeyError with 'No capabilities registered' message
- list_models_for_vendor() returns sorted model names for a vendor
(excludes '*' wildcard)
Initial population (22 entries at module load):
- 1 minimax wildcard (cost: 0.20/0.20 per Mtok)
- 4 grok (1 wildcard + 3 models; grok-2-vision has vision=True)
- 9 llama (1 wildcard + 8 models; 11b/90b vision variants have vision=True)
- 8 qwen (1 wildcard + 7 models; qwen-vl-plus/max have vision=True;
qwen-audio has notes='Text-only in v1; audio input deferred')
The plan's Task 1.3 listed 22 entries but included one impossible entry
(vendor='minimax', model='grok-2-latest'). Omitted; 21 entries shipped.
Test fix: test_fallback_to_vendor_default previously used model name
'llama-3.3-70b-specdec' which IS in the registry, so the specific entry
was returned (with default cost_tracking=True), not the wildcard. Fixed
by changing to 'llama-3.3-future-unregistered' (not in registry, so
fallback fires correctly).
3 failing tests in tests/test_vendor_capabilities.py that establish the
core behaviors of the new VendorCapability matrix:
1. test_registry_lookup_known_model: registering and looking up a specific
(vendor, model) entry returns the registered entry
2. test_fallback_to_vendor_default: looking up an unregistered model returns
the vendor's '*' default entry
3. test_unknown_vendor_raises: looking up a vendor with no entries raises
KeyError with a 'No capabilities registered' message
All 3 tests fail with ModuleNotFoundError: No module named
'src.vendor_capabilities' (confirmed via pytest). The implementation file
will be created in the next commit (Green phase).
The autouse _clean_registry fixture snapshots src.vendor_capabilities._REGISTRY
before each test and restores it after, providing test isolation for the
module-level state.
Final report for the continuation session that started after the original 25-commit run closed. Covers:
Stats:
- 17 atomic continuation commits (db5ab0d9 -> 7d6dbbd3) plus 03056a4f for the closure summary itself
- 14 unique doc files modified
- 0 source files modified (continuation was docs-only)
- 11 source files read in full; ~20 outlined
- ~250 + lines, ~190 - lines across the doc edits
What was done (14 drift clusters with detailed before/after):
- guide_hot_reload.md: example registration + trigger_key claim
- guide_app_controller.md: filename typo + fictional hot_reload() method
- guide_gui_2.md: line 155 -> 285; reload() -> reload_all()
- guide_nerv_theme.md: 5 wrong hex values; render_nerv_fx fiction; [nerv] config fiction; 0.5 Hz -> 3.18 Hz; 1.5s pulse -> no decay
- guide_shaders_and_window.md: 3 fictional [nerv] config refs
- guide_command_palette.md: 11 -> 33 commands
- guide_mma.md: 5 algorithm drift points (has_cycle iterative, topological_sort Kahn's, tick no-promote, ConductorEngine.__init__ signature)
- guide_beads.md: dispatch line range
- guide_multi_agent_conductor.md: wholesale rewrite of pre-refactor architecture
- guide_tools.md: run_powershell signature (add patch_callback)
- guide_context_curation.md: FuzzyAnchor docstring (replace 'anchor_lines' with real field names)
- guide_simulations.md: CodeOutliner doc (add [ImGui Scope], return-type suffix, count guard)
- Readme.md: 3 line-level drift (45->46 MCP, 32->33 commands, shell_runner patch_callback)
- docs/Readme.md: file tree (24->27 guides with full alphabetical list)
- conductor/index.md: 23 -> 27 guides count
Drift patterns (6, refined from the 4 in the original handoff):
1. Thread counts
2. Line numbers
3. Removed-class claims
4. Schema fields
5. NEW: Architecture rotations (the most common in this continuation)
6. NEW: Hard-coded constants described as config keys
Bucket coverage status (final):
- A (theme) DONE
- B (logging) Partial - cost_tracker and log_pruner audited; no specific doc drift
- C (commands/palette) DONE
- D (file utilities) DONE - run_powershell + CodeOutliner + FuzzyAnchor
- E (runtime/imgui) DONE
- F (MMA orchestrator) DONE
- G (beads/vendor) Partial - beads_client read, vendor_state read, dispatch line ref fixed
- H/I done in original 25-commit run
Mixed-in user files caveat (49ac008a):
- 2 user-authored files swept in from the prior_session_sepia_20260610 track
- User aware and chose to leave the commit as-is
- Theme-track agent should treat those files as owned by that track
Verbiage lesson:
- 'fictional' is a value judgment, not a technical description
- Use 'predates the refactor' / 'stale' / 'no longer matches the source' instead
- Applied in 2 user-facing doc cleanups (guide_app_controller.md:59, guide_rag.md:322)
Recommendations for the theme-track agent:
- Read guide_themes.md:87 before touching the theme system
- Do NOT touch the guide_nerv_theme.md and guide_shaders_and_window.md updates from this session (re-verified against source)
- The theme_2.py:111 comment confirms the per-frame create-and-discard FX pattern
- Run all 4 audit scripts before committing any source code change
- The markdown_table.py spec is older than the source - check both
- The _lang_map reference in the older spec is a pre-refactor claim
Open follow-ups (none blocking):
- B/G finalization
- markdown_helper.py and markdown_table.py source verification (left for theme track)
- Test count verification (322 may drift)
- Doc freshness signal
12 atomic commits added after the original 25-commit run closed:
6 small drift fixes (db5ab0d9..28172135)
- guide_hot_reload.md: example registration + trigger_key claim
- guide_app_controller.md: src/hot_reload.py -> src/hot_reloader.py + hot_reload() method
- guide_gui_2.md: line 155 -> 285; reload() -> reload_all()
- guide_nerv_theme.md: 5 wrong hex values, stale apply_nerv body, stale
render_nerv_fx example, [nerv] config that was never wired, 0.5 Hz vs
actual 3.18 Hz flicker
- guide_shaders_and_window.md: 3 fictional [nerv] config refs
- guide_app_controller.md:68: self-referential io_pool docstring claim
1 mid-size fix (81e88241)
- guide_command_palette.md: command count 11 -> 33 (full source-derived
Action column for every @registry.register decorator in src/commands.py)
2 MMA rewrites (57143b7a, 394987f8, a49e5ffb, e0368174)
- guide_mma.md: has_cycle recursive -> iterative; topological_sort DFS ->
Kahn's; tick auto-promotion claim; ConductorEngine.__init__ missing
max_workers param
- guide_beads.md: bd_ tool dispatch line range
- guide_multi_agent_conductor.md: rewrote the TrackDAG and
ExecutionEngine/ConductorEngine/WorkerPool/mma_exec sections; the prior
doc predated the conductor_engine refactor and described a different
architecture (MultiAgentConductor class that doesn't exist, ExecutionMode
enum that doesn't exist, _dispatch_loop background thread that doesn't
exist, ThreadPoolExecutor-backed WorkerPool that is actually a
dict[str, Thread] + lock + semaphore)
2 verbiage cleanups
- replaced 'fictional' with neutral phrasing ('predates the refactor' /
'stale') in 2 places where the prior session had used it in user-facing
doc text. Going forward doc-drift commits use neutral language;
'fictional' was a value judgment on the doc and its author, not a
technical description.
Bucket coverage after continuation: A (theme), C (commands/palette), E
(runtime/imgui), F (MMA orchestrator) fully covered. B (logging) and G
(beads/vendor) partial. H/I (mcp_client/ai_client deep) done in original
25-commit run. Still untouched: D (8 file utilities), shaders.py / bg
shader.py, summary_cache.py.
Caveat for next agent (theme track): commit 49ac008a accidentally swept in
2 user-authored files from the parallel prior_session_sepia_20260610 work
(conductor/tracks/prior_session_sepia_20260610/plan.md and
docs/superpowers/plans/2026-06-10-prior-session-sepia.md). The user is
aware and chose to leave them in that commit. The next agent should treat
those files as owned by the prior_session_sepia_20260610 track and not
modify them from the theme-track context.
ed