Apply ruff format across repo to fix CI format check

`ruff format --check .` flagged 43 files; run `ruff format` to normalize them.
Formatting-only changes, no behavior impact. Also gitignore local tooling
artifacts (.playwright-mcp/, home snapshots, mascot pet_state, playbook
progress) so they are never accidentally committed.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: CraftBot

.gitignore

@@ -56,4 +56,10 @@ agent_file_system/TASK_HISTORY.md
 !build_template.py
 docs/LIVING_UI_DEVELOPER_GUIDE.md
 agent_file_system/ACTIONS.md
-agent_bundle/
+agent_bundle/
+# Local tooling / runtime artifacts (never commit)
+.playwright-mcp/
+app/data/mascot/pet_state.json
+app/data/playbooks/PLAYBOOK_PROGRESS.md
+craftbot_live_home_snapshot.md
+mobile_home_snapshot.md

agent_core/core/impl/event_stream/event_stream.py

@@ -157,7 +157,8 @@ def _maybe_push_datetime(self) -> None:
         last = self._last_datetime_ts
         if (
             last is None
-            or (datetime.now(timezone.utc) - last).total_seconds() >= DATETIME_REFRESH_SECONDS
+            or (datetime.now(timezone.utc) - last).total_seconds()
+            >= DATETIME_REFRESH_SECONDS
         ):
             self._append_datetime_event()
 

agent_core/core/impl/llm/interface.py

@@ -448,9 +448,7 @@ def _call_log_to_db(
             try:
                 ctx = _llm_call_ctx.get() or {}
                 start = ctx.get("start")
-                latency_ms = (
-                    int((time.perf_counter() - start) * 1000) if start else 0
-                )
+                latency_ms = int((time.perf_counter() - start) * 1000) if start else 0
                 self._record_llm_call(
                     LLMCallRecord(
                         provider=self.provider or "",
@@ -701,7 +699,8 @@ def create_session_cache(
             (self.provider == "byteplus" and self._byteplus_cache_manager)
             or (self.provider == "gemini" and self._gemini_cache_manager)
             or (
-                self.provider in ("openai", "deepseek", "grok", "openrouter", "glm", "fugu")
+                self.provider
+                in ("openai", "deepseek", "grok", "openrouter", "glm", "fugu")
                 and self.client
             )  # OpenAI/DeepSeek/Grok/OpenRouter use automatic caching with prompt_cache_key (and cache_control for Anthropic-routed OpenRouter models)
             or (
@@ -857,7 +856,8 @@ def has_session_cache(self, task_id: str, call_type: str) -> bool:
             if self.provider == "gemini" and self._gemini_cache_manager:
                 return True
             if (
-                self.provider in ("openai", "deepseek", "grok", "openrouter", "glm", "fugu")
+                self.provider
+                in ("openai", "deepseek", "grok", "openrouter", "glm", "fugu")
                 and self.client
             ):
                 return True
@@ -1111,9 +1111,7 @@ def _generate_response_with_session_sync(
                     {"role": "system", "content": effective_system_prompt}
                 ]
                 for msg in history:
-                    oa_messages.append(
-                        {"role": msg["role"], "content": msg["content"]}
-                    )
+                    oa_messages.append({"role": msg["role"], "content": msg["content"]})
                 oa_messages.append({"role": "user", "content": user_prompt})
 
                 logger.debug(
@@ -1131,9 +1129,7 @@ def _generate_response_with_session_sync(
                 assistant_content = response.get("content", "")
                 if assistant_content and not response.get("error"):
                     history.append({"role": "user", "content": user_prompt})
-                    history.append(
-                        {"role": "assistant", "content": assistant_content}
-                    )
+                    history.append({"role": "assistant", "content": assistant_content})
 
             return self._finalize_session_response(response, log_response)
 
@@ -1516,9 +1512,7 @@ def generate_response_with_session(
             log_response: Whether to log the response.
             prompt_name: Identity of the named prompt, for capture/profiling.
         """
-        self._begin_call(
-            prompt_name=prompt_name, call_type=call_type, task_id=task_id
-        )
+        self._begin_call(prompt_name=prompt_name, call_type=call_type, task_id=task_id)
         return self._generate_response_with_session_sync(
             task_id, call_type, user_prompt, system_prompt_for_new_session, log_response
         )
@@ -1545,9 +1539,7 @@ async def generate_response_with_session_async(
         """
         # Stamp here (caller's context) so asyncio.to_thread copies it into the
         # worker thread where capture runs.
-        self._begin_call(
-            prompt_name=prompt_name, call_type=call_type, task_id=task_id
-        )
+        self._begin_call(prompt_name=prompt_name, call_type=call_type, task_id=task_id)
         return await asyncio.to_thread(
             self._generate_response_with_session_sync,
             task_id,
@@ -1871,7 +1863,9 @@ def _generate_openai(
             if prompt_tokens_details:
                 cached_tokens = getattr(prompt_tokens_details, "cached_tokens", 0) or 0
             if not cached_tokens:
-                cached_tokens = getattr(response.usage, "prompt_cache_hit_tokens", 0) or 0
+                cached_tokens = (
+                    getattr(response.usage, "prompt_cache_hit_tokens", 0) or 0
+                )
 
             # Record cache metrics
             provider_label = self.provider  # "openai", "grok", "deepseek", etc.

agent_core/core/impl/memory/bm25_index.py

@@ -16,6 +16,7 @@
 
 try:
     from rank_bm25 import BM25Okapi
+
     _HAS_BM25 = True
 except ImportError:
     BM25Okapi = None

agent_core/core/impl/memory/entity_extractor.py

@@ -21,22 +21,89 @@
 from typing import List
 
 _STOP = {
-    "the", "a", "an", "and", "or", "but", "of", "in", "on", "at", "to", "for",
-    "with", "by", "from", "as", "is", "are", "was", "were", "be", "been", "being",
-    "have", "has", "had", "do", "does", "did", "will", "would", "should", "could",
-    "may", "might", "must", "can", "i", "you", "he", "she", "it", "we", "they",
-    "this", "that", "these", "those", "user", "agent", "task", "action", "event",
-    "memory", "system", "note", "today", "yesterday", "tomorrow", "monday",
-    "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday",
-    "january", "february", "march", "april", "may", "june", "july", "august",
-    "september", "october", "november", "december",
+    "the",
+    "a",
+    "an",
+    "and",
+    "or",
+    "but",
+    "of",
+    "in",
+    "on",
+    "at",
+    "to",
+    "for",
+    "with",
+    "by",
+    "from",
+    "as",
+    "is",
+    "are",
+    "was",
+    "were",
+    "be",
+    "been",
+    "being",
+    "have",
+    "has",
+    "had",
+    "do",
+    "does",
+    "did",
+    "will",
+    "would",
+    "should",
+    "could",
+    "may",
+    "might",
+    "must",
+    "can",
+    "i",
+    "you",
+    "he",
+    "she",
+    "it",
+    "we",
+    "they",
+    "this",
+    "that",
+    "these",
+    "those",
+    "user",
+    "agent",
+    "task",
+    "action",
+    "event",
+    "memory",
+    "system",
+    "note",
+    "today",
+    "yesterday",
+    "tomorrow",
+    "monday",
+    "tuesday",
+    "wednesday",
+    "thursday",
+    "friday",
+    "saturday",
+    "sunday",
+    "january",
+    "february",
+    "march",
+    "april",
+    "may",
+    "june",
+    "july",
+    "august",
+    "september",
+    "october",
+    "november",
+    "december",
 }
 
 # Capitalised words (incl. CamelCase), optionally chained: "Trading View",
 # "OpenAI", "CraftBot", "John Doe"
-_PROPER_NOUN_RE = re.compile(
-    r"\b[A-Z][A-Za-z0-9]*(?:[ \-_][A-Z][A-Za-z0-9]*)*\b"
-)
+_PROPER_NOUN_RE = re.compile(r"\b[A-Z][A-Za-z0-9]*(?:[ \-_][A-Z][A-Za-z0-9]*)*\b")
 
 # Quoted strings (single or double)
 _QUOTED_RE = re.compile(r"\"([^\"]{2,40})\"|'([^']{2,40})'")

agent_core/core/impl/memory/injector.py

@@ -38,6 +38,7 @@ def _is_memory_enabled() -> bool:
     outside the CraftBot app)."""
     try:
         from app.ui_layer.settings.memory_settings import is_memory_enabled
+
         return is_memory_enabled()
     except ImportError:
         return True

agent_core/core/impl/memory/manager.py

@@ -70,6 +70,7 @@ def _is_embedding_function_conflict(err: Exception) -> bool:
         "conflict" in msg or "already exists" in msg
     )
 
+
 # ───────────────────────── Embedding Model ─────────────────────────
 # ChromaDB's default is sentence-transformers/all-MiniLM-L6-v2 (22M params,
 # 2021). Verbatim self-similarity scores ~0.65; topical matches sit at
@@ -86,6 +87,7 @@ def _is_embedding_function_conflict(err: Exception) -> bool:
 # sentence-transformers model. Set to "default" to use ChromaDB's
 # bundled ONNX MiniLM.
 import os as _os
+
 MEMORY_EMBEDDING_MODEL = _os.environ.get(
     "MEMORY_EMBEDDING_MODEL", "BAAI/bge-small-en-v1.5"
 )
@@ -325,6 +327,7 @@ def _build_embedding_function():
             from chromadb.utils.embedding_functions import (
                 SentenceTransformerEmbeddingFunction,
             )
+
             return SentenceTransformerEmbeddingFunction(
                 model_name=MEMORY_EMBEDDING_MODEL
             )
@@ -488,8 +491,7 @@ def retrieve(
                     metadata={
                         k: v
                         for k, v in meta.items()
-                        if k
-                        not in ("file_path", "section_path", "title", "summary")
+                        if k not in ("file_path", "section_path", "title", "summary")
                     },
                 )
             )
@@ -753,9 +755,7 @@ def _chunk_markdown(self, content: str, file_path: str) -> List[MemoryChunk]:
             return self._chunk_memory_log(content, file_path)
         return self._chunk_by_sections(content, file_path)
 
-    def _chunk_memory_log(
-        self, content: str, file_path: str
-    ) -> List[MemoryChunk]:
+    def _chunk_memory_log(self, content: str, file_path: str) -> List[MemoryChunk]:
         """One chunk per ``[ts] [cat] content`` line.
 
         Each line is short enough on its own (memory items are capped at
@@ -813,9 +813,7 @@ def _chunk_memory_log(
 
         return chunks
 
-    def _chunk_by_sections(
-        self, content: str, file_path: str
-    ) -> List[MemoryChunk]:
+    def _chunk_by_sections(self, content: str, file_path: str) -> List[MemoryChunk]:
         """Original header-based chunker. Preserves existing behaviour for
         non-list markdown (AGENT.md, USER.md, PROACTIVE.md, ...).
         """

agent_core/core/impl/task/manager.py

@@ -383,6 +383,7 @@ def create_task(
         # tasks it's the trigger description. inject_memory_event no-ops if
         # nothing passes min_relevance, so noise is filtered automatically.
         from agent_core.core.impl.memory.injector import inject_memory_event
+
         inject_memory_event(query=task_instruction, session_id=task_id)
 
         self._set_agent_property("current_task_id", task_id)

agent_core/core/impl/video_gen/interface.py

@@ -524,7 +524,9 @@ def _openai_generate(
 
         if not paths:
             raise RuntimeError(
-                _classify_error("openai", first_error or RuntimeError("no result"), self.model)
+                _classify_error(
+                    "openai", first_error or RuntimeError("no result"), self.model
+                )
             )
         return paths
 
@@ -767,7 +769,9 @@ def _gemini_generate(
                 try:
                     data = self._gemini_client.download_video(uri, timeout=180)
                 except Exception as exc:
-                    raise RuntimeError(_classify_error("gemini", exc, self.model)) from exc
+                    raise RuntimeError(
+                        _classify_error("gemini", exc, self.model)
+                    ) from exc
             elif inline:
                 data = base64.b64decode(inline)
             else:
@@ -944,7 +948,9 @@ def _byteplus_generate(
 
         if not paths:
             raise RuntimeError(
-                _classify_error("byteplus", first_error or RuntimeError("no result"), self.model)
+                _classify_error(
+                    "byteplus", first_error or RuntimeError("no result"), self.model
+                )
             )
         return paths
 
@@ -1008,7 +1014,9 @@ def _byteplus_poll(
                 )
                 r.raise_for_status()
             except Exception as exc:
-                raise RuntimeError(_classify_error("byteplus", exc, self.model)) from exc
+                raise RuntimeError(
+                    _classify_error("byteplus", exc, self.model)
+                ) from exc
 
             data = r.json()
             status = (data.get("status") or "").lower()

agent_core/core/models/chatgpt_subscription_client.py

@@ -106,9 +106,7 @@ def __init__(
 class _Message:
     __slots__ = ("role", "content", "tool_calls")
 
-    def __init__(
-        self, role: str = "assistant", content: str = "", tool_calls=None
-    ):
+    def __init__(self, role: str = "assistant", content: str = "", tool_calls=None):
         self.role = role
         self.content = content
         self.tool_calls = tool_calls
@@ -319,7 +317,9 @@ def _consume_stream(stream: Any) -> Dict[str, Any]:
         elif etype == "response.failed":
             err_resp = getattr(event, "response", None)
             err = getattr(err_resp, "error", None) if err_resp else None
-            failure_payload = err or f"response.failed (no error attached, event={event!r})"
+            failure_payload = (
+                err or f"response.failed (no error attached, event={event!r})"
+            )
         elif etype == "error":
             failure_payload = getattr(event, "error", None) or repr(event)
         elif etype == "response.output_text.delta":
@@ -522,7 +522,9 @@ def _wrap_response(
         status = getattr(resp, "status", None)
         incomplete = getattr(resp, "incomplete_details", None)
         if embedded_error:
-            raise RuntimeError(f"Codex returned an error in the response body: {embedded_error}")
+            raise RuntimeError(
+                f"Codex returned an error in the response body: {embedded_error}"
+            )
         if status and status != "completed":
             raise RuntimeError(
                 f"Codex response ended with status={status!r}"
@@ -566,6 +568,7 @@ def _translate_backend_error(exc: Exception, model: str) -> Exception:
     plan = ""
     try:
         from craftos_integrations.integrations.llm_oauth.chatgpt import load as _load
+
         cred = _load()
         if cred is not None:
             plan = (getattr(cred, "plan", "") or "").lower()