agentic

Author: nicoloboschi

hindsight-api/hindsight_api/engine/memory_engine.py

@@ -3590,7 +3590,7 @@ async def refresh_mental_model(
         }
 
         # Build query for the agent - instruct to explore multiple facets
-        query = f"""Generate a comprehensive summary about '{model['name']}': {model['description']}
+        query = f"""Generate a comprehensive summary about '{model["name"]}': {model["description"]}
 
 To create a thorough summary:
 1. Use multiple recall queries to explore different aspects and facets of this topic

hindsight-api/hindsight_api/engine/reflect/agent.py

@@ -133,7 +133,9 @@ def _log_completion(answer: str, iterations: int, forced: bool = False):
             )
             llm_trace.append({"scope": f"agent_{iteration + 1}", "duration_ms": int((time.time() - llm_start) * 1000)})
         except Exception as e:
-            llm_trace.append({"scope": f"agent_{iteration + 1}_err", "duration_ms": int((time.time() - llm_start) * 1000)})
+            llm_trace.append(
+                {"scope": f"agent_{iteration + 1}_err", "duration_ms": int((time.time() - llm_start) * 1000)}
+            )
             logger.warning(f"[REFLECT {reflect_id}] LLM call failed: {e}, forcing final response")
             prompt = build_final_prompt(query, context_history, bank_profile, context)
             llm_start = time.time()