fix(memory): keep numpy scalars out of metadata and API JSON

plugins/_memory/AGENTS.md

@@ -21,6 +21,7 @@
 
 ## Work Guidance
 
+- Keep dashboard metadata JSON-safe without changing shared API serialization.
 - Coordinate tool, prompt, and consolidation changes so saved memories remain useful and bounded.
 
 ## Verification

plugins/_memory/api/memory_dashboard.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 from helpers.api import ApiHandler, Request, Response
 from helpers import files
 from helpers.localization import Localization
@@ -216,7 +218,10 @@ def get_sort_key(m):
 
     def _format_memory_for_dashboard(self, m: Document) -> dict:
         """Format a memory document for the dashboard."""
-        metadata = m.metadata
+        metadata = dict(m.metadata)
+        similarity = metadata.get("_consolidation_similarity")
+        if isinstance(similarity, np.generic):
+            metadata["_consolidation_similarity"] = float(similarity)
         timestamp = self._serialize_memory_timestamp(metadata.get("timestamp", "unknown"))
         return {
             "id": metadata.get("id", "unknown"),

plugins/_memory/helpers/memory.py

@@ -594,7 +594,7 @@ def _cosine_normalizer(val: float) -> float:
         res = max(
             0, min(1, res)
         )  # float precision can cause values like 1.0000000596046448
-        return res
+        return float(res)  # native float, not numpy scalar (JSON serializable)
 
     @staticmethod
     def format_docs_plain(docs: list[Document]) -> list[str]:

plugins/_memory/helpers/memory_consolidation.py

@@ -344,7 +344,7 @@ async def _find_similar_memories(
             filter=f"area == '{area}'"
         )
         for doc, score in semantic_results:
-            doc.metadata['_consolidation_similarity'] = score
+            doc.metadata['_consolidation_similarity'] = float(score) if score is not None else 0.0
             all_similar.append(doc)
 
         # Step 3: Keyword-based searches with real scores
@@ -358,7 +358,7 @@ async def _find_similar_memories(
                     filter=f"area == '{area}'"
                 )
                 for doc, score in keyword_results:
-                    doc.metadata['_consolidation_similarity'] = score
+                    doc.metadata['_consolidation_similarity'] = float(score) if score is not None else 0.0
                     all_similar.append(doc)
 
         # Step 4: Deduplicate by document ID, keep highest score per memory ID

tests/test_memory_json_serialization.py

@@ -0,0 +1,33 @@
+import json
+import threading
+
+import numpy as np
+from langchain_core.documents import Document
+
+from plugins._memory.api.memory_dashboard import MemoryDashboard
+from plugins._memory.helpers.memory import Memory
+
+
+def test_cosine_normalizer_returns_native_float():
+    score = Memory._cosine_normalizer(np.float32(0.8))
+
+    assert type(score) is float
+
+
+def test_memory_dashboard_serializes_legacy_numpy_similarity():
+    dashboard = MemoryDashboard(app=None, thread_lock=threading.RLock())
+    document = Document(
+        page_content="legacy memory",
+        metadata={
+            "id": "memory-1",
+            "area": "main",
+            "timestamp": "unknown",
+            "_consolidation_similarity": np.float32(0.75),
+        },
+    )
+
+    formatted = dashboard._format_memory_for_dashboard(document)
+
+    assert type(formatted["metadata"]["_consolidation_similarity"]) is float
+    assert isinstance(document.metadata["_consolidation_similarity"], np.float32)
+    json.dumps(formatted)