feat(memory): add search score explanations (#5102)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

docs/core-concepts/memory-operations/search.mdx

@@ -156,6 +156,33 @@ const memories = memory.search("food preferences", {
   On Mem0 Platform v3, time-aware queries use Temporal Reasoning internally while preserving the normal search response shape. See <Link href="/platform/features/temporal-reasoning">Temporal Reasoning</Link>.
 </Note>
 
+### Explain OSS search scores
+
+OSS search combines semantic similarity with optional keyword and entity signals. Pass `explain=True` when tuning retrieval quality or debugging why a memory ranked where it did:
+
+<CodeGroup>
+```python Python
+results = m.search(
+    "food preferences",
+    filters={"user_id": "alice"},
+    explain=True,
+)
+
+print(results["results"][0]["score_details"])
+```
+
+```javascript JavaScript
+const results = await memory.search("food preferences", {
+    filters: { user_id: "alice" },
+    explain: true,
+});
+
+console.log(results.results[0].score_details);
+```
+</CodeGroup>
+
+Each result includes `score_details` with the semantic score, normalized BM25 score, entity boost, raw combined score, maximum possible score, final score, and threshold used for filtering. The field is omitted unless `explain` is enabled, so existing response shapes stay unchanged.
+
 ## Filter patterns
 
 Filters help narrow down search results. Common use cases:

docs/open-source/features/rest-api.mdx

@@ -226,6 +226,20 @@ curl -X POST http://localhost:8888/search \
   }'
 ```
 
+Set `explain` to inspect the scoring signals used by OSS hybrid search:
+
+```bash
+curl -X POST http://localhost:8888/search \
+  -H "Content-Type: application/json" \
+  -d '{
+    "query": "vegetable pizza",
+    "user_id": "alice",
+    "explain": true
+  }'
+```
+
+Each returned memory includes `score_details` only when explanation mode is enabled.
+
 ### Explore with OpenAPI docs
 
 1. Navigate to `http://localhost:8888/docs` (Compose) or `http://localhost:8000/docs` (raw Docker / uvicorn).

mem0-ts/src/oss/src/memory/index.ts

@@ -1064,7 +1064,7 @@ export class Memory {
       : {};
 
     await this._ensureInitialized();
-    const { topK = 20, threshold = 0.1 } = config;
+    const { topK = 20, threshold = 0.1, explain = false } = config;
 
     await this._captureEvent("search", {
       query_length: query.length,
@@ -1243,6 +1243,7 @@ export class Memory {
       entityBoosts,
       threshold ?? 0.1,
       topK,
+      explain,
     );
 
     // Step 9: Format results
@@ -1275,6 +1276,7 @@ export class Memory {
           ...(payload.user_id && { user_id: payload.user_id }),
           ...(payload.agent_id && { agent_id: payload.agent_id }),
           ...(payload.run_id && { run_id: payload.run_id }),
+          ...(scored.scoreDetails && { score_details: scored.scoreDetails }),
         };
       });
 

mem0-ts/src/oss/src/memory/memory.types.ts

@@ -17,6 +17,7 @@ export interface SearchMemoryOptions {
   topK?: number;
   filters?: SearchFilters;
   threshold?: number;
+  explain?: boolean;
 }
 
 export interface GetAllMemoryOptions {

mem0-ts/src/oss/src/utils/scoring.ts

@@ -56,10 +56,21 @@ export function normalizeBm25(
   return 1.0 / (1.0 + Math.exp(-steepness * (rawScore - midpoint)));
 }
 
+export interface ScoreDetails {
+  semanticScore: number;
+  bm25Score: number;
+  entityBoost: number;
+  rawScore: number;
+  maxPossibleScore: number;
+  finalScore: number;
+  threshold: number;
+}
+
 export interface ScoredResult {
   id: string;
   score: number;
   payload: Record<string, any>;
+  scoreDetails?: ScoreDetails;
 }
 
 /**
@@ -82,6 +93,7 @@ export interface ScoredResult {
  * @param entityBoosts - Map of memory ID to entity boost score.
  * @param threshold - Minimum semantic score to include a candidate.
  * @param topK - Maximum number of results to return.
+ * @param explain - Include scoreDetails in each result when true.
  * @returns Sorted list of scored results, highest score first.
  */
 export function scoreAndRank(
@@ -94,6 +106,7 @@ export function scoreAndRank(
   entityBoosts: Record<string, number>,
   threshold: number,
   topK: number,
+  explain: boolean = false,
 ): ScoredResult[] {
   const hasBm25 = Object.keys(bm25Scores).length > 0;
   const hasEntity = Object.keys(entityBoosts).length > 0;
@@ -126,11 +139,23 @@ export function scoreAndRank(
     const rawCombined = semanticScore + bm25Score + entityBoost;
     const combined = Math.min(rawCombined / maxPossible, 1.0);
 
-    scored.push({
+    const entry: ScoredResult = {
       id: memIdStr,
       score: combined,
       payload: result.payload,
-    });
+    };
+    if (explain) {
+      entry.scoreDetails = {
+        semanticScore,
+        bm25Score,
+        entityBoost,
+        rawScore: rawCombined,
+        maxPossibleScore: maxPossible,
+        finalScore: combined,
+        threshold,
+      };
+    }
+    scored.push(entry);
   }
 
   scored.sort((a, b) => b.score - a.score);

mem0-ts/src/oss/tests/scoring.test.ts

@@ -0,0 +1,49 @@
+/// <reference types="jest" />
+
+import { scoreAndRank } from "../src/utils/scoring";
+
+describe("scoreAndRank", () => {
+  const results = [
+    { id: "a", score: 0.8, payload: { data: "mem a" } },
+    { id: "b", score: 0.5, payload: { data: "mem b" } },
+  ];
+
+  it("omits scoreDetails by default", () => {
+    const scored = scoreAndRank(results, {}, {}, 0.1, 10);
+    expect(scored[0].scoreDetails).toBeUndefined();
+    expect(scored[1].scoreDetails).toBeUndefined();
+  });
+
+  it("omits scoreDetails when explain is false", () => {
+    const scored = scoreAndRank(results, {}, {}, 0.1, 10, false);
+    expect(scored[0].scoreDetails).toBeUndefined();
+  });
+
+  it("includes scoreDetails when explain is true", () => {
+    const bm25 = { a: 0.6 };
+    const entity = { a: 0.3 };
+    const scored = scoreAndRank(results, bm25, entity, 0.1, 10, true);
+
+    const details = scored[0].scoreDetails!;
+    expect(details).toBeDefined();
+    expect(details.semanticScore).toBe(0.8);
+    expect(details.bm25Score).toBe(0.6);
+    expect(details.entityBoost).toBe(0.3);
+    expect(details.rawScore).toBeCloseTo(1.7);
+    expect(details.maxPossibleScore).toBe(2.5);
+    expect(details.finalScore).toBeCloseTo(0.68);
+    expect(details.threshold).toBe(0.1);
+  });
+
+  it("includes scoreDetails for results without bm25/entity signals", () => {
+    const scored = scoreAndRank(results, {}, {}, 0.1, 10, true);
+
+    const details = scored[0].scoreDetails!;
+    expect(details.semanticScore).toBe(0.8);
+    expect(details.bm25Score).toBe(0);
+    expect(details.entityBoost).toBe(0);
+    expect(details.rawScore).toBe(0.8);
+    expect(details.maxPossibleScore).toBe(1.0);
+    expect(details.finalScore).toBe(0.8);
+  });
+});

mem0/memory/main.py

@@ -1132,6 +1132,7 @@ class Memory(MemoryBase):
         filters: Optional[Dict[str, Any]] = None,
         threshold: float = 0.1,
         rerank: bool = False,
+        explain: bool = False,
         **kwargs,
     ):
         """
@@ -1162,6 +1163,7 @@ class Memory(MemoryBase):
                 - {"NOT": [filter1]} - logical NOT
             threshold (float, optional): Minimum score for a memory to be included. Defaults to 0.1.
             rerank (bool, optional): Whether to rerank results. Defaults to False.
+            explain (bool, optional): Whether to include score_details for each result. Defaults to False.
 
         Returns:
             dict: A dictionary containing the search results under a "results" key.
@@ -1221,11 +1223,12 @@ class Memory(MemoryBase):
                 "encoded_ids": encoded_ids,
                 "sync_type": "sync",
                 "threshold": threshold,
+                "explain": explain,
                 "advanced_filters": bool(filters and self._has_advanced_operators(filters)),
             },
         )
 
-        original_memories = self._search_vector_store(query, effective_filters, limit, threshold)
+        original_memories = self._search_vector_store(query, effective_filters, limit, threshold, explain=explain)
 
         # Apply reranking if enabled and reranker is available
         if rerank and self.reranker and original_memories:
@@ -1341,7 +1344,7 @@ class Memory(MemoryBase):
                 return True
         return False
 
-    def _search_vector_store(self, query, filters, limit, threshold=0.1):
+    def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=False):
         # Guard against None threshold (backward compat)
         if threshold is None:
             threshold = 0.1
@@ -1396,6 +1399,7 @@ class Memory(MemoryBase):
             entity_boosts=entity_boosts,
             threshold=threshold,
             top_k=limit,
+            explain=explain,
         )
 
         # Step 9: Format results
@@ -1433,6 +1437,8 @@ class Memory(MemoryBase):
                 if not memory_item_dict.get("metadata"):
                     memory_item_dict["metadata"] = {}
                 memory_item_dict["metadata"].update(additional_metadata)
+            if explain and "score_details" in scored:
+                memory_item_dict["score_details"] = scored["score_details"]
 
             original_memories.append(memory_item_dict)
 
@@ -2568,6 +2574,7 @@ class AsyncMemory(MemoryBase):
         filters: Optional[Dict[str, Any]] = None,
         threshold: float = 0.1,
         rerank: bool = False,
+        explain: bool = False,
         **kwargs,
     ):
         """
@@ -2598,6 +2605,7 @@ class AsyncMemory(MemoryBase):
                 - {"NOT": [filter1]} - logical NOT
             threshold (float, optional): Minimum score for a memory to be included. Defaults to 0.1.
             rerank (bool, optional): Whether to rerank results. Defaults to False.
+            explain (bool, optional): Whether to include score_details for each result. Defaults to False.
 
         Returns:
             dict: A dictionary containing the search results under a "results" key.
@@ -2659,11 +2667,12 @@ class AsyncMemory(MemoryBase):
                 "encoded_ids": encoded_ids,
                 "sync_type": "async",
                 "threshold": threshold,
+                "explain": explain,
                 "advanced_filters": bool(filters and self._has_advanced_operators(filters)),
             },
         )
 
-        original_memories = await self._search_vector_store(query, effective_filters, limit, threshold)
+        original_memories = await self._search_vector_store(query, effective_filters, limit, threshold, explain=explain)
 
         # Apply reranking if enabled and reranker is available
         if rerank and self.reranker and original_memories:
@@ -2782,7 +2791,7 @@ class AsyncMemory(MemoryBase):
                 return True
         return False
 
-    async def _search_vector_store(self, query, filters, limit, threshold=0.1):
+    async def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=False):
         if threshold is None:
             threshold = 0.1
 
@@ -2836,6 +2845,7 @@ class AsyncMemory(MemoryBase):
             entity_boosts=entity_boosts,
             threshold=threshold,
             top_k=limit,
+            explain=explain,
         )
 
         # Step 9: Format results
@@ -2872,6 +2882,8 @@ class AsyncMemory(MemoryBase):
                 if not memory_item_dict.get("metadata"):
                     memory_item_dict["metadata"] = {}
                 memory_item_dict["metadata"].update(additional_metadata)
+            if explain and "score_details" in scored:
+                memory_item_dict["score_details"] = scored["score_details"]
 
             original_memories.append(memory_item_dict)
 

mem0/utils/scoring.py

@@ -63,6 +63,7 @@ def score_and_rank(
     entity_boosts: Dict[str, float],
     threshold: float,
     top_k: int,
+    explain: bool = False,
 ) -> List[Dict[str, Any]]:
     """Score candidates additively and return top-k results.
 
@@ -79,6 +80,14 @@ def score_and_rank(
         - Semantic + BM25 + entity: max_possible = 2.5
         - Semantic + entity (no BM25): max_possible = 1.5
 
+    Args:
+        semantic_results: Candidate memories from vector search.
+        bm25_scores: Normalized keyword scores keyed by memory ID.
+        entity_boosts: Entity-link boosts keyed by memory ID.
+        threshold: Minimum semantic score required before hybrid scoring.
+        top_k: Maximum number of results to return.
+        explain: Include score_details in each result when true.
+
     Returns:
         List of scored result dicts sorted by combined score descending.
     """
@@ -109,13 +118,22 @@ def score_and_rank(
         raw_combined = semantic_score + bm25_score + entity_boost
         combined = min(raw_combined / max_possible, 1.0)
 
-        scored.append(
-            {
+        scored_result = {
             "id": mem_id_str,
             "score": combined,
             "payload": result.get("payload"),
         }
-        )
+        if explain:
+            scored_result["score_details"] = {
+                "semantic_score": semantic_score,
+                "bm25_score": bm25_score,
+                "entity_boost": entity_boost,
+                "raw_score": raw_combined,
+                "max_possible_score": max_possible,
+                "final_score": combined,
+                "threshold": threshold,
+            }
+        scored.append(scored_result)
 
     scored.sort(key=lambda x: x["score"], reverse=True)
     return scored[:top_k]

server/main.py

@@ -199,6 +199,7 @@ class SearchRequest(BaseModel):
     filters: Optional[Dict[str, Any]] = None
     top_k: Optional[int] = Field(None, description="Maximum number of results to return.")
     threshold: Optional[float] = Field(None, description="Minimum similarity score for results.")
+    explain: Optional[bool] = Field(None, description="Include score details for each search result.")
 
 
 class GenerateInstructionsRequest(BaseModel):

tests/test_memory.py

@@ -143,6 +143,42 @@ def test_search_handles_incomplete_payloads(mock_sqlite, mock_llm_factory, mock_
     assert result[0]["memory"] == "content"
 
 
+@patch('mem0.memory.main.extract_entities', return_value=[])
+@patch('mem0.utils.factory.EmbedderFactory.create')
+@patch('mem0.utils.factory.VectorStoreFactory.create')
+@patch('mem0.utils.factory.LlmFactory.create')
+@patch('mem0.memory.storage.SQLiteManager')
+def test_search_explain_includes_score_details(
+    mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory, _mock_extract_entities
+):
+    mock_embedder = MagicMock()
+    mock_embedder.embed.return_value = [0.1, 0.2, 0.3]
+    mock_embedder_factory.return_value = mock_embedder
+
+    mock_vector_store = MagicMock()
+    mock_vector_store.search.return_value = [
+        MockVectorMemory("mem_1", {"data": "content", "user_id": "test"}, score=0.8)
+    ]
+    mock_vector_store.keyword_search.return_value = [
+        MockVectorMemory("mem_1", {"data": "content", "user_id": "test"}, score=5.0)
+    ]
+    mock_vector_factory.return_value = mock_vector_store
+    mock_llm_factory.return_value = MagicMock()
+    mock_sqlite.return_value = MagicMock()
+
+    from mem0.memory.main import Memory as MemoryClass
+    memory = MemoryClass(MemoryConfig())
+
+    result = memory.search("test query", filters={"user_id": "test"}, explain=True)
+
+    details = result["results"][0]["score_details"]
+    assert details["semantic_score"] == 0.8
+    assert details["bm25_score"] > 0
+    assert details["entity_boost"] == 0.0
+    assert details["final_score"] == result["results"][0]["score"]
+    assert details["threshold"] == 0.1
+
+
 @patch('mem0.utils.factory.EmbedderFactory.create')
 @patch('mem0.utils.factory.VectorStoreFactory.create')
 @patch('mem0.utils.factory.LlmFactory.create')
@@ -934,4 +970,3 @@ async def test_async_create_memory_stores_text_lemmatized(mock_sqlite, mock_llm_
         "AsyncMemory._create_memory must store text_lemmatized for BM25 keyword search"
     )
     assert payload[0]["text_lemmatized"] != "", "text_lemmatized must not be empty"
-

tests/utils/test_scoring.py

@@ -126,6 +126,28 @@ class TestScoreAndRank:
         scored = score_and_rank(results, bm25, entity, threshold=0.1, top_k=10)
         assert scored[0]["score"] <= 1.0
 
+    def test_explain_includes_score_details(self):
+        results = [{"id": "a", "score": 0.8, "payload": {"data": "mem a"}}]
+        bm25 = {"a": 0.6}
+        entity = {"a": 0.3}
+        scored = score_and_rank(results, bm25, entity, threshold=0.1, top_k=10, explain=True)
+
+        details = scored[0]["score_details"]
+        assert details == {
+            "semantic_score": 0.8,
+            "bm25_score": 0.6,
+            "entity_boost": 0.3,
+            "raw_score": pytest.approx(1.7),
+            "max_possible_score": 2.5,
+            "final_score": pytest.approx(0.68),
+            "threshold": 0.1,
+        }
+
+    def test_score_details_are_omitted_by_default(self):
+        results = [{"id": "a", "score": 0.8, "payload": {"data": "mem a"}}]
+        scored = score_and_rank(results, {}, {}, threshold=0.1, top_k=10)
+        assert "score_details" not in scored[0]
+
 
 class TestEntityBoostWeight:
     def test_weight_value(self):