Merge branch 'dev' into main

Author: fridayL

README.md

@@ -251,6 +251,7 @@ MemOS is licensed under the [Apache 2.0 License](./LICENSE).
 
 Stay up to date with the latest MemOS announcements, releases, and community highlights.
 
+
 - **2025-08-07** - 🎉 *MemOS v1.0.0 (MemCube Release)*: First MemCube with word game demo, LongMemEval evaluation, BochaAISearchRetriever integration, NebulaGraph support, enhanced search capabilities, and official Playground launch.
 - **2025-07-29** – 🎉 *MemOS v0.2.2 (Nebula Update)*: Internet search+Nebula DB integration, refactored memory scheduler, KV Cache stress tests, MemCube Cookbook release (CN/EN), and 4b/1.7b/0.6b memory ops models.
 - **2025-07-21** – 🎉 *MemOS v0.2.1 (Neo Release)*: Lightweight Neo version with plaintext+KV Cache functionality, Docker/multi-tenant support, MCP expansion, and new Cookbook/Mud game examples.

src/memos/api/config.py

@@ -136,7 +136,7 @@ def get_internet_config() -> dict[str, Any]:
                                 "max_tokens": 5000,
                                 "top_p": 0.95,
                                 "top_k": 20,
-                                "api_key": "EMPTY",
+                                "api_key": os.getenv("MEMRADER_API_KEY", "EMPTY"),
                                 "api_base": os.getenv("MEMRADER_API_BASE"),
                                 "remove_think_prefix": True,
                                 "extra_body": {"chat_template_kwargs": {"enable_thinking": False}},

src/memos/graph_dbs/nebular.py

@@ -1519,7 +1519,6 @@ def _parse_value(self, value: Any) -> Any:
 
         return prim  # already a Python primitive
 
-    @timed
     def _parse_node(self, props: dict[str, Any]) -> dict[str, Any]:
         parsed = {k: self._parse_value(v) for k, v in props.items()}
 

src/memos/memories/textual/tree_text_memory/organize/reorganizer.py

@@ -242,7 +242,7 @@ def optimize_structure(
                     except Exception as e:
                         logger.warning(
                             f"[Reorganize] Cluster processing "
-                            f"failed: {e}, trace: {traceback.format_exc()}"
+                            f"failed: {e}, cluster_nodes: {cluster_nodes}, trace: {traceback.format_exc()}"
                         )
                 logger.info("[GraphStructure Reorganize] Structure optimization finished.")
 
@@ -333,7 +333,9 @@ def _process_cluster_and_write(
 
             logger.info("[Reorganizer] Cluster relation/reasoning done.")
 
-    def _local_subcluster(self, cluster_nodes: list[GraphDBNode]) -> list[list[GraphDBNode]]:
+    def _local_subcluster(
+        self, cluster_nodes: list[GraphDBNode], max_length: int = 8000
+    ) -> (list)[list[GraphDBNode]]:
         """
         Use LLM to split a large cluster into semantically coherent sub-clusters.
         """
@@ -347,7 +349,9 @@ def _local_subcluster(self, cluster_nodes: list[GraphDBNode]) -> list[list[Graph
             scene_lines.append(line)
 
         joined_scene = "\n".join(scene_lines)
-        prompt = LOCAL_SUBCLUSTER_PROMPT.replace("{joined_scene}", joined_scene)
+        if len(joined_scene) > max_length:
+            logger.warning(f"Sub-cluster too long: {joined_scene}")
+        prompt = LOCAL_SUBCLUSTER_PROMPT.replace("{joined_scene}", joined_scene[:max_length])
 
         messages = [{"role": "user", "content": prompt}]
         response_text = self.llm.generate(messages)
@@ -394,41 +398,73 @@ def _partition(self, nodes, min_cluster_size: int = 10, max_cluster_size: int =
             )
             return [nodes]
 
-        def recursive_clustering(nodes_list):
+        def recursive_clustering(nodes_list, depth=0):
             """Recursively split clusters until each is <= max_cluster_size."""
+            indent = "  " * depth
+            logger.info(
+                f"{indent}[Recursive] Start clustering {len(nodes_list)} nodes at depth {depth}"
+            )
+
             if len(nodes_list) <= max_cluster_size:
+                logger.info(
+                    f"{indent}[Recursive] Node count <= {max_cluster_size}, stop splitting."
+                )
                 return [nodes_list]
-
             # Try kmeans with k = ceil(len(nodes) / max_cluster_size)
-            x = np.array([n.metadata.embedding for n in nodes_list if n.metadata.embedding])
+            x_nodes = [n for n in nodes_list if n.metadata.embedding]
+            x = np.array([n.metadata.embedding for n in x_nodes])
+
             if len(x) < min_cluster_size:
+                logger.info(
+                    f"{indent}[Recursive] Too few embeddings ({len(x)}), skipping clustering."
+                )
                 return [nodes_list]
 
             k = min(len(x), (len(nodes_list) + max_cluster_size - 1) // max_cluster_size)
-            k = max(1, min(k, len(x)))
+            k = max(1, k)
 
             try:
+                logger.info(f"{indent}[Recursive] Clustering with k={k} on {len(x)} points.")
                 kmeans = MiniBatchKMeans(n_clusters=k, batch_size=256, random_state=42)
                 labels = kmeans.fit_predict(x)
 
                 label_groups = defaultdict(list)
-                for node, label in zip(nodes_list, labels, strict=False):
+                for node, label in zip(x_nodes, labels, strict=False):
                     label_groups[label].append(node)
 
+                # Map: label -> nodes with no embedding (fallback group)
+                no_embedding_nodes = [n for n in nodes_list if not n.metadata.embedding]
+                if no_embedding_nodes:
+                    logger.warning(
+                        f"{indent}[Recursive] {len(no_embedding_nodes)} nodes have no embedding. Added to largest cluster."
+                    )
+                    # Assign to largest cluster
+                    largest_label = max(label_groups.items(), key=lambda kv: len(kv[1]))[0]
+                    label_groups[largest_label].extend(no_embedding_nodes)
+
                 result = []
-                for sub_group in label_groups.values():
-                    result.extend(recursive_clustering(sub_group))
+                for label, sub_group in label_groups.items():
+                    logger.info(f"{indent}  Cluster-{label}: {len(sub_group)} nodes")
+                    result.extend(recursive_clustering(sub_group, depth=depth + 1))
                 return result
+
             except Exception as e:
-                logger.warning(f"Clustering failed: {e}, falling back to single cluster.")
+                logger.warning(
+                    f"{indent}[Recursive] Clustering failed: {e}, fallback to one cluster."
+                )
                 return [nodes_list]
 
         raw_clusters = recursive_clustering(nodes)
         filtered_clusters = [c for c in raw_clusters if len(c) > min_cluster_size]
+
+        logger.info(f"[KMeansPartition] Total clusters before filtering: {len(raw_clusters)}")
+        for i, cluster in enumerate(raw_clusters):
+            logger.info(f"[KMeansPartition]   Cluster-{i}: {len(cluster)} nodes")
+
         logger.info(
-            f"[KMeansPartition] Total clusters created: {len(raw_clusters)}, "
-            f"kept {len(filtered_clusters)} (>{min_cluster_size})."
+            f"[KMeansPartition] Clusters after filtering (>{min_cluster_size}): {len(filtered_clusters)}"
         )
+
         return filtered_clusters
 
     def _summarize_cluster(self, cluster_nodes: list[GraphDBNode], scope: str) -> GraphDBNode:

src/memos/memories/textual/tree_text_memory/retrieve/searcher.py

@@ -145,19 +145,18 @@ def _retrieve_paths(self, query, parsed_goal, query_embedding, info, top_k, mode
                     memory_type,
                 )
             )
-            if parsed_goal.internet_search:
-                tasks.append(
-                    executor.submit(
-                        self._retrieve_from_internet,
-                        query,
-                        parsed_goal,
-                        query_embedding,
-                        top_k,
-                        info,
-                        mode,
-                        memory_type,
-                    )
+            tasks.append(
+                executor.submit(
+                    self._retrieve_from_internet,
+                    query,
+                    parsed_goal,
+                    query_embedding,
+                    top_k,
+                    info,
+                    mode,
+                    memory_type,
                 )
+            )
 
             results = []
             for t in tasks:
@@ -223,16 +222,16 @@ def _retrieve_from_internet(
         self, query, parsed_goal, query_embedding, top_k, info, mode, memory_type
     ):
         """Retrieve and rerank from Internet source"""
-        if not self.internet_retriever or mode == "fast" or not parsed_goal.internet_search:
-            logger.info(
-                f"[PATH-C] '{query}' Skipped (no retriever, fast mode, or no internet_search flag)"
-            )
+        if not self.internet_retriever or mode == "fast":
+            logger.info(f"[PATH-C] '{query}' Skipped (no retriever, fast mode)")
             return []
         if memory_type not in ["All"]:
             return []
+        logger.info(f"[PATH-C] '{query}' Retrieving from internet...")
         items = self.internet_retriever.retrieve_from_internet(
             query=query, top_k=top_k, parsed_goal=parsed_goal, info=info
         )
+        logger.info(f"[PATH-C] '{query}' Retrieved from internet {len(items)} items: {items}")
         return self.reranker.rerank(
             query=query,
             query_embedding=query_embedding[0],