chore: bump version to v1.0.0

.github/workflows/python-tests.yml

@@ -41,7 +41,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
     - name: Install poetry
-      run: pipx install poetry
+      # This is a temporary fix to ensure compatibility with Poetry & virtualenv
+      # Revert to the original installation method once the poetry==2.1.4 is released
+      run: |
+        echo "virtualenv==20.32.0" > constraints.txt
+        pipx install poetry==2.1.3 --pip-args="--constraint=constraints.txt"
+        rm constraints.txt
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
       with:

.gitignore

@@ -17,6 +17,9 @@ evaluation/configs/*
 .env
 evaluation/scripts/personamem
 
+# benchmarks
+benchmarks/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -251,6 +251,11 @@ 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.
+- **2025-07-11** – 🎉 *MemOS v0.2.0 (Cross-Platform)*: Added doc search/bilingual UI, MemReader-4B (local deploy), full Win/Mac/Linux support, and playground end-to-end connection.
 - **2025-07-07** – 🎉 *MemOS 1.0 (Stellar) Preview Release*: A SOTA Memory OS for LLMs is now open-sourced.
 - **2025-07-04** – 🎉 *MemOS Paper Released*: [MemOS: A Memory OS for AI System](https://arxiv.org/abs/2507.03724) was published on arXiv.
 - **2025-05-28** – 🎉 *Short Paper Uploaded*: [MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models](https://arxiv.org/abs/2505.22101) was published on arXiv.

docs/openapi.json

@@ -884,7 +884,7 @@
             "type": "string",
             "title": "Session Id",
             "description": "Session ID for the MOS. This is used to distinguish between different dialogue",
-            "default": "842877f4-c3f7-4c22-ad38-5950026870fe"
+            "default": "0ce84b9c-0615-4b9d-83dd-fba50537d5d3"
           },
           "chat_model": {
             "$ref": "#/components/schemas/LLMConfigFactory",

evaluation/scripts/run_locomo_eval.sh

@@ -6,12 +6,12 @@ VERSION="072001"
 WORKERS=10
 TOPK=20
 
-# echo "Running locomo_ingestion.py..."
-# CUDA_VISIBLE_DEVICES=0 python scripts/locomo/locomo_ingestion.py --lib $LIB --version $VERSION --workers $WORKERS
-# if [ $? -ne 0 ]; then
-#     echo "Error running locomo_ingestion.py"
-#     exit 1
-# fi
+ echo "Running locomo_ingestion.py..."
+ CUDA_VISIBLE_DEVICES=0 python scripts/locomo/locomo_ingestion.py --lib $LIB --version $VERSION --workers $WORKERS
+ if [ $? -ne 0 ]; then
+     echo "Error running locomo_ingestion.py"
+     exit 1
+ fi
 
 echo "Running locomo_search.py..."
 CUDA_VISIBLE_DEVICES=0 python scripts/locomo/locomo_search.py --lib $LIB --version $VERSION --top_k $TOPK --workers $WORKERS

examples/basic_modules/nebular_example.py

@@ -23,7 +23,7 @@ def show(nebular_data):
 
     tree_config = Neo4jGraphDBConfig.from_json_file("../../examples/data/config/neo4j_config.json")
     tree_config.use_multi_db = True
-    tree_config.db_name = "nebular-show"
+    tree_config.db_name = "nebular-show2"
 
     neo4j_db = Neo4jGraphDB(tree_config)
     neo4j_db.clear()
@@ -42,6 +42,7 @@ def show(nebular_data):
     }
 )
 embedder = EmbedderFactory.from_config(embedder_config)
+embedder_dimension = 3072
 
 
 def embed_memory_item(memory: str) -> list[float]:
@@ -62,7 +63,7 @@ def example_multi_db(db_name: str = "paper"):
             "space": db_name,
             "use_multi_db": True,
             "auto_create": True,
-            "embedding_dimension": 3072,
+            "embedding_dimension": embedder_dimension,
         },
     )
 
@@ -121,7 +122,7 @@ def example_shared_db(db_name: str = "shared-traval-group"):
                 "user_name": user_name,
                 "use_multi_db": False,
                 "auto_create": True,
-                "embedding_dimension": 3072,
+                "embedding_dimension": embedder_dimension,
             },
         )
 
@@ -208,7 +209,7 @@ def example_shared_db(db_name: str = "shared-traval-group"):
             "space": db_name,
             "user_name": user_list[0],
             "auto_create": True,
-            "embedding_dimension": 3072,
+            "embedding_dimension": embedder_dimension,
             "use_multi_db": False,
         },
     )
@@ -238,7 +239,7 @@ def run_user_session(
             "user_name": user_name,
             "use_multi_db": False,
             "auto_create": True,
-            "embedding_dimension": 3072,
+            "embedding_dimension": embedder_dimension,
         },
     )
     graph = GraphStoreFactory.from_config(config)
@@ -404,10 +405,10 @@ def example_complex_shared_db(db_name: str = "shared-traval-group-complex"):
 
 if __name__ == "__main__":
     print("\n=== Example: Multi-DB ===")
-    example_multi_db(db_name="paper")
+    example_multi_db(db_name="paper-new")
 
     print("\n=== Example: Single-DB ===")
-    example_shared_db(db_name="shared_traval_group")
+    example_shared_db(db_name="shared_traval_group-new")
 
     print("\n=== Example: Single-DB-Complex ===")
-    example_complex_shared_db(db_name="shared-traval-group-complex-new11")
+    example_complex_shared_db(db_name="shared-traval-group-complex-new2")

examples/basic_modules/textual_memory_internet_search_example.py

@@ -0,0 +1,52 @@
+"""
+Example: Using InternetRetrieverFactory with BochaAISearchRetriever
+"""
+
+from memos.configs.embedder import EmbedderConfigFactory
+from memos.configs.internet_retriever import InternetRetrieverConfigFactory
+from memos.embedders.factory import EmbedderFactory
+from memos.memories.textual.tree_text_memory.retrieve.internet_retriever_factory import (
+    InternetRetrieverFactory,
+)
+
+
+# ========= 1. Create an embedder =========
+embedder_config = EmbedderConfigFactory.model_validate(
+    {
+        "backend": "ollama",  # Or "sentence_transformer", etc.
+        "config": {
+            "model_name_or_path": "nomic-embed-text:latest",
+        },
+    }
+)
+embedder = EmbedderFactory.from_config(embedder_config)
+
+# ========= 2. Create retriever config for BochaAI =========
+retriever_config = InternetRetrieverConfigFactory.model_validate(
+    {
+        "backend": "bocha",
+        "config": {
+            "api_key": "sk-xxx",  # Your BochaAI API Key
+            "max_results": 5,
+            "reader": {  # Reader config for chunking web content
+                "backend": "simple_struct",
+                "config": {  # your simple struct reader config
+                },
+            },
+        },
+    }
+)
+
+# ========= 3. Build retriever instance via factory =========
+retriever = InternetRetrieverFactory.from_config(retriever_config, embedder)
+
+# ========= 4. Run BochaAI Web Search =========
+print("=== Scenario 1: Web Search (BochaAI) ===")
+query_web = "Alibaba 2024 ESG report"
+results_web = retriever.retrieve_from_internet(query_web)
+
+print(f"Retrieved {len(results_web)} memory items.")
+for idx, item in enumerate(results_web, 1):
+    print(f"[{idx}] {item.memory[:500]}...")  # preview first 100 chars
+
+print("==" * 20)

examples/core_memories/tree_textual_memory.py

@@ -1,32 +1,15 @@
 import time
 
 from memos import log
-from memos.configs.embedder import EmbedderConfigFactory
 from memos.configs.mem_reader import SimpleStructMemReaderConfig
 from memos.configs.memory import TreeTextMemoryConfig
-from memos.embedders.factory import EmbedderFactory
 from memos.mem_reader.simple_struct import SimpleStructMemReader
 from memos.memories.textual.tree import TreeTextMemory
 
 
 logger = log.get_logger(__name__)
 
 
-embedder_config = EmbedderConfigFactory.model_validate(
-    {
-        "backend": "ollama",
-        "config": {
-            "model_name_or_path": "nomic-embed-text:latest",
-        },
-    }
-)
-embedder = EmbedderFactory.from_config(embedder_config)
-
-
-def embed_memory_item(memory: str) -> list[float]:
-    return embedder.embed([memory])[0]
-
-
 tree_config = TreeTextMemoryConfig.from_json_file(
     "examples/data/config/tree_config_shared_database.json"
 )

examples/data/config/mem_scheduler/general_scheduler_config.yaml

@@ -1,9 +1,11 @@
 backend: general_scheduler
 config:
   top_k: 10
-  top_n: 10
   act_mem_update_interval: 30
   context_window_size: 10
   thread_pool_max_workers: 5
   consume_interval_seconds: 1
+  working_mem_monitor_capacity: 20
+  activation_mem_monitor_capacity: 5
   enable_parallel_dispatch: true
+  enable_activation_memory: true

examples/data/config/mem_scheduler/memos_config_w_scheduler.yaml

@@ -33,12 +33,14 @@ mem_scheduler:
   backend: "general_scheduler"
   config:
     top_k: 10
-    top_n: 10
     act_mem_update_interval: 30
     context_window_size: 10
     thread_pool_max_workers: 10
     consume_interval_seconds: 1
+    working_mem_monitor_capacity: 20
+    activation_mem_monitor_capacity: 5
     enable_parallel_dispatch: true
+    enable_activation_memory: true
 max_turns_window: 20
 top_k: 5
 enable_textual_memory: true

examples/data/config/mem_scheduler/memos_config_w_scheduler_and_openai.yaml

@@ -35,13 +35,14 @@ mem_scheduler:
   backend: "general_scheduler"
   config:
     top_k: 10
-    top_n: 10
     act_mem_update_interval: 30
     context_window_size: 10
     thread_pool_max_workers: 10
     consume_interval_seconds: 1
+    working_mem_monitor_capacity: 20
+    activation_mem_monitor_capacity: 5
     enable_parallel_dispatch: true
-    enable_act_memory_update: false
+    enable_activation_memory: true
 max_turns_window: 20
 top_k: 5
 enable_textual_memory: true