docs: add obvec related info

README.md

@@ -506,7 +506,7 @@ async def main():
             "dimensions": 1024,
         },
         default_vector_store_config={
-            "backend": "local",  # Supports local/chroma/qdrant/elasticsearch
+            "backend": "local",  # Supports local/chroma/qdrant/elasticsearch/obvec
         },
     )
     await reme.start()

README_ZH.md

@@ -486,7 +486,7 @@ async def main():
             "dimensions": 1024,
         },
         default_vector_store_config={
-            "backend": "local",  # 支持 local/chroma/qdrant/elasticsearch
+            "backend": "local",  # 支持 local/chroma/qdrant/elasticsearch/obvec
         },
     )
     await reme.start()

docs/README_0_2_x.md

@@ -66,6 +66,7 @@ Agent Memory = Long-Term Memory + Short-Term Memory
 - **[2025-09]** 🧪 Validated effectiveness in appworld, bfcl(v3), and frozenlake ([Experiments](docs/cookbook))
 - **[2025-08]** 🚀 MCP protocol support ([Quick Start](docs/mcp_quick_start.md))
 - **[2025-06]** 🚀 Multiple backend vector storage (Elasticsearch & ChromaDB) ([Guide](docs/vector_store_api_guide.md))
+- **[2026-04]** 🚀 ObVec vector storage (OceanBase / seekdb via pyobvector) ([Guide](docs/vector_store_api_guide.md))
 - **[2024-09]** 🧠 Personalized and time-aware memory storage
 
 ---
@@ -814,7 +815,7 @@ You can find more details in [tool_bench.md](docs/tool_memory/tool_bench.md) and
 
 ### Advanced Topics
 - **[Operator Pipelines](reme_ai/config/default.yaml)**: Customize memory processing workflows by modifying operator chains
-- **[Vector Store Backends](docs/vector_store_api_guide.md)**: Configure local, Elasticsearch, Qdrant, or ChromaDB storage
+- **[Vector Store Backends](docs/vector_store_api_guide.md)**: Configure local, Elasticsearch, Qdrant, ChromaDB, or ObVec (OceanBase / seekdb) storage
 - **[Example Collection](./cookbook)**: Real-world use cases and best practices
 
 ---

docs/README_0_2_x_ZH.md

@@ -67,6 +67,7 @@ Agent Memory = Long-Term Memory + Short-Term Memory
 - **[2025-09]** 🧪 在 appworld、bfcl(v3)、frozenlake 等环境中验证有效性（[实验文档](docs/cookbook)）
 - **[2025-08]** 🚀 支持 MCP 协议（[快速开始](docs/mcp_quick_start.md)）
 - **[2025-06]** 🚀 支持多种向量存储后端（Elasticsearch & ChromaDB）（[向量库指南](docs/vector_store_api_guide.md)）
+- **[2026-04]** 🚀 支持 ObVec 向量存储（OceanBase / seekdb，基于 pyobvector）（[向量库指南](docs/vector_store_api_guide.md)）
 - **[2024-09]** 🧠 支持个性化与时间敏感的记忆存储
 
 ---
@@ -820,7 +821,7 @@ Pass@K 衡量在生成 K 个候选中，至少一个成功完成任务（score=1
 
 ### 高级主题
 - **[算子管道](reme_ai/config/default.yaml)**：通过修改算子链来自定义记忆处理工作流
-- **[向量存储后端](docs/vector_store_api_guide.md)**：配置本地、Elasticsearch、Qdrant 或 ChromaDB 存储
+- **[向量存储后端](docs/vector_store_api_guide.md)**：配置本地、Elasticsearch、Qdrant、ChromaDB 或 ObVec（OceanBase / seekdb）存储
 - **[案例集](./cookbook)**：真实场景的用例和最佳实践
 
 ---

docs/index.md

@@ -139,7 +139,7 @@ response = requests.post("http://localhost:8002/retrieve_task_memory", json={
 ## 📚 Resources
 
 - **[Installation Guide](installation.md)**, **[Quick Start](quick_start.md)**: Get started quickly with practical examples
-- **[Vector Storage Setup](vector_store_api_guide.md)**: Configure local/vector databases and usage
+- **[Vector Storage Setup](vector_store_api_guide.md)**: Configure local, Elasticsearch, Qdrant, ChromaDB, or ObVec (OceanBase / seekdb via pyobvector) storage and usage
 - **[MCP Guide](mcp_quick_start.md)**: Create MCP services
 - **[Personal Memory](personal_memory/personal_memory.md)**, **[Task Memory](task_memory/task_memory.md)** & **[Tool Memory](tool_memory/tool_memory.md)**: Operators used in personal memory, task memory and tool memory. You can modify the config to customize the pipelines.
 - **[Example Collection](./cookbook/appworld/quickstart.md)**: Real use cases and best practices

docs/vector_store_api_guide.md

@@ -33,8 +33,9 @@ FlowLLM provides multiple Vector Store implementations tailored to different use
 - **QdrantVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/qdrant_vector_store.py)): Built on the Qdrant vector database, supporting high-performance vector search. Recommended for large-scale production environments.
 - **ChromaVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/chroma_vector_store.py)): Based on ChromaDB, providing persistent storage and metadata filtering capabilities.
 - **EsVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/es_vector_store.py)): Built on Elasticsearch, enabling powerful combined full-text and vector search functionalities.
+- **ObVecVectorStore** ([source code](https://github.com/agentscope-ai/ReMe/blob/main/reme/core/vector_store/obvec_vector_store.py)): Uses [pyobvector](https://pypi.org/project/pyobvector/) against **OceanBase** or **seekdb** (MySQL-compatible wire protocol). Suitable when you already run OceanBase/seekdb or need a SQL-native vector table with HNSW-style ANN search and JSON metadata filters.
 
-All Vector Store implementations inherit from **BaseVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/base_vector_store.py)), ensuring a consistent interface specification.
+All Vector Store implementations inherit from **BaseVectorStore** ([source code](https://github.com/agentscope-ai/ReMe/blob/main/reme/core/vector_store/base_vector_store.py)) in ReMe, ensuring a consistent interface specification.
 
 ## Core Features
 
@@ -108,6 +109,31 @@ The asynchronous interface is particularly useful in the following scenarios:
 - **hosts**: Elasticsearch host address(es), either a string or a list (default: `http://localhost:9200`).
 - **basic_auth**: Basic authentication credentials (username and password).
 
+### ObVecVectorStore Configuration
+
+- **uri**: Server address as `host:port` (default: `127.0.0.1:2881`).
+- **user**: MySQL-compatible user. seekdb single-tenant images often use `root`; OceanBase multi-tenant setups typically use `root@<tenant>` (e.g. `root@test`).
+- **password**: Database password (seekdb Docker images commonly set this via `ROOT_PASSWORD`).
+- **database**: Logical database name (default: `test`).
+- **index_type**: Vector index family (default: `HNSW`).
+- **index_metric**: Distance metric for the vector index: `cosine`, `l2`, or `ip` (inner product); default `cosine`.
+- **index_ef_search**: HNSW `ef_search` parameter passed to pyobvector (default: `100`).
+- **collection_name**: Table name for the collection (from `VectorStoreConfig`, default `reme`). Use lowercase names if your deployment restricts identifiers.
+
+**Local seekdb via Docker** (repository root):
+
+```text
+docker compose -f docker-compose.obvec.yml up -d
+```
+
+**Integration tests** (requires a running server, embedding API credentials in `.env`, and matching DB password):
+
+```shell
+OBVEC_PASSWORD=<your_root_password> python tests/test_vector_store.py --obvec
+```
+
+**Dependencies**: `pyobvector` is declared in ReMe’s `pyproject.toml`. A compatible `sqlglot` range is pinned so the pyobvector client imports cleanly.
+
 ## Configuration File Examples
 
 Configure Vector Store in `flowllm/config/default.yaml` under the `vector_store` section. The basic structure is as follows:
@@ -128,7 +154,7 @@ vector_store.default.params.<param_name>=<param_value>
 
 ### Configuration Field Descriptions
 
-- **`backend`** (required): Vector store backend type. Options: `local`, `memory`, `chroma`, `qdrant`, `elasticsearch`.
+- **`backend`** (required): Vector store backend type. Options: `local`, `memory`, `chroma`, `qdrant`, `elasticsearch`, `obvec`.
 - **`embedding_model`** (required): Name of the embedding model configuration, referencing the `embedding_model` section.
 - **`params`** (optional): Dictionary of backend-specific parameters passed to the vector store constructor.
 
@@ -295,6 +321,35 @@ vector_store.default.backend=elasticsearch
 vector_store.default.params.hosts='["http://es-node1:9200", "http://es-node2:9200", "http://es-node3:9200"]'
 ```
 
+#### 6. ObVecVectorStore Configuration (OceanBase / seekdb)
+
+**Implementation**: [`reme/core/vector_store/obvec_vector_store.py`](https://github.com/agentscope-ai/ReMe/blob/main/reme/core/vector_store/obvec_vector_store.py)
+
+**Example (seekdb on localhost)**:
+
+```yaml
+vector_stores:
+  default:
+    backend: obvec
+    embedding_model: default
+    collection_name: reme
+    uri: "127.0.0.1:2881"
+    user: "root"
+    password: "your-root-password"
+    database: "test"
+    index_metric: "cosine"
+    index_ef_search: 100
+```
+
+```shell
+vector_stores.default.backend=obvec
+vector_stores.default.uri=127.0.0.1:2881
+vector_stores.default.user=root
+vector_stores.default.password=your-root-password
+```
+
+ReMe service YAML uses the key `vector_stores` (plural); CLI overrides use the same nested paths.
+
 ### Complete Configuration Example
 
 Below is a complete `default.yaml` example including both embedding model and vector store configurations:
@@ -352,8 +407,9 @@ Two types of metadata filtering are supported:
 
 - **Development & Testing**: Use MemoryVectorStore or LocalVectorStore—no additional services required.
 - **Small-Scale Applications**: Use LocalVectorStore or ChromaVectorStore for simplicity and ease of use.
-- **Production Environments**: Use QdrantVectorStore or EsVectorStore for high performance and scalability.
+- **Production Environments**: Use QdrantVectorStore, EsVectorStore, or ObVecVectorStore (OceanBase/seekdb) for high performance and scalability, depending on your existing infrastructure.
 - **Hybrid Search**: Use EsVectorStore to combine vector search with full-text search capabilities.
+- **OceanBase / seekdb**: Use ObVecVectorStore when you standardize on pyobvector and SQL-accessible vector tables.
 
 ## Important Notes
 

reme/core/vector_store/obvec_vector_store.py

@@ -1,4 +1,4 @@
-"""OceanBase / SeekDB vector store for ReMe (pyobvector).
+"""OceanBase / seekdb vector store for ReMe (pyobvector).
 
 Dense vectors, kNN via ``ObVecClient.ann_search``, JSON metadata filters, and
 helpers for coercion / metrics live in this module (pyobvector-aligned).
@@ -139,7 +139,7 @@ def _vector_node_from_db_row(row: tuple[Any, ...]) -> VectorNode:
 
 
 class ObVecVectorStore(BaseVectorStore):
-    """OceanBase or SeekDB vector store for dense vectors and kNN search."""
+    """OceanBase or seekdb vector store for dense vectors and kNN search."""
 
     def __init__(
         self,

tests/test_vector_store.py

@@ -76,7 +76,7 @@ class TestConfig:
     CHROMA_TENANT = None  # Set for ChromaDB Cloud tenant
     CHROMA_DATABASE = None  # Set for ChromaDB Cloud database
 
-    # ObVecVectorStore: SeekDB uses user `root`; OceanBase multi-tenant often uses `root@test`.
+    # ObVecVectorStore: seekdb uses user `root`; OceanBase multi-tenant often uses `root@test`.
     # Defaults match docker-compose.obvec.yml (ROOT_PASSWORD=root).
     OBVEC_URI = os.environ.get("OBVEC_URI", "127.0.0.1:2881")
     OBVEC_USER = os.environ.get("OBVEC_USER", "root")