search_memories
Search stored conversation memories using natural language queries and filters to find relevant information across users and agents.
Instructions
Run a semantic search over existing memories.
Use filters to narrow results. Common filter patterns:
- Single user: {"AND": [{"user_id": "john"}]}
- Agent memories: {"AND": [{"agent_id": "agent_name"}]}
- Recent memories: {"AND": [{"user_id": "john"}, {"created_at": {"gte": "2024-01-01"}}]}
- Multiple users: {"AND": [{"user_id": {"in": ["john", "jane"]}}]}
- Cross-entity: {"OR": [{"user_id": "john"}, {"agent_id": "agent_name"}]}
user_id is automatically added to filters if not provided.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language description of what to find. | |
| filters | No | Additional filter clauses (user_id injected automatically). | |
| limit | No | Maximum number of results to return. | |
| enable_graph | No | Set true only when the user explicitly wants graph-derived memories. |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/mem0_mcp_server/server.py:246-290 (handler)The handler function for the 'search_memories' MCP tool. It is registered via the @server.tool decorator, validates input using SearchMemoriesArgs, injects default user_id into filters, and invokes the Mem0 MemoryClient.search method to perform semantic search on memories.
@server.tool( description="""Run a semantic search over existing memories. Use filters to narrow results. Common filter patterns: - Single user: {"AND": [{"user_id": "john"}]} - Agent memories: {"AND": [{"agent_id": "agent_name"}]} - Recent memories: {"AND": [{"user_id": "john"}, {"created_at": {"gte": "2024-01-01"}}]} - Multiple users: {"AND": [{"user_id": {"in": ["john", "jane"]}}]} - Cross-entity: {"OR": [{"user_id": "john"}, {"agent_id": "agent_name"}]} user_id is automatically added to filters if not provided. """ ) def search_memories( query: Annotated[str, Field(description="Natural language description of what to find.")], filters: Annotated[ Optional[Dict[str, Any]], Field(default=None, description="Additional filter clauses (user_id injected automatically)."), ] = None, limit: Annotated[ Optional[int], Field(default=None, description="Maximum number of results to return.") ] = None, enable_graph: Annotated[ Optional[bool], Field( default=None, description="Set true only when the user explicitly wants graph-derived memories.", ), ] = None, ctx: Context | None = None, ) -> str: """Semantic search against existing memories.""" api_key, default_user, graph_default = _resolve_settings(ctx) args = SearchMemoriesArgs( query=query, filters=filters, limit=limit, enable_graph=_default_enable_graph(enable_graph, graph_default), ) payload = args.model_dump(exclude_none=True) payload["filters"] = _with_default_filters(default_user, payload.get("filters")) payload.setdefault("enable_graph", graph_default) client = _mem0_client(api_key) return _mem0_call(client.search, **payload) - src/mem0_mcp_server/schemas.py:50-58 (schema)Pydantic model defining the input schema (arguments) for the search_memories tool, used for validation in the handler.
class SearchMemoriesArgs(BaseModel): query: str = Field(..., description="Describe what you want to find.") filters: Optional[Dict[str, Any]] = Field( None, description="Additional filter clauses; user_id is injected automatically." ) limit: Optional[int] = Field(None, description="Optional maximum number of matches.") enable_graph: Optional[bool] = Field( None, description="Set True only when the user asks for graph knowledge." )