Wire thread_visibility to agent; memory tools use private or shared backend by visibility

surfsense_backend/app/agents/new_chat/chat_deepagent.py

@@ -22,6 +22,7 @@ from app.agents.new_chat.system_prompt import (
     build_surfsense_system_prompt,
 )
 from app.agents.new_chat.tools.registry import build_tools_async
+from app.db import ChatVisibility
 from app.services.connector_service import ConnectorService
 
 # =============================================================================
@@ -126,6 +127,7 @@ async def create_surfsense_deep_agent(
     disabled_tools: list[str] | None = None,
     additional_tools: Sequence[BaseTool] | None = None,
     firecrawl_api_key: str | None = None,
+    thread_visibility: ChatVisibility | None = None,
 ):
     """
     Create a SurfSense deep agent with configurable tools and prompts.
@@ -227,15 +229,15 @@ async def create_surfsense_deep_agent(
 
         logging.warning(f"Failed to discover available connectors/document types: {e}")
 
-    # Build dependencies dict for the tools registry
+    visibility = thread_visibility or ChatVisibility.PRIVATE
     dependencies = {
         "search_space_id": search_space_id,
         "db_session": db_session,
         "connector_service": connector_service,
         "firecrawl_api_key": firecrawl_api_key,
-        "user_id": user_id,  # Required for memory tools
+        "user_id": user_id,
-        "thread_id": thread_id,  # For podcast tool
+        "thread_id": thread_id,
-        # Dynamic connector/document type discovery for knowledge base tool
+        "thread_visibility": visibility,
         "available_connectors": available_connectors,
         "available_document_types": available_document_types,
     }

surfsense_backend/app/agents/new_chat/tools/registry.py

@@ -51,8 +51,14 @@ from .mcp_tool import load_mcp_tools
 from .podcast import create_generate_podcast_tool
 from .scrape_webpage import create_scrape_webpage_tool
 from .search_surfsense_docs import create_search_surfsense_docs_tool
+from .shared_memory import (
+    create_recall_shared_memory_tool,
+    create_save_shared_memory_tool,
+)
 from .user_memory import create_recall_memory_tool, create_save_memory_tool
 
+from app.db import ChatVisibility
+
 # =============================================================================
 # Tool Definition
 # =============================================================================
@@ -156,29 +162,42 @@ BUILTIN_TOOLS: list[ToolDefinition] = [
         requires=["db_session"],
     ),
     # =========================================================================
-    # USER MEMORY TOOLS - Claude-like memory feature
+    # USER MEMORY TOOLS - private or team store by thread_visibility
     # =========================================================================
-    # Save memory tool - stores facts/preferences about the user
     ToolDefinition(
         name="save_memory",
-        description="Save facts, preferences, or context about the user for personalized responses",
+        description="Save facts, preferences, or context for personalized or team responses",
-        factory=lambda deps: create_save_memory_tool(
+        factory=lambda deps: (
+            create_save_shared_memory_tool(
+                search_space_id=deps["search_space_id"],
+                created_by_id=deps["user_id"],
+                db_session=deps["db_session"],
+            )
+            if deps["thread_visibility"] == ChatVisibility.SEARCH_SPACE
+            else create_save_memory_tool(
                 user_id=deps["user_id"],
                 search_space_id=deps["search_space_id"],
                 db_session=deps["db_session"],
+            )
         ),
-        requires=["user_id", "search_space_id", "db_session"],
+        requires=["user_id", "search_space_id", "db_session", "thread_visibility"],
     ),
-    # Recall memory tool - retrieves relevant user memories
     ToolDefinition(
         name="recall_memory",
-        description="Recall user memories for personalized and contextual responses",
+        description="Recall relevant memories (personal or team) for context",
-        factory=lambda deps: create_recall_memory_tool(
+        factory=lambda deps: (
+            create_recall_shared_memory_tool(
+                search_space_id=deps["search_space_id"],
+                db_session=deps["db_session"],
+            )
+            if deps["thread_visibility"] == ChatVisibility.SEARCH_SPACE
+            else create_recall_memory_tool(
                 user_id=deps["user_id"],
                 search_space_id=deps["search_space_id"],
                 db_session=deps["db_session"],
+            )
         ),
-        requires=["user_id", "search_space_id", "db_session"],
+        requires=["user_id", "search_space_id", "db_session", "thread_visibility"],
     ),
     # =========================================================================
     # ADD YOUR CUSTOM TOOLS BELOW

surfsense_backend/app/agents/new_chat/tools/shared_memory.py

@@ -4,6 +4,7 @@ import logging
 from typing import Any
 from uuid import UUID
 
+from langchain_core.tools import tool
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -177,3 +178,101 @@ def format_shared_memories_for_context(
         )
     parts.append("</team_memories>")
     return "\n".join(parts)
+
+
+def create_save_shared_memory_tool(
+    search_space_id: int,
+    created_by_id: str | UUID,
+    db_session: AsyncSession,
+):
+    """
+    Factory function to create the save_memory tool for shared (team) chats.
+
+    Args:
+        search_space_id: The search space ID
+        created_by_id: The user ID of the person adding the memory
+        db_session: Database session for executing queries
+
+    Returns:
+        A configured tool function for saving team memories
+    """
+
+    @tool
+    async def save_memory(
+        content: str,
+        category: str = "fact",
+    ) -> dict[str, Any]:
+        """
+        Save a fact, preference, or context to the team's shared memory for future reference.
+
+        Use this tool when:
+        - User or a team member says "remember this", "keep this in mind", or similar in this shared chat
+        - The team agrees on something to remember (e.g., decisions, conventions, where things live)
+        - Someone shares a preference or fact that should be visible to the whole team
+
+        The saved information will be available in future shared conversations in this space.
+
+        Args:
+            content: The fact/preference/context to remember.
+                    Phrase it clearly, e.g., "API keys are stored in Vault",
+                    "The team prefers weekly demos on Fridays"
+            category: Type of memory. One of:
+                    - "preference": Team or workspace preferences
+                    - "fact": Facts the team agreed on (e.g., processes, locations)
+                    - "instruction": Standing instructions for the team
+                    - "context": Current context (e.g., ongoing projects, goals)
+
+        Returns:
+            A dictionary with the save status and memory details
+        """
+        return await save_shared_memory(
+            db_session, search_space_id, created_by_id, content, category
+        )
+
+    return save_memory
+
+
+def create_recall_shared_memory_tool(
+    search_space_id: int,
+    db_session: AsyncSession,
+):
+    """
+    Factory function to create the recall_memory tool for shared (team) chats.
+
+    Args:
+        search_space_id: The search space ID
+        db_session: Database session for executing queries
+
+    Returns:
+        A configured tool function for recalling team memories
+    """
+
+    @tool
+    async def recall_memory(
+        query: str | None = None,
+        category: str | None = None,
+        top_k: int = DEFAULT_RECALL_TOP_K,
+    ) -> dict[str, Any]:
+        """
+        Recall relevant team memories for this space to provide contextual responses.
+
+        Use this tool when:
+        - You need team context to answer (e.g., "where do we store X?", "what did we decide about Y?")
+        - Someone asks about something the team agreed to remember
+        - Team preferences or conventions would improve the response
+
+        Args:
+            query: Optional search query to find specific memories.
+                  If not provided, returns the most recent memories.
+            category: Optional category filter. One of:
+                     "preference", "fact", "instruction", "context"
+            top_k: Number of memories to retrieve (default: 5, max: 20)
+
+        Returns:
+            A dictionary containing relevant memories and formatted context
+        """
+        return await recall_shared_memory(
+            db_session, search_space_id, query, category, top_k
+        )
+
+    return recall_memory