Merge pull request #799 from CREDO23/sur-152-impr-split-private-and-shared-memory

[Feat] Split private vs shared chat memory and add team prompt/attribution

surfsense_backend/alembic/versions/96_add_shared_memories_table.py

@@ -0,0 +1,77 @@
+"""Add shared_memories table (SUR-152)."""
+
+from collections.abc import Sequence
+
+from alembic import op
+from app.config import config
+
+revision: str = "96"
+down_revision: str | None = "95"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+EMBEDDING_DIM = config.embedding_model_instance.dimension
+
+
+def upgrade() -> None:
+    op.execute(
+        f"""
+        DO $$
+        BEGIN
+            IF NOT EXISTS (
+                SELECT FROM information_schema.tables
+                WHERE table_name = 'shared_memories'
+            ) THEN
+                CREATE TABLE shared_memories (
+                    id SERIAL PRIMARY KEY,
+                    created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(),
+                    updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(),
+                    search_space_id INTEGER NOT NULL REFERENCES searchspaces(id) ON DELETE CASCADE,
+                    created_by_id UUID NOT NULL REFERENCES "user"(id) ON DELETE CASCADE,
+                    memory_text TEXT NOT NULL,
+                    category memorycategory NOT NULL DEFAULT 'fact',
+                    embedding vector({EMBEDDING_DIM})
+                );
+            END IF;
+        END$$;
+        """
+    )
+    op.execute(
+        """
+        DO $$
+        BEGIN
+            IF NOT EXISTS (
+                SELECT 1 FROM pg_indexes
+                WHERE tablename = 'shared_memories' AND indexname = 'ix_shared_memories_search_space_id'
+            ) THEN
+                CREATE INDEX ix_shared_memories_search_space_id ON shared_memories(search_space_id);
+            END IF;
+            IF NOT EXISTS (
+                SELECT 1 FROM pg_indexes
+                WHERE tablename = 'shared_memories' AND indexname = 'ix_shared_memories_updated_at'
+            ) THEN
+                CREATE INDEX ix_shared_memories_updated_at ON shared_memories(updated_at);
+            END IF;
+            IF NOT EXISTS (
+                SELECT 1 FROM pg_indexes
+                WHERE tablename = 'shared_memories' AND indexname = 'ix_shared_memories_created_by_id'
+            ) THEN
+                CREATE INDEX ix_shared_memories_created_by_id ON shared_memories(created_by_id);
+            END IF;
+        END$$;
+        """
+    )
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS shared_memories_vector_index
+        ON shared_memories USING hnsw (embedding public.vector_cosine_ops);
+        """
+    )
+
+
+def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS shared_memories_vector_index;")
+    op.execute("DROP INDEX IF EXISTS ix_shared_memories_created_by_id;")
+    op.execute("DROP INDEX IF EXISTS ix_shared_memories_updated_at;")
+    op.execute("DROP INDEX IF EXISTS ix_shared_memories_search_space_id;")
+    op.execute("DROP TABLE IF EXISTS shared_memories CASCADE;")

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.
@@ -228,14 +230,15 @@ 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,
     }
@@ -255,10 +258,12 @@ async def create_surfsense_deep_agent(
             custom_system_instructions=agent_config.system_instructions,
             use_default_system_instructions=agent_config.use_default_system_instructions,
             citations_enabled=agent_config.citations_enabled,
+            thread_visibility=thread_visibility,
         )
     else:
-        # Use default prompt (with citations enabled)
+        system_prompt = build_surfsense_system_prompt(
-        system_prompt = build_surfsense_system_prompt()
+            thread_visibility=thread_visibility,
+        )
 
     # Create the deep agent with system prompt and checkpointer
     # Note: TodoListMiddleware (write_todos) is included by default in create_deep_agent

surfsense_backend/app/agents/new_chat/system_prompt.py

@@ -12,6 +12,8 @@ The prompt is composed of three parts:
 
 from datetime import UTC, datetime
 
+from app.db import ChatVisibility
+
 # Default system instructions - can be overridden via NewLLMConfig.system_instructions
 SURFSENSE_SYSTEM_INSTRUCTIONS = """
 <system_instruction>
@@ -22,7 +24,34 @@ Today's date (UTC): {resolved_today}
 </system_instruction>
 """
 
-SURFSENSE_TOOLS_INSTRUCTIONS = """
+# Default system instructions for shared (team) threads: team context + message format for attribution
+_SYSTEM_INSTRUCTIONS_SHARED = """
+<system_instruction>
+You are SurfSense, a reasoning and acting AI agent designed to answer questions in this team space using the team's shared knowledge base.
+
+In this team thread, each message is prefixed with **[DisplayName of the author]**. Use this to attribute and reference the author of anything in the discussion (who asked a question, made a suggestion, or contributed an idea) and to cite who said what in your answers.
+
+Today's date (UTC): {resolved_today}
+
+</system_instruction>
+"""
+
+
+def _get_system_instructions(
+    thread_visibility: ChatVisibility | None = None, today: datetime | None = None
+) -> str:
+    """Build system instructions based on thread visibility (private vs shared)."""
+
+    resolved_today = (today or datetime.now(UTC)).astimezone(UTC).date().isoformat()
+    visibility = thread_visibility or ChatVisibility.PRIVATE
+    if visibility == ChatVisibility.SEARCH_SPACE:
+        return _SYSTEM_INSTRUCTIONS_SHARED.format(resolved_today=resolved_today)
+    else:
+        return SURFSENSE_SYSTEM_INSTRUCTIONS.format(resolved_today=resolved_today)
+
+
+# Tools 0-6 (common to both private and shared prompts)
+_TOOLS_INSTRUCTIONS_COMMON = """
 <tools>
 You have access to the following tools:
 
@@ -138,7 +167,11 @@ You have access to the following tools:
     * Prioritize showing: diagrams, charts, infographics, key illustrations, or images that help explain the content.
     * Don't show every image - just the most relevant 1-3 images that enhance understanding.
 
-7. save_memory: Save facts, preferences, or context about the user for personalized responses.
+"""
+
+# Private (user) memory: tools 7-8 + memory-specific examples
+_TOOLS_INSTRUCTIONS_MEMORY_PRIVATE = """
+7. save_memory: Save facts, preferences, or context for personalized responses.
   - Use this when the user explicitly or implicitly shares information worth remembering.
   - Trigger scenarios:
     * User says "remember this", "keep this in mind", "note that", or similar
@@ -178,6 +211,75 @@ You have access to the following tools:
     stating "Based on your memory..." - integrate the context seamlessly.
 </tools>
 <tool_call_examples>
+- User: "Remember that I prefer TypeScript over JavaScript"
+  - Call: `save_memory(content="User prefers TypeScript over JavaScript for development", category="preference")`
+
+- User: "I'm a data scientist working on ML pipelines"
+  - Call: `save_memory(content="User is a data scientist working on ML pipelines", category="fact")`
+
+- User: "Always give me code examples in Python"
+  - Call: `save_memory(content="User wants code examples to be written in Python", category="instruction")`
+
+- User: "What programming language should I use for this project?"
+  - First recall: `recall_memory(query="programming language preferences")`
+  - Then provide a personalized recommendation based on their preferences
+
+- User: "What do you know about me?"
+  - Call: `recall_memory(top_k=10)`
+  - Then summarize the stored memories
+
+"""
+
+# Shared (team) memory: tools 7-8 + team memory examples
+_TOOLS_INSTRUCTIONS_MEMORY_SHARED = """
+7. save_memory: Save a fact, preference, or context to the team's shared memory for future reference.
+  - Use this when the user or a team member says "remember this", "keep this in mind", or similar in this shared chat.
+  - Use when the team agrees on something to remember (e.g., decisions, conventions).
+  - 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: Confirmation of saved memory; returned context may include who added it (added_by).
+  - IMPORTANT: Only save information that would be genuinely useful for future team conversations in this space.
+
+8. recall_memory: Recall relevant team memories for this space to provide contextual responses.
+  - Use when you need team context to answer (e.g., "where do we store X?", "what did we decide about Y?").
+  - Use when someone asks about something the team agreed to remember.
+  - Use when 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 filter by category ("preference", "fact", "instruction", "context")
+    - top_k: Number of memories to retrieve (default: 5, max: 20)
+  - Returns: Relevant team memories and formatted context (may include added_by). Integrate naturally without saying "Based on team memory...".
+</tools>
+<tool_call_examples>
+- User: "Remember that API keys are stored in Vault"
+  - Call: `save_memory(content="API keys are stored in Vault", category="fact")`
+
+- User: "Let's remember that the team prefers weekly demos on Fridays"
+  - Call: `save_memory(content="The team prefers weekly demos on Fridays", category="preference")`
+
+- User: "What did we decide about the release date?"
+  - First recall: `recall_memory(query="release date decision")`
+  - Then answer based on the team memories
+
+- User: "Where do we document onboarding?"
+  - Call: `recall_memory(query="onboarding documentation")`
+  - Then answer using the recalled team context
+
+- User: "What have we agreed to remember about our deployment process?"
+  - Call: `recall_memory(query="deployment process", top_k=10)`
+  - Then summarize the relevant team memories
+
+"""
+
+# Examples shared by both private and shared prompts (knowledge base, docs, podcast, links, images, etc.)
+_TOOLS_INSTRUCTIONS_EXAMPLES_COMMON = """
 - User: "What time is the team meeting today?"
   - Call: `search_knowledge_base(query="team meeting time today")` (searches ALL sources - calendar, notes, Obsidian, etc.)
   - DO NOT limit to just calendar - the info might be in notes!
@@ -209,23 +311,6 @@ You have access to the following tools:
 - User: "What's in my Obsidian vault about project ideas?"
   - Call: `search_knowledge_base(query="project ideas", connectors_to_search=["OBSIDIAN_CONNECTOR"])`
 
-- User: "Remember that I prefer TypeScript over JavaScript"
-  - Call: `save_memory(content="User prefers TypeScript over JavaScript for development", category="preference")`
-
-- User: "I'm a data scientist working on ML pipelines"
-  - Call: `save_memory(content="User is a data scientist working on ML pipelines", category="fact")`
-
-- User: "Always give me code examples in Python"
-  - Call: `save_memory(content="User wants code examples to be written in Python", category="instruction")`
-
-- User: "What programming language should I use for this project?"
-  - First recall: `recall_memory(query="programming language preferences")`
-  - Then provide a personalized recommendation based on their preferences
-
-- User: "What do you know about me?"
-  - Call: `recall_memory(top_k=10)`
-  - Then summarize the stored memories
-
 - User: "Give me a podcast about AI trends based on what we discussed"
   - First search for relevant content, then call: `generate_podcast(source_content="Based on our conversation and search results: [detailed summary of chat + search findings]", podcast_title="AI Trends Podcast")`
 
@@ -315,6 +400,31 @@ You have access to the following tools:
 </tool_call_examples>
 """
 
+# Reassemble so existing callers see no change (same full prompt)
+SURFSENSE_TOOLS_INSTRUCTIONS = (
+    _TOOLS_INSTRUCTIONS_COMMON
+    + _TOOLS_INSTRUCTIONS_MEMORY_PRIVATE
+    + _TOOLS_INSTRUCTIONS_EXAMPLES_COMMON
+)
+
+
+def _get_tools_instructions(thread_visibility: ChatVisibility | None = None) -> str:
+    """Build tools instructions based on thread visibility (private vs shared).
+
+    For private chats: use user-focused memory wording and examples.
+    For shared chats: use team memory wording and examples.
+    """
+    visibility = thread_visibility or ChatVisibility.PRIVATE
+    memory_block = (
+        _TOOLS_INSTRUCTIONS_MEMORY_SHARED
+        if visibility == ChatVisibility.SEARCH_SPACE
+        else _TOOLS_INSTRUCTIONS_MEMORY_PRIVATE
+    )
+    return (
+        _TOOLS_INSTRUCTIONS_COMMON + memory_block + _TOOLS_INSTRUCTIONS_EXAMPLES_COMMON
+    )
+
+
 SURFSENSE_CITATION_INSTRUCTIONS = """
 <citation_instructions>
 CRITICAL CITATION REQUIREMENTS:
@@ -413,6 +523,7 @@ Your goal is to provide helpful, informative answers in a clean, readable format
 
 def build_surfsense_system_prompt(
     today: datetime | None = None,
+    thread_visibility: ChatVisibility | None = None,
 ) -> str:
     """
     Build the SurfSense system prompt with default settings.
@@ -424,17 +535,17 @@ def build_surfsense_system_prompt(
 
     Args:
         today: Optional datetime for today's date (defaults to current UTC date)
+        thread_visibility: Optional; when provided, used for conditional prompt (e.g. private vs shared memory wording). Defaults to private behavior when None.
 
     Returns:
         Complete system prompt string
     """
-    resolved_today = (today or datetime.now(UTC)).astimezone(UTC).date().isoformat()
 
-    return (
+    visibility = thread_visibility or ChatVisibility.PRIVATE
-        SURFSENSE_SYSTEM_INSTRUCTIONS.format(resolved_today=resolved_today)
+    system_instructions = _get_system_instructions(visibility, today)
-        + SURFSENSE_TOOLS_INSTRUCTIONS
+    tools_instructions = _get_tools_instructions(visibility)
-        + SURFSENSE_CITATION_INSTRUCTIONS
+    citation_instructions = SURFSENSE_CITATION_INSTRUCTIONS
-    )
+    return system_instructions + tools_instructions + citation_instructions
 
 
 def build_configurable_system_prompt(
@@ -442,6 +553,7 @@ def build_configurable_system_prompt(
     use_default_system_instructions: bool = True,
     citations_enabled: bool = True,
     today: datetime | None = None,
+    thread_visibility: ChatVisibility | None = None,
 ) -> str:
     """
     Build a configurable SurfSense system prompt based on NewLLMConfig settings.
@@ -460,6 +572,7 @@ def build_configurable_system_prompt(
         citations_enabled: Whether to include citation instructions (True) or
                           anti-citation instructions (False).
         today: Optional datetime for today's date (defaults to current UTC date)
+        thread_visibility: Optional; when provided, used for conditional prompt (e.g. private vs shared memory wording). Defaults to private behavior when None.
 
     Returns:
         Complete system prompt string
@@ -473,16 +586,14 @@ def build_configurable_system_prompt(
             resolved_today=resolved_today
         )
     elif use_default_system_instructions:
-        # Use default instructions
+        visibility = thread_visibility or ChatVisibility.PRIVATE
-        system_instructions = SURFSENSE_SYSTEM_INSTRUCTIONS.format(
+        system_instructions = _get_system_instructions(visibility, today)
-            resolved_today=resolved_today
-        )
     else:
         # No system instructions (edge case)
         system_instructions = ""
 
-    # Tools instructions are always included
+    # Tools instructions: conditional on thread_visibility (private vs shared memory wording)
-    tools_instructions = SURFSENSE_TOOLS_INSTRUCTIONS
+    tools_instructions = _get_tools_instructions(thread_visibility)
 
     # Citation instructions based on toggle
     citation_instructions = (

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: (
-            user_id=deps["user_id"],
+            create_save_shared_memory_tool(
-            search_space_id=deps["search_space_id"],
+                search_space_id=deps["search_space_id"],
-            db_session=deps["db_session"],
+                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: (
-            user_id=deps["user_id"],
+            create_recall_shared_memory_tool(
-            search_space_id=deps["search_space_id"],
+                search_space_id=deps["search_space_id"],
-            db_session=deps["db_session"],
+                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

@@ -0,0 +1,278 @@
+"""Shared (team) memory backend for search-space-scoped AI context."""
+
+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
+
+from app.config import config
+from app.db import MemoryCategory, SharedMemory, User
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_RECALL_TOP_K = 5
+MAX_MEMORIES_PER_SEARCH_SPACE = 250
+
+
+async def get_shared_memory_count(
+    db_session: AsyncSession,
+    search_space_id: int,
+) -> int:
+    result = await db_session.execute(
+        select(SharedMemory).where(SharedMemory.search_space_id == search_space_id)
+    )
+    return len(result.scalars().all())
+
+
+async def delete_oldest_shared_memory(
+    db_session: AsyncSession,
+    search_space_id: int,
+) -> None:
+    result = await db_session.execute(
+        select(SharedMemory)
+        .where(SharedMemory.search_space_id == search_space_id)
+        .order_by(SharedMemory.updated_at.asc())
+        .limit(1)
+    )
+    oldest = result.scalars().first()
+    if oldest:
+        await db_session.delete(oldest)
+        await db_session.commit()
+
+
+def _to_uuid(value: str | UUID) -> UUID:
+    if isinstance(value, UUID):
+        return value
+    return UUID(value)
+
+
+async def save_shared_memory(
+    db_session: AsyncSession,
+    search_space_id: int,
+    created_by_id: str | UUID,
+    content: str,
+    category: str = "fact",
+) -> dict[str, Any]:
+    category = category.lower() if category else "fact"
+    valid = ["preference", "fact", "instruction", "context"]
+    if category not in valid:
+        category = "fact"
+    try:
+        count = await get_shared_memory_count(db_session, search_space_id)
+        if count >= MAX_MEMORIES_PER_SEARCH_SPACE:
+            await delete_oldest_shared_memory(db_session, search_space_id)
+        embedding = config.embedding_model_instance.embed(content)
+        row = SharedMemory(
+            search_space_id=search_space_id,
+            created_by_id=_to_uuid(created_by_id),
+            memory_text=content,
+            category=MemoryCategory(category),
+            embedding=embedding,
+        )
+        db_session.add(row)
+        await db_session.commit()
+        await db_session.refresh(row)
+        return {
+            "status": "saved",
+            "memory_id": row.id,
+            "memory_text": content,
+            "category": category,
+            "message": f"I'll remember: {content}",
+        }
+    except Exception as e:
+        logger.exception("Failed to save shared memory: %s", e)
+        await db_session.rollback()
+        return {
+            "status": "error",
+            "error": str(e),
+            "message": "Failed to save memory. Please try again.",
+        }
+
+
+async def recall_shared_memory(
+    db_session: AsyncSession,
+    search_space_id: int,
+    query: str | None = None,
+    category: str | None = None,
+    top_k: int = DEFAULT_RECALL_TOP_K,
+) -> dict[str, Any]:
+    top_k = min(max(top_k, 1), 20)
+    try:
+        valid_categories = ["preference", "fact", "instruction", "context"]
+        stmt = select(SharedMemory).where(
+            SharedMemory.search_space_id == search_space_id
+        )
+        if category and category in valid_categories:
+            stmt = stmt.where(SharedMemory.category == MemoryCategory(category))
+        if query:
+            query_embedding = config.embedding_model_instance.embed(query)
+            stmt = stmt.order_by(
+                SharedMemory.embedding.op("<=>")(query_embedding)
+            ).limit(top_k)
+        else:
+            stmt = stmt.order_by(SharedMemory.updated_at.desc()).limit(top_k)
+        result = await db_session.execute(stmt)
+        rows = result.scalars().all()
+        memory_list = [
+            {
+                "id": m.id,
+                "memory_text": m.memory_text,
+                "category": m.category.value if m.category else "unknown",
+                "updated_at": m.updated_at.isoformat() if m.updated_at else None,
+                "created_by_id": str(m.created_by_id) if m.created_by_id else None,
+            }
+            for m in rows
+        ]
+        created_by_ids = list({m["created_by_id"] for m in memory_list if m["created_by_id"]})
+        created_by_map: dict[str, str] = {}
+        if created_by_ids:
+            uuids = [UUID(uid) for uid in created_by_ids]
+            users_result = await db_session.execute(
+                select(User).where(User.id.in_(uuids))
+            )
+            for u in users_result.scalars().all():
+                created_by_map[str(u.id)] = u.display_name or "A team member"
+        formatted_context = format_shared_memories_for_context(
+            memory_list, created_by_map
+        )
+        return {
+            "status": "success",
+            "count": len(memory_list),
+            "memories": memory_list,
+            "formatted_context": formatted_context,
+        }
+    except Exception as e:
+        logger.exception("Failed to recall shared memory: %s", e)
+        await db_session.rollback()
+        return {
+            "status": "error",
+            "error": str(e),
+            "memories": [],
+            "formatted_context": "Failed to recall memories.",
+        }
+
+
+def format_shared_memories_for_context(
+    memories: list[dict[str, Any]],
+    created_by_map: dict[str, str] | None = None,
+) -> str:
+    if not memories:
+        return "No relevant team memories found."
+    created_by_map = created_by_map or {}
+    parts = ["<team_memories>"]
+    for memory in memories:
+        category = memory.get("category", "unknown")
+        text = memory.get("memory_text", "")
+        updated = memory.get("updated_at", "")
+        created_by_id = memory.get("created_by_id")
+        added_by = (
+            created_by_map.get(str(created_by_id), "A team member")
+            if created_by_id is not None
+            else "A team member"
+        )
+        parts.append(
+            f"  <memory category='{category}' updated='{updated}' added_by='{added_by}'>{text}</memory>"
+        )
+    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

surfsense_backend/app/db.py

@@ -801,9 +801,8 @@ class MemoryCategory(str, Enum):
 
 class UserMemory(BaseModel, TimestampMixin):
     """
-    Stores facts, preferences, and context about users for personalized AI responses.
+    Private memory: facts, preferences, context per user per search space.
-    Similar to Claude's memory feature - enables the AI to remember user information
+    Used only for private chats (not shared/team chats).
-    across conversations.
     """
 
     __tablename__ = "user_memories"
@@ -847,6 +846,40 @@ class UserMemory(BaseModel, TimestampMixin):
     search_space = relationship("SearchSpace", back_populates="user_memories")
 
 
+class SharedMemory(BaseModel, TimestampMixin):
+    __tablename__ = "shared_memories"
+
+    search_space_id = Column(
+        Integer,
+        ForeignKey("searchspaces.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    created_by_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey("user.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    memory_text = Column(Text, nullable=False)
+    category = Column(
+        SQLAlchemyEnum(MemoryCategory),
+        nullable=False,
+        default=MemoryCategory.fact,
+    )
+    embedding = Column(Vector(config.embedding_model_instance.dimension))
+    updated_at = Column(
+        TIMESTAMP(timezone=True),
+        nullable=False,
+        default=lambda: datetime.now(UTC),
+        onupdate=lambda: datetime.now(UTC),
+        index=True,
+    )
+
+    search_space = relationship("SearchSpace", back_populates="shared_memories")
+    created_by = relationship("User")
+
+
 class Document(BaseModel, TimestampMixin):
     __tablename__ = "documents"
 
@@ -1209,6 +1242,12 @@ class SearchSpace(BaseModel, TimestampMixin):
         order_by="UserMemory.updated_at.desc()",
         cascade="all, delete-orphan",
     )
+    shared_memories = relationship(
+        "SharedMemory",
+        back_populates="search_space",
+        order_by="SharedMemory.updated_at.desc()",
+        cascade="all, delete-orphan",
+    )
 
 
 class SearchSourceConnector(BaseModel, TimestampMixin):
@@ -1258,7 +1297,7 @@ class NewLLMConfig(BaseModel, TimestampMixin):
     - Configurable system instructions (defaults to SURFSENSE_SYSTEM_INSTRUCTIONS)
     - Citation toggle (enable/disable citation instructions)
 
-    Note: SURFSENSE_TOOLS_INSTRUCTIONS is always used and not configurable.
+    Note: Tools instructions are built by get_tools_instructions(thread_visibility) (personal vs shared memory).
     """
 
     __tablename__ = "new_llm_configs"

surfsense_backend/app/routes/new_chat_routes.py

@@ -1045,12 +1045,14 @@ async def handle_new_chat(
                 search_space_id=request.search_space_id,
                 chat_id=request.chat_id,
                 session=session,
-                user_id=str(user.id),  # Pass user ID for memory tools and session state
+                user_id=str(user.id),
                 llm_config_id=llm_config_id,
                 attachments=request.attachments,
                 mentioned_document_ids=request.mentioned_document_ids,
                 mentioned_surfsense_doc_ids=request.mentioned_surfsense_doc_ids,
                 needs_history_bootstrap=thread.needs_history_bootstrap,
+                thread_visibility=thread.visibility,
+                current_user_display_name=user.display_name or "A team member",
             ),
             media_type="text/event-stream",
             headers={
@@ -1281,6 +1283,8 @@ async def regenerate_response(
                     mentioned_surfsense_doc_ids=request.mentioned_surfsense_doc_ids,
                     checkpoint_id=target_checkpoint_id,
                     needs_history_bootstrap=thread.needs_history_bootstrap,
+                    thread_visibility=thread.visibility,
+                    current_user_display_name=user.display_name or "A team member",
                 ):
                     yield chunk
                 # If we get here, streaming completed successfully

surfsense_backend/app/tasks/chat/stream_new_chat.py

@@ -26,7 +26,7 @@ from app.agents.new_chat.llm_config import (
     load_agent_config,
     load_llm_config_from_yaml,
 )
-from app.db import Document, SurfsenseDocsDocument
+from app.db import ChatVisibility, Document, SurfsenseDocsDocument
 from app.prompts import TITLE_GENERATION_PROMPT_TEMPLATE
 from app.schemas.new_chat import ChatAttachment
 from app.services.chat_session_state_service import (
@@ -208,6 +208,8 @@ async def stream_new_chat(
     mentioned_surfsense_doc_ids: list[int] | None = None,
     checkpoint_id: str | None = None,
     needs_history_bootstrap: bool = False,
+    thread_visibility: ChatVisibility | None = None,
+    current_user_display_name: str | None = None,
 ) -> AsyncGenerator[str, None]:
     """
     Stream chat responses from the new SurfSense deep agent.
@@ -295,17 +297,18 @@ async def stream_new_chat(
         # Get the PostgreSQL checkpointer for persistent conversation memory
         checkpointer = await get_checkpointer()
 
-        # Create the deep agent with checkpointer and configurable prompts
+        visibility = thread_visibility or ChatVisibility.PRIVATE
         agent = await create_surfsense_deep_agent(
             llm=llm,
             search_space_id=search_space_id,
             db_session=session,
             connector_service=connector_service,
             checkpointer=checkpointer,
-            user_id=user_id,  # Pass user ID for memory tools
+            user_id=user_id,
-            thread_id=chat_id,  # Pass chat ID for podcast association
+            thread_id=chat_id,
-            agent_config=agent_config,  # Pass prompt configuration
+            agent_config=agent_config,
-            firecrawl_api_key=firecrawl_api_key,  # Pass Firecrawl API key if configured
+            firecrawl_api_key=firecrawl_api_key,
+            thread_visibility=visibility,
         )
 
         # Build input with message history
@@ -313,7 +316,9 @@ async def stream_new_chat(
 
         # Bootstrap history for cloned chats (no LangGraph checkpoint exists yet)
         if needs_history_bootstrap:
-            langchain_messages = await bootstrap_history_from_db(session, chat_id)
+            langchain_messages = await bootstrap_history_from_db(
+                session, chat_id, thread_visibility=visibility
+            )
 
             # Clear the flag so we don't bootstrap again on next message
             from app.db import NewChatThread
@@ -376,6 +381,9 @@ async def stream_new_chat(
             context = "\n\n".join(context_parts)
             final_query = f"{context}\n\n<user_query>{user_query}</user_query>"
 
+        if visibility == ChatVisibility.SEARCH_SPACE and current_user_display_name:
+            final_query = f"**[{current_user_display_name}]:** {final_query}"
+
         # if messages:
         #     # Convert frontend messages to LangChain format
         #     for msg in messages:

surfsense_backend/app/utils/content_utils.py

@@ -12,6 +12,7 @@ These utilities help extract and transform content for different use cases.
 from langchain_core.messages import AIMessage, HumanMessage
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import selectinload
 
 
 def extract_text_content(content: str | dict | list) -> str:
@@ -38,6 +39,7 @@ def extract_text_content(content: str | dict | list) -> str:
 async def bootstrap_history_from_db(
     session: AsyncSession,
     thread_id: int,
+    thread_visibility: "ChatVisibility | None" = None,
 ) -> list[HumanMessage | AIMessage]:
     """
     Load message history from database and convert to LangChain format.
@@ -45,20 +47,28 @@ async def bootstrap_history_from_db(
     Used for cloned chats where the LangGraph checkpointer has no state,
     but we have messages in the database that should be used as context.
 
+    When thread_visibility is SEARCH_SPACE, user messages are prefixed with
+    the author's display name so the LLM sees who said what.
+
     Args:
         session: Database session
         thread_id: The chat thread ID
+        thread_visibility: When SEARCH_SPACE, user messages get author prefix
 
     Returns:
         List of LangChain messages (HumanMessage/AIMessage)
     """
-    from app.db import NewChatMessage
+    from app.db import ChatVisibility, NewChatMessage
 
-    result = await session.execute(
+    is_shared = thread_visibility == ChatVisibility.SEARCH_SPACE
+    stmt = (
         select(NewChatMessage)
         .filter(NewChatMessage.thread_id == thread_id)
         .order_by(NewChatMessage.created_at)
     )
+    if is_shared:
+        stmt = stmt.options(selectinload(NewChatMessage.author))
+    result = await session.execute(stmt)
     db_messages = result.scalars().all()
 
     langchain_messages: list[HumanMessage | AIMessage] = []
@@ -68,6 +78,11 @@ async def bootstrap_history_from_db(
         if not text_content:
             continue
         if msg.role == "user":
+            if is_shared:
+                author_name = (
+                    (msg.author.display_name if msg.author else None) or "A team member"
+                )
+                text_content = f"**[{author_name}]:** {text_content}"
             langchain_messages.append(HumanMessage(content=text_content))
         elif msg.role == "assistant":
             langchain_messages.append(AIMessage(content=text_content))

surfsense_web/atoms/user/user-query.atoms.ts

@@ -1,16 +1,14 @@
 import { atomWithQuery } from "jotai-tanstack-query";
 import { userApiService } from "@/lib/apis/user-api.service";
-import { getBearerToken } from "@/lib/auth-utils";
+import { getBearerToken, isPublicRoute } from "@/lib/auth-utils";
 import { cacheKeys } from "@/lib/query-client/cache-keys";
 
 export const currentUserAtom = atomWithQuery(() => {
+	const pathname = typeof window !== "undefined" ? window.location.pathname : null;
 	return {
 		queryKey: cacheKeys.user.current(),
 		staleTime: 5 * 60 * 1000, // 5 minutes
-		// Only fetch user data when a bearer token is present
+		enabled: !!getBearerToken() && pathname !== null && !isPublicRoute(pathname),
-		enabled: !!getBearerToken(),
+		queryFn: async () => userApiService.getMe(),
-		queryFn: async () => {
-			return userApiService.getMe();
-		},
 	};
 });

surfsense_web/lib/auth-utils.ts

@@ -10,28 +10,53 @@ const REFRESH_TOKEN_KEY = "surfsense_refresh_token";
 let isRefreshing = false;
 let refreshPromise: Promise<string | null> | null = null;
 
+/** Path prefixes for routes that do not require auth (no current-user fetch, no redirect on 401) */
+const PUBLIC_ROUTE_PREFIXES = [
+	"/login",
+	"/register",
+	"/auth",
+	"/docs",
+	"/public",
+	"/invite",
+	"/contact",
+	"/pricing",
+	"/privacy",
+	"/terms",
+	"/changelog",
+];
+
 /**
- * Saves the current path and redirects to login page
+ * Returns true if the pathname is a public route where we should not run auth checks
- * Call this when a 401 response is received
+ * or redirect to login on 401.
+ */
+export function isPublicRoute(pathname: string): boolean {
+	if (pathname === "/" || pathname === "") return true;
+	return PUBLIC_ROUTE_PREFIXES.some((prefix) => pathname.startsWith(prefix));
+}
+
+/**
+ * Clears tokens and optionally redirects to login.
+ * Call this when a 401 response is received.
+ * Only redirects when the current route is protected; on public routes we just clear tokens.
  */
 export function handleUnauthorized(): void {
 	if (typeof window === "undefined") return;
 
-	// Save the current path (including search params and hash) for redirect after login
+	const pathname = window.location.pathname;
-	const currentPath = window.location.pathname + window.location.search + window.location.hash;
 
-	// Don't save auth-related paths
+	// Always clear tokens
-	const excludedPaths = ["/auth", "/auth/callback", "/"];
-	if (!excludedPaths.includes(window.location.pathname)) {
-		localStorage.setItem(REDIRECT_PATH_KEY, currentPath);
-	}
-
-	// Clear both tokens
 	localStorage.removeItem(BEARER_TOKEN_KEY);
 	localStorage.removeItem(REFRESH_TOKEN_KEY);
 
-	// Redirect to home page (which has login options)
+	// Only redirect on protected routes; stay on public pages (e.g. /docs)
-	window.location.href = "/login";
+	if (!isPublicRoute(pathname)) {
+		const currentPath = pathname + window.location.search + window.location.hash;
+		const excludedPaths = ["/auth", "/auth/callback", "/"];
+		if (!excludedPaths.includes(pathname)) {
+			localStorage.setItem(REDIRECT_PATH_KEY, currentPath);
+		}
+		window.location.href = "/login";
+	}
 }
 
 /**
@@ -179,7 +204,6 @@ export function getAuthHeaders(additionalHeaders?: Record<string, string>): Reco
 /**
  * Attempts to refresh the access token using the stored refresh token.
  * Returns the new access token if successful, null otherwise.
- * Exported for use by API services.
  */
 export async function refreshAccessToken(): Promise<string | null> {
 	// If already refreshing, wait for that request to complete