Merge branch 'dev' into ntindle/waitlist

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.github/workflows/platform-frontend-ci.yml

@@ -128,7 +128,7 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           exitOnceUploaded: true
 
-  e2e_test:
+  test:
     runs-on: big-boi
     needs: setup
     strategy:
@@ -258,39 +258,3 @@ jobs:
       - name: Print Final Docker Compose logs
         if: always()
         run: docker compose -f ../docker-compose.yml logs
-
-  integration_test:
-    runs-on: ubuntu-latest
-    needs: setup
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          submodules: recursive
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22.18.0"
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Restore dependencies cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.pnpm-store
-          key: ${{ needs.setup.outputs.cache-key }}
-          restore-keys: |
-            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
-            ${{ runner.os }}-pnpm-
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Generate API client
-        run: pnpm generate:api
-
-      - name: Run Integration Tests
-        run: pnpm test:unit

AGENTS.md

@@ -16,32 +16,6 @@ See `docs/content/platform/getting-started.md` for setup instructions.
 - Format Python code with `poetry run format`.
 - Format frontend code using `pnpm format`.
 
-
-## Frontend guidelines:
-
-See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
-
-1. **Pages**: Create in `src/app/(platform)/feature-name/page.tsx`
-   - Add `usePageName.ts` hook for logic
-   - Put sub-components in local `components/` folder
-2. **Components**: Structure as `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
-   - Use design system components from `src/components/` (atoms, molecules, organisms)
-   - Never use `src/components/__legacy__/*`
-3. **Data fetching**: Use generated API hooks from `@/app/api/__generated__/endpoints/`
-   - Regenerate with `pnpm generate:api`
-   - Pattern: `use{Method}{Version}{OperationName}`
-4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
-5. **Testing**: Add Storybook stories for new components, Playwright for E2E
-6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
-- Component props should be `interface Props { ... }` (not exported) unless the interface needs to be used outside the component
-- Separate render logic from business logic (component.tsx + useComponent.ts + helpers.ts)
-- Colocate state when possible and avoid creating large components, use sub-components ( local `/components` folder next to the parent component ) when sensible
-- Avoid large hooks, abstract logic into `helpers.ts` files when sensible
-- Use function declarations for components, arrow functions only for callbacks
-- No barrel files or `index.ts` re-exports
-- Do not use `useCallback` or `useMemo` unless strictly needed
-- Avoid comments at all times unless the code is very complex
-
 ## Testing
 
 - Backend: `poetry run test` (runs pytest with a docker based postgres + prisma).

autogpt_platform/CLAUDE.md

@@ -201,7 +201,7 @@ If you get any pushback or hit complex block conditions check the new_blocks gui
 3. Write tests alongside the route file
 4. Run `poetry run test` to verify
 
-### Frontend guidelines:
+**Frontend feature development:**
 
 See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 
@@ -217,14 +217,6 @@ See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
 5. **Testing**: Add Storybook stories for new components, Playwright for E2E
 6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
-- Component props should be `interface Props { ... }` (not exported) unless the interface needs to be used outside the component
-- Separate render logic from business logic (component.tsx + useComponent.ts + helpers.ts)
-- Colocate state when possible and avoid creating large components, use sub-components ( local `/components` folder next to the parent component ) when sensible
-- Avoid large hooks, abstract logic into `helpers.ts` files when sensible
-- Use function declarations for components, arrow functions only for callbacks
-- No barrel files or `index.ts` re-exports
-- Do not use `useCallback` or `useMemo` unless strictly needed
-- Avoid comments at all times unless the code is very complex
 
 ### Security Implementation
 

autogpt_platform/backend/backend/api/features/admin/waitlist_admin_routes.py

@@ -0,0 +1,251 @@
+import logging
+
+import autogpt_libs.auth
+import fastapi
+import fastapi.responses
+
+import backend.api.features.store.db as store_db
+import backend.api.features.store.model as store_model
+
+logger = logging.getLogger(__name__)
+
+router = fastapi.APIRouter(
+    prefix="/admin/waitlist",
+    tags=["store", "admin", "waitlist"],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_admin_user)],
+)
+
+
+@router.post(
+    "",
+    summary="Create Waitlist",
+    response_model=store_model.WaitlistAdminResponse,
+)
+async def create_waitlist(
+    request: store_model.WaitlistCreateRequest,
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
+):
+    """
+    Create a new waitlist (admin only).
+
+    Args:
+        request: Waitlist creation details
+        user_id: Authenticated admin user creating the waitlist
+
+    Returns:
+        WaitlistAdminResponse with the created waitlist details
+    """
+    try:
+        waitlist = await store_db.create_waitlist_admin(
+            admin_user_id=user_id,
+            data=request,
+        )
+        return waitlist
+    except Exception as e:
+        logger.exception("Error creating waitlist: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while creating the waitlist"},
+        )
+
+
+@router.get(
+    "",
+    summary="List All Waitlists",
+    response_model=store_model.WaitlistAdminListResponse,
+)
+async def list_waitlists():
+    """
+    Get all waitlists with admin details (admin only).
+
+    Returns:
+        WaitlistAdminListResponse with all waitlists
+    """
+    try:
+        return await store_db.get_waitlists_admin()
+    except Exception as e:
+        logger.exception("Error listing waitlists: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while fetching waitlists"},
+        )
+
+
+@router.get(
+    "/{waitlist_id}",
+    summary="Get Waitlist Details",
+    response_model=store_model.WaitlistAdminResponse,
+)
+async def get_waitlist(
+    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
+):
+    """
+    Get a single waitlist with admin details (admin only).
+
+    Args:
+        waitlist_id: ID of the waitlist to retrieve
+
+    Returns:
+        WaitlistAdminResponse with waitlist details
+    """
+    try:
+        return await store_db.get_waitlist_admin(waitlist_id)
+    except ValueError:
+        logger.warning("Waitlist not found: %s", waitlist_id)
+        return fastapi.responses.JSONResponse(
+            status_code=404,
+            content={"detail": "Waitlist not found"},
+        )
+    except Exception as e:
+        logger.exception("Error fetching waitlist: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while fetching the waitlist"},
+        )
+
+
+@router.put(
+    "/{waitlist_id}",
+    summary="Update Waitlist",
+    response_model=store_model.WaitlistAdminResponse,
+)
+async def update_waitlist(
+    request: store_model.WaitlistUpdateRequest,
+    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
+):
+    """
+    Update a waitlist (admin only).
+
+    Args:
+        waitlist_id: ID of the waitlist to update
+        request: Fields to update
+
+    Returns:
+        WaitlistAdminResponse with updated waitlist details
+    """
+    try:
+        return await store_db.update_waitlist_admin(waitlist_id, request)
+    except ValueError:
+        logger.warning("Waitlist not found for update: %s", waitlist_id)
+        return fastapi.responses.JSONResponse(
+            status_code=404,
+            content={"detail": "Waitlist not found"},
+        )
+    except Exception as e:
+        logger.exception("Error updating waitlist: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while updating the waitlist"},
+        )
+
+
+@router.delete(
+    "/{waitlist_id}",
+    summary="Delete Waitlist",
+)
+async def delete_waitlist(
+    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
+):
+    """
+    Soft delete a waitlist (admin only).
+
+    Args:
+        waitlist_id: ID of the waitlist to delete
+
+    Returns:
+        Success message
+    """
+    try:
+        await store_db.delete_waitlist_admin(waitlist_id)
+        return {"message": "Waitlist deleted successfully"}
+    except ValueError:
+        logger.warning(f"Waitlist not found for deletion: {waitlist_id}")
+        return fastapi.responses.JSONResponse(
+            status_code=404,
+            content={"detail": "Waitlist not found"},
+        )
+    except Exception as e:
+        logger.exception("Error deleting waitlist: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while deleting the waitlist"},
+        )
+
+
+@router.get(
+    "/{waitlist_id}/signups",
+    summary="Get Waitlist Signups",
+    response_model=store_model.WaitlistSignupListResponse,
+)
+async def get_waitlist_signups(
+    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
+):
+    """
+    Get all signups for a waitlist (admin only).
+
+    Args:
+        waitlist_id: ID of the waitlist
+
+    Returns:
+        WaitlistSignupListResponse with all signups
+    """
+    try:
+        return await store_db.get_waitlist_signups_admin(waitlist_id)
+    except ValueError:
+        logger.warning("Waitlist not found for signups: %s", waitlist_id)
+        return fastapi.responses.JSONResponse(
+            status_code=404,
+            content={"detail": "Waitlist not found"},
+        )
+    except Exception as e:
+        logger.exception("Error fetching waitlist signups: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while fetching waitlist signups"},
+        )
+
+
+@router.post(
+    "/{waitlist_id}/link",
+    summary="Link Waitlist to Store Listing",
+    response_model=store_model.WaitlistAdminResponse,
+)
+async def link_waitlist_to_listing(
+    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
+    store_listing_id: str = fastapi.Body(
+        ..., embed=True, description="The ID of the store listing"
+    ),
+):
+    """
+    Link a waitlist to a store listing (admin only).
+
+    When the linked store listing is approved/published, waitlist users
+    will be automatically notified.
+
+    Args:
+        waitlist_id: ID of the waitlist
+        store_listing_id: ID of the store listing to link
+
+    Returns:
+        WaitlistAdminResponse with updated waitlist details
+    """
+    try:
+        return await store_db.link_waitlist_to_listing_admin(
+            waitlist_id, store_listing_id
+        )
+    except ValueError:
+        logger.warning(
+            "Link failed - waitlist or listing not found: %s, %s",
+            waitlist_id,
+            store_listing_id,
+        )
+        return fastapi.responses.JSONResponse(
+            status_code=404,
+            content={"detail": "Waitlist or store listing not found"},
+        )
+    except Exception as e:
+        logger.exception("Error linking waitlist to listing: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while linking the waitlist"},
+        )

autogpt_platform/backend/backend/api/features/chat/model.py

@@ -290,11 +290,6 @@ async def _cache_session(session: ChatSession) -> None:
     await async_redis.setex(redis_key, config.session_ttl, session.model_dump_json())
 
 
-async def cache_chat_session(session: ChatSession) -> None:
-    """Cache a chat session without persisting to the database."""
-    await _cache_session(session)
-
-
 async def _get_session_from_db(session_id: str) -> ChatSession | None:
     """Get a chat session from the database."""
     prisma_session = await chat_db.get_chat_session(session_id)

autogpt_platform/backend/backend/api/features/chat/routes.py

@@ -172,12 +172,12 @@ async def get_session(
         user_id: The optional authenticated user ID, or None for anonymous access.
 
     Returns:
-        SessionDetailResponse: Details for the requested session, or None if not found.
+        SessionDetailResponse: Details for the requested session; raises NotFoundError if not found.
 
     """
     session = await get_chat_session(session_id, user_id)
     if not session:
-        raise NotFoundError(f"Session {session_id} not found.")
+        raise NotFoundError(f"Session {session_id} not found")
 
     messages = [message.model_dump() for message in session.messages]
     logger.info(
@@ -222,8 +222,6 @@ async def stream_chat_post(
     session = await _validate_and_get_session(session_id, user_id)
 
     async def event_generator() -> AsyncGenerator[str, None]:
-        chunk_count = 0
-        first_chunk_type: str | None = None
         async for chunk in chat_service.stream_chat_completion(
             session_id,
             request.message,
@@ -232,26 +230,7 @@ async def stream_chat_post(
             session=session,  # Pass pre-fetched session to avoid double-fetch
             context=request.context,
         ):
-            if chunk_count < 3:
-                logger.info(
-                    "Chat stream chunk",
-                    extra={
-                        "session_id": session_id,
-                        "chunk_type": str(chunk.type),
-                    },
-                )
-            if not first_chunk_type:
-                first_chunk_type = str(chunk.type)
-            chunk_count += 1
             yield chunk.to_sse()
-        logger.info(
-            "Chat stream completed",
-            extra={
-                "session_id": session_id,
-                "chunk_count": chunk_count,
-                "first_chunk_type": first_chunk_type,
-            },
-        )
         # AI SDK protocol termination
         yield "data: [DONE]\n\n"
 
@@ -296,8 +275,6 @@ async def stream_chat_get(
     session = await _validate_and_get_session(session_id, user_id)
 
     async def event_generator() -> AsyncGenerator[str, None]:
-        chunk_count = 0
-        first_chunk_type: str | None = None
         async for chunk in chat_service.stream_chat_completion(
             session_id,
             message,
@@ -305,26 +282,7 @@ async def stream_chat_get(
             user_id=user_id,
             session=session,  # Pass pre-fetched session to avoid double-fetch
         ):
-            if chunk_count < 3:
-                logger.info(
-                    "Chat stream chunk",
-                    extra={
-                        "session_id": session_id,
-                        "chunk_type": str(chunk.type),
-                    },
-                )
-            if not first_chunk_type:
-                first_chunk_type = str(chunk.type)
-            chunk_count += 1
             yield chunk.to_sse()
-        logger.info(
-            "Chat stream completed",
-            extra={
-                "session_id": session_id,
-                "chunk_count": chunk_count,
-                "first_chunk_type": first_chunk_type,
-            },
-        )
         # AI SDK protocol termination
         yield "data: [DONE]\n\n"
 

autogpt_platform/backend/backend/api/features/chat/service.py

@@ -1,20 +1,12 @@
 import asyncio
 import logging
-import time
-from asyncio import CancelledError
 from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
 from langfuse import get_client, propagate_attributes
 from langfuse.openai import openai  # type: ignore
-from openai import (
-    APIConnectionError,
-    APIError,
-    APIStatusError,
-    PermissionDeniedError,
-    RateLimitError,
-)
+from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -29,7 +21,6 @@ from .model import (
     ChatMessage,
     ChatSession,
     Usage,
-    cache_chat_session,
     get_chat_session,
     update_session_title,
     upsert_chat_session,
@@ -305,10 +296,6 @@ async def stream_chat_completion(
                 content="",
             )
             accumulated_tool_calls: list[dict[str, Any]] = []
-            has_saved_assistant_message = False
-            has_appended_streaming_message = False
-            last_cache_time = 0.0
-            last_cache_content_len = 0
 
             # Wrap main logic in try/finally to ensure Langfuse observations are always ended
             has_yielded_end = False
@@ -345,23 +332,6 @@ async def stream_chat_completion(
                         assert assistant_response.content is not None
                         assistant_response.content += delta
                         has_received_text = True
-                        if not has_appended_streaming_message:
-                            session.messages.append(assistant_response)
-                            has_appended_streaming_message = True
-                        current_time = time.monotonic()
-                        content_len = len(assistant_response.content)
-                        if (
-                            current_time - last_cache_time >= 1.0
-                            and content_len > last_cache_content_len
-                        ):
-                            try:
-                                await cache_chat_session(session)
-                            except Exception as e:
-                                logger.warning(
-                                    f"Failed to cache partial session {session.session_id}: {e}"
-                                )
-                            last_cache_time = current_time
-                            last_cache_content_len = content_len
                         yield chunk
                     elif isinstance(chunk, StreamTextEnd):
                         # Emit text-end after text completes
@@ -420,42 +390,10 @@ async def stream_chat_completion(
                             if has_received_text and not text_streaming_ended:
                                 yield StreamTextEnd(id=text_block_id)
                                 text_streaming_ended = True
-
-                            # Save assistant message before yielding finish to ensure it's persisted
-                            # even if client disconnects immediately after receiving StreamFinish
-                            if not has_saved_assistant_message:
-                                messages_to_save_early: list[ChatMessage] = []
-                                if accumulated_tool_calls:
-                                    assistant_response.tool_calls = (
-                                        accumulated_tool_calls
-                                    )
-                                if not has_appended_streaming_message and (
-                                    assistant_response.content
-                                    or assistant_response.tool_calls
-                                ):
-                                    messages_to_save_early.append(assistant_response)
-                                messages_to_save_early.extend(tool_response_messages)
-
-                                if messages_to_save_early:
-                                    session.messages.extend(messages_to_save_early)
-                                    logger.info(
-                                        f"Saving assistant message before StreamFinish: "
-                                        f"content_len={len(assistant_response.content or '')}, "
-                                        f"tool_calls={len(assistant_response.tool_calls or [])}, "
-                                        f"tool_responses={len(tool_response_messages)}"
-                                    )
-                                if (
-                                    messages_to_save_early
-                                    or has_appended_streaming_message
-                                ):
-                                    await upsert_chat_session(session)
-                                    has_saved_assistant_message = True
-
                             has_yielded_end = True
                             yield chunk
                     elif isinstance(chunk, StreamError):
                         has_yielded_error = True
-                        yield chunk
                     elif isinstance(chunk, StreamUsage):
                         session.usage.append(
                             Usage(
@@ -475,27 +413,6 @@ async def stream_chat_completion(
                     langfuse.update_current_trace(output=str(tool_response_messages))
                     langfuse.update_current_span(output=str(tool_response_messages))
 
-            except CancelledError:
-                if not has_saved_assistant_message:
-                    if accumulated_tool_calls:
-                        assistant_response.tool_calls = accumulated_tool_calls
-                    if assistant_response.content:
-                        assistant_response.content = (
-                            f"{assistant_response.content}\n\n[interrupted]"
-                        )
-                    else:
-                        assistant_response.content = "[interrupted]"
-                    if not has_appended_streaming_message:
-                        session.messages.append(assistant_response)
-                    if tool_response_messages:
-                        session.messages.extend(tool_response_messages)
-                    try:
-                        await upsert_chat_session(session)
-                    except Exception as e:
-                        logger.warning(
-                            f"Failed to save interrupted session {session.session_id}: {e}"
-                        )
-                raise
             except Exception as e:
                 logger.error(f"Error during stream: {e!s}", exc_info=True)
 
@@ -517,19 +434,14 @@ async def stream_chat_completion(
                     # Add assistant message if it has content or tool calls
                     if accumulated_tool_calls:
                         assistant_response.tool_calls = accumulated_tool_calls
-                    if not has_appended_streaming_message and (
-                        assistant_response.content or assistant_response.tool_calls
-                    ):
+                    if assistant_response.content or assistant_response.tool_calls:
                         messages_to_save.append(assistant_response)
 
                     # Add tool response messages after assistant message
                     messages_to_save.extend(tool_response_messages)
 
-                    if not has_saved_assistant_message:
-                        if messages_to_save:
-                            session.messages.extend(messages_to_save)
-                        if messages_to_save or has_appended_streaming_message:
-                            await upsert_chat_session(session)
+                    session.messages.extend(messages_to_save)
+                    await upsert_chat_session(session)
 
                     if not has_yielded_error:
                         error_message = str(e)
@@ -560,49 +472,38 @@ async def stream_chat_completion(
                 return  # Exit after retry to avoid double-saving in finally block
 
             # Normal completion path - save session and handle tool call continuation
-            # Only save if we haven't already saved when StreamFinish was received
-            if not has_saved_assistant_message:
+            logger.info(
+                f"Normal completion path: session={session.session_id}, "
+                f"current message_count={len(session.messages)}"
+            )
+
+            # Build the messages list in the correct order
+            messages_to_save: list[ChatMessage] = []
+
+            # Add assistant message with tool_calls if any
+            if accumulated_tool_calls:
+                assistant_response.tool_calls = accumulated_tool_calls
                 logger.info(
-                    f"Normal completion path: session={session.session_id}, "
-                    f"current message_count={len(session.messages)}"
+                    f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+                )
+            if assistant_response.content or assistant_response.tool_calls:
+                messages_to_save.append(assistant_response)
+                logger.info(
+                    f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
                 )
 
-                # Build the messages list in the correct order
-                messages_to_save: list[ChatMessage] = []
+            # Add tool response messages after assistant message
+            messages_to_save.extend(tool_response_messages)
+            logger.info(
+                f"Saving {len(tool_response_messages)} tool response messages, "
+                f"total_to_save={len(messages_to_save)}"
+            )
 
-                # Add assistant message with tool_calls if any
-                if accumulated_tool_calls:
-                    assistant_response.tool_calls = accumulated_tool_calls
-                    logger.info(
-                        f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
-                    )
-                if not has_appended_streaming_message and (
-                    assistant_response.content or assistant_response.tool_calls
-                ):
-                    messages_to_save.append(assistant_response)
-                    logger.info(
-                        f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
-                    )
-
-                # Add tool response messages after assistant message
-                messages_to_save.extend(tool_response_messages)
-                logger.info(
-                    f"Saving {len(tool_response_messages)} tool response messages, "
-                    f"total_to_save={len(messages_to_save)}"
-                )
-
-                if messages_to_save:
-                    session.messages.extend(messages_to_save)
-                    logger.info(
-                        f"Extended session messages, new message_count={len(session.messages)}"
-                    )
-                if messages_to_save or has_appended_streaming_message:
-                    await upsert_chat_session(session)
-            else:
-                logger.info(
-                    "Assistant message already saved when StreamFinish was received, "
-                    "skipping duplicate save"
-                )
+            session.messages.extend(messages_to_save)
+            logger.info(
+                f"Extended session messages, new message_count={len(session.messages)}"
+            )
+            await upsert_chat_session(session)
 
             # If we did a tool call, stream the chat completion again to get the next response
             if has_done_tool_call:
@@ -644,12 +545,6 @@ def _is_retryable_error(error: Exception) -> bool:
     return False
 
 
-def _is_region_blocked_error(error: Exception) -> bool:
-    if isinstance(error, PermissionDeniedError):
-        return "not available in your region" in str(error).lower()
-    return "not available in your region" in str(error).lower()
-
-
 async def _stream_chat_chunks(
     session: ChatSession,
     tools: list[ChatCompletionToolParam],
@@ -842,18 +737,7 @@ async def _stream_chat_chunks(
                         f"Error in stream (not retrying): {e!s}",
                         exc_info=True,
                     )
-                    error_code = None
-                    error_text = str(e)
-                    if _is_region_blocked_error(e):
-                        error_code = "MODEL_NOT_AVAILABLE_REGION"
-                        error_text = (
-                            "This model is not available in your region. "
-                            "Please connect via VPN and try again."
-                        )
-                    error_response = StreamError(
-                        errorText=error_text,
-                        code=error_code,
-                    )
+                    error_response = StreamError(errorText=str(e))
                     yield error_response
                     yield StreamFinish()
                     return

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -218,7 +218,6 @@ async def save_agent_to_library(
     library_agents = await library_db.create_library_agent(
         graph=created_graph,
         user_id=user_id,
-        sensitive_action_safe_mode=True,
         create_library_agents_for_sub_graphs=False,
     )
 

autogpt_platform/backend/backend/api/features/library/db.py

@@ -401,11 +401,27 @@ async def add_generated_agent_image(
     )
 
 
+def _initialize_graph_settings(graph: graph_db.GraphModel) -> GraphSettings:
+    """
+    Initialize GraphSettings based on graph content.
+
+    Args:
+        graph: The graph to analyze
+
+    Returns:
+        GraphSettings with appropriate human_in_the_loop_safe_mode value
+    """
+    if graph.has_human_in_the_loop:
+        # Graph has HITL blocks - set safe mode to True by default
+        return GraphSettings(human_in_the_loop_safe_mode=True)
+    else:
+        # Graph has no HITL blocks - keep None
+        return GraphSettings(human_in_the_loop_safe_mode=None)
+
+
 async def create_library_agent(
     graph: graph_db.GraphModel,
     user_id: str,
-    hitl_safe_mode: bool = True,
-    sensitive_action_safe_mode: bool = False,
     create_library_agents_for_sub_graphs: bool = True,
 ) -> list[library_model.LibraryAgent]:
     """
@@ -414,8 +430,6 @@ async def create_library_agent(
     Args:
         agent: The agent/Graph to add to the library.
         user_id: The user to whom the agent will be added.
-        hitl_safe_mode: Whether HITL blocks require manual review (default True).
-        sensitive_action_safe_mode: Whether sensitive action blocks require review.
         create_library_agents_for_sub_graphs: If True, creates LibraryAgent records for sub-graphs as well.
 
     Returns:
@@ -451,11 +465,7 @@ async def create_library_agent(
                             }
                         },
                         settings=SafeJson(
-                            GraphSettings.from_graph(
-                                graph_entry,
-                                hitl_safe_mode=hitl_safe_mode,
-                                sensitive_action_safe_mode=sensitive_action_safe_mode,
-                            ).model_dump()
+                            _initialize_graph_settings(graph_entry).model_dump()
                         ),
                     ),
                     include=library_agent_include(
@@ -617,6 +627,33 @@ async def update_library_agent(
         raise DatabaseError("Failed to update library agent") from e
 
 
+async def update_library_agent_settings(
+    user_id: str,
+    agent_id: str,
+    settings: GraphSettings,
+) -> library_model.LibraryAgent:
+    """
+    Updates the settings for a specific LibraryAgent.
+
+    Args:
+        user_id: The owner of the LibraryAgent.
+        agent_id: The ID of the LibraryAgent to update.
+        settings: New GraphSettings to apply.
+
+    Returns:
+        The updated LibraryAgent.
+
+    Raises:
+        NotFoundError: If the specified LibraryAgent does not exist.
+        DatabaseError: If there's an error in the update operation.
+    """
+    return await update_library_agent(
+        library_agent_id=agent_id,
+        user_id=user_id,
+        settings=settings,
+    )
+
+
 async def delete_library_agent(
     library_agent_id: str, user_id: str, soft_delete: bool = True
 ) -> None:
@@ -801,7 +838,7 @@ async def add_store_agent_to_library(
                 "isCreatedByUser": False,
                 "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
-                    GraphSettings.from_graph(graph_model).model_dump()
+                    _initialize_graph_settings(graph_model).model_dump()
                 ),
             },
             include=library_agent_include(
@@ -1191,15 +1228,8 @@ async def fork_library_agent(
         )
         new_graph = await on_graph_activate(new_graph, user_id=user_id)
 
-        # Create a library agent for the new graph, preserving safe mode settings
-        return (
-            await create_library_agent(
-                new_graph,
-                user_id,
-                hitl_safe_mode=original_agent.settings.human_in_the_loop_safe_mode,
-                sensitive_action_safe_mode=original_agent.settings.sensitive_action_safe_mode,
-            )
-        )[0]
+        # Create a library agent for the new graph
+        return (await create_library_agent(new_graph, user_id))[0]
     except prisma.errors.PrismaError as e:
         logger.error(f"Database error cloning library agent: {e}")
         raise DatabaseError("Failed to fork library agent") from e

autogpt_platform/backend/backend/api/features/library/model.py

@@ -73,12 +73,6 @@ class LibraryAgent(pydantic.BaseModel):
     has_external_trigger: bool = pydantic.Field(
         description="Whether the agent has an external trigger (e.g. webhook) node"
     )
-    has_human_in_the_loop: bool = pydantic.Field(
-        description="Whether the agent has human-in-the-loop blocks"
-    )
-    has_sensitive_action: bool = pydantic.Field(
-        description="Whether the agent has sensitive action blocks"
-    )
     trigger_setup_info: Optional[GraphTriggerInfo] = None
 
     # Indicates whether there's a new output (based on recent runs)
@@ -186,8 +180,6 @@ class LibraryAgent(pydantic.BaseModel):
                 graph.credentials_input_schema if sub_graphs is not None else None
             ),
             has_external_trigger=graph.has_external_trigger,
-            has_human_in_the_loop=graph.has_human_in_the_loop,
-            has_sensitive_action=graph.has_sensitive_action,
             trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
             can_access_graph=can_access_graph,

autogpt_platform/backend/backend/api/features/library/routes_test.py

@@ -52,8 +52,6 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
-                has_human_in_the_loop=False,
-                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -77,8 +75,6 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
-                has_human_in_the_loop=False,
-                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -154,8 +150,6 @@ async def test_get_favorite_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
-                has_human_in_the_loop=False,
-                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -224,8 +218,6 @@ def test_add_agent_to_library_success(
         output_schema={"type": "object", "properties": {}},
         credentials_input_schema={"type": "object", "properties": {}},
         has_external_trigger=False,
-        has_human_in_the_loop=False,
-        has_sensitive_action=False,
         status=library_model.LibraryAgentStatus.COMPLETED,
         new_output=False,
         can_access_graph=True,

autogpt_platform/backend/backend/api/features/store/db.py

@@ -22,6 +22,7 @@ from backend.data.notifications import (
     AgentApprovalData,
     AgentRejectionData,
     NotificationEventModel,
+    WaitlistLaunchData,
 )
 from backend.notifications.notifications import queue_notification_async
 from backend.util.exceptions import DatabaseError
@@ -1717,6 +1718,29 @@ async def review_store_submission(
                 # Don't fail the review process if email sending fails
                 pass
 
+        # Notify waitlist users if this is an approval and has a linked waitlist
+        if is_approved and submission.StoreListing:
+            try:
+                frontend_base_url = (
+                    settings.config.frontend_base_url
+                    or settings.config.platform_base_url
+                )
+                store_agent = (
+                    await prisma.models.StoreAgent.prisma().find_first_or_raise(
+                        where={"storeListingVersionId": submission.id}
+                    )
+                )
+                creator_username = store_agent.creator_username or "unknown"
+                store_url = f"{frontend_base_url}/marketplace/agent/{creator_username}/{store_agent.slug}"
+                await notify_waitlist_users_on_launch(
+                    store_listing_id=submission.StoreListing.id,
+                    agent_name=submission.name,
+                    store_url=store_url,
+                )
+            except Exception as e:
+                logger.error(f"Failed to notify waitlist users on agent approval: {e}")
+                # Don't fail the approval process
+
         # Convert to Pydantic model for consistency
         return store_model.StoreSubmission(
             listing_id=(submission.StoreListing.id if submission.StoreListing else ""),
@@ -1964,3 +1988,552 @@ async def get_agent_as_admin(
         )
 
     return graph
+
+
+def _waitlist_to_store_entry(
+    waitlist: prisma.models.WaitlistEntry,
+) -> store_model.StoreWaitlistEntry:
+    """Convert a WaitlistEntry to StoreWaitlistEntry for public display."""
+    return store_model.StoreWaitlistEntry(
+        waitlistId=waitlist.id,
+        slug=waitlist.slug,
+        name=waitlist.name,
+        subHeading=waitlist.subHeading,
+        videoUrl=waitlist.videoUrl,
+        agentOutputDemoUrl=waitlist.agentOutputDemoUrl,
+        imageUrls=waitlist.imageUrls or [],
+        description=waitlist.description,
+        categories=waitlist.categories,
+    )
+
+
+async def get_waitlist() -> list[store_model.StoreWaitlistEntry]:
+    """Get all active waitlists for public display."""
+    try:
+        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
+            where=prisma.types.WaitlistEntryWhereInput(isDeleted=False),
+        )
+
+        # Filter out closed/done waitlists and sort by votes (descending)
+        excluded_statuses = {
+            prisma.enums.WaitlistExternalStatus.CANCELED,
+            prisma.enums.WaitlistExternalStatus.DONE,
+        }
+        active_waitlists = [w for w in waitlists if w.status not in excluded_statuses]
+        sorted_list = sorted(active_waitlists, key=lambda x: x.votes, reverse=True)
+
+        return [_waitlist_to_store_entry(w) for w in sorted_list]
+    except Exception as e:
+        logger.error(f"Error fetching waitlists: {e}")
+        raise DatabaseError("Failed to fetch waitlists") from e
+
+
+async def get_user_waitlist_memberships(user_id: str) -> list[str]:
+    """Get all waitlist IDs that a user has joined."""
+    try:
+        user = await prisma.models.User.prisma().find_unique(
+            where={"id": user_id},
+            include={"joinedWaitlists": True},
+        )
+        if not user or not user.joinedWaitlists:
+            return []
+        return [w.id for w in user.joinedWaitlists]
+    except Exception as e:
+        logger.error(f"Error fetching user waitlist memberships: {e}")
+        raise DatabaseError("Failed to fetch waitlist memberships") from e
+
+
+async def add_user_to_waitlist(
+    waitlist_id: str, user_id: str | None, email: str | None
+) -> store_model.StoreWaitlistEntry:
+    """
+    Add a user to a waitlist.
+
+    For logged-in users: connects via joinedUsers relation
+    For anonymous users: adds email to unaffiliatedEmailUsers array
+    """
+    if not user_id and not email:
+        raise ValueError("Either user_id or email must be provided")
+
+    try:
+        # Find the waitlist
+        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
+            where={"id": waitlist_id},
+            include={"joinedUsers": True},
+        )
+
+        if not waitlist:
+            raise ValueError(f"Waitlist {waitlist_id} not found")
+
+        if waitlist.isDeleted:
+            raise ValueError(f"Waitlist {waitlist_id} is no longer available")
+
+        if waitlist.status in [
+            prisma.enums.WaitlistExternalStatus.CANCELED,
+            prisma.enums.WaitlistExternalStatus.DONE,
+        ]:
+            raise ValueError(f"Waitlist {waitlist_id} is closed")
+
+        if user_id:
+            # Check if user already joined
+            joined_user_ids = [u.id for u in (waitlist.joinedUsers or [])]
+            if user_id in joined_user_ids:
+                # Already joined - return waitlist info
+                logger.debug(f"User {user_id} already joined waitlist {waitlist_id}")
+            else:
+                # Connect user to waitlist
+                await prisma.models.WaitlistEntry.prisma().update(
+                    where={"id": waitlist_id},
+                    data={"joinedUsers": {"connect": [{"id": user_id}]}},
+                )
+                logger.info(f"User {user_id} joined waitlist {waitlist_id}")
+
+            # If user was previously in email list, remove them
+            # Use transaction to prevent race conditions
+            if email:
+                async with transaction() as tx:
+                    current_waitlist = await tx.waitlistentry.find_unique(
+                        where={"id": waitlist_id}
+                    )
+                    if current_waitlist and email in (
+                        current_waitlist.unaffiliatedEmailUsers or []
+                    ):
+                        updated_emails: list[str] = [
+                            e
+                            for e in (current_waitlist.unaffiliatedEmailUsers or [])
+                            if e != email
+                        ]
+                        await tx.waitlistentry.update(
+                            where={"id": waitlist_id},
+                            data={"unaffiliatedEmailUsers": updated_emails},
+                        )
+        elif email:
+            # Add email to unaffiliated list if not already present
+            # Use transaction to prevent race conditions with concurrent signups
+            async with transaction() as tx:
+                # Re-fetch within transaction to get latest state
+                current_waitlist = await tx.waitlistentry.find_unique(
+                    where={"id": waitlist_id}
+                )
+                if current_waitlist:
+                    current_emails: list[str] = list(
+                        current_waitlist.unaffiliatedEmailUsers or []
+                    )
+                    if email not in current_emails:
+                        current_emails.append(email)
+                        await tx.waitlistentry.update(
+                            where={"id": waitlist_id},
+                            data={"unaffiliatedEmailUsers": current_emails},
+                        )
+                        logger.info(f"Email {email} added to waitlist {waitlist_id}")
+                    else:
+                        logger.debug(f"Email {email} already on waitlist {waitlist_id}")
+
+        # Re-fetch to return updated data
+        updated_waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
+            where={"id": waitlist_id}
+        )
+        return _waitlist_to_store_entry(updated_waitlist or waitlist)
+
+    except ValueError:
+        raise
+    except Exception as e:
+        logger.error(f"Error adding user to waitlist: {e}")
+        raise DatabaseError("Failed to add user to waitlist") from e
+
+
+# ============== Admin Waitlist Functions ==============
+
+
+def _waitlist_to_admin_response(
+    waitlist: prisma.models.WaitlistEntry,
+) -> store_model.WaitlistAdminResponse:
+    """Convert a WaitlistEntry to WaitlistAdminResponse."""
+    joined_count = len(waitlist.joinedUsers) if waitlist.joinedUsers else 0
+    email_count = (
+        len(waitlist.unaffiliatedEmailUsers) if waitlist.unaffiliatedEmailUsers else 0
+    )
+
+    return store_model.WaitlistAdminResponse(
+        id=waitlist.id,
+        createdAt=waitlist.createdAt.isoformat() if waitlist.createdAt else "",
+        updatedAt=waitlist.updatedAt.isoformat() if waitlist.updatedAt else "",
+        slug=waitlist.slug,
+        name=waitlist.name,
+        subHeading=waitlist.subHeading,
+        description=waitlist.description,
+        categories=waitlist.categories,
+        imageUrls=waitlist.imageUrls or [],
+        videoUrl=waitlist.videoUrl,
+        agentOutputDemoUrl=waitlist.agentOutputDemoUrl,
+        status=waitlist.status or prisma.enums.WaitlistExternalStatus.NOT_STARTED,
+        votes=waitlist.votes,
+        signupCount=joined_count + email_count,
+        storeListingId=waitlist.storeListingId,
+        owningUserId=waitlist.owningUserId,
+    )
+
+
+async def create_waitlist_admin(
+    admin_user_id: str,
+    data: store_model.WaitlistCreateRequest,
+) -> store_model.WaitlistAdminResponse:
+    """Create a new waitlist (admin only)."""
+    logger.info(f"Admin {admin_user_id} creating waitlist: {data.name}")
+
+    try:
+        waitlist = await prisma.models.WaitlistEntry.prisma().create(
+            data=prisma.types.WaitlistEntryCreateInput(
+                name=data.name,
+                slug=data.slug,
+                subHeading=data.subHeading,
+                description=data.description,
+                categories=data.categories,
+                imageUrls=data.imageUrls,
+                videoUrl=data.videoUrl,
+                agentOutputDemoUrl=data.agentOutputDemoUrl,
+                owningUserId=admin_user_id,
+                status=prisma.enums.WaitlistExternalStatus.NOT_STARTED,
+            ),
+            include={"joinedUsers": True},
+        )
+
+        return _waitlist_to_admin_response(waitlist)
+    except Exception as e:
+        logger.error(f"Error creating waitlist: {e}")
+        raise DatabaseError("Failed to create waitlist") from e
+
+
+async def get_waitlists_admin() -> store_model.WaitlistAdminListResponse:
+    """Get all waitlists with admin details."""
+    try:
+        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
+            where=prisma.types.WaitlistEntryWhereInput(isDeleted=False),
+            include={"joinedUsers": True},
+            order={"createdAt": "desc"},
+        )
+
+        return store_model.WaitlistAdminListResponse(
+            waitlists=[_waitlist_to_admin_response(w) for w in waitlists],
+            totalCount=len(waitlists),
+        )
+    except Exception as e:
+        logger.error(f"Error fetching waitlists for admin: {e}")
+        raise DatabaseError("Failed to fetch waitlists") from e
+
+
+async def get_waitlist_admin(
+    waitlist_id: str,
+) -> store_model.WaitlistAdminResponse:
+    """Get a single waitlist with admin details."""
+    try:
+        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
+            where={"id": waitlist_id},
+            include={"joinedUsers": True},
+        )
+
+        if not waitlist:
+            raise ValueError(f"Waitlist {waitlist_id} not found")
+
+        if waitlist.isDeleted:
+            raise ValueError(f"Waitlist {waitlist_id} has been deleted")
+
+        return _waitlist_to_admin_response(waitlist)
+    except ValueError:
+        raise
+    except Exception as e:
+        logger.error(f"Error fetching waitlist {waitlist_id}: {e}")
+        raise DatabaseError("Failed to fetch waitlist") from e
+
+
+async def update_waitlist_admin(
+    waitlist_id: str,
+    data: store_model.WaitlistUpdateRequest,
+) -> store_model.WaitlistAdminResponse:
+    """Update a waitlist (admin only)."""
+    logger.info(f"Updating waitlist {waitlist_id}")
+
+    try:
+        # Check if waitlist exists first
+        existing = await prisma.models.WaitlistEntry.prisma().find_unique(
+            where={"id": waitlist_id}
+        )
+
+        if not existing:
+            raise ValueError(f"Waitlist {waitlist_id} not found")
+
+        if existing.isDeleted:
+            raise ValueError(f"Waitlist {waitlist_id} has been deleted")
+
+        # Build update data from explicitly provided fields
+        # Use model_fields_set to allow clearing fields by setting them to None
+        field_mappings = {
+            "name": data.name,
+            "slug": data.slug,
+            "subHeading": data.subHeading,
+            "description": data.description,
+            "categories": data.categories,
+            "imageUrls": data.imageUrls,
+            "videoUrl": data.videoUrl,
+            "agentOutputDemoUrl": data.agentOutputDemoUrl,
+            "storeListingId": data.storeListingId,
+        }
+        update_data: dict[str, Any] = {
+            k: v for k, v in field_mappings.items() if k in data.model_fields_set
+        }
+
+        # Add status if provided (already validated as enum by Pydantic)
+        if "status" in data.model_fields_set and data.status is not None:
+            update_data["status"] = data.status
+
+        if not update_data:
+            # No updates, just return current data
+            return await get_waitlist_admin(waitlist_id)
+
+        waitlist = await prisma.models.WaitlistEntry.prisma().update(
+            where={"id": waitlist_id},
+            data=prisma.types.WaitlistEntryUpdateInput(**update_data),
+            include={"joinedUsers": True},
+        )
+
+        # We already verified existence above, so this should never be None
+        assert waitlist is not None
+        return _waitlist_to_admin_response(waitlist)
+    except ValueError:
+        raise
+    except Exception as e:
+        logger.error(f"Error updating waitlist {waitlist_id}: {e}")
+        raise DatabaseError("Failed to update waitlist") from e
+
+
+async def delete_waitlist_admin(waitlist_id: str) -> None:
+    """Soft delete a waitlist (admin only)."""
+    logger.info(f"Soft deleting waitlist {waitlist_id}")
+
+    try:
+        # Check if waitlist exists first
+        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
+            where={"id": waitlist_id},
+        )
+
+        if not waitlist:
+            raise ValueError(f"Waitlist {waitlist_id} not found")
+
+        if waitlist.isDeleted:
+            raise ValueError(f"Waitlist {waitlist_id} has already been deleted")
+
+        await prisma.models.WaitlistEntry.prisma().update(
+            where={"id": waitlist_id},
+            data={"isDeleted": True},
+        )
+    except ValueError:
+        raise
+    except Exception as e:
+        logger.error(f"Error deleting waitlist {waitlist_id}: {e}")
+        raise DatabaseError("Failed to delete waitlist") from e
+
+
+async def get_waitlist_signups_admin(
+    waitlist_id: str,
+) -> store_model.WaitlistSignupListResponse:
+    """Get all signups for a waitlist (admin only)."""
+    try:
+        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
+            where={"id": waitlist_id},
+            include={"joinedUsers": True},
+        )
+
+        if not waitlist:
+            raise ValueError(f"Waitlist {waitlist_id} not found")
+
+        signups: list[store_model.WaitlistSignup] = []
+
+        # Add user signups
+        for user in waitlist.joinedUsers or []:
+            signups.append(
+                store_model.WaitlistSignup(
+                    type="user",
+                    userId=user.id,
+                    email=user.email,
+                    username=user.name,
+                )
+            )
+
+        # Add email signups
+        for email in waitlist.unaffiliatedEmailUsers or []:
+            signups.append(
+                store_model.WaitlistSignup(
+                    type="email",
+                    email=email,
+                )
+            )
+
+        return store_model.WaitlistSignupListResponse(
+            waitlistId=waitlist_id,
+            signups=signups,
+            totalCount=len(signups),
+        )
+    except ValueError:
+        raise
+    except Exception as e:
+        logger.error(f"Error fetching signups for waitlist {waitlist_id}: {e}")
+        raise DatabaseError("Failed to fetch waitlist signups") from e
+
+
+async def link_waitlist_to_listing_admin(
+    waitlist_id: str,
+    store_listing_id: str,
+) -> store_model.WaitlistAdminResponse:
+    """Link a waitlist to a store listing (admin only)."""
+    logger.info(f"Linking waitlist {waitlist_id} to listing {store_listing_id}")
+
+    try:
+        # Verify the waitlist exists
+        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
+            where={"id": waitlist_id}
+        )
+
+        if not waitlist:
+            raise ValueError(f"Waitlist {waitlist_id} not found")
+
+        if waitlist.isDeleted:
+            raise ValueError(f"Waitlist {waitlist_id} has been deleted")
+
+        # Verify the store listing exists
+        listing = await prisma.models.StoreListing.prisma().find_unique(
+            where={"id": store_listing_id}
+        )
+
+        if not listing:
+            raise ValueError(f"Store listing {store_listing_id} not found")
+
+        updated_waitlist = await prisma.models.WaitlistEntry.prisma().update(
+            where={"id": waitlist_id},
+            data={"StoreListing": {"connect": {"id": store_listing_id}}},
+            include={"joinedUsers": True},
+        )
+
+        # We already verified existence above, so this should never be None
+        assert updated_waitlist is not None
+        return _waitlist_to_admin_response(updated_waitlist)
+    except ValueError:
+        raise
+    except Exception as e:
+        logger.error(f"Error linking waitlist to listing: {e}")
+        raise DatabaseError("Failed to link waitlist to listing") from e
+
+
+async def notify_waitlist_users_on_launch(
+    store_listing_id: str,
+    agent_name: str,
+    store_url: str,
+) -> int:
+    """
+    Notify all users on waitlists linked to a store listing when the agent is launched.
+
+    Args:
+        store_listing_id: The ID of the store listing that was approved
+        agent_name: The name of the approved agent
+        store_url: The URL to the agent's store page
+
+    Returns:
+        The number of notifications sent
+    """
+    logger.info(f"Notifying waitlist users for store listing {store_listing_id}")
+
+    try:
+        # Find all active waitlists linked to this store listing
+        # Exclude DONE and CANCELED to prevent duplicate notifications on re-approval
+        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
+            where={
+                "storeListingId": store_listing_id,
+                "isDeleted": False,
+                "status": {
+                    "not_in": [
+                        prisma.enums.WaitlistExternalStatus.DONE,
+                        prisma.enums.WaitlistExternalStatus.CANCELED,
+                    ]
+                },
+            },
+            include={"joinedUsers": True},
+        )
+
+        if not waitlists:
+            logger.info(
+                f"No active waitlists found for store listing {store_listing_id}"
+            )
+            return 0
+
+        notification_count = 0
+        launched_at = datetime.now(tz=timezone.utc)
+
+        for waitlist in waitlists:
+            # Track notification results for this waitlist
+            users_to_notify = waitlist.joinedUsers or []
+            failed_user_ids: list[str] = []
+
+            # Notify registered users
+            for user in users_to_notify:
+                try:
+                    notification_data = WaitlistLaunchData(
+                        agent_name=agent_name,
+                        waitlist_name=waitlist.name,
+                        store_url=store_url,
+                        launched_at=launched_at,
+                    )
+
+                    notification_event = NotificationEventModel[WaitlistLaunchData](
+                        user_id=user.id,
+                        type=prisma.enums.NotificationType.WAITLIST_LAUNCH,
+                        data=notification_data,
+                    )
+
+                    await queue_notification_async(notification_event)
+                    notification_count += 1
+                except Exception as e:
+                    logger.error(
+                        f"Failed to send waitlist launch notification to user {user.id}: {e}"
+                    )
+                    failed_user_ids.append(user.id)
+
+            # Note: For unaffiliated email users, you would need to send emails directly
+            # since they don't have user IDs for the notification system.
+            # This could be done via a separate email service.
+            # For now, we log these for potential manual follow-up or future implementation.
+            has_pending_email_users = bool(waitlist.unaffiliatedEmailUsers)
+            if has_pending_email_users:
+                logger.info(
+                    f"Waitlist {waitlist.id} has {len(waitlist.unaffiliatedEmailUsers)} "
+                    f"unaffiliated email users that need email notifications"
+                )
+
+            # Only mark waitlist as DONE if all registered user notifications succeeded
+            # AND there are no unaffiliated email users still waiting for notifications
+            if not failed_user_ids and not has_pending_email_users:
+                await prisma.models.WaitlistEntry.prisma().update(
+                    where={"id": waitlist.id},
+                    data={"status": prisma.enums.WaitlistExternalStatus.DONE},
+                )
+                logger.info(f"Updated waitlist {waitlist.id} status to DONE")
+            elif failed_user_ids:
+                logger.warning(
+                    f"Waitlist {waitlist.id} not marked as DONE due to "
+                    f"{len(failed_user_ids)} failed notifications"
+                )
+            elif has_pending_email_users:
+                logger.warning(
+                    f"Waitlist {waitlist.id} not marked as DONE due to "
+                    f"{len(waitlist.unaffiliatedEmailUsers)} pending email-only users"
+                )
+
+        logger.info(
+            f"Sent {notification_count} waitlist launch notifications for store listing {store_listing_id}"
+        )
+        return notification_count
+
+    except Exception as e:
+        logger.error(
+            f"Error notifying waitlist users for store listing {store_listing_id}: {e}"
+        )
+        # Don't raise - we don't want to fail the approval process
+        return 0

autogpt_platform/backend/backend/api/features/store/embeddings.py

@@ -154,16 +154,16 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-        # Use unqualified ::vector - pgvector is in search_path on all environments
+        # Use {pgvector_schema}.vector for explicit pgvector type qualification
         await execute_raw_with_schema(
             """
             INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
                 "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
             )
-            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::{pgvector_schema}.vector, $5, $6::jsonb, NOW(), NOW())
             ON CONFLICT ("contentType", "contentId", "userId")
             DO UPDATE SET
-                "embedding" = $4::vector,
+                "embedding" = $4::{pgvector_schema}.vector,
                 "searchableText" = $5,
                 "metadata" = $6::jsonb,
                 "updatedAt" = NOW()
@@ -879,7 +879,8 @@ async def semantic_search(
         min_similarity_idx = len(params) + 1
         params.append(min_similarity)
 
-        # Use unqualified ::vector and <=> operator - pgvector is in search_path on all environments
+        # Use regular string (not f-string) for template to preserve {schema_prefix} and {schema} placeholders
+        # Use OPERATOR({pgvector_schema}.<=>) for explicit operator schema qualification
         sql = (
             """
             SELECT
@@ -887,9 +888,9 @@ async def semantic_search(
                 "contentType" as content_type,
                 "searchableText" as searchable_text,
                 metadata,
-                1 - (embedding <=> '"""
+                1 - (embedding OPERATOR({pgvector_schema}.<=>) '"""
             + embedding_str
-            + """'::vector) as similarity
+            + """'::{pgvector_schema}.vector) as similarity
             FROM {schema_prefix}"UnifiedContentEmbedding"
             WHERE "contentType" IN ("""
             + content_type_placeholders
@@ -897,9 +898,9 @@ async def semantic_search(
             """
             + user_filter
             + """
-            AND 1 - (embedding <=> '"""
+            AND 1 - (embedding OPERATOR({pgvector_schema}.<=>) '"""
             + embedding_str
-            + """'::vector) >= $"""
+            + """'::{pgvector_schema}.vector) >= $"""
             + str(min_similarity_idx)
             + """
             ORDER BY similarity DESC

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -295,7 +295,7 @@ async def unified_hybrid_search(
                 FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
                 WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
                 {user_filter}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
+                ORDER BY uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector
                 LIMIT 200
             )
         ),
@@ -307,7 +307,7 @@ async def unified_hybrid_search(
                 uce.metadata,
                 uce."updatedAt" as updated_at,
                 -- Semantic score: cosine similarity (1 - distance)
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector), 0) as semantic_score,
                 -- Lexical score: ts_rank_cd
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match from metadata
@@ -583,7 +583,7 @@ async def hybrid_search(
                 WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
                 AND uce."userId" IS NULL
                 AND {where_clause}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
+                ORDER BY uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector
                 LIMIT 200
             ) uce
         ),
@@ -605,7 +605,7 @@ async def hybrid_search(
                 -- Searchable text for BM25 reranking
                 COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
                 -- Semantic score
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector), 0) as semantic_score,
                 -- Lexical score (raw, will normalize)
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match

autogpt_platform/backend/backend/api/features/store/model.py

@@ -223,6 +223,102 @@ class ReviewSubmissionRequest(pydantic.BaseModel):
     internal_comments: str | None = None  # Private admin notes
 
 
+class StoreWaitlistEntry(pydantic.BaseModel):
+    """Public waitlist entry - no PII fields exposed."""
+
+    waitlistId: str
+    slug: str
+
+    # Content fields
+    name: str
+    subHeading: str
+    videoUrl: str | None = None
+    agentOutputDemoUrl: str | None = None
+    imageUrls: list[str]
+    description: str
+    categories: list[str]
+
+
+class StoreWaitlistsAllResponse(pydantic.BaseModel):
+    listings: list[StoreWaitlistEntry]
+
+
+# Admin Waitlist Models
+
+
+class WaitlistCreateRequest(pydantic.BaseModel):
+    """Request model for creating a new waitlist."""
+
+    name: str
+    slug: str
+    subHeading: str
+    description: str
+    categories: list[str] = []
+    imageUrls: list[str] = []
+    videoUrl: str | None = None
+    agentOutputDemoUrl: str | None = None
+
+
+class WaitlistUpdateRequest(pydantic.BaseModel):
+    """Request model for updating a waitlist."""
+
+    name: str | None = None
+    slug: str | None = None
+    subHeading: str | None = None
+    description: str | None = None
+    categories: list[str] | None = None
+    imageUrls: list[str] | None = None
+    videoUrl: str | None = None
+    agentOutputDemoUrl: str | None = None
+    status: prisma.enums.WaitlistExternalStatus | None = None
+    storeListingId: str | None = None  # Link to a store listing
+
+
+class WaitlistAdminResponse(pydantic.BaseModel):
+    """Admin response model with full waitlist details including internal data."""
+
+    id: str
+    createdAt: str
+    updatedAt: str
+    slug: str
+    name: str
+    subHeading: str
+    description: str
+    categories: list[str]
+    imageUrls: list[str]
+    videoUrl: str | None = None
+    agentOutputDemoUrl: str | None = None
+    status: prisma.enums.WaitlistExternalStatus
+    votes: int
+    signupCount: int  # Total count of joinedUsers + unaffiliatedEmailUsers
+    storeListingId: str | None = None
+    owningUserId: str
+
+
+class WaitlistSignup(pydantic.BaseModel):
+    """Individual signup entry for a waitlist."""
+
+    type: str  # "user" or "email"
+    userId: str | None = None
+    email: str | None = None
+    username: str | None = None  # For user signups
+
+
+class WaitlistSignupListResponse(pydantic.BaseModel):
+    """Response model for listing waitlist signups."""
+
+    waitlistId: str
+    signups: list[WaitlistSignup]
+    totalCount: int
+
+
+class WaitlistAdminListResponse(pydantic.BaseModel):
+    """Response model for listing all waitlists (admin view)."""
+
+    waitlists: list[WaitlistAdminResponse]
+    totalCount: int
+
+
 class UnifiedSearchResult(pydantic.BaseModel):
     """A single result from unified hybrid search across all content types."""
 

autogpt_platform/backend/backend/api/features/store/routes.py

@@ -8,6 +8,7 @@ import autogpt_libs.auth
 import fastapi
 import fastapi.responses
 import prisma.enums
+from autogpt_libs.auth.dependencies import get_optional_user_id
 
 import backend.data.graph
 import backend.util.json
@@ -81,6 +82,74 @@ async def update_or_create_profile(
     return updated_profile
 
 
+##############################################
+############## Waitlist Endpoints ############
+##############################################
+@router.get(
+    "/waitlist",
+    summary="Get the agent waitlist",
+    tags=["store", "public"],
+    response_model=store_model.StoreWaitlistsAllResponse,
+)
+async def get_waitlist():
+    """
+    Get all active waitlists for public display.
+    """
+    waitlists = await store_db.get_waitlist()
+    return store_model.StoreWaitlistsAllResponse(listings=waitlists)
+
+
+@router.get(
+    "/waitlist/my-memberships",
+    summary="Get waitlist IDs the current user has joined",
+    tags=["store", "private"],
+)
+async def get_my_waitlist_memberships(
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
+) -> list[str]:
+    """Returns list of waitlist IDs the authenticated user has joined."""
+    return await store_db.get_user_waitlist_memberships(user_id)
+
+
+@router.post(
+    path="/waitlist/{waitlist_id}/join",
+    summary="Add self to the agent waitlist",
+    tags=["store", "public"],
+    response_model=store_model.StoreWaitlistEntry,
+)
+async def add_self_to_waitlist(
+    user_id: str | None = fastapi.Security(get_optional_user_id),
+    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist to join"),
+    email: str | None = fastapi.Body(
+        default=None, embed=True, description="Email address for unauthenticated users"
+    ),
+):
+    """
+    Add the current user to the agent waitlist.
+    """
+    if not user_id and not email:
+        raise fastapi.HTTPException(
+            status_code=400,
+            detail="Either user authentication or email address is required",
+        )
+
+    try:
+        waitlist_entry = await store_db.add_user_to_waitlist(
+            waitlist_id=waitlist_id, user_id=user_id, email=email
+        )
+        return waitlist_entry
+    except ValueError as e:
+        error_msg = str(e)
+        if "not found" in error_msg:
+            raise fastapi.HTTPException(status_code=404, detail="Waitlist not found")
+        # Waitlist exists but is closed or unavailable
+        raise fastapi.HTTPException(status_code=400, detail=error_msg)
+    except Exception:
+        raise fastapi.HTTPException(
+            status_code=500, detail="An error occurred while joining the waitlist"
+        )
+
+
 ##############################################
 ############### Agent Endpoints ##############
 ##############################################

autogpt_platform/backend/backend/api/features/v1.py

@@ -761,8 +761,10 @@ async def create_new_graph(
     graph.reassign_ids(user_id=user_id, reassign_graph_id=True)
     graph.validate_graph(for_run=False)
 
+    # The return value of the create graph & library function is intentionally not used here,
+    # as the graph already valid and no sub-graphs are returned back.
     await graph_db.create_graph(graph, user_id=user_id)
-    await library_db.create_library_agent(graph, user_id)
+    await library_db.create_library_agent(graph, user_id=user_id)
     activated_graph = await on_graph_activate(graph, user_id=user_id)
 
     if create_graph.source == "builder":
@@ -886,19 +888,21 @@ async def set_graph_active_version(
 async def _update_library_agent_version_and_settings(
     user_id: str, agent_graph: graph_db.GraphModel
 ) -> library_model.LibraryAgent:
+    # Keep the library agent up to date with the new active version
     library = await library_db.update_agent_version_in_library(
         user_id, agent_graph.id, agent_graph.version
     )
-    updated_settings = GraphSettings.from_graph(
-        graph=agent_graph,
-        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
-        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
-    )
-    if updated_settings != library.settings:
-        library = await library_db.update_library_agent(
-            library_agent_id=library.id,
+    # If the graph has HITL node, initialize the setting if it's not already set.
+    if (
+        agent_graph.has_human_in_the_loop
+        and library.settings.human_in_the_loop_safe_mode is None
+    ):
+        await library_db.update_library_agent_settings(
             user_id=user_id,
-            settings=updated_settings,
+            agent_id=library.id,
+            settings=library.settings.model_copy(
+                update={"human_in_the_loop_safe_mode": True}
+            ),
         )
     return library
 
@@ -915,18 +919,21 @@ async def update_graph_settings(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> GraphSettings:
     """Update graph settings for the user's library agent."""
+    # Get the library agent for this graph
     library_agent = await library_db.get_library_agent_by_graph_id(
         graph_id=graph_id, user_id=user_id
     )
     if not library_agent:
         raise HTTPException(404, f"Graph #{graph_id} not found in user's library")
 
-    updated_agent = await library_db.update_library_agent(
-        library_agent_id=library_agent.id,
+    # Update the library agent settings
+    updated_agent = await library_db.update_library_agent_settings(
         user_id=user_id,
+        agent_id=library_agent.id,
         settings=settings,
     )
 
+    # Return the updated settings
     return GraphSettings.model_validate(updated_agent.settings)
 
 

autogpt_platform/backend/backend/api/rest_api.py

@@ -19,6 +19,7 @@ from prisma.errors import PrismaError
 import backend.api.features.admin.credit_admin_routes
 import backend.api.features.admin.execution_analytics_routes
 import backend.api.features.admin.store_admin_routes
+import backend.api.features.admin.waitlist_admin_routes
 import backend.api.features.builder
 import backend.api.features.builder.routes
 import backend.api.features.chat.routes as chat_routes
@@ -283,6 +284,11 @@ app.include_router(
     tags=["v2", "admin"],
     prefix="/api/store",
 )
+app.include_router(
+    backend.api.features.admin.waitlist_admin_routes.router,
+    tags=["v2", "admin"],
+    prefix="/api/store",
+)
 app.include_router(
     backend.api.features.admin.credit_admin_routes.router,
     tags=["v2", "admin"],

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -84,7 +84,7 @@ class HITLReviewHelper:
             Exception: If review creation or status update fails
         """
         # Skip review if safe mode is disabled - return auto-approved result
-        if not execution_context.human_in_the_loop_safe_mode:
+        if not execution_context.safe_mode:
             logger.info(
                 f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -104,7 +104,7 @@ class HumanInTheLoopBlock(Block):
         execution_context: ExecutionContext,
         **_kwargs,
     ) -> BlockOutput:
-        if not execution_context.human_in_the_loop_safe_mode:
+        if not execution_context.safe_mode:
             logger.info(
                 f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/llm.py

@@ -79,10 +79,6 @@ class ModelMetadata(NamedTuple):
     provider: str
     context_window: int
     max_output_tokens: int | None
-    display_name: str
-    provider_name: str
-    creator_name: str
-    price_tier: Literal[1, 2, 3]
 
 
 class LlmModelMeta(EnumMeta):
@@ -175,26 +171,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     V0_1_5_LG = "v0-1.5-lg"
     V0_1_0_MD = "v0-1.0-md"
 
-    @classmethod
-    def __get_pydantic_json_schema__(cls, schema, handler):
-        json_schema = handler(schema)
-        llm_model_metadata = {}
-        for model in cls:
-            model_name = model.value
-            metadata = model.metadata
-            llm_model_metadata[model_name] = {
-                "creator": metadata.creator_name,
-                "creator_name": metadata.creator_name,
-                "title": metadata.display_name,
-                "provider": metadata.provider,
-                "provider_name": metadata.provider_name,
-                "name": model_name,
-                "price_tier": metadata.price_tier,
-            }
-        json_schema["llm_model"] = True
-        json_schema["llm_model_metadata"] = llm_model_metadata
-        return json_schema
-
     @property
     def metadata(self) -> ModelMetadata:
         return MODEL_METADATA[self]
@@ -214,291 +190,119 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
 
 MODEL_METADATA = {
     # https://platform.openai.com/docs/models
-    LlmModel.O3: ModelMetadata("openai", 200000, 100000, "O3", "OpenAI", "OpenAI", 2),
-    LlmModel.O3_MINI: ModelMetadata(
-        "openai", 200000, 100000, "O3 Mini", "OpenAI", "OpenAI", 1
-    ),  # o3-mini-2025-01-31
-    LlmModel.O1: ModelMetadata(
-        "openai", 200000, 100000, "O1", "OpenAI", "OpenAI", 3
-    ),  # o1-2024-12-17
-    LlmModel.O1_MINI: ModelMetadata(
-        "openai", 128000, 65536, "O1 Mini", "OpenAI", "OpenAI", 2
-    ),  # o1-mini-2024-09-12
+    LlmModel.O3: ModelMetadata("openai", 200000, 100000),
+    LlmModel.O3_MINI: ModelMetadata("openai", 200000, 100000),  # o3-mini-2025-01-31
+    LlmModel.O1: ModelMetadata("openai", 200000, 100000),  # o1-2024-12-17
+    LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
     # GPT-5 models
-    LlmModel.GPT5_2: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5.2", "OpenAI", "OpenAI", 3
-    ),
-    LlmModel.GPT5_1: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5.1", "OpenAI", "OpenAI", 2
-    ),
-    LlmModel.GPT5: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT5_MINI: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5 Mini", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT5_NANO: ModelMetadata(
-        "openai", 400000, 128000, "GPT-5 Nano", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT5_CHAT: ModelMetadata(
-        "openai", 400000, 16384, "GPT-5 Chat Latest", "OpenAI", "OpenAI", 2
-    ),
-    LlmModel.GPT41: ModelMetadata(
-        "openai", 1047576, 32768, "GPT-4.1", "OpenAI", "OpenAI", 1
-    ),
-    LlmModel.GPT41_MINI: ModelMetadata(
-        "openai", 1047576, 32768, "GPT-4.1 Mini", "OpenAI", "OpenAI", 1
-    ),
+    LlmModel.GPT5_2: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_MINI: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_NANO: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_CHAT: ModelMetadata("openai", 400000, 16384),
+    LlmModel.GPT41: ModelMetadata("openai", 1047576, 32768),
+    LlmModel.GPT41_MINI: ModelMetadata("openai", 1047576, 32768),
     LlmModel.GPT4O_MINI: ModelMetadata(
-        "openai", 128000, 16384, "GPT-4o Mini", "OpenAI", "OpenAI", 1
+        "openai", 128000, 16384
     ),  # gpt-4o-mini-2024-07-18
-    LlmModel.GPT4O: ModelMetadata(
-        "openai", 128000, 16384, "GPT-4o", "OpenAI", "OpenAI", 2
-    ),  # gpt-4o-2024-08-06
+    LlmModel.GPT4O: ModelMetadata("openai", 128000, 16384),  # gpt-4o-2024-08-06
     LlmModel.GPT4_TURBO: ModelMetadata(
-        "openai", 128000, 4096, "GPT-4 Turbo", "OpenAI", "OpenAI", 3
+        "openai", 128000, 4096
     ),  # gpt-4-turbo-2024-04-09
-    LlmModel.GPT3_5_TURBO: ModelMetadata(
-        "openai", 16385, 4096, "GPT-3.5 Turbo", "OpenAI", "OpenAI", 1
-    ),  # gpt-3.5-turbo-0125
+    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, 4096),  # gpt-3.5-turbo-0125
     # https://docs.anthropic.com/en/docs/about-claude/models
     LlmModel.CLAUDE_4_1_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000, "Claude Opus 4.1", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 32000
     ),  # claude-opus-4-1-20250805
     LlmModel.CLAUDE_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000, "Claude Opus 4", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 32000
     ),  # claude-4-opus-20250514
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
+        "anthropic", 200000, 64000
     ),  # claude-4-sonnet-20250514
     LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 64000
     ),  # claude-opus-4-5-20251101
     LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Sonnet 4.5", "Anthropic", "Anthropic", 3
+        "anthropic", 200000, 64000
     ),  # claude-sonnet-4-5-20250929
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
+        "anthropic", 200000, 64000
     ),  # claude-haiku-4-5-20251001
     LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
+        "anthropic", 200000, 64000
     ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
-        "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
+        "anthropic", 200000, 4096
     ),  # claude-3-haiku-20240307
     # https://docs.aimlapi.com/api-overview/model-database/text-models
-    LlmModel.AIML_API_QWEN2_5_72B: ModelMetadata(
-        "aiml_api", 32000, 8000, "Qwen 2.5 72B Instruct Turbo", "AI/ML", "Qwen", 1
-    ),
-    LlmModel.AIML_API_LLAMA3_1_70B: ModelMetadata(
-        "aiml_api",
-        128000,
-        40000,
-        "Llama 3.1 Nemotron 70B Instruct",
-        "AI/ML",
-        "Nvidia",
-        1,
-    ),
-    LlmModel.AIML_API_LLAMA3_3_70B: ModelMetadata(
-        "aiml_api", 128000, None, "Llama 3.3 70B Instruct Turbo", "AI/ML", "Meta", 1
-    ),
-    LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata(
-        "aiml_api", 131000, 2000, "Llama 3.1 70B Instruct Turbo", "AI/ML", "Meta", 1
-    ),
-    LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata(
-        "aiml_api", 128000, None, "Llama 3.2 3B Instruct Turbo", "AI/ML", "Meta", 1
-    ),
+    LlmModel.AIML_API_QWEN2_5_72B: ModelMetadata("aiml_api", 32000, 8000),
+    LlmModel.AIML_API_LLAMA3_1_70B: ModelMetadata("aiml_api", 128000, 40000),
+    LlmModel.AIML_API_LLAMA3_3_70B: ModelMetadata("aiml_api", 128000, None),
+    LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata("aiml_api", 131000, 2000),
+    LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata("aiml_api", 128000, None),
     # https://console.groq.com/docs/models
-    LlmModel.LLAMA3_3_70B: ModelMetadata(
-        "groq", 128000, 32768, "Llama 3.3 70B Versatile", "Groq", "Meta", 1
-    ),
-    LlmModel.LLAMA3_1_8B: ModelMetadata(
-        "groq", 128000, 8192, "Llama 3.1 8B Instant", "Groq", "Meta", 1
-    ),
+    LlmModel.LLAMA3_3_70B: ModelMetadata("groq", 128000, 32768),
+    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 128000, 8192),
     # https://ollama.com/library
-    LlmModel.OLLAMA_LLAMA3_3: ModelMetadata(
-        "ollama", 8192, None, "Llama 3.3", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata(
-        "ollama", 8192, None, "Llama 3.2", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata(
-        "ollama", 8192, None, "Llama 3", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata(
-        "ollama", 8192, None, "Llama 3.1 405B", "Ollama", "Meta", 1
-    ),
-    LlmModel.OLLAMA_DOLPHIN: ModelMetadata(
-        "ollama", 32768, None, "Dolphin Mistral Latest", "Ollama", "Mistral AI", 1
-    ),
+    LlmModel.OLLAMA_LLAMA3_3: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192, None),
+    LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
     # https://openrouter.ai/models
-    LlmModel.GEMINI_2_5_PRO: ModelMetadata(
-        "open_router",
-        1050000,
-        8192,
-        "Gemini 2.5 Pro Preview 03.25",
-        "OpenRouter",
-        "Google",
-        2,
-    ),
-    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata(
-        "open_router", 1048576, 65535, "Gemini 3 Pro Preview", "OpenRouter", "Google", 2
-    ),
-    LlmModel.GEMINI_2_5_FLASH: ModelMetadata(
-        "open_router", 1048576, 65535, "Gemini 2.5 Flash", "OpenRouter", "Google", 1
-    ),
-    LlmModel.GEMINI_2_0_FLASH: ModelMetadata(
-        "open_router", 1048576, 8192, "Gemini 2.0 Flash 001", "OpenRouter", "Google", 1
-    ),
+    LlmModel.GEMINI_2_5_PRO: ModelMetadata("open_router", 1050000, 8192),
+    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata("open_router", 1048576, 65535),
+    LlmModel.GEMINI_2_5_FLASH: ModelMetadata("open_router", 1048576, 65535),
+    LlmModel.GEMINI_2_0_FLASH: ModelMetadata("open_router", 1048576, 8192),
     LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelMetadata(
-        "open_router",
-        1048576,
-        65535,
-        "Gemini 2.5 Flash Lite Preview 06.17",
-        "OpenRouter",
-        "Google",
-        1,
-    ),
-    LlmModel.GEMINI_2_0_FLASH_LITE: ModelMetadata(
-        "open_router",
-        1048576,
-        8192,
-        "Gemini 2.0 Flash Lite 001",
-        "OpenRouter",
-        "Google",
-        1,
-    ),
-    LlmModel.MISTRAL_NEMO: ModelMetadata(
-        "open_router", 128000, 4096, "Mistral Nemo", "OpenRouter", "Mistral AI", 1
-    ),
-    LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata(
-        "open_router", 128000, 4096, "Command R 08.2024", "OpenRouter", "Cohere", 1
-    ),
-    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata(
-        "open_router", 128000, 4096, "Command R Plus 08.2024", "OpenRouter", "Cohere", 2
-    ),
-    LlmModel.DEEPSEEK_CHAT: ModelMetadata(
-        "open_router", 64000, 2048, "DeepSeek Chat", "OpenRouter", "DeepSeek", 1
-    ),
-    LlmModel.DEEPSEEK_R1_0528: ModelMetadata(
-        "open_router", 163840, 163840, "DeepSeek R1 0528", "OpenRouter", "DeepSeek", 1
-    ),
-    LlmModel.PERPLEXITY_SONAR: ModelMetadata(
-        "open_router", 127000, 8000, "Sonar", "OpenRouter", "Perplexity", 1
-    ),
-    LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata(
-        "open_router", 200000, 8000, "Sonar Pro", "OpenRouter", "Perplexity", 2
+        "open_router", 1048576, 65535
     ),
+    LlmModel.GEMINI_2_0_FLASH_LITE: ModelMetadata("open_router", 1048576, 8192),
+    LlmModel.MISTRAL_NEMO: ModelMetadata("open_router", 128000, 4096),
+    LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata("open_router", 128000, 4096),
+    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata("open_router", 128000, 4096),
+    LlmModel.DEEPSEEK_CHAT: ModelMetadata("open_router", 64000, 2048),
+    LlmModel.DEEPSEEK_R1_0528: ModelMetadata("open_router", 163840, 163840),
+    LlmModel.PERPLEXITY_SONAR: ModelMetadata("open_router", 127000, 8000),
+    LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata("open_router", 200000, 8000),
     LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: ModelMetadata(
         "open_router",
         128000,
         16000,
-        "Sonar Deep Research",
-        "OpenRouter",
-        "Perplexity",
-        3,
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B: ModelMetadata(
-        "open_router",
-        131000,
-        4096,
-        "Hermes 3 Llama 3.1 405B",
-        "OpenRouter",
-        "Nous Research",
-        1,
+        "open_router", 131000, 4096
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B: ModelMetadata(
-        "open_router",
-        12288,
-        12288,
-        "Hermes 3 Llama 3.1 70B",
-        "OpenRouter",
-        "Nous Research",
-        1,
-    ),
-    LlmModel.OPENAI_GPT_OSS_120B: ModelMetadata(
-        "open_router", 131072, 131072, "GPT-OSS 120B", "OpenRouter", "OpenAI", 1
-    ),
-    LlmModel.OPENAI_GPT_OSS_20B: ModelMetadata(
-        "open_router", 131072, 32768, "GPT-OSS 20B", "OpenRouter", "OpenAI", 1
-    ),
-    LlmModel.AMAZON_NOVA_LITE_V1: ModelMetadata(
-        "open_router", 300000, 5120, "Nova Lite V1", "OpenRouter", "Amazon", 1
-    ),
-    LlmModel.AMAZON_NOVA_MICRO_V1: ModelMetadata(
-        "open_router", 128000, 5120, "Nova Micro V1", "OpenRouter", "Amazon", 1
-    ),
-    LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata(
-        "open_router", 300000, 5120, "Nova Pro V1", "OpenRouter", "Amazon", 1
-    ),
-    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata(
-        "open_router", 65536, 4096, "WizardLM 2 8x22B", "OpenRouter", "Microsoft", 1
-    ),
-    LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata(
-        "open_router", 4096, 4096, "MythoMax L2 13B", "OpenRouter", "Gryphe", 1
-    ),
-    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata(
-        "open_router", 131072, 131072, "Llama 4 Scout", "OpenRouter", "Meta", 1
-    ),
-    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata(
-        "open_router", 1048576, 1000000, "Llama 4 Maverick", "OpenRouter", "Meta", 1
-    ),
-    LlmModel.GROK_4: ModelMetadata(
-        "open_router", 256000, 256000, "Grok 4", "OpenRouter", "xAI", 3
-    ),
-    LlmModel.GROK_4_FAST: ModelMetadata(
-        "open_router", 2000000, 30000, "Grok 4 Fast", "OpenRouter", "xAI", 1
-    ),
-    LlmModel.GROK_4_1_FAST: ModelMetadata(
-        "open_router", 2000000, 30000, "Grok 4.1 Fast", "OpenRouter", "xAI", 1
-    ),
-    LlmModel.GROK_CODE_FAST_1: ModelMetadata(
-        "open_router", 256000, 10000, "Grok Code Fast 1", "OpenRouter", "xAI", 1
-    ),
-    LlmModel.KIMI_K2: ModelMetadata(
-        "open_router", 131000, 131000, "Kimi K2", "OpenRouter", "Moonshot AI", 1
-    ),
-    LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata(
-        "open_router",
-        262144,
-        262144,
-        "Qwen 3 235B A22B Thinking 2507",
-        "OpenRouter",
-        "Qwen",
-        1,
-    ),
-    LlmModel.QWEN3_CODER: ModelMetadata(
-        "open_router", 262144, 262144, "Qwen 3 Coder", "OpenRouter", "Qwen", 3
+        "open_router", 12288, 12288
     ),
+    LlmModel.OPENAI_GPT_OSS_120B: ModelMetadata("open_router", 131072, 131072),
+    LlmModel.OPENAI_GPT_OSS_20B: ModelMetadata("open_router", 131072, 32768),
+    LlmModel.AMAZON_NOVA_LITE_V1: ModelMetadata("open_router", 300000, 5120),
+    LlmModel.AMAZON_NOVA_MICRO_V1: ModelMetadata("open_router", 128000, 5120),
+    LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata("open_router", 300000, 5120),
+    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata("open_router", 65536, 4096),
+    LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata("open_router", 4096, 4096),
+    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata("open_router", 131072, 131072),
+    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata("open_router", 1048576, 1000000),
+    LlmModel.GROK_4: ModelMetadata("open_router", 256000, 256000),
+    LlmModel.GROK_4_FAST: ModelMetadata("open_router", 2000000, 30000),
+    LlmModel.GROK_4_1_FAST: ModelMetadata("open_router", 2000000, 30000),
+    LlmModel.GROK_CODE_FAST_1: ModelMetadata("open_router", 256000, 10000),
+    LlmModel.KIMI_K2: ModelMetadata("open_router", 131000, 131000),
+    LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata("open_router", 262144, 262144),
+    LlmModel.QWEN3_CODER: ModelMetadata("open_router", 262144, 262144),
     # Llama API models
-    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata(
-        "llama_api",
-        128000,
-        4028,
-        "Llama 4 Scout 17B 16E Instruct FP8",
-        "Llama API",
-        "Meta",
-        1,
-    ),
-    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata(
-        "llama_api",
-        128000,
-        4028,
-        "Llama 4 Maverick 17B 128E Instruct FP8",
-        "Llama API",
-        "Meta",
-        1,
-    ),
-    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata(
-        "llama_api", 128000, 4028, "Llama 3.3 8B Instruct", "Llama API", "Meta", 1
-    ),
-    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata(
-        "llama_api", 128000, 4028, "Llama 3.3 70B Instruct", "Llama API", "Meta", 1
-    ),
+    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata("llama_api", 128000, 4028),
+    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata("llama_api", 128000, 4028),
+    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata("llama_api", 128000, 4028),
+    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata("llama_api", 128000, 4028),
     # v0 by Vercel models
-    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000, "v0 1.5 MD", "V0", "V0", 1),
-    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000, "v0 1.5 LG", "V0", "V0", 1),
-    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000, "v0 1.0 MD", "V0", "V0", 1),
+    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000),
+    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000),
+    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000),
 }
 
 DEFAULT_LLM_MODEL = LlmModel.GPT5_2

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -242,7 +242,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -343,7 +343,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -409,7 +409,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -471,7 +471,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -535,7 +535,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -658,7 +658,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -730,7 +730,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -786,7 +786,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -905,7 +905,7 @@ async def test_smart_decision_maker_agent_mode():
         # Create a mock execution context
 
         mock_execution_context = ExecutionContext(
-            human_in_the_loop_safe_mode=False,
+            safe_mode=False,
         )
 
         # Create a mock execution processor for agent mode tests
@@ -1027,7 +1027,7 @@ async def test_smart_decision_maker_traditional_mode_default():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+        mock_execution_context = ExecutionContext(safe_mode=False)
 
         # Create a mock execution processor for tests
 

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -386,7 +386,7 @@ async def test_output_yielding_with_dynamic_fields():
             outputs = {}
             from backend.data.execution import ExecutionContext
 
-            mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
+            mock_execution_context = ExecutionContext(safe_mode=False)
             mock_execution_processor = MagicMock()
 
             async for output_name, output_value in block.run(
@@ -609,9 +609,7 @@ async def test_validation_errors_dont_pollute_conversation():
                 outputs = {}
                 from backend.data.execution import ExecutionContext
 
-                mock_execution_context = ExecutionContext(
-                    human_in_the_loop_safe_mode=False
-                )
+                mock_execution_context = ExecutionContext(safe_mode=False)
 
                 # Create a proper mock execution processor for agent mode
                 from collections import defaultdict

autogpt_platform/backend/backend/data/block.py

@@ -474,7 +474,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.block_type = block_type
         self.webhook_config = webhook_config
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
-        self.is_sensitive_action: bool = False
+        self.requires_human_review: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -637,9 +637,8 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             - should_pause: True if execution should be paused for review
             - input_data_to_use: The input data to use (may be modified by reviewer)
         """
-        if not (
-            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
-        ):
+        # Skip review if not required or safe mode is disabled
+        if not self.requires_human_review or not execution_context.safe_mode:
             return False, input_data
 
         from backend.blocks.helpers.review import HITLReviewHelper

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -99,15 +99,10 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.OPENAI_GPT_OSS_20B: 1,
     LlmModel.GEMINI_2_5_PRO: 4,
     LlmModel.GEMINI_3_PRO_PREVIEW: 5,
-    LlmModel.GEMINI_2_5_FLASH: 1,
-    LlmModel.GEMINI_2_0_FLASH: 1,
-    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: 1,
-    LlmModel.GEMINI_2_0_FLASH_LITE: 1,
     LlmModel.MISTRAL_NEMO: 1,
     LlmModel.COHERE_COMMAND_R_08_2024: 1,
     LlmModel.COHERE_COMMAND_R_PLUS_08_2024: 3,
     LlmModel.DEEPSEEK_CHAT: 2,
-    LlmModel.DEEPSEEK_R1_0528: 1,
     LlmModel.PERPLEXITY_SONAR: 1,
     LlmModel.PERPLEXITY_SONAR_PRO: 5,
     LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: 10,
@@ -131,6 +126,11 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.KIMI_K2: 1,
     LlmModel.QWEN3_235B_A22B_THINKING: 1,
     LlmModel.QWEN3_CODER: 9,
+    LlmModel.GEMINI_2_5_FLASH: 1,
+    LlmModel.GEMINI_2_0_FLASH: 1,
+    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: 1,
+    LlmModel.GEMINI_2_0_FLASH_LITE: 1,
+    LlmModel.DEEPSEEK_R1_0528: 1,
     # v0 by Vercel models
     LlmModel.V0_1_5_MD: 1,
     LlmModel.V0_1_5_LG: 2,

autogpt_platform/backend/backend/data/db.py

@@ -121,14 +121,10 @@ async def _raw_with_schema(
     Supports placeholders:
         - {schema_prefix}: Table/type prefix (e.g., "platform".)
         - {schema}: Raw schema name for application tables (e.g., platform)
-
-    Note on pgvector types:
-        Use unqualified ::vector and <=> operator in queries. PostgreSQL resolves
-        these via search_path, which includes the schema where pgvector is installed
-        on all environments (local, CI, dev).
+        - {pgvector_schema}: Schema where pgvector is installed (defaults to "public")
 
     Args:
-        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
+        query_template: SQL query with {schema_prefix}, {schema}, and/or {pgvector_schema} placeholders
         *args: Query parameters
         execute: If False, executes SELECT query. If True, executes INSERT/UPDATE/DELETE.
         client: Optional Prisma client for transactions (only used when execute=True).
@@ -139,16 +135,20 @@ async def _raw_with_schema(
 
     Example with vector type:
         await execute_raw_with_schema(
-            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::vector)',
+            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::{pgvector_schema}.vector)',
             embedding_data
         )
     """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
+    # pgvector extension is typically installed in "public" schema
+    # On Supabase it may be in "extensions" but "public" is the common default
+    pgvector_schema = "public"
 
     formatted_query = query_template.format(
         schema_prefix=schema_prefix,
         schema=schema,
+        pgvector_schema=pgvector_schema,
     )
 
     import prisma as prisma_module

autogpt_platform/backend/backend/data/event_bus.py

@@ -103,18 +103,8 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
         return redis.get_redis()
 
     def publish_event(self, event: M, channel_key: str):
-        """
-        Publish an event to Redis. Gracefully handles connection failures
-        by logging the error instead of raising exceptions.
-        """
-        try:
-            message, full_channel_name = self._serialize_message(event, channel_key)
-            self.connection.publish(full_channel_name, message)
-        except Exception:
-            logger.exception(
-                f"Failed to publish event to Redis channel {channel_key}. "
-                "Event bus operation will continue without Redis connectivity."
-            )
+        message, full_channel_name = self._serialize_message(event, channel_key)
+        self.connection.publish(full_channel_name, message)
 
     def listen_events(self, channel_key: str) -> Generator[M, None, None]:
         pubsub, full_channel_name = self._get_pubsub_channel(
@@ -138,19 +128,9 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
         return await redis.get_redis_async()
 
     async def publish_event(self, event: M, channel_key: str):
-        """
-        Publish an event to Redis. Gracefully handles connection failures
-        by logging the error instead of raising exceptions.
-        """
-        try:
-            message, full_channel_name = self._serialize_message(event, channel_key)
-            connection = await self.connection
-            await connection.publish(full_channel_name, message)
-        except Exception:
-            logger.exception(
-                f"Failed to publish event to Redis channel {channel_key}. "
-                "Event bus operation will continue without Redis connectivity."
-            )
+        message, full_channel_name = self._serialize_message(event, channel_key)
+        connection = await self.connection
+        await connection.publish(full_channel_name, message)
 
     async def listen_events(self, channel_key: str) -> AsyncGenerator[M, None]:
         pubsub, full_channel_name = self._get_pubsub_channel(

autogpt_platform/backend/backend/data/event_bus_test.py

@@ -1,56 +0,0 @@
-"""
-Tests for event_bus graceful degradation when Redis is unavailable.
-"""
-
-from unittest.mock import AsyncMock, patch
-
-import pytest
-from pydantic import BaseModel
-
-from backend.data.event_bus import AsyncRedisEventBus
-
-
-class TestEvent(BaseModel):
-    """Test event model."""
-
-    message: str
-
-
-class TestNotificationBus(AsyncRedisEventBus[TestEvent]):
-    """Test implementation of AsyncRedisEventBus."""
-
-    Model = TestEvent
-
-    @property
-    def event_bus_name(self) -> str:
-        return "test_event_bus"
-
-
-@pytest.mark.asyncio
-async def test_publish_event_handles_connection_failure_gracefully():
-    """Test that publish_event logs exception instead of raising when Redis is unavailable."""
-    bus = TestNotificationBus()
-    event = TestEvent(message="test message")
-
-    # Mock get_redis_async to raise connection error
-    with patch(
-        "backend.data.event_bus.redis.get_redis_async",
-        side_effect=ConnectionError("Authentication required."),
-    ):
-        # Should not raise exception
-        await bus.publish_event(event, "test_channel")
-
-
-@pytest.mark.asyncio
-async def test_publish_event_works_with_redis_available():
-    """Test that publish_event works normally when Redis is available."""
-    bus = TestNotificationBus()
-    event = TestEvent(message="test message")
-
-    # Mock successful Redis connection
-    mock_redis = AsyncMock()
-    mock_redis.publish = AsyncMock()
-
-    with patch("backend.data.event_bus.redis.get_redis_async", return_value=mock_redis):
-        await bus.publish_event(event, "test_channel")
-        mock_redis.publish.assert_called_once()

autogpt_platform/backend/backend/data/execution.py

@@ -81,10 +81,7 @@ class ExecutionContext(BaseModel):
     This includes information needed by blocks, sub-graphs, and execution management.
     """
 
-    model_config = {"extra": "ignore"}
-
-    human_in_the_loop_safe_mode: bool = True
-    sensitive_action_safe_mode: bool = False
+    safe_mode: bool = True
     user_timezone: str = "UTC"
     root_execution_id: Optional[str] = None
     parent_execution_id: Optional[str] = None

autogpt_platform/backend/backend/data/graph.py

@@ -3,7 +3,7 @@ import logging
 import uuid
 from collections import defaultdict
 from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Annotated, Any, Literal, Optional, cast
+from typing import TYPE_CHECKING, Any, Literal, Optional, cast
 
 from prisma.enums import SubmissionStatus
 from prisma.models import (
@@ -20,7 +20,7 @@ from prisma.types import (
     AgentNodeLinkCreateInput,
     StoreListingVersionWhereInput,
 )
-from pydantic import BaseModel, BeforeValidator, Field, create_model
+from pydantic import BaseModel, Field, create_model
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
@@ -62,31 +62,7 @@ logger = logging.getLogger(__name__)
 
 
 class GraphSettings(BaseModel):
-    # Use Annotated with BeforeValidator to coerce None to default values.
-    # This handles cases where the database has null values for these fields.
-    model_config = {"extra": "ignore"}
-
-    human_in_the_loop_safe_mode: Annotated[
-        bool, BeforeValidator(lambda v: v if v is not None else True)
-    ] = True
-    sensitive_action_safe_mode: Annotated[
-        bool, BeforeValidator(lambda v: v if v is not None else False)
-    ] = False
-
-    @classmethod
-    def from_graph(
-        cls,
-        graph: "GraphModel",
-        hitl_safe_mode: bool | None = None,
-        sensitive_action_safe_mode: bool = False,
-    ) -> "GraphSettings":
-        # Default to True if not explicitly set
-        if hitl_safe_mode is None:
-            hitl_safe_mode = True
-        return cls(
-            human_in_the_loop_safe_mode=hitl_safe_mode,
-            sensitive_action_safe_mode=sensitive_action_safe_mode,
-        )
+    human_in_the_loop_safe_mode: bool | None = None
 
 
 class Link(BaseDbModel):
@@ -268,14 +244,10 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-        )
-
-    @computed_field
-    @property
-    def has_sensitive_action(self) -> bool:
-        return any(
-            node.block_id for node in self.nodes if node.block.is_sensitive_action
+            if (
+                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+                or node.block.requires_human_review
+            )
         )
 
     @property

autogpt_platform/backend/backend/data/notifications.py

@@ -211,6 +211,22 @@ class AgentRejectionData(BaseNotificationData):
         return value
 
 
+class WaitlistLaunchData(BaseNotificationData):
+    """Notification data for when an agent from a waitlist is launched."""
+
+    agent_name: str
+    waitlist_name: str
+    store_url: str
+    launched_at: datetime
+
+    @field_validator("launched_at")
+    @classmethod
+    def validate_timezone(cls, value: datetime):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
+
+
 NotificationData = Annotated[
     Union[
         AgentRunData,
@@ -223,6 +239,7 @@ NotificationData = Annotated[
         DailySummaryData,
         RefundRequestData,
         BaseSummaryData,
+        WaitlistLaunchData,
     ],
     Field(discriminator="type"),
 ]
@@ -273,6 +290,7 @@ def get_notif_data_type(
         NotificationType.REFUND_PROCESSED: RefundRequestData,
         NotificationType.AGENT_APPROVED: AgentApprovalData,
         NotificationType.AGENT_REJECTED: AgentRejectionData,
+        NotificationType.WAITLIST_LAUNCH: WaitlistLaunchData,
     }[notification_type]
 
 
@@ -318,6 +336,7 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: QueueType.ADMIN,
             NotificationType.AGENT_APPROVED: QueueType.IMMEDIATE,
             NotificationType.AGENT_REJECTED: QueueType.IMMEDIATE,
+            NotificationType.WAITLIST_LAUNCH: QueueType.IMMEDIATE,
         }
         return BATCHING_RULES.get(self.notification_type, QueueType.IMMEDIATE)
 
@@ -337,6 +356,7 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: "refund_processed.html",
             NotificationType.AGENT_APPROVED: "agent_approved.html",
             NotificationType.AGENT_REJECTED: "agent_rejected.html",
+            NotificationType.WAITLIST_LAUNCH: "waitlist_launch.html",
         }[self.notification_type]
 
     @property
@@ -354,6 +374,7 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: "Refund for ${{data.amount / 100}} to {{data.user_name}} has been processed",
             NotificationType.AGENT_APPROVED: "🎉 Your agent '{{data.agent_name}}' has been approved!",
             NotificationType.AGENT_REJECTED: "Your agent '{{data.agent_name}}' needs some updates",
+            NotificationType.WAITLIST_LAUNCH: "🚀 {{data.agent_name}} is now available!",
         }[self.notification_type]
 
 

autogpt_platform/backend/backend/executor/scheduler.py

@@ -309,7 +309,7 @@ def ensure_embeddings_coverage():
 
         # Process in batches until no more missing embeddings
         while True:
-            result = db_client.backfill_missing_embeddings(batch_size=100)
+            result = db_client.backfill_missing_embeddings(batch_size=10)
 
             total_processed += result["processed"]
             total_success += result["success"]

autogpt_platform/backend/backend/executor/utils.py

@@ -873,8 +873,11 @@ async def add_graph_execution(
         settings = await gdb.get_graph_settings(user_id=user_id, graph_id=graph_id)
 
         execution_context = ExecutionContext(
-            human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
-            sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
+            safe_mode=(
+                settings.human_in_the_loop_safe_mode
+                if settings.human_in_the_loop_safe_mode is not None
+                else True
+            ),
             user_timezone=(
                 user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
             ),

autogpt_platform/backend/backend/executor/utils_test.py

@@ -386,7 +386,6 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     mock_user.timezone = "UTC"
     mock_settings = mocker.MagicMock()
     mock_settings.human_in_the_loop_safe_mode = True
-    mock_settings.sensitive_action_safe_mode = False
 
     mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
     mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)
@@ -652,7 +651,6 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     mock_user.timezone = "UTC"
     mock_settings = mocker.MagicMock()
     mock_settings.human_in_the_loop_safe_mode = True
-    mock_settings.sensitive_action_safe_mode = False
 
     mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
     mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)

autogpt_platform/backend/backend/notifications/templates/waitlist_launch.html.jinja2

@@ -0,0 +1,59 @@
+{# Waitlist Launch Notification Email Template #}
+{#
+  Template variables:
+  data.agent_name: the name of the launched agent
+  data.waitlist_name: the name of the waitlist the user joined
+  data.store_url: URL to view the agent in the store
+  data.launched_at: when the agent was launched
+
+  Subject: {{ data.agent_name }} is now available!
+#}
+
+{% block content %}
+<h1 style="color: #7c3aed; font-size: 32px; font-weight: 700; margin: 0 0 24px 0; text-align: center;">
+  The wait is over!
+</h1>
+
+<p style="color: #586069; font-size: 18px; text-align: center; margin: 0 0 24px 0;">
+  <strong>'{{ data.agent_name }}'</strong> is now live in the AutoGPT Store!
+</p>
+
+<div style="height: 32px; background: transparent;"></div>
+
+<div style="background: #f3e8ff; border: 1px solid #d8b4fe; border-radius: 8px; padding: 20px; margin: 0;">
+  <h3 style="color: #6b21a8; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
+    You're one of the first to know!
+  </h3>
+  <p style="color: #6b21a8; margin: 0; font-size: 16px; line-height: 1.5;">
+    You signed up for the <strong>{{ data.waitlist_name }}</strong> waitlist, and we're excited to let you know that this agent is now ready for you to use.
+  </p>
+</div>
+
+<div style="height: 32px; background: transparent;"></div>
+
+<div style="text-align: center; margin: 24px 0;">
+  <a href="{{ data.store_url }}" style="display: inline-block; background: linear-gradient(135deg, #7c3aed 0%, #5b21b6 100%); color: white; text-decoration: none; padding: 14px 28px; border-radius: 6px; font-weight: 600; font-size: 16px;">
+    Get {{ data.agent_name }} Now
+  </a>
+</div>
+
+<div style="height: 32px; background: transparent;"></div>
+
+<div style="background: #d1ecf1; border: 1px solid #bee5eb; border-radius: 8px; padding: 20px; margin: 0;">
+  <h3 style="color: #0c5460; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
+    What can you do now?
+  </h3>
+  <ul style="color: #0c5460; margin: 0; padding-left: 18px; font-size: 16px; line-height: 1.6;">
+    <li>Visit the store to learn more about what this agent can do</li>
+    <li>Install and start using the agent right away</li>
+    <li>Share it with others who might find it useful</li>
+  </ul>
+</div>
+
+<div style="height: 32px; background: transparent;"></div>
+
+<p style="color: #6a737d; font-size: 14px; text-align: center; margin: 24px 0;">
+  Thank you for helping us prioritize what to build! Your interest made this happen.
+</p>
+
+{% endblock %}

autogpt_platform/backend/migrations/20260108032037_add_waitlist_launch_notification/migration.sql

@@ -0,0 +1,53 @@
+-- CreateEnum
+CREATE TYPE "WaitlistExternalStatus" AS ENUM ('DONE', 'NOT_STARTED', 'CANCELED', 'WORK_IN_PROGRESS');
+
+-- AlterEnum
+ALTER TYPE "NotificationType" ADD VALUE 'WAITLIST_LAUNCH';
+
+-- CreateTable
+CREATE TABLE "WaitlistEntry" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "storeListingId" TEXT,
+    "owningUserId" TEXT NOT NULL,
+    "slug" TEXT NOT NULL,
+    "search" tsvector DEFAULT ''::tsvector,
+    "name" TEXT NOT NULL,
+    "subHeading" TEXT NOT NULL,
+    "videoUrl" TEXT,
+    "agentOutputDemoUrl" TEXT,
+    "imageUrls" TEXT[],
+    "description" TEXT NOT NULL,
+    "categories" TEXT[],
+    "status" "WaitlistExternalStatus" NOT NULL DEFAULT 'NOT_STARTED',
+    "votes" INTEGER NOT NULL DEFAULT 0,
+    "unaffiliatedEmailUsers" TEXT[] DEFAULT ARRAY[]::TEXT[],
+    "isDeleted" BOOLEAN NOT NULL DEFAULT false,
+
+    CONSTRAINT "WaitlistEntry_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateTable
+CREATE TABLE "_joinedWaitlists" (
+    "A" TEXT NOT NULL,
+    "B" TEXT NOT NULL
+);
+
+-- CreateIndex
+CREATE UNIQUE INDEX "_joinedWaitlists_AB_unique" ON "_joinedWaitlists"("A", "B");
+
+-- CreateIndex
+CREATE INDEX "_joinedWaitlists_B_index" ON "_joinedWaitlists"("B");
+
+-- AddForeignKey
+ALTER TABLE "WaitlistEntry" ADD CONSTRAINT "WaitlistEntry_storeListingId_fkey" FOREIGN KEY ("storeListingId") REFERENCES "StoreListing"("id") ON DELETE SET NULL ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "WaitlistEntry" ADD CONSTRAINT "WaitlistEntry_owningUserId_fkey" FOREIGN KEY ("owningUserId") REFERENCES "User"("id") ON DELETE RESTRICT ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "_joinedWaitlists" ADD CONSTRAINT "_joinedWaitlists_A_fkey" FOREIGN KEY ("A") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "_joinedWaitlists" ADD CONSTRAINT "_joinedWaitlists_B_fkey" FOREIGN KEY ("B") REFERENCES "WaitlistEntry"("id") ON DELETE CASCADE ON UPDATE CASCADE;

autogpt_platform/backend/migrations/20260109181714_add_docs_embedding/migration.sql

@@ -1,10 +1,9 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Creates extension in current schema (determined by search_path from DATABASE_URL ?schema= param)
--- This ensures vector type is in the same schema as tables, making ::vector work without explicit qualification
+-- Create in public schema so vector type is available across all schemas
 DO $$
 BEGIN
-    CREATE EXTENSION IF NOT EXISTS "vector";
+    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
 EXCEPTION WHEN OTHERS THEN
     RAISE NOTICE 'vector extension not available or already exists, skipping';
 END $$;
@@ -20,7 +19,7 @@ CREATE TABLE "UnifiedContentEmbedding" (
     "contentType" "ContentType" NOT NULL,
     "contentId" TEXT NOT NULL,
     "userId" TEXT,
-    "embedding" vector(1536) NOT NULL,
+    "embedding" public.vector(1536) NOT NULL,
     "searchableText" TEXT NOT NULL,
     "metadata" JSONB NOT NULL DEFAULT '{}',
 
@@ -46,4 +45,4 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
 -- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
 DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
-CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

autogpt_platform/backend/schema.prisma

@@ -69,6 +69,10 @@ model User {
   OAuthAuthorizationCodes OAuthAuthorizationCode[]
   OAuthAccessTokens       OAuthAccessToken[]
   OAuthRefreshTokens      OAuthRefreshToken[]
+
+  // Waitlist relations
+  waitlistEntries       WaitlistEntry[]
+  joinedWaitlists       WaitlistEntry[] @relation("joinedWaitlists")
 }
 
 enum OnboardingStep {
@@ -295,6 +299,7 @@ enum NotificationType {
   REFUND_PROCESSED
   AGENT_APPROVED
   AGENT_REJECTED
+  WAITLIST_LAUNCH
 }
 
 model NotificationEvent {
@@ -901,7 +906,8 @@ model StoreListing {
   OwningUser   User   @relation(fields: [owningUserId], references: [id])
 
   // Relations
-  Versions StoreListingVersion[] @relation("ListingVersions")
+  Versions        StoreListingVersion[] @relation("ListingVersions")
+  waitlistEntries WaitlistEntry[]
 
   // Unique index on agentId to ensure only one listing per agent, regardless of number of versions the agent has.
   @@unique([agentGraphId])
@@ -1033,6 +1039,47 @@ model StoreListingReview {
   @@index([reviewByUserId])
 }
 
+enum WaitlistExternalStatus {
+  DONE
+  NOT_STARTED
+  CANCELED
+  WORK_IN_PROGRESS
+}
+
+model WaitlistEntry {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  storeListingId String?
+  StoreListing   StoreListing? @relation(fields: [storeListingId], references: [id], onDelete: SetNull)
+
+  owningUserId String
+  OwningUser   User   @relation(fields: [owningUserId], references: [id])
+
+  slug   String
+  search Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
+
+  // Content fields
+  name               String
+  subHeading         String
+  videoUrl           String?
+  agentOutputDemoUrl String?
+  imageUrls          String[]
+  description        String
+  categories         String[]
+
+  //Waitlist specific fields
+  status                 WaitlistExternalStatus @default(NOT_STARTED)
+  votes                  Int                    @default(0) // Hide from frontend api
+  joinedUsers            User[]                 @relation("joinedWaitlists")
+  // NOTE: DO NOT DOUBLE SEND TO THESE USERS, IF THEY HAVE SIGNED UP SINCE THEY MAY HAVE ALREADY RECEIVED AN EMAIL
+  // DOUBLE CHECK WHEN SENDING THAT THEY ARE NOT IN THE JOINED USERS LIST ALSO
+  unaffiliatedEmailUsers String[]               @default([])
+
+  isDeleted Boolean @default(false)
+}
+
 enum SubmissionStatus {
   DRAFT // Being prepared, not yet submitted
   PENDING // Submitted, awaiting review

autogpt_platform/backend/scripts/generate_block_docs.py

@@ -366,12 +366,12 @@ def generate_block_markdown(
     lines.append("")
 
     # What it is (full description)
-    lines.append("### What it is")
+    lines.append(f"### What it is")
     lines.append(block.description or "No description available.")
     lines.append("")
 
     # How it works (manual section)
-    lines.append("### How it works")
+    lines.append(f"### How it works")
     how_it_works = manual_content.get(
         "how_it_works", "_Add technical explanation here._"
     )
@@ -383,7 +383,7 @@ def generate_block_markdown(
     # Inputs table (auto-generated)
     visible_inputs = [f for f in block.inputs if not f.hidden]
     if visible_inputs:
-        lines.append("### Inputs")
+        lines.append(f"### Inputs")
         lines.append("")
         lines.append("| Input | Description | Type | Required |")
         lines.append("|-------|-------------|------|----------|")
@@ -400,7 +400,7 @@ def generate_block_markdown(
     # Outputs table (auto-generated)
     visible_outputs = [f for f in block.outputs if not f.hidden]
     if visible_outputs:
-        lines.append("### Outputs")
+        lines.append(f"### Outputs")
         lines.append("")
         lines.append("| Output | Description | Type |")
         lines.append("|--------|-------------|------|")
@@ -414,7 +414,7 @@ def generate_block_markdown(
         lines.append("")
 
     # Possible use case (manual section)
-    lines.append("### Possible use case")
+    lines.append(f"### Possible use case")
     use_case = manual_content.get("use_case", "_Add practical use case examples here._")
     lines.append("<!-- MANUAL: use_case -->")
     lines.append(use_case)

autogpt_platform/backend/snapshots/grph_single

@@ -11,7 +11,6 @@
   "forked_from_version": null,
   "has_external_trigger": false,
   "has_human_in_the_loop": false,
-  "has_sensitive_action": false,
   "id": "graph-123",
   "input_schema": {
     "properties": {},

autogpt_platform/backend/snapshots/grphs_all

@@ -11,7 +11,6 @@
     "forked_from_version": null,
     "has_external_trigger": false,
     "has_human_in_the_loop": false,
-    "has_sensitive_action": false,
     "id": "graph-123",
     "input_schema": {
       "properties": {},

autogpt_platform/backend/snapshots/lib_agts_search

@@ -27,8 +27,6 @@
         "properties": {}
       },
       "has_external_trigger": false,
-      "has_human_in_the_loop": false,
-      "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
       "can_access_graph": true,
@@ -36,8 +34,7 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": true,
-        "sensitive_action_safe_mode": false
+        "human_in_the_loop_safe_mode": null
       },
       "marketplace_listing": null
     },
@@ -68,8 +65,6 @@
         "properties": {}
       },
       "has_external_trigger": false,
-      "has_human_in_the_loop": false,
-      "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
       "can_access_graph": false,
@@ -77,8 +72,7 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": true,
-        "sensitive_action_safe_mode": false
+        "human_in_the_loop_safe_mode": null
       },
       "marketplace_listing": null
     }

autogpt_platform/frontend/.env.default

@@ -29,4 +29,4 @@ NEXT_PUBLIC_CLOUDFLARE_TURNSTILE_SITE_KEY=
 NEXT_PUBLIC_TURNSTILE=disabled
 
 # PR previews
-NEXT_PUBLIC_PREVIEW_STEALING_DEV=
+NEXT_PUBLIC_PREVIEW_STEALING_DEV=

autogpt_platform/frontend/CONTRIBUTING.md

@@ -175,8 +175,6 @@ While server components and actions are cool and cutting-edge, they introduce a
 
 - Prefer [React Query](https://tanstack.com/query/latest/docs/framework/react/overview) for server state, colocated near consumers (see [state colocation](https://kentcdodds.com/blog/state-colocation-will-make-your-react-app-faster))
 - Co-locate UI state inside components/hooks; keep global state minimal
-- Avoid `useMemo` and `useCallback` unless you have a measured performance issue
-- Do not abuse `useEffect`; prefer state colocation and derive values directly when possible
 
 ### Styling and components
 
@@ -551,48 +549,9 @@ Files:
 Types:
 
 - Prefer `interface` for object shapes
-- Component props should be `interface Props { ... }` (not exported)
-- Only use specific exported names (e.g., `export interface MyComponentProps`) when the interface needs to be used outside the component
-- Keep type definitions inline with the component - do not create separate `types.ts` files unless types are shared across multiple files
+- Component props should be `interface Props { ... }`
 - Use precise types; avoid `any` and unsafe casts
 
-**Props naming examples:**
-
-```tsx
-// ✅ Good - internal props, not exported
-interface Props {
-  title: string;
-  onClose: () => void;
-}
-
-export function Modal({ title, onClose }: Props) {
-  // ...
-}
-
-// ✅ Good - exported when needed externally
-export interface ModalProps {
-  title: string;
-  onClose: () => void;
-}
-
-export function Modal({ title, onClose }: ModalProps) {
-  // ...
-}
-
-// ❌ Bad - unnecessarily specific name for internal use
-interface ModalComponentProps {
-  title: string;
-  onClose: () => void;
-}
-
-// ❌ Bad - separate types.ts file for single component
-// types.ts
-export interface ModalProps { ... }
-
-// Modal.tsx
-import type { ModalProps } from './types';
-```
-
 Parameters:
 
 - If more than one parameter is needed, pass a single `Args` object for clarity

autogpt_platform/frontend/orval.config.ts

@@ -16,12 +16,6 @@ export default defineConfig({
       client: "react-query",
       httpClient: "fetch",
       indexFiles: false,
-      mock: {
-        type: "msw",
-        baseUrl: "http://localhost:3000/api/proxy",
-        generateEachHttpStatus: true,
-        delay: 0,
-      },
       override: {
         mutator: {
           path: "./mutators/custom-mutator.ts",

autogpt_platform/frontend/package.json

@@ -15,8 +15,6 @@
     "types": "tsc --noEmit",
     "test": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test",
     "test-ui": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test --ui",
-    "test:unit": "vitest run",
-    "test:unit:watch": "vitest",
     "test:no-build": "playwright test",
     "gentests": "playwright codegen http://localhost:3000",
     "storybook": "storybook dev -p 6006",
@@ -129,9 +127,6 @@
     "@storybook/nextjs": "9.1.5",
     "@tanstack/eslint-plugin-query": "5.91.2",
     "@tanstack/react-query-devtools": "5.90.2",
-    "@testing-library/dom": "10.4.1",
-    "@testing-library/jest-dom": "6.9.1",
-    "@testing-library/react": "16.3.2",
     "@types/canvas-confetti": "1.9.0",
     "@types/lodash": "4.17.20",
     "@types/negotiator": "0.6.4",
@@ -140,7 +135,6 @@
     "@types/react-dom": "18.3.5",
     "@types/react-modal": "3.16.3",
     "@types/react-window": "1.8.8",
-    "@vitejs/plugin-react": "5.1.2",
     "axe-playwright": "2.2.2",
     "chromatic": "13.3.3",
     "concurrently": "9.2.1",
@@ -148,7 +142,6 @@
     "eslint": "8.57.1",
     "eslint-config-next": "15.5.7",
     "eslint-plugin-storybook": "9.1.5",
-    "happy-dom": "20.3.4",
     "import-in-the-middle": "2.0.2",
     "msw": "2.11.6",
     "msw-storybook-addon": "2.0.6",
@@ -160,9 +153,7 @@
     "require-in-the-middle": "8.0.1",
     "storybook": "9.1.5",
     "tailwindcss": "3.4.17",
-    "typescript": "5.9.3",
-    "vite-tsconfig-paths": "6.0.4",
-    "vitest": "4.0.17"
+    "typescript": "5.9.3"
   },
   "msw": {
     "workerDirectory": [

autogpt_platform/frontend/src/app/(no-navbar)/logout/page.tsx

@@ -1,58 +0,0 @@
-"use client";
-
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { Text } from "@/components/atoms/Text/Text";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { useRouter } from "next/navigation";
-import { useEffect, useRef } from "react";
-
-const LOGOUT_REDIRECT_DELAY_MS = 400;
-
-function wait(ms: number): Promise<void> {
-  return new Promise(function resolveAfterDelay(resolve) {
-    setTimeout(resolve, ms);
-  });
-}
-
-export default function LogoutPage() {
-  const { logOut } = useSupabase();
-  const { toast } = useToast();
-  const router = useRouter();
-  const hasStartedRef = useRef(false);
-
-  useEffect(
-    function handleLogoutEffect() {
-      if (hasStartedRef.current) return;
-      hasStartedRef.current = true;
-
-      async function runLogout() {
-        try {
-          await logOut();
-        } catch {
-          toast({
-            title: "Failed to log out. Redirecting to login.",
-            variant: "destructive",
-          });
-        } finally {
-          await wait(LOGOUT_REDIRECT_DELAY_MS);
-          router.replace("/login");
-        }
-      }
-
-      void runLogout();
-    },
-    [logOut, router, toast],
-  );
-
-  return (
-    <div className="flex min-h-screen items-center justify-center px-4">
-      <div className="flex flex-col items-center justify-center gap-4 py-8">
-        <LoadingSpinner size="large" />
-        <Text variant="body" className="text-center">
-          Logging you out...
-        </Text>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/layout.tsx

@@ -1,5 +1,5 @@
 import { Sidebar } from "@/components/__legacy__/Sidebar";
-import { Users, DollarSign, UserSearch, FileText } from "lucide-react";
+import { Users, DollarSign, UserSearch, FileText, Clock } from "lucide-react";
 
 import { IconSliders } from "@/components/__legacy__/ui/icons";
 
@@ -11,6 +11,11 @@ const sidebarLinkGroups = [
         href: "/admin/marketplace",
         icon: <Users className="h-6 w-6" />,
       },
+      {
+        text: "Waitlist Management",
+        href: "/admin/waitlist",
+        icon: <Clock className="h-6 w-6" />,
+      },
       {
         text: "User Spending",
         href: "/admin/spending",

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/CreateWaitlistButton.tsx

@@ -0,0 +1,217 @@
+"use client";
+
+import { useState } from "react";
+import { useQueryClient } from "@tanstack/react-query";
+import { Button } from "@/components/atoms/Button/Button";
+import { Input } from "@/components/atoms/Input/Input";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import {
+  usePostV2CreateWaitlist,
+  getGetV2ListAllWaitlistsQueryKey,
+} from "@/app/api/__generated__/endpoints/admin/admin";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { Plus } from "@phosphor-icons/react";
+
+export function CreateWaitlistButton() {
+  const [open, setOpen] = useState(false);
+  const { toast } = useToast();
+  const queryClient = useQueryClient();
+
+  const createWaitlistMutation = usePostV2CreateWaitlist({
+    mutation: {
+      onSuccess: (response) => {
+        if (response.status === 200) {
+          toast({
+            title: "Success",
+            description: "Waitlist created successfully",
+          });
+          setOpen(false);
+          setFormData({
+            name: "",
+            slug: "",
+            subHeading: "",
+            description: "",
+            categories: "",
+            imageUrls: "",
+            videoUrl: "",
+            agentOutputDemoUrl: "",
+          });
+          queryClient.invalidateQueries({
+            queryKey: getGetV2ListAllWaitlistsQueryKey(),
+          });
+        } else {
+          toast({
+            variant: "destructive",
+            title: "Error",
+            description: "Failed to create waitlist",
+          });
+        }
+      },
+      onError: (error) => {
+        console.error("Error creating waitlist:", error);
+        toast({
+          variant: "destructive",
+          title: "Error",
+          description: "Failed to create waitlist",
+        });
+      },
+    },
+  });
+
+  const [formData, setFormData] = useState({
+    name: "",
+    slug: "",
+    subHeading: "",
+    description: "",
+    categories: "",
+    imageUrls: "",
+    videoUrl: "",
+    agentOutputDemoUrl: "",
+  });
+
+  function handleInputChange(id: string, value: string) {
+    setFormData((prev) => ({
+      ...prev,
+      [id]: value,
+    }));
+  }
+
+  function generateSlug(name: string) {
+    return name
+      .toLowerCase()
+      .replace(/[^a-z0-9]+/g, "-")
+      .replace(/^-|-$/g, "");
+  }
+
+  function handleSubmit(e: React.FormEvent) {
+    e.preventDefault();
+
+    createWaitlistMutation.mutate({
+      data: {
+        name: formData.name,
+        slug: formData.slug || generateSlug(formData.name),
+        subHeading: formData.subHeading,
+        description: formData.description,
+        categories: formData.categories
+          ? formData.categories.split(",").map((c) => c.trim())
+          : [],
+        imageUrls: formData.imageUrls
+          ? formData.imageUrls.split(",").map((u) => u.trim())
+          : [],
+        videoUrl: formData.videoUrl || null,
+        agentOutputDemoUrl: formData.agentOutputDemoUrl || null,
+      },
+    });
+  }
+
+  return (
+    <>
+      <Button onClick={() => setOpen(true)}>
+        <Plus size={16} className="mr-2" />
+        Create Waitlist
+      </Button>
+
+      <Dialog
+        title="Create New Waitlist"
+        controlled={{
+          isOpen: open,
+          set: async (isOpen) => setOpen(isOpen),
+        }}
+        onClose={() => setOpen(false)}
+        styling={{ maxWidth: "600px" }}
+      >
+        <Dialog.Content>
+          <p className="mb-4 text-sm text-zinc-500">
+            Create a new waitlist for an upcoming agent. Users can sign up to be
+            notified when it launches.
+          </p>
+          <form onSubmit={handleSubmit} className="flex flex-col gap-2">
+            <Input
+              id="name"
+              label="Name"
+              value={formData.name}
+              onChange={(e) => handleInputChange("name", e.target.value)}
+              placeholder="SEO Analysis Agent"
+              required
+            />
+
+            <Input
+              id="slug"
+              label="Slug"
+              value={formData.slug}
+              onChange={(e) => handleInputChange("slug", e.target.value)}
+              placeholder="seo-analysis-agent (auto-generated if empty)"
+            />
+
+            <Input
+              id="subHeading"
+              label="Subheading"
+              value={formData.subHeading}
+              onChange={(e) => handleInputChange("subHeading", e.target.value)}
+              placeholder="Analyze your website's SEO in minutes"
+              required
+            />
+
+            <Input
+              id="description"
+              label="Description"
+              type="textarea"
+              value={formData.description}
+              onChange={(e) => handleInputChange("description", e.target.value)}
+              placeholder="Detailed description of what this agent does..."
+              rows={4}
+              required
+            />
+
+            <Input
+              id="categories"
+              label="Categories (comma-separated)"
+              value={formData.categories}
+              onChange={(e) => handleInputChange("categories", e.target.value)}
+              placeholder="SEO, Marketing, Analysis"
+            />
+
+            <Input
+              id="imageUrls"
+              label="Image URLs (comma-separated)"
+              value={formData.imageUrls}
+              onChange={(e) => handleInputChange("imageUrls", e.target.value)}
+              placeholder="https://example.com/image1.jpg, https://example.com/image2.jpg"
+            />
+
+            <Input
+              id="videoUrl"
+              label="Video URL (optional)"
+              value={formData.videoUrl}
+              onChange={(e) => handleInputChange("videoUrl", e.target.value)}
+              placeholder="https://youtube.com/watch?v=..."
+            />
+
+            <Input
+              id="agentOutputDemoUrl"
+              label="Output Demo URL (optional)"
+              value={formData.agentOutputDemoUrl}
+              onChange={(e) =>
+                handleInputChange("agentOutputDemoUrl", e.target.value)
+              }
+              placeholder="https://example.com/demo-output.mp4"
+            />
+
+            <Dialog.Footer>
+              <Button
+                type="button"
+                variant="secondary"
+                onClick={() => setOpen(false)}
+              >
+                Cancel
+              </Button>
+              <Button type="submit" loading={createWaitlistMutation.isPending}>
+                Create Waitlist
+              </Button>
+            </Dialog.Footer>
+          </form>
+        </Dialog.Content>
+      </Dialog>
+    </>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/EditWaitlistDialog.tsx

@@ -0,0 +1,221 @@
+"use client";
+
+import { useState } from "react";
+import { Button } from "@/components/atoms/Button/Button";
+import { Input } from "@/components/atoms/Input/Input";
+import { Select } from "@/components/atoms/Select/Select";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { usePutV2UpdateWaitlist } from "@/app/api/__generated__/endpoints/admin/admin";
+import type { WaitlistAdminResponse } from "@/app/api/__generated__/models/waitlistAdminResponse";
+import type { WaitlistUpdateRequest } from "@/app/api/__generated__/models/waitlistUpdateRequest";
+import { WaitlistExternalStatus } from "@/app/api/__generated__/models/waitlistExternalStatus";
+
+type EditWaitlistDialogProps = {
+  waitlist: WaitlistAdminResponse;
+  onClose: () => void;
+  onSave: () => void;
+};
+
+const STATUS_OPTIONS = [
+  { value: WaitlistExternalStatus.NOT_STARTED, label: "Not Started" },
+  { value: WaitlistExternalStatus.WORK_IN_PROGRESS, label: "Work In Progress" },
+  { value: WaitlistExternalStatus.DONE, label: "Done" },
+  { value: WaitlistExternalStatus.CANCELED, label: "Canceled" },
+];
+
+export function EditWaitlistDialog({
+  waitlist,
+  onClose,
+  onSave,
+}: EditWaitlistDialogProps) {
+  const { toast } = useToast();
+  const updateWaitlistMutation = usePutV2UpdateWaitlist();
+
+  const [formData, setFormData] = useState({
+    name: waitlist.name,
+    slug: waitlist.slug,
+    subHeading: waitlist.subHeading,
+    description: waitlist.description,
+    categories: waitlist.categories.join(", "),
+    imageUrls: waitlist.imageUrls.join(", "),
+    videoUrl: waitlist.videoUrl || "",
+    agentOutputDemoUrl: waitlist.agentOutputDemoUrl || "",
+    status: waitlist.status,
+    storeListingId: waitlist.storeListingId || "",
+  });
+
+  function handleInputChange(id: string, value: string) {
+    setFormData((prev) => ({
+      ...prev,
+      [id]: value,
+    }));
+  }
+
+  function handleStatusChange(value: string) {
+    setFormData((prev) => ({
+      ...prev,
+      status: value as WaitlistExternalStatus,
+    }));
+  }
+
+  async function handleSubmit(e: React.FormEvent) {
+    e.preventDefault();
+
+    const updateData: WaitlistUpdateRequest = {
+      name: formData.name,
+      slug: formData.slug,
+      subHeading: formData.subHeading,
+      description: formData.description,
+      categories: formData.categories
+        ? formData.categories.split(",").map((c) => c.trim())
+        : [],
+      imageUrls: formData.imageUrls
+        ? formData.imageUrls.split(",").map((u) => u.trim())
+        : [],
+      videoUrl: formData.videoUrl || null,
+      agentOutputDemoUrl: formData.agentOutputDemoUrl || null,
+      status: formData.status,
+      storeListingId: formData.storeListingId || null,
+    };
+
+    updateWaitlistMutation.mutate(
+      { waitlistId: waitlist.id, data: updateData },
+      {
+        onSuccess: (response) => {
+          if (response.status === 200) {
+            toast({
+              title: "Success",
+              description: "Waitlist updated successfully",
+            });
+            onSave();
+          } else {
+            toast({
+              variant: "destructive",
+              title: "Error",
+              description: "Failed to update waitlist",
+            });
+          }
+        },
+        onError: () => {
+          toast({
+            variant: "destructive",
+            title: "Error",
+            description: "Failed to update waitlist",
+          });
+        },
+      },
+    );
+  }
+
+  return (
+    <Dialog
+      title="Edit Waitlist"
+      controlled={{
+        isOpen: true,
+        set: async (open) => {
+          if (!open) onClose();
+        },
+      }}
+      onClose={onClose}
+      styling={{ maxWidth: "600px" }}
+    >
+      <Dialog.Content>
+        <p className="mb-4 text-sm text-zinc-500">
+          Update the waitlist details. Changes will be reflected immediately.
+        </p>
+        <form onSubmit={handleSubmit} className="flex flex-col gap-2">
+          <Input
+            id="name"
+            label="Name"
+            value={formData.name}
+            onChange={(e) => handleInputChange("name", e.target.value)}
+            required
+          />
+
+          <Input
+            id="slug"
+            label="Slug"
+            value={formData.slug}
+            onChange={(e) => handleInputChange("slug", e.target.value)}
+          />
+
+          <Input
+            id="subHeading"
+            label="Subheading"
+            value={formData.subHeading}
+            onChange={(e) => handleInputChange("subHeading", e.target.value)}
+            required
+          />
+
+          <Input
+            id="description"
+            label="Description"
+            type="textarea"
+            value={formData.description}
+            onChange={(e) => handleInputChange("description", e.target.value)}
+            rows={4}
+            required
+          />
+
+          <Select
+            id="status"
+            label="Status"
+            value={formData.status}
+            onValueChange={handleStatusChange}
+            options={STATUS_OPTIONS}
+          />
+
+          <Input
+            id="categories"
+            label="Categories (comma-separated)"
+            value={formData.categories}
+            onChange={(e) => handleInputChange("categories", e.target.value)}
+          />
+
+          <Input
+            id="imageUrls"
+            label="Image URLs (comma-separated)"
+            value={formData.imageUrls}
+            onChange={(e) => handleInputChange("imageUrls", e.target.value)}
+          />
+
+          <Input
+            id="videoUrl"
+            label="Video URL"
+            value={formData.videoUrl}
+            onChange={(e) => handleInputChange("videoUrl", e.target.value)}
+          />
+
+          <Input
+            id="agentOutputDemoUrl"
+            label="Output Demo URL"
+            value={formData.agentOutputDemoUrl}
+            onChange={(e) =>
+              handleInputChange("agentOutputDemoUrl", e.target.value)
+            }
+          />
+
+          <Input
+            id="storeListingId"
+            label="Store Listing ID (for linking)"
+            value={formData.storeListingId}
+            onChange={(e) =>
+              handleInputChange("storeListingId", e.target.value)
+            }
+            placeholder="Leave empty if not linked"
+          />
+
+          <Dialog.Footer>
+            <Button type="button" variant="secondary" onClick={onClose}>
+              Cancel
+            </Button>
+            <Button type="submit" loading={updateWaitlistMutation.isPending}>
+              Save Changes
+            </Button>
+          </Dialog.Footer>
+        </form>
+      </Dialog.Content>
+    </Dialog>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/WaitlistSignupsDialog.tsx

@@ -0,0 +1,156 @@
+"use client";
+
+import { Button } from "@/components/atoms/Button/Button";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { User, Envelope, DownloadSimple } from "@phosphor-icons/react";
+import { useGetV2GetWaitlistSignups } from "@/app/api/__generated__/endpoints/admin/admin";
+
+type WaitlistSignupsDialogProps = {
+  waitlistId: string;
+  onClose: () => void;
+};
+
+export function WaitlistSignupsDialog({
+  waitlistId,
+  onClose,
+}: WaitlistSignupsDialogProps) {
+  const {
+    data: signupsResponse,
+    isLoading,
+    isError,
+  } = useGetV2GetWaitlistSignups(waitlistId);
+
+  const signups = signupsResponse?.status === 200 ? signupsResponse.data : null;
+
+  function exportToCSV() {
+    if (!signups) return;
+
+    const headers = ["Type", "Email", "User ID", "Username"];
+    const rows = signups.signups.map((signup) => [
+      signup.type,
+      signup.email || "",
+      signup.userId || "",
+      signup.username || "",
+    ]);
+
+    const escapeCell = (cell: string) => `"${cell.replace(/"/g, '""')}"`;
+
+    const csvContent = [
+      headers.join(","),
+      ...rows.map((row) => row.map(escapeCell).join(",")),
+    ].join("\n");
+
+    const blob = new Blob([csvContent], { type: "text/csv" });
+    const url = window.URL.createObjectURL(blob);
+    const a = document.createElement("a");
+    a.href = url;
+    a.download = `waitlist-${waitlistId}-signups.csv`;
+    a.click();
+    window.URL.revokeObjectURL(url);
+  }
+
+  function renderContent() {
+    if (isLoading) {
+      return <div className="py-10 text-center">Loading signups...</div>;
+    }
+
+    if (isError) {
+      return (
+        <div className="py-10 text-center text-red-500">
+          Failed to load signups. Please try again.
+        </div>
+      );
+    }
+
+    if (!signups || signups.signups.length === 0) {
+      return (
+        <div className="py-10 text-center text-gray-500">
+          No signups yet for this waitlist.
+        </div>
+      );
+    }
+
+    return (
+      <>
+        <div className="flex justify-end">
+          <Button variant="secondary" size="small" onClick={exportToCSV}>
+            <DownloadSimple className="mr-2 h-4 w-4" size={16} />
+            Export CSV
+          </Button>
+        </div>
+        <div className="max-h-[400px] overflow-y-auto rounded-md border">
+          <table className="w-full">
+            <thead className="bg-gray-50 dark:bg-gray-800">
+              <tr>
+                <th className="px-4 py-3 text-left text-sm font-medium">
+                  Type
+                </th>
+                <th className="px-4 py-3 text-left text-sm font-medium">
+                  Email / Username
+                </th>
+                <th className="px-4 py-3 text-left text-sm font-medium">
+                  User ID
+                </th>
+              </tr>
+            </thead>
+            <tbody className="divide-y">
+              {signups.signups.map((signup, index) => (
+                <tr key={index}>
+                  <td className="px-4 py-3">
+                    {signup.type === "user" ? (
+                      <span className="flex items-center gap-1 text-blue-600">
+                        <User className="h-4 w-4" size={16} /> User
+                      </span>
+                    ) : (
+                      <span className="flex items-center gap-1 text-gray-600">
+                        <Envelope className="h-4 w-4" size={16} /> Email
+                      </span>
+                    )}
+                  </td>
+                  <td className="px-4 py-3">
+                    {signup.type === "user"
+                      ? signup.username || signup.email
+                      : signup.email}
+                  </td>
+                  <td className="px-4 py-3 font-mono text-sm">
+                    {signup.userId || "-"}
+                  </td>
+                </tr>
+              ))}
+            </tbody>
+          </table>
+        </div>
+      </>
+    );
+  }
+
+  return (
+    <Dialog
+      title="Waitlist Signups"
+      controlled={{
+        isOpen: true,
+        set: async (open) => {
+          if (!open) onClose();
+        },
+      }}
+      onClose={onClose}
+      styling={{ maxWidth: "700px" }}
+    >
+      <Dialog.Content>
+        <p className="mb-4 text-sm text-zinc-500">
+          {signups
+            ? `${signups.totalCount} total signups`
+            : "Loading signups..."}
+        </p>
+
+        {renderContent()}
+
+        <Dialog.Footer>
+          <Button variant="secondary" onClick={onClose}>
+            Close
+          </Button>
+        </Dialog.Footer>
+      </Dialog.Content>
+    </Dialog>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/WaitlistTable.tsx

@@ -0,0 +1,206 @@
+"use client";
+
+import { useState } from "react";
+import { useQueryClient } from "@tanstack/react-query";
+import {
+  Table,
+  TableBody,
+  TableCell,
+  TableHead,
+  TableHeader,
+  TableRow,
+} from "@/components/__legacy__/ui/table";
+import { Button } from "@/components/atoms/Button/Button";
+import {
+  useGetV2ListAllWaitlists,
+  useDeleteV2DeleteWaitlist,
+  getGetV2ListAllWaitlistsQueryKey,
+} from "@/app/api/__generated__/endpoints/admin/admin";
+import type { WaitlistAdminResponse } from "@/app/api/__generated__/models/waitlistAdminResponse";
+import { EditWaitlistDialog } from "./EditWaitlistDialog";
+import { WaitlistSignupsDialog } from "./WaitlistSignupsDialog";
+import { Trash, PencilSimple, Users, Link } from "@phosphor-icons/react";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+
+export function WaitlistTable() {
+  const [editingWaitlist, setEditingWaitlist] =
+    useState<WaitlistAdminResponse | null>(null);
+  const [viewingSignups, setViewingSignups] = useState<string | null>(null);
+  const { toast } = useToast();
+  const queryClient = useQueryClient();
+
+  const { data: response, isLoading, error } = useGetV2ListAllWaitlists();
+
+  const deleteWaitlistMutation = useDeleteV2DeleteWaitlist({
+    mutation: {
+      onSuccess: () => {
+        toast({
+          title: "Success",
+          description: "Waitlist deleted successfully",
+        });
+        queryClient.invalidateQueries({
+          queryKey: getGetV2ListAllWaitlistsQueryKey(),
+        });
+      },
+      onError: (error) => {
+        console.error("Error deleting waitlist:", error);
+        toast({
+          variant: "destructive",
+          title: "Error",
+          description: "Failed to delete waitlist",
+        });
+      },
+    },
+  });
+
+  function handleDelete(waitlistId: string) {
+    if (!confirm("Are you sure you want to delete this waitlist?")) return;
+    deleteWaitlistMutation.mutate({ waitlistId });
+  }
+
+  function handleWaitlistSaved() {
+    setEditingWaitlist(null);
+    queryClient.invalidateQueries({
+      queryKey: getGetV2ListAllWaitlistsQueryKey(),
+    });
+  }
+
+  function formatStatus(status: string) {
+    const statusColors: Record<string, string> = {
+      NOT_STARTED: "bg-gray-100 text-gray-800",
+      WORK_IN_PROGRESS: "bg-blue-100 text-blue-800",
+      DONE: "bg-green-100 text-green-800",
+      CANCELED: "bg-red-100 text-red-800",
+    };
+
+    return (
+      <span
+        className={`rounded-full px-2 py-1 text-xs font-medium ${statusColors[status] || "bg-gray-100 text-gray-700"}`}
+      >
+        {status.replace(/_/g, " ")}
+      </span>
+    );
+  }
+
+  function formatDate(dateStr: string) {
+    if (!dateStr) return "-";
+    return new Intl.DateTimeFormat("en-US", {
+      month: "short",
+      day: "numeric",
+      year: "numeric",
+    }).format(new Date(dateStr));
+  }
+
+  if (isLoading) {
+    return <div className="py-10 text-center">Loading waitlists...</div>;
+  }
+
+  if (error) {
+    return (
+      <div className="py-10 text-center text-red-500">
+        Error loading waitlists. Please try again.
+      </div>
+    );
+  }
+
+  const waitlists = response?.status === 200 ? response.data.waitlists : [];
+
+  if (waitlists.length === 0) {
+    return (
+      <div className="py-10 text-center text-gray-500">
+        No waitlists found. Create one to get started!
+      </div>
+    );
+  }
+
+  return (
+    <>
+      <div className="rounded-md border bg-white">
+        <Table>
+          <TableHeader className="bg-gray-50">
+            <TableRow>
+              <TableHead className="font-medium">Name</TableHead>
+              <TableHead className="font-medium">Status</TableHead>
+              <TableHead className="font-medium">Signups</TableHead>
+              <TableHead className="font-medium">Votes</TableHead>
+              <TableHead className="font-medium">Created</TableHead>
+              <TableHead className="font-medium">Linked Agent</TableHead>
+              <TableHead className="font-medium">Actions</TableHead>
+            </TableRow>
+          </TableHeader>
+          <TableBody>
+            {waitlists.map((waitlist) => (
+              <TableRow key={waitlist.id}>
+                <TableCell>
+                  <div>
+                    <div className="font-medium">{waitlist.name}</div>
+                    <div className="text-sm text-gray-500">
+                      {waitlist.subHeading}
+                    </div>
+                  </div>
+                </TableCell>
+                <TableCell>{formatStatus(waitlist.status)}</TableCell>
+                <TableCell>{waitlist.signupCount}</TableCell>
+                <TableCell>{waitlist.votes}</TableCell>
+                <TableCell>{formatDate(waitlist.createdAt)}</TableCell>
+                <TableCell>
+                  {waitlist.storeListingId ? (
+                    <span className="text-green-600">
+                      <Link size={16} className="inline" /> Linked
+                    </span>
+                  ) : (
+                    <span className="text-gray-400">Not linked</span>
+                  )}
+                </TableCell>
+                <TableCell>
+                  <div className="flex gap-2">
+                    <Button
+                      variant="ghost"
+                      size="small"
+                      onClick={() => setViewingSignups(waitlist.id)}
+                      title="View signups"
+                    >
+                      <Users size={16} />
+                    </Button>
+                    <Button
+                      variant="ghost"
+                      size="small"
+                      onClick={() => setEditingWaitlist(waitlist)}
+                      title="Edit"
+                    >
+                      <PencilSimple size={16} />
+                    </Button>
+                    <Button
+                      variant="ghost"
+                      size="small"
+                      onClick={() => handleDelete(waitlist.id)}
+                      title="Delete"
+                      disabled={deleteWaitlistMutation.isPending}
+                    >
+                      <Trash size={16} className="text-red-500" />
+                    </Button>
+                  </div>
+                </TableCell>
+              </TableRow>
+            ))}
+          </TableBody>
+        </Table>
+      </div>
+
+      {editingWaitlist && (
+        <EditWaitlistDialog
+          waitlist={editingWaitlist}
+          onClose={() => setEditingWaitlist(null)}
+          onSave={handleWaitlistSaved}
+        />
+      )}
+
+      {viewingSignups && (
+        <WaitlistSignupsDialog
+          waitlistId={viewingSignups}
+          onClose={() => setViewingSignups(null)}
+        />
+      )}
+    </>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/page.tsx

@@ -0,0 +1,52 @@
+import { withRoleAccess } from "@/lib/withRoleAccess";
+import { Suspense } from "react";
+import { WaitlistTable } from "./components/WaitlistTable";
+import { CreateWaitlistButton } from "./components/CreateWaitlistButton";
+import { Warning } from "@phosphor-icons/react/dist/ssr";
+
+function WaitlistDashboard() {
+  return (
+    <div className="mx-auto p-6">
+      <div className="flex flex-col gap-4">
+        <div className="flex items-center justify-between">
+          <div>
+            <h1 className="text-3xl font-bold">Waitlist Management</h1>
+            <p className="text-gray-500">
+              Manage upcoming agent waitlists and track signups
+            </p>
+          </div>
+          <CreateWaitlistButton />
+        </div>
+
+        <div className="flex items-start gap-3 rounded-lg border border-amber-300 bg-amber-50 p-4 dark:border-amber-700 dark:bg-amber-950">
+          <Warning
+            className="mt-0.5 h-5 w-5 flex-shrink-0 text-amber-600 dark:text-amber-400"
+            weight="fill"
+          />
+          <div className="text-sm text-amber-800 dark:text-amber-200">
+            <p className="font-medium">TODO: Email-only signup notifications</p>
+            <p className="mt-1 text-amber-700 dark:text-amber-300">
+              Notifications for email-only signups (users who weren&apos;t
+              logged in) have not been implemented yet. Currently only
+              registered users will receive launch emails.
+            </p>
+          </div>
+        </div>
+
+        <Suspense
+          fallback={
+            <div className="py-10 text-center">Loading waitlists...</div>
+          }
+        >
+          <WaitlistTable />
+        </Suspense>
+      </div>
+    </div>
+  );
+}
+
+export default async function WaitlistDashboardPage() {
+  const withAdminAccess = await withRoleAccess(["admin"]);
+  const ProtectedWaitlistDashboard = await withAdminAccess(WaitlistDashboard);
+  return <ProtectedWaitlistDashboard />;
+}

autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts

@@ -9,7 +9,7 @@ export async function GET(request: Request) {
   const { searchParams, origin } = new URL(request.url);
   const code = searchParams.get("code");
 
-  let next = "/";
+  let next = "/marketplace";
 
   if (code) {
     const supabase = await getServerSupabase();

autogpt_platform/frontend/src/app/(platform)/build/components/FloatingSafeModeToogle.tsx

@@ -18,118 +18,69 @@ interface Props {
   fullWidth?: boolean;
 }
 
-interface SafeModeButtonProps {
-  isEnabled: boolean;
-  label: string;
-  tooltipEnabled: string;
-  tooltipDisabled: string;
-  onToggle: () => void;
-  isPending: boolean;
-  fullWidth?: boolean;
-}
-
-function SafeModeButton({
-  isEnabled,
-  label,
-  tooltipEnabled,
-  tooltipDisabled,
-  onToggle,
-  isPending,
-  fullWidth = false,
-}: SafeModeButtonProps) {
-  return (
-    <Tooltip delayDuration={100}>
-      <TooltipTrigger asChild>
-        <Button
-          variant={isEnabled ? "primary" : "outline"}
-          size="small"
-          onClick={onToggle}
-          disabled={isPending}
-          className={cn("justify-start", fullWidth ? "w-full" : "")}
-        >
-          {isEnabled ? (
-            <>
-              <ShieldCheckIcon weight="bold" size={16} />
-              <Text variant="body" className="text-zinc-200">
-                {label}: ON
-              </Text>
-            </>
-          ) : (
-            <>
-              <ShieldIcon weight="bold" size={16} />
-              <Text variant="body" className="text-zinc-600">
-                {label}: OFF
-              </Text>
-            </>
-          )}
-        </Button>
-      </TooltipTrigger>
-      <TooltipContent>
-        <div className="text-center">
-          <div className="font-medium">
-            {label}: {isEnabled ? "ON" : "OFF"}
-          </div>
-          <div className="mt-1 text-xs text-muted-foreground">
-            {isEnabled ? tooltipEnabled : tooltipDisabled}
-          </div>
-        </div>
-      </TooltipContent>
-    </Tooltip>
-  );
-}
-
 export function FloatingSafeModeToggle({
   graph,
   className,
   fullWidth = false,
 }: Props) {
   const {
-    currentHITLSafeMode,
-    showHITLToggle,
-    isHITLStateUndetermined,
-    handleHITLToggle,
-    currentSensitiveActionSafeMode,
-    showSensitiveActionToggle,
-    handleSensitiveActionToggle,
+    currentSafeMode,
     isPending,
     shouldShowToggle,
+    isStateUndetermined,
+    handleToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle || isPending) {
-    return null;
-  }
-
-  const showHITL = showHITLToggle && !isHITLStateUndetermined;
-  const showSensitive = showSensitiveActionToggle;
-
-  if (!showHITL && !showSensitive) {
+  if (!shouldShowToggle || isStateUndetermined || isPending) {
     return null;
   }
 
   return (
-    <div className={cn("fixed z-50 flex flex-col gap-2", className)}>
-      {showHITL && (
-        <SafeModeButton
-          isEnabled={currentHITLSafeMode}
-          label="Human in the loop block approval"
-          tooltipEnabled="The agent will pause at human-in-the-loop blocks and wait for your approval"
-          tooltipDisabled="Human in the loop blocks will proceed automatically"
-          onToggle={handleHITLToggle}
-          isPending={isPending}
-          fullWidth={fullWidth}
-        />
-      )}
-      {showSensitive && (
-        <SafeModeButton
-          isEnabled={currentSensitiveActionSafeMode}
-          label="Sensitive actions blocks approval"
-          tooltipEnabled="The agent will pause at sensitive action blocks and wait for your approval"
-          tooltipDisabled="Sensitive action blocks will proceed automatically"
-          onToggle={handleSensitiveActionToggle}
-          isPending={isPending}
-          fullWidth={fullWidth}
-        />
-      )}
+    <div className={cn("fixed z-50", className)}>
+      <Tooltip delayDuration={100}>
+        <TooltipTrigger asChild>
+          <Button
+            variant={currentSafeMode! ? "primary" : "outline"}
+            key={graph.id}
+            size="small"
+            title={
+              currentSafeMode!
+                ? "Safe Mode: ON. Human in the loop blocks require manual review"
+                : "Safe Mode: OFF. Human in the loop blocks proceed automatically"
+            }
+            onClick={handleToggle}
+            className={cn(fullWidth ? "w-full" : "")}
+          >
+            {currentSafeMode! ? (
+              <>
+                <ShieldCheckIcon weight="bold" size={16} />
+                <Text variant="body" className="text-zinc-200">
+                  Safe Mode: ON
+                </Text>
+              </>
+            ) : (
+              <>
+                <ShieldIcon weight="bold" size={16} />
+                <Text variant="body" className="text-zinc-600">
+                  Safe Mode: OFF
+                </Text>
+              </>
+            )}
+          </Button>
+        </TooltipTrigger>
+        <TooltipContent>
+          <div className="text-center">
+            <div className="font-medium">
+              Safe Mode: {currentSafeMode! ? "ON" : "OFF"}
+            </div>
+            <div className="mt-1 text-xs text-muted-foreground">
+              {currentSafeMode!
+                ? "Human in the loop blocks require manual review"
+                : "Human in the loop blocks proceed automatically"}
+            </div>
+          </div>
+        </TooltipContent>
+      </Tooltip>
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/Chat.tsx

@@ -0,0 +1,134 @@
+"use client";
+
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+import { List } from "@phosphor-icons/react";
+import React, { useState } from "react";
+import { ChatContainer } from "./components/ChatContainer/ChatContainer";
+import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
+import { ChatLoadingState } from "./components/ChatLoadingState/ChatLoadingState";
+import { SessionsDrawer } from "./components/SessionsDrawer/SessionsDrawer";
+import { useChat } from "./useChat";
+
+export interface ChatProps {
+  className?: string;
+  headerTitle?: React.ReactNode;
+  showHeader?: boolean;
+  showSessionInfo?: boolean;
+  showNewChatButton?: boolean;
+  onNewChat?: () => void;
+  headerActions?: React.ReactNode;
+}
+
+export function Chat({
+  className,
+  headerTitle = "AutoGPT Copilot",
+  showHeader = true,
+  showSessionInfo = true,
+  showNewChatButton = true,
+  onNewChat,
+  headerActions,
+}: ChatProps) {
+  const {
+    messages,
+    isLoading,
+    isCreating,
+    error,
+    sessionId,
+    createSession,
+    clearSession,
+    loadSession,
+  } = useChat();
+
+  const [isSessionsDrawerOpen, setIsSessionsDrawerOpen] = useState(false);
+
+  const handleNewChat = () => {
+    clearSession();
+    onNewChat?.();
+  };
+
+  const handleSelectSession = async (sessionId: string) => {
+    try {
+      await loadSession(sessionId);
+    } catch (err) {
+      console.error("Failed to load session:", err);
+    }
+  };
+
+  return (
+    <div className={cn("flex h-full flex-col", className)}>
+      {/* Header */}
+      {showHeader && (
+        <header className="shrink-0 border-t border-zinc-200 bg-white p-3">
+          <div className="flex items-center justify-between">
+            <div className="flex items-center gap-3">
+              <button
+                aria-label="View sessions"
+                onClick={() => setIsSessionsDrawerOpen(true)}
+                className="flex size-8 items-center justify-center rounded hover:bg-zinc-100"
+              >
+                <List width="1.25rem" height="1.25rem" />
+              </button>
+              {typeof headerTitle === "string" ? (
+                <Text variant="h2" className="text-lg font-semibold">
+                  {headerTitle}
+                </Text>
+              ) : (
+                headerTitle
+              )}
+            </div>
+            <div className="flex items-center gap-3">
+              {showSessionInfo && sessionId && (
+                <>
+                  {showNewChatButton && (
+                    <Button
+                      variant="outline"
+                      size="small"
+                      onClick={handleNewChat}
+                    >
+                      New Chat
+                    </Button>
+                  )}
+                </>
+              )}
+              {headerActions}
+            </div>
+          </div>
+        </header>
+      )}
+
+      {/* Main Content */}
+      <main className="flex min-h-0 flex-1 flex-col overflow-hidden">
+        {/* Loading State - show when explicitly loading/creating OR when we don't have a session yet and no error */}
+        {(isLoading || isCreating || (!sessionId && !error)) && (
+          <ChatLoadingState
+            message={isCreating ? "Creating session..." : "Loading..."}
+          />
+        )}
+
+        {/* Error State */}
+        {error && !isLoading && (
+          <ChatErrorState error={error} onRetry={createSession} />
+        )}
+
+        {/* Session Content */}
+        {sessionId && !isLoading && !error && (
+          <ChatContainer
+            sessionId={sessionId}
+            initialMessages={messages}
+            className="flex-1"
+          />
+        )}
+      </main>
+
+      {/* Sessions Drawer */}
+      <SessionsDrawer
+        isOpen={isSessionsDrawerOpen}
+        onClose={() => setIsSessionsDrawerOpen(false)}
+        onSelectSession={handleSelectSession}
+        currentSessionId={sessionId}
+      />
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/AuthPromptWidget/AuthPromptWidget.tsx

@@ -21,7 +21,7 @@ export function AuthPromptWidget({
   message,
   sessionId,
   agentInfo,
-  returnUrl = "/copilot/chat",
+  returnUrl = "/chat",
   className,
 }: AuthPromptWidgetProps) {
   const router = useRouter();

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/ChatContainer.tsx

@@ -0,0 +1,88 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import { cn } from "@/lib/utils";
+import { useCallback } from "react";
+import { usePageContext } from "../../usePageContext";
+import { ChatInput } from "../ChatInput/ChatInput";
+import { MessageList } from "../MessageList/MessageList";
+import { QuickActionsWelcome } from "../QuickActionsWelcome/QuickActionsWelcome";
+import { useChatContainer } from "./useChatContainer";
+
+export interface ChatContainerProps {
+  sessionId: string | null;
+  initialMessages: SessionDetailResponse["messages"];
+  className?: string;
+}
+
+export function ChatContainer({
+  sessionId,
+  initialMessages,
+  className,
+}: ChatContainerProps) {
+  const { messages, streamingChunks, isStreaming, sendMessage } =
+    useChatContainer({
+      sessionId,
+      initialMessages,
+    });
+  const { capturePageContext } = usePageContext();
+
+  // Wrap sendMessage to automatically capture page context
+  const sendMessageWithContext = useCallback(
+    async (content: string, isUserMessage: boolean = true) => {
+      const context = capturePageContext();
+      await sendMessage(content, isUserMessage, context);
+    },
+    [sendMessage, capturePageContext],
+  );
+
+  const quickActions = [
+    "Find agents for social media management",
+    "Show me agents for content creation",
+    "Help me automate my business",
+    "What can you help me with?",
+  ];
+
+  return (
+    <div
+      className={cn("flex h-full min-h-0 flex-col", className)}
+      style={{
+        backgroundColor: "#ffffff",
+        backgroundImage:
+          "radial-gradient(#e5e5e5 0.5px, transparent 0.5px), radial-gradient(#e5e5e5 0.5px, #ffffff 0.5px)",
+        backgroundSize: "20px 20px",
+        backgroundPosition: "0 0, 10px 10px",
+      }}
+    >
+      {/* Messages or Welcome Screen */}
+      <div className="flex min-h-0 flex-1 flex-col overflow-hidden pb-24">
+        {messages.length === 0 ? (
+          <QuickActionsWelcome
+            title="Welcome to AutoGPT Copilot"
+            description="Start a conversation to discover and run AI agents."
+            actions={quickActions}
+            onActionClick={sendMessageWithContext}
+            disabled={isStreaming || !sessionId}
+          />
+        ) : (
+          <MessageList
+            messages={messages}
+            streamingChunks={streamingChunks}
+            isStreaming={isStreaming}
+            onSendMessage={sendMessageWithContext}
+            className="flex-1"
+          />
+        )}
+      </div>
+
+      {/* Input - Always visible */}
+      <div className="fixed bottom-0 left-0 right-0 z-50 border-t border-zinc-200 bg-white p-4">
+        <ChatInput
+          onSend={sendMessageWithContext}
+          disabled={isStreaming || !sessionId}
+          placeholder={
+            sessionId ? "Type your message..." : "Creating session..."
+          }
+        />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/createStreamEventDispatcher.ts

@@ -1,6 +1,6 @@
 import { toast } from "sonner";
 import { StreamChunk } from "../../useChatStream";
-import type { HandlerDependencies } from "./handlers";
+import type { HandlerDependencies } from "./useChatContainer.handlers";
 import {
   handleError,
   handleLoginNeeded,
@@ -9,30 +9,12 @@ import {
   handleTextEnded,
   handleToolCallStart,
   handleToolResponse,
-  isRegionBlockedError,
-} from "./handlers";
+} from "./useChatContainer.handlers";
 
 export function createStreamEventDispatcher(
   deps: HandlerDependencies,
 ): (chunk: StreamChunk) => void {
   return function dispatchStreamEvent(chunk: StreamChunk): void {
-    if (
-      chunk.type === "text_chunk" ||
-      chunk.type === "tool_call_start" ||
-      chunk.type === "tool_response" ||
-      chunk.type === "login_needed" ||
-      chunk.type === "need_login" ||
-      chunk.type === "error"
-    ) {
-      if (!deps.hasResponseRef.current) {
-        console.info("[ChatStream] First response chunk:", {
-          type: chunk.type,
-          sessionId: deps.sessionId,
-        });
-      }
-      deps.hasResponseRef.current = true;
-    }
-
     switch (chunk.type) {
       case "text_chunk":
         handleTextChunk(chunk, deps);
@@ -56,23 +38,15 @@ export function createStreamEventDispatcher(
         break;
 
       case "stream_end":
-        console.info("[ChatStream] Stream ended:", {
-          sessionId: deps.sessionId,
-          hasResponse: deps.hasResponseRef.current,
-          chunkCount: deps.streamingChunksRef.current.length,
-        });
         handleStreamEnd(chunk, deps);
         break;
 
       case "error":
-        const isRegionBlocked = isRegionBlockedError(chunk);
         handleError(chunk, deps);
         // Show toast at dispatcher level to avoid circular dependencies
-        if (!isRegionBlocked) {
-          toast.error("Chat Error", {
-            description: chunk.message || chunk.content || "An error occurred",
-          });
-        }
+        toast.error("Chat Error", {
+          description: chunk.message || chunk.content || "An error occurred",
+        });
         break;
 
       case "usage":

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/helpers.ts

@@ -1,33 +1,6 @@
-import { SessionKey, sessionStorage } from "@/services/storage/session-storage";
 import type { ToolResult } from "@/types/chat";
 import type { ChatMessageData } from "../ChatMessage/useChatMessage";
 
-export function hasSentInitialPrompt(sessionId: string): boolean {
-  try {
-    const sent = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_SENT_INITIAL_PROMPTS) || "{}",
-    );
-    return sent[sessionId] === true;
-  } catch {
-    return false;
-  }
-}
-
-export function markInitialPromptSent(sessionId: string): void {
-  try {
-    const sent = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_SENT_INITIAL_PROMPTS) || "{}",
-    );
-    sent[sessionId] = true;
-    sessionStorage.set(
-      SessionKey.CHAT_SENT_INITIAL_PROMPTS,
-      JSON.stringify(sent),
-    );
-  } catch {
-    // Ignore storage errors
-  }
-}
-
 export function removePageContext(content: string): string {
   // Remove "Page URL: ..." pattern at start of line (case insensitive, handles various formats)
   let cleaned = content.replace(/^\s*Page URL:\s*[^\n\r]*/gim, "");
@@ -234,22 +207,12 @@ export function parseToolResponse(
     if (responseType === "setup_requirements") {
       return null;
     }
-    if (responseType === "understanding_updated") {
-      return {
-        type: "tool_response",
-        toolId,
-        toolName,
-        result: (parsedResult || result) as ToolResult,
-        success: true,
-        timestamp: timestamp || new Date(),
-      };
-    }
   }
   return {
     type: "tool_response",
     toolId,
     toolName,
-    result: parsedResult ? (parsedResult as ToolResult) : result,
+    result,
     success: true,
     timestamp: timestamp || new Date(),
   };

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/useChatContainer.handlers.ts

@@ -7,30 +7,15 @@ import {
   parseToolResponse,
 } from "./helpers";
 
-function isToolCallMessage(
-  message: ChatMessageData,
-): message is Extract<ChatMessageData, { type: "tool_call" }> {
-  return message.type === "tool_call";
-}
-
 export interface HandlerDependencies {
   setHasTextChunks: Dispatch<SetStateAction<boolean>>;
   setStreamingChunks: Dispatch<SetStateAction<string[]>>;
   streamingChunksRef: MutableRefObject<string[]>;
-  hasResponseRef: MutableRefObject<boolean>;
   setMessages: Dispatch<SetStateAction<ChatMessageData[]>>;
   setIsStreamingInitiated: Dispatch<SetStateAction<boolean>>;
-  setIsRegionBlockedModalOpen: Dispatch<SetStateAction<boolean>>;
   sessionId: string;
 }
 
-export function isRegionBlockedError(chunk: StreamChunk): boolean {
-  if (chunk.code === "MODEL_NOT_AVAILABLE_REGION") return true;
-  const message = chunk.message || chunk.content;
-  if (typeof message !== "string") return false;
-  return message.toLowerCase().includes("not available in your region");
-}
-
 export function handleTextChunk(chunk: StreamChunk, deps: HandlerDependencies) {
   if (!chunk.content) return;
   deps.setHasTextChunks(true);
@@ -45,17 +30,16 @@ export function handleTextEnded(
   _chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
+  console.log("[Text Ended] Saving streamed text as assistant message");
   const completedText = deps.streamingChunksRef.current.join("");
   if (completedText.trim()) {
-    deps.setMessages((prev) => {
-      const assistantMessage: ChatMessageData = {
-        type: "message",
-        role: "assistant",
-        content: completedText,
-        timestamp: new Date(),
-      };
-      return [...prev, assistantMessage];
-    });
+    const assistantMessage: ChatMessageData = {
+      type: "message",
+      role: "assistant",
+      content: completedText,
+      timestamp: new Date(),
+    };
+    deps.setMessages((prev) => [...prev, assistantMessage]);
   }
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
@@ -66,45 +50,30 @@ export function handleToolCallStart(
   chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
-  const toolCallMessage: Extract<ChatMessageData, { type: "tool_call" }> = {
+  const toolCallMessage: ChatMessageData = {
     type: "tool_call",
     toolId: chunk.tool_id || `tool-${Date.now()}-${chunk.idx || 0}`,
-    toolName: chunk.tool_name || "Executing",
+    toolName: chunk.tool_name || "Executing...",
     arguments: chunk.arguments || {},
     timestamp: new Date(),
   };
-
-  function updateToolCallMessages(prev: ChatMessageData[]) {
-    const existingIndex = prev.findIndex(function findToolCallIndex(msg) {
-      return isToolCallMessage(msg) && msg.toolId === toolCallMessage.toolId;
-    });
-    if (existingIndex === -1) {
-      return [...prev, toolCallMessage];
-    }
-    const nextMessages = [...prev];
-    const existing = nextMessages[existingIndex];
-    if (!isToolCallMessage(existing)) return prev;
-    const nextArguments =
-      toolCallMessage.arguments &&
-      Object.keys(toolCallMessage.arguments).length > 0
-        ? toolCallMessage.arguments
-        : existing.arguments;
-    nextMessages[existingIndex] = {
-      ...existing,
-      toolName: toolCallMessage.toolName || existing.toolName,
-      arguments: nextArguments,
-      timestamp: toolCallMessage.timestamp,
-    };
-    return nextMessages;
-  }
-
-  deps.setMessages(updateToolCallMessages);
+  deps.setMessages((prev) => [...prev, toolCallMessage]);
+  console.log("[Tool Call Start]", {
+    toolId: toolCallMessage.toolId,
+    toolName: toolCallMessage.toolName,
+    timestamp: new Date().toISOString(),
+  });
 }
 
 export function handleToolResponse(
   chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
+  console.log("[Tool Response] Received:", {
+    toolId: chunk.tool_id,
+    toolName: chunk.tool_name,
+    timestamp: new Date().toISOString(),
+  });
   let toolName = chunk.tool_name || "unknown";
   if (!chunk.tool_name || chunk.tool_name === "unknown") {
     deps.setMessages((prev) => {
@@ -158,15 +127,22 @@ export function handleToolResponse(
     const toolCallIndex = prev.findIndex(
       (msg) => msg.type === "tool_call" && msg.toolId === chunk.tool_id,
     );
-    const hasResponse = prev.some(
-      (msg) => msg.type === "tool_response" && msg.toolId === chunk.tool_id,
-    );
-    if (hasResponse) return prev;
     if (toolCallIndex !== -1) {
       const newMessages = [...prev];
-      newMessages.splice(toolCallIndex + 1, 0, responseMessage);
+      newMessages[toolCallIndex] = responseMessage;
+      console.log(
+        "[Tool Response] Replaced tool_call with matching tool_id:",
+        chunk.tool_id,
+        "at index:",
+        toolCallIndex,
+      );
       return newMessages;
     }
+    console.warn(
+      "[Tool Response] No tool_call found with tool_id:",
+      chunk.tool_id,
+      "appending instead",
+    );
     return [...prev, responseMessage];
   });
 }
@@ -191,38 +167,55 @@ export function handleStreamEnd(
   deps: HandlerDependencies,
 ) {
   const completedContent = deps.streamingChunksRef.current.join("");
-  if (!completedContent.trim() && !deps.hasResponseRef.current) {
-    deps.setMessages((prev) => [
-      ...prev,
-      {
-        type: "message",
-        role: "assistant",
-        content: "No response received. Please try again.",
-        timestamp: new Date(),
-      },
-    ]);
-  }
+  // Only save message if there are uncommitted chunks
+  // (text_ended already saved if there were tool calls)
   if (completedContent.trim()) {
+    console.log(
+      "[Stream End] Saving remaining streamed text as assistant message",
+    );
     const assistantMessage: ChatMessageData = {
       type: "message",
       role: "assistant",
       content: completedContent,
       timestamp: new Date(),
     };
-    deps.setMessages((prev) => [...prev, assistantMessage]);
+    deps.setMessages((prev) => {
+      const updated = [...prev, assistantMessage];
+      console.log("[Stream End] Final state:", {
+        localMessages: updated.map((m) => ({
+          type: m.type,
+          ...(m.type === "message" && {
+            role: m.role,
+            contentLength: m.content.length,
+          }),
+          ...(m.type === "tool_call" && {
+            toolId: m.toolId,
+            toolName: m.toolName,
+          }),
+          ...(m.type === "tool_response" && {
+            toolId: m.toolId,
+            toolName: m.toolName,
+            success: m.success,
+          }),
+        })),
+        streamingChunks: deps.streamingChunksRef.current,
+        timestamp: new Date().toISOString(),
+      });
+      return updated;
+    });
+  } else {
+    console.log("[Stream End] No uncommitted chunks, message already saved");
   }
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
   deps.setHasTextChunks(false);
   deps.setIsStreamingInitiated(false);
+  console.log("[Stream End] Stream complete, messages in local state");
 }
 
 export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
   const errorMessage = chunk.message || chunk.content || "An error occurred";
   console.error("Stream error:", errorMessage);
-  if (isRegionBlockedError(chunk)) {
-    deps.setIsRegionBlockedModalOpen(true);
-  }
   deps.setIsStreamingInitiated(false);
   deps.setHasTextChunks(false);
   deps.setStreamingChunks([]);

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/useChatContainer.ts

@@ -1,17 +1,14 @@
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import { useCallback, useMemo, useRef, useState } from "react";
 import { toast } from "sonner";
 import { useChatStream } from "../../useChatStream";
-import { usePageContext } from "../../usePageContext";
 import type { ChatMessageData } from "../ChatMessage/useChatMessage";
 import { createStreamEventDispatcher } from "./createStreamEventDispatcher";
 import {
   createUserMessage,
   filterAuthMessages,
-  hasSentInitialPrompt,
   isToolCallArray,
   isValidMessage,
-  markInitialPromptSent,
   parseToolResponse,
   removePageContext,
 } from "./helpers";
@@ -19,45 +16,20 @@ import {
 interface Args {
   sessionId: string | null;
   initialMessages: SessionDetailResponse["messages"];
-  initialPrompt?: string;
 }
 
-export function useChatContainer({
-  sessionId,
-  initialMessages,
-  initialPrompt,
-}: Args) {
+export function useChatContainer({ sessionId, initialMessages }: Args) {
   const [messages, setMessages] = useState<ChatMessageData[]>([]);
   const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
   const [hasTextChunks, setHasTextChunks] = useState(false);
   const [isStreamingInitiated, setIsStreamingInitiated] = useState(false);
-  const [isRegionBlockedModalOpen, setIsRegionBlockedModalOpen] =
-    useState(false);
-  const hasResponseRef = useRef(false);
   const streamingChunksRef = useRef<string[]>([]);
-  const previousSessionIdRef = useRef<string | null>(null);
-  const {
-    error,
-    sendMessage: sendStreamMessage,
-    stopStreaming,
-  } = useChatStream();
+  const { error, sendMessage: sendStreamMessage } = useChatStream();
   const isStreaming = isStreamingInitiated || hasTextChunks;
 
-  useEffect(() => {
-    if (sessionId !== previousSessionIdRef.current) {
-      stopStreaming(previousSessionIdRef.current ?? undefined, true);
-      previousSessionIdRef.current = sessionId;
-      setMessages([]);
-      setStreamingChunks([]);
-      streamingChunksRef.current = [];
-      setHasTextChunks(false);
-      setIsStreamingInitiated(false);
-      hasResponseRef.current = false;
-    }
-  }, [sessionId, stopStreaming]);
-
   const allMessages = useMemo(() => {
     const processedInitialMessages: ChatMessageData[] = [];
+    // Map to track tool calls by their ID so we can look up tool names for tool responses
     const toolCallMap = new Map<string, string>();
 
     for (const msg of initialMessages) {
@@ -73,9 +45,13 @@ export function useChatContainer({
         ? new Date(msg.timestamp as string)
         : undefined;
 
+      // Remove page context from user messages when loading existing sessions
       if (role === "user") {
         content = removePageContext(content);
-        if (!content.trim()) continue;
+        // Skip user messages that become empty after removing page context
+        if (!content.trim()) {
+          continue;
+        }
         processedInitialMessages.push({
           type: "message",
           role: "user",
@@ -85,15 +61,19 @@ export function useChatContainer({
         continue;
       }
 
+      // Handle assistant messages first (before tool messages) to build tool call map
       if (role === "assistant") {
+        // Strip <thinking> tags from content
         content = content
           .replace(/<thinking>[\s\S]*?<\/thinking>/gi, "")
           .trim();
 
+        // If assistant has tool calls, create tool_call messages for each
         if (toolCalls && isToolCallArray(toolCalls) && toolCalls.length > 0) {
           for (const toolCall of toolCalls) {
             const toolName = toolCall.function.name;
             const toolId = toolCall.id;
+            // Store tool name for later lookup
             toolCallMap.set(toolId, toolName);
 
             try {
@@ -116,6 +96,7 @@ export function useChatContainer({
               });
             }
           }
+          // Only add assistant message if there's content after stripping thinking tags
           if (content.trim()) {
             processedInitialMessages.push({
               type: "message",
@@ -125,6 +106,7 @@ export function useChatContainer({
             });
           }
         } else if (content.trim()) {
+          // Assistant message without tool calls, but with content
           processedInitialMessages.push({
             type: "message",
             role: "assistant",
@@ -135,6 +117,7 @@ export function useChatContainer({
         continue;
       }
 
+      // Handle tool messages - look up tool name from tool call map
       if (role === "tool") {
         const toolCallId = (msg.tool_call_id as string) || "";
         const toolName = toolCallMap.get(toolCallId) || "unknown";
@@ -150,6 +133,7 @@ export function useChatContainer({
         continue;
       }
 
+      // Handle other message types (system, etc.)
       if (content.trim()) {
         processedInitialMessages.push({
           type: "message",
@@ -170,10 +154,9 @@ export function useChatContainer({
       context?: { url: string; content: string },
     ) {
       if (!sessionId) {
-        console.error("[useChatContainer] Cannot send message: no session ID");
+        console.error("Cannot send message: no session ID");
         return;
       }
-      setIsRegionBlockedModalOpen(false);
       if (isUserMessage) {
         const userMessage = createUserMessage(content);
         setMessages((prev) => [...filterAuthMessages(prev), userMessage]);
@@ -184,19 +167,14 @@ export function useChatContainer({
       streamingChunksRef.current = [];
       setHasTextChunks(false);
       setIsStreamingInitiated(true);
-      hasResponseRef.current = false;
-
       const dispatcher = createStreamEventDispatcher({
         setHasTextChunks,
         setStreamingChunks,
         streamingChunksRef,
-        hasResponseRef,
         setMessages,
-        setIsRegionBlockedModalOpen,
         sessionId,
         setIsStreamingInitiated,
       });
-
       try {
         await sendStreamMessage(
           sessionId,
@@ -206,12 +184,8 @@ export function useChatContainer({
           context,
         );
       } catch (err) {
-        console.error("[useChatContainer] Failed to send message:", err);
+        console.error("Failed to send message:", err);
         setIsStreamingInitiated(false);
-
-        // Don't show error toast for AbortError (expected during cleanup)
-        if (err instanceof Error && err.name === "AbortError") return;
-
         const errorMessage =
           err instanceof Error ? err.message : "Failed to send message";
         toast.error("Failed to send message", {
@@ -222,63 +196,11 @@ export function useChatContainer({
     [sessionId, sendStreamMessage],
   );
 
-  const handleStopStreaming = useCallback(() => {
-    stopStreaming();
-    setStreamingChunks([]);
-    streamingChunksRef.current = [];
-    setHasTextChunks(false);
-    setIsStreamingInitiated(false);
-  }, [stopStreaming]);
-
-  const { capturePageContext } = usePageContext();
-
-  // Send initial prompt if provided (for new sessions from homepage)
-  useEffect(
-    function handleInitialPrompt() {
-      if (!initialPrompt || !sessionId) return;
-      if (initialMessages.length > 0) return;
-      if (hasSentInitialPrompt(sessionId)) return;
-
-      markInitialPromptSent(sessionId);
-      const context = capturePageContext();
-      sendMessage(initialPrompt, true, context);
-    },
-    [
-      initialPrompt,
-      sessionId,
-      initialMessages.length,
-      sendMessage,
-      capturePageContext,
-    ],
-  );
-
-  async function sendMessageWithContext(
-    content: string,
-    isUserMessage: boolean = true,
-  ) {
-    const context = capturePageContext();
-    await sendMessage(content, isUserMessage, context);
-  }
-
-  function handleRegionModalOpenChange(open: boolean) {
-    setIsRegionBlockedModalOpen(open);
-  }
-
-  function handleRegionModalClose() {
-    setIsRegionBlockedModalOpen(false);
-  }
-
   return {
     messages: allMessages,
     streamingChunks,
     isStreaming,
     error,
-    isRegionBlockedModalOpen,
-    setIsRegionBlockedModalOpen,
-    sendMessageWithContext,
-    handleRegionModalOpenChange,
-    handleRegionModalClose,
     sendMessage,
-    stopStreaming: handleStopStreaming,
   };
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatInput/ChatInput.tsx

@@ -0,0 +1,64 @@
+import { Input } from "@/components/atoms/Input/Input";
+import { cn } from "@/lib/utils";
+import { ArrowUpIcon } from "@phosphor-icons/react";
+import { useChatInput } from "./useChatInput";
+
+export interface ChatInputProps {
+  onSend: (message: string) => void;
+  disabled?: boolean;
+  placeholder?: string;
+  className?: string;
+}
+
+export function ChatInput({
+  onSend,
+  disabled = false,
+  placeholder = "Type your message...",
+  className,
+}: ChatInputProps) {
+  const inputId = "chat-input";
+  const { value, setValue, handleKeyDown, handleSend } = useChatInput({
+    onSend,
+    disabled,
+    maxRows: 5,
+    inputId,
+  });
+
+  return (
+    <div className={cn("relative flex-1", className)}>
+      <Input
+        id={inputId}
+        label="Chat message input"
+        hideLabel
+        type="textarea"
+        value={value}
+        onChange={(e) => setValue(e.target.value)}
+        onKeyDown={handleKeyDown}
+        placeholder={placeholder}
+        disabled={disabled}
+        rows={1}
+        wrapperClassName="mb-0 relative"
+        className="pr-12"
+      />
+      <span id="chat-input-hint" className="sr-only">
+        Press Enter to send, Shift+Enter for new line
+      </span>
+
+      <button
+        onClick={handleSend}
+        disabled={disabled || !value.trim()}
+        className={cn(
+          "absolute right-3 top-1/2 flex h-8 w-8 -translate-y-1/2 items-center justify-center rounded-full",
+          "border border-zinc-800 bg-zinc-800 text-white",
+          "hover:border-zinc-900 hover:bg-zinc-900",
+          "disabled:border-zinc-200 disabled:bg-zinc-200 disabled:text-white disabled:opacity-50",
+          "transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-neutral-950",
+          "disabled:pointer-events-none",
+        )}
+        aria-label="Send message"
+      >
+        <ArrowUpIcon className="h-3 w-3" weight="bold" />
+      </button>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatInput/useChatInput.ts

@@ -0,0 +1,60 @@
+import { KeyboardEvent, useCallback, useEffect, useState } from "react";
+
+interface UseChatInputArgs {
+  onSend: (message: string) => void;
+  disabled?: boolean;
+  maxRows?: number;
+  inputId?: string;
+}
+
+export function useChatInput({
+  onSend,
+  disabled = false,
+  maxRows = 5,
+  inputId = "chat-input",
+}: UseChatInputArgs) {
+  const [value, setValue] = useState("");
+
+  useEffect(() => {
+    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
+    if (!textarea) return;
+    textarea.style.height = "auto";
+    const lineHeight = parseInt(
+      window.getComputedStyle(textarea).lineHeight,
+      10,
+    );
+    const maxHeight = lineHeight * maxRows;
+    const newHeight = Math.min(textarea.scrollHeight, maxHeight);
+    textarea.style.height = `${newHeight}px`;
+    textarea.style.overflowY =
+      textarea.scrollHeight > maxHeight ? "auto" : "hidden";
+  }, [value, maxRows, inputId]);
+
+  const handleSend = useCallback(() => {
+    if (disabled || !value.trim()) return;
+    onSend(value.trim());
+    setValue("");
+    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
+    if (textarea) {
+      textarea.style.height = "auto";
+    }
+  }, [value, onSend, disabled, inputId]);
+
+  const handleKeyDown = useCallback(
+    (event: KeyboardEvent<HTMLInputElement | HTMLTextAreaElement>) => {
+      if (event.key === "Enter" && !event.shiftKey) {
+        event.preventDefault();
+        handleSend();
+      }
+      // Shift+Enter allows default behavior (new line) - no need to handle explicitly
+    },
+    [handleSend],
+  );
+
+  return {
+    value,
+    setValue,
+    handleKeyDown,
+    handleSend,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatMessage/ChatMessage.tsx

@@ -1,65 +1,48 @@
 "use client";
 
+import { useGetV2GetUserProfile } from "@/app/api/__generated__/endpoints/store/store";
+import Avatar, {
+  AvatarFallback,
+  AvatarImage,
+} from "@/components/atoms/Avatar/Avatar";
 import { Button } from "@/components/atoms/Button/Button";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { cn } from "@/lib/utils";
 import {
-  ArrowsClockwiseIcon,
+  ArrowClockwise,
   CheckCircleIcon,
   CheckIcon,
   CopyIcon,
+  RobotIcon,
 } from "@phosphor-icons/react";
 import { useRouter } from "next/navigation";
 import { useCallback, useState } from "react";
+import { getToolActionPhrase } from "../../helpers";
 import { AgentCarouselMessage } from "../AgentCarouselMessage/AgentCarouselMessage";
-import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
 import { AuthPromptWidget } from "../AuthPromptWidget/AuthPromptWidget";
 import { ChatCredentialsSetup } from "../ChatCredentialsSetup/ChatCredentialsSetup";
 import { ExecutionStartedMessage } from "../ExecutionStartedMessage/ExecutionStartedMessage";
 import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
 import { NoResultsMessage } from "../NoResultsMessage/NoResultsMessage";
 import { ToolCallMessage } from "../ToolCallMessage/ToolCallMessage";
 import { ToolResponseMessage } from "../ToolResponseMessage/ToolResponseMessage";
-import { UserChatBubble } from "../UserChatBubble/UserChatBubble";
 import { useChatMessage, type ChatMessageData } from "./useChatMessage";
-
-function stripInternalReasoning(content: string): string {
-  const cleaned = content.replace(
-    /<internal_reasoning>[\s\S]*?<\/internal_reasoning>/gi,
-    "",
-  );
-  return cleaned.replace(/\n{3,}/g, "\n\n").trim();
-}
-
-function getDisplayContent(message: ChatMessageData, isUser: boolean): string {
-  if (message.type !== "message") return "";
-  if (isUser) return message.content;
-  return stripInternalReasoning(message.content);
-}
-
 export interface ChatMessageProps {
   message: ChatMessageData;
-  messages?: ChatMessageData[];
-  index?: number;
-  isStreaming?: boolean;
   className?: string;
   onDismissLogin?: () => void;
   onDismissCredentials?: () => void;
   onSendMessage?: (content: string, isUserMessage?: boolean) => void;
   agentOutput?: ChatMessageData;
-  isFinalMessage?: boolean;
 }
 
 export function ChatMessage({
   message,
-  messages = [],
-  index = -1,
-  isStreaming = false,
   className,
   onDismissCredentials,
   onSendMessage,
   agentOutput,
-  isFinalMessage = true,
 }: ChatMessageProps) {
   const { user } = useSupabase();
   const router = useRouter();
@@ -71,7 +54,14 @@ export function ChatMessage({
     isLoginNeeded,
     isCredentialsNeeded,
   } = useChatMessage(message);
-  const displayContent = getDisplayContent(message, isUser);
+
+  const { data: profile } = useGetV2GetUserProfile({
+    query: {
+      select: (res) => (res.status === 200 ? res.data : null),
+      enabled: isUser && !!user,
+      queryKey: ["/api/store/profile", user?.id],
+    },
+  });
 
   const handleAllCredentialsComplete = useCallback(
     function handleAllCredentialsComplete() {
@@ -97,25 +87,17 @@ export function ChatMessage({
     }
   }
 
-  const handleCopy = useCallback(
-    async function handleCopy() {
-      if (message.type !== "message") return;
-      if (!displayContent) return;
+  const handleCopy = useCallback(async () => {
+    if (message.type !== "message") return;
 
-      try {
-        await navigator.clipboard.writeText(displayContent);
-        setCopied(true);
-        setTimeout(() => setCopied(false), 2000);
-      } catch (error) {
-        console.error("Failed to copy:", error);
-      }
-    },
-    [displayContent, message],
-  );
-
-  function isLongResponse(content: string): boolean {
-    return content.split("\n").length > 5;
-  }
+    try {
+      await navigator.clipboard.writeText(message.content);
+      setCopied(true);
+      setTimeout(() => setCopied(false), 2000);
+    } catch (error) {
+      console.error("Failed to copy:", error);
+    }
+  }, [message]);
 
   const handleTryAgain = useCallback(() => {
     if (message.type !== "message" || !onSendMessage) return;
@@ -187,45 +169,9 @@ export function ChatMessage({
 
   // Render tool call messages
   if (isToolCall && message.type === "tool_call") {
-    // Check if this tool call is currently streaming
-    // A tool call is streaming if:
-    // 1. isStreaming is true
-    // 2. This is the last tool_call message
-    // 3. There's no tool_response for this tool call yet
-    const isToolCallStreaming =
-      isStreaming &&
-      index >= 0 &&
-      (() => {
-        // Find the last tool_call index
-        let lastToolCallIndex = -1;
-        for (let i = messages.length - 1; i >= 0; i--) {
-          if (messages[i].type === "tool_call") {
-            lastToolCallIndex = i;
-            break;
-          }
-        }
-        // Check if this is the last tool_call and there's no response yet
-        if (index === lastToolCallIndex) {
-          // Check if there's a tool_response for this tool call
-          const hasResponse = messages
-            .slice(index + 1)
-            .some(
-              (msg) =>
-                msg.type === "tool_response" && msg.toolId === message.toolId,
-            );
-          return !hasResponse;
-        }
-        return false;
-      })();
-
     return (
       <div className={cn("px-4 py-2", className)}>
-        <ToolCallMessage
-          toolId={message.toolId}
-          toolName={message.toolName}
-          arguments={message.arguments}
-          isStreaming={isToolCallStreaming}
-        />
+        <ToolCallMessage toolName={message.toolName} />
       </div>
     );
   }
@@ -272,11 +218,27 @@ export function ChatMessage({
 
   // Render tool response messages (but skip agent_output if it's being rendered inside assistant message)
   if (isToolResponse && message.type === "tool_response") {
+    // Check if this is an agent_output that should be rendered inside assistant message
+    if (message.result) {
+      let parsedResult: Record<string, unknown> | null = null;
+      try {
+        parsedResult =
+          typeof message.result === "string"
+            ? JSON.parse(message.result)
+            : (message.result as Record<string, unknown>);
+      } catch {
+        parsedResult = null;
+      }
+      if (parsedResult?.type === "agent_output") {
+        // Skip rendering - this will be rendered inside the assistant message
+        return null;
+      }
+    }
+
     return (
       <div className={cn("px-4 py-2", className)}>
         <ToolResponseMessage
-          toolId={message.toolId}
-          toolName={message.toolName}
+          toolName={getToolActionPhrase(message.toolName)}
           result={message.result}
         />
       </div>
@@ -294,33 +256,40 @@ export function ChatMessage({
         )}
       >
         <div className="flex w-full max-w-3xl gap-3">
+          {!isUser && (
+            <div className="flex-shrink-0">
+              <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
+                <RobotIcon className="h-4 w-4 text-indigo-50" />
+              </div>
+            </div>
+          )}
+
           <div
             className={cn(
               "flex min-w-0 flex-1 flex-col",
               isUser && "items-end",
             )}
           >
-            {isUser ? (
-              <UserChatBubble>
-                <MarkdownContent content={displayContent} />
-              </UserChatBubble>
-            ) : (
-              <AIChatBubble>
-                <MarkdownContent content={displayContent} />
-                {agentOutput && agentOutput.type === "tool_response" && (
+            <MessageBubble variant={isUser ? "user" : "assistant"}>
+              <MarkdownContent content={message.content} />
+              {agentOutput &&
+                agentOutput.type === "tool_response" &&
+                !isUser && (
                   <div className="mt-4">
                     <ToolResponseMessage
-                      toolId={agentOutput.toolId}
-                      toolName={agentOutput.toolName || "Agent Output"}
+                      toolName={
+                        agentOutput.toolName
+                          ? getToolActionPhrase(agentOutput.toolName)
+                          : "Agent Output"
+                      }
                       result={agentOutput.result}
                     />
                   </div>
                 )}
-              </AIChatBubble>
-            )}
+            </MessageBubble>
             <div
               className={cn(
-                "flex gap-0",
+                "mt-1 flex gap-1",
                 isUser ? "justify-end" : "justify-start",
               )}
             >
@@ -331,25 +300,37 @@ export function ChatMessage({
                   onClick={handleTryAgain}
                   aria-label="Try again"
                 >
-                  <ArrowsClockwiseIcon className="size-4 text-zinc-600" />
-                </Button>
-              )}
-              {!isUser && isFinalMessage && isLongResponse(displayContent) && (
-                <Button
-                  variant="ghost"
-                  size="icon"
-                  onClick={handleCopy}
-                  aria-label="Copy message"
-                >
-                  {copied ? (
-                    <CheckIcon className="size-4 text-green-600" />
-                  ) : (
-                    <CopyIcon className="size-4 text-zinc-600" />
-                  )}
+                  <ArrowClockwise className="size-3 text-neutral-500" />
                 </Button>
               )}
+              <Button
+                variant="ghost"
+                size="icon"
+                onClick={handleCopy}
+                aria-label="Copy message"
+              >
+                {copied ? (
+                  <CheckIcon className="size-3 text-green-600" />
+                ) : (
+                  <CopyIcon className="size-3 text-neutral-500" />
+                )}
+              </Button>
             </div>
           </div>
+
+          {isUser && (
+            <div className="flex-shrink-0">
+              <Avatar className="h-7 w-7">
+                <AvatarImage
+                  src={profile?.avatar_url ?? ""}
+                  alt={profile?.username ?? "User"}
+                />
+                <AvatarFallback className="rounded-lg bg-neutral-200 text-neutral-600">
+                  {profile?.username?.charAt(0)?.toUpperCase() || "U"}
+                </AvatarFallback>
+              </Avatar>
+            </div>
+          )}
         </div>
       </div>
     );

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/MessageBubble/MessageBubble.tsx

@@ -13,9 +13,10 @@ export function MessageBubble({
   className,
 }: MessageBubbleProps) {
   const userTheme = {
-    bg: "bg-purple-100",
-    border: "border-purple-100",
-    text: "text-slate-900",
+    bg: "bg-slate-900",
+    border: "border-slate-800",
+    gradient: "from-slate-900/30 via-slate-800/20 to-transparent",
+    text: "text-slate-50",
   };
 
   const assistantTheme = {
@@ -39,7 +40,9 @@ export function MessageBubble({
       )}
     >
       {/* Gradient flare background */}
-      <div className={cn("absolute inset-0 bg-gradient-to-br")} />
+      <div
+        className={cn("absolute inset-0 bg-gradient-to-br", theme.gradient)}
+      />
       <div
         className={cn(
           "relative z-10 transition-all duration-500 ease-in-out",

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/MessageList/MessageList.tsx

@@ -0,0 +1,121 @@
+"use client";
+
+import { cn } from "@/lib/utils";
+import { ChatMessage } from "../ChatMessage/ChatMessage";
+import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+import { StreamingMessage } from "../StreamingMessage/StreamingMessage";
+import { ThinkingMessage } from "../ThinkingMessage/ThinkingMessage";
+import { useMessageList } from "./useMessageList";
+
+export interface MessageListProps {
+  messages: ChatMessageData[];
+  streamingChunks?: string[];
+  isStreaming?: boolean;
+  className?: string;
+  onStreamComplete?: () => void;
+  onSendMessage?: (content: string) => void;
+}
+
+export function MessageList({
+  messages,
+  streamingChunks = [],
+  isStreaming = false,
+  className,
+  onStreamComplete,
+  onSendMessage,
+}: MessageListProps) {
+  const { messagesEndRef, messagesContainerRef } = useMessageList({
+    messageCount: messages.length,
+    isStreaming,
+  });
+
+  return (
+    <div
+      ref={messagesContainerRef}
+      className={cn(
+        "flex-1 overflow-y-auto",
+        "scrollbar-thin scrollbar-track-transparent scrollbar-thumb-zinc-300",
+        className,
+      )}
+    >
+      <div className="mx-auto flex max-w-3xl flex-col py-4">
+        {/* Render all persisted messages */}
+        {messages.map((message, index) => {
+          // Check if current message is an agent_output tool_response
+          // and if previous message is an assistant message
+          let agentOutput: ChatMessageData | undefined;
+
+          if (message.type === "tool_response" && message.result) {
+            let parsedResult: Record<string, unknown> | null = null;
+            try {
+              parsedResult =
+                typeof message.result === "string"
+                  ? JSON.parse(message.result)
+                  : (message.result as Record<string, unknown>);
+            } catch {
+              parsedResult = null;
+            }
+            if (parsedResult?.type === "agent_output") {
+              const prevMessage = messages[index - 1];
+              if (
+                prevMessage &&
+                prevMessage.type === "message" &&
+                prevMessage.role === "assistant"
+              ) {
+                // This agent output will be rendered inside the previous assistant message
+                // Skip rendering this message separately
+                return null;
+              }
+            }
+          }
+
+          // Check if next message is an agent_output tool_response to include in current assistant message
+          if (message.type === "message" && message.role === "assistant") {
+            const nextMessage = messages[index + 1];
+            if (
+              nextMessage &&
+              nextMessage.type === "tool_response" &&
+              nextMessage.result
+            ) {
+              let parsedResult: Record<string, unknown> | null = null;
+              try {
+                parsedResult =
+                  typeof nextMessage.result === "string"
+                    ? JSON.parse(nextMessage.result)
+                    : (nextMessage.result as Record<string, unknown>);
+              } catch {
+                parsedResult = null;
+              }
+              if (parsedResult?.type === "agent_output") {
+                agentOutput = nextMessage;
+              }
+            }
+          }
+
+          return (
+            <ChatMessage
+              key={index}
+              message={message}
+              onSendMessage={onSendMessage}
+              agentOutput={agentOutput}
+            />
+          );
+        })}
+
+        {/* Render thinking message when streaming but no chunks yet */}
+        {isStreaming && streamingChunks.length === 0 && <ThinkingMessage />}
+
+        {/* Render streaming message if active */}
+        {isStreaming && streamingChunks.length > 0 && (
+          <StreamingMessage
+            chunks={streamingChunks}
+            onComplete={onStreamComplete}
+          />
+        )}
+
+        {/* Invisible div to scroll to */}
+        <div ref={messagesEndRef} />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/SessionsDrawer/SessionsDrawer.tsx

@@ -81,9 +81,9 @@ export function SessionsDrawer({
                 </Text>
               </div>
             ) : sessions.length === 0 ? (
-              <div className="flex h-full items-center justify-center">
+              <div className="flex items-center justify-center py-8">
                 <Text variant="body" className="text-zinc-500">
-                  You don&apos;t have previously started chats
+                  No sessions found
                 </Text>
               </div>
             ) : (

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/StreamingMessage/StreamingMessage.tsx

@@ -1,6 +1,7 @@
 import { cn } from "@/lib/utils";
-import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
+import { RobotIcon } from "@phosphor-icons/react";
 import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
 import { useStreamingMessage } from "./useStreamingMessage";
 
 export interface StreamingMessageProps {
@@ -24,10 +25,16 @@ export function StreamingMessage({
       )}
     >
       <div className="flex w-full max-w-3xl gap-3">
+        <div className="flex-shrink-0">
+          <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-600">
+            <RobotIcon className="h-4 w-4 text-indigo-50" />
+          </div>
+        </div>
+
         <div className="flex min-w-0 flex-1 flex-col">
-          <AIChatBubble>
+          <MessageBubble variant="assistant">
             <MarkdownContent content={displayText} />
-          </AIChatBubble>
+          </MessageBubble>
         </div>
       </div>
     </div>

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ThinkingMessage/ThinkingMessage.tsx

@@ -1,7 +1,7 @@
 import { cn } from "@/lib/utils";
+import { RobotIcon } from "@phosphor-icons/react";
 import { useEffect, useRef, useState } from "react";
-import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
-import { ChatLoader } from "../ChatLoader/ChatLoader";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
 
 export interface ThinkingMessageProps {
   className?: string;
@@ -34,11 +34,22 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
       )}
     >
       <div className="flex w-full max-w-3xl gap-3">
+        <div className="flex-shrink-0">
+          <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
+            <RobotIcon className="h-4 w-4 text-indigo-50" />
+          </div>
+        </div>
+
         <div className="flex min-w-0 flex-1 flex-col">
-          <AIChatBubble>
+          <MessageBubble variant="assistant">
             <div className="transition-all duration-500 ease-in-out">
               {showSlowLoader ? (
-                <ChatLoader />
+                <div className="flex flex-col items-center gap-3 py-2">
+                  <div className="loader" style={{ flexShrink: 0 }} />
+                  <p className="text-sm text-slate-700">
+                    Taking a bit longer to think, wait a moment please
+                  </p>
+                </div>
               ) : (
                 <span
                   className="inline-block bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-clip-text text-transparent"
@@ -51,7 +62,7 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
                 </span>
               )}
             </div>
-          </AIChatBubble>
+          </MessageBubble>
         </div>
       </div>
     </div>

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ToolCallMessage/ToolCallMessage.tsx

@@ -0,0 +1,24 @@
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+import { WrenchIcon } from "@phosphor-icons/react";
+import { getToolActionPhrase } from "../../helpers";
+
+export interface ToolCallMessageProps {
+  toolName: string;
+  className?: string;
+}
+
+export function ToolCallMessage({ toolName, className }: ToolCallMessageProps) {
+  return (
+    <div className={cn("flex items-center justify-center gap-2", className)}>
+      <WrenchIcon
+        size={14}
+        weight="bold"
+        className="flex-shrink-0 text-neutral-500"
+      />
+      <Text variant="small" className="text-neutral-500">
+        {getToolActionPhrase(toolName)}...
+      </Text>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ToolResponseMessage/ToolResponseMessage.tsx

@@ -0,0 +1,260 @@
+import { Text } from "@/components/atoms/Text/Text";
+import "@/components/contextual/OutputRenderers";
+import {
+  globalRegistry,
+  OutputItem,
+} from "@/components/contextual/OutputRenderers";
+import { cn } from "@/lib/utils";
+import type { ToolResult } from "@/types/chat";
+import { WrenchIcon } from "@phosphor-icons/react";
+import { getToolActionPhrase } from "../../helpers";
+
+export interface ToolResponseMessageProps {
+  toolName: string;
+  result?: ToolResult;
+  success?: boolean;
+  className?: string;
+}
+
+export function ToolResponseMessage({
+  toolName,
+  result,
+  success: _success = true,
+  className,
+}: ToolResponseMessageProps) {
+  if (!result) {
+    return (
+      <div className={cn("flex items-center justify-center gap-2", className)}>
+        <WrenchIcon
+          size={14}
+          weight="bold"
+          className="flex-shrink-0 text-neutral-500"
+        />
+        <Text variant="small" className="text-neutral-500">
+          {getToolActionPhrase(toolName)}...
+        </Text>
+      </div>
+    );
+  }
+
+  let parsedResult: Record<string, unknown> | null = null;
+  try {
+    parsedResult =
+      typeof result === "string"
+        ? JSON.parse(result)
+        : (result as Record<string, unknown>);
+  } catch {
+    parsedResult = null;
+  }
+
+  if (parsedResult && typeof parsedResult === "object") {
+    const responseType = parsedResult.type as string | undefined;
+
+    if (responseType === "agent_output") {
+      const execution = parsedResult.execution as
+        | {
+            outputs?: Record<string, unknown[]>;
+          }
+        | null
+        | undefined;
+      const outputs = execution?.outputs || {};
+      const message = parsedResult.message as string | undefined;
+
+      return (
+        <div className={cn("space-y-4 px-4 py-2", className)}>
+          <div className="flex items-center gap-2">
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {getToolActionPhrase(toolName)}
+            </Text>
+          </div>
+          {message && (
+            <div className="rounded border p-4">
+              <Text variant="small" className="text-neutral-600">
+                {message}
+              </Text>
+            </div>
+          )}
+          {Object.keys(outputs).length > 0 && (
+            <div className="space-y-4">
+              {Object.entries(outputs).map(([outputName, values]) =>
+                values.map((value, index) => {
+                  const renderer = globalRegistry.getRenderer(value);
+                  if (renderer) {
+                    return (
+                      <OutputItem
+                        key={`${outputName}-${index}`}
+                        value={value}
+                        renderer={renderer}
+                        label={outputName}
+                      />
+                    );
+                  }
+                  return (
+                    <div
+                      key={`${outputName}-${index}`}
+                      className="rounded border p-4"
+                    >
+                      <Text variant="large-medium" className="mb-2 capitalize">
+                        {outputName}
+                      </Text>
+                      <pre className="overflow-auto text-sm">
+                        {JSON.stringify(value, null, 2)}
+                      </pre>
+                    </div>
+                  );
+                }),
+              )}
+            </div>
+          )}
+        </div>
+      );
+    }
+
+    if (responseType === "block_output" && parsedResult.outputs) {
+      const outputs = parsedResult.outputs as Record<string, unknown[]>;
+
+      return (
+        <div className={cn("space-y-4 px-4 py-2", className)}>
+          <div className="flex items-center gap-2">
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {getToolActionPhrase(toolName)}
+            </Text>
+          </div>
+          <div className="space-y-4">
+            {Object.entries(outputs).map(([outputName, values]) =>
+              values.map((value, index) => {
+                const renderer = globalRegistry.getRenderer(value);
+                if (renderer) {
+                  return (
+                    <OutputItem
+                      key={`${outputName}-${index}`}
+                      value={value}
+                      renderer={renderer}
+                      label={outputName}
+                    />
+                  );
+                }
+                return (
+                  <div
+                    key={`${outputName}-${index}`}
+                    className="rounded border p-4"
+                  >
+                    <Text variant="large-medium" className="mb-2 capitalize">
+                      {outputName}
+                    </Text>
+                    <pre className="overflow-auto text-sm">
+                      {JSON.stringify(value, null, 2)}
+                    </pre>
+                  </div>
+                );
+              }),
+            )}
+          </div>
+        </div>
+      );
+    }
+
+    // Handle other response types with a message field (e.g., understanding_updated)
+    if (parsedResult.message && typeof parsedResult.message === "string") {
+      // Format tool name from snake_case to Title Case
+      const formattedToolName = toolName
+        .split("_")
+        .map((word) => word.charAt(0).toUpperCase() + word.slice(1))
+        .join(" ");
+
+      // Clean up message - remove incomplete user_name references
+      let cleanedMessage = parsedResult.message;
+      // Remove "Updated understanding with: user_name" pattern if user_name is just a placeholder
+      cleanedMessage = cleanedMessage.replace(
+        /Updated understanding with:\s*user_name\.?\s*/gi,
+        "",
+      );
+      // Remove standalone user_name references
+      cleanedMessage = cleanedMessage.replace(/\buser_name\b\.?\s*/gi, "");
+      cleanedMessage = cleanedMessage.trim();
+
+      // Only show message if it has content after cleaning
+      if (!cleanedMessage) {
+        return (
+          <div
+            className={cn(
+              "flex items-center justify-center gap-2 px-4 py-2",
+              className,
+            )}
+          >
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {formattedToolName}
+            </Text>
+          </div>
+        );
+      }
+
+      return (
+        <div className={cn("space-y-2 px-4 py-2", className)}>
+          <div className="flex items-center justify-center gap-2">
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {formattedToolName}
+            </Text>
+          </div>
+          <div className="rounded border p-4">
+            <Text variant="small" className="text-neutral-600">
+              {cleanedMessage}
+            </Text>
+          </div>
+        </div>
+      );
+    }
+  }
+
+  const renderer = globalRegistry.getRenderer(result);
+  if (renderer) {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <div className="mb-2 flex items-center gap-2">
+          <WrenchIcon
+            size={14}
+            weight="bold"
+            className="flex-shrink-0 text-neutral-500"
+          />
+          <Text variant="small" className="text-neutral-500">
+            {getToolActionPhrase(toolName)}
+          </Text>
+        </div>
+        <OutputItem value={result} renderer={renderer} />
+      </div>
+    );
+  }
+
+  return (
+    <div className={cn("flex items-center justify-center gap-2", className)}>
+      <WrenchIcon
+        size={14}
+        weight="bold"
+        className="flex-shrink-0 text-neutral-500"
+      />
+      <Text variant="small" className="text-neutral-500">
+        {getToolActionPhrase(toolName)}...
+      </Text>
+    </div>
+  );
+}