fix(frontend): reset input value when auto-approve toggle changes

- Pass external data value from parent to child component
- Add useEffect to sync child's internal state with parent's reviewDataMap
- Fixes issue where toggling auto-approve didn't reset edited input values
- Input now correctly reverts to original payload when auto-approve is enabled

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

@@ -128,7 +128,7 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           exitOnceUploaded: true
 
-  test:
+  e2e_test:
     runs-on: big-boi
     needs: setup
     strategy:
@@ -258,3 +258,39 @@ 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

@@ -16,6 +16,32 @@ 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 feature development:**
+### Frontend guidelines:
 
 See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 
@@ -217,6 +217,14 @@ 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

@@ -1,251 +0,0 @@
-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

@@ -290,6 +290,11 @@ 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; raises NotFoundError if not found.
+        SessionDetailResponse: Details for the requested session, or None 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(
@@ -222,6 +222,8 @@ 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,
@@ -230,7 +232,26 @@ 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"
 
@@ -275,6 +296,8 @@ 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,
@@ -282,7 +305,26 @@ 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

@@ -1,12 +1,20 @@
 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, RateLimitError
+from openai import (
+    APIConnectionError,
+    APIError,
+    APIStatusError,
+    PermissionDeniedError,
+    RateLimitError,
+)
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -21,6 +29,7 @@ from .model import (
     ChatMessage,
     ChatSession,
     Usage,
+    cache_chat_session,
     get_chat_session,
     update_session_title,
     upsert_chat_session,
@@ -296,6 +305,10 @@ 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
@@ -332,6 +345,23 @@ 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
@@ -390,10 +420,42 @@ 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(
@@ -413,6 +475,27 @@ 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)
 
@@ -434,14 +517,19 @@ 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 assistant_response.content or assistant_response.tool_calls:
+                    if not has_appended_streaming_message and (
+                        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)
 
-                    session.messages.extend(messages_to_save)
-                    await upsert_chat_session(session)
+                    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)
 
                     if not has_yielded_error:
                         error_message = str(e)
@@ -472,38 +560,49 @@ 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
-            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
+            # Only save if we haven't already saved when StreamFinish was received
+            if not has_saved_assistant_message:
                 logger.info(
-                    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 [])}"
+                    f"Normal completion path: session={session.session_id}, "
+                    f"current message_count={len(session.messages)}"
                 )
 
-            # 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)}"
-            )
+                # Build the messages list in the correct order
+                messages_to_save: list[ChatMessage] = []
 
-            session.messages.extend(messages_to_save)
-            logger.info(
-                f"Extended session messages, new message_count={len(session.messages)}"
-            )
-            await upsert_chat_session(session)
+                # 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"
+                )
 
             # If we did a tool call, stream the chat completion again to get the next response
             if has_done_tool_call:
@@ -545,6 +644,12 @@ 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],
@@ -737,7 +842,18 @@ async def _stream_chat_chunks(
                         f"Error in stream (not retrying): {e!s}",
                         exc_info=True,
                     )
-                    error_response = StreamError(errorText=str(e))
+                    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,
+                    )
                     yield error_response
                     yield StreamFinish()
                     return

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

@@ -218,6 +218,7 @@ 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/executions/review/model.py

@@ -23,6 +23,7 @@ class PendingHumanReviewModel(BaseModel):
         id: Unique identifier for the review record
         user_id: ID of the user who must perform the review
         node_exec_id: ID of the node execution that created this review
+        node_id: ID of the node definition (for grouping reviews from same node)
         graph_exec_id: ID of the graph execution containing the node
         graph_id: ID of the graph template being executed
         graph_version: Version number of the graph template
@@ -37,6 +38,10 @@ class PendingHumanReviewModel(BaseModel):
     """
 
     node_exec_id: str = Field(description="Node execution ID (primary key)")
+    node_id: str = Field(
+        description="Node definition ID (for grouping)",
+        default="",  # Temporary default for test compatibility
+    )
     user_id: str = Field(description="User ID associated with the review")
     graph_exec_id: str = Field(description="Graph execution ID")
     graph_id: str = Field(description="Graph ID")
@@ -66,7 +71,9 @@ class PendingHumanReviewModel(BaseModel):
     )
 
     @classmethod
-    def from_db(cls, review: "PendingHumanReview") -> "PendingHumanReviewModel":
+    def from_db(
+        cls, review: "PendingHumanReview", node_id: str
+    ) -> "PendingHumanReviewModel":
         """
         Convert a database model to a response model.
 
@@ -74,9 +81,14 @@ class PendingHumanReviewModel(BaseModel):
         payload, instructions, and editable flag.
 
         Handles invalid data gracefully by using safe defaults.
+
+        Args:
+            review: Database review object
+            node_id: Node definition ID (fetched from NodeExecution)
         """
         return cls(
             node_exec_id=review.nodeExecId,
+            node_id=node_id,
             user_id=review.userId,
             graph_exec_id=review.graphExecId,
             graph_id=review.graphId,
@@ -107,6 +119,13 @@ class ReviewItem(BaseModel):
     reviewed_data: SafeJsonData | None = Field(
         None, description="Optional edited data (ignored if approved=False)"
     )
+    auto_approve_future: bool = Field(
+        default=False,
+        description=(
+            "If true and this review is approved, future executions of this same "
+            "block (node) will be automatically approved. This only affects approved reviews."
+        ),
+    )
 
     @field_validator("reviewed_data")
     @classmethod
@@ -174,6 +193,9 @@ class ReviewRequest(BaseModel):
     This request must include ALL pending reviews for a graph execution.
     Each review will be either approved (with optional data modifications)
     or rejected (data ignored). The execution will resume only after ALL reviews are processed.
+
+    Each review item can individually specify whether to auto-approve future executions
+    of the same block via the `auto_approve_future` field on ReviewItem.
     """
 
     reviews: List[ReviewItem] = Field(

autogpt_platform/backend/backend/api/features/executions/review/review_routes_test.py

@@ -8,6 +8,12 @@ from prisma.enums import ReviewStatus
 from pytest_snapshot.plugin import Snapshot
 
 from backend.api.rest_api import handle_internal_http_error
+from backend.data.execution import (
+    ExecutionContext,
+    ExecutionStatus,
+    NodeExecutionResult,
+)
+from backend.data.graph import GraphSettings
 
 from .model import PendingHumanReviewModel
 from .routes import router
@@ -15,20 +21,24 @@ from .routes import router
 # Using a fixed timestamp for reproducible tests
 FIXED_NOW = datetime.datetime(2023, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)
 
-app = fastapi.FastAPI()
-app.include_router(router, prefix="/api/review")
-app.add_exception_handler(ValueError, handle_internal_http_error(400))
 
-client = fastapi.testclient.TestClient(app)
+@pytest.fixture
+def app():
+    """Create FastAPI app for testing"""
+    test_app = fastapi.FastAPI()
+    test_app.include_router(router, prefix="/api/review")
+    test_app.add_exception_handler(ValueError, handle_internal_http_error(400))
+    return test_app
 
 
-@pytest.fixture(autouse=True)
-def setup_app_auth(mock_jwt_user):
-    """Setup auth overrides for all tests in this module"""
+@pytest.fixture
+def client(app, mock_jwt_user):
+    """Create test client with auth overrides"""
     from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
-    yield
+    with fastapi.testclient.TestClient(app) as test_client:
+        yield test_client
     app.dependency_overrides.clear()
 
 
@@ -37,6 +47,7 @@ def sample_pending_review(test_user_id: str) -> PendingHumanReviewModel:
     """Create a sample pending review for testing"""
     return PendingHumanReviewModel(
         node_exec_id="test_node_123",
+        node_id="test_node_def_456",
         user_id=test_user_id,
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
@@ -55,6 +66,7 @@ def sample_pending_review(test_user_id: str) -> PendingHumanReviewModel:
 
 
 def test_get_pending_reviews_empty(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     snapshot: Snapshot,
     test_user_id: str,
@@ -73,6 +85,7 @@ def test_get_pending_reviews_empty(
 
 
 def test_get_pending_reviews_with_data(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     snapshot: Snapshot,
@@ -95,6 +108,7 @@ def test_get_pending_reviews_with_data(
 
 
 def test_get_pending_reviews_for_execution_success(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     snapshot: Snapshot,
@@ -123,6 +137,7 @@ def test_get_pending_reviews_for_execution_success(
 
 
 def test_get_pending_reviews_for_execution_not_available(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
 ) -> None:
     """Test access denied when user doesn't own the execution"""
@@ -138,6 +153,7 @@ def test_get_pending_reviews_for_execution_not_available(
 
 
 def test_process_review_action_approve_success(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
@@ -145,6 +161,12 @@ def test_process_review_action_approve_success(
     """Test successful review approval"""
     # Mock the route functions
 
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = sample_pending_review
+
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
@@ -173,6 +195,14 @@ def test_process_review_action_approve_success(
     )
     mock_process_all_reviews.return_value = {"test_node_123": approved_review}
 
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
     mock_has_pending = mocker.patch(
         "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
     )
@@ -202,6 +232,7 @@ def test_process_review_action_approve_success(
 
 
 def test_process_review_action_reject_success(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
@@ -209,6 +240,20 @@ def test_process_review_action_reject_success(
     """Test successful review rejection"""
     # Mock the route functions
 
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = sample_pending_review
+
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
@@ -262,6 +307,7 @@ def test_process_review_action_reject_success(
 
 
 def test_process_review_action_mixed_success(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
@@ -288,6 +334,12 @@ def test_process_review_action_mixed_success(
 
     # Mock the route functions
 
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = sample_pending_review
+
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
@@ -337,6 +389,14 @@ def test_process_review_action_mixed_success(
         "test_node_456": rejected_review,
     }
 
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
     mock_has_pending = mocker.patch(
         "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
     )
@@ -369,6 +429,7 @@ def test_process_review_action_mixed_success(
 
 
 def test_process_review_action_empty_request(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     test_user_id: str,
 ) -> None:
@@ -386,10 +447,45 @@ def test_process_review_action_empty_request(
 
 
 def test_process_review_action_review_not_found(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
+    sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
 ) -> None:
     """Test error when review is not found"""
+    # Create a review with the nonexistent_node ID so the route can find the graph_exec_id
+    nonexistent_review = PendingHumanReviewModel(
+        node_exec_id="nonexistent_node",
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        payload={"data": "test"},
+        instructions="Review",
+        editable=True,
+        status=ReviewStatus.WAITING,
+        review_message=None,
+        was_edited=None,
+        processed=False,
+        created_at=FIXED_NOW,
+        updated_at=None,
+        reviewed_at=None,
+    )
+
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = nonexistent_review
+
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
     # Mock the functions that extract graph execution ID from the request
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
@@ -422,11 +518,26 @@ def test_process_review_action_review_not_found(
 
 
 def test_process_review_action_partial_failure(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
 ) -> None:
     """Test handling of partial failures in review processing"""
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = sample_pending_review
+
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
     # Mock the route functions
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
@@ -456,16 +567,50 @@ def test_process_review_action_partial_failure(
 
 
 def test_process_review_action_invalid_node_exec_id(
+    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
 ) -> None:
     """Test failure when trying to process review with invalid node execution ID"""
+    # Create a review with the invalid-node-format ID so the route can find the graph_exec_id
+    invalid_review = PendingHumanReviewModel(
+        node_exec_id="invalid-node-format",
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        payload={"data": "test"},
+        instructions="Review",
+        editable=True,
+        status=ReviewStatus.WAITING,
+        review_message=None,
+        was_edited=None,
+        processed=False,
+        created_at=FIXED_NOW,
+        updated_at=None,
+        reviewed_at=None,
+    )
+
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = invalid_review
+
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
     # Mock the route functions
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
-    mock_get_reviews_for_execution.return_value = [sample_pending_review]
+    mock_get_reviews_for_execution.return_value = [invalid_review]
 
     # Mock validation failure - this should return 400, not 500
     mock_process_all_reviews = mocker.patch(
@@ -490,3 +635,595 @@ def test_process_review_action_invalid_node_exec_id(
     # Should be a 400 Bad Request, not 500 Internal Server Error
     assert response.status_code == 400
     assert "Invalid node execution ID format" in response.json()["detail"]
+
+
+def test_process_review_action_auto_approve_creates_auto_approval_records(
+    client: fastapi.testclient.TestClient,
+    mocker: pytest_mock.MockerFixture,
+    sample_pending_review: PendingHumanReviewModel,
+    test_user_id: str,
+) -> None:
+    """Test that auto_approve_future_actions flag creates auto-approval records"""
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = sample_pending_review
+
+    # Mock process_all_reviews
+    mock_process_all_reviews = mocker.patch(
+        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
+    )
+    approved_review = PendingHumanReviewModel(
+        node_exec_id="test_node_123",
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        payload={"data": "test payload"},
+        instructions="Please review",
+        editable=True,
+        status=ReviewStatus.APPROVED,
+        review_message="Approved",
+        was_edited=False,
+        processed=False,
+        created_at=FIXED_NOW,
+        updated_at=FIXED_NOW,
+        reviewed_at=FIXED_NOW,
+    )
+    mock_process_all_reviews.return_value = {"test_node_123": approved_review}
+
+    # Mock get_node_execution to return node_id
+    mock_get_node_execution = mocker.patch(
+        "backend.api.features.executions.review.routes.get_node_execution"
+    )
+    mock_node_exec = mocker.Mock(spec=NodeExecutionResult)
+    mock_node_exec.node_id = "test_node_def_456"
+    mock_get_node_execution.return_value = mock_node_exec
+
+    # Mock create_auto_approval_record
+    mock_create_auto_approval = mocker.patch(
+        "backend.api.features.executions.review.routes.create_auto_approval_record"
+    )
+
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
+    # Mock has_pending_reviews_for_graph_exec
+    mock_has_pending = mocker.patch(
+        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
+    )
+    mock_has_pending.return_value = False
+
+    # Mock get_graph_settings to return custom settings
+    mock_get_settings = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_settings"
+    )
+    mock_get_settings.return_value = GraphSettings(
+        human_in_the_loop_safe_mode=True,
+        sensitive_action_safe_mode=True,
+    )
+
+    # Mock get_user_by_id to prevent database access
+    mock_get_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_user_by_id"
+    )
+    mock_user = mocker.Mock()
+    mock_user.timezone = "UTC"
+    mock_get_user.return_value = mock_user
+
+    # Mock add_graph_execution
+    mock_add_execution = mocker.patch(
+        "backend.api.features.executions.review.routes.add_graph_execution"
+    )
+
+    request_data = {
+        "reviews": [
+            {
+                "node_exec_id": "test_node_123",
+                "approved": True,
+                "message": "Approved",
+                "auto_approve_future": True,
+            }
+        ],
+    }
+
+    response = client.post("/api/review/action", json=request_data)
+
+    assert response.status_code == 200
+
+    # Verify process_all_reviews_for_execution was called (without auto_approve param)
+    mock_process_all_reviews.assert_called_once()
+
+    # Verify create_auto_approval_record was called for the approved review
+    mock_create_auto_approval.assert_called_once_with(
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        node_id="test_node_def_456",
+        payload={"data": "test payload"},
+    )
+
+    # Verify get_graph_settings was called with correct parameters
+    mock_get_settings.assert_called_once_with(
+        user_id=test_user_id, graph_id="test_graph_789"
+    )
+
+    # Verify add_graph_execution was called with proper ExecutionContext
+    mock_add_execution.assert_called_once()
+    call_kwargs = mock_add_execution.call_args.kwargs
+    execution_context = call_kwargs["execution_context"]
+
+    assert isinstance(execution_context, ExecutionContext)
+    assert execution_context.human_in_the_loop_safe_mode is True
+    assert execution_context.sensitive_action_safe_mode is True
+
+
+def test_process_review_action_without_auto_approve_still_loads_settings(
+    client: fastapi.testclient.TestClient,
+    mocker: pytest_mock.MockerFixture,
+    sample_pending_review: PendingHumanReviewModel,
+    test_user_id: str,
+) -> None:
+    """Test that execution context is created with settings even without auto-approve"""
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = sample_pending_review
+
+    # Mock process_all_reviews
+    mock_process_all_reviews = mocker.patch(
+        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
+    )
+    approved_review = PendingHumanReviewModel(
+        node_exec_id="test_node_123",
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        payload={"data": "test payload"},
+        instructions="Please review",
+        editable=True,
+        status=ReviewStatus.APPROVED,
+        review_message="Approved",
+        was_edited=False,
+        processed=False,
+        created_at=FIXED_NOW,
+        updated_at=FIXED_NOW,
+        reviewed_at=FIXED_NOW,
+    )
+    mock_process_all_reviews.return_value = {"test_node_123": approved_review}
+
+    # Mock create_auto_approval_record - should NOT be called when auto_approve is False
+    mock_create_auto_approval = mocker.patch(
+        "backend.api.features.executions.review.routes.create_auto_approval_record"
+    )
+
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
+    # Mock has_pending_reviews_for_graph_exec
+    mock_has_pending = mocker.patch(
+        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
+    )
+    mock_has_pending.return_value = False
+
+    # Mock get_graph_settings with sensitive_action_safe_mode enabled
+    mock_get_settings = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_settings"
+    )
+    mock_get_settings.return_value = GraphSettings(
+        human_in_the_loop_safe_mode=False,
+        sensitive_action_safe_mode=True,
+    )
+
+    # Mock get_user_by_id to prevent database access
+    mock_get_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_user_by_id"
+    )
+    mock_user = mocker.Mock()
+    mock_user.timezone = "UTC"
+    mock_get_user.return_value = mock_user
+
+    # Mock add_graph_execution
+    mock_add_execution = mocker.patch(
+        "backend.api.features.executions.review.routes.add_graph_execution"
+    )
+
+    # Request WITHOUT auto_approve_future (defaults to False)
+    request_data = {
+        "reviews": [
+            {
+                "node_exec_id": "test_node_123",
+                "approved": True,
+                "message": "Approved",
+                # auto_approve_future defaults to False
+            }
+        ],
+    }
+
+    response = client.post("/api/review/action", json=request_data)
+
+    assert response.status_code == 200
+
+    # Verify process_all_reviews_for_execution was called
+    mock_process_all_reviews.assert_called_once()
+
+    # Verify create_auto_approval_record was NOT called (auto_approve_future=False)
+    mock_create_auto_approval.assert_not_called()
+
+    # Verify settings were loaded
+    mock_get_settings.assert_called_once()
+
+    # Verify ExecutionContext has proper settings
+    mock_add_execution.assert_called_once()
+    call_kwargs = mock_add_execution.call_args.kwargs
+    execution_context = call_kwargs["execution_context"]
+
+    assert isinstance(execution_context, ExecutionContext)
+    assert execution_context.human_in_the_loop_safe_mode is False
+    assert execution_context.sensitive_action_safe_mode is True
+
+
+def test_process_review_action_auto_approve_only_applies_to_approved_reviews(
+    client: fastapi.testclient.TestClient,
+    mocker: pytest_mock.MockerFixture,
+    test_user_id: str,
+) -> None:
+    """Test that auto_approve record is created only for approved reviews"""
+    # Create two reviews - one approved, one rejected
+    approved_review = PendingHumanReviewModel(
+        node_exec_id="node_exec_approved",
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        payload={"data": "approved"},
+        instructions="Review",
+        editable=True,
+        status=ReviewStatus.APPROVED,
+        review_message=None,
+        was_edited=False,
+        processed=False,
+        created_at=FIXED_NOW,
+        updated_at=FIXED_NOW,
+        reviewed_at=FIXED_NOW,
+    )
+    rejected_review = PendingHumanReviewModel(
+        node_exec_id="node_exec_rejected",
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        payload={"data": "rejected"},
+        instructions="Review",
+        editable=True,
+        status=ReviewStatus.REJECTED,
+        review_message="Rejected",
+        was_edited=False,
+        processed=False,
+        created_at=FIXED_NOW,
+        updated_at=FIXED_NOW,
+        reviewed_at=FIXED_NOW,
+    )
+
+    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+    mock_get_reviews_for_user.return_value = approved_review
+
+    # Mock process_all_reviews
+    mock_process_all_reviews = mocker.patch(
+        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
+    )
+    mock_process_all_reviews.return_value = {
+        "node_exec_approved": approved_review,
+        "node_exec_rejected": rejected_review,
+    }
+
+    # Mock get_node_execution to return node_id (only called for approved review)
+    mock_get_node_execution = mocker.patch(
+        "backend.api.features.executions.review.routes.get_node_execution"
+    )
+    mock_node_exec = mocker.Mock(spec=NodeExecutionResult)
+    mock_node_exec.node_id = "test_node_def_approved"
+    mock_get_node_execution.return_value = mock_node_exec
+
+    # Mock create_auto_approval_record
+    mock_create_auto_approval = mocker.patch(
+        "backend.api.features.executions.review.routes.create_auto_approval_record"
+    )
+
+    # Mock get_graph_execution_meta to return execution in REVIEW status
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
+    # Mock has_pending_reviews_for_graph_exec
+    mock_has_pending = mocker.patch(
+        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
+    )
+    mock_has_pending.return_value = False
+
+    # Mock get_graph_settings
+    mock_get_settings = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_settings"
+    )
+    mock_get_settings.return_value = GraphSettings()
+
+    # Mock get_user_by_id to prevent database access
+    mock_get_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_user_by_id"
+    )
+    mock_user = mocker.Mock()
+    mock_user.timezone = "UTC"
+    mock_get_user.return_value = mock_user
+
+    # Mock add_graph_execution
+    mock_add_execution = mocker.patch(
+        "backend.api.features.executions.review.routes.add_graph_execution"
+    )
+
+    request_data = {
+        "reviews": [
+            {
+                "node_exec_id": "node_exec_approved",
+                "approved": True,
+                "auto_approve_future": True,
+            },
+            {
+                "node_exec_id": "node_exec_rejected",
+                "approved": False,
+                "auto_approve_future": True,  # Should be ignored since rejected
+            },
+        ],
+    }
+
+    response = client.post("/api/review/action", json=request_data)
+
+    assert response.status_code == 200
+
+    # Verify process_all_reviews_for_execution was called
+    mock_process_all_reviews.assert_called_once()
+
+    # Verify create_auto_approval_record was called ONLY for the approved review
+    # (not for the rejected one)
+    mock_create_auto_approval.assert_called_once_with(
+        user_id=test_user_id,
+        graph_exec_id="test_graph_exec_456",
+        graph_id="test_graph_789",
+        graph_version=1,
+        node_id="test_node_def_approved",
+        payload={"data": "approved"},
+    )
+
+    # Verify get_node_execution was called only for approved review
+    mock_get_node_execution.assert_called_once_with("node_exec_approved")
+
+    # Verify ExecutionContext was created (auto-approval is now DB-based)
+    call_kwargs = mock_add_execution.call_args.kwargs
+    execution_context = call_kwargs["execution_context"]
+    assert isinstance(execution_context, ExecutionContext)
+
+
+def test_process_review_action_per_review_auto_approve_granularity(
+    client: fastapi.testclient.TestClient,
+    mocker: pytest_mock.MockerFixture,
+    sample_pending_review: PendingHumanReviewModel,
+    test_user_id: str,
+) -> None:
+    """Test that auto-approval can be set per-review (granular control)"""
+    # Mock get_pending_review_by_node_exec_id - return different reviews based on node_exec_id
+    mock_get_reviews_for_user = mocker.patch(
+        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
+    )
+
+    # Create a mapping of node_exec_id to review
+    review_map = {
+        "node_1_auto": PendingHumanReviewModel(
+            node_exec_id="node_1_auto",
+            user_id=test_user_id,
+            graph_exec_id="test_graph_exec",
+            graph_id="test_graph",
+            graph_version=1,
+            payload={"data": "node1"},
+            instructions="Review 1",
+            editable=True,
+            status=ReviewStatus.WAITING,
+            review_message=None,
+            was_edited=False,
+            processed=False,
+            created_at=FIXED_NOW,
+        ),
+        "node_2_manual": PendingHumanReviewModel(
+            node_exec_id="node_2_manual",
+            user_id=test_user_id,
+            graph_exec_id="test_graph_exec",
+            graph_id="test_graph",
+            graph_version=1,
+            payload={"data": "node2"},
+            instructions="Review 2",
+            editable=True,
+            status=ReviewStatus.WAITING,
+            review_message=None,
+            was_edited=False,
+            processed=False,
+            created_at=FIXED_NOW,
+        ),
+        "node_3_auto": PendingHumanReviewModel(
+            node_exec_id="node_3_auto",
+            user_id=test_user_id,
+            graph_exec_id="test_graph_exec",
+            graph_id="test_graph",
+            graph_version=1,
+            payload={"data": "node3"},
+            instructions="Review 3",
+            editable=True,
+            status=ReviewStatus.WAITING,
+            review_message=None,
+            was_edited=False,
+            processed=False,
+            created_at=FIXED_NOW,
+        ),
+    }
+
+    # Use side_effect to return different reviews based on node_exec_id parameter
+    def mock_get_review_by_id(node_exec_id: str, _user_id: str):
+        return review_map.get(node_exec_id)
+
+    mock_get_reviews_for_user.side_effect = mock_get_review_by_id
+
+    # Mock process_all_reviews - return 3 approved reviews
+    mock_process_all_reviews = mocker.patch(
+        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
+    )
+    mock_process_all_reviews.return_value = {
+        "node_1_auto": PendingHumanReviewModel(
+            node_exec_id="node_1_auto",
+            user_id=test_user_id,
+            graph_exec_id="test_graph_exec",
+            graph_id="test_graph",
+            graph_version=1,
+            payload={"data": "node1"},
+            instructions="Review 1",
+            editable=True,
+            status=ReviewStatus.APPROVED,
+            review_message=None,
+            was_edited=False,
+            processed=False,
+            created_at=FIXED_NOW,
+            updated_at=FIXED_NOW,
+            reviewed_at=FIXED_NOW,
+        ),
+        "node_2_manual": PendingHumanReviewModel(
+            node_exec_id="node_2_manual",
+            user_id=test_user_id,
+            graph_exec_id="test_graph_exec",
+            graph_id="test_graph",
+            graph_version=1,
+            payload={"data": "node2"},
+            instructions="Review 2",
+            editable=True,
+            status=ReviewStatus.APPROVED,
+            review_message=None,
+            was_edited=False,
+            processed=False,
+            created_at=FIXED_NOW,
+            updated_at=FIXED_NOW,
+            reviewed_at=FIXED_NOW,
+        ),
+        "node_3_auto": PendingHumanReviewModel(
+            node_exec_id="node_3_auto",
+            user_id=test_user_id,
+            graph_exec_id="test_graph_exec",
+            graph_id="test_graph",
+            graph_version=1,
+            payload={"data": "node3"},
+            instructions="Review 3",
+            editable=True,
+            status=ReviewStatus.APPROVED,
+            review_message=None,
+            was_edited=False,
+            processed=False,
+            created_at=FIXED_NOW,
+            updated_at=FIXED_NOW,
+            reviewed_at=FIXED_NOW,
+        ),
+    }
+
+    # Mock get_node_execution
+    mock_get_node_execution = mocker.patch(
+        "backend.api.features.executions.review.routes.get_node_execution"
+    )
+
+    def mock_get_node(node_exec_id: str):
+        mock_node = mocker.Mock(spec=NodeExecutionResult)
+        mock_node.node_id = f"node_def_{node_exec_id}"
+        return mock_node
+
+    mock_get_node_execution.side_effect = mock_get_node
+
+    # Mock create_auto_approval_record
+    mock_create_auto_approval = mocker.patch(
+        "backend.api.features.executions.review.routes.create_auto_approval_record"
+    )
+
+    # Mock get_graph_execution_meta
+    mock_get_graph_exec = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_execution_meta"
+    )
+    mock_graph_exec_meta = mocker.Mock()
+    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
+    mock_get_graph_exec.return_value = mock_graph_exec_meta
+
+    # Mock has_pending_reviews_for_graph_exec
+    mock_has_pending = mocker.patch(
+        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
+    )
+    mock_has_pending.return_value = False
+
+    # Mock settings and execution
+    mock_get_settings = mocker.patch(
+        "backend.api.features.executions.review.routes.get_graph_settings"
+    )
+    mock_get_settings.return_value = GraphSettings(
+        human_in_the_loop_safe_mode=False, sensitive_action_safe_mode=False
+    )
+
+    mocker.patch("backend.api.features.executions.review.routes.add_graph_execution")
+    mocker.patch("backend.api.features.executions.review.routes.get_user_by_id")
+
+    # Request with granular auto-approval:
+    # - node_1_auto: auto_approve_future=True
+    # - node_2_manual: auto_approve_future=False (explicit)
+    # - node_3_auto: auto_approve_future=True
+    request_data = {
+        "reviews": [
+            {
+                "node_exec_id": "node_1_auto",
+                "approved": True,
+                "auto_approve_future": True,
+            },
+            {
+                "node_exec_id": "node_2_manual",
+                "approved": True,
+                "auto_approve_future": False,  # Don't auto-approve this one
+            },
+            {
+                "node_exec_id": "node_3_auto",
+                "approved": True,
+                "auto_approve_future": True,
+            },
+        ],
+    }
+
+    response = client.post("/api/review/action", json=request_data)
+
+    assert response.status_code == 200
+
+    # Verify create_auto_approval_record was called ONLY for reviews with auto_approve_future=True
+    assert mock_create_auto_approval.call_count == 2
+
+    # Check that it was called for node_1 and node_3, but NOT node_2
+    call_args_list = [call.kwargs for call in mock_create_auto_approval.call_args_list]
+    node_ids_with_auto_approval = [args["node_id"] for args in call_args_list]
+
+    assert "node_def_node_1_auto" in node_ids_with_auto_approval
+    assert "node_def_node_3_auto" in node_ids_with_auto_approval
+    assert "node_def_node_2_manual" not in node_ids_with_auto_approval

autogpt_platform/backend/backend/api/features/executions/review/routes.py

@@ -5,13 +5,23 @@ import autogpt_libs.auth as autogpt_auth_lib
 from fastapi import APIRouter, HTTPException, Query, Security, status
 from prisma.enums import ReviewStatus
 
-from backend.data.execution import get_graph_execution_meta
+from backend.data.execution import (
+    ExecutionContext,
+    ExecutionStatus,
+    get_graph_execution_meta,
+    get_node_execution,
+)
+from backend.data.graph import get_graph_settings
 from backend.data.human_review import (
+    create_auto_approval_record,
+    get_pending_review_by_node_exec_id,
     get_pending_reviews_for_execution,
     get_pending_reviews_for_user,
     has_pending_reviews_for_graph_exec,
     process_all_reviews_for_execution,
 )
+from backend.data.model import USER_TIMEZONE_NOT_SET
+from backend.data.user import get_user_by_id
 from backend.executor.utils import add_graph_execution
 
 from .model import PendingHumanReviewModel, ReviewRequest, ReviewResponse
@@ -127,17 +137,80 @@ async def process_review_action(
             detail="At least one review must be provided",
         )
 
-    # Build review decisions map
+    # Get graph execution ID by looking up all requested reviews
+    # Use direct lookup to avoid pagination issues (can't miss reviews beyond first page)
+    # Also validate that all reviews belong to the same execution
+    matching_review = None
+    graph_exec_ids: set[str] = set()
+
+    for node_exec_id in all_request_node_ids:
+        review = await get_pending_review_by_node_exec_id(node_exec_id, user_id)
+        if not review:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=f"No pending review found for node execution {node_exec_id}",
+            )
+        if matching_review is None:
+            matching_review = review
+        graph_exec_ids.add(review.graph_exec_id)
+
+    # Ensure all reviews belong to the same execution
+    if len(graph_exec_ids) > 1:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="All reviews in a single request must belong to the same execution.",
+        )
+
+    # Safety check (matching_review should never be None here due to validation above)
+    if matching_review is None:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="Internal error: No matching review found despite validation",
+        )
+
+    graph_exec_id = matching_review.graph_exec_id
+
+    # Validate execution status before processing reviews
+    graph_exec_meta = await get_graph_execution_meta(
+        user_id=user_id, execution_id=graph_exec_id
+    )
+
+    if not graph_exec_meta:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=f"Graph execution #{graph_exec_id} not found",
+        )
+
+    # Only allow processing reviews if execution is paused for review
+    # or incomplete (partial execution with some reviews already processed)
+    if graph_exec_meta.status not in (
+        ExecutionStatus.REVIEW,
+        ExecutionStatus.INCOMPLETE,
+    ):
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=f"Cannot process reviews while execution status is {graph_exec_meta.status}. "
+            f"Reviews can only be processed when execution is paused (REVIEW status). "
+            f"Current status: {graph_exec_meta.status}",
+        )
+
+    # Build review decisions map and track which reviews requested auto-approval
+    # Auto-approved reviews use original data (no modifications allowed)
     review_decisions = {}
+    auto_approve_requests = {}  # Map node_exec_id -> auto_approve_future flag
+
     for review in request.reviews:
         review_status = (
             ReviewStatus.APPROVED if review.approved else ReviewStatus.REJECTED
         )
+        # If this review requested auto-approval, don't allow data modifications
+        reviewed_data = None if review.auto_approve_future else review.reviewed_data
         review_decisions[review.node_exec_id] = (
             review_status,
-            review.reviewed_data,
+            reviewed_data,
             review.message,
         )
+        auto_approve_requests[review.node_exec_id] = review.auto_approve_future
 
     # Process all reviews
     updated_reviews = await process_all_reviews_for_execution(
@@ -145,6 +218,32 @@ async def process_review_action(
         review_decisions=review_decisions,
     )
 
+    # Create auto-approval records for approved reviews that requested it
+    # Note: Processing sequentially to avoid event loop issues in tests
+    for node_exec_id, review_result in updated_reviews.items():
+        # Only create auto-approval if:
+        # 1. This review was approved
+        # 2. The review requested auto-approval
+        if review_result.status == ReviewStatus.APPROVED and auto_approve_requests.get(
+            node_exec_id, False
+        ):
+            try:
+                node_exec = await get_node_execution(node_exec_id)
+                if node_exec:
+                    await create_auto_approval_record(
+                        user_id=user_id,
+                        graph_exec_id=review_result.graph_exec_id,
+                        graph_id=review_result.graph_id,
+                        graph_version=review_result.graph_version,
+                        node_id=node_exec.node_id,
+                        payload=review_result.payload,
+                    )
+            except Exception as e:
+                logger.error(
+                    f"Failed to create auto-approval record for {node_exec_id}",
+                    exc_info=e,
+                )
+
     # Count results
     approved_count = sum(
         1
@@ -157,22 +256,37 @@ async def process_review_action(
         if review.status == ReviewStatus.REJECTED
     )
 
-    # Resume execution if we processed some reviews
+    # Resume execution only if ALL pending reviews for this execution have been processed
     if updated_reviews:
-        # Get graph execution ID from any processed review
-        first_review = next(iter(updated_reviews.values()))
-        graph_exec_id = first_review.graph_exec_id
-
-        # Check if any pending reviews remain for this execution
         still_has_pending = await has_pending_reviews_for_graph_exec(graph_exec_id)
 
         if not still_has_pending:
-            # Resume execution
+            # Get the graph_id from any processed review
+            first_review = next(iter(updated_reviews.values()))
+
             try:
+                # Fetch user and settings to build complete execution context
+                user = await get_user_by_id(user_id)
+                settings = await get_graph_settings(
+                    user_id=user_id, graph_id=first_review.graph_id
+                )
+
+                # Preserve user's timezone preference when resuming execution
+                user_timezone = (
+                    user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
+                )
+
+                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,
+                    user_timezone=user_timezone,
+                )
+
                 await add_graph_execution(
                     graph_id=first_review.graph_id,
                     user_id=user_id,
                     graph_exec_id=graph_exec_id,
+                    execution_context=execution_context,
                 )
                 logger.info(f"Resumed execution {graph_exec_id}")
             except Exception as e:

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

@@ -401,27 +401,11 @@ 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]:
     """
@@ -430,6 +414,8 @@ 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:
@@ -465,7 +451,11 @@ async def create_library_agent(
                             }
                         },
                         settings=SafeJson(
-                            _initialize_graph_settings(graph_entry).model_dump()
+                            GraphSettings.from_graph(
+                                graph_entry,
+                                hitl_safe_mode=hitl_safe_mode,
+                                sensitive_action_safe_mode=sensitive_action_safe_mode,
+                            ).model_dump()
                         ),
                     ),
                     include=library_agent_include(
@@ -627,33 +617,6 @@ 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:
@@ -838,7 +801,7 @@ async def add_store_agent_to_library(
                 "isCreatedByUser": False,
                 "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
-                    _initialize_graph_settings(graph_model).model_dump()
+                    GraphSettings.from_graph(graph_model).model_dump()
                 ),
             },
             include=library_agent_include(
@@ -1228,8 +1191,15 @@ 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
-        return (await create_library_agent(new_graph, user_id))[0]
+        # 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]
     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

@@ -73,6 +73,12 @@ 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)
@@ -180,6 +186,8 @@ 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

@@ -52,6 +52,8 @@ 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,
@@ -75,6 +77,8 @@ 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,
@@ -150,6 +154,8 @@ 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,
@@ -218,6 +224,8 @@ 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

@@ -22,7 +22,6 @@ from backend.data.notifications import (
     AgentApprovalData,
     AgentRejectionData,
     NotificationEventModel,
-    WaitlistLaunchData,
 )
 from backend.notifications.notifications import queue_notification_async
 from backend.util.exceptions import DatabaseError
@@ -1718,29 +1717,6 @@ 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 ""),
@@ -1988,552 +1964,3 @@ 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

@@ -6,6 +6,7 @@ Handles generation and storage of OpenAI embeddings for all content types
 """
 
 import asyncio
+import contextvars
 import logging
 import time
 from typing import Any
@@ -21,6 +22,11 @@ from backend.util.json import dumps
 
 logger = logging.getLogger(__name__)
 
+# Context variable to track errors logged in the current task/operation
+# This prevents spamming the same error multiple times when processing batches
+_logged_errors: contextvars.ContextVar[set[str]] = contextvars.ContextVar(
+    "_logged_errors"
+)
 
 # OpenAI embedding model configuration
 EMBEDDING_MODEL = "text-embedding-3-small"
@@ -31,6 +37,42 @@ EMBEDDING_DIM = 1536
 EMBEDDING_MAX_TOKENS = 8191
 
 
+def log_once_per_task(error_key: str, log_fn, message: str, **kwargs) -> bool:
+    """
+    Log an error/warning only once per task/operation to avoid log spam.
+
+    Uses contextvars to track what has been logged in the current async context.
+    Useful when processing batches where the same error might occur for many items.
+
+    Args:
+        error_key: Unique identifier for this error type
+        log_fn: Logger function to call (e.g., logger.error, logger.warning)
+        message: Message to log
+        **kwargs: Additional arguments to pass to log_fn
+
+    Returns:
+        True if the message was logged, False if it was suppressed (already logged)
+
+    Example:
+        log_once_per_task("missing_api_key", logger.error, "API key not set")
+    """
+    # Get current logged errors, or create a new set if this is the first call in this context
+    logged = _logged_errors.get(None)
+    if logged is None:
+        logged = set()
+        _logged_errors.set(logged)
+
+    if error_key in logged:
+        return False
+
+    # Log the message with a note that it will only appear once
+    log_fn(f"{message} (This message will only be shown once per task.)", **kwargs)
+
+    # Mark as logged
+    logged.add(error_key)
+    return True
+
+
 def build_searchable_text(
     name: str,
     description: str,
@@ -73,7 +115,11 @@ async def generate_embedding(text: str) -> list[float] | None:
     try:
         client = get_openai_client()
         if not client:
-            logger.error("openai_internal_api_key not set, cannot generate embedding")
+            log_once_per_task(
+                "openai_api_key_missing",
+                logger.error,
+                "openai_internal_api_key not set, cannot generate embeddings",
+            )
             return None
 
         # Truncate text to token limit using tiktoken
@@ -154,16 +200,16 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-        # Use {pgvector_schema}.vector for explicit pgvector type qualification
+        # Use unqualified ::vector - pgvector is in search_path on all environments
         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::{pgvector_schema}.vector, $5, $6::jsonb, NOW(), NOW())
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
             ON CONFLICT ("contentType", "contentId", "userId")
             DO UPDATE SET
-                "embedding" = $4::{pgvector_schema}.vector,
+                "embedding" = $4::vector,
                 "searchableText" = $5,
                 "metadata" = $6::jsonb,
                 "updatedAt" = NOW()
@@ -290,7 +336,12 @@ async def ensure_embedding(
         # Generate new embedding
         embedding = await generate_embedding(searchable_text)
         if embedding is None:
-            logger.warning(f"Could not generate embedding for version {version_id}")
+            log_once_per_task(
+                "embedding_generation_failed",
+                logger.warning,
+                "Could not generate embeddings (missing API key or service unavailable). "
+                "Embedding generation is disabled for this task.",
+            )
             return False
 
         # Store the embedding with metadata using new function
@@ -609,8 +660,11 @@ async def ensure_content_embedding(
         # Generate new embedding
         embedding = await generate_embedding(searchable_text)
         if embedding is None:
-            logger.warning(
-                f"Could not generate embedding for {content_type}:{content_id}"
+            log_once_per_task(
+                "embedding_generation_failed",
+                logger.warning,
+                "Could not generate embeddings (missing API key or service unavailable). "
+                "Embedding generation is disabled for this task.",
             )
             return False
 
@@ -879,8 +933,7 @@ async def semantic_search(
         min_similarity_idx = len(params) + 1
         params.append(min_similarity)
 
-        # Use regular string (not f-string) for template to preserve {schema_prefix} and {schema} placeholders
-        # Use OPERATOR({pgvector_schema}.<=>) for explicit operator schema qualification
+        # Use unqualified ::vector and <=> operator - pgvector is in search_path on all environments
         sql = (
             """
             SELECT
@@ -888,9 +941,9 @@ async def semantic_search(
                 "contentType" as content_type,
                 "searchableText" as searchable_text,
                 metadata,
-                1 - (embedding OPERATOR({pgvector_schema}.<=>) '"""
+                1 - (embedding <=> '"""
             + embedding_str
-            + """'::{pgvector_schema}.vector) as similarity
+            + """'::vector) as similarity
             FROM {schema_prefix}"UnifiedContentEmbedding"
             WHERE "contentType" IN ("""
             + content_type_placeholders
@@ -898,9 +951,9 @@ async def semantic_search(
             """
             + user_filter
             + """
-            AND 1 - (embedding OPERATOR({pgvector_schema}.<=>) '"""
+            AND 1 - (embedding <=> '"""
             + embedding_str
-            + """'::{pgvector_schema}.vector) >= $"""
+            + """'::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 OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector
+                ORDER BY uce.embedding <=> {embedding_param}::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 OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::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 OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector
+                ORDER BY uce.embedding <=> {embedding_param}::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 OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::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

@@ -223,102 +223,6 @@ 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

@@ -8,7 +8,6 @@ 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
@@ -82,74 +81,6 @@ 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

@@ -761,10 +761,8 @@ 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=user_id)
+    await library_db.create_library_agent(graph, user_id)
     activated_graph = await on_graph_activate(graph, user_id=user_id)
 
     if create_graph.source == "builder":
@@ -888,21 +886,19 @@ 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
     )
-    # 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(
+    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,
             user_id=user_id,
-            agent_id=library.id,
-            settings=library.settings.model_copy(
-                update={"human_in_the_loop_safe_mode": True}
-            ),
+            settings=updated_settings,
         )
     return library
 
@@ -919,21 +915,18 @@ 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")
 
-    # Update the library agent settings
-    updated_agent = await library_db.update_library_agent_settings(
+    updated_agent = await library_db.update_library_agent(
+        library_agent_id=library_agent.id,
         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

@@ -19,7 +19,6 @@ 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
@@ -284,11 +283,6 @@ 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/basic.py

@@ -116,6 +116,7 @@ class PrintToConsoleBlock(Block):
             input_schema=PrintToConsoleBlock.Input,
             output_schema=PrintToConsoleBlock.Output,
             test_input={"text": "Hello, World!"},
+            is_sensitive_action=True,
             test_output=[
                 ("output", "Hello, World!"),
                 ("status", "printed"),

autogpt_platform/backend/backend/blocks/claude_code.py

@@ -0,0 +1,659 @@
+import json
+import shlex
+import uuid
+from typing import Literal, Optional
+
+from e2b import AsyncSandbox as BaseAsyncSandbox
+from pydantic import BaseModel, SecretStr
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.model import (
+    APIKeyCredentials,
+    CredentialsField,
+    CredentialsMetaInput,
+    SchemaField,
+)
+from backend.integrations.providers import ProviderName
+
+
+class ClaudeCodeExecutionError(Exception):
+    """Exception raised when Claude Code execution fails.
+
+    Carries the sandbox_id so it can be returned to the user for cleanup
+    when dispose_sandbox=False.
+    """
+
+    def __init__(self, message: str, sandbox_id: str = ""):
+        super().__init__(message)
+        self.sandbox_id = sandbox_id
+
+
+# Test credentials for E2B
+TEST_E2B_CREDENTIALS = APIKeyCredentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="e2b",
+    api_key=SecretStr("mock-e2b-api-key"),
+    title="Mock E2B API key",
+    expires_at=None,
+)
+TEST_E2B_CREDENTIALS_INPUT = {
+    "provider": TEST_E2B_CREDENTIALS.provider,
+    "id": TEST_E2B_CREDENTIALS.id,
+    "type": TEST_E2B_CREDENTIALS.type,
+    "title": TEST_E2B_CREDENTIALS.title,
+}
+
+# Test credentials for Anthropic
+TEST_ANTHROPIC_CREDENTIALS = APIKeyCredentials(
+    id="2e568a2b-b2ea-475a-8564-9a676bf31c56",
+    provider="anthropic",
+    api_key=SecretStr("mock-anthropic-api-key"),
+    title="Mock Anthropic API key",
+    expires_at=None,
+)
+TEST_ANTHROPIC_CREDENTIALS_INPUT = {
+    "provider": TEST_ANTHROPIC_CREDENTIALS.provider,
+    "id": TEST_ANTHROPIC_CREDENTIALS.id,
+    "type": TEST_ANTHROPIC_CREDENTIALS.type,
+    "title": TEST_ANTHROPIC_CREDENTIALS.title,
+}
+
+
+class ClaudeCodeBlock(Block):
+    """
+    Execute tasks using Claude Code (Anthropic's AI coding assistant) in an E2B sandbox.
+
+    Claude Code can create files, install tools, run commands, and perform complex
+    coding tasks autonomously within a secure sandbox environment.
+    """
+
+    # Use base template - we'll install Claude Code ourselves for latest version
+    DEFAULT_TEMPLATE = "base"
+
+    class Input(BlockSchemaInput):
+        e2b_credentials: CredentialsMetaInput[
+            Literal[ProviderName.E2B], Literal["api_key"]
+        ] = CredentialsField(
+            description=(
+                "API key for the E2B platform to create the sandbox. "
+                "Get one on the [e2b website](https://e2b.dev/docs)"
+            ),
+        )
+
+        anthropic_credentials: CredentialsMetaInput[
+            Literal[ProviderName.ANTHROPIC], Literal["api_key"]
+        ] = CredentialsField(
+            description=(
+                "API key for Anthropic to power Claude Code. "
+                "Get one at [Anthropic's website](https://console.anthropic.com)"
+            ),
+        )
+
+        prompt: str = SchemaField(
+            description=(
+                "The task or instruction for Claude Code to execute. "
+                "Claude Code can create files, install packages, run commands, "
+                "and perform complex coding tasks."
+            ),
+            placeholder="Create a hello world index.html file",
+            default="",
+            advanced=False,
+        )
+
+        timeout: int = SchemaField(
+            description=(
+                "Sandbox timeout in seconds. Claude Code tasks can take "
+                "a while, so set this appropriately for your task complexity. "
+                "Note: This only applies when creating a new sandbox. "
+                "When reconnecting to an existing sandbox via sandbox_id, "
+                "the original timeout is retained."
+            ),
+            default=300,  # 5 minutes default
+            advanced=True,
+        )
+
+        setup_commands: list[str] = SchemaField(
+            description=(
+                "Optional shell commands to run before executing Claude Code. "
+                "Useful for installing dependencies or setting up the environment."
+            ),
+            default_factory=list,
+            advanced=True,
+        )
+
+        working_directory: str = SchemaField(
+            description="Working directory for Claude Code to operate in.",
+            default="/home/user",
+            advanced=True,
+        )
+
+        # Session/continuation support
+        session_id: str = SchemaField(
+            description=(
+                "Session ID to resume a previous conversation. "
+                "Leave empty for a new conversation. "
+                "Use the session_id from a previous run to continue that conversation."
+            ),
+            default="",
+            advanced=True,
+        )
+
+        sandbox_id: str = SchemaField(
+            description=(
+                "Sandbox ID to reconnect to an existing sandbox. "
+                "Required when resuming a session (along with session_id). "
+                "Use the sandbox_id from a previous run where dispose_sandbox was False."
+            ),
+            default="",
+            advanced=True,
+        )
+
+        conversation_history: str = SchemaField(
+            description=(
+                "Previous conversation history to continue from. "
+                "Use this to restore context on a fresh sandbox if the previous one timed out. "
+                "Pass the conversation_history output from a previous run."
+            ),
+            default="",
+            advanced=True,
+        )
+
+        dispose_sandbox: bool = SchemaField(
+            description=(
+                "Whether to dispose of the sandbox immediately after execution. "
+                "Set to False if you want to continue the conversation later "
+                "(you'll need both sandbox_id and session_id from the output)."
+            ),
+            default=True,
+            advanced=True,
+        )
+
+    class FileOutput(BaseModel):
+        """A file extracted from the sandbox."""
+
+        path: str
+        relative_path: str  # Path relative to working directory (for GitHub, etc.)
+        name: str
+        content: str
+
+    class Output(BlockSchemaOutput):
+        response: str = SchemaField(
+            description="The output/response from Claude Code execution"
+        )
+        files: list["ClaudeCodeBlock.FileOutput"] = SchemaField(
+            description=(
+                "List of text files created/modified by Claude Code during this execution. "
+                "Each file has 'path', 'relative_path', 'name', and 'content' fields."
+            )
+        )
+        conversation_history: str = SchemaField(
+            description=(
+                "Full conversation history including this turn. "
+                "Pass this to conversation_history input to continue on a fresh sandbox "
+                "if the previous sandbox timed out."
+            )
+        )
+        session_id: str = SchemaField(
+            description=(
+                "Session ID for this conversation. "
+                "Pass this back along with sandbox_id to continue the conversation."
+            )
+        )
+        sandbox_id: Optional[str] = SchemaField(
+            description=(
+                "ID of the sandbox instance. "
+                "Pass this back along with session_id to continue the conversation. "
+                "This is None if dispose_sandbox was True (sandbox was disposed)."
+            ),
+            default=None,
+        )
+        error: str = SchemaField(description="Error message if execution failed")
+
+    def __init__(self):
+        super().__init__(
+            id="4e34f4a5-9b89-4326-ba77-2dd6750b7194",
+            description=(
+                "Execute tasks using Claude Code in an E2B sandbox. "
+                "Claude Code can create files, install tools, run commands, "
+                "and perform complex coding tasks autonomously."
+            ),
+            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.AI},
+            input_schema=ClaudeCodeBlock.Input,
+            output_schema=ClaudeCodeBlock.Output,
+            test_credentials={
+                "e2b_credentials": TEST_E2B_CREDENTIALS,
+                "anthropic_credentials": TEST_ANTHROPIC_CREDENTIALS,
+            },
+            test_input={
+                "e2b_credentials": TEST_E2B_CREDENTIALS_INPUT,
+                "anthropic_credentials": TEST_ANTHROPIC_CREDENTIALS_INPUT,
+                "prompt": "Create a hello world HTML file",
+                "timeout": 300,
+                "setup_commands": [],
+                "working_directory": "/home/user",
+                "session_id": "",
+                "sandbox_id": "",
+                "conversation_history": "",
+                "dispose_sandbox": True,
+            },
+            test_output=[
+                ("response", "Created index.html with hello world content"),
+                (
+                    "files",
+                    [
+                        {
+                            "path": "/home/user/index.html",
+                            "relative_path": "index.html",
+                            "name": "index.html",
+                            "content": "<html>Hello World</html>",
+                        }
+                    ],
+                ),
+                (
+                    "conversation_history",
+                    "User: Create a hello world HTML file\n"
+                    "Claude: Created index.html with hello world content",
+                ),
+                ("session_id", str),
+                ("sandbox_id", None),  # None because dispose_sandbox=True in test_input
+            ],
+            test_mock={
+                "execute_claude_code": lambda *args, **kwargs: (
+                    "Created index.html with hello world content",  # response
+                    [
+                        ClaudeCodeBlock.FileOutput(
+                            path="/home/user/index.html",
+                            relative_path="index.html",
+                            name="index.html",
+                            content="<html>Hello World</html>",
+                        )
+                    ],  # files
+                    "User: Create a hello world HTML file\n"
+                    "Claude: Created index.html with hello world content",  # conversation_history
+                    "test-session-id",  # session_id
+                    "sandbox_id",  # sandbox_id
+                ),
+            },
+        )
+
+    async def execute_claude_code(
+        self,
+        e2b_api_key: str,
+        anthropic_api_key: str,
+        prompt: str,
+        timeout: int,
+        setup_commands: list[str],
+        working_directory: str,
+        session_id: str,
+        existing_sandbox_id: str,
+        conversation_history: str,
+        dispose_sandbox: bool,
+    ) -> tuple[str, list["ClaudeCodeBlock.FileOutput"], str, str, str]:
+        """
+        Execute Claude Code in an E2B sandbox.
+
+        Returns:
+            Tuple of (response, files, conversation_history, session_id, sandbox_id)
+        """
+
+        # Validate that sandbox_id is provided when resuming a session
+        if session_id and not existing_sandbox_id:
+            raise ValueError(
+                "sandbox_id is required when resuming a session with session_id. "
+                "The session state is stored in the original sandbox. "
+                "If the sandbox has timed out, use conversation_history instead "
+                "to restore context on a fresh sandbox."
+            )
+
+        sandbox = None
+        sandbox_id = ""
+
+        try:
+            # Either reconnect to existing sandbox or create a new one
+            if existing_sandbox_id:
+                # Reconnect to existing sandbox for conversation continuation
+                sandbox = await BaseAsyncSandbox.connect(
+                    sandbox_id=existing_sandbox_id,
+                    api_key=e2b_api_key,
+                )
+            else:
+                # Create new sandbox
+                sandbox = await BaseAsyncSandbox.create(
+                    template=self.DEFAULT_TEMPLATE,
+                    api_key=e2b_api_key,
+                    timeout=timeout,
+                    envs={"ANTHROPIC_API_KEY": anthropic_api_key},
+                )
+
+                # Install Claude Code from npm (ensures we get the latest version)
+                install_result = await sandbox.commands.run(
+                    "npm install -g @anthropic-ai/claude-code@latest",
+                    timeout=120,  # 2 min timeout for install
+                )
+                if install_result.exit_code != 0:
+                    raise Exception(
+                        f"Failed to install Claude Code: {install_result.stderr}"
+                    )
+
+                # Run any user-provided setup commands
+                for cmd in setup_commands:
+                    setup_result = await sandbox.commands.run(cmd)
+                    if setup_result.exit_code != 0:
+                        raise Exception(
+                            f"Setup command failed: {cmd}\n"
+                            f"Exit code: {setup_result.exit_code}\n"
+                            f"Stdout: {setup_result.stdout}\n"
+                            f"Stderr: {setup_result.stderr}"
+                        )
+
+            # Capture sandbox_id immediately after creation/connection
+            # so it's available for error recovery if dispose_sandbox=False
+            sandbox_id = sandbox.sandbox_id
+
+            # Generate or use provided session ID
+            current_session_id = session_id if session_id else str(uuid.uuid4())
+
+            # Build base Claude flags
+            base_flags = "-p --dangerously-skip-permissions --output-format json"
+
+            # Add conversation history context if provided (for fresh sandbox continuation)
+            history_flag = ""
+            if conversation_history and not session_id:
+                # Inject previous conversation as context via system prompt
+                # Use consistent escaping via _escape_prompt helper
+                escaped_history = self._escape_prompt(
+                    f"Previous conversation context: {conversation_history}"
+                )
+                history_flag = f" --append-system-prompt {escaped_history}"
+
+            # Build Claude command based on whether we're resuming or starting new
+            # Use shlex.quote for working_directory and session IDs to prevent injection
+            safe_working_dir = shlex.quote(working_directory)
+            if session_id:
+                # Resuming existing session (sandbox still alive)
+                safe_session_id = shlex.quote(session_id)
+                claude_command = (
+                    f"cd {safe_working_dir} && "
+                    f"echo {self._escape_prompt(prompt)} | "
+                    f"claude --resume {safe_session_id} {base_flags}"
+                )
+            else:
+                # New session with specific ID
+                safe_current_session_id = shlex.quote(current_session_id)
+                claude_command = (
+                    f"cd {safe_working_dir} && "
+                    f"echo {self._escape_prompt(prompt)} | "
+                    f"claude --session-id {safe_current_session_id} {base_flags}{history_flag}"
+                )
+
+            # Capture timestamp before running Claude Code to filter files later
+            # Capture timestamp 1 second in the past to avoid race condition with file creation
+            timestamp_result = await sandbox.commands.run(
+                "date -u -d '1 second ago' +%Y-%m-%dT%H:%M:%S"
+            )
+            if timestamp_result.exit_code != 0:
+                raise RuntimeError(
+                    f"Failed to capture timestamp: {timestamp_result.stderr}"
+                )
+            start_timestamp = (
+                timestamp_result.stdout.strip() if timestamp_result.stdout else None
+            )
+
+            result = await sandbox.commands.run(
+                claude_command,
+                timeout=0,  # No command timeout - let sandbox timeout handle it
+            )
+
+            # Check for command failure
+            if result.exit_code != 0:
+                error_msg = result.stderr or result.stdout or "Unknown error"
+                raise Exception(
+                    f"Claude Code command failed with exit code {result.exit_code}:\n"
+                    f"{error_msg}"
+                )
+
+            raw_output = result.stdout or ""
+
+            # Parse JSON output to extract response and build conversation history
+            response = ""
+            new_conversation_history = conversation_history or ""
+
+            try:
+                # The JSON output contains the result
+                output_data = json.loads(raw_output)
+                response = output_data.get("result", raw_output)
+
+                # Build conversation history entry
+                turn_entry = f"User: {prompt}\nClaude: {response}"
+                if new_conversation_history:
+                    new_conversation_history = (
+                        f"{new_conversation_history}\n\n{turn_entry}"
+                    )
+                else:
+                    new_conversation_history = turn_entry
+
+            except json.JSONDecodeError:
+                # If not valid JSON, use raw output
+                response = raw_output
+                turn_entry = f"User: {prompt}\nClaude: {response}"
+                if new_conversation_history:
+                    new_conversation_history = (
+                        f"{new_conversation_history}\n\n{turn_entry}"
+                    )
+                else:
+                    new_conversation_history = turn_entry
+
+            # Extract files created/modified during this run
+            files = await self._extract_files(
+                sandbox, working_directory, start_timestamp
+            )
+
+            return (
+                response,
+                files,
+                new_conversation_history,
+                current_session_id,
+                sandbox_id,
+            )
+
+        except Exception as e:
+            # Wrap exception with sandbox_id so caller can access/cleanup
+            # the preserved sandbox when dispose_sandbox=False
+            raise ClaudeCodeExecutionError(str(e), sandbox_id) from e
+
+        finally:
+            if dispose_sandbox and sandbox:
+                await sandbox.kill()
+
+    async def _extract_files(
+        self,
+        sandbox: BaseAsyncSandbox,
+        working_directory: str,
+        since_timestamp: str | None = None,
+    ) -> list["ClaudeCodeBlock.FileOutput"]:
+        """
+        Extract text files created/modified during this Claude Code execution.
+
+        Args:
+            sandbox: The E2B sandbox instance
+            working_directory: Directory to search for files
+            since_timestamp: ISO timestamp - only return files modified after this time
+
+        Returns:
+            List of FileOutput objects with path, relative_path, name, and content
+        """
+        files: list[ClaudeCodeBlock.FileOutput] = []
+
+        # Text file extensions we can safely read as text
+        text_extensions = {
+            ".txt",
+            ".md",
+            ".html",
+            ".htm",
+            ".css",
+            ".js",
+            ".ts",
+            ".jsx",
+            ".tsx",
+            ".json",
+            ".xml",
+            ".yaml",
+            ".yml",
+            ".toml",
+            ".ini",
+            ".cfg",
+            ".conf",
+            ".py",
+            ".rb",
+            ".php",
+            ".java",
+            ".c",
+            ".cpp",
+            ".h",
+            ".hpp",
+            ".cs",
+            ".go",
+            ".rs",
+            ".swift",
+            ".kt",
+            ".scala",
+            ".sh",
+            ".bash",
+            ".zsh",
+            ".sql",
+            ".graphql",
+            ".env",
+            ".gitignore",
+            ".dockerfile",
+            "Dockerfile",
+            ".vue",
+            ".svelte",
+            ".astro",
+            ".mdx",
+            ".rst",
+            ".tex",
+            ".csv",
+            ".log",
+        }
+
+        try:
+            # List files recursively using find command
+            # Exclude node_modules and .git directories, but allow hidden files
+            # like .env and .gitignore (they're filtered by text_extensions later)
+            # Filter by timestamp to only get files created/modified during this run
+            safe_working_dir = shlex.quote(working_directory)
+            timestamp_filter = ""
+            if since_timestamp:
+                timestamp_filter = f"-newermt {shlex.quote(since_timestamp)} "
+            find_result = await sandbox.commands.run(
+                f"find {safe_working_dir} -type f "
+                f"{timestamp_filter}"
+                f"-not -path '*/node_modules/*' "
+                f"-not -path '*/.git/*' "
+                f"2>/dev/null"
+            )
+
+            if find_result.stdout:
+                for file_path in find_result.stdout.strip().split("\n"):
+                    if not file_path:
+                        continue
+
+                    # Check if it's a text file we can read
+                    is_text = any(
+                        file_path.endswith(ext) for ext in text_extensions
+                    ) or file_path.endswith("Dockerfile")
+
+                    if is_text:
+                        try:
+                            content = await sandbox.files.read(file_path)
+                            # Handle bytes or string
+                            if isinstance(content, bytes):
+                                content = content.decode("utf-8", errors="replace")
+
+                            # Extract filename from path
+                            file_name = file_path.split("/")[-1]
+
+                            # Calculate relative path by stripping working directory
+                            relative_path = file_path
+                            if file_path.startswith(working_directory):
+                                relative_path = file_path[len(working_directory) :]
+                                # Remove leading slash if present
+                                if relative_path.startswith("/"):
+                                    relative_path = relative_path[1:]
+
+                            files.append(
+                                ClaudeCodeBlock.FileOutput(
+                                    path=file_path,
+                                    relative_path=relative_path,
+                                    name=file_name,
+                                    content=content,
+                                )
+                            )
+                        except Exception:
+                            # Skip files that can't be read
+                            pass
+
+        except Exception:
+            # If file extraction fails, return empty results
+            pass
+
+        return files
+
+    def _escape_prompt(self, prompt: str) -> str:
+        """Escape the prompt for safe shell execution."""
+        # Use single quotes and escape any single quotes in the prompt
+        escaped = prompt.replace("'", "'\"'\"'")
+        return f"'{escaped}'"
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        e2b_credentials: APIKeyCredentials,
+        anthropic_credentials: APIKeyCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            (
+                response,
+                files,
+                conversation_history,
+                session_id,
+                sandbox_id,
+            ) = await self.execute_claude_code(
+                e2b_api_key=e2b_credentials.api_key.get_secret_value(),
+                anthropic_api_key=anthropic_credentials.api_key.get_secret_value(),
+                prompt=input_data.prompt,
+                timeout=input_data.timeout,
+                setup_commands=input_data.setup_commands,
+                working_directory=input_data.working_directory,
+                session_id=input_data.session_id,
+                existing_sandbox_id=input_data.sandbox_id,
+                conversation_history=input_data.conversation_history,
+                dispose_sandbox=input_data.dispose_sandbox,
+            )
+
+            yield "response", response
+            # Always yield files (empty list if none) to match Output schema
+            yield "files", [f.model_dump() for f in files]
+            # Always yield conversation_history so user can restore context on fresh sandbox
+            yield "conversation_history", conversation_history
+            # Always yield session_id so user can continue conversation
+            yield "session_id", session_id
+            # Always yield sandbox_id (None if disposed) to match Output schema
+            yield "sandbox_id", sandbox_id if not input_data.dispose_sandbox else None
+
+        except ClaudeCodeExecutionError as e:
+            yield "error", str(e)
+            # If sandbox was preserved (dispose_sandbox=False), yield sandbox_id
+            # so user can reconnect to or clean up the orphaned sandbox
+            if not input_data.dispose_sandbox and e.sandbox_id:
+                yield "sandbox_id", e.sandbox_id
+        except Exception as e:
+            yield "error", str(e)

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

@@ -9,7 +9,7 @@ from typing import Any, Optional
 from prisma.enums import ReviewStatus
 from pydantic import BaseModel
 
-from backend.data.execution import ExecutionContext, ExecutionStatus
+from backend.data.execution import ExecutionStatus
 from backend.data.human_review import ReviewResult
 from backend.executor.manager import async_update_node_execution_status
 from backend.util.clients import get_database_manager_async_client
@@ -28,6 +28,11 @@ class ReviewDecision(BaseModel):
 class HITLReviewHelper:
     """Helper class for Human-In-The-Loop review operations."""
 
+    @staticmethod
+    async def check_approval(**kwargs) -> Optional[ReviewResult]:
+        """Check if there's an existing approval for this node execution."""
+        return await get_database_manager_async_client().check_approval(**kwargs)
+
     @staticmethod
     async def get_or_create_human_review(**kwargs) -> Optional[ReviewResult]:
         """Create or retrieve a human review from the database."""
@@ -55,11 +60,11 @@ class HITLReviewHelper:
     async def _handle_review_request(
         input_data: Any,
         user_id: str,
+        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
         graph_version: int,
-        execution_context: ExecutionContext,
         block_name: str = "Block",
         editable: bool = False,
     ) -> Optional[ReviewResult]:
@@ -69,11 +74,11 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
+            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
             graph_version: Version of the graph
-            execution_context: Current execution context
             block_name: Name of the block requesting review
             editable: Whether the reviewer can edit the data
 
@@ -83,15 +88,41 @@ class HITLReviewHelper:
         Raises:
             Exception: If review creation or status update fails
         """
-        # Skip review if safe mode is disabled - return auto-approved result
-        if not execution_context.safe_mode:
+        # Note: Safe mode checks (human_in_the_loop_safe_mode, sensitive_action_safe_mode)
+        # are handled by the caller:
+        # - HITL blocks check human_in_the_loop_safe_mode in their run() method
+        # - Sensitive action blocks check sensitive_action_safe_mode in is_block_exec_need_review()
+        # This function only handles checking for existing approvals.
+
+        # Check if this node has already been approved (normal or auto-approval)
+        if approval_result := await HITLReviewHelper.check_approval(
+            node_exec_id=node_exec_id,
+            graph_exec_id=graph_exec_id,
+            node_id=node_id,
+            user_id=user_id,
+            input_data=input_data,
+        ):
             logger.info(
-                f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
+                f"Block {block_name} skipping review for node {node_exec_id} - "
+                f"found existing approval"
+            )
+            # Return a new ReviewResult with the current node_exec_id but approved status
+            # For auto-approvals, always use current input_data
+            # For normal approvals, use approval_result.data unless it's None
+            is_auto_approval = approval_result.node_exec_id != node_exec_id
+            approved_data = (
+                input_data
+                if is_auto_approval
+                else (
+                    approval_result.data
+                    if approval_result.data is not None
+                    else input_data
+                )
             )
             return ReviewResult(
-                data=input_data,
+                data=approved_data,
                 status=ReviewStatus.APPROVED,
-                message="Auto-approved (safe mode disabled)",
+                message=approval_result.message,
                 processed=True,
                 node_exec_id=node_exec_id,
             )
@@ -129,11 +160,11 @@ class HITLReviewHelper:
     async def handle_review_decision(
         input_data: Any,
         user_id: str,
+        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
         graph_version: int,
-        execution_context: ExecutionContext,
         block_name: str = "Block",
         editable: bool = False,
     ) -> Optional[ReviewDecision]:
@@ -143,11 +174,11 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
+            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
             graph_version: Version of the graph
-            execution_context: Current execution context
             block_name: Name of the block requesting review
             editable: Whether the reviewer can edit the data
 
@@ -158,11 +189,11 @@ class HITLReviewHelper:
         review_result = await HITLReviewHelper._handle_review_request(
             input_data=input_data,
             user_id=user_id,
+            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
-            execution_context=execution_context,
             block_name=block_name,
             editable=editable,
         )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -97,6 +97,7 @@ class HumanInTheLoopBlock(Block):
         input_data: Input,
         *,
         user_id: str,
+        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -104,7 +105,7 @@ class HumanInTheLoopBlock(Block):
         execution_context: ExecutionContext,
         **_kwargs,
     ) -> BlockOutput:
-        if not execution_context.safe_mode:
+        if not execution_context.human_in_the_loop_safe_mode:
             logger.info(
                 f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
             )
@@ -115,11 +116,11 @@ class HumanInTheLoopBlock(Block):
         decision = await self.handle_review_decision(
             input_data=input_data.data,
             user_id=user_id,
+            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
-            execution_context=execution_context,
             block_name=self.name,
             editable=input_data.editable,
         )

autogpt_platform/backend/backend/blocks/llm.py

@@ -79,6 +79,10 @@ 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):
@@ -171,6 +175,26 @@ 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]
@@ -190,119 +214,291 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
 
 MODEL_METADATA = {
     # https://platform.openai.com/docs/models
-    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
+    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
     # GPT-5 models
-    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.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.GPT4O_MINI: ModelMetadata(
-        "openai", 128000, 16384
+        "openai", 128000, 16384, "GPT-4o Mini", "OpenAI", "OpenAI", 1
     ),  # gpt-4o-mini-2024-07-18
-    LlmModel.GPT4O: ModelMetadata("openai", 128000, 16384),  # gpt-4o-2024-08-06
+    LlmModel.GPT4O: ModelMetadata(
+        "openai", 128000, 16384, "GPT-4o", "OpenAI", "OpenAI", 2
+    ),  # gpt-4o-2024-08-06
     LlmModel.GPT4_TURBO: ModelMetadata(
-        "openai", 128000, 4096
+        "openai", 128000, 4096, "GPT-4 Turbo", "OpenAI", "OpenAI", 3
     ),  # gpt-4-turbo-2024-04-09
-    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, 4096),  # gpt-3.5-turbo-0125
+    LlmModel.GPT3_5_TURBO: ModelMetadata(
+        "openai", 16385, 4096, "GPT-3.5 Turbo", "OpenAI", "OpenAI", 1
+    ),  # gpt-3.5-turbo-0125
     # https://docs.anthropic.com/en/docs/about-claude/models
     LlmModel.CLAUDE_4_1_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000
+        "anthropic", 200000, 32000, "Claude Opus 4.1", "Anthropic", "Anthropic", 3
     ),  # claude-opus-4-1-20250805
     LlmModel.CLAUDE_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000
+        "anthropic", 200000, 32000, "Claude Opus 4", "Anthropic", "Anthropic", 3
     ),  # claude-4-opus-20250514
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
     ),  # claude-4-sonnet-20250514
     LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
     ),  # claude-opus-4-5-20251101
     LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Sonnet 4.5", "Anthropic", "Anthropic", 3
     ),  # claude-sonnet-4-5-20250929
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
     ),  # claude-haiku-4-5-20251001
     LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
     ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
-        "anthropic", 200000, 4096
+        "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
     ),  # 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),
-    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),
-    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 128000, 8192),
-    # https://ollama.com/library
-    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),
-    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
+    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
+    ),
+    # 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
+    ),
+    # 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
+    ),
+    # 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_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
     ),
-    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
+        "open_router",
+        131000,
+        4096,
+        "Hermes 3 Llama 3.1 405B",
+        "OpenRouter",
+        "Nous Research",
+        1,
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B: ModelMetadata(
-        "open_router", 12288, 12288
+        "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
     ),
-    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),
-    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),
+    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
+    ),
     # v0 by Vercel models
-    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),
+    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),
 }
 
 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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(
-            safe_mode=False,
+            human_in_the_loop_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(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_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(safe_mode=False)
+            mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
             mock_execution_processor = MagicMock()
 
             async for output_name, output_value in block.run(
@@ -609,7 +609,9 @@ async def test_validation_errors_dont_pollute_conversation():
                 outputs = {}
                 from backend.data.execution import ExecutionContext
 
-                mock_execution_context = ExecutionContext(safe_mode=False)
+                mock_execution_context = ExecutionContext(
+                    human_in_the_loop_safe_mode=False
+                )
 
                 # Create a proper mock execution processor for agent mode
                 from collections import defaultdict

autogpt_platform/backend/backend/data/block.py

@@ -441,6 +441,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         static_output: bool = False,
         block_type: BlockType = BlockType.STANDARD,
         webhook_config: Optional[BlockWebhookConfig | BlockManualWebhookConfig] = None,
+        is_sensitive_action: bool = False,
     ):
         """
         Initialize the block with the given schema.
@@ -473,8 +474,8 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.static_output = static_output
         self.block_type = block_type
         self.webhook_config = webhook_config
+        self.is_sensitive_action = is_sensitive_action
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
-        self.requires_human_review: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -622,6 +623,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         input_data: BlockInput,
         *,
         user_id: str,
+        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -637,8 +639,9 @@ 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)
         """
-        # Skip review if not required or safe mode is disabled
-        if not self.requires_human_review or not execution_context.safe_mode:
+        if not (
+            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
+        ):
             return False, input_data
 
         from backend.blocks.helpers.review import HITLReviewHelper
@@ -647,11 +650,11 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         decision = await HITLReviewHelper.handle_review_decision(
             input_data=input_data,
             user_id=user_id,
+            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
-            execution_context=execution_context,
             block_name=self.name,
             editable=True,
         )

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -99,10 +99,15 @@ 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,
@@ -126,11 +131,6 @@ 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

@@ -121,10 +121,14 @@ 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)
-        - {pgvector_schema}: Schema where pgvector is installed (defaults to "public")
+
+    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).
 
     Args:
-        query_template: SQL query with {schema_prefix}, {schema}, and/or {pgvector_schema} placeholders
+        query_template: SQL query with {schema_prefix} and/or {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).
@@ -135,20 +139,16 @@ async def _raw_with_schema(
 
     Example with vector type:
         await execute_raw_with_schema(
-            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::{pgvector_schema}.vector)',
+            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::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

@@ -103,8 +103,18 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
         return redis.get_redis()
 
     def publish_event(self, event: M, channel_key: str):
-        message, full_channel_name = self._serialize_message(event, channel_key)
-        self.connection.publish(full_channel_name, message)
+        """
+        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."
+            )
 
     def listen_events(self, channel_key: str) -> Generator[M, None, None]:
         pubsub, full_channel_name = self._get_pubsub_channel(
@@ -128,9 +138,19 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
         return await redis.get_redis_async()
 
     async def publish_event(self, event: M, channel_key: str):
-        message, full_channel_name = self._serialize_message(event, channel_key)
-        connection = await self.connection
-        await connection.publish(full_channel_name, message)
+        """
+        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."
+            )
 
     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

@@ -0,0 +1,56 @@
+"""
+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

@@ -81,7 +81,10 @@ class ExecutionContext(BaseModel):
     This includes information needed by blocks, sub-graphs, and execution management.
     """
 
-    safe_mode: bool = True
+    model_config = {"extra": "ignore"}
+
+    human_in_the_loop_safe_mode: bool = True
+    sensitive_action_safe_mode: bool = False
     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, Any, Literal, Optional, cast
+from typing import TYPE_CHECKING, Annotated, 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, Field, create_model
+from pydantic import BaseModel, BeforeValidator, Field, create_model
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
@@ -62,7 +62,31 @@ logger = logging.getLogger(__name__)
 
 
 class GraphSettings(BaseModel):
-    human_in_the_loop_safe_mode: bool | None = None
+    # 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,
+        )
 
 
 class Link(BaseDbModel):
@@ -244,10 +268,14 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if (
-                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-                or node.block.requires_human_review
-            )
+            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
         )
 
     @property

autogpt_platform/backend/backend/data/human_review.py

@@ -6,7 +6,7 @@ Handles all database operations for pending human reviews.
 import asyncio
 import logging
 from datetime import datetime, timezone
-from typing import Optional
+from typing import TYPE_CHECKING, Optional
 
 from prisma.enums import ReviewStatus
 from prisma.models import PendingHumanReview
@@ -17,8 +17,12 @@ from backend.api.features.executions.review.model import (
     PendingHumanReviewModel,
     SafeJsonData,
 )
+from backend.data.execution import get_graph_execution_meta
 from backend.util.json import SafeJson
 
+if TYPE_CHECKING:
+    pass
+
 logger = logging.getLogger(__name__)
 
 
@@ -32,6 +36,125 @@ class ReviewResult(BaseModel):
     node_exec_id: str
 
 
+def get_auto_approve_key(graph_exec_id: str, node_id: str) -> str:
+    """Generate the special nodeExecId key for auto-approval records."""
+    return f"auto_approve_{graph_exec_id}_{node_id}"
+
+
+async def check_approval(
+    node_exec_id: str,
+    graph_exec_id: str,
+    node_id: str,
+    user_id: str,
+    input_data: SafeJsonData | None = None,
+) -> Optional[ReviewResult]:
+    """
+    Check if there's an existing approval for this node execution.
+
+    Checks both:
+    1. Normal approval by node_exec_id (previous run of the same node execution)
+    2. Auto-approval by special key pattern "auto_approve_{graph_exec_id}_{node_id}"
+
+    Args:
+        node_exec_id: ID of the node execution
+        graph_exec_id: ID of the graph execution
+        node_id: ID of the node definition (not execution)
+        user_id: ID of the user (for data isolation)
+        input_data: Current input data (used for auto-approvals to avoid stale data)
+
+    Returns:
+        ReviewResult if approval found (either normal or auto), None otherwise
+    """
+    auto_approve_key = get_auto_approve_key(graph_exec_id, node_id)
+
+    # Check for either normal approval or auto-approval in a single query
+    existing_review = await PendingHumanReview.prisma().find_first(
+        where={
+            "OR": [
+                {"nodeExecId": node_exec_id},
+                {"nodeExecId": auto_approve_key},
+            ],
+            "status": ReviewStatus.APPROVED,
+            "userId": user_id,
+        },
+    )
+
+    if existing_review:
+        is_auto_approval = existing_review.nodeExecId == auto_approve_key
+        logger.info(
+            f"Found {'auto-' if is_auto_approval else ''}approval for node {node_id} "
+            f"(exec: {node_exec_id}) in execution {graph_exec_id}"
+        )
+        # For auto-approvals, use current input_data to avoid replaying stale payload
+        # For normal approvals, use the stored payload (which may have been edited)
+        return ReviewResult(
+            data=(
+                input_data
+                if is_auto_approval and input_data is not None
+                else existing_review.payload
+            ),
+            status=ReviewStatus.APPROVED,
+            message=(
+                "Auto-approved (user approved all future actions for this node)"
+                if is_auto_approval
+                else existing_review.reviewMessage or ""
+            ),
+            processed=True,
+            node_exec_id=existing_review.nodeExecId,
+        )
+
+    return None
+
+
+async def create_auto_approval_record(
+    user_id: str,
+    graph_exec_id: str,
+    graph_id: str,
+    graph_version: int,
+    node_id: str,
+    payload: SafeJsonData,
+) -> None:
+    """
+    Create an auto-approval record for a node in this execution.
+
+    This is stored as a PendingHumanReview with a special nodeExecId pattern
+    and status=APPROVED, so future executions of the same node can skip review.
+
+    Raises:
+        ValueError: If the graph execution doesn't belong to the user
+    """
+    # Validate that the graph execution belongs to this user (defense in depth)
+    graph_exec = await get_graph_execution_meta(
+        user_id=user_id, execution_id=graph_exec_id
+    )
+    if not graph_exec:
+        raise ValueError(
+            f"Graph execution {graph_exec_id} not found or doesn't belong to user {user_id}"
+        )
+
+    auto_approve_key = get_auto_approve_key(graph_exec_id, node_id)
+
+    await PendingHumanReview.prisma().upsert(
+        where={"nodeExecId": auto_approve_key},
+        data={
+            "create": {
+                "nodeExecId": auto_approve_key,
+                "userId": user_id,
+                "graphExecId": graph_exec_id,
+                "graphId": graph_id,
+                "graphVersion": graph_version,
+                "payload": SafeJson(payload),
+                "instructions": "Auto-approval record",
+                "editable": False,
+                "status": ReviewStatus.APPROVED,
+                "processed": True,
+                "reviewedAt": datetime.now(timezone.utc),
+            },
+            "update": {},  # Already exists, no update needed
+        },
+    )
+
+
 async def get_or_create_human_review(
     user_id: str,
     node_exec_id: str,
@@ -108,6 +231,38 @@ async def get_or_create_human_review(
         )
 
 
+async def get_pending_review_by_node_exec_id(
+    node_exec_id: str, user_id: str
+) -> Optional["PendingHumanReviewModel"]:
+    """
+    Get a pending review by its node execution ID.
+
+    Args:
+        node_exec_id: The node execution ID to look up
+        user_id: User ID for authorization (only returns if review belongs to this user)
+
+    Returns:
+        The pending review if found and belongs to user, None otherwise
+    """
+    review = await PendingHumanReview.prisma().find_first(
+        where={
+            "nodeExecId": node_exec_id,
+            "userId": user_id,
+            "status": ReviewStatus.WAITING,
+        }
+    )
+
+    if not review:
+        return None
+
+    # Local import to avoid event loop conflicts in tests
+    from backend.data.execution import get_node_execution
+
+    node_exec = await get_node_execution(review.nodeExecId)
+    node_id = node_exec.node_id if node_exec else review.nodeExecId
+    return PendingHumanReviewModel.from_db(review, node_id=node_id)
+
+
 async def has_pending_reviews_for_graph_exec(graph_exec_id: str) -> bool:
     """
     Check if a graph execution has any pending reviews.
@@ -137,8 +292,11 @@ async def get_pending_reviews_for_user(
         page_size: Number of reviews per page
 
     Returns:
-        List of pending review models
+        List of pending review models with node_id included
     """
+    # Local import to avoid event loop conflicts in tests
+    from backend.data.execution import get_node_execution
+
     # Calculate offset for pagination
     offset = (page - 1) * page_size
 
@@ -149,7 +307,14 @@ async def get_pending_reviews_for_user(
         take=page_size,
     )
 
-    return [PendingHumanReviewModel.from_db(review) for review in reviews]
+    # Fetch node_id for each review from NodeExecution
+    result = []
+    for review in reviews:
+        node_exec = await get_node_execution(review.nodeExecId)
+        node_id = node_exec.node_id if node_exec else review.nodeExecId
+        result.append(PendingHumanReviewModel.from_db(review, node_id=node_id))
+
+    return result
 
 
 async def get_pending_reviews_for_execution(
@@ -163,8 +328,11 @@ async def get_pending_reviews_for_execution(
         user_id: User ID for security validation
 
     Returns:
-        List of pending review models
+        List of pending review models with node_id included
     """
+    # Local import to avoid event loop conflicts in tests
+    from backend.data.execution import get_node_execution
+
     reviews = await PendingHumanReview.prisma().find_many(
         where={
             "userId": user_id,
@@ -174,7 +342,14 @@ async def get_pending_reviews_for_execution(
         order={"createdAt": "asc"},
     )
 
-    return [PendingHumanReviewModel.from_db(review) for review in reviews]
+    # Fetch node_id for each review from NodeExecution
+    result = []
+    for review in reviews:
+        node_exec = await get_node_execution(review.nodeExecId)
+        node_id = node_exec.node_id if node_exec else review.nodeExecId
+        result.append(PendingHumanReviewModel.from_db(review, node_id=node_id))
+
+    return result
 
 
 async def process_all_reviews_for_execution(
@@ -244,11 +419,19 @@ async def process_all_reviews_for_execution(
     # Note: Execution resumption is now handled at the API layer after ALL reviews
     # for an execution are processed (both approved and rejected)
 
-    # Return as dict for easy access
-    return {
-        review.nodeExecId: PendingHumanReviewModel.from_db(review)
-        for review in updated_reviews
-    }
+    # Fetch node_id for each review and return as dict for easy access
+    # Local import to avoid event loop conflicts in tests
+    from backend.data.execution import get_node_execution
+
+    result = {}
+    for review in updated_reviews:
+        node_exec = await get_node_execution(review.nodeExecId)
+        node_id = node_exec.node_id if node_exec else review.nodeExecId
+        result[review.nodeExecId] = PendingHumanReviewModel.from_db(
+            review, node_id=node_id
+        )
+
+    return result
 
 
 async def update_review_processed_status(node_exec_id: str, processed: bool) -> None:
@@ -256,3 +439,44 @@ async def update_review_processed_status(node_exec_id: str, processed: bool) ->
     await PendingHumanReview.prisma().update(
         where={"nodeExecId": node_exec_id}, data={"processed": processed}
     )
+
+
+async def cancel_pending_reviews_for_execution(graph_exec_id: str, user_id: str) -> int:
+    """
+    Cancel all pending reviews for a graph execution (e.g., when execution is stopped).
+
+    Marks all WAITING reviews as REJECTED with a message indicating the execution was stopped.
+
+    Args:
+        graph_exec_id: The graph execution ID
+        user_id: User ID who owns the execution (for security validation)
+
+    Returns:
+        Number of reviews cancelled
+
+    Raises:
+        ValueError: If the graph execution doesn't belong to the user
+    """
+    # Validate user ownership before cancelling reviews
+    graph_exec = await get_graph_execution_meta(
+        user_id=user_id, execution_id=graph_exec_id
+    )
+    if not graph_exec:
+        raise ValueError(
+            f"Graph execution {graph_exec_id} not found or doesn't belong to user {user_id}"
+        )
+
+    result = await PendingHumanReview.prisma().update_many(
+        where={
+            "graphExecId": graph_exec_id,
+            "userId": user_id,
+            "status": ReviewStatus.WAITING,
+        },
+        data={
+            "status": ReviewStatus.REJECTED,
+            "reviewMessage": "Execution was stopped by user",
+            "processed": True,
+            "reviewedAt": datetime.now(timezone.utc),
+        },
+    )
+    return result

autogpt_platform/backend/backend/data/human_review_test.py

@@ -46,8 +46,8 @@ async def test_get_or_create_human_review_new(
     sample_db_review.status = ReviewStatus.WAITING
     sample_db_review.processed = False
 
-    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
+    mock_prisma = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
+    mock_prisma.return_value.upsert = AsyncMock(return_value=sample_db_review)
 
     result = await get_or_create_human_review(
         user_id="test-user-123",
@@ -75,8 +75,8 @@ async def test_get_or_create_human_review_approved(
     sample_db_review.processed = False
     sample_db_review.reviewMessage = "Looks good"
 
-    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
+    mock_prisma = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
+    mock_prisma.return_value.upsert = AsyncMock(return_value=sample_db_review)
 
     result = await get_or_create_human_review(
         user_id="test-user-123",
@@ -131,10 +131,19 @@ async def test_get_pending_reviews_for_user(
     mock_find_many = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
     mock_find_many.return_value.find_many = AsyncMock(return_value=[sample_db_review])
 
+    # Mock get_node_execution to return node with node_id (async function)
+    mock_node_exec = Mock()
+    mock_node_exec.node_id = "test_node_def_789"
+    mocker.patch(
+        "backend.data.execution.get_node_execution",
+        new=AsyncMock(return_value=mock_node_exec),
+    )
+
     result = await get_pending_reviews_for_user("test_user", page=2, page_size=10)
 
     assert len(result) == 1
     assert result[0].node_exec_id == "test_node_123"
+    assert result[0].node_id == "test_node_def_789"
 
     # Verify pagination parameters
     call_args = mock_find_many.return_value.find_many.call_args
@@ -151,12 +160,21 @@ async def test_get_pending_reviews_for_execution(
     mock_find_many = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
     mock_find_many.return_value.find_many = AsyncMock(return_value=[sample_db_review])
 
+    # Mock get_node_execution to return node with node_id (async function)
+    mock_node_exec = Mock()
+    mock_node_exec.node_id = "test_node_def_789"
+    mocker.patch(
+        "backend.data.execution.get_node_execution",
+        new=AsyncMock(return_value=mock_node_exec),
+    )
+
     result = await get_pending_reviews_for_execution(
         "test_graph_exec_456", "test-user-123"
     )
 
     assert len(result) == 1
     assert result[0].graph_exec_id == "test_graph_exec_456"
+    assert result[0].node_id == "test_node_def_789"
 
     # Verify it filters by execution and user
     call_args = mock_find_many.return_value.find_many.call_args
@@ -201,6 +219,14 @@ async def test_process_all_reviews_for_execution_success(
         new=AsyncMock(return_value=[updated_review]),
     )
 
+    # Mock get_node_execution to return node with node_id (async function)
+    mock_node_exec = Mock()
+    mock_node_exec.node_id = "test_node_def_789"
+    mocker.patch(
+        "backend.data.execution.get_node_execution",
+        new=AsyncMock(return_value=mock_node_exec),
+    )
+
     result = await process_all_reviews_for_execution(
         user_id="test-user-123",
         review_decisions={
@@ -211,6 +237,7 @@ async def test_process_all_reviews_for_execution_success(
     assert len(result) == 1
     assert "test_node_123" in result
     assert result["test_node_123"].status == ReviewStatus.APPROVED
+    assert result["test_node_123"].node_id == "test_node_def_789"
 
 
 @pytest.mark.asyncio
@@ -329,6 +356,14 @@ async def test_process_all_reviews_mixed_approval_rejection(
         new=AsyncMock(return_value=[approved_review, rejected_review]),
     )
 
+    # Mock get_node_execution to return node with node_id (async function)
+    mock_node_exec = Mock()
+    mock_node_exec.node_id = "test_node_def_789"
+    mocker.patch(
+        "backend.data.execution.get_node_execution",
+        new=AsyncMock(return_value=mock_node_exec),
+    )
+
     result = await process_all_reviews_for_execution(
         user_id="test-user-123",
         review_decisions={
@@ -340,3 +375,5 @@ async def test_process_all_reviews_mixed_approval_rejection(
     assert len(result) == 2
     assert "test_node_123" in result
     assert "test_node_456" in result
+    assert result["test_node_123"].node_id == "test_node_def_789"
+    assert result["test_node_456"].node_id == "test_node_def_789"

autogpt_platform/backend/backend/data/notifications.py

@@ -211,22 +211,6 @@ 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,
@@ -239,7 +223,6 @@ NotificationData = Annotated[
         DailySummaryData,
         RefundRequestData,
         BaseSummaryData,
-        WaitlistLaunchData,
     ],
     Field(discriminator="type"),
 ]
@@ -290,7 +273,6 @@ def get_notif_data_type(
         NotificationType.REFUND_PROCESSED: RefundRequestData,
         NotificationType.AGENT_APPROVED: AgentApprovalData,
         NotificationType.AGENT_REJECTED: AgentRejectionData,
-        NotificationType.WAITLIST_LAUNCH: WaitlistLaunchData,
     }[notification_type]
 
 
@@ -336,7 +318,6 @@ 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)
 
@@ -356,7 +337,6 @@ 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
@@ -374,7 +354,6 @@ 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/database.py

@@ -50,6 +50,8 @@ from backend.data.graph import (
     validate_graph_execution_permissions,
 )
 from backend.data.human_review import (
+    cancel_pending_reviews_for_execution,
+    check_approval,
     get_or_create_human_review,
     has_pending_reviews_for_graph_exec,
     update_review_processed_status,
@@ -190,6 +192,8 @@ class DatabaseManager(AppService):
     get_user_notification_preference = _(get_user_notification_preference)
 
     # Human In The Loop
+    cancel_pending_reviews_for_execution = _(cancel_pending_reviews_for_execution)
+    check_approval = _(check_approval)
     get_or_create_human_review = _(get_or_create_human_review)
     has_pending_reviews_for_graph_exec = _(has_pending_reviews_for_graph_exec)
     update_review_processed_status = _(update_review_processed_status)
@@ -313,6 +317,8 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     set_execution_kv_data = d.set_execution_kv_data
 
     # Human In The Loop
+    cancel_pending_reviews_for_execution = d.cancel_pending_reviews_for_execution
+    check_approval = d.check_approval
     get_or_create_human_review = d.get_or_create_human_review
     update_review_processed_status = d.update_review_processed_status
 

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=10)
+            result = db_client.backfill_missing_embeddings(batch_size=100)
 
             total_processed += result["processed"]
             total_success += result["success"]

autogpt_platform/backend/backend/executor/utils.py

@@ -10,6 +10,7 @@ from pydantic import BaseModel, JsonValue, ValidationError
 
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
+from backend.data import human_review as human_review_db
 from backend.data import onboarding as onboarding_db
 from backend.data import user as user_db
 from backend.data.block import (
@@ -749,9 +750,27 @@ async def stop_graph_execution(
         if graph_exec.status in [
             ExecutionStatus.QUEUED,
             ExecutionStatus.INCOMPLETE,
+            ExecutionStatus.REVIEW,
         ]:
-            # If the graph is still on the queue, we can prevent them from being executed
-            # by setting the status to TERMINATED.
+            # If the graph is queued/incomplete/paused for review, terminate immediately
+            # No need to wait for executor since it's not actively running
+
+            # If graph is in REVIEW status, clean up pending reviews before terminating
+            if graph_exec.status == ExecutionStatus.REVIEW:
+                # Use human_review_db if Prisma connected, else database manager
+                review_db = (
+                    human_review_db
+                    if prisma.is_connected()
+                    else get_database_manager_async_client()
+                )
+                # Mark all pending reviews as rejected/cancelled
+                cancelled_count = await review_db.cancel_pending_reviews_for_execution(
+                    graph_exec_id, user_id
+                )
+                logger.info(
+                    f"Cancelled {cancelled_count} pending review(s) for stopped execution {graph_exec_id}"
+                )
+
             graph_exec.status = ExecutionStatus.TERMINATED
 
             await asyncio.gather(
@@ -873,11 +892,8 @@ async def add_graph_execution(
         settings = await gdb.get_graph_settings(user_id=user_id, graph_id=graph_id)
 
         execution_context = ExecutionContext(
-            safe_mode=(
-                settings.human_in_the_loop_safe_mode
-                if settings.human_in_the_loop_safe_mode is not None
-                else True
-            ),
+            human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
+            sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
             user_timezone=(
                 user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
             ),
@@ -890,9 +906,28 @@ async def add_graph_execution(
             nodes_to_skip=nodes_to_skip,
             execution_context=execution_context,
         )
-        logger.info(f"Publishing execution {graph_exec.id} to execution queue")
+        logger.info(f"Queueing execution {graph_exec.id}")
+
+        # Update execution status to QUEUED BEFORE publishing to prevent race condition
+        # where two concurrent requests could both publish the same execution
+        updated_exec = await edb.update_graph_execution_stats(
+            graph_exec_id=graph_exec.id,
+            status=ExecutionStatus.QUEUED,
+        )
+
+        # Verify the status update succeeded (prevents duplicate queueing in race conditions)
+        # If another request already updated the status, this execution will not be QUEUED
+        if not updated_exec or updated_exec.status != ExecutionStatus.QUEUED:
+            logger.warning(
+                f"Skipping queue publish for execution {graph_exec.id} - "
+                f"status update failed or execution already queued by another request"
+            )
+            return graph_exec
+
+        graph_exec.status = ExecutionStatus.QUEUED
 
         # Publish to execution queue for executor to pick up
+        # This happens AFTER status update to ensure only one request publishes
         exec_queue = await get_async_execution_queue()
         await exec_queue.publish_message(
             routing_key=GRAPH_EXECUTION_ROUTING_KEY,
@@ -900,13 +935,6 @@ async def add_graph_execution(
             exchange=GRAPH_EXECUTION_EXCHANGE,
         )
         logger.info(f"Published execution {graph_exec.id} to RabbitMQ queue")
-
-        # Update execution status to QUEUED
-        graph_exec.status = ExecutionStatus.QUEUED
-        await edb.update_graph_execution_stats(
-            graph_exec_id=graph_exec.id,
-            status=graph_exec.status,
-        )
     except BaseException as e:
         err = str(e) or type(e).__name__
         if not graph_exec:

autogpt_platform/backend/backend/executor/utils_test.py

@@ -4,6 +4,7 @@ import pytest
 from pytest_mock import MockerFixture
 
 from backend.data.dynamic_fields import merge_execution_input, parse_execution_output
+from backend.data.execution import ExecutionStatus
 from backend.util.mock import MockObject
 
 
@@ -346,6 +347,7 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []  # Add this to avoid AttributeError
+    mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
     mock_graph_exec.to_graph_execution_entry.return_value = mocker.MagicMock()
 
     # Mock the queue and event bus
@@ -386,6 +388,7 @@ 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)
@@ -610,6 +613,7 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []
+    mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
 
     # Track what's passed to to_graph_execution_entry
     captured_kwargs = {}
@@ -651,6 +655,7 @@ 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)
@@ -668,3 +673,232 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     # Verify nodes_to_skip was passed to to_graph_execution_entry
     assert "nodes_to_skip" in captured_kwargs
     assert captured_kwargs["nodes_to_skip"] == nodes_to_skip
+
+
+@pytest.mark.asyncio
+async def test_stop_graph_execution_in_review_status_cancels_pending_reviews(
+    mocker: MockerFixture,
+):
+    """Test that stopping an execution in REVIEW status cancels pending reviews."""
+    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
+    from backend.executor.utils import stop_graph_execution
+
+    user_id = "test-user"
+    graph_exec_id = "test-exec-123"
+
+    # Mock graph execution in REVIEW status
+    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionMeta)
+    mock_graph_exec.id = graph_exec_id
+    mock_graph_exec.status = ExecutionStatus.REVIEW
+
+    # Mock dependencies
+    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
+    mock_queue_client = mocker.AsyncMock()
+    mock_get_queue.return_value = mock_queue_client
+
+    mock_prisma = mocker.patch("backend.executor.utils.prisma")
+    mock_prisma.is_connected.return_value = True
+
+    mock_human_review_db = mocker.patch("backend.executor.utils.human_review_db")
+    mock_human_review_db.cancel_pending_reviews_for_execution = mocker.AsyncMock(
+        return_value=2  # 2 reviews cancelled
+    )
+
+    mock_execution_db = mocker.patch("backend.executor.utils.execution_db")
+    mock_execution_db.get_graph_execution_meta = mocker.AsyncMock(
+        return_value=mock_graph_exec
+    )
+    mock_execution_db.update_graph_execution_stats = mocker.AsyncMock()
+
+    mock_get_event_bus = mocker.patch(
+        "backend.executor.utils.get_async_execution_event_bus"
+    )
+    mock_event_bus = mocker.MagicMock()
+    mock_event_bus.publish = mocker.AsyncMock()
+    mock_get_event_bus.return_value = mock_event_bus
+
+    mock_get_child_executions = mocker.patch(
+        "backend.executor.utils._get_child_executions"
+    )
+    mock_get_child_executions.return_value = []  # No children
+
+    # Call stop_graph_execution with timeout to allow status check
+    await stop_graph_execution(
+        user_id=user_id,
+        graph_exec_id=graph_exec_id,
+        wait_timeout=1.0,  # Wait to allow status check
+        cascade=True,
+    )
+
+    # Verify pending reviews were cancelled
+    mock_human_review_db.cancel_pending_reviews_for_execution.assert_called_once_with(
+        graph_exec_id, user_id
+    )
+
+    # Verify execution status was updated to TERMINATED
+    mock_execution_db.update_graph_execution_stats.assert_called_once()
+    call_kwargs = mock_execution_db.update_graph_execution_stats.call_args[1]
+    assert call_kwargs["graph_exec_id"] == graph_exec_id
+    assert call_kwargs["status"] == ExecutionStatus.TERMINATED
+
+
+@pytest.mark.asyncio
+async def test_stop_graph_execution_with_database_manager_when_prisma_disconnected(
+    mocker: MockerFixture,
+):
+    """Test that stop uses database manager when Prisma is not connected."""
+    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
+    from backend.executor.utils import stop_graph_execution
+
+    user_id = "test-user"
+    graph_exec_id = "test-exec-456"
+
+    # Mock graph execution in REVIEW status
+    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionMeta)
+    mock_graph_exec.id = graph_exec_id
+    mock_graph_exec.status = ExecutionStatus.REVIEW
+
+    # Mock dependencies
+    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
+    mock_queue_client = mocker.AsyncMock()
+    mock_get_queue.return_value = mock_queue_client
+
+    # Prisma is NOT connected
+    mock_prisma = mocker.patch("backend.executor.utils.prisma")
+    mock_prisma.is_connected.return_value = False
+
+    # Mock database manager client
+    mock_get_db_manager = mocker.patch(
+        "backend.executor.utils.get_database_manager_async_client"
+    )
+    mock_db_manager = mocker.AsyncMock()
+    mock_db_manager.get_graph_execution_meta = mocker.AsyncMock(
+        return_value=mock_graph_exec
+    )
+    mock_db_manager.cancel_pending_reviews_for_execution = mocker.AsyncMock(
+        return_value=3  # 3 reviews cancelled
+    )
+    mock_db_manager.update_graph_execution_stats = mocker.AsyncMock()
+    mock_get_db_manager.return_value = mock_db_manager
+
+    mock_get_event_bus = mocker.patch(
+        "backend.executor.utils.get_async_execution_event_bus"
+    )
+    mock_event_bus = mocker.MagicMock()
+    mock_event_bus.publish = mocker.AsyncMock()
+    mock_get_event_bus.return_value = mock_event_bus
+
+    mock_get_child_executions = mocker.patch(
+        "backend.executor.utils._get_child_executions"
+    )
+    mock_get_child_executions.return_value = []  # No children
+
+    # Call stop_graph_execution with timeout
+    await stop_graph_execution(
+        user_id=user_id,
+        graph_exec_id=graph_exec_id,
+        wait_timeout=1.0,
+        cascade=True,
+    )
+
+    # Verify database manager was used for cancel_pending_reviews
+    mock_db_manager.cancel_pending_reviews_for_execution.assert_called_once_with(
+        graph_exec_id, user_id
+    )
+
+    # Verify execution status was updated via database manager
+    mock_db_manager.update_graph_execution_stats.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_stop_graph_execution_cascades_to_child_with_reviews(
+    mocker: MockerFixture,
+):
+    """Test that stopping parent execution cascades to children and cancels their reviews."""
+    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
+    from backend.executor.utils import stop_graph_execution
+
+    user_id = "test-user"
+    parent_exec_id = "parent-exec"
+    child_exec_id = "child-exec"
+
+    # Mock parent execution in RUNNING status
+    mock_parent_exec = mocker.MagicMock(spec=GraphExecutionMeta)
+    mock_parent_exec.id = parent_exec_id
+    mock_parent_exec.status = ExecutionStatus.RUNNING
+
+    # Mock child execution in REVIEW status
+    mock_child_exec = mocker.MagicMock(spec=GraphExecutionMeta)
+    mock_child_exec.id = child_exec_id
+    mock_child_exec.status = ExecutionStatus.REVIEW
+
+    # Mock dependencies
+    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
+    mock_queue_client = mocker.AsyncMock()
+    mock_get_queue.return_value = mock_queue_client
+
+    mock_prisma = mocker.patch("backend.executor.utils.prisma")
+    mock_prisma.is_connected.return_value = True
+
+    mock_human_review_db = mocker.patch("backend.executor.utils.human_review_db")
+    mock_human_review_db.cancel_pending_reviews_for_execution = mocker.AsyncMock(
+        return_value=1  # 1 child review cancelled
+    )
+
+    # Mock execution_db to return different status based on which execution is queried
+    mock_execution_db = mocker.patch("backend.executor.utils.execution_db")
+
+    # Track call count to simulate status transition
+    call_count = {"count": 0}
+
+    async def get_exec_meta_side_effect(execution_id, user_id):
+        call_count["count"] += 1
+        if execution_id == parent_exec_id:
+            # After a few calls (child processing happens), transition parent to TERMINATED
+            # This simulates the executor service processing the stop request
+            if call_count["count"] > 3:
+                mock_parent_exec.status = ExecutionStatus.TERMINATED
+            return mock_parent_exec
+        elif execution_id == child_exec_id:
+            return mock_child_exec
+        return None
+
+    mock_execution_db.get_graph_execution_meta = mocker.AsyncMock(
+        side_effect=get_exec_meta_side_effect
+    )
+    mock_execution_db.update_graph_execution_stats = mocker.AsyncMock()
+
+    mock_get_event_bus = mocker.patch(
+        "backend.executor.utils.get_async_execution_event_bus"
+    )
+    mock_event_bus = mocker.MagicMock()
+    mock_event_bus.publish = mocker.AsyncMock()
+    mock_get_event_bus.return_value = mock_event_bus
+
+    # Mock _get_child_executions to return the child
+    mock_get_child_executions = mocker.patch(
+        "backend.executor.utils._get_child_executions"
+    )
+
+    def get_children_side_effect(parent_id):
+        if parent_id == parent_exec_id:
+            return [mock_child_exec]
+        return []
+
+    mock_get_child_executions.side_effect = get_children_side_effect
+
+    # Call stop_graph_execution on parent with cascade=True
+    await stop_graph_execution(
+        user_id=user_id,
+        graph_exec_id=parent_exec_id,
+        wait_timeout=1.0,
+        cascade=True,
+    )
+
+    # Verify child reviews were cancelled
+    mock_human_review_db.cancel_pending_reviews_for_execution.assert_called_once_with(
+        child_exec_id, user_id
+    )
+
+    # Verify both parent and child status updates
+    assert mock_execution_db.update_graph_execution_stats.call_count >= 1

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

@@ -1,59 +0,0 @@
-{# 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/backend/util/test.py

@@ -1,3 +1,4 @@
+import asyncio
 import inspect
 import logging
 import time
@@ -58,6 +59,11 @@ class SpinTestServer:
         self.db_api.__exit__(exc_type, exc_val, exc_tb)
         self.notif_manager.__exit__(exc_type, exc_val, exc_tb)
 
+        # Give services time to fully shut down
+        #  This prevents event loop issues where services haven't fully cleaned up
+        # before the next test starts
+        await asyncio.sleep(0.5)
+
     def setup_dependency_overrides(self):
         # Override get_user_id for testing
         self.agent_server.set_test_dependency_overrides(

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

@@ -1,53 +0,0 @@
--- 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

@@ -1,11 +1,37 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Create in public schema so vector type is available across all schemas
+-- Ensures vector extension is in the current schema (from DATABASE_URL ?schema= param)
+-- If it exists in a different schema (e.g., public), we drop and recreate it in the current schema
+-- This ensures vector type is in the same schema as tables, making ::vector work without explicit qualification
 DO $$
+DECLARE
+    current_schema_name text;
+    vector_schema text;
 BEGIN
-    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'vector extension not available or already exists, skipping';
+    -- Get the current schema from search_path
+    SELECT current_schema() INTO current_schema_name;
+
+    -- Check if vector extension exists and which schema it's in
+    SELECT n.nspname INTO vector_schema
+    FROM pg_extension e
+    JOIN pg_namespace n ON e.extnamespace = n.oid
+    WHERE e.extname = 'vector';
+
+    -- Handle removal if in wrong schema
+    IF vector_schema IS NOT NULL AND vector_schema != current_schema_name THEN
+        BEGIN
+            -- Vector exists in a different schema, drop it first
+            RAISE WARNING 'pgvector found in schema "%" but need it in "%". Dropping and reinstalling...',
+                vector_schema, current_schema_name;
+            EXECUTE 'DROP EXTENSION IF EXISTS vector CASCADE';
+        EXCEPTION WHEN OTHERS THEN
+            RAISE EXCEPTION 'Failed to drop pgvector from schema "%": %. You may need to drop it manually.',
+                vector_schema, SQLERRM;
+        END;
+    END IF;
+
+    -- Create extension in current schema (let it fail naturally if not available)
+    EXECUTE format('CREATE EXTENSION IF NOT EXISTS vector SCHEMA %I', current_schema_name);
 END $$;
 
 -- CreateEnum
@@ -19,7 +45,7 @@ CREATE TABLE "UnifiedContentEmbedding" (
     "contentType" "ContentType" NOT NULL,
     "contentId" TEXT NOT NULL,
     "userId" TEXT,
-    "embedding" public.vector(1536) NOT NULL,
+    "embedding" vector(1536) NOT NULL,
     "searchableText" TEXT NOT NULL,
     "metadata" JSONB NOT NULL DEFAULT '{}',
 
@@ -45,4 +71,4 @@ 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" public.vector_cosine_ops);
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);

autogpt_platform/backend/migrations/20260112173500_add_supabase_extensions_to_platform_schema/migration.sql

@@ -1,71 +0,0 @@
--- Acknowledge Supabase-managed extensions to prevent drift warnings
--- These extensions are pre-installed by Supabase in specific schemas
--- This migration ensures they exist where available (Supabase) or skips gracefully (CI)
-
--- Create schemas (safe in both CI and Supabase)
-CREATE SCHEMA IF NOT EXISTS "extensions";
-
--- Extensions that exist in both CI and Supabase
-DO $$
-BEGIN
-    CREATE EXTENSION IF NOT EXISTS "pgcrypto" WITH SCHEMA "extensions";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'pgcrypto extension not available, skipping';
-END $$;
-
-DO $$
-BEGIN
-    CREATE EXTENSION IF NOT EXISTS "uuid-ossp" WITH SCHEMA "extensions";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'uuid-ossp extension not available, skipping';
-END $$;
-
--- Supabase-specific extensions (skip gracefully in CI)
-DO $$
-BEGIN
-    CREATE EXTENSION IF NOT EXISTS "pg_stat_statements" WITH SCHEMA "extensions";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'pg_stat_statements extension not available, skipping';
-END $$;
-
-DO $$
-BEGIN
-    CREATE EXTENSION IF NOT EXISTS "pg_net" WITH SCHEMA "extensions";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'pg_net extension not available, skipping';
-END $$;
-
-DO $$
-BEGIN
-    CREATE EXTENSION IF NOT EXISTS "pgjwt" WITH SCHEMA "extensions";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'pgjwt extension not available, skipping';
-END $$;
-
-DO $$
-BEGIN
-    CREATE SCHEMA IF NOT EXISTS "graphql";
-    CREATE EXTENSION IF NOT EXISTS "pg_graphql" WITH SCHEMA "graphql";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'pg_graphql extension not available, skipping';
-END $$;
-
-DO $$
-BEGIN
-    CREATE SCHEMA IF NOT EXISTS "pgsodium";
-    CREATE EXTENSION IF NOT EXISTS "pgsodium" WITH SCHEMA "pgsodium";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'pgsodium extension not available, skipping';
-END $$;
-
-DO $$
-BEGIN
-    CREATE SCHEMA IF NOT EXISTS "vault";
-    CREATE EXTENSION IF NOT EXISTS "supabase_vault" WITH SCHEMA "vault";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'supabase_vault extension not available, skipping';
-END $$;
-
-
--- Return to platform
-CREATE SCHEMA IF NOT EXISTS "platform";

autogpt_platform/backend/migrations/20260121200000_remove_node_execution_fk_from_pending_human_review/migration.sql

@@ -0,0 +1,7 @@
+-- Remove NodeExecution foreign key from PendingHumanReview
+-- The nodeExecId column remains as the primary key, but we remove the FK constraint
+-- to AgentNodeExecution since PendingHumanReview records can persist after node
+-- execution records are deleted.
+
+-- Drop foreign key constraint that linked PendingHumanReview.nodeExecId to AgentNodeExecution.id
+ALTER TABLE "PendingHumanReview" DROP CONSTRAINT IF EXISTS "PendingHumanReview_nodeExecId_fkey";

autogpt_platform/backend/schema.prisma

@@ -69,10 +69,6 @@ model User {
   OAuthAuthorizationCodes OAuthAuthorizationCode[]
   OAuthAccessTokens       OAuthAccessToken[]
   OAuthRefreshTokens      OAuthRefreshToken[]
-
-  // Waitlist relations
-  waitlistEntries       WaitlistEntry[]
-  joinedWaitlists       WaitlistEntry[] @relation("joinedWaitlists")
 }
 
 enum OnboardingStep {
@@ -299,7 +295,6 @@ enum NotificationType {
   REFUND_PROCESSED
   AGENT_APPROVED
   AGENT_REJECTED
-  WAITLIST_LAUNCH
 }
 
 model NotificationEvent {
@@ -522,8 +517,6 @@ model AgentNodeExecution {
 
   stats Json?
 
-  PendingHumanReview PendingHumanReview?
-
   @@index([agentGraphExecutionId, agentNodeId, executionStatus])
   @@index([agentNodeId, executionStatus])
   @@index([addedTime, queuedTime])
@@ -572,6 +565,7 @@ enum ReviewStatus {
 }
 
 // Pending human reviews for Human-in-the-loop blocks
+// Also stores auto-approval records with special nodeExecId patterns (e.g., "auto_approve_{graph_exec_id}_{node_id}")
 model PendingHumanReview {
   nodeExecId    String       @id
   userId        String
@@ -590,7 +584,6 @@ model PendingHumanReview {
   reviewedAt    DateTime?
 
   User           User                @relation(fields: [userId], references: [id], onDelete: Cascade)
-  NodeExecution  AgentNodeExecution  @relation(fields: [nodeExecId], references: [id], onDelete: Cascade)
   GraphExecution AgentGraphExecution @relation(fields: [graphExecId], references: [id], onDelete: Cascade)
 
   @@unique([nodeExecId]) // One pending review per node execution
@@ -906,8 +899,7 @@ model StoreListing {
   OwningUser   User   @relation(fields: [owningUserId], references: [id])
 
   // Relations
-  Versions        StoreListingVersion[] @relation("ListingVersions")
-  waitlistEntries WaitlistEntry[]
+  Versions StoreListingVersion[] @relation("ListingVersions")
 
   // Unique index on agentId to ensure only one listing per agent, regardless of number of versions the agent has.
   @@unique([agentGraphId])
@@ -1039,47 +1031,6 @@ 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

@@ -34,7 +34,10 @@ logger = logging.getLogger(__name__)
 
 # Default output directory relative to repo root
 DEFAULT_OUTPUT_DIR = (
-    Path(__file__).parent.parent.parent.parent / "docs" / "integrations"
+    Path(__file__).parent.parent.parent.parent
+    / "docs"
+    / "integrations"
+    / "block-integrations"
 )
 
 
@@ -366,12 +369,12 @@ def generate_block_markdown(
     lines.append("")
 
     # What it is (full description)
-    lines.append(f"### What it is")
+    lines.append("### What it is")
     lines.append(block.description or "No description available.")
     lines.append("")
 
     # How it works (manual section)
-    lines.append(f"### How it works")
+    lines.append("### How it works")
     how_it_works = manual_content.get(
         "how_it_works", "_Add technical explanation here._"
     )
@@ -383,7 +386,7 @@ 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(f"### Inputs")
+        lines.append("### Inputs")
         lines.append("")
         lines.append("| Input | Description | Type | Required |")
         lines.append("|-------|-------------|------|----------|")
@@ -400,7 +403,7 @@ 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(f"### Outputs")
+        lines.append("### Outputs")
         lines.append("")
         lines.append("| Output | Description | Type |")
         lines.append("|--------|-------------|------|")
@@ -414,13 +417,21 @@ def generate_block_markdown(
         lines.append("")
 
     # Possible use case (manual section)
-    lines.append(f"### Possible use case")
+    lines.append("### 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)
     lines.append("<!-- END MANUAL -->")
     lines.append("")
 
+    # Optional per-block extras (only include if has content)
+    extras = manual_content.get("extras", "")
+    if extras:
+        lines.append("<!-- MANUAL: extras -->")
+        lines.append(extras)
+        lines.append("<!-- END MANUAL -->")
+        lines.append("")
+
     lines.append("---")
     lines.append("")
 
@@ -456,25 +467,52 @@ def get_block_file_mapping(blocks: list[BlockDoc]) -> dict[str, list[BlockDoc]]:
     return dict(file_mapping)
 
 
-def generate_overview_table(blocks: list[BlockDoc]) -> str:
-    """Generate the overview table markdown (blocks.md)."""
+def generate_overview_table(blocks: list[BlockDoc], block_dir_prefix: str = "") -> str:
+    """Generate the overview table markdown (blocks.md).
+
+    Args:
+        blocks: List of block documentation objects
+        block_dir_prefix: Prefix for block file links (e.g., "block-integrations/")
+    """
     lines = []
 
+    # GitBook YAML frontmatter
+    lines.append("---")
+    lines.append("layout:")
+    lines.append("  width: default")
+    lines.append("  title:")
+    lines.append("    visible: true")
+    lines.append("  description:")
+    lines.append("    visible: true")
+    lines.append("  tableOfContents:")
+    lines.append("    visible: false")
+    lines.append("  outline:")
+    lines.append("    visible: true")
+    lines.append("  pagination:")
+    lines.append("    visible: true")
+    lines.append("  metadata:")
+    lines.append("    visible: true")
+    lines.append("---")
+    lines.append("")
+
     lines.append("# AutoGPT Blocks Overview")
     lines.append("")
     lines.append(
         'AutoGPT uses a modular approach with various "blocks" to handle different tasks. These blocks are the building blocks of AutoGPT workflows, allowing users to create complex automations by combining simple, specialized components.'
     )
     lines.append("")
-    lines.append('!!! info "Creating Your Own Blocks"')
-    lines.append("    Want to create your own custom blocks? Check out our guides:")
-    lines.append("    ")
+    lines.append('{% hint style="info" %}')
+    lines.append("**Creating Your Own Blocks**")
+    lines.append("")
+    lines.append("Want to create your own custom blocks? Check out our guides:")
+    lines.append("")
     lines.append(
-        "    - [Build your own Blocks](https://docs.agpt.co/platform/new_blocks/) - Step-by-step tutorial with examples"
+        "* [Build your own Blocks](https://docs.agpt.co/platform/new_blocks/) - Step-by-step tutorial with examples"
     )
     lines.append(
-        "    - [Block SDK Guide](https://docs.agpt.co/platform/block-sdk-guide/) - Advanced SDK patterns with OAuth, webhooks, and provider configuration"
+        "* [Block SDK Guide](https://docs.agpt.co/platform/block-sdk-guide/) - Advanced SDK patterns with OAuth, webhooks, and provider configuration"
     )
+    lines.append("{% endhint %}")
     lines.append("")
     lines.append(
         "Below is a comprehensive list of all available blocks, categorized by their primary function. Click on any block name to view its detailed documentation."
@@ -537,7 +575,8 @@ def generate_overview_table(blocks: list[BlockDoc]) -> str:
                     else "No description"
                 )
                 short_desc = short_desc.replace("\n", " ").replace("|", "\\|")
-                lines.append(f"| [{block.name}]({file_path}#{anchor}) | {short_desc} |")
+                link_path = f"{block_dir_prefix}{file_path}"
+                lines.append(f"| [{block.name}]({link_path}#{anchor}) | {short_desc} |")
             lines.append("")
             continue
 
@@ -563,13 +602,55 @@ def generate_overview_table(blocks: list[BlockDoc]) -> str:
             )
             short_desc = short_desc.replace("\n", " ").replace("|", "\\|")
 
-            lines.append(f"| [{block.name}]({file_path}#{anchor}) | {short_desc} |")
+            link_path = f"{block_dir_prefix}{file_path}"
+            lines.append(f"| [{block.name}]({link_path}#{anchor}) | {short_desc} |")
 
         lines.append("")
 
     return "\n".join(lines)
 
 
+def generate_summary_md(
+    blocks: list[BlockDoc], root_dir: Path, block_dir_prefix: str = ""
+) -> str:
+    """Generate SUMMARY.md for GitBook navigation.
+
+    Args:
+        blocks: List of block documentation objects
+        root_dir: The root docs directory (e.g., docs/integrations/)
+        block_dir_prefix: Prefix for block file links (e.g., "block-integrations/")
+    """
+    lines = []
+    lines.append("# Table of contents")
+    lines.append("")
+    lines.append("* [AutoGPT Blocks Overview](README.md)")
+    lines.append("")
+
+    # Check for guides/ directory at the root level (docs/integrations/guides/)
+    guides_dir = root_dir / "guides"
+    if guides_dir.exists():
+        lines.append("## Guides")
+        lines.append("")
+        for guide_file in sorted(guides_dir.glob("*.md")):
+            # Use just the file name for title (replace hyphens/underscores with spaces)
+            title = file_path_to_title(guide_file.stem.replace("-", "_") + ".md")
+            lines.append(f"* [{title}](guides/{guide_file.name})")
+        lines.append("")
+
+    lines.append("## Block Integrations")
+    lines.append("")
+
+    file_mapping = get_block_file_mapping(blocks)
+    for file_path in sorted(file_mapping.keys()):
+        title = file_path_to_title(file_path)
+        link_path = f"{block_dir_prefix}{file_path}"
+        lines.append(f"* [{title}]({link_path})")
+
+    lines.append("")
+
+    return "\n".join(lines)
+
+
 def load_all_blocks_for_docs() -> list[BlockDoc]:
     """Load all blocks and extract documentation."""
     from backend.blocks import load_all_blocks
@@ -653,6 +734,16 @@ def write_block_docs(
                 )
             )
 
+        # Add file-level additional_content section if present
+        file_additional = extract_manual_content(existing_content).get(
+            "additional_content", ""
+        )
+        if file_additional:
+            content_parts.append("<!-- MANUAL: additional_content -->")
+            content_parts.append(file_additional)
+            content_parts.append("<!-- END MANUAL -->")
+            content_parts.append("")
+
         full_content = file_header + "\n" + "\n".join(content_parts)
         generated_files[str(file_path)] = full_content
 
@@ -661,14 +752,28 @@ def write_block_docs(
 
         full_path.write_text(full_content)
 
-    # Generate overview file
-    overview_content = generate_overview_table(blocks)
-    overview_path = output_dir / "README.md"
+    # Generate overview file at the parent directory (docs/integrations/)
+    # with links prefixed to point into block-integrations/
+    root_dir = output_dir.parent
+    block_dir_name = output_dir.name  # "block-integrations"
+    block_dir_prefix = f"{block_dir_name}/"
+
+    overview_content = generate_overview_table(blocks, block_dir_prefix)
+    overview_path = root_dir / "README.md"
     generated_files["README.md"] = overview_content
     overview_path.write_text(overview_content)
 
     if verbose:
-        print("  Writing README.md (overview)")
+        print("  Writing README.md (overview) to parent directory")
+
+    # Generate SUMMARY.md for GitBook navigation at the parent directory
+    summary_content = generate_summary_md(blocks, root_dir, block_dir_prefix)
+    summary_path = root_dir / "SUMMARY.md"
+    generated_files["SUMMARY.md"] = summary_content
+    summary_path.write_text(summary_content)
+
+    if verbose:
+        print("  Writing SUMMARY.md (navigation) to parent directory")
 
     return generated_files
 
@@ -748,6 +853,16 @@ def check_docs_in_sync(output_dir: Path, blocks: list[BlockDoc]) -> bool:
             elif block_match.group(1).strip() != expected_block_content.strip():
                 mismatched_blocks.append(block.name)
 
+        # Add file-level additional_content to expected content (matches write_block_docs)
+        file_additional = extract_manual_content(existing_content).get(
+            "additional_content", ""
+        )
+        if file_additional:
+            content_parts.append("<!-- MANUAL: additional_content -->")
+            content_parts.append(file_additional)
+            content_parts.append("<!-- END MANUAL -->")
+            content_parts.append("")
+
         expected_content = file_header + "\n" + "\n".join(content_parts)
 
         if existing_content.strip() != expected_content.strip():
@@ -757,11 +872,15 @@ def check_docs_in_sync(output_dir: Path, blocks: list[BlockDoc]) -> bool:
             out_of_sync_details.append((file_path, mismatched_blocks))
             all_match = False
 
-    # Check overview
-    overview_path = output_dir / "README.md"
+    # Check overview at the parent directory (docs/integrations/)
+    root_dir = output_dir.parent
+    block_dir_name = output_dir.name  # "block-integrations"
+    block_dir_prefix = f"{block_dir_name}/"
+
+    overview_path = root_dir / "README.md"
     if overview_path.exists():
         existing_overview = overview_path.read_text()
-        expected_overview = generate_overview_table(blocks)
+        expected_overview = generate_overview_table(blocks, block_dir_prefix)
         if existing_overview.strip() != expected_overview.strip():
             print("OUT OF SYNC: README.md (overview)")
             print("  The blocks overview table needs regeneration")
@@ -772,6 +891,21 @@ def check_docs_in_sync(output_dir: Path, blocks: list[BlockDoc]) -> bool:
         out_of_sync_details.append(("README.md", ["overview table"]))
         all_match = False
 
+    # Check SUMMARY.md at the parent directory
+    summary_path = root_dir / "SUMMARY.md"
+    if summary_path.exists():
+        existing_summary = summary_path.read_text()
+        expected_summary = generate_summary_md(blocks, root_dir, block_dir_prefix)
+        if existing_summary.strip() != expected_summary.strip():
+            print("OUT OF SYNC: SUMMARY.md (navigation)")
+            print("  The GitBook navigation needs regeneration")
+            out_of_sync_details.append(("SUMMARY.md", ["navigation"]))
+            all_match = False
+    else:
+        print("MISSING: SUMMARY.md (navigation)")
+        out_of_sync_details.append(("SUMMARY.md", ["navigation"]))
+        all_match = False
+
     # Check for unfilled manual sections
     unfilled_patterns = [
         "_Add a description of this category of blocks._",

autogpt_platform/backend/snapshots/grph_single

@@ -11,6 +11,7 @@
   "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

@@ -11,6 +11,7 @@
     "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

@@ -27,6 +27,8 @@
         "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,
@@ -34,7 +36,8 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": null
+        "human_in_the_loop_safe_mode": true,
+        "sensitive_action_safe_mode": false
       },
       "marketplace_listing": null
     },
@@ -65,6 +68,8 @@
         "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,
@@ -72,7 +77,8 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": null
+        "human_in_the_loop_safe_mode": true,
+        "sensitive_action_safe_mode": false
       },
       "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

@@ -175,6 +175,8 @@ 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
 
@@ -549,9 +551,48 @@ Files:
 Types:
 
 - Prefer `interface` for object shapes
-- Component props should be `interface Props { ... }`
+- 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
 - 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

@@ -16,6 +16,12 @@ 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

@@ -15,6 +15,8 @@
     "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",
@@ -118,6 +120,7 @@
   },
   "devDependencies": {
     "@chromatic-com/storybook": "4.1.2",
+    "happy-dom": "20.3.4",
     "@opentelemetry/instrumentation": "0.209.0",
     "@playwright/test": "1.56.1",
     "@storybook/addon-a11y": "9.1.5",
@@ -127,6 +130,8 @@
     "@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/react": "16.3.2",
     "@types/canvas-confetti": "1.9.0",
     "@types/lodash": "4.17.20",
     "@types/negotiator": "0.6.4",
@@ -135,6 +140,7 @@
     "@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",
@@ -153,7 +159,9 @@
     "require-in-the-middle": "8.0.1",
     "storybook": "9.1.5",
     "tailwindcss": "3.4.17",
-    "typescript": "5.9.3"
+    "typescript": "5.9.3",
+    "vite-tsconfig-paths": "6.0.4",
+    "vitest": "4.0.17"
   },
   "msw": {
     "workerDirectory": [

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

@@ -0,0 +1,58 @@
+"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, Clock } from "lucide-react";
+import { Users, DollarSign, UserSearch, FileText } from "lucide-react";
 
 import { IconSliders } from "@/components/__legacy__/ui/icons";
 
@@ -11,11 +11,6 @@ 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

@@ -1,217 +0,0 @@
-"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

@@ -1,221 +0,0 @@
-"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

@@ -1,156 +0,0 @@
-"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

@@ -1,206 +0,0 @@
-"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

@@ -1,52 +0,0 @@
-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 = "/marketplace";
+  let next = "/";
 
   if (code) {
     const supabase = await getServerSupabase();

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

@@ -18,69 +18,110 @@ 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 {
-    currentSafeMode,
+    currentHITLSafeMode,
+    showHITLToggle,
+    handleHITLToggle,
+    currentSensitiveActionSafeMode,
+    showSensitiveActionToggle,
+    handleSensitiveActionToggle,
     isPending,
     shouldShowToggle,
-    isStateUndetermined,
-    handleToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle || isStateUndetermined || isPending) {
+  if (!shouldShowToggle || isPending) {
     return null;
   }
 
   return (
-    <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 className={cn("fixed z-50 flex flex-col gap-2", className)}>
+      {showHITLToggle && (
+        <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}
+        />
+      )}
+      {showSensitiveActionToggle && (
+        <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>
   );
 }

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

@@ -1,134 +0,0 @@
-"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/ChatContainer/ChatContainer.tsx

@@ -1,88 +0,0 @@
-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/ChatInput/ChatInput.tsx

@@ -1,64 +0,0 @@
-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

@@ -1,60 +0,0 @@
-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/MessageList/MessageList.tsx

@@ -1,121 +0,0 @@
-"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/ThinkingMessage/ThinkingMessage.tsx

@@ -1,70 +0,0 @@
-import { cn } from "@/lib/utils";
-import { RobotIcon } from "@phosphor-icons/react";
-import { useEffect, useRef, useState } from "react";
-import { MessageBubble } from "../MessageBubble/MessageBubble";
-
-export interface ThinkingMessageProps {
-  className?: string;
-}
-
-export function ThinkingMessage({ className }: ThinkingMessageProps) {
-  const [showSlowLoader, setShowSlowLoader] = useState(false);
-  const timerRef = useRef<NodeJS.Timeout | null>(null);
-
-  useEffect(() => {
-    if (timerRef.current === null) {
-      timerRef.current = setTimeout(() => {
-        setShowSlowLoader(true);
-      }, 8000);
-    }
-
-    return () => {
-      if (timerRef.current) {
-        clearTimeout(timerRef.current);
-        timerRef.current = null;
-      }
-    };
-  }, []);
-
-  return (
-    <div
-      className={cn(
-        "group relative flex w-full justify-start gap-3 px-4 py-3",
-        className,
-      )}
-    >
-      <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">
-          <MessageBubble variant="assistant">
-            <div className="transition-all duration-500 ease-in-out">
-              {showSlowLoader ? (
-                <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"
-                  style={{
-                    backgroundSize: "200% 100%",
-                    animation: "shimmer 2s ease-in-out infinite",
-                  }}
-                >
-                  Thinking...
-                </span>
-              )}
-            </div>
-          </MessageBubble>
-        </div>
-      </div>
-    </div>
-  );
-}

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

@@ -1,24 +0,0 @@
-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

@@ -1,260 +0,0 @@
-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>
-  );
-}

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

@@ -1,66 +0,0 @@
-/**
- * Maps internal tool names to user-friendly display names with emojis.
- * @deprecated Use getToolActionPhrase or getToolCompletionPhrase for status messages
- *
- * @param toolName - The internal tool name from the backend
- * @returns A user-friendly display name with an emoji prefix
- */
-export function getToolDisplayName(toolName: string): string {
-  const toolDisplayNames: Record<string, string> = {
-    find_agent: "🔍 Search Marketplace",
-    get_agent_details: "📋 Get Agent Details",
-    check_credentials: "🔑 Check Credentials",
-    setup_agent: "⚙️ Setup Agent",
-    run_agent: "▶️ Run Agent",
-    get_required_setup_info: "📝 Get Setup Requirements",
-  };
-  return toolDisplayNames[toolName] || toolName;
-}
-
-/**
- * Maps internal tool names to human-friendly action phrases (present continuous).
- * Used for tool call messages to indicate what action is currently happening.
- *
- * @param toolName - The internal tool name from the backend
- * @returns A human-friendly action phrase in present continuous tense
- */
-export function getToolActionPhrase(toolName: string): string {
-  const toolActionPhrases: Record<string, string> = {
-    find_agent: "Looking for agents in the marketplace",
-    agent_carousel: "Looking for agents in the marketplace",
-    get_agent_details: "Learning about the agent",
-    check_credentials: "Checking your credentials",
-    setup_agent: "Setting up the agent",
-    execution_started: "Running the agent",
-    run_agent: "Running the agent",
-    get_required_setup_info: "Getting setup requirements",
-    schedule_agent: "Scheduling the agent to run",
-  };
-
-  // Return mapped phrase or generate human-friendly fallback
-  return toolActionPhrases[toolName] || toolName;
-}
-
-/**
- * Maps internal tool names to human-friendly completion phrases (past tense).
- * Used for tool response messages to indicate what action was completed.
- *
- * @param toolName - The internal tool name from the backend
- * @returns A human-friendly completion phrase in past tense
- */
-export function getToolCompletionPhrase(toolName: string): string {
-  const toolCompletionPhrases: Record<string, string> = {
-    find_agent: "Finished searching the marketplace",
-    get_agent_details: "Got agent details",
-    check_credentials: "Checked credentials",
-    setup_agent: "Agent setup complete",
-    run_agent: "Agent execution started",
-    get_required_setup_info: "Got setup requirements",
-  };
-
-  // Return mapped phrase or generate human-friendly fallback
-  return (
-    toolCompletionPhrases[toolName] ||
-    `Finished ${toolName.replace(/_/g, " ").replace("...", "")}`
-  );
-}

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

@@ -1,271 +0,0 @@
-import {
-  getGetV2GetSessionQueryKey,
-  getGetV2GetSessionQueryOptions,
-  postV2CreateSession,
-  useGetV2GetSession,
-  usePatchV2SessionAssignUser,
-  usePostV2CreateSession,
-} from "@/app/api/__generated__/endpoints/chat/chat";
-import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-import { okData } from "@/app/api/helpers";
-import { isValidUUID } from "@/lib/utils";
-import { Key, storage } from "@/services/storage/local-storage";
-import { useQueryClient } from "@tanstack/react-query";
-import { useCallback, useEffect, useMemo, useRef, useState } from "react";
-import { toast } from "sonner";
-
-interface UseChatSessionArgs {
-  urlSessionId?: string | null;
-  autoCreate?: boolean;
-}
-
-export function useChatSession({
-  urlSessionId,
-  autoCreate = false,
-}: UseChatSessionArgs = {}) {
-  const queryClient = useQueryClient();
-  const [sessionId, setSessionId] = useState<string | null>(null);
-  const [error, setError] = useState<Error | null>(null);
-  const justCreatedSessionIdRef = useRef<string | null>(null);
-
-  useEffect(() => {
-    if (urlSessionId) {
-      if (!isValidUUID(urlSessionId)) {
-        console.error("Invalid session ID format:", urlSessionId);
-        toast.error("Invalid session ID", {
-          description:
-            "The session ID in the URL is not valid. Starting a new session...",
-        });
-        setSessionId(null);
-        storage.clean(Key.CHAT_SESSION_ID);
-        return;
-      }
-      setSessionId(urlSessionId);
-      storage.set(Key.CHAT_SESSION_ID, urlSessionId);
-    } else {
-      const storedSessionId = storage.get(Key.CHAT_SESSION_ID);
-      if (storedSessionId) {
-        if (!isValidUUID(storedSessionId)) {
-          console.error("Invalid stored session ID:", storedSessionId);
-          storage.clean(Key.CHAT_SESSION_ID);
-          setSessionId(null);
-        } else {
-          setSessionId(storedSessionId);
-        }
-      } else if (autoCreate) {
-        setSessionId(null);
-      }
-    }
-  }, [urlSessionId, autoCreate]);
-
-  const {
-    mutateAsync: createSessionMutation,
-    isPending: isCreating,
-    error: createError,
-  } = usePostV2CreateSession();
-
-  const {
-    data: sessionData,
-    isLoading: isLoadingSession,
-    error: loadError,
-    refetch,
-  } = useGetV2GetSession(sessionId || "", {
-    query: {
-      enabled: !!sessionId,
-      select: okData,
-      staleTime: Infinity, // Never mark as stale
-      refetchOnMount: false, // Don't refetch on component mount
-      refetchOnWindowFocus: false, // Don't refetch when window regains focus
-      refetchOnReconnect: false, // Don't refetch when network reconnects
-      retry: 1,
-    },
-  });
-
-  const { mutateAsync: claimSessionMutation } = usePatchV2SessionAssignUser();
-
-  const session = useMemo(() => {
-    if (sessionData) return sessionData;
-
-    if (sessionId && justCreatedSessionIdRef.current === sessionId) {
-      return {
-        id: sessionId,
-        user_id: null,
-        messages: [],
-        created_at: new Date().toISOString(),
-        updated_at: new Date().toISOString(),
-      } as SessionDetailResponse;
-    }
-    return null;
-  }, [sessionData, sessionId]);
-
-  const messages = session?.messages || [];
-  const isLoading = isCreating || isLoadingSession;
-
-  useEffect(() => {
-    if (createError) {
-      setError(
-        createError instanceof Error
-          ? createError
-          : new Error("Failed to create session"),
-      );
-    } else if (loadError) {
-      setError(
-        loadError instanceof Error
-          ? loadError
-          : new Error("Failed to load session"),
-      );
-    } else {
-      setError(null);
-    }
-  }, [createError, loadError]);
-
-  const createSession = useCallback(
-    async function createSession() {
-      try {
-        setError(null);
-        const response = await postV2CreateSession({
-          body: JSON.stringify({}),
-        });
-        if (response.status !== 200) {
-          throw new Error("Failed to create session");
-        }
-        const newSessionId = response.data.id;
-        setSessionId(newSessionId);
-        storage.set(Key.CHAT_SESSION_ID, newSessionId);
-        justCreatedSessionIdRef.current = newSessionId;
-        setTimeout(() => {
-          if (justCreatedSessionIdRef.current === newSessionId) {
-            justCreatedSessionIdRef.current = null;
-          }
-        }, 10000);
-        return newSessionId;
-      } catch (err) {
-        const error =
-          err instanceof Error ? err : new Error("Failed to create session");
-        setError(error);
-        toast.error("Failed to create chat session", {
-          description: error.message,
-        });
-        throw error;
-      }
-    },
-    [createSessionMutation],
-  );
-
-  const loadSession = useCallback(
-    async function loadSession(id: string) {
-      try {
-        setError(null);
-        // Invalidate the query cache for this session to force a fresh fetch
-        await queryClient.invalidateQueries({
-          queryKey: getGetV2GetSessionQueryKey(id),
-        });
-        // Set sessionId after invalidation to ensure the hook refetches
-        setSessionId(id);
-        storage.set(Key.CHAT_SESSION_ID, id);
-        // Force fetch with fresh data (bypass cache)
-        const queryOptions = getGetV2GetSessionQueryOptions(id, {
-          query: {
-            staleTime: 0, // Force fresh fetch
-            retry: 1,
-          },
-        });
-        const result = await queryClient.fetchQuery(queryOptions);
-        if (!result || ("status" in result && result.status !== 200)) {
-          console.warn("Session not found on server, clearing local state");
-          storage.clean(Key.CHAT_SESSION_ID);
-          setSessionId(null);
-          throw new Error("Session not found");
-        }
-      } catch (err) {
-        const error =
-          err instanceof Error ? err : new Error("Failed to load session");
-        setError(error);
-        throw error;
-      }
-    },
-    [queryClient],
-  );
-
-  const refreshSession = useCallback(
-    async function refreshSession() {
-      if (!sessionId) {
-        console.log("[refreshSession] Skipping - no session ID");
-        return;
-      }
-      try {
-        setError(null);
-        await refetch();
-      } catch (err) {
-        const error =
-          err instanceof Error ? err : new Error("Failed to refresh session");
-        setError(error);
-        throw error;
-      }
-    },
-    [sessionId, refetch],
-  );
-
-  const claimSession = useCallback(
-    async function claimSession(id: string) {
-      try {
-        setError(null);
-        await claimSessionMutation({ sessionId: id });
-        if (justCreatedSessionIdRef.current === id) {
-          justCreatedSessionIdRef.current = null;
-        }
-        await queryClient.invalidateQueries({
-          queryKey: getGetV2GetSessionQueryKey(id),
-        });
-        await refetch();
-        toast.success("Session claimed successfully", {
-          description: "Your chat history has been saved to your account",
-        });
-      } catch (err: unknown) {
-        const error =
-          err instanceof Error ? err : new Error("Failed to claim session");
-        const is404 =
-          (typeof err === "object" &&
-            err !== null &&
-            "status" in err &&
-            err.status === 404) ||
-          (typeof err === "object" &&
-            err !== null &&
-            "response" in err &&
-            typeof err.response === "object" &&
-            err.response !== null &&
-            "status" in err.response &&
-            err.response.status === 404);
-        if (!is404) {
-          setError(error);
-          toast.error("Failed to claim session", {
-            description: error.message || "Unable to claim session",
-          });
-        }
-        throw error;
-      }
-    },
-    [claimSessionMutation, queryClient, refetch],
-  );
-
-  const clearSession = useCallback(function clearSession() {
-    setSessionId(null);
-    setError(null);
-    storage.clean(Key.CHAT_SESSION_ID);
-    justCreatedSessionIdRef.current = null;
-  }, []);
-
-  return {
-    session,
-    sessionId,
-    messages,
-    isLoading,
-    isCreating,
-    error,
-    createSession,
-    loadSession,
-    refreshSession,
-    claimSession,
-    clearSession,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/chat/page.tsx

@@ -1,27 +0,0 @@
-"use client";
-
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
-import { useRouter } from "next/navigation";
-import { useEffect } from "react";
-import { Chat } from "./components/Chat/Chat";
-
-export default function ChatPage() {
-  const isChatEnabled = useGetFlag(Flag.CHAT);
-  const router = useRouter();
-
-  useEffect(() => {
-    if (isChatEnabled === false) {
-      router.push("/marketplace");
-    }
-  }, [isChatEnabled, router]);
-
-  if (isChatEnabled === null || isChatEnabled === false) {
-    return null;
-  }
-
-  return (
-    <div className="flex h-full flex-col">
-      <Chat className="flex-1" />
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/NewChatContext.tsx

@@ -0,0 +1,41 @@
+"use client";
+
+import { createContext, useContext, useRef, type ReactNode } from "react";
+
+interface NewChatContextValue {
+  onNewChatClick: () => void;
+  setOnNewChatClick: (handler?: () => void) => void;
+  performNewChat?: () => void;
+  setPerformNewChat: (handler?: () => void) => void;
+}
+
+const NewChatContext = createContext<NewChatContextValue | null>(null);
+
+export function NewChatProvider({ children }: { children: ReactNode }) {
+  const onNewChatRef = useRef<(() => void) | undefined>();
+  const performNewChatRef = useRef<(() => void) | undefined>();
+  const contextValueRef = useRef<NewChatContextValue>({
+    onNewChatClick() {
+      onNewChatRef.current?.();
+    },
+    setOnNewChatClick(handler?: () => void) {
+      onNewChatRef.current = handler;
+    },
+    performNewChat() {
+      performNewChatRef.current?.();
+    },
+    setPerformNewChat(handler?: () => void) {
+      performNewChatRef.current = handler;
+    },
+  });
+
+  return (
+    <NewChatContext.Provider value={contextValueRef.current}>
+      {children}
+    </NewChatContext.Provider>
+  );
+}
+
+export function useNewChat() {
+  return useContext(NewChatContext);
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx

@@ -0,0 +1,105 @@
+"use client";
+
+import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
+import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
+import type { ReactNode } from "react";
+import { useEffect } from "react";
+import { useNewChat } from "../../NewChatContext";
+import { DesktopSidebar } from "./components/DesktopSidebar/DesktopSidebar";
+import { LoadingState } from "./components/LoadingState/LoadingState";
+import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
+import { MobileHeader } from "./components/MobileHeader/MobileHeader";
+import { useCopilotShell } from "./useCopilotShell";
+
+interface Props {
+  children: ReactNode;
+}
+
+export function CopilotShell({ children }: Props) {
+  const {
+    isMobile,
+    isDrawerOpen,
+    isLoading,
+    isLoggedIn,
+    hasActiveSession,
+    sessions,
+    currentSessionId,
+    handleSelectSession,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+    handleNewChat,
+    hasNextPage,
+    isFetchingNextPage,
+    fetchNextPage,
+    isReadyToShowContent,
+  } = useCopilotShell();
+
+  const newChatContext = useNewChat();
+  const handleNewChatClickWrapper =
+    newChatContext?.onNewChatClick || handleNewChat;
+
+  useEffect(
+    function registerNewChatHandler() {
+      if (!newChatContext) return;
+      newChatContext.setPerformNewChat(handleNewChat);
+      return function cleanup() {
+        newChatContext.setPerformNewChat(undefined);
+      };
+    },
+    [newChatContext, handleNewChat],
+  );
+
+  if (!isLoggedIn) {
+    return (
+      <div className="flex h-full items-center justify-center">
+        <ChatLoader />
+      </div>
+    );
+  }
+
+  return (
+    <div
+      className="flex overflow-hidden bg-[#EFEFF0]"
+      style={{ height: `calc(100vh - ${NAVBAR_HEIGHT_PX}px)` }}
+    >
+      {!isMobile && (
+        <DesktopSidebar
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={handleSelectSession}
+          onFetchNextPage={fetchNextPage}
+          onNewChat={handleNewChatClickWrapper}
+          hasActiveSession={Boolean(hasActiveSession)}
+        />
+      )}
+
+      <div className="relative flex min-h-0 flex-1 flex-col">
+        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
+        <div className="flex min-h-0 flex-1 flex-col">
+          {isReadyToShowContent ? children : <LoadingState />}
+        </div>
+      </div>
+
+      {isMobile && (
+        <MobileDrawer
+          isOpen={isDrawerOpen}
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={handleSelectSession}
+          onFetchNextPage={fetchNextPage}
+          onNewChat={handleNewChatClickWrapper}
+          onClose={handleCloseDrawer}
+          onOpenChange={handleDrawerOpenChange}
+          hasActiveSession={Boolean(hasActiveSession)}
+        />
+      )}
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx

@@ -0,0 +1,70 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { scrollbarStyles } from "@/components/styles/scrollbars";
+import { cn } from "@/lib/utils";
+import { Plus } from "@phosphor-icons/react";
+import { SessionsList } from "../SessionsList/SessionsList";
+
+interface Props {
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+  onNewChat: () => void;
+  hasActiveSession: boolean;
+}
+
+export function DesktopSidebar({
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+  onNewChat,
+  hasActiveSession,
+}: Props) {
+  return (
+    <aside className="flex h-full w-80 flex-col border-r border-zinc-100 bg-zinc-50">
+      <div className="shrink-0 px-6 py-4">
+        <Text variant="h3" size="body-medium">
+          Your chats
+        </Text>
+      </div>
+      <div
+        className={cn(
+          "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
+          scrollbarStyles,
+        )}
+      >
+        <SessionsList
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={onSelectSession}
+          onFetchNextPage={onFetchNextPage}
+        />
+      </div>
+      {hasActiveSession && (
+        <div className="shrink-0 bg-zinc-50 p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
+          <Button
+            variant="primary"
+            size="small"
+            onClick={onNewChat}
+            className="w-full"
+            leftIcon={<Plus width="1rem" height="1rem" />}
+          >
+            New Chat
+          </Button>
+        </div>
+      )}
+    </aside>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/LoadingState/LoadingState.tsx

@@ -0,0 +1,15 @@
+import { Text } from "@/components/atoms/Text/Text";
+import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
+
+export function LoadingState() {
+  return (
+    <div className="flex flex-1 items-center justify-center">
+      <div className="flex flex-col items-center gap-4">
+        <ChatLoader />
+        <Text variant="body" className="text-zinc-500">
+          Loading your chats...
+        </Text>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx

@@ -0,0 +1,91 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Button } from "@/components/atoms/Button/Button";
+import { scrollbarStyles } from "@/components/styles/scrollbars";
+import { cn } from "@/lib/utils";
+import { PlusIcon, X } from "@phosphor-icons/react";
+import { Drawer } from "vaul";
+import { SessionsList } from "../SessionsList/SessionsList";
+
+interface Props {
+  isOpen: boolean;
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+  onNewChat: () => void;
+  onClose: () => void;
+  onOpenChange: (open: boolean) => void;
+  hasActiveSession: boolean;
+}
+
+export function MobileDrawer({
+  isOpen,
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+  onNewChat,
+  onClose,
+  onOpenChange,
+  hasActiveSession,
+}: Props) {
+  return (
+    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
+      <Drawer.Portal>
+        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
+        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
+          <div className="shrink-0 border-b border-zinc-200 p-4">
+            <div className="flex items-center justify-between">
+              <Drawer.Title className="text-lg font-semibold text-zinc-800">
+                Your chats
+              </Drawer.Title>
+              <Button
+                variant="icon"
+                size="icon"
+                aria-label="Close sessions"
+                onClick={onClose}
+              >
+                <X width="1.25rem" height="1.25rem" />
+              </Button>
+            </div>
+          </div>
+          <div
+            className={cn(
+              "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
+              scrollbarStyles,
+            )}
+          >
+            <SessionsList
+              sessions={sessions}
+              currentSessionId={currentSessionId}
+              isLoading={isLoading}
+              hasNextPage={hasNextPage}
+              isFetchingNextPage={isFetchingNextPage}
+              onSelectSession={onSelectSession}
+              onFetchNextPage={onFetchNextPage}
+            />
+          </div>
+          {hasActiveSession && (
+            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
+              <Button
+                variant="primary"
+                size="small"
+                onClick={onNewChat}
+                className="w-full"
+                leftIcon={<PlusIcon width="1rem" height="1rem" />}
+              >
+                New Chat
+              </Button>
+            </div>
+          )}
+        </Drawer.Content>
+      </Drawer.Portal>
+    </Drawer.Root>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts

@@ -0,0 +1,24 @@
+import { useState } from "react";
+
+export function useMobileDrawer() {
+  const [isDrawerOpen, setIsDrawerOpen] = useState(false);
+
+  function handleOpenDrawer() {
+    setIsDrawerOpen(true);
+  }
+
+  function handleCloseDrawer() {
+    setIsDrawerOpen(false);
+  }
+
+  function handleDrawerOpenChange(open: boolean) {
+    setIsDrawerOpen(open);
+  }
+
+  return {
+    isDrawerOpen,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileHeader/MobileHeader.tsx

@@ -0,0 +1,22 @@
+import { Button } from "@/components/atoms/Button/Button";
+import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
+import { ListIcon } from "@phosphor-icons/react";
+
+interface Props {
+  onOpenDrawer: () => void;
+}
+
+export function MobileHeader({ onOpenDrawer }: Props) {
+  return (
+    <Button
+      variant="icon"
+      size="icon"
+      aria-label="Open sessions"
+      onClick={onOpenDrawer}
+      className="fixed z-50 bg-white shadow-md"
+      style={{ left: "1rem", top: `${NAVBAR_HEIGHT_PX + 20}px` }}
+    >
+      <ListIcon width="1.25rem" height="1.25rem" />
+    </Button>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx

@@ -0,0 +1,80 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Skeleton } from "@/components/__legacy__/ui/skeleton";
+import { Text } from "@/components/atoms/Text/Text";
+import { InfiniteList } from "@/components/molecules/InfiniteList/InfiniteList";
+import { cn } from "@/lib/utils";
+import { getSessionTitle } from "../../helpers";
+
+interface Props {
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+}
+
+export function SessionsList({
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+}: Props) {
+  if (isLoading) {
+    return (
+      <div className="space-y-1">
+        {Array.from({ length: 5 }).map((_, i) => (
+          <div key={i} className="rounded-lg px-3 py-2.5">
+            <Skeleton className="h-5 w-full" />
+          </div>
+        ))}
+      </div>
+    );
+  }
+
+  if (sessions.length === 0) {
+    return (
+      <div className="flex h-full items-center justify-center">
+        <Text variant="body" className="text-zinc-500">
+          You don&apos;t have previous chats
+        </Text>
+      </div>
+    );
+  }
+
+  return (
+    <InfiniteList
+      items={sessions}
+      hasMore={hasNextPage}
+      isFetchingMore={isFetchingNextPage}
+      onEndReached={onFetchNextPage}
+      className="space-y-1"
+      renderItem={(session) => {
+        const isActive = session.id === currentSessionId;
+        return (
+          <button
+            onClick={() => onSelectSession(session.id)}
+            className={cn(
+              "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
+              isActive ? "bg-zinc-100" : "hover:bg-zinc-50",
+            )}
+          >
+            <Text
+              variant="body"
+              className={cn(
+                "font-normal",
+                isActive ? "text-zinc-600" : "text-zinc-800",
+              )}
+            >
+              {getSessionTitle(session)}
+            </Text>
+          </button>
+        );
+      }}
+    />
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts

@@ -0,0 +1,92 @@
+import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { okData } from "@/app/api/helpers";
+import { useEffect, useMemo, useState } from "react";
+
+const PAGE_SIZE = 50;
+
+export interface UseSessionsPaginationArgs {
+  enabled: boolean;
+}
+
+export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
+  const [offset, setOffset] = useState(0);
+  const [accumulatedSessions, setAccumulatedSessions] = useState<
+    SessionSummaryResponse[]
+  >([]);
+  const [totalCount, setTotalCount] = useState<number | null>(null);
+
+  const { data, isLoading, isFetching, isError } = useGetV2ListSessions(
+    { limit: PAGE_SIZE, offset },
+    {
+      query: {
+        enabled: enabled && offset >= 0,
+      },
+    },
+  );
+
+  useEffect(() => {
+    const responseData = okData(data);
+    if (responseData) {
+      const newSessions = responseData.sessions;
+      const total = responseData.total;
+      setTotalCount(total);
+
+      if (offset === 0) {
+        setAccumulatedSessions(newSessions);
+      } else {
+        setAccumulatedSessions((prev) => [...prev, ...newSessions]);
+      }
+    } else if (!enabled) {
+      setAccumulatedSessions([]);
+      setTotalCount(null);
+    }
+  }, [data, offset, enabled]);
+
+  const hasNextPage = useMemo(() => {
+    if (totalCount === null) return false;
+    return accumulatedSessions.length < totalCount;
+  }, [accumulatedSessions.length, totalCount]);
+
+  const areAllSessionsLoaded = useMemo(() => {
+    if (totalCount === null) return false;
+    return (
+      accumulatedSessions.length >= totalCount && !isFetching && !isLoading
+    );
+  }, [accumulatedSessions.length, totalCount, isFetching, isLoading]);
+
+  useEffect(() => {
+    if (
+      hasNextPage &&
+      !isFetching &&
+      !isLoading &&
+      !isError &&
+      totalCount !== null
+    ) {
+      setOffset((prev) => prev + PAGE_SIZE);
+    }
+  }, [hasNextPage, isFetching, isLoading, isError, totalCount]);
+
+  function fetchNextPage() {
+    if (hasNextPage && !isFetching) {
+      setOffset((prev) => prev + PAGE_SIZE);
+    }
+  }
+
+  function reset() {
+    setOffset(0);
+    setAccumulatedSessions([]);
+    setTotalCount(null);
+  }
+
+  return {
+    sessions: accumulatedSessions,
+    isLoading,
+    isFetching,
+    hasNextPage,
+    areAllSessionsLoaded,
+    totalCount,
+    fetchNextPage,
+    reset,
+  };
+}