feat(frontend): enhance UI components with animations and accessibility improvements

### Changes 🏗️
- Integrated `FadeIn` animations in `AgentsSection`, `FeaturedCreators`, `FeaturedSection`, `HeroSection`, and `BecomeACreator` components for improved visual appeal.
- Replaced static elements with `StaggeredList` in `FeaturedCreators` and `AgentsSection` for a more dynamic layout.
- Updated `SearchBar` to use `type="search"` and added `aria-label` for better accessibility.
- Enhanced `StoreCard` with focus-visible styles and keyboard navigation support.
- Refactored `FilterChips` to utilize `FilterChip` component for a more consistent design.

### Checklist 📋
- [x] Verified animations function correctly across components.
- [x] Ensured accessibility improvements are in place and tested.
- [x] Confirmed UI consistency with design specifications.

AGENTS.md

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

autogpt_platform/CLAUDE.md

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

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py

@@ -33,7 +33,7 @@ from .models import (
     UserReadiness,
 )
 from .utils import (
-    build_missing_credentials_from_graph,
+    check_user_has_required_credentials,
     extract_credentials_from_schema,
     fetch_graph_from_store_slug,
     get_or_create_library_agent,
@@ -237,13 +237,15 @@ class RunAgentTool(BaseTool):
                 # Return credentials needed response with input data info
                 # The UI handles credential setup automatically, so the message
                 # focuses on asking about input data
-                requirements_creds_dict = build_missing_credentials_from_graph(
-                    graph, None
+                credentials = extract_credentials_from_schema(
+                    graph.credentials_input_schema
                 )
-                missing_credentials_dict = build_missing_credentials_from_graph(
-                    graph, graph_credentials
+                missing_creds_check = await check_user_has_required_credentials(
+                    user_id, credentials
                 )
-                requirements_creds_list = list(requirements_creds_dict.values())
+                missing_credentials_dict = {
+                    c.id: c.model_dump() for c in missing_creds_check
+                }
 
                 return SetupRequirementsResponse(
                     message=self._build_inputs_message(graph, MSG_WHAT_VALUES_TO_USE),
@@ -257,7 +259,7 @@ class RunAgentTool(BaseTool):
                             ready_to_run=False,
                         ),
                         requirements={
-                            "credentials": requirements_creds_list,
+                            "credentials": [c.model_dump() for c in credentials],
                             "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },

autogpt_platform/backend/backend/api/features/chat/tools/run_block.py

@@ -22,7 +22,6 @@ from .models import (
     ToolResponseBase,
     UserReadiness,
 )
-from .utils import build_missing_credentials_from_field_info
 
 logger = logging.getLogger(__name__)
 
@@ -190,11 +189,7 @@ class RunBlockTool(BaseTool):
 
         if missing_credentials:
             # Return setup requirements response with missing credentials
-            credentials_fields_info = block.input_schema.get_credentials_fields_info()
-            missing_creds_dict = build_missing_credentials_from_field_info(
-                credentials_fields_info, set(matched_credentials.keys())
-            )
-            missing_creds_list = list(missing_creds_dict.values())
+            missing_creds_dict = {c.id: c.model_dump() for c in missing_credentials}
 
             return SetupRequirementsResponse(
                 message=(
@@ -211,7 +206,7 @@ class RunBlockTool(BaseTool):
                         ready_to_run=False,
                     ),
                     requirements={
-                        "credentials": missing_creds_list,
+                        "credentials": [c.model_dump() for c in missing_credentials],
                         "inputs": self._get_inputs_list(block),
                         "execution_modes": ["immediate"],
                     },

autogpt_platform/backend/backend/api/features/chat/tools/utils.py

@@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -89,59 +89,6 @@ def extract_credentials_from_schema(
     return credentials
 
 
-def _serialize_missing_credential(
-    field_key: str, field_info: CredentialsFieldInfo
-) -> dict[str, Any]:
-    """
-    Convert credential field info into a serializable dict that preserves all supported
-    credential types (e.g., api_key + oauth2) so the UI can offer multiple options.
-    """
-    supported_types = sorted(field_info.supported_types)
-    provider = next(iter(field_info.provider), "unknown")
-    scopes = sorted(field_info.required_scopes or [])
-
-    return {
-        "id": field_key,
-        "title": field_key.replace("_", " ").title(),
-        "provider": provider,
-        "provider_name": provider.replace("_", " ").title(),
-        "type": supported_types[0] if supported_types else "api_key",
-        "types": supported_types,
-        "scopes": scopes,
-    }
-
-
-def build_missing_credentials_from_graph(
-    graph: GraphModel, matched_credentials: dict[str, CredentialsMetaInput] | None
-) -> dict[str, Any]:
-    """
-    Build a missing_credentials mapping from a graph's aggregated credentials inputs,
-    preserving all supported credential types for each field.
-    """
-    matched_keys = set(matched_credentials.keys()) if matched_credentials else set()
-    aggregated_fields = graph.aggregate_credentials_inputs()
-
-    return {
-        field_key: _serialize_missing_credential(field_key, field_info)
-        for field_key, (field_info, _node_fields) in aggregated_fields.items()
-        if field_key not in matched_keys
-    }
-
-
-def build_missing_credentials_from_field_info(
-    credential_fields: dict[str, CredentialsFieldInfo],
-    matched_keys: set[str],
-) -> dict[str, Any]:
-    """
-    Build missing_credentials mapping from a simple credentials field info dictionary.
-    """
-    return {
-        field_key: _serialize_missing_credential(field_key, field_info)
-        for field_key, field_info in credential_fields.items()
-        if field_key not in matched_keys
-    }
-
-
 def extract_credentials_as_dict(
     credentials_input_schema: dict[str, Any] | None,
 ) -> dict[str, CredentialsMetaInput]:

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

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

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

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

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

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

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

@@ -154,7 +154,6 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-        # Use unqualified ::vector - pgvector is in search_path on all environments
         await execute_raw_with_schema(
             """
             INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
@@ -178,6 +177,7 @@ async def store_content_embedding(
             searchable_text,
             metadata_json,
             client=client,
+            set_public_search_path=True,
         )
 
         logger.info(f"Stored embedding for {content_type}:{content_id}")
@@ -236,6 +236,7 @@ async def get_content_embedding(
             content_type,
             content_id,
             user_id,
+            set_public_search_path=True,
         )
 
         if result and len(result) > 0:
@@ -870,45 +871,31 @@ async def semantic_search(
         # Add content type parameters and build placeholders dynamically
         content_type_start_idx = len(params) + 1
         content_type_placeholders = ", ".join(
-            "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
+            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
             for i in range(len(content_types))
         )
         params.extend([ct.value for ct in content_types])
 
-        # Build min_similarity param index before appending
-        min_similarity_idx = len(params) + 1
-        params.append(min_similarity)
-
-        # Use unqualified ::vector and <=> operator - pgvector is in search_path on all environments
-        sql = (
-            """
+        sql = f"""
             SELECT
                 "contentId" as content_id,
                 "contentType" as content_type,
                 "searchableText" as searchable_text,
                 metadata,
-                1 - (embedding <=> '"""
-            + embedding_str
-            + """'::vector) as similarity
-            FROM {schema_prefix}"UnifiedContentEmbedding"
-            WHERE "contentType" IN ("""
-            + content_type_placeholders
-            + """)
-            """
-            + user_filter
-            + """
-            AND 1 - (embedding <=> '"""
-            + embedding_str
-            + """'::vector) >= $"""
-            + str(min_similarity_idx)
-            + """
+                1 - (embedding <=> '{embedding_str}'::vector) as similarity
+            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+            WHERE "contentType" IN ({content_type_placeholders})
+            {user_filter}
+            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
             ORDER BY similarity DESC
             LIMIT $1
         """
-        )
+        params.append(min_similarity)
 
         try:
-            results = await query_raw_with_schema(sql, *params)
+            results = await query_raw_with_schema(
+                sql, *params, set_public_search_path=True
+            )
             return [
                 {
                     "content_id": row["content_id"],
@@ -935,41 +922,31 @@ async def semantic_search(
     # Add content type parameters and build placeholders dynamically
     content_type_start_idx = len(params_lexical) + 1
     content_type_placeholders_lexical = ", ".join(
-        "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
+        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
         for i in range(len(content_types))
     )
     params_lexical.extend([ct.value for ct in content_types])
 
-    # Build query param index before appending
-    query_param_idx = len(params_lexical) + 1
-    params_lexical.append(f"%{query}%")
-
-    # Use regular string (not f-string) for template to preserve {schema_prefix} placeholders
-    sql_lexical = (
-        """
+    sql_lexical = f"""
         SELECT
             "contentId" as content_id,
             "contentType" as content_type,
             "searchableText" as searchable_text,
             metadata,
             0.0 as similarity
-        FROM {schema_prefix}"UnifiedContentEmbedding"
-        WHERE "contentType" IN ("""
-        + content_type_placeholders_lexical
-        + """)
-        """
-        + user_filter
-        + """
-        AND "searchableText" ILIKE $"""
-        + str(query_param_idx)
-        + """
+        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+        WHERE "contentType" IN ({content_type_placeholders_lexical})
+        {user_filter}
+        AND "searchableText" ILIKE ${len(params_lexical) + 1}
         ORDER BY "updatedAt" DESC
         LIMIT $1
     """
-    )
+    params_lexical.append(f"%{query}%")
 
     try:
-        results = await query_raw_with_schema(sql_lexical, *params_lexical)
+        results = await query_raw_with_schema(
+            sql_lexical, *params_lexical, set_public_search_path=True
+        )
         return [
             {
                 "content_id": row["content_id"],

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

@@ -155,14 +155,18 @@ async def test_store_embedding_success(mocker):
     )
 
     assert result is True
-    # execute_raw is called once for INSERT (no separate SET search_path needed)
-    assert mock_client.execute_raw.call_count == 1
+    # execute_raw is called twice: once for SET search_path, once for INSERT
+    assert mock_client.execute_raw.call_count == 2
 
-    # Verify the INSERT query with the actual data
-    call_args = mock_client.execute_raw.call_args_list[0][0]
-    assert "test-version-id" in call_args
-    assert "[0.1,0.2,0.3]" in call_args
-    assert None in call_args  # userId should be None for store agents
+    # First call: SET search_path
+    first_call_args = mock_client.execute_raw.call_args_list[0][0]
+    assert "SET search_path" in first_call_args[0]
+
+    # Second call: INSERT query with the actual data
+    second_call_args = mock_client.execute_raw.call_args_list[1][0]
+    assert "test-version-id" in second_call_args
+    assert "[0.1,0.2,0.3]" in second_call_args
+    assert None in second_call_args  # userId should be None for store agents
 
 
 @pytest.mark.asyncio(loop_scope="session")

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

@@ -12,7 +12,7 @@ from dataclasses import dataclass
 from typing import Any, Literal
 
 from prisma.enums import ContentType
-from rank_bm25 import BM25Okapi  # type: ignore[import-untyped]
+from rank_bm25 import BM25Okapi
 
 from backend.api.features.store.embeddings import (
     EMBEDDING_DIM,
@@ -363,7 +363,9 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -686,7 +688,9 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
 
     total = results[0]["total_count"] if results else 0
 

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

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

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -680,58 +680,3 @@ class ListIsEmptyBlock(Block):
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         yield "is_empty", len(input_data.list) == 0
-
-
-class ConcatenateListsBlock(Block):
-    class Input(BlockSchemaInput):
-        lists: List[List[Any]] = SchemaField(
-            description="A list of lists to concatenate together. All lists will be combined in order into a single list.",
-            placeholder="e.g., [[1, 2], [3, 4], [5, 6]]",
-        )
-
-    class Output(BlockSchemaOutput):
-        concatenated_list: List[Any] = SchemaField(
-            description="The concatenated list containing all elements from all input lists in order."
-        )
-        error: str = SchemaField(
-            description="Error message if concatenation failed due to invalid input types."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3cf9298b-5817-4141-9d80-7c2cc5199c8e",
-            description="Concatenates multiple lists into a single list. All elements from all input lists are combined in order.",
-            categories={BlockCategory.BASIC},
-            input_schema=ConcatenateListsBlock.Input,
-            output_schema=ConcatenateListsBlock.Output,
-            test_input=[
-                {"lists": [[1, 2, 3], [4, 5, 6]]},
-                {"lists": [["a", "b"], ["c"], ["d", "e", "f"]]},
-                {"lists": [[1, 2], []]},
-                {"lists": []},
-            ],
-            test_output=[
-                ("concatenated_list", [1, 2, 3, 4, 5, 6]),
-                ("concatenated_list", ["a", "b", "c", "d", "e", "f"]),
-                ("concatenated_list", [1, 2]),
-                ("concatenated_list", []),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        concatenated = []
-        for idx, lst in enumerate(input_data.lists):
-            if lst is None:
-                # Skip None values to avoid errors
-                continue
-            if not isinstance(lst, list):
-                # Type validation: each item must be a list
-                # Strings are iterable and would cause extend() to iterate character-by-character
-                # Non-iterable types would raise TypeError
-                yield "error", (
-                    f"Invalid input at index {idx}: expected a list, got {type(lst).__name__}. "
-                    f"All items in 'lists' must be lists (e.g., [[1, 2], [3, 4]])."
-                )
-                return
-            concatenated.extend(lst)
-        yield "concatenated_list", concatenated

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

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

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

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

autogpt_platform/backend/backend/blocks/llm.py

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

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

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

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

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

autogpt_platform/backend/backend/data/block.py

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

autogpt_platform/backend/backend/data/block_cost_config.py

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

autogpt_platform/backend/backend/data/db.py

@@ -38,6 +38,20 @@ POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
 if POOL_TIMEOUT:
     DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
 
+# Add public schema to search_path for pgvector type access
+# The vector extension is in public schema, but search_path is determined by schema parameter
+# Extract the schema from DATABASE_URL or default to 'public' (matching get_database_schema())
+parsed_url = urlparse(DATABASE_URL)
+url_params = dict(parse_qsl(parsed_url.query))
+db_schema = url_params.get("schema", "public")
+# Build search_path, avoiding duplicates if db_schema is already 'public'
+search_path_schemas = list(
+    dict.fromkeys([db_schema, "public"])
+)  # Preserves order, removes duplicates
+search_path = ",".join(search_path_schemas)
+# This allows using ::vector without schema qualification
+DATABASE_URL = add_param(DATABASE_URL, "options", f"-c search_path={search_path}")
+
 HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
 
 prisma = Prisma(
@@ -113,48 +127,38 @@ async def _raw_with_schema(
     *args,
     execute: bool = False,
     client: Prisma | None = None,
+    set_public_search_path: bool = False,
 ) -> list[dict] | int:
     """Internal: Execute raw SQL with proper schema handling.
 
     Use query_raw_with_schema() or execute_raw_with_schema() instead.
 
-    Supports placeholders:
-        - {schema_prefix}: Table/type prefix (e.g., "platform".)
-        - {schema}: Raw schema name for application tables (e.g., platform)
-
-    Note on pgvector types:
-        Use unqualified ::vector and <=> operator in queries. PostgreSQL resolves
-        these via search_path, which includes the schema where pgvector is installed
-        on all environments (local, CI, dev).
-
     Args:
-        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
+        query_template: SQL query with {schema_prefix} placeholder
         *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).
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
 
     Returns:
         - list[dict] if execute=False (query results)
         - int if execute=True (number of affected rows)
-
-    Example with vector type:
-        await execute_raw_with_schema(
-            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::vector)',
-            embedding_data
-        )
     """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
-
-    formatted_query = query_template.format(
-        schema_prefix=schema_prefix,
-        schema=schema,
-    )
+    formatted_query = query_template.format(schema_prefix=schema_prefix)
 
     import prisma as prisma_module
 
     db_client = client if client else prisma_module.get_client()
 
+    # Set search_path to include public schema if requested
+    # Prisma doesn't support the 'options' connection parameter, so we set it per-session
+    # This is idempotent and safe to call multiple times
+    if set_public_search_path:
+        await db_client.execute_raw(f"SET search_path = {schema}, public")  # type: ignore
+
     if execute:
         result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
     else:
@@ -163,12 +167,16 @@ async def _raw_with_schema(
     return result
 
 
-async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
+async def query_raw_with_schema(
+    query_template: str, *args, set_public_search_path: bool = False
+) -> list[dict]:
     """Execute raw SQL SELECT query with proper schema handling.
 
     Args:
-        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
+        query_template: SQL query with {schema_prefix} placeholder
         *args: Query parameters
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
 
     Returns:
         List of result rows as dictionaries
@@ -179,20 +187,23 @@ async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
             user_id
         )
     """
-    return await _raw_with_schema(query_template, *args, execute=False)  # type: ignore
+    return await _raw_with_schema(query_template, *args, execute=False, set_public_search_path=set_public_search_path)  # type: ignore
 
 
 async def execute_raw_with_schema(
     query_template: str,
     *args,
     client: Prisma | None = None,
+    set_public_search_path: bool = False,
 ) -> int:
     """Execute raw SQL command (INSERT/UPDATE/DELETE) with proper schema handling.
 
     Args:
-        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
+        query_template: SQL query with {schema_prefix} placeholder
         *args: Query parameters
         client: Optional Prisma client for transactions
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
 
     Returns:
         Number of affected rows
@@ -204,7 +215,7 @@ async def execute_raw_with_schema(
             client=tx  # Optional transaction client
         )
     """
-    return await _raw_with_schema(query_template, *args, execute=True, client=client)  # type: ignore
+    return await _raw_with_schema(query_template, *args, execute=True, client=client, set_public_search_path=set_public_search_path)  # type: ignore
 
 
 class BaseDbModel(BaseModel):

autogpt_platform/backend/backend/data/event_bus.py

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

autogpt_platform/backend/backend/data/event_bus_test.py

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

autogpt_platform/backend/backend/data/execution.py

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

autogpt_platform/backend/backend/data/graph.py

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

autogpt_platform/backend/backend/executor/scheduler.py

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

autogpt_platform/backend/backend/executor/utils.py

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

autogpt_platform/backend/backend/executor/utils_test.py

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

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

@@ -1,37 +1,11 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- 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
+-- Create in public schema so vector type is available across all schemas
 DO $$
-DECLARE
-    current_schema_name text;
-    vector_schema text;
 BEGIN
-    -- Get the current schema from search_path
-    SELECT current_schema() INTO current_schema_name;
-
-    -- Check if vector extension exists and which schema it's in
-    SELECT n.nspname INTO vector_schema
-    FROM pg_extension e
-    JOIN pg_namespace n ON e.extnamespace = n.oid
-    WHERE e.extname = 'vector';
-
-    -- Handle removal if in wrong schema
-    IF vector_schema IS NOT NULL AND vector_schema != current_schema_name THEN
-        BEGIN
-            -- Vector exists in a different schema, drop it first
-            RAISE WARNING 'pgvector found in schema "%" but need it in "%". Dropping and reinstalling...',
-                vector_schema, current_schema_name;
-            EXECUTE 'DROP EXTENSION IF EXISTS vector CASCADE';
-        EXCEPTION WHEN OTHERS THEN
-            RAISE EXCEPTION 'Failed to drop pgvector from schema "%": %. You may need to drop it manually.',
-                vector_schema, SQLERRM;
-        END;
-    END IF;
-
-    -- Create extension in current schema (let it fail naturally if not available)
-    EXECUTE format('CREATE EXTENSION IF NOT EXISTS vector SCHEMA %I', current_schema_name);
+    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'vector extension not available or already exists, skipping';
 END $$;
 
 -- CreateEnum
@@ -45,7 +19,7 @@ CREATE TABLE "UnifiedContentEmbedding" (
     "contentType" "ContentType" NOT NULL,
     "contentId" TEXT NOT NULL,
     "userId" TEXT,
-    "embedding" vector(1536) NOT NULL,
+    "embedding" public.vector(1536) NOT NULL,
     "searchableText" TEXT NOT NULL,
     "metadata" JSONB NOT NULL DEFAULT '{}',
 
@@ -71,4 +45,4 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
 -- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
 DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
-CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

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

@@ -0,0 +1,71 @@
+-- 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/scripts/generate_block_docs.py

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

autogpt_platform/backend/snapshots/grph_single

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

autogpt_platform/backend/snapshots/grphs_all

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

autogpt_platform/backend/snapshots/lib_agts_search

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

autogpt_platform/frontend/CONTRIBUTING.md

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

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

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

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

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

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

@@ -5,11 +5,10 @@ import {
   TooltipContent,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
-import { CircleNotchIcon, PlayIcon, StopIcon } from "@phosphor-icons/react";
+import { PlayIcon, StopIcon } from "@phosphor-icons/react";
 import { useShallow } from "zustand/react/shallow";
 import { RunInputDialog } from "../RunInputDialog/RunInputDialog";
 import { useRunGraph } from "./useRunGraph";
-import { cn } from "@/lib/utils";
 
 export const RunGraph = ({ flowID }: { flowID: string | null }) => {
   const {
@@ -25,31 +24,6 @@ export const RunGraph = ({ flowID }: { flowID: string | null }) => {
     useShallow((state) => state.isGraphRunning),
   );
 
-  const isLoading = isExecutingGraph || isTerminatingGraph || isSaving;
-
-  // Determine which icon to show with proper animation
-  const renderIcon = () => {
-    const iconClass = cn(
-      "size-4 transition-transform duration-200 ease-out",
-      !isLoading && "group-hover:scale-110",
-    );
-
-    if (isLoading) {
-      return (
-        <CircleNotchIcon
-          className={cn(iconClass, "animate-spin")}
-          weight="bold"
-        />
-      );
-    }
-
-    if (isGraphRunning) {
-      return <StopIcon className={iconClass} weight="fill" />;
-    }
-
-    return <PlayIcon className={iconClass} weight="fill" />;
-  };
-
   return (
     <>
       <Tooltip>
@@ -59,18 +33,18 @@ export const RunGraph = ({ flowID }: { flowID: string | null }) => {
             variant={isGraphRunning ? "destructive" : "primary"}
             data-id={isGraphRunning ? "stop-graph-button" : "run-graph-button"}
             onClick={isGraphRunning ? handleStopGraph : handleRunGraph}
-            disabled={!flowID || isLoading}
-            className="group"
+            disabled={!flowID || isExecutingGraph || isTerminatingGraph}
+            loading={isExecutingGraph || isTerminatingGraph || isSaving}
           >
-            {renderIcon()}
+            {!isGraphRunning ? (
+              <PlayIcon className="size-4" />
+            ) : (
+              <StopIcon className="size-4" />
+            )}
           </Button>
         </TooltipTrigger>
         <TooltipContent>
-          {isLoading
-            ? "Processing..."
-            : isGraphRunning
-              ? "Stop agent"
-              : "Run agent"}
+          {isGraphRunning ? "Stop agent" : "Run agent"}
         </TooltipContent>
       </Tooltip>
       <RunInputDialog

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

@@ -61,67 +61,63 @@ export const RunInputDialog = ({
           isOpen,
           set: setIsOpen,
         }}
-        styling={{ maxWidth: "700px", minWidth: "700px" }}
+        styling={{ maxWidth: "600px", minWidth: "600px" }}
       >
         <Dialog.Content>
-          <div
-            className="grid grid-cols-[1fr_auto] gap-10 p-1"
-            data-id="run-input-dialog-content"
-          >
-            <div className="space-y-6">
-              {/* Credentials Section */}
-              {hasCredentials() && credentialFields.length > 0 && (
-                <div data-id="run-input-credentials-section">
-                  <div className="mb-4">
-                    <Text variant="h4" className="text-gray-900">
-                      Credentials
-                    </Text>
-                  </div>
-                  <div className="px-2" data-id="run-input-credentials-form">
-                    <CredentialsGroupedView
-                      credentialFields={credentialFields}
-                      requiredCredentials={requiredCredentials}
-                      inputCredentials={credentialValues}
-                      inputValues={inputValues}
-                      onCredentialChange={handleCredentialFieldChange}
-                    />
-                  </div>
+          <div className="space-y-6 p-1" data-id="run-input-dialog-content">
+            {/* Credentials Section */}
+            {hasCredentials() && credentialFields.length > 0 && (
+              <div data-id="run-input-credentials-section">
+                <div className="mb-4">
+                  <Text variant="h4" className="text-gray-900">
+                    Credentials
+                  </Text>
                 </div>
-              )}
-
-              {/* Inputs Section */}
-              {hasInputs() && (
-                <div data-id="run-input-inputs-section">
-                  <div className="mb-4">
-                    <Text variant="h4" className="text-gray-900">
-                      Inputs
-                    </Text>
-                  </div>
-                  <div data-id="run-input-inputs-form">
-                    <FormRenderer
-                      jsonSchema={inputSchema as RJSFSchema}
-                      handleChange={(v) => handleInputChange(v.formData)}
-                      uiSchema={uiSchema}
-                      initialValues={{}}
-                      formContext={{
-                        showHandles: false,
-                        size: "large",
-                      }}
-                    />
-                  </div>
+                <div className="px-2" data-id="run-input-credentials-form">
+                  <CredentialsGroupedView
+                    credentialFields={credentialFields}
+                    requiredCredentials={requiredCredentials}
+                    inputCredentials={credentialValues}
+                    inputValues={inputValues}
+                    onCredentialChange={handleCredentialFieldChange}
+                  />
                 </div>
-              )}
-            </div>
+              </div>
+            )}
 
+            {/* Inputs Section */}
+            {hasInputs() && (
+              <div data-id="run-input-inputs-section">
+                <div className="mb-4">
+                  <Text variant="h4" className="text-gray-900">
+                    Inputs
+                  </Text>
+                </div>
+                <div data-id="run-input-inputs-form">
+                  <FormRenderer
+                    jsonSchema={inputSchema as RJSFSchema}
+                    handleChange={(v) => handleInputChange(v.formData)}
+                    uiSchema={uiSchema}
+                    initialValues={{}}
+                    formContext={{
+                      showHandles: false,
+                      size: "large",
+                    }}
+                  />
+                </div>
+              </div>
+            )}
+
+            {/* Action Button */}
             <div
-              className="flex flex-col items-end justify-start"
+              className="flex justify-end pt-2"
               data-id="run-input-actions-section"
             >
               {purpose === "run" && (
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2 px-10"
+                  className="group h-fit min-w-0 gap-2"
                   onClick={handleManualRun}
                   loading={isExecutingGraph}
                   data-id="run-input-manual-run-button"
@@ -136,7 +132,7 @@ export const RunInputDialog = ({
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2 px-10"
+                  className="group h-fit min-w-0 gap-2"
                   onClick={() => setOpenCronSchedulerDialog(true)}
                   data-id="run-input-schedule-button"
                 >

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

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

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/components/CustomControl.tsx

@@ -53,14 +53,14 @@ export const CustomControls = memo(
     const controls = [
       {
         id: "zoom-in-button",
-        icon: <PlusIcon className="size-3.5 text-zinc-600" />,
+        icon: <PlusIcon className="size-4" />,
         label: "Zoom In",
         onClick: () => zoomIn(),
         className: "h-10 w-10 border-none",
       },
       {
         id: "zoom-out-button",
-        icon: <MinusIcon className="size-3.5 text-zinc-600" />,
+        icon: <MinusIcon className="size-4" />,
         label: "Zoom Out",
         onClick: () => zoomOut(),
         className: "h-10 w-10 border-none",
@@ -68,9 +68,9 @@ export const CustomControls = memo(
       {
         id: "tutorial-button",
         icon: isTutorialLoading ? (
-          <CircleNotchIcon className="size-3.5 animate-spin text-zinc-600" />
+          <CircleNotchIcon className="size-4 animate-spin" />
         ) : (
-          <ChalkboardIcon className="size-3.5 text-zinc-600" />
+          <ChalkboardIcon className="size-4" />
         ),
         label: isTutorialLoading ? "Loading Tutorial..." : "Start Tutorial",
         onClick: handleTutorialClick,
@@ -79,7 +79,7 @@ export const CustomControls = memo(
       },
       {
         id: "fit-view-button",
-        icon: <FrameCornersIcon className="size-3.5 text-zinc-600" />,
+        icon: <FrameCornersIcon className="size-4" />,
         label: "Fit View",
         onClick: () => fitView({ padding: 0.2, duration: 800, maxZoom: 1 }),
         className: "h-10 w-10 border-none",
@@ -87,9 +87,9 @@ export const CustomControls = memo(
       {
         id: "lock-button",
         icon: !isLocked ? (
-          <LockOpenIcon className="size-3.5 text-zinc-600" />
+          <LockOpenIcon className="size-4" />
         ) : (
-          <LockIcon className="size-3.5 text-zinc-600" />
+          <LockIcon className="size-4" />
         ),
         label: "Toggle Lock",
         onClick: () => setIsLocked(!isLocked),

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/edges/CustomEdge.tsx

@@ -19,8 +19,6 @@ export type CustomEdgeData = {
   beadUp?: number;
   beadDown?: number;
   beadData?: Map<string, NodeExecutionResult["status"]>;
-  edgeColorClass?: string;
-  edgeHexColor?: string;
 };
 
 export type CustomEdge = XYEdge<CustomEdgeData, "custom">;
@@ -38,6 +36,7 @@ const CustomEdge = ({
   selected,
 }: EdgeProps<CustomEdge>) => {
   const removeConnection = useEdgeStore((state) => state.removeEdge);
+  // Subscribe to the brokenEdgeIDs map and check if this edge is broken across any node
   const isBroken = useNodeStore((state) => state.isEdgeBroken(id));
   const [isHovered, setIsHovered] = useState(false);
 
@@ -53,7 +52,6 @@ const CustomEdge = ({
   const isStatic = data?.isStatic ?? false;
   const beadUp = data?.beadUp ?? 0;
   const beadDown = data?.beadDown ?? 0;
-  const edgeColorClass = data?.edgeColorClass;
 
   const handleRemoveEdge = () => {
     removeConnection(id);
@@ -72,9 +70,7 @@ const CustomEdge = ({
             ? "!stroke-red-500 !stroke-[2px] [stroke-dasharray:4]"
             : selected
               ? "stroke-zinc-800"
-              : edgeColorClass
-                ? cn(edgeColorClass, "opacity-70 hover:opacity-100")
-                : "stroke-zinc-500/50 hover:stroke-zinc-500",
+              : "stroke-zinc-500/50 hover:stroke-zinc-500",
         )}
       />
       <JSBeads

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/edges/useCustomEdge.ts

@@ -8,7 +8,6 @@ import { useCallback } from "react";
 import { useNodeStore } from "../../../stores/nodeStore";
 import { useHistoryStore } from "../../../stores/historyStore";
 import { CustomEdge } from "./CustomEdge";
-import { getEdgeColorFromOutputType } from "../nodes/helpers";
 
 export const useCustomEdge = () => {
   const edges = useEdgeStore((s) => s.edges);
@@ -35,13 +34,8 @@ export const useCustomEdge = () => {
       if (exists) return;
 
       const nodes = useNodeStore.getState().nodes;
-      const sourceNode = nodes.find((n) => n.id === conn.source);
-      const isStatic = sourceNode?.data?.staticOutput;
-
-      const { colorClass, hexColor } = getEdgeColorFromOutputType(
-        sourceNode?.data?.outputSchema,
-        conn.sourceHandle,
-      );
+      const isStatic = nodes.find((n) => n.id === conn.source)?.data
+        ?.staticOutput;
 
       addEdge({
         source: conn.source,
@@ -50,8 +44,6 @@ export const useCustomEdge = () => {
         targetHandle: conn.targetHandle,
         data: {
           isStatic,
-          edgeColorClass: colorClass,
-          edgeHexColor: hexColor,
         },
       });
     },

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/helpers.ts

@@ -187,38 +187,3 @@ export const getTypeDisplayInfo = (schema: any) => {
     hexColor,
   };
 };
-
-export function getEdgeColorFromOutputType(
-  outputSchema: RJSFSchema | undefined,
-  sourceHandle: string,
-): { colorClass: string; hexColor: string } {
-  const defaultColor = {
-    colorClass: "stroke-zinc-500/50",
-    hexColor: "#6b7280",
-  };
-
-  if (!outputSchema?.properties) return defaultColor;
-
-  const properties = outputSchema.properties as Record<string, unknown>;
-  const handleParts = sourceHandle.split("_#_");
-  let currentSchema: Record<string, unknown> = properties;
-
-  for (let i = 0; i < handleParts.length; i++) {
-    const part = handleParts[i];
-    const fieldSchema = currentSchema[part] as Record<string, unknown>;
-    if (!fieldSchema) return defaultColor;
-
-    if (i === handleParts.length - 1) {
-      const { hexColor, colorClass } = getTypeDisplayInfo(fieldSchema);
-      return { colorClass: colorClass.replace("!text-", "stroke-"), hexColor };
-    }
-
-    if (fieldSchema.properties) {
-      currentSchema = fieldSchema.properties as Record<string, unknown>;
-    } else {
-      return defaultColor;
-    }
-  }
-
-  return defaultColor;
-}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/tutorial/icons.ts

@@ -1,32 +1,7 @@
-type IconOptions = {
-  size?: number;
-  color?: string;
-};
-
-const DEFAULT_SIZE = 16;
-const DEFAULT_COLOR = "#52525b"; // zinc-600
-
-const iconPaths = {
-  ClickIcon: `M88,24V16a8,8,0,0,1,16,0v8a8,8,0,0,1-16,0ZM16,104h8a8,8,0,0,0,0-16H16a8,8,0,0,0,0,16ZM124.42,39.16a8,8,0,0,0,10.74-3.58l8-16a8,8,0,0,0-14.31-7.16l-8,16A8,8,0,0,0,124.42,39.16Zm-96,81.69-16,8a8,8,0,0,0,7.16,14.31l16-8a8,8,0,1,0-7.16-14.31ZM219.31,184a16,16,0,0,1,0,22.63l-12.68,12.68a16,16,0,0,1-22.63,0L132.7,168,115,214.09c0,.1-.08.21-.13.32a15.83,15.83,0,0,1-14.6,9.59l-.79,0a15.83,15.83,0,0,1-14.41-11L32.8,52.92A16,16,0,0,1,52.92,32.8L213,85.07a16,16,0,0,1,1.41,29.8l-.32.13L168,132.69ZM208,195.31,156.69,144h0a16,16,0,0,1,4.93-26l.32-.14,45.95-17.64L48,48l52.2,159.86,17.65-46c0-.11.08-.22.13-.33a16,16,0,0,1,11.69-9.34,16.72,16.72,0,0,1,3-.28,16,16,0,0,1,11.3,4.69L195.31,208Z`,
-  Keyboard: `M224,48H32A16,16,0,0,0,16,64V192a16,16,0,0,0,16,16H224a16,16,0,0,0,16-16V64A16,16,0,0,0,224,48Zm0,144H32V64H224V192Zm-16-64a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,128Zm0-32a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,96ZM72,160a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16h8A8,8,0,0,1,72,160Zm96,0a8,8,0,0,1-8,8H96a8,8,0,0,1,0-16h64A8,8,0,0,1,168,160Zm40,0a8,8,0,0,1-8,8h-8a8,8,0,0,1,0-16h8A8,8,0,0,1,208,160Z`,
-  Drag: `M188,80a27.79,27.79,0,0,0-13.36,3.4,28,28,0,0,0-46.64-11A28,28,0,0,0,80,92v20H68a28,28,0,0,0-28,28v12a88,88,0,0,0,176,0V108A28,28,0,0,0,188,80Zm12,72a72,72,0,0,1-144,0V140a12,12,0,0,1,12-12H80v24a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V108a12,12,0,0,1,24,0Z`,
-};
-
-function createIcon(path: string, options: IconOptions = {}): string {
-  const size = options.size ?? DEFAULT_SIZE;
-  const color = options.color ?? DEFAULT_COLOR;
-  return `<svg xmlns="http://www.w3.org/2000/svg" width="${size}" height="${size}" fill="${color}" viewBox="0 0 256 256"><path d="${path}"></path></svg>`;
-}
+// These are SVG Phosphor icons
 
 export const ICONS = {
-  ClickIcon: createIcon(iconPaths.ClickIcon),
-  Keyboard: createIcon(iconPaths.Keyboard),
-  Drag: createIcon(iconPaths.Drag),
+  ClickIcon: `<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#000000" viewBox="0 0 256 256"><path d="M88,24V16a8,8,0,0,1,16,0v8a8,8,0,0,1-16,0ZM16,104h8a8,8,0,0,0,0-16H16a8,8,0,0,0,0,16ZM124.42,39.16a8,8,0,0,0,10.74-3.58l8-16a8,8,0,0,0-14.31-7.16l-8,16A8,8,0,0,0,124.42,39.16Zm-96,81.69-16,8a8,8,0,0,0,7.16,14.31l16-8a8,8,0,1,0-7.16-14.31ZM219.31,184a16,16,0,0,1,0,22.63l-12.68,12.68a16,16,0,0,1-22.63,0L132.7,168,115,214.09c0,.1-.08.21-.13.32a15.83,15.83,0,0,1-14.6,9.59l-.79,0a15.83,15.83,0,0,1-14.41-11L32.8,52.92A16,16,0,0,1,52.92,32.8L213,85.07a16,16,0,0,1,1.41,29.8l-.32.13L168,132.69ZM208,195.31,156.69,144h0a16,16,0,0,1,4.93-26l.32-.14,45.95-17.64L48,48l52.2,159.86,17.65-46c0-.11.08-.22.13-.33a16,16,0,0,1,11.69-9.34,16.72,16.72,0,0,1,3-.28,16,16,0,0,1,11.3,4.69L195.31,208Z"></path></svg>`,
+  Keyboard: `<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#000000" viewBox="0 0 256 256"><path d="M224,48H32A16,16,0,0,0,16,64V192a16,16,0,0,0,16,16H224a16,16,0,0,0,16-16V64A16,16,0,0,0,224,48Zm0,144H32V64H224V192Zm-16-64a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,128Zm0-32a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16H200A8,8,0,0,1,208,96ZM72,160a8,8,0,0,1-8,8H56a8,8,0,0,1,0-16h8A8,8,0,0,1,72,160Zm96,0a8,8,0,0,1-8,8H96a8,8,0,0,1,0-16h64A8,8,0,0,1,168,160Zm40,0a8,8,0,0,1-8,8h-8a8,8,0,0,1,0-16h8A8,8,0,0,1,208,160Z"></path></svg>`,
+  Drag: `<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#000000" viewBox="0 0 256 256"><path d="M188,80a27.79,27.79,0,0,0-13.36,3.4,28,28,0,0,0-46.64-11A28,28,0,0,0,80,92v20H68a28,28,0,0,0-28,28v12a88,88,0,0,0,176,0V108A28,28,0,0,0,188,80Zm12,72a72,72,0,0,1-144,0V140a12,12,0,0,1,12-12H80v24a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V92a12,12,0,0,1,24,0v28a8,8,0,0,0,16,0V108a12,12,0,0,1,24,0Z"></path></svg>`,
 };
-
-export function getIcon(
-  name: keyof typeof iconPaths,
-  options?: IconOptions,
-): string {
-  return createIcon(iconPaths[name], options);
-}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/tutorial/index.ts

@@ -11,7 +11,6 @@ import {
 } from "./helpers";
 import { useNodeStore } from "../../../stores/nodeStore";
 import { useEdgeStore } from "../../../stores/edgeStore";
-import { useTutorialStore } from "../../../stores/tutorialStore";
 
 let isTutorialLoading = false;
 let tutorialLoadingCallback: ((loading: boolean) => void) | null = null;
@@ -61,14 +60,12 @@ export const startTutorial = async () => {
     handleTutorialComplete();
     removeTutorialStyles();
     clearPrefetchedBlocks();
-    useTutorialStore.getState().setIsTutorialRunning(false);
   });
 
   tour.on("cancel", () => {
     handleTutorialCancel(tour);
     removeTutorialStyles();
     clearPrefetchedBlocks();
-    useTutorialStore.getState().setIsTutorialRunning(false);
   });
 
   for (const step of tour.steps) {

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

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

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

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

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

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

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

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

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

@@ -1,33 +1,6 @@
-import { SessionKey, sessionStorage } from "@/services/storage/session-storage";
 import type { ToolResult } from "@/types/chat";
 import type { ChatMessageData } from "../ChatMessage/useChatMessage";
 
-export function hasSentInitialPrompt(sessionId: string): boolean {
-  try {
-    const sent = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_SENT_INITIAL_PROMPTS) || "{}",
-    );
-    return sent[sessionId] === true;
-  } catch {
-    return false;
-  }
-}
-
-export function markInitialPromptSent(sessionId: string): void {
-  try {
-    const sent = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_SENT_INITIAL_PROMPTS) || "{}",
-    );
-    sent[sessionId] = true;
-    sessionStorage.set(
-      SessionKey.CHAT_SENT_INITIAL_PROMPTS,
-      JSON.stringify(sent),
-    );
-  } catch {
-    // Ignore storage errors
-  }
-}
-
 export function removePageContext(content: string): string {
   // Remove "Page URL: ..." pattern at start of line (case insensitive, handles various formats)
   let cleaned = content.replace(/^\s*Page URL:\s*[^\n\r]*/gim, "");
@@ -234,22 +207,12 @@ export function parseToolResponse(
     if (responseType === "setup_requirements") {
       return null;
     }
-    if (responseType === "understanding_updated") {
-      return {
-        type: "tool_response",
-        toolId,
-        toolName,
-        result: (parsedResult || result) as ToolResult,
-        success: true,
-        timestamp: timestamp || new Date(),
-      };
-    }
   }
   return {
     type: "tool_response",
     toolId,
     toolName,
-    result: parsedResult ? (parsedResult as ToolResult) : result,
+    result,
     success: true,
     timestamp: timestamp || new Date(),
   };
@@ -304,34 +267,23 @@ export function extractCredentialsNeeded(
       | undefined;
     if (missingCreds && Object.keys(missingCreds).length > 0) {
       const agentName = (setupInfo?.agent_name as string) || "this block";
-      const credentials = Object.values(missingCreds).map((credInfo) => {
-        // Normalize to array at boundary - prefer 'types' array, fall back to single 'type'
-        const typesArray = credInfo.types as
-          | Array<"api_key" | "oauth2" | "user_password" | "host_scoped">
-          | undefined;
-        const singleType =
+      const credentials = Object.values(missingCreds).map((credInfo) => ({
+        provider: (credInfo.provider as string) || "unknown",
+        providerName:
+          (credInfo.provider_name as string) ||
+          (credInfo.provider as string) ||
+          "Unknown Provider",
+        credentialType:
           (credInfo.type as
             | "api_key"
             | "oauth2"
             | "user_password"
-            | "host_scoped"
-            | undefined) || "api_key";
-        const credentialTypes =
-          typesArray && typesArray.length > 0 ? typesArray : [singleType];
-
-        return {
-          provider: (credInfo.provider as string) || "unknown",
-          providerName:
-            (credInfo.provider_name as string) ||
-            (credInfo.provider as string) ||
-            "Unknown Provider",
-          credentialTypes,
-          title:
-            (credInfo.title as string) ||
-            `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
-          scopes: credInfo.scopes as string[] | undefined,
-        };
-      });
+            | "host_scoped") || "api_key",
+        title:
+          (credInfo.title as string) ||
+          `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
+        scopes: credInfo.scopes as string[] | undefined,
+      }));
       return {
         type: "credentials_needed",
         toolName,
@@ -406,14 +358,11 @@ export function extractInputsNeeded(
       credentials.forEach((cred) => {
         const id = cred.id as string;
         if (id) {
-          const credentialTypes = Array.isArray(cred.types)
-            ? cred.types
-            : [(cred.type as string) || "api_key"];
           credentialsSchema[id] = {
             type: "object",
             properties: {},
             credentials_provider: [cred.provider as string],
-            credentials_types: credentialTypes,
+            credentials_types: [(cred.type as string) || "api_key"],
             credentials_scopes: cred.scopes as string[] | undefined,
           };
         }

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

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

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

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

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

@@ -9,9 +9,7 @@ import { useChatCredentialsSetup } from "./useChatCredentialsSetup";
 export interface CredentialInfo {
   provider: string;
   providerName: string;
-  credentialTypes: Array<
-    "api_key" | "oauth2" | "user_password" | "host_scoped"
-  >;
+  credentialType: "api_key" | "oauth2" | "user_password" | "host_scoped";
   title: string;
   scopes?: string[];
 }
@@ -32,7 +30,7 @@ function createSchemaFromCredentialInfo(
     type: "object",
     properties: {},
     credentials_provider: [credential.provider],
-    credentials_types: credential.credentialTypes,
+    credentials_types: [credential.credentialType],
     credentials_scopes: credential.scopes,
     discriminator: undefined,
     discriminator_mapping: undefined,

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

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

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

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

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

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

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

@@ -41,9 +41,7 @@ export type ChatMessageData =
       credentials: Array<{
         provider: string;
         providerName: string;
-        credentialTypes: Array<
-          "api_key" | "oauth2" | "user_password" | "host_scoped"
-        >;
+        credentialType: "api_key" | "oauth2" | "user_password" | "host_scoped";
         title: string;
         scopes?: string[];
       }>;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,66 @@
+/**
+ * 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/useChat.ts

@@ -1,20 +1,17 @@
 "use client";
 
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { useEffect, useRef, useState } from "react";
+import { useEffect, useRef } from "react";
 import { toast } from "sonner";
 import { useChatSession } from "./useChatSession";
 import { useChatStream } from "./useChatStream";
 
-interface UseChatArgs {
-  urlSessionId?: string | null;
-}
-
-export function useChat({ urlSessionId }: UseChatArgs = {}) {
+export function useChat() {
+  const hasCreatedSessionRef = useRef(false);
   const hasClaimedSessionRef = useRef(false);
   const { user } = useSupabase();
   const { sendMessage: sendStreamMessage } = useChatStream();
-  const [showLoader, setShowLoader] = useState(false);
+
   const {
     session,
     sessionId: sessionIdFromHook,
@@ -22,16 +19,27 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     isLoading,
     isCreating,
     error,
-    isSessionNotFound,
     createSession,
     claimSession,
     clearSession: clearSessionBase,
     loadSession,
   } = useChatSession({
-    urlSessionId,
+    urlSessionId: null,
     autoCreate: false,
   });
 
+  useEffect(
+    function autoCreateSession() {
+      if (!hasCreatedSessionRef.current && !isCreating && !sessionIdFromHook) {
+        hasCreatedSessionRef.current = true;
+        createSession().catch((_err) => {
+          hasCreatedSessionRef.current = false;
+        });
+      }
+    },
+    [isCreating, sessionIdFromHook, createSession],
+  );
+
   useEffect(
     function autoClaimSession() {
       if (
@@ -67,17 +75,6 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     ],
   );
 
-  useEffect(() => {
-    if (isLoading || isCreating) {
-      const timer = setTimeout(() => {
-        setShowLoader(true);
-      }, 300);
-      return () => clearTimeout(timer);
-    } else {
-      setShowLoader(false);
-    }
-  }, [isLoading, isCreating]);
-
   useEffect(function monitorNetworkStatus() {
     function handleOnline() {
       toast.success("Connection restored", {
@@ -102,6 +99,7 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
 
   function clearSession() {
     clearSessionBase();
+    hasCreatedSessionRef.current = false;
     hasClaimedSessionRef.current = false;
   }
 
@@ -111,11 +109,9 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     isLoading,
     isCreating,
     error,
-    isSessionNotFound,
     createSession,
     clearSession,
     loadSession,
     sessionId: sessionIdFromHook,
-    showLoader,
   };
 }

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

@@ -0,0 +1,271 @@
+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/components/Chat/useChatStream.ts

@@ -21,8 +21,6 @@ export interface StreamChunk {
   timestamp?: string;
   content?: string;
   message?: string;
-  code?: string;
-  details?: Record<string, unknown>;
   tool_id?: string;
   tool_name?: string;
   arguments?: ToolArguments;
@@ -140,18 +138,8 @@ function normalizeStreamChunk(
       return { type: "stream_end" };
     case "start":
     case "text-start":
-      return null;
     case "tool-input-start":
-      const toolInputStart = chunk as Extract<
-        VercelStreamChunk,
-        { type: "tool-input-start" }
-      >;
-      return {
-        type: "tool_call_start",
-        tool_id: toolInputStart.toolCallId,
-        tool_name: toolInputStart.toolName,
-        arguments: {},
-      };
+      return null;
   }
 }
 
@@ -161,103 +149,22 @@ export function useChatStream() {
   const retryCountRef = useRef<number>(0);
   const retryTimeoutRef = useRef<NodeJS.Timeout | null>(null);
   const abortControllerRef = useRef<AbortController | null>(null);
-  const currentSessionIdRef = useRef<string | null>(null);
-  const requestStartTimeRef = useRef<number | null>(null);
 
-  const stopStreaming = useCallback(
-    (sessionId?: string, force: boolean = false) => {
-      console.log("[useChatStream] stopStreaming called", {
-        hasAbortController: !!abortControllerRef.current,
-        isAborted: abortControllerRef.current?.signal.aborted,
-        currentSessionId: currentSessionIdRef.current,
-        requestedSessionId: sessionId,
-        requestStartTime: requestStartTimeRef.current,
-        timeSinceStart: requestStartTimeRef.current
-          ? Date.now() - requestStartTimeRef.current
-          : null,
-        force,
-        stack: new Error().stack,
-      });
-
-      if (
-        sessionId &&
-        currentSessionIdRef.current &&
-        currentSessionIdRef.current !== sessionId
-      ) {
-        console.log(
-          "[useChatStream] Session changed, aborting previous stream",
-          {
-            oldSessionId: currentSessionIdRef.current,
-            newSessionId: sessionId,
-          },
-        );
-      }
-
-      const controller = abortControllerRef.current;
-      if (controller) {
-        const timeSinceStart = requestStartTimeRef.current
-          ? Date.now() - requestStartTimeRef.current
-          : null;
-
-        if (!force && timeSinceStart !== null && timeSinceStart < 100) {
-          console.log(
-            "[useChatStream] Request just started (<100ms), skipping abort to prevent race condition",
-            {
-              timeSinceStart,
-            },
-          );
-          return;
-        }
-
-        try {
-          const signal = controller.signal;
-
-          if (
-            signal &&
-            typeof signal.aborted === "boolean" &&
-            !signal.aborted
-          ) {
-            console.log("[useChatStream] Aborting stream");
-            controller.abort();
-          } else {
-            console.log(
-              "[useChatStream] Stream already aborted or signal invalid",
-            );
-          }
-        } catch (error) {
-          if (error instanceof Error && error.name === "AbortError") {
-            console.log(
-              "[useChatStream] AbortError caught (expected during cleanup)",
-            );
-          } else {
-            console.warn("[useChatStream] Error aborting stream:", error);
-          }
-        } finally {
-          abortControllerRef.current = null;
-          requestStartTimeRef.current = null;
-        }
-      }
-      if (retryTimeoutRef.current) {
-        clearTimeout(retryTimeoutRef.current);
-        retryTimeoutRef.current = null;
-      }
-      setIsStreaming(false);
-    },
-    [],
-  );
+  const stopStreaming = useCallback(() => {
+    if (abortControllerRef.current) {
+      abortControllerRef.current.abort();
+      abortControllerRef.current = null;
+    }
+    if (retryTimeoutRef.current) {
+      clearTimeout(retryTimeoutRef.current);
+      retryTimeoutRef.current = null;
+    }
+    setIsStreaming(false);
+  }, []);
 
   useEffect(() => {
-    console.log("[useChatStream] Component mounted");
     return () => {
-      const sessionIdAtUnmount = currentSessionIdRef.current;
-      console.log(
-        "[useChatStream] Component unmounting, calling stopStreaming",
-        {
-          sessionIdAtUnmount,
-        },
-      );
-      stopStreaming(undefined, false);
-      currentSessionIdRef.current = null;
+      stopStreaming();
     };
   }, [stopStreaming]);
 
@@ -270,32 +177,12 @@ export function useChatStream() {
       context?: { url: string; content: string },
       isRetry: boolean = false,
     ) => {
-      console.log("[useChatStream] sendMessage called", {
-        sessionId,
-        message: message.substring(0, 50),
-        isUserMessage,
-        isRetry,
-        stack: new Error().stack,
-      });
-
-      const previousSessionId = currentSessionIdRef.current;
-      stopStreaming(sessionId, true);
-      currentSessionIdRef.current = sessionId;
+      stopStreaming();
 
       const abortController = new AbortController();
       abortControllerRef.current = abortController;
-      requestStartTimeRef.current = Date.now();
-      console.log("[useChatStream] Created new AbortController", {
-        sessionId,
-        previousSessionId,
-        requestStartTime: requestStartTimeRef.current,
-      });
 
       if (abortController.signal.aborted) {
-        console.warn(
-          "[useChatStream] AbortController was aborted before request started",
-        );
-        requestStartTimeRef.current = null;
         return Promise.reject(new Error("Request aborted"));
       }
 
@@ -323,34 +210,18 @@ export function useChatStream() {
           signal: abortController.signal,
         });
 
-        console.info("[useChatStream] Stream response", {
-          sessionId,
-          status: response.status,
-          ok: response.ok,
-          contentType: response.headers.get("content-type"),
-        });
-
         if (!response.ok) {
           const errorText = await response.text();
-          console.warn("[useChatStream] Stream response error", {
-            sessionId,
-            status: response.status,
-            errorText,
-          });
           throw new Error(errorText || `HTTP ${response.status}`);
         }
 
         if (!response.body) {
-          console.warn("[useChatStream] Response body is null", { sessionId });
           throw new Error("Response body is null");
         }
 
         const reader = response.body.getReader();
         const decoder = new TextDecoder();
         let buffer = "";
-        let receivedChunkCount = 0;
-        let firstChunkAt: number | null = null;
-        let loggedLineCount = 0;
 
         return new Promise<void>((resolve, reject) => {
           let didDispatchStreamEnd = false;
@@ -374,13 +245,6 @@ export function useChatStream() {
 
                 if (done) {
                   cleanup();
-                  console.info("[useChatStream] Stream closed", {
-                    sessionId,
-                    receivedChunkCount,
-                    timeSinceStart: requestStartTimeRef.current
-                      ? Date.now() - requestStartTimeRef.current
-                      : null,
-                  });
                   dispatchStreamEnd();
                   retryCountRef.current = 0;
                   stopStreaming();
@@ -395,23 +259,8 @@ export function useChatStream() {
                 for (const line of lines) {
                   if (line.startsWith("data: ")) {
                     const data = line.slice(6);
-                    if (loggedLineCount < 3) {
-                      console.info("[useChatStream] Raw stream line", {
-                        sessionId,
-                        data:
-                          data.length > 300 ? `${data.slice(0, 300)}...` : data,
-                      });
-                      loggedLineCount += 1;
-                    }
                     if (data === "[DONE]") {
                       cleanup();
-                      console.info("[useChatStream] Stream done marker", {
-                        sessionId,
-                        receivedChunkCount,
-                        timeSinceStart: requestStartTimeRef.current
-                          ? Date.now() - requestStartTimeRef.current
-                          : null,
-                      });
                       dispatchStreamEnd();
                       retryCountRef.current = 0;
                       stopStreaming();
@@ -428,18 +277,6 @@ export function useChatStream() {
                         continue;
                       }
 
-                      if (!firstChunkAt) {
-                        firstChunkAt = Date.now();
-                        console.info("[useChatStream] First stream chunk", {
-                          sessionId,
-                          chunkType: chunk.type,
-                          timeSinceStart: requestStartTimeRef.current
-                            ? firstChunkAt - requestStartTimeRef.current
-                            : null,
-                        });
-                      }
-                      receivedChunkCount += 1;
-
                       // Call the chunk handler
                       onChunk(chunk);
 
@@ -447,13 +284,6 @@ export function useChatStream() {
                       if (chunk.type === "stream_end") {
                         didDispatchStreamEnd = true;
                         cleanup();
-                        console.info("[useChatStream] Stream end chunk", {
-                          sessionId,
-                          receivedChunkCount,
-                          timeSinceStart: requestStartTimeRef.current
-                            ? Date.now() - requestStartTimeRef.current
-                            : null,
-                        });
                         retryCountRef.current = 0;
                         stopStreaming();
                         resolve();
@@ -477,9 +307,6 @@ export function useChatStream() {
             } catch (err) {
               if (err instanceof Error && err.name === "AbortError") {
                 cleanup();
-                dispatchStreamEnd();
-                stopStreaming();
-                resolve();
                 return;
               }
 
@@ -525,10 +352,6 @@ export function useChatStream() {
           readStream();
         });
       } catch (err) {
-        if (err instanceof Error && err.name === "AbortError") {
-          setIsStreaming(false);
-          return Promise.resolve();
-        }
         const streamError =
           err instanceof Error ? err : new Error("Failed to start stream");
         setError(streamError);