remove langfuse tracing

## Changes 🏗️

- Make the loading UX better when switching between chats or loading a
new chat
- Make session/chat management logic more manageable
- Improving "Deep thinking" loading states
- Fix bug that happened when returning to chat after navigating away

## Checklist 📋

### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Run the app locally and test the above

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

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

AGENTS.md

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

autogpt_platform/CLAUDE.md

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

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

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

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

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

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

@@ -1,12 +1,20 @@
 import asyncio
 import logging
+import time
+from asyncio import CancelledError
 from collections.abc import AsyncGenerator
 from typing import Any
 
+import openai
 import orjson
-from langfuse import get_client, propagate_attributes
-from langfuse.openai import openai  # type: ignore
-from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
+from langfuse import get_client
+from openai import (
+    APIConnectionError,
+    APIError,
+    APIStatusError,
+    PermissionDeniedError,
+    RateLimitError,
+)
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -21,6 +29,7 @@ from .model import (
     ChatMessage,
     ChatSession,
     Usage,
+    cache_chat_session,
     get_chat_session,
     update_session_title,
     upsert_chat_session,
@@ -267,257 +276,301 @@ async def stream_chat_completion(
     # Build system prompt with business understanding
     system_prompt, understanding = await _build_system_prompt(user_id)
 
-    # Create Langfuse trace for this LLM call (each call gets its own trace, grouped by session_id)
-    # Using v3 SDK: start_observation creates a root span, update_trace sets trace-level attributes
-    input = message
-    if not message and tool_call_response:
-        input = tool_call_response
+    # Initialize variables for streaming
+    assistant_response = ChatMessage(
+        role="assistant",
+        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
 
-    langfuse = get_client()
-    with langfuse.start_as_current_observation(
-        as_type="span",
-        name="user-copilot-request",
-        input=input,
-    ) as span:
-        with propagate_attributes(
-            session_id=session_id,
-            user_id=user_id,
-            tags=["copilot"],
-            metadata={
-                "users_information": format_understanding_for_prompt(understanding)[
-                    :200
-                ]  # langfuse only accepts upto to 200 chars
-            },
+    has_yielded_end = False
+    has_yielded_error = False
+    has_done_tool_call = False
+    has_received_text = False
+    text_streaming_ended = False
+    tool_response_messages: list[ChatMessage] = []
+    should_retry = False
+
+    # Generate unique IDs for AI SDK protocol
+    import uuid as uuid_module
+
+    message_id = str(uuid_module.uuid4())
+    text_block_id = str(uuid_module.uuid4())
+
+    # Yield message start
+    yield StreamStart(messageId=message_id)
+
+    try:
+        async for chunk in _stream_chat_chunks(
+            session=session,
+            tools=tools,
+            system_prompt=system_prompt,
+            text_block_id=text_block_id,
         ):
 
-            # Initialize variables that will be used in finally block (must be defined before try)
-            assistant_response = ChatMessage(
-                role="assistant",
-                content="",
-            )
-            accumulated_tool_calls: list[dict[str, Any]] = []
-
-            # Wrap main logic in try/finally to ensure Langfuse observations are always ended
-            has_yielded_end = False
-            has_yielded_error = False
-            has_done_tool_call = False
-            has_received_text = False
-            text_streaming_ended = False
-            tool_response_messages: list[ChatMessage] = []
-            should_retry = False
-
-            # Generate unique IDs for AI SDK protocol
-            import uuid as uuid_module
-
-            message_id = str(uuid_module.uuid4())
-            text_block_id = str(uuid_module.uuid4())
-
-            # Yield message start
-            yield StreamStart(messageId=message_id)
-
-            try:
-                async for chunk in _stream_chat_chunks(
-                    session=session,
-                    tools=tools,
-                    system_prompt=system_prompt,
-                    text_block_id=text_block_id,
-                ):
-
-                    if isinstance(chunk, StreamTextStart):
-                        # Emit text-start before first text delta
-                        if not has_received_text:
-                            yield chunk
-                    elif isinstance(chunk, StreamTextDelta):
-                        delta = chunk.delta or ""
-                        assert assistant_response.content is not None
-                        assistant_response.content += delta
-                        has_received_text = True
-                        yield chunk
-                    elif isinstance(chunk, StreamTextEnd):
-                        # Emit text-end after text completes
-                        if has_received_text and not text_streaming_ended:
-                            text_streaming_ended = True
-                            if assistant_response.content:
-                                logger.warn(
-                                    f"StreamTextEnd: Attempting to set output {assistant_response.content}"
-                                )
-                                span.update_trace(output=assistant_response.content)
-                                span.update(output=assistant_response.content)
-                            yield chunk
-                    elif isinstance(chunk, StreamToolInputStart):
-                        # Emit text-end before first tool call, but only if we've received text
-                        if has_received_text and not text_streaming_ended:
-                            yield StreamTextEnd(id=text_block_id)
-                            text_streaming_ended = True
-                        yield chunk
-                    elif isinstance(chunk, StreamToolInputAvailable):
-                        # Accumulate tool calls in OpenAI format
-                        accumulated_tool_calls.append(
-                            {
-                                "id": chunk.toolCallId,
-                                "type": "function",
-                                "function": {
-                                    "name": chunk.toolName,
-                                    "arguments": orjson.dumps(chunk.input).decode(
-                                        "utf-8"
-                                    ),
-                                },
-                            }
-                        )
-                    elif isinstance(chunk, StreamToolOutputAvailable):
-                        result_content = (
-                            chunk.output
-                            if isinstance(chunk.output, str)
-                            else orjson.dumps(chunk.output).decode("utf-8")
-                        )
-                        tool_response_messages.append(
-                            ChatMessage(
-                                role="tool",
-                                content=result_content,
-                                tool_call_id=chunk.toolCallId,
-                            )
-                        )
-                        has_done_tool_call = True
-                        # Track if any tool execution failed
-                        if not chunk.success:
-                            logger.warning(
-                                f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
-                            )
-                        yield chunk
-                    elif isinstance(chunk, StreamFinish):
-                        if not has_done_tool_call:
-                            # Emit text-end before finish if we received text but haven't closed it
-                            if has_received_text and not text_streaming_ended:
-                                yield StreamTextEnd(id=text_block_id)
-                                text_streaming_ended = True
-                            has_yielded_end = True
-                            yield chunk
-                    elif isinstance(chunk, StreamError):
-                        has_yielded_error = True
-                    elif isinstance(chunk, StreamUsage):
-                        session.usage.append(
-                            Usage(
-                                prompt_tokens=chunk.promptTokens,
-                                completion_tokens=chunk.completionTokens,
-                                total_tokens=chunk.totalTokens,
-                            )
-                        )
-                    else:
-                        logger.error(
-                            f"Unknown chunk type: {type(chunk)}", exc_info=True
-                        )
-                if assistant_response.content:
-                    langfuse.update_current_trace(output=assistant_response.content)
-                    langfuse.update_current_span(output=assistant_response.content)
-                elif tool_response_messages:
-                    langfuse.update_current_trace(output=str(tool_response_messages))
-                    langfuse.update_current_span(output=str(tool_response_messages))
-
-            except Exception as e:
-                logger.error(f"Error during stream: {e!s}", exc_info=True)
-
-                # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
-                is_retryable = isinstance(
-                    e, (orjson.JSONDecodeError, KeyError, TypeError)
-                )
-
-                if is_retryable and retry_count < config.max_retries:
-                    logger.info(
-                        f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
-                    )
-                    should_retry = True
-                else:
-                    # Non-retryable error or max retries exceeded
-                    # Save any partial progress before reporting error
-                    messages_to_save: list[ChatMessage] = []
-
-                    # Add assistant message if it has content or tool calls
-                    if accumulated_tool_calls:
-                        assistant_response.tool_calls = accumulated_tool_calls
-                    if assistant_response.content or assistant_response.tool_calls:
-                        messages_to_save.append(assistant_response)
-
-                    # Add tool response messages after assistant message
-                    messages_to_save.extend(tool_response_messages)
-
-                    session.messages.extend(messages_to_save)
-                    await upsert_chat_session(session)
-
-                    if not has_yielded_error:
-                        error_message = str(e)
-                        if not is_retryable:
-                            error_message = f"Non-retryable error: {error_message}"
-                        elif retry_count >= config.max_retries:
-                            error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
-
-                        error_response = StreamError(errorText=error_message)
-                        yield error_response
-                    if not has_yielded_end:
-                        yield StreamFinish()
-                    return
-
-            # Handle retry outside of exception handler to avoid nesting
-            if should_retry and retry_count < config.max_retries:
-                logger.info(
-                    f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
-                )
-                async for chunk in stream_chat_completion(
-                    session_id=session.session_id,
-                    user_id=user_id,
-                    retry_count=retry_count + 1,
-                    session=session,
-                    context=context,
-                ):
+            if isinstance(chunk, StreamTextStart):
+                # Emit text-start before first text delta
+                if not has_received_text:
                     yield chunk
-                return  # Exit after retry to avoid double-saving in finally block
+            elif isinstance(chunk, StreamTextDelta):
+                delta = chunk.delta or ""
+                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
+                if has_received_text and not text_streaming_ended:
+                    text_streaming_ended = True
+                    yield chunk
+            elif isinstance(chunk, StreamToolInputStart):
+                # Emit text-end before first tool call, but only if we've received text
+                if has_received_text and not text_streaming_ended:
+                    yield StreamTextEnd(id=text_block_id)
+                    text_streaming_ended = True
+                yield chunk
+            elif isinstance(chunk, StreamToolInputAvailable):
+                # Accumulate tool calls in OpenAI format
+                accumulated_tool_calls.append(
+                    {
+                        "id": chunk.toolCallId,
+                        "type": "function",
+                        "function": {
+                            "name": chunk.toolName,
+                            "arguments": orjson.dumps(chunk.input).decode("utf-8"),
+                        },
+                    }
+                )
+            elif isinstance(chunk, StreamToolOutputAvailable):
+                result_content = (
+                    chunk.output
+                    if isinstance(chunk.output, str)
+                    else orjson.dumps(chunk.output).decode("utf-8")
+                )
+                tool_response_messages.append(
+                    ChatMessage(
+                        role="tool",
+                        content=result_content,
+                        tool_call_id=chunk.toolCallId,
+                    )
+                )
+                has_done_tool_call = True
+                # Track if any tool execution failed
+                if not chunk.success:
+                    logger.warning(
+                        f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
+                    )
+                yield chunk
+            elif isinstance(chunk, StreamFinish):
+                if not has_done_tool_call:
+                    # Emit text-end before finish if we received text but haven't closed it
+                    if has_received_text and not text_streaming_ended:
+                        yield StreamTextEnd(id=text_block_id)
+                        text_streaming_ended = True
 
-            # Normal completion path - save session and handle tool call continuation
-            logger.info(
-                f"Normal completion path: session={session.session_id}, "
-                f"current message_count={len(session.messages)}"
-            )
+                    # 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)
 
-            # Build the messages list in the correct order
-            messages_to_save: list[ChatMessage] = []
+                        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
 
-            # Add assistant message with tool_calls if any
+                    has_yielded_end = True
+                    yield chunk
+            elif isinstance(chunk, StreamError):
+                has_yielded_error = True
+                yield chunk
+            elif isinstance(chunk, StreamUsage):
+                session.usage.append(
+                    Usage(
+                        prompt_tokens=chunk.promptTokens,
+                        completion_tokens=chunk.completionTokens,
+                        total_tokens=chunk.totalTokens,
+                    )
+                )
+            else:
+                logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
+
+    except CancelledError:
+        if not has_saved_assistant_message:
             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 assistant_response.content:
+                assistant_response.content = (
+                    f"{assistant_response.content}\n\n[interrupted]"
                 )
-            if assistant_response.content or assistant_response.tool_calls:
+            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)
+
+        # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
+        is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
+
+        if is_retryable and retry_count < config.max_retries:
+            logger.info(
+                f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
+            )
+            should_retry = True
+        else:
+            # Non-retryable error or max retries exceeded
+            # Save any partial progress before reporting error
+            messages_to_save: list[ChatMessage] = []
+
+            # 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
+            ):
                 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)
+
+            if not has_saved_assistant_message:
+                if messages_to_save:
+                    session.messages.extend(messages_to_save)
+                if messages_to_save or has_appended_streaming_message:
+                    await upsert_chat_session(session)
+
+            if not has_yielded_error:
+                error_message = str(e)
+                if not is_retryable:
+                    error_message = f"Non-retryable error: {error_message}"
+                elif retry_count >= config.max_retries:
+                    error_message = (
+                        f"Max retries ({config.max_retries}) exceeded: {error_message}"
+                    )
+
+                error_response = StreamError(errorText=error_message)
+                yield error_response
+            if not has_yielded_end:
+                yield StreamFinish()
+            return
+
+    # Handle retry outside of exception handler to avoid nesting
+    if should_retry and retry_count < config.max_retries:
+        logger.info(
+            f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
+        )
+        async for chunk in stream_chat_completion(
+            session_id=session.session_id,
+            user_id=user_id,
+            retry_count=retry_count + 1,
+            session=session,
+            context=context,
+        ):
+            yield chunk
+        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"Saving {len(tool_response_messages)} tool response messages, "
-                f"total_to_save={len(messages_to_save)}"
+                f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+            )
+        if not has_appended_streaming_message and (
+            assistant_response.content or assistant_response.tool_calls
+        ):
+            messages_to_save.append(assistant_response)
+            logger.info(
+                f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
             )
 
+        # Add tool response messages after assistant message
+        messages_to_save.extend(tool_response_messages)
+        logger.info(
+            f"Saving {len(tool_response_messages)} tool response messages, "
+            f"total_to_save={len(messages_to_save)}"
+        )
+
+        if messages_to_save:
             session.messages.extend(messages_to_save)
             logger.info(
                 f"Extended session messages, new message_count={len(session.messages)}"
             )
+        if messages_to_save or has_appended_streaming_message:
             await upsert_chat_session(session)
+    else:
+        logger.info(
+            "Assistant message already saved when StreamFinish was received, "
+            "skipping duplicate save"
+        )
 
-            # If we did a tool call, stream the chat completion again to get the next response
-            if has_done_tool_call:
-                logger.info(
-                    "Tool call executed, streaming chat completion again to get assistant response"
-                )
-                async for chunk in stream_chat_completion(
-                    session_id=session.session_id,
-                    user_id=user_id,
-                    session=session,  # Pass session object to avoid Redis refetch
-                    context=context,
-                    tool_call_response=str(tool_response_messages),
-                ):
-                    yield chunk
+    # If we did a tool call, stream the chat completion again to get the next response
+    if has_done_tool_call:
+        logger.info(
+            "Tool call executed, streaming chat completion again to get assistant response"
+        )
+        async for chunk in stream_chat_completion(
+            session_id=session.session_id,
+            user_id=user_id,
+            session=session,  # Pass session object to avoid Redis refetch
+            context=context,
+            tool_call_response=str(tool_response_messages),
+        ):
+            yield chunk
 
 
 # Retry configuration for OpenAI API calls
@@ -545,6 +598,12 @@ def _is_retryable_error(error: Exception) -> bool:
     return False
 
 
+def _is_region_blocked_error(error: Exception) -> bool:
+    if isinstance(error, PermissionDeniedError):
+        return "not available in your region" in str(error).lower()
+    return "not available in your region" in str(error).lower()
+
+
 async def _stream_chat_chunks(
     session: ChatSession,
     tools: list[ChatCompletionToolParam],
@@ -737,7 +796,18 @@ async def _stream_chat_chunks(
                         f"Error in stream (not retrying): {e!s}",
                         exc_info=True,
                     )
-                    error_response = StreamError(errorText=str(e))
+                    error_code = None
+                    error_text = str(e)
+                    if _is_region_blocked_error(e):
+                        error_code = "MODEL_NOT_AVAILABLE_REGION"
+                        error_text = (
+                            "This model is not available in your region. "
+                            "Please connect via VPN and try again."
+                        )
+                    error_response = StreamError(
+                        errorText=error_text,
+                        code=error_code,
+                    )
                     yield error_response
                     yield StreamFinish()
                     return

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

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.data.understanding import (
     BusinessUnderstandingInput,
@@ -61,7 +59,6 @@ and automations for the user's specific needs."""
         """Requires authentication to store user-specific data."""
         return True
 
-    @observe(as_type="tool", name="add_understanding")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -5,7 +5,6 @@ import re
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
@@ -329,7 +328,6 @@ class AgentOutputTool(BaseTool):
             total_executions=len(available_executions) if available_executions else 1,
         )
 
-    @observe(as_type="tool", name="view_agent_output")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -80,7 +78,6 @@ class CreateAgentTool(BaseTool):
             "required": ["description"],
         }
 
-    @observe(as_type="tool", name="create_agent")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -87,7 +85,6 @@ class EditAgentTool(BaseTool):
             "required": ["agent_id", "changes"],
         }
 
-    @observe(as_type="tool", name="edit_agent")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -37,7 +35,6 @@ class FindAgentTool(BaseTool):
             "required": ["query"],
         }
 
-    @observe(as_type="tool", name="find_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -1,7 +1,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -56,7 +55,6 @@ class FindBlockTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @observe(as_type="tool", name="find_block")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -43,7 +41,6 @@ class FindLibraryAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @observe(as_type="tool", name="find_library_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -4,8 +4,6 @@ import logging
 from pathlib import Path
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.chat.tools.base import BaseTool
 from backend.api.features.chat.tools.models import (
@@ -73,7 +71,6 @@ class GetDocPageTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
-    @observe(as_type="tool", name="get_doc_page")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -3,7 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
@@ -155,7 +154,6 @@ class RunAgentTool(BaseTool):
         """All operations require authentication."""
         return True
 
-    @observe(as_type="tool", name="run_agent")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -4,8 +4,6 @@ import logging
 from collections import defaultdict
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
@@ -130,7 +128,6 @@ class RunBlockTool(BaseTool):
 
         return matched_credentials, missing_credentials
 
-    @observe(as_type="tool", name="run_block")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -3,7 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -88,7 +87,6 @@ class SearchDocsTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
-    @observe(as_type="tool", name="search_docs")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -107,13 +107,6 @@ class ReviewItem(BaseModel):
     reviewed_data: SafeJsonData | None = Field(
         None, description="Optional edited data (ignored if approved=False)"
     )
-    auto_approve_future: bool = Field(
-        default=False,
-        description=(
-            "If true and this review is approved, future executions of this same "
-            "block (node) will be automatically approved. This only affects approved reviews."
-        ),
-    )
 
     @field_validator("reviewed_data")
     @classmethod
@@ -181,9 +174,6 @@ class ReviewRequest(BaseModel):
     This request must include ALL pending reviews for a graph execution.
     Each review will be either approved (with optional data modifications)
     or rejected (data ignored). The execution will resume only after ALL reviews are processed.
-
-    Each review item can individually specify whether to auto-approve future executions
-    of the same block via the `auto_approve_future` field on ReviewItem.
     """
 
     reviews: List[ReviewItem] = Field(

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

@@ -8,12 +8,6 @@ from prisma.enums import ReviewStatus
 from pytest_snapshot.plugin import Snapshot
 
 from backend.api.rest_api import handle_internal_http_error
-from backend.data.execution import (
-    ExecutionContext,
-    ExecutionStatus,
-    NodeExecutionResult,
-)
-from backend.data.graph import GraphSettings
 
 from .model import PendingHumanReviewModel
 from .routes import router
@@ -21,24 +15,20 @@ from .routes import router
 # Using a fixed timestamp for reproducible tests
 FIXED_NOW = datetime.datetime(2023, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)
 
+app = fastapi.FastAPI()
+app.include_router(router, prefix="/api/review")
+app.add_exception_handler(ValueError, handle_internal_http_error(400))
 
-@pytest.fixture
-def app():
-    """Create FastAPI app for testing"""
-    test_app = fastapi.FastAPI()
-    test_app.include_router(router, prefix="/api/review")
-    test_app.add_exception_handler(ValueError, handle_internal_http_error(400))
-    return test_app
+client = fastapi.testclient.TestClient(app)
 
 
-@pytest.fixture
-def client(app, mock_jwt_user):
-    """Create test client with auth overrides"""
+@pytest.fixture(autouse=True)
+def setup_app_auth(mock_jwt_user):
+    """Setup auth overrides for all tests in this module"""
     from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
-    with fastapi.testclient.TestClient(app) as test_client:
-        yield test_client
+    yield
     app.dependency_overrides.clear()
 
 
@@ -65,7 +55,6 @@ def sample_pending_review(test_user_id: str) -> PendingHumanReviewModel:
 
 
 def test_get_pending_reviews_empty(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     snapshot: Snapshot,
     test_user_id: str,
@@ -84,7 +73,6 @@ def test_get_pending_reviews_empty(
 
 
 def test_get_pending_reviews_with_data(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     snapshot: Snapshot,
@@ -107,7 +95,6 @@ def test_get_pending_reviews_with_data(
 
 
 def test_get_pending_reviews_for_execution_success(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     snapshot: Snapshot,
@@ -136,7 +123,6 @@ def test_get_pending_reviews_for_execution_success(
 
 
 def test_get_pending_reviews_for_execution_not_available(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
 ) -> None:
     """Test access denied when user doesn't own the execution"""
@@ -152,7 +138,6 @@ def test_get_pending_reviews_for_execution_not_available(
 
 
 def test_process_review_action_approve_success(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
@@ -160,12 +145,6 @@ def test_process_review_action_approve_success(
     """Test successful review approval"""
     # Mock the route functions
 
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = sample_pending_review
-
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
@@ -194,14 +173,6 @@ def test_process_review_action_approve_success(
     )
     mock_process_all_reviews.return_value = {"test_node_123": approved_review}
 
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
     mock_has_pending = mocker.patch(
         "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
     )
@@ -231,7 +202,6 @@ def test_process_review_action_approve_success(
 
 
 def test_process_review_action_reject_success(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
@@ -239,20 +209,6 @@ def test_process_review_action_reject_success(
     """Test successful review rejection"""
     # Mock the route functions
 
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = sample_pending_review
-
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
@@ -306,7 +262,6 @@ def test_process_review_action_reject_success(
 
 
 def test_process_review_action_mixed_success(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
@@ -333,12 +288,6 @@ def test_process_review_action_mixed_success(
 
     # Mock the route functions
 
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = sample_pending_review
-
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
@@ -388,14 +337,6 @@ def test_process_review_action_mixed_success(
         "test_node_456": rejected_review,
     }
 
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
     mock_has_pending = mocker.patch(
         "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
     )
@@ -428,7 +369,6 @@ def test_process_review_action_mixed_success(
 
 
 def test_process_review_action_empty_request(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     test_user_id: str,
 ) -> None:
@@ -446,45 +386,10 @@ def test_process_review_action_empty_request(
 
 
 def test_process_review_action_review_not_found(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
-    sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
 ) -> None:
     """Test error when review is not found"""
-    # Create a review with the nonexistent_node ID so the route can find the graph_exec_id
-    nonexistent_review = PendingHumanReviewModel(
-        node_exec_id="nonexistent_node",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "test"},
-        instructions="Review",
-        editable=True,
-        status=ReviewStatus.WAITING,
-        review_message=None,
-        was_edited=None,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=None,
-        reviewed_at=None,
-    )
-
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = nonexistent_review
-
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
     # Mock the functions that extract graph execution ID from the request
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
@@ -517,26 +422,11 @@ def test_process_review_action_review_not_found(
 
 
 def test_process_review_action_partial_failure(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
 ) -> None:
     """Test handling of partial failures in review processing"""
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = sample_pending_review
-
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
     # Mock the route functions
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
@@ -566,50 +456,16 @@ def test_process_review_action_partial_failure(
 
 
 def test_process_review_action_invalid_node_exec_id(
-    client: fastapi.testclient.TestClient,
     mocker: pytest_mock.MockerFixture,
     sample_pending_review: PendingHumanReviewModel,
     test_user_id: str,
 ) -> None:
     """Test failure when trying to process review with invalid node execution ID"""
-    # Create a review with the invalid-node-format ID so the route can find the graph_exec_id
-    invalid_review = PendingHumanReviewModel(
-        node_exec_id="invalid-node-format",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "test"},
-        instructions="Review",
-        editable=True,
-        status=ReviewStatus.WAITING,
-        review_message=None,
-        was_edited=None,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=None,
-        reviewed_at=None,
-    )
-
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = invalid_review
-
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
     # Mock the route functions
     mock_get_reviews_for_execution = mocker.patch(
         "backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
     )
-    mock_get_reviews_for_execution.return_value = [invalid_review]
+    mock_get_reviews_for_execution.return_value = [sample_pending_review]
 
     # Mock validation failure - this should return 400, not 500
     mock_process_all_reviews = mocker.patch(
@@ -634,595 +490,3 @@ def test_process_review_action_invalid_node_exec_id(
     # Should be a 400 Bad Request, not 500 Internal Server Error
     assert response.status_code == 400
     assert "Invalid node execution ID format" in response.json()["detail"]
-
-
-def test_process_review_action_auto_approve_creates_auto_approval_records(
-    client: fastapi.testclient.TestClient,
-    mocker: pytest_mock.MockerFixture,
-    sample_pending_review: PendingHumanReviewModel,
-    test_user_id: str,
-) -> None:
-    """Test that auto_approve_future_actions flag creates auto-approval records"""
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = sample_pending_review
-
-    # Mock process_all_reviews
-    mock_process_all_reviews = mocker.patch(
-        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
-    )
-    approved_review = PendingHumanReviewModel(
-        node_exec_id="test_node_123",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "test payload"},
-        instructions="Please review",
-        editable=True,
-        status=ReviewStatus.APPROVED,
-        review_message="Approved",
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-    mock_process_all_reviews.return_value = {"test_node_123": approved_review}
-
-    # Mock get_node_execution to return node_id
-    mock_get_node_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.get_node_execution"
-    )
-    mock_node_exec = mocker.Mock(spec=NodeExecutionResult)
-    mock_node_exec.node_id = "test_node_def_456"
-    mock_get_node_execution.return_value = mock_node_exec
-
-    # Mock create_auto_approval_record
-    mock_create_auto_approval = mocker.patch(
-        "backend.api.features.executions.review.routes.create_auto_approval_record"
-    )
-
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
-    # Mock has_pending_reviews_for_graph_exec
-    mock_has_pending = mocker.patch(
-        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
-    )
-    mock_has_pending.return_value = False
-
-    # Mock get_graph_settings to return custom settings
-    mock_get_settings = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_settings"
-    )
-    mock_get_settings.return_value = GraphSettings(
-        human_in_the_loop_safe_mode=True,
-        sensitive_action_safe_mode=True,
-    )
-
-    # Mock get_user_by_id to prevent database access
-    mock_get_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_user_by_id"
-    )
-    mock_user = mocker.Mock()
-    mock_user.timezone = "UTC"
-    mock_get_user.return_value = mock_user
-
-    # Mock add_graph_execution
-    mock_add_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.add_graph_execution"
-    )
-
-    request_data = {
-        "reviews": [
-            {
-                "node_exec_id": "test_node_123",
-                "approved": True,
-                "message": "Approved",
-                "auto_approve_future": True,
-            }
-        ],
-    }
-
-    response = client.post("/api/review/action", json=request_data)
-
-    assert response.status_code == 200
-
-    # Verify process_all_reviews_for_execution was called (without auto_approve param)
-    mock_process_all_reviews.assert_called_once()
-
-    # Verify create_auto_approval_record was called for the approved review
-    mock_create_auto_approval.assert_called_once_with(
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        node_id="test_node_def_456",
-        payload={"data": "test payload"},
-    )
-
-    # Verify get_graph_settings was called with correct parameters
-    mock_get_settings.assert_called_once_with(
-        user_id=test_user_id, graph_id="test_graph_789"
-    )
-
-    # Verify add_graph_execution was called with proper ExecutionContext
-    mock_add_execution.assert_called_once()
-    call_kwargs = mock_add_execution.call_args.kwargs
-    execution_context = call_kwargs["execution_context"]
-
-    assert isinstance(execution_context, ExecutionContext)
-    assert execution_context.human_in_the_loop_safe_mode is True
-    assert execution_context.sensitive_action_safe_mode is True
-
-
-def test_process_review_action_without_auto_approve_still_loads_settings(
-    client: fastapi.testclient.TestClient,
-    mocker: pytest_mock.MockerFixture,
-    sample_pending_review: PendingHumanReviewModel,
-    test_user_id: str,
-) -> None:
-    """Test that execution context is created with settings even without auto-approve"""
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = sample_pending_review
-
-    # Mock process_all_reviews
-    mock_process_all_reviews = mocker.patch(
-        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
-    )
-    approved_review = PendingHumanReviewModel(
-        node_exec_id="test_node_123",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "test payload"},
-        instructions="Please review",
-        editable=True,
-        status=ReviewStatus.APPROVED,
-        review_message="Approved",
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-    mock_process_all_reviews.return_value = {"test_node_123": approved_review}
-
-    # Mock create_auto_approval_record - should NOT be called when auto_approve is False
-    mock_create_auto_approval = mocker.patch(
-        "backend.api.features.executions.review.routes.create_auto_approval_record"
-    )
-
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
-    # Mock has_pending_reviews_for_graph_exec
-    mock_has_pending = mocker.patch(
-        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
-    )
-    mock_has_pending.return_value = False
-
-    # Mock get_graph_settings with sensitive_action_safe_mode enabled
-    mock_get_settings = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_settings"
-    )
-    mock_get_settings.return_value = GraphSettings(
-        human_in_the_loop_safe_mode=False,
-        sensitive_action_safe_mode=True,
-    )
-
-    # Mock get_user_by_id to prevent database access
-    mock_get_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_user_by_id"
-    )
-    mock_user = mocker.Mock()
-    mock_user.timezone = "UTC"
-    mock_get_user.return_value = mock_user
-
-    # Mock add_graph_execution
-    mock_add_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.add_graph_execution"
-    )
-
-    # Request WITHOUT auto_approve_future (defaults to False)
-    request_data = {
-        "reviews": [
-            {
-                "node_exec_id": "test_node_123",
-                "approved": True,
-                "message": "Approved",
-                # auto_approve_future defaults to False
-            }
-        ],
-    }
-
-    response = client.post("/api/review/action", json=request_data)
-
-    assert response.status_code == 200
-
-    # Verify process_all_reviews_for_execution was called
-    mock_process_all_reviews.assert_called_once()
-
-    # Verify create_auto_approval_record was NOT called (auto_approve_future=False)
-    mock_create_auto_approval.assert_not_called()
-
-    # Verify settings were loaded
-    mock_get_settings.assert_called_once()
-
-    # Verify ExecutionContext has proper settings
-    mock_add_execution.assert_called_once()
-    call_kwargs = mock_add_execution.call_args.kwargs
-    execution_context = call_kwargs["execution_context"]
-
-    assert isinstance(execution_context, ExecutionContext)
-    assert execution_context.human_in_the_loop_safe_mode is False
-    assert execution_context.sensitive_action_safe_mode is True
-
-
-def test_process_review_action_auto_approve_only_applies_to_approved_reviews(
-    client: fastapi.testclient.TestClient,
-    mocker: pytest_mock.MockerFixture,
-    test_user_id: str,
-) -> None:
-    """Test that auto_approve record is created only for approved reviews"""
-    # Create two reviews - one approved, one rejected
-    approved_review = PendingHumanReviewModel(
-        node_exec_id="node_exec_approved",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "approved"},
-        instructions="Review",
-        editable=True,
-        status=ReviewStatus.APPROVED,
-        review_message=None,
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-    rejected_review = PendingHumanReviewModel(
-        node_exec_id="node_exec_rejected",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "rejected"},
-        instructions="Review",
-        editable=True,
-        status=ReviewStatus.REJECTED,
-        review_message="Rejected",
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-
-    # Mock get_pending_review_by_node_exec_id (called to find the graph_exec_id)
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-    mock_get_reviews_for_user.return_value = approved_review
-
-    # Mock process_all_reviews
-    mock_process_all_reviews = mocker.patch(
-        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
-    )
-    mock_process_all_reviews.return_value = {
-        "node_exec_approved": approved_review,
-        "node_exec_rejected": rejected_review,
-    }
-
-    # Mock get_node_execution to return node_id (only called for approved review)
-    mock_get_node_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.get_node_execution"
-    )
-    mock_node_exec = mocker.Mock(spec=NodeExecutionResult)
-    mock_node_exec.node_id = "test_node_def_approved"
-    mock_get_node_execution.return_value = mock_node_exec
-
-    # Mock create_auto_approval_record
-    mock_create_auto_approval = mocker.patch(
-        "backend.api.features.executions.review.routes.create_auto_approval_record"
-    )
-
-    # Mock get_graph_execution_meta to return execution in REVIEW status
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
-    # Mock has_pending_reviews_for_graph_exec
-    mock_has_pending = mocker.patch(
-        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
-    )
-    mock_has_pending.return_value = False
-
-    # Mock get_graph_settings
-    mock_get_settings = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_settings"
-    )
-    mock_get_settings.return_value = GraphSettings()
-
-    # Mock get_user_by_id to prevent database access
-    mock_get_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_user_by_id"
-    )
-    mock_user = mocker.Mock()
-    mock_user.timezone = "UTC"
-    mock_get_user.return_value = mock_user
-
-    # Mock add_graph_execution
-    mock_add_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.add_graph_execution"
-    )
-
-    request_data = {
-        "reviews": [
-            {
-                "node_exec_id": "node_exec_approved",
-                "approved": True,
-                "auto_approve_future": True,
-            },
-            {
-                "node_exec_id": "node_exec_rejected",
-                "approved": False,
-                "auto_approve_future": True,  # Should be ignored since rejected
-            },
-        ],
-    }
-
-    response = client.post("/api/review/action", json=request_data)
-
-    assert response.status_code == 200
-
-    # Verify process_all_reviews_for_execution was called
-    mock_process_all_reviews.assert_called_once()
-
-    # Verify create_auto_approval_record was called ONLY for the approved review
-    # (not for the rejected one)
-    mock_create_auto_approval.assert_called_once_with(
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        node_id="test_node_def_approved",
-        payload={"data": "approved"},
-    )
-
-    # Verify get_node_execution was called only for approved review
-    mock_get_node_execution.assert_called_once_with("node_exec_approved")
-
-    # Verify ExecutionContext was created (auto-approval is now DB-based)
-    call_kwargs = mock_add_execution.call_args.kwargs
-    execution_context = call_kwargs["execution_context"]
-    assert isinstance(execution_context, ExecutionContext)
-
-
-def test_process_review_action_per_review_auto_approve_granularity(
-    client: fastapi.testclient.TestClient,
-    mocker: pytest_mock.MockerFixture,
-    sample_pending_review: PendingHumanReviewModel,
-    test_user_id: str,
-) -> None:
-    """Test that auto-approval can be set per-review (granular control)"""
-    # Mock get_pending_review_by_node_exec_id - return different reviews based on node_exec_id
-    mock_get_reviews_for_user = mocker.patch(
-        "backend.api.features.executions.review.routes.get_pending_review_by_node_exec_id"
-    )
-
-    # Create a mapping of node_exec_id to review
-    review_map = {
-        "node_1_auto": PendingHumanReviewModel(
-            node_exec_id="node_1_auto",
-            user_id=test_user_id,
-            graph_exec_id="test_graph_exec",
-            graph_id="test_graph",
-            graph_version=1,
-            payload={"data": "node1"},
-            instructions="Review 1",
-            editable=True,
-            status=ReviewStatus.WAITING,
-            review_message=None,
-            was_edited=False,
-            processed=False,
-            created_at=FIXED_NOW,
-        ),
-        "node_2_manual": PendingHumanReviewModel(
-            node_exec_id="node_2_manual",
-            user_id=test_user_id,
-            graph_exec_id="test_graph_exec",
-            graph_id="test_graph",
-            graph_version=1,
-            payload={"data": "node2"},
-            instructions="Review 2",
-            editable=True,
-            status=ReviewStatus.WAITING,
-            review_message=None,
-            was_edited=False,
-            processed=False,
-            created_at=FIXED_NOW,
-        ),
-        "node_3_auto": PendingHumanReviewModel(
-            node_exec_id="node_3_auto",
-            user_id=test_user_id,
-            graph_exec_id="test_graph_exec",
-            graph_id="test_graph",
-            graph_version=1,
-            payload={"data": "node3"},
-            instructions="Review 3",
-            editable=True,
-            status=ReviewStatus.WAITING,
-            review_message=None,
-            was_edited=False,
-            processed=False,
-            created_at=FIXED_NOW,
-        ),
-    }
-
-    # Use side_effect to return different reviews based on node_exec_id parameter
-    def mock_get_review_by_id(node_exec_id: str, _user_id: str):
-        return review_map.get(node_exec_id)
-
-    mock_get_reviews_for_user.side_effect = mock_get_review_by_id
-
-    # Mock process_all_reviews - return 3 approved reviews
-    mock_process_all_reviews = mocker.patch(
-        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
-    )
-    mock_process_all_reviews.return_value = {
-        "node_1_auto": PendingHumanReviewModel(
-            node_exec_id="node_1_auto",
-            user_id=test_user_id,
-            graph_exec_id="test_graph_exec",
-            graph_id="test_graph",
-            graph_version=1,
-            payload={"data": "node1"},
-            instructions="Review 1",
-            editable=True,
-            status=ReviewStatus.APPROVED,
-            review_message=None,
-            was_edited=False,
-            processed=False,
-            created_at=FIXED_NOW,
-            updated_at=FIXED_NOW,
-            reviewed_at=FIXED_NOW,
-        ),
-        "node_2_manual": PendingHumanReviewModel(
-            node_exec_id="node_2_manual",
-            user_id=test_user_id,
-            graph_exec_id="test_graph_exec",
-            graph_id="test_graph",
-            graph_version=1,
-            payload={"data": "node2"},
-            instructions="Review 2",
-            editable=True,
-            status=ReviewStatus.APPROVED,
-            review_message=None,
-            was_edited=False,
-            processed=False,
-            created_at=FIXED_NOW,
-            updated_at=FIXED_NOW,
-            reviewed_at=FIXED_NOW,
-        ),
-        "node_3_auto": PendingHumanReviewModel(
-            node_exec_id="node_3_auto",
-            user_id=test_user_id,
-            graph_exec_id="test_graph_exec",
-            graph_id="test_graph",
-            graph_version=1,
-            payload={"data": "node3"},
-            instructions="Review 3",
-            editable=True,
-            status=ReviewStatus.APPROVED,
-            review_message=None,
-            was_edited=False,
-            processed=False,
-            created_at=FIXED_NOW,
-            updated_at=FIXED_NOW,
-            reviewed_at=FIXED_NOW,
-        ),
-    }
-
-    # Mock get_node_execution
-    mock_get_node_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.get_node_execution"
-    )
-
-    def mock_get_node(node_exec_id: str):
-        mock_node = mocker.Mock(spec=NodeExecutionResult)
-        mock_node.node_id = f"node_def_{node_exec_id}"
-        return mock_node
-
-    mock_get_node_execution.side_effect = mock_get_node
-
-    # Mock create_auto_approval_record
-    mock_create_auto_approval = mocker.patch(
-        "backend.api.features.executions.review.routes.create_auto_approval_record"
-    )
-
-    # Mock get_graph_execution_meta
-    mock_get_graph_exec = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_execution_meta"
-    )
-    mock_graph_exec_meta = mocker.Mock()
-    mock_graph_exec_meta.status = ExecutionStatus.REVIEW
-    mock_get_graph_exec.return_value = mock_graph_exec_meta
-
-    # Mock has_pending_reviews_for_graph_exec
-    mock_has_pending = mocker.patch(
-        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
-    )
-    mock_has_pending.return_value = False
-
-    # Mock settings and execution
-    mock_get_settings = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_settings"
-    )
-    mock_get_settings.return_value = GraphSettings(
-        human_in_the_loop_safe_mode=False, sensitive_action_safe_mode=False
-    )
-
-    mocker.patch("backend.api.features.executions.review.routes.add_graph_execution")
-    mocker.patch("backend.api.features.executions.review.routes.get_user_by_id")
-
-    # Request with granular auto-approval:
-    # - node_1_auto: auto_approve_future=True
-    # - node_2_manual: auto_approve_future=False (explicit)
-    # - node_3_auto: auto_approve_future=True
-    request_data = {
-        "reviews": [
-            {
-                "node_exec_id": "node_1_auto",
-                "approved": True,
-                "auto_approve_future": True,
-            },
-            {
-                "node_exec_id": "node_2_manual",
-                "approved": True,
-                "auto_approve_future": False,  # Don't auto-approve this one
-            },
-            {
-                "node_exec_id": "node_3_auto",
-                "approved": True,
-                "auto_approve_future": True,
-            },
-        ],
-    }
-
-    response = client.post("/api/review/action", json=request_data)
-
-    assert response.status_code == 200
-
-    # Verify create_auto_approval_record was called ONLY for reviews with auto_approve_future=True
-    assert mock_create_auto_approval.call_count == 2
-
-    # Check that it was called for node_1 and node_3, but NOT node_2
-    call_args_list = [call.kwargs for call in mock_create_auto_approval.call_args_list]
-    node_ids_with_auto_approval = [args["node_id"] for args in call_args_list]
-
-    assert "node_def_node_1_auto" in node_ids_with_auto_approval
-    assert "node_def_node_3_auto" in node_ids_with_auto_approval
-    assert "node_def_node_2_manual" not in node_ids_with_auto_approval

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

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

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

@@ -6,7 +6,6 @@ Handles generation and storage of OpenAI embeddings for all content types
 """
 
 import asyncio
-import contextvars
 import logging
 import time
 from typing import Any
@@ -22,11 +21,6 @@ from backend.util.json import dumps
 
 logger = logging.getLogger(__name__)
 
-# Context variable to track errors logged in the current task/operation
-# This prevents spamming the same error multiple times when processing batches
-_logged_errors: contextvars.ContextVar[set[str]] = contextvars.ContextVar(
-    "_logged_errors"
-)
 
 # OpenAI embedding model configuration
 EMBEDDING_MODEL = "text-embedding-3-small"
@@ -37,42 +31,6 @@ EMBEDDING_DIM = 1536
 EMBEDDING_MAX_TOKENS = 8191
 
 
-def log_once_per_task(error_key: str, log_fn, message: str, **kwargs) -> bool:
-    """
-    Log an error/warning only once per task/operation to avoid log spam.
-
-    Uses contextvars to track what has been logged in the current async context.
-    Useful when processing batches where the same error might occur for many items.
-
-    Args:
-        error_key: Unique identifier for this error type
-        log_fn: Logger function to call (e.g., logger.error, logger.warning)
-        message: Message to log
-        **kwargs: Additional arguments to pass to log_fn
-
-    Returns:
-        True if the message was logged, False if it was suppressed (already logged)
-
-    Example:
-        log_once_per_task("missing_api_key", logger.error, "API key not set")
-    """
-    # Get current logged errors, or create a new set if this is the first call in this context
-    logged = _logged_errors.get(None)
-    if logged is None:
-        logged = set()
-        _logged_errors.set(logged)
-
-    if error_key in logged:
-        return False
-
-    # Log the message with a note that it will only appear once
-    log_fn(f"{message} (This message will only be shown once per task.)", **kwargs)
-
-    # Mark as logged
-    logged.add(error_key)
-    return True
-
-
 def build_searchable_text(
     name: str,
     description: str,
@@ -115,11 +73,7 @@ async def generate_embedding(text: str) -> list[float] | None:
     try:
         client = get_openai_client()
         if not client:
-            log_once_per_task(
-                "openai_api_key_missing",
-                logger.error,
-                "openai_internal_api_key not set, cannot generate embeddings",
-            )
+            logger.error("openai_internal_api_key not set, cannot generate embedding")
             return None
 
         # Truncate text to token limit using tiktoken
@@ -336,12 +290,7 @@ async def ensure_embedding(
         # Generate new embedding
         embedding = await generate_embedding(searchable_text)
         if embedding is None:
-            log_once_per_task(
-                "embedding_generation_failed",
-                logger.warning,
-                "Could not generate embeddings (missing API key or service unavailable). "
-                "Embedding generation is disabled for this task.",
-            )
+            logger.warning(f"Could not generate embedding for version {version_id}")
             return False
 
         # Store the embedding with metadata using new function
@@ -660,11 +609,8 @@ async def ensure_content_embedding(
         # Generate new embedding
         embedding = await generate_embedding(searchable_text)
         if embedding is None:
-            log_once_per_task(
-                "embedding_generation_failed",
-                logger.warning,
-                "Could not generate embeddings (missing API key or service unavailable). "
-                "Embedding generation is disabled for this task.",
+            logger.warning(
+                f"Could not generate embedding for {content_type}:{content_id}"
             )
             return False
 

autogpt_platform/backend/backend/blocks/basic.py

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

autogpt_platform/backend/backend/blocks/claude_code.py

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

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

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

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -97,7 +97,6 @@ class HumanInTheLoopBlock(Block):
         input_data: Input,
         *,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -116,11 +115,11 @@ class HumanInTheLoopBlock(Block):
         decision = await self.handle_review_decision(
             input_data=input_data.data,
             user_id=user_id,
-            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
+            execution_context=execution_context,
             block_name=self.name,
             editable=input_data.editable,
         )

autogpt_platform/backend/backend/data/block.py

@@ -441,7 +441,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         static_output: bool = False,
         block_type: BlockType = BlockType.STANDARD,
         webhook_config: Optional[BlockWebhookConfig | BlockManualWebhookConfig] = None,
-        is_sensitive_action: bool = False,
     ):
         """
         Initialize the block with the given schema.
@@ -474,8 +473,8 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.static_output = static_output
         self.block_type = block_type
         self.webhook_config = webhook_config
-        self.is_sensitive_action = is_sensitive_action
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
+        self.is_sensitive_action: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -623,7 +622,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         input_data: BlockInput,
         *,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -650,11 +648,11 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         decision = await HITLReviewHelper.handle_review_decision(
             input_data=input_data,
             user_id=user_id,
-            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
+            execution_context=execution_context,
             block_name=self.name,
             editable=True,
         )

autogpt_platform/backend/backend/data/human_review.py

@@ -17,7 +17,6 @@ from backend.api.features.executions.review.model import (
     PendingHumanReviewModel,
     SafeJsonData,
 )
-from backend.data.execution import get_graph_execution_meta
 from backend.util.json import SafeJson
 
 logger = logging.getLogger(__name__)
@@ -33,125 +32,6 @@ class ReviewResult(BaseModel):
     node_exec_id: str
 
 
-def get_auto_approve_key(graph_exec_id: str, node_id: str) -> str:
-    """Generate the special nodeExecId key for auto-approval records."""
-    return f"auto_approve_{graph_exec_id}_{node_id}"
-
-
-async def check_approval(
-    node_exec_id: str,
-    graph_exec_id: str,
-    node_id: str,
-    user_id: str,
-    input_data: SafeJsonData | None = None,
-) -> Optional[ReviewResult]:
-    """
-    Check if there's an existing approval for this node execution.
-
-    Checks both:
-    1. Normal approval by node_exec_id (previous run of the same node execution)
-    2. Auto-approval by special key pattern "auto_approve_{graph_exec_id}_{node_id}"
-
-    Args:
-        node_exec_id: ID of the node execution
-        graph_exec_id: ID of the graph execution
-        node_id: ID of the node definition (not execution)
-        user_id: ID of the user (for data isolation)
-        input_data: Current input data (used for auto-approvals to avoid stale data)
-
-    Returns:
-        ReviewResult if approval found (either normal or auto), None otherwise
-    """
-    auto_approve_key = get_auto_approve_key(graph_exec_id, node_id)
-
-    # Check for either normal approval or auto-approval in a single query
-    existing_review = await PendingHumanReview.prisma().find_first(
-        where={
-            "OR": [
-                {"nodeExecId": node_exec_id},
-                {"nodeExecId": auto_approve_key},
-            ],
-            "status": ReviewStatus.APPROVED,
-            "userId": user_id,
-        },
-    )
-
-    if existing_review:
-        is_auto_approval = existing_review.nodeExecId == auto_approve_key
-        logger.info(
-            f"Found {'auto-' if is_auto_approval else ''}approval for node {node_id} "
-            f"(exec: {node_exec_id}) in execution {graph_exec_id}"
-        )
-        # For auto-approvals, use current input_data to avoid replaying stale payload
-        # For normal approvals, use the stored payload (which may have been edited)
-        return ReviewResult(
-            data=(
-                input_data
-                if is_auto_approval and input_data is not None
-                else existing_review.payload
-            ),
-            status=ReviewStatus.APPROVED,
-            message=(
-                "Auto-approved (user approved all future actions for this node)"
-                if is_auto_approval
-                else existing_review.reviewMessage or ""
-            ),
-            processed=True,
-            node_exec_id=existing_review.nodeExecId,
-        )
-
-    return None
-
-
-async def create_auto_approval_record(
-    user_id: str,
-    graph_exec_id: str,
-    graph_id: str,
-    graph_version: int,
-    node_id: str,
-    payload: SafeJsonData,
-) -> None:
-    """
-    Create an auto-approval record for a node in this execution.
-
-    This is stored as a PendingHumanReview with a special nodeExecId pattern
-    and status=APPROVED, so future executions of the same node can skip review.
-
-    Raises:
-        ValueError: If the graph execution doesn't belong to the user
-    """
-    # Validate that the graph execution belongs to this user (defense in depth)
-    graph_exec = await get_graph_execution_meta(
-        user_id=user_id, execution_id=graph_exec_id
-    )
-    if not graph_exec:
-        raise ValueError(
-            f"Graph execution {graph_exec_id} not found or doesn't belong to user {user_id}"
-        )
-
-    auto_approve_key = get_auto_approve_key(graph_exec_id, node_id)
-
-    await PendingHumanReview.prisma().upsert(
-        where={"nodeExecId": auto_approve_key},
-        data={
-            "create": {
-                "nodeExecId": auto_approve_key,
-                "userId": user_id,
-                "graphExecId": graph_exec_id,
-                "graphId": graph_id,
-                "graphVersion": graph_version,
-                "payload": SafeJson(payload),
-                "instructions": "Auto-approval record",
-                "editable": False,
-                "status": ReviewStatus.APPROVED,
-                "processed": True,
-                "reviewedAt": datetime.now(timezone.utc),
-            },
-            "update": {},  # Already exists, no update needed
-        },
-    )
-
-
 async def get_or_create_human_review(
     user_id: str,
     node_exec_id: str,
@@ -228,33 +108,6 @@ async def get_or_create_human_review(
         )
 
 
-async def get_pending_review_by_node_exec_id(
-    node_exec_id: str, user_id: str
-) -> Optional["PendingHumanReviewModel"]:
-    """
-    Get a pending review by its node execution ID.
-
-    Args:
-        node_exec_id: The node execution ID to look up
-        user_id: User ID for authorization (only returns if review belongs to this user)
-
-    Returns:
-        The pending review if found and belongs to user, None otherwise
-    """
-    review = await PendingHumanReview.prisma().find_first(
-        where={
-            "nodeExecId": node_exec_id,
-            "userId": user_id,
-            "status": ReviewStatus.WAITING,
-        }
-    )
-
-    if not review:
-        return None
-
-    return PendingHumanReviewModel.from_db(review)
-
-
 async def has_pending_reviews_for_graph_exec(graph_exec_id: str) -> bool:
     """
     Check if a graph execution has any pending reviews.
@@ -403,44 +256,3 @@ async def update_review_processed_status(node_exec_id: str, processed: bool) ->
     await PendingHumanReview.prisma().update(
         where={"nodeExecId": node_exec_id}, data={"processed": processed}
     )
-
-
-async def cancel_pending_reviews_for_execution(graph_exec_id: str, user_id: str) -> int:
-    """
-    Cancel all pending reviews for a graph execution (e.g., when execution is stopped).
-
-    Marks all WAITING reviews as REJECTED with a message indicating the execution was stopped.
-
-    Args:
-        graph_exec_id: The graph execution ID
-        user_id: User ID who owns the execution (for security validation)
-
-    Returns:
-        Number of reviews cancelled
-
-    Raises:
-        ValueError: If the graph execution doesn't belong to the user
-    """
-    # Validate user ownership before cancelling reviews
-    graph_exec = await get_graph_execution_meta(
-        user_id=user_id, execution_id=graph_exec_id
-    )
-    if not graph_exec:
-        raise ValueError(
-            f"Graph execution {graph_exec_id} not found or doesn't belong to user {user_id}"
-        )
-
-    result = await PendingHumanReview.prisma().update_many(
-        where={
-            "graphExecId": graph_exec_id,
-            "userId": user_id,
-            "status": ReviewStatus.WAITING,
-        },
-        data={
-            "status": ReviewStatus.REJECTED,
-            "reviewMessage": "Execution was stopped by user",
-            "processed": True,
-            "reviewedAt": datetime.now(timezone.utc),
-        },
-    )
-    return result

autogpt_platform/backend/backend/data/human_review_test.py

@@ -46,8 +46,8 @@ async def test_get_or_create_human_review_new(
     sample_db_review.status = ReviewStatus.WAITING
     sample_db_review.processed = False
 
-    mock_prisma = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_prisma.return_value.upsert = AsyncMock(return_value=sample_db_review)
+    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
+    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
 
     result = await get_or_create_human_review(
         user_id="test-user-123",
@@ -75,8 +75,8 @@ async def test_get_or_create_human_review_approved(
     sample_db_review.processed = False
     sample_db_review.reviewMessage = "Looks good"
 
-    mock_prisma = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_prisma.return_value.upsert = AsyncMock(return_value=sample_db_review)
+    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
+    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
 
     result = await get_or_create_human_review(
         user_id="test-user-123",

autogpt_platform/backend/backend/executor/database.py

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

autogpt_platform/backend/backend/executor/utils.py

@@ -10,7 +10,6 @@ from pydantic import BaseModel, JsonValue, ValidationError
 
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
-from backend.data import human_review as human_review_db
 from backend.data import onboarding as onboarding_db
 from backend.data import user as user_db
 from backend.data.block import (
@@ -750,27 +749,9 @@ async def stop_graph_execution(
         if graph_exec.status in [
             ExecutionStatus.QUEUED,
             ExecutionStatus.INCOMPLETE,
-            ExecutionStatus.REVIEW,
         ]:
-            # If the graph is queued/incomplete/paused for review, terminate immediately
-            # No need to wait for executor since it's not actively running
-
-            # If graph is in REVIEW status, clean up pending reviews before terminating
-            if graph_exec.status == ExecutionStatus.REVIEW:
-                # Use human_review_db if Prisma connected, else database manager
-                review_db = (
-                    human_review_db
-                    if prisma.is_connected()
-                    else get_database_manager_async_client()
-                )
-                # Mark all pending reviews as rejected/cancelled
-                cancelled_count = await review_db.cancel_pending_reviews_for_execution(
-                    graph_exec_id, user_id
-                )
-                logger.info(
-                    f"Cancelled {cancelled_count} pending review(s) for stopped execution {graph_exec_id}"
-                )
-
+            # If the graph is still on the queue, we can prevent them from being executed
+            # by setting the status to TERMINATED.
             graph_exec.status = ExecutionStatus.TERMINATED
 
             await asyncio.gather(
@@ -906,28 +887,9 @@ async def add_graph_execution(
             nodes_to_skip=nodes_to_skip,
             execution_context=execution_context,
         )
-        logger.info(f"Queueing execution {graph_exec.id}")
-
-        # Update execution status to QUEUED BEFORE publishing to prevent race condition
-        # where two concurrent requests could both publish the same execution
-        updated_exec = await edb.update_graph_execution_stats(
-            graph_exec_id=graph_exec.id,
-            status=ExecutionStatus.QUEUED,
-        )
-
-        # Verify the status update succeeded (prevents duplicate queueing in race conditions)
-        # If another request already updated the status, this execution will not be QUEUED
-        if not updated_exec or updated_exec.status != ExecutionStatus.QUEUED:
-            logger.warning(
-                f"Skipping queue publish for execution {graph_exec.id} - "
-                f"status update failed or execution already queued by another request"
-            )
-            return graph_exec
-
-        graph_exec.status = ExecutionStatus.QUEUED
+        logger.info(f"Publishing execution {graph_exec.id} to execution queue")
 
         # Publish to execution queue for executor to pick up
-        # This happens AFTER status update to ensure only one request publishes
         exec_queue = await get_async_execution_queue()
         await exec_queue.publish_message(
             routing_key=GRAPH_EXECUTION_ROUTING_KEY,
@@ -935,6 +897,13 @@ async def add_graph_execution(
             exchange=GRAPH_EXECUTION_EXCHANGE,
         )
         logger.info(f"Published execution {graph_exec.id} to RabbitMQ queue")
+
+        # Update execution status to QUEUED
+        graph_exec.status = ExecutionStatus.QUEUED
+        await edb.update_graph_execution_stats(
+            graph_exec_id=graph_exec.id,
+            status=graph_exec.status,
+        )
     except BaseException as e:
         err = str(e) or type(e).__name__
         if not graph_exec:

autogpt_platform/backend/backend/executor/utils_test.py

@@ -4,7 +4,6 @@ import pytest
 from pytest_mock import MockerFixture
 
 from backend.data.dynamic_fields import merge_execution_input, parse_execution_output
-from backend.data.execution import ExecutionStatus
 from backend.util.mock import MockObject
 
 
@@ -347,7 +346,6 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []  # Add this to avoid AttributeError
-    mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
     mock_graph_exec.to_graph_execution_entry.return_value = mocker.MagicMock()
 
     # Mock the queue and event bus
@@ -613,7 +611,6 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []
-    mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
 
     # Track what's passed to to_graph_execution_entry
     captured_kwargs = {}
@@ -673,232 +670,3 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     # Verify nodes_to_skip was passed to to_graph_execution_entry
     assert "nodes_to_skip" in captured_kwargs
     assert captured_kwargs["nodes_to_skip"] == nodes_to_skip
-
-
-@pytest.mark.asyncio
-async def test_stop_graph_execution_in_review_status_cancels_pending_reviews(
-    mocker: MockerFixture,
-):
-    """Test that stopping an execution in REVIEW status cancels pending reviews."""
-    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
-    from backend.executor.utils import stop_graph_execution
-
-    user_id = "test-user"
-    graph_exec_id = "test-exec-123"
-
-    # Mock graph execution in REVIEW status
-    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionMeta)
-    mock_graph_exec.id = graph_exec_id
-    mock_graph_exec.status = ExecutionStatus.REVIEW
-
-    # Mock dependencies
-    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
-    mock_queue_client = mocker.AsyncMock()
-    mock_get_queue.return_value = mock_queue_client
-
-    mock_prisma = mocker.patch("backend.executor.utils.prisma")
-    mock_prisma.is_connected.return_value = True
-
-    mock_human_review_db = mocker.patch("backend.executor.utils.human_review_db")
-    mock_human_review_db.cancel_pending_reviews_for_execution = mocker.AsyncMock(
-        return_value=2  # 2 reviews cancelled
-    )
-
-    mock_execution_db = mocker.patch("backend.executor.utils.execution_db")
-    mock_execution_db.get_graph_execution_meta = mocker.AsyncMock(
-        return_value=mock_graph_exec
-    )
-    mock_execution_db.update_graph_execution_stats = mocker.AsyncMock()
-
-    mock_get_event_bus = mocker.patch(
-        "backend.executor.utils.get_async_execution_event_bus"
-    )
-    mock_event_bus = mocker.MagicMock()
-    mock_event_bus.publish = mocker.AsyncMock()
-    mock_get_event_bus.return_value = mock_event_bus
-
-    mock_get_child_executions = mocker.patch(
-        "backend.executor.utils._get_child_executions"
-    )
-    mock_get_child_executions.return_value = []  # No children
-
-    # Call stop_graph_execution with timeout to allow status check
-    await stop_graph_execution(
-        user_id=user_id,
-        graph_exec_id=graph_exec_id,
-        wait_timeout=1.0,  # Wait to allow status check
-        cascade=True,
-    )
-
-    # Verify pending reviews were cancelled
-    mock_human_review_db.cancel_pending_reviews_for_execution.assert_called_once_with(
-        graph_exec_id, user_id
-    )
-
-    # Verify execution status was updated to TERMINATED
-    mock_execution_db.update_graph_execution_stats.assert_called_once()
-    call_kwargs = mock_execution_db.update_graph_execution_stats.call_args[1]
-    assert call_kwargs["graph_exec_id"] == graph_exec_id
-    assert call_kwargs["status"] == ExecutionStatus.TERMINATED
-
-
-@pytest.mark.asyncio
-async def test_stop_graph_execution_with_database_manager_when_prisma_disconnected(
-    mocker: MockerFixture,
-):
-    """Test that stop uses database manager when Prisma is not connected."""
-    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
-    from backend.executor.utils import stop_graph_execution
-
-    user_id = "test-user"
-    graph_exec_id = "test-exec-456"
-
-    # Mock graph execution in REVIEW status
-    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionMeta)
-    mock_graph_exec.id = graph_exec_id
-    mock_graph_exec.status = ExecutionStatus.REVIEW
-
-    # Mock dependencies
-    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
-    mock_queue_client = mocker.AsyncMock()
-    mock_get_queue.return_value = mock_queue_client
-
-    # Prisma is NOT connected
-    mock_prisma = mocker.patch("backend.executor.utils.prisma")
-    mock_prisma.is_connected.return_value = False
-
-    # Mock database manager client
-    mock_get_db_manager = mocker.patch(
-        "backend.executor.utils.get_database_manager_async_client"
-    )
-    mock_db_manager = mocker.AsyncMock()
-    mock_db_manager.get_graph_execution_meta = mocker.AsyncMock(
-        return_value=mock_graph_exec
-    )
-    mock_db_manager.cancel_pending_reviews_for_execution = mocker.AsyncMock(
-        return_value=3  # 3 reviews cancelled
-    )
-    mock_db_manager.update_graph_execution_stats = mocker.AsyncMock()
-    mock_get_db_manager.return_value = mock_db_manager
-
-    mock_get_event_bus = mocker.patch(
-        "backend.executor.utils.get_async_execution_event_bus"
-    )
-    mock_event_bus = mocker.MagicMock()
-    mock_event_bus.publish = mocker.AsyncMock()
-    mock_get_event_bus.return_value = mock_event_bus
-
-    mock_get_child_executions = mocker.patch(
-        "backend.executor.utils._get_child_executions"
-    )
-    mock_get_child_executions.return_value = []  # No children
-
-    # Call stop_graph_execution with timeout
-    await stop_graph_execution(
-        user_id=user_id,
-        graph_exec_id=graph_exec_id,
-        wait_timeout=1.0,
-        cascade=True,
-    )
-
-    # Verify database manager was used for cancel_pending_reviews
-    mock_db_manager.cancel_pending_reviews_for_execution.assert_called_once_with(
-        graph_exec_id, user_id
-    )
-
-    # Verify execution status was updated via database manager
-    mock_db_manager.update_graph_execution_stats.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_stop_graph_execution_cascades_to_child_with_reviews(
-    mocker: MockerFixture,
-):
-    """Test that stopping parent execution cascades to children and cancels their reviews."""
-    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
-    from backend.executor.utils import stop_graph_execution
-
-    user_id = "test-user"
-    parent_exec_id = "parent-exec"
-    child_exec_id = "child-exec"
-
-    # Mock parent execution in RUNNING status
-    mock_parent_exec = mocker.MagicMock(spec=GraphExecutionMeta)
-    mock_parent_exec.id = parent_exec_id
-    mock_parent_exec.status = ExecutionStatus.RUNNING
-
-    # Mock child execution in REVIEW status
-    mock_child_exec = mocker.MagicMock(spec=GraphExecutionMeta)
-    mock_child_exec.id = child_exec_id
-    mock_child_exec.status = ExecutionStatus.REVIEW
-
-    # Mock dependencies
-    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
-    mock_queue_client = mocker.AsyncMock()
-    mock_get_queue.return_value = mock_queue_client
-
-    mock_prisma = mocker.patch("backend.executor.utils.prisma")
-    mock_prisma.is_connected.return_value = True
-
-    mock_human_review_db = mocker.patch("backend.executor.utils.human_review_db")
-    mock_human_review_db.cancel_pending_reviews_for_execution = mocker.AsyncMock(
-        return_value=1  # 1 child review cancelled
-    )
-
-    # Mock execution_db to return different status based on which execution is queried
-    mock_execution_db = mocker.patch("backend.executor.utils.execution_db")
-
-    # Track call count to simulate status transition
-    call_count = {"count": 0}
-
-    async def get_exec_meta_side_effect(execution_id, user_id):
-        call_count["count"] += 1
-        if execution_id == parent_exec_id:
-            # After a few calls (child processing happens), transition parent to TERMINATED
-            # This simulates the executor service processing the stop request
-            if call_count["count"] > 3:
-                mock_parent_exec.status = ExecutionStatus.TERMINATED
-            return mock_parent_exec
-        elif execution_id == child_exec_id:
-            return mock_child_exec
-        return None
-
-    mock_execution_db.get_graph_execution_meta = mocker.AsyncMock(
-        side_effect=get_exec_meta_side_effect
-    )
-    mock_execution_db.update_graph_execution_stats = mocker.AsyncMock()
-
-    mock_get_event_bus = mocker.patch(
-        "backend.executor.utils.get_async_execution_event_bus"
-    )
-    mock_event_bus = mocker.MagicMock()
-    mock_event_bus.publish = mocker.AsyncMock()
-    mock_get_event_bus.return_value = mock_event_bus
-
-    # Mock _get_child_executions to return the child
-    mock_get_child_executions = mocker.patch(
-        "backend.executor.utils._get_child_executions"
-    )
-
-    def get_children_side_effect(parent_id):
-        if parent_id == parent_exec_id:
-            return [mock_child_exec]
-        return []
-
-    mock_get_child_executions.side_effect = get_children_side_effect
-
-    # Call stop_graph_execution on parent with cascade=True
-    await stop_graph_execution(
-        user_id=user_id,
-        graph_exec_id=parent_exec_id,
-        wait_timeout=1.0,
-        cascade=True,
-    )
-
-    # Verify child reviews were cancelled
-    mock_human_review_db.cancel_pending_reviews_for_execution.assert_called_once_with(
-        child_exec_id, user_id
-    )
-
-    # Verify both parent and child status updates
-    assert mock_execution_db.update_graph_execution_stats.call_count >= 1

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

@@ -1,12 +1,37 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Creates extension in current schema (determined by search_path from DATABASE_URL ?schema= param)
+-- Ensures vector extension is in the current schema (from DATABASE_URL ?schema= param)
+-- If it exists in a different schema (e.g., public), we drop and recreate it in the current schema
 -- This ensures vector type is in the same schema as tables, making ::vector work without explicit qualification
 DO $$
+DECLARE
+    current_schema_name text;
+    vector_schema text;
 BEGIN
-    CREATE EXTENSION IF NOT EXISTS "vector";
-EXCEPTION WHEN OTHERS THEN
-    RAISE NOTICE 'vector extension not available or already exists, skipping';
+    -- Get the current schema from search_path
+    SELECT current_schema() INTO current_schema_name;
+
+    -- Check if vector extension exists and which schema it's in
+    SELECT n.nspname INTO vector_schema
+    FROM pg_extension e
+    JOIN pg_namespace n ON e.extnamespace = n.oid
+    WHERE e.extname = 'vector';
+
+    -- Handle removal if in wrong schema
+    IF vector_schema IS NOT NULL AND vector_schema != current_schema_name THEN
+        BEGIN
+            -- Vector exists in a different schema, drop it first
+            RAISE WARNING 'pgvector found in schema "%" but need it in "%". Dropping and reinstalling...',
+                vector_schema, current_schema_name;
+            EXECUTE 'DROP EXTENSION IF EXISTS vector CASCADE';
+        EXCEPTION WHEN OTHERS THEN
+            RAISE EXCEPTION 'Failed to drop pgvector from schema "%": %. You may need to drop it manually.',
+                vector_schema, SQLERRM;
+        END;
+    END IF;
+
+    -- Create extension in current schema (let it fail naturally if not available)
+    EXECUTE format('CREATE EXTENSION IF NOT EXISTS vector SCHEMA %I', current_schema_name);
 END $$;
 
 -- CreateEnum

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

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

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

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

autogpt_platform/backend/schema.prisma

@@ -517,6 +517,8 @@ model AgentNodeExecution {
 
   stats Json?
 
+  PendingHumanReview PendingHumanReview?
+
   @@index([agentGraphExecutionId, agentNodeId, executionStatus])
   @@index([agentNodeId, executionStatus])
   @@index([addedTime, queuedTime])
@@ -565,7 +567,6 @@ enum ReviewStatus {
 }
 
 // Pending human reviews for Human-in-the-loop blocks
-// Also stores auto-approval records with special nodeExecId patterns (e.g., "auto_approve_{graph_exec_id}_{node_id}")
 model PendingHumanReview {
   nodeExecId    String       @id
   userId        String
@@ -584,6 +585,7 @@ model PendingHumanReview {
   reviewedAt    DateTime?
 
   User           User                @relation(fields: [userId], references: [id], onDelete: Cascade)
+  NodeExecution  AgentNodeExecution  @relation(fields: [nodeExecId], references: [id], onDelete: Cascade)
   GraphExecution AgentGraphExecution @relation(fields: [graphExecId], references: [id], onDelete: Cascade)
 
   @@unique([nodeExecId]) // One pending review per node execution

autogpt_platform/backend/scripts/generate_block_docs.py

@@ -34,7 +34,10 @@ logger = logging.getLogger(__name__)
 
 # Default output directory relative to repo root
 DEFAULT_OUTPUT_DIR = (
-    Path(__file__).parent.parent.parent.parent / "docs" / "integrations"
+    Path(__file__).parent.parent.parent.parent
+    / "docs"
+    / "integrations"
+    / "block-integrations"
 )
 
 
@@ -421,6 +424,14 @@ def generate_block_markdown(
     lines.append("<!-- END MANUAL -->")
     lines.append("")
 
+    # Optional per-block extras (only include if has content)
+    extras = manual_content.get("extras", "")
+    if extras:
+        lines.append("<!-- MANUAL: extras -->")
+        lines.append(extras)
+        lines.append("<!-- END MANUAL -->")
+        lines.append("")
+
     lines.append("---")
     lines.append("")
 
@@ -456,25 +467,52 @@ def get_block_file_mapping(blocks: list[BlockDoc]) -> dict[str, list[BlockDoc]]:
     return dict(file_mapping)
 
 
-def generate_overview_table(blocks: list[BlockDoc]) -> str:
-    """Generate the overview table markdown (blocks.md)."""
+def generate_overview_table(blocks: list[BlockDoc], block_dir_prefix: str = "") -> str:
+    """Generate the overview table markdown (blocks.md).
+
+    Args:
+        blocks: List of block documentation objects
+        block_dir_prefix: Prefix for block file links (e.g., "block-integrations/")
+    """
     lines = []
 
+    # GitBook YAML frontmatter
+    lines.append("---")
+    lines.append("layout:")
+    lines.append("  width: default")
+    lines.append("  title:")
+    lines.append("    visible: true")
+    lines.append("  description:")
+    lines.append("    visible: true")
+    lines.append("  tableOfContents:")
+    lines.append("    visible: false")
+    lines.append("  outline:")
+    lines.append("    visible: true")
+    lines.append("  pagination:")
+    lines.append("    visible: true")
+    lines.append("  metadata:")
+    lines.append("    visible: true")
+    lines.append("---")
+    lines.append("")
+
     lines.append("# AutoGPT Blocks Overview")
     lines.append("")
     lines.append(
         'AutoGPT uses a modular approach with various "blocks" to handle different tasks. These blocks are the building blocks of AutoGPT workflows, allowing users to create complex automations by combining simple, specialized components.'
     )
     lines.append("")
-    lines.append('!!! info "Creating Your Own Blocks"')
-    lines.append("    Want to create your own custom blocks? Check out our guides:")
-    lines.append("    ")
+    lines.append('{% hint style="info" %}')
+    lines.append("**Creating Your Own Blocks**")
+    lines.append("")
+    lines.append("Want to create your own custom blocks? Check out our guides:")
+    lines.append("")
     lines.append(
-        "    - [Build your own Blocks](https://docs.agpt.co/platform/new_blocks/) - Step-by-step tutorial with examples"
+        "* [Build your own Blocks](https://docs.agpt.co/platform/new_blocks/) - Step-by-step tutorial with examples"
     )
     lines.append(
-        "    - [Block SDK Guide](https://docs.agpt.co/platform/block-sdk-guide/) - Advanced SDK patterns with OAuth, webhooks, and provider configuration"
+        "* [Block SDK Guide](https://docs.agpt.co/platform/block-sdk-guide/) - Advanced SDK patterns with OAuth, webhooks, and provider configuration"
     )
+    lines.append("{% endhint %}")
     lines.append("")
     lines.append(
         "Below is a comprehensive list of all available blocks, categorized by their primary function. Click on any block name to view its detailed documentation."
@@ -537,7 +575,8 @@ def generate_overview_table(blocks: list[BlockDoc]) -> str:
                     else "No description"
                 )
                 short_desc = short_desc.replace("\n", " ").replace("|", "\\|")
-                lines.append(f"| [{block.name}]({file_path}#{anchor}) | {short_desc} |")
+                link_path = f"{block_dir_prefix}{file_path}"
+                lines.append(f"| [{block.name}]({link_path}#{anchor}) | {short_desc} |")
             lines.append("")
             continue
 
@@ -563,13 +602,55 @@ def generate_overview_table(blocks: list[BlockDoc]) -> str:
             )
             short_desc = short_desc.replace("\n", " ").replace("|", "\\|")
 
-            lines.append(f"| [{block.name}]({file_path}#{anchor}) | {short_desc} |")
+            link_path = f"{block_dir_prefix}{file_path}"
+            lines.append(f"| [{block.name}]({link_path}#{anchor}) | {short_desc} |")
 
         lines.append("")
 
     return "\n".join(lines)
 
 
+def generate_summary_md(
+    blocks: list[BlockDoc], root_dir: Path, block_dir_prefix: str = ""
+) -> str:
+    """Generate SUMMARY.md for GitBook navigation.
+
+    Args:
+        blocks: List of block documentation objects
+        root_dir: The root docs directory (e.g., docs/integrations/)
+        block_dir_prefix: Prefix for block file links (e.g., "block-integrations/")
+    """
+    lines = []
+    lines.append("# Table of contents")
+    lines.append("")
+    lines.append("* [AutoGPT Blocks Overview](README.md)")
+    lines.append("")
+
+    # Check for guides/ directory at the root level (docs/integrations/guides/)
+    guides_dir = root_dir / "guides"
+    if guides_dir.exists():
+        lines.append("## Guides")
+        lines.append("")
+        for guide_file in sorted(guides_dir.glob("*.md")):
+            # Use just the file name for title (replace hyphens/underscores with spaces)
+            title = file_path_to_title(guide_file.stem.replace("-", "_") + ".md")
+            lines.append(f"* [{title}](guides/{guide_file.name})")
+        lines.append("")
+
+    lines.append("## Block Integrations")
+    lines.append("")
+
+    file_mapping = get_block_file_mapping(blocks)
+    for file_path in sorted(file_mapping.keys()):
+        title = file_path_to_title(file_path)
+        link_path = f"{block_dir_prefix}{file_path}"
+        lines.append(f"* [{title}]({link_path})")
+
+    lines.append("")
+
+    return "\n".join(lines)
+
+
 def load_all_blocks_for_docs() -> list[BlockDoc]:
     """Load all blocks and extract documentation."""
     from backend.blocks import load_all_blocks
@@ -653,6 +734,16 @@ def write_block_docs(
                 )
             )
 
+        # Add file-level additional_content section if present
+        file_additional = extract_manual_content(existing_content).get(
+            "additional_content", ""
+        )
+        if file_additional:
+            content_parts.append("<!-- MANUAL: additional_content -->")
+            content_parts.append(file_additional)
+            content_parts.append("<!-- END MANUAL -->")
+            content_parts.append("")
+
         full_content = file_header + "\n" + "\n".join(content_parts)
         generated_files[str(file_path)] = full_content
 
@@ -661,14 +752,28 @@ def write_block_docs(
 
         full_path.write_text(full_content)
 
-    # Generate overview file
-    overview_content = generate_overview_table(blocks)
-    overview_path = output_dir / "README.md"
+    # Generate overview file at the parent directory (docs/integrations/)
+    # with links prefixed to point into block-integrations/
+    root_dir = output_dir.parent
+    block_dir_name = output_dir.name  # "block-integrations"
+    block_dir_prefix = f"{block_dir_name}/"
+
+    overview_content = generate_overview_table(blocks, block_dir_prefix)
+    overview_path = root_dir / "README.md"
     generated_files["README.md"] = overview_content
     overview_path.write_text(overview_content)
 
     if verbose:
-        print("  Writing README.md (overview)")
+        print("  Writing README.md (overview) to parent directory")
+
+    # Generate SUMMARY.md for GitBook navigation at the parent directory
+    summary_content = generate_summary_md(blocks, root_dir, block_dir_prefix)
+    summary_path = root_dir / "SUMMARY.md"
+    generated_files["SUMMARY.md"] = summary_content
+    summary_path.write_text(summary_content)
+
+    if verbose:
+        print("  Writing SUMMARY.md (navigation) to parent directory")
 
     return generated_files
 
@@ -748,6 +853,16 @@ def check_docs_in_sync(output_dir: Path, blocks: list[BlockDoc]) -> bool:
             elif block_match.group(1).strip() != expected_block_content.strip():
                 mismatched_blocks.append(block.name)
 
+        # Add file-level additional_content to expected content (matches write_block_docs)
+        file_additional = extract_manual_content(existing_content).get(
+            "additional_content", ""
+        )
+        if file_additional:
+            content_parts.append("<!-- MANUAL: additional_content -->")
+            content_parts.append(file_additional)
+            content_parts.append("<!-- END MANUAL -->")
+            content_parts.append("")
+
         expected_content = file_header + "\n" + "\n".join(content_parts)
 
         if existing_content.strip() != expected_content.strip():
@@ -757,11 +872,15 @@ def check_docs_in_sync(output_dir: Path, blocks: list[BlockDoc]) -> bool:
             out_of_sync_details.append((file_path, mismatched_blocks))
             all_match = False
 
-    # Check overview
-    overview_path = output_dir / "README.md"
+    # Check overview at the parent directory (docs/integrations/)
+    root_dir = output_dir.parent
+    block_dir_name = output_dir.name  # "block-integrations"
+    block_dir_prefix = f"{block_dir_name}/"
+
+    overview_path = root_dir / "README.md"
     if overview_path.exists():
         existing_overview = overview_path.read_text()
-        expected_overview = generate_overview_table(blocks)
+        expected_overview = generate_overview_table(blocks, block_dir_prefix)
         if existing_overview.strip() != expected_overview.strip():
             print("OUT OF SYNC: README.md (overview)")
             print("  The blocks overview table needs regeneration")
@@ -772,6 +891,21 @@ def check_docs_in_sync(output_dir: Path, blocks: list[BlockDoc]) -> bool:
         out_of_sync_details.append(("README.md", ["overview table"]))
         all_match = False
 
+    # Check SUMMARY.md at the parent directory
+    summary_path = root_dir / "SUMMARY.md"
+    if summary_path.exists():
+        existing_summary = summary_path.read_text()
+        expected_summary = generate_summary_md(blocks, root_dir, block_dir_prefix)
+        if existing_summary.strip() != expected_summary.strip():
+            print("OUT OF SYNC: SUMMARY.md (navigation)")
+            print("  The GitBook navigation needs regeneration")
+            out_of_sync_details.append(("SUMMARY.md", ["navigation"]))
+            all_match = False
+    else:
+        print("MISSING: SUMMARY.md (navigation)")
+        out_of_sync_details.append(("SUMMARY.md", ["navigation"]))
+        all_match = False
+
     # Check for unfilled manual sections
     unfilled_patterns = [
         "_Add a description of this category of blocks._",

autogpt_platform/frontend/.env.default

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

autogpt_platform/frontend/CONTRIBUTING.md

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

autogpt_platform/frontend/orval.config.ts

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

autogpt_platform/frontend/package.json

@@ -15,6 +15,8 @@
     "types": "tsc --noEmit",
     "test": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test",
     "test-ui": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test --ui",
+    "test:unit": "vitest run",
+    "test:unit:watch": "vitest",
     "test:no-build": "playwright test",
     "gentests": "playwright codegen http://localhost:3000",
     "storybook": "storybook dev -p 6006",
@@ -118,6 +120,7 @@
   },
   "devDependencies": {
     "@chromatic-com/storybook": "4.1.2",
+    "happy-dom": "20.3.4",
     "@opentelemetry/instrumentation": "0.209.0",
     "@playwright/test": "1.56.1",
     "@storybook/addon-a11y": "9.1.5",
@@ -127,6 +130,8 @@
     "@storybook/nextjs": "9.1.5",
     "@tanstack/eslint-plugin-query": "5.91.2",
     "@tanstack/react-query-devtools": "5.90.2",
+    "@testing-library/dom": "10.4.1",
+    "@testing-library/react": "16.3.2",
     "@types/canvas-confetti": "1.9.0",
     "@types/lodash": "4.17.20",
     "@types/negotiator": "0.6.4",
@@ -135,6 +140,7 @@
     "@types/react-dom": "18.3.5",
     "@types/react-modal": "3.16.3",
     "@types/react-window": "1.8.8",
+    "@vitejs/plugin-react": "5.1.2",
     "axe-playwright": "2.2.2",
     "chromatic": "13.3.3",
     "concurrently": "9.2.1",
@@ -153,7 +159,9 @@
     "require-in-the-middle": "8.0.1",
     "storybook": "9.1.5",
     "tailwindcss": "3.4.17",
-    "typescript": "5.9.3"
+    "typescript": "5.9.3",
+    "vite-tsconfig-paths": "6.0.4",
+    "vitest": "4.0.17"
   },
   "msw": {
     "workerDirectory": [

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

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

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

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

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

@@ -86,6 +86,7 @@ export function FloatingSafeModeToggle({
   const {
     currentHITLSafeMode,
     showHITLToggle,
+    isHITLStateUndetermined,
     handleHITLToggle,
     currentSensitiveActionSafeMode,
     showSensitiveActionToggle,
@@ -98,9 +99,16 @@ export function FloatingSafeModeToggle({
     return null;
   }
 
+  const showHITL = showHITLToggle && !isHITLStateUndetermined;
+  const showSensitive = showSensitiveActionToggle;
+
+  if (!showHITL && !showSensitive) {
+    return null;
+  }
+
   return (
     <div className={cn("fixed z-50 flex flex-col gap-2", className)}>
-      {showHITLToggle && (
+      {showHITL && (
         <SafeModeButton
           isEnabled={currentHITLSafeMode}
           label="Human in the loop block approval"
@@ -111,7 +119,7 @@ export function FloatingSafeModeToggle({
           fullWidth={fullWidth}
         />
       )}
-      {showSensitiveActionToggle && (
+      {showSensitive && (
         <SafeModeButton
           isEnabled={currentSensitiveActionSafeMode}
           label="Sensitive actions blocks approval"

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

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

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

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

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

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

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

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

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

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

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

@@ -1,70 +0,0 @@
-import { cn } from "@/lib/utils";
-import { RobotIcon } from "@phosphor-icons/react";
-import { useEffect, useRef, useState } from "react";
-import { MessageBubble } from "../MessageBubble/MessageBubble";
-
-export interface ThinkingMessageProps {
-  className?: string;
-}
-
-export function ThinkingMessage({ className }: ThinkingMessageProps) {
-  const [showSlowLoader, setShowSlowLoader] = useState(false);
-  const timerRef = useRef<NodeJS.Timeout | null>(null);
-
-  useEffect(() => {
-    if (timerRef.current === null) {
-      timerRef.current = setTimeout(() => {
-        setShowSlowLoader(true);
-      }, 8000);
-    }
-
-    return () => {
-      if (timerRef.current) {
-        clearTimeout(timerRef.current);
-        timerRef.current = null;
-      }
-    };
-  }, []);
-
-  return (
-    <div
-      className={cn(
-        "group relative flex w-full justify-start gap-3 px-4 py-3",
-        className,
-      )}
-    >
-      <div className="flex w-full max-w-3xl gap-3">
-        <div className="flex-shrink-0">
-          <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
-            <RobotIcon className="h-4 w-4 text-indigo-50" />
-          </div>
-        </div>
-
-        <div className="flex min-w-0 flex-1 flex-col">
-          <MessageBubble variant="assistant">
-            <div className="transition-all duration-500 ease-in-out">
-              {showSlowLoader ? (
-                <div className="flex flex-col items-center gap-3 py-2">
-                  <div className="loader" style={{ flexShrink: 0 }} />
-                  <p className="text-sm text-slate-700">
-                    Taking a bit longer to think, wait a moment please
-                  </p>
-                </div>
-              ) : (
-                <span
-                  className="inline-block bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-clip-text text-transparent"
-                  style={{
-                    backgroundSize: "200% 100%",
-                    animation: "shimmer 2s ease-in-out infinite",
-                  }}
-                >
-                  Thinking...
-                </span>
-              )}
-            </div>
-          </MessageBubble>
-        </div>
-      </div>
-    </div>
-  );
-}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,165 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { format, formatDistanceToNow, isToday } from "date-fns";
+
+export function convertSessionDetailToSummary(
+  session: SessionDetailResponse,
+): SessionSummaryResponse {
+  return {
+    id: session.id,
+    created_at: session.created_at,
+    updated_at: session.updated_at,
+    title: undefined,
+  };
+}
+
+export function filterVisibleSessions(
+  sessions: SessionSummaryResponse[],
+): SessionSummaryResponse[] {
+  return sessions.filter(
+    (session) => session.updated_at !== session.created_at,
+  );
+}
+
+export function getSessionTitle(session: SessionSummaryResponse): string {
+  if (session.title) return session.title;
+  const isNewSession = session.updated_at === session.created_at;
+  if (isNewSession) {
+    const createdDate = new Date(session.created_at);
+    if (isToday(createdDate)) {
+      return "Today";
+    }
+    return format(createdDate, "MMM d, yyyy");
+  }
+  return "Untitled Chat";
+}
+
+export function getSessionUpdatedLabel(
+  session: SessionSummaryResponse,
+): string {
+  if (!session.updated_at) return "";
+  return formatDistanceToNow(new Date(session.updated_at), { addSuffix: true });
+}
+
+export function mergeCurrentSessionIntoList(
+  accumulatedSessions: SessionSummaryResponse[],
+  currentSessionId: string | null,
+  currentSessionData: SessionDetailResponse | null | undefined,
+): SessionSummaryResponse[] {
+  const filteredSessions: SessionSummaryResponse[] = [];
+
+  if (accumulatedSessions.length > 0) {
+    const visibleSessions = filterVisibleSessions(accumulatedSessions);
+
+    if (currentSessionId) {
+      const currentInAll = accumulatedSessions.find(
+        (s) => s.id === currentSessionId,
+      );
+      if (currentInAll) {
+        const isInVisible = visibleSessions.some(
+          (s) => s.id === currentSessionId,
+        );
+        if (!isInVisible) {
+          filteredSessions.push(currentInAll);
+        }
+      }
+    }
+
+    filteredSessions.push(...visibleSessions);
+  }
+
+  if (currentSessionId && currentSessionData) {
+    const isCurrentInList = filteredSessions.some(
+      (s) => s.id === currentSessionId,
+    );
+    if (!isCurrentInList) {
+      const summarySession = convertSessionDetailToSummary(currentSessionData);
+      filteredSessions.unshift(summarySession);
+    }
+  }
+
+  return filteredSessions;
+}
+
+export function getCurrentSessionId(
+  searchParams: URLSearchParams,
+): string | null {
+  return searchParams.get("sessionId");
+}
+
+export function shouldAutoSelectSession(
+  areAllSessionsLoaded: boolean,
+  hasAutoSelectedSession: boolean,
+  paramSessionId: string | null,
+  visibleSessions: SessionSummaryResponse[],
+  accumulatedSessions: SessionSummaryResponse[],
+  isLoading: boolean,
+  totalCount: number | null,
+): {
+  shouldSelect: boolean;
+  sessionIdToSelect: string | null;
+  shouldCreate: boolean;
+} {
+  if (!areAllSessionsLoaded || hasAutoSelectedSession) {
+    return {
+      shouldSelect: false,
+      sessionIdToSelect: null,
+      shouldCreate: false,
+    };
+  }
+
+  if (paramSessionId) {
+    return {
+      shouldSelect: false,
+      sessionIdToSelect: null,
+      shouldCreate: false,
+    };
+  }
+
+  if (visibleSessions.length > 0) {
+    return {
+      shouldSelect: true,
+      sessionIdToSelect: visibleSessions[0].id,
+      shouldCreate: false,
+    };
+  }
+
+  if (accumulatedSessions.length === 0 && !isLoading && totalCount === 0) {
+    return { shouldSelect: false, sessionIdToSelect: null, shouldCreate: true };
+  }
+
+  if (totalCount === 0) {
+    return {
+      shouldSelect: false,
+      sessionIdToSelect: null,
+      shouldCreate: false,
+    };
+  }
+
+  return { shouldSelect: false, sessionIdToSelect: null, shouldCreate: false };
+}
+
+export function checkReadyToShowContent(
+  areAllSessionsLoaded: boolean,
+  paramSessionId: string | null,
+  accumulatedSessions: SessionSummaryResponse[],
+  isCurrentSessionLoading: boolean,
+  currentSessionData: SessionDetailResponse | null | undefined,
+  hasAutoSelectedSession: boolean,
+): boolean {
+  if (!areAllSessionsLoaded) return false;
+
+  if (paramSessionId) {
+    const sessionFound = accumulatedSessions.some(
+      (s) => s.id === paramSessionId,
+    );
+    return (
+      sessionFound ||
+      (!isCurrentSessionLoading &&
+        currentSessionData !== undefined &&
+        currentSessionData !== null)
+    );
+  }
+
+  return hasAutoSelectedSession;
+}

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

@@ -0,0 +1,172 @@
+"use client";
+
+import {
+  getGetV2ListSessionsQueryKey,
+  useGetV2GetSession,
+} from "@/app/api/__generated__/endpoints/chat/chat";
+import { okData } from "@/app/api/helpers";
+import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import { useQueryClient } from "@tanstack/react-query";
+import { usePathname, useRouter, useSearchParams } from "next/navigation";
+import { useEffect, useRef, useState } from "react";
+import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
+import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
+import {
+  checkReadyToShowContent,
+  filterVisibleSessions,
+  getCurrentSessionId,
+  mergeCurrentSessionIntoList,
+} from "./helpers";
+
+export function useCopilotShell() {
+  const router = useRouter();
+  const pathname = usePathname();
+  const searchParams = useSearchParams();
+  const queryClient = useQueryClient();
+  const breakpoint = useBreakpoint();
+  const { isLoggedIn } = useSupabase();
+  const isMobile =
+    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
+
+  const isOnHomepage = pathname === "/copilot";
+  const paramSessionId = searchParams.get("sessionId");
+
+  const {
+    isDrawerOpen,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+  } = useMobileDrawer();
+
+  const paginationEnabled = !isMobile || isDrawerOpen || !!paramSessionId;
+
+  const {
+    sessions: accumulatedSessions,
+    isLoading: isSessionsLoading,
+    isFetching: isSessionsFetching,
+    hasNextPage,
+    areAllSessionsLoaded,
+    fetchNextPage,
+    reset: resetPagination,
+  } = useSessionsPagination({
+    enabled: paginationEnabled,
+  });
+
+  const currentSessionId = getCurrentSessionId(searchParams);
+
+  const { data: currentSessionData, isLoading: isCurrentSessionLoading } =
+    useGetV2GetSession(currentSessionId || "", {
+      query: {
+        enabled: !!currentSessionId,
+        select: okData,
+      },
+    });
+
+  const [hasAutoSelectedSession, setHasAutoSelectedSession] = useState(false);
+  const hasAutoSelectedRef = useRef(false);
+
+  // Mark as auto-selected when sessionId is in URL
+  useEffect(() => {
+    if (paramSessionId && !hasAutoSelectedRef.current) {
+      hasAutoSelectedRef.current = true;
+      setHasAutoSelectedSession(true);
+    }
+  }, [paramSessionId]);
+
+  // On homepage without sessionId, mark as ready immediately
+  useEffect(() => {
+    if (isOnHomepage && !paramSessionId && !hasAutoSelectedRef.current) {
+      hasAutoSelectedRef.current = true;
+      setHasAutoSelectedSession(true);
+    }
+  }, [isOnHomepage, paramSessionId]);
+
+  // Invalidate sessions list when navigating to homepage (to show newly created sessions)
+  useEffect(() => {
+    if (isOnHomepage && !paramSessionId) {
+      queryClient.invalidateQueries({
+        queryKey: getGetV2ListSessionsQueryKey(),
+      });
+    }
+  }, [isOnHomepage, paramSessionId, queryClient]);
+
+  // Reset pagination when query becomes disabled
+  const prevPaginationEnabledRef = useRef(paginationEnabled);
+  useEffect(() => {
+    if (prevPaginationEnabledRef.current && !paginationEnabled) {
+      resetPagination();
+      resetAutoSelect();
+    }
+    prevPaginationEnabledRef.current = paginationEnabled;
+  }, [paginationEnabled, resetPagination]);
+
+  const sessions = mergeCurrentSessionIntoList(
+    accumulatedSessions,
+    currentSessionId,
+    currentSessionData,
+  );
+
+  const visibleSessions = filterVisibleSessions(sessions);
+
+  const sidebarSelectedSessionId =
+    isOnHomepage && !paramSessionId ? null : currentSessionId;
+
+  const isReadyToShowContent = isOnHomepage
+    ? true
+    : checkReadyToShowContent(
+        areAllSessionsLoaded,
+        paramSessionId,
+        accumulatedSessions,
+        isCurrentSessionLoading,
+        currentSessionData,
+        hasAutoSelectedSession,
+      );
+
+  function handleSelectSession(sessionId: string) {
+    // Navigate using replaceState to avoid full page reload
+    window.history.replaceState(null, "", `/copilot?sessionId=${sessionId}`);
+    // Force a re-render by updating the URL through router
+    router.replace(`/copilot?sessionId=${sessionId}`);
+    if (isMobile) handleCloseDrawer();
+  }
+
+  function handleNewChat() {
+    resetAutoSelect();
+    resetPagination();
+    // Invalidate and refetch sessions list to ensure newly created sessions appear
+    queryClient.invalidateQueries({
+      queryKey: getGetV2ListSessionsQueryKey(),
+    });
+    window.history.replaceState(null, "", "/copilot");
+    router.replace("/copilot");
+    if (isMobile) handleCloseDrawer();
+  }
+
+  function resetAutoSelect() {
+    hasAutoSelectedRef.current = false;
+    setHasAutoSelectedSession(false);
+  }
+
+  const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
+
+  return {
+    isMobile,
+    isDrawerOpen,
+    isLoggedIn,
+    hasActiveSession:
+      Boolean(currentSessionId) && (!isOnHomepage || Boolean(paramSessionId)),
+    isLoading,
+    sessions: visibleSessions,
+    currentSessionId: sidebarSelectedSessionId,
+    handleSelectSession,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+    handleNewChat,
+    hasNextPage,
+    isFetchingNextPage: isSessionsFetching,
+    fetchNextPage,
+    isReadyToShowContent,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts

@@ -0,0 +1,56 @@
+import type { User } from "@supabase/supabase-js";
+
+export type PageState =
+  | { type: "welcome" }
+  | { type: "newChat" }
+  | { type: "creating"; prompt: string }
+  | { type: "chat"; sessionId: string; initialPrompt?: string };
+
+export function getInitialPromptFromState(
+  pageState: PageState,
+  storedInitialPrompt: string | undefined,
+) {
+  if (storedInitialPrompt) return storedInitialPrompt;
+  if (pageState.type === "creating") return pageState.prompt;
+  if (pageState.type === "chat") return pageState.initialPrompt;
+}
+
+export function shouldResetToWelcome(pageState: PageState) {
+  return (
+    pageState.type !== "newChat" &&
+    pageState.type !== "creating" &&
+    pageState.type !== "welcome"
+  );
+}
+
+export function getGreetingName(user?: User | null): string {
+  if (!user) return "there";
+  const metadata = user.user_metadata as Record<string, unknown> | undefined;
+  const fullName = metadata?.full_name;
+  const name = metadata?.name;
+  if (typeof fullName === "string" && fullName.trim()) {
+    return fullName.split(" ")[0];
+  }
+  if (typeof name === "string" && name.trim()) {
+    return name.split(" ")[0];
+  }
+  if (user.email) {
+    return user.email.split("@")[0];
+  }
+  return "there";
+}
+
+export function buildCopilotChatUrl(prompt: string): string {
+  const trimmed = prompt.trim();
+  if (!trimmed) return "/copilot/chat";
+  const encoded = encodeURIComponent(trimmed);
+  return `/copilot/chat?prompt=${encoded}`;
+}
+
+export function getQuickActions(): string[] {
+  return [
+    "Show me what I can automate",
+    "Design a custom workflow",
+    "Help me with content creation",
+  ];
+}

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

@@ -0,0 +1,11 @@
+import type { ReactNode } from "react";
+import { NewChatProvider } from "./NewChatContext";
+import { CopilotShell } from "./components/CopilotShell/CopilotShell";
+
+export default function CopilotLayout({ children }: { children: ReactNode }) {
+  return (
+    <NewChatProvider>
+      <CopilotShell>{children}</CopilotShell>
+    </NewChatProvider>
+  );
+}

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

@@ -0,0 +1,169 @@
+"use client";
+
+import { Skeleton } from "@/components/__legacy__/ui/skeleton";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { Chat } from "@/components/contextual/Chat/Chat";
+import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
+import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { useCopilotPage } from "./useCopilotPage";
+
+export default function CopilotPage() {
+  const { state, handlers } = useCopilotPage();
+  const {
+    greetingName,
+    quickActions,
+    isLoading,
+    pageState,
+    isNewChatModalOpen,
+    isReady,
+  } = state;
+  const {
+    handleQuickAction,
+    startChatWithPrompt,
+    handleSessionNotFound,
+    handleStreamingChange,
+    handleCancelNewChat,
+    proceedWithNewChat,
+    handleNewChatModalOpen,
+  } = handlers;
+
+  if (!isReady) {
+    return null;
+  }
+
+  // Show Chat when we have an active session
+  if (pageState.type === "chat") {
+    return (
+      <div className="flex h-full flex-col">
+        <Chat
+          key={pageState.sessionId ?? "welcome"}
+          className="flex-1"
+          urlSessionId={pageState.sessionId}
+          initialPrompt={pageState.initialPrompt}
+          onSessionNotFound={handleSessionNotFound}
+          onStreamingChange={handleStreamingChange}
+        />
+        <Dialog
+          title="Interrupt current chat?"
+          styling={{ maxWidth: 300, width: "100%" }}
+          controlled={{
+            isOpen: isNewChatModalOpen,
+            set: handleNewChatModalOpen,
+          }}
+          onClose={handleCancelNewChat}
+        >
+          <Dialog.Content>
+            <div className="flex flex-col gap-4">
+              <Text variant="body">
+                The current chat response will be interrupted. Are you sure you
+                want to start a new chat?
+              </Text>
+              <Dialog.Footer>
+                <Button
+                  type="button"
+                  variant="outline"
+                  onClick={handleCancelNewChat}
+                >
+                  Cancel
+                </Button>
+                <Button
+                  type="button"
+                  variant="primary"
+                  onClick={proceedWithNewChat}
+                >
+                  Start new chat
+                </Button>
+              </Dialog.Footer>
+            </div>
+          </Dialog.Content>
+        </Dialog>
+      </div>
+    );
+  }
+
+  if (pageState.type === "newChat") {
+    return (
+      <div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
+        <div className="flex flex-col items-center gap-4">
+          <ChatLoader />
+          <Text variant="body" className="text-zinc-500">
+            Loading your chats...
+          </Text>
+        </div>
+      </div>
+    );
+  }
+
+  // Show loading state while creating session and sending first message
+  if (pageState.type === "creating") {
+    return (
+      <div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
+        <div className="flex flex-col items-center gap-4">
+          <ChatLoader />
+          <Text variant="body" className="text-zinc-500">
+            Loading your chats...
+          </Text>
+        </div>
+      </div>
+    );
+  }
+
+  // Show Welcome screen
+  return (
+    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-6 py-10">
+      <div className="w-full text-center">
+        {isLoading ? (
+          <div className="mx-auto max-w-2xl">
+            <Skeleton className="mx-auto mb-3 h-8 w-64" />
+            <Skeleton className="mx-auto mb-8 h-6 w-80" />
+            <div className="mb-8">
+              <Skeleton className="mx-auto h-14 w-full rounded-lg" />
+            </div>
+            <div className="flex flex-wrap items-center justify-center gap-3">
+              {Array.from({ length: 4 }).map((_, i) => (
+                <Skeleton key={i} className="h-9 w-48 rounded-md" />
+              ))}
+            </div>
+          </div>
+        ) : (
+          <>
+            <div className="mx-auto max-w-2xl">
+              <Text
+                variant="h3"
+                className="mb-3 !text-[1.375rem] text-zinc-700"
+              >
+                Hey, <span className="text-violet-600">{greetingName}</span>
+              </Text>
+              <Text variant="h3" className="mb-8 !font-normal">
+                What do you want to automate?
+              </Text>
+
+              <div className="mb-6">
+                <ChatInput
+                  onSend={startChatWithPrompt}
+                  placeholder='You can search or just ask - e.g. "create a blog post outline"'
+                />
+              </div>
+            </div>
+            <div className="flex flex-nowrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
+              {quickActions.map((action) => (
+                <Button
+                  key={action}
+                  type="button"
+                  variant="outline"
+                  size="small"
+                  onClick={() => handleQuickAction(action)}
+                  className="h-auto shrink-0 border-zinc-600 !px-4 !py-2 text-[1rem] text-zinc-600"
+                >
+                  {action}
+                </Button>
+              ))}
+            </div>
+          </>
+        )}
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts

@@ -0,0 +1,266 @@
+import { postV2CreateSession } from "@/app/api/__generated__/endpoints/chat/chat";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { getHomepageRoute } from "@/lib/constants";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import {
+  Flag,
+  type FlagValues,
+  useGetFlag,
+} from "@/services/feature-flags/use-get-flag";
+import * as Sentry from "@sentry/nextjs";
+import { useFlags } from "launchdarkly-react-client-sdk";
+import { useRouter } from "next/navigation";
+import { useEffect, useReducer } from "react";
+import { useNewChat } from "./NewChatContext";
+import { getGreetingName, getQuickActions, type PageState } from "./helpers";
+import { useCopilotURLState } from "./useCopilotURLState";
+
+type CopilotState = {
+  pageState: PageState;
+  isStreaming: boolean;
+  isNewChatModalOpen: boolean;
+  initialPrompts: Record<string, string>;
+  previousSessionId: string | null;
+};
+
+type CopilotAction =
+  | { type: "setPageState"; pageState: PageState }
+  | { type: "setStreaming"; isStreaming: boolean }
+  | { type: "setNewChatModalOpen"; isOpen: boolean }
+  | { type: "setInitialPrompt"; sessionId: string; prompt: string }
+  | { type: "setPreviousSessionId"; sessionId: string | null };
+
+function isSamePageState(next: PageState, current: PageState) {
+  if (next.type !== current.type) return false;
+  if (next.type === "creating" && current.type === "creating") {
+    return next.prompt === current.prompt;
+  }
+  if (next.type === "chat" && current.type === "chat") {
+    return (
+      next.sessionId === current.sessionId &&
+      next.initialPrompt === current.initialPrompt
+    );
+  }
+  return true;
+}
+
+function copilotReducer(
+  state: CopilotState,
+  action: CopilotAction,
+): CopilotState {
+  if (action.type === "setPageState") {
+    if (isSamePageState(action.pageState, state.pageState)) return state;
+    return { ...state, pageState: action.pageState };
+  }
+  if (action.type === "setStreaming") {
+    if (action.isStreaming === state.isStreaming) return state;
+    return { ...state, isStreaming: action.isStreaming };
+  }
+  if (action.type === "setNewChatModalOpen") {
+    if (action.isOpen === state.isNewChatModalOpen) return state;
+    return { ...state, isNewChatModalOpen: action.isOpen };
+  }
+  if (action.type === "setInitialPrompt") {
+    if (state.initialPrompts[action.sessionId] === action.prompt) return state;
+    return {
+      ...state,
+      initialPrompts: {
+        ...state.initialPrompts,
+        [action.sessionId]: action.prompt,
+      },
+    };
+  }
+  if (action.type === "setPreviousSessionId") {
+    if (state.previousSessionId === action.sessionId) return state;
+    return { ...state, previousSessionId: action.sessionId };
+  }
+  return state;
+}
+
+export function useCopilotPage() {
+  const router = useRouter();
+  const { user, isLoggedIn, isUserLoading } = useSupabase();
+  const { toast } = useToast();
+
+  const isChatEnabled = useGetFlag(Flag.CHAT);
+  const flags = useFlags<FlagValues>();
+  const homepageRoute = getHomepageRoute(isChatEnabled);
+  const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
+  const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
+  const isLaunchDarklyConfigured = envEnabled && Boolean(clientId);
+  const isFlagReady =
+    !isLaunchDarklyConfigured || flags[Flag.CHAT] !== undefined;
+
+  const [state, dispatch] = useReducer(copilotReducer, {
+    pageState: { type: "welcome" },
+    isStreaming: false,
+    isNewChatModalOpen: false,
+    initialPrompts: {},
+    previousSessionId: null,
+  });
+
+  const newChatContext = useNewChat();
+  const greetingName = getGreetingName(user);
+  const quickActions = getQuickActions();
+
+  function setPageState(pageState: PageState) {
+    dispatch({ type: "setPageState", pageState });
+  }
+
+  function setInitialPrompt(sessionId: string, prompt: string) {
+    dispatch({ type: "setInitialPrompt", sessionId, prompt });
+  }
+
+  function setPreviousSessionId(sessionId: string | null) {
+    dispatch({ type: "setPreviousSessionId", sessionId });
+  }
+
+  const { setUrlSessionId } = useCopilotURLState({
+    pageState: state.pageState,
+    initialPrompts: state.initialPrompts,
+    previousSessionId: state.previousSessionId,
+    setPageState,
+    setInitialPrompt,
+    setPreviousSessionId,
+  });
+
+  useEffect(
+    function registerNewChatHandler() {
+      if (!newChatContext) return;
+      newChatContext.setOnNewChatClick(handleNewChatClick);
+      return function cleanup() {
+        newChatContext.setOnNewChatClick(undefined);
+      };
+    },
+    [newChatContext, handleNewChatClick],
+  );
+
+  useEffect(
+    function transitionNewChatToWelcome() {
+      if (state.pageState.type === "newChat") {
+        function setWelcomeState() {
+          dispatch({ type: "setPageState", pageState: { type: "welcome" } });
+        }
+
+        const timer = setTimeout(setWelcomeState, 300);
+
+        return function cleanup() {
+          clearTimeout(timer);
+        };
+      }
+    },
+    [state.pageState.type],
+  );
+
+  useEffect(
+    function ensureAccess() {
+      if (!isFlagReady) return;
+      if (isChatEnabled === false) {
+        router.replace(homepageRoute);
+      }
+    },
+    [homepageRoute, isChatEnabled, isFlagReady, router],
+  );
+
+  async function startChatWithPrompt(prompt: string) {
+    if (!prompt?.trim()) return;
+    if (state.pageState.type === "creating") return;
+
+    const trimmedPrompt = prompt.trim();
+    dispatch({
+      type: "setPageState",
+      pageState: { type: "creating", prompt: trimmedPrompt },
+    });
+
+    try {
+      const sessionResponse = await postV2CreateSession({
+        body: JSON.stringify({}),
+      });
+
+      if (sessionResponse.status !== 200 || !sessionResponse.data?.id) {
+        throw new Error("Failed to create session");
+      }
+
+      const sessionId = sessionResponse.data.id;
+
+      dispatch({
+        type: "setInitialPrompt",
+        sessionId,
+        prompt: trimmedPrompt,
+      });
+
+      await setUrlSessionId(sessionId, { shallow: false });
+      dispatch({
+        type: "setPageState",
+        pageState: { type: "chat", sessionId, initialPrompt: trimmedPrompt },
+      });
+    } catch (error) {
+      console.error("[CopilotPage] Failed to start chat:", error);
+      toast({ title: "Failed to start chat", variant: "destructive" });
+      Sentry.captureException(error);
+      dispatch({ type: "setPageState", pageState: { type: "welcome" } });
+    }
+  }
+
+  function handleQuickAction(action: string) {
+    startChatWithPrompt(action);
+  }
+
+  function handleSessionNotFound() {
+    router.replace("/copilot");
+  }
+
+  function handleStreamingChange(isStreamingValue: boolean) {
+    dispatch({ type: "setStreaming", isStreaming: isStreamingValue });
+  }
+
+  async function proceedWithNewChat() {
+    dispatch({ type: "setNewChatModalOpen", isOpen: false });
+    if (newChatContext?.performNewChat) {
+      newChatContext.performNewChat();
+      return;
+    }
+    try {
+      await setUrlSessionId(null, { shallow: false });
+    } catch (error) {
+      console.error("[CopilotPage] Failed to clear session:", error);
+    }
+    router.replace("/copilot");
+  }
+
+  function handleCancelNewChat() {
+    dispatch({ type: "setNewChatModalOpen", isOpen: false });
+  }
+
+  function handleNewChatModalOpen(isOpen: boolean) {
+    dispatch({ type: "setNewChatModalOpen", isOpen });
+  }
+
+  function handleNewChatClick() {
+    if (state.isStreaming) {
+      dispatch({ type: "setNewChatModalOpen", isOpen: true });
+    } else {
+      proceedWithNewChat();
+    }
+  }
+
+  return {
+    state: {
+      greetingName,
+      quickActions,
+      isLoading: isUserLoading,
+      pageState: state.pageState,
+      isNewChatModalOpen: state.isNewChatModalOpen,
+      isReady: isFlagReady && isChatEnabled !== false && isLoggedIn,
+    },
+    handlers: {
+      handleQuickAction,
+      startChatWithPrompt,
+      handleSessionNotFound,
+      handleStreamingChange,
+      handleCancelNewChat,
+      proceedWithNewChat,
+      handleNewChatModalOpen,
+    },
+  };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotURLState.ts

@@ -0,0 +1,80 @@
+import { parseAsString, useQueryState } from "nuqs";
+import { useLayoutEffect } from "react";
+import {
+  getInitialPromptFromState,
+  type PageState,
+  shouldResetToWelcome,
+} from "./helpers";
+
+interface UseCopilotUrlStateArgs {
+  pageState: PageState;
+  initialPrompts: Record<string, string>;
+  previousSessionId: string | null;
+  setPageState: (pageState: PageState) => void;
+  setInitialPrompt: (sessionId: string, prompt: string) => void;
+  setPreviousSessionId: (sessionId: string | null) => void;
+}
+
+export function useCopilotURLState({
+  pageState,
+  initialPrompts,
+  previousSessionId,
+  setPageState,
+  setInitialPrompt,
+  setPreviousSessionId,
+}: UseCopilotUrlStateArgs) {
+  const [urlSessionId, setUrlSessionId] = useQueryState(
+    "sessionId",
+    parseAsString,
+  );
+
+  function syncSessionFromUrl() {
+    if (urlSessionId) {
+      if (pageState.type === "chat" && pageState.sessionId === urlSessionId) {
+        setPreviousSessionId(urlSessionId);
+        return;
+      }
+
+      const storedInitialPrompt = initialPrompts[urlSessionId];
+      const currentInitialPrompt = getInitialPromptFromState(
+        pageState,
+        storedInitialPrompt,
+      );
+
+      if (currentInitialPrompt) {
+        setInitialPrompt(urlSessionId, currentInitialPrompt);
+      }
+
+      setPageState({
+        type: "chat",
+        sessionId: urlSessionId,
+        initialPrompt: currentInitialPrompt,
+      });
+      setPreviousSessionId(urlSessionId);
+      return;
+    }
+
+    const wasInChat = previousSessionId !== null && pageState.type === "chat";
+    setPreviousSessionId(null);
+    if (wasInChat) {
+      setPageState({ type: "newChat" });
+      return;
+    }
+
+    if (shouldResetToWelcome(pageState)) {
+      setPageState({ type: "welcome" });
+    }
+  }
+
+  useLayoutEffect(syncSessionFromUrl, [
+    urlSessionId,
+    pageState.type,
+    previousSessionId,
+    initialPrompts,
+  ]);
+
+  return {
+    urlSessionId,
+    setUrlSessionId,
+  };
+}

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

@@ -1,6 +1,8 @@
 "use client";
 
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
+import { getHomepageRoute } from "@/lib/constants";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { useSearchParams } from "next/navigation";
 import { Suspense } from "react";
 import { getErrorDetails } from "./helpers";
@@ -9,6 +11,8 @@ function ErrorPageContent() {
   const searchParams = useSearchParams();
   const errorMessage = searchParams.get("message");
   const errorDetails = getErrorDetails(errorMessage);
+  const isChatEnabled = useGetFlag(Flag.CHAT);
+  const homepageRoute = getHomepageRoute(isChatEnabled);
 
   function handleRetry() {
     // Auth-related errors should redirect to login
@@ -25,8 +29,8 @@ function ErrorPageContent() {
         window.location.reload();
       }, 2000);
     } else {
-      // For server/network errors, go to marketplace
-      window.location.href = "/marketplace";
+      // For server/network errors, go to home
+      window.location.href = homepageRoute;
     }
   }
 

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/RunAgentModal.tsx

@@ -14,10 +14,6 @@ import {
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { useEffect, useRef, useState } from "react";
 import { ScheduleAgentModal } from "../ScheduleAgentModal/ScheduleAgentModal";
-import {
-  AIAgentSafetyPopup,
-  useAIAgentSafetyPopup,
-} from "./components/AIAgentSafetyPopup/AIAgentSafetyPopup";
 import { ModalHeader } from "./components/ModalHeader/ModalHeader";
 import { ModalRunSection } from "./components/ModalRunSection/ModalRunSection";
 import { RunActions } from "./components/RunActions/RunActions";
@@ -87,18 +83,8 @@ export function RunAgentModal({
 
   const [isScheduleModalOpen, setIsScheduleModalOpen] = useState(false);
   const [hasOverflow, setHasOverflow] = useState(false);
-  const [isSafetyPopupOpen, setIsSafetyPopupOpen] = useState(false);
-  const [pendingRunAction, setPendingRunAction] = useState<(() => void) | null>(
-    null,
-  );
   const contentRef = useRef<HTMLDivElement>(null);
 
-  const { shouldShowPopup, dismissPopup } = useAIAgentSafetyPopup(
-    agent.id,
-    agent.has_sensitive_action,
-    agent.has_human_in_the_loop,
-  );
-
   const hasAnySetupFields =
     Object.keys(agentInputFields || {}).length > 0 ||
     Object.keys(agentCredentialsInputFields || {}).length > 0;
@@ -179,24 +165,6 @@ export function RunAgentModal({
     onScheduleCreated?.(schedule);
   }
 
-  function handleRunWithSafetyCheck() {
-    if (shouldShowPopup) {
-      setPendingRunAction(() => handleRun);
-      setIsSafetyPopupOpen(true);
-    } else {
-      handleRun();
-    }
-  }
-
-  function handleSafetyPopupAcknowledge() {
-    setIsSafetyPopupOpen(false);
-    dismissPopup();
-    if (pendingRunAction) {
-      pendingRunAction();
-      setPendingRunAction(null);
-    }
-  }
-
   return (
     <>
       <Dialog
@@ -212,7 +180,7 @@ export function RunAgentModal({
 
               {/* Content */}
               {hasAnySetupFields ? (
-                <div className="mt-10 pb-32">
+                <div className="mt-4 pb-10">
                   <RunAgentModalContextProvider
                     value={{
                       agent,
@@ -280,7 +248,7 @@ export function RunAgentModal({
                 )}
                 <RunActions
                   defaultRunType={defaultRunType}
-                  onRun={handleRunWithSafetyCheck}
+                  onRun={handleRun}
                   isExecuting={isExecuting}
                   isSettingUpTrigger={isSettingUpTrigger}
                   isRunReady={allRequiredInputsAreSet}
@@ -298,12 +266,6 @@ export function RunAgentModal({
           </div>
         </Dialog.Content>
       </Dialog>
-
-      <AIAgentSafetyPopup
-        agentId={agent.id}
-        isOpen={isSafetyPopupOpen}
-        onAcknowledge={handleSafetyPopupAcknowledge}
-      />
     </>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/AIAgentSafetyPopup/AIAgentSafetyPopup.tsx

@@ -1,108 +0,0 @@
-"use client";
-
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Key, storage } from "@/services/storage/local-storage";
-import { ShieldCheckIcon } from "@phosphor-icons/react";
-import { useCallback, useEffect, useState } from "react";
-
-interface Props {
-  agentId: string;
-  onAcknowledge: () => void;
-  isOpen: boolean;
-}
-
-export function AIAgentSafetyPopup({ agentId, onAcknowledge, isOpen }: Props) {
-  function handleAcknowledge() {
-    // Add this agent to the list of agents for which popup has been shown
-    const seenAgentsJson = storage.get(Key.AI_AGENT_SAFETY_POPUP_SHOWN);
-    const seenAgents: string[] = seenAgentsJson
-      ? JSON.parse(seenAgentsJson)
-      : [];
-
-    if (!seenAgents.includes(agentId)) {
-      seenAgents.push(agentId);
-      storage.set(Key.AI_AGENT_SAFETY_POPUP_SHOWN, JSON.stringify(seenAgents));
-    }
-
-    onAcknowledge();
-  }
-
-  if (!isOpen) return null;
-
-  return (
-    <Dialog
-      controlled={{ isOpen, set: () => {} }}
-      styling={{ maxWidth: "480px" }}
-    >
-      <Dialog.Content>
-        <div className="flex flex-col items-center p-6 text-center">
-          <div className="mb-6 flex h-16 w-16 items-center justify-center rounded-full bg-blue-50">
-            <ShieldCheckIcon
-              weight="fill"
-              size={32}
-              className="text-blue-600"
-            />
-          </div>
-
-          <Text variant="h3" className="mb-4">
-            Safety Checks Enabled
-          </Text>
-
-          <Text variant="body" className="mb-2 text-zinc-700">
-            AI-generated agents may take actions that affect your data or
-            external systems.
-          </Text>
-
-          <Text variant="body" className="mb-8 text-zinc-700">
-            AutoGPT includes safety checks so you&apos;ll always have the
-            opportunity to review and approve sensitive actions before they
-            happen.
-          </Text>
-
-          <Button
-            variant="primary"
-            size="large"
-            className="w-full"
-            onClick={handleAcknowledge}
-          >
-            Got it
-          </Button>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}
-
-export function useAIAgentSafetyPopup(
-  agentId: string,
-  hasSensitiveAction: boolean,
-  hasHumanInTheLoop: boolean,
-) {
-  const [shouldShowPopup, setShouldShowPopup] = useState(false);
-  const [hasChecked, setHasChecked] = useState(false);
-
-  useEffect(() => {
-    if (hasChecked) return;
-
-    const seenAgentsJson = storage.get(Key.AI_AGENT_SAFETY_POPUP_SHOWN);
-    const seenAgents: string[] = seenAgentsJson
-      ? JSON.parse(seenAgentsJson)
-      : [];
-    const hasSeenPopupForThisAgent = seenAgents.includes(agentId);
-    const isRelevantAgent = hasSensitiveAction || hasHumanInTheLoop;
-
-    setShouldShowPopup(!hasSeenPopupForThisAgent && isRelevantAgent);
-    setHasChecked(true);
-  }, [agentId, hasSensitiveAction, hasHumanInTheLoop, hasChecked]);
-
-  const dismissPopup = useCallback(() => {
-    setShouldShowPopup(false);
-  }, []);
-
-  return {
-    shouldShowPopup,
-    dismissPopup,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/ModalHeader/ModalHeader.tsx

@@ -29,7 +29,7 @@ export function ModalHeader({ agent }: ModalHeaderProps) {
           <ShowMoreText
             previewLimit={400}
             variant="small"
-            className="mt-4 !text-zinc-700"
+            className="mb-2 mt-4 !text-zinc-700"
           >
             {agent.description}
           </ShowMoreText>
@@ -40,6 +40,8 @@ export function ModalHeader({ agent }: ModalHeaderProps) {
             <Text variant="lead-semibold" className="text-blue-600">
               Tip
             </Text>
+            <div className="h-px w-full bg-blue-100" />
+
             <Text variant="body">
               For best results, run this agent{" "}
               {humanizeCronExpression(
@@ -50,7 +52,7 @@ export function ModalHeader({ agent }: ModalHeaderProps) {
         ) : null}
 
         {agent.instructions ? (
-          <div className="flex flex-col gap-4 rounded-medium border border-purple-100 bg-[#F1EBFE/5] p-4">
+          <div className="mt-4 flex flex-col gap-4 rounded-medium border border-purple-100 bg-[#f1ebfe80] p-4">
             <Text variant="lead-semibold" className="text-purple-600">
               Instructions
             </Text>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedRunView/components/SafeModeToggle.tsx

@@ -69,6 +69,7 @@ export function SafeModeToggle({ graph, className }: Props) {
   const {
     currentHITLSafeMode,
     showHITLToggle,
+    isHITLStateUndetermined,
     handleHITLToggle,
     currentSensitiveActionSafeMode,
     showSensitiveActionToggle,
@@ -77,13 +78,20 @@ export function SafeModeToggle({ graph, className }: Props) {
     shouldShowToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle) {
+  if (!shouldShowToggle || isHITLStateUndetermined) {
+    return null;
+  }
+
+  const showHITL = showHITLToggle && !isHITLStateUndetermined;
+  const showSensitive = showSensitiveActionToggle;
+
+  if (!showHITL && !showSensitive) {
     return null;
   }
 
   return (
     <div className={cn("flex gap-1", className)}>
-      {showHITLToggle && (
+      {showHITL && (
         <SafeModeIconButton
           isEnabled={currentHITLSafeMode}
           label="Human-in-the-loop"
@@ -93,7 +101,7 @@ export function SafeModeToggle({ graph, className }: Props) {
           isPending={isPending}
         />
       )}
-      {showSensitiveActionToggle && (
+      {showSensitive && (
         <SafeModeIconButton
           isEnabled={currentSensitiveActionSafeMode}
           label="Sensitive actions"

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryAgentCard/useLibraryAgentCard.ts

@@ -8,6 +8,8 @@ import { useGetV2GetUserProfile } from "@/app/api/__generated__/endpoints/store/
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { okData } from "@/app/api/helpers";
 import { useToast } from "@/components/molecules/Toast/use-toast";
+import { isLogoutInProgress } from "@/lib/autogpt-server-api/helpers";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { updateFavoriteInQueries } from "./helpers";
 
 interface Props {
@@ -23,10 +25,14 @@ export function useLibraryAgentCard({ agent }: Props) {
   const { toast } = useToast();
   const queryClient = getQueryClient();
   const { mutateAsync: updateLibraryAgent } = usePatchV2UpdateLibraryAgent();
+  const { user, isLoggedIn } = useSupabase();
+  const logoutInProgress = isLogoutInProgress();
 
   const { data: profile } = useGetV2GetUserProfile({
     query: {
       select: okData,
+      enabled: isLoggedIn && !!user && !logoutInProgress,
+      queryKey: ["/api/store/profile", user?.id],
     },
   });
 

autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts

@@ -1,6 +1,8 @@
 import { useToast } from "@/components/molecules/Toast/use-toast";
+import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { environment } from "@/services/environment";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { loginFormSchema, LoginProvider } from "@/types/auth";
 import { zodResolver } from "@hookform/resolvers/zod";
 import { useRouter, useSearchParams } from "next/navigation";
@@ -20,15 +22,17 @@ export function useLoginPage() {
   const [isGoogleLoading, setIsGoogleLoading] = useState(false);
   const [showNotAllowedModal, setShowNotAllowedModal] = useState(false);
   const isCloudEnv = environment.isCloud();
+  const isChatEnabled = useGetFlag(Flag.CHAT);
+  const homepageRoute = getHomepageRoute(isChatEnabled);
 
   // Get redirect destination from 'next' query parameter
   const nextUrl = searchParams.get("next");
 
   useEffect(() => {
     if (isLoggedIn && !isLoggingIn) {
-      router.push(nextUrl || "/marketplace");
+      router.push(nextUrl || homepageRoute);
     }
-  }, [isLoggedIn, isLoggingIn, nextUrl, router]);
+  }, [homepageRoute, isLoggedIn, isLoggingIn, nextUrl, router]);
 
   const form = useForm<z.infer<typeof loginFormSchema>>({
     resolver: zodResolver(loginFormSchema),
@@ -98,7 +102,7 @@ export function useLoginPage() {
       } else if (result.onboarding) {
         router.replace("/onboarding");
       } else {
-        router.replace("/marketplace");
+        router.replace(homepageRoute);
       }
     } catch (error) {
       toast({

autogpt_platform/frontend/src/app/(platform)/marketplace/components/MainMarketplacePage/__tests__/main.test.tsx

@@ -0,0 +1,15 @@
+import { expect, test } from "vitest";
+import { render, screen } from "@/tests/integrations/test-utils";
+import { MainMarkeplacePage } from "../MainMarketplacePage";
+import { server } from "@/mocks/mock-server";
+import { getDeleteV2DeleteStoreSubmissionMockHandler422 } from "@/app/api/__generated__/endpoints/store/store.msw";
+
+// Only for CI testing purpose, will remove it in future PR
+test("MainMarketplacePage", async () => {
+  server.use(getDeleteV2DeleteStoreSubmissionMockHandler422());
+
+  render(<MainMarkeplacePage />);
+  expect(
+    await screen.findByText("Featured agents", { exact: false }),
+  ).toBeDefined();
+});

autogpt_platform/frontend/src/app/(platform)/profile/(user)/page.tsx

@@ -3,12 +3,14 @@
 import { useGetV2GetUserProfile } from "@/app/api/__generated__/endpoints/store/store";
 import { ProfileInfoForm } from "@/components/__legacy__/ProfileInfoForm";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
+import { isLogoutInProgress } from "@/lib/autogpt-server-api/helpers";
 import { ProfileDetails } from "@/lib/autogpt-server-api/types";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { ProfileLoading } from "./ProfileLoading";
 
 export default function UserProfilePage() {
   const { user } = useSupabase();
+  const logoutInProgress = isLogoutInProgress();
 
   const {
     data: profile,
@@ -18,7 +20,7 @@ export default function UserProfilePage() {
     refetch,
   } = useGetV2GetUserProfile<ProfileDetails | null>({
     query: {
-      enabled: !!user,
+      enabled: !!user && !logoutInProgress,
       select: (res) => {
         if (res.status === 200) {
           return {

autogpt_platform/frontend/src/app/(platform)/signup/actions.ts

@@ -1,5 +1,6 @@
 "use server";
 
+import { getHomepageRoute } from "@/lib/constants";
 import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
 import { signupFormSchema } from "@/types/auth";
 import * as Sentry from "@sentry/nextjs";
@@ -11,6 +12,7 @@ export async function signup(
   password: string,
   confirmPassword: string,
   agreeToTerms: boolean,
+  isChatEnabled: boolean,
 ) {
   try {
     const parsed = signupFormSchema.safeParse({
@@ -58,7 +60,9 @@ export async function signup(
     }
 
     const isOnboardingEnabled = await shouldShowOnboarding();
-    const next = isOnboardingEnabled ? "/onboarding" : "/";
+    const next = isOnboardingEnabled
+      ? "/onboarding"
+      : getHomepageRoute(isChatEnabled);
 
     return { success: true, next };
   } catch (err) {

autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts

@@ -1,6 +1,8 @@
 import { useToast } from "@/components/molecules/Toast/use-toast";
+import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { environment } from "@/services/environment";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { LoginProvider, signupFormSchema } from "@/types/auth";
 import { zodResolver } from "@hookform/resolvers/zod";
 import { useRouter, useSearchParams } from "next/navigation";
@@ -20,15 +22,17 @@ export function useSignupPage() {
   const [isGoogleLoading, setIsGoogleLoading] = useState(false);
   const [showNotAllowedModal, setShowNotAllowedModal] = useState(false);
   const isCloudEnv = environment.isCloud();
+  const isChatEnabled = useGetFlag(Flag.CHAT);
+  const homepageRoute = getHomepageRoute(isChatEnabled);
 
   // Get redirect destination from 'next' query parameter
   const nextUrl = searchParams.get("next");
 
   useEffect(() => {
     if (isLoggedIn && !isSigningUp) {
-      router.push(nextUrl || "/marketplace");
+      router.push(nextUrl || homepageRoute);
     }
-  }, [isLoggedIn, isSigningUp, nextUrl, router]);
+  }, [homepageRoute, isLoggedIn, isSigningUp, nextUrl, router]);
 
   const form = useForm<z.infer<typeof signupFormSchema>>({
     resolver: zodResolver(signupFormSchema),
@@ -104,6 +108,7 @@ export function useSignupPage() {
         data.password,
         data.confirmPassword,
         data.agreeToTerms,
+        isChatEnabled === true,
       );
 
       setIsLoading(false);
@@ -129,7 +134,7 @@ export function useSignupPage() {
       }
 
       // Prefer the URL's next parameter, then result.next (for onboarding), then default
-      const redirectTo = nextUrl || result.next || "/";
+      const redirectTo = nextUrl || result.next || homepageRoute;
       router.replace(redirectTo);
     } catch (error) {
       setIsLoading(false);

autogpt_platform/frontend/src/app/api/mutators/custom-mutator.ts

@@ -4,12 +4,12 @@ import {
   getServerAuthToken,
 } from "@/lib/autogpt-server-api/helpers";
 
-import { transformDates } from "./date-transformer";
-import { environment } from "@/services/environment";
 import {
   IMPERSONATION_HEADER_NAME,
   IMPERSONATION_STORAGE_KEY,
 } from "@/lib/constants";
+import { environment } from "@/services/environment";
+import { transformDates } from "./date-transformer";
 
 const FRONTEND_BASE_URL =
   process.env.NEXT_PUBLIC_FRONTEND_BASE_URL || "http://localhost:3000";

autogpt_platform/frontend/src/app/api/openapi.json

@@ -1022,7 +1022,7 @@
       "get": {
         "tags": ["v2", "chat", "chat"],
         "summary": "Get Session",
-        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session; raises NotFoundError if not found.",
+        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session, or None if not found.",
         "operationId": "getV2GetSession",
         "security": [{ "HTTPBearerJWT": [] }],
         "parameters": [
@@ -9411,12 +9411,6 @@
             ],
             "title": "Reviewed Data",
             "description": "Optional edited data (ignored if approved=False)"
-          },
-          "auto_approve_future": {
-            "type": "boolean",
-            "title": "Auto Approve Future",
-            "description": "If true and this review is approved, future executions of this same block (node) will be automatically approved. This only affects approved reviews.",
-            "default": false
           }
         },
         "type": "object",
@@ -9436,7 +9430,7 @@
         "type": "object",
         "required": ["reviews"],
         "title": "ReviewRequest",
-        "description": "Request model for processing ALL pending reviews for an execution.\n\nThis request must include ALL pending reviews for a graph execution.\nEach review will be either approved (with optional data modifications)\nor rejected (data ignored). The execution will resume only after ALL reviews are processed.\n\nEach review item can individually specify whether to auto-approve future executions\nof the same block via the `auto_approve_future` field on ReviewItem."
+        "description": "Request model for processing ALL pending reviews for an execution.\n\nThis request must include ALL pending reviews for a graph execution.\nEach review will be either approved (with optional data modifications)\nor rejected (data ignored). The execution will resume only after ALL reviews are processed."
       },
       "ReviewResponse": {
         "properties": {

autogpt_platform/frontend/src/app/globals.css

@@ -141,52 +141,6 @@
   }
 }
 
-@keyframes shimmer {
-  0% {
-    background-position: -200% 0;
-  }
-  100% {
-    background-position: 200% 0;
-  }
-}
-
-@keyframes l3 {
-  25% {
-    background-position:
-      0 0,
-      100% 100%,
-      100% calc(100% - 5px);
-  }
-  50% {
-    background-position:
-      0 100%,
-      100% 100%,
-      0 calc(100% - 5px);
-  }
-  75% {
-    background-position:
-      0 100%,
-      100% 0,
-      100% 5px;
-  }
-}
-
-.loader {
-  width: 80px;
-  height: 70px;
-  border: 5px solid rgb(241 245 249);
-  padding: 0 8px;
-  box-sizing: border-box;
-  background:
-    linear-gradient(rgb(15 23 42) 0 0) 0 0/8px 20px,
-    linear-gradient(rgb(15 23 42) 0 0) 100% 0/8px 20px,
-    radial-gradient(farthest-side, rgb(15 23 42) 90%, #0000) 0 5px/8px 8px
-      content-box,
-    transparent;
-  background-repeat: no-repeat;
-  animation: l3 2s infinite linear;
-}
-
 input[type="number"]::-webkit-outer-spin-button,
 input[type="number"]::-webkit-inner-spin-button {
   -webkit-appearance: none;

autogpt_platform/frontend/src/app/page.tsx

@@ -1,5 +1,27 @@
-import { redirect } from "next/navigation";
+"use client";
+
+import { getHomepageRoute } from "@/lib/constants";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
+import { useRouter } from "next/navigation";
+import { useEffect } from "react";
 
 export default function Page() {
-  redirect("/marketplace");
+  const isChatEnabled = useGetFlag(Flag.CHAT);
+  const router = useRouter();
+  const homepageRoute = getHomepageRoute(isChatEnabled);
+  const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
+  const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
+  const isLaunchDarklyConfigured = envEnabled && Boolean(clientId);
+  const isFlagReady =
+    !isLaunchDarklyConfigured || typeof isChatEnabled === "boolean";
+
+  useEffect(
+    function redirectToHomepage() {
+      if (!isFlagReady) return;
+      router.replace(homepageRoute);
+    },
+    [homepageRoute, isFlagReady, router],
+  );
+
+  return null;
 }

autogpt_platform/frontend/src/components/atoms/Badge/Badge.test.tsx

@@ -1,81 +0,0 @@
-// import { render, screen } from "@testing-library/react";
-// import { describe, expect, it } from "vitest";
-// import { Badge } from "./Badge";
-
-// describe("Badge Component", () => {
-//   it("renders badge with content", () => {
-//     render(<Badge variant="success">Success</Badge>);
-
-//     expect(screen.getByText("Success")).toBeInTheDocument();
-//   });
-
-//   it("applies correct variant styles", () => {
-//     const { rerender } = render(<Badge variant="success">Success</Badge>);
-//     let badge = screen.getByText("Success");
-//     expect(badge).toHaveClass("bg-green-100", "text-green-800");
-
-//     rerender(<Badge variant="error">Error</Badge>);
-//     badge = screen.getByText("Error");
-//     expect(badge).toHaveClass("bg-red-100", "text-red-800");
-
-//     rerender(<Badge variant="info">Info</Badge>);
-//     badge = screen.getByText("Info");
-//     expect(badge).toHaveClass("bg-slate-100", "text-slate-800");
-//   });
-
-//   it("applies custom className", () => {
-//     render(
-//       <Badge variant="success" className="custom-class">
-//         Success
-//       </Badge>,
-//     );
-
-//     const badge = screen.getByText("Success");
-//     expect(badge).toHaveClass("custom-class");
-//   });
-
-//   it("renders as span element", () => {
-//     render(<Badge variant="success">Success</Badge>);
-
-//     const badge = screen.getByText("Success");
-//     expect(badge.tagName).toBe("SPAN");
-//   });
-
-//   it("renders children correctly", () => {
-//     render(
-//       <Badge variant="success">
-//         <span>Custom</span> Content
-//       </Badge>,
-//     );
-
-//     expect(screen.getByText("Custom")).toBeInTheDocument();
-//     expect(screen.getByText("Content")).toBeInTheDocument();
-//   });
-
-//   it("supports all badge variants", () => {
-//     const variants = ["success", "error", "info"] as const;
-
-//     variants.forEach((variant) => {
-//       const { unmount } = render(
-//         <Badge variant={variant} data-testid={`badge-${variant}`}>
-//           {variant}
-//         </Badge>,
-//       );
-
-//       expect(screen.getByTestId(`badge-${variant}`)).toBeInTheDocument();
-//       unmount();
-//     });
-//   });
-
-//   it("handles long text content", () => {
-//     render(
-//       <Badge variant="info">
-//         Very long text that should be handled properly by the component
-//       </Badge>,
-//     );
-
-//     const badge = screen.getByText(/Very long text/);
-//     expect(badge).toBeInTheDocument();
-//     expect(badge).toHaveClass("overflow-hidden", "text-ellipsis");
-//   });
-// });

autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx

@@ -0,0 +1,84 @@
+"use client";
+
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+import { useEffect, useRef } from "react";
+import { ChatContainer } from "./components/ChatContainer/ChatContainer";
+import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
+import { ChatLoader } from "./components/ChatLoader/ChatLoader";
+import { useChat } from "./useChat";
+
+export interface ChatProps {
+  className?: string;
+  urlSessionId?: string | null;
+  initialPrompt?: string;
+  onSessionNotFound?: () => void;
+  onStreamingChange?: (isStreaming: boolean) => void;
+}
+
+export function Chat({
+  className,
+  urlSessionId,
+  initialPrompt,
+  onSessionNotFound,
+  onStreamingChange,
+}: ChatProps) {
+  const hasHandledNotFoundRef = useRef(false);
+  const {
+    messages,
+    isLoading,
+    isCreating,
+    error,
+    isSessionNotFound,
+    sessionId,
+    createSession,
+    showLoader,
+  } = useChat({ urlSessionId });
+
+  useEffect(
+    function handleMissingSession() {
+      if (!onSessionNotFound) return;
+      if (!urlSessionId) return;
+      if (!isSessionNotFound || isLoading || isCreating) return;
+      if (hasHandledNotFoundRef.current) return;
+      hasHandledNotFoundRef.current = true;
+      onSessionNotFound();
+    },
+    [onSessionNotFound, urlSessionId, isSessionNotFound, isLoading, isCreating],
+  );
+
+  return (
+    <div className={cn("flex h-full flex-col", className)}>
+      {/* Main Content */}
+      <main className="flex min-h-0 w-full flex-1 flex-col overflow-hidden bg-[#f8f8f9]">
+        {/* Loading State */}
+        {showLoader && (isLoading || isCreating) && (
+          <div className="flex flex-1 items-center justify-center">
+            <div className="flex flex-col items-center gap-4">
+              <ChatLoader />
+              <Text variant="body" className="text-zinc-500">
+                Loading your chats...
+              </Text>
+            </div>
+          </div>
+        )}
+
+        {/* Error State */}
+        {error && !isLoading && (
+          <ChatErrorState error={error} onRetry={createSession} />
+        )}
+
+        {/* Session Content */}
+        {sessionId && !isLoading && !error && (
+          <ChatContainer
+            sessionId={sessionId}
+            initialMessages={messages}
+            initialPrompt={initialPrompt}
+            className="flex-1"
+            onStreamingChange={onStreamingChange}
+          />
+        )}
+      </main>
+    </div>
+  );
+}

autogpt_platform/frontend/src/components/contextual/Chat/components/AIChatBubble/AIChatBubble.tsx

@@ -0,0 +1,15 @@
+import { cn } from "@/lib/utils";
+import { ReactNode } from "react";
+
+export interface AIChatBubbleProps {
+  children: ReactNode;
+  className?: string;
+}
+
+export function AIChatBubble({ children, className }: AIChatBubbleProps) {
+  return (
+    <div className={cn("text-left text-[1rem] leading-relaxed", className)}>
+      {children}
+    </div>
+  );
+}

autogpt_platform/frontend/src/components/contextual/Chat/components/AuthPromptWidget/AuthPromptWidget.tsx

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

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx

@@ -0,0 +1,113 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
+import { cn } from "@/lib/utils";
+import { useEffect } from "react";
+import { ChatInput } from "../ChatInput/ChatInput";
+import { MessageList } from "../MessageList/MessageList";
+import { useChatContainer } from "./useChatContainer";
+
+export interface ChatContainerProps {
+  sessionId: string | null;
+  initialMessages: SessionDetailResponse["messages"];
+  initialPrompt?: string;
+  className?: string;
+  onStreamingChange?: (isStreaming: boolean) => void;
+}
+
+export function ChatContainer({
+  sessionId,
+  initialMessages,
+  initialPrompt,
+  className,
+  onStreamingChange,
+}: ChatContainerProps) {
+  const {
+    messages,
+    streamingChunks,
+    isStreaming,
+    stopStreaming,
+    isRegionBlockedModalOpen,
+    sendMessageWithContext,
+    handleRegionModalOpenChange,
+    handleRegionModalClose,
+  } = useChatContainer({
+    sessionId,
+    initialMessages,
+    initialPrompt,
+  });
+
+  useEffect(() => {
+    onStreamingChange?.(isStreaming);
+  }, [isStreaming, onStreamingChange]);
+
+  const breakpoint = useBreakpoint();
+  const isMobile =
+    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
+
+  return (
+    <div
+      className={cn(
+        "mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col bg-[#f8f8f9]",
+        className,
+      )}
+    >
+      <Dialog
+        title="Service unavailable"
+        controlled={{
+          isOpen: isRegionBlockedModalOpen,
+          set: handleRegionModalOpenChange,
+        }}
+        onClose={handleRegionModalClose}
+      >
+        <Dialog.Content>
+          <div className="flex flex-col gap-4">
+            <Text variant="body">
+              This model is not available in your region. Please connect via VPN
+              and try again.
+            </Text>
+            <div className="flex justify-end">
+              <Button
+                type="button"
+                variant="primary"
+                onClick={handleRegionModalClose}
+              >
+                Got it
+              </Button>
+            </div>
+          </div>
+        </Dialog.Content>
+      </Dialog>
+      {/* Messages - Scrollable */}
+      <div className="relative flex min-h-0 flex-1 flex-col">
+        <div className="flex min-h-full flex-col justify-end">
+          <MessageList
+            messages={messages}
+            streamingChunks={streamingChunks}
+            isStreaming={isStreaming}
+            onSendMessage={sendMessageWithContext}
+            className="flex-1"
+          />
+        </div>
+      </div>
+
+      {/* Input - Fixed at bottom */}
+      <div className="relative px-3 pb-6 pt-2">
+        <div className="pointer-events-none absolute top-[-18px] z-10 h-6 w-full bg-gradient-to-b from-transparent to-[#f8f8f9]" />
+        <ChatInput
+          onSend={sendMessageWithContext}
+          disabled={isStreaming || !sessionId}
+          isStreaming={isStreaming}
+          onStop={stopStreaming}
+          placeholder={
+            isMobile
+              ? "You can search or just ask"
+              : 'You can search or just ask — e.g. "create a blog post outline"'
+          }
+        />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/createStreamEventDispatcher.ts

@@ -1,6 +1,6 @@
 import { toast } from "sonner";
 import { StreamChunk } from "../../useChatStream";
-import type { HandlerDependencies } from "./useChatContainer.handlers";
+import type { HandlerDependencies } from "./handlers";
 import {
   handleError,
   handleLoginNeeded,
@@ -9,12 +9,30 @@ import {
   handleTextEnded,
   handleToolCallStart,
   handleToolResponse,
-} from "./useChatContainer.handlers";
+  isRegionBlockedError,
+} from "./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);
@@ -38,15 +56,23 @@ 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
-        toast.error("Chat Error", {
-          description: chunk.message || chunk.content || "An error occurred",
-        });
+        if (!isRegionBlocked) {
+          toast.error("Chat Error", {
+            description: chunk.message || chunk.content || "An error occurred",
+          });
+        }
         break;
 
       case "usage":

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts

@@ -7,15 +7,30 @@ 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);
@@ -30,16 +45,17 @@ 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()) {
-    const assistantMessage: ChatMessageData = {
-      type: "message",
-      role: "assistant",
-      content: completedText,
-      timestamp: new Date(),
-    };
-    deps.setMessages((prev) => [...prev, assistantMessage]);
+    deps.setMessages((prev) => {
+      const assistantMessage: ChatMessageData = {
+        type: "message",
+        role: "assistant",
+        content: completedText,
+        timestamp: new Date(),
+      };
+      return [...prev, assistantMessage];
+    });
   }
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
@@ -50,30 +66,45 @@ export function handleToolCallStart(
   chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
-  const toolCallMessage: ChatMessageData = {
+  const toolCallMessage: Extract<ChatMessageData, { type: "tool_call" }> = {
     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(),
   };
-  deps.setMessages((prev) => [...prev, toolCallMessage]);
-  console.log("[Tool Call Start]", {
-    toolId: toolCallMessage.toolId,
-    toolName: toolCallMessage.toolName,
-    timestamp: new Date().toISOString(),
-  });
+
+  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);
 }
 
 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) => {
@@ -127,22 +158,15 @@ 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[toolCallIndex] = responseMessage;
-      console.log(
-        "[Tool Response] Replaced tool_call with matching tool_id:",
-        chunk.tool_id,
-        "at index:",
-        toolCallIndex,
-      );
+      newMessages.splice(toolCallIndex + 1, 0, responseMessage);
       return newMessages;
     }
-    console.warn(
-      "[Tool Response] No tool_call found with tool_id:",
-      chunk.tool_id,
-      "appending instead",
-    );
     return [...prev, responseMessage];
   });
 }
@@ -167,55 +191,38 @@ export function handleStreamEnd(
   deps: HandlerDependencies,
 ) {
   const completedContent = deps.streamingChunksRef.current.join("");
-  // Only save message if there are uncommitted chunks
-  // (text_ended already saved if there were tool calls)
+  if (!completedContent.trim() && !deps.hasResponseRef.current) {
+    deps.setMessages((prev) => [
+      ...prev,
+      {
+        type: "message",
+        role: "assistant",
+        content: "No response received. Please try again.",
+        timestamp: new Date(),
+      },
+    ]);
+  }
   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) => {
-      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.setMessages((prev) => [...prev, assistantMessage]);
   }
   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/components/contextual/Chat/components/ChatContainer/helpers.ts

@@ -1,6 +1,33 @@
+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, "");
@@ -207,12 +234,22 @@ 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,
+    result: parsedResult ? (parsedResult as ToolResult) : result,
     success: true,
     timestamp: timestamp || new Date(),
   };

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts

@@ -1,14 +1,17 @@
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-import { useCallback, useMemo, useRef, useState } from "react";
+import { useCallback, useEffect, 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";
@@ -16,20 +19,45 @@ import {
 interface Args {
   sessionId: string | null;
   initialMessages: SessionDetailResponse["messages"];
+  initialPrompt?: string;
 }
 
-export function useChatContainer({ sessionId, initialMessages }: Args) {
+export function useChatContainer({
+  sessionId,
+  initialMessages,
+  initialPrompt,
+}: 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 { error, sendMessage: sendStreamMessage } = useChatStream();
+  const previousSessionIdRef = useRef<string | null>(null);
+  const {
+    error,
+    sendMessage: sendStreamMessage,
+    stopStreaming,
+  } = 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) {
@@ -45,13 +73,9 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
         ? new Date(msg.timestamp as string)
         : undefined;
 
-      // Remove page context from user messages when loading existing sessions
       if (role === "user") {
         content = removePageContext(content);
-        // Skip user messages that become empty after removing page context
-        if (!content.trim()) {
-          continue;
-        }
+        if (!content.trim()) continue;
         processedInitialMessages.push({
           type: "message",
           role: "user",
@@ -61,19 +85,15 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
         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 {
@@ -96,7 +116,6 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
               });
             }
           }
-          // Only add assistant message if there's content after stripping thinking tags
           if (content.trim()) {
             processedInitialMessages.push({
               type: "message",
@@ -106,7 +125,6 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
             });
           }
         } else if (content.trim()) {
-          // Assistant message without tool calls, but with content
           processedInitialMessages.push({
             type: "message",
             role: "assistant",
@@ -117,7 +135,6 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
         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";
@@ -133,7 +150,6 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
         continue;
       }
 
-      // Handle other message types (system, etc.)
       if (content.trim()) {
         processedInitialMessages.push({
           type: "message",
@@ -154,9 +170,10 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
       context?: { url: string; content: string },
     ) {
       if (!sessionId) {
-        console.error("Cannot send message: no session ID");
+        console.error("[useChatContainer] Cannot send message: no session ID");
         return;
       }
+      setIsRegionBlockedModalOpen(false);
       if (isUserMessage) {
         const userMessage = createUserMessage(content);
         setMessages((prev) => [...filterAuthMessages(prev), userMessage]);
@@ -167,14 +184,19 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
       streamingChunksRef.current = [];
       setHasTextChunks(false);
       setIsStreamingInitiated(true);
+      hasResponseRef.current = false;
+
       const dispatcher = createStreamEventDispatcher({
         setHasTextChunks,
         setStreamingChunks,
         streamingChunksRef,
+        hasResponseRef,
         setMessages,
+        setIsRegionBlockedModalOpen,
         sessionId,
         setIsStreamingInitiated,
       });
+
       try {
         await sendStreamMessage(
           sessionId,
@@ -184,8 +206,12 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
           context,
         );
       } catch (err) {
-        console.error("Failed to send message:", err);
+        console.error("[useChatContainer] 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", {
@@ -196,11 +222,63 @@ export function useChatContainer({ sessionId, initialMessages }: Args) {
     [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,
   };
 }