test(backend): Update test to match removed SET search_path call

The SET search_path call was removed in favor of explicit schema
qualification, so the test now expects only 1 execute_raw call.

AGENTS.md

@@ -16,20 +16,6 @@ See `docs/content/platform/getting-started.md` for setup instructions.
 - Format Python code with `poetry run format`.
 - Format frontend code using `pnpm format`.
 
-## Frontend-specific guidelines
-
-**When working on files in `autogpt_platform/frontend/`, always read and follow the conventions in `autogpt_platform/frontend/CONTRIBUTING.md`.**
-
-Key frontend conventions:
-- 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 create 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 and handlers, arrow functions only for small inline callbacks
-- No barrel files or `index.ts` re-exports
-
-See `autogpt_platform/frontend/CONTRIBUTING.md` for complete frontend architecture, patterns, and conventions.
-
 ## Testing
 
 - Backend: `poetry run test` (runs pytest with a docker based postgres + prisma).

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

@@ -4,14 +4,9 @@ from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
-from langfuse import Langfuse
-from openai import (
-    APIConnectionError,
-    APIError,
-    APIStatusError,
-    AsyncOpenAI,
-    RateLimitError,
-)
+from langfuse import get_client, propagate_attributes
+from langfuse.openai import openai  # type: ignore
+from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -21,7 +16,6 @@ from backend.data.understanding import (
 from backend.util.exceptions import NotFoundError
 from backend.util.settings import Settings
 
-from . import db as chat_db
 from .config import ChatConfig
 from .model import (
     ChatMessage,
@@ -50,10 +44,10 @@ logger = logging.getLogger(__name__)
 
 config = ChatConfig()
 settings = Settings()
-client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-# Langfuse client (lazy initialization)
-_langfuse_client: Langfuse | None = None
+
+langfuse = get_client()
 
 
 class LangfuseNotConfiguredError(Exception):
@@ -69,65 +63,6 @@ def _is_langfuse_configured() -> bool:
     )
 
 
-def _get_langfuse_client() -> Langfuse:
-    """Get or create the Langfuse client for prompt management and tracing."""
-    global _langfuse_client
-    if _langfuse_client is None:
-        if not _is_langfuse_configured():
-            raise LangfuseNotConfiguredError(
-                "Langfuse is not configured. The chat feature requires Langfuse for prompt management. "
-                "Please set the LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
-            )
-        _langfuse_client = Langfuse(
-            public_key=settings.secrets.langfuse_public_key,
-            secret_key=settings.secrets.langfuse_secret_key,
-            host=settings.secrets.langfuse_host or "https://cloud.langfuse.com",
-        )
-    return _langfuse_client
-
-
-def _get_environment() -> str:
-    """Get the current environment name for Langfuse tagging."""
-    return settings.config.app_env.value
-
-
-def _get_langfuse_prompt() -> str:
-    """Fetch the latest production prompt from Langfuse.
-
-    Returns:
-        The compiled prompt text from Langfuse.
-
-    Raises:
-        Exception: If Langfuse is unavailable or prompt fetch fails.
-    """
-    try:
-        langfuse = _get_langfuse_client()
-        # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
-        prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
-        compiled = prompt.compile()
-        logger.info(
-            f"Fetched prompt '{config.langfuse_prompt_name}' from Langfuse "
-            f"(version: {prompt.version})"
-        )
-        return compiled
-    except Exception as e:
-        logger.error(f"Failed to fetch prompt from Langfuse: {e}")
-        raise
-
-
-async def _is_first_session(user_id: str) -> bool:
-    """Check if this is the user's first chat session.
-
-    Returns True if the user has 1 or fewer sessions (meaning this is their first).
-    """
-    try:
-        session_count = await chat_db.get_user_session_count(user_id)
-        return session_count <= 1
-    except Exception as e:
-        logger.warning(f"Failed to check session count for user {user_id}: {e}")
-        return False  # Default to non-onboarding if we can't check
-
-
 async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
@@ -139,8 +74,6 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         Tuple of (compiled prompt string, Langfuse prompt object for tracing)
     """
 
-    langfuse = _get_langfuse_client()
-
     # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
     prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
 
@@ -158,7 +91,7 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
     compiled = prompt.compile(users_information=context)
-    return compiled, prompt
+    return compiled, understanding
 
 
 async def _generate_session_title(message: str) -> str | None:
@@ -217,6 +150,7 @@ async def assign_user_to_session(
 async def stream_chat_completion(
     session_id: str,
     message: str | None = None,
+    tool_call_response: str | None = None,
     is_user_message: bool = True,
     user_id: str | None = None,
     retry_count: int = 0,
@@ -256,11 +190,6 @@ async def stream_chat_completion(
         yield StreamFinish()
         return
 
-    # Langfuse observations will be created after session is loaded (need messages for input)
-    # Initialize to None so finally block can safely check and end them
-    trace = None
-    generation = None
-
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
         session = await get_chat_session(session_id, user_id)
@@ -336,297 +265,259 @@ async def stream_chat_completion(
             asyncio.create_task(_update_title())
 
     # Build system prompt with business understanding
-    system_prompt, langfuse_prompt = await _build_system_prompt(user_id)
-
-    # Build input messages including system prompt for complete Langfuse logging
-    trace_input_messages = [{"role": "system", "content": system_prompt}] + [
-        m.model_dump() for m in session.messages
-    ]
+    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
-    try:
-        langfuse = _get_langfuse_client()
-        env = _get_environment()
-        trace = langfuse.start_observation(
-            name="chat_completion",
-            input={"messages": trace_input_messages},
-            metadata={
-                "environment": env,
-                "model": config.model,
-                "message_count": len(session.messages),
-                "prompt_name": langfuse_prompt.name if langfuse_prompt else None,
-                "prompt_version": langfuse_prompt.version if langfuse_prompt else None,
-            },
-        )
-        # Set trace-level attributes (session_id, user_id, tags)
-        trace.update_trace(
+    input = message
+    if not message and tool_call_response:
+        input = tool_call_response
+
+    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=[env, "copilot"],
-        )
-    except Exception as e:
-        logger.warning(f"Failed to create Langfuse trace: {e}")
+            tags=["copilot"],
+            metadata={
+                "users_information": format_understanding_for_prompt(understanding)[
+                    :200
+                ]  # langfuse only accepts upto to 200 chars
+            },
+        ):
 
-    # 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
-    try:
-        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)
-
-        # Create Langfuse generation for each LLM call, linked to the prompt
-        # Using v3 SDK: start_observation with as_type="generation"
-        generation = (
-            trace.start_observation(
-                as_type="generation",
-                name="llm_call",
-                model=config.model,
-                input={"messages": trace_input_messages},
-                prompt=langfuse_prompt,
+            # Initialize variables that will be used in finally block (must be defined before try)
+            assistant_response = ChatMessage(
+                role="assistant",
+                content="",
             )
-            if trace
-            else None
-        )
+            accumulated_tool_calls: list[dict[str, Any]] = []
 
-        try:
-            async for chunk in _stream_chat_chunks(
-                session=session,
-                tools=tools,
-                system_prompt=system_prompt,
-                text_block_id=text_block_id,
-            ):
+            # 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
 
-                if isinstance(chunk, StreamTextStart):
-                    # Emit text-start before first text delta
-                    if not has_received_text:
+            # 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, 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
-                        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
+                    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
-                        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,
+                    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:
-                    logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
-        except Exception as e:
-            logger.error(f"Error during stream: {e!s}", exc_info=True)
+                    # Non-retryable error or max retries exceeded
+                    # Save any partial progress before reporting error
+                    messages_to_save: list[ChatMessage] = []
 
-            # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
-            is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
+                    # 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)
 
-            if is_retryable and retry_count < config.max_retries:
+                    # 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"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
+                    f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
                 )
-                should_retry = True
-            else:
-                # Non-retryable error or max retries exceeded
-                # Save any partial progress before reporting error
-                messages_to_save: list[ChatMessage] = []
+                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
 
-                # 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:
+            # Normal completion path - save session and handle tool call continuation
             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
-        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"Added {len(accumulated_tool_calls)} tool calls to assistant message"
-            )
-        if assistant_response.content or assistant_response.tool_calls:
-            messages_to_save.append(assistant_response)
-            logger.info(
-                f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
+                f"Normal completion path: session={session.session_id}, "
+                f"current message_count={len(session.messages)}"
             )
 
-        # Add tool response messages after assistant message
-        messages_to_save.extend(tool_response_messages)
-        logger.info(
-            f"Saving {len(tool_response_messages)} tool response messages, "
-            f"total_to_save={len(messages_to_save)}"
-        )
+            # Build the messages list in the correct order
+            messages_to_save: list[ChatMessage] = []
 
-        session.messages.extend(messages_to_save)
-        logger.info(
-            f"Extended session messages, new message_count={len(session.messages)}"
-        )
-        await upsert_chat_session(session)
-
-        # 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,
-            ):
-                yield chunk
-
-    finally:
-        # Always end Langfuse observations to prevent resource leaks
-        # Guard against None and catch errors to avoid masking original exceptions
-        if generation is not None:
-            try:
-                latest_usage = session.usage[-1] if session.usage else None
-                generation.update(
-                    model=config.model,
-                    output={
-                        "content": assistant_response.content,
-                        "tool_calls": accumulated_tool_calls or None,
-                    },
-                    usage_details=(
-                        {
-                            "input": latest_usage.prompt_tokens,
-                            "output": latest_usage.completion_tokens,
-                            "total": latest_usage.total_tokens,
-                        }
-                        if latest_usage
-                        else None
-                    ),
+            # Add assistant message with tool_calls if any
+            if accumulated_tool_calls:
+                assistant_response.tool_calls = accumulated_tool_calls
+                logger.info(
+                    f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+                )
+            if 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 [])}"
                 )
-                generation.end()
-            except Exception as e:
-                logger.warning(f"Failed to end Langfuse generation: {e}")
 
-        if trace is not None:
-            try:
-                if accumulated_tool_calls:
-                    trace.update_trace(output={"tool_calls": accumulated_tool_calls})
-                else:
-                    trace.update_trace(output={"response": assistant_response.content})
-                trace.end()
-            except Exception as e:
-                logger.warning(f"Failed to end Langfuse trace: {e}")
+            # 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)}"
+            )
+
+            session.messages.extend(messages_to_save)
+            logger.info(
+                f"Extended session messages, new message_count={len(session.messages)}"
+            )
+            await upsert_chat_session(session)
+
+            # If we did a tool call, stream the chat completion again to get the next response
+            if has_done_tool_call:
+                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
@@ -900,5 +791,4 @@ async def _yield_tool_call(
         session=session,
     )
 
-    logger.info(f"Yielding Tool execution response: {tool_execution_response}")
     yield tool_execution_response

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

@@ -30,7 +30,7 @@ TOOL_REGISTRY: dict[str, BaseTool] = {
     "find_library_agent": FindLibraryAgentTool(),
     "run_agent": RunAgentTool(),
     "run_block": RunBlockTool(),
-    "agent_output": AgentOutputTool(),
+    "view_agent_output": AgentOutputTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
 }

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

@@ -3,6 +3,8 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 from backend.data.understanding import (
     BusinessUnderstandingInput,
@@ -59,6 +61,7 @@ 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

@@ -5,6 +5,7 @@ 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
@@ -103,7 +104,7 @@ class AgentOutputTool(BaseTool):
 
     @property
     def name(self) -> str:
-        return "agent_output"
+        return "view_agent_output"
 
     @property
     def description(self) -> str:
@@ -328,6 +329,7 @@ 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

@@ -3,6 +3,8 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -78,6 +80,7 @@ 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

@@ -3,6 +3,8 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -85,6 +87,7 @@ 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

@@ -2,6 +2,8 @@
 
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -35,6 +37,7 @@ 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

@@ -1,6 +1,7 @@
 import logging
 from typing import Any
 
+from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -55,6 +56,7 @@ 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

@@ -2,6 +2,8 @@
 
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -41,6 +43,7 @@ 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

@@ -4,6 +4,8 @@ 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 (
@@ -71,6 +73,7 @@ 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

@@ -3,6 +3,7 @@
 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
@@ -154,6 +155,7 @@ 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

@@ -4,6 +4,8 @@ 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
@@ -127,6 +129,7 @@ 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

@@ -3,6 +3,7 @@
 import logging
 from typing import Any
 
+from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -87,6 +88,7 @@ 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/store/embeddings.py

@@ -154,15 +154,16 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
+        # Use {schema}.vector for explicit pgvector type qualification
         await execute_raw_with_schema(
             """
             INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
                 "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
             )
-            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::{schema}.vector, $5, $6::jsonb, NOW(), NOW())
             ON CONFLICT ("contentType", "contentId", "userId")
             DO UPDATE SET
-                "embedding" = $4::vector,
+                "embedding" = $4::{schema}.vector,
                 "searchableText" = $5,
                 "metadata" = $6::jsonb,
                 "updatedAt" = NOW()
@@ -177,7 +178,6 @@ async def store_content_embedding(
             searchable_text,
             metadata_json,
             client=client,
-            set_public_search_path=True,
         )
 
         logger.info(f"Stored embedding for {content_type}:{content_id}")
@@ -236,7 +236,6 @@ async def get_content_embedding(
             content_type,
             content_id,
             user_id,
-            set_public_search_path=True,
         )
 
         if result and len(result) > 0:
@@ -871,31 +870,34 @@ async def semantic_search(
         # Add content type parameters and build placeholders dynamically
         content_type_start_idx = len(params) + 1
         content_type_placeholders = ", ".join(
-            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+            '$' + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
             for i in range(len(content_types))
         )
         params.extend([ct.value for ct in content_types])
 
-        sql = f"""
+        # Build min_similarity param index before appending
+        min_similarity_idx = len(params) + 1
+        params.append(min_similarity)
+
+        # Use regular string (not f-string) for template to preserve {schema_prefix} and {schema} placeholders
+        # Use OPERATOR({schema}.<=>) for explicit operator schema qualification
+        sql = """
             SELECT
                 "contentId" as content_id,
                 "contentType" as content_type,
                 "searchableText" as searchable_text,
                 metadata,
-                1 - (embedding <=> '{embedding_str}'::vector) as similarity
-            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
-            WHERE "contentType" IN ({content_type_placeholders})
-            {user_filter}
-            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
+                1 - (embedding OPERATOR({schema}.<=>) '""" + embedding_str + """'::{schema}.vector) as similarity
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" IN (""" + content_type_placeholders + """)
+            """ + user_filter + """
+            AND 1 - (embedding OPERATOR({schema}.<=>) '""" + embedding_str + """'::{schema}.vector) >= $""" + str(min_similarity_idx) + """
             ORDER BY similarity DESC
             LIMIT $1
         """
-        params.append(min_similarity)
 
         try:
-            results = await query_raw_with_schema(
-                sql, *params, set_public_search_path=True
-            )
+            results = await query_raw_with_schema(sql, *params)
             return [
                 {
                     "content_id": row["content_id"],
@@ -922,31 +924,33 @@ async def semantic_search(
     # Add content type parameters and build placeholders dynamically
     content_type_start_idx = len(params_lexical) + 1
     content_type_placeholders_lexical = ", ".join(
-        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+        '$' + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
         for i in range(len(content_types))
     )
     params_lexical.extend([ct.value for ct in content_types])
 
-    sql_lexical = f"""
+    # Build query param index before appending
+    query_param_idx = len(params_lexical) + 1
+    params_lexical.append(f"%{query}%")
+
+    # Use regular string (not f-string) for template to preserve {schema_prefix} placeholders
+    sql_lexical = """
         SELECT
             "contentId" as content_id,
             "contentType" as content_type,
             "searchableText" as searchable_text,
             metadata,
             0.0 as similarity
-        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
-        WHERE "contentType" IN ({content_type_placeholders_lexical})
-        {user_filter}
-        AND "searchableText" ILIKE ${len(params_lexical) + 1}
+        FROM {schema_prefix}"UnifiedContentEmbedding"
+        WHERE "contentType" IN (""" + content_type_placeholders_lexical + """)
+        """ + user_filter + """
+        AND "searchableText" ILIKE $""" + str(query_param_idx) + """
         ORDER BY "updatedAt" DESC
         LIMIT $1
     """
-    params_lexical.append(f"%{query}%")
 
     try:
-        results = await query_raw_with_schema(
-            sql_lexical, *params_lexical, set_public_search_path=True
-        )
+        results = await query_raw_with_schema(sql_lexical, *params_lexical)
         return [
             {
                 "content_id": row["content_id"],

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

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

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

@@ -295,7 +295,7 @@ async def unified_hybrid_search(
                 FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
                 WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
                 {user_filter}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
+                ORDER BY uce.embedding OPERATOR({{schema}}.<=>)  {embedding_param}::{{schema}}.vector
                 LIMIT 200
             )
         ),
@@ -307,7 +307,7 @@ async def unified_hybrid_search(
                 uce.metadata,
                 uce."updatedAt" as updated_at,
                 -- Semantic score: cosine similarity (1 - distance)
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding OPERATOR({{schema}}.<=>)  {embedding_param}::{{schema}}.vector), 0) as semantic_score,
                 -- Lexical score: ts_rank_cd
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match from metadata
@@ -363,9 +363,7 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(
-        sql_query, *params, set_public_search_path=True
-    )
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -585,7 +583,7 @@ async def hybrid_search(
                 WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
                 AND uce."userId" IS NULL
                 AND {where_clause}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
+                ORDER BY uce.embedding OPERATOR({{schema}}.<=>)  {embedding_param}::{{schema}}.vector
                 LIMIT 200
             ) uce
         ),
@@ -607,7 +605,7 @@ async def hybrid_search(
                 -- Searchable text for BM25 reranking
                 COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
                 -- Semantic score
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding OPERATOR({{schema}}.<=>)  {embedding_param}::{{schema}}.vector), 0) as semantic_score,
                 -- Lexical score (raw, will normalize)
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match
@@ -688,9 +686,7 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(
-        sql_query, *params, set_public_search_path=True
-    )
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
 

autogpt_platform/backend/backend/data/db.py

@@ -38,20 +38,6 @@ POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
 if POOL_TIMEOUT:
     DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
 
-# Add public schema to search_path for pgvector type access
-# The vector extension is in public schema, but search_path is determined by schema parameter
-# Extract the schema from DATABASE_URL or default to 'public' (matching get_database_schema())
-parsed_url = urlparse(DATABASE_URL)
-url_params = dict(parse_qsl(parsed_url.query))
-db_schema = url_params.get("schema", "public")
-# Build search_path, avoiding duplicates if db_schema is already 'public'
-search_path_schemas = list(
-    dict.fromkeys([db_schema, "public"])
-)  # Preserves order, removes duplicates
-search_path = ",".join(search_path_schemas)
-# This allows using ::vector without schema qualification
-DATABASE_URL = add_param(DATABASE_URL, "options", f"-c search_path={search_path}")
-
 HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
 
 prisma = Prisma(
@@ -127,38 +113,43 @@ async def _raw_with_schema(
     *args,
     execute: bool = False,
     client: Prisma | None = None,
-    set_public_search_path: bool = False,
 ) -> list[dict] | int:
     """Internal: Execute raw SQL with proper schema handling.
 
     Use query_raw_with_schema() or execute_raw_with_schema() instead.
 
+    Supports placeholders:
+        - {schema_prefix}: Table/type prefix (e.g., "platform".)
+        - {schema}: Raw schema name (e.g., platform) for pgvector types and operators
+
     Args:
-        query_template: SQL query with {schema_prefix} placeholder
+        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
         *args: Query parameters
         execute: If False, executes SELECT query. If True, executes INSERT/UPDATE/DELETE.
         client: Optional Prisma client for transactions (only used when execute=True).
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
 
     Returns:
         - list[dict] if execute=False (query results)
         - int if execute=True (number of affected rows)
+
+    Example with vector type:
+        await execute_raw_with_schema(
+            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::{schema}.vector)',
+            embedding_data
+        )
     """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
-    formatted_query = query_template.format(schema_prefix=schema_prefix)
+
+    formatted_query = query_template.format(
+        schema_prefix=schema_prefix,
+        schema=schema,
+    )
 
     import prisma as prisma_module
 
     db_client = client if client else prisma_module.get_client()
 
-    # Set search_path to include public schema if requested
-    # Prisma doesn't support the 'options' connection parameter, so we set it per-session
-    # This is idempotent and safe to call multiple times
-    if set_public_search_path:
-        await db_client.execute_raw(f"SET search_path = {schema}, public")  # type: ignore
-
     if execute:
         result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
     else:
@@ -167,16 +158,12 @@ async def _raw_with_schema(
     return result
 
 
-async def query_raw_with_schema(
-    query_template: str, *args, set_public_search_path: bool = False
-) -> list[dict]:
+async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
     """Execute raw SQL SELECT query with proper schema handling.
 
     Args:
-        query_template: SQL query with {schema_prefix} placeholder
+        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
         *args: Query parameters
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
 
     Returns:
         List of result rows as dictionaries
@@ -187,23 +174,20 @@ async def query_raw_with_schema(
             user_id
         )
     """
-    return await _raw_with_schema(query_template, *args, execute=False, set_public_search_path=set_public_search_path)  # type: ignore
+    return await _raw_with_schema(query_template, *args, execute=False)  # type: ignore
 
 
 async def execute_raw_with_schema(
     query_template: str,
     *args,
     client: Prisma | None = None,
-    set_public_search_path: bool = False,
 ) -> int:
     """Execute raw SQL command (INSERT/UPDATE/DELETE) with proper schema handling.
 
     Args:
-        query_template: SQL query with {schema_prefix} placeholder
+        query_template: SQL query with {schema_prefix} and/or {schema} placeholders
         *args: Query parameters
         client: Optional Prisma client for transactions
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
 
     Returns:
         Number of affected rows
@@ -215,7 +199,7 @@ async def execute_raw_with_schema(
             client=tx  # Optional transaction client
         )
     """
-    return await _raw_with_schema(query_template, *args, execute=True, client=client, set_public_search_path=set_public_search_path)  # type: ignore
+    return await _raw_with_schema(query_template, *args, execute=True, client=client)  # type: ignore
 
 
 class BaseDbModel(BaseModel):

autogpt_platform/backend/backend/data/understanding.py

@@ -328,6 +328,8 @@ async def clear_business_understanding(user_id: str) -> bool:
 
 def format_understanding_for_prompt(understanding: BusinessUnderstanding) -> str:
     """Format business understanding as text for system prompt injection."""
+    if not understanding:
+        return ""
     sections = []
 
     # User info section

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

@@ -1,9 +1,10 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Create in public schema so vector type is available across all schemas
+-- Creates extension in current schema (determined by search_path)
+-- The extension may already exist in a different schema (e.g., Supabase pre-enables it)
 DO $$
 BEGIN
-    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
+    CREATE EXTENSION IF NOT EXISTS "vector";
 EXCEPTION WHEN OTHERS THEN
     RAISE NOTICE 'vector extension not available or already exists, skipping';
 END $$;
@@ -12,6 +13,7 @@ END $$;
 CREATE TYPE "ContentType" AS ENUM ('STORE_AGENT', 'BLOCK', 'INTEGRATION', 'DOCUMENTATION', 'LIBRARY_AGENT');
 
 -- CreateTable
+-- Note: vector type is unqualified - relies on search_path including the schema where pgvector is installed
 CREATE TABLE "UnifiedContentEmbedding" (
     "id" TEXT NOT NULL,
     "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
@@ -19,7 +21,7 @@ CREATE TABLE "UnifiedContentEmbedding" (
     "contentType" "ContentType" NOT NULL,
     "contentId" TEXT NOT NULL,
     "userId" TEXT,
-    "embedding" public.vector(1536) NOT NULL,
+    "embedding" vector(1536) NOT NULL,
     "searchableText" TEXT NOT NULL,
     "metadata" JSONB NOT NULL DEFAULT '{}',
 
@@ -45,4 +47,4 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
 -- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
 DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
-CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);

autogpt_platform/frontend/CONTRIBUTING.md

@@ -549,48 +549,9 @@ Files:
 Types:
 
 - Prefer `interface` for object shapes
-- Component props should be `interface Props { ... }` (not exported)
-- Only use specific exported names (e.g., `export interface MyComponentProps`) when the interface needs to be used outside the component
-- Keep type definitions inline with the component - do not create separate `types.ts` files unless types are shared across multiple files
+- Component props should be `interface Props { ... }`
 - Use precise types; avoid `any` and unsafe casts
 
-**Props naming examples:**
-
-```tsx
-// ✅ Good - internal props, not exported
-interface Props {
-  title: string;
-  onClose: () => void;
-}
-
-export function Modal({ title, onClose }: Props) {
-  // ...
-}
-
-// ✅ Good - exported when needed externally
-export interface ModalProps {
-  title: string;
-  onClose: () => void;
-}
-
-export function Modal({ title, onClose }: ModalProps) {
-  // ...
-}
-
-// ❌ Bad - unnecessarily specific name for internal use
-interface ModalComponentProps {
-  title: string;
-  onClose: () => void;
-}
-
-// ❌ Bad - separate types.ts file for single component
-// types.ts
-export interface ModalProps { ... }
-
-// Modal.tsx
-import type { ModalProps } from './types';
-```
-
 Parameters:
 
 - If more than one parameter is needed, pass a single `Args` object for clarity

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

@@ -1,45 +0,0 @@
-"use client";
-
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { Text } from "@/components/atoms/Text/Text";
-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 router = useRouter();
-  const hasStartedRef = useRef(false);
-
-  useEffect(function handleLogoutEffect() {
-    if (hasStartedRef.current) return;
-    hasStartedRef.current = true;
-
-    async function runLogout() {
-      await logOut();
-      await wait(LOGOUT_REDIRECT_DELAY_MS);
-      router.replace("/login");
-    }
-
-    void runLogout();
-  }, []);
-
-  return (
-    <div className="flex min-h-screen items-center justify-center px-4">
-      <div className="flex flex-col items-center justify-center gap-4 py-8">
-        <LoadingSpinner size="large" />
-        <Text variant="body" className="text-center">
-          Logging you out...
-        </Text>
-      </div>
-    </div>
-  );
-}

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

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

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

@@ -3,32 +3,32 @@
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
-import type { ReactNode } from "react";
+import { List } from "@phosphor-icons/react";
+import React, { useState } from "react";
 import { ChatContainer } from "./components/ChatContainer/ChatContainer";
 import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
-import { ChatLoader } from "./components/ChatLoader/ChatLoader";
+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?: ReactNode;
-  urlSessionId?: string | null;
-  initialPrompt?: string | null;
+  headerActions?: React.ReactNode;
 }
 
 export function Chat({
   className,
+  headerTitle = "AutoGPT Copilot",
   showHeader = true,
   showSessionInfo = true,
   showNewChatButton = true,
   onNewChat,
   headerActions,
-  urlSessionId,
-  initialPrompt,
 }: ChatProps) {
   const {
     messages,
@@ -38,20 +38,46 @@ export function Chat({
     sessionId,
     createSession,
     clearSession,
-    showLoader,
-  } = useChat({ urlSessionId });
+    loadSession,
+  } = useChat();
 
-  function handleNewChat() {
+  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 bg-[#f8f8f9] p-3">
+        <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 && (
                 <>
@@ -73,17 +99,12 @@ export function Chat({
       )}
 
       {/* Main Content */}
-      <main className="flex min-h-0 w-full flex-1 flex-col overflow-hidden">
-        {/* Loading State - show loader when loading or creating a session (with 300ms delay) */}
-        {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>
+      <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 */}
@@ -96,11 +117,18 @@ export function Chat({
           <ChatContainer
             sessionId={sessionId}
             initialMessages={messages}
-            initialPrompt={initialPrompt}
             className="flex-1"
           />
         )}
       </main>
+
+      {/* Sessions Drawer */}
+      <SessionsDrawer
+        isOpen={isSessionsDrawerOpen}
+        onClose={() => setIsSessionsDrawerOpen(false)}
+        onSelectSession={handleSelectSession}
+        currentSessionId={sessionId}
+      />
     </div>
   );
 }

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

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

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

@@ -1,23 +1,22 @@
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
 import { cn } from "@/lib/utils";
-import { useCallback, useEffect, useRef } from "react";
+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;
-  initialPrompt?: string | null;
 }
 
 export function ChatContainer({
   sessionId,
   initialMessages,
   className,
-  initialPrompt,
 }: ChatContainerProps) {
   const { messages, streamingChunks, isStreaming, sendMessage } =
     useChatContainer({
@@ -25,7 +24,6 @@ export function ChatContainer({
       initialMessages,
     });
   const { capturePageContext } = usePageContext();
-  const hasSentInitialRef = useRef(false);
 
   // Wrap sendMessage to automatically capture page context
   const sendMessageWithContext = useCallback(
@@ -36,28 +34,35 @@ export function ChatContainer({
     [sendMessage, capturePageContext],
   );
 
-  useEffect(
-    function handleInitialPrompt() {
-      if (!initialPrompt) return;
-      if (hasSentInitialRef.current) return;
-      if (!sessionId) return;
-      if (messages.length > 0) return;
-      hasSentInitialRef.current = true;
-      void sendMessageWithContext(initialPrompt);
-    },
-    [initialPrompt, messages.length, sendMessageWithContext, sessionId],
-  );
+  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(
-        "mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col bg-[#f8f8f9]",
-        className,
-      )}
+      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 - Scrollable */}
-      <div className="relative flex min-h-0 flex-1 flex-col overflow-y-auto">
-        <div className="flex min-h-full flex-col justify-end">
+      {/* 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}
@@ -65,16 +70,17 @@ export function ChatContainer({
             onSendMessage={sendMessageWithContext}
             className="flex-1"
           />
-        </div>
+        )}
       </div>
 
-      {/* Input - Fixed at bottom */}
-      <div className="relative pb-4 pt-2">
-        <div className="pointer-events-none absolute top-[-18px] z-10 h-6 w-full bg-gradient-to-b from-transparent to-[#f8f8f9]" />
+      {/* 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="You can search or just ask — e.g. “create a blog post outline”"
+          placeholder={
+            sessionId ? "Type your message..." : "Creating session..."
+          }
         />
       </div>
     </div>

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

@@ -33,23 +33,13 @@ export function handleTextEnded(
   console.log("[Text Ended] Saving streamed text as assistant message");
   const completedText = deps.streamingChunksRef.current.join("");
   if (completedText.trim()) {
-    deps.setMessages((prev) => {
-      const lastMessage = prev[prev.length - 1];
-      console.log("[Text Ended] Previous message:", {
-        type: lastMessage?.type,
-        toolName:
-          lastMessage?.type === "tool_call" ? lastMessage.toolName : undefined,
-        content: completedText.substring(0, 200),
-      });
-
-      const assistantMessage: ChatMessageData = {
-        type: "message",
-        role: "assistant",
-        content: completedText,
-        timestamp: new Date(),
-      };
-      return [...prev, assistantMessage];
-    });
+    const assistantMessage: ChatMessageData = {
+      type: "message",
+      role: "assistant",
+      content: completedText,
+      timestamp: new Date(),
+    };
+    deps.setMessages((prev) => [...prev, assistantMessage]);
   }
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];

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

@@ -3,7 +3,7 @@ import { cn } from "@/lib/utils";
 import { ArrowUpIcon } from "@phosphor-icons/react";
 import { useChatInput } from "./useChatInput";
 
-export interface Props {
+export interface ChatInputProps {
   onSend: (message: string) => void;
   disabled?: boolean;
   placeholder?: string;
@@ -15,7 +15,7 @@ export function ChatInput({
   disabled = false,
   placeholder = "Type your message...",
   className,
-}: Props) {
+}: ChatInputProps) {
   const inputId = "chat-input";
   const { value, setValue, handleKeyDown, handleSend } = useChatInput({
     onSend,

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

@@ -1,5 +1,10 @@
 "use client";
 
+import { useGetV2GetUserProfile } from "@/app/api/__generated__/endpoints/store/store";
+import Avatar, {
+  AvatarFallback,
+  AvatarImage,
+} from "@/components/atoms/Avatar/Avatar";
 import { Button } from "@/components/atoms/Button/Button";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { cn } from "@/lib/utils";
@@ -8,19 +13,20 @@ import {
   CheckCircleIcon,
   CheckIcon,
   CopyIcon,
+  RobotIcon,
 } from "@phosphor-icons/react";
 import { useRouter } from "next/navigation";
 import { useCallback, useState } from "react";
+import { getToolActionPhrase } from "../../helpers";
 import { AgentCarouselMessage } from "../AgentCarouselMessage/AgentCarouselMessage";
-import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
 import { AuthPromptWidget } from "../AuthPromptWidget/AuthPromptWidget";
 import { ChatCredentialsSetup } from "../ChatCredentialsSetup/ChatCredentialsSetup";
 import { ExecutionStartedMessage } from "../ExecutionStartedMessage/ExecutionStartedMessage";
 import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
 import { NoResultsMessage } from "../NoResultsMessage/NoResultsMessage";
 import { ToolCallMessage } from "../ToolCallMessage/ToolCallMessage";
 import { ToolResponseMessage } from "../ToolResponseMessage/ToolResponseMessage";
-import { UserChatBubble } from "../UserChatBubble/UserChatBubble";
 import { useChatMessage, type ChatMessageData } from "./useChatMessage";
 export interface ChatMessageProps {
   message: ChatMessageData;
@@ -29,7 +35,6 @@ export interface ChatMessageProps {
   onDismissCredentials?: () => void;
   onSendMessage?: (content: string, isUserMessage?: boolean) => void;
   agentOutput?: ChatMessageData;
-  isFinalMessage?: boolean;
 }
 
 export function ChatMessage({
@@ -38,7 +43,6 @@ export function ChatMessage({
   onDismissCredentials,
   onSendMessage,
   agentOutput,
-  isFinalMessage = true,
 }: ChatMessageProps) {
   const { user } = useSupabase();
   const router = useRouter();
@@ -51,6 +55,14 @@ export function ChatMessage({
     isCredentialsNeeded,
   } = useChatMessage(message);
 
+  const { data: profile } = useGetV2GetUserProfile({
+    query: {
+      select: (res) => (res.status === 200 ? res.data : null),
+      enabled: isUser && !!user,
+      queryKey: ["/api/store/profile", user?.id],
+    },
+  });
+
   const handleAllCredentialsComplete = useCallback(
     function handleAllCredentialsComplete() {
       // Send a user message that explicitly asks to retry the setup
@@ -159,11 +171,7 @@ export function ChatMessage({
   if (isToolCall && message.type === "tool_call") {
     return (
       <div className={cn("px-4 py-2", className)}>
-        <ToolCallMessage
-          toolId={message.toolId}
-          toolName={message.toolName}
-          arguments={message.arguments}
-        />
+        <ToolCallMessage toolName={message.toolName} />
       </div>
     );
   }
@@ -210,11 +218,27 @@ export function ChatMessage({
 
   // Render tool response messages (but skip agent_output if it's being rendered inside assistant message)
   if (isToolResponse && message.type === "tool_response") {
+    // Check if this is an agent_output that should be rendered inside assistant message
+    if (message.result) {
+      let parsedResult: Record<string, unknown> | null = null;
+      try {
+        parsedResult =
+          typeof message.result === "string"
+            ? JSON.parse(message.result)
+            : (message.result as Record<string, unknown>);
+      } catch {
+        parsedResult = null;
+      }
+      if (parsedResult?.type === "agent_output") {
+        // Skip rendering - this will be rendered inside the assistant message
+        return null;
+      }
+    }
+
     return (
       <div className={cn("px-4 py-2", className)}>
         <ToolResponseMessage
-          toolId={message.toolId}
-          toolName={message.toolName}
+          toolName={getToolActionPhrase(message.toolName)}
           result={message.result}
         />
       </div>
@@ -232,33 +256,40 @@ export function ChatMessage({
         )}
       >
         <div className="flex w-full max-w-3xl gap-3">
+          {!isUser && (
+            <div className="flex-shrink-0">
+              <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
+                <RobotIcon className="h-4 w-4 text-indigo-50" />
+              </div>
+            </div>
+          )}
+
           <div
             className={cn(
               "flex min-w-0 flex-1 flex-col",
               isUser && "items-end",
             )}
           >
-            {isUser ? (
-              <UserChatBubble>
-                <MarkdownContent content={message.content} />
-              </UserChatBubble>
-            ) : (
-              <AIChatBubble>
-                <MarkdownContent content={message.content} />
-                {agentOutput && agentOutput.type === "tool_response" && (
+            <MessageBubble variant={isUser ? "user" : "assistant"}>
+              <MarkdownContent content={message.content} />
+              {agentOutput &&
+                agentOutput.type === "tool_response" &&
+                !isUser && (
                   <div className="mt-4">
                     <ToolResponseMessage
-                      toolId={agentOutput.toolId}
-                      toolName={agentOutput.toolName || "Agent Output"}
+                      toolName={
+                        agentOutput.toolName
+                          ? getToolActionPhrase(agentOutput.toolName)
+                          : "Agent Output"
+                      }
                       result={agentOutput.result}
                     />
                   </div>
                 )}
-              </AIChatBubble>
-            )}
+            </MessageBubble>
             <div
               className={cn(
-                "flex gap-1",
+                "mt-1 flex gap-1",
                 isUser ? "justify-end" : "justify-start",
               )}
             >
@@ -272,22 +303,34 @@ export function ChatMessage({
                   <ArrowClockwise className="size-3 text-neutral-500" />
                 </Button>
               )}
-              {(isUser || isFinalMessage) && (
-                <Button
-                  variant="ghost"
-                  size="icon"
-                  onClick={handleCopy}
-                  aria-label="Copy message"
-                >
-                  {copied ? (
-                    <CheckIcon className="size-3 text-green-600" />
-                  ) : (
-                    <CopyIcon className="size-3 text-neutral-500" />
-                  )}
-                </Button>
-              )}
+              <Button
+                variant="ghost"
+                size="icon"
+                onClick={handleCopy}
+                aria-label="Copy message"
+              >
+                {copied ? (
+                  <CheckIcon className="size-3 text-green-600" />
+                ) : (
+                  <CopyIcon className="size-3 text-neutral-500" />
+                )}
+              </Button>
             </div>
           </div>
+
+          {isUser && (
+            <div className="flex-shrink-0">
+              <Avatar className="h-7 w-7">
+                <AvatarImage
+                  src={profile?.avatar_url ?? ""}
+                  alt={profile?.username ?? "User"}
+                />
+                <AvatarFallback className="rounded-lg bg-neutral-200 text-neutral-600">
+                  {profile?.username?.charAt(0)?.toUpperCase() || "U"}
+                </AvatarFallback>
+              </Avatar>
+            </div>
+          )}
         </div>
       </div>
     );

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

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

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

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

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

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

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

@@ -1,6 +1,7 @@
 import { cn } from "@/lib/utils";
+import { RobotIcon } from "@phosphor-icons/react";
 import { useEffect, useRef, useState } from "react";
-import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
 
 export interface ThinkingMessageProps {
   className?: string;
@@ -33,8 +34,14 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
       )}
     >
       <div className="flex w-full max-w-3xl gap-3">
+        <div className="flex-shrink-0">
+          <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
+            <RobotIcon className="h-4 w-4 text-indigo-50" />
+          </div>
+        </div>
+
         <div className="flex min-w-0 flex-1 flex-col">
-          <AIChatBubble>
+          <MessageBubble variant="assistant">
             <div className="transition-all duration-500 ease-in-out">
               {showSlowLoader ? (
                 <div className="flex flex-col items-center gap-3 py-2">
@@ -55,7 +62,7 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
                 </span>
               )}
             </div>
-          </AIChatBubble>
+          </MessageBubble>
         </div>
       </div>
     </div>

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

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

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

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

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

@@ -1,20 +1,17 @@
 "use client";
 
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { useEffect, useRef, useState } from "react";
+import { useEffect, useRef } from "react";
 import { toast } from "sonner";
 import { useChatSession } from "./useChatSession";
 import { useChatStream } from "./useChatStream";
 
-interface UseChatArgs {
-  urlSessionId?: string | null;
-}
-
-export function useChat({ urlSessionId }: UseChatArgs = {}) {
+export function useChat() {
+  const hasCreatedSessionRef = useRef(false);
   const hasClaimedSessionRef = useRef(false);
   const { user } = useSupabase();
   const { sendMessage: sendStreamMessage } = useChatStream();
-  const [showLoader, setShowLoader] = useState(false);
+
   const {
     session,
     sessionId: sessionIdFromHook,
@@ -27,10 +24,22 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     clearSession: clearSessionBase,
     loadSession,
   } = useChatSession({
-    urlSessionId,
+    urlSessionId: null,
     autoCreate: false,
   });
 
+  useEffect(
+    function autoCreateSession() {
+      if (!hasCreatedSessionRef.current && !isCreating && !sessionIdFromHook) {
+        hasCreatedSessionRef.current = true;
+        createSession().catch((_err) => {
+          hasCreatedSessionRef.current = false;
+        });
+      }
+    },
+    [isCreating, sessionIdFromHook, createSession],
+  );
+
   useEffect(
     function autoClaimSession() {
       if (
@@ -66,17 +75,6 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     ],
   );
 
-  useEffect(() => {
-    if (isLoading || isCreating) {
-      const timer = setTimeout(() => {
-        setShowLoader(true);
-      }, 300);
-      return () => clearTimeout(timer);
-    } else {
-      setShowLoader(false);
-    }
-  }, [isLoading, isCreating]);
-
   useEffect(function monitorNetworkStatus() {
     function handleOnline() {
       toast.success("Connection restored", {
@@ -101,6 +99,7 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
 
   function clearSession() {
     clearSessionBase();
+    hasCreatedSessionRef.current = false;
     hasClaimedSessionRef.current = false;
   }
 
@@ -114,6 +113,5 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     clearSession,
     loadSession,
     sessionId: sessionIdFromHook,
-    showLoader,
   };
 }

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

@@ -0,0 +1,27 @@
+"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/chat/page.tsx

@@ -1,24 +0,0 @@
-"use client";
-
-import { Chat } from "@/components/contextual/Chat/Chat";
-import { useCopilotChatPage } from "./useCopilotChatPage";
-
-export default function CopilotChatPage() {
-  const { isFlagReady, isChatEnabled, sessionId, prompt } =
-    useCopilotChatPage();
-
-  if (!isFlagReady || isChatEnabled === false) {
-    return null;
-  }
-
-  return (
-    <div className="flex h-full flex-col">
-      <Chat
-        className="flex-1"
-        urlSessionId={sessionId}
-        initialPrompt={prompt}
-        showNewChatButton={false}
-      />
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/chat/useCopilotChatPage.ts

@@ -1,44 +0,0 @@
-"use client";
-
-import { getHomepageRoute } from "@/lib/constants";
-import {
-  Flag,
-  type FlagValues,
-  useGetFlag,
-} from "@/services/feature-flags/use-get-flag";
-import { useFlags } from "launchdarkly-react-client-sdk";
-import { useRouter, useSearchParams } from "next/navigation";
-import { useEffect } from "react";
-
-export function useCopilotChatPage() {
-  const router = useRouter();
-  const searchParams = useSearchParams();
-  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 sessionId = searchParams.get("sessionId");
-  const prompt = searchParams.get("prompt");
-
-  useEffect(
-    function guardAccess() {
-      if (!isFlagReady) return;
-      if (isChatEnabled === false) {
-        router.replace(homepageRoute);
-      }
-    },
-    [homepageRoute, isChatEnabled, isFlagReady, router],
-  );
-
-  return {
-    isFlagReady,
-    isChatEnabled,
-    sessionId,
-    prompt,
-  };
-}

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

@@ -1,75 +0,0 @@
-"use client";
-
-import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
-import type { ReactNode } from "react";
-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,
-    sessions,
-    currentSessionId,
-    handleSelectSession,
-    handleOpenDrawer,
-    handleCloseDrawer,
-    handleDrawerOpenChange,
-    handleNewChat,
-    hasNextPage,
-    isFetchingNextPage,
-    fetchNextPage,
-    isReadyToShowContent,
-  } = useCopilotShell();
-
-  return (
-    <div
-      className="flex overflow-hidden bg-zinc-50"
-      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={handleNewChat}
-        />
-      )}
-
-      <div className="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={handleNewChat}
-          onClose={handleCloseDrawer}
-          onOpenChange={handleDrawerOpenChange}
-        />
-      )}
-    </div>
-  );
-}

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

@@ -1,66 +0,0 @@
-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;
-}
-
-export function DesktopSidebar({
-  sessions,
-  currentSessionId,
-  isLoading,
-  hasNextPage,
-  isFetchingNextPage,
-  onSelectSession,
-  onFetchNextPage,
-  onNewChat,
-}: Props) {
-  return (
-    <aside className="flex h-full w-80 flex-col border-r border-zinc-100 bg-white">
-      <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>
-      <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={<Plus width="1rem" height="1rem" />}
-        >
-          New Chat
-        </Button>
-      </div>
-    </aside>
-  );
-}

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

@@ -1,15 +0,0 @@
-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

@@ -1,87 +0,0 @@
-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 { Plus, 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;
-}
-
-export function MobileDrawer({
-  isOpen,
-  sessions,
-  currentSessionId,
-  isLoading,
-  hasNextPage,
-  isFetchingNextPage,
-  onSelectSession,
-  onFetchNextPage,
-  onNewChat,
-  onClose,
-  onOpenChange,
-}: 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-white">
-          <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 tasks
-              </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>
-          <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={<Plus 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

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

@@ -1,21 +0,0 @@
-import { Button } from "@/components/atoms/Button/Button";
-import { List } from "@phosphor-icons/react";
-
-interface Props {
-  onOpenDrawer: () => void;
-}
-
-export function MobileHeader({ onOpenDrawer }: Props) {
-  return (
-    <header className="flex items-center justify-between px-4 py-3">
-      <Button
-        variant="icon"
-        size="icon"
-        aria-label="Open sessions"
-        onClick={onOpenDrawer}
-      >
-        <List width="1.25rem" height="1.25rem" />
-      </Button>
-    </header>
-  );
-}

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

@@ -1,80 +0,0 @@
-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 items-center justify-center py-8">
-        <Text variant="body" className="text-zinc-500">
-          No sessions found
-        </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

@@ -1,89 +0,0 @@
-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]);
-      }
-    }
-  }, [data, offset]);
-
-  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

@@ -1,167 +0,0 @@
-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 | 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,
-  storedSessionId: string | null,
-): string | null {
-  const paramSessionId = searchParams.get("sessionId");
-  if (paramSessionId) return paramSessionId;
-  if (storedSessionId) return storedSessionId;
-  return null;
-}
-
-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 | undefined,
-  hasAutoSelectedSession: boolean,
-): boolean {
-  if (!areAllSessionsLoaded) return false;
-
-  if (paramSessionId) {
-    const sessionFound = accumulatedSessions.some(
-      (s) => s.id === paramSessionId,
-    );
-    return (
-      sessionFound ||
-      (!isCurrentSessionLoading && currentSessionData !== undefined)
-    );
-  }
-
-  return hasAutoSelectedSession;
-}

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

@@ -1,202 +0,0 @@
-"use client";
-
-import {
-  postV2CreateSession,
-  useGetV2GetSession,
-} from "@/app/api/__generated__/endpoints/chat/chat";
-import { okData } from "@/app/api/helpers";
-import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
-import { Key, storage } from "@/services/storage/local-storage";
-import { useRouter, useSearchParams } from "next/navigation";
-import { useCallback, useEffect, useMemo, useRef, useState } from "react";
-import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
-import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
-import {
-  checkReadyToShowContent,
-  filterVisibleSessions,
-  getCurrentSessionId,
-  mergeCurrentSessionIntoList,
-  shouldAutoSelectSession,
-} from "./helpers";
-
-export function useCopilotShell() {
-  const router = useRouter();
-  const searchParams = useSearchParams();
-  const breakpoint = useBreakpoint();
-  const isMobile =
-    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
-
-  const {
-    isDrawerOpen,
-    handleOpenDrawer,
-    handleCloseDrawer,
-    handleDrawerOpenChange,
-  } = useMobileDrawer();
-
-  const paginationEnabled = !isMobile || isDrawerOpen;
-
-  const {
-    sessions: accumulatedSessions,
-    isLoading: isSessionsLoading,
-    isFetching: isSessionsFetching,
-    hasNextPage,
-    areAllSessionsLoaded,
-    totalCount,
-    fetchNextPage,
-    reset: resetPagination,
-  } = useSessionsPagination({
-    enabled: paginationEnabled,
-  });
-
-  const storedSessionId = storage.get(Key.CHAT_SESSION_ID) ?? null;
-  const currentSessionId = useMemo(
-    () => getCurrentSessionId(searchParams, storedSessionId),
-    [searchParams, storedSessionId],
-  );
-
-  const { data: currentSessionData, isLoading: isCurrentSessionLoading } =
-    useGetV2GetSession(currentSessionId || "", {
-      query: {
-        enabled: !!currentSessionId && paginationEnabled,
-        select: okData,
-      },
-    });
-
-  const [hasAutoSelectedSession, setHasAutoSelectedSession] = useState(false);
-  const hasCreatedSessionRef = useRef(false);
-  const paramSessionId = searchParams.get("sessionId");
-
-  const createSessionAndNavigate = useCallback(
-    function createSessionAndNavigate() {
-      postV2CreateSession({ body: JSON.stringify({}) })
-        .then((response) => {
-          if (response.status === 200 && response.data) {
-            router.push(`/copilot/chat?sessionId=${response.data.id}`);
-            setHasAutoSelectedSession(true);
-          }
-        })
-        .catch(() => {
-          hasCreatedSessionRef.current = false;
-        });
-    },
-    [router],
-  );
-
-  useEffect(() => {
-    if (!areAllSessionsLoaded || hasAutoSelectedSession) return;
-
-    const visibleSessions = filterVisibleSessions(accumulatedSessions);
-    const autoSelect = shouldAutoSelectSession(
-      areAllSessionsLoaded,
-      hasAutoSelectedSession,
-      paramSessionId,
-      visibleSessions,
-      accumulatedSessions,
-      isSessionsLoading,
-      totalCount,
-    );
-
-    if (paramSessionId) {
-      setHasAutoSelectedSession(true);
-      return;
-    }
-
-    if (autoSelect.shouldSelect && autoSelect.sessionIdToSelect) {
-      setHasAutoSelectedSession(true);
-      router.push(`/copilot/chat?sessionId=${autoSelect.sessionIdToSelect}`);
-    } else if (autoSelect.shouldCreate && !hasCreatedSessionRef.current) {
-      hasCreatedSessionRef.current = true;
-      createSessionAndNavigate();
-    } else if (totalCount === 0) {
-      setHasAutoSelectedSession(true);
-    }
-  }, [
-    areAllSessionsLoaded,
-    accumulatedSessions,
-    paramSessionId,
-    hasAutoSelectedSession,
-    router,
-    isSessionsLoading,
-    totalCount,
-    createSessionAndNavigate,
-  ]);
-
-  useEffect(() => {
-    if (paramSessionId) {
-      setHasAutoSelectedSession(true);
-    }
-  }, [paramSessionId]);
-
-  function resetAutoSelect() {
-    setHasAutoSelectedSession(false);
-    hasCreatedSessionRef.current = false;
-  }
-
-  // Reset pagination and auto-selection when query becomes disabled
-  useEffect(() => {
-    if (!paginationEnabled) {
-      resetPagination();
-      resetAutoSelect();
-    }
-  }, [paginationEnabled, resetPagination]);
-
-  const sessions = useMemo(
-    function getSessions() {
-      return mergeCurrentSessionIntoList(
-        accumulatedSessions,
-        currentSessionId,
-        currentSessionData,
-      );
-    },
-    [accumulatedSessions, currentSessionId, currentSessionData],
-  );
-
-  function handleSelectSession(sessionId: string) {
-    router.push(`/copilot/chat?sessionId=${sessionId}`);
-    if (isMobile) handleCloseDrawer();
-  }
-
-  function handleNewChat() {
-    storage.clean(Key.CHAT_SESSION_ID);
-    resetAutoSelect();
-    createSessionAndNavigate();
-    if (isMobile) handleCloseDrawer();
-  }
-
-  const isReadyToShowContent = useMemo(
-    () =>
-      checkReadyToShowContent(
-        areAllSessionsLoaded,
-        paramSessionId,
-        accumulatedSessions,
-        isCurrentSessionLoading,
-        currentSessionData,
-        hasAutoSelectedSession,
-      ),
-    [
-      areAllSessionsLoaded,
-      paramSessionId,
-      accumulatedSessions,
-      isCurrentSessionLoading,
-      currentSessionData,
-      hasAutoSelectedSession,
-    ],
-  );
-
-  return {
-    isMobile,
-    isDrawerOpen,
-    isLoading: isSessionsLoading || !areAllSessionsLoaded,
-    sessions,
-    currentSessionId,
-    handleSelectSession,
-    handleOpenDrawer,
-    handleCloseDrawer,
-    handleDrawerOpenChange,
-    handleNewChat,
-    hasNextPage,
-    isFetchingNextPage: isSessionsFetching,
-    fetchNextPage,
-    isReadyToShowContent,
-  };
-}

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

@@ -1,33 +0,0 @@
-import type { User } from "@supabase/supabase-js";
-
-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

@@ -1,6 +0,0 @@
-import type { ReactNode } from "react";
-import { CopilotShell } from "./components/CopilotShell/CopilotShell";
-
-export default function CopilotLayout({ children }: { children: ReactNode }) {
-  return <CopilotShell>{children}</CopilotShell>;
-}

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

@@ -1,102 +0,0 @@
-"use client";
-
-import { Skeleton } from "@/components/__legacy__/ui/skeleton";
-import { Button } from "@/components/atoms/Button/Button";
-import { Input } from "@/components/atoms/Input/Input";
-import { Text } from "@/components/atoms/Text/Text";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { ArrowUpIcon } from "@phosphor-icons/react";
-import { useCopilotHome } from "./useCopilotHome";
-
-export default function CopilotPage() {
-  const {
-    greetingName,
-    value,
-    quickActions,
-    isFlagReady,
-    isChatEnabled,
-    handleChange,
-    handleSubmit,
-    handleKeyDown,
-    handleQuickAction,
-  } = useCopilotHome();
-  const { isUserLoading } = useSupabase();
-
-  if (!isFlagReady || isChatEnabled === false) {
-    return null;
-  }
-
-  const isLoading = isUserLoading;
-
-  return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto px-6 py-10">
-      <div className="w-full max-w-2xl text-center">
-        {isLoading ? (
-          <>
-            <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 max-w-2xl 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>
-          </>
-        ) : (
-          <>
-            <Text variant="h2" className="mb-3 text-zinc-700">
-              Hey, <span className="text-violet-600">{greetingName}</span>
-            </Text>
-            <Text variant="h3" className="mb-8 text-zinc-900">
-              What do you want to automate?
-            </Text>
-
-            <form onSubmit={handleSubmit} className="mb-8">
-              <div className="relative">
-                <Input
-                  id="copilot-prompt"
-                  label="Copilot prompt"
-                  hideLabel
-                  type="textarea"
-                  value={value}
-                  onChange={handleChange}
-                  onKeyDown={handleKeyDown}
-                  rows={1}
-                  placeholder='You can search or just ask - e.g. "create a blog post outline"'
-                  wrapperClassName="mb-0"
-                  className="min-h-[3.5rem] pr-12 text-base"
-                />
-                <Button
-                  type="submit"
-                  variant="icon"
-                  size="icon"
-                  aria-label="Submit prompt"
-                  className="absolute right-2 top-1/2 -translate-y-1/2 border-zinc-800 bg-zinc-800 text-white hover:border-zinc-900 hover:bg-zinc-900"
-                  disabled={!value.trim()}
-                >
-                  <ArrowUpIcon className="h-4 w-4" weight="bold" />
-                </Button>
-              </div>
-            </form>
-
-            <div className="flex flex-wrap items-center justify-center gap-3">
-              {quickActions.map((action) => (
-                <Button
-                  key={action}
-                  variant="outline"
-                  size="small"
-                  onClick={() => handleQuickAction(action)}
-                  className="border-zinc-300 text-zinc-700 hover:border-zinc-400 hover:bg-zinc-50"
-                >
-                  {action}
-                </Button>
-              ))}
-            </div>
-          </>
-        )}
-      </div>
-    </div>
-  );
-}

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

@@ -1,90 +0,0 @@
-"use client";
-
-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 { useFlags } from "launchdarkly-react-client-sdk";
-import { useRouter } from "next/navigation";
-import { useEffect, useMemo, useState } from "react";
-import {
-  buildCopilotChatUrl,
-  getGreetingName,
-  getQuickActions,
-} from "./helpers";
-
-export function useCopilotHome() {
-  const router = useRouter();
-  const { user } = useSupabase();
-  const [value, setValue] = useState("");
-  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 greetingName = useMemo(
-    function getName() {
-      return getGreetingName(user);
-    },
-    [user],
-  );
-
-  const quickActions = useMemo(function getActions() {
-    return getQuickActions();
-  }, []);
-
-  useEffect(
-    function ensureAccess() {
-      if (!isFlagReady) return;
-      if (isChatEnabled === false) {
-        router.replace(homepageRoute);
-      }
-    },
-    [homepageRoute, isChatEnabled, isFlagReady, router],
-  );
-
-  function handleChange(
-    event: React.ChangeEvent<HTMLInputElement | HTMLTextAreaElement>,
-  ) {
-    setValue(event.target.value);
-  }
-
-  function handleSubmit(event: React.FormEvent<HTMLFormElement>) {
-    event.preventDefault();
-    if (!value.trim()) return;
-    router.push(buildCopilotChatUrl(value));
-  }
-
-  function handleKeyDown(
-    event: React.KeyboardEvent<HTMLInputElement | HTMLTextAreaElement>,
-  ) {
-    if (event.key !== "Enter") return;
-    if (event.shiftKey) return;
-    event.preventDefault();
-    if (!value.trim()) return;
-    router.push(buildCopilotChatUrl(value));
-  }
-
-  function handleQuickAction(action: string) {
-    router.push(buildCopilotChatUrl(action));
-  }
-
-  return {
-    greetingName,
-    value,
-    quickActions,
-    isFlagReady,
-    isChatEnabled,
-    handleChange,
-    handleSubmit,
-    handleKeyDown,
-    handleQuickAction,
-  };
-}

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

@@ -1,8 +1,6 @@
 "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";
@@ -11,8 +9,6 @@ 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
@@ -29,8 +25,8 @@ function ErrorPageContent() {
         window.location.reload();
       }, 2000);
     } else {
-      // For server/network errors, go to home
-      window.location.href = homepageRoute;
+      // For server/network errors, go to marketplace
+      window.location.href = "/marketplace";
     }
   }
 

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

@@ -180,7 +180,7 @@ export function RunAgentModal({
 
               {/* Content */}
               {hasAnySetupFields ? (
-                <div className="mt-4 pb-10">
+                <div className="mt-10 pb-32">
                   <RunAgentModalContextProvider
                     value={{
                       agent,

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="mb-2 mt-4 !text-zinc-700"
+            className="mt-4 !text-zinc-700"
           >
             {agent.description}
           </ShowMoreText>
@@ -40,8 +40,6 @@ 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(
@@ -52,7 +50,7 @@ export function ModalHeader({ agent }: ModalHeaderProps) {
         ) : null}
 
         {agent.instructions ? (
-          <div className="mt-4 flex flex-col gap-4 rounded-medium border border-purple-100 bg-[#f1ebfe80] p-4">
+          <div className="flex flex-col gap-4 rounded-medium border border-purple-100 bg-[#F1EBFE/5] p-4">
             <Text variant="lead-semibold" className="text-purple-600">
               Instructions
             </Text>

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

@@ -8,8 +8,6 @@ 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 {
@@ -25,14 +23,10 @@ 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

@@ -1,8 +1,6 @@
 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";
@@ -22,17 +20,15 @@ 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 || homepageRoute);
+      router.push(nextUrl || "/marketplace");
     }
-  }, [homepageRoute, isLoggedIn, isLoggingIn, nextUrl, router]);
+  }, [isLoggedIn, isLoggingIn, nextUrl, router]);
 
   const form = useForm<z.infer<typeof loginFormSchema>>({
     resolver: zodResolver(loginFormSchema),
@@ -102,7 +98,7 @@ export function useLoginPage() {
       } else if (result.onboarding) {
         router.replace("/onboarding");
       } else {
-        router.replace(homepageRoute);
+        router.replace("/marketplace");
       }
     } catch (error) {
       toast({

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

@@ -3,14 +3,12 @@
 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,
@@ -20,7 +18,7 @@ export default function UserProfilePage() {
     refetch,
   } = useGetV2GetUserProfile<ProfileDetails | null>({
     query: {
-      enabled: !!user && !logoutInProgress,
+      enabled: !!user,
       select: (res) => {
         if (res.status === 200) {
           return {

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

@@ -1,6 +1,5 @@
 "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";
@@ -59,7 +58,7 @@ export async function signup(
     }
 
     const isOnboardingEnabled = await shouldShowOnboarding();
-    const next = isOnboardingEnabled ? "/onboarding" : getHomepageRoute();
+    const next = isOnboardingEnabled ? "/onboarding" : "/";
 
     return { success: true, next };
   } catch (err) {

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

@@ -1,8 +1,6 @@
 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";
@@ -22,17 +20,15 @@ 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 || homepageRoute);
+      router.push(nextUrl || "/marketplace");
     }
-  }, [homepageRoute, isLoggedIn, isSigningUp, nextUrl, router]);
+  }, [isLoggedIn, isSigningUp, nextUrl, router]);
 
   const form = useForm<z.infer<typeof signupFormSchema>>({
     resolver: zodResolver(signupFormSchema),
@@ -133,7 +129,7 @@ export function useSignupPage() {
       }
 
       // Prefer the URL's next parameter, then result.next (for onboarding), then default
-      const redirectTo = nextUrl || result.next || homepageRoute;
+      const redirectTo = nextUrl || result.next || "/";
       router.replace(redirectTo);
     } catch (error) {
       setIsLoading(false);

autogpt_platform/frontend/src/app/page.tsx

@@ -1,33 +1,5 @@
-"use client";
-
-import { getHomepageRoute } from "@/lib/constants";
-import {
-  Flag,
-  type FlagValues,
-  useGetFlag,
-} from "@/services/feature-flags/use-get-flag";
-import { useFlags } from "launchdarkly-react-client-sdk";
-import { useRouter } from "next/navigation";
-import { useEffect } from "react";
+import { redirect } from "next/navigation";
 
 export default function Page() {
-  const isChatEnabled = useGetFlag(Flag.CHAT);
-  const flags = useFlags<FlagValues>();
-  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 || flags[Flag.CHAT] !== undefined;
-
-  useEffect(
-    function redirectToHomepage() {
-      if (!isFlagReady) return;
-      router.replace(homepageRoute);
-    },
-    [homepageRoute, isFlagReady, router],
-  );
-
-  return null;
+  redirect("/marketplace");
 }

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

@@ -1,15 +0,0 @@
-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-sm leading-relaxed", className)}>
-      {children}
-    </div>
-  );
-}

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatLoader/ChatLoader.module.css

@@ -1,13 +0,0 @@
-.loader {
-  width: 20px;
-  aspect-ratio: 1;
-  border-radius: 50%;
-  background: #000;
-  box-shadow: 0 0 0 0 #0004;
-  animation: l1 1s infinite;
-}
-@keyframes l1 {
-  100% {
-    box-shadow: 0 0 0 30px #0000;
-  }
-}

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

@@ -1,5 +0,0 @@
-import styles from "./ChatLoader.module.css";
-
-export function ChatLoader() {
-  return <div className={styles.loader} />;
-}

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

@@ -1,105 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import type { ChatMessageData } from "../ChatMessage/useChatMessage";
-import { StreamingMessage } from "../StreamingMessage/StreamingMessage";
-import { ThinkingMessage } from "../ThinkingMessage/ThinkingMessage";
-import { LastToolResponse } from "./components/LastToolResponse/LastToolResponse";
-import { MessageItem } from "./components/MessageItem/MessageItem";
-import { findLastMessageIndex, shouldSkipAgentOutput } from "./helpers";
-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 overflow-x-hidden",
-        "scrollbar-thin scrollbar-track-transparent scrollbar-thumb-zinc-300",
-        className,
-      )}
-    >
-      <div className="mx-auto flex min-w-0 flex-col hyphens-auto break-words py-4">
-        {/* Render all persisted messages */}
-        {(() => {
-          const lastAssistantMessageIndex = findLastMessageIndex(
-            messages,
-            (msg) => msg.type === "message" && msg.role === "assistant",
-          );
-
-          const lastToolResponseIndex = findLastMessageIndex(
-            messages,
-            (msg) => msg.type === "tool_response",
-          );
-
-          return messages.map((message, index) => {
-            // Skip agent_output tool_responses that should be rendered inside assistant messages
-            if (shouldSkipAgentOutput(message, messages[index - 1])) {
-              return null;
-            }
-
-            // Render last tool_response as AIChatBubble
-            if (
-              message.type === "tool_response" &&
-              index === lastToolResponseIndex
-            ) {
-              return (
-                <LastToolResponse
-                  key={index}
-                  message={message}
-                  prevMessage={messages[index - 1]}
-                />
-              );
-            }
-
-            return (
-              <MessageItem
-                key={index}
-                message={message}
-                messages={messages}
-                index={index}
-                lastAssistantMessageIndex={lastAssistantMessageIndex}
-                onSendMessage={onSendMessage}
-              />
-            );
-          });
-        })()}
-
-        {/* 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/components/contextual/Chat/components/MessageList/components/LastToolResponse/LastToolResponse.tsx

@@ -1,29 +0,0 @@
-import { AIChatBubble } from "../../../AIChatBubble/AIChatBubble";
-import type { ChatMessageData } from "../../../ChatMessage/useChatMessage";
-import { MarkdownContent } from "../../../MarkdownContent/MarkdownContent";
-import { formatToolResultValue, shouldSkipAgentOutput } from "../../helpers";
-
-export interface LastToolResponseProps {
-  message: ChatMessageData;
-  prevMessage: ChatMessageData | undefined;
-}
-
-export function LastToolResponse({
-  message,
-  prevMessage,
-}: LastToolResponseProps) {
-  if (message.type !== "tool_response") return null;
-
-  // Skip if this is an agent_output that should be rendered inside assistant message
-  if (shouldSkipAgentOutput(message, prevMessage)) return null;
-
-  const resultValue = formatToolResultValue(message.result);
-
-  return (
-    <div className="min-w-0 overflow-x-hidden hyphens-auto break-words px-4 py-2">
-      <AIChatBubble>
-        <MarkdownContent content={resultValue} />
-      </AIChatBubble>
-    </div>
-  );
-}

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

@@ -1,35 +0,0 @@
-import { ChatMessage } from "../../../ChatMessage/ChatMessage";
-import type { ChatMessageData } from "../../../ChatMessage/useChatMessage";
-import { useMessageItem } from "./useMessageItem";
-
-export interface MessageItemProps {
-  message: ChatMessageData;
-  messages: ChatMessageData[];
-  index: number;
-  lastAssistantMessageIndex: number;
-  onSendMessage?: (content: string) => void;
-}
-
-export function MessageItem({
-  message,
-  messages,
-  index,
-  lastAssistantMessageIndex,
-  onSendMessage,
-}: MessageItemProps) {
-  const { messageToRender, agentOutput, isFinalMessage } = useMessageItem({
-    message,
-    messages,
-    index,
-    lastAssistantMessageIndex,
-  });
-
-  return (
-    <ChatMessage
-      message={messageToRender}
-      onSendMessage={onSendMessage}
-      agentOutput={agentOutput}
-      isFinalMessage={isFinalMessage}
-    />
-  );
-}

autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/components/MessageItem/useMessageItem.ts

@@ -1,83 +0,0 @@
-import type { ChatMessageData } from "../../../ChatMessage/useChatMessage";
-import { isAgentOutputResult, isToolOutputPattern } from "../../helpers";
-
-export interface UseMessageItemArgs {
-  message: ChatMessageData;
-  messages: ChatMessageData[];
-  index: number;
-  lastAssistantMessageIndex: number;
-}
-
-export function useMessageItem({
-  message,
-  messages,
-  index,
-  lastAssistantMessageIndex,
-}: UseMessageItemArgs) {
-  let agentOutput: ChatMessageData | undefined;
-  let messageToRender: ChatMessageData = message;
-
-  // Check if assistant message follows a tool_call and looks like a tool output
-  if (message.type === "message" && message.role === "assistant") {
-    const prevMessage = messages[index - 1];
-
-    // Check if next message is an agent_output tool_response to include in current assistant message
-    const nextMessage = messages[index + 1];
-    if (
-      nextMessage &&
-      nextMessage.type === "tool_response" &&
-      nextMessage.result
-    ) {
-      if (isAgentOutputResult(nextMessage.result)) {
-        agentOutput = nextMessage;
-      }
-    }
-
-    // Only convert to tool_response if it follows a tool_call AND looks like a tool output
-    if (prevMessage && prevMessage.type === "tool_call") {
-      if (isToolOutputPattern(message.content)) {
-        // Convert this message to a tool_response format for rendering
-        messageToRender = {
-          type: "tool_response",
-          toolId: prevMessage.toolId,
-          toolName: prevMessage.toolName,
-          result: message.content,
-          success: true,
-          timestamp: message.timestamp,
-        } as ChatMessageData;
-
-        console.log(
-          "[MessageItem] Converting assistant message to tool output:",
-          {
-            content: message.content.substring(0, 100),
-            prevToolName: prevMessage.toolName,
-          },
-        );
-      }
-    }
-
-    // Log for debugging
-    if (message.type === "message" && message.role === "assistant") {
-      const prevMessageToolName =
-        prevMessage?.type === "tool_call" ? prevMessage.toolName : undefined;
-      console.log("[MessageItem] Assistant message:", {
-        index,
-        content: message.content.substring(0, 200),
-        fullContent: message.content,
-        prevMessageType: prevMessage?.type,
-        prevMessageToolName,
-      });
-    }
-  }
-
-  const isFinalMessage =
-    messageToRender.type !== "message" ||
-    messageToRender.role !== "assistant" ||
-    index === lastAssistantMessageIndex;
-
-  return {
-    messageToRender,
-    agentOutput,
-    isFinalMessage,
-  };
-}

autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/helpers.ts

@@ -1,68 +0,0 @@
-import type { ChatMessageData } from "../ChatMessage/useChatMessage";
-
-export function parseToolResult(
-  result: unknown,
-): Record<string, unknown> | null {
-  try {
-    return typeof result === "string"
-      ? JSON.parse(result)
-      : (result as Record<string, unknown>);
-  } catch {
-    return null;
-  }
-}
-
-export function isAgentOutputResult(result: unknown): boolean {
-  const parsed = parseToolResult(result);
-  return parsed?.type === "agent_output";
-}
-
-export function isToolOutputPattern(content: string): boolean {
-  const normalizedContent = content.toLowerCase().trim();
-
-  return (
-    normalizedContent.startsWith("no agents found") ||
-    normalizedContent.startsWith("no results found") ||
-    normalizedContent.includes("no agents found matching") ||
-    !!normalizedContent.match(/^no \w+ found/i) ||
-    (content.length < 150 && normalizedContent.includes("try different")) ||
-    (content.length < 200 &&
-      !normalizedContent.includes("i'll") &&
-      !normalizedContent.includes("let me") &&
-      !normalizedContent.includes("i can") &&
-      !normalizedContent.includes("i will"))
-  );
-}
-
-export function formatToolResultValue(result: unknown): string {
-  return typeof result === "string"
-    ? result
-    : result
-      ? JSON.stringify(result, null, 2)
-      : "";
-}
-
-export function findLastMessageIndex(
-  messages: ChatMessageData[],
-  predicate: (msg: ChatMessageData) => boolean,
-): number {
-  for (let i = messages.length - 1; i >= 0; i--) {
-    if (predicate(messages[i])) return i;
-  }
-  return -1;
-}
-
-export function shouldSkipAgentOutput(
-  message: ChatMessageData,
-  prevMessage: ChatMessageData | undefined,
-): boolean {
-  if (message.type !== "tool_response" || !message.result) return false;
-
-  const isAgentOutput = isAgentOutputResult(message.result);
-  return (
-    isAgentOutput &&
-    !!prevMessage &&
-    prevMessage.type === "message" &&
-    prevMessage.role === "assistant"
-  );
-}

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

@@ -1,33 +0,0 @@
-import { Text } from "@/components/atoms/Text/Text";
-import type { ToolArguments } from "@/types/chat";
-import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
-
-export interface ToolCallMessageProps {
-  toolId?: string;
-  toolName: string;
-  arguments?: ToolArguments;
-  className?: string;
-}
-
-export function ToolCallMessage({
-  toolId,
-  toolName,
-  arguments: toolArguments,
-  className,
-}: ToolCallMessageProps) {
-  const displayKey = toolName || toolId;
-
-  const displayData = toolArguments
-    ? JSON.stringify(toolArguments)
-    : "No arguments";
-
-  const displayText = `${displayKey}: ${displayData}`;
-
-  return (
-    <AIChatBubble className={className}>
-      <Text variant="small" className="text-neutral-500">
-        {displayText}
-      </Text>
-    </AIChatBubble>
-  );
-}

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

@@ -1,36 +0,0 @@
-import { Text } from "@/components/atoms/Text/Text";
-import type { ToolResult } from "@/types/chat";
-import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
-
-export interface ToolResponseMessageProps {
-  toolId?: string;
-  toolName: string;
-  result?: ToolResult;
-  success?: boolean;
-  className?: string;
-}
-
-export function ToolResponseMessage({
-  toolId,
-  toolName,
-  result: _result,
-  success: _success = true,
-  className,
-}: ToolResponseMessageProps) {
-  const displayKey = toolId || toolName;
-  const resultValue =
-    typeof _result === "string"
-      ? _result
-      : _result
-        ? JSON.stringify(_result)
-        : toolName;
-  const displayText = `${displayKey}: ${resultValue}`;
-
-  return (
-    <AIChatBubble className={className}>
-      <Text variant="small" className="text-neutral-500">
-        {displayText}
-      </Text>
-    </AIChatBubble>
-  );
-}

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

@@ -1,25 +0,0 @@
-import { cn } from "@/lib/utils";
-import { ReactNode } from "react";
-
-export interface UserChatBubbleProps {
-  children: ReactNode;
-  className?: string;
-}
-
-export function UserChatBubble({ children, className }: UserChatBubbleProps) {
-  return (
-    <div
-      className={cn(
-        "group relative min-w-20 overflow-hidden rounded-xl bg-purple-100 px-3 text-right text-sm leading-relaxed transition-all duration-500 ease-in-out",
-        className,
-      )}
-      style={{
-        borderBottomRightRadius: 0,
-      }}
-    >
-      <div className="relative z-10 text-slate-900 transition-all duration-500 ease-in-out">
-        {children}
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/components/contextual/PublishAgentModal/components/AgentSelectStep/useAgentSelectStep.ts

@@ -1,7 +1,6 @@
 import * as React from "react";
 import { useGetV2GetMyAgents } from "@/app/api/__generated__/endpoints/store/store";
 import { okData } from "@/app/api/helpers";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 
 export interface Agent {
   name: string;
@@ -37,7 +36,6 @@ export function useAgentSelectStep({
   const [selectedAgentVersion, setSelectedAgentVersion] = React.useState<
     number | null
   >(null);
-  const { isLoggedIn } = useSupabase();
 
   const {
     data: _myAgents,
@@ -45,7 +43,6 @@ export function useAgentSelectStep({
     error,
   } = useGetV2GetMyAgents(undefined, {
     query: {
-      enabled: isLoggedIn,
       select: (res) =>
         okData(res)
           ?.agents.map(

autogpt_platform/frontend/src/components/contextual/PublishAgentModal/usePublishAgentModal.ts

@@ -11,7 +11,6 @@ import {
 import { okData } from "@/app/api/helpers";
 import type { MyAgent } from "@/app/api/__generated__/models/myAgent";
 import { useQueryClient } from "@tanstack/react-query";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 
 const defaultTargetState: PublishState = {
   isOpen: false,
@@ -69,19 +68,10 @@ export function usePublishAgentModal({
 
   const router = useRouter();
   const queryClient = useQueryClient();
-  const { isLoggedIn } = useSupabase();
 
   // Fetch agent data for pre-populating form when agent is pre-selected
-  const { data: myAgents } = useGetV2GetMyAgents(undefined, {
-    query: {
-      enabled: isLoggedIn,
-    },
-  });
-  const { data: mySubmissions } = useGetV2ListMySubmissions(undefined, {
-    query: {
-      enabled: isLoggedIn,
-    },
-  });
+  const { data: myAgents } = useGetV2GetMyAgents();
+  const { data: mySubmissions } = useGetV2ListMySubmissions();
 
   // Sync currentState with targetState when it changes from outside
   useEffect(() => {

autogpt_platform/frontend/src/components/layout/Navbar/Navbar.tsx

@@ -4,8 +4,6 @@ import { useGetV2GetUserProfile } from "@/app/api/__generated__/endpoints/store/
 import { okData } from "@/app/api/helpers";
 import { IconAutoGPTLogo, IconType } from "@/components/__legacy__/ui/icons";
 import { PreviewBanner } from "@/components/layout/Navbar/components/PreviewBanner/PreviewBanner";
-import { isLogoutInProgress } from "@/lib/autogpt-server-api/helpers";
-import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
 import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { environment } from "@/services/environment";
@@ -26,13 +24,12 @@ export function Navbar() {
   const dynamicMenuItems = getAccountMenuItems(user?.role);
   const isChatEnabled = useGetFlag(Flag.CHAT);
   const previewBranchName = environment.getPreviewStealingDev();
-  const logoutInProgress = isLogoutInProgress();
 
   const { data: profile, isLoading: isProfileLoading } = useGetV2GetUserProfile(
     {
       query: {
         select: okData,
-        enabled: isLoggedIn && !!user && !logoutInProgress,
+        enabled: isLoggedIn && !!user,
         // Include user ID in query key to ensure cache invalidation when user changes
         queryKey: ["/api/store/profile", user?.id],
       },
@@ -43,13 +40,10 @@ export function Navbar() {
 
   const shouldShowPreviewBanner = Boolean(isLoggedIn && previewBranchName);
 
-  const homeHref = isChatEnabled === true ? "/copilot" : "/library";
-
-  const actualLoggedInLinks = [
-    { name: "Home", href: homeHref },
-    ...(isChatEnabled === true ? [{ name: "Tasks", href: "/library" }] : []),
-    ...loggedInLinks,
-  ];
+  const actualLoggedInLinks =
+    isChatEnabled === true
+      ? loggedInLinks.concat([{ name: "Chat", href: "/chat" }])
+      : loggedInLinks;
 
   if (isUserLoading) {
     return <NavbarLoading />;
@@ -61,10 +55,7 @@ export function Navbar() {
         {shouldShowPreviewBanner && previewBranchName ? (
           <PreviewBanner branchName={previewBranchName} />
         ) : null}
-        <nav
-          className="border-zinc-[#EFEFF0] inline-flex w-full items-center border border-[#EFEFF0] bg-[#F3F4F6]/20 p-3 backdrop-blur-[26px]"
-          style={{ height: NAVBAR_HEIGHT_PX }}
-        >
+        <nav className="border-zinc-[#EFEFF0] inline-flex h-[60px] w-full items-center border border-[#EFEFF0] bg-[#F3F4F6]/20 p-3 backdrop-blur-[26px]">
           {/* Left section */}
           {!isSmallScreen ? (
             <div className="flex flex-1 items-center gap-5">
@@ -126,17 +117,21 @@ export function Navbar() {
                   groupName: "Navigation",
                   items: actualLoggedInLinks
                     .map((link) => {
+                      if (link.name === "Chat" && !isChatEnabled) {
+                        return null;
+                      }
+
                       return {
                         icon:
-                          link.href === "/marketplace"
+                          link.name === "Marketplace"
                             ? IconType.Marketplace
-                            : link.href === "/build"
-                              ? IconType.Builder
-                              : link.href === "/copilot"
-                                ? IconType.Chat
-                                : link.href === "/library"
-                                  ? IconType.Library
-                                  : link.href === "/monitor"
+                            : link.name === "Library"
+                              ? IconType.Library
+                              : link.name === "Build"
+                                ? IconType.Builder
+                                : link.name === "Chat"
+                                  ? IconType.Chat
+                                  : link.name === "Monitor"
                                     ? IconType.Library
                                     : IconType.LayoutDashboard,
                         text: link.name,