chore(deps): bump peter-evans/create-pull-request from 7 to 8

Bumps [peter-evans/create-pull-request](https://github.com/peter-evans/create-pull-request) from 7 to 8.
- [Release notes](https://github.com/peter-evans/create-pull-request/releases)
- [Commits](https://github.com/peter-evans/create-pull-request/compare/v7...v8)

---
updated-dependencies:
- dependency-name: peter-evans/create-pull-request
  dependency-version: '8'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

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

@@ -49,7 +49,7 @@ jobs:
 
       - name: Create PR ${{ env.BUILD_BRANCH }} -> ${{ github.ref_name }}
         if: github.event_name == 'push'
-        uses: peter-evans/create-pull-request@v7
+        uses: peter-evans/create-pull-request@v8
         with:
           add-paths: classic/frontend/build/web
           base: ${{ github.ref_name }}

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

@@ -4,9 +4,14 @@ from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
-from langfuse import get_client, propagate_attributes
-from langfuse.openai import openai  # type: ignore
-from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
+from langfuse import Langfuse
+from openai import (
+    APIConnectionError,
+    APIError,
+    APIStatusError,
+    AsyncOpenAI,
+    RateLimitError,
+)
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -16,6 +21,7 @@ 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,
@@ -44,10 +50,10 @@ logger = logging.getLogger(__name__)
 
 config = ChatConfig()
 settings = Settings()
-client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-
-langfuse = get_client()
+# Langfuse client (lazy initialization)
+_langfuse_client: Langfuse | None = None
 
 
 class LangfuseNotConfiguredError(Exception):
@@ -63,6 +69,65 @@ 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.
 
@@ -74,6 +139,8 @@ 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)
 
@@ -91,7 +158,7 @@ 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, understanding
+    return compiled, prompt
 
 
 async def _generate_session_title(message: str) -> str | None:
@@ -150,7 +217,6 @@ 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,
@@ -190,6 +256,11 @@ 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)
@@ -265,259 +336,297 @@ async def stream_chat_completion(
             asyncio.create_task(_update_title())
 
     # Build system prompt with business understanding
-    system_prompt, understanding = await _build_system_prompt(user_id)
+    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
+    ]
 
     # Create Langfuse trace for this LLM call (each call gets its own trace, grouped by session_id)
     # Using v3 SDK: start_observation creates a root span, update_trace sets trace-level attributes
-    input = message
-    if not message and tool_call_response:
-        input = tool_call_response
-
-    langfuse = get_client()
-    with langfuse.start_as_current_observation(
-        as_type="span",
-        name="user-copilot-request",
-        input=input,
-    ) as span:
-        with propagate_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(
             session_id=session_id,
             user_id=user_id,
-            tags=["copilot"],
-            metadata={
-                "users_information": format_understanding_for_prompt(understanding)[
-                    :200
-                ]  # langfuse only accepts upto to 200 chars
-            },
-        ):
+            tags=[env, "copilot"],
+        )
+    except Exception as e:
+        logger.warning(f"Failed to create Langfuse trace: {e}")
 
-            # Initialize variables that will be used in finally block (must be defined before try)
-            assistant_response = ChatMessage(
-                role="assistant",
-                content="",
+    # 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,
             )
-            accumulated_tool_calls: list[dict[str, Any]] = []
+            if trace
+            else None
+        )
 
-            # 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
+        try:
+            async for chunk in _stream_chat_chunks(
+                session=session,
+                tools=tools,
+                system_prompt=system_prompt,
+                text_block_id=text_block_id,
+            ):
 
-            # 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
+                if isinstance(chunk, StreamTextStart):
+                    # Emit text-start before first text delta
+                    if not has_received_text:
                         yield chunk
-                    elif isinstance(chunk, StreamTextEnd):
-                        # Emit text-end after text completes
-                        if has_received_text and not text_streaming_ended:
-                            text_streaming_ended = True
-                            if assistant_response.content:
-                                logger.warn(
-                                    f"StreamTextEnd: Attempting to set output {assistant_response.content}"
-                                )
-                                span.update_trace(output=assistant_response.content)
-                                span.update(output=assistant_response.content)
-                            yield chunk
-                    elif isinstance(chunk, StreamToolInputStart):
-                        # Emit text-end before first tool call, but only if we've received text
+                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
                         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, 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, 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, StreamToolOutputAvailable):
-                        result_content = (
-                            chunk.output
-                            if isinstance(chunk.output, str)
-                            else orjson.dumps(chunk.output).decode("utf-8")
-                        )
-                        tool_response_messages.append(
-                            ChatMessage(
-                                role="tool",
-                                content=result_content,
-                                tool_call_id=chunk.toolCallId,
-                            )
-                        )
-                        has_done_tool_call = True
-                        # Track if any tool execution failed
-                        if not chunk.success:
-                            logger.warning(
-                                f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
-                            )
-                        yield chunk
-                    elif isinstance(chunk, StreamFinish):
-                        if not has_done_tool_call:
-                            # Emit text-end before finish if we received text but haven't closed it
-                            if has_received_text and not text_streaming_ended:
-                                yield StreamTextEnd(id=text_block_id)
-                                text_streaming_ended = True
-                            has_yielded_end = True
-                            yield chunk
-                    elif isinstance(chunk, StreamError):
-                        has_yielded_error = True
-                    elif isinstance(chunk, StreamUsage):
-                        session.usage.append(
-                            Usage(
-                                prompt_tokens=chunk.promptTokens,
-                                completion_tokens=chunk.completionTokens,
-                                total_tokens=chunk.totalTokens,
-                            )
-                        )
-                    else:
-                        logger.error(
-                            f"Unknown chunk type: {type(chunk)}", exc_info=True
-                        )
-                if assistant_response.content:
-                    langfuse.update_current_trace(output=assistant_response.content)
-                    langfuse.update_current_span(output=assistant_response.content)
-                elif tool_response_messages:
-                    langfuse.update_current_trace(output=str(tool_response_messages))
-                    langfuse.update_current_span(output=str(tool_response_messages))
-
-            except Exception as e:
-                logger.error(f"Error during stream: {e!s}", exc_info=True)
-
-                # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
-                is_retryable = isinstance(
-                    e, (orjson.JSONDecodeError, KeyError, TypeError)
-                )
-
-                if is_retryable and retry_count < config.max_retries:
-                    logger.info(
-                        f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
                     )
-                    should_retry = True
                 else:
-                    # Non-retryable error or max retries exceeded
-                    # Save any partial progress before reporting error
-                    messages_to_save: list[ChatMessage] = []
+                    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)
 
-                    # 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)
+            # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
+            is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
 
-                    # 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:
+            if is_retryable and retry_count < config.max_retries:
                 logger.info(
-                    f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
+                    f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
                 )
-                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
+                should_retry = True
+            else:
+                # Non-retryable error or max retries exceeded
+                # Save any partial progress before reporting error
+                messages_to_save: list[ChatMessage] = []
 
-            # Normal completion path - save session and handle tool call continuation
+                # Add assistant message if it has content or tool calls
+                if accumulated_tool_calls:
+                    assistant_response.tool_calls = accumulated_tool_calls
+                if assistant_response.content or assistant_response.tool_calls:
+                    messages_to_save.append(assistant_response)
+
+                # Add tool response messages after assistant message
+                messages_to_save.extend(tool_response_messages)
+
+                session.messages.extend(messages_to_save)
+                await upsert_chat_session(session)
+
+                if not has_yielded_error:
+                    error_message = str(e)
+                    if not is_retryable:
+                        error_message = f"Non-retryable error: {error_message}"
+                    elif retry_count >= config.max_retries:
+                        error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
+
+                    error_response = StreamError(errorText=error_message)
+                    yield error_response
+                if not has_yielded_end:
+                    yield StreamFinish()
+                return
+
+        # Handle retry outside of exception handler to avoid nesting
+        if should_retry and retry_count < config.max_retries:
             logger.info(
-                f"Normal completion path: session={session.session_id}, "
-                f"current message_count={len(session.messages)}"
+                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 [])}"
             )
 
-            # Build the messages list in the correct order
-            messages_to_save: list[ChatMessage] = []
+        # Add tool response messages after assistant message
+        messages_to_save.extend(tool_response_messages)
+        logger.info(
+            f"Saving {len(tool_response_messages)} tool response messages, "
+            f"total_to_save={len(messages_to_save)}"
+        )
 
-            # Add assistant message with tool_calls if any
-            if accumulated_tool_calls:
-                assistant_response.tool_calls = accumulated_tool_calls
-                logger.info(
-                    f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
-                )
-            if 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 [])}"
-                )
+        session.messages.extend(messages_to_save)
+        logger.info(
+            f"Extended session messages, new message_count={len(session.messages)}"
+        )
+        await upsert_chat_session(session)
 
-            # Add tool response messages after assistant message
-            messages_to_save.extend(tool_response_messages)
+        # If we did a tool call, stream the chat completion again to get the next response
+        if has_done_tool_call:
             logger.info(
-                f"Saving {len(tool_response_messages)} tool response messages, "
-                f"total_to_save={len(messages_to_save)}"
+                "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
 
-            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"
+    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
+                    ),
                 )
-                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
+                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}")
 
 
 # Retry configuration for OpenAI API calls
@@ -791,4 +900,5 @@ 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(),
-    "view_agent_output": AgentOutputTool(),
+    "agent_output": AgentOutputTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
 }

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

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

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

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

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

@@ -5,7 +5,6 @@ import re
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
@@ -104,7 +103,7 @@ class AgentOutputTool(BaseTool):
 
     @property
     def name(self) -> str:
-        return "view_agent_output"
+        return "agent_output"
 
     @property
     def description(self) -> str:
@@ -329,7 +328,6 @@ class AgentOutputTool(BaseTool):
             total_executions=len(available_executions) if available_executions else 1,
         )
 
-    @observe(as_type="tool", name="view_agent_output")
     async def _execute(
         self,
         user_id: str | None,

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -3,7 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
@@ -33,7 +32,7 @@ from .models import (
     UserReadiness,
 )
 from .utils import (
-    build_missing_credentials_from_graph,
+    check_user_has_required_credentials,
     extract_credentials_from_schema,
     fetch_graph_from_store_slug,
     get_or_create_library_agent,
@@ -155,7 +154,6 @@ class RunAgentTool(BaseTool):
         """All operations require authentication."""
         return True
 
-    @observe(as_type="tool", name="run_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -237,13 +235,15 @@ class RunAgentTool(BaseTool):
                 # Return credentials needed response with input data info
                 # The UI handles credential setup automatically, so the message
                 # focuses on asking about input data
-                requirements_creds_dict = build_missing_credentials_from_graph(
-                    graph, None
+                credentials = extract_credentials_from_schema(
+                    graph.credentials_input_schema
                 )
-                missing_credentials_dict = build_missing_credentials_from_graph(
-                    graph, graph_credentials
+                missing_creds_check = await check_user_has_required_credentials(
+                    user_id, credentials
                 )
-                requirements_creds_list = list(requirements_creds_dict.values())
+                missing_credentials_dict = {
+                    c.id: c.model_dump() for c in missing_creds_check
+                }
 
                 return SetupRequirementsResponse(
                     message=self._build_inputs_message(graph, MSG_WHAT_VALUES_TO_USE),
@@ -257,7 +257,7 @@ class RunAgentTool(BaseTool):
                             ready_to_run=False,
                         ),
                         requirements={
-                            "credentials": requirements_creds_list,
+                            "credentials": [c.model_dump() for c in credentials],
                             "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },

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

@@ -4,8 +4,6 @@ import logging
 from collections import defaultdict
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
@@ -22,7 +20,6 @@ from .models import (
     ToolResponseBase,
     UserReadiness,
 )
-from .utils import build_missing_credentials_from_field_info
 
 logger = logging.getLogger(__name__)
 
@@ -130,7 +127,6 @@ class RunBlockTool(BaseTool):
 
         return matched_credentials, missing_credentials
 
-    @observe(as_type="tool", name="run_block")
     async def _execute(
         self,
         user_id: str | None,
@@ -190,11 +186,7 @@ class RunBlockTool(BaseTool):
 
         if missing_credentials:
             # Return setup requirements response with missing credentials
-            credentials_fields_info = block.input_schema.get_credentials_fields_info()
-            missing_creds_dict = build_missing_credentials_from_field_info(
-                credentials_fields_info, set(matched_credentials.keys())
-            )
-            missing_creds_list = list(missing_creds_dict.values())
+            missing_creds_dict = {c.id: c.model_dump() for c in missing_credentials}
 
             return SetupRequirementsResponse(
                 message=(
@@ -211,7 +203,7 @@ class RunBlockTool(BaseTool):
                         ready_to_run=False,
                     ),
                     requirements={
-                        "credentials": missing_creds_list,
+                        "credentials": [c.model_dump() for c in missing_credentials],
                         "inputs": self._get_inputs_list(block),
                         "execution_modes": ["immediate"],
                     },

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

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

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

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

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

@@ -41,7 +41,6 @@ class PendingHumanReviewModel(BaseModel):
     graph_exec_id: str = Field(description="Graph execution ID")
     graph_id: str = Field(description="Graph ID")
     graph_version: int = Field(description="Graph version")
-    node_id: str = Field(description="Node ID in the graph definition")
     payload: SafeJsonData = Field(description="The actual data payload awaiting review")
     instructions: str | None = Field(
         description="Instructions or message for the reviewer", default=None
@@ -82,7 +81,6 @@ class PendingHumanReviewModel(BaseModel):
             graph_exec_id=review.graphExecId,
             graph_id=review.graphId,
             graph_version=review.graphVersion,
-            node_id=review.nodeId,
             payload=review.payload,
             instructions=review.instructions,
             editable=review.editable,
@@ -181,15 +179,6 @@ class ReviewRequest(BaseModel):
     reviews: List[ReviewItem] = Field(
         description="All reviews with their approval status, data, and messages"
     )
-    auto_approve_node_ids: List[str] = Field(
-        default_factory=list,
-        description=(
-            "List of node IDs (from the graph definition) to auto-approve for "
-            "the remainder of this execution. Future reviews from these specific "
-            "nodes will be automatically approved. This only affects the current "
-            "execution run."
-        ),
-    )
 
     @model_validator(mode="after")
     def validate_review_completeness(self):

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

@@ -41,7 +41,6 @@ def sample_pending_review(test_user_id: str) -> PendingHumanReviewModel:
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_123",
         payload={"data": "test payload", "value": 42},
         instructions="Please review this data",
         editable=True,
@@ -161,7 +160,6 @@ def test_process_review_action_approve_success(
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_123",
         payload={"data": "modified payload", "value": 50},
         instructions="Please review this data",
         editable=True,
@@ -225,7 +223,6 @@ def test_process_review_action_reject_success(
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_123",
         payload={"data": "test payload"},
         instructions="Please review",
         editable=True,
@@ -277,7 +274,6 @@ def test_process_review_action_mixed_success(
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_456",
         payload={"data": "second payload"},
         instructions="Second review",
         editable=False,
@@ -307,7 +303,6 @@ def test_process_review_action_mixed_success(
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_123",
         payload={"data": "modified"},
         instructions="Please review",
         editable=True,
@@ -326,7 +321,6 @@ def test_process_review_action_mixed_success(
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_456",
         payload={"data": "second payload"},
         instructions="Second review",
         editable=False,

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

@@ -5,7 +5,7 @@ import autogpt_libs.auth as autogpt_auth_lib
 from fastapi import APIRouter, HTTPException, Query, Security, status
 from prisma.enums import ReviewStatus
 
-from backend.data.execution import ExecutionContext, get_graph_execution_meta
+from backend.data.execution import get_graph_execution_meta
 from backend.data.human_review import (
     get_pending_reviews_for_execution,
     get_pending_reviews_for_user,
@@ -169,23 +169,10 @@ async def process_review_action(
         if not still_has_pending:
             # Resume execution
             try:
-                # If auto_approve_node_ids is set, create a context that will
-                # automatically approve future reviews from these specific nodes
-                execution_context = None
-                if request.auto_approve_node_ids:
-                    execution_context = ExecutionContext(
-                        auto_approved_node_ids=set(request.auto_approve_node_ids),
-                    )
-                    logger.info(
-                        f"Auto-approving future reviews for nodes "
-                        f"{request.auto_approve_node_ids} in execution {graph_exec_id}"
-                    )
-
                 await add_graph_execution(
                     graph_id=first_review.graph_id,
                     user_id=user_id,
                     graph_exec_id=graph_exec_id,
-                    execution_context=execution_context,
                 )
                 logger.info(f"Resumed execution {graph_exec_id}")
             except Exception as e:

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

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

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

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

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

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

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

@@ -154,16 +154,15 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-        # Use {pgvector_schema}.vector for explicit pgvector type qualification
         await execute_raw_with_schema(
             """
             INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
                 "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
             )
-            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::{pgvector_schema}.vector, $5, $6::jsonb, NOW(), NOW())
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
             ON CONFLICT ("contentType", "contentId", "userId")
             DO UPDATE SET
-                "embedding" = $4::{pgvector_schema}.vector,
+                "embedding" = $4::vector,
                 "searchableText" = $5,
                 "metadata" = $6::jsonb,
                 "updatedAt" = NOW()
@@ -178,6 +177,7 @@ async def store_content_embedding(
             searchable_text,
             metadata_json,
             client=client,
+            set_public_search_path=True,
         )
 
         logger.info(f"Stored embedding for {content_type}:{content_id}")
@@ -236,6 +236,7 @@ async def get_content_embedding(
             content_type,
             content_id,
             user_id,
+            set_public_search_path=True,
         )
 
         if result and len(result) > 0:
@@ -870,46 +871,31 @@ async def semantic_search(
         # Add content type parameters and build placeholders dynamically
         content_type_start_idx = len(params) + 1
         content_type_placeholders = ", ".join(
-            "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
+            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
             for i in range(len(content_types))
         )
         params.extend([ct.value for ct in content_types])
 
-        # Build min_similarity param index before appending
-        min_similarity_idx = len(params) + 1
-        params.append(min_similarity)
-
-        # Use regular string (not f-string) for template to preserve {schema_prefix} and {schema} placeholders
-        # Use OPERATOR({pgvector_schema}.<=>) for explicit operator schema qualification
-        sql = (
-            """
+        sql = f"""
             SELECT
                 "contentId" as content_id,
                 "contentType" as content_type,
                 "searchableText" as searchable_text,
                 metadata,
-                1 - (embedding OPERATOR({pgvector_schema}.<=>) '"""
-            + embedding_str
-            + """'::{pgvector_schema}.vector) as similarity
-            FROM {schema_prefix}"UnifiedContentEmbedding"
-            WHERE "contentType" IN ("""
-            + content_type_placeholders
-            + """)
-            """
-            + user_filter
-            + """
-            AND 1 - (embedding OPERATOR({pgvector_schema}.<=>) '"""
-            + embedding_str
-            + """'::{pgvector_schema}.vector) >= $"""
-            + str(min_similarity_idx)
-            + """
+                1 - (embedding <=> '{embedding_str}'::vector) as similarity
+            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+            WHERE "contentType" IN ({content_type_placeholders})
+            {user_filter}
+            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
             ORDER BY similarity DESC
             LIMIT $1
         """
-        )
+        params.append(min_similarity)
 
         try:
-            results = await query_raw_with_schema(sql, *params)
+            results = await query_raw_with_schema(
+                sql, *params, set_public_search_path=True
+            )
             return [
                 {
                     "content_id": row["content_id"],
@@ -936,41 +922,31 @@ async def semantic_search(
     # Add content type parameters and build placeholders dynamically
     content_type_start_idx = len(params_lexical) + 1
     content_type_placeholders_lexical = ", ".join(
-        "$" + str(content_type_start_idx + i) + '::{schema_prefix}"ContentType"'
+        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
         for i in range(len(content_types))
     )
     params_lexical.extend([ct.value for ct in content_types])
 
-    # Build query param index before appending
-    query_param_idx = len(params_lexical) + 1
-    params_lexical.append(f"%{query}%")
-
-    # Use regular string (not f-string) for template to preserve {schema_prefix} placeholders
-    sql_lexical = (
-        """
+    sql_lexical = f"""
         SELECT
             "contentId" as content_id,
             "contentType" as content_type,
             "searchableText" as searchable_text,
             metadata,
             0.0 as similarity
-        FROM {schema_prefix}"UnifiedContentEmbedding"
-        WHERE "contentType" IN ("""
-        + content_type_placeholders_lexical
-        + """)
-        """
-        + user_filter
-        + """
-        AND "searchableText" ILIKE $"""
-        + str(query_param_idx)
-        + """
+        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+        WHERE "contentType" IN ({content_type_placeholders_lexical})
+        {user_filter}
+        AND "searchableText" ILIKE ${len(params_lexical) + 1}
         ORDER BY "updatedAt" DESC
         LIMIT $1
     """
-    )
+    params_lexical.append(f"%{query}%")
 
     try:
-        results = await query_raw_with_schema(sql_lexical, *params_lexical)
+        results = await query_raw_with_schema(
+            sql_lexical, *params_lexical, set_public_search_path=True
+        )
         return [
             {
                 "content_id": row["content_id"],

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

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

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

@@ -12,7 +12,7 @@ from dataclasses import dataclass
 from typing import Any, Literal
 
 from prisma.enums import ContentType
-from rank_bm25 import BM25Okapi  # type: ignore[import-untyped]
+from rank_bm25 import BM25Okapi
 
 from backend.api.features.store.embeddings import (
     EMBEDDING_DIM,
@@ -295,7 +295,7 @@ async def unified_hybrid_search(
                 FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
                 WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
                 {user_filter}
-                ORDER BY uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector
+                ORDER BY uce.embedding <=> {embedding_param}::vector
                 LIMIT 200
             )
         ),
@@ -307,7 +307,7 @@ async def unified_hybrid_search(
                 uce.metadata,
                 uce."updatedAt" as updated_at,
                 -- Semantic score: cosine similarity (1 - distance)
-                COALESCE(1 - (uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
                 -- Lexical score: ts_rank_cd
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match from metadata
@@ -363,7 +363,9 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -583,7 +585,7 @@ async def hybrid_search(
                 WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
                 AND uce."userId" IS NULL
                 AND {where_clause}
-                ORDER BY uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector
+                ORDER BY uce.embedding <=> {embedding_param}::vector
                 LIMIT 200
             ) uce
         ),
@@ -605,7 +607,7 @@ async def hybrid_search(
                 -- Searchable text for BM25 reranking
                 COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
                 -- Semantic score
-                COALESCE(1 - (uce.embedding OPERATOR({{pgvector_schema}}.<=>)  {embedding_param}::{{pgvector_schema}}.vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
                 -- Lexical score (raw, will normalize)
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match
@@ -686,7 +688,9 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
 
     total = results[0]["total_count"] if results else 0
 

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

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

autogpt_platform/backend/backend/blocks/data_manipulation.py

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

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

@@ -55,7 +55,6 @@ class HITLReviewHelper:
     async def _handle_review_request(
         input_data: Any,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -63,7 +62,6 @@ class HITLReviewHelper:
         execution_context: ExecutionContext,
         block_name: str = "Block",
         editable: bool = False,
-        skip_safe_mode_check: bool = False,
     ) -> Optional[ReviewResult]:
         """
         Handle a review request for a block that requires human review.
@@ -71,7 +69,6 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
-            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
@@ -79,8 +76,6 @@ class HITLReviewHelper:
             execution_context: Current execution context
             block_name: Name of the block requesting review
             editable: Whether the reviewer can edit the data
-            skip_safe_mode_check: If True, skip the safe mode check (caller already
-                verified). Used by sensitive action blocks that check their own flag.
 
         Returns:
             ReviewResult if review is complete, None if waiting for human input
@@ -89,11 +84,7 @@ class HITLReviewHelper:
             Exception: If review creation or status update fails
         """
         # Skip review if safe mode is disabled - return auto-approved result
-        # (unless caller already checked and wants to skip this check)
-        if (
-            not skip_safe_mode_check
-            and not execution_context.human_in_the_loop_safe_mode
-        ):
+        if not execution_context.safe_mode:
             logger.info(
                 f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
             )
@@ -105,27 +96,12 @@ class HITLReviewHelper:
                 node_exec_id=node_exec_id,
             )
 
-        # Skip review if this specific node has been auto-approved by the user
-        if node_id in execution_context.auto_approved_node_ids:
-            logger.info(
-                f"Block {block_name} skipping review for node {node_exec_id} - "
-                f"node {node_id} is auto-approved"
-            )
-            return ReviewResult(
-                data=input_data,
-                status=ReviewStatus.APPROVED,
-                message="Auto-approved (user approved all future actions for this block)",
-                processed=True,
-                node_exec_id=node_exec_id,
-            )
-
         result = await HITLReviewHelper.get_or_create_human_review(
             user_id=user_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
-            node_id=node_id,
             input_data=input_data,
             message=f"Review required for {block_name} execution",
             editable=editable,
@@ -153,7 +129,6 @@ class HITLReviewHelper:
     async def handle_review_decision(
         input_data: Any,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -161,7 +136,6 @@ class HITLReviewHelper:
         execution_context: ExecutionContext,
         block_name: str = "Block",
         editable: bool = False,
-        skip_safe_mode_check: bool = False,
     ) -> Optional[ReviewDecision]:
         """
         Handle a review request and return the decision in a single call.
@@ -169,7 +143,6 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
-            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
@@ -177,8 +150,6 @@ class HITLReviewHelper:
             execution_context: Current execution context
             block_name: Name of the block requesting review
             editable: Whether the reviewer can edit the data
-            skip_safe_mode_check: If True, skip the safe mode check (caller already
-                verified). Used by sensitive action blocks that check their own flag.
 
         Returns:
             ReviewDecision if review is complete (approved/rejected),
@@ -187,7 +158,6 @@ class HITLReviewHelper:
         review_result = await HITLReviewHelper._handle_review_request(
             input_data=input_data,
             user_id=user_id,
-            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
@@ -195,7 +165,6 @@ class HITLReviewHelper:
             execution_context=execution_context,
             block_name=block_name,
             editable=editable,
-            skip_safe_mode_check=skip_safe_mode_check,
         )
 
         if review_result is None:

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -97,7 +97,6 @@ class HumanInTheLoopBlock(Block):
         input_data: Input,
         *,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -105,17 +104,7 @@ class HumanInTheLoopBlock(Block):
         execution_context: ExecutionContext,
         **_kwargs,
     ) -> BlockOutput:
-        # Check if this specific node has been auto-approved by the user
-        if node_id in execution_context.auto_approved_node_ids:
-            logger.info(
-                f"HITL block skipping review for node {node_exec_id} - "
-                f"node {node_id} is auto-approved"
-            )
-            yield "approved_data", input_data.data
-            yield "review_message", "Auto-approved (user approved all future actions for this block)"
-            return
-
-        if not execution_context.human_in_the_loop_safe_mode:
+        if not execution_context.safe_mode:
             logger.info(
                 f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
             )
@@ -126,7 +115,6 @@ class HumanInTheLoopBlock(Block):
         decision = await self.handle_review_decision(
             input_data=input_data.data,
             user_id=user_id,
-            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,

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

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

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

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

autogpt_platform/backend/backend/data/block.py

@@ -474,7 +474,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.block_type = block_type
         self.webhook_config = webhook_config
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
-        self.is_sensitive_action: bool = False
+        self.requires_human_review: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -622,7 +622,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         input_data: BlockInput,
         *,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -638,9 +637,8 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             - should_pause: True if execution should be paused for review
             - input_data_to_use: The input data to use (may be modified by reviewer)
         """
-        if not (
-            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
-        ):
+        # Skip review if not required or safe mode is disabled
+        if not self.requires_human_review or not execution_context.safe_mode:
             return False, input_data
 
         from backend.blocks.helpers.review import HITLReviewHelper
@@ -649,7 +647,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         decision = await HITLReviewHelper.handle_review_decision(
             input_data=input_data,
             user_id=user_id,
-            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,

autogpt_platform/backend/backend/data/db.py

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

autogpt_platform/backend/backend/data/execution.py

@@ -81,12 +81,10 @@ class ExecutionContext(BaseModel):
     This includes information needed by blocks, sub-graphs, and execution management.
     """
 
-    human_in_the_loop_safe_mode: bool = True
-    sensitive_action_safe_mode: bool = False
+    safe_mode: bool = True
     user_timezone: str = "UTC"
     root_execution_id: Optional[str] = None
     parent_execution_id: Optional[str] = None
-    auto_approved_node_ids: set[str] = Field(default_factory=set)
 
 
 # -------------------------- Models -------------------------- #

autogpt_platform/backend/backend/data/graph.py

@@ -62,23 +62,7 @@ logger = logging.getLogger(__name__)
 
 
 class GraphSettings(BaseModel):
-    human_in_the_loop_safe_mode: bool = True
-    sensitive_action_safe_mode: bool = False
-
-    @classmethod
-    def from_graph(
-        cls,
-        graph: "GraphModel",
-        hitl_safe_mode: bool | None = None,
-        sensitive_action_safe_mode: bool = False,
-    ) -> "GraphSettings":
-        # Default to True if not explicitly set
-        if hitl_safe_mode is None:
-            hitl_safe_mode = True
-        return cls(
-            human_in_the_loop_safe_mode=hitl_safe_mode,
-            sensitive_action_safe_mode=sensitive_action_safe_mode,
-        )
+    human_in_the_loop_safe_mode: bool | None = None
 
 
 class Link(BaseDbModel):
@@ -260,14 +244,10 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-        )
-
-    @computed_field
-    @property
-    def has_sensitive_action(self) -> bool:
-        return any(
-            node.block_id for node in self.nodes if node.block.is_sensitive_action
+            if (
+                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+                or node.block.requires_human_review
+            )
         )
 
     @property

autogpt_platform/backend/backend/data/human_review.py

@@ -38,7 +38,6 @@ async def get_or_create_human_review(
     graph_exec_id: str,
     graph_id: str,
     graph_version: int,
-    node_id: str,
     input_data: SafeJsonData,
     message: str,
     editable: bool,
@@ -54,7 +53,6 @@ async def get_or_create_human_review(
         graph_exec_id: ID of the graph execution
         graph_id: ID of the graph template
         graph_version: Version of the graph template
-        node_id: ID of the node in the graph definition
         input_data: The data to be reviewed
         message: Instructions for the reviewer
         editable: Whether the data can be edited
@@ -75,7 +73,6 @@ async def get_or_create_human_review(
                     "graphExecId": graph_exec_id,
                     "graphId": graph_id,
                     "graphVersion": graph_version,
-                    "nodeId": node_id,
                     "payload": SafeJson(input_data),
                     "instructions": message,
                     "editable": editable,

autogpt_platform/backend/backend/data/human_review_test.py

@@ -23,7 +23,6 @@ def sample_db_review():
     mock_review.graphExecId = "test_graph_exec_456"
     mock_review.graphId = "test_graph_789"
     mock_review.graphVersion = 1
-    mock_review.nodeId = "node_def_123"
     mock_review.payload = {"data": "test payload"}
     mock_review.instructions = "Please review"
     mock_review.editable = True
@@ -56,7 +55,6 @@ async def test_get_or_create_human_review_new(
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_123",
         input_data={"data": "test payload"},
         message="Please review",
         editable=True,
@@ -86,7 +84,6 @@ async def test_get_or_create_human_review_approved(
         graph_exec_id="test_graph_exec_456",
         graph_id="test_graph_789",
         graph_version=1,
-        node_id="node_def_123",
         input_data={"data": "test payload"},
         message="Please review",
         editable=True,
@@ -186,7 +183,6 @@ async def test_process_all_reviews_for_execution_success(
     updated_review.graphExecId = "test_graph_exec_456"
     updated_review.graphId = "test_graph_789"
     updated_review.graphVersion = 1
-    updated_review.nodeId = "node_def_123"
     updated_review.payload = {"data": "modified"}
     updated_review.instructions = "Please review"
     updated_review.editable = True
@@ -276,7 +272,6 @@ async def test_process_all_reviews_mixed_approval_rejection(
     second_review.graphExecId = "test_graph_exec_456"
     second_review.graphId = "test_graph_789"
     second_review.graphVersion = 1
-    second_review.nodeId = "node_def_456"
     second_review.payload = {"data": "original"}
     second_review.instructions = "Second review"
     second_review.editable = True
@@ -301,7 +296,6 @@ async def test_process_all_reviews_mixed_approval_rejection(
     approved_review.graphExecId = "test_graph_exec_456"
     approved_review.graphId = "test_graph_789"
     approved_review.graphVersion = 1
-    approved_review.nodeId = "node_def_123"
     approved_review.payload = {"data": "modified"}
     approved_review.instructions = "Please review"
     approved_review.editable = True
@@ -319,7 +313,6 @@ async def test_process_all_reviews_mixed_approval_rejection(
     rejected_review.graphExecId = "test_graph_exec_456"
     rejected_review.graphId = "test_graph_789"
     rejected_review.graphVersion = 1
-    rejected_review.nodeId = "node_def_456"
     rejected_review.payload = {"data": "original"}
     rejected_review.instructions = "Please review"
     rejected_review.editable = True

autogpt_platform/backend/backend/data/understanding.py

@@ -328,8 +328,6 @@ 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/backend/executor/scheduler.py

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

autogpt_platform/backend/backend/executor/utils.py

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

autogpt_platform/backend/backend/executor/utils_test.py

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

autogpt_platform/backend/migrations/20260121000000_add_node_id_to_pending_human_review/migration.sql

@@ -1,2 +0,0 @@
--- AlterTable
-ALTER TABLE "PendingHumanReview" ADD COLUMN "nodeId" TEXT NOT NULL DEFAULT '';

autogpt_platform/backend/schema.prisma

@@ -573,7 +573,6 @@ model PendingHumanReview {
   graphExecId   String
   graphId       String
   graphVersion  Int
-  nodeId        String       // The node ID in the graph definition (for auto-approval tracking)
   payload       Json // The actual payload data to be reviewed
   instructions  String? // Instructions/message for the reviewer
   editable      Boolean      @default(true) // Whether the reviewer can edit the data

autogpt_platform/backend/scripts/generate_block_docs.py

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

autogpt_platform/backend/snapshots/grph_single

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

autogpt_platform/backend/snapshots/grphs_all

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

autogpt_platform/backend/snapshots/lib_agts_search

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

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

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

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

@@ -10,7 +10,6 @@ import { useRunInputDialog } from "./useRunInputDialog";
 import { CronSchedulerDialog } from "../CronSchedulerDialog/CronSchedulerDialog";
 import { useTutorialStore } from "@/app/(platform)/build/stores/tutorialStore";
 import { useEffect } from "react";
-import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
 
 export const RunInputDialog = ({
   isOpen,
@@ -24,17 +23,19 @@ export const RunInputDialog = ({
   const hasInputs = useGraphStore((state) => state.hasInputs);
   const hasCredentials = useGraphStore((state) => state.hasCredentials);
   const inputSchema = useGraphStore((state) => state.inputSchema);
+  const credentialsSchema = useGraphStore(
+    (state) => state.credentialsInputSchema,
+  );
 
   const {
-    credentialFields,
-    requiredCredentials,
+    credentialsUiSchema,
     handleManualRun,
     handleInputChange,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,
     inputValues,
     credentialValues,
-    handleCredentialFieldChange,
+    handleCredentialChange,
     isExecutingGraph,
   } = useRunInputDialog({ setIsOpen });
 
@@ -61,67 +62,67 @@ export const RunInputDialog = ({
           isOpen,
           set: setIsOpen,
         }}
-        styling={{ maxWidth: "700px", minWidth: "700px" }}
+        styling={{ maxWidth: "600px", minWidth: "600px" }}
       >
         <Dialog.Content>
-          <div
-            className="grid grid-cols-[1fr_auto] gap-10 p-1"
-            data-id="run-input-dialog-content"
-          >
-            <div className="space-y-6">
-              {/* Credentials Section */}
-              {hasCredentials() && credentialFields.length > 0 && (
-                <div data-id="run-input-credentials-section">
-                  <div className="mb-4">
-                    <Text variant="h4" className="text-gray-900">
-                      Credentials
-                    </Text>
-                  </div>
-                  <div className="px-2" data-id="run-input-credentials-form">
-                    <CredentialsGroupedView
-                      credentialFields={credentialFields}
-                      requiredCredentials={requiredCredentials}
-                      inputCredentials={credentialValues}
-                      inputValues={inputValues}
-                      onCredentialChange={handleCredentialFieldChange}
-                    />
-                  </div>
+          <div className="space-y-6 p-1" data-id="run-input-dialog-content">
+            {/* Credentials Section */}
+            {hasCredentials() && (
+              <div data-id="run-input-credentials-section">
+                <div className="mb-4">
+                  <Text variant="h4" className="text-gray-900">
+                    Credentials
+                  </Text>
                 </div>
-              )}
-
-              {/* Inputs Section */}
-              {hasInputs() && (
-                <div data-id="run-input-inputs-section">
-                  <div className="mb-4">
-                    <Text variant="h4" className="text-gray-900">
-                      Inputs
-                    </Text>
-                  </div>
-                  <div data-id="run-input-inputs-form">
-                    <FormRenderer
-                      jsonSchema={inputSchema as RJSFSchema}
-                      handleChange={(v) => handleInputChange(v.formData)}
-                      uiSchema={uiSchema}
-                      initialValues={{}}
-                      formContext={{
-                        showHandles: false,
-                        size: "large",
-                      }}
-                    />
-                  </div>
+                <div className="px-2" data-id="run-input-credentials-form">
+                  <FormRenderer
+                    jsonSchema={credentialsSchema as RJSFSchema}
+                    handleChange={(v) => handleCredentialChange(v.formData)}
+                    uiSchema={credentialsUiSchema}
+                    initialValues={{}}
+                    formContext={{
+                      showHandles: false,
+                      size: "large",
+                      showOptionalToggle: false,
+                    }}
+                  />
                 </div>
-              )}
-            </div>
+              </div>
+            )}
 
+            {/* Inputs Section */}
+            {hasInputs() && (
+              <div data-id="run-input-inputs-section">
+                <div className="mb-4">
+                  <Text variant="h4" className="text-gray-900">
+                    Inputs
+                  </Text>
+                </div>
+                <div data-id="run-input-inputs-form">
+                  <FormRenderer
+                    jsonSchema={inputSchema as RJSFSchema}
+                    handleChange={(v) => handleInputChange(v.formData)}
+                    uiSchema={uiSchema}
+                    initialValues={{}}
+                    formContext={{
+                      showHandles: false,
+                      size: "large",
+                    }}
+                  />
+                </div>
+              </div>
+            )}
+
+            {/* Action Button */}
             <div
-              className="flex flex-col items-end justify-start"
+              className="flex justify-end pt-2"
               data-id="run-input-actions-section"
             >
               {purpose === "run" && (
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2 px-10"
+                  className="group h-fit min-w-0 gap-2"
                   onClick={handleManualRun}
                   loading={isExecutingGraph}
                   data-id="run-input-manual-run-button"
@@ -136,7 +137,7 @@ export const RunInputDialog = ({
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2 px-10"
+                  className="group h-fit min-w-0 gap-2"
                   onClick={() => setOpenCronSchedulerDialog(true)}
                   data-id="run-input-schedule-button"
                 >

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

@@ -7,11 +7,12 @@ import {
   GraphExecutionMeta,
 } from "@/lib/autogpt-server-api";
 import { parseAsInteger, parseAsString, useQueryStates } from "nuqs";
-import { useCallback, useMemo, useState } from "react";
+import { useMemo, useState } from "react";
+import { uiSchema } from "../../../FlowEditor/nodes/uiSchema";
+import { isCredentialFieldSchema } from "@/components/renderers/InputRenderer/custom/CredentialField/helpers";
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { useReactFlow } from "@xyflow/react";
-import type { CredentialField } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
 
 export const useRunInputDialog = ({
   setIsOpen,
@@ -119,32 +120,27 @@ export const useRunInputDialog = ({
       },
     });
 
-  // Convert credentials schema to credential fields array for CredentialsGroupedView
-  const credentialFields: CredentialField[] = useMemo(() => {
-    if (!credentialsSchema?.properties) return [];
-    return Object.entries(credentialsSchema.properties);
-  }, [credentialsSchema]);
+  // We are rendering the credentials field differently compared to other fields.
+  // In the node, we have the field name as "credential" - so our library catches it and renders it differently.
+  // But here we have a different name, something like `Firecrawl credentials`, so here we are telling the library that this field is a credential field type.
 
-  // Get required credentials as a Set
-  const requiredCredentials = useMemo(() => {
-    return new Set<string>(credentialsSchema?.required || []);
-  }, [credentialsSchema]);
+  const credentialsUiSchema = useMemo(() => {
+    const dynamicUiSchema: any = { ...uiSchema };
 
-  // Handler for individual credential changes
-  const handleCredentialFieldChange = useCallback(
-    (key: string, value?: CredentialsMetaInput) => {
-      setCredentialValues((prev) => {
-        if (value) {
-          return { ...prev, [key]: value };
-        } else {
-          const next = { ...prev };
-          delete next[key];
-          return next;
+    if (credentialsSchema?.properties) {
+      Object.keys(credentialsSchema.properties).forEach((fieldName) => {
+        const fieldSchema = credentialsSchema.properties[fieldName];
+        if (isCredentialFieldSchema(fieldSchema)) {
+          dynamicUiSchema[fieldName] = {
+            ...dynamicUiSchema[fieldName],
+            "ui:field": "custom/credential_field",
+          };
         }
       });
-    },
-    [],
-  );
+    }
+
+    return dynamicUiSchema;
+  }, [credentialsSchema]);
 
   const handleManualRun = async () => {
     // Filter out incomplete credentials (those without a valid id)
@@ -177,14 +173,12 @@ export const useRunInputDialog = ({
   };
 
   return {
-    credentialFields,
-    requiredCredentials,
+    credentialsUiSchema,
     inputValues,
     credentialValues,
     isExecutingGraph,
     handleInputChange,
     handleCredentialChange,
-    handleCredentialFieldChange,
     handleManualRun,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,

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

@@ -18,110 +18,69 @@ interface Props {
   fullWidth?: boolean;
 }
 
-interface SafeModeButtonProps {
-  isEnabled: boolean;
-  label: string;
-  tooltipEnabled: string;
-  tooltipDisabled: string;
-  onToggle: () => void;
-  isPending: boolean;
-  fullWidth?: boolean;
-}
-
-function SafeModeButton({
-  isEnabled,
-  label,
-  tooltipEnabled,
-  tooltipDisabled,
-  onToggle,
-  isPending,
-  fullWidth = false,
-}: SafeModeButtonProps) {
-  return (
-    <Tooltip delayDuration={100}>
-      <TooltipTrigger asChild>
-        <Button
-          variant={isEnabled ? "primary" : "outline"}
-          size="small"
-          onClick={onToggle}
-          disabled={isPending}
-          className={cn("justify-start", fullWidth ? "w-full" : "")}
-        >
-          {isEnabled ? (
-            <>
-              <ShieldCheckIcon weight="bold" size={16} />
-              <Text variant="body" className="text-zinc-200">
-                {label}: ON
-              </Text>
-            </>
-          ) : (
-            <>
-              <ShieldIcon weight="bold" size={16} />
-              <Text variant="body" className="text-zinc-600">
-                {label}: OFF
-              </Text>
-            </>
-          )}
-        </Button>
-      </TooltipTrigger>
-      <TooltipContent>
-        <div className="text-center">
-          <div className="font-medium">
-            {label}: {isEnabled ? "ON" : "OFF"}
-          </div>
-          <div className="mt-1 text-xs text-muted-foreground">
-            {isEnabled ? tooltipEnabled : tooltipDisabled}
-          </div>
-        </div>
-      </TooltipContent>
-    </Tooltip>
-  );
-}
-
 export function FloatingSafeModeToggle({
   graph,
   className,
   fullWidth = false,
 }: Props) {
   const {
-    currentHITLSafeMode,
-    showHITLToggle,
-    handleHITLToggle,
-    currentSensitiveActionSafeMode,
-    showSensitiveActionToggle,
-    handleSensitiveActionToggle,
+    currentSafeMode,
     isPending,
     shouldShowToggle,
+    isStateUndetermined,
+    handleToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle || isPending) {
+  if (!shouldShowToggle || isStateUndetermined || isPending) {
     return null;
   }
 
   return (
-    <div className={cn("fixed z-50 flex flex-col gap-2", className)}>
-      {showHITLToggle && (
-        <SafeModeButton
-          isEnabled={currentHITLSafeMode}
-          label="Human in the loop block approval"
-          tooltipEnabled="The agent will pause at human-in-the-loop blocks and wait for your approval"
-          tooltipDisabled="Human in the loop blocks will proceed automatically"
-          onToggle={handleHITLToggle}
-          isPending={isPending}
-          fullWidth={fullWidth}
-        />
-      )}
-      {showSensitiveActionToggle && (
-        <SafeModeButton
-          isEnabled={currentSensitiveActionSafeMode}
-          label="Sensitive actions blocks approval"
-          tooltipEnabled="The agent will pause at sensitive action blocks and wait for your approval"
-          tooltipDisabled="Sensitive action blocks will proceed automatically"
-          onToggle={handleSensitiveActionToggle}
-          isPending={isPending}
-          fullWidth={fullWidth}
-        />
-      )}
+    <div className={cn("fixed z-50", className)}>
+      <Tooltip delayDuration={100}>
+        <TooltipTrigger asChild>
+          <Button
+            variant={currentSafeMode! ? "primary" : "outline"}
+            key={graph.id}
+            size="small"
+            title={
+              currentSafeMode!
+                ? "Safe Mode: ON. Human in the loop blocks require manual review"
+                : "Safe Mode: OFF. Human in the loop blocks proceed automatically"
+            }
+            onClick={handleToggle}
+            className={cn(fullWidth ? "w-full" : "")}
+          >
+            {currentSafeMode! ? (
+              <>
+                <ShieldCheckIcon weight="bold" size={16} />
+                <Text variant="body" className="text-zinc-200">
+                  Safe Mode: ON
+                </Text>
+              </>
+            ) : (
+              <>
+                <ShieldIcon weight="bold" size={16} />
+                <Text variant="body" className="text-zinc-600">
+                  Safe Mode: OFF
+                </Text>
+              </>
+            )}
+          </Button>
+        </TooltipTrigger>
+        <TooltipContent>
+          <div className="text-center">
+            <div className="font-medium">
+              Safe Mode: {currentSafeMode! ? "ON" : "OFF"}
+            </div>
+            <div className="mt-1 text-xs text-muted-foreground">
+              {currentSafeMode!
+                ? "Human in the loop blocks require manual review"
+                : "Human in the loop blocks proceed automatically"}
+            </div>
+          </div>
+        </TooltipContent>
+      </Tooltip>
     </div>
   );
 }

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

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

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/useFlow.ts

@@ -139,6 +139,14 @@ export const useFlow = () => {
       useNodeStore.getState().setNodes([]);
       useNodeStore.getState().clearResolutionState();
       addNodes(customNodes);
+
+      // Sync hardcoded values with handle IDs.
+      // If a key–value field has a key without a value, the backend omits it from hardcoded values.
+      // But if a handleId exists for that key, it causes inconsistency.
+      // This ensures hardcoded values stay in sync with handle IDs.
+      customNodes.forEach((node) => {
+        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
+      });
     }
   }, [customNodes, addNodes]);
 
@@ -150,14 +158,6 @@ export const useFlow = () => {
     }
   }, [graph?.links, addLinks]);
 
-  useEffect(() => {
-    if (customNodes.length > 0 && graph?.links) {
-      customNodes.forEach((node) => {
-        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
-      });
-    }
-  }, [customNodes, graph?.links]);
-
   // update node execution status in nodes
   useEffect(() => {
     if (

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

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

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

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

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeOutput/NodeOutput.tsx

@@ -1,21 +1,22 @@
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
-import {
-  Accordion,
-  AccordionContent,
-  AccordionItem,
-  AccordionTrigger,
-} from "@/components/molecules/Accordion/Accordion";
 import { beautifyString, cn } from "@/lib/utils";
-import { CopyIcon, CheckIcon } from "@phosphor-icons/react";
+import { CaretDownIcon, CopyIcon, CheckIcon } from "@phosphor-icons/react";
 import { NodeDataViewer } from "./components/NodeDataViewer/NodeDataViewer";
 import { ContentRenderer } from "./components/ContentRenderer";
 import { useNodeOutput } from "./useNodeOutput";
 import { ViewMoreData } from "./components/ViewMoreData";
 
 export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
-  const { outputData, copiedKey, handleCopy, executionResultId, inputData } =
-    useNodeOutput(nodeId);
+  const {
+    outputData,
+    isExpanded,
+    setIsExpanded,
+    copiedKey,
+    handleCopy,
+    executionResultId,
+    inputData,
+  } = useNodeOutput(nodeId);
 
   if (Object.keys(outputData).length === 0) {
     return null;
@@ -24,117 +25,122 @@ export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
   return (
     <div
       data-tutorial-id={`node-output`}
-      className="rounded-b-xl border-t border-zinc-200 px-4 py-2"
+      className="flex flex-col gap-3 rounded-b-xl border-t border-zinc-200 px-4 py-4"
     >
-      <Accordion type="single" collapsible defaultValue="node-output">
-        <AccordionItem value="node-output" className="border-none">
-          <AccordionTrigger className="py-2 hover:no-underline">
-            <Text
-              variant="body-medium"
-              className="!font-semibold text-slate-700"
-            >
-              Node Output
-            </Text>
-          </AccordionTrigger>
-          <AccordionContent className="pt-2">
-            <div className="flex max-w-[350px] flex-col gap-4">
-              <div className="space-y-2">
-                <Text variant="small-medium">Input</Text>
+      <div className="flex items-center justify-between">
+        <Text variant="body-medium" className="!font-semibold text-slate-700">
+          Node Output
+        </Text>
+        <Button
+          variant="ghost"
+          size="small"
+          onClick={() => setIsExpanded(!isExpanded)}
+          className="h-fit min-w-0 p-1 text-slate-600 hover:text-slate-900"
+        >
+          <CaretDownIcon
+            size={16}
+            weight="bold"
+            className={`transition-transform ${isExpanded ? "rotate-180" : ""}`}
+          />
+        </Button>
+      </div>
 
-                <ContentRenderer value={inputData} shortContent={false} />
+      {isExpanded && (
+        <>
+          <div className="flex max-w-[350px] flex-col gap-4">
+            <div className="space-y-2">
+              <Text variant="small-medium">Input</Text>
 
-                <div className="mt-1 flex justify-end gap-1">
-                  <NodeDataViewer
-                    data={inputData}
-                    pinName="Input"
-                    execId={executionResultId}
-                  />
-                  <Button
-                    variant="secondary"
-                    size="small"
-                    onClick={() => handleCopy("input", inputData)}
-                    className={cn(
-                      "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
-                      copiedKey === "input" &&
-                        "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
-                    )}
-                  >
-                    {copiedKey === "input" ? (
-                      <CheckIcon size={12} className="text-green-600" />
-                    ) : (
-                      <CopyIcon size={12} />
-                    )}
-                  </Button>
-                </div>
+              <ContentRenderer value={inputData} shortContent={false} />
+
+              <div className="mt-1 flex justify-end gap-1">
+                <NodeDataViewer
+                  data={inputData}
+                  pinName="Input"
+                  execId={executionResultId}
+                />
+                <Button
+                  variant="secondary"
+                  size="small"
+                  onClick={() => handleCopy("input", inputData)}
+                  className={cn(
+                    "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
+                    copiedKey === "input" &&
+                      "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
+                  )}
+                >
+                  {copiedKey === "input" ? (
+                    <CheckIcon size={12} className="text-green-600" />
+                  ) : (
+                    <CopyIcon size={12} />
+                  )}
+                </Button>
               </div>
+            </div>
 
-              {Object.entries(outputData)
-                .slice(0, 2)
-                .map(([key, value]) => (
-                  <div key={key} className="flex flex-col gap-2">
-                    <div className="flex items-center gap-2">
-                      <Text
-                        variant="small-medium"
-                        className="!font-semibold text-slate-600"
-                      >
-                        Pin:
-                      </Text>
-                      <Text variant="small" className="text-slate-700">
-                        {beautifyString(key)}
-                      </Text>
-                    </div>
-                    <div className="w-full space-y-2">
-                      <Text
-                        variant="small"
-                        className="!font-semibold text-slate-600"
-                      >
-                        Data:
-                      </Text>
-                      <div className="relative space-y-2">
-                        {value.map((item, index) => (
-                          <div key={index}>
-                            <ContentRenderer value={item} shortContent={true} />
-                          </div>
-                        ))}
-
-                        <div className="mt-1 flex justify-end gap-1">
-                          <NodeDataViewer
-                            data={value}
-                            pinName={key}
-                            execId={executionResultId}
-                          />
-                          <Button
-                            variant="secondary"
-                            size="small"
-                            onClick={() => handleCopy(key, value)}
-                            className={cn(
-                              "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
-                              copiedKey === key &&
-                                "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
-                            )}
-                          >
-                            {copiedKey === key ? (
-                              <CheckIcon size={12} className="text-green-600" />
-                            ) : (
-                              <CopyIcon size={12} />
-                            )}
-                          </Button>
+            {Object.entries(outputData)
+              .slice(0, 2)
+              .map(([key, value]) => (
+                <div key={key} className="flex flex-col gap-2">
+                  <div className="flex items-center gap-2">
+                    <Text
+                      variant="small-medium"
+                      className="!font-semibold text-slate-600"
+                    >
+                      Pin:
+                    </Text>
+                    <Text variant="small" className="text-slate-700">
+                      {beautifyString(key)}
+                    </Text>
+                  </div>
+                  <div className="w-full space-y-2">
+                    <Text
+                      variant="small"
+                      className="!font-semibold text-slate-600"
+                    >
+                      Data:
+                    </Text>
+                    <div className="relative space-y-2">
+                      {value.map((item, index) => (
+                        <div key={index}>
+                          <ContentRenderer value={item} shortContent={true} />
                         </div>
+                      ))}
+
+                      <div className="mt-1 flex justify-end gap-1">
+                        <NodeDataViewer
+                          data={value}
+                          pinName={key}
+                          execId={executionResultId}
+                        />
+                        <Button
+                          variant="secondary"
+                          size="small"
+                          onClick={() => handleCopy(key, value)}
+                          className={cn(
+                            "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
+                            copiedKey === key &&
+                              "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
+                          )}
+                        >
+                          {copiedKey === key ? (
+                            <CheckIcon size={12} className="text-green-600" />
+                          ) : (
+                            <CopyIcon size={12} />
+                          )}
+                        </Button>
                       </div>
                     </div>
                   </div>
-                ))}
-            </div>
+                </div>
+              ))}
+          </div>
 
-            {Object.keys(outputData).length > 2 && (
-              <ViewMoreData
-                outputData={outputData}
-                execId={executionResultId}
-              />
-            )}
-          </AccordionContent>
-        </AccordionItem>
-      </Accordion>
+          {Object.keys(outputData).length > 2 && (
+            <ViewMoreData outputData={outputData} execId={executionResultId} />
+          )}
+        </>
+      )}
     </div>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeOutput/useNodeOutput.tsx

@@ -4,6 +4,7 @@ import { useShallow } from "zustand/react/shallow";
 import { useState } from "react";
 
 export const useNodeOutput = (nodeId: string) => {
+  const [isExpanded, setIsExpanded] = useState(true);
   const [copiedKey, setCopiedKey] = useState<string | null>(null);
   const { toast } = useToast();
 
@@ -36,10 +37,13 @@ export const useNodeOutput = (nodeId: string) => {
     }
   };
   return {
-    outputData,
-    inputData,
-    copiedKey,
-    handleCopy,
+    outputData: outputData,
+    inputData: inputData,
+    isExpanded: isExpanded,
+    setIsExpanded: setIsExpanded,
+    copiedKey: copiedKey,
+    setCopiedKey: setCopiedKey,
+    handleCopy: handleCopy,
     executionResultId: nodeExecutionResult?.node_exec_id,
   };
 };

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

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

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

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

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

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

autogpt_platform/frontend/src/app/(platform)/build/components/helper.ts

@@ -61,18 +61,12 @@ export const convertNodesPlusBlockInfoIntoCustomNodes = (
   return customNode;
 };
 
-const isToolSourceName = (sourceName: string): boolean =>
-  sourceName.startsWith("tools_^_");
-
-const cleanupSourceName = (sourceName: string): string =>
-  isToolSourceName(sourceName) ? "tools" : sourceName;
-
 export const linkToCustomEdge = (link: Link): CustomEdge => ({
   id: link.id ?? "",
   type: "custom" as const,
   source: link.source_id,
   target: link.sink_id,
-  sourceHandle: cleanupSourceName(link.source_name),
+  sourceHandle: link.source_name,
   targetHandle: link.sink_name,
   data: {
     isStatic: link.is_static,

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

@@ -267,34 +267,23 @@ export function extractCredentialsNeeded(
       | undefined;
     if (missingCreds && Object.keys(missingCreds).length > 0) {
       const agentName = (setupInfo?.agent_name as string) || "this block";
-      const credentials = Object.values(missingCreds).map((credInfo) => {
-        // Normalize to array at boundary - prefer 'types' array, fall back to single 'type'
-        const typesArray = credInfo.types as
-          | Array<"api_key" | "oauth2" | "user_password" | "host_scoped">
-          | undefined;
-        const singleType =
+      const credentials = Object.values(missingCreds).map((credInfo) => ({
+        provider: (credInfo.provider as string) || "unknown",
+        providerName:
+          (credInfo.provider_name as string) ||
+          (credInfo.provider as string) ||
+          "Unknown Provider",
+        credentialType:
           (credInfo.type as
             | "api_key"
             | "oauth2"
             | "user_password"
-            | "host_scoped"
-            | undefined) || "api_key";
-        const credentialTypes =
-          typesArray && typesArray.length > 0 ? typesArray : [singleType];
-
-        return {
-          provider: (credInfo.provider as string) || "unknown",
-          providerName:
-            (credInfo.provider_name as string) ||
-            (credInfo.provider as string) ||
-            "Unknown Provider",
-          credentialTypes,
-          title:
-            (credInfo.title as string) ||
-            `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
-          scopes: credInfo.scopes as string[] | undefined,
-        };
-      });
+            | "host_scoped") || "api_key",
+        title:
+          (credInfo.title as string) ||
+          `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
+        scopes: credInfo.scopes as string[] | undefined,
+      }));
       return {
         type: "credentials_needed",
         toolName,
@@ -369,14 +358,11 @@ export function extractInputsNeeded(
       credentials.forEach((cred) => {
         const id = cred.id as string;
         if (id) {
-          const credentialTypes = Array.isArray(cred.types)
-            ? cred.types
-            : [(cred.type as string) || "api_key"];
           credentialsSchema[id] = {
             type: "object",
             properties: {},
             credentials_provider: [cred.provider as string],
-            credentials_types: credentialTypes,
+            credentials_types: [(cred.type as string) || "api_key"],
             credentials_scopes: cred.scopes as string[] | undefined,
           };
         }

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

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

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

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

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

@@ -31,18 +31,10 @@ export function AgentSettingsModal({
     }
   }
 
-  const {
-    currentHITLSafeMode,
-    showHITLToggle,
-    handleHITLToggle,
-    currentSensitiveActionSafeMode,
-    showSensitiveActionToggle,
-    handleSensitiveActionToggle,
-    isPending,
-    shouldShowToggle,
-  } = useAgentSafeMode(agent);
+  const { currentSafeMode, isPending, hasHITLBlocks, handleToggle } =
+    useAgentSafeMode(agent);
 
-  if (!shouldShowToggle) return null;
+  if (!hasHITLBlocks) return null;
 
   return (
     <Dialog
@@ -65,48 +57,23 @@ export function AgentSettingsModal({
       )}
       <Dialog.Content>
         <div className="space-y-6">
-          {showHITLToggle && (
-            <div className="flex w-full flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
-              <div className="flex w-full items-start justify-between gap-4">
-                <div className="flex-1">
-                  <Text variant="large-semibold">
-                    Human-in-the-loop approval
-                  </Text>
-                  <Text variant="large" className="mt-1 text-zinc-900">
-                    The agent will pause at human-in-the-loop blocks and wait
-                    for your review before continuing
-                  </Text>
-                </div>
-                <Switch
-                  checked={currentHITLSafeMode || false}
-                  onCheckedChange={handleHITLToggle}
-                  disabled={isPending}
-                  className="mt-1"
-                />
+          <div className="flex w-full flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
+            <div className="flex w-full items-start justify-between gap-4">
+              <div className="flex-1">
+                <Text variant="large-semibold">Require human approval</Text>
+                <Text variant="large" className="mt-1 text-zinc-900">
+                  The agent will pause and wait for your review before
+                  continuing
+                </Text>
               </div>
+              <Switch
+                checked={currentSafeMode || false}
+                onCheckedChange={handleToggle}
+                disabled={isPending}
+                className="mt-1"
+              />
             </div>
-          )}
-          {showSensitiveActionToggle && (
-            <div className="flex w-full flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
-              <div className="flex w-full items-start justify-between gap-4">
-                <div className="flex-1">
-                  <Text variant="large-semibold">
-                    Sensitive action approval
-                  </Text>
-                  <Text variant="large" className="mt-1 text-zinc-900">
-                    The agent will pause at sensitive action blocks and wait for
-                    your review before continuing
-                  </Text>
-                </div>
-                <Switch
-                  checked={currentSensitiveActionSafeMode}
-                  onCheckedChange={handleSensitiveActionToggle}
-                  disabled={isPending}
-                  className="mt-1"
-                />
-              </div>
-            </div>
-          )}
+          </div>
         </div>
       </Dialog.Content>
     </Dialog>

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

@@ -14,10 +14,6 @@ import {
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { useEffect, useRef, useState } from "react";
 import { ScheduleAgentModal } from "../ScheduleAgentModal/ScheduleAgentModal";
-import {
-  AIAgentSafetyPopup,
-  useAIAgentSafetyPopup,
-} from "./components/AIAgentSafetyPopup/AIAgentSafetyPopup";
 import { ModalHeader } from "./components/ModalHeader/ModalHeader";
 import { ModalRunSection } from "./components/ModalRunSection/ModalRunSection";
 import { RunActions } from "./components/RunActions/RunActions";
@@ -87,17 +83,8 @@ export function RunAgentModal({
 
   const [isScheduleModalOpen, setIsScheduleModalOpen] = useState(false);
   const [hasOverflow, setHasOverflow] = useState(false);
-  const [isSafetyPopupOpen, setIsSafetyPopupOpen] = useState(false);
-  const [pendingRunAction, setPendingRunAction] = useState<(() => void) | null>(
-    null,
-  );
   const contentRef = useRef<HTMLDivElement>(null);
 
-  const { shouldShowPopup, dismissPopup } = useAIAgentSafetyPopup(
-    agent.has_sensitive_action,
-    agent.has_human_in_the_loop,
-  );
-
   const hasAnySetupFields =
     Object.keys(agentInputFields || {}).length > 0 ||
     Object.keys(agentCredentialsInputFields || {}).length > 0;
@@ -178,24 +165,6 @@ export function RunAgentModal({
     onScheduleCreated?.(schedule);
   }
 
-  function handleRunWithSafetyCheck() {
-    if (shouldShowPopup) {
-      setPendingRunAction(() => handleRun);
-      setIsSafetyPopupOpen(true);
-    } else {
-      handleRun();
-    }
-  }
-
-  function handleSafetyPopupAcknowledge() {
-    setIsSafetyPopupOpen(false);
-    dismissPopup();
-    if (pendingRunAction) {
-      pendingRunAction();
-      setPendingRunAction(null);
-    }
-  }
-
   return (
     <>
       <Dialog
@@ -279,7 +248,7 @@ export function RunAgentModal({
                 )}
                 <RunActions
                   defaultRunType={defaultRunType}
-                  onRun={handleRunWithSafetyCheck}
+                  onRun={handleRun}
                   isExecuting={isExecuting}
                   isSettingUpTrigger={isSettingUpTrigger}
                   isRunReady={allRequiredInputsAreSet}
@@ -297,11 +266,6 @@ export function RunAgentModal({
           </div>
         </Dialog.Content>
       </Dialog>
-
-      <AIAgentSafetyPopup
-        isOpen={isSafetyPopupOpen}
-        onAcknowledge={handleSafetyPopupAcknowledge}
-      />
     </>
   );
 }

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

@@ -1,95 +0,0 @@
-"use client";
-
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Key, storage } from "@/services/storage/local-storage";
-import { ShieldCheckIcon } from "@phosphor-icons/react";
-import { useCallback, useEffect, useState } from "react";
-
-interface Props {
-  onAcknowledge: () => void;
-  isOpen: boolean;
-}
-
-export function AIAgentSafetyPopup({ onAcknowledge, isOpen }: Props) {
-  function handleAcknowledge() {
-    // Mark popup as shown so it won't appear again
-    storage.set(Key.AI_AGENT_SAFETY_POPUP_SHOWN, "true");
-    onAcknowledge();
-  }
-
-  if (!isOpen) return null;
-
-  return (
-    <Dialog
-      controlled={{ isOpen, set: () => {} }}
-      styling={{ maxWidth: "480px" }}
-    >
-      <Dialog.Content>
-        <div className="flex flex-col items-center p-6 text-center">
-          <div className="mb-6 flex h-16 w-16 items-center justify-center rounded-full bg-blue-50">
-            <ShieldCheckIcon
-              weight="fill"
-              size={32}
-              className="text-blue-600"
-            />
-          </div>
-
-          <Text variant="h3" className="mb-4">
-            Safety Checks Enabled
-          </Text>
-
-          <Text variant="body" className="mb-2 text-zinc-700">
-            AI-generated agents may take actions that affect your data or
-            external systems.
-          </Text>
-
-          <Text variant="body" className="mb-8 text-zinc-700">
-            AutoGPT includes safety checks so you&apos;ll always have the
-            opportunity to review and approve sensitive actions before they
-            happen.
-          </Text>
-
-          <Button
-            variant="primary"
-            size="large"
-            className="w-full"
-            onClick={handleAcknowledge}
-          >
-            Got it
-          </Button>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}
-
-export function useAIAgentSafetyPopup(
-  hasSensitiveAction: boolean,
-  hasHumanInTheLoop: boolean,
-) {
-  const [shouldShowPopup, setShouldShowPopup] = useState(false);
-  const [hasChecked, setHasChecked] = useState(false);
-
-  useEffect(() => {
-    // Only check once after mount (to avoid SSR issues)
-    if (hasChecked) return;
-
-    const hasSeenPopup =
-      storage.get(Key.AI_AGENT_SAFETY_POPUP_SHOWN) === "true";
-    const isRelevantAgent = hasSensitiveAction || hasHumanInTheLoop;
-
-    setShouldShowPopup(!hasSeenPopup && isRelevantAgent);
-    setHasChecked(true);
-  }, [hasSensitiveAction, hasHumanInTheLoop, hasChecked]);
-
-  const dismissPopup = useCallback(() => {
-    setShouldShowPopup(false);
-  }, []);
-
-  return {
-    shouldShowPopup,
-    dismissPopup,
-  };
-}

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

@@ -5,37 +5,30 @@ import {
   AccordionItem,
   AccordionTrigger,
 } from "@/components/molecules/Accordion/Accordion";
-import {
-  CredentialsMetaInput,
-  CredentialsType,
-} from "@/lib/autogpt-server-api/types";
 import { CredentialsProvidersContext } from "@/providers/agent-credentials/credentials-provider";
-import { SlidersHorizontalIcon } from "@phosphor-icons/react";
+import { SlidersHorizontal } from "@phosphor-icons/react";
 import { useContext, useEffect, useMemo, useRef } from "react";
+import { useRunAgentModalContext } from "../../context";
 import {
   areSystemCredentialProvidersLoading,
   CredentialField,
   findSavedCredentialByProviderAndType,
   hasMissingRequiredSystemCredentials,
   splitCredentialFieldsBySystem,
-} from "./helpers";
+} from "../helpers";
 
 type Props = {
   credentialFields: CredentialField[];
   requiredCredentials: Set<string>;
-  inputCredentials: Record<string, CredentialsMetaInput | undefined>;
-  inputValues: Record<string, any>;
-  onCredentialChange: (key: string, value?: CredentialsMetaInput) => void;
 };
 
 export function CredentialsGroupedView({
   credentialFields,
   requiredCredentials,
-  inputCredentials,
-  inputValues,
-  onCredentialChange,
 }: Props) {
   const allProviders = useContext(CredentialsProvidersContext);
+  const { inputCredentials, setInputCredentialsValue, inputValues } =
+    useRunAgentModalContext();
 
   const { userCredentialFields, systemCredentialFields } = useMemo(
     () =>
@@ -94,11 +87,11 @@ export function CredentialsGroupedView({
       );
 
       if (savedCredential) {
-        onCredentialChange(key, {
+        setInputCredentialsValue(key, {
           id: savedCredential.id,
           provider: savedCredential.provider,
-          type: savedCredential.type as CredentialsType,
-          title: savedCredential.title,
+          type: savedCredential.type,
+          title: (savedCredential as { title?: string }).title,
         });
       }
     }
@@ -110,7 +103,7 @@ export function CredentialsGroupedView({
     systemCredentialFields,
     requiredCredentials,
     inputCredentials,
-    onCredentialChange,
+    setInputCredentialsValue,
     isLoadingProviders,
   ]);
 
@@ -130,7 +123,7 @@ export function CredentialsGroupedView({
                   }
                   selectedCredentials={selectedCred}
                   onSelectCredentials={(value) => {
-                    onCredentialChange(key, value);
+                    setInputCredentialsValue(key, value);
                   }}
                   siblingInputs={inputValues}
                   isOptional={!requiredCredentials.has(key)}
@@ -150,8 +143,7 @@ export function CredentialsGroupedView({
           <AccordionItem value="system-credentials" className="border-none">
             <AccordionTrigger className="py-2 text-sm text-muted-foreground hover:no-underline">
               <div className="flex items-center gap-1">
-                <SlidersHorizontalIcon size={16} weight="bold" /> System
-                credentials
+                <SlidersHorizontal size={16} weight="bold" /> System credentials
                 {hasMissingSystemCredentials && (
                   <span className="text-destructive">(missing)</span>
                 )}
@@ -171,7 +163,7 @@ export function CredentialsGroupedView({
                         }
                         selectedCredentials={selectedCred}
                         onSelectCredentials={(value) => {
-                          onCredentialChange(key, value);
+                          setInputCredentialsValue(key, value);
                         }}
                         siblingInputs={inputValues}
                         isOptional={!requiredCredentials.has(key)}

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

@@ -1,9 +1,9 @@
 import { Input } from "@/components/atoms/Input/Input";
-import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import { useMemo } from "react";
 import { RunAgentInputs } from "../../../RunAgentInputs/RunAgentInputs";
 import { useRunAgentModalContext } from "../../context";
+import { CredentialsGroupedView } from "../CredentialsGroupedView/CredentialsGroupedView";
 import { ModalSection } from "../ModalSection/ModalSection";
 import { WebhookTriggerBanner } from "../WebhookTriggerBanner/WebhookTriggerBanner";
 
@@ -19,8 +19,6 @@ export function ModalRunSection() {
     setInputValue,
     agentInputFields,
     agentCredentialsInputFields,
-    inputCredentials,
-    setInputCredentialsValue,
   } = useRunAgentModalContext();
 
   const inputFields = Object.entries(agentInputFields || {});
@@ -104,9 +102,6 @@ export function ModalRunSection() {
           <CredentialsGroupedView
             credentialFields={credentialFields}
             requiredCredentials={requiredCredentials}
-            inputCredentials={inputCredentials}
-            inputValues={inputValues}
-            onCredentialChange={setInputCredentialsValue}
           />
         </ModalSection>
       ) : null}

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

@@ -1,5 +1,5 @@
 import { CredentialsProvidersContextType } from "@/providers/agent-credentials/credentials-provider";
-import { getSystemCredentials } from "../../helpers";
+import { getSystemCredentials } from "../../../../../../../../../../../components/contextual/CredentialsInput/helpers";
 
 export type CredentialField = [string, any];
 

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

@@ -5,104 +5,48 @@ import { Graph } from "@/lib/autogpt-server-api/types";
 import { cn } from "@/lib/utils";
 import { ShieldCheckIcon, ShieldIcon } from "@phosphor-icons/react";
 import { useAgentSafeMode } from "@/hooks/useAgentSafeMode";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipTrigger,
-} from "@/components/atoms/Tooltip/BaseTooltip";
 
 interface Props {
   graph: GraphModel | LibraryAgent | Graph;
   className?: string;
+  fullWidth?: boolean;
 }
 
-interface SafeModeIconButtonProps {
-  isEnabled: boolean;
-  label: string;
-  tooltipEnabled: string;
-  tooltipDisabled: string;
-  onToggle: () => void;
-  isPending: boolean;
-}
-
-function SafeModeIconButton({
-  isEnabled,
-  label,
-  tooltipEnabled,
-  tooltipDisabled,
-  onToggle,
-  isPending,
-}: SafeModeIconButtonProps) {
-  return (
-    <Tooltip delayDuration={100}>
-      <TooltipTrigger asChild>
-        <Button
-          variant="icon"
-          size="icon"
-          aria-label={`${label}: ${isEnabled ? "ON" : "OFF"}. ${isEnabled ? tooltipEnabled : tooltipDisabled}`}
-          onClick={onToggle}
-          disabled={isPending}
-          className={cn(isPending ? "opacity-0" : "opacity-100")}
-        >
-          {isEnabled ? (
-            <ShieldCheckIcon weight="bold" size={16} />
-          ) : (
-            <ShieldIcon weight="bold" size={16} />
-          )}
-        </Button>
-      </TooltipTrigger>
-      <TooltipContent>
-        <div className="text-center">
-          <div className="font-medium">
-            {label}: {isEnabled ? "ON" : "OFF"}
-          </div>
-          <div className="mt-1 text-xs text-muted-foreground">
-            {isEnabled ? tooltipEnabled : tooltipDisabled}
-          </div>
-        </div>
-      </TooltipContent>
-    </Tooltip>
-  );
-}
-
-export function SafeModeToggle({ graph, className }: Props) {
+export function SafeModeToggle({ graph }: Props) {
   const {
-    currentHITLSafeMode,
-    showHITLToggle,
-    handleHITLToggle,
-    currentSensitiveActionSafeMode,
-    showSensitiveActionToggle,
-    handleSensitiveActionToggle,
+    currentSafeMode,
     isPending,
     shouldShowToggle,
+    isStateUndetermined,
+    handleToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle) {
+  if (!shouldShowToggle || isStateUndetermined) {
     return null;
   }
 
   return (
-    <div className={cn("flex gap-1", className)}>
-      {showHITLToggle && (
-        <SafeModeIconButton
-          isEnabled={currentHITLSafeMode}
-          label="Human-in-the-loop"
-          tooltipEnabled="The agent will pause at human-in-the-loop blocks and wait for your approval"
-          tooltipDisabled="Human-in-the-loop blocks will proceed automatically"
-          onToggle={handleHITLToggle}
-          isPending={isPending}
-        />
+    <Button
+      variant="icon"
+      key={graph.id}
+      size="icon"
+      aria-label={
+        currentSafeMode!
+          ? "Safe Mode: ON. Human in the loop blocks require manual review"
+          : "Safe Mode: OFF. Human in the loop blocks proceed automatically"
+      }
+      onClick={handleToggle}
+      className={cn(isPending ? "opacity-0" : "opacity-100")}
+    >
+      {currentSafeMode! ? (
+        <>
+          <ShieldCheckIcon weight="bold" size={16} />
+        </>
+      ) : (
+        <>
+          <ShieldIcon weight="bold" size={16} />
+        </>
       )}
-      {showSensitiveActionToggle && (
-        <SafeModeIconButton
-          isEnabled={currentSensitiveActionSafeMode}
-          label="Sensitive actions"
-          tooltipEnabled="The agent will pause at sensitive action blocks and wait for your approval"
-          tooltipDisabled="Sensitive action blocks will proceed automatically"
-          onToggle={handleSensitiveActionToggle}
-          isPending={isPending}
-        />
-      )}
-    </div>
+    </Button>
   );
 }

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

@@ -13,16 +13,8 @@ interface Props {
 }
 
 export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
-  const {
-    currentHITLSafeMode,
-    showHITLToggle,
-    handleHITLToggle,
-    currentSensitiveActionSafeMode,
-    showSensitiveActionToggle,
-    handleSensitiveActionToggle,
-    isPending,
-    shouldShowToggle,
-  } = useAgentSafeMode(agent);
+  const { currentSafeMode, isPending, hasHITLBlocks, handleToggle } =
+    useAgentSafeMode(agent);
 
   return (
     <SelectedViewLayout agent={agent}>
@@ -42,51 +34,24 @@ export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
         </div>
 
         <div className={`${AGENT_LIBRARY_SECTION_PADDING_X} space-y-6`}>
-          {shouldShowToggle ? (
-            <>
-              {showHITLToggle && (
-                <div className="flex w-full max-w-2xl flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
-                  <div className="flex w-full items-start justify-between gap-4">
-                    <div className="flex-1">
-                      <Text variant="large-semibold">
-                        Human-in-the-loop approval
-                      </Text>
-                      <Text variant="large" className="mt-1 text-zinc-900">
-                        The agent will pause at human-in-the-loop blocks and
-                        wait for your review before continuing
-                      </Text>
-                    </div>
-                    <Switch
-                      checked={currentHITLSafeMode || false}
-                      onCheckedChange={handleHITLToggle}
-                      disabled={isPending}
-                      className="mt-1"
-                    />
-                  </div>
+          {hasHITLBlocks ? (
+            <div className="flex w-full max-w-2xl flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
+              <div className="flex w-full items-start justify-between gap-4">
+                <div className="flex-1">
+                  <Text variant="large-semibold">Require human approval</Text>
+                  <Text variant="large" className="mt-1 text-zinc-900">
+                    The agent will pause and wait for your review before
+                    continuing
+                  </Text>
                 </div>
-              )}
-              {showSensitiveActionToggle && (
-                <div className="flex w-full max-w-2xl flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
-                  <div className="flex w-full items-start justify-between gap-4">
-                    <div className="flex-1">
-                      <Text variant="large-semibold">
-                        Sensitive action approval
-                      </Text>
-                      <Text variant="large" className="mt-1 text-zinc-900">
-                        The agent will pause at sensitive action blocks and wait
-                        for your review before continuing
-                      </Text>
-                    </div>
-                    <Switch
-                      checked={currentSensitiveActionSafeMode}
-                      onCheckedChange={handleSensitiveActionToggle}
-                      disabled={isPending}
-                      className="mt-1"
-                    />
-                  </div>
-                </div>
-              )}
-            </>
+                <Switch
+                  checked={currentSafeMode || false}
+                  onCheckedChange={handleToggle}
+                  disabled={isPending}
+                  className="mt-1"
+                />
+              </div>
+            </div>
           ) : (
             <div className="rounded-xl border border-zinc-100 bg-white p-6">
               <Text variant="body" className="text-muted-foreground">

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryUploadAgentDialog/LibraryUploadAgentDialog.tsx

@@ -2,7 +2,6 @@
 import { Button } from "@/components/atoms/Button/Button";
 import { FileInput } from "@/components/atoms/FileInput/FileInput";
 import { Input } from "@/components/atoms/Input/Input";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import {
   Form,
@@ -121,7 +120,7 @@ export default function LibraryUploadAgentDialog() {
           >
             {isUploading ? (
               <div className="flex items-center gap-2">
-                <LoadingSpinner size="small" className="text-white" />
+                <div className="h-4 w-4 animate-spin rounded-full border-b-2 border-t-2 border-white"></div>
                 <span>Uploading...</span>
               </div>
             ) : (

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

@@ -6383,11 +6383,6 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
-          "has_sensitive_action": {
-            "type": "boolean",
-            "title": "Has Sensitive Action",
-            "readOnly": true
-          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -6404,7 +6399,6 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
-          "has_sensitive_action",
           "trigger_setup_info"
         ],
         "title": "BaseGraph"
@@ -7635,11 +7629,6 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
-          "has_sensitive_action": {
-            "type": "boolean",
-            "title": "Has Sensitive Action",
-            "readOnly": true
-          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7663,7 +7652,6 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
-          "has_sensitive_action",
           "trigger_setup_info",
           "credentials_input_schema"
         ],
@@ -7742,11 +7730,6 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
-          "has_sensitive_action": {
-            "type": "boolean",
-            "title": "Has Sensitive Action",
-            "readOnly": true
-          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7771,7 +7754,6 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
-          "has_sensitive_action",
           "trigger_setup_info",
           "credentials_input_schema"
         ],
@@ -7780,14 +7762,8 @@
       "GraphSettings": {
         "properties": {
           "human_in_the_loop_safe_mode": {
-            "type": "boolean",
-            "title": "Human In The Loop Safe Mode",
-            "default": true
-          },
-          "sensitive_action_safe_mode": {
-            "type": "boolean",
-            "title": "Sensitive Action Safe Mode",
-            "default": false
+            "anyOf": [{ "type": "boolean" }, { "type": "null" }],
+            "title": "Human In The Loop Safe Mode"
           }
         },
         "type": "object",
@@ -7945,16 +7921,6 @@
             "title": "Has External Trigger",
             "description": "Whether the agent has an external trigger (e.g. webhook) node"
           },
-          "has_human_in_the_loop": {
-            "type": "boolean",
-            "title": "Has Human In The Loop",
-            "description": "Whether the agent has human-in-the-loop blocks"
-          },
-          "has_sensitive_action": {
-            "type": "boolean",
-            "title": "Has Sensitive Action",
-            "description": "Whether the agent has sensitive action blocks"
-          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -8001,8 +7967,6 @@
           "output_schema",
           "credentials_input_schema",
           "has_external_trigger",
-          "has_human_in_the_loop",
-          "has_sensitive_action",
           "new_output",
           "can_access_graph",
           "is_latest_version",
@@ -8829,11 +8793,6 @@
             "title": "Graph Version",
             "description": "Graph version"
           },
-          "node_id": {
-            "type": "string",
-            "title": "Node Id",
-            "description": "Node ID in the graph definition"
-          },
           "payload": {
             "anyOf": [
               { "additionalProperties": true, "type": "object" },
@@ -8907,7 +8866,6 @@
           "graph_exec_id",
           "graph_id",
           "graph_version",
-          "node_id",
           "payload",
           "editable",
           "status",
@@ -9431,12 +9389,6 @@
             "type": "array",
             "title": "Reviews",
             "description": "All reviews with their approval status, data, and messages"
-          },
-          "auto_approve_node_ids": {
-            "items": { "type": "string" },
-            "type": "array",
-            "title": "Auto Approve Node Ids",
-            "description": "List of node IDs (from the graph definition) to auto-approve for the remainder of this execution. Future reviews from these specific nodes will be automatically approved. This only affects the current execution run."
           }
         },
         "type": "object",

autogpt_platform/frontend/src/components/organisms/PendingReviewsList/PendingReviewsList.tsx

@@ -37,7 +37,7 @@ export function PendingReviewsList({
   >({});
 
   const [pendingAction, setPendingAction] = useState<
-    "approve" | "approve-all" | "reject" | null
+    "approve" | "reject" | null
   >(null);
 
   const { toast } = useToast();
@@ -92,10 +92,7 @@ export function PendingReviewsList({
     setReviewMessageMap((prev) => ({ ...prev, [nodeExecId]: message }));
   }
 
-  function processReviews(
-    approved: boolean,
-    autoApproveFutureActions: boolean = false,
-  ) {
+  function processReviews(approved: boolean) {
     if (reviews.length === 0) {
       toast({
         title: "No reviews to process",
@@ -105,13 +102,7 @@ export function PendingReviewsList({
       return;
     }
 
-    setPendingAction(
-      autoApproveFutureActions
-        ? "approve-all"
-        : approved
-          ? "approve"
-          : "reject",
-    );
+    setPendingAction(approved ? "approve" : "reject");
     const reviewItems = [];
 
     for (const review of reviews) {
@@ -143,15 +134,9 @@ export function PendingReviewsList({
       });
     }
 
-    // Collect unique node_ids if auto-approving future actions
-    const autoApproveNodeIds = autoApproveFutureActions
-      ? [...new Set(reviews.map((r) => r.node_id))]
-      : [];
-
     reviewActionMutation.mutate({
       data: {
         reviews: reviewItems,
-        auto_approve_node_ids: autoApproveNodeIds,
       },
     });
   }
@@ -206,8 +191,12 @@ export function PendingReviewsList({
         ))}
       </div>
 
-      <div className="space-y-4">
-        <div className="flex flex-wrap gap-2">
+      <div className="space-y-7">
+        <Text variant="body" className="text-textGrey">
+          Note: Changes you make here apply only to this task
+        </Text>
+
+        <div className="flex gap-2">
           <Button
             onClick={() => processReviews(true)}
             disabled={reviewActionMutation.isPending || reviews.length === 0}
@@ -219,17 +208,6 @@ export function PendingReviewsList({
           >
             Approve
           </Button>
-          <Button
-            onClick={() => processReviews(true, true)}
-            disabled={reviewActionMutation.isPending || reviews.length === 0}
-            variant="secondary"
-            className="flex items-center justify-center gap-2 rounded-full px-4 py-3"
-            loading={
-              pendingAction === "approve-all" && reviewActionMutation.isPending
-            }
-          >
-            Approve all future actions
-          </Button>
           <Button
             onClick={() => processReviews(false)}
             disabled={reviewActionMutation.isPending || reviews.length === 0}
@@ -242,11 +220,6 @@ export function PendingReviewsList({
             Reject
           </Button>
         </div>
-
-        <Text variant="small" className="text-textGrey">
-          You can turn auto-approval on or off anytime in this agent&apos;s
-          settings.
-        </Text>
       </div>
     </div>
   );

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/components/CredentialFieldTitle.tsx

@@ -35,13 +35,12 @@ export const CredentialFieldTitle = (props: {
     uiOptions,
   );
 
-  const provider = getCredentialProviderFromSchema(
-    useNodeStore.getState().getHardCodedValues(nodeId),
-    schema as BlockIOCredentialsSubSchema,
+  const credentialProvider = toDisplayName(
+    getCredentialProviderFromSchema(
+      useNodeStore.getState().getHardCodedValues(nodeId),
+      schema as BlockIOCredentialsSubSchema,
+    ) ?? "",
   );
-  const credentialProvider = provider
-    ? `${toDisplayName(provider)} credential`
-    : "credential";
 
   const updatedUiSchema = updateUiOption(uiSchema, {
     showHandles: false,

autogpt_platform/frontend/src/hooks/useAgentSafeMode.ts

@@ -20,15 +20,11 @@ function hasHITLBlocks(graph: GraphModel | LibraryAgent | Graph): boolean {
   if ("has_human_in_the_loop" in graph) {
     return !!graph.has_human_in_the_loop;
   }
-  return false;
-}
 
-function hasSensitiveActionBlocks(
-  graph: GraphModel | LibraryAgent | Graph,
-): boolean {
-  if ("has_sensitive_action" in graph) {
-    return !!graph.has_sensitive_action;
+  if (isLibraryAgent(graph)) {
+    return graph.settings?.human_in_the_loop_safe_mode !== null;
   }
+
   return false;
 }
 
@@ -44,9 +40,7 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
 
   const graphId = getGraphId(graph);
   const isAgent = isLibraryAgent(graph);
-  const showHITLToggle = hasHITLBlocks(graph);
-  const showSensitiveActionToggle = hasSensitiveActionBlocks(graph);
-  const shouldShowToggle = showHITLToggle || showSensitiveActionToggle;
+  const shouldShowToggle = hasHITLBlocks(graph);
 
   const { mutateAsync: updateGraphSettings, isPending } =
     usePatchV1UpdateGraphSettings();
@@ -62,37 +56,27 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
     },
   );
 
-  const [localHITLSafeMode, setLocalHITLSafeMode] = useState<boolean>(true);
-  const [localSensitiveActionSafeMode, setLocalSensitiveActionSafeMode] =
-    useState<boolean>(false);
-  const [isLocalStateLoaded, setIsLocalStateLoaded] = useState<boolean>(false);
+  const [localSafeMode, setLocalSafeMode] = useState<boolean | null>(null);
 
   useEffect(() => {
     if (!isAgent && libraryAgent) {
-      setLocalHITLSafeMode(
-        libraryAgent.settings?.human_in_the_loop_safe_mode ?? true,
-      );
-      setLocalSensitiveActionSafeMode(
-        libraryAgent.settings?.sensitive_action_safe_mode ?? false,
-      );
-      setIsLocalStateLoaded(true);
+      const backendValue = libraryAgent.settings?.human_in_the_loop_safe_mode;
+      if (backendValue !== undefined) {
+        setLocalSafeMode(backendValue);
+      }
     }
   }, [isAgent, libraryAgent]);
 
-  const currentHITLSafeMode = isAgent
-    ? (graph.settings?.human_in_the_loop_safe_mode ?? true)
-    : localHITLSafeMode;
+  const currentSafeMode = isAgent
+    ? graph.settings?.human_in_the_loop_safe_mode
+    : localSafeMode;
 
-  const currentSensitiveActionSafeMode = isAgent
-    ? (graph.settings?.sensitive_action_safe_mode ?? false)
-    : localSensitiveActionSafeMode;
+  const isStateUndetermined = isAgent
+    ? graph.settings?.human_in_the_loop_safe_mode == null
+    : isLoading || localSafeMode === null;
 
-  const isHITLStateUndetermined = isAgent
-    ? false
-    : isLoading || !isLocalStateLoaded;
-
-  const handleHITLToggle = useCallback(async () => {
-    const newSafeMode = !currentHITLSafeMode;
+  const handleToggle = useCallback(async () => {
+    const newSafeMode = !currentSafeMode;
 
     try {
       await updateGraphSettings({
@@ -101,7 +85,7 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
       });
 
       if (!isAgent) {
-        setLocalHITLSafeMode(newSafeMode);
+        setLocalSafeMode(newSafeMode);
       }
 
       if (isAgent) {
@@ -117,62 +101,37 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
       queryClient.invalidateQueries({ queryKey: ["v2", "executions"] });
 
       toast({
-        title: `HITL safe mode ${newSafeMode ? "enabled" : "disabled"}`,
+        title: `Safe mode ${newSafeMode ? "enabled" : "disabled"}`,
         description: newSafeMode
           ? "Human-in-the-loop blocks will require manual review"
           : "Human-in-the-loop blocks will proceed automatically",
         duration: 2000,
       });
     } catch (error) {
-      handleToggleError(error, isAgent, toast);
-    }
-  }, [
-    currentHITLSafeMode,
-    graphId,
-    isAgent,
-    graph.id,
-    updateGraphSettings,
-    queryClient,
-    toast,
-  ]);
+      const isNotFoundError =
+        error instanceof Error &&
+        (error.message.includes("404") || error.message.includes("not found"));
 
-  const handleSensitiveActionToggle = useCallback(async () => {
-    const newSafeMode = !currentSensitiveActionSafeMode;
-
-    try {
-      await updateGraphSettings({
-        graphId,
-        data: { sensitive_action_safe_mode: newSafeMode },
-      });
-
-      if (!isAgent) {
-        setLocalSensitiveActionSafeMode(newSafeMode);
-      }
-
-      if (isAgent) {
-        queryClient.invalidateQueries({
-          queryKey: getGetV2GetLibraryAgentQueryOptions(graph.id.toString())
-            .queryKey,
+      if (!isAgent && isNotFoundError) {
+        toast({
+          title: "Safe mode not available",
+          description:
+            "To configure safe mode, please save this graph to your library first.",
+          variant: "destructive",
+        });
+      } else {
+        toast({
+          title: "Failed to update safe mode",
+          description:
+            error instanceof Error
+              ? error.message
+              : "An unexpected error occurred.",
+          variant: "destructive",
         });
       }
-
-      queryClient.invalidateQueries({
-        queryKey: ["v1", "graphs", graphId, "executions"],
-      });
-      queryClient.invalidateQueries({ queryKey: ["v2", "executions"] });
-
-      toast({
-        title: `Sensitive action safe mode ${newSafeMode ? "enabled" : "disabled"}`,
-        description: newSafeMode
-          ? "Sensitive action blocks will require manual review"
-          : "Sensitive action blocks will proceed automatically",
-        duration: 2000,
-      });
-    } catch (error) {
-      handleToggleError(error, isAgent, toast);
     }
   }, [
-    currentSensitiveActionSafeMode,
+    currentSafeMode,
     graphId,
     isAgent,
     graph.id,
@@ -182,53 +141,11 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
   ]);
 
   return {
-    // HITL safe mode
-    currentHITLSafeMode,
-    showHITLToggle,
-    isHITLStateUndetermined,
-    handleHITLToggle,
-
-    // Sensitive action safe mode
-    currentSensitiveActionSafeMode,
-    showSensitiveActionToggle,
-    handleSensitiveActionToggle,
-
-    // General
+    currentSafeMode,
     isPending,
     shouldShowToggle,
-
-    // Backwards compatibility
-    currentSafeMode: currentHITLSafeMode,
-    isStateUndetermined: isHITLStateUndetermined,
-    handleToggle: handleHITLToggle,
-    hasHITLBlocks: showHITLToggle,
+    isStateUndetermined,
+    handleToggle,
+    hasHITLBlocks: shouldShowToggle,
   };
 }
-
-function handleToggleError(
-  error: unknown,
-  isAgent: boolean,
-  toast: ReturnType<typeof useToast>["toast"],
-) {
-  const isNotFoundError =
-    error instanceof Error &&
-    (error.message.includes("404") || error.message.includes("not found"));
-
-  if (!isAgent && isNotFoundError) {
-    toast({
-      title: "Safe mode not available",
-      description:
-        "To configure safe mode, please save this graph to your library first.",
-      variant: "destructive",
-    });
-  } else {
-    toast({
-      title: "Failed to update safe mode",
-      description:
-        error instanceof Error
-          ? error.message
-          : "An unexpected error occurred.",
-      variant: "destructive",
-    });
-  }
-}

autogpt_platform/frontend/src/lib/dexie/draft-utils.ts

@@ -5,7 +5,7 @@ import isEqual from "lodash/isEqual";
 export function cleanNode(node: CustomNode) {
   return {
     id: node.id,
-    // Note: position is intentionally excluded to prevent draft saves when dragging nodes
+    position: node.position,
     data: {
       hardcodedValues: node.data.hardcodedValues,
       title: node.data.title,

autogpt_platform/frontend/src/services/storage/local-storage.ts

@@ -10,7 +10,6 @@ export enum Key {
   LIBRARY_AGENTS_CACHE = "library-agents-cache",
   CHAT_SESSION_ID = "chat_session_id",
   COOKIE_CONSENT = "autogpt_cookie_consent",
-  AI_AGENT_SAFETY_POPUP_SHOWN = "ai-agent-safety-popup-shown",
 }
 
 function get(key: Key) {

docs/CLAUDE.md

@@ -1,44 +0,0 @@
-# Documentation Guidelines
-
-## Block Documentation Manual Sections
-
-When updating manual sections (`<!-- MANUAL: ... -->`) in block documentation files (e.g., `docs/integrations/basic.md`), follow these formats:
-
-### How It Works Section
-
-Provide a technical explanation of how the block functions:
-- Describe the processing logic in 1-2 paragraphs
-- Mention any validation, error handling, or edge cases
-- Use code examples with backticks when helpful (e.g., `[[1, 2], [3, 4]]` becomes `[1, 2, 3, 4]`)
-
-Example:
-```markdown
-<!-- MANUAL: how_it_works -->
-The block iterates through each list in the input and extends a result list with all elements from each one. It processes lists in order, so `[[1, 2], [3, 4]]` becomes `[1, 2, 3, 4]`.
-
-The block includes validation to ensure each item is actually a list. If a non-list value is encountered, the block outputs an error message instead of proceeding.
-<!-- END MANUAL -->
-```
-
-### Use Case Section
-
-Provide 3 practical use cases in this format:
-- **Bold Heading**: Short one-sentence description
-
-Example:
-```markdown
-<!-- MANUAL: use_case -->
-**Paginated API Merging**: Combine results from multiple API pages into a single list for batch processing or display.
-
-**Parallel Task Aggregation**: Merge outputs from parallel workflow branches that each produce a list of results.
-
-**Multi-Source Data Collection**: Combine data collected from different sources (like multiple RSS feeds or API endpoints) into one unified list.
-<!-- END MANUAL -->
-```
-
-### Style Guidelines
-
-- Keep descriptions concise and action-oriented
-- Focus on practical, real-world scenarios
-- Use consistent terminology with other blocks
-- Avoid overly technical jargon unless necessary

docs/integrations/README.md

@@ -31,7 +31,6 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Agent Time Input](basic.md#agent-time-input) | Block for time input |
 | [Agent Toggle Input](basic.md#agent-toggle-input) | Block for boolean toggle input |
 | [Block Installation](basic.md#block-installation) | Given a code string, this block allows the verification and installation of a block code into the system |
-| [Concatenate Lists](basic.md#concatenate-lists) | Concatenates multiple lists into a single list |
 | [Dictionary Is Empty](basic.md#dictionary-is-empty) | Checks if a dictionary is empty |
 | [File Store](basic.md#file-store) | Stores the input file in the temporary directory |
 | [Find In Dictionary](basic.md#find-in-dictionary) | A block that looks up a value in a dictionary, list, or object by key or index and returns the corresponding value |

docs/integrations/basic.md

@@ -634,42 +634,6 @@ This enables extensibility by allowing custom blocks to be added without modifyi
 
 ---
 
-## Concatenate Lists
-
-### What it is
-Concatenates multiple lists into a single list. All elements from all input lists are combined in order.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block iterates through each list in the input and extends a result list with all elements from each one. It processes lists in order, so `[[1, 2], [3, 4]]` becomes `[1, 2, 3, 4]`.
-
-The block includes validation to ensure each item is actually a list. If a non-list value (like a string or number) is encountered, the block outputs an error message instead of proceeding. None values are skipped automatically.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| lists | A list of lists to concatenate together. All lists will be combined in order into a single list. | List[List[Any]] | Yes |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if concatenation failed due to invalid input types. | str |
-| concatenated_list | The concatenated list containing all elements from all input lists in order. | List[Any] |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-**Paginated API Merging**: Combine results from multiple API pages into a single list for batch processing or display.
-
-**Parallel Task Aggregation**: Merge outputs from parallel workflow branches that each produce a list of results.
-
-**Multi-Source Data Collection**: Combine data collected from different sources (like multiple RSS feeds or API endpoints) into one unified list.
-<!-- END MANUAL -->
-
----
-
 ## Dictionary Is Empty
 
 ### What it is