fix: wrap generate_agent call in try/except for consistency

Add exception handler for AgentGeneratorNotConfiguredError in generate_agent
call for defensive consistency, even though decompose_goal would typically
catch it first.

Addresses CodeRabbit review suggestion.

.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@v8
+        uses: peter-evans/create-pull-request@v7
         with:
           add-paths: classic/frontend/build/web
           base: ${{ github.ref_name }}

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

@@ -4,14 +4,9 @@ from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
-from langfuse import Langfuse
-from openai import (
-    APIConnectionError,
-    APIError,
-    APIStatusError,
-    AsyncOpenAI,
-    RateLimitError,
-)
+from langfuse import get_client, propagate_attributes
+from langfuse.openai import openai  # type: ignore
+from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -21,7 +16,6 @@ from backend.data.understanding import (
 from backend.util.exceptions import NotFoundError
 from backend.util.settings import Settings
 
-from . import db as chat_db
 from .config import ChatConfig
 from .model import (
     ChatMessage,
@@ -50,10 +44,10 @@ logger = logging.getLogger(__name__)
 
 config = ChatConfig()
 settings = Settings()
-client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-# Langfuse client (lazy initialization)
-_langfuse_client: Langfuse | None = None
+
+langfuse = get_client()
 
 
 class LangfuseNotConfiguredError(Exception):
@@ -69,65 +63,6 @@ def _is_langfuse_configured() -> bool:
     )
 
 
-def _get_langfuse_client() -> Langfuse:
-    """Get or create the Langfuse client for prompt management and tracing."""
-    global _langfuse_client
-    if _langfuse_client is None:
-        if not _is_langfuse_configured():
-            raise LangfuseNotConfiguredError(
-                "Langfuse is not configured. The chat feature requires Langfuse for prompt management. "
-                "Please set the LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
-            )
-        _langfuse_client = Langfuse(
-            public_key=settings.secrets.langfuse_public_key,
-            secret_key=settings.secrets.langfuse_secret_key,
-            host=settings.secrets.langfuse_host or "https://cloud.langfuse.com",
-        )
-    return _langfuse_client
-
-
-def _get_environment() -> str:
-    """Get the current environment name for Langfuse tagging."""
-    return settings.config.app_env.value
-
-
-def _get_langfuse_prompt() -> str:
-    """Fetch the latest production prompt from Langfuse.
-
-    Returns:
-        The compiled prompt text from Langfuse.
-
-    Raises:
-        Exception: If Langfuse is unavailable or prompt fetch fails.
-    """
-    try:
-        langfuse = _get_langfuse_client()
-        # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
-        prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
-        compiled = prompt.compile()
-        logger.info(
-            f"Fetched prompt '{config.langfuse_prompt_name}' from Langfuse "
-            f"(version: {prompt.version})"
-        )
-        return compiled
-    except Exception as e:
-        logger.error(f"Failed to fetch prompt from Langfuse: {e}")
-        raise
-
-
-async def _is_first_session(user_id: str) -> bool:
-    """Check if this is the user's first chat session.
-
-    Returns True if the user has 1 or fewer sessions (meaning this is their first).
-    """
-    try:
-        session_count = await chat_db.get_user_session_count(user_id)
-        return session_count <= 1
-    except Exception as e:
-        logger.warning(f"Failed to check session count for user {user_id}: {e}")
-        return False  # Default to non-onboarding if we can't check
-
-
 async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
@@ -139,8 +74,6 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         Tuple of (compiled prompt string, Langfuse prompt object for tracing)
     """
 
-    langfuse = _get_langfuse_client()
-
     # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
     prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
 
@@ -158,7 +91,7 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
     compiled = prompt.compile(users_information=context)
-    return compiled, prompt
+    return compiled, understanding
 
 
 async def _generate_session_title(message: str) -> str | None:
@@ -217,6 +150,7 @@ async def assign_user_to_session(
 async def stream_chat_completion(
     session_id: str,
     message: str | None = None,
+    tool_call_response: str | None = None,
     is_user_message: bool = True,
     user_id: str | None = None,
     retry_count: int = 0,
@@ -256,11 +190,6 @@ async def stream_chat_completion(
         yield StreamFinish()
         return
 
-    # Langfuse observations will be created after session is loaded (need messages for input)
-    # Initialize to None so finally block can safely check and end them
-    trace = None
-    generation = None
-
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
         session = await get_chat_session(session_id, user_id)
@@ -336,297 +265,259 @@ async def stream_chat_completion(
             asyncio.create_task(_update_title())
 
     # Build system prompt with business understanding
-    system_prompt, langfuse_prompt = await _build_system_prompt(user_id)
-
-    # Build input messages including system prompt for complete Langfuse logging
-    trace_input_messages = [{"role": "system", "content": system_prompt}] + [
-        m.model_dump() for m in session.messages
-    ]
+    system_prompt, understanding = await _build_system_prompt(user_id)
 
     # Create Langfuse trace for this LLM call (each call gets its own trace, grouped by session_id)
     # Using v3 SDK: start_observation creates a root span, update_trace sets trace-level attributes
-    try:
-        langfuse = _get_langfuse_client()
-        env = _get_environment()
-        trace = langfuse.start_observation(
-            name="chat_completion",
-            input={"messages": trace_input_messages},
-            metadata={
-                "environment": env,
-                "model": config.model,
-                "message_count": len(session.messages),
-                "prompt_name": langfuse_prompt.name if langfuse_prompt else None,
-                "prompt_version": langfuse_prompt.version if langfuse_prompt else None,
-            },
-        )
-        # Set trace-level attributes (session_id, user_id, tags)
-        trace.update_trace(
+    input = message
+    if not message and tool_call_response:
+        input = tool_call_response
+
+    langfuse = get_client()
+    with langfuse.start_as_current_observation(
+        as_type="span",
+        name="user-copilot-request",
+        input=input,
+    ) as span:
+        with propagate_attributes(
             session_id=session_id,
             user_id=user_id,
-            tags=[env, "copilot"],
-        )
-    except Exception as e:
-        logger.warning(f"Failed to create Langfuse trace: {e}")
+            tags=["copilot"],
+            metadata={
+                "users_information": format_understanding_for_prompt(understanding)[
+                    :200
+                ]  # langfuse only accepts upto to 200 chars
+            },
+        ):
 
-    # Initialize variables that will be used in finally block (must be defined before try)
-    assistant_response = ChatMessage(
-        role="assistant",
-        content="",
-    )
-    accumulated_tool_calls: list[dict[str, Any]] = []
-
-    # Wrap main logic in try/finally to ensure Langfuse observations are always ended
-    try:
-        has_yielded_end = False
-        has_yielded_error = False
-        has_done_tool_call = False
-        has_received_text = False
-        text_streaming_ended = False
-        tool_response_messages: list[ChatMessage] = []
-        should_retry = False
-
-        # Generate unique IDs for AI SDK protocol
-        import uuid as uuid_module
-
-        message_id = str(uuid_module.uuid4())
-        text_block_id = str(uuid_module.uuid4())
-
-        # Yield message start
-        yield StreamStart(messageId=message_id)
-
-        # Create Langfuse generation for each LLM call, linked to the prompt
-        # Using v3 SDK: start_observation with as_type="generation"
-        generation = (
-            trace.start_observation(
-                as_type="generation",
-                name="llm_call",
-                model=config.model,
-                input={"messages": trace_input_messages},
-                prompt=langfuse_prompt,
+            # Initialize variables that will be used in finally block (must be defined before try)
+            assistant_response = ChatMessage(
+                role="assistant",
+                content="",
             )
-            if trace
-            else None
-        )
+            accumulated_tool_calls: list[dict[str, Any]] = []
 
-        try:
-            async for chunk in _stream_chat_chunks(
-                session=session,
-                tools=tools,
-                system_prompt=system_prompt,
-                text_block_id=text_block_id,
-            ):
+            # Wrap main logic in try/finally to ensure Langfuse observations are always ended
+            has_yielded_end = False
+            has_yielded_error = False
+            has_done_tool_call = False
+            has_received_text = False
+            text_streaming_ended = False
+            tool_response_messages: list[ChatMessage] = []
+            should_retry = False
 
-                if isinstance(chunk, StreamTextStart):
-                    # Emit text-start before first text delta
-                    if not has_received_text:
+            # Generate unique IDs for AI SDK protocol
+            import uuid as uuid_module
+
+            message_id = str(uuid_module.uuid4())
+            text_block_id = str(uuid_module.uuid4())
+
+            # Yield message start
+            yield StreamStart(messageId=message_id)
+
+            try:
+                async for chunk in _stream_chat_chunks(
+                    session=session,
+                    tools=tools,
+                    system_prompt=system_prompt,
+                    text_block_id=text_block_id,
+                ):
+
+                    if isinstance(chunk, StreamTextStart):
+                        # Emit text-start before first text delta
+                        if not has_received_text:
+                            yield chunk
+                    elif isinstance(chunk, StreamTextDelta):
+                        delta = chunk.delta or ""
+                        assert assistant_response.content is not None
+                        assistant_response.content += delta
+                        has_received_text = True
                         yield chunk
-                elif isinstance(chunk, StreamTextDelta):
-                    delta = chunk.delta or ""
-                    assert assistant_response.content is not None
-                    assistant_response.content += delta
-                    has_received_text = True
-                    yield chunk
-                elif isinstance(chunk, StreamTextEnd):
-                    # Emit text-end after text completes
-                    if has_received_text and not text_streaming_ended:
-                        text_streaming_ended = True
-                        yield chunk
-                elif isinstance(chunk, StreamToolInputStart):
-                    # Emit text-end before first tool call, but only if we've received text
-                    if has_received_text and not text_streaming_ended:
-                        yield StreamTextEnd(id=text_block_id)
-                        text_streaming_ended = True
-                    yield chunk
-                elif isinstance(chunk, StreamToolInputAvailable):
-                    # Accumulate tool calls in OpenAI format
-                    accumulated_tool_calls.append(
-                        {
-                            "id": chunk.toolCallId,
-                            "type": "function",
-                            "function": {
-                                "name": chunk.toolName,
-                                "arguments": orjson.dumps(chunk.input).decode("utf-8"),
-                            },
-                        }
-                    )
-                elif isinstance(chunk, StreamToolOutputAvailable):
-                    result_content = (
-                        chunk.output
-                        if isinstance(chunk.output, str)
-                        else orjson.dumps(chunk.output).decode("utf-8")
-                    )
-                    tool_response_messages.append(
-                        ChatMessage(
-                            role="tool",
-                            content=result_content,
-                            tool_call_id=chunk.toolCallId,
-                        )
-                    )
-                    has_done_tool_call = True
-                    # Track if any tool execution failed
-                    if not chunk.success:
-                        logger.warning(
-                            f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
-                        )
-                    yield chunk
-                elif isinstance(chunk, StreamFinish):
-                    if not has_done_tool_call:
-                        # Emit text-end before finish if we received text but haven't closed it
+                    elif isinstance(chunk, StreamTextEnd):
+                        # Emit text-end after text completes
+                        if has_received_text and not text_streaming_ended:
+                            text_streaming_ended = True
+                            if assistant_response.content:
+                                logger.warn(
+                                    f"StreamTextEnd: Attempting to set output {assistant_response.content}"
+                                )
+                                span.update_trace(output=assistant_response.content)
+                                span.update(output=assistant_response.content)
+                            yield chunk
+                    elif isinstance(chunk, StreamToolInputStart):
+                        # Emit text-end before first tool call, but only if we've received text
                         if has_received_text and not text_streaming_ended:
                             yield StreamTextEnd(id=text_block_id)
                             text_streaming_ended = True
-                        has_yielded_end = True
                         yield chunk
-                elif isinstance(chunk, StreamError):
-                    has_yielded_error = True
-                elif isinstance(chunk, StreamUsage):
-                    session.usage.append(
-                        Usage(
-                            prompt_tokens=chunk.promptTokens,
-                            completion_tokens=chunk.completionTokens,
-                            total_tokens=chunk.totalTokens,
+                    elif isinstance(chunk, StreamToolInputAvailable):
+                        # Accumulate tool calls in OpenAI format
+                        accumulated_tool_calls.append(
+                            {
+                                "id": chunk.toolCallId,
+                                "type": "function",
+                                "function": {
+                                    "name": chunk.toolName,
+                                    "arguments": orjson.dumps(chunk.input).decode(
+                                        "utf-8"
+                                    ),
+                                },
+                            }
                         )
+                    elif isinstance(chunk, StreamToolOutputAvailable):
+                        result_content = (
+                            chunk.output
+                            if isinstance(chunk.output, str)
+                            else orjson.dumps(chunk.output).decode("utf-8")
+                        )
+                        tool_response_messages.append(
+                            ChatMessage(
+                                role="tool",
+                                content=result_content,
+                                tool_call_id=chunk.toolCallId,
+                            )
+                        )
+                        has_done_tool_call = True
+                        # Track if any tool execution failed
+                        if not chunk.success:
+                            logger.warning(
+                                f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
+                            )
+                        yield chunk
+                    elif isinstance(chunk, StreamFinish):
+                        if not has_done_tool_call:
+                            # Emit text-end before finish if we received text but haven't closed it
+                            if has_received_text and not text_streaming_ended:
+                                yield StreamTextEnd(id=text_block_id)
+                                text_streaming_ended = True
+                            has_yielded_end = True
+                            yield chunk
+                    elif isinstance(chunk, StreamError):
+                        has_yielded_error = True
+                    elif isinstance(chunk, StreamUsage):
+                        session.usage.append(
+                            Usage(
+                                prompt_tokens=chunk.promptTokens,
+                                completion_tokens=chunk.completionTokens,
+                                total_tokens=chunk.totalTokens,
+                            )
+                        )
+                    else:
+                        logger.error(
+                            f"Unknown chunk type: {type(chunk)}", exc_info=True
+                        )
+                if assistant_response.content:
+                    langfuse.update_current_trace(output=assistant_response.content)
+                    langfuse.update_current_span(output=assistant_response.content)
+                elif tool_response_messages:
+                    langfuse.update_current_trace(output=str(tool_response_messages))
+                    langfuse.update_current_span(output=str(tool_response_messages))
+
+            except Exception as e:
+                logger.error(f"Error during stream: {e!s}", exc_info=True)
+
+                # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
+                is_retryable = isinstance(
+                    e, (orjson.JSONDecodeError, KeyError, TypeError)
+                )
+
+                if is_retryable and retry_count < config.max_retries:
+                    logger.info(
+                        f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
                     )
+                    should_retry = True
                 else:
-                    logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
-        except Exception as e:
-            logger.error(f"Error during stream: {e!s}", exc_info=True)
+                    # Non-retryable error or max retries exceeded
+                    # Save any partial progress before reporting error
+                    messages_to_save: list[ChatMessage] = []
 
-            # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
-            is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
+                    # Add assistant message if it has content or tool calls
+                    if accumulated_tool_calls:
+                        assistant_response.tool_calls = accumulated_tool_calls
+                    if assistant_response.content or assistant_response.tool_calls:
+                        messages_to_save.append(assistant_response)
 
-            if is_retryable and retry_count < config.max_retries:
+                    # Add tool response messages after assistant message
+                    messages_to_save.extend(tool_response_messages)
+
+                    session.messages.extend(messages_to_save)
+                    await upsert_chat_session(session)
+
+                    if not has_yielded_error:
+                        error_message = str(e)
+                        if not is_retryable:
+                            error_message = f"Non-retryable error: {error_message}"
+                        elif retry_count >= config.max_retries:
+                            error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
+
+                        error_response = StreamError(errorText=error_message)
+                        yield error_response
+                    if not has_yielded_end:
+                        yield StreamFinish()
+                    return
+
+            # Handle retry outside of exception handler to avoid nesting
+            if should_retry and retry_count < config.max_retries:
                 logger.info(
-                    f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
+                    f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
                 )
-                should_retry = True
-            else:
-                # Non-retryable error or max retries exceeded
-                # Save any partial progress before reporting error
-                messages_to_save: list[ChatMessage] = []
+                async for chunk in stream_chat_completion(
+                    session_id=session.session_id,
+                    user_id=user_id,
+                    retry_count=retry_count + 1,
+                    session=session,
+                    context=context,
+                ):
+                    yield chunk
+                return  # Exit after retry to avoid double-saving in finally block
 
-                # Add assistant message if it has content or tool calls
-                if accumulated_tool_calls:
-                    assistant_response.tool_calls = accumulated_tool_calls
-                if assistant_response.content or assistant_response.tool_calls:
-                    messages_to_save.append(assistant_response)
-
-                # Add tool response messages after assistant message
-                messages_to_save.extend(tool_response_messages)
-
-                session.messages.extend(messages_to_save)
-                await upsert_chat_session(session)
-
-                if not has_yielded_error:
-                    error_message = str(e)
-                    if not is_retryable:
-                        error_message = f"Non-retryable error: {error_message}"
-                    elif retry_count >= config.max_retries:
-                        error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
-
-                    error_response = StreamError(errorText=error_message)
-                    yield error_response
-                if not has_yielded_end:
-                    yield StreamFinish()
-                return
-
-        # Handle retry outside of exception handler to avoid nesting
-        if should_retry and retry_count < config.max_retries:
+            # Normal completion path - save session and handle tool call continuation
             logger.info(
-                f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
-            )
-            async for chunk in stream_chat_completion(
-                session_id=session.session_id,
-                user_id=user_id,
-                retry_count=retry_count + 1,
-                session=session,
-                context=context,
-            ):
-                yield chunk
-            return  # Exit after retry to avoid double-saving in finally block
-
-        # Normal completion path - save session and handle tool call continuation
-        logger.info(
-            f"Normal completion path: session={session.session_id}, "
-            f"current message_count={len(session.messages)}"
-        )
-
-        # Build the messages list in the correct order
-        messages_to_save: list[ChatMessage] = []
-
-        # Add assistant message with tool_calls if any
-        if accumulated_tool_calls:
-            assistant_response.tool_calls = accumulated_tool_calls
-            logger.info(
-                f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
-            )
-        if assistant_response.content or assistant_response.tool_calls:
-            messages_to_save.append(assistant_response)
-            logger.info(
-                f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
+                f"Normal completion path: session={session.session_id}, "
+                f"current message_count={len(session.messages)}"
             )
 
-        # Add tool response messages after assistant message
-        messages_to_save.extend(tool_response_messages)
-        logger.info(
-            f"Saving {len(tool_response_messages)} tool response messages, "
-            f"total_to_save={len(messages_to_save)}"
-        )
+            # Build the messages list in the correct order
+            messages_to_save: list[ChatMessage] = []
 
-        session.messages.extend(messages_to_save)
-        logger.info(
-            f"Extended session messages, new message_count={len(session.messages)}"
-        )
-        await upsert_chat_session(session)
-
-        # If we did a tool call, stream the chat completion again to get the next response
-        if has_done_tool_call:
-            logger.info(
-                "Tool call executed, streaming chat completion again to get assistant response"
-            )
-            async for chunk in stream_chat_completion(
-                session_id=session.session_id,
-                user_id=user_id,
-                session=session,  # Pass session object to avoid Redis refetch
-                context=context,
-            ):
-                yield chunk
-
-    finally:
-        # Always end Langfuse observations to prevent resource leaks
-        # Guard against None and catch errors to avoid masking original exceptions
-        if generation is not None:
-            try:
-                latest_usage = session.usage[-1] if session.usage else None
-                generation.update(
-                    model=config.model,
-                    output={
-                        "content": assistant_response.content,
-                        "tool_calls": accumulated_tool_calls or None,
-                    },
-                    usage_details=(
-                        {
-                            "input": latest_usage.prompt_tokens,
-                            "output": latest_usage.completion_tokens,
-                            "total": latest_usage.total_tokens,
-                        }
-                        if latest_usage
-                        else None
-                    ),
+            # Add assistant message with tool_calls if any
+            if accumulated_tool_calls:
+                assistant_response.tool_calls = accumulated_tool_calls
+                logger.info(
+                    f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+                )
+            if assistant_response.content or assistant_response.tool_calls:
+                messages_to_save.append(assistant_response)
+                logger.info(
+                    f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
                 )
-                generation.end()
-            except Exception as e:
-                logger.warning(f"Failed to end Langfuse generation: {e}")
 
-        if trace is not None:
-            try:
-                if accumulated_tool_calls:
-                    trace.update_trace(output={"tool_calls": accumulated_tool_calls})
-                else:
-                    trace.update_trace(output={"response": assistant_response.content})
-                trace.end()
-            except Exception as e:
-                logger.warning(f"Failed to end Langfuse trace: {e}")
+            # Add tool response messages after assistant message
+            messages_to_save.extend(tool_response_messages)
+            logger.info(
+                f"Saving {len(tool_response_messages)} tool response messages, "
+                f"total_to_save={len(messages_to_save)}"
+            )
+
+            session.messages.extend(messages_to_save)
+            logger.info(
+                f"Extended session messages, new message_count={len(session.messages)}"
+            )
+            await upsert_chat_session(session)
+
+            # If we did a tool call, stream the chat completion again to get the next response
+            if has_done_tool_call:
+                logger.info(
+                    "Tool call executed, streaming chat completion again to get assistant response"
+                )
+                async for chunk in stream_chat_completion(
+                    session_id=session.session_id,
+                    user_id=user_id,
+                    session=session,  # Pass session object to avoid Redis refetch
+                    context=context,
+                    tool_call_response=str(tool_response_messages),
+                ):
+                    yield chunk
 
 
 # Retry configuration for OpenAI API calls
@@ -900,5 +791,4 @@ async def _yield_tool_call(
         session=session,
     )
 
-    logger.info(f"Yielding Tool execution response: {tool_execution_response}")
     yield tool_execution_response

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

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

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

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

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

@@ -1,29 +1,28 @@
 """Agent generator package - Creates agents from natural language."""
 
 from .core import (
-    apply_agent_patch,
+    AgentGeneratorNotConfiguredError,
     decompose_goal,
     generate_agent,
     generate_agent_patch,
     get_agent_as_json,
+    json_to_graph,
     save_agent_to_library,
 )
-from .fixer import apply_all_fixes
-from .utils import get_blocks_info
-from .validator import validate_agent
+from .service import health_check as check_external_service_health
+from .service import is_external_service_configured
 
 __all__ = [
     # Core functions
     "decompose_goal",
     "generate_agent",
     "generate_agent_patch",
-    "apply_agent_patch",
     "save_agent_to_library",
     "get_agent_as_json",
-    # Fixer
-    "apply_all_fixes",
-    # Validator
-    "validate_agent",
-    # Utils
-    "get_blocks_info",
+    "json_to_graph",
+    # Exceptions
+    "AgentGeneratorNotConfiguredError",
+    # Service
+    "is_external_service_configured",
+    "check_external_service_health",
 ]

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

@@ -1,25 +0,0 @@
-"""OpenRouter client configuration for agent generation."""
-
-import os
-
-from openai import AsyncOpenAI
-
-# Configuration - use OPEN_ROUTER_API_KEY for consistency with chat/config.py
-OPENROUTER_API_KEY = os.getenv("OPEN_ROUTER_API_KEY")
-AGENT_GENERATOR_MODEL = os.getenv("AGENT_GENERATOR_MODEL", "anthropic/claude-opus-4.5")
-
-# OpenRouter client (OpenAI-compatible API)
-_client: AsyncOpenAI | None = None
-
-
-def get_client() -> AsyncOpenAI:
-    """Get or create the OpenRouter client."""
-    global _client
-    if _client is None:
-        if not OPENROUTER_API_KEY:
-            raise ValueError("OPENROUTER_API_KEY environment variable is required")
-        _client = AsyncOpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=OPENROUTER_API_KEY,
-        )
-    return _client

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

@@ -1,7 +1,5 @@
 """Core agent generation functions."""
 
-import copy
-import json
 import logging
 import uuid
 from typing import Any
@@ -9,13 +7,35 @@ from typing import Any
 from backend.api.features.library import db as library_db
 from backend.data.graph import Graph, Link, Node, create_graph
 
-from .client import AGENT_GENERATOR_MODEL, get_client
-from .prompts import DECOMPOSITION_PROMPT, GENERATION_PROMPT, PATCH_PROMPT
-from .utils import get_block_summaries, parse_json_from_llm
+from .service import (
+    decompose_goal_external,
+    generate_agent_external,
+    generate_agent_patch_external,
+    is_external_service_configured,
+)
 
 logger = logging.getLogger(__name__)
 
 
+class AgentGeneratorNotConfiguredError(Exception):
+    """Raised when the external Agent Generator service is not configured."""
+
+    pass
+
+
+def _check_service_configured() -> None:
+    """Check if the external Agent Generator service is configured.
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the service is not configured.
+    """
+    if not is_external_service_configured():
+        raise AgentGeneratorNotConfiguredError(
+            "Agent Generator service is not configured. "
+            "Set AGENTGENERATOR_HOST environment variable to enable agent generation."
+        )
+
+
 async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
     """Break down a goal into steps or return clarifying questions.
 
@@ -28,40 +48,13 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any]
         - {"type": "clarifying_questions", "questions": [...]}
         - {"type": "instructions", "steps": [...]}
         Or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = DECOMPOSITION_PROMPT.format(block_summaries=get_block_summaries())
-
-    full_description = description
-    if context:
-        full_description = f"{description}\n\nAdditional context:\n{context}"
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": full_description},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for decomposition")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse decomposition response: {content[:200]}")
-            return None
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error decomposing goal: {e}")
-        return None
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for decompose_goal")
+    return await decompose_goal_external(description, context)
 
 
 async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
@@ -72,31 +65,14 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
 
     Returns:
         Agent JSON dict or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = GENERATION_PROMPT.format(block_summaries=get_block_summaries())
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": json.dumps(instructions, indent=2)},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for agent generation")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse agent JSON: {content[:200]}")
-            return None
-
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent")
+    result = await generate_agent_external(instructions)
+    if result:
         # Ensure required fields
         if "id" not in result:
             result["id"] = str(uuid.uuid4())
@@ -104,12 +80,7 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
             result["version"] = 1
         if "is_active" not in result:
             result["is_active"] = True
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error generating agent: {e}")
-        return None
+    return result
 
 
 def json_to_graph(agent_json: dict[str, Any]) -> Graph:
@@ -218,6 +189,7 @@ async def save_agent_to_library(
     library_agents = await library_db.create_library_agent(
         graph=created_graph,
         user_id=user_id,
+        sensitive_action_safe_mode=True,
         create_library_agents_for_sub_graphs=False,
     )
 
@@ -283,108 +255,23 @@ async def get_agent_as_json(
 async def generate_agent_patch(
     update_request: str, current_agent: dict[str, Any]
 ) -> dict[str, Any] | None:
-    """Generate a patch to update an existing agent.
+    """Update an existing agent using natural language.
+
+    The external Agent Generator service handles:
+    - Generating the patch
+    - Applying the patch
+    - Fixing and validating the result
 
     Args:
         update_request: Natural language description of changes
         current_agent: Current agent JSON
 
     Returns:
-        Patch dict or clarifying questions, or None on error
+        Updated agent JSON, clarifying questions dict, or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = PATCH_PROMPT.format(
-        current_agent=json.dumps(current_agent, indent=2),
-        block_summaries=get_block_summaries(),
-    )
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": update_request},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for patch generation")
-            return None
-
-        return parse_json_from_llm(content)
-
-    except Exception as e:
-        logger.error(f"Error generating patch: {e}")
-        return None
-
-
-def apply_agent_patch(
-    current_agent: dict[str, Any], patch: dict[str, Any]
-) -> dict[str, Any]:
-    """Apply a patch to an existing agent.
-
-    Args:
-        current_agent: Current agent JSON
-        patch: Patch dict with operations
-
-    Returns:
-        Updated agent JSON
-    """
-    agent = copy.deepcopy(current_agent)
-    patches = patch.get("patches", [])
-
-    for p in patches:
-        patch_type = p.get("type")
-
-        if patch_type == "modify":
-            node_id = p.get("node_id")
-            changes = p.get("changes", {})
-
-            for node in agent.get("nodes", []):
-                if node["id"] == node_id:
-                    _deep_update(node, changes)
-                    logger.debug(f"Modified node {node_id}")
-                    break
-
-        elif patch_type == "add":
-            new_nodes = p.get("new_nodes", [])
-            new_links = p.get("new_links", [])
-
-            agent["nodes"] = agent.get("nodes", []) + new_nodes
-            agent["links"] = agent.get("links", []) + new_links
-            logger.debug(f"Added {len(new_nodes)} nodes, {len(new_links)} links")
-
-        elif patch_type == "remove":
-            node_ids_to_remove = set(p.get("node_ids", []))
-            link_ids_to_remove = set(p.get("link_ids", []))
-
-            # Remove nodes
-            agent["nodes"] = [
-                n for n in agent.get("nodes", []) if n["id"] not in node_ids_to_remove
-            ]
-
-            # Remove links (both explicit and those referencing removed nodes)
-            agent["links"] = [
-                link
-                for link in agent.get("links", [])
-                if link["id"] not in link_ids_to_remove
-                and link["source_id"] not in node_ids_to_remove
-                and link["sink_id"] not in node_ids_to_remove
-            ]
-
-            logger.debug(
-                f"Removed {len(node_ids_to_remove)} nodes, {len(link_ids_to_remove)} links"
-            )
-
-    return agent
-
-
-def _deep_update(target: dict, source: dict) -> None:
-    """Recursively update a dict with another dict."""
-    for key, value in source.items():
-        if key in target and isinstance(target[key], dict) and isinstance(value, dict):
-            _deep_update(target[key], value)
-        else:
-            target[key] = value
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent_patch")
+    return await generate_agent_patch_external(update_request, current_agent)

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

@@ -1,606 +0,0 @@
-"""Agent fixer - Fixes common LLM generation errors."""
-
-import logging
-import re
-import uuid
-from typing import Any
-
-from .utils import (
-    ADDTODICTIONARY_BLOCK_ID,
-    ADDTOLIST_BLOCK_ID,
-    CODE_EXECUTION_BLOCK_ID,
-    CONDITION_BLOCK_ID,
-    CREATEDICT_BLOCK_ID,
-    CREATELIST_BLOCK_ID,
-    DATA_SAMPLING_BLOCK_ID,
-    DOUBLE_CURLY_BRACES_BLOCK_IDS,
-    GET_CURRENT_DATE_BLOCK_ID,
-    STORE_VALUE_BLOCK_ID,
-    UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-    get_blocks_info,
-    is_valid_uuid,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def fix_agent_ids(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix invalid UUIDs in agent and link IDs."""
-    # Fix agent ID
-    if not is_valid_uuid(agent.get("id", "")):
-        agent["id"] = str(uuid.uuid4())
-        logger.debug(f"Fixed agent ID: {agent['id']}")
-
-    # Fix node IDs
-    id_mapping = {}  # Old ID -> New ID
-    for node in agent.get("nodes", []):
-        if not is_valid_uuid(node.get("id", "")):
-            old_id = node.get("id", "")
-            new_id = str(uuid.uuid4())
-            id_mapping[old_id] = new_id
-            node["id"] = new_id
-            logger.debug(f"Fixed node ID: {old_id} -> {new_id}")
-
-    # Fix link IDs and update references
-    for link in agent.get("links", []):
-        if not is_valid_uuid(link.get("id", "")):
-            link["id"] = str(uuid.uuid4())
-            logger.debug(f"Fixed link ID: {link['id']}")
-
-        # Update source/sink IDs if they were remapped
-        if link.get("source_id") in id_mapping:
-            link["source_id"] = id_mapping[link["source_id"]]
-        if link.get("sink_id") in id_mapping:
-            link["sink_id"] = id_mapping[link["sink_id"]]
-
-    return agent
-
-
-def fix_double_curly_braces(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix single curly braces to double in template blocks."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") not in DOUBLE_CURLY_BRACES_BLOCK_IDS:
-            continue
-
-        input_data = node.get("input_default", {})
-        for key in ("prompt", "format"):
-            if key in input_data and isinstance(input_data[key], str):
-                original = input_data[key]
-                # Fix simple variable references: {var} -> {{var}}
-                fixed = re.sub(
-                    r"(?<!\{)\{([a-zA-Z_][a-zA-Z0-9_]*)\}(?!\})",
-                    r"{{\1}}",
-                    original,
-                )
-                if fixed != original:
-                    input_data[key] = fixed
-                    logger.debug(f"Fixed curly braces in {key}")
-
-    return agent
-
-
-def fix_storevalue_before_condition(agent: dict[str, Any]) -> dict[str, Any]:
-    """Add StoreValueBlock before ConditionBlock if needed for value2."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    # Find all ConditionBlock nodes
-    condition_node_ids = {
-        node["id"] for node in nodes if node.get("block_id") == CONDITION_BLOCK_ID
-    }
-
-    if not condition_node_ids:
-        return agent
-
-    new_nodes = []
-    new_links = []
-    processed_conditions = set()
-
-    for link in links:
-        sink_id = link.get("sink_id")
-        sink_name = link.get("sink_name")
-
-        # Check if this link goes to a ConditionBlock's value2
-        if sink_id in condition_node_ids and sink_name == "value2":
-            source_node = next(
-                (n for n in nodes if n["id"] == link.get("source_id")), None
-            )
-
-            # Skip if source is already a StoreValueBlock
-            if source_node and source_node.get("block_id") == STORE_VALUE_BLOCK_ID:
-                continue
-
-            # Skip if we already processed this condition
-            if sink_id in processed_conditions:
-                continue
-
-            processed_conditions.add(sink_id)
-
-            # Create StoreValueBlock
-            store_node_id = str(uuid.uuid4())
-            store_node = {
-                "id": store_node_id,
-                "block_id": STORE_VALUE_BLOCK_ID,
-                "input_default": {"data": None},
-                "metadata": {"position": {"x": 0, "y": -100}},
-            }
-            new_nodes.append(store_node)
-
-            # Create link: original source -> StoreValueBlock
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": store_node_id,
-                    "sink_name": "input",
-                    "is_static": False,
-                }
-            )
-
-            # Update original link: StoreValueBlock -> ConditionBlock
-            link["source_id"] = store_node_id
-            link["source_name"] = "output"
-
-            logger.debug(f"Added StoreValueBlock before ConditionBlock {sink_id}")
-
-    if new_nodes:
-        agent["nodes"] = nodes + new_nodes
-
-    return agent
-
-
-def fix_addtolist_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToList blocks by adding prerequisite empty AddToList block.
-
-    When an AddToList block is found:
-    1. Checks if there's a CreateListBlock before it
-    2. Removes CreateListBlock if linked directly to AddToList
-    3. Adds an empty AddToList block before the original
-    4. Ensures the original has a self-referencing link
-    """
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    new_nodes = []
-    original_addtolist_ids = set()
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    # First pass: identify CreateListBlock nodes to remove
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATELIST_BLOCK_ID
-            and sink_node.get("block_id") == ADDTOLIST_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateListBlock {source_node.get('id')}")
-
-    # Second pass: process AddToList blocks
-    filtered_nodes = []
-    for node in nodes:
-        if node.get("id") in nodes_to_remove:
-            continue
-
-        if node.get("block_id") == ADDTOLIST_BLOCK_ID:
-            original_addtolist_ids.add(node.get("id"))
-            node_id = node.get("id")
-            pos = node.get("metadata", {}).get("position", {"x": 0, "y": 0})
-
-            # Check if already has prerequisite
-            has_prereq = any(
-                link.get("sink_id") == node_id
-                and link.get("sink_name") == "list"
-                and link.get("source_name") == "updated_list"
-                for link in links
-            )
-
-            if not has_prereq:
-                # Remove links to "list" input (except self-reference)
-                for link in links:
-                    if (
-                        link.get("sink_id") == node_id
-                        and link.get("sink_name") == "list"
-                        and link.get("source_id") != node_id
-                        and link not in links_to_remove
-                    ):
-                        links_to_remove.append(link)
-
-                # Create prerequisite AddToList block
-                prereq_id = str(uuid.uuid4())
-                prereq_node = {
-                    "id": prereq_id,
-                    "block_id": ADDTOLIST_BLOCK_ID,
-                    "input_default": {"list": [], "entry": None, "entries": []},
-                    "metadata": {
-                        "position": {"x": pos.get("x", 0) - 800, "y": pos.get("y", 0)}
-                    },
-                }
-                new_nodes.append(prereq_node)
-
-                # Link prerequisite to original
-                links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": prereq_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added prerequisite AddToList block for {node_id}")
-
-        filtered_nodes.append(node)
-
-    # Remove marked links
-    filtered_links = [link for link in links if link not in links_to_remove]
-
-    # Add self-referencing links for original AddToList blocks
-    for node in filtered_nodes + new_nodes:
-        if (
-            node.get("block_id") == ADDTOLIST_BLOCK_ID
-            and node.get("id") in original_addtolist_ids
-        ):
-            node_id = node.get("id")
-            has_self_ref = any(
-                link["source_id"] == node_id
-                and link["sink_id"] == node_id
-                and link["source_name"] == "updated_list"
-                and link["sink_name"] == "list"
-                for link in filtered_links
-            )
-            if not has_self_ref:
-                filtered_links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": node_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added self-reference for AddToList {node_id}")
-
-    agent["nodes"] = filtered_nodes + new_nodes
-    agent["links"] = filtered_links
-    return agent
-
-
-def fix_addtodictionary_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToDictionary blocks by removing empty CreateDictionary nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATEDICT_BLOCK_ID
-            and sink_node.get("block_id") == ADDTODICTIONARY_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateDictionary {source_node.get('id')}")
-
-    agent["nodes"] = [n for n in nodes if n.get("id") not in nodes_to_remove]
-    agent["links"] = [link for link in links if link not in links_to_remove]
-    return agent
-
-
-def fix_code_execution_output(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix CodeExecutionBlock output: change 'response' to 'stdout_logs'."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        if (
-            source_node
-            and source_node.get("block_id") == CODE_EXECUTION_BLOCK_ID
-            and link.get("source_name") == "response"
-        ):
-            link["source_name"] = "stdout_logs"
-            logger.debug("Fixed CodeExecutionBlock output: response -> stdout_logs")
-
-    return agent
-
-
-def fix_data_sampling_sample_size(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix DataSamplingBlock by setting sample_size to 1 as default."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    links_to_remove = []
-
-    for node in nodes:
-        if node.get("block_id") == DATA_SAMPLING_BLOCK_ID:
-            node_id = node.get("id")
-            input_default = node.get("input_default", {})
-
-            # Remove links to sample_size
-            for link in links:
-                if (
-                    link.get("sink_id") == node_id
-                    and link.get("sink_name") == "sample_size"
-                ):
-                    links_to_remove.append(link)
-
-            # Set default
-            input_default["sample_size"] = 1
-            node["input_default"] = input_default
-            logger.debug(f"Fixed DataSamplingBlock {node_id} sample_size to 1")
-
-    if links_to_remove:
-        agent["links"] = [link for link in links if link not in links_to_remove]
-
-    return agent
-
-
-def fix_node_x_coordinates(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix node x-coordinates to ensure 800+ unit spacing between linked nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    for link in links:
-        source_id = link.get("source_id")
-        sink_id = link.get("sink_id")
-
-        source_node = node_lookup.get(source_id)
-        sink_node = node_lookup.get(sink_id)
-
-        if not source_node or not sink_node:
-            continue
-
-        source_pos = source_node.get("metadata", {}).get("position", {})
-        sink_pos = sink_node.get("metadata", {}).get("position", {})
-
-        source_x = source_pos.get("x", 0)
-        sink_x = sink_pos.get("x", 0)
-
-        if abs(sink_x - source_x) < 800:
-            new_x = source_x + 800
-            if "metadata" not in sink_node:
-                sink_node["metadata"] = {}
-            if "position" not in sink_node["metadata"]:
-                sink_node["metadata"]["position"] = {}
-            sink_node["metadata"]["position"]["x"] = new_x
-            logger.debug(f"Fixed node {sink_id} x: {sink_x} -> {new_x}")
-
-    return agent
-
-
-def fix_getcurrentdate_offset(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix GetCurrentDateBlock offset to ensure it's positive."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") == GET_CURRENT_DATE_BLOCK_ID:
-            input_default = node.get("input_default", {})
-            if "offset" in input_default:
-                offset = input_default["offset"]
-                if isinstance(offset, (int, float)) and offset < 0:
-                    input_default["offset"] = abs(offset)
-                    logger.debug(f"Fixed offset: {offset} -> {abs(offset)}")
-
-    return agent
-
-
-def fix_ai_model_parameter(
-    agent: dict[str, Any],
-    blocks_info: list[dict[str, Any]],
-    default_model: str = "gpt-4o",
-) -> dict[str, Any]:
-    """Add default model parameter to AI blocks if missing."""
-    block_map = {b.get("id"): b for b in blocks_info}
-
-    for node in agent.get("nodes", []):
-        block_id = node.get("block_id")
-        block = block_map.get(block_id)
-
-        if not block:
-            continue
-
-        # Check if block has AI category
-        categories = block.get("categories", [])
-        is_ai_block = any(
-            cat.get("category") == "AI" for cat in categories if isinstance(cat, dict)
-        )
-
-        if is_ai_block:
-            input_default = node.get("input_default", {})
-            if "model" not in input_default:
-                input_default["model"] = default_model
-                node["input_default"] = input_default
-                logger.debug(
-                    f"Added model '{default_model}' to AI block {node.get('id')}"
-                )
-
-    return agent
-
-
-def fix_link_static_properties(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix is_static property based on source block's staticOutput."""
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-    for link in agent.get("links", []):
-        source_node = node_lookup.get(link.get("source_id"))
-        if not source_node:
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        if not source_block:
-            continue
-
-        static_output = source_block.get("staticOutput", False)
-        if link.get("is_static") != static_output:
-            link["is_static"] = static_output
-            logger.debug(f"Fixed link {link.get('id')} is_static to {static_output}")
-
-    return agent
-
-
-def fix_data_type_mismatch(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix data type mismatches by inserting UniversalTypeConverterBlock."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    def get_property_type(schema: dict, name: str) -> str | None:
-        if "_#_" in name:
-            parent, child = name.split("_#_", 1)
-            parent_schema = schema.get(parent, {})
-            if "properties" in parent_schema:
-                return parent_schema["properties"].get(child, {}).get("type")
-            return None
-        return schema.get(name, {}).get("type")
-
-    def are_types_compatible(src: str, sink: str) -> bool:
-        if {src, sink} <= {"integer", "number"}:
-            return True
-        return src == sink
-
-    type_mapping = {
-        "string": "string",
-        "text": "string",
-        "integer": "number",
-        "number": "number",
-        "float": "number",
-        "boolean": "boolean",
-        "bool": "boolean",
-        "array": "list",
-        "list": "list",
-        "object": "dictionary",
-        "dict": "dictionary",
-        "dictionary": "dictionary",
-    }
-
-    new_links = []
-    nodes_to_add = []
-
-    for link in links:
-        source_node = node_lookup.get(link.get("source_id"))
-        sink_node = node_lookup.get(link.get("sink_id"))
-
-        if not source_node or not sink_node:
-            new_links.append(link)
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        sink_block = block_map.get(sink_node.get("block_id"))
-
-        if not source_block or not sink_block:
-            new_links.append(link)
-            continue
-
-        source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-        sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-        source_type = get_property_type(source_outputs, link.get("source_name", ""))
-        sink_type = get_property_type(sink_inputs, link.get("sink_name", ""))
-
-        if (
-            source_type
-            and sink_type
-            and not are_types_compatible(source_type, sink_type)
-        ):
-            # Insert type converter
-            converter_id = str(uuid.uuid4())
-            target_type = type_mapping.get(sink_type, sink_type)
-
-            converter_node = {
-                "id": converter_id,
-                "block_id": UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-                "input_default": {"type": target_type},
-                "metadata": {"position": {"x": 0, "y": 100}},
-            }
-            nodes_to_add.append(converter_node)
-
-            # source -> converter
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": converter_id,
-                    "sink_name": "value",
-                    "is_static": False,
-                }
-            )
-
-            # converter -> sink
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": converter_id,
-                    "source_name": "value",
-                    "sink_id": link["sink_id"],
-                    "sink_name": link["sink_name"],
-                    "is_static": False,
-                }
-            )
-
-            logger.debug(f"Inserted type converter: {source_type} -> {target_type}")
-        else:
-            new_links.append(link)
-
-    if nodes_to_add:
-        agent["nodes"] = nodes + nodes_to_add
-        agent["links"] = new_links
-
-    return agent
-
-
-def apply_all_fixes(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> dict[str, Any]:
-    """Apply all fixes to an agent JSON.
-
-    Args:
-        agent: Agent JSON dict
-        blocks_info: Optional list of block info dicts for advanced fixes
-
-    Returns:
-        Fixed agent JSON
-    """
-    # Basic fixes (no block info needed)
-    agent = fix_agent_ids(agent)
-    agent = fix_double_curly_braces(agent)
-    agent = fix_storevalue_before_condition(agent)
-    agent = fix_addtolist_blocks(agent)
-    agent = fix_addtodictionary_blocks(agent)
-    agent = fix_code_execution_output(agent)
-    agent = fix_data_sampling_sample_size(agent)
-    agent = fix_node_x_coordinates(agent)
-    agent = fix_getcurrentdate_offset(agent)
-
-    # Advanced fixes (require block info)
-    if blocks_info is None:
-        blocks_info = get_blocks_info()
-
-    agent = fix_ai_model_parameter(agent, blocks_info)
-    agent = fix_link_static_properties(agent, blocks_info)
-    agent = fix_data_type_mismatch(agent, blocks_info)
-
-    return agent

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

@@ -1,225 +0,0 @@
-"""Prompt templates for agent generation."""
-
-DECOMPOSITION_PROMPT = """
-You are an expert AutoGPT Workflow Decomposer. Your task is to analyze a user's high-level goal and break it down into a clear, step-by-step plan using the available blocks.
-
-Each step should represent a distinct, automatable action suitable for execution by an AI automation system.
-
----
-
-FIRST: Analyze the user's goal and determine:
-1) Design-time configuration (fixed settings that won't change per run)
-2) Runtime inputs (values the agent's end-user will provide each time it runs)
-
-For anything that can vary per run (email addresses, names, dates, search terms, etc.):
-- DO NOT ask for the actual value
-- Instead, define it as an Agent Input with a clear name, type, and description
-
-Only ask clarifying questions about design-time config that affects how you build the workflow:
-- Which external service to use (e.g., "Gmail vs Outlook", "Notion vs Google Docs")
-- Required formats or structures (e.g., "CSV, JSON, or PDF output?")
-- Business rules that must be hard-coded
-
-IMPORTANT CLARIFICATIONS POLICY:
-- Ask no more than five essential questions
-- Do not ask for concrete values that can be provided at runtime as Agent Inputs
-- Do not ask for API keys or credentials; the platform handles those directly
-- If there is enough information to infer reasonable defaults, prefer to propose defaults
-
----
-
-GUIDELINES:
-1. List each step as a numbered item
-2. Describe the action clearly and specify inputs/outputs
-3. Ensure steps are in logical, sequential order
-4. Mention block names naturally (e.g., "Use GetWeatherByLocationBlock to...")
-5. Help the user reach their goal efficiently
-
----
-
-RULES:
-1. OUTPUT FORMAT: Only output either clarifying questions OR step-by-step instructions, not both
-2. USE ONLY THE BLOCKS PROVIDED
-3. ALL required_input fields must be provided
-4. Data types of linked properties must match
-5. Write expert-level prompts for AI-related blocks
-
----
-
-CRITICAL BLOCK RESTRICTIONS:
-1. AddToListBlock: Outputs updated list EVERY addition, not after all additions
-2. SendEmailBlock: Draft the email for user review; set SMTP config based on email type
-3. ConditionBlock: value2 is reference, value1 is contrast
-4. CodeExecutionBlock: DO NOT USE - use AI blocks instead
-5. ReadCsvBlock: Only use the 'rows' output, not 'row'
-
----
-
-OUTPUT FORMAT:
-
-If more information is needed:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "Which email provider should be used? (Gmail, Outlook, custom SMTP)",
-      "keyword": "email_provider",
-      "example": "Gmail"
-    }}
-  ]
-}}
-```
-
-If ready to proceed:
-```json
-{{
-  "type": "instructions",
-  "steps": [
-    {{
-      "step_number": 1,
-      "block_name": "AgentShortTextInputBlock",
-      "description": "Get the URL of the content to analyze.",
-      "inputs": [{{"name": "name", "value": "URL"}}],
-      "outputs": [{{"name": "result", "description": "The URL entered by user"}}]
-    }}
-  ]
-}}
-```
-
----
-
-AVAILABLE BLOCKS:
-{block_summaries}
-"""
-
-GENERATION_PROMPT = """
-You are an expert AI workflow builder. Generate a valid agent JSON from the given instructions.
-
----
-
-NODES:
-Each node must include:
-- `id`: Unique UUID v4 (e.g. `a8f5b1e2-c3d4-4e5f-8a9b-0c1d2e3f4a5b`)
-- `block_id`: The block identifier (must match an Allowed Block)
-- `input_default`: Dict of inputs (can be empty if no static inputs needed)
-- `metadata`: Must contain:
-  - `position`: {{"x": number, "y": number}} - adjacent nodes should differ by 800+ in X
-  - `customized_name`: Clear name describing this block's purpose in the workflow
-
----
-
-LINKS:
-Each link connects a source node's output to a sink node's input:
-- `id`: MUST be UUID v4 (NOT "link-1", "link-2", etc.)
-- `source_id`: ID of the source node
-- `source_name`: Output field name from the source block
-- `sink_id`: ID of the sink node
-- `sink_name`: Input field name on the sink block
-- `is_static`: true only if source block has static_output: true
-
-CRITICAL: All IDs must be valid UUID v4 format!
-
----
-
-AGENT (GRAPH):
-Wrap nodes and links in:
-- `id`: UUID of the agent
-- `name`: Short, generic name (avoid specific company names, URLs)
-- `description`: Short, generic description
-- `nodes`: List of all nodes
-- `links`: List of all links
-- `version`: 1
-- `is_active`: true
-
----
-
-TIPS:
-- All required_input fields must be provided via input_default or a valid link
-- Ensure consistent source_id and sink_id references
-- Avoid dangling links
-- Input/output pins must match block schemas
-- Do not invent unknown block_ids
-
----
-
-ALLOWED BLOCKS:
-{block_summaries}
-
----
-
-Generate the complete agent JSON. Output ONLY valid JSON, no explanation.
-"""
-
-PATCH_PROMPT = """
-You are an expert at modifying AutoGPT agent workflows. Given the current agent and a modification request, generate a JSON patch to update the agent.
-
-CURRENT AGENT:
-{current_agent}
-
-AVAILABLE BLOCKS:
-{block_summaries}
-
----
-
-PATCH FORMAT:
-Return a JSON object with the following structure:
-
-```json
-{{
-  "type": "patch",
-  "intent": "Brief description of what the patch does",
-  "patches": [
-    {{
-      "type": "modify",
-      "node_id": "uuid-of-node-to-modify",
-      "changes": {{
-        "input_default": {{"field": "new_value"}},
-        "metadata": {{"customized_name": "New Name"}}
-      }}
-    }},
-    {{
-      "type": "add",
-      "new_nodes": [
-        {{
-          "id": "new-uuid",
-          "block_id": "block-uuid",
-          "input_default": {{}},
-          "metadata": {{"position": {{"x": 0, "y": 0}}, "customized_name": "Name"}}
-        }}
-      ],
-      "new_links": [
-        {{
-          "id": "link-uuid",
-          "source_id": "source-node-id",
-          "source_name": "output_field",
-          "sink_id": "sink-node-id",
-          "sink_name": "input_field"
-        }}
-      ]
-    }},
-    {{
-      "type": "remove",
-      "node_ids": ["uuid-of-node-to-remove"],
-      "link_ids": ["uuid-of-link-to-remove"]
-    }}
-  ]
-}}
-```
-
-If you need more information, return:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "What specific change do you want?",
-      "keyword": "change_type",
-      "example": "Add error handling"
-    }}
-  ]
-}}
-```
-
-Generate the minimal patch needed. Output ONLY valid JSON.
-"""

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

@@ -0,0 +1,269 @@
+"""External Agent Generator service client.
+
+This module provides a client for communicating with the external Agent Generator
+microservice. When AGENTGENERATOR_HOST is configured, the agent generation functions
+will delegate to the external service instead of using the built-in LLM-based implementation.
+"""
+
+import logging
+from typing import Any
+
+import httpx
+
+from backend.util.settings import Settings
+
+logger = logging.getLogger(__name__)
+
+_client: httpx.AsyncClient | None = None
+_settings: Settings | None = None
+
+
+def _get_settings() -> Settings:
+    """Get or create settings singleton."""
+    global _settings
+    if _settings is None:
+        _settings = Settings()
+    return _settings
+
+
+def is_external_service_configured() -> bool:
+    """Check if external Agent Generator service is configured."""
+    settings = _get_settings()
+    return bool(settings.config.agentgenerator_host)
+
+
+def _get_base_url() -> str:
+    """Get the base URL for the external service."""
+    settings = _get_settings()
+    host = settings.config.agentgenerator_host
+    port = settings.config.agentgenerator_port
+    return f"http://{host}:{port}"
+
+
+def _get_client() -> httpx.AsyncClient:
+    """Get or create the HTTP client for the external service."""
+    global _client
+    if _client is None:
+        settings = _get_settings()
+        _client = httpx.AsyncClient(
+            base_url=_get_base_url(),
+            timeout=httpx.Timeout(settings.config.agentgenerator_timeout),
+        )
+    return _client
+
+
+async def decompose_goal_external(
+    description: str, context: str = ""
+) -> dict[str, Any] | None:
+    """Call the external service to decompose a goal.
+
+    Args:
+        description: Natural language goal description
+        context: Additional context (e.g., answers to previous questions)
+
+    Returns:
+        Dict with either:
+        - {"type": "clarifying_questions", "questions": [...]}
+        - {"type": "instructions", "steps": [...]}
+        - {"type": "unachievable_goal", ...}
+        - {"type": "vague_goal", ...}
+        Or None on error
+    """
+    client = _get_client()
+
+    # Build the request payload
+    payload: dict[str, Any] = {"description": description}
+    if context:
+        # The external service uses user_instruction for additional context
+        payload["user_instruction"] = context
+
+    try:
+        response = await client.post("/api/decompose-description", json=payload)
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Map the response to the expected format
+        response_type = data.get("type")
+        if response_type == "instructions":
+            return {"type": "instructions", "steps": data.get("steps", [])}
+        elif response_type == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+        elif response_type == "unachievable_goal":
+            return {
+                "type": "unachievable_goal",
+                "reason": data.get("reason"),
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        elif response_type == "vague_goal":
+            return {
+                "type": "vague_goal",
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        else:
+            logger.error(
+                f"Unknown response type from external service: {response_type}"
+            )
+            return None
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_external(
+    instructions: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate an agent from instructions.
+
+    Args:
+        instructions: Structured instructions from decompose_goal
+
+    Returns:
+        Agent JSON dict or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/generate-agent", json={"instructions": instructions}
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_patch_external(
+    update_request: str, current_agent: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate a patch for an existing agent.
+
+    Args:
+        update_request: Natural language description of changes
+        current_agent: Current agent JSON
+
+    Returns:
+        Updated agent JSON, clarifying questions dict, or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/update-agent",
+            json={
+                "update_request": update_request,
+                "current_agent_json": current_agent,
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Check if it's clarifying questions
+        if data.get("type") == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+
+        # Otherwise return the updated agent JSON
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def get_blocks_external() -> list[dict[str, Any]] | None:
+    """Get available blocks from the external service.
+
+    Returns:
+        List of block info dicts or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.get("/api/blocks")
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error("External service returned error getting blocks")
+            return None
+
+        return data.get("blocks", [])
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error getting blocks from external service: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error getting blocks from external service: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error getting blocks from external service: {e}")
+        return None
+
+
+async def health_check() -> bool:
+    """Check if the external service is healthy.
+
+    Returns:
+        True if healthy, False otherwise
+    """
+    if not is_external_service_configured():
+        return False
+
+    client = _get_client()
+
+    try:
+        response = await client.get("/health")
+        response.raise_for_status()
+        data = response.json()
+        return data.get("status") == "healthy" and data.get("blocks_loaded", False)
+    except Exception as e:
+        logger.warning(f"External agent generator health check failed: {e}")
+        return False
+
+
+async def close_client() -> None:
+    """Close the HTTP client."""
+    global _client
+    if _client is not None:
+        await _client.aclose()
+        _client = None

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

@@ -1,213 +0,0 @@
-"""Utilities for agent generation."""
-
-import json
-import re
-from typing import Any
-
-from backend.data.block import get_blocks
-
-# UUID validation regex
-UUID_REGEX = re.compile(
-    r"^[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$"
-)
-
-# Block IDs for various fixes
-STORE_VALUE_BLOCK_ID = "1ff065e9-88e8-4358-9d82-8dc91f622ba9"
-CONDITION_BLOCK_ID = "715696a0-e1da-45c8-b209-c2fa9c3b0be6"
-ADDTOLIST_BLOCK_ID = "aeb08fc1-2fc1-4141-bc8e-f758f183a822"
-ADDTODICTIONARY_BLOCK_ID = "31d1064e-7446-4693-a7d4-65e5ca1180d1"
-CREATELIST_BLOCK_ID = "a912d5c7-6e00-4542-b2a9-8034136930e4"
-CREATEDICT_BLOCK_ID = "b924ddf4-de4f-4b56-9a85-358930dcbc91"
-CODE_EXECUTION_BLOCK_ID = "0b02b072-abe7-11ef-8372-fb5d162dd712"
-DATA_SAMPLING_BLOCK_ID = "4a448883-71fa-49cf-91cf-70d793bd7d87"
-UNIVERSAL_TYPE_CONVERTER_BLOCK_ID = "95d1b990-ce13-4d88-9737-ba5c2070c97b"
-GET_CURRENT_DATE_BLOCK_ID = "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1"
-
-DOUBLE_CURLY_BRACES_BLOCK_IDS = [
-    "44f6c8ad-d75c-4ae1-8209-aad1c0326928",  # FillTextTemplateBlock
-    "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-    "90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
-    "363ae599-353e-4804-937e-b2ee3cef3da4",  # AgentOutputBlock
-    "3b191d9f-356f-482d-8238-ba04b6d18381",
-    "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-    "3a7c4b8d-6e2f-4a5d-b9c1-f8d23c5a9b0e",
-    "ed1ae7a0-b770-4089-b520-1f0005fad19a",
-    "a892b8d9-3e4e-4e9c-9c1e-75f8efcf1bfa",
-    "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1",
-    "716a67b3-6760-42e7-86dc-18645c6e00fc",
-    "530cf046-2ce0-4854-ae2c-659db17c7a46",
-    "ed55ac19-356e-4243-a6cb-bc599e9b716f",
-    "1f292d4a-41a4-4977-9684-7c8d560b9f91",  # LLM blocks
-    "32a87eab-381e-4dd4-bdb8-4c47151be35a",
-]
-
-
-def is_valid_uuid(value: str) -> bool:
-    """Check if a string is a valid UUID v4."""
-    return isinstance(value, str) and UUID_REGEX.match(value) is not None
-
-
-def _compact_schema(schema: dict) -> dict[str, str]:
-    """Extract compact type info from a JSON schema properties dict.
-
-    Returns a dict of {field_name: type_string} for essential info only.
-    """
-    props = schema.get("properties", {})
-    result = {}
-
-    for name, prop in props.items():
-        # Skip internal/complex fields
-        if name.startswith("_"):
-            continue
-
-        # Get type string
-        type_str = prop.get("type", "any")
-
-        # Handle anyOf/oneOf (optional types)
-        if "anyOf" in prop:
-            types = [t.get("type", "?") for t in prop["anyOf"] if t.get("type")]
-            type_str = "|".join(types) if types else "any"
-        elif "allOf" in prop:
-            type_str = "object"
-
-        # Add array item type if present
-        if type_str == "array" and "items" in prop:
-            items = prop["items"]
-            if isinstance(items, dict):
-                item_type = items.get("type", "any")
-                type_str = f"array[{item_type}]"
-
-        result[name] = type_str
-
-    return result
-
-
-def get_block_summaries(include_schemas: bool = True) -> str:
-    """Generate compact block summaries for prompts.
-
-    Args:
-        include_schemas: Whether to include input/output type info
-
-    Returns:
-        Formatted string of block summaries (compact format)
-    """
-    blocks = get_blocks()
-    summaries = []
-
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        name = block.name
-        desc = getattr(block, "description", "") or ""
-
-        # Truncate description
-        if len(desc) > 150:
-            desc = desc[:147] + "..."
-
-        if not include_schemas:
-            summaries.append(f"- {name} (id: {block_id}): {desc}")
-        else:
-            # Compact format with type info only
-            inputs = {}
-            outputs = {}
-            required = []
-
-            if hasattr(block, "input_schema"):
-                try:
-                    schema = block.input_schema.jsonschema()
-                    inputs = _compact_schema(schema)
-                    required = schema.get("required", [])
-                except Exception:
-                    pass
-
-            if hasattr(block, "output_schema"):
-                try:
-                    schema = block.output_schema.jsonschema()
-                    outputs = _compact_schema(schema)
-                except Exception:
-                    pass
-
-            # Build compact line format
-            # Format: NAME (id): desc | in: {field:type, ...} [required] | out: {field:type}
-            in_str = ", ".join(f"{k}:{v}" for k, v in inputs.items())
-            out_str = ", ".join(f"{k}:{v}" for k, v in outputs.items())
-            req_str = f" req=[{','.join(required)}]" if required else ""
-
-            static = " [static]" if getattr(block, "static_output", False) else ""
-
-            line = f"- {name} (id: {block_id}): {desc}"
-            if in_str:
-                line += f"\n  in: {{{in_str}}}{req_str}"
-            if out_str:
-                line += f"\n  out: {{{out_str}}}{static}"
-
-            summaries.append(line)
-
-    return "\n".join(summaries)
-
-
-def get_blocks_info() -> list[dict[str, Any]]:
-    """Get block information with schemas for validation and fixing."""
-    blocks = get_blocks()
-    blocks_info = []
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        blocks_info.append(
-            {
-                "id": block_id,
-                "name": block.name,
-                "description": getattr(block, "description", ""),
-                "categories": getattr(block, "categories", []),
-                "staticOutput": getattr(block, "static_output", False),
-                "inputSchema": (
-                    block.input_schema.jsonschema()
-                    if hasattr(block, "input_schema")
-                    else {}
-                ),
-                "outputSchema": (
-                    block.output_schema.jsonschema()
-                    if hasattr(block, "output_schema")
-                    else {}
-                ),
-            }
-        )
-    return blocks_info
-
-
-def parse_json_from_llm(text: str) -> dict[str, Any] | None:
-    """Extract JSON from LLM response (handles markdown code blocks)."""
-    if not text:
-        return None
-
-    # Try fenced code block
-    match = re.search(r"```(?:json)?\s*([\s\S]*?)```", text, re.IGNORECASE)
-    if match:
-        try:
-            return json.loads(match.group(1).strip())
-        except json.JSONDecodeError:
-            pass
-
-    # Try raw text
-    try:
-        return json.loads(text.strip())
-    except json.JSONDecodeError:
-        pass
-
-    # Try finding {...} span
-    start = text.find("{")
-    end = text.rfind("}")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    # Try finding [...] span
-    start = text.find("[")
-    end = text.rfind("]")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    return None

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

@@ -1,279 +0,0 @@
-"""Agent validator - Validates agent structure and connections."""
-
-import logging
-import re
-from typing import Any
-
-from .utils import get_blocks_info
-
-logger = logging.getLogger(__name__)
-
-
-class AgentValidator:
-    """Validator for AutoGPT agents with detailed error reporting."""
-
-    def __init__(self):
-        self.errors: list[str] = []
-
-    def add_error(self, error: str) -> None:
-        """Add an error message."""
-        self.errors.append(error)
-
-    def validate_block_existence(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate all block IDs exist in the blocks library."""
-        valid = True
-        valid_block_ids = {b.get("id") for b in blocks_info if b.get("id")}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            node_id = node.get("id")
-
-            if not block_id:
-                self.add_error(f"Node '{node_id}' is missing 'block_id' field.")
-                valid = False
-                continue
-
-            if block_id not in valid_block_ids:
-                self.add_error(
-                    f"Node '{node_id}' references block_id '{block_id}' which does not exist."
-                )
-                valid = False
-
-        return valid
-
-    def validate_link_node_references(self, agent: dict[str, Any]) -> bool:
-        """Validate all node IDs referenced in links exist."""
-        valid = True
-        valid_node_ids = {n.get("id") for n in agent.get("nodes", []) if n.get("id")}
-
-        for link in agent.get("links", []):
-            link_id = link.get("id", "Unknown")
-            source_id = link.get("source_id")
-            sink_id = link.get("sink_id")
-
-            if not source_id:
-                self.add_error(f"Link '{link_id}' is missing 'source_id'.")
-                valid = False
-            elif source_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent source_id '{source_id}'."
-                )
-                valid = False
-
-            if not sink_id:
-                self.add_error(f"Link '{link_id}' is missing 'sink_id'.")
-                valid = False
-            elif sink_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent sink_id '{sink_id}'."
-                )
-                valid = False
-
-        return valid
-
-    def validate_required_inputs(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate required inputs are provided."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            block = block_map.get(block_id)
-
-            if not block:
-                continue
-
-            required_inputs = block.get("inputSchema", {}).get("required", [])
-            input_defaults = node.get("input_default", {})
-            node_id = node.get("id")
-
-            # Get linked inputs
-            linked_inputs = {
-                link["sink_name"]
-                for link in agent.get("links", [])
-                if link.get("sink_id") == node_id
-            }
-
-            for req_input in required_inputs:
-                if (
-                    req_input not in input_defaults
-                    and req_input not in linked_inputs
-                    and req_input != "credentials"
-                ):
-                    block_name = block.get("name", "Unknown Block")
-                    self.add_error(
-                        f"Node '{node_id}' ({block_name}) is missing required input '{req_input}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate_data_type_compatibility(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate linked data types are compatible."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        def get_type(schema: dict, name: str) -> str | None:
-            if "_#_" in name:
-                parent, child = name.split("_#_", 1)
-                parent_schema = schema.get(parent, {})
-                if "properties" in parent_schema:
-                    return parent_schema["properties"].get(child, {}).get("type")
-                return None
-            return schema.get(name, {}).get("type")
-
-        def are_compatible(src: str, sink: str) -> bool:
-            if {src, sink} <= {"integer", "number"}:
-                return True
-            return src == sink
-
-        for link in agent.get("links", []):
-            source_node = node_lookup.get(link.get("source_id"))
-            sink_node = node_lookup.get(link.get("sink_id"))
-
-            if not source_node or not sink_node:
-                continue
-
-            source_block = block_map.get(source_node.get("block_id"))
-            sink_block = block_map.get(sink_node.get("block_id"))
-
-            if not source_block or not sink_block:
-                continue
-
-            source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-            sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-            source_type = get_type(source_outputs, link.get("source_name", ""))
-            sink_type = get_type(sink_inputs, link.get("sink_name", ""))
-
-            if source_type and sink_type and not are_compatible(source_type, sink_type):
-                self.add_error(
-                    f"Type mismatch: {source_block.get('name')} output '{link['source_name']}' "
-                    f"({source_type}) -> {sink_block.get('name')} input '{link['sink_name']}' ({sink_type})."
-                )
-                valid = False
-
-        return valid
-
-    def validate_nested_sink_links(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate nested sink links (with _#_ notation)."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        for link in agent.get("links", []):
-            sink_name = link.get("sink_name", "")
-
-            if "_#_" in sink_name:
-                parent, child = sink_name.split("_#_", 1)
-
-                sink_node = node_lookup.get(link.get("sink_id"))
-                if not sink_node:
-                    continue
-
-                block = block_map.get(sink_node.get("block_id"))
-                if not block:
-                    continue
-
-                input_props = block.get("inputSchema", {}).get("properties", {})
-                parent_schema = input_props.get(parent)
-
-                if not parent_schema:
-                    self.add_error(
-                        f"Invalid nested link '{sink_name}': parent '{parent}' not found."
-                    )
-                    valid = False
-                    continue
-
-                if not parent_schema.get("additionalProperties"):
-                    if not (
-                        isinstance(parent_schema, dict)
-                        and "properties" in parent_schema
-                        and child in parent_schema.get("properties", {})
-                    ):
-                        self.add_error(
-                            f"Invalid nested link '{sink_name}': child '{child}' not found in '{parent}'."
-                        )
-                        valid = False
-
-        return valid
-
-    def validate_prompt_spaces(self, agent: dict[str, Any]) -> bool:
-        """Validate prompts don't have spaces in template variables."""
-        valid = True
-
-        for node in agent.get("nodes", []):
-            input_default = node.get("input_default", {})
-            prompt = input_default.get("prompt", "")
-
-            if not isinstance(prompt, str):
-                continue
-
-            # Find {{...}} with spaces
-            matches = re.finditer(r"\{\{([^}]+)\}\}", prompt)
-            for match in matches:
-                content = match.group(1)
-                if " " in content:
-                    self.add_error(
-                        f"Node '{node.get('id')}' has spaces in template variable: "
-                        f"'{{{{{content}}}}}' should be '{{{{{content.replace(' ', '_')}}}}}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-    ) -> tuple[bool, str | None]:
-        """Run all validations.
-
-        Returns:
-            Tuple of (is_valid, error_message)
-        """
-        self.errors = []
-
-        if blocks_info is None:
-            blocks_info = get_blocks_info()
-
-        checks = [
-            self.validate_block_existence(agent, blocks_info),
-            self.validate_link_node_references(agent),
-            self.validate_required_inputs(agent, blocks_info),
-            self.validate_data_type_compatibility(agent, blocks_info),
-            self.validate_nested_sink_links(agent, blocks_info),
-            self.validate_prompt_spaces(agent),
-        ]
-
-        all_passed = all(checks)
-
-        if all_passed:
-            logger.info("Agent validation successful")
-            return True, None
-
-        error_message = "Agent validation failed:\n"
-        for i, error in enumerate(self.errors, 1):
-            error_message += f"{i}. {error}\n"
-
-        logger.warning(f"Agent validation failed with {len(self.errors)} errors")
-        return False, error_message
-
-
-def validate_agent(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> tuple[bool, str | None]:
-    """Convenience function to validate an agent.
-
-    Returns:
-        Tuple of (is_valid, error_message)
-    """
-    validator = AgentValidator()
-    return validator.validate(agent, blocks_info)

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

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

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

@@ -3,15 +3,15 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    apply_all_fixes,
+    AgentGeneratorNotConfiguredError,
     decompose_goal,
     generate_agent,
-    get_blocks_info,
     save_agent_to_library,
-    validate_agent,
 )
 from .base import BaseTool
 from .models import (
@@ -25,9 +25,6 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
-# Maximum retries for agent generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
 
 class CreateAgentTool(BaseTool):
     """Tool for creating agents from natural language descriptions."""
@@ -78,6 +75,7 @@ class CreateAgentTool(BaseTool):
             "required": ["description"],
         }
 
+    @observe(as_type="tool", name="create_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -88,9 +86,8 @@ class CreateAgentTool(BaseTool):
 
         Flow:
         1. Decompose the description into steps (may return clarifying questions)
-        2. Generate agent JSON from the steps
-        3. Apply fixes to correct common LLM errors
-        4. Preview or save based on the save parameter
+        2. Generate agent JSON (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
         """
         description = kwargs.get("description", "").strip()
         context = kwargs.get("context", "")
@@ -107,11 +104,13 @@ class CreateAgentTool(BaseTool):
         # Step 1: Decompose goal into steps
         try:
             decomposition_result = await decompose_goal(description, context)
-        except ValueError as e:
-            # Handle missing API key or configuration errors
+        except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
-                message=f"Agent generation is not configured: {str(e)}",
-                error="configuration_error",
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
                 session_id=session_id,
             )
 
@@ -168,72 +167,32 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 2: Generate agent JSON with retry on validation failure
-        blocks_info = get_blocks_info()
-        agent_json = None
-        validation_errors = None
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate agent (include validation errors from previous attempt)
-            if attempt == 0:
-                agent_json = await generate_agent(decomposition_result)
-            else:
-                # Retry with validation error feedback
-                logger.info(
-                    f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                )
-                retry_instructions = {
-                    **decomposition_result,
-                    "previous_errors": validation_errors,
-                    "retry_instructions": (
-                        "The previous generation had validation errors. "
-                        "Please fix these issues in the new generation:\n"
-                        f"{validation_errors}"
-                    ),
-                }
-                agent_json = await generate_agent(retry_instructions)
-
-            if agent_json is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate the agent. Please try again.",
-                        error="Generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Step 3: Apply fixes to correct common errors
-            agent_json = apply_all_fixes(agent_json, blocks_info)
-
-            # Step 4: Validate the agent
-            is_valid, validation_errors = validate_agent(agent_json, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent generated successfully on attempt {attempt + 1}")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
+        # Step 2: Generate agent JSON (external service handles fixing and validation)
+        try:
+            agent_json = await generate_agent(decomposition_result)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
             )
 
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Generated agent has validation errors after {MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the workflow."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
+        if agent_json is None:
+            return ErrorResponse(
+                message="Failed to generate the agent. Please try again.",
+                error="Generation failed",
+                session_id=session_id,
+            )
 
         agent_name = agent_json.get("name", "Generated Agent")
         agent_description = agent_json.get("description", "")
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
-        # Step 4: Preview or save
+        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(

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

@@ -3,16 +3,15 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    apply_agent_patch,
-    apply_all_fixes,
+    AgentGeneratorNotConfiguredError,
     generate_agent_patch,
     get_agent_as_json,
-    get_blocks_info,
     save_agent_to_library,
-    validate_agent,
 )
 from .base import BaseTool
 from .models import (
@@ -26,9 +25,6 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
-# Maximum retries for patch generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
 
 class EditAgentTool(BaseTool):
     """Tool for editing existing agents using natural language."""
@@ -41,7 +37,7 @@ class EditAgentTool(BaseTool):
     def description(self) -> str:
         return (
             "Edit an existing agent from the user's library using natural language. "
-            "Generates a patch to update the agent while preserving unchanged parts."
+            "Generates updates to the agent while preserving unchanged parts."
         )
 
     @property
@@ -85,6 +81,7 @@ class EditAgentTool(BaseTool):
             "required": ["agent_id", "changes"],
         }
 
+    @observe(as_type="tool", name="edit_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -95,9 +92,8 @@ class EditAgentTool(BaseTool):
 
         Flow:
         1. Fetch the current agent
-        2. Generate a patch based on the requested changes
-        3. Apply the patch to create an updated agent
-        4. Preview or save based on the save parameter
+        2. Generate updated agent (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
         """
         agent_id = kwargs.get("agent_id", "").strip()
         changes = kwargs.get("changes", "").strip()
@@ -134,121 +130,58 @@ class EditAgentTool(BaseTool):
         if context:
             update_request = f"{changes}\n\nAdditional context:\n{context}"
 
-        # Step 2: Generate patch with retry on validation failure
-        blocks_info = get_blocks_info()
-        updated_agent = None
-        validation_errors = None
-        intent = "Applied requested changes"
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate patch (include validation errors from previous attempt)
-            try:
-                if attempt == 0:
-                    patch_result = await generate_agent_patch(
-                        update_request, current_agent
-                    )
-                else:
-                    # Retry with validation error feedback
-                    logger.info(
-                        f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                    )
-                    retry_request = (
-                        f"{update_request}\n\n"
-                        f"IMPORTANT: The previous edit had validation errors. "
-                        f"Please fix these issues:\n{validation_errors}"
-                    )
-                    patch_result = await generate_agent_patch(
-                        retry_request, current_agent
-                    )
-            except ValueError as e:
-                # Handle missing API key or configuration errors
-                return ErrorResponse(
-                    message=f"Agent generation is not configured: {str(e)}",
-                    error="configuration_error",
-                    session_id=session_id,
-                )
-
-            if patch_result is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate changes. Please try rephrasing.",
-                        error="Patch generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Check if LLM returned clarifying questions
-            if patch_result.get("type") == "clarifying_questions":
-                questions = patch_result.get("questions", [])
-                return ClarificationNeededResponse(
-                    message=(
-                        "I need some more information about the changes. "
-                        "Please answer the following questions:"
-                    ),
-                    questions=[
-                        ClarifyingQuestion(
-                            question=q.get("question", ""),
-                            keyword=q.get("keyword", ""),
-                            example=q.get("example"),
-                        )
-                        for q in questions
-                    ],
-                    session_id=session_id,
-                )
-
-            # Step 3: Apply patch and fixes
-            try:
-                updated_agent = apply_agent_patch(current_agent, patch_result)
-                updated_agent = apply_all_fixes(updated_agent, blocks_info)
-            except Exception as e:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message=f"Failed to apply changes: {str(e)}",
-                        error="patch_apply_failed",
-                        details={"exception": str(e)},
-                        session_id=session_id,
-                    )
-                validation_errors = str(e)
-                continue
-
-            # Step 4: Validate the updated agent
-            is_valid, validation_errors = validate_agent(updated_agent, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent edited successfully on attempt {attempt + 1}")
-                intent = patch_result.get("intent", "Applied requested changes")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
+        # Step 2: Generate updated agent (external service handles fixing and validation)
+        try:
+            result = await generate_agent_patch(update_request, current_agent)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent editing is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
             )
 
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Updated agent has validation errors after "
-                        f"{MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the changes."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
+        if result is None:
+            return ErrorResponse(
+                message="Failed to generate changes. Please try rephrasing.",
+                error="Update generation failed",
+                session_id=session_id,
+            )
 
-        # At this point, updated_agent is guaranteed to be set (we return on all failure paths)
-        assert updated_agent is not None
+        # Check if LLM returned clarifying questions
+        if result.get("type") == "clarifying_questions":
+            questions = result.get("questions", [])
+            return ClarificationNeededResponse(
+                message=(
+                    "I need some more information about the changes. "
+                    "Please answer the following questions:"
+                ),
+                questions=[
+                    ClarifyingQuestion(
+                        question=q.get("question", ""),
+                        keyword=q.get("keyword", ""),
+                        example=q.get("example"),
+                    )
+                    for q in questions
+                ],
+                session_id=session_id,
+            )
+
+        # Result is the updated agent JSON
+        updated_agent = result
 
         agent_name = updated_agent.get("name", "Updated Agent")
         agent_description = updated_agent.get("description", "")
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
-        # Step 5: Preview or save
+        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
-                    f"I've updated the agent. Changes: {intent}. "
+                    f"I've updated the agent. "
                     f"The agent now has {node_count} blocks. "
                     f"Review it and call edit_agent with save=true to save the changes."
                 ),
@@ -274,10 +207,7 @@ class EditAgentTool(BaseTool):
             )
 
             return AgentSavedResponse(
-                message=(
-                    f"Updated agent '{created_graph.name}' has been saved to your library! "
-                    f"Changes: {intent}"
-                ),
+                message=f"Updated agent '{created_graph.name}' has been saved to your library!",
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,

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

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

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

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

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

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

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

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

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

@@ -3,6 +3,7 @@
 import logging
 from typing import Any
 
+from langfuse import observe
 from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
@@ -32,7 +33,7 @@ from .models import (
     UserReadiness,
 )
 from .utils import (
-    check_user_has_required_credentials,
+    build_missing_credentials_from_graph,
     extract_credentials_from_schema,
     fetch_graph_from_store_slug,
     get_or_create_library_agent,
@@ -154,6 +155,7 @@ class RunAgentTool(BaseTool):
         """All operations require authentication."""
         return True
 
+    @observe(as_type="tool", name="run_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -235,15 +237,13 @@ 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
-                credentials = extract_credentials_from_schema(
-                    graph.credentials_input_schema
+                requirements_creds_dict = build_missing_credentials_from_graph(
+                    graph, None
                 )
-                missing_creds_check = await check_user_has_required_credentials(
-                    user_id, credentials
+                missing_credentials_dict = build_missing_credentials_from_graph(
+                    graph, graph_credentials
                 )
-                missing_credentials_dict = {
-                    c.id: c.model_dump() for c in missing_creds_check
-                }
+                requirements_creds_list = list(requirements_creds_dict.values())
 
                 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": [c.model_dump() for c in credentials],
+                            "credentials": requirements_creds_list,
                             "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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/data_manipulation.py

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

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

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

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

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

autogpt_platform/backend/backend/blocks/llm.py

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

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

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

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

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

autogpt_platform/backend/backend/data/block.py

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

autogpt_platform/backend/backend/data/block_cost_config.py

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

autogpt_platform/backend/backend/data/db.py

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

autogpt_platform/backend/backend/data/event_bus.py

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

autogpt_platform/backend/backend/data/event_bus_test.py

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

autogpt_platform/backend/backend/data/execution.py

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

autogpt_platform/backend/backend/data/graph.py

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

autogpt_platform/backend/backend/data/understanding.py

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

autogpt_platform/backend/backend/executor/scheduler.py

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

autogpt_platform/backend/backend/executor/utils.py

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

autogpt_platform/backend/backend/executor/utils_test.py

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

autogpt_platform/backend/backend/util/settings.py

@@ -350,6 +350,19 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="Whether to mark failed scans as clean or not",
     )
 
+    agentgenerator_host: str = Field(
+        default="",
+        description="The host for the Agent Generator service (empty to use built-in)",
+    )
+    agentgenerator_port: int = Field(
+        default=8000,
+        description="The port for the Agent Generator service",
+    )
+    agentgenerator_timeout: int = Field(
+        default=120,
+        description="The timeout in seconds for Agent Generator service requests",
+    )
+
     enable_example_blocks: bool = Field(
         default=False,
         description="Whether to enable example blocks in production",

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

@@ -1,9 +1,10 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Create in public schema so vector type is available across all schemas
+-- Creates extension in current schema (determined by search_path from DATABASE_URL ?schema= param)
+-- This ensures vector type is in the same schema as tables, making ::vector work without explicit qualification
 DO $$
 BEGIN
-    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
+    CREATE EXTENSION IF NOT EXISTS "vector";
 EXCEPTION WHEN OTHERS THEN
     RAISE NOTICE 'vector extension not available or already exists, skipping';
 END $$;
@@ -19,7 +20,7 @@ CREATE TABLE "UnifiedContentEmbedding" (
     "contentType" "ContentType" NOT NULL,
     "contentId" TEXT NOT NULL,
     "userId" TEXT,
-    "embedding" public.vector(1536) NOT NULL,
+    "embedding" vector(1536) NOT NULL,
     "searchableText" TEXT NOT NULL,
     "metadata" JSONB NOT NULL DEFAULT '{}',
 
@@ -45,4 +46,4 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
 -- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
 DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
-CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);

autogpt_platform/backend/scripts/generate_block_docs.py

@@ -366,12 +366,12 @@ def generate_block_markdown(
     lines.append("")
 
     # What it is (full description)
-    lines.append(f"### What it is")
+    lines.append("### What it is")
     lines.append(block.description or "No description available.")
     lines.append("")
 
     # How it works (manual section)
-    lines.append(f"### How it works")
+    lines.append("### How it works")
     how_it_works = manual_content.get(
         "how_it_works", "_Add technical explanation here._"
     )
@@ -383,7 +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(f"### Inputs")
+        lines.append("### 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(f"### Outputs")
+        lines.append("### 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(f"### Possible use case")
+    lines.append("### Possible use case")
     use_case = manual_content.get("use_case", "_Add practical use case examples here._")
     lines.append("<!-- MANUAL: use_case -->")
     lines.append(use_case)

autogpt_platform/backend/snapshots/grph_single

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

autogpt_platform/backend/snapshots/grphs_all

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

autogpt_platform/backend/snapshots/lib_agts_search

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

autogpt_platform/backend/test/agent_generator/__init__.py

@@ -0,0 +1 @@
+"""Tests for agent generator module."""

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -0,0 +1,273 @@
+"""
+Tests for the Agent Generator core module.
+
+This test suite verifies that the core functions correctly delegate to
+the external Agent Generator service.
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import core
+from backend.api.features.chat.tools.agent_generator.core import (
+    AgentGeneratorNotConfiguredError,
+)
+
+
+class TestServiceNotConfigured:
+    """Test that functions raise AgentGeneratorNotConfiguredError when service is not configured."""
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_raises_when_not_configured(self):
+        """Test that decompose_goal raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.decompose_goal("Build a chatbot")
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_raises_when_not_configured(self):
+        """Test that generate_agent raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.generate_agent({"steps": []})
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_patch_raises_when_not_configured(self):
+        """Test that generate_agent_patch raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.generate_agent_patch("Add a node", {"nodes": []})
+
+
+class TestDecomposeGoal:
+    """Test decompose_goal function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that decompose_goal calls the external service."""
+        expected_result = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            result = await core.decompose_goal("Build a chatbot")
+
+            mock_external.assert_called_once_with("Build a chatbot", "")
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_passes_context_to_external_service(self):
+        """Test that decompose_goal passes context to external service."""
+        expected_result = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            await core.decompose_goal("Build a chatbot", "Use Python")
+
+            mock_external.assert_called_once_with("Build a chatbot", "Use Python")
+
+    @pytest.mark.asyncio
+    async def test_returns_none_on_service_failure(self):
+        """Test that decompose_goal returns None when external service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.decompose_goal("Build a chatbot")
+
+            assert result is None
+
+
+class TestGenerateAgent:
+    """Test generate_agent function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that generate_agent calls the external service."""
+        expected_result = {"name": "Test Agent", "nodes": [], "links": []}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            instructions = {"type": "instructions", "steps": ["Step 1"]}
+            result = await core.generate_agent(instructions)
+
+            mock_external.assert_called_once_with(instructions)
+            # Result should have id, version, is_active added if not present
+            assert result is not None
+            assert result["name"] == "Test Agent"
+            assert "id" in result
+            assert result["version"] == 1
+            assert result["is_active"] is True
+
+    @pytest.mark.asyncio
+    async def test_preserves_existing_id_and_version(self):
+        """Test that external service result preserves existing id and version."""
+        expected_result = {
+            "id": "existing-id",
+            "version": 3,
+            "is_active": False,
+            "name": "Test Agent",
+        }
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result.copy()
+
+            result = await core.generate_agent({"steps": []})
+
+            assert result is not None
+            assert result["id"] == "existing-id"
+            assert result["version"] == 3
+            assert result["is_active"] is False
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_external_service_fails(self):
+        """Test that generate_agent returns None when external service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.generate_agent({"steps": []})
+
+            assert result is None
+
+
+class TestGenerateAgentPatch:
+    """Test generate_agent_patch function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that generate_agent_patch calls the external service."""
+        expected_result = {"name": "Updated Agent", "nodes": [], "links": []}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            current_agent = {"nodes": [], "links": []}
+            result = await core.generate_agent_patch("Add a node", current_agent)
+
+            mock_external.assert_called_once_with("Add a node", current_agent)
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_returns_clarifying_questions(self):
+        """Test that generate_agent_patch returns clarifying questions."""
+        expected_result = {
+            "type": "clarifying_questions",
+            "questions": [{"question": "What type of node?"}],
+        }
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            result = await core.generate_agent_patch("Add a node", {"nodes": []})
+
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_external_service_fails(self):
+        """Test that generate_agent_patch returns None when service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.generate_agent_patch("Add a node", {"nodes": []})
+
+            assert result is None
+
+
+class TestJsonToGraph:
+    """Test json_to_graph function."""
+
+    def test_converts_agent_json_to_graph(self):
+        """Test conversion of agent JSON to Graph model."""
+        agent_json = {
+            "id": "test-id",
+            "version": 2,
+            "is_active": True,
+            "name": "Test Agent",
+            "description": "A test agent",
+            "nodes": [
+                {
+                    "id": "node1",
+                    "block_id": "block1",
+                    "input_default": {"key": "value"},
+                    "metadata": {"x": 100},
+                }
+            ],
+            "links": [
+                {
+                    "id": "link1",
+                    "source_id": "node1",
+                    "sink_id": "output",
+                    "source_name": "result",
+                    "sink_name": "input",
+                    "is_static": False,
+                }
+            ],
+        }
+
+        graph = core.json_to_graph(agent_json)
+
+        assert graph.id == "test-id"
+        assert graph.version == 2
+        assert graph.is_active is True
+        assert graph.name == "Test Agent"
+        assert graph.description == "A test agent"
+        assert len(graph.nodes) == 1
+        assert graph.nodes[0].id == "node1"
+        assert graph.nodes[0].block_id == "block1"
+        assert len(graph.links) == 1
+        assert graph.links[0].source_id == "node1"
+
+    def test_generates_ids_if_missing(self):
+        """Test that missing IDs are generated."""
+        agent_json = {
+            "name": "Test Agent",
+            "nodes": [{"block_id": "block1"}],
+            "links": [],
+        }
+
+        graph = core.json_to_graph(agent_json)
+
+        assert graph.id is not None
+        assert graph.nodes[0].id is not None
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -0,0 +1,422 @@
+"""
+Tests for the Agent Generator external service client.
+
+This test suite verifies the external Agent Generator service integration,
+including service detection, API calls, and error handling.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import service
+
+
+class TestServiceConfiguration:
+    """Test service configuration detection."""
+
+    def setup_method(self):
+        """Reset settings singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    def test_external_service_not_configured_when_host_empty(self):
+        """Test that external service is not configured when host is empty."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = ""
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            assert service.is_external_service_configured() is False
+
+    def test_external_service_configured_when_host_set(self):
+        """Test that external service is configured when host is set."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = "agent-generator.local"
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            assert service.is_external_service_configured() is True
+
+    def test_get_base_url(self):
+        """Test base URL construction."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = "agent-generator.local"
+        mock_settings.config.agentgenerator_port = 8000
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            url = service._get_base_url()
+            assert url == "http://agent-generator.local:8000"
+
+
+class TestDecomposeGoalExternal:
+    """Test decompose_goal_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_instructions(self):
+        """Test successful decomposition returning instructions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1", "Step 2"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result == {"type": "instructions", "steps": ["Step 1", "Step 2"]}
+        mock_client.post.assert_called_once_with(
+            "/api/decompose-description", json={"description": "Build a chatbot"}
+        )
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_clarifying_questions(self):
+        """Test decomposition returning clarifying questions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "clarifying_questions",
+            "questions": ["What platform?", "What language?"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build something")
+
+        assert result == {
+            "type": "clarifying_questions",
+            "questions": ["What platform?", "What language?"],
+        }
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_with_context(self):
+        """Test decomposition with additional context."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.decompose_goal_external(
+                "Build a chatbot", context="Use Python"
+            )
+
+        mock_client.post.assert_called_once_with(
+            "/api/decompose-description",
+            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
+        )
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_unachievable_goal(self):
+        """Test decomposition returning unachievable goal response."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "unachievable_goal",
+            "reason": "Cannot do X",
+            "suggested_goal": "Try Y instead",
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Do something impossible")
+
+        assert result == {
+            "type": "unachievable_goal",
+            "reason": "Cannot do X",
+            "suggested_goal": "Try Y instead",
+        }
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_http_error(self):
+        """Test decomposition handles HTTP errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.HTTPStatusError(
+            "Server error", request=MagicMock(), response=MagicMock()
+        )
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_request_error(self):
+        """Test decomposition handles request errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_service_error(self):
+        """Test decomposition handles service returning error."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": False,
+            "error": "Internal error",
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+
+class TestGenerateAgentExternal:
+    """Test generate_agent_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_success(self):
+        """Test successful agent generation."""
+        agent_json = {
+            "name": "Test Agent",
+            "nodes": [],
+            "links": [],
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": agent_json,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        instructions = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_external(instructions)
+
+        assert result == agent_json
+        mock_client.post.assert_called_once_with(
+            "/api/generate-agent", json={"instructions": instructions}
+        )
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_handles_error(self):
+        """Test agent generation handles errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_external({"steps": []})
+
+        assert result is None
+
+
+class TestGenerateAgentPatchExternal:
+    """Test generate_agent_patch_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_generate_patch_returns_updated_agent(self):
+        """Test successful patch generation returning updated agent."""
+        updated_agent = {
+            "name": "Updated Agent",
+            "nodes": [{"id": "1", "block_id": "test"}],
+            "links": [],
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": updated_agent,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        current_agent = {"name": "Old Agent", "nodes": [], "links": []}
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_patch_external(
+                "Add a new node", current_agent
+            )
+
+        assert result == updated_agent
+        mock_client.post.assert_called_once_with(
+            "/api/update-agent",
+            json={
+                "update_request": "Add a new node",
+                "current_agent_json": current_agent,
+            },
+        )
+
+    @pytest.mark.asyncio
+    async def test_generate_patch_returns_clarifying_questions(self):
+        """Test patch generation returning clarifying questions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "clarifying_questions",
+            "questions": ["What type of node?"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_patch_external(
+                "Add something", {"nodes": []}
+            )
+
+        assert result == {
+            "type": "clarifying_questions",
+            "questions": ["What type of node?"],
+        }
+
+
+class TestHealthCheck:
+    """Test health_check function."""
+
+    def setup_method(self):
+        """Reset singletons before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_when_not_configured(self):
+        """Test health check returns False when service not configured."""
+        with patch.object(
+            service, "is_external_service_configured", return_value=False
+        ):
+            result = await service.health_check()
+            assert result is False
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_true_when_healthy(self):
+        """Test health check returns True when service is healthy."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "status": "healthy",
+            "blocks_loaded": True,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is True
+        mock_client.get.assert_called_once_with("/health")
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_when_not_healthy(self):
+        """Test health check returns False when service is not healthy."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "status": "unhealthy",
+            "blocks_loaded": False,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is False
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_on_error(self):
+        """Test health check returns False on connection error."""
+        mock_client = AsyncMock()
+        mock_client.get.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is False
+
+
+class TestGetBlocksExternal:
+    """Test get_blocks_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_get_blocks_success(self):
+        """Test successful blocks retrieval."""
+        blocks = [
+            {"id": "block1", "name": "Block 1"},
+            {"id": "block2", "name": "Block 2"},
+        ]
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "blocks": blocks,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.get_blocks_external()
+
+        assert result == blocks
+        mock_client.get.assert_called_once_with("/api/blocks")
+
+    @pytest.mark.asyncio
+    async def test_get_blocks_handles_error(self):
+        """Test blocks retrieval handles errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.get.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.get_blocks_external()
+
+        assert result is None
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

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

@@ -5,10 +5,11 @@ import {
   TooltipContent,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
-import { PlayIcon, StopIcon } from "@phosphor-icons/react";
+import { CircleNotchIcon, 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 {
@@ -24,6 +25,31 @@ 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>
@@ -33,18 +59,18 @@ 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 || isExecutingGraph || isTerminatingGraph}
-            loading={isExecutingGraph || isTerminatingGraph || isSaving}
+            disabled={!flowID || isLoading}
+            className="group"
           >
-            {!isGraphRunning ? (
-              <PlayIcon className="size-4" />
-            ) : (
-              <StopIcon className="size-4" />
-            )}
+            {renderIcon()}
           </Button>
         </TooltipTrigger>
         <TooltipContent>
-          {isGraphRunning ? "Stop agent" : "Run agent"}
+          {isLoading
+            ? "Processing..."
+            : isGraphRunning
+              ? "Stop agent"
+              : "Run agent"}
         </TooltipContent>
       </Tooltip>
       <RunInputDialog

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

@@ -10,6 +10,7 @@ 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,
@@ -23,19 +24,17 @@ 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 {
-    credentialsUiSchema,
+    credentialFields,
+    requiredCredentials,
     handleManualRun,
     handleInputChange,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,
     inputValues,
     credentialValues,
-    handleCredentialChange,
+    handleCredentialFieldChange,
     isExecutingGraph,
   } = useRunInputDialog({ setIsOpen });
 
@@ -62,67 +61,67 @@ export const RunInputDialog = ({
           isOpen,
           set: setIsOpen,
         }}
-        styling={{ maxWidth: "600px", minWidth: "600px" }}
+        styling={{ maxWidth: "700px", minWidth: "700px" }}
       >
         <Dialog.Content>
-          <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
+            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>
-                <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>
-            )}
+              )}
 
-            {/* Inputs Section */}
-            {hasInputs() && (
-              <div data-id="run-input-inputs-section">
-                <div className="mb-4">
-                  <Text variant="h4" className="text-gray-900">
-                    Inputs
-                  </Text>
+              {/* 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>
-                <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>
-            )}
+              )}
+            </div>
 
-            {/* Action Button */}
             <div
-              className="flex justify-end pt-2"
+              className="flex flex-col items-end justify-start"
               data-id="run-input-actions-section"
             >
               {purpose === "run" && (
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2"
+                  className="group h-fit min-w-0 gap-2 px-10"
                   onClick={handleManualRun}
                   loading={isExecutingGraph}
                   data-id="run-input-manual-run-button"
@@ -137,7 +136,7 @@ export const RunInputDialog = ({
                 <Button
                   variant="primary"
                   size="large"
-                  className="group h-fit min-w-0 gap-2"
+                  className="group h-fit min-w-0 gap-2 px-10"
                   onClick={() => setOpenCronSchedulerDialog(true)}
                   data-id="run-input-schedule-button"
                 >

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

@@ -7,12 +7,11 @@ import {
   GraphExecutionMeta,
 } from "@/lib/autogpt-server-api";
 import { parseAsInteger, parseAsString, useQueryStates } from "nuqs";
-import { useMemo, useState } from "react";
-import { uiSchema } from "../../../FlowEditor/nodes/uiSchema";
-import { isCredentialFieldSchema } from "@/components/renderers/InputRenderer/custom/CredentialField/helpers";
+import { useCallback, useMemo, useState } from "react";
 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,
@@ -120,27 +119,32 @@ export const useRunInputDialog = ({
       },
     });
 
-  // 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.
+  // Convert credentials schema to credential fields array for CredentialsGroupedView
+  const credentialFields: CredentialField[] = useMemo(() => {
+    if (!credentialsSchema?.properties) return [];
+    return Object.entries(credentialsSchema.properties);
+  }, [credentialsSchema]);
 
-  const credentialsUiSchema = useMemo(() => {
-    const dynamicUiSchema: any = { ...uiSchema };
+  // Get required credentials as a Set
+  const requiredCredentials = useMemo(() => {
+    return new Set<string>(credentialsSchema?.required || []);
+  }, [credentialsSchema]);
 
-    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",
-          };
+  // 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;
         }
       });
-    }
-
-    return dynamicUiSchema;
-  }, [credentialsSchema]);
+    },
+    [],
+  );
 
   const handleManualRun = async () => {
     // Filter out incomplete credentials (those without a valid id)
@@ -173,12 +177,14 @@ export const useRunInputDialog = ({
   };
 
   return {
-    credentialsUiSchema,
+    credentialFields,
+    requiredCredentials,
     inputValues,
     credentialValues,
     isExecutingGraph,
     handleInputChange,
     handleCredentialChange,
+    handleCredentialFieldChange,
     handleManualRun,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,

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

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

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-4" />,
+        icon: <PlusIcon className="size-3.5 text-zinc-600" />,
         label: "Zoom In",
         onClick: () => zoomIn(),
         className: "h-10 w-10 border-none",
       },
       {
         id: "zoom-out-button",
-        icon: <MinusIcon className="size-4" />,
+        icon: <MinusIcon className="size-3.5 text-zinc-600" />,
         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-4 animate-spin" />
+          <CircleNotchIcon className="size-3.5 animate-spin text-zinc-600" />
         ) : (
-          <ChalkboardIcon className="size-4" />
+          <ChalkboardIcon className="size-3.5 text-zinc-600" />
         ),
         label: isTutorialLoading ? "Loading Tutorial..." : "Start Tutorial",
         onClick: handleTutorialClick,
@@ -79,7 +79,7 @@ export const CustomControls = memo(
       },
       {
         id: "fit-view-button",
-        icon: <FrameCornersIcon className="size-4" />,
+        icon: <FrameCornersIcon className="size-3.5 text-zinc-600" />,
         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-4" />
+          <LockOpenIcon className="size-3.5 text-zinc-600" />
         ) : (
-          <LockIcon className="size-4" />
+          <LockIcon className="size-3.5 text-zinc-600" />
         ),
         label: "Toggle Lock",
         onClick: () => setIsLocked(!isLocked),

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

@@ -139,14 +139,6 @@ 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]);
 
@@ -158,6 +150,14 @@ 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

@@ -19,6 +19,8 @@ export type CustomEdgeData = {
   beadUp?: number;
   beadDown?: number;
   beadData?: Map<string, NodeExecutionResult["status"]>;
+  edgeColorClass?: string;
+  edgeHexColor?: string;
 };
 
 export type CustomEdge = XYEdge<CustomEdgeData, "custom">;
@@ -36,7 +38,6 @@ 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);
 
@@ -52,6 +53,7 @@ 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);
@@ -70,7 +72,9 @@ const CustomEdge = ({
             ? "!stroke-red-500 !stroke-[2px] [stroke-dasharray:4]"
             : selected
               ? "stroke-zinc-800"
-              : "stroke-zinc-500/50 hover:stroke-zinc-500",
+              : edgeColorClass
+                ? cn(edgeColorClass, "opacity-70 hover:opacity-100")
+                : "stroke-zinc-500/50 hover:stroke-zinc-500",
         )}
       />
       <JSBeads

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

@@ -8,6 +8,7 @@ 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);
@@ -34,8 +35,13 @@ export const useCustomEdge = () => {
       if (exists) return;
 
       const nodes = useNodeStore.getState().nodes;
-      const isStatic = nodes.find((n) => n.id === conn.source)?.data
-        ?.staticOutput;
+      const sourceNode = nodes.find((n) => n.id === conn.source);
+      const isStatic = sourceNode?.data?.staticOutput;
+
+      const { colorClass, hexColor } = getEdgeColorFromOutputType(
+        sourceNode?.data?.outputSchema,
+        conn.sourceHandle,
+      );
 
       addEdge({
         source: conn.source,
@@ -44,6 +50,8 @@ 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

@@ -1,22 +1,21 @@
 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 { CaretDownIcon, CopyIcon, CheckIcon } from "@phosphor-icons/react";
+import { 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,
-    isExpanded,
-    setIsExpanded,
-    copiedKey,
-    handleCopy,
-    executionResultId,
-    inputData,
-  } = useNodeOutput(nodeId);
+  const { outputData, copiedKey, handleCopy, executionResultId, inputData } =
+    useNodeOutput(nodeId);
 
   if (Object.keys(outputData).length === 0) {
     return null;
@@ -25,122 +24,117 @@ export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
   return (
     <div
       data-tutorial-id={`node-output`}
-      className="flex flex-col gap-3 rounded-b-xl border-t border-zinc-200 px-4 py-4"
+      className="rounded-b-xl border-t border-zinc-200 px-4 py-2"
     >
-      <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>
+      <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>
 
-      {isExpanded && (
-        <>
-          <div className="flex max-w-[350px] flex-col gap-4">
-            <div className="space-y-2">
-              <Text variant="small-medium">Input</Text>
+                <ContentRenderer value={inputData} shortContent={false} />
 
-              <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 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>
-            </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} />
+              {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 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} />
-          )}
-        </>
-      )}
+            {Object.keys(outputData).length > 2 && (
+              <ViewMoreData
+                outputData={outputData}
+                execId={executionResultId}
+              />
+            )}
+          </AccordionContent>
+        </AccordionItem>
+      </Accordion>
     </div>
   );
 };

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

@@ -4,7 +4,6 @@ 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();
 
@@ -37,13 +36,10 @@ export const useNodeOutput = (nodeId: string) => {
     }
   };
   return {
-    outputData: outputData,
-    inputData: inputData,
-    isExpanded: isExpanded,
-    setIsExpanded: setIsExpanded,
-    copiedKey: copiedKey,
-    setCopiedKey: setCopiedKey,
-    handleCopy: handleCopy,
+    outputData,
+    inputData,
+    copiedKey,
+    handleCopy,
     executionResultId: nodeExecutionResult?.node_exec_id,
   };
 };

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

@@ -187,3 +187,38 @@ 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

@@ -1,7 +1,32 @@
-// These are SVG Phosphor icons
+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>`;
+}
 
 export const ICONS = {
-  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>`,
+  ClickIcon: createIcon(iconPaths.ClickIcon),
+  Keyboard: createIcon(iconPaths.Keyboard),
+  Drag: createIcon(iconPaths.Drag),
 };
+
+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

@@ -11,6 +11,7 @@ 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;
@@ -60,12 +61,14 @@ 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

@@ -61,12 +61,18 @@ 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: link.source_name,
+  sourceHandle: cleanupSourceName(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

@@ -267,23 +267,34 @@ 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) => ({
-        provider: (credInfo.provider as string) || "unknown",
-        providerName:
-          (credInfo.provider_name as string) ||
-          (credInfo.provider as string) ||
-          "Unknown Provider",
-        credentialType:
+      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 =
           (credInfo.type as
             | "api_key"
             | "oauth2"
             | "user_password"
-            | "host_scoped") || "api_key",
-        title:
-          (credInfo.title as string) ||
-          `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
-        scopes: credInfo.scopes as string[] | undefined,
-      }));
+            | "host_scoped"
+            | undefined) || "api_key";
+        const credentialTypes =
+          typesArray && typesArray.length > 0 ? typesArray : [singleType];
+
+        return {
+          provider: (credInfo.provider as string) || "unknown",
+          providerName:
+            (credInfo.provider_name as string) ||
+            (credInfo.provider as string) ||
+            "Unknown Provider",
+          credentialTypes,
+          title:
+            (credInfo.title as string) ||
+            `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
+          scopes: credInfo.scopes as string[] | undefined,
+        };
+      });
       return {
         type: "credentials_needed",
         toolName,
@@ -358,11 +369,14 @@ 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: [(cred.type as string) || "api_key"],
+            credentials_types: credentialTypes,
             credentials_scopes: cred.scopes as string[] | undefined,
           };
         }

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

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

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

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

@@ -31,10 +31,18 @@ export function AgentSettingsModal({
     }
   }
 
-  const { currentSafeMode, isPending, hasHITLBlocks, handleToggle } =
-    useAgentSafeMode(agent);
+  const {
+    currentHITLSafeMode,
+    showHITLToggle,
+    handleHITLToggle,
+    currentSensitiveActionSafeMode,
+    showSensitiveActionToggle,
+    handleSensitiveActionToggle,
+    isPending,
+    shouldShowToggle,
+  } = useAgentSafeMode(agent);
 
-  if (!hasHITLBlocks) return null;
+  if (!shouldShowToggle) return null;
 
   return (
     <Dialog
@@ -57,23 +65,48 @@ export function AgentSettingsModal({
       )}
       <Dialog.Content>
         <div className="space-y-6">
-          <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>
+          {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>
-              <Switch
-                checked={currentSafeMode || false}
-                onCheckedChange={handleToggle}
-                disabled={isPending}
-                className="mt-1"
-              />
             </div>
-          </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>
       </Dialog.Content>
     </Dialog>

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";
 
@@ -19,6 +19,8 @@ export function ModalRunSection() {
     setInputValue,
     agentInputFields,
     agentCredentialsInputFields,
+    inputCredentials,
+    setInputCredentialsValue,
   } = useRunAgentModalContext();
 
   const inputFields = Object.entries(agentInputFields || {});
@@ -102,6 +104,9 @@ 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/selected-views/SelectedRunView/components/SafeModeToggle.tsx

@@ -5,48 +5,112 @@ 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;
 }
 
-export function SafeModeToggle({ graph }: Props) {
+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) {
   const {
-    currentSafeMode,
+    currentHITLSafeMode,
+    showHITLToggle,
+    isHITLStateUndetermined,
+    handleHITLToggle,
+    currentSensitiveActionSafeMode,
+    showSensitiveActionToggle,
+    handleSensitiveActionToggle,
     isPending,
     shouldShowToggle,
-    isStateUndetermined,
-    handleToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle || isStateUndetermined) {
+  if (!shouldShowToggle || isHITLStateUndetermined) {
+    return null;
+  }
+
+  const showHITL = showHITLToggle && !isHITLStateUndetermined;
+  const showSensitive = showSensitiveActionToggle;
+
+  if (!showHITL && !showSensitive) {
     return null;
   }
 
   return (
-    <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} />
-        </>
+    <div className={cn("flex gap-1", className)}>
+      {showHITL && (
+        <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>
+      {showSensitive && (
+        <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>
   );
 }

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

@@ -13,8 +13,16 @@ interface Props {
 }
 
 export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
-  const { currentSafeMode, isPending, hasHITLBlocks, handleToggle } =
-    useAgentSafeMode(agent);
+  const {
+    currentHITLSafeMode,
+    showHITLToggle,
+    handleHITLToggle,
+    currentSensitiveActionSafeMode,
+    showSensitiveActionToggle,
+    handleSensitiveActionToggle,
+    isPending,
+    shouldShowToggle,
+  } = useAgentSafeMode(agent);
 
   return (
     <SelectedViewLayout agent={agent}>
@@ -34,24 +42,51 @@ export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
         </div>
 
         <div className={`${AGENT_LIBRARY_SECTION_PADDING_X} space-y-6`}>
-          {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>
+          {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>
                 </div>
-                <Switch
-                  checked={currentSafeMode || false}
-                  onCheckedChange={handleToggle}
-                  disabled={isPending}
-                  className="mt-1"
-                />
-              </div>
-            </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>
+              )}
+            </>
           ) : (
             <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/agents/[id]/components/OldAgentLibraryView/components/agent-run-draft-view.tsx

@@ -1,8 +1,15 @@
 "use client";
-import React, { useCallback, useEffect, useMemo, useState } from "react";
+import React, {
+  useCallback,
+  useContext,
+  useEffect,
+  useMemo,
+  useState,
+} from "react";
 
 import {
   CredentialsMetaInput,
+  CredentialsType,
   GraphExecutionID,
   GraphMeta,
   LibraryAgentPreset,
@@ -29,7 +36,11 @@ import {
 } from "@/components/__legacy__/ui/icons";
 import { Input } from "@/components/__legacy__/ui/input";
 import { Button } from "@/components/atoms/Button/Button";
-import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
+import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
+import {
+  findSavedCredentialByProviderAndType,
+  findSavedUserCredentialByProviderAndType,
+} from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import {
   useToast,
@@ -37,6 +48,7 @@ import {
 } from "@/components/molecules/Toast/use-toast";
 import { humanizeCronExpression } from "@/lib/cron-expression-utils";
 import { cn, isEmpty } from "@/lib/utils";
+import { CredentialsProvidersContext } from "@/providers/agent-credentials/credentials-provider";
 import { ClockIcon, CopyIcon, InfoIcon } from "@phosphor-icons/react";
 import { CalendarClockIcon, Trash2Icon } from "lucide-react";
 
@@ -90,6 +102,7 @@ export function AgentRunDraftView({
   const api = useBackendAPI();
   const { toast } = useToast();
   const toastOnFail = useToastOnFail();
+  const allProviders = useContext(CredentialsProvidersContext);
 
   const [inputValues, setInputValues] = useState<Record<string, any>>({});
   const [inputCredentials, setInputCredentials] = useState<
@@ -128,6 +141,77 @@ export function AgentRunDraftView({
     () => graph.credentials_input_schema.properties,
     [graph],
   );
+  const credentialFields = useMemo(
+    function getCredentialFields() {
+      return Object.entries(agentCredentialsInputFields);
+    },
+    [agentCredentialsInputFields],
+  );
+  const requiredCredentials = useMemo(
+    function getRequiredCredentials() {
+      return new Set(
+        (graph.credentials_input_schema?.required as string[]) || [],
+      );
+    },
+    [graph.credentials_input_schema?.required],
+  );
+
+  useEffect(
+    function initializeDefaultCredentials() {
+      if (!allProviders) return;
+      if (!graph.credentials_input_schema?.properties) return;
+      if (requiredCredentials.size === 0) return;
+
+      setInputCredentials(function updateCredentials(currentCreds) {
+        const next = { ...currentCreds };
+        let didAdd = false;
+
+        for (const key of requiredCredentials) {
+          if (next[key]) continue;
+          const schema = graph.credentials_input_schema.properties[key];
+          if (!schema) continue;
+
+          const providerNames = schema.credentials_provider || [];
+          const credentialTypes = schema.credentials_types || [];
+          const requiredScopes = schema.credentials_scopes;
+
+          const userCredential = findSavedUserCredentialByProviderAndType(
+            providerNames,
+            credentialTypes,
+            requiredScopes,
+            allProviders,
+          );
+
+          const savedCredential =
+            userCredential ||
+            findSavedCredentialByProviderAndType(
+              providerNames,
+              credentialTypes,
+              requiredScopes,
+              allProviders,
+            );
+
+          if (!savedCredential) continue;
+
+          next[key] = {
+            id: savedCredential.id,
+            provider: savedCredential.provider,
+            type: savedCredential.type as CredentialsType,
+            title: savedCredential.title,
+          };
+          didAdd = true;
+        }
+
+        if (!didAdd) return currentCreds;
+        return next;
+      });
+    },
+    [
+      allProviders,
+      graph.credentials_input_schema?.properties,
+      requiredCredentials,
+    ],
+  );
 
   const [allRequiredInputsAreSet, missingInputs] = useMemo(() => {
     const nonEmptyInputs = new Set(
@@ -145,18 +229,35 @@ export function AgentRunDraftView({
     );
     return [isSuperset, difference];
   }, [agentInputSchema.required, inputValues]);
-  const [allCredentialsAreSet, missingCredentials] = useMemo(() => {
-    const availableCredentials = new Set(Object.keys(inputCredentials));
-    const allCredentials = new Set(Object.keys(agentCredentialsInputFields));
-    // Backwards-compatible implementation of isSupersetOf and difference
-    const isSuperset = Array.from(allCredentials).every((item) =>
-      availableCredentials.has(item),
-    );
-    const difference = Array.from(allCredentials).filter(
-      (item) => !availableCredentials.has(item),
-    );
-    return [isSuperset, difference];
-  }, [agentCredentialsInputFields, inputCredentials]);
+  const [allCredentialsAreSet, missingCredentials] = useMemo(
+    function getCredentialStatus() {
+      const missing = Array.from(requiredCredentials).filter((key) => {
+        const cred = inputCredentials[key];
+        return !cred || !cred.id;
+      });
+      return [missing.length === 0, missing];
+    },
+    [requiredCredentials, inputCredentials],
+  );
+  function addChangedCredentials(prev: Set<keyof LibraryAgentPresetUpdatable>) {
+    const next = new Set(prev);
+    next.add("credentials");
+    return next;
+  }
+
+  function handleCredentialChange(key: string, value?: CredentialsMetaInput) {
+    setInputCredentials(function updateInputCredentials(currentCreds) {
+      const next = { ...currentCreds };
+      if (value === undefined) {
+        delete next[key];
+        return next;
+      }
+      next[key] = value;
+      return next;
+    });
+    setChangedPresetAttributes(addChangedCredentials);
+  }
+
   const notifyMissingInputs = useCallback(
     (needPresetName: boolean = true) => {
       const allMissingFields = (
@@ -649,35 +750,6 @@ export function AgentRunDraftView({
               </>
             )}
 
-            {/* Credentials inputs */}
-            {Object.entries(agentCredentialsInputFields).map(
-              ([key, inputSubSchema]) => (
-                <CredentialsInput
-                  key={key}
-                  schema={{ ...inputSubSchema, discriminator: undefined }}
-                  selectedCredentials={
-                    inputCredentials[key] ?? inputSubSchema.default
-                  }
-                  onSelectCredentials={(value) => {
-                    setInputCredentials((obj) => {
-                      const newObj = { ...obj };
-                      if (value === undefined) {
-                        delete newObj[key];
-                        return newObj;
-                      }
-                      return {
-                        ...obj,
-                        [key]: value,
-                      };
-                    });
-                    setChangedPresetAttributes((prev) =>
-                      prev.add("credentials"),
-                    );
-                  }}
-                />
-              ),
-            )}
-
             {/* Regular inputs */}
             {Object.entries(agentInputFields).map(([key, inputSubSchema]) => (
               <RunAgentInputs
@@ -695,6 +767,17 @@ export function AgentRunDraftView({
                 data-testid={`agent-input-${key}`}
               />
             ))}
+
+            {/* Credentials inputs */}
+            {credentialFields.length > 0 && (
+              <CredentialsGroupedView
+                credentialFields={credentialFields}
+                requiredCredentials={requiredCredentials}
+                inputCredentials={inputCredentials}
+                inputValues={inputValues}
+                onCredentialChange={handleCredentialChange}
+              />
+            )}
           </CardContent>
         </Card>
       </div>

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

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

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

@@ -6383,6 +6383,11 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "readOnly": true
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -6399,6 +6404,7 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
+          "has_sensitive_action",
           "trigger_setup_info"
         ],
         "title": "BaseGraph"
@@ -7629,6 +7635,11 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "readOnly": true
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7652,6 +7663,7 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
+          "has_sensitive_action",
           "trigger_setup_info",
           "credentials_input_schema"
         ],
@@ -7730,6 +7742,11 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "readOnly": true
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7754,6 +7771,7 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
+          "has_sensitive_action",
           "trigger_setup_info",
           "credentials_input_schema"
         ],
@@ -7762,8 +7780,14 @@
       "GraphSettings": {
         "properties": {
           "human_in_the_loop_safe_mode": {
-            "anyOf": [{ "type": "boolean" }, { "type": "null" }],
-            "title": "Human In The Loop Safe Mode"
+            "type": "boolean",
+            "title": "Human In The Loop Safe Mode",
+            "default": true
+          },
+          "sensitive_action_safe_mode": {
+            "type": "boolean",
+            "title": "Sensitive Action Safe Mode",
+            "default": false
           }
         },
         "type": "object",
@@ -7921,6 +7945,16 @@
             "title": "Has External Trigger",
             "description": "Whether the agent has an external trigger (e.g. webhook) node"
           },
+          "has_human_in_the_loop": {
+            "type": "boolean",
+            "title": "Has Human In The Loop",
+            "description": "Whether the agent has human-in-the-loop blocks"
+          },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "description": "Whether the agent has sensitive action blocks"
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7967,6 +8001,8 @@
           "output_schema",
           "credentials_input_schema",
           "has_external_trigger",
+          "has_human_in_the_loop",
+          "has_sensitive_action",
           "new_output",
           "can_access_graph",
           "is_latest_version",

autogpt_platform/frontend/src/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView.tsx

@@ -5,30 +5,37 @@ 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 { SlidersHorizontal } from "@phosphor-icons/react";
+import { SlidersHorizontalIcon } 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(
     () =>
@@ -87,11 +94,11 @@ export function CredentialsGroupedView({
       );
 
       if (savedCredential) {
-        setInputCredentialsValue(key, {
+        onCredentialChange(key, {
           id: savedCredential.id,
           provider: savedCredential.provider,
-          type: savedCredential.type,
-          title: (savedCredential as { title?: string }).title,
+          type: savedCredential.type as CredentialsType,
+          title: savedCredential.title,
         });
       }
     }
@@ -103,7 +110,7 @@ export function CredentialsGroupedView({
     systemCredentialFields,
     requiredCredentials,
     inputCredentials,
-    setInputCredentialsValue,
+    onCredentialChange,
     isLoadingProviders,
   ]);
 
@@ -123,7 +130,7 @@ export function CredentialsGroupedView({
                   }
                   selectedCredentials={selectedCred}
                   onSelectCredentials={(value) => {
-                    setInputCredentialsValue(key, value);
+                    onCredentialChange(key, value);
                   }}
                   siblingInputs={inputValues}
                   isOptional={!requiredCredentials.has(key)}
@@ -143,7 +150,8 @@ 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">
-                <SlidersHorizontal size={16} weight="bold" /> System credentials
+                <SlidersHorizontalIcon size={16} weight="bold" /> System
+                credentials
                 {hasMissingSystemCredentials && (
                   <span className="text-destructive">(missing)</span>
                 )}
@@ -163,7 +171,7 @@ export function CredentialsGroupedView({
                         }
                         selectedCredentials={selectedCred}
                         onSelectCredentials={(value) => {
-                          setInputCredentialsValue(key, value);
+                          onCredentialChange(key, value);
                         }}
                         siblingInputs={inputValues}
                         isOptional={!requiredCredentials.has(key)}

autogpt_platform/frontend/src/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers.ts

@@ -1,5 +1,5 @@
 import { CredentialsProvidersContextType } from "@/providers/agent-credentials/credentials-provider";
-import { getSystemCredentials } from "../../../../../../../../../../../components/contextual/CredentialsInput/helpers";
+import { filterSystemCredentials, getSystemCredentials } from "../../helpers";
 
 export type CredentialField = [string, any];
 
@@ -208,3 +208,42 @@ export function findSavedCredentialByProviderAndType(
 
   return undefined;
 }
+
+export function findSavedUserCredentialByProviderAndType(
+  providerNames: string[],
+  credentialTypes: string[],
+  requiredScopes: string[] | undefined,
+  allProviders: CredentialsProvidersContextType | null,
+): SavedCredential | undefined {
+  for (const providerName of providerNames) {
+    const providerData = allProviders?.[providerName];
+    if (!providerData) continue;
+
+    const userCredentials = filterSystemCredentials(
+      providerData.savedCredentials ?? [],
+    );
+
+    const matchingCredentials: SavedCredential[] = [];
+
+    for (const credential of userCredentials) {
+      const typeMatches =
+        credentialTypes.length === 0 ||
+        credentialTypes.includes(credential.type);
+      const scopesMatch = hasRequiredScopes(credential, requiredScopes);
+
+      if (!typeMatches) continue;
+      if (!scopesMatch) continue;
+
+      matchingCredentials.push(credential as SavedCredential);
+    }
+
+    if (matchingCredentials.length === 1) {
+      return matchingCredentials[0];
+    }
+    if (matchingCredentials.length > 1) {
+      return undefined;
+    }
+  }
+
+  return undefined;
+}

autogpt_platform/frontend/src/components/contextual/CredentialsInput/useCredentialsInput.ts

@@ -98,24 +98,20 @@ export function useCredentialsInput({
 
   // Auto-select the first available credential on initial mount
   // Once a user has made a selection, we don't override it
-  useEffect(() => {
-    if (readOnly) return;
-    if (!credentials || !("savedCredentials" in credentials)) return;
+  useEffect(
+    function autoSelectCredential() {
+      if (readOnly) return;
+      if (!credentials || !("savedCredentials" in credentials)) return;
+      if (selectedCredential?.id) return;
 
-    // If already selected, don't auto-select
-    if (selectedCredential?.id) return;
+      const savedCreds = credentials.savedCredentials;
+      if (savedCreds.length === 0) return;
 
-    // Only attempt auto-selection once
-    if (hasAttemptedAutoSelect.current) return;
-    hasAttemptedAutoSelect.current = true;
+      if (hasAttemptedAutoSelect.current) return;
+      hasAttemptedAutoSelect.current = true;
 
-    // If optional, don't auto-select (user can choose "None")
-    if (isOptional) return;
+      if (isOptional) return;
 
-    const savedCreds = credentials.savedCredentials;
-
-    // Auto-select the first credential if any are available
-    if (savedCreds.length > 0) {
       const cred = savedCreds[0];
       onSelectCredential({
         id: cred.id,
@@ -123,14 +119,15 @@ export function useCredentialsInput({
         provider: credentials.provider,
         title: (cred as any).title,
       });
-    }
-  }, [
-    credentials,
-    selectedCredential?.id,
-    readOnly,
-    isOptional,
-    onSelectCredential,
-  ]);
+    },
+    [
+      credentials,
+      selectedCredential?.id,
+      readOnly,
+      isOptional,
+      onSelectCredential,
+    ],
+  );
 
   if (
     !credentials ||

autogpt_platform/frontend/src/components/molecules/Popover/Popover.tsx

@@ -0,0 +1,33 @@
+"use client";
+
+import * as PopoverPrimitive from "@radix-ui/react-popover";
+import * as React from "react";
+
+import { cn } from "@/lib/utils";
+
+const Popover = PopoverPrimitive.Root;
+
+const PopoverTrigger = PopoverPrimitive.Trigger;
+
+const PopoverAnchor = PopoverPrimitive.Anchor;
+
+const PopoverContent = React.forwardRef<
+  React.ElementRef<typeof PopoverPrimitive.Content>,
+  React.ComponentPropsWithoutRef<typeof PopoverPrimitive.Content>
+>(({ className, align = "center", sideOffset = 4, ...props }, ref) => (
+  <PopoverPrimitive.Portal>
+    <PopoverPrimitive.Content
+      ref={ref}
+      align={align}
+      sideOffset={sideOffset}
+      className={cn(
+        "z-50 w-72 rounded-lg border border-zinc-200 bg-white p-4 text-zinc-900 shadow-md outline-none data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
+        className,
+      )}
+      {...props}
+    />
+  </PopoverPrimitive.Portal>
+));
+PopoverContent.displayName = PopoverPrimitive.Content.displayName;
+
+export { Popover, PopoverAnchor, PopoverContent, PopoverTrigger };

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

@@ -35,12 +35,13 @@ export const CredentialFieldTitle = (props: {
     uiOptions,
   );
 
-  const credentialProvider = toDisplayName(
-    getCredentialProviderFromSchema(
-      useNodeStore.getState().getHardCodedValues(nodeId),
-      schema as BlockIOCredentialsSubSchema,
-    ) ?? "",
+  const provider = 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/components/renderers/InputRenderer/custom/LlmModelField/LlmModelField.tsx

@@ -0,0 +1,92 @@
+"use client";
+
+import {
+  descriptionId,
+  FieldProps,
+  getTemplate,
+  RJSFSchema,
+  titleId,
+} from "@rjsf/utils";
+import { useMemo } from "react";
+import { LlmModelPicker } from "./components/LlmModelPicker";
+import { LlmModelMetadataMap } from "./types";
+import { updateUiOption } from "../../helpers";
+
+type LlmModelSchema = RJSFSchema & {
+  llm_model_metadata?: LlmModelMetadataMap;
+};
+
+export function LlmModelField(props: FieldProps) {
+  const { schema, formData, onChange, disabled, readonly, fieldPathId } = props;
+
+  const metadata = useMemo(() => {
+    return (schema as LlmModelSchema)?.llm_model_metadata ?? {};
+  }, [schema]);
+
+  const models = useMemo(() => {
+    return Object.values(metadata);
+  }, [metadata]);
+
+  const selectedName =
+    typeof formData === "string"
+      ? formData
+      : typeof schema.default === "string"
+        ? schema.default
+        : "";
+
+  const selectedModel = selectedName
+    ? (metadata[selectedName] ??
+      models.find((model) => model.name === selectedName))
+    : undefined;
+
+  const recommendedName =
+    typeof schema.default === "string" ? schema.default : models[0]?.name;
+
+  const recommendedModel =
+    recommendedName && metadata[recommendedName]
+      ? metadata[recommendedName]
+      : undefined;
+
+  if (models.length === 0) {
+    return null;
+  }
+
+  const TitleFieldTemplate = getTemplate("TitleFieldTemplate", props.registry);
+  const DescriptionFieldTemplate = getTemplate(
+    "DescriptionFieldTemplate",
+    props.registry,
+  );
+
+  const updatedUiSchema = updateUiOption(props.uiSchema, {
+    showHandles: false,
+  });
+
+  return (
+    <>
+      <div className="flex items-center gap-2">
+        <TitleFieldTemplate
+          id={titleId(fieldPathId)}
+          title={schema.title || ""}
+          required={true}
+          schema={schema}
+          uiSchema={updatedUiSchema}
+          registry={props.registry}
+        />
+        <DescriptionFieldTemplate
+          id={descriptionId(fieldPathId)}
+          description={schema.description || ""}
+          schema={schema}
+          registry={props.registry}
+        />
+      </div>
+
+      <LlmModelPicker
+        models={models}
+        selectedModel={selectedModel}
+        recommendedModel={recommendedModel}
+        onSelect={(value) => onChange(value, fieldPathId?.path)}
+        disabled={disabled || readonly}
+      />
+    </>
+  );
+}

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/LlmModelField/components/LlmIcon.tsx

@@ -0,0 +1,66 @@
+"use client";
+
+import Image from "next/image";
+import { Text } from "@/components/atoms/Text/Text";
+
+const creatorIconMap: Record<string, string> = {
+  anthropic: "/integrations/anthropic-color.png",
+  openai: "/integrations/openai.png",
+  google: "/integrations/gemini.png",
+  nvidia: "/integrations/nvidia.png",
+  groq: "/integrations/groq.png",
+  ollama: "/integrations/ollama.png",
+  openrouter: "/integrations/open_router.png",
+  v0: "/integrations/v0.png",
+  xai: "/integrations/xai.webp",
+  meta: "/integrations/llama_api.png",
+  amazon: "/integrations/amazon.png",
+  cohere: "/integrations/cohere.png",
+  deepseek: "/integrations/deepseek.png",
+  gryphe: "/integrations/gryphe.png",
+  microsoft: "/integrations/microsoft.webp",
+  moonshotai: "/integrations/moonshot.png",
+  mistral: "/integrations/mistral.png",
+  mistralai: "/integrations/mistral.png",
+  nousresearch: "/integrations/nousresearch.avif",
+  perplexity: "/integrations/perplexity.webp",
+  qwen: "/integrations/qwen.png",
+};
+
+type Props = {
+  value: string;
+  size?: number;
+};
+
+export function LlmIcon({ value, size = 20 }: Props) {
+  const normalized = value.trim().toLowerCase().replace(/\s+/g, "");
+  const src = creatorIconMap[normalized];
+  if (src) {
+    return (
+      <div
+        className="flex items-center justify-center overflow-hidden rounded-xsmall"
+        style={{ width: size, height: size }}
+      >
+        <Image
+          src={src}
+          alt={value}
+          width={size}
+          height={size}
+          className="h-full w-full object-cover"
+        />
+      </div>
+    );
+  }
+
+  const fallback = value?.trim().slice(0, 1).toUpperCase() || "?";
+  return (
+    <div
+      className="flex items-center justify-center rounded-xsmall bg-zinc-100"
+      style={{ width: size, height: size }}
+    >
+      <Text variant="small" className="text-zinc-500">
+        {fallback}
+      </Text>
+    </div>
+  );
+}