fix(backend): Remove _credentials_id key on fork instead of setting to None

Setting _credentials_id to None on fork was ambiguous — both "forked,
needs re-auth" and "chained data from upstream" were represented as None.
This caused _acquire_auto_credentials to silently skip credential
acquisition for forked agents, leading to confusing TypeErrors at runtime.

Now the key is deleted entirely, making the three states unambiguous:
- Present with value: user-selected credentials
- Present as None: chained data from upstream block
- Absent: forked/needs re-authentication

Also adds pre-run validation for the missing key case and makes error
messages provider-agnostic.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

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

@@ -27,11 +27,20 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       cache-key: ${{ steps.cache-key.outputs.key }}
+      components-changed: ${{ steps.filter.outputs.components }}
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
+      - name: Check for component changes
+        uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            components:
+              - 'autogpt_platform/frontend/src/components/**'
+
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
@@ -90,8 +99,11 @@ jobs:
   chromatic:
     runs-on: ubuntu-latest
     needs: setup
-    # Only run on dev branch pushes or PRs targeting dev
-    if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
+    # Disabled: to re-enable, remove 'false &&' from the condition below
+    if: >-
+      false
+      && (github.ref == 'refs/heads/dev' || github.base_ref == 'dev')
+      && needs.setup.outputs.components-changed == 'true'
 
     steps:
       - name: Checkout repository

autogpt_platform/backend/.env.default

@@ -152,6 +152,7 @@ REPLICATE_API_KEY=
 REVID_API_KEY=
 SCREENSHOTONE_API_KEY=
 UNREAL_SPEECH_API_KEY=
+ELEVENLABS_API_KEY=
 
 # Data & Search Services
 E2B_API_KEY=

autogpt_platform/backend/.gitignore

@@ -19,3 +19,6 @@ load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
 migrations/*/rollback*.sql
+
+# Workspace files
+workspaces/

autogpt_platform/backend/Dockerfile

@@ -62,10 +62,12 @@ ENV POETRY_HOME=/opt/poetry \
     DEBIAN_FRONTEND=noninteractive
 ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python without upgrading system-managed packages
+# Install Python, FFmpeg, and ImageMagick (required for video processing blocks)
 RUN apt-get update && apt-get install -y \
     python3.13 \
     python3-pip \
+    ffmpeg \
+    imagemagick \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy only necessary files from builder

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

@@ -11,7 +11,7 @@ class ChatConfig(BaseSettings):
 
     # OpenAI API Configuration
     model: str = Field(
-        default="anthropic/claude-opus-4.5", description="Default model to use"
+        default="anthropic/claude-opus-4.6", description="Default model to use"
     )
     title_model: str = Field(
         default="openai/gpt-4o-mini",

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

@@ -18,10 +18,6 @@ class ResponseType(str, Enum):
     START = "start"
     FINISH = "finish"
 
-    # Step lifecycle (one LLM API call within a message)
-    START_STEP = "start-step"
-    FINISH_STEP = "finish-step"
-
     # Text streaming
     TEXT_START = "text-start"
     TEXT_DELTA = "text-delta"
@@ -61,16 +57,6 @@ class StreamStart(StreamBaseResponse):
         description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-protocol fields like taskId."""
-        import json
-
-        data: dict[str, Any] = {
-            "type": self.type.value,
-            "messageId": self.messageId,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-
 
 class StreamFinish(StreamBaseResponse):
     """End of message/stream."""
@@ -78,26 +64,6 @@ class StreamFinish(StreamBaseResponse):
     type: ResponseType = ResponseType.FINISH
 
 
-class StreamStartStep(StreamBaseResponse):
-    """Start of a step (one LLM API call within a message).
-
-    The AI SDK uses this to add a step-start boundary to message.parts,
-    enabling visual separation between multiple LLM calls in a single message.
-    """
-
-    type: ResponseType = ResponseType.START_STEP
-
-
-class StreamFinishStep(StreamBaseResponse):
-    """End of a step (one LLM API call within a message).
-
-    The AI SDK uses this to reset activeTextParts and activeReasoningParts,
-    so the next LLM call in a tool-call continuation starts with clean state.
-    """
-
-    type: ResponseType = ResponseType.FINISH_STEP
-
-
 # ========== Text Streaming ==========
 
 
@@ -151,7 +117,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
     type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
     toolCallId: str = Field(..., description="Tool call ID this responds to")
     output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Keep these for internal backend use
+    # Additional fields for internal use (not part of AI SDK spec but useful)
     toolName: str | None = Field(
         default=None, description="Name of the tool that was executed"
     )
@@ -159,17 +125,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
         default=True, description="Whether the tool execution succeeded"
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-spec fields."""
-        import json
-
-        data = {
-            "type": self.type.value,
-            "toolCallId": self.toolCallId,
-            "output": self.output,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-
 
 # ========== Other ==========
 

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

@@ -6,7 +6,7 @@ from collections.abc import AsyncGenerator
 from typing import Annotated
 
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
@@ -17,29 +17,7 @@ from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-from .response_model import StreamFinish, StreamHeartbeat
-from .tools.models import (
-    AgentDetailsResponse,
-    AgentOutputResponse,
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    AgentsFoundResponse,
-    BlockListResponse,
-    BlockOutputResponse,
-    ClarificationNeededResponse,
-    DocPageResponse,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    ExecutionStartedResponse,
-    InputValidationErrorResponse,
-    NeedLoginResponse,
-    NoResultsResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
-    SetupRequirementsResponse,
-    UnderstandingUpdatedResponse,
-)
+from .response_model import StreamFinish, StreamHeartbeat, StreamStart
 
 config = ChatConfig()
 
@@ -306,6 +284,10 @@ async def stream_chat_post(
     # Background task that runs the AI generation independently of SSE connection
     async def run_ai_generation():
         try:
+            # Emit a start event with task_id for reconnection
+            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
+            await stream_registry.publish_chunk(task_id, start_chunk)
+
             async for chunk in chat_service.stream_chat_completion(
                 session_id,
                 request.message,
@@ -313,7 +295,6 @@ async def stream_chat_post(
                 user_id=user_id,
                 session=session,  # Pass pre-fetched session to avoid double-fetch
                 context=request.context,
-                _task_id=task_id,  # Pass task_id so service emits start with taskId for reconnection
             ):
                 # Write to Redis (subscribers will receive via XREAD)
                 await stream_registry.publish_chunk(task_id, chunk)
@@ -393,73 +374,63 @@ async def stream_chat_post(
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def resume_session_stream(
+async def stream_chat_get(
     session_id: str,
+    message: Annotated[str, Query(min_length=1, max_length=10000)],
     user_id: str | None = Depends(auth.get_user_id),
+    is_user_message: bool = Query(default=True),
 ):
     """
-    Resume an active stream for a session.
+    Stream chat responses for a session (GET - legacy endpoint).
 
-    Called by the AI SDK's ``useChat(resume: true)`` on page load.
-    Checks for an active (in-progress) task on the session and either replays
-    the full SSE stream or returns 204 No Content if nothing is running.
+    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
+      - Text fragments as they are generated
+      - Tool call UI elements (if invoked)
+      - Tool execution results
 
     Args:
-        session_id: The chat session identifier.
+        session_id: The chat session identifier to associate with the streamed messages.
+        message: The user's new message to process.
         user_id: Optional authenticated user ID.
-
+        is_user_message: Whether the message is a user message.
     Returns:
-        StreamingResponse (SSE) when an active stream exists,
-        or 204 No Content when there is nothing to resume.
+        StreamingResponse: SSE-formatted response chunks.
+
     """
-    import asyncio
-
-    active_task, _last_id = await stream_registry.get_active_task_for_session(
-        session_id, user_id
-    )
-
-    if not active_task:
-        return Response(status_code=204)
-
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=active_task.task_id,
-        user_id=user_id,
-        last_message_id="0-0",  # Full replay so useChat rebuilds the message
-    )
-
-    if subscriber_queue is None:
-        return Response(status_code=204)
+    session = await _validate_and_get_session(session_id, user_id)
 
     async def event_generator() -> AsyncGenerator[str, None]:
-        try:
-            while True:
-                try:
-                    chunk = await asyncio.wait_for(
-                        subscriber_queue.get(), timeout=30.0
-                    )
-                    yield chunk.to_sse()
-
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    yield StreamHeartbeat().to_sse()
-        except GeneratorExit:
-            pass
-        except Exception as e:
-            logger.error(
-                f"Error in resume stream for session {session_id}: {e}"
-            )
-        finally:
-            try:
-                await stream_registry.unsubscribe_from_task(
-                    active_task.task_id, subscriber_queue
+        chunk_count = 0
+        first_chunk_type: str | None = None
+        async for chunk in chat_service.stream_chat_completion(
+            session_id,
+            message,
+            is_user_message=is_user_message,
+            user_id=user_id,
+            session=session,  # Pass pre-fetched session to avoid double-fetch
+        ):
+            if chunk_count < 3:
+                logger.info(
+                    "Chat stream chunk",
+                    extra={
+                        "session_id": session_id,
+                        "chunk_type": str(chunk.type),
+                    },
                 )
-            except Exception as unsub_err:
-                logger.error(
-                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
-                    exc_info=True,
-                )
-            yield "data: [DONE]\n\n"
+            if not first_chunk_type:
+                first_chunk_type = str(chunk.type)
+            chunk_count += 1
+            yield chunk.to_sse()
+        logger.info(
+            "Chat stream completed",
+            extra={
+                "session_id": session_id,
+                "chunk_count": chunk_count,
+                "first_chunk_type": first_chunk_type,
+            },
+        )
+        # AI SDK protocol termination
+        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -467,8 +438,8 @@ async def resume_session_stream(
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
+            "X-Accel-Buffering": "no",  # Disable nginx buffering
+            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
         },
     )
 
@@ -780,42 +751,3 @@ async def health_check() -> dict:
         "service": "chat",
         "version": "0.1.0",
     }
-
-
-# ========== Schema Export (for OpenAPI / Orval codegen) ==========
-
-ToolResponseUnion = (
-    AgentsFoundResponse
-    | NoResultsResponse
-    | AgentDetailsResponse
-    | SetupRequirementsResponse
-    | ExecutionStartedResponse
-    | NeedLoginResponse
-    | ErrorResponse
-    | InputValidationErrorResponse
-    | AgentOutputResponse
-    | UnderstandingUpdatedResponse
-    | AgentPreviewResponse
-    | AgentSavedResponse
-    | ClarificationNeededResponse
-    | BlockListResponse
-    | BlockOutputResponse
-    | DocSearchResultsResponse
-    | DocPageResponse
-    | OperationStartedResponse
-    | OperationPendingResponse
-    | OperationInProgressResponse
-)
-
-
-@router.get(
-    "/schema/tool-responses",
-    response_model=ToolResponseUnion,
-    include_in_schema=True,
-    summary="[Dummy] Tool response type export for codegen",
-    description="This endpoint is not meant to be called. It exists solely to "
-    "expose tool response models in the OpenAPI schema for frontend codegen.",
-)
-async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
-    """Never called at runtime. Exists only so Orval generates TS types."""
-    raise HTTPException(status_code=501, detail="Schema-only endpoint")

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

@@ -52,10 +52,8 @@ from .response_model import (
     StreamBaseResponse,
     StreamError,
     StreamFinish,
-    StreamFinishStep,
     StreamHeartbeat,
     StreamStart,
-    StreamStartStep,
     StreamTextDelta,
     StreamTextEnd,
     StreamTextStart,
@@ -353,10 +351,6 @@ async def stream_chat_completion(
     retry_count: int = 0,
     session: ChatSession | None = None,
     context: dict[str, str] | None = None,  # {url: str, content: str}
-    _continuation_message_id: (
-        str | None
-    ) = None,  # Internal: reuse message ID for tool call continuations
-    _task_id: str | None = None,  # Internal: task ID for SSE reconnection support
 ) -> AsyncGenerator[StreamBaseResponse, None]:
     """Main entry point for streaming chat completions with database handling.
 
@@ -485,17 +479,11 @@ async def stream_chat_completion(
     # Generate unique IDs for AI SDK protocol
     import uuid as uuid_module
 
-    is_continuation = _continuation_message_id is not None
-    message_id = _continuation_message_id or str(uuid_module.uuid4())
+    message_id = str(uuid_module.uuid4())
     text_block_id = str(uuid_module.uuid4())
 
-    # Only yield message start for the initial call, not for continuations.
-    # This is the single place where StreamStart is emitted (removed from routes.py).
-    if not is_continuation:
-        yield StreamStart(messageId=message_id, taskId=_task_id)
-
-    # Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
-    yield StreamStartStep()
+    # Yield message start
+    yield StreamStart(messageId=message_id)
 
     try:
         async for chunk in _stream_chat_chunks(
@@ -597,10 +585,6 @@ async def stream_chat_completion(
                     )
                 yield chunk
             elif isinstance(chunk, StreamFinish):
-                if has_done_tool_call:
-                    # Tool calls happened — close the step but don't send message-level finish.
-                    # The continuation will open a new step, and finish will come at the end.
-                    yield StreamFinishStep()
                 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:
@@ -632,8 +616,6 @@ async def stream_chat_completion(
                             has_saved_assistant_message = True
 
                     has_yielded_end = True
-                    # Emit finish-step before finish (resets AI SDK text/reasoning state)
-                    yield StreamFinishStep()
                     yield chunk
             elif isinstance(chunk, StreamError):
                 has_yielded_error = True
@@ -718,7 +700,6 @@ async def stream_chat_completion(
                 error_response = StreamError(errorText=error_message)
                 yield error_response
             if not has_yielded_end:
-                yield StreamFinishStep()
                 yield StreamFinish()
             return
 
@@ -733,8 +714,6 @@ async def stream_chat_completion(
             retry_count=retry_count + 1,
             session=session,
             context=context,
-            _continuation_message_id=message_id,  # Reuse message ID since start was already sent
-            _task_id=_task_id,
         ):
             yield chunk
         return  # Exit after retry to avoid double-saving in finally block
@@ -804,8 +783,6 @@ async def stream_chat_completion(
             session=session,  # Pass session object to avoid Redis refetch
             context=context,
             tool_call_response=str(tool_response_messages),
-            _continuation_message_id=message_id,  # Reuse message ID to avoid duplicates
-            _task_id=_task_id,
         ):
             yield chunk
 
@@ -1588,7 +1565,6 @@ async def _execute_long_running_tool_with_streaming(
             task_id,
             StreamError(errorText=str(e)),
         )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
         await stream_registry.publish_chunk(task_id, StreamFinish())
 
         await _update_pending_operation(
@@ -1846,7 +1822,6 @@ async def _generate_llm_continuation_with_streaming(
 
         # Publish start event
         await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
-        await stream_registry.publish_chunk(task_id, StreamStartStep())
         await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
 
         # Stream the response
@@ -1870,7 +1845,6 @@ async def _generate_llm_continuation_with_streaming(
 
         # Publish end events
         await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
 
         if assistant_content:
             # Reload session from DB to avoid race condition with user messages
@@ -1912,5 +1886,4 @@ async def _generate_llm_continuation_with_streaming(
             task_id,
             StreamError(errorText=f"Failed to generate response: {e}"),
         )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
         await stream_registry.publish_chunk(task_id, StreamFinish())

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

@@ -598,10 +598,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
         ResponseType,
         StreamError,
         StreamFinish,
-        StreamFinishStep,
         StreamHeartbeat,
         StreamStart,
-        StreamStartStep,
         StreamTextDelta,
         StreamTextEnd,
         StreamTextStart,
@@ -615,8 +613,6 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
     type_to_class: dict[str, type[StreamBaseResponse]] = {
         ResponseType.START.value: StreamStart,
         ResponseType.FINISH.value: StreamFinish,
-        ResponseType.START_STEP.value: StreamStartStep,
-        ResponseType.FINISH_STEP.value: StreamFinishStep,
         ResponseType.TEXT_START.value: StreamTextStart,
         ResponseType.TEXT_DELTA.value: StreamTextDelta,
         ResponseType.TEXT_END.value: StreamTextEnd,

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

@@ -7,15 +7,7 @@ from typing import Any, NotRequired, TypedDict
 
 from backend.api.features.library import db as library_db
 from backend.api.features.store import db as store_db
-from backend.data.graph import (
-    Graph,
-    Link,
-    Node,
-    create_graph,
-    get_graph,
-    get_graph_all_versions,
-    get_store_listed_graphs,
-)
+from backend.data.graph import Graph, Link, Node, get_graph, get_store_listed_graphs
 from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .service import (
@@ -28,8 +20,6 @@ from .service import (
 
 logger = logging.getLogger(__name__)
 
-AGENT_EXECUTOR_BLOCK_ID = "e189baac-8c20-45a1-94a7-55177ea42565"
-
 
 class ExecutionSummary(TypedDict):
     """Summary of a single execution for quality assessment."""
@@ -669,45 +659,6 @@ def json_to_graph(agent_json: dict[str, Any]) -> Graph:
     )
 
 
-def _reassign_node_ids(graph: Graph) -> None:
-    """Reassign all node and link IDs to new UUIDs.
-
-    This is needed when creating a new version to avoid unique constraint violations.
-    """
-    id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
-
-    for node in graph.nodes:
-        node.id = id_map[node.id]
-
-    for link in graph.links:
-        link.id = str(uuid.uuid4())
-        if link.source_id in id_map:
-            link.source_id = id_map[link.source_id]
-        if link.sink_id in id_map:
-            link.sink_id = id_map[link.sink_id]
-
-
-def _populate_agent_executor_user_ids(agent_json: dict[str, Any], user_id: str) -> None:
-    """Populate user_id in AgentExecutorBlock nodes.
-
-    The external agent generator creates AgentExecutorBlock nodes with empty user_id.
-    This function fills in the actual user_id so sub-agents run with correct permissions.
-
-    Args:
-        agent_json: Agent JSON dict (modified in place)
-        user_id: User ID to set
-    """
-    for node in agent_json.get("nodes", []):
-        if node.get("block_id") == AGENT_EXECUTOR_BLOCK_ID:
-            input_default = node.get("input_default") or {}
-            if not input_default.get("user_id"):
-                input_default["user_id"] = user_id
-                node["input_default"] = input_default
-                logger.debug(
-                    f"Set user_id for AgentExecutorBlock node {node.get('id')}"
-                )
-
-
 async def save_agent_to_library(
     agent_json: dict[str, Any], user_id: str, is_update: bool = False
 ) -> tuple[Graph, Any]:
@@ -721,35 +672,10 @@ async def save_agent_to_library(
     Returns:
         Tuple of (created Graph, LibraryAgent)
     """
-    # Populate user_id in AgentExecutorBlock nodes before conversion
-    _populate_agent_executor_user_ids(agent_json, user_id)
-
     graph = json_to_graph(agent_json)
-
     if is_update:
-        if graph.id:
-            existing_versions = await get_graph_all_versions(graph.id, user_id)
-            if existing_versions:
-                latest_version = max(v.version for v in existing_versions)
-                graph.version = latest_version + 1
-                _reassign_node_ids(graph)
-                logger.info(f"Updating agent {graph.id} to version {graph.version}")
-    else:
-        graph.id = str(uuid.uuid4())
-        graph.version = 1
-        _reassign_node_ids(graph)
-        logger.info(f"Creating new agent with ID {graph.id}")
-
-    created_graph = await create_graph(graph, user_id)
-
-    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,
-    )
-
-    return created_graph, library_agents[0]
+        return await library_db.update_graph_in_library(graph, user_id)
+    return await library_db.create_graph_in_library(graph, user_id)
 
 
 def graph_to_json(graph: Graph) -> dict[str, Any]:

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

@@ -206,9 +206,9 @@ async def search_agents(
             ]
         )
         no_results_msg = (
-            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
+            f"No agents found matching '{query}'. Let the user know they can try different keywords or browse the marketplace. Also let them know you can create a custom agent for them based on their needs."
             if source == "marketplace"
-            else f"No agents matching '{query}' found in your library."
+            else f"No agents matching '{query}' found in your library. Let the user know you can create a custom agent for them based on their needs."
         )
         return NoResultsResponse(
             message=no_results_msg, session_id=session_id, suggestions=suggestions
@@ -224,10 +224,10 @@ async def search_agents(
     message = (
         "Now you have found some options for the user to choose from. "
         "You can add a link to a recommended agent at: /marketplace/agent/agent_id "
-        "Please ask the user if they would like to use any of these agents."
+        "Please ask the user if they would like to use any of these agents. Let the user know we can create a custom agent for them based on their needs."
         if source == "marketplace"
         else "Found agents in the user's library. You can provide a link to view an agent at: "
-        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
+        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute. Let the user know we can create a custom agent for them based on their needs."
     )
 
     return AgentsFoundResponse(

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

@@ -6,7 +6,6 @@ from typing import Any
 from backend.api.features.library import db as library_db
 from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
-from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
     CredentialsFieldInfo,
@@ -44,14 +43,8 @@ async def fetch_graph_from_store_slug(
         return None, None
 
     # Get the graph from store listing version
-    graph_meta = await store_db.get_available_graph(
-        store_agent.store_listing_version_id
-    )
-    graph = await graph_db.get_graph(
-        graph_id=graph_meta.id,
-        version=graph_meta.version,
-        user_id=None,  # Public access
-        include_subgraphs=True,
+    graph = await store_db.get_available_graph(
+        store_agent.store_listing_version_id, hide_nodes=False
     )
     return graph, store_agent
 
@@ -124,11 +117,11 @@ def build_missing_credentials_from_graph(
     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()
+    aggregated_fields = graph.regular_credentials_inputs
 
     return {
         field_key: _serialize_missing_credential(field_key, field_info)
-        for field_key, (field_info, _node_fields) in aggregated_fields.items()
+        for field_key, (field_info, _, _) in aggregated_fields.items()
         if field_key not in matched_keys
     }
 
@@ -251,7 +244,7 @@ async def match_user_credentials_to_graph(
     missing_creds: list[str] = []
 
     # Get aggregated credentials requirements from the graph
-    aggregated_creds = graph.aggregate_credentials_inputs()
+    aggregated_creds = graph.regular_credentials_inputs
     logger.debug(
         f"Matching credentials for graph {graph.id}: {len(aggregated_creds)} required"
     )
@@ -269,7 +262,8 @@ async def match_user_credentials_to_graph(
     # provider is in the set of acceptable providers.
     for credential_field_name, (
         credential_requirements,
-        _node_fields,
+        _,
+        _,
     ) in aggregated_creds.items():
         # Find first matching credential by provider, type, and scopes
         matching_cred = next(

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

@@ -0,0 +1,78 @@
+"""Tests for chat tools utility functions."""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from backend.data.model import CredentialsFieldInfo
+
+
+def _make_regular_field() -> CredentialsFieldInfo:
+    return CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["github"],
+            "credentials_types": ["api_key"],
+            "is_auto_credential": False,
+        },
+        by_alias=True,
+    )
+
+
+def test_build_missing_credentials_excludes_auto_creds():
+    """
+    build_missing_credentials_from_graph() should use regular_credentials_inputs
+    and thus exclude auto_credentials from the "missing" set.
+    """
+    from backend.api.features.chat.tools.utils import (
+        build_missing_credentials_from_graph,
+    )
+
+    regular_field = _make_regular_field()
+
+    mock_graph = MagicMock()
+    # regular_credentials_inputs should only return the non-auto field
+    mock_graph.regular_credentials_inputs = {
+        "github_api_key": (regular_field, {("node-1", "credentials")}, True),
+    }
+
+    result = build_missing_credentials_from_graph(mock_graph, matched_credentials=None)
+
+    # Should include the regular credential
+    assert "github_api_key" in result
+    # Should NOT include the auto_credential (not in regular_credentials_inputs)
+    assert "google_oauth2" not in result
+
+
+@pytest.mark.asyncio
+async def test_match_user_credentials_excludes_auto_creds():
+    """
+    match_user_credentials_to_graph() should use regular_credentials_inputs
+    and thus exclude auto_credentials from matching.
+    """
+    from backend.api.features.chat.tools.utils import match_user_credentials_to_graph
+
+    regular_field = _make_regular_field()
+
+    mock_graph = MagicMock()
+    mock_graph.id = "test-graph"
+    # regular_credentials_inputs returns only non-auto fields
+    mock_graph.regular_credentials_inputs = {
+        "github_api_key": (regular_field, {("node-1", "credentials")}, True),
+    }
+
+    # Mock the credentials manager to return no credentials
+    with patch(
+        "backend.api.features.chat.tools.utils.IntegrationCredentialsManager"
+    ) as MockCredsMgr:
+        mock_store = AsyncMock()
+        mock_store.get_all_creds.return_value = []
+        MockCredsMgr.return_value.store = mock_store
+
+        matched, missing = await match_user_credentials_to_graph(
+            user_id="test-user", graph=mock_graph
+        )
+
+    # No credentials available, so github should be missing
+    assert len(matched) == 0
+    assert len(missing) == 1
+    assert "github_api_key" in missing[0]

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

@@ -19,7 +19,10 @@ from backend.data.graph import GraphSettings
 from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
 from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
+from backend.integrations.webhooks.graph_lifecycle_hooks import (
+    on_graph_activate,
+    on_graph_deactivate,
+)
 from backend.util.clients import get_scheduler_client
 from backend.util.exceptions import DatabaseError, InvalidInputError, NotFoundError
 from backend.util.json import SafeJson
@@ -371,7 +374,7 @@ async def get_library_agent_by_graph_id(
 
 
 async def add_generated_agent_image(
-    graph: graph_db.BaseGraph,
+    graph: graph_db.GraphBaseMeta,
     user_id: str,
     library_agent_id: str,
 ) -> Optional[prisma.models.LibraryAgent]:
@@ -537,6 +540,92 @@ async def update_agent_version_in_library(
     return library_model.LibraryAgent.from_db(lib)
 
 
+async def create_graph_in_library(
+    graph: graph_db.Graph,
+    user_id: str,
+) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
+    """Create a new graph and add it to the user's library."""
+    graph.version = 1
+    graph_model = graph_db.make_graph_model(graph, user_id)
+    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=True)
+
+    created_graph = await graph_db.create_graph(graph_model, user_id)
+
+    library_agents = await create_library_agent(
+        graph=created_graph,
+        user_id=user_id,
+        sensitive_action_safe_mode=True,
+        create_library_agents_for_sub_graphs=False,
+    )
+
+    if created_graph.is_active:
+        created_graph = await on_graph_activate(created_graph, user_id=user_id)
+
+    return created_graph, library_agents[0]
+
+
+async def update_graph_in_library(
+    graph: graph_db.Graph,
+    user_id: str,
+) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
+    """Create a new version of an existing graph and update the library entry."""
+    existing_versions = await graph_db.get_graph_all_versions(graph.id, user_id)
+    current_active_version = (
+        next((v for v in existing_versions if v.is_active), None)
+        if existing_versions
+        else None
+    )
+    graph.version = (
+        max(v.version for v in existing_versions) + 1 if existing_versions else 1
+    )
+
+    graph_model = graph_db.make_graph_model(graph, user_id)
+    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=False)
+
+    created_graph = await graph_db.create_graph(graph_model, user_id)
+
+    library_agent = await get_library_agent_by_graph_id(user_id, created_graph.id)
+    if not library_agent:
+        raise NotFoundError(f"Library agent not found for graph {created_graph.id}")
+
+    library_agent = await update_library_agent_version_and_settings(
+        user_id, created_graph
+    )
+
+    if created_graph.is_active:
+        created_graph = await on_graph_activate(created_graph, user_id=user_id)
+        await graph_db.set_graph_active_version(
+            graph_id=created_graph.id,
+            version=created_graph.version,
+            user_id=user_id,
+        )
+        if current_active_version:
+            await on_graph_deactivate(current_active_version, user_id=user_id)
+
+    return created_graph, library_agent
+
+
+async def update_library_agent_version_and_settings(
+    user_id: str, agent_graph: graph_db.GraphModel
+) -> library_model.LibraryAgent:
+    """Update library agent to point to new graph version and sync settings."""
+    library = await update_agent_version_in_library(
+        user_id, agent_graph.id, agent_graph.version
+    )
+    updated_settings = GraphSettings.from_graph(
+        graph=agent_graph,
+        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
+        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
+    )
+    if updated_settings != library.settings:
+        library = await update_library_agent(
+            library_agent_id=library.id,
+            user_id=user_id,
+            settings=updated_settings,
+        )
+    return library
+
+
 async def update_library_agent(
     library_agent_id: str,
     user_id: str,
@@ -1014,7 +1103,7 @@ async def create_preset_from_graph_execution(
             raise NotFoundError(
                 f"Graph #{graph_execution.graph_id} not found or accessible"
             )
-        elif len(graph.aggregate_credentials_inputs()) > 0:
+        elif len(graph.regular_credentials_inputs) > 0:
             raise ValueError(
                 f"Graph execution #{graph_exec_id} can't be turned into a preset "
                 "because it was run before this feature existed "

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

@@ -1,7 +1,7 @@
 import asyncio
 import logging
 from datetime import datetime, timezone
-from typing import Any, Literal
+from typing import Any, Literal, overload
 
 import fastapi
 import prisma.enums
@@ -11,8 +11,8 @@ import prisma.types
 
 from backend.data.db import transaction
 from backend.data.graph import (
-    GraphMeta,
     GraphModel,
+    GraphModelWithoutNodes,
     get_graph,
     get_graph_as_admin,
     get_sub_graphs,
@@ -334,7 +334,22 @@ async def get_store_agent_details(
         raise DatabaseError("Failed to fetch agent details") from e
 
 
-async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
+@overload
+async def get_available_graph(
+    store_listing_version_id: str, hide_nodes: Literal[False]
+) -> GraphModel: ...
+
+
+@overload
+async def get_available_graph(
+    store_listing_version_id: str, hide_nodes: Literal[True] = True
+) -> GraphModelWithoutNodes: ...
+
+
+async def get_available_graph(
+    store_listing_version_id: str,
+    hide_nodes: bool = True,
+) -> GraphModelWithoutNodes | GraphModel:
     try:
         # Get avaialble, non-deleted store listing version
         store_listing_version = (
@@ -344,7 +359,7 @@ async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
                     "isAvailable": True,
                     "isDeleted": False,
                 },
-                include={"AgentGraph": {"include": {"Nodes": True}}},
+                include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}},
             )
         )
 
@@ -354,7 +369,9 @@ async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
                 detail=f"Store listing version {store_listing_version_id} not found",
             )
 
-        return GraphModel.from_db(store_listing_version.AgentGraph).meta()
+        return (GraphModelWithoutNodes if hide_nodes else GraphModel).from_db(
+            store_listing_version.AgentGraph
+        )
 
     except Exception as e:
         logger.error(f"Error getting agent: {e}")

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

@@ -16,7 +16,7 @@ from backend.blocks.ideogram import (
     StyleType,
     UpscaleOption,
 )
-from backend.data.graph import BaseGraph
+from backend.data.graph import GraphBaseMeta
 from backend.data.model import CredentialsMetaInput, ProviderName
 from backend.integrations.credentials_store import ideogram_credentials
 from backend.util.request import Requests
@@ -34,14 +34,14 @@ class ImageStyle(str, Enum):
     DIGITAL_ART = "digital art"
 
 
-async def generate_agent_image(agent: BaseGraph | AgentGraph) -> io.BytesIO:
+async def generate_agent_image(agent: GraphBaseMeta | AgentGraph) -> io.BytesIO:
     if settings.config.use_agent_image_generation_v2:
         return await generate_agent_image_v2(graph=agent)
     else:
         return await generate_agent_image_v1(agent=agent)
 
 
-async def generate_agent_image_v2(graph: BaseGraph | AgentGraph) -> io.BytesIO:
+async def generate_agent_image_v2(graph: GraphBaseMeta | AgentGraph) -> io.BytesIO:
     """
     Generate an image for an agent using Ideogram model.
     Returns:
@@ -54,14 +54,17 @@ async def generate_agent_image_v2(graph: BaseGraph | AgentGraph) -> io.BytesIO:
     description = f"{name} ({graph.description})" if graph.description else name
 
     prompt = (
-        f"Create a visually striking retro-futuristic vector pop art illustration prominently featuring "
-        f'"{name}" in bold typography. The image clearly and literally depicts a {description}, '
-        f"along with recognizable objects directly associated with the primary function of a {name}. "
-        f"Ensure the imagery is concrete, intuitive, and immediately understandable, clearly conveying the "
-        f"purpose of a {name}. Maintain vibrant, limited-palette colors, sharp vector lines, geometric "
-        f"shapes, flat illustration techniques, and solid colors without gradients or shading. Preserve a "
-        f"retro-futuristic aesthetic influenced by mid-century futurism and 1960s psychedelia, "
-        f"prioritizing clear visual storytelling and thematic clarity above all else."
+        "Create a visually striking retro-futuristic vector pop art illustration "
+        f'prominently featuring "{name}" in bold typography. The image clearly and '
+        f"literally depicts a {description}, along with recognizable objects directly "
+        f"associated with the primary function of a {name}. "
+        f"Ensure the imagery is concrete, intuitive, and immediately understandable, "
+        f"clearly conveying the purpose of a {name}. "
+        "Maintain vibrant, limited-palette colors, sharp vector lines, "
+        "geometric shapes, flat illustration techniques, and solid colors "
+        "without gradients or shading. Preserve a retro-futuristic aesthetic "
+        "influenced by mid-century futurism and 1960s psychedelia, "
+        "prioritizing clear visual storytelling and thematic clarity above all else."
     )
 
     custom_colors = [
@@ -99,12 +102,12 @@ async def generate_agent_image_v2(graph: BaseGraph | AgentGraph) -> io.BytesIO:
     return io.BytesIO(response.content)
 
 
-async def generate_agent_image_v1(agent: BaseGraph | AgentGraph) -> io.BytesIO:
+async def generate_agent_image_v1(agent: GraphBaseMeta | AgentGraph) -> io.BytesIO:
     """
     Generate an image for an agent using Flux model via Replicate API.
 
     Args:
-        agent (Graph): The agent to generate an image for
+        agent (GraphBaseMeta | AgentGraph): The agent to generate an image for
 
     Returns:
         io.BytesIO: The generated image as bytes
@@ -114,7 +117,13 @@ async def generate_agent_image_v1(agent: BaseGraph | AgentGraph) -> io.BytesIO:
             raise ValueError("Missing Replicate API key in settings")
 
         # Construct prompt from agent details
-        prompt = f"Create a visually engaging app store thumbnail for the AI agent that highlights what it does in a clear and captivating way:\n- **Name**: {agent.name}\n- **Description**: {agent.description}\nFocus on showcasing its core functionality with an appealing design."
+        prompt = (
+            "Create a visually engaging app store thumbnail for the AI agent "
+            "that highlights what it does in a clear and captivating way:\n"
+            f"- **Name**: {agent.name}\n"
+            f"- **Description**: {agent.description}\n"
+            f"Focus on showcasing its core functionality with an appealing design."
+        )
 
         # Set up Replicate client
         client = ReplicateClient(api_token=settings.secrets.replicate_api_key)

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

@@ -278,7 +278,7 @@ async def get_agent(
 )
 async def get_graph_meta_by_store_listing_version_id(
     store_listing_version_id: str,
-) -> backend.data.graph.GraphMeta:
+) -> backend.data.graph.GraphModelWithoutNodes:
     """
     Get Agent Graph from Store Listing Version ID.
     """

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

@@ -101,7 +101,6 @@ from backend.util.timezone_utils import (
 from backend.util.virus_scanner import scan_content_safe
 
 from .library import db as library_db
-from .library import model as library_model
 from .store.model import StoreAgentDetails
 
 
@@ -823,18 +822,16 @@ async def update_graph(
     graph: graph_db.Graph,
     user_id: Annotated[str, Security(get_user_id)],
 ) -> graph_db.GraphModel:
-    # Sanity check
     if graph.id and graph.id != graph_id:
         raise HTTPException(400, detail="Graph ID does not match ID in URI")
 
-    # Determine new version
     existing_versions = await graph_db.get_graph_all_versions(graph_id, user_id=user_id)
     if not existing_versions:
         raise HTTPException(404, detail=f"Graph #{graph_id} not found")
-    latest_version_number = max(g.version for g in existing_versions)
-    graph.version = latest_version_number + 1
 
+    graph.version = max(g.version for g in existing_versions) + 1
     current_active_version = next((v for v in existing_versions if v.is_active), None)
+
     graph = graph_db.make_graph_model(graph, user_id)
     graph.reassign_ids(user_id=user_id, reassign_graph_id=False)
     graph.validate_graph(for_run=False)
@@ -842,27 +839,23 @@ async def update_graph(
     new_graph_version = await graph_db.create_graph(graph, user_id=user_id)
 
     if new_graph_version.is_active:
-        # Keep the library agent up to date with the new active version
-        await _update_library_agent_version_and_settings(user_id, new_graph_version)
-
-        # Handle activation of the new graph first to ensure continuity
+        await library_db.update_library_agent_version_and_settings(
+            user_id, new_graph_version
+        )
         new_graph_version = await on_graph_activate(new_graph_version, user_id=user_id)
-        # Ensure new version is the only active version
         await graph_db.set_graph_active_version(
             graph_id=graph_id, version=new_graph_version.version, user_id=user_id
         )
         if current_active_version:
-            # Handle deactivation of the previously active version
             await on_graph_deactivate(current_active_version, user_id=user_id)
 
-    # Fetch new graph version *with sub-graphs* (needed for credentials input schema)
     new_graph_version_with_subgraphs = await graph_db.get_graph(
         graph_id,
         new_graph_version.version,
         user_id=user_id,
         include_subgraphs=True,
     )
-    assert new_graph_version_with_subgraphs  # make type checker happy
+    assert new_graph_version_with_subgraphs
     return new_graph_version_with_subgraphs
 
 
@@ -900,33 +893,15 @@ async def set_graph_active_version(
     )
 
     # Keep the library agent up to date with the new active version
-    await _update_library_agent_version_and_settings(user_id, new_active_graph)
+    await library_db.update_library_agent_version_and_settings(
+        user_id, new_active_graph
+    )
 
     if current_active_graph and current_active_graph.version != new_active_version:
         # Handle deactivation of the previously active version
         await on_graph_deactivate(current_active_graph, user_id=user_id)
 
 
-async def _update_library_agent_version_and_settings(
-    user_id: str, agent_graph: graph_db.GraphModel
-) -> library_model.LibraryAgent:
-    library = await library_db.update_agent_version_in_library(
-        user_id, agent_graph.id, agent_graph.version
-    )
-    updated_settings = GraphSettings.from_graph(
-        graph=agent_graph,
-        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
-        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
-    )
-    if updated_settings != library.settings:
-        library = await library_db.update_library_agent(
-            library_agent_id=library.id,
-            user_id=user_id,
-            settings=updated_settings,
-        )
-    return library
-
-
 @v1_router.patch(
     path="/graphs/{graph_id}/settings",
     summary="Update graph settings",

autogpt_platform/backend/backend/blocks/elevenlabs/_auth.py

@@ -0,0 +1,28 @@
+"""ElevenLabs integration blocks - test credentials and shared utilities."""
+
+from typing import Literal
+
+from pydantic import SecretStr
+
+from backend.data.model import APIKeyCredentials, CredentialsMetaInput
+from backend.integrations.providers import ProviderName
+
+TEST_CREDENTIALS = APIKeyCredentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="elevenlabs",
+    api_key=SecretStr("mock-elevenlabs-api-key"),
+    title="Mock ElevenLabs API key",
+    expires_at=None,
+)
+
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.title,
+}
+
+ElevenLabsCredentials = APIKeyCredentials
+ElevenLabsCredentialsInput = CredentialsMetaInput[
+    Literal[ProviderName.ELEVENLABS], Literal["api_key"]
+]

autogpt_platform/backend/backend/blocks/encoder_block.py

@@ -0,0 +1,77 @@
+"""Text encoding block for converting special characters to escape sequences."""
+
+import codecs
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.model import SchemaField
+
+
+class TextEncoderBlock(Block):
+    """
+    Encodes a string by converting special characters into escape sequences.
+
+    This block is the inverse of TextDecoderBlock. It takes text containing
+    special characters (like newlines, tabs, etc.) and converts them into
+    their escape sequence representations (e.g., newline becomes \\n).
+    """
+
+    class Input(BlockSchemaInput):
+        """Input schema for TextEncoderBlock."""
+
+        text: str = SchemaField(
+            description="A string containing special characters to be encoded",
+            placeholder="Your text with newlines and quotes to encode",
+        )
+
+    class Output(BlockSchemaOutput):
+        """Output schema for TextEncoderBlock."""
+
+        encoded_text: str = SchemaField(
+            description="The encoded text with special characters converted to escape sequences"
+        )
+        error: str = SchemaField(description="Error message if encoding fails")
+
+    def __init__(self):
+        super().__init__(
+            id="5185f32e-4b65-4ecf-8fbb-873f003f09d6",
+            description="Encodes a string by converting special characters into escape sequences",
+            categories={BlockCategory.TEXT},
+            input_schema=TextEncoderBlock.Input,
+            output_schema=TextEncoderBlock.Output,
+            test_input={
+                "text": """Hello
+World!
+This is a "quoted" string."""
+            },
+            test_output=[
+                (
+                    "encoded_text",
+                    """Hello\\nWorld!\\nThis is a "quoted" string.""",
+                )
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        """
+        Encode the input text by converting special characters to escape sequences.
+
+        Args:
+            input_data: The input containing the text to encode.
+            **kwargs: Additional keyword arguments (unused).
+
+        Yields:
+            The encoded text with escape sequences, or an error message if encoding fails.
+        """
+        try:
+            encoded_text = codecs.encode(input_data.text, "unicode_escape").decode(
+                "utf-8"
+            )
+            yield "encoded_text", encoded_text
+        except Exception as e:
+            yield "error", f"Encoding error: {str(e)}"

autogpt_platform/backend/backend/blocks/llm.py

@@ -115,6 +115,7 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
     CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
     CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
+    CLAUDE_4_6_OPUS = "claude-opus-4-6"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
     # AI/ML API models
     AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
@@ -270,6 +271,9 @@ MODEL_METADATA = {
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
     ),  # claude-4-sonnet-20250514
+    LlmModel.CLAUDE_4_6_OPUS: ModelMetadata(
+        "anthropic", 200000, 128000, "Claude Opus 4.6", "Anthropic", "Anthropic", 3
+    ),  # claude-opus-4-6
     LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
     ),  # claude-opus-4-5-20251101

autogpt_platform/backend/backend/blocks/media.py

@@ -1,246 +0,0 @@
-import os
-import tempfile
-from typing import Optional
-
-from moviepy.audio.io.AudioFileClip import AudioFileClip
-from moviepy.video.fx.Loop import Loop
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.execution import ExecutionContext
-from backend.data.model import SchemaField
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class MediaDurationBlock(Block):
-
-    class Input(BlockSchemaInput):
-        media_in: MediaFileType = SchemaField(
-            description="Media input (URL, data URI, or local path)."
-        )
-        is_video: bool = SchemaField(
-            description="Whether the media is a video (True) or audio (False).",
-            default=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        duration: float = SchemaField(
-            description="Duration of the media file (in seconds)."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
-            description="Block to get the duration of a media file.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=MediaDurationBlock.Input,
-            output_schema=MediaDurationBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
-        # 1) Store the input media locally
-        local_media_path = await store_media_file(
-            file=input_data.media_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-        assert execution_context.graph_exec_id is not None
-        media_abspath = get_exec_file_path(
-            execution_context.graph_exec_id, local_media_path
-        )
-
-        # 2) Load the clip
-        if input_data.is_video:
-            clip = VideoFileClip(media_abspath)
-        else:
-            clip = AudioFileClip(media_abspath)
-
-        yield "duration", clip.duration
-
-
-class LoopVideoBlock(Block):
-    """
-    Block for looping (repeating) a video clip until a given duration or number of loops.
-    """
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="The input video (can be a URL, data URI, or local path)."
-        )
-        # Provide EITHER a `duration` or `n_loops` or both. We'll demonstrate `duration`.
-        duration: Optional[float] = SchemaField(
-            description="Target duration (in seconds) to loop the video to. If omitted, defaults to no looping.",
-            default=None,
-            ge=0.0,
-        )
-        n_loops: Optional[int] = SchemaField(
-            description="Number of times to repeat the video. If omitted, defaults to 1 (no repeat).",
-            default=None,
-            ge=1,
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: str = SchemaField(
-            description="Looped video returned either as a relative path or a data URI."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8bf9eef6-5451-4213-b265-25306446e94b",
-            description="Block to loop a video to a given duration or number of repeats.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=LoopVideoBlock.Input,
-            output_schema=LoopVideoBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
-        assert execution_context.graph_exec_id is not None
-        assert execution_context.node_exec_id is not None
-        graph_exec_id = execution_context.graph_exec_id
-        node_exec_id = execution_context.node_exec_id
-
-        # 1) Store the input video locally
-        local_video_path = await store_media_file(
-            file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
-
-        # 2) Load the clip
-        clip = VideoFileClip(input_abspath)
-
-        # 3) Apply the loop effect
-        looped_clip = clip
-        if input_data.duration:
-            # Loop until we reach the specified duration
-            looped_clip = looped_clip.with_effects([Loop(duration=input_data.duration)])
-        elif input_data.n_loops:
-            looped_clip = looped_clip.with_effects([Loop(n=input_data.n_loops)])
-        else:
-            raise ValueError("Either 'duration' or 'n_loops' must be provided.")
-
-        assert isinstance(looped_clip, VideoFileClip)
-
-        # 4) Save the looped output
-        output_filename = MediaFileType(
-            f"{node_exec_id}_looped_{os.path.basename(local_video_path)}"
-        )
-        output_abspath = get_exec_file_path(graph_exec_id, output_filename)
-
-        looped_clip = looped_clip.with_audio(clip.audio)
-        looped_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
-
-        # Return output - for_block_output returns workspace:// if available, else data URI
-        video_out = await store_media_file(
-            file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-        yield "video_out", video_out
-
-
-class AddAudioToVideoBlock(Block):
-    """
-    Block that adds (attaches) an audio track to an existing video.
-    Optionally scale the volume of the new track.
-    """
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="Video input (URL, data URI, or local path)."
-        )
-        audio_in: MediaFileType = SchemaField(
-            description="Audio input (URL, data URI, or local path)."
-        )
-        volume: float = SchemaField(
-            description="Volume scale for the newly attached audio track (1.0 = original).",
-            default=1.0,
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: MediaFileType = SchemaField(
-            description="Final video (with attached audio), as a path or data URI."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3503748d-62b6-4425-91d6-725b064af509",
-            description="Block to attach an audio file to a video file using moviepy.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=AddAudioToVideoBlock.Input,
-            output_schema=AddAudioToVideoBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
-        assert execution_context.graph_exec_id is not None
-        assert execution_context.node_exec_id is not None
-        graph_exec_id = execution_context.graph_exec_id
-        node_exec_id = execution_context.node_exec_id
-
-        # 1) Store the inputs locally
-        local_video_path = await store_media_file(
-            file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-        local_audio_path = await store_media_file(
-            file=input_data.audio_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-
-        abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
-        video_abspath = os.path.join(abs_temp_dir, local_video_path)
-        audio_abspath = os.path.join(abs_temp_dir, local_audio_path)
-
-        # 2) Load video + audio with moviepy
-        video_clip = VideoFileClip(video_abspath)
-        audio_clip = AudioFileClip(audio_abspath)
-        # Optionally scale volume
-        if input_data.volume != 1.0:
-            audio_clip = audio_clip.with_volume_scaled(input_data.volume)
-
-        # 3) Attach the new audio track
-        final_clip = video_clip.with_audio(audio_clip)
-
-        # 4) Write to output file
-        output_filename = MediaFileType(
-            f"{node_exec_id}_audio_attached_{os.path.basename(local_video_path)}"
-        )
-        output_abspath = os.path.join(abs_temp_dir, output_filename)
-        final_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
-
-        # 5) Return output - for_block_output returns workspace:// if available, else data URI
-        video_out = await store_media_file(
-            file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-
-        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/test/test_text_encoder.py

@@ -0,0 +1,77 @@
+import pytest
+
+from backend.blocks.encoder_block import TextEncoderBlock
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_basic():
+    """Test basic encoding of newlines and special characters."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="Hello\nWorld")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert result[0][1] == "Hello\\nWorld"
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_multiple_escapes():
+    """Test encoding of multiple escape sequences."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(
+        TextEncoderBlock.Input(text="Line1\nLine2\tTabbed\rCarriage")
+    ):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert "\\n" in result[0][1]
+    assert "\\t" in result[0][1]
+    assert "\\r" in result[0][1]
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_unicode():
+    """Test that unicode characters are handled correctly."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="Hello 世界\n")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    # Unicode characters should be escaped as \uXXXX sequences
+    assert "\\n" in result[0][1]
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_empty_string():
+    """Test encoding of an empty string."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert result[0][1] == ""
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_error_handling():
+    """Test that encoding errors are handled gracefully."""
+    from unittest.mock import patch
+
+    block = TextEncoderBlock()
+    result = []
+
+    with patch("codecs.encode", side_effect=Exception("Mocked encoding error")):
+        async for output in block.run(TextEncoderBlock.Input(text="test")):
+            result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "error"
+    assert "Mocked encoding error" in result[0][1]

autogpt_platform/backend/backend/blocks/video/__init__.py

@@ -0,0 +1,37 @@
+"""Video editing blocks for AutoGPT Platform.
+
+This module provides blocks for:
+- Downloading videos from URLs (YouTube, Vimeo, news sites, direct links)
+- Clipping/trimming video segments
+- Concatenating multiple videos
+- Adding text overlays
+- Adding AI-generated narration
+- Getting media duration
+- Looping videos
+- Adding audio to videos
+
+Dependencies:
+- yt-dlp: For video downloading
+- moviepy: For video editing operations
+- elevenlabs: For AI narration (optional)
+"""
+
+from backend.blocks.video.add_audio import AddAudioToVideoBlock
+from backend.blocks.video.clip import VideoClipBlock
+from backend.blocks.video.concat import VideoConcatBlock
+from backend.blocks.video.download import VideoDownloadBlock
+from backend.blocks.video.duration import MediaDurationBlock
+from backend.blocks.video.loop import LoopVideoBlock
+from backend.blocks.video.narration import VideoNarrationBlock
+from backend.blocks.video.text_overlay import VideoTextOverlayBlock
+
+__all__ = [
+    "AddAudioToVideoBlock",
+    "LoopVideoBlock",
+    "MediaDurationBlock",
+    "VideoClipBlock",
+    "VideoConcatBlock",
+    "VideoDownloadBlock",
+    "VideoNarrationBlock",
+    "VideoTextOverlayBlock",
+]

autogpt_platform/backend/backend/blocks/video/_utils.py

@@ -0,0 +1,131 @@
+"""Shared utilities for video blocks."""
+
+from __future__ import annotations
+
+import logging
+import os
+import re
+import subprocess
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+# Known operation tags added by video blocks
+_VIDEO_OPS = (
+    r"(?:clip|overlay|narrated|looped|concat|audio_attached|with_audio|narration)"
+)
+
+# Matches: {node_exec_id}_{operation}_ where node_exec_id contains a UUID
+_BLOCK_PREFIX_RE = re.compile(
+    r"^[a-zA-Z0-9_-]*"
+    r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
+    r"[a-zA-Z0-9_-]*"
+    r"_" + _VIDEO_OPS + r"_"
+)
+
+# Matches: a lone {node_exec_id}_ prefix (no operation keyword, e.g. download output)
+_UUID_PREFIX_RE = re.compile(
+    r"^[a-zA-Z0-9_-]*"
+    r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
+    r"[a-zA-Z0-9_-]*_"
+)
+
+
+def extract_source_name(input_path: str, max_length: int = 50) -> str:
+    """Extract the original source filename by stripping block-generated prefixes.
+
+    Iteratively removes {node_exec_id}_{operation}_ prefixes that accumulate
+    when chaining video blocks, recovering the original human-readable name.
+
+    Safe for plain filenames (no UUID -> no stripping).
+    Falls back to "video" if everything is stripped.
+    """
+    stem = Path(input_path).stem
+
+    # Pass 1: strip {node_exec_id}_{operation}_ prefixes iteratively
+    while _BLOCK_PREFIX_RE.match(stem):
+        stem = _BLOCK_PREFIX_RE.sub("", stem, count=1)
+
+    # Pass 2: strip a lone {node_exec_id}_ prefix (e.g. from download block)
+    if _UUID_PREFIX_RE.match(stem):
+        stem = _UUID_PREFIX_RE.sub("", stem, count=1)
+
+    if not stem:
+        return "video"
+
+    return stem[:max_length]
+
+
+def get_video_codecs(output_path: str) -> tuple[str, str]:
+    """Get appropriate video and audio codecs based on output file extension.
+
+    Args:
+        output_path: Path to the output file (used to determine extension)
+
+    Returns:
+        Tuple of (video_codec, audio_codec)
+
+    Codec mappings:
+        - .mp4: H.264 + AAC (universal compatibility)
+        - .webm: VP8 + Vorbis (web streaming)
+        - .mkv: H.264 + AAC (container supports many codecs)
+        - .mov: H.264 + AAC (Apple QuickTime, widely compatible)
+        - .m4v: H.264 + AAC (Apple iTunes/devices)
+        - .avi: MPEG-4 + MP3 (legacy Windows)
+    """
+    ext = os.path.splitext(output_path)[1].lower()
+
+    codec_map: dict[str, tuple[str, str]] = {
+        ".mp4": ("libx264", "aac"),
+        ".webm": ("libvpx", "libvorbis"),
+        ".mkv": ("libx264", "aac"),
+        ".mov": ("libx264", "aac"),
+        ".m4v": ("libx264", "aac"),
+        ".avi": ("mpeg4", "libmp3lame"),
+    }
+
+    return codec_map.get(ext, ("libx264", "aac"))
+
+
+def strip_chapters_inplace(video_path: str) -> None:
+    """Strip chapter metadata from a media file in-place using ffmpeg.
+
+    MoviePy 2.x crashes with IndexError when parsing files with embedded
+    chapter metadata (https://github.com/Zulko/moviepy/issues/2419).
+    This strips chapters without re-encoding.
+
+    Args:
+        video_path: Absolute path to the media file to strip chapters from.
+    """
+    base, ext = os.path.splitext(video_path)
+    tmp_path = base + ".tmp" + ext
+    try:
+        result = subprocess.run(
+            [
+                "ffmpeg",
+                "-y",
+                "-i",
+                video_path,
+                "-map_chapters",
+                "-1",
+                "-codec",
+                "copy",
+                tmp_path,
+            ],
+            capture_output=True,
+            text=True,
+            timeout=300,
+        )
+        if result.returncode != 0:
+            logger.warning(
+                "ffmpeg chapter strip failed (rc=%d): %s",
+                result.returncode,
+                result.stderr,
+            )
+            return
+        os.replace(tmp_path, video_path)
+    except FileNotFoundError:
+        logger.warning("ffmpeg not found; skipping chapter strip")
+    finally:
+        if os.path.exists(tmp_path):
+            os.unlink(tmp_path)

autogpt_platform/backend/backend/blocks/video/add_audio.py

@@ -0,0 +1,113 @@
+"""AddAudioToVideoBlock - Attach an audio track to a video file."""
+
+from moviepy.audio.io.AudioFileClip import AudioFileClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import extract_source_name, strip_chapters_inplace
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class AddAudioToVideoBlock(Block):
+    """Add (attach) an audio track to an existing video."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="Video input (URL, data URI, or local path)."
+        )
+        audio_in: MediaFileType = SchemaField(
+            description="Audio input (URL, data URI, or local path)."
+        )
+        volume: float = SchemaField(
+            description="Volume scale for the newly attached audio track (1.0 = original).",
+            default=1.0,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Final video (with attached audio), as a path or data URI."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3503748d-62b6-4425-91d6-725b064af509",
+            description="Block to attach an audio file to a video file using moviepy.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=AddAudioToVideoBlock.Input,
+            output_schema=AddAudioToVideoBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
+        assert execution_context.graph_exec_id is not None
+        assert execution_context.node_exec_id is not None
+        graph_exec_id = execution_context.graph_exec_id
+        node_exec_id = execution_context.node_exec_id
+
+        # 1) Store the inputs locally
+        local_video_path = await store_media_file(
+            file=input_data.video_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        local_audio_path = await store_media_file(
+            file=input_data.audio_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+        video_abspath = get_exec_file_path(graph_exec_id, local_video_path)
+        audio_abspath = get_exec_file_path(graph_exec_id, local_audio_path)
+
+        # 2) Load video + audio with moviepy
+        strip_chapters_inplace(video_abspath)
+        strip_chapters_inplace(audio_abspath)
+        video_clip = None
+        audio_clip = None
+        final_clip = None
+        try:
+            video_clip = VideoFileClip(video_abspath)
+            audio_clip = AudioFileClip(audio_abspath)
+            # Optionally scale volume
+            if input_data.volume != 1.0:
+                audio_clip = audio_clip.with_volume_scaled(input_data.volume)
+
+            # 3) Attach the new audio track
+            final_clip = video_clip.with_audio(audio_clip)
+
+            # 4) Write to output file
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_with_audio_{source}.mp4")
+            output_abspath = get_exec_file_path(graph_exec_id, output_filename)
+            final_clip.write_videofile(
+                output_abspath, codec="libx264", audio_codec="aac"
+            )
+        finally:
+            if final_clip:
+                final_clip.close()
+            if audio_clip:
+                audio_clip.close()
+            if video_clip:
+                video_clip.close()
+
+        # 5) Return output - for_block_output returns workspace:// if available, else data URI
+        video_out = await store_media_file(
+            file=output_filename,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/video/clip.py

@@ -0,0 +1,167 @@
+"""VideoClipBlock - Extract a segment from a video file."""
+
+from typing import Literal
+
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoClipBlock(Block):
+    """Extract a time segment from a video."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="Input video (URL, data URI, or local path)"
+        )
+        start_time: float = SchemaField(description="Start time in seconds", ge=0.0)
+        end_time: float = SchemaField(description="End time in seconds", ge=0.0)
+        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
+            description="Output format", default="mp4", advanced=True
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Clipped video file (path or data URI)"
+        )
+        duration: float = SchemaField(description="Clip duration in seconds")
+
+    def __init__(self):
+        super().__init__(
+            id="8f539119-e580-4d86-ad41-86fbcb22abb1",
+            description="Extract a time segment from a video",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            test_input={
+                "video_in": "/tmp/test.mp4",
+                "start_time": 0.0,
+                "end_time": 10.0,
+            },
+            test_output=[("video_out", str), ("duration", float)],
+            test_mock={
+                "_clip_video": lambda *args: 10.0,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "clip_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _clip_video(
+        self,
+        video_abspath: str,
+        output_abspath: str,
+        start_time: float,
+        end_time: float,
+    ) -> float:
+        """Extract a clip from a video. Extracted for testability."""
+        clip = None
+        subclip = None
+        try:
+            strip_chapters_inplace(video_abspath)
+            clip = VideoFileClip(video_abspath)
+            subclip = clip.subclipped(start_time, end_time)
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            subclip.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+            return subclip.duration
+        finally:
+            if subclip:
+                subclip.close()
+            if clip:
+                clip.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        # Validate time range
+        if input_data.end_time <= input_data.start_time:
+            raise BlockExecutionError(
+                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
+                block_name=self.name,
+                block_id=str(self.id),
+            )
+
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store the input video locally
+            local_video_path = await self._store_input_video(
+                execution_context, input_data.video_in
+            )
+            video_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, local_video_path
+            )
+
+            # Build output path
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(
+                f"{node_exec_id}_clip_{source}.{input_data.output_format}"
+            )
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            duration = self._clip_video(
+                video_abspath,
+                output_abspath,
+                input_data.start_time,
+                input_data.end_time,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+
+            yield "video_out", video_out
+            yield "duration", duration
+
+        except BlockExecutionError:
+            raise
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to clip video: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/concat.py

@@ -0,0 +1,227 @@
+"""VideoConcatBlock - Concatenate multiple video clips into one."""
+
+from typing import Literal
+
+from moviepy import concatenate_videoclips
+from moviepy.video.fx import CrossFadeIn, CrossFadeOut, FadeIn, FadeOut
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoConcatBlock(Block):
+    """Merge multiple video clips into one continuous video."""
+
+    class Input(BlockSchemaInput):
+        videos: list[MediaFileType] = SchemaField(
+            description="List of video files to concatenate (in order)"
+        )
+        transition: Literal["none", "crossfade", "fade_black"] = SchemaField(
+            description="Transition between clips", default="none"
+        )
+        transition_duration: int = SchemaField(
+            description="Transition duration in seconds",
+            default=1,
+            ge=0,
+            advanced=True,
+        )
+        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
+            description="Output format", default="mp4", advanced=True
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Concatenated video file (path or data URI)"
+        )
+        total_duration: float = SchemaField(description="Total duration in seconds")
+
+    def __init__(self):
+        super().__init__(
+            id="9b0f531a-1118-487f-aeec-3fa63ea8900a",
+            description="Merge multiple video clips into one continuous video",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            test_input={
+                "videos": ["/tmp/a.mp4", "/tmp/b.mp4"],
+            },
+            test_output=[
+                ("video_out", str),
+                ("total_duration", float),
+            ],
+            test_mock={
+                "_concat_videos": lambda *args: 20.0,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "concat_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _concat_videos(
+        self,
+        video_abspaths: list[str],
+        output_abspath: str,
+        transition: str,
+        transition_duration: int,
+    ) -> float:
+        """Concatenate videos. Extracted for testability.
+
+        Returns:
+            Total duration of the concatenated video.
+        """
+        clips = []
+        faded_clips = []
+        final = None
+        try:
+            # Load clips
+            for v in video_abspaths:
+                strip_chapters_inplace(v)
+                clips.append(VideoFileClip(v))
+
+            # Validate transition_duration against shortest clip
+            if transition in {"crossfade", "fade_black"} and transition_duration > 0:
+                min_duration = min(c.duration for c in clips)
+                if transition_duration >= min_duration:
+                    raise BlockExecutionError(
+                        message=(
+                            f"transition_duration ({transition_duration}s) must be "
+                            f"shorter than the shortest clip ({min_duration:.2f}s)"
+                        ),
+                        block_name=self.name,
+                        block_id=str(self.id),
+                    )
+
+            if transition == "crossfade":
+                for i, clip in enumerate(clips):
+                    effects = []
+                    if i > 0:
+                        effects.append(CrossFadeIn(transition_duration))
+                    if i < len(clips) - 1:
+                        effects.append(CrossFadeOut(transition_duration))
+                    if effects:
+                        clip = clip.with_effects(effects)
+                    faded_clips.append(clip)
+                final = concatenate_videoclips(
+                    faded_clips,
+                    method="compose",
+                    padding=-transition_duration,
+                )
+            elif transition == "fade_black":
+                for clip in clips:
+                    faded = clip.with_effects(
+                        [FadeIn(transition_duration), FadeOut(transition_duration)]
+                    )
+                    faded_clips.append(faded)
+                final = concatenate_videoclips(faded_clips)
+            else:
+                final = concatenate_videoclips(clips)
+
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            final.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+
+            return final.duration
+        finally:
+            if final:
+                final.close()
+            for clip in faded_clips:
+                clip.close()
+            for clip in clips:
+                clip.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        # Validate minimum clips
+        if len(input_data.videos) < 2:
+            raise BlockExecutionError(
+                message="At least 2 videos are required for concatenation",
+                block_name=self.name,
+                block_id=str(self.id),
+            )
+
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store all input videos locally
+            video_abspaths = []
+            for video in input_data.videos:
+                local_path = await self._store_input_video(execution_context, video)
+                video_abspaths.append(
+                    get_exec_file_path(execution_context.graph_exec_id, local_path)
+                )
+
+            # Build output path
+            source = (
+                extract_source_name(video_abspaths[0]) if video_abspaths else "video"
+            )
+            output_filename = MediaFileType(
+                f"{node_exec_id}_concat_{source}.{input_data.output_format}"
+            )
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            total_duration = self._concat_videos(
+                video_abspaths,
+                output_abspath,
+                input_data.transition,
+                input_data.transition_duration,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+
+            yield "video_out", video_out
+            yield "total_duration", total_duration
+
+        except BlockExecutionError:
+            raise
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to concatenate videos: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/download.py

@@ -0,0 +1,172 @@
+"""VideoDownloadBlock - Download video from URL (YouTube, Vimeo, news sites, direct links)."""
+
+import os
+import typing
+from typing import Literal
+
+import yt_dlp
+
+if typing.TYPE_CHECKING:
+    from yt_dlp import _Params
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoDownloadBlock(Block):
+    """Download video from URL using yt-dlp."""
+
+    class Input(BlockSchemaInput):
+        url: str = SchemaField(
+            description="URL of the video to download (YouTube, Vimeo, direct link, etc.)",
+            placeholder="https://www.youtube.com/watch?v=...",
+        )
+        quality: Literal["best", "1080p", "720p", "480p", "audio_only"] = SchemaField(
+            description="Video quality preference", default="720p"
+        )
+        output_format: Literal["mp4", "webm", "mkv"] = SchemaField(
+            description="Output video format", default="mp4", advanced=True
+        )
+
+    class Output(BlockSchemaOutput):
+        video_file: MediaFileType = SchemaField(
+            description="Downloaded video (path or data URI)"
+        )
+        duration: float = SchemaField(description="Video duration in seconds")
+        title: str = SchemaField(description="Video title from source")
+        source_url: str = SchemaField(description="Original source URL")
+
+    def __init__(self):
+        super().__init__(
+            id="c35daabb-cd60-493b-b9ad-51f1fe4b50c4",
+            description="Download video from URL (YouTube, Vimeo, news sites, direct links)",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            disabled=True,  # Disable until we can sandbox yt-dlp and handle security implications
+            test_input={
+                "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
+                "quality": "480p",
+            },
+            test_output=[
+                ("video_file", str),
+                ("duration", float),
+                ("title", str),
+                ("source_url", str),
+            ],
+            test_mock={
+                "_download_video": lambda *args: (
+                    "video.mp4",
+                    212.0,
+                    "Test Video",
+                ),
+                "_store_output_video": lambda *args, **kwargs: "video.mp4",
+            },
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _get_format_string(self, quality: str) -> str:
+        formats = {
+            "best": "bestvideo+bestaudio/best",
+            "1080p": "bestvideo[height<=1080]+bestaudio/best[height<=1080]",
+            "720p": "bestvideo[height<=720]+bestaudio/best[height<=720]",
+            "480p": "bestvideo[height<=480]+bestaudio/best[height<=480]",
+            "audio_only": "bestaudio/best",
+        }
+        return formats.get(quality, formats["720p"])
+
+    def _download_video(
+        self,
+        url: str,
+        quality: str,
+        output_format: str,
+        output_dir: str,
+        node_exec_id: str,
+    ) -> tuple[str, float, str]:
+        """Download video. Extracted for testability."""
+        output_template = os.path.join(
+            output_dir, f"{node_exec_id}_%(title).50s.%(ext)s"
+        )
+
+        ydl_opts: "_Params" = {
+            "format": f"{self._get_format_string(quality)}/best",
+            "outtmpl": output_template,
+            "merge_output_format": output_format,
+            "quiet": True,
+            "no_warnings": True,
+        }
+
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            info = ydl.extract_info(url, download=True)
+            video_path = ydl.prepare_filename(info)
+
+            # Handle format conversion in filename
+            if not video_path.endswith(f".{output_format}"):
+                video_path = video_path.rsplit(".", 1)[0] + f".{output_format}"
+
+            # Return just the filename, not the full path
+            filename = os.path.basename(video_path)
+
+            return (
+                filename,
+                info.get("duration") or 0.0,
+                info.get("title") or "Unknown",
+            )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Get the exec file directory
+            output_dir = get_exec_file_path(execution_context.graph_exec_id, "")
+            os.makedirs(output_dir, exist_ok=True)
+
+            filename, duration, title = self._download_video(
+                input_data.url,
+                input_data.quality,
+                input_data.output_format,
+                output_dir,
+                node_exec_id,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, MediaFileType(filename)
+            )
+
+            yield "video_file", video_out
+            yield "duration", duration
+            yield "title", title
+            yield "source_url", input_data.url
+
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to download video: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/duration.py

@@ -0,0 +1,77 @@
+"""MediaDurationBlock - Get the duration of a media file."""
+
+from moviepy.audio.io.AudioFileClip import AudioFileClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import strip_chapters_inplace
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class MediaDurationBlock(Block):
+    """Get the duration of a media file (video or audio)."""
+
+    class Input(BlockSchemaInput):
+        media_in: MediaFileType = SchemaField(
+            description="Media input (URL, data URI, or local path)."
+        )
+        is_video: bool = SchemaField(
+            description="Whether the media is a video (True) or audio (False).",
+            default=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        duration: float = SchemaField(
+            description="Duration of the media file (in seconds)."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
+            description="Block to get the duration of a media file.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=MediaDurationBlock.Input,
+            output_schema=MediaDurationBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
+        # 1) Store the input media locally
+        local_media_path = await store_media_file(
+            file=input_data.media_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        assert execution_context.graph_exec_id is not None
+        media_abspath = get_exec_file_path(
+            execution_context.graph_exec_id, local_media_path
+        )
+
+        # 2) Strip chapters to avoid MoviePy crash, then load the clip
+        strip_chapters_inplace(media_abspath)
+        clip = None
+        try:
+            if input_data.is_video:
+                clip = VideoFileClip(media_abspath)
+            else:
+                clip = AudioFileClip(media_abspath)
+
+            duration = clip.duration
+        finally:
+            if clip:
+                clip.close()
+
+        yield "duration", duration

autogpt_platform/backend/backend/blocks/video/loop.py

@@ -0,0 +1,115 @@
+"""LoopVideoBlock - Loop a video to a given duration or number of repeats."""
+
+from typing import Optional
+
+from moviepy.video.fx.Loop import Loop
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import extract_source_name, strip_chapters_inplace
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class LoopVideoBlock(Block):
+    """Loop (repeat) a video clip until a given duration or number of loops."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="The input video (can be a URL, data URI, or local path)."
+        )
+        duration: Optional[float] = SchemaField(
+            description="Target duration (in seconds) to loop the video to. Either duration or n_loops must be provided.",
+            default=None,
+            ge=0.0,
+            le=3600.0,  # Max 1 hour to prevent disk exhaustion
+        )
+        n_loops: Optional[int] = SchemaField(
+            description="Number of times to repeat the video. Either n_loops or duration must be provided.",
+            default=None,
+            ge=1,
+            le=10,  # Max 10 loops to prevent disk exhaustion
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Looped video returned either as a relative path or a data URI."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="8bf9eef6-5451-4213-b265-25306446e94b",
+            description="Block to loop a video to a given duration or number of repeats.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=LoopVideoBlock.Input,
+            output_schema=LoopVideoBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
+        assert execution_context.graph_exec_id is not None
+        assert execution_context.node_exec_id is not None
+        graph_exec_id = execution_context.graph_exec_id
+        node_exec_id = execution_context.node_exec_id
+
+        # 1) Store the input video locally
+        local_video_path = await store_media_file(
+            file=input_data.video_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
+
+        # 2) Load the clip
+        strip_chapters_inplace(input_abspath)
+        clip = None
+        looped_clip = None
+        try:
+            clip = VideoFileClip(input_abspath)
+
+            # 3) Apply the loop effect
+            if input_data.duration:
+                # Loop until we reach the specified duration
+                looped_clip = clip.with_effects([Loop(duration=input_data.duration)])
+            elif input_data.n_loops:
+                looped_clip = clip.with_effects([Loop(n=input_data.n_loops)])
+            else:
+                raise ValueError("Either 'duration' or 'n_loops' must be provided.")
+
+            assert isinstance(looped_clip, VideoFileClip)
+
+            # 4) Save the looped output
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_looped_{source}.mp4")
+            output_abspath = get_exec_file_path(graph_exec_id, output_filename)
+
+            looped_clip = looped_clip.with_audio(clip.audio)
+            looped_clip.write_videofile(
+                output_abspath, codec="libx264", audio_codec="aac"
+            )
+        finally:
+            if looped_clip:
+                looped_clip.close()
+            if clip:
+                clip.close()
+
+        # Return output - for_block_output returns workspace:// if available, else data URI
+        video_out = await store_media_file(
+            file=output_filename,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/video/narration.py

@@ -0,0 +1,267 @@
+"""VideoNarrationBlock - Generate AI voice narration and add to video."""
+
+import os
+from typing import Literal
+
+from elevenlabs import ElevenLabs
+from moviepy import CompositeAudioClip
+from moviepy.audio.io.AudioFileClip import AudioFileClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.elevenlabs._auth import (
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    ElevenLabsCredentials,
+    ElevenLabsCredentialsInput,
+)
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import CredentialsField, SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoNarrationBlock(Block):
+    """Generate AI narration and add to video."""
+
+    class Input(BlockSchemaInput):
+        credentials: ElevenLabsCredentialsInput = CredentialsField(
+            description="ElevenLabs API key for voice synthesis"
+        )
+        video_in: MediaFileType = SchemaField(
+            description="Input video (URL, data URI, or local path)"
+        )
+        script: str = SchemaField(description="Narration script text")
+        voice_id: str = SchemaField(
+            description="ElevenLabs voice ID", default="21m00Tcm4TlvDq8ikWAM"  # Rachel
+        )
+        model_id: Literal[
+            "eleven_multilingual_v2",
+            "eleven_flash_v2_5",
+            "eleven_turbo_v2_5",
+            "eleven_turbo_v2",
+        ] = SchemaField(
+            description="ElevenLabs TTS model",
+            default="eleven_multilingual_v2",
+        )
+        mix_mode: Literal["replace", "mix", "ducking"] = SchemaField(
+            description="How to combine with original audio. 'ducking' applies stronger attenuation than 'mix'.",
+            default="ducking",
+        )
+        narration_volume: float = SchemaField(
+            description="Narration volume (0.0 to 2.0)",
+            default=1.0,
+            ge=0.0,
+            le=2.0,
+            advanced=True,
+        )
+        original_volume: float = SchemaField(
+            description="Original audio volume when mixing (0.0 to 1.0)",
+            default=0.3,
+            ge=0.0,
+            le=1.0,
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Video with narration (path or data URI)"
+        )
+        audio_file: MediaFileType = SchemaField(
+            description="Generated audio file (path or data URI)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3d036b53-859c-4b17-9826-ca340f736e0e",
+            description="Generate AI narration and add to video",
+            categories={BlockCategory.MULTIMEDIA, BlockCategory.AI},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            test_input={
+                "video_in": "/tmp/test.mp4",
+                "script": "Hello world",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("video_out", str), ("audio_file", str)],
+            test_mock={
+                "_generate_narration_audio": lambda *args: b"mock audio content",
+                "_add_narration_to_video": lambda *args: None,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "narrated_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _generate_narration_audio(
+        self, api_key: str, script: str, voice_id: str, model_id: str
+    ) -> bytes:
+        """Generate narration audio via ElevenLabs API."""
+        client = ElevenLabs(api_key=api_key)
+        audio_generator = client.text_to_speech.convert(
+            voice_id=voice_id,
+            text=script,
+            model_id=model_id,
+        )
+        # The SDK returns a generator, collect all chunks
+        return b"".join(audio_generator)
+
+    def _add_narration_to_video(
+        self,
+        video_abspath: str,
+        audio_abspath: str,
+        output_abspath: str,
+        mix_mode: str,
+        narration_volume: float,
+        original_volume: float,
+    ) -> None:
+        """Add narration audio to video. Extracted for testability."""
+        video = None
+        final = None
+        narration_original = None
+        narration_scaled = None
+        original = None
+
+        try:
+            strip_chapters_inplace(video_abspath)
+            video = VideoFileClip(video_abspath)
+            narration_original = AudioFileClip(audio_abspath)
+            narration_scaled = narration_original.with_volume_scaled(narration_volume)
+            narration = narration_scaled
+
+            if mix_mode == "replace":
+                final_audio = narration
+            elif mix_mode == "mix":
+                if video.audio:
+                    original = video.audio.with_volume_scaled(original_volume)
+                    final_audio = CompositeAudioClip([original, narration])
+                else:
+                    final_audio = narration
+            else:  # ducking - apply stronger attenuation
+                if video.audio:
+                    # Ducking uses a much lower volume for original audio
+                    ducking_volume = original_volume * 0.3
+                    original = video.audio.with_volume_scaled(ducking_volume)
+                    final_audio = CompositeAudioClip([original, narration])
+                else:
+                    final_audio = narration
+
+            final = video.with_audio(final_audio)
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            final.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+
+        finally:
+            if original:
+                original.close()
+            if narration_scaled:
+                narration_scaled.close()
+            if narration_original:
+                narration_original.close()
+            if final:
+                final.close()
+            if video:
+                video.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: ElevenLabsCredentials,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store the input video locally
+            local_video_path = await self._store_input_video(
+                execution_context, input_data.video_in
+            )
+            video_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, local_video_path
+            )
+
+            # Generate narration audio via ElevenLabs
+            audio_content = self._generate_narration_audio(
+                credentials.api_key.get_secret_value(),
+                input_data.script,
+                input_data.voice_id,
+                input_data.model_id,
+            )
+
+            # Save audio to exec file path
+            audio_filename = MediaFileType(f"{node_exec_id}_narration.mp3")
+            audio_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, audio_filename
+            )
+            os.makedirs(os.path.dirname(audio_abspath), exist_ok=True)
+            with open(audio_abspath, "wb") as f:
+                f.write(audio_content)
+
+            # Add narration to video
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_narrated_{source}.mp4")
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            self._add_narration_to_video(
+                video_abspath,
+                audio_abspath,
+                output_abspath,
+                input_data.mix_mode,
+                input_data.narration_volume,
+                input_data.original_volume,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+            audio_out = await self._store_output_video(
+                execution_context, audio_filename
+            )
+
+            yield "video_out", video_out
+            yield "audio_file", audio_out
+
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to add narration: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/text_overlay.py

@@ -0,0 +1,231 @@
+"""VideoTextOverlayBlock - Add text overlay to video."""
+
+from typing import Literal
+
+from moviepy import CompositeVideoClip, TextClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoTextOverlayBlock(Block):
+    """Add text overlay/caption to video."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="Input video (URL, data URI, or local path)"
+        )
+        text: str = SchemaField(description="Text to overlay on video")
+        position: Literal[
+            "top",
+            "center",
+            "bottom",
+            "top-left",
+            "top-right",
+            "bottom-left",
+            "bottom-right",
+        ] = SchemaField(description="Position of text on screen", default="bottom")
+        start_time: float | None = SchemaField(
+            description="When to show text (seconds). None = entire video",
+            default=None,
+            advanced=True,
+        )
+        end_time: float | None = SchemaField(
+            description="When to hide text (seconds). None = until end",
+            default=None,
+            advanced=True,
+        )
+        font_size: int = SchemaField(
+            description="Font size", default=48, ge=12, le=200, advanced=True
+        )
+        font_color: str = SchemaField(
+            description="Font color (hex or name)", default="white", advanced=True
+        )
+        bg_color: str | None = SchemaField(
+            description="Background color behind text (None for transparent)",
+            default=None,
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Video with text overlay (path or data URI)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="8ef14de6-cc90-430a-8cfa-3a003be92454",
+            description="Add text overlay/caption to video",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            disabled=True,  # Disable until we can lockdown imagemagick security policy
+            test_input={"video_in": "/tmp/test.mp4", "text": "Hello World"},
+            test_output=[("video_out", str)],
+            test_mock={
+                "_add_text_overlay": lambda *args: None,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "overlay_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _add_text_overlay(
+        self,
+        video_abspath: str,
+        output_abspath: str,
+        text: str,
+        position: str,
+        start_time: float | None,
+        end_time: float | None,
+        font_size: int,
+        font_color: str,
+        bg_color: str | None,
+    ) -> None:
+        """Add text overlay to video. Extracted for testability."""
+        video = None
+        final = None
+        txt_clip = None
+        try:
+            strip_chapters_inplace(video_abspath)
+            video = VideoFileClip(video_abspath)
+
+            txt_clip = TextClip(
+                text=text,
+                font_size=font_size,
+                color=font_color,
+                bg_color=bg_color,
+            )
+
+            # Position mapping
+            pos_map = {
+                "top": ("center", "top"),
+                "center": ("center", "center"),
+                "bottom": ("center", "bottom"),
+                "top-left": ("left", "top"),
+                "top-right": ("right", "top"),
+                "bottom-left": ("left", "bottom"),
+                "bottom-right": ("right", "bottom"),
+            }
+
+            txt_clip = txt_clip.with_position(pos_map[position])
+
+            # Set timing
+            start = start_time or 0
+            end = end_time or video.duration
+            duration = max(0, end - start)
+            txt_clip = txt_clip.with_start(start).with_end(end).with_duration(duration)
+
+            final = CompositeVideoClip([video, txt_clip])
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            final.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+
+        finally:
+            if txt_clip:
+                txt_clip.close()
+            if final:
+                final.close()
+            if video:
+                video.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        # Validate time range if both are provided
+        if (
+            input_data.start_time is not None
+            and input_data.end_time is not None
+            and input_data.end_time <= input_data.start_time
+        ):
+            raise BlockExecutionError(
+                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
+                block_name=self.name,
+                block_id=str(self.id),
+            )
+
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store the input video locally
+            local_video_path = await self._store_input_video(
+                execution_context, input_data.video_in
+            )
+            video_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, local_video_path
+            )
+
+            # Build output path
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_overlay_{source}.mp4")
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            self._add_text_overlay(
+                video_abspath,
+                output_abspath,
+                input_data.text,
+                input_data.position,
+                input_data.start_time,
+                input_data.end_time,
+                input_data.font_size,
+                input_data.font_color,
+                input_data.bg_color,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+
+            yield "video_out", video_out
+
+        except BlockExecutionError:
+            raise
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to add text overlay: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/data/block.py

@@ -246,7 +246,9 @@ class BlockSchema(BaseModel):
                         f"is not of type {CredentialsMetaInput.__name__}"
                     )
 
-                credentials_fields[field_name].validate_credentials_field_schema(cls)
+                CredentialsMetaInput.validate_credentials_field_schema(
+                    cls.get_field_schema(field_name), field_name
+                )
 
             elif field_name in credentials_fields:
                 raise KeyError(
@@ -317,6 +319,8 @@ class BlockSchema(BaseModel):
                 "credentials_provider": [config.get("provider", "google")],
                 "credentials_types": [config.get("type", "oauth2")],
                 "credentials_scopes": config.get("scopes"),
+                "is_auto_credential": True,
+                "input_field_name": info["field_name"],
             }
             result[kwarg_name] = CredentialsFieldInfo.model_validate(
                 auto_schema, by_alias=True

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -36,12 +36,14 @@ from backend.blocks.replicate.replicate_block import ReplicateModelBlock
 from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
 from backend.blocks.text_to_speech_block import UnrealTextToSpeechBlock
+from backend.blocks.video.narration import VideoNarrationBlock
 from backend.data.block import Block, BlockCost, BlockCostType
 from backend.integrations.credentials_store import (
     aiml_api_credentials,
     anthropic_credentials,
     apollo_credentials,
     did_credentials,
+    elevenlabs_credentials,
     enrichlayer_credentials,
     groq_credentials,
     ideogram_credentials,
@@ -78,6 +80,7 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_1_OPUS: 21,
     LlmModel.CLAUDE_4_OPUS: 21,
     LlmModel.CLAUDE_4_SONNET: 5,
+    LlmModel.CLAUDE_4_6_OPUS: 14,
     LlmModel.CLAUDE_4_5_HAIKU: 4,
     LlmModel.CLAUDE_4_5_OPUS: 14,
     LlmModel.CLAUDE_4_5_SONNET: 9,
@@ -639,4 +642,16 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
             },
         ),
     ],
+    VideoNarrationBlock: [
+        BlockCost(
+            cost_amount=5,  # ElevenLabs TTS cost
+            cost_filter={
+                "credentials": {
+                    "id": elevenlabs_credentials.id,
+                    "provider": elevenlabs_credentials.provider,
+                    "type": elevenlabs_credentials.type,
+                }
+            },
+        )
+    ],
 }

autogpt_platform/backend/backend/data/graph.py

@@ -3,7 +3,7 @@ import logging
 import uuid
 from collections import defaultdict
 from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Annotated, Any, Literal, Optional, cast
+from typing import TYPE_CHECKING, Annotated, Any, Literal, Optional, Self, cast
 
 from prisma.enums import SubmissionStatus
 from prisma.models import (
@@ -20,7 +20,7 @@ from prisma.types import (
     AgentNodeLinkCreateInput,
     StoreListingVersionWhereInput,
 )
-from pydantic import BaseModel, BeforeValidator, Field, create_model
+from pydantic import BaseModel, BeforeValidator, Field
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
@@ -30,7 +30,6 @@ from backend.data.db import prisma as db
 from backend.data.dynamic_fields import is_tool_pin, sanitize_pin_name
 from backend.data.includes import MAX_GRAPH_VERSIONS_FETCH
 from backend.data.model import (
-    CredentialsField,
     CredentialsFieldInfo,
     CredentialsMetaInput,
     is_credentials_field_name,
@@ -45,7 +44,6 @@ from .block import (
     AnyBlockSchema,
     Block,
     BlockInput,
-    BlockSchema,
     BlockType,
     EmptySchema,
     get_block,
@@ -113,10 +111,12 @@ class Link(BaseDbModel):
 
 class Node(BaseDbModel):
     block_id: str
-    input_default: BlockInput = {}  # dict[input_name, default_value]
-    metadata: dict[str, Any] = {}
-    input_links: list[Link] = []
-    output_links: list[Link] = []
+    input_default: BlockInput = Field(  # dict[input_name, default_value]
+        default_factory=dict
+    )
+    metadata: dict[str, Any] = Field(default_factory=dict)
+    input_links: list[Link] = Field(default_factory=list)
+    output_links: list[Link] = Field(default_factory=list)
 
     @property
     def credentials_optional(self) -> bool:
@@ -221,18 +221,33 @@ class NodeModel(Node):
         return result
 
 
-class BaseGraph(BaseDbModel):
+class GraphBaseMeta(BaseDbModel):
+    """
+    Shared base for `GraphMeta` and `BaseGraph`, with core graph metadata fields.
+    """
+
     version: int = 1
     is_active: bool = True
     name: str
     description: str
     instructions: str | None = None
     recommended_schedule_cron: str | None = None
-    nodes: list[Node] = []
-    links: list[Link] = []
     forked_from_id: str | None = None
     forked_from_version: int | None = None
 
+
+class BaseGraph(GraphBaseMeta):
+    """
+    Graph with nodes, links, and computed I/O schema fields.
+
+    Used to represent sub-graphs within a `Graph`. Contains the full graph
+    structure including nodes and links, plus computed fields for schemas
+    and trigger info. Does NOT include user_id or created_at (see GraphModel).
+    """
+
+    nodes: list[Node] = Field(default_factory=list)
+    links: list[Link] = Field(default_factory=list)
+
     @computed_field
     @property
     def input_schema(self) -> dict[str, Any]:
@@ -361,44 +376,78 @@ class GraphTriggerInfo(BaseModel):
 
 
 class Graph(BaseGraph):
-    sub_graphs: list[BaseGraph] = []  # Flattened sub-graphs
+    """Creatable graph model used in API create/update endpoints."""
+
+    sub_graphs: list[BaseGraph] = Field(default_factory=list)  # Flattened sub-graphs
+
+
+class GraphMeta(GraphBaseMeta):
+    """
+    Lightweight graph metadata model representing an existing graph from the database,
+    for use in listings and summaries.
+
+    Lacks `GraphModel`'s nodes, links, and expensive computed fields.
+    Use for list endpoints where full graph data is not needed and performance matters.
+    """
+
+    id: str  # type: ignore
+    version: int  # type: ignore
+    user_id: str
+    created_at: datetime
+
+    @classmethod
+    def from_db(cls, graph: "AgentGraph") -> Self:
+        return cls(
+            id=graph.id,
+            version=graph.version,
+            is_active=graph.isActive,
+            name=graph.name or "",
+            description=graph.description or "",
+            instructions=graph.instructions,
+            recommended_schedule_cron=graph.recommendedScheduleCron,
+            forked_from_id=graph.forkedFromId,
+            forked_from_version=graph.forkedFromVersion,
+            user_id=graph.userId,
+            created_at=graph.createdAt,
+        )
+
+
+class GraphModel(Graph, GraphMeta):
+    """
+    Full graph model representing an existing graph from the database.
+
+    This is the primary model for working with persisted graphs. Includes all
+    graph data (nodes, links, sub_graphs) plus user ownership and timestamps.
+    Provides computed fields (input_schema, output_schema, etc.) used during
+    set-up (frontend) and execution (backend).
+
+    Inherits from:
+    - `Graph`: provides structure (nodes, links, sub_graphs) and computed schemas
+    - `GraphMeta`: provides user_id, created_at for database records
+    """
+
+    nodes: list[NodeModel] = Field(default_factory=list)  # type: ignore
+
+    @property
+    def starting_nodes(self) -> list[NodeModel]:
+        outbound_nodes = {link.sink_id for link in self.links}
+        input_nodes = {
+            node.id for node in self.nodes if node.block.block_type == BlockType.INPUT
+        }
+        return [
+            node
+            for node in self.nodes
+            if node.id not in outbound_nodes or node.id in input_nodes
+        ]
+
+    @property
+    def webhook_input_node(self) -> NodeModel | None:  # type: ignore
+        return cast(NodeModel, super().webhook_input_node)
 
     @computed_field
     @property
     def credentials_input_schema(self) -> dict[str, Any]:
-        schema = self._credentials_input_schema.jsonschema()
-
-        # Determine which credential fields are required based on credentials_optional metadata
-        graph_credentials_inputs = self.aggregate_credentials_inputs()
-        required_fields = []
-
-        # Build a map of node_id -> node for quick lookup
-        all_nodes = {node.id: node for node in self.nodes}
-        for sub_graph in self.sub_graphs:
-            for node in sub_graph.nodes:
-                all_nodes[node.id] = node
-
-        for field_key, (
-            _field_info,
-            node_field_pairs,
-        ) in graph_credentials_inputs.items():
-            # A field is required if ANY node using it has credentials_optional=False
-            is_required = False
-            for node_id, _field_name in node_field_pairs:
-                node = all_nodes.get(node_id)
-                if node and not node.credentials_optional:
-                    is_required = True
-                    break
-
-            if is_required:
-                required_fields.append(field_key)
-
-        schema["required"] = required_fields
-        return schema
-
-    @property
-    def _credentials_input_schema(self) -> type[BlockSchema]:
-        graph_credentials_inputs = self.aggregate_credentials_inputs()
+        graph_credentials_inputs = self.regular_credentials_inputs
         logger.debug(
             f"Combined credentials input fields for graph #{self.id} ({self.name}): "
             f"{graph_credentials_inputs}"
@@ -406,8 +455,8 @@ class Graph(BaseGraph):
 
         # Warn if same-provider credentials inputs can't be combined (= bad UX)
         graph_cred_fields = list(graph_credentials_inputs.values())
-        for i, (field, keys) in enumerate(graph_cred_fields):
-            for other_field, other_keys in list(graph_cred_fields)[i + 1 :]:
+        for i, (field, keys, _) in enumerate(graph_cred_fields):
+            for other_field, other_keys, _ in list(graph_cred_fields)[i + 1 :]:
                 if field.provider != other_field.provider:
                     continue
                 if ProviderName.HTTP in field.provider:
@@ -423,31 +472,78 @@ class Graph(BaseGraph):
                     f"keys: {keys} <> {other_keys}."
                 )
 
-        fields: dict[str, tuple[type[CredentialsMetaInput], CredentialsMetaInput]] = {
-            agg_field_key: (
-                CredentialsMetaInput[
-                    Literal[tuple(field_info.provider)],  # type: ignore
-                    Literal[tuple(field_info.supported_types)],  # type: ignore
-                ],
-                CredentialsField(
-                    required_scopes=set(field_info.required_scopes or []),
-                    discriminator=field_info.discriminator,
-                    discriminator_mapping=field_info.discriminator_mapping,
-                    discriminator_values=field_info.discriminator_values,
-                ),
-            )
-            for agg_field_key, (field_info, _) in graph_credentials_inputs.items()
-        }
+        # Build JSON schema directly to avoid expensive create_model + validation overhead
+        properties = {}
+        required_fields = []
 
-        return create_model(
-            self.name.replace(" ", "") + "CredentialsInputSchema",
-            __base__=BlockSchema,
-            **fields,  # type: ignore
-        )
+        for agg_field_key, (
+            field_info,
+            _,
+            is_required,
+        ) in graph_credentials_inputs.items():
+            providers = list(field_info.provider)
+            cred_types = list(field_info.supported_types)
+
+            field_schema: dict[str, Any] = {
+                "credentials_provider": providers,
+                "credentials_types": cred_types,
+                "type": "object",
+                "properties": {
+                    "id": {"title": "Id", "type": "string"},
+                    "title": {
+                        "anyOf": [{"type": "string"}, {"type": "null"}],
+                        "default": None,
+                        "title": "Title",
+                    },
+                    "provider": {
+                        "title": "Provider",
+                        "type": "string",
+                        **(
+                            {"enum": providers}
+                            if len(providers) > 1
+                            else {"const": providers[0]}
+                        ),
+                    },
+                    "type": {
+                        "title": "Type",
+                        "type": "string",
+                        **(
+                            {"enum": cred_types}
+                            if len(cred_types) > 1
+                            else {"const": cred_types[0]}
+                        ),
+                    },
+                },
+                "required": ["id", "provider", "type"],
+            }
+
+            # Add other (optional) field info items
+            field_schema.update(
+                field_info.model_dump(
+                    by_alias=True,
+                    exclude_defaults=True,
+                    exclude={"provider", "supported_types"},  # already included above
+                )
+            )
+
+            # Ensure field schema is well-formed
+            CredentialsMetaInput.validate_credentials_field_schema(
+                field_schema, agg_field_key
+            )
+
+            properties[agg_field_key] = field_schema
+            if is_required:
+                required_fields.append(agg_field_key)
+
+        return {
+            "type": "object",
+            "properties": properties,
+            "required": required_fields,
+        }
 
     def aggregate_credentials_inputs(
         self,
-    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]]]]:
+    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
         """
         Returns:
             dict[aggregated_field_key, tuple(
@@ -455,13 +551,19 @@ class Graph(BaseGraph):
                     (now includes discriminator_values from matching nodes)
                 set[(node_id, field_name)]: Node credentials fields that are
                     compatible with this aggregated field spec
+                bool: True if the field is required (any node has credentials_optional=False)
             )]
         """
         # First collect all credential field data with input defaults
-        node_credential_data = []
+        # Track (field_info, (node_id, field_name), is_required) for each credential field
+        node_credential_data: list[tuple[CredentialsFieldInfo, tuple[str, str]]] = []
+        node_required_map: dict[str, bool] = {}  # node_id -> is_required
 
         for graph in [self] + self.sub_graphs:
             for node in graph.nodes:
+                # Track if this node requires credentials (credentials_optional=False means required)
+                node_required_map[node.id] = not node.credentials_optional
+
                 for (
                     field_name,
                     field_info,
@@ -485,37 +587,43 @@ class Graph(BaseGraph):
                     )
 
         # Combine credential field info (this will merge discriminator_values automatically)
-        return CredentialsFieldInfo.combine(*node_credential_data)
+        combined = CredentialsFieldInfo.combine(*node_credential_data)
 
-
-class GraphModel(Graph):
-    user_id: str
-    nodes: list[NodeModel] = []  # type: ignore
-
-    created_at: datetime
-
-    @property
-    def starting_nodes(self) -> list[NodeModel]:
-        outbound_nodes = {link.sink_id for link in self.links}
-        input_nodes = {
-            node.id for node in self.nodes if node.block.block_type == BlockType.INPUT
+        # Add is_required flag to each aggregated field
+        # A field is required if ANY node using it has credentials_optional=False
+        return {
+            key: (
+                field_info,
+                node_field_pairs,
+                any(
+                    node_required_map.get(node_id, True)
+                    for node_id, _ in node_field_pairs
+                ),
+            )
+            for key, (field_info, node_field_pairs) in combined.items()
         }
-        return [
-            node
-            for node in self.nodes
-            if node.id not in outbound_nodes or node.id in input_nodes
-        ]
 
     @property
-    def webhook_input_node(self) -> NodeModel | None:  # type: ignore
-        return cast(NodeModel, super().webhook_input_node)
+    def regular_credentials_inputs(
+        self,
+    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
+        """Credentials that need explicit user mapping (CredentialsMetaInput fields)."""
+        return {
+            k: v
+            for k, v in self.aggregate_credentials_inputs().items()
+            if not v[0].is_auto_credential
+        }
 
-    def meta(self) -> "GraphMeta":
-        """
-        Returns a GraphMeta object with metadata about the graph.
-        This is used to return metadata about the graph without exposing nodes and links.
-        """
-        return GraphMeta.from_graph(self)
+    @property
+    def auto_credentials_inputs(
+        self,
+    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
+        """Credentials embedded in file fields (_credentials_id), resolved at execution time."""
+        return {
+            k: v
+            for k, v in self.aggregate_credentials_inputs().items()
+            if v[0].is_auto_credential
+        }
 
     def reassign_ids(self, user_id: str, reassign_graph_id: bool = False):
         """
@@ -567,6 +675,16 @@ class GraphModel(Graph):
             ) and graph_id in graph_id_map:
                 node.input_default["graph_id"] = graph_id_map[graph_id]
 
+        # Clear auto-credentials references (e.g., _credentials_id in
+        # GoogleDriveFile fields) so the new user must re-authenticate
+        # with their own account
+        for node in graph.nodes:
+            if not node.input_default:
+                continue
+            for key, value in node.input_default.items():
+                if isinstance(value, dict) and "_credentials_id" in value:
+                    del value["_credentials_id"]
+
     def validate_graph(
         self,
         for_run: bool = False,
@@ -799,13 +917,14 @@ class GraphModel(Graph):
             if is_static_output_block(link.source_id):
                 link.is_static = True  # Each value block output should be static.
 
-    @staticmethod
-    def from_db(
+    @classmethod
+    def from_db(  # type: ignore[reportIncompatibleMethodOverride]
+        cls,
         graph: AgentGraph,
         for_export: bool = False,
         sub_graphs: list[AgentGraph] | None = None,
-    ) -> "GraphModel":
-        return GraphModel(
+    ) -> Self:
+        return cls(
             id=graph.id,
             user_id=graph.userId if not for_export else "",
             version=graph.version,
@@ -831,17 +950,28 @@ class GraphModel(Graph):
             ],
         )
 
+    def hide_nodes(self) -> "GraphModelWithoutNodes":
+        """
+        Returns a copy of the `GraphModel` with nodes, links, and sub-graphs hidden
+        (excluded from serialization). They are still present in the model instance
+        so all computed fields (e.g. `credentials_input_schema`) still work.
+        """
+        return GraphModelWithoutNodes.model_validate(self, from_attributes=True)
 
-class GraphMeta(Graph):
-    user_id: str
 
-    # Easy work-around to prevent exposing nodes and links in the API response
-    nodes: list[NodeModel] = Field(default=[], exclude=True)  # type: ignore
-    links: list[Link] = Field(default=[], exclude=True)
+class GraphModelWithoutNodes(GraphModel):
+    """
+    GraphModel variant that excludes nodes, links, and sub-graphs from serialization.
 
-    @staticmethod
-    def from_graph(graph: GraphModel) -> "GraphMeta":
-        return GraphMeta(**graph.model_dump())
+    Used in contexts like the store where exposing internal graph structure
+    is not desired. Inherits all computed fields from GraphModel but marks
+    nodes and links as excluded from JSON output.
+    """
+
+    nodes: list[NodeModel] = Field(default_factory=list, exclude=True)
+    links: list[Link] = Field(default_factory=list, exclude=True)
+
+    sub_graphs: list[BaseGraph] = Field(default_factory=list, exclude=True)
 
 
 class GraphsPaginated(BaseModel):
@@ -912,21 +1042,11 @@ async def list_graphs_paginated(
         where=where_clause,
         distinct=["id"],
         order={"version": "desc"},
-        include=AGENT_GRAPH_INCLUDE,
         skip=offset,
         take=page_size,
     )
 
-    graph_models: list[GraphMeta] = []
-    for graph in graphs:
-        try:
-            graph_meta = GraphModel.from_db(graph).meta()
-            # Trigger serialization to validate that the graph is well formed
-            graph_meta.model_dump()
-            graph_models.append(graph_meta)
-        except Exception as e:
-            logger.error(f"Error processing graph {graph.id}: {e}")
-            continue
+    graph_models = [GraphMeta.from_db(graph) for graph in graphs]
 
     return GraphsPaginated(
         graphs=graph_models,

autogpt_platform/backend/backend/data/graph_test.py

@@ -463,3 +463,328 @@ def test_node_credentials_optional_with_other_metadata():
     assert node.credentials_optional is True
     assert node.metadata["position"] == {"x": 100, "y": 200}
     assert node.metadata["customized_name"] == "My Custom Node"
+
+
+# ============================================================================
+# Tests for _reassign_ids credential clearing (Fix 3: SECRT-1772)
+def test_combine_preserves_is_auto_credential_flag():
+    """
+    CredentialsFieldInfo.combine() must propagate is_auto_credential and
+    input_field_name to the combined result. Regression test for reviewer
+    finding that combine() dropped these fields.
+    """
+    from backend.data.model import CredentialsFieldInfo
+
+    auto_field = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["google"],
+            "credentials_types": ["oauth2"],
+            "credentials_scopes": ["drive.readonly"],
+            "is_auto_credential": True,
+            "input_field_name": "spreadsheet",
+        },
+        by_alias=True,
+    )
+
+    # combine() takes *args of (field_info, key) tuples
+    combined = CredentialsFieldInfo.combine(
+        (auto_field, ("node-1", "credentials")),
+        (auto_field, ("node-2", "credentials")),
+    )
+
+    assert len(combined) == 1
+    group_key = next(iter(combined))
+    combined_info, combined_keys = combined[group_key]
+
+    assert combined_info.is_auto_credential is True
+    assert combined_info.input_field_name == "spreadsheet"
+    assert combined_keys == {("node-1", "credentials"), ("node-2", "credentials")}
+
+
+def test_combine_preserves_regular_credential_defaults():
+    """Regular credentials should have is_auto_credential=False after combine()."""
+    from backend.data.model import CredentialsFieldInfo
+
+    regular_field = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["github"],
+            "credentials_types": ["api_key"],
+            "is_auto_credential": False,
+        },
+        by_alias=True,
+    )
+
+    combined = CredentialsFieldInfo.combine(
+        (regular_field, ("node-1", "credentials")),
+    )
+
+    group_key = next(iter(combined))
+    combined_info, _ = combined[group_key]
+
+    assert combined_info.is_auto_credential is False
+    assert combined_info.input_field_name is None
+
+
+# ============================================================================
+
+
+def test_reassign_ids_clears_credentials_id():
+    """
+    [SECRT-1772] _reassign_ids should clear _credentials_id from
+    GoogleDriveFile-style input_default fields so forked agents
+    don't retain the original creator's credential references.
+    """
+    from backend.data.graph import GraphModel
+
+    node = Node(
+        id="node-1",
+        block_id=StoreValueBlock().id,
+        input_default={
+            "spreadsheet": {
+                "_credentials_id": "original-cred-id",
+                "id": "file-123",
+                "name": "test.xlsx",
+                "mimeType": "application/vnd.google-apps.spreadsheet",
+                "url": "https://docs.google.com/spreadsheets/d/file-123",
+            },
+        },
+    )
+
+    graph = Graph(
+        id="test-graph",
+        name="Test",
+        description="Test",
+        nodes=[node],
+        links=[],
+    )
+
+    GraphModel._reassign_ids(graph, user_id="new-user", graph_id_map={})
+
+    # _credentials_id key should be removed (not set to None) so that
+    # _acquire_auto_credentials correctly errors instead of treating it as chained data
+    assert "_credentials_id" not in graph.nodes[0].input_default["spreadsheet"]
+
+
+def test_reassign_ids_preserves_non_credential_fields():
+    """
+    Regression guard: _reassign_ids should NOT modify non-credential fields
+    like name, mimeType, id, url.
+    """
+    from backend.data.graph import GraphModel
+
+    node = Node(
+        id="node-1",
+        block_id=StoreValueBlock().id,
+        input_default={
+            "spreadsheet": {
+                "_credentials_id": "cred-abc",
+                "id": "file-123",
+                "name": "test.xlsx",
+                "mimeType": "application/vnd.google-apps.spreadsheet",
+                "url": "https://docs.google.com/spreadsheets/d/file-123",
+            },
+        },
+    )
+
+    graph = Graph(
+        id="test-graph",
+        name="Test",
+        description="Test",
+        nodes=[node],
+        links=[],
+    )
+
+    GraphModel._reassign_ids(graph, user_id="new-user", graph_id_map={})
+
+    field = graph.nodes[0].input_default["spreadsheet"]
+    assert field["id"] == "file-123"
+    assert field["name"] == "test.xlsx"
+    assert field["mimeType"] == "application/vnd.google-apps.spreadsheet"
+    assert field["url"] == "https://docs.google.com/spreadsheets/d/file-123"
+
+
+def test_reassign_ids_handles_no_credentials():
+    """
+    Regression guard: _reassign_ids should not error when input_default
+    has no dict fields with _credentials_id.
+    """
+    from backend.data.graph import GraphModel
+
+    node = Node(
+        id="node-1",
+        block_id=StoreValueBlock().id,
+        input_default={
+            "input": "some value",
+            "another_input": 42,
+        },
+    )
+
+    graph = Graph(
+        id="test-graph",
+        name="Test",
+        description="Test",
+        nodes=[node],
+        links=[],
+    )
+
+    GraphModel._reassign_ids(graph, user_id="new-user", graph_id_map={})
+
+    # Should not error, fields unchanged
+    assert graph.nodes[0].input_default["input"] == "some value"
+    assert graph.nodes[0].input_default["another_input"] == 42
+
+
+def test_reassign_ids_handles_multiple_credential_fields():
+    """
+    [SECRT-1772] When a node has multiple dict fields with _credentials_id,
+    ALL of them should be cleared.
+    """
+    from backend.data.graph import GraphModel
+
+    node = Node(
+        id="node-1",
+        block_id=StoreValueBlock().id,
+        input_default={
+            "spreadsheet": {
+                "_credentials_id": "cred-1",
+                "id": "file-1",
+                "name": "file1.xlsx",
+            },
+            "doc_file": {
+                "_credentials_id": "cred-2",
+                "id": "file-2",
+                "name": "file2.docx",
+            },
+            "plain_input": "not a dict",
+        },
+    )
+
+    graph = Graph(
+        id="test-graph",
+        name="Test",
+        description="Test",
+        nodes=[node],
+        links=[],
+    )
+
+    GraphModel._reassign_ids(graph, user_id="new-user", graph_id_map={})
+
+    assert "_credentials_id" not in graph.nodes[0].input_default["spreadsheet"]
+    assert "_credentials_id" not in graph.nodes[0].input_default["doc_file"]
+    assert graph.nodes[0].input_default["plain_input"] == "not a dict"
+
+
+# ============================================================================
+# Tests for discriminate() field propagation
+def test_discriminate_preserves_is_auto_credential_flag():
+    """
+    CredentialsFieldInfo.discriminate() must propagate is_auto_credential and
+    input_field_name to the discriminated result. Regression test for
+    discriminate() dropping these fields (same class of bug as combine()).
+    """
+    from backend.data.model import CredentialsFieldInfo
+
+    auto_field = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["google", "openai"],
+            "credentials_types": ["oauth2"],
+            "credentials_scopes": ["drive.readonly"],
+            "is_auto_credential": True,
+            "input_field_name": "spreadsheet",
+            "discriminator": "model",
+            "discriminator_mapping": {"gpt-4": "openai", "gemini": "google"},
+        },
+        by_alias=True,
+    )
+
+    discriminated = auto_field.discriminate("gemini")
+
+    assert discriminated.is_auto_credential is True
+    assert discriminated.input_field_name == "spreadsheet"
+    assert discriminated.provider == frozenset(["google"])
+
+
+def test_discriminate_preserves_regular_credential_defaults():
+    """Regular credentials should have is_auto_credential=False after discriminate()."""
+    from backend.data.model import CredentialsFieldInfo
+
+    regular_field = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["google", "openai"],
+            "credentials_types": ["api_key"],
+            "is_auto_credential": False,
+            "discriminator": "model",
+            "discriminator_mapping": {"gpt-4": "openai", "gemini": "google"},
+        },
+        by_alias=True,
+    )
+
+    discriminated = regular_field.discriminate("gpt-4")
+
+    assert discriminated.is_auto_credential is False
+    assert discriminated.input_field_name is None
+    assert discriminated.provider == frozenset(["openai"])
+
+
+# ============================================================================
+# Tests for credentials_input_schema excluding auto_credentials
+def test_credentials_input_schema_excludes_auto_creds():
+    """
+    GraphModel.credentials_input_schema should exclude auto_credentials
+    (is_auto_credential=True) from the schema. Auto_credentials are
+    transparently resolved at execution time via file picker data.
+    """
+    from datetime import datetime, timezone
+    from unittest.mock import PropertyMock, patch
+
+    from backend.data.graph import GraphModel, NodeModel
+    from backend.data.model import CredentialsFieldInfo
+
+    regular_field_info = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["github"],
+            "credentials_types": ["api_key"],
+            "is_auto_credential": False,
+        },
+        by_alias=True,
+    )
+
+    graph = GraphModel(
+        id="test-graph",
+        version=1,
+        name="Test",
+        description="Test",
+        user_id="test-user",
+        created_at=datetime.now(timezone.utc),
+        nodes=[
+            NodeModel(
+                id="node-1",
+                block_id=StoreValueBlock().id,
+                input_default={},
+                graph_id="test-graph",
+                graph_version=1,
+            ),
+        ],
+        links=[],
+    )
+
+    # Mock regular_credentials_inputs to return only the non-auto field (3-tuple)
+    regular_only = {
+        "github_credentials": (
+            regular_field_info,
+            {("node-1", "credentials")},
+            True,
+        ),
+    }
+
+    with patch.object(
+        type(graph),
+        "regular_credentials_inputs",
+        new_callable=PropertyMock,
+        return_value=regular_only,
+    ):
+        schema = graph.credentials_input_schema
+        field_names = set(schema.get("properties", {}).keys())
+        # Should include regular credential but NOT auto_credential
+        assert "github_credentials" in field_names
+        assert "google_credentials" not in field_names

autogpt_platform/backend/backend/data/model.py

@@ -163,7 +163,6 @@ class User(BaseModel):
 if TYPE_CHECKING:
     from prisma.models import User as PrismaUser
 
-    from backend.data.block import BlockSchema
 
 T = TypeVar("T")
 logger = logging.getLogger(__name__)
@@ -508,15 +507,13 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
     def allowed_cred_types(cls) -> tuple[CredentialsType, ...]:
         return get_args(cls.model_fields["type"].annotation)
 
-    @classmethod
-    def validate_credentials_field_schema(cls, model: type["BlockSchema"]):
+    @staticmethod
+    def validate_credentials_field_schema(
+        field_schema: dict[str, Any], field_name: str
+    ):
         """Validates the schema of a credentials input field"""
-        field_name = next(
-            name for name, type in model.get_credentials_fields().items() if type is cls
-        )
-        field_schema = model.jsonschema()["properties"][field_name]
         try:
-            schema_extra = CredentialsFieldInfo[CP, CT].model_validate(field_schema)
+            field_info = CredentialsFieldInfo[CP, CT].model_validate(field_schema)
         except ValidationError as e:
             if "Field required [type=missing" not in str(e):
                 raise
@@ -526,11 +523,11 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
                 f"{field_schema}"
             ) from e
 
-        providers = cls.allowed_providers()
+        providers = field_info.provider
         if (
             providers is not None
             and len(providers) > 1
-            and not schema_extra.discriminator
+            and not field_info.discriminator
         ):
             raise TypeError(
                 f"Multi-provider CredentialsField '{field_name}' "
@@ -574,6 +571,8 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
     discriminator: Optional[str] = None
     discriminator_mapping: Optional[dict[str, CP]] = None
     discriminator_values: set[Any] = Field(default_factory=set)
+    is_auto_credential: bool = False
+    input_field_name: Optional[str] = None
 
     @classmethod
     def combine(
@@ -654,6 +653,9 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
                 + "_credentials"
             )
 
+            # Propagate is_auto_credential from the combined field.
+            # All fields in a group should share the same is_auto_credential
+            # value since auto and regular credentials serve different purposes.
             result[group_key] = (
                 CredentialsFieldInfo[CP, CT](
                     credentials_provider=combined.provider,
@@ -662,6 +664,8 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
                     discriminator=combined.discriminator,
                     discriminator_mapping=combined.discriminator_mapping,
                     discriminator_values=set(all_discriminator_values),
+                    is_auto_credential=combined.is_auto_credential,
+                    input_field_name=combined.input_field_name,
                 ),
                 combined_keys,
             )
@@ -687,6 +691,8 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
             discriminator=self.discriminator,
             discriminator_mapping=self.discriminator_mapping,
             discriminator_values=self.discriminator_values,
+            is_auto_credential=self.is_auto_credential,
+            input_field_name=self.input_field_name,
         )
 
 

autogpt_platform/backend/backend/executor/manager.py

@@ -172,6 +172,81 @@ def execute_graph(
 T = TypeVar("T")
 
 
+async def _acquire_auto_credentials(
+    input_model: type[BlockSchema],
+    input_data: dict[str, Any],
+    creds_manager: "IntegrationCredentialsManager",
+    user_id: str,
+) -> tuple[dict[str, Any], list[AsyncRedisLock]]:
+    """
+    Resolve auto_credentials from GoogleDriveFileField-style inputs.
+
+    Returns:
+        (extra_exec_kwargs, locks): kwargs to inject into block execution, and
+        credential locks to release after execution completes.
+    """
+    extra_exec_kwargs: dict[str, Any] = {}
+    locks: list[AsyncRedisLock] = []
+
+    # NOTE: If a block ever has multiple auto-credential fields, a ValueError
+    # on a later field will strand locks acquired for earlier fields. They'll
+    # auto-expire via Redis TTL, but add a try/except to release partial locks
+    # if that becomes a real scenario.
+    for kwarg_name, info in input_model.get_auto_credentials_fields().items():
+        field_name = info["field_name"]
+        field_data = input_data.get(field_name)
+
+        if field_data and isinstance(field_data, dict):
+            # Check if _credentials_id key exists in the field data
+            if "_credentials_id" in field_data:
+                cred_id = field_data["_credentials_id"]
+                if cred_id:
+                    # Credential ID provided - acquire credentials
+                    provider = info.get("config", {}).get(
+                        "provider", "external service"
+                    )
+                    file_name = field_data.get("name", "selected file")
+                    try:
+                        credentials, lock = await creds_manager.acquire(
+                            user_id, cred_id
+                        )
+                        locks.append(lock)
+                        extra_exec_kwargs[kwarg_name] = credentials
+                    except ValueError:
+                        raise ValueError(
+                            f"{provider.capitalize()} credentials for "
+                            f"'{file_name}' in field '{field_name}' are not "
+                            f"available in your account. "
+                            f"This can happen if the agent was created by another "
+                            f"user or the credentials were deleted. "
+                            f"Please open the agent in the builder and re-select "
+                            f"the file to authenticate with your own account."
+                        )
+                # else: _credentials_id is explicitly None, skip (chained data)
+            else:
+                # _credentials_id key missing entirely - this is an error
+                provider = info.get("config", {}).get("provider", "external service")
+                file_name = field_data.get("name", "selected file")
+                raise ValueError(
+                    f"Authentication missing for '{file_name}' in field "
+                    f"'{field_name}'. Please re-select the file to authenticate "
+                    f"with {provider.capitalize()}."
+                )
+        elif field_data is None and field_name not in input_data:
+            # Field not in input_data at all = connected from upstream block, skip
+            pass
+        else:
+            # field_data is None/empty but key IS in input_data = user didn't select
+            provider = info.get("config", {}).get("provider", "external service")
+            raise ValueError(
+                f"No file selected for '{field_name}'. "
+                f"Please select a file to provide "
+                f"{provider.capitalize()} authentication."
+            )
+
+    return extra_exec_kwargs, locks
+
+
 async def execute_node(
     node: Node,
     data: NodeExecutionEntry,
@@ -271,41 +346,14 @@ async def execute_node(
         extra_exec_kwargs[field_name] = credentials
 
     # Handle auto-generated credentials (e.g., from GoogleDriveFileInput)
-    for kwarg_name, info in input_model.get_auto_credentials_fields().items():
-        field_name = info["field_name"]
-        field_data = input_data.get(field_name)
-        if field_data and isinstance(field_data, dict):
-            # Check if _credentials_id key exists in the field data
-            if "_credentials_id" in field_data:
-                cred_id = field_data["_credentials_id"]
-                if cred_id:
-                    # Credential ID provided - acquire credentials
-                    provider = info.get("config", {}).get(
-                        "provider", "external service"
-                    )
-                    file_name = field_data.get("name", "selected file")
-                    try:
-                        credentials, lock = await creds_manager.acquire(
-                            user_id, cred_id
-                        )
-                        creds_locks.append(lock)
-                        extra_exec_kwargs[kwarg_name] = credentials
-                    except ValueError:
-                        # Credential was deleted or doesn't exist
-                        raise ValueError(
-                            f"Authentication expired for '{file_name}' in field '{field_name}'. "
-                            f"The saved {provider.capitalize()} credentials no longer exist. "
-                            f"Please re-select the file to re-authenticate."
-                        )
-                # else: _credentials_id is explicitly None, skip credentials (for chained data)
-            else:
-                # _credentials_id key missing entirely - this is an error
-                provider = info.get("config", {}).get("provider", "external service")
-                file_name = field_data.get("name", "selected file")
-                raise ValueError(
-                    f"Authentication missing for '{file_name}' in field '{field_name}'. "
-                    f"Please re-select the file to authenticate with {provider.capitalize()}."
-                )
+    auto_extra_kwargs, auto_locks = await _acquire_auto_credentials(
+        input_model=input_model,
+        input_data=input_data,
+        creds_manager=creds_manager,
+        user_id=user_id,
+    )
+    extra_exec_kwargs.update(auto_extra_kwargs)
+    creds_locks.extend(auto_locks)
 
     output_size = 0
 

autogpt_platform/backend/backend/executor/manager_auto_credentials_test.py

@@ -0,0 +1,320 @@
+"""
+Tests for auto_credentials handling in execute_node().
+
+These test the _acquire_auto_credentials() helper function extracted from
+execute_node() (manager.py lines 273-308).
+"""
+
+import pytest
+from pytest_mock import MockerFixture
+
+
+@pytest.fixture
+def google_drive_file_data():
+    return {
+        "valid": {
+            "_credentials_id": "cred-id-123",
+            "id": "file-123",
+            "name": "test.xlsx",
+            "mimeType": "application/vnd.google-apps.spreadsheet",
+        },
+        "chained": {
+            "_credentials_id": None,
+            "id": "file-456",
+            "name": "chained.xlsx",
+            "mimeType": "application/vnd.google-apps.spreadsheet",
+        },
+        "missing_key": {
+            "id": "file-789",
+            "name": "bad.xlsx",
+            "mimeType": "application/vnd.google-apps.spreadsheet",
+        },
+    }
+
+
+@pytest.fixture
+def mock_input_model(mocker: MockerFixture):
+    """Create a mock input model with get_auto_credentials_fields() returning one field."""
+    input_model = mocker.MagicMock()
+    input_model.get_auto_credentials_fields.return_value = {
+        "credentials": {
+            "field_name": "spreadsheet",
+            "config": {
+                "provider": "google",
+                "type": "oauth2",
+                "scopes": ["https://www.googleapis.com/auth/drive.readonly"],
+            },
+        }
+    }
+    return input_model
+
+
+@pytest.fixture
+def mock_creds_manager(mocker: MockerFixture):
+    manager = mocker.AsyncMock()
+    mock_lock = mocker.AsyncMock()
+    mock_creds = mocker.MagicMock()
+    mock_creds.id = "cred-id-123"
+    mock_creds.provider = "google"
+    manager.acquire.return_value = (mock_creds, mock_lock)
+    return manager, mock_creds, mock_lock
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_happy_path(
+    mocker: MockerFixture,
+    google_drive_file_data,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """When field_data has a valid _credentials_id, credentials should be acquired."""
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, mock_creds, mock_lock = mock_creds_manager
+    input_data = {"spreadsheet": google_drive_file_data["valid"]}
+
+    extra_kwargs, locks = await _acquire_auto_credentials(
+        input_model=mock_input_model,
+        input_data=input_data,
+        creds_manager=manager,
+        user_id="user-1",
+    )
+
+    manager.acquire.assert_called_once_with("user-1", "cred-id-123")
+    assert extra_kwargs["credentials"] == mock_creds
+    assert mock_lock in locks
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_field_none_static_raises(
+    mocker: MockerFixture,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """
+    [THE BUG FIX TEST — OPEN-2895]
+    When field_data is None and the key IS in input_data (user didn't select a file),
+    should raise ValueError instead of silently skipping.
+    """
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, _, _ = mock_creds_manager
+    # Key is present but value is None = user didn't select a file
+    input_data = {"spreadsheet": None}
+
+    with pytest.raises(ValueError, match="No file selected"):
+        await _acquire_auto_credentials(
+            input_model=mock_input_model,
+            input_data=input_data,
+            creds_manager=manager,
+            user_id="user-1",
+        )
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_field_absent_skips(
+    mocker: MockerFixture,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """
+    When the field key is NOT in input_data at all (upstream connection),
+    should skip without error.
+    """
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, _, _ = mock_creds_manager
+    # Key not present = connected from upstream block
+    input_data = {}
+
+    extra_kwargs, locks = await _acquire_auto_credentials(
+        input_model=mock_input_model,
+        input_data=input_data,
+        creds_manager=manager,
+        user_id="user-1",
+    )
+
+    manager.acquire.assert_not_called()
+    assert "credentials" not in extra_kwargs
+    assert locks == []
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_chained_cred_id_none(
+    mocker: MockerFixture,
+    google_drive_file_data,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """
+    When _credentials_id is explicitly None (chained data from upstream),
+    should skip credential acquisition.
+    """
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, _, _ = mock_creds_manager
+    input_data = {"spreadsheet": google_drive_file_data["chained"]}
+
+    extra_kwargs, locks = await _acquire_auto_credentials(
+        input_model=mock_input_model,
+        input_data=input_data,
+        creds_manager=manager,
+        user_id="user-1",
+    )
+
+    manager.acquire.assert_not_called()
+    assert "credentials" not in extra_kwargs
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_missing_cred_id_key_raises(
+    mocker: MockerFixture,
+    google_drive_file_data,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """
+    When _credentials_id key is missing entirely from field_data dict,
+    should raise ValueError.
+    """
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, _, _ = mock_creds_manager
+    input_data = {"spreadsheet": google_drive_file_data["missing_key"]}
+
+    with pytest.raises(ValueError, match="Authentication missing"):
+        await _acquire_auto_credentials(
+            input_model=mock_input_model,
+            input_data=input_data,
+            creds_manager=manager,
+            user_id="user-1",
+        )
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_ownership_mismatch_error(
+    mocker: MockerFixture,
+    google_drive_file_data,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """
+    [SECRT-1772] When acquire() raises ValueError (credential belongs to another user),
+    the error message should mention 'not available' (not 'expired').
+    """
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, _, _ = mock_creds_manager
+    manager.acquire.side_effect = ValueError(
+        "Credentials #cred-id-123 for user #user-2 not found"
+    )
+    input_data = {"spreadsheet": google_drive_file_data["valid"]}
+
+    with pytest.raises(ValueError, match="not available in your account"):
+        await _acquire_auto_credentials(
+            input_model=mock_input_model,
+            input_data=input_data,
+            creds_manager=manager,
+            user_id="user-2",
+        )
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_deleted_credential_error(
+    mocker: MockerFixture,
+    google_drive_file_data,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """
+    [SECRT-1772] When acquire() raises ValueError (credential was deleted),
+    the error message should mention 'not available' (not 'expired').
+    """
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, _, _ = mock_creds_manager
+    manager.acquire.side_effect = ValueError(
+        "Credentials #cred-id-123 for user #user-1 not found"
+    )
+    input_data = {"spreadsheet": google_drive_file_data["valid"]}
+
+    with pytest.raises(ValueError, match="not available in your account"):
+        await _acquire_auto_credentials(
+            input_model=mock_input_model,
+            input_data=input_data,
+            creds_manager=manager,
+            user_id="user-1",
+        )
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_lock_appended(
+    mocker: MockerFixture,
+    google_drive_file_data,
+    mock_input_model,
+    mock_creds_manager,
+):
+    """Lock from acquire() should be included in returned locks list."""
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, _, mock_lock = mock_creds_manager
+    input_data = {"spreadsheet": google_drive_file_data["valid"]}
+
+    extra_kwargs, locks = await _acquire_auto_credentials(
+        input_model=mock_input_model,
+        input_data=input_data,
+        creds_manager=manager,
+        user_id="user-1",
+    )
+
+    assert len(locks) == 1
+    assert locks[0] is mock_lock
+
+
+@pytest.mark.asyncio
+async def test_auto_credentials_multiple_fields(
+    mocker: MockerFixture,
+    mock_creds_manager,
+):
+    """When there are multiple auto_credentials fields, only valid ones should acquire."""
+    from backend.executor.manager import _acquire_auto_credentials
+
+    manager, mock_creds, mock_lock = mock_creds_manager
+
+    input_model = mocker.MagicMock()
+    input_model.get_auto_credentials_fields.return_value = {
+        "credentials": {
+            "field_name": "spreadsheet",
+            "config": {"provider": "google", "type": "oauth2"},
+        },
+        "credentials2": {
+            "field_name": "doc_file",
+            "config": {"provider": "google", "type": "oauth2"},
+        },
+    }
+
+    input_data = {
+        "spreadsheet": {
+            "_credentials_id": "cred-id-123",
+            "id": "file-1",
+            "name": "file1.xlsx",
+        },
+        "doc_file": {
+            "_credentials_id": None,
+            "id": "file-2",
+            "name": "chained.doc",
+        },
+    }
+
+    extra_kwargs, locks = await _acquire_auto_credentials(
+        input_model=input_model,
+        input_data=input_data,
+        creds_manager=manager,
+        user_id="user-1",
+    )
+
+    # Only the first field should have acquired credentials
+    manager.acquire.assert_called_once_with("user-1", "cred-id-123")
+    assert "credentials" in extra_kwargs
+    assert "credentials2" not in extra_kwargs
+    assert len(locks) == 1

autogpt_platform/backend/backend/executor/utils.py

@@ -259,7 +259,8 @@ async def _validate_node_input_credentials(
 
         # Find any fields of type CredentialsMetaInput
         credentials_fields = block.input_schema.get_credentials_fields()
-        if not credentials_fields:
+        auto_credentials_fields = block.input_schema.get_auto_credentials_fields()
+        if not credentials_fields and not auto_credentials_fields:
             continue
 
         # Track if any credential field is missing for this node
@@ -339,6 +340,47 @@ async def _validate_node_input_credentials(
                 ] = "Invalid credentials: type/provider mismatch"
                 continue
 
+        # Validate auto-credentials (GoogleDriveFileField-based)
+        # These have _credentials_id embedded in the file field data
+        if auto_credentials_fields:
+            for _kwarg_name, info in auto_credentials_fields.items():
+                field_name = info["field_name"]
+                # Check input_default and nodes_input_masks for the field value
+                field_value = node.input_default.get(field_name)
+                if nodes_input_masks and node.id in nodes_input_masks:
+                    field_value = nodes_input_masks[node.id].get(
+                        field_name, field_value
+                    )
+
+                if field_value and isinstance(field_value, dict):
+                    if "_credentials_id" not in field_value:
+                        # Key removed (e.g., on fork) — needs re-auth
+                        has_missing_credentials = True
+                        credential_errors[node.id][field_name] = (
+                            "Authentication missing for the selected file. "
+                            "Please re-select the file to authenticate with "
+                            "your own account."
+                        )
+                        continue
+                    cred_id = field_value.get("_credentials_id")
+                    if cred_id and isinstance(cred_id, str):
+                        try:
+                            creds_store = get_integration_credentials_store()
+                            creds = await creds_store.get_creds_by_id(user_id, cred_id)
+                        except Exception as e:
+                            has_missing_credentials = True
+                            credential_errors[node.id][
+                                field_name
+                            ] = f"Credentials not available: {e}"
+                            continue
+                        if not creds:
+                            has_missing_credentials = True
+                            credential_errors[node.id][field_name] = (
+                                "The saved credentials are not available "
+                                "for your account. Please re-select the file to "
+                                "authenticate with your own account."
+                            )
+
         # If node has optional credentials and any are missing, mark for skipping
         # But only if there are no other errors for this node
         if (
@@ -370,10 +412,11 @@ def make_node_credentials_input_map(
     """
     result: dict[str, dict[str, JsonValue]] = {}
 
-    # Get aggregated credentials fields for the graph
-    graph_cred_inputs = graph.aggregate_credentials_inputs()
+    # Only map regular credentials (not auto_credentials, which are resolved
+    # at execution time from _credentials_id in file field data)
+    graph_cred_inputs = graph.regular_credentials_inputs
 
-    for graph_input_name, (_, compatible_node_fields) in graph_cred_inputs.items():
+    for graph_input_name, (_, compatible_node_fields, _) in graph_cred_inputs.items():
         # Best-effort map: skip missing items
         if graph_input_name not in graph_credentials_input:
             continue

autogpt_platform/backend/backend/executor/utils_test.py

@@ -907,3 +907,335 @@ async def test_stop_graph_execution_cascades_to_child_with_reviews(
 
     # Verify both parent and child status updates
     assert mock_execution_db.update_graph_execution_stats.call_count >= 1
+
+
+# ============================================================================
+# Tests for auto_credentials validation in _validate_node_input_credentials
+# (Fix 3: SECRT-1772 + Fix 4: Path 4)
+# ============================================================================
+
+
+@pytest.mark.asyncio
+async def test_validate_node_input_credentials_auto_creds_valid(
+    mocker: MockerFixture,
+):
+    """
+    [SECRT-1772] When a node has auto_credentials with a valid _credentials_id
+    that exists in the store, validation should pass without errors.
+    """
+    from backend.executor.utils import _validate_node_input_credentials
+
+    mock_node = mocker.MagicMock()
+    mock_node.id = "node-with-auto-creds"
+    mock_node.credentials_optional = False
+    mock_node.input_default = {
+        "spreadsheet": {
+            "_credentials_id": "valid-cred-id",
+            "id": "file-123",
+            "name": "test.xlsx",
+        }
+    }
+
+    mock_block = mocker.MagicMock()
+    # No regular credentials fields
+    mock_block.input_schema.get_credentials_fields.return_value = {}
+    # Has auto_credentials fields
+    mock_block.input_schema.get_auto_credentials_fields.return_value = {
+        "credentials": {
+            "field_name": "spreadsheet",
+            "config": {"provider": "google", "type": "oauth2"},
+        }
+    }
+    mock_node.block = mock_block
+
+    mock_graph = mocker.MagicMock()
+    mock_graph.nodes = [mock_node]
+
+    # Mock the credentials store to return valid credentials
+    mock_store = mocker.MagicMock()
+    mock_creds = mocker.MagicMock()
+    mock_creds.id = "valid-cred-id"
+    mock_store.get_creds_by_id = mocker.AsyncMock(return_value=mock_creds)
+    mocker.patch(
+        "backend.executor.utils.get_integration_credentials_store",
+        return_value=mock_store,
+    )
+
+    errors, nodes_to_skip = await _validate_node_input_credentials(
+        graph=mock_graph,
+        user_id="test-user",
+        nodes_input_masks=None,
+    )
+
+    assert mock_node.id not in errors
+    assert mock_node.id not in nodes_to_skip
+
+
+@pytest.mark.asyncio
+async def test_validate_node_input_credentials_auto_creds_missing(
+    mocker: MockerFixture,
+):
+    """
+    [SECRT-1772] When a node has auto_credentials with a _credentials_id
+    that doesn't exist for the current user, validation should report an error.
+    """
+    from backend.executor.utils import _validate_node_input_credentials
+
+    mock_node = mocker.MagicMock()
+    mock_node.id = "node-with-bad-auto-creds"
+    mock_node.credentials_optional = False
+    mock_node.input_default = {
+        "spreadsheet": {
+            "_credentials_id": "other-users-cred-id",
+            "id": "file-123",
+            "name": "test.xlsx",
+        }
+    }
+
+    mock_block = mocker.MagicMock()
+    mock_block.input_schema.get_credentials_fields.return_value = {}
+    mock_block.input_schema.get_auto_credentials_fields.return_value = {
+        "credentials": {
+            "field_name": "spreadsheet",
+            "config": {"provider": "google", "type": "oauth2"},
+        }
+    }
+    mock_node.block = mock_block
+
+    mock_graph = mocker.MagicMock()
+    mock_graph.nodes = [mock_node]
+
+    # Mock the credentials store to return None (cred not found for this user)
+    mock_store = mocker.MagicMock()
+    mock_store.get_creds_by_id = mocker.AsyncMock(return_value=None)
+    mocker.patch(
+        "backend.executor.utils.get_integration_credentials_store",
+        return_value=mock_store,
+    )
+
+    errors, nodes_to_skip = await _validate_node_input_credentials(
+        graph=mock_graph,
+        user_id="different-user",
+        nodes_input_masks=None,
+    )
+
+    assert mock_node.id in errors
+    assert "spreadsheet" in errors[mock_node.id]
+    assert "not available" in errors[mock_node.id]["spreadsheet"].lower()
+
+
+@pytest.mark.asyncio
+async def test_validate_node_input_credentials_both_regular_and_auto(
+    mocker: MockerFixture,
+):
+    """
+    [SECRT-1772] A node that has BOTH regular credentials AND auto_credentials
+    should have both validated.
+    """
+    from backend.executor.utils import _validate_node_input_credentials
+
+    mock_node = mocker.MagicMock()
+    mock_node.id = "node-with-both-creds"
+    mock_node.credentials_optional = False
+    mock_node.input_default = {
+        "credentials": {
+            "id": "regular-cred-id",
+            "provider": "github",
+            "type": "api_key",
+        },
+        "spreadsheet": {
+            "_credentials_id": "auto-cred-id",
+            "id": "file-123",
+            "name": "test.xlsx",
+        },
+    }
+
+    mock_credentials_field_type = mocker.MagicMock()
+    mock_credentials_meta = mocker.MagicMock()
+    mock_credentials_meta.id = "regular-cred-id"
+    mock_credentials_meta.provider = "github"
+    mock_credentials_meta.type = "api_key"
+    mock_credentials_field_type.model_validate.return_value = mock_credentials_meta
+
+    mock_block = mocker.MagicMock()
+    # Regular credentials field
+    mock_block.input_schema.get_credentials_fields.return_value = {
+        "credentials": mock_credentials_field_type,
+    }
+    # Auto-credentials field
+    mock_block.input_schema.get_auto_credentials_fields.return_value = {
+        "auto_credentials": {
+            "field_name": "spreadsheet",
+            "config": {"provider": "google", "type": "oauth2"},
+        }
+    }
+    mock_node.block = mock_block
+
+    mock_graph = mocker.MagicMock()
+    mock_graph.nodes = [mock_node]
+
+    # Mock the credentials store to return valid credentials for both
+    mock_store = mocker.MagicMock()
+    mock_regular_creds = mocker.MagicMock()
+    mock_regular_creds.id = "regular-cred-id"
+    mock_regular_creds.provider = "github"
+    mock_regular_creds.type = "api_key"
+
+    mock_auto_creds = mocker.MagicMock()
+    mock_auto_creds.id = "auto-cred-id"
+
+    def get_creds_side_effect(user_id, cred_id):
+        if cred_id == "regular-cred-id":
+            return mock_regular_creds
+        elif cred_id == "auto-cred-id":
+            return mock_auto_creds
+        return None
+
+    mock_store.get_creds_by_id = mocker.AsyncMock(side_effect=get_creds_side_effect)
+    mocker.patch(
+        "backend.executor.utils.get_integration_credentials_store",
+        return_value=mock_store,
+    )
+
+    errors, nodes_to_skip = await _validate_node_input_credentials(
+        graph=mock_graph,
+        user_id="test-user",
+        nodes_input_masks=None,
+    )
+
+    # Both should validate successfully - no errors
+    assert mock_node.id not in errors
+    assert mock_node.id not in nodes_to_skip
+
+
+@pytest.mark.asyncio
+async def test_validate_node_input_credentials_auto_creds_skipped_when_none(
+    mocker: MockerFixture,
+):
+    """
+    When a node has auto_credentials but the field value has _credentials_id=None
+    (e.g., from upstream connection), validation should skip it without error.
+    """
+    from backend.executor.utils import _validate_node_input_credentials
+
+    mock_node = mocker.MagicMock()
+    mock_node.id = "node-with-chained-auto-creds"
+    mock_node.credentials_optional = False
+    mock_node.input_default = {
+        "spreadsheet": {
+            "_credentials_id": None,
+            "id": "file-123",
+            "name": "test.xlsx",
+        }
+    }
+
+    mock_block = mocker.MagicMock()
+    mock_block.input_schema.get_credentials_fields.return_value = {}
+    mock_block.input_schema.get_auto_credentials_fields.return_value = {
+        "credentials": {
+            "field_name": "spreadsheet",
+            "config": {"provider": "google", "type": "oauth2"},
+        }
+    }
+    mock_node.block = mock_block
+
+    mock_graph = mocker.MagicMock()
+    mock_graph.nodes = [mock_node]
+
+    errors, nodes_to_skip = await _validate_node_input_credentials(
+        graph=mock_graph,
+        user_id="test-user",
+        nodes_input_masks=None,
+    )
+
+    # No error - chained data with None cred_id is valid
+    assert mock_node.id not in errors
+
+
+# ============================================================================
+# Tests for CredentialsFieldInfo auto_credential tag (Fix 4: Path 4)
+# ============================================================================
+
+
+def test_credentials_field_info_auto_credential_tag():
+    """
+    [Path 4] CredentialsFieldInfo should support is_auto_credential and
+    input_field_name fields for distinguishing auto from regular credentials.
+    """
+    from backend.data.model import CredentialsFieldInfo
+
+    # Regular credential should have is_auto_credential=False by default
+    regular = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["github"],
+            "credentials_types": ["api_key"],
+        },
+        by_alias=True,
+    )
+    assert regular.is_auto_credential is False
+    assert regular.input_field_name is None
+
+    # Auto credential should have is_auto_credential=True
+    auto = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["google"],
+            "credentials_types": ["oauth2"],
+            "is_auto_credential": True,
+            "input_field_name": "spreadsheet",
+        },
+        by_alias=True,
+    )
+    assert auto.is_auto_credential is True
+    assert auto.input_field_name == "spreadsheet"
+
+
+def test_make_node_credentials_input_map_excludes_auto_creds(
+    mocker: MockerFixture,
+):
+    """
+    [Path 4] make_node_credentials_input_map should only include regular credentials,
+    not auto_credentials (which are resolved at execution time).
+    """
+    from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+    from backend.executor.utils import make_node_credentials_input_map
+    from backend.integrations.providers import ProviderName
+
+    # Create a mock graph with aggregate_credentials_inputs that returns
+    # both regular and auto credentials
+    mock_graph = mocker.MagicMock()
+
+    regular_field_info = CredentialsFieldInfo.model_validate(
+        {
+            "credentials_provider": ["github"],
+            "credentials_types": ["api_key"],
+            "is_auto_credential": False,
+        },
+        by_alias=True,
+    )
+
+    # Mock regular_credentials_inputs property (auto_credentials are excluded)
+    mock_graph.regular_credentials_inputs = {
+        "github_creds": (regular_field_info, {("node-1", "credentials")}, True),
+    }
+
+    graph_credentials_input = {
+        "github_creds": CredentialsMetaInput(
+            id="cred-123",
+            provider=ProviderName("github"),
+            type="api_key",
+        ),
+    }
+
+    result = make_node_credentials_input_map(mock_graph, graph_credentials_input)
+
+    # Regular credentials should be mapped
+    assert "node-1" in result
+    assert "credentials" in result["node-1"]
+
+    # Auto credentials should NOT appear in the result
+    # (they would have been mapped to the kwarg_name "credentials" not "spreadsheet")
+    for node_id, fields in result.items():
+        for field_name, value in fields.items():
+            # Verify no auto-credential phantom entries
+            if isinstance(value, dict):
+                assert "_credentials_id" not in value

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -224,6 +224,14 @@ openweathermap_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
+elevenlabs_credentials = APIKeyCredentials(
+    id="f4a8b6c2-3d1e-4f5a-9b8c-7d6e5f4a3b2c",
+    provider="elevenlabs",
+    api_key=SecretStr(settings.secrets.elevenlabs_api_key),
+    title="Use Credits for ElevenLabs",
+    expires_at=None,
+)
+
 DEFAULT_CREDENTIALS = [
     ollama_credentials,
     revid_credentials,
@@ -252,6 +260,7 @@ DEFAULT_CREDENTIALS = [
     v0_credentials,
     webshare_proxy_credentials,
     openweathermap_credentials,
+    elevenlabs_credentials,
 ]
 
 SYSTEM_CREDENTIAL_IDS = {cred.id for cred in DEFAULT_CREDENTIALS}
@@ -366,6 +375,8 @@ class IntegrationCredentialsStore:
             all_credentials.append(webshare_proxy_credentials)
         if settings.secrets.openweathermap_api_key:
             all_credentials.append(openweathermap_credentials)
+        if settings.secrets.elevenlabs_api_key:
+            all_credentials.append(elevenlabs_credentials)
         return all_credentials
 
     async def get_creds_by_id(

autogpt_platform/backend/backend/integrations/providers.py

@@ -18,6 +18,7 @@ class ProviderName(str, Enum):
     DISCORD = "discord"
     D_ID = "d_id"
     E2B = "e2b"
+    ELEVENLABS = "elevenlabs"
     FAL = "fal"
     GITHUB = "github"
     GOOGLE = "google"

autogpt_platform/backend/backend/util/file.py

@@ -8,6 +8,8 @@ from pathlib import Path
 from typing import TYPE_CHECKING, Literal
 from urllib.parse import urlparse
 
+from pydantic import BaseModel
+
 from backend.util.cloud_storage import get_cloud_storage_handler
 from backend.util.request import Requests
 from backend.util.settings import Config
@@ -17,6 +19,35 @@ from backend.util.virus_scanner import scan_content_safe
 if TYPE_CHECKING:
     from backend.data.execution import ExecutionContext
 
+
+class WorkspaceUri(BaseModel):
+    """Parsed workspace:// URI."""
+
+    file_ref: str  # File ID or path (e.g. "abc123" or "/path/to/file.txt")
+    mime_type: str | None = None  # MIME type from fragment (e.g. "video/mp4")
+    is_path: bool = False  # True if file_ref is a path (starts with "/")
+
+
+def parse_workspace_uri(uri: str) -> WorkspaceUri:
+    """Parse a workspace:// URI into its components.
+
+    Examples:
+        "workspace://abc123"            → WorkspaceUri(file_ref="abc123", mime_type=None, is_path=False)
+        "workspace://abc123#video/mp4"  → WorkspaceUri(file_ref="abc123", mime_type="video/mp4", is_path=False)
+        "workspace:///path/to/file.txt" → WorkspaceUri(file_ref="/path/to/file.txt", mime_type=None, is_path=True)
+    """
+    raw = uri.removeprefix("workspace://")
+    mime_type: str | None = None
+    if "#" in raw:
+        raw, fragment = raw.split("#", 1)
+        mime_type = fragment or None
+    return WorkspaceUri(
+        file_ref=raw,
+        mime_type=mime_type,
+        is_path=raw.startswith("/"),
+    )
+
+
 # Return format options for store_media_file
 # - "for_local_processing": Returns local file path - use with ffmpeg, MoviePy, PIL, etc.
 # - "for_external_api": Returns data URI (base64) - use when sending content to external APIs
@@ -183,22 +214,20 @@ async def store_media_file(
                 "This file type is only available in CoPilot sessions."
             )
 
-        # Parse workspace reference
-        # workspace://abc123 - by file ID
-        # workspace:///path/to/file.txt - by virtual path
-        file_ref = file[12:]  # Remove "workspace://"
+        # Parse workspace reference (strips #mimeType fragment from file ID)
+        ws = parse_workspace_uri(file)
 
-        if file_ref.startswith("/"):
-            # Path reference
-            workspace_content = await workspace_manager.read_file(file_ref)
-            file_info = await workspace_manager.get_file_info_by_path(file_ref)
+        if ws.is_path:
+            # Path reference: workspace:///path/to/file.txt
+            workspace_content = await workspace_manager.read_file(ws.file_ref)
+            file_info = await workspace_manager.get_file_info_by_path(ws.file_ref)
             filename = sanitize_filename(
                 file_info.name if file_info else f"{uuid.uuid4()}.bin"
             )
         else:
-            # ID reference
-            workspace_content = await workspace_manager.read_file_by_id(file_ref)
-            file_info = await workspace_manager.get_file_info(file_ref)
+            # ID reference: workspace://abc123 or workspace://abc123#video/mp4
+            workspace_content = await workspace_manager.read_file_by_id(ws.file_ref)
+            file_info = await workspace_manager.get_file_info(ws.file_ref)
             filename = sanitize_filename(
                 file_info.name if file_info else f"{uuid.uuid4()}.bin"
             )
@@ -334,7 +363,21 @@ async def store_media_file(
 
         # Don't re-save if input was already from workspace
         if is_from_workspace:
-            # Return original workspace reference
+            # Return original workspace reference, ensuring MIME type fragment
+            ws = parse_workspace_uri(file)
+            if not ws.mime_type:
+                # Add MIME type fragment if missing (older refs without it)
+                try:
+                    if ws.is_path:
+                        info = await workspace_manager.get_file_info_by_path(
+                            ws.file_ref
+                        )
+                    else:
+                        info = await workspace_manager.get_file_info(ws.file_ref)
+                    if info:
+                        return MediaFileType(f"{file}#{info.mimeType}")
+                except Exception:
+                    pass
             return MediaFileType(file)
 
         # Save new content to workspace
@@ -346,7 +389,7 @@ async def store_media_file(
             filename=filename,
             overwrite=True,
         )
-        return MediaFileType(f"workspace://{file_record.id}")
+        return MediaFileType(f"workspace://{file_record.id}#{file_record.mimeType}")
 
     else:
         raise ValueError(f"Invalid return_format: {return_format}")

autogpt_platform/backend/backend/util/settings.py

@@ -656,6 +656,7 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
     e2b_api_key: str = Field(default="", description="E2B API key")
     nvidia_api_key: str = Field(default="", description="Nvidia API key")
     mem0_api_key: str = Field(default="", description="Mem0 API key")
+    elevenlabs_api_key: str = Field(default="", description="ElevenLabs API key")
 
     linear_client_id: str = Field(default="", description="Linear client ID")
     linear_client_secret: str = Field(default="", description="Linear client secret")

autogpt_platform/backend/backend/util/workspace.py

@@ -22,6 +22,7 @@ from backend.data.workspace import (
     soft_delete_workspace_file,
 )
 from backend.util.settings import Config
+from backend.util.virus_scanner import scan_content_safe
 from backend.util.workspace_storage import compute_file_checksum, get_workspace_storage
 
 logger = logging.getLogger(__name__)
@@ -187,6 +188,9 @@ class WorkspaceManager:
                 f"{Config().max_file_size_mb}MB limit"
             )
 
+        # Virus scan content before persisting (defense in depth)
+        await scan_content_safe(content, filename=filename)
+
         # Determine path with session scoping
         if path is None:
             path = f"/{filename}"

autogpt_platform/backend/poetry.lock

@@ -1169,6 +1169,29 @@ attrs = ">=21.3.0"
 e2b = ">=1.5.4,<2.0.0"
 httpx = ">=0.20.0,<1.0.0"
 
+[[package]]
+name = "elevenlabs"
+version = "1.59.0"
+description = ""
+optional = false
+python-versions = "<4.0,>=3.8"
+groups = ["main"]
+files = [
+    {file = "elevenlabs-1.59.0-py3-none-any.whl", hash = "sha256:468145db81a0bc867708b4a8619699f75583e9481b395ec1339d0b443da771ed"},
+    {file = "elevenlabs-1.59.0.tar.gz", hash = "sha256:16e735bd594e86d415dd445d249c8cc28b09996cfd627fbc10102c0a84698859"},
+]
+
+[package.dependencies]
+httpx = ">=0.21.2"
+pydantic = ">=1.9.2"
+pydantic-core = ">=2.18.2,<3.0.0"
+requests = ">=2.20"
+typing_extensions = ">=4.0.0"
+websockets = ">=11.0"
+
+[package.extras]
+pyaudio = ["pyaudio (>=0.2.14)"]
+
 [[package]]
 name = "email-validator"
 version = "2.2.0"
@@ -7361,6 +7384,28 @@ files = [
 defusedxml = ">=0.7.1,<0.8.0"
 requests = "*"
 
+[[package]]
+name = "yt-dlp"
+version = "2025.12.8"
+description = "A feature-rich command-line audio/video downloader"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "yt_dlp-2025.12.8-py3-none-any.whl", hash = "sha256:36e2584342e409cfbfa0b5e61448a1c5189e345cf4564294456ee509e7d3e065"},
+    {file = "yt_dlp-2025.12.8.tar.gz", hash = "sha256:b773c81bb6b71cb2c111cfb859f453c7a71cf2ef44eff234ff155877184c3e4f"},
+]
+
+[package.extras]
+build = ["build", "hatchling (>=1.27.0)", "pip", "setuptools (>=71.0.2)", "wheel"]
+curl-cffi = ["curl-cffi (>=0.5.10,<0.6.dev0 || >=0.10.dev0,<0.14) ; implementation_name == \"cpython\""]
+default = ["brotli ; implementation_name == \"cpython\"", "brotlicffi ; implementation_name != \"cpython\"", "certifi", "mutagen", "pycryptodomex", "requests (>=2.32.2,<3)", "urllib3 (>=2.0.2,<3)", "websockets (>=13.0)", "yt-dlp-ejs (==0.3.2)"]
+dev = ["autopep8 (>=2.0,<3.0)", "pre-commit", "pytest (>=8.1,<9.0)", "pytest-rerunfailures (>=14.0,<15.0)", "ruff (>=0.14.0,<0.15.0)"]
+pyinstaller = ["pyinstaller (>=6.17.0)"]
+secretstorage = ["cffi", "secretstorage"]
+static-analysis = ["autopep8 (>=2.0,<3.0)", "ruff (>=0.14.0,<0.15.0)"]
+test = ["pytest (>=8.1,<9.0)", "pytest-rerunfailures (>=14.0,<15.0)"]
+
 [[package]]
 name = "zerobouncesdk"
 version = "1.1.2"
@@ -7512,4 +7557,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "ee5742dc1a9df50dfc06d4b26a1682cbb2b25cab6b79ce5625ec272f93e4f4bf"
+content-hash = "8239323f9ae6713224dffd1fe8ba8b449fe88b6c3c7a90940294a74f43a0387a"

autogpt_platform/backend/pyproject.toml

@@ -20,6 +20,7 @@ click = "^8.2.0"
 cryptography = "^45.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
+elevenlabs = "^1.50.0"
 fastapi = "^0.116.1"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
@@ -71,6 +72,7 @@ tweepy = "^4.16.0"
 uvicorn = { extras = ["standard"], version = "^0.35.0" }
 websockets = "^15.0"
 youtube-transcript-api = "^1.2.1"
+yt-dlp = "2025.12.08"
 zerobouncesdk = "^1.1.2"
 # NOTE: please insert new dependencies in their alphabetical location
 pytest-snapshot = "^0.9.0"

autogpt_platform/backend/snapshots/grph_single

@@ -3,7 +3,6 @@
   "credentials_input_schema": {
     "properties": {},
     "required": [],
-    "title": "TestGraphCredentialsInputSchema",
     "type": "object"
   },
   "description": "A test graph",

autogpt_platform/backend/snapshots/grphs_all

@@ -1,34 +1,14 @@
 [
   {
-    "credentials_input_schema": {
-      "properties": {},
-      "required": [],
-      "title": "TestGraphCredentialsInputSchema",
-      "type": "object"
-    },
+    "created_at": "2025-09-04T13:37:00",
     "description": "A test graph",
     "forked_from_id": null,
     "forked_from_version": null,
-    "has_external_trigger": false,
-    "has_human_in_the_loop": false,
-    "has_sensitive_action": false,
     "id": "graph-123",
-    "input_schema": {
-      "properties": {},
-      "required": [],
-      "type": "object"
-    },
     "instructions": null,
     "is_active": true,
     "name": "Test Graph",
-    "output_schema": {
-      "properties": {},
-      "required": [],
-      "type": "object"
-    },
     "recommended_schedule_cron": null,
-    "sub_graphs": [],
-    "trigger_setup_info": null,
     "user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
     "version": 1
   }

autogpt_platform/frontend/package.json

@@ -30,7 +30,6 @@
     "defaults"
   ],
   "dependencies": {
-    "@ai-sdk/react": "3.0.61",
     "@faker-js/faker": "10.0.0",
     "@hookform/resolvers": "5.2.2",
     "@next/third-parties": "15.4.6",
@@ -61,10 +60,6 @@
     "@rjsf/utils": "6.1.2",
     "@rjsf/validator-ajv8": "6.1.2",
     "@sentry/nextjs": "10.27.0",
-    "@streamdown/cjk": "1.0.1",
-    "@streamdown/code": "1.0.1",
-    "@streamdown/math": "1.0.1",
-    "@streamdown/mermaid": "1.0.1",
     "@supabase/ssr": "0.7.0",
     "@supabase/supabase-js": "2.78.0",
     "@tanstack/react-query": "5.90.6",
@@ -73,7 +68,6 @@
     "@vercel/analytics": "1.5.0",
     "@vercel/speed-insights": "1.2.0",
     "@xyflow/react": "12.9.2",
-    "ai": "6.0.59",
     "boring-avatars": "1.11.2",
     "class-variance-authority": "0.7.1",
     "clsx": "2.1.1",
@@ -118,11 +112,9 @@
     "remark-math": "6.0.0",
     "shepherd.js": "14.5.1",
     "sonner": "2.0.7",
-    "streamdown": "2.1.0",
     "tailwind-merge": "2.6.0",
     "tailwind-scrollbar": "3.1.0",
     "tailwindcss-animate": "1.0.7",
-    "use-stick-to-bottom": "1.1.2",
     "uuid": "11.1.0",
     "vaul": "1.1.2",
     "zod": "3.25.76",

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/components/AgentOnboardingCredentials/AgentOnboardingCredentials.tsx

@@ -1,5 +1,5 @@
 import { CredentialsMetaInput } from "@/app/api/__generated__/models/credentialsMetaInput";
-import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
+import { GraphModel } from "@/app/api/__generated__/models/graphModel";
 import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
 import { useState } from "react";
 import { getSchemaDefaultCredentials } from "../../helpers";
@@ -9,7 +9,7 @@ type Credential = CredentialsMetaInput | undefined;
 type Credentials = Record<string, Credential>;
 
 type Props = {
-  agent: GraphMeta | null;
+  agent: GraphModel | null;
   siblingInputs?: Record<string, any>;
   onCredentialsChange: (
     credentials: Record<string, CredentialsMetaInput>,

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/components/AgentOnboardingCredentials/helpers.ts

@@ -1,9 +1,9 @@
 import { CredentialsMetaInput } from "@/app/api/__generated__/models/credentialsMetaInput";
-import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
+import { GraphModel } from "@/app/api/__generated__/models/graphModel";
 import { BlockIOCredentialsSubSchema } from "@/lib/autogpt-server-api/types";
 
 export function getCredentialFields(
-  agent: GraphMeta | null,
+  agent: GraphModel | null,
 ): AgentCredentialsFields {
   if (!agent) return {};
 

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/helpers.ts

@@ -3,10 +3,10 @@ import type {
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
 import type { InputValues } from "./types";
-import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
+import { GraphModel } from "@/app/api/__generated__/models/graphModel";
 
 export function computeInitialAgentInputs(
-  agent: GraphMeta | null,
+  agent: GraphModel | null,
   existingInputs?: InputValues | null,
 ): InputValues {
   const properties = agent?.input_schema?.properties || {};
@@ -29,7 +29,7 @@ export function computeInitialAgentInputs(
 }
 
 type IsRunDisabledParams = {
-  agent: GraphMeta | null;
+  agent: GraphModel | null;
   isRunning: boolean;
   agentInputs: InputValues | null | undefined;
 };

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/BlocksControl.tsx

@@ -30,6 +30,8 @@ import {
 } from "@/components/atoms/Tooltip/BaseTooltip";
 import { GraphMeta } from "@/lib/autogpt-server-api";
 import jaro from "jaro-winkler";
+import { getV1GetSpecificGraph } from "@/app/api/__generated__/endpoints/graphs/graphs";
+import { okData } from "@/app/api/helpers";
 
 type _Block = Omit<Block, "inputSchema" | "outputSchema"> & {
   uiKey?: string;
@@ -107,6 +109,8 @@ export function BlocksControl({
       .filter((b) => b.uiType !== BlockUIType.AGENT)
       .sort((a, b) => a.name.localeCompare(b.name));
 
+    // Agent blocks are created from GraphMeta which doesn't include schemas.
+    // Schemas will be fetched on-demand when the block is actually added.
     const agentBlockList = flows
       .map((flow): _Block => {
         return {
@@ -116,8 +120,9 @@ export function BlocksControl({
             `Ver.${flow.version}` +
             (flow.description ? ` | ${flow.description}` : ""),
           categories: [{ category: "AGENT", description: "" }],
-          inputSchema: flow.input_schema,
-          outputSchema: flow.output_schema,
+          // Empty schemas - will be populated when block is added
+          inputSchema: { type: "object", properties: {} },
+          outputSchema: { type: "object", properties: {} },
           staticOutput: false,
           uiType: BlockUIType.AGENT,
           costs: [],
@@ -125,8 +130,7 @@ export function BlocksControl({
           hardcodedValues: {
             graph_id: flow.id,
             graph_version: flow.version,
-            input_schema: flow.input_schema,
-            output_schema: flow.output_schema,
+            // Schemas will be fetched on-demand when block is added
           },
         };
       })
@@ -182,6 +186,37 @@ export function BlocksControl({
     setSelectedCategory(null);
   }, []);
 
+  // Handler to add a block, fetching graph data on-demand for agent blocks
+  const handleAddBlock = useCallback(
+    async (block: _Block & { notAvailable: string | null }) => {
+      if (block.notAvailable) return;
+
+      // For agent blocks, fetch the full graph to get schemas
+      if (block.uiType === BlockUIType.AGENT && block.hardcodedValues) {
+        const graphID = block.hardcodedValues.graph_id as string;
+        const graphVersion = block.hardcodedValues.graph_version as number;
+        const graphData = okData(
+          await getV1GetSpecificGraph(graphID, { version: graphVersion }),
+        );
+
+        if (graphData) {
+          addBlock(block.id, block.name, {
+            ...block.hardcodedValues,
+            input_schema: graphData.input_schema,
+            output_schema: graphData.output_schema,
+          });
+        } else {
+          // Fallback: add without schemas (will be incomplete)
+          console.error("Failed to fetch graph data for agent block");
+          addBlock(block.id, block.name, block.hardcodedValues || {});
+        }
+      } else {
+        addBlock(block.id, block.name, block.hardcodedValues || {});
+      }
+    },
+    [addBlock],
+  );
+
   // Extract unique categories from blocks
   const categories = useMemo(() => {
     return Array.from(
@@ -303,10 +338,7 @@ export function BlocksControl({
                       }),
                     );
                   }}
-                  onClick={() =>
-                    !block.notAvailable &&
-                    addBlock(block.id, block.name, block?.hardcodedValues || {})
-                  }
+                  onClick={() => handleAddBlock(block)}
                   title={block.notAvailable ?? undefined}
                 >
                   <div

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/DataTable.tsx

@@ -1,6 +1,6 @@
 import { beautifyString } from "@/lib/utils";
 import { Clipboard, Maximize2 } from "lucide-react";
-import React, { useState } from "react";
+import React, { useMemo, useState } from "react";
 import { Button } from "../../../../../components/__legacy__/ui/button";
 import { ContentRenderer } from "../../../../../components/__legacy__/ui/render";
 import {
@@ -11,6 +11,12 @@ import {
   TableHeader,
   TableRow,
 } from "../../../../../components/__legacy__/ui/table";
+import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
+import {
+  globalRegistry,
+  OutputItem,
+} from "@/components/contextual/OutputRenderers";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { useToast } from "../../../../../components/molecules/Toast/use-toast";
 import ExpandableOutputDialog from "./ExpandableOutputDialog";
 
@@ -26,6 +32,9 @@ export default function DataTable({
   data,
 }: DataTableProps) {
   const { toast } = useToast();
+  const enableEnhancedOutputHandling = useGetFlag(
+    Flag.ENABLE_ENHANCED_OUTPUT_HANDLING,
+  );
   const [expandedDialog, setExpandedDialog] = useState<{
     isOpen: boolean;
     execId: string;
@@ -33,6 +42,15 @@ export default function DataTable({
     data: any[];
   } | null>(null);
 
+  // Prepare renderers for each item when enhanced mode is enabled
+  const getItemRenderer = useMemo(() => {
+    if (!enableEnhancedOutputHandling) return null;
+    return (item: unknown) => {
+      const metadata: OutputMetadata = {};
+      return globalRegistry.getRenderer(item, metadata);
+    };
+  }, [enableEnhancedOutputHandling]);
+
   const copyData = (pin: string, data: string) => {
     navigator.clipboard.writeText(data).then(() => {
       toast({
@@ -102,15 +120,31 @@ export default function DataTable({
                       <Clipboard size={18} />
                     </Button>
                   </div>
-                  {value.map((item, index) => (
-                    <React.Fragment key={index}>
-                      <ContentRenderer
-                        value={item}
-                        truncateLongData={truncateLongData}
-                      />
-                      {index < value.length - 1 && ", "}
-                    </React.Fragment>
-                  ))}
+                  {value.map((item, index) => {
+                    const renderer = getItemRenderer?.(item);
+                    if (enableEnhancedOutputHandling && renderer) {
+                      const metadata: OutputMetadata = {};
+                      return (
+                        <React.Fragment key={index}>
+                          <OutputItem
+                            value={item}
+                            metadata={metadata}
+                            renderer={renderer}
+                          />
+                          {index < value.length - 1 && ", "}
+                        </React.Fragment>
+                      );
+                    }
+                    return (
+                      <React.Fragment key={index}>
+                        <ContentRenderer
+                          value={item}
+                          truncateLongData={truncateLongData}
+                        />
+                        {index < value.length - 1 && ", "}
+                      </React.Fragment>
+                    );
+                  })}
                 </div>
               </TableCell>
             </TableRow>

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/Flow/Flow.tsx

@@ -29,13 +29,17 @@ import "@xyflow/react/dist/style.css";
 import { ConnectedEdge, CustomNode } from "../CustomNode/CustomNode";
 import "./flow.css";
 import {
+  BlockIORootSchema,
   BlockUIType,
   formatEdgeID,
   GraphExecutionID,
   GraphID,
   GraphMeta,
   LibraryAgent,
+  SpecialBlockID,
 } from "@/lib/autogpt-server-api";
+import { getV1GetSpecificGraph } from "@/app/api/__generated__/endpoints/graphs/graphs";
+import { okData } from "@/app/api/helpers";
 import { IncompatibilityInfo } from "../../../hooks/useSubAgentUpdate/types";
 import { Key, storage } from "@/services/storage/local-storage";
 import { findNewlyAddedBlockCoordinates, getTypeColor } from "@/lib/utils";
@@ -687,8 +691,94 @@ const FlowEditor: React.FC<{
     [getNode, updateNode, nodes],
   );
 
+  /* Shared helper to create and add a node */
+  const createAndAddNode = useCallback(
+    async (
+      blockID: string,
+      blockName: string,
+      hardcodedValues: Record<string, any>,
+      position: { x: number; y: number },
+    ): Promise<CustomNode | null> => {
+      const nodeSchema = availableBlocks.find((node) => node.id === blockID);
+      if (!nodeSchema) {
+        console.error(`Schema not found for block ID: ${blockID}`);
+        return null;
+      }
+
+      // For agent blocks, fetch the full graph to get schemas
+      let inputSchema: BlockIORootSchema = nodeSchema.inputSchema;
+      let outputSchema: BlockIORootSchema = nodeSchema.outputSchema;
+      let finalHardcodedValues = hardcodedValues;
+
+      if (blockID === SpecialBlockID.AGENT) {
+        const graphID = hardcodedValues.graph_id as string;
+        const graphVersion = hardcodedValues.graph_version as number;
+        const graphData = okData(
+          await getV1GetSpecificGraph(graphID, { version: graphVersion }),
+        );
+
+        if (graphData) {
+          inputSchema = graphData.input_schema as BlockIORootSchema;
+          outputSchema = graphData.output_schema as BlockIORootSchema;
+          finalHardcodedValues = {
+            ...hardcodedValues,
+            input_schema: graphData.input_schema,
+            output_schema: graphData.output_schema,
+          };
+        } else {
+          console.error("Failed to fetch graph data for agent block");
+        }
+      }
+
+      const newNode: CustomNode = {
+        id: nodeId.toString(),
+        type: "custom",
+        position,
+        data: {
+          blockType: blockName,
+          blockCosts: nodeSchema.costs || [],
+          title: `${blockName} ${nodeId}`,
+          description: nodeSchema.description,
+          categories: nodeSchema.categories,
+          inputSchema: inputSchema,
+          outputSchema: outputSchema,
+          hardcodedValues: finalHardcodedValues,
+          connections: [],
+          isOutputOpen: false,
+          block_id: blockID,
+          isOutputStatic: nodeSchema.staticOutput,
+          uiType: nodeSchema.uiType,
+        },
+      };
+
+      addNodes(newNode);
+      setNodeId((prevId) => prevId + 1);
+      clearNodesStatusAndOutput();
+
+      history.push({
+        type: "ADD_NODE",
+        payload: { node: { ...newNode, ...newNode.data } },
+        undo: () => deleteElements({ nodes: [{ id: newNode.id }] }),
+        redo: () => addNodes(newNode),
+      });
+
+      return newNode;
+    },
+    [
+      availableBlocks,
+      nodeId,
+      addNodes,
+      deleteElements,
+      clearNodesStatusAndOutput,
+    ],
+  );
+
   const addNode = useCallback(
-    (blockId: string, nodeType: string, hardcodedValues: any = {}) => {
+    async (
+      blockId: string,
+      nodeType: string,
+      hardcodedValues: Record<string, any> = {},
+    ) => {
       const nodeSchema = availableBlocks.find((node) => node.id === blockId);
       if (!nodeSchema) {
         console.error(`Schema not found for block ID: ${blockId}`);
@@ -707,73 +797,42 @@ const FlowEditor: React.FC<{
       // Alternative: We could also use D3 force, Intersection for this (React flow Pro examples)
 
       const { x, y } = getViewport();
-      const viewportCoordinates =
+      const position =
         nodeDimensions && Object.keys(nodeDimensions).length > 0
-          ? // we will get all the dimension of nodes, then store
-            findNewlyAddedBlockCoordinates(
+          ? findNewlyAddedBlockCoordinates(
               nodeDimensions,
               nodeSchema.uiType == BlockUIType.NOTE ? 300 : 500,
               60,
               1.0,
             )
-          : // we will get all the dimension of nodes, then store
-            {
+          : {
               x: window.innerWidth / 2 - x,
               y: window.innerHeight / 2 - y,
             };
 
-      const newNode: CustomNode = {
-        id: nodeId.toString(),
-        type: "custom",
-        position: viewportCoordinates, // Set the position to the calculated viewport center
-        data: {
-          blockType: nodeType,
-          blockCosts: nodeSchema.costs,
-          title: `${nodeType} ${nodeId}`,
-          description: nodeSchema.description,
-          categories: nodeSchema.categories,
-          inputSchema: nodeSchema.inputSchema,
-          outputSchema: nodeSchema.outputSchema,
-          hardcodedValues: hardcodedValues,
-          connections: [],
-          isOutputOpen: false,
-          block_id: blockId,
-          isOutputStatic: nodeSchema.staticOutput,
-          uiType: nodeSchema.uiType,
-        },
-      };
-
-      addNodes(newNode);
-      setNodeId((prevId) => prevId + 1);
-      clearNodesStatusAndOutput(); // Clear status and output when a new node is added
+      const newNode = await createAndAddNode(
+        blockId,
+        nodeType,
+        hardcodedValues,
+        position,
+      );
+      if (!newNode) return;
 
       setViewport(
         {
-          // Rough estimate of the dimension of the node is: 500x400px.
-          // Though we skip shifting the X, considering the block menu side-bar.
-          x: -viewportCoordinates.x * 0.8 + (window.innerWidth - 0.0) / 2,
-          y: -viewportCoordinates.y * 0.8 + (window.innerHeight - 400) / 2,
+          x: -position.x * 0.8 + (window.innerWidth - 0.0) / 2,
+          y: -position.y * 0.8 + (window.innerHeight - 400) / 2,
           zoom: 0.8,
         },
         { duration: 500 },
       );
-
-      history.push({
-        type: "ADD_NODE",
-        payload: { node: { ...newNode, ...newNode.data } },
-        undo: () => deleteElements({ nodes: [{ id: newNode.id }] }),
-        redo: () => addNodes(newNode),
-      });
     },
     [
-      nodeId,
       getViewport,
       setViewport,
       availableBlocks,
-      addNodes,
       nodeDimensions,
-      deleteElements,
-      clearNodesStatusAndOutput,
+      createAndAddNode,
     ],
   );
 
@@ -920,7 +979,7 @@ const FlowEditor: React.FC<{
   }, []);
 
   const onDrop = useCallback(
-    (event: React.DragEvent) => {
+    async (event: React.DragEvent) => {
       event.preventDefault();
 
       const blockData = event.dataTransfer.getData("application/reactflow");
@@ -935,62 +994,17 @@ const FlowEditor: React.FC<{
           y: event.clientY,
         });
 
-        // Find the block schema
-        const nodeSchema = availableBlocks.find((node) => node.id === blockId);
-        if (!nodeSchema) {
-          console.error(`Schema not found for block ID: ${blockId}`);
-          return;
-        }
-
-        // Create the new node at the drop position
-        const newNode: CustomNode = {
-          id: nodeId.toString(),
-          type: "custom",
+        await createAndAddNode(
+          blockId,
+          blockName,
+          hardcodedValues || {},
           position,
-          data: {
-            blockType: blockName,
-            blockCosts: nodeSchema.costs || [],
-            title: `${blockName} ${nodeId}`,
-            description: nodeSchema.description,
-            categories: nodeSchema.categories,
-            inputSchema: nodeSchema.inputSchema,
-            outputSchema: nodeSchema.outputSchema,
-            hardcodedValues: hardcodedValues,
-            connections: [],
-            isOutputOpen: false,
-            block_id: blockId,
-            uiType: nodeSchema.uiType,
-          },
-        };
-
-        history.push({
-          type: "ADD_NODE",
-          payload: { node: { ...newNode, ...newNode.data } },
-          undo: () => {
-            deleteElements({ nodes: [{ id: newNode.id } as any], edges: [] });
-          },
-          redo: () => {
-            addNodes([newNode]);
-          },
-        });
-        addNodes([newNode]);
-        clearNodesStatusAndOutput();
-
-        setNodeId((prevId) => prevId + 1);
+        );
       } catch (error) {
         console.error("Failed to drop block:", error);
       }
     },
-    [
-      nodeId,
-      availableBlocks,
-      nodes,
-      edges,
-      addNodes,
-      screenToFlowPosition,
-      deleteElements,
-      clearNodesStatusAndOutput,
-    ],
+    [screenToFlowPosition, createAndAddNode],
   );
 
   const buildContextValue: BuilderContextType = useMemo(

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeOutputs.tsx

@@ -1,8 +1,14 @@
-import React, { useContext, useState } from "react";
+import React, { useContext, useMemo, useState } from "react";
 import { Button } from "@/components/__legacy__/ui/button";
 import { Maximize2 } from "lucide-react";
 import * as Separator from "@radix-ui/react-separator";
 import { ContentRenderer } from "@/components/__legacy__/ui/render";
+import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
+import {
+  globalRegistry,
+  OutputItem,
+} from "@/components/contextual/OutputRenderers";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 
 import { beautifyString } from "@/lib/utils";
 
@@ -21,6 +27,9 @@ export default function NodeOutputs({
   data,
 }: NodeOutputsProps) {
   const builderContext = useContext(BuilderContext);
+  const enableEnhancedOutputHandling = useGetFlag(
+    Flag.ENABLE_ENHANCED_OUTPUT_HANDLING,
+  );
 
   const [expandedDialog, setExpandedDialog] = useState<{
     isOpen: boolean;
@@ -37,6 +46,15 @@ export default function NodeOutputs({
 
   const { getNodeTitle } = builderContext;
 
+  // Prepare renderers for each item when enhanced mode is enabled
+  const getItemRenderer = useMemo(() => {
+    if (!enableEnhancedOutputHandling) return null;
+    return (item: unknown) => {
+      const metadata: OutputMetadata = {};
+      return globalRegistry.getRenderer(item, metadata);
+    };
+  }, [enableEnhancedOutputHandling]);
+
   const getBeautifiedPinName = (pin: string) => {
     if (!pin.startsWith("tools_^_")) {
       return beautifyString(pin);
@@ -87,15 +105,31 @@ export default function NodeOutputs({
           <div className="mt-2">
             <strong className="mr-2">Data:</strong>
             <div className="mt-1">
-              {dataArray.slice(0, 10).map((item, index) => (
-                <React.Fragment key={index}>
-                  <ContentRenderer
-                    value={item}
-                    truncateLongData={truncateLongData}
-                  />
-                  {index < Math.min(dataArray.length, 10) - 1 && ", "}
-                </React.Fragment>
-              ))}
+              {dataArray.slice(0, 10).map((item, index) => {
+                const renderer = getItemRenderer?.(item);
+                if (enableEnhancedOutputHandling && renderer) {
+                  const metadata: OutputMetadata = {};
+                  return (
+                    <React.Fragment key={index}>
+                      <OutputItem
+                        value={item}
+                        metadata={metadata}
+                        renderer={renderer}
+                      />
+                      {index < Math.min(dataArray.length, 10) - 1 && ", "}
+                    </React.Fragment>
+                  );
+                }
+                return (
+                  <React.Fragment key={index}>
+                    <ContentRenderer
+                      value={item}
+                      truncateLongData={truncateLongData}
+                    />
+                    {index < Math.min(dataArray.length, 10) - 1 && ", "}
+                  </React.Fragment>
+                );
+              })}
               {dataArray.length > 10 && (
                 <span style={{ color: "#888" }}>
                   <br />

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/RunnerInputUI.tsx

@@ -4,13 +4,13 @@ import { AgentRunDraftView } from "@/app/(platform)/library/agents/[id]/componen
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import type {
   CredentialsMetaInput,
-  GraphMeta,
+  Graph,
 } from "@/lib/autogpt-server-api/types";
 
 interface RunInputDialogProps {
   isOpen: boolean;
   doClose: () => void;
-  graph: GraphMeta;
+  graph: Graph;
   doRun?: (
     inputs: Record<string, any>,
     credentialsInputs: Record<string, CredentialsMetaInput>,

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/RunnerUIWrapper.tsx

@@ -9,13 +9,13 @@ import { CustomNodeData } from "@/app/(platform)/build/components/legacy-builder
 import {
   BlockUIType,
   CredentialsMetaInput,
-  GraphMeta,
+  Graph,
 } from "@/lib/autogpt-server-api/types";
 import RunnerOutputUI, { OutputNodeInfo } from "./RunnerOutputUI";
 import { RunnerInputDialog } from "./RunnerInputUI";
 
 interface RunnerUIWrapperProps {
-  graph: GraphMeta;
+  graph: Graph;
   nodes: Node<CustomNodeData>[];
   graphExecutionError?: string | null;
   saveAndRun: (

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSubAgentUpdate/helpers.ts

@@ -1,5 +1,5 @@
 import { GraphInputSchema } from "@/lib/autogpt-server-api";
-import { GraphMetaLike, IncompatibilityInfo } from "./types";
+import { GraphLike, IncompatibilityInfo } from "./types";
 
 // Helper type for schema properties - the generated types are too loose
 type SchemaProperties = Record<string, GraphInputSchema["properties"][string]>;
@@ -36,7 +36,7 @@ export function getSchemaRequired(schema: unknown): SchemaRequired {
  */
 export function createUpdatedAgentNodeInputs(
   currentInputs: Record<string, unknown>,
-  latestSubGraphVersion: GraphMetaLike,
+  latestSubGraphVersion: GraphLike,
 ): Record<string, unknown> {
   return {
     ...currentInputs,

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSubAgentUpdate/types.ts

@@ -1,7 +1,11 @@
-import type { GraphMeta as LegacyGraphMeta } from "@/lib/autogpt-server-api";
+import type {
+  Graph as LegacyGraph,
+  GraphMeta as LegacyGraphMeta,
+} from "@/lib/autogpt-server-api";
+import type { GraphModel as GeneratedGraph } from "@/app/api/__generated__/models/graphModel";
 import type { GraphMeta as GeneratedGraphMeta } from "@/app/api/__generated__/models/graphMeta";
 
-export type SubAgentUpdateInfo<T extends GraphMetaLike = GraphMetaLike> = {
+export type SubAgentUpdateInfo<T extends GraphLike = GraphLike> = {
   hasUpdate: boolean;
   currentVersion: number;
   latestVersion: number;
@@ -10,7 +14,10 @@ export type SubAgentUpdateInfo<T extends GraphMetaLike = GraphMetaLike> = {
   incompatibilities: IncompatibilityInfo | null;
 };
 
-// Union type for GraphMeta that works with both legacy and new builder
+// Union type for Graph (with schemas) that works with both legacy and new builder
+export type GraphLike = LegacyGraph | GeneratedGraph;
+
+// Union type for GraphMeta (without schemas) for version detection
 export type GraphMetaLike = LegacyGraphMeta | GeneratedGraphMeta;
 
 export type IncompatibilityInfo = {

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSubAgentUpdate/useSubAgentUpdate.ts

@@ -1,5 +1,11 @@
 import { useMemo } from "react";
-import { GraphInputSchema, GraphOutputSchema } from "@/lib/autogpt-server-api";
+import type {
+  GraphInputSchema,
+  GraphOutputSchema,
+} from "@/lib/autogpt-server-api";
+import type { GraphModel } from "@/app/api/__generated__/models/graphModel";
+import { useGetV1GetSpecificGraph } from "@/app/api/__generated__/endpoints/graphs/graphs";
+import { okData } from "@/app/api/helpers";
 import { getEffectiveType } from "@/lib/utils";
 import { EdgeLike, getSchemaProperties, getSchemaRequired } from "./helpers";
 import {
@@ -11,26 +17,38 @@ import {
 /**
  * Checks if a newer version of a sub-agent is available and determines compatibility
  */
-export function useSubAgentUpdate<T extends GraphMetaLike>(
+export function useSubAgentUpdate(
   nodeID: string,
   graphID: string | undefined,
   graphVersion: number | undefined,
   currentInputSchema: GraphInputSchema | undefined,
   currentOutputSchema: GraphOutputSchema | undefined,
   connections: EdgeLike[],
-  availableGraphs: T[],
-): SubAgentUpdateInfo<T> {
+  availableGraphs: GraphMetaLike[],
+): SubAgentUpdateInfo<GraphModel> {
   // Find the latest version of the same graph
-  const latestGraph = useMemo(() => {
+  const latestGraphInfo = useMemo(() => {
     if (!graphID) return null;
     return availableGraphs.find((graph) => graph.id === graphID) || null;
   }, [graphID, availableGraphs]);
 
-  // Check if there's an update available
+  // Check if there's a newer version available
   const hasUpdate = useMemo(() => {
-    if (!latestGraph || graphVersion === undefined) return false;
-    return latestGraph.version! > graphVersion;
-  }, [latestGraph, graphVersion]);
+    if (!latestGraphInfo || graphVersion === undefined) return false;
+    return latestGraphInfo.version! > graphVersion;
+  }, [latestGraphInfo, graphVersion]);
+
+  // Fetch full graph IF an update is detected
+  const { data: latestGraph } = useGetV1GetSpecificGraph(
+    graphID ?? "",
+    { version: latestGraphInfo?.version },
+    {
+      query: {
+        enabled: hasUpdate && !!graphID && !!latestGraphInfo?.version,
+        select: okData,
+      },
+    },
+  );
 
   // Get connected input and output handles for this specific node
   const connectedHandles = useMemo(() => {
@@ -152,8 +170,8 @@ export function useSubAgentUpdate<T extends GraphMetaLike>(
   return {
     hasUpdate,
     currentVersion: graphVersion || 0,
-    latestVersion: latestGraph?.version || 0,
-    latestGraph,
+    latestVersion: latestGraphInfo?.version || 0,
+    latestGraph: latestGraph || null,
     isCompatible: compatibilityResult.isCompatible,
     incompatibilities: compatibilityResult.incompatibilities,
   };

autogpt_platform/frontend/src/app/(platform)/build/stores/graphStore.ts

@@ -18,7 +18,7 @@ interface GraphStore {
     outputSchema: Record<string, any> | null,
   ) => void;
 
-  // Available graphs; used for sub-graph updates
+  // Available graphs; used for sub-graph updated version detection
   availableSubGraphs: GraphMeta[];
   setAvailableSubGraphs: (graphs: GraphMeta[]) => void;
 

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/ChatContainer/ChatContainer.tsx

@@ -1,71 +0,0 @@
-"use client";
-import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
-import { UIDataTypes, UIMessage, UITools } from "ai";
-import { LayoutGroup, motion } from "framer-motion";
-import { ChatMessagesContainer } from "../ChatMessagesContainer/ChatMessagesContainer";
-import { CopilotChatActionsProvider } from "../CopilotChatActionsProvider/CopilotChatActionsProvider";
-import { EmptySession } from "../EmptySession/EmptySession";
-
-export interface ChatContainerProps {
-  messages: UIMessage<unknown, UIDataTypes, UITools>[];
-  status: string;
-  error: Error | undefined;
-  sessionId: string | null;
-  isLoadingSession: boolean;
-  isCreatingSession: boolean;
-  onCreateSession: () => void | Promise<string>;
-  onSend: (message: string) => void | Promise<void>;
-}
-export const ChatContainer = ({
-  messages,
-  status,
-  error,
-  sessionId,
-  isLoadingSession,
-  isCreatingSession,
-  onCreateSession,
-  onSend,
-}: ChatContainerProps) => {
-  const inputLayoutId = "copilot-2-chat-input";
-
-  return (
-    <CopilotChatActionsProvider onSend={onSend}>
-      <LayoutGroup id="copilot-2-chat-layout">
-        <div className="flex h-full min-h-0 w-full flex-col bg-[#f8f8f9] px-2 lg:px-0">
-          {sessionId ? (
-            <div className="mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col">
-              <ChatMessagesContainer
-                messages={messages}
-                status={status}
-                error={error}
-                isLoading={isLoadingSession}
-              />
-              <motion.div
-                layoutId={inputLayoutId}
-                transition={{ type: "spring", bounce: 0.2, duration: 0.65 }}
-                className="relative px-3 pb-2 pt-2"
-              >
-                <div className="pointer-events-none absolute left-0 right-0 top-[-18px] z-10 h-6 bg-gradient-to-b from-transparent to-[#f8f8f9]" />
-                <ChatInput
-                  inputId="chat-input-session"
-                  onSend={onSend}
-                  disabled={status === "streaming"}
-                  isStreaming={status === "streaming"}
-                  onStop={() => {}}
-                  placeholder="What else can I help with?"
-                />
-              </motion.div>
-            </div>
-          ) : (
-            <EmptySession
-              inputLayoutId={inputLayoutId}
-              isCreatingSession={isCreatingSession}
-              onCreateSession={onCreateSession}
-              onSend={onSend}
-            />
-          )}
-        </div>
-      </LayoutGroup>
-    </CopilotChatActionsProvider>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/ChatMessagesContainer/ChatMessagesContainer.tsx

@@ -1,144 +0,0 @@
-import {
-  Conversation,
-  ConversationContent,
-  ConversationScrollButton,
-} from "@/components/ai-elements/conversation";
-import {
-  Message,
-  MessageContent,
-  MessageResponse,
-} from "@/components/ai-elements/message";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { UIDataTypes, UIMessage, UITools, ToolUIPart } from "ai";
-import { FindBlocksTool } from "../../tools/FindBlocks/FindBlocks";
-import { FindAgentsTool } from "../../tools/FindAgents/FindAgents";
-import { SearchDocsTool } from "../../tools/SearchDocs/SearchDocs";
-import { RunBlockTool } from "../../tools/RunBlock/RunBlock";
-import { RunAgentTool } from "../../tools/RunAgent/RunAgent";
-import { ViewAgentOutputTool } from "../../tools/ViewAgentOutput/ViewAgentOutput";
-import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
-import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
-
-interface ChatMessagesContainerProps {
-  messages: UIMessage<unknown, UIDataTypes, UITools>[];
-  status: string;
-  error: Error | undefined;
-  isLoading: boolean;
-}
-
-export const ChatMessagesContainer = ({
-  messages,
-  status,
-  error,
-  isLoading,
-}: ChatMessagesContainerProps) => {
-  return (
-    <Conversation className="min-h-0 flex-1">
-      <ConversationContent className="gap-6 px-3 py-6">
-        {isLoading && messages.length === 0 && (
-          <div className="flex flex-1 items-center justify-center">
-            <LoadingSpinner size="large" className="text-neutral-400" />
-          </div>
-        )}
-        {messages.map((message) => (
-          <Message from={message.role} key={message.id}>
-            <MessageContent
-              className={
-                "text-[1rem] leading-relaxed " +
-                "group-[.is-user]:rounded-xl group-[.is-user]:bg-purple-100 group-[.is-user]:px-3 group-[.is-user]:py-2.5 group-[.is-user]:text-slate-900 group-[.is-user]:[border-bottom-right-radius:0] " +
-                "group-[.is-assistant]:bg-transparent group-[.is-assistant]:text-slate-900"
-              }
-            >
-              {message.parts.map((part, i) => {
-                switch (part.type) {
-                  case "text":
-                    return (
-                      <MessageResponse key={`${message.id}-${i}`}>
-                        {part.text}
-                      </MessageResponse>
-                    );
-                  case "tool-find_block":
-                    return (
-                      <FindBlocksTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  case "tool-find_agent":
-                  case "tool-find_library_agent":
-                    return (
-                      <FindAgentsTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  case "tool-search_docs":
-                  case "tool-get_doc_page":
-                    return (
-                      <SearchDocsTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  case "tool-run_block":
-                    return (
-                      <RunBlockTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  case "tool-run_agent":
-                  case "tool-schedule_agent":
-                    return (
-                      <RunAgentTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  case "tool-create_agent":
-                    return (
-                      <CreateAgentTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  case "tool-edit_agent":
-                    return (
-                      <EditAgentTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  case "tool-view_agent_output":
-                    return (
-                      <ViewAgentOutputTool
-                        key={`${message.id}-${i}`}
-                        part={part as ToolUIPart}
-                      />
-                    );
-                  default:
-                    return null;
-                }
-              })}
-            </MessageContent>
-          </Message>
-        ))}
-        {status === "submitted" && (
-          <Message from="assistant">
-            <MessageContent className="text-[1rem] leading-relaxed">
-              <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
-                Thinking...
-              </span>
-            </MessageContent>
-          </Message>
-        )}
-        {error && (
-          <div className="rounded-lg bg-red-50 p-3 text-red-600">
-            Error: {error.message}
-          </div>
-        )}
-      </ConversationContent>
-      <ConversationScrollButton />
-    </Conversation>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/ChatSidebar/ChatSidebar.tsx

@@ -1,191 +0,0 @@
-"use client";
-import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import {
-  Sidebar,
-  SidebarContent,
-  SidebarFooter,
-  SidebarHeader,
-  SidebarTrigger,
-  useSidebar,
-} from "@/components/ui/sidebar";
-import { cn } from "@/lib/utils";
-import {
-  PlusCircleIcon,
-  PlusIcon,
-  SpinnerGapIcon,
-} from "@phosphor-icons/react";
-import { motion } from "framer-motion";
-import { parseAsString, useQueryState } from "nuqs";
-
-export function ChatSidebar() {
-  const { state } = useSidebar();
-  const isCollapsed = state === "collapsed";
-  const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
-
-  const { data: sessionsResponse, isLoading: isLoadingSessions } =
-    useGetV2ListSessions({ limit: 50 });
-
-  const sessions =
-    sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
-
-  function handleNewChat() {
-    setSessionId(null);
-  }
-
-  function handleSelectSession(id: string) {
-    setSessionId(id);
-  }
-
-  function formatDate(dateString: string) {
-    const date = new Date(dateString);
-    const now = new Date();
-    const diffMs = now.getTime() - date.getTime();
-    const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
-
-    if (diffDays === 0) return "Today";
-    if (diffDays === 1) return "Yesterday";
-    if (diffDays < 7) return `${diffDays} days ago`;
-
-    const day = date.getDate();
-    const ordinal =
-      day % 10 === 1 && day !== 11
-        ? "st"
-        : day % 10 === 2 && day !== 12
-          ? "nd"
-          : day % 10 === 3 && day !== 13
-            ? "rd"
-            : "th";
-    const month = date.toLocaleDateString("en-US", { month: "short" });
-    const year = date.getFullYear();
-
-    return `${day}${ordinal} ${month} ${year}`;
-  }
-
-  return (
-    <Sidebar
-      variant="inset"
-      collapsible="icon"
-      className="!top-[50px] !h-[calc(100vh-50px)] border-r border-zinc-100 px-0"
-    >
-      {isCollapsed && (
-        <SidebarHeader
-          className={cn(
-            "flex",
-            isCollapsed
-              ? "flex-row items-center justify-between gap-y-4 md:flex-col md:items-start md:justify-start"
-              : "flex-row items-center justify-between",
-          )}
-        >
-          <motion.div
-            key={isCollapsed ? "header-collapsed" : "header-expanded"}
-            className="flex flex-col items-center gap-3 pt-4"
-            initial={{ opacity: 0, filter: "blur(3px)" }}
-            animate={{ opacity: 1, filter: "blur(0px)" }}
-            transition={{ type: "spring", bounce: 0.2 }}
-          >
-            <div className="flex flex-col items-center gap-2">
-              <SidebarTrigger />
-              <Button
-                variant="ghost"
-                onClick={handleNewChat}
-                style={{ minWidth: "auto", width: "auto" }}
-              >
-                <PlusCircleIcon className="!size-5" />
-                <span className="sr-only">New Chat</span>
-              </Button>
-            </div>
-          </motion.div>
-        </SidebarHeader>
-      )}
-      <SidebarContent className="gap-4 overflow-y-auto px-4 py-4 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
-        {!isCollapsed && (
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.1 }}
-            className="flex items-center justify-between px-3"
-          >
-            <Text variant="h3" size="body-medium">
-              Your chats
-            </Text>
-            <div className="relative left-6">
-              <SidebarTrigger />
-            </div>
-          </motion.div>
-        )}
-
-        {!isCollapsed && (
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.15 }}
-            className="mt-4 flex flex-col gap-1"
-          >
-            {isLoadingSessions ? (
-              <div className="flex items-center justify-center py-4">
-                <SpinnerGapIcon className="h-5 w-5 animate-spin text-neutral-400" />
-              </div>
-            ) : sessions.length === 0 ? (
-              <p className="py-4 text-center text-sm text-neutral-500">
-                No conversations yet
-              </p>
-            ) : (
-              sessions.map((session) => (
-                <button
-                  key={session.id}
-                  onClick={() => handleSelectSession(session.id)}
-                  className={cn(
-                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
-                    session.id === sessionId
-                      ? "bg-zinc-100"
-                      : "hover:bg-zinc-50",
-                  )}
-                >
-                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
-                    <div className="min-w-0 max-w-full">
-                      <Text
-                        variant="body"
-                        className={cn(
-                          "truncate font-normal",
-                          session.id === sessionId
-                            ? "text-zinc-600"
-                            : "text-zinc-800",
-                        )}
-                      >
-                        {session.title || `Untitled chat`}
-                      </Text>
-                    </div>
-                    <Text variant="small" className="text-neutral-400">
-                      {formatDate(session.updated_at)}
-                    </Text>
-                  </div>
-                </button>
-              ))
-            )}
-          </motion.div>
-        )}
-      </SidebarContent>
-      {!isCollapsed && sessionId && (
-        <SidebarFooter className="shrink-0 bg-zinc-50 p-3 pb-1 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.2 }}
-          >
-            <Button
-              variant="primary"
-              size="small"
-              onClick={handleNewChat}
-              className="w-full"
-              leftIcon={<PlusIcon className="h-4 w-4" weight="bold" />}
-            >
-              New Chat
-            </Button>
-          </motion.div>
-        </SidebarFooter>
-      )}
-    </Sidebar>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx

@@ -1,16 +0,0 @@
-"use client";
-
-import { CopilotChatActionsContext } from "./useCopilotChatActions";
-
-interface Props {
-  onSend: (message: string) => void | Promise<void>;
-  children: React.ReactNode;
-}
-
-export function CopilotChatActionsProvider({ onSend, children }: Props) {
-  return (
-    <CopilotChatActionsContext.Provider value={{ onSend }}>
-      {children}
-    </CopilotChatActionsContext.Provider>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/CopilotChatActionsProvider/useCopilotChatActions.ts

@@ -1,23 +0,0 @@
-"use client";
-
-import { createContext, useContext } from "react";
-
-interface CopilotChatActions {
-  onSend: (message: string) => void | Promise<void>;
-}
-
-const CopilotChatActionsContext = createContext<CopilotChatActions | null>(
-  null,
-);
-
-export function useCopilotChatActions(): CopilotChatActions {
-  const ctx = useContext(CopilotChatActionsContext);
-  if (!ctx) {
-    throw new Error(
-      "useCopilotChatActions must be used within CopilotChatActionsProvider",
-    );
-  }
-  return ctx;
-}
-
-export { CopilotChatActionsContext };

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/EmptySession/EmptySession.tsx

@@ -1,111 +0,0 @@
-"use client";
-
-import {
-  getGreetingName,
-  getInputPlaceholder,
-  getQuickActions,
-} from "@/app/(platform)/copilot/helpers";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { SpinnerGapIcon } from "@phosphor-icons/react";
-import { motion } from "framer-motion";
-import { useEffect, useState } from "react";
-
-interface Props {
-  inputLayoutId: string;
-  isCreatingSession: boolean;
-  onCreateSession: () => void | Promise<string>;
-  onSend: (message: string) => void | Promise<void>;
-}
-
-export function EmptySession({
-  inputLayoutId,
-  isCreatingSession,
-  onSend,
-}: Props) {
-  const { user } = useSupabase();
-  const greetingName = getGreetingName(user);
-  const quickActions = getQuickActions();
-  const [loadingAction, setLoadingAction] = useState<string | null>(null);
-  const [inputPlaceholder, setInputPlaceholder] = useState(
-    getInputPlaceholder(),
-  );
-
-  useEffect(() => {
-    setInputPlaceholder(getInputPlaceholder(window.innerWidth));
-  }, [window.innerWidth]);
-
-  async function handleQuickActionClick(action: string) {
-    if (isCreatingSession || loadingAction) return;
-
-    setLoadingAction(action);
-    try {
-      await onSend(action);
-    } finally {
-      setLoadingAction(null);
-    }
-  }
-
-  return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-0 py-5 md:px-6 md:py-10">
-      <motion.div
-        className="w-full max-w-3xl text-center"
-        initial={{ opacity: 0, y: 14, filter: "blur(6px)" }}
-        animate={{ opacity: 1, y: 0, filter: "blur(0px)" }}
-        transition={{ type: "spring", bounce: 0.2, duration: 0.7 }}
-      >
-        <div className="mx-auto max-w-3xl">
-          <Text variant="h3" className="mb-1 !text-[1.375rem] text-zinc-700">
-            Hey, <span className="text-violet-600">{greetingName}</span>
-          </Text>
-          <Text variant="h3" className="mb-8 !font-normal">
-            Tell me about your work — I&apos;ll find what to automate.
-          </Text>
-
-          <div className="mb-6">
-            <motion.div
-              layoutId={inputLayoutId}
-              transition={{ type: "spring", bounce: 0.2, duration: 0.65 }}
-              className="w-full px-2"
-            >
-              <ChatInput
-                inputId="chat-input-empty"
-                onSend={onSend}
-                disabled={isCreatingSession}
-                placeholder={inputPlaceholder}
-                className="w-full"
-              />
-            </motion.div>
-          </div>
-        </div>
-
-        <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
-          {quickActions.map((action) => (
-            <Button
-              key={action}
-              type="button"
-              variant="outline"
-              size="small"
-              onClick={() => void handleQuickActionClick(action)}
-              disabled={isCreatingSession || loadingAction !== null}
-              aria-busy={loadingAction === action}
-              leftIcon={
-                loadingAction === action ? (
-                  <SpinnerGapIcon
-                    className="h-4 w-4 animate-spin"
-                    weight="bold"
-                  />
-                ) : null
-              }
-              className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
-            >
-              {action}
-            </Button>
-          ))}
-        </div>
-      </motion.div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/EmptySession/helpers.ts

@@ -1,11 +0,0 @@
-export function getInputPlaceholder(width?: number) {
-  if (!width) return "What's your role and what eats up most of your day?";
-
-  if (width < 500) {
-    return "I'm a chef and I hate...";
-  }
-  if (width <= 1080) {
-    return "What's your role and what eats up most of your day?";
-  }
-  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/MobileDrawer/MobileDrawer.tsx

@@ -1,140 +0,0 @@
-import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { scrollbarStyles } from "@/components/styles/scrollbars";
-import { cn } from "@/lib/utils";
-import { PlusIcon, SpinnerGapIcon, X } from "@phosphor-icons/react";
-import { Drawer } from "vaul";
-
-interface Props {
-  isOpen: boolean;
-  sessions: SessionSummaryResponse[];
-  currentSessionId: string | null;
-  isLoading: boolean;
-  onSelectSession: (sessionId: string) => void;
-  onNewChat: () => void;
-  onClose: () => void;
-  onOpenChange: (open: boolean) => void;
-}
-
-function formatDate(dateString: string) {
-  const date = new Date(dateString);
-  const now = new Date();
-  const diffMs = now.getTime() - date.getTime();
-  const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
-
-  if (diffDays === 0) return "Today";
-  if (diffDays === 1) return "Yesterday";
-  if (diffDays < 7) return `${diffDays} days ago`;
-
-  const day = date.getDate();
-  const ordinal =
-    day % 10 === 1 && day !== 11
-      ? "st"
-      : day % 10 === 2 && day !== 12
-        ? "nd"
-        : day % 10 === 3 && day !== 13
-          ? "rd"
-          : "th";
-  const month = date.toLocaleDateString("en-US", { month: "short" });
-  const year = date.getFullYear();
-
-  return `${day}${ordinal} ${month} ${year}`;
-}
-
-export function MobileDrawer({
-  isOpen,
-  sessions,
-  currentSessionId,
-  isLoading,
-  onSelectSession,
-  onNewChat,
-  onClose,
-  onOpenChange,
-}: Props) {
-  return (
-    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
-      <Drawer.Portal>
-        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
-        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
-          <div className="shrink-0 border-b border-zinc-200 px-4 py-2">
-            <div className="flex items-center justify-between">
-              <Drawer.Title className="text-lg font-semibold text-zinc-800">
-                Your chats
-              </Drawer.Title>
-              <Button
-                variant="icon"
-                size="icon"
-                aria-label="Close sessions"
-                onClick={onClose}
-              >
-                <X width="1rem" height="1rem" />
-              </Button>
-            </div>
-          </div>
-          <div
-            className={cn(
-              "flex min-h-0 flex-1 flex-col gap-1 overflow-y-auto px-3 py-3",
-              scrollbarStyles,
-            )}
-          >
-            {isLoading ? (
-              <div className="flex items-center justify-center py-4">
-                <SpinnerGapIcon className="h-5 w-5 animate-spin text-neutral-400" />
-              </div>
-            ) : sessions.length === 0 ? (
-              <p className="py-4 text-center text-sm text-neutral-500">
-                No conversations yet
-              </p>
-            ) : (
-              sessions.map((session) => (
-                <button
-                  key={session.id}
-                  onClick={() => onSelectSession(session.id)}
-                  className={cn(
-                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
-                    session.id === currentSessionId
-                      ? "bg-zinc-100"
-                      : "hover:bg-zinc-50",
-                  )}
-                >
-                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
-                    <div className="min-w-0 max-w-full">
-                      <Text
-                        variant="body"
-                        className={cn(
-                          "truncate font-normal",
-                          session.id === currentSessionId
-                            ? "text-zinc-600"
-                            : "text-zinc-800",
-                        )}
-                      >
-                        {session.title || "Untitled chat"}
-                      </Text>
-                    </div>
-                    <Text variant="small" className="text-neutral-400">
-                      {formatDate(session.updated_at)}
-                    </Text>
-                  </div>
-                </button>
-              ))
-            )}
-          </div>
-          {currentSessionId && (
-            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
-              <Button
-                variant="primary"
-                size="small"
-                onClick={onNewChat}
-                className="w-full"
-                leftIcon={<PlusIcon width="1rem" height="1rem" />}
-              >
-                New Chat
-              </Button>
-            </div>
-          )}
-        </Drawer.Content>
-      </Drawer.Portal>
-    </Drawer.Root>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/MobileHeader/MobileHeader.tsx

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

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/MorphingTextAnimation/MorphingTextAnimation.tsx

@@ -1,54 +0,0 @@
-import { cn } from "@/lib/utils";
-import { AnimatePresence, motion } from "framer-motion";
-
-interface Props {
-  text: string;
-  className?: string;
-}
-
-export function MorphingTextAnimation({ text, className }: Props) {
-  const letters = text.split("");
-
-  return (
-    <div className={cn(className)}>
-      <AnimatePresence mode="popLayout" initial={false}>
-        <motion.div key={text} className="whitespace-nowrap">
-          <motion.span className="inline-flex overflow-hidden">
-            {letters.map((char, index) => (
-              <motion.span
-                key={`${text}-${index}`}
-                initial={{
-                  opacity: 0,
-                  y: 8,
-                  rotateX: "80deg",
-                  filter: "blur(6px)",
-                }}
-                animate={{
-                  opacity: 1,
-                  y: 0,
-                  rotateX: "0deg",
-                  filter: "blur(0px)",
-                }}
-                exit={{
-                  opacity: 0,
-                  y: -8,
-                  rotateX: "-80deg",
-                  filter: "blur(6px)",
-                }}
-                style={{ willChange: "transform" }}
-                transition={{
-                  delay: 0.015 * index,
-                  type: "spring",
-                  bounce: 0.5,
-                }}
-                className="inline-block"
-              >
-                {char === " " ? "\u00A0" : char}
-              </motion.span>
-            ))}
-          </motion.span>
-        </motion.div>
-      </AnimatePresence>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/ToolAccordion/ToolAccordion.tsx

@@ -1,92 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { CaretDownIcon } from "@phosphor-icons/react";
-import { AnimatePresence, motion, useReducedMotion } from "framer-motion";
-import { useId } from "react";
-import { useToolAccordion } from "./useToolAccordion";
-
-interface Props {
-  badgeText: string;
-  title: React.ReactNode;
-  description?: React.ReactNode;
-  children: React.ReactNode;
-  className?: string;
-  defaultExpanded?: boolean;
-  expanded?: boolean;
-  onExpandedChange?: (expanded: boolean) => void;
-}
-
-export function ToolAccordion({
-  badgeText,
-  title,
-  description,
-  children,
-  className,
-  defaultExpanded,
-  expanded,
-  onExpandedChange,
-}: Props) {
-  const shouldReduceMotion = useReducedMotion();
-  const contentId = useId();
-  const { isExpanded, toggle } = useToolAccordion({
-    expanded,
-    defaultExpanded,
-    onExpandedChange,
-  });
-
-  return (
-    <div className={cn("mt-2 w-full rounded-lg border px-3 py-2", className)}>
-      <button
-        type="button"
-        aria-expanded={isExpanded}
-        aria-controls={contentId}
-        onClick={toggle}
-        className="flex w-full items-center justify-between gap-3 py-1 text-left"
-      >
-        <div className="flex min-w-0 items-center gap-2">
-          <span className="px-2 py-0.5 text-[11px] font-medium text-muted-foreground">
-            {badgeText}
-          </span>
-          <div className="min-w-0">
-            <p className="truncate text-sm font-medium text-foreground">
-              {title}
-            </p>
-            {description && (
-              <p className="truncate text-xs text-muted-foreground">
-                {description}
-              </p>
-            )}
-          </div>
-        </div>
-        <CaretDownIcon
-          className={cn(
-            "h-4 w-4 shrink-0 text-muted-foreground transition-transform",
-            isExpanded && "rotate-180",
-          )}
-          weight="bold"
-        />
-      </button>
-
-      <AnimatePresence initial={false}>
-        {isExpanded && (
-          <motion.div
-            id={contentId}
-            initial={{ height: 0, opacity: 0, filter: "blur(10px)" }}
-            animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
-            exit={{ height: 0, opacity: 0, filter: "blur(10px)" }}
-            transition={
-              shouldReduceMotion
-                ? { duration: 0 }
-                : { type: "spring", bounce: 0.35, duration: 0.55 }
-            }
-            className="overflow-hidden"
-            style={{ willChange: "height, opacity, filter" }}
-          >
-            <div className="pb-2 pt-3">{children}</div>
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/components/ToolAccordion/useToolAccordion.ts

@@ -1,32 +0,0 @@
-import { useState } from "react";
-
-interface UseToolAccordionOptions {
-  expanded?: boolean;
-  defaultExpanded?: boolean;
-  onExpandedChange?: (expanded: boolean) => void;
-}
-
-interface UseToolAccordionResult {
-  isExpanded: boolean;
-  toggle: () => void;
-}
-
-export function useToolAccordion({
-  expanded,
-  defaultExpanded = false,
-  onExpandedChange,
-}: UseToolAccordionOptions): UseToolAccordionResult {
-  const [uncontrolledExpanded, setUncontrolledExpanded] =
-    useState(defaultExpanded);
-
-  const isControlled = typeof expanded === "boolean";
-  const isExpanded = isControlled ? expanded : uncontrolledExpanded;
-
-  function toggle() {
-    const next = !isExpanded;
-    if (!isControlled) setUncontrolledExpanded(next);
-    onExpandedChange?.(next);
-  }
-
-  return { isExpanded, toggle };
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/helpers/convertChatSessionToUiMessages.ts

@@ -1,128 +0,0 @@
-import type { UIMessage, UIDataTypes, UITools } from "ai";
-
-interface SessionChatMessage {
-  role: string;
-  content: string | null;
-  tool_call_id: string | null;
-  tool_calls: unknown[] | null;
-}
-
-function coerceSessionChatMessages(
-  rawMessages: unknown[],
-): SessionChatMessage[] {
-  return rawMessages
-    .map((m) => {
-      if (!m || typeof m !== "object") return null;
-      const msg = m as Record<string, unknown>;
-
-      const role = typeof msg.role === "string" ? msg.role : null;
-      if (!role) return null;
-
-      return {
-        role,
-        content:
-          typeof msg.content === "string"
-            ? msg.content
-            : msg.content == null
-              ? null
-              : String(msg.content),
-        tool_call_id:
-          typeof msg.tool_call_id === "string"
-            ? msg.tool_call_id
-            : msg.tool_call_id == null
-              ? null
-              : String(msg.tool_call_id),
-        tool_calls: Array.isArray(msg.tool_calls) ? msg.tool_calls : null,
-      };
-    })
-    .filter((m): m is SessionChatMessage => m !== null);
-}
-
-function safeJsonParse(value: string): unknown {
-  try {
-    return JSON.parse(value) as unknown;
-  } catch {
-    return value;
-  }
-}
-
-function toToolInput(rawArguments: unknown): unknown {
-  if (typeof rawArguments === "string") {
-    const trimmed = rawArguments.trim();
-    return trimmed ? safeJsonParse(trimmed) : {};
-  }
-  if (rawArguments && typeof rawArguments === "object") return rawArguments;
-  return {};
-}
-
-export function convertChatSessionMessagesToUiMessages(
-  sessionId: string,
-  rawMessages: unknown[],
-): UIMessage<unknown, UIDataTypes, UITools>[] {
-  const messages = coerceSessionChatMessages(rawMessages);
-  const toolOutputsByCallId = new Map<string, unknown>();
-
-  for (const msg of messages) {
-    if (msg.role !== "tool") continue;
-    if (!msg.tool_call_id) continue;
-    if (msg.content == null) continue;
-    toolOutputsByCallId.set(msg.tool_call_id, msg.content);
-  }
-
-  const uiMessages: UIMessage<unknown, UIDataTypes, UITools>[] = [];
-
-  messages.forEach((msg, index) => {
-    if (msg.role === "tool") return;
-    if (msg.role !== "user" && msg.role !== "assistant") return;
-
-    const parts: UIMessage<unknown, UIDataTypes, UITools>["parts"] = [];
-
-    if (typeof msg.content === "string" && msg.content.trim()) {
-      parts.push({ type: "text", text: msg.content, state: "done" });
-    }
-
-    if (msg.role === "assistant" && Array.isArray(msg.tool_calls)) {
-      for (const rawToolCall of msg.tool_calls) {
-        if (!rawToolCall || typeof rawToolCall !== "object") continue;
-        const toolCall = rawToolCall as {
-          id?: unknown;
-          function?: { name?: unknown; arguments?: unknown };
-        };
-
-        const toolCallId = String(toolCall.id ?? "").trim();
-        const toolName = String(toolCall.function?.name ?? "").trim();
-        if (!toolCallId || !toolName) continue;
-
-        const input = toToolInput(toolCall.function?.arguments);
-        const output = toolOutputsByCallId.get(toolCallId);
-
-        if (output !== undefined) {
-          parts.push({
-            type: `tool-${toolName}`,
-            toolCallId,
-            state: "output-available",
-            input,
-            output: typeof output === "string" ? safeJsonParse(output) : output,
-          });
-        } else {
-          parts.push({
-            type: `tool-${toolName}`,
-            toolCallId,
-            state: "input-available",
-            input,
-          });
-        }
-      }
-    }
-
-    if (parts.length === 0) return;
-
-    uiMessages.push({
-      id: `${sessionId}-${index}`,
-      role: msg.role,
-      parts,
-    });
-  });
-
-  return uiMessages;
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/page.tsx

@@ -1,67 +0,0 @@
-"use client";
-
-import { SidebarProvider } from "@/components/ui/sidebar";
-import { ChatContainer } from "./components/ChatContainer/ChatContainer";
-import { ChatSidebar } from "./components/ChatSidebar/ChatSidebar";
-import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
-import { MobileHeader } from "./components/MobileHeader/MobileHeader";
-import { useCopilotPage } from "./useCopilotPage";
-
-export default function Page() {
-  const {
-    sessionId,
-    messages,
-    status,
-    error,
-    isLoadingSession,
-    isCreatingSession,
-    createSession,
-    onSend,
-    // Mobile drawer
-    isMobile,
-    isDrawerOpen,
-    sessions,
-    isLoadingSessions,
-    handleOpenDrawer,
-    handleCloseDrawer,
-    handleDrawerOpenChange,
-    handleSelectSession,
-    handleNewChat,
-  } = useCopilotPage();
-
-  return (
-    <SidebarProvider
-      defaultOpen={true}
-      className="h-[calc(100vh-72px)] min-h-0"
-    >
-      {!isMobile && <ChatSidebar />}
-      <div className="relative flex h-full w-full flex-col overflow-hidden bg-[#f8f8f9] px-0">
-        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
-        <div className="flex-1 overflow-hidden">
-          <ChatContainer
-            messages={messages}
-            status={status}
-            error={error}
-            sessionId={sessionId}
-            isLoadingSession={isLoadingSession}
-            isCreatingSession={isCreatingSession}
-            onCreateSession={createSession}
-            onSend={onSend}
-          />
-        </div>
-      </div>
-      {isMobile && (
-        <MobileDrawer
-          isOpen={isDrawerOpen}
-          sessions={sessions}
-          currentSessionId={sessionId}
-          isLoading={isLoadingSessions}
-          onSelectSession={handleSelectSession}
-          onNewChat={handleNewChat}
-          onClose={handleCloseDrawer}
-          onOpenChange={handleDrawerOpenChange}
-        />
-      )}
-    </SidebarProvider>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/CreateAgent/CreateAgent.tsx

@@ -1,218 +0,0 @@
-"use client";
-
-import type { ToolUIPart } from "ai";
-import Link from "next/link";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import {
-  ClarificationQuestionsWidget,
-  type ClarifyingQuestion as WidgetClarifyingQuestion,
-} from "@/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget";
-import {
-  formatMaybeJson,
-  getAnimationText,
-  getCreateAgentToolOutput,
-  isAgentPreviewOutput,
-  isAgentSavedOutput,
-  isClarificationNeededOutput,
-  isErrorOutput,
-  isOperationInProgressOutput,
-  isOperationPendingOutput,
-  isOperationStartedOutput,
-  ToolIcon,
-  truncateText,
-  type CreateAgentToolOutput,
-} from "./helpers";
-
-export interface CreateAgentToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: CreateAgentToolPart;
-}
-
-function getAccordionMeta(output: CreateAgentToolOutput): {
-  badgeText: string;
-  title: string;
-  description?: string;
-} {
-  if (isAgentSavedOutput(output)) {
-    return { badgeText: "Create agent", title: output.agent_name };
-  }
-  if (isAgentPreviewOutput(output)) {
-    return {
-      badgeText: "Create agent",
-      title: output.agent_name,
-      description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
-    };
-  }
-  if (isClarificationNeededOutput(output)) {
-    const questions = output.questions ?? [];
-    return {
-      badgeText: "Create agent",
-      title: "Needs clarification",
-      description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
-    };
-  }
-  if (
-    isOperationStartedOutput(output) ||
-    isOperationPendingOutput(output) ||
-    isOperationInProgressOutput(output)
-  ) {
-    return { badgeText: "Create agent", title: "Creating agent" };
-  }
-  return { badgeText: "Create agent", title: "Error" };
-}
-
-export function CreateAgentTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const { onSend } = useCopilotChatActions();
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getCreateAgentToolOutput(part);
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output) ||
-      isAgentPreviewOutput(output) ||
-      isAgentSavedOutput(output) ||
-      isClarificationNeededOutput(output) ||
-      isErrorOutput(output));
-
-  function handleClarificationAnswers(answers: Record<string, string>) {
-    const contextMessage = Object.entries(answers)
-      .map(([keyword, answer]) => `${keyword}: ${answer}`)
-      .join("\n");
-
-    onSend(
-      `I have the answers to your questions:\n\n${contextMessage}\n\nPlease proceed with creating the agent.`,
-    );
-  }
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion
-          {...getAccordionMeta(output)}
-          defaultExpanded={isClarificationNeededOutput(output)}
-        >
-          {(isOperationStartedOutput(output) ||
-            isOperationPendingOutput(output)) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              <p className="text-xs text-muted-foreground">
-                Operation: {output.operation_id}
-              </p>
-              <p className="text-xs italic text-muted-foreground">
-                Check your library in a few minutes.
-              </p>
-            </div>
-          )}
-
-          {isOperationInProgressOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              <p className="text-xs italic text-muted-foreground">
-                Please wait for the current operation to finish.
-              </p>
-            </div>
-          )}
-
-          {isAgentSavedOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              <div className="flex flex-wrap gap-2">
-                <Link
-                  href={output.library_agent_link}
-                  className="text-xs font-medium text-purple-600 hover:text-purple-700"
-                >
-                  Open in library
-                </Link>
-                <Link
-                  href={output.agent_page_link}
-                  className="text-xs font-medium text-purple-600 hover:text-purple-700"
-                >
-                  Open in builder
-                </Link>
-              </div>
-              <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                {truncateText(
-                  formatMaybeJson({ agent_id: output.agent_id }),
-                  800,
-                )}
-              </pre>
-            </div>
-          )}
-
-          {isAgentPreviewOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.description?.trim() && (
-                <p className="text-xs text-muted-foreground">
-                  {output.description}
-                </p>
-              )}
-              <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                {truncateText(formatMaybeJson(output.agent_json), 1600)}
-              </pre>
-            </div>
-          )}
-
-          {isClarificationNeededOutput(output) && (
-            <ClarificationQuestionsWidget
-              questions={(output.questions ?? []).map((q) => {
-                const item: WidgetClarifyingQuestion = {
-                  question: q.question,
-                  keyword: q.keyword,
-                };
-                const example =
-                  typeof q.example === "string" && q.example.trim()
-                    ? q.example.trim()
-                    : null;
-                if (example) item.example = example;
-                return item;
-              })}
-              message={output.message}
-              onSubmitAnswers={handleClarificationAnswers}
-            />
-          )}
-
-          {isErrorOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.error && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.error)}
-                </pre>
-              )}
-              {output.details && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.details)}
-                </pre>
-              )}
-            </div>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/CreateAgent/helpers.tsx

@@ -1,181 +0,0 @@
-import type { ToolUIPart } from "ai";
-import { PlusIcon } from "@phosphor-icons/react";
-import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
-import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
-import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
-import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
-import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-
-export type CreateAgentToolOutput =
-  | OperationStartedResponse
-  | OperationPendingResponse
-  | OperationInProgressResponse
-  | AgentPreviewResponse
-  | AgentSavedResponse
-  | ClarificationNeededResponse
-  | ErrorResponse;
-
-function parseOutput(output: unknown): CreateAgentToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.operation_started ||
-      type === ResponseType.operation_pending ||
-      type === ResponseType.operation_in_progress ||
-      type === ResponseType.agent_preview ||
-      type === ResponseType.agent_saved ||
-      type === ResponseType.clarification_needed ||
-      type === ResponseType.error
-    ) {
-      return output as CreateAgentToolOutput;
-    }
-    if ("operation_id" in output && "tool_name" in output)
-      return output as OperationStartedResponse | OperationPendingResponse;
-    if ("tool_call_id" in output) return output as OperationInProgressResponse;
-    if ("agent_json" in output && "agent_name" in output)
-      return output as AgentPreviewResponse;
-    if ("agent_id" in output && "library_agent_id" in output)
-      return output as AgentSavedResponse;
-    if ("questions" in output) return output as ClarificationNeededResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getCreateAgentToolOutput(
-  part: unknown,
-): CreateAgentToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isOperationStartedOutput(
-  output: CreateAgentToolOutput,
-): output is OperationStartedResponse {
-  return (
-    output.type === ResponseType.operation_started ||
-    ("operation_id" in output && "tool_name" in output)
-  );
-}
-
-export function isOperationPendingOutput(
-  output: CreateAgentToolOutput,
-): output is OperationPendingResponse {
-  return output.type === ResponseType.operation_pending;
-}
-
-export function isOperationInProgressOutput(
-  output: CreateAgentToolOutput,
-): output is OperationInProgressResponse {
-  return (
-    output.type === ResponseType.operation_in_progress ||
-    "tool_call_id" in output
-  );
-}
-
-export function isAgentPreviewOutput(
-  output: CreateAgentToolOutput,
-): output is AgentPreviewResponse {
-  return output.type === ResponseType.agent_preview || "agent_json" in output;
-}
-
-export function isAgentSavedOutput(
-  output: CreateAgentToolOutput,
-): output is AgentSavedResponse {
-  return (
-    output.type === ResponseType.agent_saved || "agent_page_link" in output
-  );
-}
-
-export function isClarificationNeededOutput(
-  output: CreateAgentToolOutput,
-): output is ClarificationNeededResponse {
-  return (
-    output.type === ResponseType.clarification_needed || "questions" in output
-  );
-}
-
-export function isErrorOutput(
-  output: CreateAgentToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return "Creating a new agent";
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return "Creating a new agent";
-      if (isOperationStartedOutput(output)) return "Agent creation started";
-      if (isOperationPendingOutput(output)) return "Agent creation in progress";
-      if (isOperationInProgressOutput(output))
-        return "Agent creation already in progress";
-      if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
-      if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
-      if (isClarificationNeededOutput(output)) return "Needs clarification";
-      return "Error creating agent";
-    }
-    case "output-error":
-      return "Error creating agent";
-    default:
-      return "Creating a new agent";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  return (
-    <PlusIcon
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}
-
-export function truncateText(text: string, maxChars: number): string {
-  const trimmed = text.trim();
-  if (trimmed.length <= maxChars) return trimmed;
-  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/EditAgent/EditAgent.tsx

@@ -1,218 +0,0 @@
-"use client";
-
-import type { ToolUIPart } from "ai";
-import Link from "next/link";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import {
-  ClarificationQuestionsWidget,
-  type ClarifyingQuestion as WidgetClarifyingQuestion,
-} from "@/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget";
-import {
-  formatMaybeJson,
-  getAnimationText,
-  getEditAgentToolOutput,
-  isAgentPreviewOutput,
-  isAgentSavedOutput,
-  isClarificationNeededOutput,
-  isErrorOutput,
-  isOperationInProgressOutput,
-  isOperationPendingOutput,
-  isOperationStartedOutput,
-  ToolIcon,
-  truncateText,
-  type EditAgentToolOutput,
-} from "./helpers";
-
-export interface EditAgentToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: EditAgentToolPart;
-}
-
-function getAccordionMeta(output: EditAgentToolOutput): {
-  badgeText: string;
-  title: string;
-  description?: string;
-} {
-  if (isAgentSavedOutput(output)) {
-    return { badgeText: "Edit agent", title: output.agent_name };
-  }
-  if (isAgentPreviewOutput(output)) {
-    return {
-      badgeText: "Edit agent",
-      title: output.agent_name,
-      description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
-    };
-  }
-  if (isClarificationNeededOutput(output)) {
-    const questions = output.questions ?? [];
-    return {
-      badgeText: "Edit agent",
-      title: "Needs clarification",
-      description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
-    };
-  }
-  if (
-    isOperationStartedOutput(output) ||
-    isOperationPendingOutput(output) ||
-    isOperationInProgressOutput(output)
-  ) {
-    return { badgeText: "Edit agent", title: "Editing agent" };
-  }
-  return { badgeText: "Edit agent", title: "Error" };
-}
-
-export function EditAgentTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const { onSend } = useCopilotChatActions();
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getEditAgentToolOutput(part);
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output) ||
-      isAgentPreviewOutput(output) ||
-      isAgentSavedOutput(output) ||
-      isClarificationNeededOutput(output) ||
-      isErrorOutput(output));
-
-  function handleClarificationAnswers(answers: Record<string, string>) {
-    const contextMessage = Object.entries(answers)
-      .map(([keyword, answer]) => `${keyword}: ${answer}`)
-      .join("\n");
-
-    onSend(
-      `I have the answers to your questions:\n\n${contextMessage}\n\nPlease proceed with editing the agent.`,
-    );
-  }
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion
-          {...getAccordionMeta(output)}
-          defaultExpanded={isClarificationNeededOutput(output)}
-        >
-          {(isOperationStartedOutput(output) ||
-            isOperationPendingOutput(output)) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              <p className="text-xs text-muted-foreground">
-                Operation: {output.operation_id}
-              </p>
-              <p className="text-xs italic text-muted-foreground">
-                Check your library in a few minutes.
-              </p>
-            </div>
-          )}
-
-          {isOperationInProgressOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              <p className="text-xs italic text-muted-foreground">
-                Please wait for the current operation to finish.
-              </p>
-            </div>
-          )}
-
-          {isAgentSavedOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              <div className="flex flex-wrap gap-2">
-                <Link
-                  href={output.library_agent_link}
-                  className="text-xs font-medium text-purple-600 hover:text-purple-700"
-                >
-                  Open in library
-                </Link>
-                <Link
-                  href={output.agent_page_link}
-                  className="text-xs font-medium text-purple-600 hover:text-purple-700"
-                >
-                  Open in builder
-                </Link>
-              </div>
-              <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                {truncateText(
-                  formatMaybeJson({ agent_id: output.agent_id }),
-                  800,
-                )}
-              </pre>
-            </div>
-          )}
-
-          {isAgentPreviewOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.description?.trim() && (
-                <p className="text-xs text-muted-foreground">
-                  {output.description}
-                </p>
-              )}
-              <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                {truncateText(formatMaybeJson(output.agent_json), 1600)}
-              </pre>
-            </div>
-          )}
-
-          {isClarificationNeededOutput(output) && (
-            <ClarificationQuestionsWidget
-              questions={(output.questions ?? []).map((q) => {
-                const item: WidgetClarifyingQuestion = {
-                  question: q.question,
-                  keyword: q.keyword,
-                };
-                const example =
-                  typeof q.example === "string" && q.example.trim()
-                    ? q.example.trim()
-                    : null;
-                if (example) item.example = example;
-                return item;
-              })}
-              message={output.message}
-              onSubmitAnswers={handleClarificationAnswers}
-            />
-          )}
-
-          {isErrorOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.error && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.error)}
-                </pre>
-              )}
-              {output.details && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.details)}
-                </pre>
-              )}
-            </div>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/EditAgent/helpers.tsx

@@ -1,181 +0,0 @@
-import type { ToolUIPart } from "ai";
-import { PencilLineIcon } from "@phosphor-icons/react";
-import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
-import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
-import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
-import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
-import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-
-export type EditAgentToolOutput =
-  | OperationStartedResponse
-  | OperationPendingResponse
-  | OperationInProgressResponse
-  | AgentPreviewResponse
-  | AgentSavedResponse
-  | ClarificationNeededResponse
-  | ErrorResponse;
-
-function parseOutput(output: unknown): EditAgentToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.operation_started ||
-      type === ResponseType.operation_pending ||
-      type === ResponseType.operation_in_progress ||
-      type === ResponseType.agent_preview ||
-      type === ResponseType.agent_saved ||
-      type === ResponseType.clarification_needed ||
-      type === ResponseType.error
-    ) {
-      return output as EditAgentToolOutput;
-    }
-    if ("operation_id" in output && "tool_name" in output)
-      return output as OperationStartedResponse | OperationPendingResponse;
-    if ("tool_call_id" in output) return output as OperationInProgressResponse;
-    if ("agent_json" in output && "agent_name" in output)
-      return output as AgentPreviewResponse;
-    if ("agent_id" in output && "library_agent_id" in output)
-      return output as AgentSavedResponse;
-    if ("questions" in output) return output as ClarificationNeededResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getEditAgentToolOutput(
-  part: unknown,
-): EditAgentToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isOperationStartedOutput(
-  output: EditAgentToolOutput,
-): output is OperationStartedResponse {
-  return (
-    output.type === ResponseType.operation_started ||
-    ("operation_id" in output && "tool_name" in output)
-  );
-}
-
-export function isOperationPendingOutput(
-  output: EditAgentToolOutput,
-): output is OperationPendingResponse {
-  return output.type === ResponseType.operation_pending;
-}
-
-export function isOperationInProgressOutput(
-  output: EditAgentToolOutput,
-): output is OperationInProgressResponse {
-  return (
-    output.type === ResponseType.operation_in_progress ||
-    "tool_call_id" in output
-  );
-}
-
-export function isAgentPreviewOutput(
-  output: EditAgentToolOutput,
-): output is AgentPreviewResponse {
-  return output.type === ResponseType.agent_preview || "agent_json" in output;
-}
-
-export function isAgentSavedOutput(
-  output: EditAgentToolOutput,
-): output is AgentSavedResponse {
-  return (
-    output.type === ResponseType.agent_saved || "agent_page_link" in output
-  );
-}
-
-export function isClarificationNeededOutput(
-  output: EditAgentToolOutput,
-): output is ClarificationNeededResponse {
-  return (
-    output.type === ResponseType.clarification_needed || "questions" in output
-  );
-}
-
-export function isErrorOutput(
-  output: EditAgentToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return "Editing the agent";
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return "Editing the agent";
-      if (isOperationStartedOutput(output)) return "Agent update started";
-      if (isOperationPendingOutput(output)) return "Agent update in progress";
-      if (isOperationInProgressOutput(output))
-        return "Agent update already in progress";
-      if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
-      if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
-      if (isClarificationNeededOutput(output)) return "Needs clarification";
-      return "Error editing agent";
-    }
-    case "output-error":
-      return "Error editing agent";
-    default:
-      return "Editing the agent";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  return (
-    <PencilLineIcon
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}
-
-export function truncateText(text: string, maxChars: number): string {
-  const trimmed = text.trim();
-  if (trimmed.length <= maxChars) return trimmed;
-  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/FindAgents/FindAgents.tsx

@@ -1,131 +0,0 @@
-"use client";
-
-import { ToolUIPart } from "ai";
-import Link from "next/link";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import {
-  getAgentHref,
-  getAnimationText,
-  getFindAgentsOutput,
-  getSourceLabelFromToolType,
-  isAgentsFoundOutput,
-  isErrorOutput,
-  ToolIcon,
-} from "./helpers";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-
-export interface FindAgentsToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: FindAgentsToolPart;
-}
-
-export function FindAgentsTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const output = getFindAgentsOutput(part);
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-
-  const query =
-    typeof part.input === "object" && part.input !== null
-      ? String((part.input as { query?: unknown }).query ?? "").trim()
-      : "";
-
-  const agentsFoundOutput =
-    part.state === "output-available" && output && isAgentsFoundOutput(output)
-      ? output
-      : null;
-
-  const hasAgents =
-    !!agentsFoundOutput &&
-    agentsFoundOutput.agents.length > 0 &&
-    (typeof agentsFoundOutput.count !== "number" ||
-      agentsFoundOutput.count > 0);
-  const totalCount = agentsFoundOutput ? agentsFoundOutput.count : 0;
-  const { label: sourceLabel, source } = getSourceLabelFromToolType(part.type);
-  const scopeText =
-    source === "library"
-      ? "in your library"
-      : source === "marketplace"
-        ? "in marketplace"
-        : "";
-  const accordionDescription = `Found ${totalCount}${scopeText ? ` ${scopeText}` : ""}${
-    query ? ` for "${query}"` : ""
-  }`;
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon
-          toolType={part.type}
-          isStreaming={isStreaming}
-          isError={isError}
-        />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasAgents && agentsFoundOutput && (
-        <ToolAccordion
-          badgeText={sourceLabel}
-          title="Agent results"
-          description={accordionDescription}
-        >
-          <div className="grid gap-2 sm:grid-cols-2">
-            {agentsFoundOutput.agents.map((agent) => {
-              const href = getAgentHref(agent);
-              const agentSource =
-                agent.source === "library"
-                  ? "Library"
-                  : agent.source === "marketplace"
-                    ? "Marketplace"
-                    : null;
-              return (
-                <div
-                  key={agent.id}
-                  className="rounded-2xl border bg-background p-3"
-                >
-                  <div className="flex items-start justify-between gap-2">
-                    <div className="min-w-0">
-                      <div className="flex items-center gap-2">
-                        <p className="truncate text-sm font-medium text-foreground">
-                          {agent.name}
-                        </p>
-                        {agentSource && (
-                          <span className="shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-muted-foreground">
-                            {agentSource}
-                          </span>
-                        )}
-                      </div>
-                      <p className="mt-1 line-clamp-2 text-xs text-muted-foreground">
-                        {agent.description}
-                      </p>
-                    </div>
-                    {href && (
-                      <Link
-                        href={href}
-                        className="shrink-0 text-xs font-medium text-purple-600 hover:text-purple-700"
-                      >
-                        Open
-                      </Link>
-                    )}
-                  </div>
-                </div>
-              );
-            })}
-          </div>
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/FindAgents/helpers.tsx

@@ -1,176 +0,0 @@
-import { ToolUIPart } from "ai";
-import { MagnifyingGlassIcon, SquaresFourIcon } from "@phosphor-icons/react";
-import type { AgentInfo } from "@/app/api/__generated__/models/agentInfo";
-import type { AgentsFoundResponse } from "@/app/api/__generated__/models/agentsFoundResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-
-export interface FindAgentInput {
-  query: string;
-}
-
-export type FindAgentsOutput =
-  | AgentsFoundResponse
-  | NoResultsResponse
-  | ErrorResponse;
-
-export type FindAgentsToolType =
-  | "tool-find_agent"
-  | "tool-find_library_agent"
-  | (string & {});
-
-function parseOutput(output: unknown): FindAgentsOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.agents_found ||
-      type === ResponseType.no_results ||
-      type === ResponseType.error
-    ) {
-      return output as FindAgentsOutput;
-    }
-    if ("agents" in output && "count" in output)
-      return output as AgentsFoundResponse;
-    if ("suggestions" in output && !("error" in output))
-      return output as NoResultsResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getFindAgentsOutput(part: unknown): FindAgentsOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isAgentsFoundOutput(
-  output: FindAgentsOutput,
-): output is AgentsFoundResponse {
-  return output.type === ResponseType.agents_found || "agents" in output;
-}
-
-export function isNoResultsOutput(
-  output: FindAgentsOutput,
-): output is NoResultsResponse {
-  return (
-    output.type === ResponseType.no_results ||
-    ("suggestions" in output && !("error" in output))
-  );
-}
-
-export function isErrorOutput(
-  output: FindAgentsOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getSourceLabelFromToolType(toolType?: FindAgentsToolType): {
-  source: "marketplace" | "library" | "unknown";
-  label: string;
-} {
-  if (toolType === "tool-find_library_agent") {
-    return { source: "library", label: "Library" };
-  }
-  if (toolType === "tool-find_agent") {
-    return { source: "marketplace", label: "Marketplace" };
-  }
-  return { source: "unknown", label: "Agents" };
-}
-
-export function getAnimationText(part: {
-  type?: FindAgentsToolType;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  const { source } = getSourceLabelFromToolType(part.type);
-  const query = (part.input as FindAgentInput | undefined)?.query?.trim();
-
-  // Action phrase matching legacy ToolCallMessage
-  const actionPhrase =
-    source === "library"
-      ? "Looking for library agents"
-      : "Looking for agents in the marketplace";
-
-  const queryText = query ? ` matching "${query}"` : "";
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `${actionPhrase}${queryText}`;
-
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) {
-        return `${actionPhrase}${queryText}`;
-      }
-      if (isNoResultsOutput(output)) {
-        return `No agents found${queryText}`;
-      }
-      if (isAgentsFoundOutput(output)) {
-        const count = output.count ?? output.agents?.length ?? 0;
-        return `Found ${count} agent${count === 1 ? "" : "s"}${queryText}`;
-      }
-      if (isErrorOutput(output)) {
-        return `Error finding agents${queryText}`;
-      }
-      return `${actionPhrase}${queryText}`;
-    }
-
-    case "output-error":
-      return `Error finding agents${queryText}`;
-
-    default:
-      return actionPhrase;
-  }
-}
-
-export function getAgentHref(agent: AgentInfo): string | null {
-  if (agent.source === "library") {
-    return `/library/agents/${encodeURIComponent(agent.id)}`;
-  }
-
-  const [creator, slug, ...rest] = agent.id.split("/");
-  if (!creator || !slug || rest.length > 0) return null;
-  return `/marketplace/agent/${encodeURIComponent(creator)}/${encodeURIComponent(slug)}`;
-}
-
-export function ToolIcon({
-  toolType,
-  isStreaming,
-  isError,
-}: {
-  toolType?: FindAgentsToolType;
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  const { source } = getSourceLabelFromToolType(toolType);
-  const IconComponent =
-    source === "library" ? MagnifyingGlassIcon : SquaresFourIcon;
-
-  return (
-    <IconComponent
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/FindBlocks/FindBlocks.tsx

@@ -1,41 +0,0 @@
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
-import { ToolUIPart } from "ai";
-import { getAnimationText, ToolIcon } from "./helpers";
-
-export interface FindBlockInput {
-  query: string;
-}
-
-export type FindBlockOutput = BlockListResponse;
-
-export interface FindBlockToolPart {
-  type: string;
-  toolName?: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: FindBlockInput | unknown;
-  output?: string | FindBlockOutput | unknown;
-  title?: string;
-}
-
-interface Props {
-  part: FindBlockToolPart;
-}
-
-export function FindBlocksTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-  const isError = part.state === "output-error";
-
-  return (
-    <div className="flex items-center gap-2 py-2 text-sm text-muted-foreground">
-      <ToolIcon isStreaming={isStreaming} isError={isError} />
-      <MorphingTextAnimation
-        text={text}
-        className={isError ? "text-red-500" : undefined}
-      />
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/FindBlocks/helpers.tsx

@@ -1,72 +0,0 @@
-import { ToolUIPart } from "ai";
-import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-import { FindBlockInput, FindBlockToolPart } from "./FindBlocks";
-import { PackageIcon } from "@phosphor-icons/react";
-
-function parseOutput(output: unknown): BlockListResponse | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (type === ResponseType.block_list || "blocks" in output) {
-      return output as BlockListResponse;
-    }
-  }
-  return null;
-}
-
-export function getAnimationText(part: FindBlockToolPart): string {
-  const query = (part.input as FindBlockInput | undefined)?.query?.trim();
-  const queryText = query ? ` matching "${query}"` : "";
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `Searching for blocks${queryText}`;
-
-    case "output-available": {
-      const parsed = parseOutput(part.output);
-      if (parsed) {
-        return `Found ${parsed.count} block${parsed.count === 1 ? "" : "s"}${queryText}`;
-      }
-      return `Searching for blocks${queryText}`;
-    }
-
-    case "output-error":
-      return `Error finding blocks${queryText}`;
-
-    default:
-      return "Searching for blocks";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  return (
-    <PackageIcon
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/RunAgent/RunAgent.tsx

@@ -1,377 +0,0 @@
-"use client";
-
-import type { ToolUIPart } from "ai";
-import Link from "next/link";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import {
-  ChatCredentialsSetup,
-  type CredentialInfo,
-} from "@/components/contextual/Chat/components/ChatCredentialsSetup/ChatCredentialsSetup";
-import {
-  formatMaybeJson,
-  getAnimationText,
-  getRunAgentToolOutput,
-  isRunAgentAgentDetailsOutput,
-  isRunAgentErrorOutput,
-  isRunAgentExecutionStartedOutput,
-  isRunAgentNeedLoginOutput,
-  isRunAgentSetupRequirementsOutput,
-  ToolIcon,
-  type RunAgentToolOutput,
-} from "./helpers";
-
-export interface RunAgentToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: RunAgentToolPart;
-}
-
-function getAccordionMeta(output: RunAgentToolOutput): {
-  badgeText: string;
-  title: string;
-  description?: string;
-} {
-  if (isRunAgentExecutionStartedOutput(output)) {
-    const statusText =
-      typeof output.status === "string" && output.status.trim()
-        ? output.status.trim()
-        : "started";
-    return {
-      badgeText: "Run agent",
-      title: output.graph_name,
-      description: `Status: ${statusText}`,
-    };
-  }
-
-  if (isRunAgentAgentDetailsOutput(output)) {
-    return {
-      badgeText: "Run agent",
-      title: output.agent.name,
-      description: "Inputs required",
-    };
-  }
-
-  if (isRunAgentSetupRequirementsOutput(output)) {
-    const missingCredsCount = Object.keys(
-      (output.setup_info.user_readiness?.missing_credentials ?? {}) as Record<
-        string,
-        unknown
-      >,
-    ).length;
-    return {
-      badgeText: "Run agent",
-      title: output.setup_info.agent_name,
-      description:
-        missingCredsCount > 0
-          ? `Missing ${missingCredsCount} credential${missingCredsCount === 1 ? "" : "s"}`
-          : output.message,
-    };
-  }
-
-  if (isRunAgentNeedLoginOutput(output)) {
-    return { badgeText: "Run agent", title: "Sign in required" };
-  }
-
-  return { badgeText: "Run agent", title: "Error" };
-}
-
-function coerceMissingCredentials(
-  rawMissingCredentials: unknown,
-): CredentialInfo[] {
-  const missing =
-    rawMissingCredentials && typeof rawMissingCredentials === "object"
-      ? (rawMissingCredentials as Record<string, unknown>)
-      : {};
-
-  const validTypes = new Set([
-    "api_key",
-    "oauth2",
-    "user_password",
-    "host_scoped",
-  ]);
-
-  const results: CredentialInfo[] = [];
-
-  Object.values(missing).forEach((value) => {
-    if (!value || typeof value !== "object") return;
-    const cred = value as Record<string, unknown>;
-
-    const provider =
-      typeof cred.provider === "string" ? cred.provider.trim() : "";
-    if (!provider) return;
-
-    const providerName =
-      typeof cred.provider_name === "string" && cred.provider_name.trim()
-        ? cred.provider_name.trim()
-        : provider.replace(/_/g, " ");
-
-    const title =
-      typeof cred.title === "string" && cred.title.trim()
-        ? cred.title.trim()
-        : providerName;
-
-    const types =
-      Array.isArray(cred.types) && cred.types.length > 0
-        ? cred.types
-        : typeof cred.type === "string"
-          ? [cred.type]
-          : [];
-
-    const credentialTypes = types
-      .map((t) => (typeof t === "string" ? t.trim() : ""))
-      .filter(
-        (t): t is "api_key" | "oauth2" | "user_password" | "host_scoped" =>
-          validTypes.has(t),
-      );
-
-    if (credentialTypes.length === 0) return;
-
-    const scopes = Array.isArray(cred.scopes)
-      ? cred.scopes.filter((s): s is string => typeof s === "string")
-      : undefined;
-
-    const item: CredentialInfo = {
-      provider,
-      providerName,
-      credentialTypes,
-      title,
-    };
-    if (scopes && scopes.length > 0) {
-      item.scopes = scopes;
-    }
-    results.push(item);
-  });
-
-  return results;
-}
-
-function coerceExpectedInputs(rawInputs: unknown): Array<{
-  name: string;
-  title: string;
-  type: string;
-  description?: string;
-  required: boolean;
-}> {
-  if (!Array.isArray(rawInputs)) return [];
-  const results: Array<{
-    name: string;
-    title: string;
-    type: string;
-    description?: string;
-    required: boolean;
-  }> = [];
-
-  rawInputs.forEach((value, index) => {
-    if (!value || typeof value !== "object") return;
-    const input = value as Record<string, unknown>;
-
-    const name =
-      typeof input.name === "string" && input.name.trim()
-        ? input.name.trim()
-        : `input-${index}`;
-    const title =
-      typeof input.title === "string" && input.title.trim()
-        ? input.title.trim()
-        : name;
-    const type = typeof input.type === "string" ? input.type : "unknown";
-    const description =
-      typeof input.description === "string" && input.description.trim()
-        ? input.description.trim()
-        : undefined;
-    const required = Boolean(input.required);
-
-    const item: {
-      name: string;
-      title: string;
-      type: string;
-      description?: string;
-      required: boolean;
-    } = { name, title, type, required };
-    if (description) item.description = description;
-    results.push(item);
-  });
-
-  return results;
-}
-
-export function RunAgentTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const { onSend } = useCopilotChatActions();
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getRunAgentToolOutput(part);
-  const isError =
-    part.state === "output-error" ||
-    (!!output && isRunAgentErrorOutput(output));
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isRunAgentExecutionStartedOutput(output) ||
-      isRunAgentAgentDetailsOutput(output) ||
-      isRunAgentSetupRequirementsOutput(output) ||
-      isRunAgentNeedLoginOutput(output) ||
-      isRunAgentErrorOutput(output));
-
-  function handleAllCredentialsComplete() {
-    onSend(
-      "I've configured the required credentials. Please check if everything is ready and proceed with running the agent.",
-    );
-  }
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion
-          {...getAccordionMeta(output)}
-          defaultExpanded={
-            isRunAgentSetupRequirementsOutput(output) ||
-            isRunAgentAgentDetailsOutput(output)
-          }
-        >
-          {isRunAgentExecutionStartedOutput(output) && (
-            <div className="grid gap-2">
-              <div className="rounded-2xl border bg-background p-3">
-                <div className="flex items-start justify-between gap-3">
-                  <div className="min-w-0">
-                    <p className="text-sm font-medium text-foreground">
-                      Execution started
-                    </p>
-                    <p className="mt-0.5 truncate text-xs text-muted-foreground">
-                      {output.execution_id}
-                    </p>
-                    <p className="mt-2 text-xs text-muted-foreground">
-                      {output.message}
-                    </p>
-                  </div>
-                  {output.library_agent_link && (
-                    <Link
-                      href={output.library_agent_link}
-                      className="shrink-0 text-xs font-medium text-purple-600 hover:text-purple-700"
-                    >
-                      Open
-                    </Link>
-                  )}
-                </div>
-              </div>
-            </div>
-          )}
-
-          {isRunAgentAgentDetailsOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-
-              {output.agent.description?.trim() && (
-                <p className="text-xs text-muted-foreground">
-                  {output.agent.description}
-                </p>
-              )}
-
-              <div className="rounded-2xl border bg-background p-3">
-                <p className="text-xs font-medium text-foreground">Inputs</p>
-                <p className="mt-1 text-xs text-muted-foreground">
-                  Provide required inputs in chat, or ask to run with defaults.
-                </p>
-                <pre className="mt-2 whitespace-pre-wrap text-xs text-muted-foreground">
-                  {formatMaybeJson(output.agent.inputs)}
-                </pre>
-              </div>
-            </div>
-          )}
-
-          {isRunAgentSetupRequirementsOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-
-              {coerceMissingCredentials(
-                output.setup_info.user_readiness?.missing_credentials,
-              ).length > 0 && (
-                <ChatCredentialsSetup
-                  credentials={coerceMissingCredentials(
-                    output.setup_info.user_readiness?.missing_credentials,
-                  )}
-                  agentName={output.setup_info.agent_name}
-                  message={output.message}
-                  onAllCredentialsComplete={handleAllCredentialsComplete}
-                  onCancel={() => {}}
-                />
-              )}
-
-              {coerceExpectedInputs(
-                (output.setup_info.requirements as Record<string, unknown>)
-                  ?.inputs,
-              ).length > 0 && (
-                <div className="rounded-2xl border bg-background p-3">
-                  <p className="text-xs font-medium text-foreground">
-                    Expected inputs
-                  </p>
-                  <div className="mt-2 grid gap-2">
-                    {coerceExpectedInputs(
-                      (
-                        output.setup_info.requirements as Record<
-                          string,
-                          unknown
-                        >
-                      )?.inputs,
-                    ).map((input) => (
-                      <div key={input.name} className="rounded-xl border p-2">
-                        <div className="flex items-center justify-between gap-2">
-                          <p className="truncate text-xs font-medium text-foreground">
-                            {input.title}
-                          </p>
-                          <span className="shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-muted-foreground">
-                            {input.required ? "Required" : "Optional"}
-                          </span>
-                        </div>
-                        <p className="mt-1 text-xs text-muted-foreground">
-                          {input.name} • {input.type}
-                          {input.description ? ` • ${input.description}` : ""}
-                        </p>
-                      </div>
-                    ))}
-                  </div>
-                </div>
-              )}
-            </div>
-          )}
-
-          {isRunAgentNeedLoginOutput(output) && (
-            <p className="text-sm text-foreground">{output.message}</p>
-          )}
-
-          {isRunAgentErrorOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.error && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.error)}
-                </pre>
-              )}
-              {output.details && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.details)}
-                </pre>
-              )}
-            </div>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/RunAgent/helpers.tsx

@@ -1,191 +0,0 @@
-import type { ToolUIPart } from "ai";
-import { PlayIcon } from "@phosphor-icons/react";
-import type { AgentDetailsResponse } from "@/app/api/__generated__/models/agentDetailsResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { ExecutionStartedResponse } from "@/app/api/__generated__/models/executionStartedResponse";
-import type { NeedLoginResponse } from "@/app/api/__generated__/models/needLoginResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
-
-export interface RunAgentInput {
-  username_agent_slug?: string;
-  library_agent_id?: string;
-  inputs?: Record<string, unknown>;
-  use_defaults?: boolean;
-  schedule_name?: string;
-  cron?: string;
-  timezone?: string;
-}
-
-export type RunAgentToolOutput =
-  | SetupRequirementsResponse
-  | ExecutionStartedResponse
-  | AgentDetailsResponse
-  | NeedLoginResponse
-  | ErrorResponse;
-
-const RUN_AGENT_OUTPUT_TYPES = new Set<string>([
-  ResponseType.setup_requirements,
-  ResponseType.execution_started,
-  ResponseType.agent_details,
-  ResponseType.need_login,
-  ResponseType.error,
-]);
-
-export function isRunAgentSetupRequirementsOutput(
-  output: RunAgentToolOutput,
-): output is SetupRequirementsResponse {
-  return (
-    output.type === ResponseType.setup_requirements ||
-    ("setup_info" in output && typeof output.setup_info === "object")
-  );
-}
-
-export function isRunAgentExecutionStartedOutput(
-  output: RunAgentToolOutput,
-): output is ExecutionStartedResponse {
-  return (
-    output.type === ResponseType.execution_started || "execution_id" in output
-  );
-}
-
-export function isRunAgentAgentDetailsOutput(
-  output: RunAgentToolOutput,
-): output is AgentDetailsResponse {
-  return output.type === ResponseType.agent_details || "agent" in output;
-}
-
-export function isRunAgentNeedLoginOutput(
-  output: RunAgentToolOutput,
-): output is NeedLoginResponse {
-  return output.type === ResponseType.need_login;
-}
-
-export function isRunAgentErrorOutput(
-  output: RunAgentToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-function parseOutput(output: unknown): RunAgentToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (typeof type === "string" && RUN_AGENT_OUTPUT_TYPES.has(type)) {
-      return output as RunAgentToolOutput;
-    }
-    if ("execution_id" in output) return output as ExecutionStartedResponse;
-    if ("setup_info" in output) return output as SetupRequirementsResponse;
-    if ("agent" in output) return output as AgentDetailsResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-    if (type === ResponseType.need_login) return output as NeedLoginResponse;
-  }
-  return null;
-}
-
-export function getRunAgentToolOutput(
-  part: unknown,
-): RunAgentToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-function getAgentIdentifierText(
-  input: RunAgentInput | undefined,
-): string | null {
-  if (!input) return null;
-  const slug = input.username_agent_slug?.trim();
-  if (slug) return slug;
-  const libraryId = input.library_agent_id?.trim();
-  if (libraryId) return `Library agent ${libraryId}`;
-  return null;
-}
-
-function getExecutionModeText(input: RunAgentInput | undefined): string | null {
-  if (!input) return null;
-  const isSchedule = Boolean(input.schedule_name?.trim() || input.cron?.trim());
-  return isSchedule ? "Scheduled run" : "Run";
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  const input = part.input as RunAgentInput | undefined;
-  const agentIdentifier = getAgentIdentifierText(input);
-  const isSchedule = Boolean(
-    input?.schedule_name?.trim() || input?.cron?.trim(),
-  );
-  const actionPhrase = isSchedule
-    ? "Scheduling the agent to run"
-    : "Running the agent";
-  const identifierText = agentIdentifier ? ` "${agentIdentifier}"` : "";
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `${actionPhrase}${identifierText}`;
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return `${actionPhrase}${identifierText}`;
-      if (isRunAgentExecutionStartedOutput(output)) {
-        return `Started "${output.graph_name}"`;
-      }
-      if (isRunAgentAgentDetailsOutput(output)) {
-        return `Agent inputs needed for "${output.agent.name}"`;
-      }
-      if (isRunAgentSetupRequirementsOutput(output)) {
-        return `Setup needed for "${output.setup_info.agent_name}"`;
-      }
-      if (isRunAgentNeedLoginOutput(output))
-        return "Sign in required to run agent";
-      return "Error running agent";
-    }
-    case "output-error":
-      return "Error running agent";
-    default:
-      return actionPhrase;
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  return (
-    <PlayIcon
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/RunBlock/RunBlock.tsx

@@ -1,325 +0,0 @@
-"use client";
-
-import type { ToolUIPart } from "ai";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import {
-  ChatCredentialsSetup,
-  type CredentialInfo,
-} from "@/components/contextual/Chat/components/ChatCredentialsSetup/ChatCredentialsSetup";
-import {
-  formatMaybeJson,
-  getAnimationText,
-  getRunBlockToolOutput,
-  isRunBlockBlockOutput,
-  isRunBlockErrorOutput,
-  isRunBlockSetupRequirementsOutput,
-  ToolIcon,
-  type RunBlockToolOutput,
-} from "./helpers";
-
-export interface RunBlockToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: RunBlockToolPart;
-}
-
-function getAccordionMeta(output: RunBlockToolOutput): {
-  badgeText: string;
-  title: string;
-  description?: string;
-} {
-  if (isRunBlockBlockOutput(output)) {
-    const keys = Object.keys(output.outputs ?? {});
-    return {
-      badgeText: "Run block",
-      title: output.block_name,
-      description:
-        keys.length > 0
-          ? `${keys.length} output key${keys.length === 1 ? "" : "s"}`
-          : output.message,
-    };
-  }
-
-  if (isRunBlockSetupRequirementsOutput(output)) {
-    const missingCredsCount = Object.keys(
-      (output.setup_info.user_readiness?.missing_credentials ?? {}) as Record<
-        string,
-        unknown
-      >,
-    ).length;
-    return {
-      badgeText: "Run block",
-      title: output.setup_info.agent_name,
-      description:
-        missingCredsCount > 0
-          ? `Missing ${missingCredsCount} credential${missingCredsCount === 1 ? "" : "s"}`
-          : output.message,
-    };
-  }
-
-  return { badgeText: "Run block", title: "Error" };
-}
-
-function coerceMissingCredentials(
-  rawMissingCredentials: unknown,
-): CredentialInfo[] {
-  const missing =
-    rawMissingCredentials && typeof rawMissingCredentials === "object"
-      ? (rawMissingCredentials as Record<string, unknown>)
-      : {};
-
-  const validTypes = new Set([
-    "api_key",
-    "oauth2",
-    "user_password",
-    "host_scoped",
-  ]);
-
-  const results: CredentialInfo[] = [];
-
-  Object.values(missing).forEach((value) => {
-    if (!value || typeof value !== "object") return;
-    const cred = value as Record<string, unknown>;
-
-    const provider =
-      typeof cred.provider === "string" ? cred.provider.trim() : "";
-    if (!provider) return;
-
-    const providerName =
-      typeof cred.provider_name === "string" && cred.provider_name.trim()
-        ? cred.provider_name.trim()
-        : provider.replace(/_/g, " ");
-
-    const title =
-      typeof cred.title === "string" && cred.title.trim()
-        ? cred.title.trim()
-        : providerName;
-
-    const types =
-      Array.isArray(cred.types) && cred.types.length > 0
-        ? cred.types
-        : typeof cred.type === "string"
-          ? [cred.type]
-          : [];
-
-    const credentialTypes = types
-      .map((t) => (typeof t === "string" ? t.trim() : ""))
-      .filter(
-        (t): t is "api_key" | "oauth2" | "user_password" | "host_scoped" =>
-          validTypes.has(t),
-      );
-
-    if (credentialTypes.length === 0) return;
-
-    const scopes = Array.isArray(cred.scopes)
-      ? cred.scopes.filter((s): s is string => typeof s === "string")
-      : undefined;
-
-    const item: CredentialInfo = {
-      provider,
-      providerName,
-      credentialTypes,
-      title,
-    };
-    if (scopes && scopes.length > 0) {
-      item.scopes = scopes;
-    }
-    results.push(item);
-  });
-
-  return results;
-}
-
-function coerceExpectedInputs(rawInputs: unknown): Array<{
-  name: string;
-  title: string;
-  type: string;
-  description?: string;
-  required: boolean;
-}> {
-  if (!Array.isArray(rawInputs)) return [];
-  const results: Array<{
-    name: string;
-    title: string;
-    type: string;
-    description?: string;
-    required: boolean;
-  }> = [];
-
-  rawInputs.forEach((value, index) => {
-    if (!value || typeof value !== "object") return;
-    const input = value as Record<string, unknown>;
-
-    const name =
-      typeof input.name === "string" && input.name.trim()
-        ? input.name.trim()
-        : `input-${index}`;
-    const title =
-      typeof input.title === "string" && input.title.trim()
-        ? input.title.trim()
-        : name;
-    const type = typeof input.type === "string" ? input.type : "unknown";
-    const description =
-      typeof input.description === "string" && input.description.trim()
-        ? input.description.trim()
-        : undefined;
-    const required = Boolean(input.required);
-
-    const item: {
-      name: string;
-      title: string;
-      type: string;
-      description?: string;
-      required: boolean;
-    } = { name, title, type, required };
-    if (description) item.description = description;
-    results.push(item);
-  });
-
-  return results;
-}
-
-export function RunBlockTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const { onSend } = useCopilotChatActions();
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getRunBlockToolOutput(part);
-  const isError =
-    part.state === "output-error" ||
-    (!!output && isRunBlockErrorOutput(output));
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isRunBlockBlockOutput(output) ||
-      isRunBlockSetupRequirementsOutput(output) ||
-      isRunBlockErrorOutput(output));
-
-  function handleAllCredentialsComplete() {
-    onSend(
-      "I've configured the required credentials. Please re-run the block now.",
-    );
-  }
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion
-          {...getAccordionMeta(output)}
-          defaultExpanded={isRunBlockSetupRequirementsOutput(output)}
-        >
-          {isRunBlockBlockOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-
-              {Object.entries(output.outputs ?? {}).map(([key, items]) => (
-                <div key={key} className="rounded-2xl border bg-background p-3">
-                  <div className="flex items-center justify-between gap-2">
-                    <p className="truncate text-xs font-medium text-foreground">
-                      {key}
-                    </p>
-                    <span className="shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-muted-foreground">
-                      {items.length} item{items.length === 1 ? "" : "s"}
-                    </span>
-                  </div>
-                  <pre className="mt-2 whitespace-pre-wrap text-xs text-muted-foreground">
-                    {formatMaybeJson(items.slice(0, 3))}
-                  </pre>
-                </div>
-              ))}
-            </div>
-          )}
-
-          {isRunBlockSetupRequirementsOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-
-              {coerceMissingCredentials(
-                output.setup_info.user_readiness?.missing_credentials,
-              ).length > 0 && (
-                <ChatCredentialsSetup
-                  credentials={coerceMissingCredentials(
-                    output.setup_info.user_readiness?.missing_credentials,
-                  )}
-                  agentName={output.setup_info.agent_name}
-                  message={output.message}
-                  onAllCredentialsComplete={handleAllCredentialsComplete}
-                  onCancel={() => {}}
-                />
-              )}
-
-              {coerceExpectedInputs(
-                (output.setup_info.requirements as Record<string, unknown>)
-                  ?.inputs,
-              ).length > 0 && (
-                <div className="rounded-2xl border bg-background p-3">
-                  <p className="text-xs font-medium text-foreground">
-                    Expected inputs
-                  </p>
-                  <div className="mt-2 grid gap-2">
-                    {coerceExpectedInputs(
-                      (
-                        output.setup_info.requirements as Record<
-                          string,
-                          unknown
-                        >
-                      )?.inputs,
-                    ).map((input) => (
-                      <div key={input.name} className="rounded-xl border p-2">
-                        <div className="flex items-center justify-between gap-2">
-                          <p className="truncate text-xs font-medium text-foreground">
-                            {input.title}
-                          </p>
-                          <span className="shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-muted-foreground">
-                            {input.required ? "Required" : "Optional"}
-                          </span>
-                        </div>
-                        <p className="mt-1 text-xs text-muted-foreground">
-                          {input.name} • {input.type}
-                          {input.description ? ` • ${input.description}` : ""}
-                        </p>
-                      </div>
-                    ))}
-                  </div>
-                </div>
-              )}
-            </div>
-          )}
-
-          {isRunBlockErrorOutput(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.error && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.error)}
-                </pre>
-              )}
-              {output.details && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.details)}
-                </pre>
-              )}
-            </div>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/RunBlock/helpers.tsx

@@ -1,140 +0,0 @@
-import type { ToolUIPart } from "ai";
-import { PlayIcon } from "@phosphor-icons/react";
-import type { BlockOutputResponse } from "@/app/api/__generated__/models/blockOutputResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
-
-export interface RunBlockInput {
-  block_id?: string;
-  input_data?: Record<string, unknown>;
-}
-
-export type RunBlockToolOutput =
-  | SetupRequirementsResponse
-  | BlockOutputResponse
-  | ErrorResponse;
-
-const RUN_BLOCK_OUTPUT_TYPES = new Set<string>([
-  ResponseType.setup_requirements,
-  ResponseType.block_output,
-  ResponseType.error,
-]);
-
-export function isRunBlockSetupRequirementsOutput(
-  output: RunBlockToolOutput,
-): output is SetupRequirementsResponse {
-  return (
-    output.type === ResponseType.setup_requirements ||
-    ("setup_info" in output && typeof output.setup_info === "object")
-  );
-}
-
-export function isRunBlockBlockOutput(
-  output: RunBlockToolOutput,
-): output is BlockOutputResponse {
-  return output.type === ResponseType.block_output || "block_id" in output;
-}
-
-export function isRunBlockErrorOutput(
-  output: RunBlockToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-function parseOutput(output: unknown): RunBlockToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (typeof type === "string" && RUN_BLOCK_OUTPUT_TYPES.has(type)) {
-      return output as RunBlockToolOutput;
-    }
-    if ("block_id" in output) return output as BlockOutputResponse;
-    if ("setup_info" in output) return output as SetupRequirementsResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getRunBlockToolOutput(
-  part: unknown,
-): RunBlockToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-function getBlockLabel(input: RunBlockInput | undefined): string | null {
-  const blockId = input?.block_id?.trim();
-  if (!blockId) return null;
-  return `Block ${blockId.slice(0, 8)}…`;
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  const input = part.input as RunBlockInput | undefined;
-  const blockId = input?.block_id?.trim();
-  const blockText = blockId ? ` "${blockId}"` : "";
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `Running the block${blockText}`;
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return `Running the block${blockText}`;
-      if (isRunBlockBlockOutput(output)) return `Ran "${output.block_name}"`;
-      if (isRunBlockSetupRequirementsOutput(output)) {
-        return `Setup needed for "${output.setup_info.agent_name}"`;
-      }
-      return "Error running block";
-    }
-    case "output-error":
-      return "Error running block";
-    default:
-      return "Running the block";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  return (
-    <PlayIcon
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/SearchDocs/SearchDocs.tsx

@@ -1,197 +0,0 @@
-"use client";
-
-import type { ToolUIPart } from "ai";
-import Link from "next/link";
-import { useMemo } from "react";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import {
-  getDocsToolOutput,
-  getDocsToolTitle,
-  getToolLabel,
-  getAnimationText,
-  isDocPageOutput,
-  isDocSearchResultsOutput,
-  isErrorOutput,
-  isNoResultsOutput,
-  ToolIcon,
-  toDocsUrl,
-  type DocsToolType,
-} from "./helpers";
-
-export interface DocsToolPart {
-  type: DocsToolType;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: DocsToolPart;
-}
-
-function truncate(text: string, maxChars: number): string {
-  const trimmed = text.trim();
-  if (trimmed.length <= maxChars) return trimmed;
-  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
-}
-
-export function SearchDocsTool({ part }: Props) {
-  const output = getDocsToolOutput(part);
-  const text = getAnimationText(part);
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-
-  const normalized = useMemo(() => {
-    if (!output) return null;
-    const title = getDocsToolTitle(part.type, output);
-    const label = getToolLabel(part.type);
-    return { title, label };
-  }, [output, part.type]);
-
-  const isOutputAvailable = part.state === "output-available" && !!output;
-
-  const docSearchOutput =
-    isOutputAvailable && output && isDocSearchResultsOutput(output)
-      ? output
-      : null;
-  const docPageOutput =
-    isOutputAvailable && output && isDocPageOutput(output) ? output : null;
-  const noResultsOutput =
-    isOutputAvailable && output && isNoResultsOutput(output) ? output : null;
-  const errorOutput =
-    isOutputAvailable && output && isErrorOutput(output) ? output : null;
-
-  const hasExpandableContent =
-    isOutputAvailable &&
-    ((!!docSearchOutput && docSearchOutput.count > 0) ||
-      !!docPageOutput ||
-      !!noResultsOutput ||
-      !!errorOutput);
-
-  const accordionDescription =
-    hasExpandableContent && docSearchOutput
-      ? `Found ${docSearchOutput.count} result${docSearchOutput.count === 1 ? "" : "s"} for "${docSearchOutput.query}"`
-      : hasExpandableContent && docPageOutput
-        ? docPageOutput.path
-        : hasExpandableContent && (noResultsOutput || errorOutput)
-          ? ((noResultsOutput ?? errorOutput)?.message ?? null)
-          : null;
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon
-          toolType={part.type}
-          isStreaming={isStreaming}
-          isError={isError}
-        />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && normalized && (
-        <ToolAccordion
-          badgeText={normalized.label}
-          title={normalized.title}
-          description={accordionDescription}
-        >
-          {docSearchOutput && (
-            <div className="grid gap-2">
-              {docSearchOutput.results.map((r) => {
-                const href = r.doc_url ?? toDocsUrl(r.path);
-                return (
-                  <div
-                    key={r.path}
-                    className="rounded-2xl border bg-background p-3"
-                  >
-                    <div className="flex items-start justify-between gap-2">
-                      <div className="min-w-0">
-                        <p className="truncate text-sm font-medium text-foreground">
-                          {r.title}
-                        </p>
-                        <p className="mt-0.5 truncate text-xs text-muted-foreground">
-                          {r.path}
-                          {r.section ? ` • ${r.section}` : ""}
-                        </p>
-                        <p className="mt-2 text-xs text-muted-foreground">
-                          {truncate(r.snippet, 240)}
-                        </p>
-                      </div>
-                      <Link
-                        href={href}
-                        target="_blank"
-                        rel="noreferrer"
-                        className="shrink-0 text-xs font-medium text-purple-600 hover:text-purple-700"
-                      >
-                        Open
-                      </Link>
-                    </div>
-                  </div>
-                );
-              })}
-            </div>
-          )}
-
-          {docPageOutput && (
-            <div>
-              <div className="flex items-start justify-between gap-2">
-                <div className="min-w-0">
-                  <p className="truncate text-sm font-medium text-foreground">
-                    {docPageOutput.title}
-                  </p>
-                  <p className="mt-0.5 truncate text-xs text-muted-foreground">
-                    {docPageOutput.path}
-                  </p>
-                </div>
-                <Link
-                  href={docPageOutput.doc_url ?? toDocsUrl(docPageOutput.path)}
-                  target="_blank"
-                  rel="noreferrer"
-                  className="shrink-0 text-xs font-medium text-purple-600 hover:text-purple-700"
-                >
-                  Open
-                </Link>
-              </div>
-              <p className="mt-2 whitespace-pre-wrap text-xs text-muted-foreground">
-                {truncate(docPageOutput.content, 800)}
-              </p>
-            </div>
-          )}
-
-          {noResultsOutput && (
-            <div>
-              <p className="text-sm text-foreground">
-                {noResultsOutput.message}
-              </p>
-              {noResultsOutput.suggestions &&
-                noResultsOutput.suggestions.length > 0 && (
-                  <ul className="mt-2 list-disc space-y-1 pl-5 text-xs text-muted-foreground">
-                    {noResultsOutput.suggestions.slice(0, 5).map((s) => (
-                      <li key={s}>{s}</li>
-                    ))}
-                  </ul>
-                )}
-            </div>
-          )}
-
-          {errorOutput && (
-            <div>
-              <p className="text-sm text-foreground">{errorOutput.message}</p>
-              {errorOutput.error && (
-                <p className="mt-2 text-xs text-muted-foreground">
-                  {errorOutput.error}
-                </p>
-              )}
-            </div>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/SearchDocs/helpers.tsx

@@ -1,205 +0,0 @@
-import { ToolUIPart } from "ai";
-import { FileMagnifyingGlassIcon, FileTextIcon } from "@phosphor-icons/react";
-import type { DocPageResponse } from "@/app/api/__generated__/models/docPageResponse";
-import type { DocSearchResultsResponse } from "@/app/api/__generated__/models/docSearchResultsResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-
-export interface SearchDocsInput {
-  query: string;
-}
-
-export interface GetDocPageInput {
-  path: string;
-}
-
-export type DocsToolOutput =
-  | DocSearchResultsResponse
-  | DocPageResponse
-  | NoResultsResponse
-  | ErrorResponse;
-
-export type DocsToolType = "tool-search_docs" | "tool-get_doc_page" | string;
-
-export function getToolLabel(toolType: DocsToolType): string {
-  switch (toolType) {
-    case "tool-search_docs":
-      return "Docs";
-    case "tool-get_doc_page":
-      return "Docs page";
-    default:
-      return "Docs";
-  }
-}
-
-function parseOutput(output: unknown): DocsToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.doc_search_results ||
-      type === ResponseType.doc_page ||
-      type === ResponseType.no_results ||
-      type === ResponseType.error
-    ) {
-      return output as DocsToolOutput;
-    }
-    if ("results" in output && "query" in output)
-      return output as DocSearchResultsResponse;
-    if ("content" in output && "path" in output)
-      return output as DocPageResponse;
-    if ("suggestions" in output && !("error" in output))
-      return output as NoResultsResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getDocsToolOutput(part: unknown): DocsToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isDocSearchResultsOutput(
-  output: DocsToolOutput,
-): output is DocSearchResultsResponse {
-  return output.type === ResponseType.doc_search_results || "results" in output;
-}
-
-export function isDocPageOutput(
-  output: DocsToolOutput,
-): output is DocPageResponse {
-  return output.type === ResponseType.doc_page || "content" in output;
-}
-
-export function isNoResultsOutput(
-  output: DocsToolOutput,
-): output is NoResultsResponse {
-  return (
-    output.type === ResponseType.no_results ||
-    ("suggestions" in output && !("error" in output))
-  );
-}
-
-export function isErrorOutput(output: DocsToolOutput): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getDocsToolTitle(
-  toolType: DocsToolType,
-  output: DocsToolOutput,
-): string {
-  if (toolType === "tool-search_docs") {
-    if (isDocSearchResultsOutput(output)) return "Documentation results";
-    if (isNoResultsOutput(output)) return "No documentation found";
-    return "Documentation search error";
-  }
-
-  if (isDocPageOutput(output)) return "Documentation page";
-  if (isNoResultsOutput(output)) return "No documentation found";
-  return "Documentation page error";
-}
-
-export function getAnimationText(part: {
-  type: DocsToolType;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  switch (part.type) {
-    case "tool-search_docs": {
-      const query = (part.input as SearchDocsInput | undefined)?.query?.trim();
-      const queryText = query ? ` for "${query}"` : "";
-
-      switch (part.state) {
-        case "input-streaming":
-        case "input-available":
-          return `Searching documentation${queryText}`;
-        case "output-available": {
-          const output = parseOutput(part.output);
-          if (!output) return `Searching documentation${queryText}`;
-          if (isDocSearchResultsOutput(output)) {
-            const count = output.count ?? output.results.length;
-            return `Found ${count} result${count === 1 ? "" : "s"}${queryText}`;
-          }
-          if (isNoResultsOutput(output)) {
-            return `No results found${queryText}`;
-          }
-          return `Error searching documentation${queryText}`;
-        }
-        case "output-error":
-          return `Error searching documentation${queryText}`;
-        default:
-          return "Searching documentation";
-      }
-    }
-    case "tool-get_doc_page": {
-      const path = (part.input as GetDocPageInput | undefined)?.path?.trim();
-      const pathText = path ? ` "${path}"` : "";
-
-      switch (part.state) {
-        case "input-streaming":
-        case "input-available":
-          return `Loading documentation page${pathText}`;
-        case "output-available": {
-          const output = parseOutput(part.output);
-          if (!output) return `Loading documentation page${pathText}`;
-          if (isDocPageOutput(output)) return `Loaded "${output.title}"`;
-          if (isNoResultsOutput(output)) return "Documentation page not found";
-          return "Error loading documentation page";
-        }
-        case "output-error":
-          return "Error loading documentation page";
-        default:
-          return "Loading documentation page";
-      }
-    }
-  }
-
-  return "Processing";
-}
-
-export function ToolIcon({
-  toolType,
-  isStreaming,
-  isError,
-}: {
-  toolType: DocsToolType;
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  const IconComponent =
-    toolType === "tool-get_doc_page" ? FileTextIcon : FileMagnifyingGlassIcon;
-
-  return (
-    <IconComponent
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function toDocsUrl(path: string): string {
-  const urlPath = path.includes(".")
-    ? path.slice(0, path.lastIndexOf("."))
-    : path;
-  return `https://docs.agpt.co/${urlPath}`;
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/ViewAgentOutput/ViewAgentOutput.tsx

@@ -1,181 +0,0 @@
-"use client";
-
-import type { ToolUIPart } from "ai";
-import Link from "next/link";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import {
-  formatMaybeJson,
-  getAnimationText,
-  getViewAgentOutputToolOutput,
-  isAgentOutputResponse,
-  isErrorResponse,
-  isNoResultsResponse,
-  ToolIcon,
-  type ViewAgentOutputToolOutput,
-} from "./helpers";
-
-export interface ViewAgentOutputToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: ViewAgentOutputToolPart;
-}
-
-function getAccordionMeta(output: ViewAgentOutputToolOutput): {
-  badgeText: string;
-  title: string;
-  description?: string;
-} {
-  if (isAgentOutputResponse(output)) {
-    const status = output.execution?.status;
-    return {
-      badgeText: "Agent output",
-      title: output.agent_name,
-      description: status ? `Status: ${status}` : output.message,
-    };
-  }
-  if (isNoResultsResponse(output)) {
-    return { badgeText: "Agent output", title: "No results" };
-  }
-  return { badgeText: "Agent output", title: "Error" };
-}
-
-export function ViewAgentOutputTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getViewAgentOutputToolOutput(part);
-  const isError =
-    part.state === "output-error" || (!!output && isErrorResponse(output));
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isAgentOutputResponse(output) ||
-      isNoResultsResponse(output) ||
-      isErrorResponse(output));
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion {...getAccordionMeta(output)}>
-          {isAgentOutputResponse(output) && (
-            <div className="grid gap-2">
-              <div className="flex items-start justify-between gap-3">
-                <p className="text-sm text-foreground">{output.message}</p>
-                {output.library_agent_link && (
-                  <Link
-                    href={output.library_agent_link}
-                    className="shrink-0 text-xs font-medium text-purple-600 hover:text-purple-700"
-                  >
-                    Open
-                  </Link>
-                )}
-              </div>
-
-              {output.execution ? (
-                <div className="grid gap-2">
-                  <div className="rounded-2xl border bg-background p-3">
-                    <p className="text-xs font-medium text-foreground">
-                      Execution
-                    </p>
-                    <p className="mt-1 truncate text-xs text-muted-foreground">
-                      {output.execution.execution_id}
-                    </p>
-                    <p className="mt-1 text-xs text-muted-foreground">
-                      Status: {output.execution.status}
-                    </p>
-                  </div>
-
-                  {output.execution.inputs_summary && (
-                    <div className="rounded-2xl border bg-background p-3">
-                      <p className="text-xs font-medium text-foreground">
-                        Inputs summary
-                      </p>
-                      <pre className="mt-2 whitespace-pre-wrap text-xs text-muted-foreground">
-                        {formatMaybeJson(output.execution.inputs_summary)}
-                      </pre>
-                    </div>
-                  )}
-
-                  {Object.entries(output.execution.outputs ?? {}).map(
-                    ([key, items]) => (
-                      <div
-                        key={key}
-                        className="rounded-2xl border bg-background p-3"
-                      >
-                        <div className="flex items-center justify-between gap-2">
-                          <p className="truncate text-xs font-medium text-foreground">
-                            {key}
-                          </p>
-                          <span className="shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-muted-foreground">
-                            {items.length} item{items.length === 1 ? "" : "s"}
-                          </span>
-                        </div>
-                        <pre className="mt-2 whitespace-pre-wrap text-xs text-muted-foreground">
-                          {formatMaybeJson(items.slice(0, 3))}
-                        </pre>
-                      </div>
-                    ),
-                  )}
-                </div>
-              ) : (
-                <div className="rounded-2xl border bg-background p-3">
-                  <p className="text-sm text-foreground">
-                    No execution selected.
-                  </p>
-                  <p className="mt-1 text-xs text-muted-foreground">
-                    Try asking for a specific run or execution_id.
-                  </p>
-                </div>
-              )}
-            </div>
-          )}
-
-          {isNoResultsResponse(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.suggestions && output.suggestions.length > 0 && (
-                <ul className="mt-1 list-disc space-y-1 pl-5 text-xs text-muted-foreground">
-                  {output.suggestions.slice(0, 5).map((s) => (
-                    <li key={s}>{s}</li>
-                  ))}
-                </ul>
-              )}
-            </div>
-          )}
-
-          {isErrorResponse(output) && (
-            <div className="grid gap-2">
-              <p className="text-sm text-foreground">{output.message}</p>
-              {output.error && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.error)}
-                </pre>
-              )}
-              {output.details && (
-                <pre className="whitespace-pre-wrap rounded-2xl border bg-muted/30 p-3 text-xs text-muted-foreground">
-                  {formatMaybeJson(output.details)}
-                </pre>
-              )}
-            </div>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/tools/ViewAgentOutput/helpers.tsx

@@ -1,154 +0,0 @@
-import type { ToolUIPart } from "ai";
-import { EyeIcon } from "@phosphor-icons/react";
-import type { AgentOutputResponse } from "@/app/api/__generated__/models/agentOutputResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-
-export interface ViewAgentOutputInput {
-  agent_name?: string;
-  library_agent_id?: string;
-  store_slug?: string;
-  execution_id?: string;
-  run_time?: string;
-}
-
-export type ViewAgentOutputToolOutput =
-  | AgentOutputResponse
-  | NoResultsResponse
-  | ErrorResponse;
-
-function parseOutput(output: unknown): ViewAgentOutputToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.agent_output ||
-      type === ResponseType.no_results ||
-      type === ResponseType.error
-    ) {
-      return output as ViewAgentOutputToolOutput;
-    }
-    if ("agent_id" in output && "agent_name" in output) {
-      return output as AgentOutputResponse;
-    }
-    if ("suggestions" in output && !("error" in output)) {
-      return output as NoResultsResponse;
-    }
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function isAgentOutputResponse(
-  output: ViewAgentOutputToolOutput,
-): output is AgentOutputResponse {
-  return output.type === ResponseType.agent_output || "agent_id" in output;
-}
-
-export function isNoResultsResponse(
-  output: ViewAgentOutputToolOutput,
-): output is NoResultsResponse {
-  return (
-    output.type === ResponseType.no_results ||
-    ("suggestions" in output && !("error" in output))
-  );
-}
-
-export function isErrorResponse(
-  output: ViewAgentOutputToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getViewAgentOutputToolOutput(
-  part: unknown,
-): ViewAgentOutputToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-function getAgentIdentifierText(
-  input: ViewAgentOutputInput | undefined,
-): string | null {
-  if (!input) return null;
-  const libraryId = input.library_agent_id?.trim();
-  if (libraryId) return `Library agent ${libraryId}`;
-  const slug = input.store_slug?.trim();
-  if (slug) return slug;
-  const name = input.agent_name?.trim();
-  if (name) return name;
-  return null;
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  const input = part.input as ViewAgentOutputInput | undefined;
-  const agent = getAgentIdentifierText(input);
-  const agentText = agent ? ` "${agent}"` : "";
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `Retrieving agent output${agentText}`;
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return `Retrieving agent output${agentText}`;
-      if (isAgentOutputResponse(output)) {
-        if (output.execution)
-          return `Retrieved output (${output.execution.status})`;
-        return "Retrieved agent output";
-      }
-      if (isNoResultsResponse(output)) return "No outputs found";
-      return "Error loading agent output";
-    }
-    case "output-error":
-      return "Error loading agent output";
-    default:
-      return "Retrieving agent output";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  return (
-    <EyeIcon
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/useChatSession.ts

@@ -1,64 +0,0 @@
-import {
-  getGetV2ListSessionsQueryKey,
-  useGetV2GetSession,
-  usePostV2CreateSession,
-} from "@/app/api/__generated__/endpoints/chat/chat";
-import { useQueryClient } from "@tanstack/react-query";
-import { parseAsString, useQueryState } from "nuqs";
-import { useMemo } from "react";
-import { convertChatSessionMessagesToUiMessages } from "./helpers/convertChatSessionToUiMessages";
-
-export function useChatSession() {
-  const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
-  const queryClient = useQueryClient();
-
-  const sessionQuery = useGetV2GetSession(sessionId ?? "", {
-    query: {
-      staleTime: Infinity,
-      refetchOnWindowFocus: false,
-      refetchOnReconnect: false,
-    },
-  });
-
-  // Memoize so the effect in useCopilotPage doesn't infinite-loop on a new
-  // array reference every render. Re-derives only when query data changes.
-  const hydratedMessages = useMemo(() => {
-    if (sessionQuery.data?.status !== 200 || !sessionId) return undefined;
-    return convertChatSessionMessagesToUiMessages(
-      sessionId,
-      sessionQuery.data.data.messages ?? [],
-    );
-  }, [sessionQuery.data, sessionId]);
-
-  const { mutateAsync: createSessionMutation, isPending: isCreatingSession } =
-    usePostV2CreateSession({
-      mutation: {
-        onSuccess: (response) => {
-          if (response.status === 200 && response.data?.id) {
-            setSessionId(response.data.id);
-            queryClient.invalidateQueries({
-              queryKey: getGetV2ListSessionsQueryKey(),
-            });
-          }
-        },
-      },
-    });
-
-  async function createSession() {
-    if (sessionId) return sessionId;
-    const response = await createSessionMutation();
-    if (response.status !== 200 || !response.data?.id) {
-      throw new Error("Failed to create session");
-    }
-    return response.data.id;
-  }
-
-  return {
-    sessionId,
-    setSessionId,
-    hydratedMessages,
-    isLoadingSession: sessionQuery.isLoading,
-    createSession,
-    isCreatingSession,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/copilot-2/useCopilotPage.ts

@@ -1,137 +0,0 @@
-import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
-import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
-import { useChat } from "@ai-sdk/react";
-import { DefaultChatTransport } from "ai";
-import { useCallback, useEffect, useState } from "react";
-import { useChatSession } from "./useChatSession";
-
-export function useCopilotPage() {
-  const [isDrawerOpen, setIsDrawerOpen] = useState(false);
-  const [pendingMessage, setPendingMessage] = useState<string | null>(null);
-
-  const {
-    sessionId,
-    setSessionId,
-    hydratedMessages,
-    isLoadingSession,
-    createSession,
-    isCreatingSession,
-  } = useChatSession();
-
-  const breakpoint = useBreakpoint();
-  const isMobile =
-    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
-
-  const transport = sessionId
-    ? new DefaultChatTransport({
-        api: `/api/chat/sessions/${sessionId}/stream`,
-        prepareSendMessagesRequest: ({ messages }) => {
-          const last = messages[messages.length - 1];
-          return {
-            body: {
-              message: last.parts
-                ?.map((p) => (p.type === "text" ? p.text : ""))
-                .join(""),
-              is_user_message: last.role === "user",
-              context: null,
-            },
-          };
-        },
-        // Resume uses GET on the same endpoint (no message param → backend resumes)
-        prepareReconnectToStreamRequest: () => ({
-          api: `/api/chat/sessions/${sessionId}/stream`,
-        }),
-      })
-    : null;
-
-  const { messages, sendMessage, status, error, setMessages } = useChat({
-    id: sessionId ?? undefined,
-    transport: transport ?? undefined,
-    resume: !!sessionId,
-  });
-
-  useEffect(() => {
-    if (!hydratedMessages || hydratedMessages.length === 0) return;
-    setMessages((prev) => {
-      if (prev.length > hydratedMessages.length) return prev;
-      return hydratedMessages;
-    });
-  }, [hydratedMessages, setMessages]);
-
-  // Clear messages when session is null
-  useEffect(() => {
-    if (!sessionId) setMessages([]);
-  }, [sessionId, setMessages]);
-
-  useEffect(() => {
-    if (!sessionId || !pendingMessage) return;
-    const msg = pendingMessage;
-    setPendingMessage(null);
-    sendMessage({ text: msg });
-  }, [sessionId, pendingMessage, sendMessage]);
-
-  async function onSend(message: string) {
-    const trimmed = message.trim();
-    if (!trimmed) return;
-
-    if (sessionId) {
-      sendMessage({ text: trimmed });
-      return;
-    }
-
-    setPendingMessage(trimmed);
-    await createSession();
-  }
-
-  const { data: sessionsResponse, isLoading: isLoadingSessions } =
-    useGetV2ListSessions({ limit: 50 });
-
-  const sessions =
-    sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
-
-  const handleOpenDrawer = useCallback(() => {
-    setIsDrawerOpen(true);
-  }, []);
-
-  const handleCloseDrawer = useCallback(() => {
-    setIsDrawerOpen(false);
-  }, []);
-
-  const handleDrawerOpenChange = useCallback((open: boolean) => {
-    setIsDrawerOpen(open);
-  }, []);
-
-  const handleSelectSession = useCallback(
-    (id: string) => {
-      setSessionId(id);
-      if (isMobile) setIsDrawerOpen(false);
-    },
-    [setSessionId, isMobile],
-  );
-
-  const handleNewChat = useCallback(() => {
-    setSessionId(null);
-    if (isMobile) setIsDrawerOpen(false);
-  }, [setSessionId, isMobile]);
-
-  return {
-    sessionId,
-    messages,
-    status,
-    error,
-    isLoadingSession,
-    isCreatingSession,
-    createSession,
-    onSend,
-    // Mobile drawer
-    isMobile,
-    isDrawerOpen,
-    sessions,
-    isLoadingSessions,
-    handleOpenDrawer,
-    handleCloseDrawer,
-    handleDrawerOpenChange,
-    handleSelectSession,
-    handleNewChat,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-run-draft-view.tsx

@@ -10,8 +10,8 @@ import React, {
 import {
   CredentialsMetaInput,
   CredentialsType,
+  Graph,
   GraphExecutionID,
-  GraphMeta,
   LibraryAgentPreset,
   LibraryAgentPresetID,
   LibraryAgentPresetUpdatable,
@@ -69,7 +69,7 @@ export function AgentRunDraftView({
   className,
   recommendedScheduleCron,
 }: {
-  graph: GraphMeta;
+  graph: Graph;
   agentActions?: ButtonAction[];
   recommendedScheduleCron?: string | null;
   doRun?: (

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-schedule-details-view.tsx

@@ -2,8 +2,8 @@
 import React, { useCallback, useMemo } from "react";
 
 import {
+  Graph,
   GraphExecutionID,
-  GraphMeta,
   Schedule,
   ScheduleID,
 } from "@/lib/autogpt-server-api";
@@ -35,7 +35,7 @@ export function AgentScheduleDetailsView({
   onForcedRun,
   doDeleteSchedule,
 }: {
-  graph: GraphMeta;
+  graph: Graph;
   schedule: Schedule;
   agentActions: ButtonAction[];
   onForcedRun: (runID: GraphExecutionID) => void;