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
+    # Disabled: to re-enable, remove 'false &&' from the condition below
-    if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
+    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/admin/waitlist_admin_routes.py

@@ -1,251 +0,0 @@
-import logging
-
-import autogpt_libs.auth
-import fastapi
-import fastapi.responses
-
-import backend.api.features.store.db as store_db
-import backend.api.features.store.model as store_model
-
-logger = logging.getLogger(__name__)
-
-router = fastapi.APIRouter(
-    prefix="/admin/waitlist",
-    tags=["store", "admin", "waitlist"],
-    dependencies=[fastapi.Security(autogpt_libs.auth.requires_admin_user)],
-)
-
-
-@router.post(
-    "",
-    summary="Create Waitlist",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def create_waitlist(
-    request: store_model.WaitlistCreateRequest,
-    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
-):
-    """
-    Create a new waitlist (admin only).
-
-    Args:
-        request: Waitlist creation details
-        user_id: Authenticated admin user creating the waitlist
-
-    Returns:
-        WaitlistAdminResponse with the created waitlist details
-    """
-    try:
-        waitlist = await store_db.create_waitlist_admin(
-            admin_user_id=user_id,
-            data=request,
-        )
-        return waitlist
-    except Exception as e:
-        logger.exception("Error creating waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while creating the waitlist"},
-        )
-
-
-@router.get(
-    "",
-    summary="List All Waitlists",
-    response_model=store_model.WaitlistAdminListResponse,
-)
-async def list_waitlists():
-    """
-    Get all waitlists with admin details (admin only).
-
-    Returns:
-        WaitlistAdminListResponse with all waitlists
-    """
-    try:
-        return await store_db.get_waitlists_admin()
-    except Exception as e:
-        logger.exception("Error listing waitlists: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while fetching waitlists"},
-        )
-
-
-@router.get(
-    "/{waitlist_id}",
-    summary="Get Waitlist Details",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def get_waitlist(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Get a single waitlist with admin details (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist to retrieve
-
-    Returns:
-        WaitlistAdminResponse with waitlist details
-    """
-    try:
-        return await store_db.get_waitlist_admin(waitlist_id)
-    except ValueError:
-        logger.warning("Waitlist not found: %s", waitlist_id)
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error fetching waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while fetching the waitlist"},
-        )
-
-
-@router.put(
-    "/{waitlist_id}",
-    summary="Update Waitlist",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def update_waitlist(
-    request: store_model.WaitlistUpdateRequest,
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Update a waitlist (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist to update
-        request: Fields to update
-
-    Returns:
-        WaitlistAdminResponse with updated waitlist details
-    """
-    try:
-        return await store_db.update_waitlist_admin(waitlist_id, request)
-    except ValueError:
-        logger.warning("Waitlist not found for update: %s", waitlist_id)
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error updating waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while updating the waitlist"},
-        )
-
-
-@router.delete(
-    "/{waitlist_id}",
-    summary="Delete Waitlist",
-)
-async def delete_waitlist(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Soft delete a waitlist (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist to delete
-
-    Returns:
-        Success message
-    """
-    try:
-        await store_db.delete_waitlist_admin(waitlist_id)
-        return {"message": "Waitlist deleted successfully"}
-    except ValueError:
-        logger.warning(f"Waitlist not found for deletion: {waitlist_id}")
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error deleting waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while deleting the waitlist"},
-        )
-
-
-@router.get(
-    "/{waitlist_id}/signups",
-    summary="Get Waitlist Signups",
-    response_model=store_model.WaitlistSignupListResponse,
-)
-async def get_waitlist_signups(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Get all signups for a waitlist (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist
-
-    Returns:
-        WaitlistSignupListResponse with all signups
-    """
-    try:
-        return await store_db.get_waitlist_signups_admin(waitlist_id)
-    except ValueError:
-        logger.warning("Waitlist not found for signups: %s", waitlist_id)
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error fetching waitlist signups: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while fetching waitlist signups"},
-        )
-
-
-@router.post(
-    "/{waitlist_id}/link",
-    summary="Link Waitlist to Store Listing",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def link_waitlist_to_listing(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-    store_listing_id: str = fastapi.Body(
-        ..., embed=True, description="The ID of the store listing"
-    ),
-):
-    """
-    Link a waitlist to a store listing (admin only).
-
-    When the linked store listing is approved/published, waitlist users
-    will be automatically notified.
-
-    Args:
-        waitlist_id: ID of the waitlist
-        store_listing_id: ID of the store listing to link
-
-    Returns:
-        WaitlistAdminResponse with updated waitlist details
-    """
-    try:
-        return await store_db.link_waitlist_to_listing_admin(
-            waitlist_id, store_listing_id
-        )
-    except ValueError:
-        logger.warning(
-            "Link failed - waitlist or listing not found: %s, %s",
-            waitlist_id,
-            store_listing_id,
-        )
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist or store listing not found"},
-        )
-    except Exception as e:
-        logger.exception("Error linking waitlist to listing: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while linking the waitlist"},
-        )

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/service.py

@@ -33,7 +33,7 @@ from backend.data.understanding import (
     get_business_understanding,
 )
 from backend.util.exceptions import NotFoundError
-from backend.util.settings import Settings
+from backend.util.settings import AppEnvironment, Settings
 
 from . import db as chat_db
 from . import stream_registry
@@ -222,8 +222,18 @@ async def _get_system_prompt_template(context: str) -> str:
         try:
             # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
             # Use asyncio.to_thread to avoid blocking the event loop
+            # In non-production environments, fetch the latest prompt version
+            # instead of the production-labeled version for easier testing
+            label = (
+                None
+                if settings.config.app_env == AppEnvironment.PRODUCTION
+                else "latest"
+            )
             prompt = await asyncio.to_thread(
-                langfuse.get_prompt, config.langfuse_prompt_name, cache_ttl_seconds=0
+                langfuse.get_prompt,
+                config.langfuse_prompt_name,
+                label=label,
+                cache_ttl_seconds=0,
             )
             return prompt.compile(users_information=context)
         except Exception as e:
@@ -618,6 +628,9 @@ async def stream_chat_completion(
                         total_tokens=chunk.totalTokens,
                     )
                 )
+            elif isinstance(chunk, StreamHeartbeat):
+                # Pass through heartbeat to keep SSE connection alive
+                yield chunk
             else:
                 logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
 

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 (
+from backend.data.graph import Graph, Link, Node, get_graph, get_store_listed_graphs
-    Graph,
-    Link,
-    Node,
-    create_graph,
-    get_graph,
-    get_graph_all_versions,
-    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:
+        return await library_db.update_graph_in_library(graph, user_id)
-            existing_versions = await get_graph_all_versions(graph.id, user_id)
+    return await library_db.create_graph_in_library(graph, 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]
 
 
 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(
+    graph = await store_db.get_available_graph(
-        store_agent.store_listing_version_id
+        store_agent.store_listing_version_id, hide_nodes=False
-    )
-    graph = await graph_db.get_graph(
-        graph_id=graph_meta.id,
-        version=graph_meta.version,
-        user_id=None,  # Public access
-        include_subgraphs=True,
     )
     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,
@@ -22,7 +22,6 @@ from backend.data.notifications import (
     AgentApprovalData,
     AgentRejectionData,
     NotificationEventModel,
-    WaitlistLaunchData,
 )
 from backend.notifications.notifications import queue_notification_async
 from backend.util.exceptions import DatabaseError
@@ -335,7 +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 = (
@@ -345,7 +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}},
             )
         )
 
@@ -355,7 +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}")
@@ -1714,29 +1730,6 @@ async def review_store_submission(
                 # Don't fail the review process if email sending fails
                 pass
 
-        # Notify waitlist users if this is an approval and has a linked waitlist
-        if is_approved and submission.StoreListing:
-            try:
-                frontend_base_url = (
-                    settings.config.frontend_base_url
-                    or settings.config.platform_base_url
-                )
-                store_agent = (
-                    await prisma.models.StoreAgent.prisma().find_first_or_raise(
-                        where={"storeListingVersionId": submission.id}
-                    )
-                )
-                creator_username = store_agent.creator_username or "unknown"
-                store_url = f"{frontend_base_url}/marketplace/agent/{creator_username}/{store_agent.slug}"
-                await notify_waitlist_users_on_launch(
-                    store_listing_id=submission.StoreListing.id,
-                    agent_name=submission.name,
-                    store_url=store_url,
-                )
-            except Exception as e:
-                logger.error(f"Failed to notify waitlist users on agent approval: {e}")
-                # Don't fail the approval process
-
         # Convert to Pydantic model for consistency
         return store_model.StoreSubmission(
             listing_id=(submission.StoreListing.id if submission.StoreListing else ""),
@@ -1984,552 +1977,3 @@ async def get_agent_as_admin(
         )
 
     return graph
-
-
-def _waitlist_to_store_entry(
-    waitlist: prisma.models.WaitlistEntry,
-) -> store_model.StoreWaitlistEntry:
-    """Convert a WaitlistEntry to StoreWaitlistEntry for public display."""
-    return store_model.StoreWaitlistEntry(
-        waitlistId=waitlist.id,
-        slug=waitlist.slug,
-        name=waitlist.name,
-        subHeading=waitlist.subHeading,
-        videoUrl=waitlist.videoUrl,
-        agentOutputDemoUrl=waitlist.agentOutputDemoUrl,
-        imageUrls=waitlist.imageUrls or [],
-        description=waitlist.description,
-        categories=waitlist.categories,
-    )
-
-
-async def get_waitlist() -> list[store_model.StoreWaitlistEntry]:
-    """Get all active waitlists for public display."""
-    try:
-        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
-            where=prisma.types.WaitlistEntryWhereInput(isDeleted=False),
-        )
-
-        # Filter out closed/done waitlists and sort by votes (descending)
-        excluded_statuses = {
-            prisma.enums.WaitlistExternalStatus.CANCELED,
-            prisma.enums.WaitlistExternalStatus.DONE,
-        }
-        active_waitlists = [w for w in waitlists if w.status not in excluded_statuses]
-        sorted_list = sorted(active_waitlists, key=lambda x: x.votes, reverse=True)
-
-        return [_waitlist_to_store_entry(w) for w in sorted_list]
-    except Exception as e:
-        logger.error(f"Error fetching waitlists: {e}")
-        raise DatabaseError("Failed to fetch waitlists") from e
-
-
-async def get_user_waitlist_memberships(user_id: str) -> list[str]:
-    """Get all waitlist IDs that a user has joined."""
-    try:
-        user = await prisma.models.User.prisma().find_unique(
-            where={"id": user_id},
-            include={"joinedWaitlists": True},
-        )
-        if not user or not user.joinedWaitlists:
-            return []
-        return [w.id for w in user.joinedWaitlists]
-    except Exception as e:
-        logger.error(f"Error fetching user waitlist memberships: {e}")
-        raise DatabaseError("Failed to fetch waitlist memberships") from e
-
-
-async def add_user_to_waitlist(
-    waitlist_id: str, user_id: str | None, email: str | None
-) -> store_model.StoreWaitlistEntry:
-    """
-    Add a user to a waitlist.
-
-    For logged-in users: connects via joinedUsers relation
-    For anonymous users: adds email to unaffiliatedEmailUsers array
-    """
-    if not user_id and not email:
-        raise ValueError("Either user_id or email must be provided")
-
-    try:
-        # Find the waitlist
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id},
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        if waitlist.isDeleted:
-            raise ValueError(f"Waitlist {waitlist_id} is no longer available")
-
-        if waitlist.status in [
-            prisma.enums.WaitlistExternalStatus.CANCELED,
-            prisma.enums.WaitlistExternalStatus.DONE,
-        ]:
-            raise ValueError(f"Waitlist {waitlist_id} is closed")
-
-        if user_id:
-            # Check if user already joined
-            joined_user_ids = [u.id for u in (waitlist.joinedUsers or [])]
-            if user_id in joined_user_ids:
-                # Already joined - return waitlist info
-                logger.debug(f"User {user_id} already joined waitlist {waitlist_id}")
-            else:
-                # Connect user to waitlist
-                await prisma.models.WaitlistEntry.prisma().update(
-                    where={"id": waitlist_id},
-                    data={"joinedUsers": {"connect": [{"id": user_id}]}},
-                )
-                logger.info(f"User {user_id} joined waitlist {waitlist_id}")
-
-            # If user was previously in email list, remove them
-            # Use transaction to prevent race conditions
-            if email:
-                async with transaction() as tx:
-                    current_waitlist = await tx.waitlistentry.find_unique(
-                        where={"id": waitlist_id}
-                    )
-                    if current_waitlist and email in (
-                        current_waitlist.unaffiliatedEmailUsers or []
-                    ):
-                        updated_emails: list[str] = [
-                            e
-                            for e in (current_waitlist.unaffiliatedEmailUsers or [])
-                            if e != email
-                        ]
-                        await tx.waitlistentry.update(
-                            where={"id": waitlist_id},
-                            data={"unaffiliatedEmailUsers": updated_emails},
-                        )
-        elif email:
-            # Add email to unaffiliated list if not already present
-            # Use transaction to prevent race conditions with concurrent signups
-            async with transaction() as tx:
-                # Re-fetch within transaction to get latest state
-                current_waitlist = await tx.waitlistentry.find_unique(
-                    where={"id": waitlist_id}
-                )
-                if current_waitlist:
-                    current_emails: list[str] = list(
-                        current_waitlist.unaffiliatedEmailUsers or []
-                    )
-                    if email not in current_emails:
-                        current_emails.append(email)
-                        await tx.waitlistentry.update(
-                            where={"id": waitlist_id},
-                            data={"unaffiliatedEmailUsers": current_emails},
-                        )
-                        logger.info(f"Email {email} added to waitlist {waitlist_id}")
-                    else:
-                        logger.debug(f"Email {email} already on waitlist {waitlist_id}")
-
-        # Re-fetch to return updated data
-        updated_waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id}
-        )
-        return _waitlist_to_store_entry(updated_waitlist or waitlist)
-
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error adding user to waitlist: {e}")
-        raise DatabaseError("Failed to add user to waitlist") from e
-
-
-# ============== Admin Waitlist Functions ==============
-
-
-def _waitlist_to_admin_response(
-    waitlist: prisma.models.WaitlistEntry,
-) -> store_model.WaitlistAdminResponse:
-    """Convert a WaitlistEntry to WaitlistAdminResponse."""
-    joined_count = len(waitlist.joinedUsers) if waitlist.joinedUsers else 0
-    email_count = (
-        len(waitlist.unaffiliatedEmailUsers) if waitlist.unaffiliatedEmailUsers else 0
-    )
-
-    return store_model.WaitlistAdminResponse(
-        id=waitlist.id,
-        createdAt=waitlist.createdAt.isoformat() if waitlist.createdAt else "",
-        updatedAt=waitlist.updatedAt.isoformat() if waitlist.updatedAt else "",
-        slug=waitlist.slug,
-        name=waitlist.name,
-        subHeading=waitlist.subHeading,
-        description=waitlist.description,
-        categories=waitlist.categories,
-        imageUrls=waitlist.imageUrls or [],
-        videoUrl=waitlist.videoUrl,
-        agentOutputDemoUrl=waitlist.agentOutputDemoUrl,
-        status=waitlist.status or prisma.enums.WaitlistExternalStatus.NOT_STARTED,
-        votes=waitlist.votes,
-        signupCount=joined_count + email_count,
-        storeListingId=waitlist.storeListingId,
-        owningUserId=waitlist.owningUserId,
-    )
-
-
-async def create_waitlist_admin(
-    admin_user_id: str,
-    data: store_model.WaitlistCreateRequest,
-) -> store_model.WaitlistAdminResponse:
-    """Create a new waitlist (admin only)."""
-    logger.info(f"Admin {admin_user_id} creating waitlist: {data.name}")
-
-    try:
-        waitlist = await prisma.models.WaitlistEntry.prisma().create(
-            data=prisma.types.WaitlistEntryCreateInput(
-                name=data.name,
-                slug=data.slug,
-                subHeading=data.subHeading,
-                description=data.description,
-                categories=data.categories,
-                imageUrls=data.imageUrls,
-                videoUrl=data.videoUrl,
-                agentOutputDemoUrl=data.agentOutputDemoUrl,
-                owningUserId=admin_user_id,
-                status=prisma.enums.WaitlistExternalStatus.NOT_STARTED,
-            ),
-            include={"joinedUsers": True},
-        )
-
-        return _waitlist_to_admin_response(waitlist)
-    except Exception as e:
-        logger.error(f"Error creating waitlist: {e}")
-        raise DatabaseError("Failed to create waitlist") from e
-
-
-async def get_waitlists_admin() -> store_model.WaitlistAdminListResponse:
-    """Get all waitlists with admin details."""
-    try:
-        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
-            where=prisma.types.WaitlistEntryWhereInput(isDeleted=False),
-            include={"joinedUsers": True},
-            order={"createdAt": "desc"},
-        )
-
-        return store_model.WaitlistAdminListResponse(
-            waitlists=[_waitlist_to_admin_response(w) for w in waitlists],
-            totalCount=len(waitlists),
-        )
-    except Exception as e:
-        logger.error(f"Error fetching waitlists for admin: {e}")
-        raise DatabaseError("Failed to fetch waitlists") from e
-
-
-async def get_waitlist_admin(
-    waitlist_id: str,
-) -> store_model.WaitlistAdminResponse:
-    """Get a single waitlist with admin details."""
-    try:
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id},
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        if waitlist.isDeleted:
-            raise ValueError(f"Waitlist {waitlist_id} has been deleted")
-
-        return _waitlist_to_admin_response(waitlist)
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error fetching waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to fetch waitlist") from e
-
-
-async def update_waitlist_admin(
-    waitlist_id: str,
-    data: store_model.WaitlistUpdateRequest,
-) -> store_model.WaitlistAdminResponse:
-    """Update a waitlist (admin only)."""
-    logger.info(f"Updating waitlist {waitlist_id}")
-
-    try:
-        # Check if waitlist exists first
-        existing = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id}
-        )
-
-        if not existing:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        if existing.isDeleted:
-            raise ValueError(f"Waitlist {waitlist_id} has been deleted")
-
-        # Build update data from explicitly provided fields
-        # Use model_fields_set to allow clearing fields by setting them to None
-        field_mappings = {
-            "name": data.name,
-            "slug": data.slug,
-            "subHeading": data.subHeading,
-            "description": data.description,
-            "categories": data.categories,
-            "imageUrls": data.imageUrls,
-            "videoUrl": data.videoUrl,
-            "agentOutputDemoUrl": data.agentOutputDemoUrl,
-            "storeListingId": data.storeListingId,
-        }
-        update_data: dict[str, Any] = {
-            k: v for k, v in field_mappings.items() if k in data.model_fields_set
-        }
-
-        # Add status if provided (already validated as enum by Pydantic)
-        if "status" in data.model_fields_set and data.status is not None:
-            update_data["status"] = data.status
-
-        if not update_data:
-            # No updates, just return current data
-            return await get_waitlist_admin(waitlist_id)
-
-        waitlist = await prisma.models.WaitlistEntry.prisma().update(
-            where={"id": waitlist_id},
-            data=prisma.types.WaitlistEntryUpdateInput(**update_data),
-            include={"joinedUsers": True},
-        )
-
-        # We already verified existence above, so this should never be None
-        assert waitlist is not None
-        return _waitlist_to_admin_response(waitlist)
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error updating waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to update waitlist") from e
-
-
-async def delete_waitlist_admin(waitlist_id: str) -> None:
-    """Soft delete a waitlist (admin only)."""
-    logger.info(f"Soft deleting waitlist {waitlist_id}")
-
-    try:
-        # Check if waitlist exists first
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        if waitlist.isDeleted:
-            raise ValueError(f"Waitlist {waitlist_id} has already been deleted")
-
-        await prisma.models.WaitlistEntry.prisma().update(
-            where={"id": waitlist_id},
-            data={"isDeleted": True},
-        )
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error deleting waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to delete waitlist") from e
-
-
-async def get_waitlist_signups_admin(
-    waitlist_id: str,
-) -> store_model.WaitlistSignupListResponse:
-    """Get all signups for a waitlist (admin only)."""
-    try:
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id},
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        signups: list[store_model.WaitlistSignup] = []
-
-        # Add user signups
-        for user in waitlist.joinedUsers or []:
-            signups.append(
-                store_model.WaitlistSignup(
-                    type="user",
-                    userId=user.id,
-                    email=user.email,
-                    username=user.name,
-                )
-            )
-
-        # Add email signups
-        for email in waitlist.unaffiliatedEmailUsers or []:
-            signups.append(
-                store_model.WaitlistSignup(
-                    type="email",
-                    email=email,
-                )
-            )
-
-        return store_model.WaitlistSignupListResponse(
-            waitlistId=waitlist_id,
-            signups=signups,
-            totalCount=len(signups),
-        )
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error fetching signups for waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to fetch waitlist signups") from e
-
-
-async def link_waitlist_to_listing_admin(
-    waitlist_id: str,
-    store_listing_id: str,
-) -> store_model.WaitlistAdminResponse:
-    """Link a waitlist to a store listing (admin only)."""
-    logger.info(f"Linking waitlist {waitlist_id} to listing {store_listing_id}")
-
-    try:
-        # Verify the waitlist exists
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id}
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        if waitlist.isDeleted:
-            raise ValueError(f"Waitlist {waitlist_id} has been deleted")
-
-        # Verify the store listing exists
-        listing = await prisma.models.StoreListing.prisma().find_unique(
-            where={"id": store_listing_id}
-        )
-
-        if not listing:
-            raise ValueError(f"Store listing {store_listing_id} not found")
-
-        updated_waitlist = await prisma.models.WaitlistEntry.prisma().update(
-            where={"id": waitlist_id},
-            data={"StoreListing": {"connect": {"id": store_listing_id}}},
-            include={"joinedUsers": True},
-        )
-
-        # We already verified existence above, so this should never be None
-        assert updated_waitlist is not None
-        return _waitlist_to_admin_response(updated_waitlist)
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error linking waitlist to listing: {e}")
-        raise DatabaseError("Failed to link waitlist to listing") from e
-
-
-async def notify_waitlist_users_on_launch(
-    store_listing_id: str,
-    agent_name: str,
-    store_url: str,
-) -> int:
-    """
-    Notify all users on waitlists linked to a store listing when the agent is launched.
-
-    Args:
-        store_listing_id: The ID of the store listing that was approved
-        agent_name: The name of the approved agent
-        store_url: The URL to the agent's store page
-
-    Returns:
-        The number of notifications sent
-    """
-    logger.info(f"Notifying waitlist users for store listing {store_listing_id}")
-
-    try:
-        # Find all active waitlists linked to this store listing
-        # Exclude DONE and CANCELED to prevent duplicate notifications on re-approval
-        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
-            where={
-                "storeListingId": store_listing_id,
-                "isDeleted": False,
-                "status": {
-                    "not_in": [
-                        prisma.enums.WaitlistExternalStatus.DONE,
-                        prisma.enums.WaitlistExternalStatus.CANCELED,
-                    ]
-                },
-            },
-            include={"joinedUsers": True},
-        )
-
-        if not waitlists:
-            logger.info(
-                f"No active waitlists found for store listing {store_listing_id}"
-            )
-            return 0
-
-        notification_count = 0
-        launched_at = datetime.now(tz=timezone.utc)
-
-        for waitlist in waitlists:
-            # Track notification results for this waitlist
-            users_to_notify = waitlist.joinedUsers or []
-            failed_user_ids: list[str] = []
-
-            # Notify registered users
-            for user in users_to_notify:
-                try:
-                    notification_data = WaitlistLaunchData(
-                        agent_name=agent_name,
-                        waitlist_name=waitlist.name,
-                        store_url=store_url,
-                        launched_at=launched_at,
-                    )
-
-                    notification_event = NotificationEventModel[WaitlistLaunchData](
-                        user_id=user.id,
-                        type=prisma.enums.NotificationType.WAITLIST_LAUNCH,
-                        data=notification_data,
-                    )
-
-                    await queue_notification_async(notification_event)
-                    notification_count += 1
-                except Exception as e:
-                    logger.error(
-                        f"Failed to send waitlist launch notification to user {user.id}: {e}"
-                    )
-                    failed_user_ids.append(user.id)
-
-            # Note: For unaffiliated email users, you would need to send emails directly
-            # since they don't have user IDs for the notification system.
-            # This could be done via a separate email service.
-            # For now, we log these for potential manual follow-up or future implementation.
-            has_pending_email_users = bool(waitlist.unaffiliatedEmailUsers)
-            if has_pending_email_users:
-                logger.info(
-                    f"Waitlist {waitlist.id} has {len(waitlist.unaffiliatedEmailUsers)} "
-                    f"unaffiliated email users that need email notifications"
-                )
-
-            # Only mark waitlist as DONE if all registered user notifications succeeded
-            # AND there are no unaffiliated email users still waiting for notifications
-            if not failed_user_ids and not has_pending_email_users:
-                await prisma.models.WaitlistEntry.prisma().update(
-                    where={"id": waitlist.id},
-                    data={"status": prisma.enums.WaitlistExternalStatus.DONE},
-                )
-                logger.info(f"Updated waitlist {waitlist.id} status to DONE")
-            elif failed_user_ids:
-                logger.warning(
-                    f"Waitlist {waitlist.id} not marked as DONE due to "
-                    f"{len(failed_user_ids)} failed notifications"
-                )
-            elif has_pending_email_users:
-                logger.warning(
-                    f"Waitlist {waitlist.id} not marked as DONE due to "
-                    f"{len(waitlist.unaffiliatedEmailUsers)} pending email-only users"
-                )
-
-        logger.info(
-            f"Sent {notification_count} waitlist launch notifications for store listing {store_listing_id}"
-        )
-        return notification_count
-
-    except Exception as e:
-        logger.error(
-            f"Error notifying waitlist users for store listing {store_listing_id}: {e}"
-        )
-        # Don't raise - we don't want to fail the approval process
-        return 0

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 "
+        "Create a visually striking retro-futuristic vector pop art illustration "
-        f'"{name}" in bold typography. The image clearly and literally depicts a {description}, '
+        f'prominently featuring "{name}" in bold typography. The image clearly and '
-        f"along with recognizable objects directly associated with the primary function of a {name}. "
+        f"literally depicts a {description}, along with recognizable objects directly "
-        f"Ensure the imagery is concrete, intuitive, and immediately understandable, clearly conveying the "
+        f"associated with the primary function of a {name}. "
-        f"purpose of a {name}. Maintain vibrant, limited-palette colors, sharp vector lines, geometric "
+        f"Ensure the imagery is concrete, intuitive, and immediately understandable, "
-        f"shapes, flat illustration techniques, and solid colors without gradients or shading. Preserve a "
+        f"clearly conveying the purpose of a {name}. "
-        f"retro-futuristic aesthetic influenced by mid-century futurism and 1960s psychedelia, "
+        "Maintain vibrant, limited-palette colors, sharp vector lines, "
-        f"prioritizing clear visual storytelling and thematic clarity above all else."
+        "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/model.py

@@ -224,102 +224,6 @@ class ReviewSubmissionRequest(pydantic.BaseModel):
     internal_comments: str | None = None  # Private admin notes
 
 
-class StoreWaitlistEntry(pydantic.BaseModel):
-    """Public waitlist entry - no PII fields exposed."""
-
-    waitlistId: str
-    slug: str
-
-    # Content fields
-    name: str
-    subHeading: str
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-    imageUrls: list[str]
-    description: str
-    categories: list[str]
-
-
-class StoreWaitlistsAllResponse(pydantic.BaseModel):
-    listings: list[StoreWaitlistEntry]
-
-
-# Admin Waitlist Models
-
-
-class WaitlistCreateRequest(pydantic.BaseModel):
-    """Request model for creating a new waitlist."""
-
-    name: str
-    slug: str
-    subHeading: str
-    description: str
-    categories: list[str] = []
-    imageUrls: list[str] = []
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-
-
-class WaitlistUpdateRequest(pydantic.BaseModel):
-    """Request model for updating a waitlist."""
-
-    name: str | None = None
-    slug: str | None = None
-    subHeading: str | None = None
-    description: str | None = None
-    categories: list[str] | None = None
-    imageUrls: list[str] | None = None
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-    status: prisma.enums.WaitlistExternalStatus | None = None
-    storeListingId: str | None = None  # Link to a store listing
-
-
-class WaitlistAdminResponse(pydantic.BaseModel):
-    """Admin response model with full waitlist details including internal data."""
-
-    id: str
-    createdAt: str
-    updatedAt: str
-    slug: str
-    name: str
-    subHeading: str
-    description: str
-    categories: list[str]
-    imageUrls: list[str]
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-    status: prisma.enums.WaitlistExternalStatus
-    votes: int
-    signupCount: int  # Total count of joinedUsers + unaffiliatedEmailUsers
-    storeListingId: str | None = None
-    owningUserId: str
-
-
-class WaitlistSignup(pydantic.BaseModel):
-    """Individual signup entry for a waitlist."""
-
-    type: str  # "user" or "email"
-    userId: str | None = None
-    email: str | None = None
-    username: str | None = None  # For user signups
-
-
-class WaitlistSignupListResponse(pydantic.BaseModel):
-    """Response model for listing waitlist signups."""
-
-    waitlistId: str
-    signups: list[WaitlistSignup]
-    totalCount: int
-
-
-class WaitlistAdminListResponse(pydantic.BaseModel):
-    """Response model for listing all waitlists (admin view)."""
-
-    waitlists: list[WaitlistAdminResponse]
-    totalCount: int
-
-
 class UnifiedSearchResult(pydantic.BaseModel):
     """A single result from unified hybrid search across all content types."""
 

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

@@ -8,7 +8,6 @@ import autogpt_libs.auth
 import fastapi
 import fastapi.responses
 import prisma.enums
-from autogpt_libs.auth.dependencies import get_optional_user_id
 
 import backend.data.graph
 import backend.util.json
@@ -82,74 +81,6 @@ async def update_or_create_profile(
     return updated_profile
 
 
-##############################################
-############## Waitlist Endpoints ############
-##############################################
-@router.get(
-    "/waitlist",
-    summary="Get the agent waitlist",
-    tags=["store", "public"],
-    response_model=store_model.StoreWaitlistsAllResponse,
-)
-async def get_waitlist():
-    """
-    Get all active waitlists for public display.
-    """
-    waitlists = await store_db.get_waitlist()
-    return store_model.StoreWaitlistsAllResponse(listings=waitlists)
-
-
-@router.get(
-    "/waitlist/my-memberships",
-    summary="Get waitlist IDs the current user has joined",
-    tags=["store", "private"],
-)
-async def get_my_waitlist_memberships(
-    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
-) -> list[str]:
-    """Returns list of waitlist IDs the authenticated user has joined."""
-    return await store_db.get_user_waitlist_memberships(user_id)
-
-
-@router.post(
-    path="/waitlist/{waitlist_id}/join",
-    summary="Add self to the agent waitlist",
-    tags=["store", "public"],
-    response_model=store_model.StoreWaitlistEntry,
-)
-async def add_self_to_waitlist(
-    user_id: str | None = fastapi.Security(get_optional_user_id),
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist to join"),
-    email: str | None = fastapi.Body(
-        default=None, embed=True, description="Email address for unauthenticated users"
-    ),
-):
-    """
-    Add the current user to the agent waitlist.
-    """
-    if not user_id and not email:
-        raise fastapi.HTTPException(
-            status_code=400,
-            detail="Either user authentication or email address is required",
-        )
-
-    try:
-        waitlist_entry = await store_db.add_user_to_waitlist(
-            waitlist_id=waitlist_id, user_id=user_id, email=email
-        )
-        return waitlist_entry
-    except ValueError as e:
-        error_msg = str(e)
-        if "not found" in error_msg:
-            raise fastapi.HTTPException(status_code=404, detail="Waitlist not found")
-        # Waitlist exists but is closed or unavailable
-        raise fastapi.HTTPException(status_code=400, detail=error_msg)
-    except Exception:
-        raise fastapi.HTTPException(
-            status_code=500, detail="An error occurred while joining the waitlist"
-        )
-
-
 ##############################################
 ############### Agent Endpoints ##############
 ##############################################
@@ -347,7 +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 library_db.update_library_agent_version_and_settings(
-        await _update_library_agent_version_and_settings(user_id, new_graph_version)
+            user_id, new_graph_version
-
+        )
-        # Handle activation of the new graph first to ensure continuity
         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/api/rest_api.py

@@ -19,7 +19,6 @@ from prisma.errors import PrismaError
 import backend.api.features.admin.credit_admin_routes
 import backend.api.features.admin.execution_analytics_routes
 import backend.api.features.admin.store_admin_routes
-import backend.api.features.admin.waitlist_admin_routes
 import backend.api.features.builder
 import backend.api.features.builder.routes
 import backend.api.features.chat.routes as chat_routes
@@ -307,11 +306,6 @@ app.include_router(
     tags=["v2", "admin"],
     prefix="/api/store",
 )
-app.include_router(
-    backend.api.features.admin.waitlist_admin_routes.router,
-    tags=["v2", "admin"],
-    prefix="/api/store",
-)
 app.include_router(
     backend.api.features.admin.credit_admin_routes.router,
     tags=["v2", "admin"],

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/blocks/youtube.py

@@ -165,10 +165,13 @@ class TranscribeYoutubeVideoBlock(Block):
         credentials: WebshareProxyCredentials,
         **kwargs,
     ) -> BlockOutput:
+        try:
             video_id = self.extract_video_id(input_data.youtube_url)
-        yield "video_id", video_id
-
             transcript = self.get_transcript(video_id, credentials)
             transcript_text = self.format_transcript(transcript=transcript)
 
+            # Only yield after all operations succeed
+            yield "video_id", video_id
             yield "transcript", transcript_text
+        except Exception as e:
+            yield "error", str(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/credit_test.py

@@ -134,6 +134,16 @@ async def test_block_credit_reset(server: SpinTestServer):
         month1 = datetime.now(timezone.utc).replace(month=1, day=1)
         user_credit.time_now = lambda: month1
 
+        # IMPORTANT: Set updatedAt to December of previous year to ensure it's
+        # in a different month than month1 (January). This fixes a timing bug
+        # where if the test runs in early February, 35 days ago would be January,
+        # matching the mocked month1 and preventing the refill from triggering.
+        dec_previous_year = month1.replace(year=month1.year - 1, month=12, day=15)
+        await UserBalance.prisma().update(
+            where={"userId": DEFAULT_USER_ID},
+            data={"updatedAt": dec_previous_year},
+        )
+
         # First call in month 1 should trigger refill
         balance = await user_credit.get_credits(DEFAULT_USER_ID)
         assert balance == REFILL_VALUE  # Should get 1000 credits

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]
+    input_default: BlockInput = Field(  # dict[input_name, default_value]
-    metadata: dict[str, Any] = {}
+        default_factory=dict
-    input_links: list[Link] = []
+    )
-    output_links: list[Link] = []
+    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()
+        graph_credentials_inputs = self.regular_credentials_inputs
-
-        # 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()
         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 i, (field, keys, _) in enumerate(graph_cred_fields):
-            for other_field, other_keys in list(graph_cred_fields)[i + 1 :]:
+            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]] = {
+        # Build JSON schema directly to avoid expensive create_model + validation overhead
-            agg_field_key: (
+        properties = {}
-                CredentialsMetaInput[
+        required_fields = []
-                    Literal[tuple(field_info.provider)],  # type: ignore
+
-                    Literal[tuple(field_info.supported_types)],  # type: ignore
+        for agg_field_key, (
-                ],
+            field_info,
-                CredentialsField(
+            _,
-                    required_scopes=set(field_info.required_scopes or []),
+            is_required,
-                    discriminator=field_info.discriminator,
+        ) in graph_credentials_inputs.items():
-                    discriminator_mapping=field_info.discriminator_mapping,
+            providers = list(field_info.provider)
-                    discriminator_values=field_info.discriminator_values,
+            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]}
                         ),
-            )
+                    },
-            for agg_field_key, (field_info, _) in graph_credentials_inputs.items()
+                    "type": {
+                        "title": "Type",
+                        "type": "string",
+                        **(
+                            {"enum": cred_types}
+                            if len(cred_types) > 1
+                            else {"const": cred_types[0]}
+                        ),
+                    },
+                },
+                "required": ["id", "provider", "type"],
             }
 
-        return create_model(
+            # Add other (optional) field info items
-            self.name.replace(" ", "") + "CredentialsInputSchema",
+            field_schema.update(
-            __base__=BlockSchema,
+                field_info.model_dump(
-            **fields,  # type: ignore
+                    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)
 
-
+        # Add is_required flag to each aggregated field
-class GraphModel(Graph):
+        # A field is required if ANY node using it has credentials_optional=False
-    user_id: str
+        return {
-    nodes: list[NodeModel] = []  # type: ignore
+            key: (
-
+                field_info,
-    created_at: datetime
+                node_field_pairs,
-
+                any(
-    @property
+                    node_required_map.get(node_id, True)
-    def starting_nodes(self) -> list[NodeModel]:
+                    for node_id, _ in node_field_pairs
-        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
+            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
+    def regular_credentials_inputs(
-        return cast(NodeModel, super().webhook_input_node)
+        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":
+    @property
-        """
+    def auto_credentials_inputs(
-        Returns a GraphMeta object with metadata about the graph.
+        self,
-        This is used to return metadata about the graph without exposing nodes and links.
+    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
-        """
+        """Credentials embedded in file fields (_credentials_id), resolved at execution time."""
-        return GraphMeta.from_graph(self)
+        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
+    @classmethod
-    def from_db(
+    def from_db(  # type: ignore[reportIncompatibleMethodOverride]
+        cls,
         graph: AgentGraph,
         for_export: bool = False,
         sub_graphs: list[AgentGraph] | None = None,
-    ) -> "GraphModel":
+    ) -> Self:
-        return GraphModel(
+        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
+class GraphModelWithoutNodes(GraphModel):
-    nodes: list[NodeModel] = Field(default=[], exclude=True)  # type: ignore
+    """
-    links: list[Link] = Field(default=[], exclude=True)
+    GraphModel variant that excludes nodes, links, and sub-graphs from serialization.
 
-    @staticmethod
+    Used in contexts like the store where exposing internal graph structure
-    def from_graph(graph: GraphModel) -> "GraphMeta":
+    is not desired. Inherits all computed fields from GraphModel but marks
-        return GraphMeta(**graph.model_dump())
+    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] = []
+    graph_models = [GraphMeta.from_db(graph) for graph in graphs]
-    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
 
     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
+    @staticmethod
-    def validate_credentials_field_schema(cls, model: type["BlockSchema"]):
+    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/data/notifications.py

@@ -211,22 +211,6 @@ class AgentRejectionData(BaseNotificationData):
         return value
 
 
-class WaitlistLaunchData(BaseNotificationData):
-    """Notification data for when an agent from a waitlist is launched."""
-
-    agent_name: str
-    waitlist_name: str
-    store_url: str
-    launched_at: datetime
-
-    @field_validator("launched_at")
-    @classmethod
-    def validate_timezone(cls, value: datetime):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
-
 NotificationData = Annotated[
     Union[
         AgentRunData,
@@ -239,7 +223,6 @@ NotificationData = Annotated[
         DailySummaryData,
         RefundRequestData,
         BaseSummaryData,
-        WaitlistLaunchData,
     ],
     Field(discriminator="type"),
 ]
@@ -290,7 +273,6 @@ def get_notif_data_type(
         NotificationType.REFUND_PROCESSED: RefundRequestData,
         NotificationType.AGENT_APPROVED: AgentApprovalData,
         NotificationType.AGENT_REJECTED: AgentRejectionData,
-        NotificationType.WAITLIST_LAUNCH: WaitlistLaunchData,
     }[notification_type]
 
 
@@ -336,7 +318,6 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: QueueType.ADMIN,
             NotificationType.AGENT_APPROVED: QueueType.IMMEDIATE,
             NotificationType.AGENT_REJECTED: QueueType.IMMEDIATE,
-            NotificationType.WAITLIST_LAUNCH: QueueType.IMMEDIATE,
         }
         return BATCHING_RULES.get(self.notification_type, QueueType.IMMEDIATE)
 
@@ -356,7 +337,6 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: "refund_processed.html",
             NotificationType.AGENT_APPROVED: "agent_approved.html",
             NotificationType.AGENT_REJECTED: "agent_rejected.html",
-            NotificationType.WAITLIST_LAUNCH: "waitlist_launch.html",
         }[self.notification_type]
 
     @property
@@ -374,7 +354,6 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: "Refund for ${{data.amount / 100}} to {{data.user_name}} has been processed",
             NotificationType.AGENT_APPROVED: "🎉 Your agent '{{data.agent_name}}' has been approved!",
             NotificationType.AGENT_REJECTED: "Your agent '{{data.agent_name}}' needs some updates",
-            NotificationType.WAITLIST_LAUNCH: "🚀 {{data.agent_name}} is now available!",
         }[self.notification_type]
 
 

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():
+    auto_extra_kwargs, auto_locks = await _acquire_auto_credentials(
-        field_name = info["field_name"]
+        input_model=input_model,
-        field_data = input_data.get(field_name)
+        input_data=input_data,
-        if field_data and isinstance(field_data, dict):
+        creds_manager=creds_manager,
-            # Check if _credentials_id key exists in the field data
+        user_id=user_id,
-            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()}."
     )
+    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
+    # Only map regular credentials (not auto_credentials, which are resolved
-    graph_cred_inputs = graph.aggregate_credentials_inputs()
+    # 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/notifications/templates/waitlist_launch.html.jinja2

@@ -1,59 +0,0 @@
-{# Waitlist Launch Notification Email Template #}
-{#
-  Template variables:
-  data.agent_name: the name of the launched agent
-  data.waitlist_name: the name of the waitlist the user joined
-  data.store_url: URL to view the agent in the store
-  data.launched_at: when the agent was launched
-
-  Subject: {{ data.agent_name }} is now available!
-#}
-
-{% block content %}
-<h1 style="color: #7c3aed; font-size: 32px; font-weight: 700; margin: 0 0 24px 0; text-align: center;">
-  The wait is over!
-</h1>
-
-<p style="color: #586069; font-size: 18px; text-align: center; margin: 0 0 24px 0;">
-  <strong>'{{ data.agent_name }}'</strong> is now live in the AutoGPT Store!
-</p>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="background: #f3e8ff; border: 1px solid #d8b4fe; border-radius: 8px; padding: 20px; margin: 0;">
-  <h3 style="color: #6b21a8; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    You're one of the first to know!
-  </h3>
-  <p style="color: #6b21a8; margin: 0; font-size: 16px; line-height: 1.5;">
-    You signed up for the <strong>{{ data.waitlist_name }}</strong> waitlist, and we're excited to let you know that this agent is now ready for you to use.
-  </p>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="text-align: center; margin: 24px 0;">
-  <a href="{{ data.store_url }}" style="display: inline-block; background: linear-gradient(135deg, #7c3aed 0%, #5b21b6 100%); color: white; text-decoration: none; padding: 14px 28px; border-radius: 6px; font-weight: 600; font-size: 16px;">
-    Get {{ data.agent_name }} Now
-  </a>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="background: #d1ecf1; border: 1px solid #bee5eb; border-radius: 8px; padding: 20px; margin: 0;">
-  <h3 style="color: #0c5460; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    What can you do now?
-  </h3>
-  <ul style="color: #0c5460; margin: 0; padding-left: 18px; font-size: 16px; line-height: 1.6;">
-    <li>Visit the store to learn more about what this agent can do</li>
-    <li>Install and start using the agent right away</li>
-    <li>Share it with others who might find it useful</li>
-  </ul>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<p style="color: #6a737d; font-size: 14px; text-align: center; margin: 24px 0;">
-  Thank you for helping us prioritize what to build! Your interest made this happen.
-</p>
-
-{% endblock %}

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
+        # Parse workspace reference (strips #mimeType fragment from file ID)
-        # workspace://abc123 - by file ID
+        ws = parse_workspace_uri(file)
-        # workspace:///path/to/file.txt - by virtual path
-        file_ref = file[12:]  # Remove "workspace://"
 
-        if file_ref.startswith("/"):
+        if ws.is_path:
-            # Path reference
+            # Path reference: workspace:///path/to/file.txt
-            workspace_content = await workspace_manager.read_file(file_ref)
+            workspace_content = await workspace_manager.read_file(ws.file_ref)
-            file_info = await workspace_manager.get_file_info_by_path(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
+            # ID reference: workspace://abc123 or workspace://abc123#video/mp4
-            workspace_content = await workspace_manager.read_file_by_id(file_ref)
+            workspace_content = await workspace_manager.read_file_by_id(ws.file_ref)
-            file_info = await workspace_manager.get_file_info(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/migrations/20260108032037_add_waitlist_launch_notification/migration.sql

@@ -1,53 +0,0 @@
--- CreateEnum
-CREATE TYPE "WaitlistExternalStatus" AS ENUM ('DONE', 'NOT_STARTED', 'CANCELED', 'WORK_IN_PROGRESS');
-
--- AlterEnum
-ALTER TYPE "NotificationType" ADD VALUE 'WAITLIST_LAUNCH';
-
--- CreateTable
-CREATE TABLE "WaitlistEntry" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL,
-    "storeListingId" TEXT,
-    "owningUserId" TEXT NOT NULL,
-    "slug" TEXT NOT NULL,
-    "search" tsvector DEFAULT ''::tsvector,
-    "name" TEXT NOT NULL,
-    "subHeading" TEXT NOT NULL,
-    "videoUrl" TEXT,
-    "agentOutputDemoUrl" TEXT,
-    "imageUrls" TEXT[],
-    "description" TEXT NOT NULL,
-    "categories" TEXT[],
-    "status" "WaitlistExternalStatus" NOT NULL DEFAULT 'NOT_STARTED',
-    "votes" INTEGER NOT NULL DEFAULT 0,
-    "unaffiliatedEmailUsers" TEXT[] DEFAULT ARRAY[]::TEXT[],
-    "isDeleted" BOOLEAN NOT NULL DEFAULT false,
-
-    CONSTRAINT "WaitlistEntry_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "_joinedWaitlists" (
-    "A" TEXT NOT NULL,
-    "B" TEXT NOT NULL
-);
-
--- CreateIndex
-CREATE UNIQUE INDEX "_joinedWaitlists_AB_unique" ON "_joinedWaitlists"("A", "B");
-
--- CreateIndex
-CREATE INDEX "_joinedWaitlists_B_index" ON "_joinedWaitlists"("B");
-
--- AddForeignKey
-ALTER TABLE "WaitlistEntry" ADD CONSTRAINT "WaitlistEntry_storeListingId_fkey" FOREIGN KEY ("storeListingId") REFERENCES "StoreListing"("id") ON DELETE SET NULL ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "WaitlistEntry" ADD CONSTRAINT "WaitlistEntry_owningUserId_fkey" FOREIGN KEY ("owningUserId") REFERENCES "User"("id") ON DELETE RESTRICT ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "_joinedWaitlists" ADD CONSTRAINT "_joinedWaitlists_A_fkey" FOREIGN KEY ("A") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "_joinedWaitlists" ADD CONSTRAINT "_joinedWaitlists_B_fkey" FOREIGN KEY ("B") REFERENCES "WaitlistEntry"("id") ON DELETE CASCADE ON UPDATE CASCADE;

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/schema.prisma

@@ -70,10 +70,6 @@ model User {
   OAuthAuthorizationCodes OAuthAuthorizationCode[]
   OAuthAccessTokens       OAuthAccessToken[]
   OAuthRefreshTokens      OAuthRefreshToken[]
-
-  // Waitlist relations
-  waitlistEntries       WaitlistEntry[]
-  joinedWaitlists       WaitlistEntry[] @relation("joinedWaitlists")
 }
 
 enum OnboardingStep {
@@ -348,7 +344,6 @@ enum NotificationType {
   REFUND_PROCESSED
   AGENT_APPROVED
   AGENT_REJECTED
-  WAITLIST_LAUNCH
 }
 
 model NotificationEvent {
@@ -954,7 +949,6 @@ model StoreListing {
 
   // Relations
   Versions StoreListingVersion[] @relation("ListingVersions")
-  waitlistEntries WaitlistEntry[]
 
   // Unique index on agentId to ensure only one listing per agent, regardless of number of versions the agent has.
   @@unique([agentGraphId])
@@ -1086,47 +1080,6 @@ model StoreListingReview {
   @@index([reviewByUserId])
 }
 
-enum WaitlistExternalStatus {
-  DONE
-  NOT_STARTED
-  CANCELED
-  WORK_IN_PROGRESS
-}
-
-model WaitlistEntry {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  storeListingId String?
-  StoreListing   StoreListing? @relation(fields: [storeListingId], references: [id], onDelete: SetNull)
-
-  owningUserId String
-  OwningUser   User   @relation(fields: [owningUserId], references: [id])
-
-  slug   String
-  search Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
-
-  // Content fields
-  name               String
-  subHeading         String
-  videoUrl           String?
-  agentOutputDemoUrl String?
-  imageUrls          String[]
-  description        String
-  categories         String[]
-
-  //Waitlist specific fields
-  status                 WaitlistExternalStatus @default(NOT_STARTED)
-  votes                  Int                    @default(0) // Hide from frontend api
-  joinedUsers            User[]                 @relation("joinedWaitlists")
-  // NOTE: DO NOT DOUBLE SEND TO THESE USERS, IF THEY HAVE SIGNED UP SINCE THEY MAY HAVE ALREADY RECEIVED AN EMAIL
-  // DOUBLE CHECK WHEN SENDING THAT THEY ARE NOT IN THE JOINED USERS LIST ALSO
-  unaffiliatedEmailUsers String[]               @default([])
-
-  isDeleted Boolean @default(false)
-}
-
 enum SubmissionStatus {
   DRAFT // Being prepared, not yet submitted
   PENDING // Submitted, awaiting review

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": {
+    "created_at": "2025-09-04T13:37:00",
-      "properties": {},
-      "required": [],
-      "title": "TestGraphCredentialsInputSchema",
-      "type": "object"
-    },
     "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/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)/admin/layout.tsx

@@ -1,5 +1,5 @@
 import { Sidebar } from "@/components/__legacy__/Sidebar";
-import { Users, DollarSign, UserSearch, FileText, Clock } from "lucide-react";
+import { Users, DollarSign, UserSearch, FileText } from "lucide-react";
 
 import { IconSliders } from "@/components/__legacy__/ui/icons";
 
@@ -11,11 +11,6 @@ const sidebarLinkGroups = [
         href: "/admin/marketplace",
         icon: <Users className="h-6 w-6" />,
       },
-      {
-        text: "Waitlist Management",
-        href: "/admin/waitlist",
-        icon: <Clock className="h-6 w-6" />,
-      },
       {
         text: "User Spending",
         href: "/admin/spending",

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/CreateWaitlistButton.tsx

@@ -1,217 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useQueryClient } from "@tanstack/react-query";
-import { Button } from "@/components/atoms/Button/Button";
-import { Input } from "@/components/atoms/Input/Input";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import {
-  usePostV2CreateWaitlist,
-  getGetV2ListAllWaitlistsQueryKey,
-} from "@/app/api/__generated__/endpoints/admin/admin";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { Plus } from "@phosphor-icons/react";
-
-export function CreateWaitlistButton() {
-  const [open, setOpen] = useState(false);
-  const { toast } = useToast();
-  const queryClient = useQueryClient();
-
-  const createWaitlistMutation = usePostV2CreateWaitlist({
-    mutation: {
-      onSuccess: (response) => {
-        if (response.status === 200) {
-          toast({
-            title: "Success",
-            description: "Waitlist created successfully",
-          });
-          setOpen(false);
-          setFormData({
-            name: "",
-            slug: "",
-            subHeading: "",
-            description: "",
-            categories: "",
-            imageUrls: "",
-            videoUrl: "",
-            agentOutputDemoUrl: "",
-          });
-          queryClient.invalidateQueries({
-            queryKey: getGetV2ListAllWaitlistsQueryKey(),
-          });
-        } else {
-          toast({
-            variant: "destructive",
-            title: "Error",
-            description: "Failed to create waitlist",
-          });
-        }
-      },
-      onError: (error) => {
-        console.error("Error creating waitlist:", error);
-        toast({
-          variant: "destructive",
-          title: "Error",
-          description: "Failed to create waitlist",
-        });
-      },
-    },
-  });
-
-  const [formData, setFormData] = useState({
-    name: "",
-    slug: "",
-    subHeading: "",
-    description: "",
-    categories: "",
-    imageUrls: "",
-    videoUrl: "",
-    agentOutputDemoUrl: "",
-  });
-
-  function handleInputChange(id: string, value: string) {
-    setFormData((prev) => ({
-      ...prev,
-      [id]: value,
-    }));
-  }
-
-  function generateSlug(name: string) {
-    return name
-      .toLowerCase()
-      .replace(/[^a-z0-9]+/g, "-")
-      .replace(/^-|-$/g, "");
-  }
-
-  function handleSubmit(e: React.FormEvent) {
-    e.preventDefault();
-
-    createWaitlistMutation.mutate({
-      data: {
-        name: formData.name,
-        slug: formData.slug || generateSlug(formData.name),
-        subHeading: formData.subHeading,
-        description: formData.description,
-        categories: formData.categories
-          ? formData.categories.split(",").map((c) => c.trim())
-          : [],
-        imageUrls: formData.imageUrls
-          ? formData.imageUrls.split(",").map((u) => u.trim())
-          : [],
-        videoUrl: formData.videoUrl || null,
-        agentOutputDemoUrl: formData.agentOutputDemoUrl || null,
-      },
-    });
-  }
-
-  return (
-    <>
-      <Button onClick={() => setOpen(true)}>
-        <Plus size={16} className="mr-2" />
-        Create Waitlist
-      </Button>
-
-      <Dialog
-        title="Create New Waitlist"
-        controlled={{
-          isOpen: open,
-          set: async (isOpen) => setOpen(isOpen),
-        }}
-        onClose={() => setOpen(false)}
-        styling={{ maxWidth: "600px" }}
-      >
-        <Dialog.Content>
-          <p className="mb-4 text-sm text-zinc-500">
-            Create a new waitlist for an upcoming agent. Users can sign up to be
-            notified when it launches.
-          </p>
-          <form onSubmit={handleSubmit} className="flex flex-col gap-2">
-            <Input
-              id="name"
-              label="Name"
-              value={formData.name}
-              onChange={(e) => handleInputChange("name", e.target.value)}
-              placeholder="SEO Analysis Agent"
-              required
-            />
-
-            <Input
-              id="slug"
-              label="Slug"
-              value={formData.slug}
-              onChange={(e) => handleInputChange("slug", e.target.value)}
-              placeholder="seo-analysis-agent (auto-generated if empty)"
-            />
-
-            <Input
-              id="subHeading"
-              label="Subheading"
-              value={formData.subHeading}
-              onChange={(e) => handleInputChange("subHeading", e.target.value)}
-              placeholder="Analyze your website's SEO in minutes"
-              required
-            />
-
-            <Input
-              id="description"
-              label="Description"
-              type="textarea"
-              value={formData.description}
-              onChange={(e) => handleInputChange("description", e.target.value)}
-              placeholder="Detailed description of what this agent does..."
-              rows={4}
-              required
-            />
-
-            <Input
-              id="categories"
-              label="Categories (comma-separated)"
-              value={formData.categories}
-              onChange={(e) => handleInputChange("categories", e.target.value)}
-              placeholder="SEO, Marketing, Analysis"
-            />
-
-            <Input
-              id="imageUrls"
-              label="Image URLs (comma-separated)"
-              value={formData.imageUrls}
-              onChange={(e) => handleInputChange("imageUrls", e.target.value)}
-              placeholder="https://example.com/image1.jpg, https://example.com/image2.jpg"
-            />
-
-            <Input
-              id="videoUrl"
-              label="Video URL (optional)"
-              value={formData.videoUrl}
-              onChange={(e) => handleInputChange("videoUrl", e.target.value)}
-              placeholder="https://youtube.com/watch?v=..."
-            />
-
-            <Input
-              id="agentOutputDemoUrl"
-              label="Output Demo URL (optional)"
-              value={formData.agentOutputDemoUrl}
-              onChange={(e) =>
-                handleInputChange("agentOutputDemoUrl", e.target.value)
-              }
-              placeholder="https://example.com/demo-output.mp4"
-            />
-
-            <Dialog.Footer>
-              <Button
-                type="button"
-                variant="secondary"
-                onClick={() => setOpen(false)}
-              >
-                Cancel
-              </Button>
-              <Button type="submit" loading={createWaitlistMutation.isPending}>
-                Create Waitlist
-              </Button>
-            </Dialog.Footer>
-          </form>
-        </Dialog.Content>
-      </Dialog>
-    </>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/EditWaitlistDialog.tsx

@@ -1,221 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { Button } from "@/components/atoms/Button/Button";
-import { Input } from "@/components/atoms/Input/Input";
-import { Select } from "@/components/atoms/Select/Select";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { usePutV2UpdateWaitlist } from "@/app/api/__generated__/endpoints/admin/admin";
-import type { WaitlistAdminResponse } from "@/app/api/__generated__/models/waitlistAdminResponse";
-import type { WaitlistUpdateRequest } from "@/app/api/__generated__/models/waitlistUpdateRequest";
-import { WaitlistExternalStatus } from "@/app/api/__generated__/models/waitlistExternalStatus";
-
-type EditWaitlistDialogProps = {
-  waitlist: WaitlistAdminResponse;
-  onClose: () => void;
-  onSave: () => void;
-};
-
-const STATUS_OPTIONS = [
-  { value: WaitlistExternalStatus.NOT_STARTED, label: "Not Started" },
-  { value: WaitlistExternalStatus.WORK_IN_PROGRESS, label: "Work In Progress" },
-  { value: WaitlistExternalStatus.DONE, label: "Done" },
-  { value: WaitlistExternalStatus.CANCELED, label: "Canceled" },
-];
-
-export function EditWaitlistDialog({
-  waitlist,
-  onClose,
-  onSave,
-}: EditWaitlistDialogProps) {
-  const { toast } = useToast();
-  const updateWaitlistMutation = usePutV2UpdateWaitlist();
-
-  const [formData, setFormData] = useState({
-    name: waitlist.name,
-    slug: waitlist.slug,
-    subHeading: waitlist.subHeading,
-    description: waitlist.description,
-    categories: waitlist.categories.join(", "),
-    imageUrls: waitlist.imageUrls.join(", "),
-    videoUrl: waitlist.videoUrl || "",
-    agentOutputDemoUrl: waitlist.agentOutputDemoUrl || "",
-    status: waitlist.status,
-    storeListingId: waitlist.storeListingId || "",
-  });
-
-  function handleInputChange(id: string, value: string) {
-    setFormData((prev) => ({
-      ...prev,
-      [id]: value,
-    }));
-  }
-
-  function handleStatusChange(value: string) {
-    setFormData((prev) => ({
-      ...prev,
-      status: value as WaitlistExternalStatus,
-    }));
-  }
-
-  async function handleSubmit(e: React.FormEvent) {
-    e.preventDefault();
-
-    const updateData: WaitlistUpdateRequest = {
-      name: formData.name,
-      slug: formData.slug,
-      subHeading: formData.subHeading,
-      description: formData.description,
-      categories: formData.categories
-        ? formData.categories.split(",").map((c) => c.trim())
-        : [],
-      imageUrls: formData.imageUrls
-        ? formData.imageUrls.split(",").map((u) => u.trim())
-        : [],
-      videoUrl: formData.videoUrl || null,
-      agentOutputDemoUrl: formData.agentOutputDemoUrl || null,
-      status: formData.status,
-      storeListingId: formData.storeListingId || null,
-    };
-
-    updateWaitlistMutation.mutate(
-      { waitlistId: waitlist.id, data: updateData },
-      {
-        onSuccess: (response) => {
-          if (response.status === 200) {
-            toast({
-              title: "Success",
-              description: "Waitlist updated successfully",
-            });
-            onSave();
-          } else {
-            toast({
-              variant: "destructive",
-              title: "Error",
-              description: "Failed to update waitlist",
-            });
-          }
-        },
-        onError: () => {
-          toast({
-            variant: "destructive",
-            title: "Error",
-            description: "Failed to update waitlist",
-          });
-        },
-      },
-    );
-  }
-
-  return (
-    <Dialog
-      title="Edit Waitlist"
-      controlled={{
-        isOpen: true,
-        set: async (open) => {
-          if (!open) onClose();
-        },
-      }}
-      onClose={onClose}
-      styling={{ maxWidth: "600px" }}
-    >
-      <Dialog.Content>
-        <p className="mb-4 text-sm text-zinc-500">
-          Update the waitlist details. Changes will be reflected immediately.
-        </p>
-        <form onSubmit={handleSubmit} className="flex flex-col gap-2">
-          <Input
-            id="name"
-            label="Name"
-            value={formData.name}
-            onChange={(e) => handleInputChange("name", e.target.value)}
-            required
-          />
-
-          <Input
-            id="slug"
-            label="Slug"
-            value={formData.slug}
-            onChange={(e) => handleInputChange("slug", e.target.value)}
-          />
-
-          <Input
-            id="subHeading"
-            label="Subheading"
-            value={formData.subHeading}
-            onChange={(e) => handleInputChange("subHeading", e.target.value)}
-            required
-          />
-
-          <Input
-            id="description"
-            label="Description"
-            type="textarea"
-            value={formData.description}
-            onChange={(e) => handleInputChange("description", e.target.value)}
-            rows={4}
-            required
-          />
-
-          <Select
-            id="status"
-            label="Status"
-            value={formData.status}
-            onValueChange={handleStatusChange}
-            options={STATUS_OPTIONS}
-          />
-
-          <Input
-            id="categories"
-            label="Categories (comma-separated)"
-            value={formData.categories}
-            onChange={(e) => handleInputChange("categories", e.target.value)}
-          />
-
-          <Input
-            id="imageUrls"
-            label="Image URLs (comma-separated)"
-            value={formData.imageUrls}
-            onChange={(e) => handleInputChange("imageUrls", e.target.value)}
-          />
-
-          <Input
-            id="videoUrl"
-            label="Video URL"
-            value={formData.videoUrl}
-            onChange={(e) => handleInputChange("videoUrl", e.target.value)}
-          />
-
-          <Input
-            id="agentOutputDemoUrl"
-            label="Output Demo URL"
-            value={formData.agentOutputDemoUrl}
-            onChange={(e) =>
-              handleInputChange("agentOutputDemoUrl", e.target.value)
-            }
-          />
-
-          <Input
-            id="storeListingId"
-            label="Store Listing ID (for linking)"
-            value={formData.storeListingId}
-            onChange={(e) =>
-              handleInputChange("storeListingId", e.target.value)
-            }
-            placeholder="Leave empty if not linked"
-          />
-
-          <Dialog.Footer>
-            <Button type="button" variant="secondary" onClick={onClose}>
-              Cancel
-            </Button>
-            <Button type="submit" loading={updateWaitlistMutation.isPending}>
-              Save Changes
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/WaitlistSignupsDialog.tsx

@@ -1,156 +0,0 @@
-"use client";
-
-import { Button } from "@/components/atoms/Button/Button";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { User, Envelope, DownloadSimple } from "@phosphor-icons/react";
-import { useGetV2GetWaitlistSignups } from "@/app/api/__generated__/endpoints/admin/admin";
-
-type WaitlistSignupsDialogProps = {
-  waitlistId: string;
-  onClose: () => void;
-};
-
-export function WaitlistSignupsDialog({
-  waitlistId,
-  onClose,
-}: WaitlistSignupsDialogProps) {
-  const {
-    data: signupsResponse,
-    isLoading,
-    isError,
-  } = useGetV2GetWaitlistSignups(waitlistId);
-
-  const signups = signupsResponse?.status === 200 ? signupsResponse.data : null;
-
-  function exportToCSV() {
-    if (!signups) return;
-
-    const headers = ["Type", "Email", "User ID", "Username"];
-    const rows = signups.signups.map((signup) => [
-      signup.type,
-      signup.email || "",
-      signup.userId || "",
-      signup.username || "",
-    ]);
-
-    const escapeCell = (cell: string) => `"${cell.replace(/"/g, '""')}"`;
-
-    const csvContent = [
-      headers.join(","),
-      ...rows.map((row) => row.map(escapeCell).join(",")),
-    ].join("\n");
-
-    const blob = new Blob([csvContent], { type: "text/csv" });
-    const url = window.URL.createObjectURL(blob);
-    const a = document.createElement("a");
-    a.href = url;
-    a.download = `waitlist-${waitlistId}-signups.csv`;
-    a.click();
-    window.URL.revokeObjectURL(url);
-  }
-
-  function renderContent() {
-    if (isLoading) {
-      return <div className="py-10 text-center">Loading signups...</div>;
-    }
-
-    if (isError) {
-      return (
-        <div className="py-10 text-center text-red-500">
-          Failed to load signups. Please try again.
-        </div>
-      );
-    }
-
-    if (!signups || signups.signups.length === 0) {
-      return (
-        <div className="py-10 text-center text-gray-500">
-          No signups yet for this waitlist.
-        </div>
-      );
-    }
-
-    return (
-      <>
-        <div className="flex justify-end">
-          <Button variant="secondary" size="small" onClick={exportToCSV}>
-            <DownloadSimple className="mr-2 h-4 w-4" size={16} />
-            Export CSV
-          </Button>
-        </div>
-        <div className="max-h-[400px] overflow-y-auto rounded-md border">
-          <table className="w-full">
-            <thead className="bg-gray-50 dark:bg-gray-800">
-              <tr>
-                <th className="px-4 py-3 text-left text-sm font-medium">
-                  Type
-                </th>
-                <th className="px-4 py-3 text-left text-sm font-medium">
-                  Email / Username
-                </th>
-                <th className="px-4 py-3 text-left text-sm font-medium">
-                  User ID
-                </th>
-              </tr>
-            </thead>
-            <tbody className="divide-y">
-              {signups.signups.map((signup, index) => (
-                <tr key={index}>
-                  <td className="px-4 py-3">
-                    {signup.type === "user" ? (
-                      <span className="flex items-center gap-1 text-blue-600">
-                        <User className="h-4 w-4" size={16} /> User
-                      </span>
-                    ) : (
-                      <span className="flex items-center gap-1 text-gray-600">
-                        <Envelope className="h-4 w-4" size={16} /> Email
-                      </span>
-                    )}
-                  </td>
-                  <td className="px-4 py-3">
-                    {signup.type === "user"
-                      ? signup.username || signup.email
-                      : signup.email}
-                  </td>
-                  <td className="px-4 py-3 font-mono text-sm">
-                    {signup.userId || "-"}
-                  </td>
-                </tr>
-              ))}
-            </tbody>
-          </table>
-        </div>
-      </>
-    );
-  }
-
-  return (
-    <Dialog
-      title="Waitlist Signups"
-      controlled={{
-        isOpen: true,
-        set: async (open) => {
-          if (!open) onClose();
-        },
-      }}
-      onClose={onClose}
-      styling={{ maxWidth: "700px" }}
-    >
-      <Dialog.Content>
-        <p className="mb-4 text-sm text-zinc-500">
-          {signups
-            ? `${signups.totalCount} total signups`
-            : "Loading signups..."}
-        </p>
-
-        {renderContent()}
-
-        <Dialog.Footer>
-          <Button variant="secondary" onClick={onClose}>
-            Close
-          </Button>
-        </Dialog.Footer>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/WaitlistTable.tsx

@@ -1,206 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useQueryClient } from "@tanstack/react-query";
-import {
-  Table,
-  TableBody,
-  TableCell,
-  TableHead,
-  TableHeader,
-  TableRow,
-} from "@/components/__legacy__/ui/table";
-import { Button } from "@/components/atoms/Button/Button";
-import {
-  useGetV2ListAllWaitlists,
-  useDeleteV2DeleteWaitlist,
-  getGetV2ListAllWaitlistsQueryKey,
-} from "@/app/api/__generated__/endpoints/admin/admin";
-import type { WaitlistAdminResponse } from "@/app/api/__generated__/models/waitlistAdminResponse";
-import { EditWaitlistDialog } from "./EditWaitlistDialog";
-import { WaitlistSignupsDialog } from "./WaitlistSignupsDialog";
-import { Trash, PencilSimple, Users, Link } from "@phosphor-icons/react";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-
-export function WaitlistTable() {
-  const [editingWaitlist, setEditingWaitlist] =
-    useState<WaitlistAdminResponse | null>(null);
-  const [viewingSignups, setViewingSignups] = useState<string | null>(null);
-  const { toast } = useToast();
-  const queryClient = useQueryClient();
-
-  const { data: response, isLoading, error } = useGetV2ListAllWaitlists();
-
-  const deleteWaitlistMutation = useDeleteV2DeleteWaitlist({
-    mutation: {
-      onSuccess: () => {
-        toast({
-          title: "Success",
-          description: "Waitlist deleted successfully",
-        });
-        queryClient.invalidateQueries({
-          queryKey: getGetV2ListAllWaitlistsQueryKey(),
-        });
-      },
-      onError: (error) => {
-        console.error("Error deleting waitlist:", error);
-        toast({
-          variant: "destructive",
-          title: "Error",
-          description: "Failed to delete waitlist",
-        });
-      },
-    },
-  });
-
-  function handleDelete(waitlistId: string) {
-    if (!confirm("Are you sure you want to delete this waitlist?")) return;
-    deleteWaitlistMutation.mutate({ waitlistId });
-  }
-
-  function handleWaitlistSaved() {
-    setEditingWaitlist(null);
-    queryClient.invalidateQueries({
-      queryKey: getGetV2ListAllWaitlistsQueryKey(),
-    });
-  }
-
-  function formatStatus(status: string) {
-    const statusColors: Record<string, string> = {
-      NOT_STARTED: "bg-gray-100 text-gray-800",
-      WORK_IN_PROGRESS: "bg-blue-100 text-blue-800",
-      DONE: "bg-green-100 text-green-800",
-      CANCELED: "bg-red-100 text-red-800",
-    };
-
-    return (
-      <span
-        className={`rounded-full px-2 py-1 text-xs font-medium ${statusColors[status] || "bg-gray-100 text-gray-700"}`}
-      >
-        {status.replace(/_/g, " ")}
-      </span>
-    );
-  }
-
-  function formatDate(dateStr: string) {
-    if (!dateStr) return "-";
-    return new Intl.DateTimeFormat("en-US", {
-      month: "short",
-      day: "numeric",
-      year: "numeric",
-    }).format(new Date(dateStr));
-  }
-
-  if (isLoading) {
-    return <div className="py-10 text-center">Loading waitlists...</div>;
-  }
-
-  if (error) {
-    return (
-      <div className="py-10 text-center text-red-500">
-        Error loading waitlists. Please try again.
-      </div>
-    );
-  }
-
-  const waitlists = response?.status === 200 ? response.data.waitlists : [];
-
-  if (waitlists.length === 0) {
-    return (
-      <div className="py-10 text-center text-gray-500">
-        No waitlists found. Create one to get started!
-      </div>
-    );
-  }
-
-  return (
-    <>
-      <div className="rounded-md border bg-white">
-        <Table>
-          <TableHeader className="bg-gray-50">
-            <TableRow>
-              <TableHead className="font-medium">Name</TableHead>
-              <TableHead className="font-medium">Status</TableHead>
-              <TableHead className="font-medium">Signups</TableHead>
-              <TableHead className="font-medium">Votes</TableHead>
-              <TableHead className="font-medium">Created</TableHead>
-              <TableHead className="font-medium">Linked Agent</TableHead>
-              <TableHead className="font-medium">Actions</TableHead>
-            </TableRow>
-          </TableHeader>
-          <TableBody>
-            {waitlists.map((waitlist) => (
-              <TableRow key={waitlist.id}>
-                <TableCell>
-                  <div>
-                    <div className="font-medium">{waitlist.name}</div>
-                    <div className="text-sm text-gray-500">
-                      {waitlist.subHeading}
-                    </div>
-                  </div>
-                </TableCell>
-                <TableCell>{formatStatus(waitlist.status)}</TableCell>
-                <TableCell>{waitlist.signupCount}</TableCell>
-                <TableCell>{waitlist.votes}</TableCell>
-                <TableCell>{formatDate(waitlist.createdAt)}</TableCell>
-                <TableCell>
-                  {waitlist.storeListingId ? (
-                    <span className="text-green-600">
-                      <Link size={16} className="inline" /> Linked
-                    </span>
-                  ) : (
-                    <span className="text-gray-400">Not linked</span>
-                  )}
-                </TableCell>
-                <TableCell>
-                  <div className="flex gap-2">
-                    <Button
-                      variant="ghost"
-                      size="small"
-                      onClick={() => setViewingSignups(waitlist.id)}
-                      title="View signups"
-                    >
-                      <Users size={16} />
-                    </Button>
-                    <Button
-                      variant="ghost"
-                      size="small"
-                      onClick={() => setEditingWaitlist(waitlist)}
-                      title="Edit"
-                    >
-                      <PencilSimple size={16} />
-                    </Button>
-                    <Button
-                      variant="ghost"
-                      size="small"
-                      onClick={() => handleDelete(waitlist.id)}
-                      title="Delete"
-                      disabled={deleteWaitlistMutation.isPending}
-                    >
-                      <Trash size={16} className="text-red-500" />
-                    </Button>
-                  </div>
-                </TableCell>
-              </TableRow>
-            ))}
-          </TableBody>
-        </Table>
-      </div>
-
-      {editingWaitlist && (
-        <EditWaitlistDialog
-          waitlist={editingWaitlist}
-          onClose={() => setEditingWaitlist(null)}
-          onSave={handleWaitlistSaved}
-        />
-      )}
-
-      {viewingSignups && (
-        <WaitlistSignupsDialog
-          waitlistId={viewingSignups}
-          onClose={() => setViewingSignups(null)}
-        />
-      )}
-    </>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/page.tsx

@@ -1,52 +0,0 @@
-import { withRoleAccess } from "@/lib/withRoleAccess";
-import { Suspense } from "react";
-import { WaitlistTable } from "./components/WaitlistTable";
-import { CreateWaitlistButton } from "./components/CreateWaitlistButton";
-import { Warning } from "@phosphor-icons/react/dist/ssr";
-
-function WaitlistDashboard() {
-  return (
-    <div className="mx-auto p-6">
-      <div className="flex flex-col gap-4">
-        <div className="flex items-center justify-between">
-          <div>
-            <h1 className="text-3xl font-bold">Waitlist Management</h1>
-            <p className="text-gray-500">
-              Manage upcoming agent waitlists and track signups
-            </p>
-          </div>
-          <CreateWaitlistButton />
-        </div>
-
-        <div className="flex items-start gap-3 rounded-lg border border-amber-300 bg-amber-50 p-4 dark:border-amber-700 dark:bg-amber-950">
-          <Warning
-            className="mt-0.5 h-5 w-5 flex-shrink-0 text-amber-600 dark:text-amber-400"
-            weight="fill"
-          />
-          <div className="text-sm text-amber-800 dark:text-amber-200">
-            <p className="font-medium">TODO: Email-only signup notifications</p>
-            <p className="mt-1 text-amber-700 dark:text-amber-300">
-              Notifications for email-only signups (users who weren&apos;t
-              logged in) have not been implemented yet. Currently only
-              registered users will receive launch emails.
-            </p>
-          </div>
-        </div>
-
-        <Suspense
-          fallback={
-            <div className="py-10 text-center">Loading waitlists...</div>
-          }
-        >
-          <WaitlistTable />
-        </Suspense>
-      </div>
-    </div>
-  );
-}
-
-export default async function WaitlistDashboardPage() {
-  const withAdminAccess = await withRoleAccess(["admin"]);
-  const ProtectedWaitlistDashboard = await withAdminAccess(WaitlistDashboard);
-  return <ProtectedWaitlistDashboard />;
-}

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,
+          // Empty schemas - will be populated when block is added
-          outputSchema: flow.output_schema,
+          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,
+            // Schemas will be fetched on-demand when block is added
-            output_schema: flow.output_schema,
           },
         };
       })
@@ -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={() =>
+                  onClick={() => handleAddBlock(block)}
-                    !block.notAvailable &&
-                    addBlock(block.id, block.name, block?.hardcodedValues || {})
-                  }
                   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({
@@ -102,7 +120,22 @@ export default function DataTable({
                       <Clipboard size={18} />
                     </Button>
                   </div>
-                  {value.map((item, index) => (
+                  {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}
@@ -110,7 +143,8 @@ export default function DataTable({
                         />
                         {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 = {
+      const newNode = await createAndAddNode(
-        id: nodeId.toString(),
+        blockId,
-        type: "custom",
+        nodeType,
-        position: viewportCoordinates, // Set the position to the calculated viewport center
+        hardcodedValues,
-        data: {
+        position,
-          blockType: nodeType,
+      );
-          blockCosts: nodeSchema.costs,
+      if (!newNode) return;
-          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
 
       setViewport(
         {
-          // Rough estimate of the dimension of the node is: 500x400px.
+          x: -position.x * 0.8 + (window.innerWidth - 0.0) / 2,
-          // Though we skip shifting the X, considering the block menu side-bar.
+          y: -position.y * 0.8 + (window.innerHeight - 400) / 2,
-          x: -viewportCoordinates.x * 0.8 + (window.innerWidth - 0.0) / 2,
-          y: -viewportCoordinates.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,
+      createAndAddNode,
-      clearNodesStatusAndOutput,
     ],
   );
 
@@ -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
+        await createAndAddNode(
-        const nodeSchema = availableBlocks.find((node) => node.id === blockId);
+          blockId,
-        if (!nodeSchema) {
+          blockName,
-          console.error(`Schema not found for block ID: ${blockId}`);
+          hardcodedValues || {},
-          return;
-        }
-
-        // Create the new node at the drop position
-        const newNode: CustomNode = {
-          id: nodeId.toString(),
-          type: "custom",
           position,
-          data: {
+        );
-            blockType: blockName,
-            blockCosts: nodeSchema.costs || [],
-            title: `${blockName} ${nodeId}`,
-            description: nodeSchema.description,
-            categories: nodeSchema.categories,
-            inputSchema: 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);
       }
     },
-    [
+    [screenToFlowPosition, createAndAddNode],
-      nodeId,
-      availableBlocks,
-      nodes,
-      edges,
-      addNodes,
-      screenToFlowPosition,
-      deleteElements,
-      clearNodesStatusAndOutput,
-    ],
   );
 
   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);
@@ -87,7 +105,22 @@ 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) => (
+              {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}
@@ -95,7 +128,8 @@ export default function NodeOutputs({
                     />
                     {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[],
+  availableGraphs: GraphMetaLike[],
-): SubAgentUpdateInfo<T> {
+): 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;
+    if (!latestGraphInfo || graphVersion === undefined) return false;
-    return latestGraph.version! > graphVersion;
+    return latestGraphInfo.version! > graphVersion;
-  }, [latestGraph, 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,
+    latestVersion: latestGraphInfo?.version || 0,
-    latestGraph,
+    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)/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;

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

@@ -8,7 +8,6 @@ import { useMainMarketplacePage } from "./useMainMarketplacePage";
 import { FeaturedCreators } from "../FeaturedCreators/FeaturedCreators";
 import { MainMarketplacePageLoading } from "../MainMarketplacePageLoading";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
-import { WaitlistSection } from "../WaitlistSection/WaitlistSection";
 
 export const MainMarkeplacePage = () => {
   const { featuredAgents, topAgents, featuredCreators, isLoading, hasError } =
@@ -47,10 +46,6 @@ export const MainMarkeplacePage = () => {
         {/* 100px margin because our featured sections button are placed 40px below the container */}
         <Separator className="mb-6 mt-24" />
 
-        {/* Waitlist Section - "Help Shape What's Next" */}
-        <WaitlistSection />
-        <Separator className="mb-6 mt-12" />
-
         {topAgents && (
           <AgentsSection sectionTitle="Top Agents" agents={topAgents.agents} />
         )}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistCard/WaitlistCard.tsx

@@ -1,105 +0,0 @@
-"use client";
-
-import Image from "next/image";
-import { Button } from "@/components/atoms/Button/Button";
-import { Check } from "@phosphor-icons/react";
-
-interface WaitlistCardProps {
-  name: string;
-  subHeading: string;
-  description: string;
-  imageUrl: string | null;
-  isMember?: boolean;
-  onCardClick: () => void;
-  onJoinClick: (e: React.MouseEvent) => void;
-}
-
-export function WaitlistCard({
-  name,
-  subHeading,
-  description,
-  imageUrl,
-  isMember = false,
-  onCardClick,
-  onJoinClick,
-}: WaitlistCardProps) {
-  function handleJoinClick(e: React.MouseEvent) {
-    e.stopPropagation();
-    onJoinClick(e);
-  }
-
-  return (
-    <div
-      className="flex h-[24rem] w-full max-w-md cursor-pointer flex-col items-start rounded-3xl bg-white transition-all duration-300 hover:shadow-lg dark:bg-zinc-900 dark:hover:shadow-gray-700"
-      onClick={onCardClick}
-      data-testid="waitlist-card"
-      role="button"
-      tabIndex={0}
-      aria-label={`${name} waitlist card`}
-      onKeyDown={(e) => {
-        if (e.key === "Enter" || e.key === " ") {
-          onCardClick();
-        }
-      }}
-    >
-      {/* Image Section */}
-      <div className="relative aspect-[2/1.2] w-full overflow-hidden rounded-large md:aspect-[2.17/1]">
-        {imageUrl ? (
-          <Image
-            src={imageUrl}
-            alt={`${name} preview image`}
-            fill
-            className="object-cover"
-          />
-        ) : (
-          <div className="flex h-full w-full items-center justify-center bg-gradient-to-br from-neutral-200 to-neutral-300 dark:from-neutral-700 dark:to-neutral-800">
-            <span className="text-4xl font-bold text-neutral-400 dark:text-neutral-500">
-              {name.charAt(0)}
-            </span>
-          </div>
-        )}
-      </div>
-
-      <div className="mt-3 flex w-full flex-1 flex-col px-4">
-        {/* Name and Subheading */}
-        <div className="flex w-full flex-col">
-          <h3 className="line-clamp-1 font-poppins text-xl font-semibold text-[#272727] dark:text-neutral-100">
-            {name}
-          </h3>
-          <p className="mt-1 line-clamp-1 text-sm text-neutral-500 dark:text-neutral-400">
-            {subHeading}
-          </p>
-        </div>
-
-        {/* Description */}
-        <div className="mt-2 flex w-full flex-col">
-          <p className="line-clamp-5 text-sm font-normal leading-relaxed text-neutral-600 dark:text-neutral-400">
-            {description}
-          </p>
-        </div>
-
-        <div className="flex-grow" />
-
-        {/* Join Waitlist Button */}
-        <div className="mt-4 w-full pb-4">
-          {isMember ? (
-            <Button
-              disabled
-              className="w-full rounded-full bg-green-600 text-white hover:bg-green-600 dark:bg-green-700 dark:hover:bg-green-700"
-            >
-              <Check className="mr-2" size={16} weight="bold" />
-              On the waitlist
-            </Button>
-          ) : (
-            <Button
-              onClick={handleJoinClick}
-              className="w-full rounded-full bg-zinc-800 text-white hover:bg-zinc-700 dark:bg-zinc-700 dark:hover:bg-zinc-600"
-            >
-              Join waitlist
-            </Button>
-          )}
-        </div>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistDetailModal/WaitlistDetailModal.tsx

@@ -1,356 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import Image from "next/image";
-import { Button } from "@/components/atoms/Button/Button";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Input } from "@/components/atoms/Input/Input";
-import {
-  Carousel,
-  CarouselContent,
-  CarouselItem,
-  CarouselNext,
-  CarouselPrevious,
-} from "@/components/__legacy__/ui/carousel";
-import type { StoreWaitlistEntry } from "@/app/api/__generated__/models/storeWaitlistEntry";
-import { Check, Play } from "@phosphor-icons/react";
-import { useSupabaseStore } from "@/lib/supabase/hooks/useSupabaseStore";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { usePostV2AddSelfToTheAgentWaitlist } from "@/app/api/__generated__/endpoints/store/store";
-
-interface MediaItem {
-  type: "image" | "video";
-  url: string;
-  label?: string;
-}
-
-// Extract YouTube video ID from various URL formats
-function getYouTubeVideoId(url: string): string | null {
-  const regExp =
-    /^.*((youtu.be\/)|(v\/)|(\/u\/\w\/)|(embed\/)|(watch\?))\??v?=?([^#&?]*).*/;
-  const match = url.match(regExp);
-  return match && match[7].length === 11 ? match[7] : null;
-}
-
-// Validate video URL for security
-function isValidVideoUrl(url: string): boolean {
-  if (url.startsWith("data:video")) {
-    return true;
-  }
-  const videoExtensions = /\.(mp4|webm|ogg)$/i;
-  const youtubeRegex = /^(https?:\/\/)?(www\.)?(youtube\.com|youtu\.?be)\/.+$/;
-  const validUrl = /^(https?:\/\/)/i;
-  const cleanedUrl = url.split("?")[0];
-  return (
-    (validUrl.test(url) && videoExtensions.test(cleanedUrl)) ||
-    youtubeRegex.test(url)
-  );
-}
-
-// Video player with YouTube embed support
-function VideoPlayer({
-  url,
-  autoPlay = false,
-  className = "",
-}: {
-  url: string;
-  autoPlay?: boolean;
-  className?: string;
-}) {
-  const youtubeId = getYouTubeVideoId(url);
-
-  if (youtubeId) {
-    return (
-      <iframe
-        src={`https://www.youtube.com/embed/${youtubeId}${autoPlay ? "?autoplay=1" : ""}`}
-        title="YouTube video player"
-        className={className}
-        allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-        sandbox="allow-same-origin allow-scripts allow-presentation"
-        allowFullScreen
-      />
-    );
-  }
-
-  if (!isValidVideoUrl(url)) {
-    return (
-      <div
-        className={`flex items-center justify-center bg-zinc-800 ${className}`}
-      >
-        <span className="text-sm text-zinc-400">Invalid video URL</span>
-      </div>
-    );
-  }
-
-  return <video src={url} controls autoPlay={autoPlay} className={className} />;
-}
-
-function MediaCarousel({ waitlist }: { waitlist: StoreWaitlistEntry }) {
-  const [activeVideo, setActiveVideo] = useState<string | null>(null);
-
-  // Build media items array: videos first, then images
-  const mediaItems: MediaItem[] = [
-    ...(waitlist.videoUrl
-      ? [{ type: "video" as const, url: waitlist.videoUrl, label: "Video" }]
-      : []),
-    ...(waitlist.agentOutputDemoUrl
-      ? [
-          {
-            type: "video" as const,
-            url: waitlist.agentOutputDemoUrl,
-            label: "Demo",
-          },
-        ]
-      : []),
-    ...waitlist.imageUrls.map((url) => ({ type: "image" as const, url })),
-  ];
-
-  if (mediaItems.length === 0) return null;
-
-  // Single item - no carousel needed
-  if (mediaItems.length === 1) {
-    const item = mediaItems[0];
-    return (
-      <div className="relative aspect-[350/196] w-full overflow-hidden rounded-large">
-        {item.type === "image" ? (
-          <Image
-            src={item.url}
-            alt={`${waitlist.name} preview`}
-            fill
-            className="object-cover"
-          />
-        ) : (
-          <VideoPlayer url={item.url} className="h-full w-full object-cover" />
-        )}
-      </div>
-    );
-  }
-
-  // Multiple items - use carousel
-  return (
-    <Carousel className="w-full">
-      <CarouselContent>
-        {mediaItems.map((item, index) => (
-          <CarouselItem key={index}>
-            <div className="relative aspect-[350/196] w-full overflow-hidden rounded-large">
-              {item.type === "image" ? (
-                <Image
-                  src={item.url}
-                  alt={`${waitlist.name} preview ${index + 1}`}
-                  fill
-                  className="object-cover"
-                />
-              ) : activeVideo === item.url ? (
-                <VideoPlayer
-                  url={item.url}
-                  autoPlay
-                  className="h-full w-full object-cover"
-                />
-              ) : (
-                <button
-                  onClick={() => setActiveVideo(item.url)}
-                  className="group relative h-full w-full bg-zinc-900"
-                >
-                  <div className="absolute inset-0 flex items-center justify-center">
-                    <div className="flex h-16 w-16 items-center justify-center rounded-full bg-white/90 transition-transform group-hover:scale-110">
-                      <Play size={32} weight="fill" className="text-zinc-800" />
-                    </div>
-                  </div>
-                  <span className="absolute bottom-3 left-3 text-sm text-white">
-                    {item.label}
-                  </span>
-                </button>
-              )}
-            </div>
-          </CarouselItem>
-        ))}
-      </CarouselContent>
-      <CarouselPrevious className="left-2 top-1/2 -translate-y-1/2" />
-      <CarouselNext className="right-2 top-1/2 -translate-y-1/2" />
-    </Carousel>
-  );
-}
-
-interface WaitlistDetailModalProps {
-  waitlist: StoreWaitlistEntry;
-  isMember?: boolean;
-  onClose: () => void;
-  onJoinSuccess?: (waitlistId: string) => void;
-}
-
-export function WaitlistDetailModal({
-  waitlist,
-  isMember = false,
-  onClose,
-  onJoinSuccess,
-}: WaitlistDetailModalProps) {
-  const { user } = useSupabaseStore();
-  const [email, setEmail] = useState("");
-  const [success, setSuccess] = useState(false);
-  const { toast } = useToast();
-  const joinWaitlistMutation = usePostV2AddSelfToTheAgentWaitlist();
-
-  function handleJoin() {
-    joinWaitlistMutation.mutate(
-      {
-        waitlistId: waitlist.waitlistId,
-        data: { email: user ? undefined : email },
-      },
-      {
-        onSuccess: (response) => {
-          if (response.status === 200) {
-            setSuccess(true);
-            toast({
-              title: "You're on the waitlist!",
-              description: `We'll notify you when ${waitlist.name} goes live.`,
-            });
-            onJoinSuccess?.(waitlist.waitlistId);
-          } else {
-            toast({
-              variant: "destructive",
-              title: "Error",
-              description: "Failed to join waitlist. Please try again.",
-            });
-          }
-        },
-        onError: () => {
-          toast({
-            variant: "destructive",
-            title: "Error",
-            description: "Failed to join waitlist. Please try again.",
-          });
-        },
-      },
-    );
-  }
-
-  // Success state
-  if (success) {
-    return (
-      <Dialog
-        title=""
-        controlled={{
-          isOpen: true,
-          set: async (open) => {
-            if (!open) onClose();
-          },
-        }}
-        onClose={onClose}
-        styling={{ maxWidth: "500px" }}
-      >
-        <Dialog.Content>
-          <div className="flex flex-col items-center justify-center py-4 text-center">
-            {/* Party emoji */}
-            <span className="mb-2 text-5xl">🎉</span>
-
-            {/* Title */}
-            <h2 className="mb-2 font-poppins text-[22px] font-medium leading-7 text-zinc-900 dark:text-zinc-100">
-              You&apos;re on the waitlist
-            </h2>
-
-            {/* Subtitle */}
-            <p className="text-base leading-[26px] text-zinc-600 dark:text-zinc-400">
-              Thanks for helping us prioritize which agents to build next.
-              We&apos;ll notify you when this agent goes live in the
-              marketplace.
-            </p>
-          </div>
-
-          {/* Close button */}
-          <Dialog.Footer className="flex justify-center pb-2 pt-4">
-            <Button
-              variant="secondary"
-              onClick={onClose}
-              className="rounded-full border border-zinc-700 bg-white px-4 py-3 text-zinc-900 hover:bg-zinc-100 dark:border-zinc-500 dark:bg-zinc-800 dark:text-zinc-100 dark:hover:bg-zinc-700"
-            >
-              Close
-            </Button>
-          </Dialog.Footer>
-        </Dialog.Content>
-      </Dialog>
-    );
-  }
-
-  // Main modal - handles both member and non-member states
-  return (
-    <Dialog
-      title="Join the waitlist"
-      controlled={{
-        isOpen: true,
-        set: async (open) => {
-          if (!open) onClose();
-        },
-      }}
-      onClose={onClose}
-      styling={{ maxWidth: "500px" }}
-    >
-      <Dialog.Content>
-        {/* Subtitle */}
-        <p className="mb-6 text-center text-base text-zinc-600 dark:text-zinc-400">
-          Help us decide what to build next — and get notified when this agent
-          is ready
-        </p>
-
-        {/* Media Carousel */}
-        <MediaCarousel waitlist={waitlist} />
-
-        {/* Agent Name */}
-        <h3 className="mt-4 font-poppins text-[22px] font-medium leading-7 text-zinc-800 dark:text-zinc-100">
-          {waitlist.name}
-        </h3>
-
-        {/* Agent Description */}
-        <p className="mt-2 line-clamp-5 text-sm leading-[22px] text-zinc-500 dark:text-zinc-400">
-          {waitlist.description}
-        </p>
-
-        {/* Email input for non-logged-in users who haven't joined */}
-        {!isMember && !user && (
-          <div className="mt-4 pr-1">
-            <Input
-              id="email"
-              label="Email address"
-              type="email"
-              placeholder="you@example.com"
-              value={email}
-              onChange={(e) => setEmail(e.target.value)}
-              required
-            />
-          </div>
-        )}
-
-        {/* Footer buttons */}
-        <Dialog.Footer className="sticky bottom-0 mt-6 flex justify-center gap-3 bg-white pb-2 pt-4 dark:bg-zinc-900">
-          {isMember ? (
-            <Button
-              disabled
-              className="rounded-full bg-green-600 px-4 py-3 text-white hover:bg-green-600 dark:bg-green-700 dark:hover:bg-green-700"
-            >
-              <Check size={16} className="mr-2" />
-              You&apos;re on the waitlist
-            </Button>
-          ) : (
-            <>
-              <Button
-                onClick={handleJoin}
-                loading={joinWaitlistMutation.isPending}
-                disabled={!user && !email}
-                className="rounded-full bg-zinc-800 px-4 py-3 text-white hover:bg-zinc-700 dark:bg-zinc-700 dark:hover:bg-zinc-600"
-              >
-                Join waitlist
-              </Button>
-              <Button
-                type="button"
-                variant="secondary"
-                onClick={onClose}
-                className="rounded-full bg-zinc-200 px-4 py-3 text-zinc-900 hover:bg-zinc-300 dark:bg-zinc-700 dark:text-zinc-100 dark:hover:bg-zinc-600"
-              >
-                Not now
-              </Button>
-            </>
-          )}
-        </Dialog.Footer>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistSection/WaitlistSection.tsx

@@ -1,102 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import {
-  Carousel,
-  CarouselContent,
-  CarouselItem,
-} from "@/components/__legacy__/ui/carousel";
-import { WaitlistCard } from "../WaitlistCard/WaitlistCard";
-import { WaitlistDetailModal } from "../WaitlistDetailModal/WaitlistDetailModal";
-import type { StoreWaitlistEntry } from "@/app/api/__generated__/models/storeWaitlistEntry";
-import { useWaitlistSection } from "./useWaitlistSection";
-
-export function WaitlistSection() {
-  const { waitlists, joinedWaitlistIds, isLoading, hasError, markAsJoined } =
-    useWaitlistSection();
-  const [selectedWaitlist, setSelectedWaitlist] =
-    useState<StoreWaitlistEntry | null>(null);
-
-  function handleOpenModal(waitlist: StoreWaitlistEntry) {
-    setSelectedWaitlist(waitlist);
-  }
-
-  function handleJoinSuccess(waitlistId: string) {
-    markAsJoined(waitlistId);
-  }
-
-  // Don't render if loading, error, or no waitlists
-  if (isLoading || hasError || !waitlists || waitlists.length === 0) {
-    return null;
-  }
-
-  return (
-    <div className="flex flex-col items-center justify-center">
-      <div className="w-full max-w-[1360px]">
-        {/* Section Header */}
-        <div className="mb-6">
-          <h2 className="font-poppins text-2xl font-semibold text-[#282828] dark:text-neutral-200">
-            Help Shape What&apos;s Next
-          </h2>
-          <p className="mt-2 text-base text-neutral-600 dark:text-neutral-400">
-            These agents are in development. Your interest helps us prioritize
-            what gets built — and we&apos;ll notify you when they&apos;re ready.
-          </p>
-        </div>
-
-        {/* Mobile Carousel View */}
-        <Carousel
-          className="md:hidden"
-          opts={{
-            loop: true,
-          }}
-        >
-          <CarouselContent>
-            {waitlists.map((waitlist) => (
-              <CarouselItem
-                key={waitlist.waitlistId}
-                className="min-w-64 max-w-71"
-              >
-                <WaitlistCard
-                  name={waitlist.name}
-                  subHeading={waitlist.subHeading}
-                  description={waitlist.description}
-                  imageUrl={waitlist.imageUrls[0] || null}
-                  isMember={joinedWaitlistIds.has(waitlist.waitlistId)}
-                  onCardClick={() => handleOpenModal(waitlist)}
-                  onJoinClick={() => handleOpenModal(waitlist)}
-                />
-              </CarouselItem>
-            ))}
-          </CarouselContent>
-        </Carousel>
-
-        {/* Desktop Grid View */}
-        <div className="hidden grid-cols-1 place-items-center gap-6 md:grid md:grid-cols-2 lg:grid-cols-3">
-          {waitlists.map((waitlist) => (
-            <WaitlistCard
-              key={waitlist.waitlistId}
-              name={waitlist.name}
-              subHeading={waitlist.subHeading}
-              description={waitlist.description}
-              imageUrl={waitlist.imageUrls[0] || null}
-              isMember={joinedWaitlistIds.has(waitlist.waitlistId)}
-              onCardClick={() => handleOpenModal(waitlist)}
-              onJoinClick={() => handleOpenModal(waitlist)}
-            />
-          ))}
-        </div>
-      </div>
-
-      {/* Single Modal for both viewing and joining */}
-      {selectedWaitlist && (
-        <WaitlistDetailModal
-          waitlist={selectedWaitlist}
-          isMember={joinedWaitlistIds.has(selectedWaitlist.waitlistId)}
-          onClose={() => setSelectedWaitlist(null)}
-          onJoinSuccess={handleJoinSuccess}
-        />
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistSection/useWaitlistSection.ts

@@ -1,58 +0,0 @@
-"use client";
-
-import { useMemo } from "react";
-import { useSupabaseStore } from "@/lib/supabase/hooks/useSupabaseStore";
-import {
-  useGetV2GetTheAgentWaitlist,
-  useGetV2GetWaitlistIdsTheCurrentUserHasJoined,
-  getGetV2GetWaitlistIdsTheCurrentUserHasJoinedQueryKey,
-} from "@/app/api/__generated__/endpoints/store/store";
-import type { StoreWaitlistEntry } from "@/app/api/__generated__/models/storeWaitlistEntry";
-import { useQueryClient } from "@tanstack/react-query";
-
-export function useWaitlistSection() {
-  const { user } = useSupabaseStore();
-  const queryClient = useQueryClient();
-
-  // Fetch waitlists
-  const {
-    data: waitlistsResponse,
-    isLoading: waitlistsLoading,
-    isError: waitlistsError,
-  } = useGetV2GetTheAgentWaitlist();
-
-  // Fetch memberships if logged in
-  const { data: membershipsResponse, isLoading: membershipsLoading } =
-    useGetV2GetWaitlistIdsTheCurrentUserHasJoined({
-      query: {
-        enabled: !!user,
-      },
-    });
-
-  const waitlists: StoreWaitlistEntry[] = useMemo(() => {
-    if (waitlistsResponse?.status === 200) {
-      return waitlistsResponse.data.listings;
-    }
-    return [];
-  }, [waitlistsResponse]);
-
-  const joinedWaitlistIds: Set<string> = useMemo(() => {
-    if (membershipsResponse?.status === 200) {
-      return new Set(membershipsResponse.data);
-    }
-    return new Set();
-  }, [membershipsResponse]);
-
-  const isLoading = waitlistsLoading || (!!user && membershipsLoading);
-  const hasError = waitlistsError;
-
-  // Function to add a waitlist ID to joined set (called after successful join)
-  function markAsJoined(_waitlistId: string) {
-    // Invalidate the memberships query to refetch
-    queryClient.invalidateQueries({
-      queryKey: getGetV2GetWaitlistIdsTheCurrentUserHasJoinedQueryKey(),
-    });
-  }
-
-  return { waitlists, joinedWaitlistIds, isLoading, hasError, markAsJoined };
-}

autogpt_platform/frontend/src/components/__legacy__/ui/render.tsx

@@ -22,7 +22,7 @@ const isValidVideoUrl = (url: string): boolean => {
   if (url.startsWith("data:video")) {
     return true;
   }
-  const videoExtensions = /\.(mp4|webm|ogg)$/i;
+  const videoExtensions = /\.(mp4|webm|ogg|mov|avi|mkv|m4v)$/i;
   const youtubeRegex = /^(https?:\/\/)?(www\.)?(youtube\.com|youtu\.?be)\/.+$/;
   const cleanedUrl = url.split("?")[0];
   return (
@@ -44,11 +44,29 @@ const isValidAudioUrl = (url: string): boolean => {
   if (url.startsWith("data:audio")) {
     return true;
   }
-  const audioExtensions = /\.(mp3|wav)$/i;
+  const audioExtensions = /\.(mp3|wav|ogg|m4a|aac|flac)$/i;
   const cleanedUrl = url.split("?")[0];
   return isValidMediaUri(url) && audioExtensions.test(cleanedUrl);
 };
 
+const getVideoMimeType = (url: string): string => {
+  if (url.startsWith("data:video/")) {
+    const match = url.match(/^data:(video\/[^;]+)/);
+    return match?.[1] || "video/mp4";
+  }
+  const extension = url.split("?")[0].split(".").pop()?.toLowerCase();
+  const mimeMap: Record<string, string> = {
+    mp4: "video/mp4",
+    webm: "video/webm",
+    ogg: "video/ogg",
+    mov: "video/quicktime",
+    avi: "video/x-msvideo",
+    mkv: "video/x-matroska",
+    m4v: "video/mp4",
+  };
+  return mimeMap[extension || ""] || "video/mp4";
+};
+
 const VideoRenderer: React.FC<{ videoUrl: string }> = ({ videoUrl }) => {
   const videoId = getYouTubeVideoId(videoUrl);
   return (
@@ -63,7 +81,7 @@ const VideoRenderer: React.FC<{ videoUrl: string }> = ({ videoUrl }) => {
         ></iframe>
       ) : (
         <video controls width="100%" height="315">
-          <source src={videoUrl} type="video/mp4" />
+          <source src={videoUrl} type={getVideoMimeType(videoUrl)} />
           Your browser does not support the video tag.
         </video>
       )}

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

@@ -102,18 +102,6 @@ export function ChatMessage({
     }
   }
 
-  function handleClarificationAnswers(answers: Record<string, string>) {
-    if (onSendMessage) {
-      const contextMessage = Object.entries(answers)
-        .map(([keyword, answer]) => `${keyword}: ${answer}`)
-        .join("\n");
-
-      onSendMessage(
-        `I have the answers to your questions:\n\n${contextMessage}\n\nPlease proceed with creating the agent.`,
-      );
-    }
-  }
-
   const handleCopy = useCallback(
     async function handleCopy() {
       if (message.type !== "message") return;
@@ -162,6 +150,22 @@ export function ChatMessage({
         .slice(index + 1)
         .some((m) => m.type === "message" && m.role === "user");
 
+    const handleClarificationAnswers = (answers: Record<string, string>) => {
+      if (onSendMessage) {
+        // Iterate over questions (preserves original order) instead of answers
+        const contextMessage = message.questions
+          .map((q) => {
+            const answer = answers[q.keyword] || "";
+            return `> ${q.question}\n\n${answer}`;
+          })
+          .join("\n\n");
+
+        onSendMessage(
+          `**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with creating the agent.`,
+        );
+      }
+    };
+
     return (
       <ClarificationQuestionsWidget
         questions={message.questions}
@@ -346,6 +350,7 @@ export function ChatMessage({
           toolId={message.toolId}
           toolName={message.toolName}
           result={message.result}
+          onSendMessage={onSendMessage}
         />
       </div>
     );

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

@@ -3,7 +3,7 @@
 import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
 import { cn } from "@/lib/utils";
 import { EyeSlash } from "@phosphor-icons/react";
-import React from "react";
+import React, { useState } from "react";
 import ReactMarkdown from "react-markdown";
 import remarkGfm from "remark-gfm";
 
@@ -48,7 +48,9 @@ interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
  */
 function resolveWorkspaceUrl(src: string): string {
   if (src.startsWith("workspace://")) {
-    const fileId = src.replace("workspace://", "");
+    // Strip MIME type fragment if present (e.g., workspace://abc123#video/mp4 → abc123)
+    const withoutPrefix = src.replace("workspace://", "");
+    const fileId = withoutPrefix.split("#")[0];
     // Use the generated API URL helper to get the correct path
     const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
     // Route through the Next.js proxy (same pattern as customMutator for client-side)
@@ -65,13 +67,49 @@ function isWorkspaceImage(src: string | undefined): boolean {
   return src?.includes("/workspace/files/") ?? false;
 }
 
+/**
+ * Renders a workspace video with controls and an optional "AI cannot see" badge.
+ */
+function WorkspaceVideo({
+  src,
+  aiCannotSee,
+}: {
+  src: string;
+  aiCannotSee: boolean;
+}) {
+  return (
+    <span className="relative my-2 inline-block">
+      <video
+        controls
+        className="h-auto max-w-full rounded-md border border-zinc-200"
+        preload="metadata"
+      >
+        <source src={src} />
+        Your browser does not support the video tag.
+      </video>
+      {aiCannotSee && (
+        <span
+          className="absolute bottom-2 right-2 flex items-center gap-1 rounded bg-black/70 px-2 py-1 text-xs text-white"
+          title="The AI cannot see this video"
+        >
+          <EyeSlash size={14} />
+          <span>AI cannot see this video</span>
+        </span>
+      )}
+    </span>
+  );
+}
+
 /**
  * Custom image component that shows an indicator when the AI cannot see the image.
+ * Also handles the "video:" alt-text prefix convention to render <video> elements.
+ * For workspace files with unknown types, falls back to <video> if <img> fails.
  * Note: src is already transformed by urlTransform, so workspace:// is now /api/workspace/...
  */
 function MarkdownImage(props: Record<string, unknown>) {
   const src = props.src as string | undefined;
   const alt = props.alt as string | undefined;
+  const [imgFailed, setImgFailed] = useState(false);
 
   const aiCannotSee = isWorkspaceImage(src);
 
@@ -84,6 +122,18 @@ function MarkdownImage(props: Record<string, unknown>) {
     );
   }
 
+  // Detect video: prefix in alt text (set by formatOutputValue in helpers.ts)
+  if (alt?.startsWith("video:")) {
+    return <WorkspaceVideo src={src} aiCannotSee={aiCannotSee} />;
+  }
+
+  // If the <img> failed to load and this is a workspace file, try as video.
+  // This handles generic output keys like "file_out" where the MIME type
+  // isn't known from the key name alone.
+  if (imgFailed && aiCannotSee) {
+    return <WorkspaceVideo src={src} aiCannotSee={aiCannotSee} />;
+  }
+
   return (
     <span className="relative my-2 inline-block">
       {/* eslint-disable-next-line @next/next/no-img-element */}
@@ -92,6 +142,9 @@ function MarkdownImage(props: Record<string, unknown>) {
         alt={alt || "Image"}
         className="h-auto max-w-full rounded-md border border-zinc-200"
         loading="lazy"
+        onError={() => {
+          if (aiCannotSee) setImgFailed(true);
+        }}
       />
       {aiCannotSee && (
         <span

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

@@ -73,6 +73,7 @@ export function MessageList({
                     key={index}
                     message={message}
                     prevMessage={messages[index - 1]}
+                    onSendMessage={onSendMessage}
                   />
                 );
               }

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

@@ -5,11 +5,13 @@ import { shouldSkipAgentOutput } from "../../helpers";
 export interface LastToolResponseProps {
   message: ChatMessageData;
   prevMessage: ChatMessageData | undefined;
+  onSendMessage?: (content: string) => void;
 }
 
 export function LastToolResponse({
   message,
   prevMessage,
+  onSendMessage,
 }: LastToolResponseProps) {
   if (message.type !== "tool_response") return null;
 
@@ -21,6 +23,7 @@ export function LastToolResponse({
         toolId={message.toolId}
         toolName={message.toolName}
         result={message.result}
+        onSendMessage={onSendMessage}
       />
     </div>
   );

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

@@ -1,6 +1,8 @@
+import { Progress } from "@/components/atoms/Progress/Progress";
 import { cn } from "@/lib/utils";
 import { useEffect, useRef, useState } from "react";
 import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
+import { useAsymptoticProgress } from "../ToolCallMessage/useAsymptoticProgress";
 
 export interface ThinkingMessageProps {
   className?: string;
@@ -11,6 +13,7 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
   const [showCoffeeMessage, setShowCoffeeMessage] = useState(false);
   const timerRef = useRef<NodeJS.Timeout | null>(null);
   const coffeeTimerRef = useRef<NodeJS.Timeout | null>(null);
+  const progress = useAsymptoticProgress(showCoffeeMessage);
 
   useEffect(() => {
     if (timerRef.current === null) {
@@ -49,9 +52,18 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
           <AIChatBubble>
             <div className="transition-all duration-500 ease-in-out">
               {showCoffeeMessage ? (
+                <div className="flex flex-col items-center gap-3">
+                  <div className="flex w-full max-w-[280px] flex-col gap-1.5">
+                    <div className="flex items-center justify-between text-xs text-neutral-500">
+                      <span>Working on it...</span>
+                      <span>{Math.round(progress)}%</span>
+                    </div>
+                    <Progress value={progress} className="h-2 w-full" />
+                  </div>
                   <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">
                     This could take a few minutes, grab a coffee ☕️
                   </span>
+                </div>
               ) : showSlowLoader ? (
                 <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">
                   Taking a bit more time...

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolCallMessage/useAsymptoticProgress.ts

@@ -0,0 +1,50 @@
+import { useEffect, useRef, useState } from "react";
+
+/**
+ * Hook that returns a progress value that starts fast and slows down,
+ * asymptotically approaching but never reaching the max value.
+ *
+ * Uses a half-life formula: progress = max * (1 - 0.5^(time/halfLife))
+ * This creates the "game loading bar" effect where:
+ * - 50% is reached at halfLifeSeconds
+ * - 75% is reached at 2 * halfLifeSeconds
+ * - 87.5% is reached at 3 * halfLifeSeconds
+ * - and so on...
+ *
+ * @param isActive - Whether the progress should be animating
+ * @param halfLifeSeconds - Time in seconds to reach 50% progress (default: 30)
+ * @param maxProgress - Maximum progress value to approach (default: 100)
+ * @param intervalMs - Update interval in milliseconds (default: 100)
+ * @returns Current progress value (0-maxProgress)
+ */
+export function useAsymptoticProgress(
+  isActive: boolean,
+  halfLifeSeconds = 30,
+  maxProgress = 100,
+  intervalMs = 100,
+) {
+  const [progress, setProgress] = useState(0);
+  const elapsedTimeRef = useRef(0);
+
+  useEffect(() => {
+    if (!isActive) {
+      setProgress(0);
+      elapsedTimeRef.current = 0;
+      return;
+    }
+
+    const interval = setInterval(() => {
+      elapsedTimeRef.current += intervalMs / 1000;
+      // Half-life approach: progress = max * (1 - 0.5^(time/halfLife))
+      // At t=halfLife: 50%, at t=2*halfLife: 75%, at t=3*halfLife: 87.5%, etc.
+      const newProgress =
+        maxProgress *
+        (1 - Math.pow(0.5, elapsedTimeRef.current / halfLifeSeconds));
+      setProgress(newProgress);
+    }, intervalMs);
+
+    return () => clearInterval(interval);
+  }, [isActive, halfLifeSeconds, maxProgress, intervalMs]);
+
+  return progress;
+}

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

@@ -0,0 +1,128 @@
+"use client";
+
+import { useGetV2GetLibraryAgent } from "@/app/api/__generated__/endpoints/library/library";
+import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
+import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
+import { RunAgentModal } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/RunAgentModal";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import {
+  CheckCircleIcon,
+  PencilLineIcon,
+  PlayIcon,
+} from "@phosphor-icons/react";
+import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
+
+interface Props {
+  agentName: string;
+  libraryAgentId: string;
+  onSendMessage?: (content: string) => void;
+}
+
+export function AgentCreatedPrompt({
+  agentName,
+  libraryAgentId,
+  onSendMessage,
+}: Props) {
+  // Fetch library agent eagerly so modal is ready when user clicks
+  const { data: libraryAgentResponse, isLoading } = useGetV2GetLibraryAgent(
+    libraryAgentId,
+    {
+      query: {
+        enabled: !!libraryAgentId,
+      },
+    },
+  );
+
+  const libraryAgent =
+    libraryAgentResponse?.status === 200 ? libraryAgentResponse.data : null;
+
+  function handleRunWithPlaceholders() {
+    onSendMessage?.(
+      `Run the agent "${agentName}" with placeholder/example values so I can test it.`,
+    );
+  }
+
+  function handleRunCreated(execution: GraphExecutionMeta) {
+    onSendMessage?.(
+      `I've started the agent "${agentName}". The execution ID is ${execution.id}. Please monitor its progress and let me know when it completes.`,
+    );
+  }
+
+  function handleScheduleCreated(schedule: GraphExecutionJobInfo) {
+    const scheduleInfo = schedule.cron
+      ? `with cron schedule "${schedule.cron}"`
+      : "to run on the specified schedule";
+    onSendMessage?.(
+      `I've scheduled the agent "${agentName}" ${scheduleInfo}. The schedule ID is ${schedule.id}.`,
+    );
+  }
+
+  return (
+    <AIChatBubble>
+      <div className="flex flex-col gap-4">
+        <div className="flex items-center gap-2">
+          <div className="flex h-8 w-8 items-center justify-center rounded-full bg-green-100">
+            <CheckCircleIcon
+              size={18}
+              weight="fill"
+              className="text-green-600"
+            />
+          </div>
+          <div>
+            <Text variant="body-medium" className="text-neutral-900">
+              Agent Created Successfully
+            </Text>
+            <Text variant="small" className="text-neutral-500">
+              &quot;{agentName}&quot; is ready to test
+            </Text>
+          </div>
+        </div>
+
+        <div className="flex flex-col gap-2">
+          <Text variant="small-medium" className="text-neutral-700">
+            Ready to test?
+          </Text>
+          <div className="flex flex-wrap gap-2">
+            <Button
+              variant="outline"
+              size="small"
+              onClick={handleRunWithPlaceholders}
+              className="gap-2"
+            >
+              <PlayIcon size={16} />
+              Run with example values
+            </Button>
+            {libraryAgent ? (
+              <RunAgentModal
+                triggerSlot={
+                  <Button variant="outline" size="small" className="gap-2">
+                    <PencilLineIcon size={16} />
+                    Run with my inputs
+                  </Button>
+                }
+                agent={libraryAgent}
+                onRunCreated={handleRunCreated}
+                onScheduleCreated={handleScheduleCreated}
+              />
+            ) : (
+              <Button
+                variant="outline"
+                size="small"
+                loading={isLoading}
+                disabled
+                className="gap-2"
+              >
+                <PencilLineIcon size={16} />
+                Run with my inputs
+              </Button>
+            )}
+          </div>
+          <Text variant="small" className="text-neutral-500">
+            or just ask me
+          </Text>
+        </div>
+      </div>
+    </AIChatBubble>
+  );
+}

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

@@ -2,11 +2,13 @@ import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
 import type { ToolResult } from "@/types/chat";
 import { WarningCircleIcon } from "@phosphor-icons/react";
+import { AgentCreatedPrompt } from "./AgentCreatedPrompt";
 import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
 import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
 import {
   formatToolResponse,
   getErrorMessage,
+  isAgentSavedResponse,
   isErrorResponse,
 } from "./helpers";
 
@@ -16,6 +18,7 @@ export interface ToolResponseMessageProps {
   result?: ToolResult;
   success?: boolean;
   className?: string;
+  onSendMessage?: (content: string) => void;
 }
 
 export function ToolResponseMessage({
@@ -24,6 +27,7 @@ export function ToolResponseMessage({
   result,
   success: _success,
   className,
+  onSendMessage,
 }: ToolResponseMessageProps) {
   if (isErrorResponse(result)) {
     const errorMessage = getErrorMessage(result);
@@ -43,6 +47,18 @@ export function ToolResponseMessage({
     );
   }
 
+  // Check for agent_saved response - show special prompt
+  const agentSavedData = isAgentSavedResponse(result);
+  if (agentSavedData.isSaved) {
+    return (
+      <AgentCreatedPrompt
+        agentName={agentSavedData.agentName}
+        libraryAgentId={agentSavedData.libraryAgentId}
+        onSendMessage={onSendMessage}
+      />
+    );
+  }
+
   const formattedText = formatToolResponse(result, toolName);
 
   return (

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

@@ -6,6 +6,43 @@ function stripInternalReasoning(content: string): string {
     .trim();
 }
 
+export interface AgentSavedData {
+  isSaved: boolean;
+  agentName: string;
+  agentId: string;
+  libraryAgentId: string;
+  libraryAgentLink: string;
+}
+
+export function isAgentSavedResponse(result: unknown): AgentSavedData {
+  if (typeof result !== "object" || result === null) {
+    return {
+      isSaved: false,
+      agentName: "",
+      agentId: "",
+      libraryAgentId: "",
+      libraryAgentLink: "",
+    };
+  }
+  const response = result as Record<string, unknown>;
+  if (response.type === "agent_saved") {
+    return {
+      isSaved: true,
+      agentName: (response.agent_name as string) || "Agent",
+      agentId: (response.agent_id as string) || "",
+      libraryAgentId: (response.library_agent_id as string) || "",
+      libraryAgentLink: (response.library_agent_link as string) || "",
+    };
+  }
+  return {
+    isSaved: false,
+    agentName: "",
+    agentId: "",
+    libraryAgentId: "",
+    libraryAgentLink: "",
+  };
+}
+
 export function isErrorResponse(result: unknown): boolean {
   if (typeof result === "string") {
     const lower = result.toLowerCase();
@@ -39,33 +76,62 @@ export function getErrorMessage(result: unknown): string {
 
 /**
  * Check if a value is a workspace file reference.
+ * Format: workspace://{fileId} or workspace://{fileId}#{mimeType}
  */
 function isWorkspaceRef(value: unknown): value is string {
   return typeof value === "string" && value.startsWith("workspace://");
 }
 
 /**
- * Check if a workspace reference appears to be an image based on common patterns.
+ * Extract MIME type from a workspace reference fragment.
- * Since workspace refs don't have extensions, we check the context or assume image
+ * e.g., "workspace://abc123#video/mp4" → "video/mp4"
- * for certain block types.
+ * Returns undefined if no fragment is present.
- *
- * TODO: Replace keyword matching with MIME type encoded in workspace ref.
- * e.g., workspace://abc123#image/png or workspace://abc123#video/mp4
- * This would let frontend render correctly without fragile keyword matching.
  */
-function isLikelyImageRef(value: string, outputKey?: string): boolean {
+function getWorkspaceMimeType(value: string): string | undefined {
-  if (!isWorkspaceRef(value)) return false;
+  const hashIndex = value.indexOf("#");
+  if (hashIndex === -1) return undefined;
+  return value.slice(hashIndex + 1) || undefined;
+}
 
-  // Check output key name for video-related hints (these are NOT images)
+/**
-  const videoKeywords = ["video", "mp4", "mov", "avi", "webm", "movie", "clip"];
+ * Determine the media category of a workspace ref or data URI.
+ * Uses the MIME type fragment on workspace refs when available,
+ * falls back to output key keyword matching for older refs without it.
+ */
+function getMediaCategory(
+  value: string,
+  outputKey?: string,
+): "video" | "image" | "audio" | "unknown" {
+  // Data URIs carry their own MIME type
+  if (value.startsWith("data:video/")) return "video";
+  if (value.startsWith("data:image/")) return "image";
+  if (value.startsWith("data:audio/")) return "audio";
+
+  // Workspace refs: prefer MIME type fragment
+  if (isWorkspaceRef(value)) {
+    const mime = getWorkspaceMimeType(value);
+    if (mime) {
+      if (mime.startsWith("video/")) return "video";
+      if (mime.startsWith("image/")) return "image";
+      if (mime.startsWith("audio/")) return "audio";
+      return "unknown";
+    }
+
+    // Fallback: keyword matching on output key for older refs without fragment
     if (outputKey) {
       const lowerKey = outputKey.toLowerCase();
-    if (videoKeywords.some((kw) => lowerKey.includes(kw))) {
-      return false;
-    }
-  }
 
-  // Check output key name for image-related hints
+      const videoKeywords = [
+        "video",
+        "mp4",
+        "mov",
+        "avi",
+        "webm",
+        "movie",
+        "clip",
+      ];
+      if (videoKeywords.some((kw) => lowerKey.includes(kw))) return "video";
+
       const imageKeywords = [
         "image",
         "img",
@@ -76,32 +142,35 @@ function isLikelyImageRef(value: string, outputKey?: string): boolean {
         "icon",
         "screenshot",
       ];
-  if (outputKey) {
+      if (imageKeywords.some((kw) => lowerKey.includes(kw))) return "image";
-    const lowerKey = outputKey.toLowerCase();
-    if (imageKeywords.some((kw) => lowerKey.includes(kw))) {
-      return true;
-    }
     }
 
-  // Default to treating workspace refs as potential images
+    // Default to image for backward compatibility
-  // since that's the most common case for generated content
+    return "image";
-  return true;
+  }
+
+  return "unknown";
 }
 
 /**
- * Format a single output value, converting workspace refs to markdown images.
+ * Format a single output value, converting workspace refs to markdown images/videos.
+ * Videos use a "video:" alt-text prefix so the MarkdownContent renderer can
+ * distinguish them from images and render a <video> element.
  */
 function formatOutputValue(value: unknown, outputKey?: string): string {
-  if (isWorkspaceRef(value) && isLikelyImageRef(value, outputKey)) {
+  if (typeof value === "string") {
-    // Format as markdown image
+    const category = getMediaCategory(value, outputKey);
+
+    if (category === "video") {
+      // Format with "video:" prefix so MarkdownContent renders <video>
+      return `![video:${outputKey || "Video"}](${value})`;
+    }
+
+    if (category === "image") {
       return `![${outputKey || "Generated image"}](${value})`;
     }
 
-  if (typeof value === "string") {
+    // For audio, unknown workspace refs, data URIs, etc. - return as-is
-    // Check for data URIs (images)
-    if (value.startsWith("data:image/")) {
-      return `![${outputKey || "Generated image"}](${value})`;
-    }
     return value;
   }
 

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

@@ -26,6 +26,7 @@ export const providerIcons: Partial<
   nvidia: fallbackIcon,
   discord: FaDiscord,
   d_id: fallbackIcon,
+  elevenlabs: fallbackIcon,
   google_maps: FaGoogle,
   jina: fallbackIcon,
   ideogram: fallbackIcon,

autogpt_platform/frontend/src/components/contextual/GoogleDrivePicker/helpers.ts

@@ -4,7 +4,9 @@ import { loadScript } from "@/services/scripts/scripts";
 export async function loadGoogleAPIPicker(): Promise<void> {
   validateWindow();
 
-  await loadScript("https://apis.google.com/js/api.js");
+  await loadScript("https://apis.google.com/js/api.js", {
+    referrerPolicy: "no-referrer-when-downgrade",
+  });
 
   const googleAPI = window.gapi;
   if (!googleAPI) {
@@ -27,7 +29,9 @@ export async function loadGoogleIdentityServices(): Promise<void> {
     throw new Error("Google Identity Services cannot load on server");
   }
 
-  await loadScript("https://accounts.google.com/gsi/client");
+  await loadScript("https://accounts.google.com/gsi/client", {
+    referrerPolicy: "no-referrer-when-downgrade",
+  });
 
   const google = window.google;
   if (!google?.accounts?.oauth2) {

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

@@ -47,7 +47,7 @@ export function Navbar() {
 
   const actualLoggedInLinks = [
     { name: "Home", href: homeHref },
-    ...(isChatEnabled === true ? [{ name: "Tasks", href: "/library" }] : []),
+    ...(isChatEnabled === true ? [{ name: "Agents", href: "/library" }] : []),
     ...loggedInLinks,
   ];
 

autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts

@@ -362,25 +362,14 @@ export type GraphMeta = {
   user_id: UserID;
   version: number;
   is_active: boolean;
+  created_at: Date;
   name: string;
   description: string;
   instructions?: string | null;
   recommended_schedule_cron: string | null;
   forked_from_id?: GraphID | null;
   forked_from_version?: number | null;
-  input_schema: GraphInputSchema;
+};
-  output_schema: GraphOutputSchema;
-  credentials_input_schema: CredentialsInputSchema;
-} & (
-  | {
-      has_external_trigger: true;
-      trigger_setup_info: GraphTriggerInfo;
-    }
-  | {
-      has_external_trigger: false;
-      trigger_setup_info: null;
-    }
-);
 
 export type GraphID = Brand<string, "GraphID">;
 
@@ -447,11 +436,22 @@ export type GraphTriggerInfo = {
 
 /* Mirror of backend/data/graph.py:Graph */
 export type Graph = GraphMeta & {
-  created_at: Date;
   nodes: Node[];
   links: Link[];
   sub_graphs: Omit<Graph, "sub_graphs">[]; // Flattened sub-graphs
-};
+  input_schema: GraphInputSchema;
+  output_schema: GraphOutputSchema;
+  credentials_input_schema: CredentialsInputSchema;
+} & (
+    | {
+        has_external_trigger: true;
+        trigger_setup_info: GraphTriggerInfo;
+      }
+    | {
+        has_external_trigger: false;
+        trigger_setup_info: null;
+      }
+  );
 
 export type GraphUpdateable = Omit<
   Graph,

docs/integrations/README.md

@@ -192,6 +192,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Get Current Time](block-integrations/text.md#get-current-time) | This block outputs the current time |
 | [Match Text Pattern](block-integrations/text.md#match-text-pattern) | Matches text against a regex pattern and forwards data to positive or negative output based on the match |
 | [Text Decoder](block-integrations/text.md#text-decoder) | Decodes a string containing escape sequences into actual text |
+| [Text Encoder](block-integrations/text.md#text-encoder) | Encodes a string by converting special characters into escape sequences |
 | [Text Replace](block-integrations/text.md#text-replace) | This block is used to replace a text with a new text |
 | [Text Split](block-integrations/text.md#text-split) | This block is used to split a text into a list of strings |
 | [Word Character Count](block-integrations/text.md#word-character-count) | Counts the number of words and characters in a given text |
@@ -232,6 +233,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Stagehand Extract](block-integrations/stagehand/blocks.md#stagehand-extract) | Extract structured data from a webpage |
 | [Stagehand Observe](block-integrations/stagehand/blocks.md#stagehand-observe) | Find suggested actions for your workflows |
 | [Unreal Text To Speech](block-integrations/llm.md#unreal-text-to-speech) | Converts text to speech using the Unreal Speech API |
+| [Video Narration](block-integrations/video/narration.md#video-narration) | Generate AI narration and add to video |
 
 ## Search and Information Retrieval
 
@@ -471,9 +473,13 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 
 | Block Name | Description |
 |------------|-------------|
-| [Add Audio To Video](block-integrations/multimedia.md#add-audio-to-video) | Block to attach an audio file to a video file using moviepy |
+| [Add Audio To Video](block-integrations/video/add_audio.md#add-audio-to-video) | Block to attach an audio file to a video file using moviepy |
-| [Loop Video](block-integrations/multimedia.md#loop-video) | Block to loop a video to a given duration or number of repeats |
+| [Loop Video](block-integrations/video/loop.md#loop-video) | Block to loop a video to a given duration or number of repeats |
-| [Media Duration](block-integrations/multimedia.md#media-duration) | Block to get the duration of a media file |
+| [Media Duration](block-integrations/video/duration.md#media-duration) | Block to get the duration of a media file |
+| [Video Clip](block-integrations/video/clip.md#video-clip) | Extract a time segment from a video |
+| [Video Concat](block-integrations/video/concat.md#video-concat) | Merge multiple video clips into one continuous video |
+| [Video Download](block-integrations/video/download.md#video-download) | Download video from URL (YouTube, Vimeo, news sites, direct links) |
+| [Video Text Overlay](block-integrations/video/text_overlay.md#video-text-overlay) | Add text overlay/caption to video |
 
 ## Productivity
 

docs/integrations/SUMMARY.md

@@ -85,7 +85,6 @@
 * [LLM](block-integrations/llm.md)
 * [Logic](block-integrations/logic.md)
 * [Misc](block-integrations/misc.md)
-* [Multimedia](block-integrations/multimedia.md)
 * [Notion Create Page](block-integrations/notion/create_page.md)
 * [Notion Read Database](block-integrations/notion/read_database.md)
 * [Notion Read Page](block-integrations/notion/read_page.md)
@@ -129,5 +128,13 @@
 * [Twitter Timeline](block-integrations/twitter/timeline.md)
 * [Twitter Tweet Lookup](block-integrations/twitter/tweet_lookup.md)
 * [Twitter User Lookup](block-integrations/twitter/user_lookup.md)
+* [Video Add Audio](block-integrations/video/add_audio.md)
+* [Video Clip](block-integrations/video/clip.md)
+* [Video Concat](block-integrations/video/concat.md)
+* [Video Download](block-integrations/video/download.md)
+* [Video Duration](block-integrations/video/duration.md)
+* [Video Loop](block-integrations/video/loop.md)
+* [Video Narration](block-integrations/video/narration.md)
+* [Video Text Overlay](block-integrations/video/text_overlay.md)
 * [Wolfram LLM API](block-integrations/wolfram/llm_api.md)
 * [Zerobounce Validate Emails](block-integrations/zerobounce/validate_emails.md)