refactor(copilot): use Pydantic models and match/case in customize_agent

Addresses review feedback from ntindle:

1. Use typed parameters instead of kwargs.get():
   - Added CustomizeAgentInput Pydantic model with field_validator for stripping strings
   - Tool now uses params = CustomizeAgentInput(**kwargs) pattern

2. Use match/case for cleaner pattern matching:
   - Extracted response handling to _handle_customization_result method
   - Uses match result_type: case 'error' | 'clarifying_questions' | _

3. Improved code organization:
   - Split monolithic _execute into smaller focused methods
   - _handle_customization_result for response type handling
   - _save_or_preview_agent for final save/preview logic

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

@@ -27,20 +27,11 @@ 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:
@@ -99,11 +90,8 @@ jobs:
   chromatic:
     runs-on: ubuntu-latest
     needs: setup
-    # Disabled: to re-enable, remove 'false &&' from the condition below
+    # Only run on dev branch pushes or PRs targeting dev
-    if: >-
+    if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
-      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

@@ -152,7 +152,6 @@ 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

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

autogpt_platform/backend/Dockerfile

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

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

@@ -11,7 +11,7 @@ class ChatConfig(BaseSettings):
 
     # OpenAI API Configuration
     model: str = Field(
-        default="anthropic/claude-opus-4.6", description="Default model to use"
+        default="anthropic/claude-opus-4.5", 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 AppEnvironment, Settings
+from backend.util.settings import Settings
 
 from . import db as chat_db
 from . import stream_registry
@@ -222,18 +222,8 @@ 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,
+                langfuse.get_prompt, config.langfuse_prompt_name, cache_ttl_seconds=0
-                config.langfuse_prompt_name,
-                label=label,
-                cache_ttl_seconds=0,
             )
             return prompt.compile(users_information=context)
         except Exception as e:
@@ -628,9 +618,6 @@ 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

@@ -7,7 +7,15 @@ from typing import Any, NotRequired, TypedDict
 
 from backend.api.features.library import db as library_db
 from backend.api.features.store import db as store_db
-from backend.data.graph import Graph, Link, Node, get_graph, get_store_listed_graphs
+from backend.data.graph import (
+    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 (
@@ -20,6 +28,8 @@ 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."""
@@ -659,6 +669,45 @@ 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]:
@@ -672,10 +721,35 @@ 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:
-        return await library_db.update_graph_in_library(graph, user_id)
+        if graph.id:
-    return await library_db.create_graph_in_library(graph, user_id)
+            existing_versions = await get_graph_all_versions(graph.id, user_id)
+            if existing_versions:
+                latest_version = max(v.version for v in existing_versions)
+                graph.version = latest_version + 1
+                _reassign_node_ids(graph)
+                logger.info(f"Updating agent {graph.id} to version {graph.version}")
+    else:
+        graph.id = str(uuid.uuid4())
+        graph.version = 1
+        _reassign_node_ids(graph)
+        logger.info(f"Creating new agent with ID {graph.id}")
+
+    created_graph = await create_graph(graph, user_id)
+
+    library_agents = await library_db.create_library_agent(
+        graph=created_graph,
+        user_id=user_id,
+        sensitive_action_safe_mode=True,
+        create_library_agents_for_sub_graphs=False,
+    )
+
+    return created_graph, library_agents[0]
 
 
 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}'. 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."
+            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
             if source == "marketplace"
-            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."
+            else f"No agents matching '{query}' found in your library."
         )
         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. Let the user know we can create a custom agent for them based on their needs."
+        "Please ask the user if they would like to use any of these agents."
         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. Let the user know we can create a custom agent for them based on their needs."
+        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
     )
 
     return AgentsFoundResponse(

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

@@ -3,6 +3,8 @@
 import logging
 from typing import Any
 
+from pydantic import BaseModel, field_validator
+
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.store import db as store_db
 from backend.api.features.store.exceptions import AgentNotFoundError
@@ -27,6 +29,23 @@ from .models import (
 logger = logging.getLogger(__name__)
 
 
+class CustomizeAgentInput(BaseModel):
+    """Input parameters for the customize_agent tool."""
+
+    agent_id: str = ""
+    modifications: str = ""
+    context: str = ""
+    save: bool = True
+
+    @field_validator("agent_id", "modifications", "context", mode="before")
+    @classmethod
+    def strip_strings(cls, v: Any) -> str:
+        """Strip whitespace from string fields."""
+        if isinstance(v, str):
+            return v.strip()
+        return v if v is not None else ""
+
+
 class CustomizeAgentTool(BaseTool):
     """Tool for customizing marketplace/template agents using natural language."""
 
@@ -92,7 +111,7 @@ class CustomizeAgentTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
     ) -> ToolResponseBase:
         """Execute the customize_agent tool.
 
@@ -102,20 +121,17 @@ class CustomizeAgentTool(BaseTool):
         3. Call customize_template with the modification request
         4. Preview or save based on the save parameter
         """
-        agent_id = kwargs.get("agent_id", "").strip()
+        params = CustomizeAgentInput(**kwargs)
-        modifications = kwargs.get("modifications", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
-        if not agent_id:
+        if not params.agent_id:
             return ErrorResponse(
                 message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').",
                 error="missing_agent_id",
                 session_id=session_id,
             )
 
-        if not modifications:
+        if not params.modifications:
             return ErrorResponse(
                 message="Please describe how you want to customize this agent.",
                 error="missing_modifications",
@@ -123,11 +139,11 @@ class CustomizeAgentTool(BaseTool):
             )
 
         # Parse agent_id in format "creator/slug"
-        parts = [p.strip() for p in agent_id.split("/")]
+        parts = params.agent_id.split("/")
         if len(parts) != 2 or not parts[0] or not parts[1]:
             return ErrorResponse(
                 message=(
-                    f"Invalid agent ID format: '{agent_id}'. "
+                    f"Invalid agent ID format: '{params.agent_id}'. "
                     "Expected format is 'creator/agent-name' "
                     "(e.g., 'autogpt/newsletter-writer')."
                 ),
@@ -145,14 +161,14 @@ class CustomizeAgentTool(BaseTool):
         except AgentNotFoundError:
             return ErrorResponse(
                 message=(
-                    f"Could not find marketplace agent '{agent_id}'. "
+                    f"Could not find marketplace agent '{params.agent_id}'. "
                     "Please check the agent ID and try again."
                 ),
                 error="agent_not_found",
                 session_id=session_id,
             )
         except Exception as e:
-            logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
+            logger.error(f"Error fetching marketplace agent {params.agent_id}: {e}")
             return ErrorResponse(
                 message="Failed to fetch the marketplace agent. Please try again.",
                 error="fetch_error",
@@ -162,7 +178,7 @@ class CustomizeAgentTool(BaseTool):
         if not agent_details.store_listing_version_id:
             return ErrorResponse(
                 message=(
-                    f"The agent '{agent_id}' does not have an available version. "
+                    f"The agent '{params.agent_id}' does not have an available version. "
                     "Please try a different agent."
                 ),
                 error="no_version_available",
@@ -174,7 +190,7 @@ class CustomizeAgentTool(BaseTool):
             graph = await store_db.get_agent(agent_details.store_listing_version_id)
             template_agent = graph_to_json(graph)
         except Exception as e:
-            logger.error(f"Error fetching agent graph for {agent_id}: {e}")
+            logger.error(f"Error fetching agent graph for {params.agent_id}: {e}")
             return ErrorResponse(
                 message="Failed to fetch the agent configuration. Please try again.",
                 error="graph_fetch_error",
@@ -185,8 +201,8 @@ class CustomizeAgentTool(BaseTool):
         try:
             result = await customize_template(
                 template_agent=template_agent,
-                modification_request=modifications,
+                modification_request=params.modifications,
-                context=context,
+                context=params.context,
             )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
@@ -198,7 +214,7 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
         except Exception as e:
-            logger.error(f"Error calling customize_template for {agent_id}: {e}")
+            logger.error(f"Error calling customize_template for {params.agent_id}: {e}")
             return ErrorResponse(
                 message=(
                     "Failed to customize the agent due to a service error. "
@@ -219,55 +235,25 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Handle error response
+        # Handle response using match/case for cleaner pattern matching
-        if isinstance(result, dict) and result.get("type") == "error":
+        return await self._handle_customization_result(
-            error_msg = result.get("error", "Unknown error")
+            result=result,
-            error_type = result.get("error_type", "unknown")
+            params=params,
-            user_message = get_user_message_for_error(
+            agent_details=agent_details,
-                error_type,
+            user_id=user_id,
-                operation="customize the agent",
+            session_id=session_id,
-                llm_parse_message=(
+        )
-                    "The AI had trouble customizing the agent. "
-                    "Please try again or simplify your request."
-                ),
-                validation_message=(
-                    "The customized agent failed validation. "
-                    "Please try rephrasing your request."
-                ),
-                error_details=error_msg,
-            )
-            return ErrorResponse(
-                message=user_message,
-                error=f"customization_failed:{error_type}",
-                session_id=session_id,
-            )
 
-        # Handle clarifying questions
+    async def _handle_customization_result(
-        if isinstance(result, dict) and result.get("type") == "clarifying_questions":
+        self,
-            questions = result.get("questions") or []
+        result: dict[str, Any],
-            if not isinstance(questions, list):
+        params: CustomizeAgentInput,
-                logger.error(
+        agent_details: Any,
-                    f"Unexpected clarifying questions format: {type(questions)}"
+        user_id: str | None,
-                )
+        session_id: str | None,
-                questions = []
+    ) -> ToolResponseBase:
-            return ClarificationNeededResponse(
+        """Handle the result from customize_template using pattern matching."""
-                message=(
+        # Ensure result is a dict
-                    "I need some more information to customize this agent. "
-                    "Please answer the following questions:"
-                ),
-                questions=[
-                    ClarifyingQuestion(
-                        question=q.get("question", ""),
-                        keyword=q.get("keyword", ""),
-                        example=q.get("example"),
-                    )
-                    for q in questions
-                    if isinstance(q, dict)
-                ],
-                session_id=session_id,
-            )
-
-        # Result should be the customized agent JSON
         if not isinstance(result, dict):
             logger.error(f"Unexpected customize_template response type: {type(result)}")
             return ErrorResponse(
@@ -276,8 +262,77 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        customized_agent = result
+        result_type = result.get("type")
 
+        match result_type:
+            case "error":
+                error_msg = result.get("error", "Unknown error")
+                error_type = result.get("error_type", "unknown")
+                user_message = get_user_message_for_error(
+                    error_type,
+                    operation="customize the agent",
+                    llm_parse_message=(
+                        "The AI had trouble customizing the agent. "
+                        "Please try again or simplify your request."
+                    ),
+                    validation_message=(
+                        "The customized agent failed validation. "
+                        "Please try rephrasing your request."
+                    ),
+                    error_details=error_msg,
+                )
+                return ErrorResponse(
+                    message=user_message,
+                    error=f"customization_failed:{error_type}",
+                    session_id=session_id,
+                )
+
+            case "clarifying_questions":
+                questions_data = result.get("questions") or []
+                if not isinstance(questions_data, list):
+                    logger.error(
+                        f"Unexpected clarifying questions format: {type(questions_data)}"
+                    )
+                    questions_data = []
+
+                questions = [
+                    ClarifyingQuestion(
+                        question=q.get("question", "") if isinstance(q, dict) else "",
+                        keyword=q.get("keyword", "") if isinstance(q, dict) else "",
+                        example=q.get("example") if isinstance(q, dict) else None,
+                    )
+                    for q in questions_data
+                    if isinstance(q, dict)
+                ]
+
+                return ClarificationNeededResponse(
+                    message=(
+                        "I need some more information to customize this agent. "
+                        "Please answer the following questions:"
+                    ),
+                    questions=questions,
+                    session_id=session_id,
+                )
+
+            case _:
+                # Default case: result is the customized agent JSON
+                return await self._save_or_preview_agent(
+                    customized_agent=result,
+                    params=params,
+                    agent_details=agent_details,
+                    user_id=user_id,
+                    session_id=session_id,
+                )
+
+    async def _save_or_preview_agent(
+        self,
+        customized_agent: dict[str, Any],
+        params: CustomizeAgentInput,
+        agent_details: Any,
+        user_id: str | None,
+        session_id: str | None,
+    ) -> ToolResponseBase:
+        """Save or preview the customized agent based on params.save."""
         agent_name = customized_agent.get(
             "name", f"Customized {agent_details.agent_name}"
         )
@@ -287,7 +342,7 @@ class CustomizeAgentTool(BaseTool):
         node_count = len(nodes) if isinstance(nodes, list) else 0
         link_count = len(links) if isinstance(links, list) else 0
 
-        if not save:
+        if not params.save:
             return AgentPreviewResponse(
                 message=(
                     f"I've customized the agent '{agent_details.agent_name}'. "

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

@@ -6,13 +6,9 @@ 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 (
+from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput
-    CredentialsFieldInfo,
-    CredentialsMetaInput,
-    HostScopedCredentials,
-    OAuth2Credentials,
-)
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -43,8 +39,14 @@ async def fetch_graph_from_store_slug(
         return None, None
 
     # Get the graph from store listing version
-    graph = await store_db.get_available_graph(
+    graph_meta = await store_db.get_available_graph(
-        store_agent.store_listing_version_id, hide_nodes=False
+        store_agent.store_listing_version_id
+    )
+    graph = await graph_db.get_graph(
+        graph_id=graph_meta.id,
+        version=graph_meta.version,
+        user_id=None,  # Public access
+        include_subgraphs=True,
     )
     return graph, store_agent
 
@@ -117,11 +119,11 @@ 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.regular_credentials_inputs
+    aggregated_fields = graph.aggregate_credentials_inputs()
 
     return {
         field_key: _serialize_missing_credential(field_key, field_info)
-        for field_key, (field_info, _, _) in aggregated_fields.items()
+        for field_key, (field_info, _node_fields) in aggregated_fields.items()
         if field_key not in matched_keys
     }
 
@@ -244,7 +246,7 @@ async def match_user_credentials_to_graph(
     missing_creds: list[str] = []
 
     # Get aggregated credentials requirements from the graph
-    aggregated_creds = graph.regular_credentials_inputs
+    aggregated_creds = graph.aggregate_credentials_inputs()
     logger.debug(
         f"Matching credentials for graph {graph.id}: {len(aggregated_creds)} required"
     )
@@ -262,8 +264,7 @@ 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(
@@ -272,14 +273,7 @@ async def match_user_credentials_to_graph(
                 for cred in available_creds
                 if cred.provider in credential_requirements.provider
                 and cred.type in credential_requirements.supported_types
-                and (
+                and _credential_has_required_scopes(cred, credential_requirements)
-                    cred.type != "oauth2"
-                    or _credential_has_required_scopes(cred, credential_requirements)
-                )
-                and (
-                    cred.type != "host_scoped"
-                    or _credential_is_for_host(cred, credential_requirements)
-                )
             ),
             None,
         )
@@ -324,10 +318,19 @@ async def match_user_credentials_to_graph(
 
 
 def _credential_has_required_scopes(
-    credential: OAuth2Credentials,
+    credential: Credentials,
     requirements: CredentialsFieldInfo,
 ) -> bool:
-    """Check if an OAuth2 credential has all the scopes required by the input."""
+    """
+    Check if a credential has all the scopes required by the block.
+
+    For OAuth2 credentials, verifies that the credential's scopes are a superset
+    of the required scopes. For other credential types, returns True (no scope check).
+    """
+    # Only OAuth2 credentials have scopes to check
+    if credential.type != "oauth2":
+        return True
+
     # If no scopes are required, any credential matches
     if not requirements.required_scopes:
         return True
@@ -336,22 +339,6 @@ def _credential_has_required_scopes(
     return set(credential.scopes).issuperset(requirements.required_scopes)
 
 
-def _credential_is_for_host(
-    credential: HostScopedCredentials,
-    requirements: CredentialsFieldInfo,
-) -> bool:
-    """Check if a host-scoped credential matches the host required by the input."""
-    # We need to know the host to match host-scoped credentials to.
-    # Graph.aggregate_credentials_inputs() adds the node's set URL value (if any)
-    # to discriminator_values. No discriminator_values -> no host to match against.
-    if not requirements.discriminator_values:
-        return True
-
-    # Check that credential host matches required host.
-    # Host-scoped credential inputs are grouped by host, so any item from the set works.
-    return credential.matches_url(list(requirements.discriminator_values)[0])
-
-
 async def check_user_has_required_credentials(
     user_id: str,
     required_credentials: list[CredentialsMetaInput],

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

@@ -1,78 +0,0 @@
-"""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

@@ -19,10 +19,7 @@ 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 (
+from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
-    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
@@ -374,7 +371,7 @@ async def get_library_agent_by_graph_id(
 
 
 async def add_generated_agent_image(
-    graph: graph_db.GraphBaseMeta,
+    graph: graph_db.BaseGraph,
     user_id: str,
     library_agent_id: str,
 ) -> Optional[prisma.models.LibraryAgent]:
@@ -540,92 +537,6 @@ 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,
@@ -1103,7 +1014,7 @@ async def create_preset_from_graph_execution(
             raise NotFoundError(
                 f"Graph #{graph_execution.graph_id} not found or accessible"
             )
-        elif len(graph.regular_credentials_inputs) > 0:
+        elif len(graph.aggregate_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, overload
+from typing import Any, Literal
 
 import fastapi
 import prisma.enums
@@ -11,8 +11,8 @@ import prisma.types
 
 from backend.data.db import transaction
 from backend.data.graph import (
+    GraphMeta,
     GraphModel,
-    GraphModelWithoutNodes,
     get_graph,
     get_graph_as_admin,
     get_sub_graphs,
@@ -334,22 +334,7 @@ async def get_store_agent_details(
         raise DatabaseError("Failed to fetch agent details") from e
 
 
-@overload
+async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
-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 = (
@@ -359,7 +344,7 @@ async def get_available_graph(
                     "isAvailable": True,
                     "isDeleted": False,
                 },
-                include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}},
+                include={"AgentGraph": {"include": {"Nodes": True}}},
             )
         )
 
@@ -369,9 +354,7 @@ async def get_available_graph(
                 detail=f"Store listing version {store_listing_version_id} not found",
             )
 
-        return (GraphModelWithoutNodes if hide_nodes else GraphModel).from_db(
+        return GraphModel.from_db(store_listing_version.AgentGraph).meta()
-            store_listing_version.AgentGraph
-        )
 
     except Exception as e:
         logger.error(f"Error getting agent: {e}")

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

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

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

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

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

@@ -101,6 +101,7 @@ 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
 
 
@@ -822,16 +823,18 @@ 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)
@@ -839,23 +842,27 @@ async def update_graph(
     new_graph_version = await graph_db.create_graph(graph, user_id=user_id)
 
     if new_graph_version.is_active:
-        await library_db.update_library_agent_version_and_settings(
+        # Keep the library agent up to date with the new active version
-            user_id, new_graph_version
+        await _update_library_agent_version_and_settings(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
+    assert new_graph_version_with_subgraphs  # make type checker happy
     return new_graph_version_with_subgraphs
 
 
@@ -893,15 +900,33 @@ async def set_graph_active_version(
     )
 
     # 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_active_graph)
-        user_id, new_active_graph
-    )
 
     if current_active_graph and current_active_graph.version != new_active_version:
         # Handle deactivation of the previously active version
         await on_graph_deactivate(current_active_graph, user_id=user_id)
 
 
+async def _update_library_agent_version_and_settings(
+    user_id: str, agent_graph: graph_db.GraphModel
+) -> library_model.LibraryAgent:
+    library = await library_db.update_agent_version_in_library(
+        user_id, agent_graph.id, agent_graph.version
+    )
+    updated_settings = GraphSettings.from_graph(
+        graph=agent_graph,
+        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
+        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
+    )
+    if updated_settings != library.settings:
+        library = await library_db.update_library_agent(
+            library_agent_id=library.id,
+            user_id=user_id,
+            settings=updated_settings,
+        )
+    return library
+
+
 @v1_router.patch(
     path="/graphs/{graph_id}/settings",
     summary="Update graph settings",

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

@@ -1,28 +0,0 @@
-"""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

@@ -1,77 +0,0 @@
-"""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/linear/_api.py

@@ -162,16 +162,8 @@ class LinearClient:
                 "searchTerm": team_name,
             }
 
-            result = await self.query(query, variables)
+            team_id = await self.query(query, variables)
-            nodes = result["teams"]["nodes"]
+            return team_id["teams"]["nodes"][0]["id"]
-
-            if not nodes:
-                raise LinearAPIException(
-                    f"Team '{team_name}' not found. Check the team name or key and try again.",
-                    status_code=404,
-                )
-
-            return nodes[0]["id"]
         except LinearAPIException as e:
             raise e
 
@@ -248,44 +240,17 @@ class LinearClient:
         except LinearAPIException as e:
             raise e
 
-    async def try_search_issues(
+    async def try_search_issues(self, term: str) -> list[Issue]:
-        self,
-        term: str,
-        max_results: int = 10,
-        team_id: str | None = None,
-    ) -> list[Issue]:
         try:
             query = """
-                query SearchIssues(
+                query SearchIssues($term: String!, $includeComments: Boolean!) {
-                    $term: String!,
+                    searchIssues(term: $term, includeComments: $includeComments) {
-                    $first: Int,
-                    $teamId: String
-                ) {
-                    searchIssues(
-                        term: $term,
-                        first: $first,
-                        teamId: $teamId
-                    ) {
                         nodes {
                             id
                             identifier
                             title
                             description
                             priority
-                            createdAt
-                            state {
-                                id
-                                name
-                                type
-                            }
-                            project {
-                                id
-                                name
-                            }
-                            assignee {
-                                id
-                                name
-                            }
                         }
                     }
                 }
@@ -293,8 +258,7 @@ class LinearClient:
 
             variables: dict[str, Any] = {
                 "term": term,
-                "first": max_results,
+                "includeComments": True,
-                "teamId": team_id,
             }
 
             issues = await self.query(query, variables)

autogpt_platform/backend/backend/blocks/linear/issues.py

@@ -17,7 +17,7 @@ from ._config import (
     LinearScope,
     linear,
 )
-from .models import CreateIssueResponse, Issue, State
+from .models import CreateIssueResponse, Issue
 
 
 class LinearCreateIssueBlock(Block):
@@ -135,20 +135,9 @@ class LinearSearchIssuesBlock(Block):
             description="Linear credentials with read permissions",
             required_scopes={LinearScope.READ},
         )
-        max_results: int = SchemaField(
-            description="Maximum number of results to return",
-            default=10,
-            ge=1,
-            le=100,
-        )
-        team_name: str | None = SchemaField(
-            description="Optional team name to filter results (e.g., 'Internal', 'Open Source')",
-            default=None,
-        )
 
     class Output(BlockSchemaOutput):
         issues: list[Issue] = SchemaField(description="List of issues")
-        error: str = SchemaField(description="Error message if the search failed")
 
     def __init__(self):
         super().__init__(
@@ -156,11 +145,8 @@ class LinearSearchIssuesBlock(Block):
             description="Searches for issues on Linear",
             input_schema=self.Input,
             output_schema=self.Output,
-            categories={BlockCategory.PRODUCTIVITY, BlockCategory.ISSUE_TRACKING},
             test_input={
                 "term": "Test issue",
-                "max_results": 10,
-                "team_name": None,
                 "credentials": TEST_CREDENTIALS_INPUT_OAUTH,
             },
             test_credentials=TEST_CREDENTIALS_OAUTH,
@@ -170,14 +156,10 @@ class LinearSearchIssuesBlock(Block):
                     [
                         Issue(
                             id="abc123",
-                            identifier="TST-123",
+                            identifier="abc123",
                             title="Test issue",
                             description="Test description",
                             priority=1,
-                            state=State(
-                                id="state1", name="In Progress", type="started"
-                            ),
-                            createdAt="2026-01-15T10:00:00.000Z",
                         )
                     ],
                 )
@@ -186,12 +168,10 @@ class LinearSearchIssuesBlock(Block):
                 "search_issues": lambda *args, **kwargs: [
                     Issue(
                         id="abc123",
-                        identifier="TST-123",
+                        identifier="abc123",
                         title="Test issue",
                         description="Test description",
                         priority=1,
-                        state=State(id="state1", name="In Progress", type="started"),
-                        createdAt="2026-01-15T10:00:00.000Z",
                     )
                 ]
             },
@@ -201,22 +181,10 @@ class LinearSearchIssuesBlock(Block):
     async def search_issues(
         credentials: OAuth2Credentials | APIKeyCredentials,
         term: str,
-        max_results: int = 10,
-        team_name: str | None = None,
     ) -> list[Issue]:
         client = LinearClient(credentials=credentials)
-
+        response: list[Issue] = await client.try_search_issues(term=term)
-        # Resolve team name to ID if provided
+        return response
-        # Raises LinearAPIException with descriptive message if team not found
-        team_id: str | None = None
-        if team_name:
-            team_id = await client.try_get_team_by_name(team_name=team_name)
-
-        return await client.try_search_issues(
-            term=term,
-            max_results=max_results,
-            team_id=team_id,
-        )
 
     async def run(
         self,
@@ -228,10 +196,7 @@ class LinearSearchIssuesBlock(Block):
         """Execute the issue search"""
         try:
             issues = await self.search_issues(
-                credentials=credentials,
+                credentials=credentials, term=input_data.term
-                term=input_data.term,
-                max_results=input_data.max_results,
-                team_name=input_data.team_name,
             )
             yield "issues", issues
         except LinearAPIException as e:

autogpt_platform/backend/backend/blocks/linear/models.py

@@ -36,21 +36,12 @@ class Project(BaseModel):
     content: str | None = None
 
 
-class State(BaseModel):
-    id: str
-    name: str
-    type: str | None = (
-        None  # Workflow state type (e.g., "triage", "backlog", "started", "completed", "canceled")
-    )
-
-
 class Issue(BaseModel):
     id: str
     identifier: str
     title: str
     description: str | None
     priority: int
-    state: State | None = None
     project: Project | None = None
     createdAt: str | None = None
     comments: list[Comment] | None = None

autogpt_platform/backend/backend/blocks/llm.py

@@ -115,7 +115,6 @@ 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"
@@ -271,9 +270,6 @@ 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

@@ -0,0 +1,246 @@
+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

@@ -1,77 +0,0 @@
-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

@@ -1,37 +0,0 @@
-"""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

@@ -1,131 +0,0 @@
-"""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

@@ -1,113 +0,0 @@
-"""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

@@ -1,167 +0,0 @@
-"""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

@@ -1,227 +0,0 @@
-"""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

@@ -1,172 +0,0 @@
-"""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

@@ -1,77 +0,0 @@
-"""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

@@ -1,115 +0,0 @@
-"""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

@@ -1,267 +0,0 @@
-"""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

@@ -1,231 +0,0 @@
-"""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

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

autogpt_platform/backend/backend/data/block.py

@@ -246,9 +246,7 @@ class BlockSchema(BaseModel):
                         f"is not of type {CredentialsMetaInput.__name__}"
                     )
 
-                CredentialsMetaInput.validate_credentials_field_schema(
+                credentials_fields[field_name].validate_credentials_field_schema(cls)
-                    cls.get_field_schema(field_name), field_name
-                )
 
             elif field_name in credentials_fields:
                 raise KeyError(
@@ -319,8 +317,6 @@ 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

@@ -36,14 +36,12 @@ 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,
@@ -80,7 +78,6 @@ 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,
@@ -642,16 +639,4 @@ 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

@@ -134,16 +134,6 @@ 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, Self, cast
+from typing import TYPE_CHECKING, Annotated, Any, Literal, Optional, cast
 
 from prisma.enums import SubmissionStatus
 from prisma.models import (
@@ -20,7 +20,7 @@ from prisma.types import (
     AgentNodeLinkCreateInput,
     StoreListingVersionWhereInput,
 )
-from pydantic import BaseModel, BeforeValidator, Field
+from pydantic import BaseModel, BeforeValidator, Field, create_model
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
@@ -30,6 +30,7 @@ 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,
@@ -44,6 +45,7 @@ from .block import (
     AnyBlockSchema,
     Block,
     BlockInput,
+    BlockSchema,
     BlockType,
     EmptySchema,
     get_block,
@@ -111,12 +113,10 @@ class Link(BaseDbModel):
 
 class Node(BaseDbModel):
     block_id: str
-    input_default: BlockInput = Field(  # dict[input_name, default_value]
+    input_default: BlockInput = {}  # dict[input_name, default_value]
-        default_factory=dict
+    metadata: dict[str, Any] = {}
-    )
+    input_links: list[Link] = []
-    metadata: dict[str, Any] = Field(default_factory=dict)
+    output_links: list[Link] = []
-    input_links: list[Link] = Field(default_factory=list)
-    output_links: list[Link] = Field(default_factory=list)
 
     @property
     def credentials_optional(self) -> bool:
@@ -221,33 +221,18 @@ class NodeModel(Node):
         return result
 
 
-class GraphBaseMeta(BaseDbModel):
+class BaseGraph(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]:
@@ -376,78 +361,44 @@ class GraphTriggerInfo(BaseModel):
 
 
 class Graph(BaseGraph):
-    """Creatable graph model used in API create/update endpoints."""
+    sub_graphs: list[BaseGraph] = []  # Flattened sub-graphs
-
-    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]:
-        graph_credentials_inputs = self.regular_credentials_inputs
+        schema = self._credentials_input_schema.jsonschema()
+
+        # Determine which credential fields are required based on credentials_optional metadata
+        graph_credentials_inputs = self.aggregate_credentials_inputs()
+        required_fields = []
+
+        # Build a map of node_id -> node for quick lookup
+        all_nodes = {node.id: node for node in self.nodes}
+        for sub_graph in self.sub_graphs:
+            for node in sub_graph.nodes:
+                all_nodes[node.id] = node
+
+        for field_key, (
+            _field_info,
+            node_field_pairs,
+        ) in graph_credentials_inputs.items():
+            # A field is required if ANY node using it has credentials_optional=False
+            is_required = False
+            for node_id, _field_name in node_field_pairs:
+                node = all_nodes.get(node_id)
+                if node and not node.credentials_optional:
+                    is_required = True
+                    break
+
+            if is_required:
+                required_fields.append(field_key)
+
+        schema["required"] = required_fields
+        return schema
+
+    @property
+    def _credentials_input_schema(self) -> type[BlockSchema]:
+        graph_credentials_inputs = self.aggregate_credentials_inputs()
         logger.debug(
             f"Combined credentials input fields for graph #{self.id} ({self.name}): "
             f"{graph_credentials_inputs}"
@@ -455,8 +406,8 @@ class GraphModel(Graph, GraphMeta):
 
         # 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:
@@ -472,78 +423,31 @@ class GraphModel(Graph, GraphMeta):
                     f"keys: {keys} <> {other_keys}."
                 )
 
-        # Build JSON schema directly to avoid expensive create_model + validation overhead
+        fields: dict[str, tuple[type[CredentialsMetaInput], CredentialsMetaInput]] = {
-        properties = {}
+            agg_field_key: (
-        required_fields = []
+                CredentialsMetaInput[
-
+                    Literal[tuple(field_info.provider)],  # type: ignore
-        for agg_field_key, (
+                    Literal[tuple(field_info.supported_types)],  # type: ignore
-            field_info,
+                ],
-            _,
+                CredentialsField(
-            is_required,
+                    required_scopes=set(field_info.required_scopes or []),
-        ) in graph_credentials_inputs.items():
+                    discriminator=field_info.discriminator,
-            providers = list(field_info.provider)
+                    discriminator_mapping=field_info.discriminator_mapping,
-            cred_types = list(field_info.supported_types)
+                    discriminator_values=field_info.discriminator_values,
-
+                ),
-            field_schema: dict[str, Any] = {
-                "credentials_provider": providers,
-                "credentials_types": cred_types,
-                "type": "object",
-                "properties": {
-                    "id": {"title": "Id", "type": "string"},
-                    "title": {
-                        "anyOf": [{"type": "string"}, {"type": "null"}],
-                        "default": None,
-                        "title": "Title",
-                    },
-                    "provider": {
-                        "title": "Provider",
-                        "type": "string",
-                        **(
-                            {"enum": providers}
-                            if len(providers) > 1
-                            else {"const": providers[0]}
-                        ),
-                    },
-                    "type": {
-                        "title": "Type",
-                        "type": "string",
-                        **(
-                            {"enum": cred_types}
-                            if len(cred_types) > 1
-                            else {"const": cred_types[0]}
-                        ),
-                    },
-                },
-                "required": ["id", "provider", "type"],
-            }
-
-            # Add other (optional) field info items
-            field_schema.update(
-                field_info.model_dump(
-                    by_alias=True,
-                    exclude_defaults=True,
-                    exclude={"provider", "supported_types"},  # already included above
-                )
             )
-
+            for agg_field_key, (field_info, _) in graph_credentials_inputs.items()
-            # 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,
         }
 
+        return create_model(
+            self.name.replace(" ", "") + "CredentialsInputSchema",
+            __base__=BlockSchema,
+            **fields,  # type: ignore
+        )
+
     def aggregate_credentials_inputs(
         self,
-    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
+    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]]]]:
         """
         Returns:
             dict[aggregated_field_key, tuple(
@@ -551,19 +455,13 @@ class GraphModel(Graph, GraphMeta):
                     (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
-        # Track (field_info, (node_id, field_name), is_required) for each credential field
+        node_credential_data = []
-        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,
@@ -587,43 +485,37 @@ class GraphModel(Graph, GraphMeta):
                     )
 
         # Combine credential field info (this will merge discriminator_values automatically)
-        combined = CredentialsFieldInfo.combine(*node_credential_data)
+        return CredentialsFieldInfo.combine(*node_credential_data)
 
-        # Add is_required flag to each aggregated field
+
-        # A field is required if ANY node using it has credentials_optional=False
+class GraphModel(Graph):
-        return {
+    user_id: str
-            key: (
+    nodes: list[NodeModel] = []  # type: ignore
-                field_info,
+
-                node_field_pairs,
+    created_at: datetime
-                any(
-                    node_required_map.get(node_id, True)
-                    for node_id, _ in node_field_pairs
-                ),
-            )
-            for key, (field_info, node_field_pairs) in combined.items()
-        }
 
     @property
-    def regular_credentials_inputs(
+    def starting_nodes(self) -> list[NodeModel]:
-        self,
+        outbound_nodes = {link.sink_id for link in self.links}
-    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
+        input_nodes = {
-        """Credentials that need explicit user mapping (CredentialsMetaInput fields)."""
+            node.id for node in self.nodes if node.block.block_type == BlockType.INPUT
-        return {
-            k: v
-            for k, v in self.aggregate_credentials_inputs().items()
-            if not v[0].is_auto_credential
         }
+        return [
+            node
+            for node in self.nodes
+            if node.id not in outbound_nodes or node.id in input_nodes
+        ]
 
     @property
-    def auto_credentials_inputs(
+    def webhook_input_node(self) -> NodeModel | None:  # type: ignore
-        self,
+        return cast(NodeModel, super().webhook_input_node)
-    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
+
-        """Credentials embedded in file fields (_credentials_id), resolved at execution time."""
+    def meta(self) -> "GraphMeta":
-        return {
+        """
-            k: v
+        Returns a GraphMeta object with metadata about the graph.
-            for k, v in self.aggregate_credentials_inputs().items()
+        This is used to return metadata about the graph without exposing nodes and links.
-            if v[0].is_auto_credential
+        """
-        }
+        return GraphMeta.from_graph(self)
 
     def reassign_ids(self, user_id: str, reassign_graph_id: bool = False):
         """
@@ -675,16 +567,6 @@ class GraphModel(Graph, GraphMeta):
             ) 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,
@@ -917,14 +799,13 @@ class GraphModel(Graph, GraphMeta):
             if is_static_output_block(link.source_id):
                 link.is_static = True  # Each value block output should be static.
 
-    @classmethod
+    @staticmethod
-    def from_db(  # type: ignore[reportIncompatibleMethodOverride]
+    def from_db(
-        cls,
         graph: AgentGraph,
         for_export: bool = False,
         sub_graphs: list[AgentGraph] | None = None,
-    ) -> Self:
+    ) -> "GraphModel":
-        return cls(
+        return GraphModel(
             id=graph.id,
             user_id=graph.userId if not for_export else "",
             version=graph.version,
@@ -950,28 +831,17 @@ class GraphModel(Graph, GraphMeta):
             ],
         )
 
-    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
 
-class GraphModelWithoutNodes(GraphModel):
+    # Easy work-around to prevent exposing nodes and links in the API response
-    """
+    nodes: list[NodeModel] = Field(default=[], exclude=True)  # type: ignore
-    GraphModel variant that excludes nodes, links, and sub-graphs from serialization.
+    links: list[Link] = Field(default=[], exclude=True)
 
-    Used in contexts like the store where exposing internal graph structure
+    @staticmethod
-    is not desired. Inherits all computed fields from GraphModel but marks
+    def from_graph(graph: GraphModel) -> "GraphMeta":
-    nodes and links as excluded from JSON output.
+        return GraphMeta(**graph.model_dump())
-    """
-
-    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):
@@ -1042,11 +912,21 @@ async def list_graphs_paginated(
         where=where_clause,
         distinct=["id"],
         order={"version": "desc"},
+        include=AGENT_GRAPH_INCLUDE,
         skip=offset,
         take=page_size,
     )
 
-    graph_models = [GraphMeta.from_db(graph) for graph in graphs]
+    graph_models: list[GraphMeta] = []
+    for graph in graphs:
+        try:
+            graph_meta = GraphModel.from_db(graph).meta()
+            # Trigger serialization to validate that the graph is well formed
+            graph_meta.model_dump()
+            graph_models.append(graph_meta)
+        except Exception as e:
+            logger.error(f"Error processing graph {graph.id}: {e}")
+            continue
 
     return GraphsPaginated(
         graphs=graph_models,

autogpt_platform/backend/backend/data/graph_test.py

@@ -463,328 +463,3 @@ 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

@@ -19,6 +19,7 @@ from typing import (
     cast,
     get_args,
 )
+from urllib.parse import urlparse
 from uuid import uuid4
 
 from prisma.enums import CreditTransactionType, OnboardingStep
@@ -41,7 +42,6 @@ from typing_extensions import TypedDict
 
 from backend.integrations.providers import ProviderName
 from backend.util.json import loads as json_loads
-from backend.util.request import parse_url
 from backend.util.settings import Secrets
 
 # Type alias for any provider name (including custom ones)
@@ -163,6 +163,7 @@ 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__)
@@ -396,25 +397,19 @@ class HostScopedCredentials(_BaseCredentials):
     def matches_url(self, url: str) -> bool:
         """Check if this credential should be applied to the given URL."""
 
-        request_host, request_port = _extract_host_from_url(url)
+        parsed_url = urlparse(url)
-        cred_scope_host, cred_scope_port = _extract_host_from_url(self.host)
+        # Extract hostname without port
+        request_host = parsed_url.hostname
         if not request_host:
             return False
 
-        # If a port is specified in credential host, the request host port must match
+        # Simple host matching - exact match or wildcard subdomain match
-        if cred_scope_port is not None and request_port != cred_scope_port:
+        if self.host == request_host:
-            return False
-        # Non-standard ports are only allowed if explicitly specified in credential host
-        elif cred_scope_port is None and request_port not in (80, 443, None):
-            return False
-
-        # Simple host matching
-        if cred_scope_host == request_host:
             return True
 
         # Support wildcard matching (e.g., "*.example.com" matches "api.example.com")
-        if cred_scope_host.startswith("*."):
+        if self.host.startswith("*."):
-            domain = cred_scope_host[2:]  # Remove "*."
+            domain = self.host[2:]  # Remove "*."
             return request_host.endswith(f".{domain}") or request_host == domain
 
         return False
@@ -507,13 +502,15 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
     def allowed_cred_types(cls) -> tuple[CredentialsType, ...]:
         return get_args(cls.model_fields["type"].annotation)
 
-    @staticmethod
+    @classmethod
-    def validate_credentials_field_schema(
+    def validate_credentials_field_schema(cls, model: type["BlockSchema"]):
-        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:
-            field_info = CredentialsFieldInfo[CP, CT].model_validate(field_schema)
+            schema_extra = CredentialsFieldInfo[CP, CT].model_validate(field_schema)
         except ValidationError as e:
             if "Field required [type=missing" not in str(e):
                 raise
@@ -523,11 +520,11 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
                 f"{field_schema}"
             ) from e
 
-        providers = field_info.provider
+        providers = cls.allowed_providers()
         if (
             providers is not None
             and len(providers) > 1
-            and not field_info.discriminator
+            and not schema_extra.discriminator
         ):
             raise TypeError(
                 f"Multi-provider CredentialsField '{field_name}' "
@@ -554,13 +551,13 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
     )
 
 
-def _extract_host_from_url(url: str) -> tuple[str, int | None]:
+def _extract_host_from_url(url: str) -> str:
-    """Extract host and port from URL for grouping host-scoped credentials."""
+    """Extract host from URL for grouping host-scoped credentials."""
     try:
-        parsed = parse_url(url)
+        parsed = urlparse(url)
-        return parsed.hostname or url, parsed.port
+        return parsed.hostname or url
     except Exception:
-        return "", None
+        return ""
 
 
 class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
@@ -571,8 +568,6 @@ 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(
@@ -611,7 +606,7 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
                 providers = frozenset(
                     [cast(CP, "http")]
                     + [
-                        cast(CP, parse_url(str(value)).netloc)
+                        cast(CP, _extract_host_from_url(str(value)))
                         for value in field.discriminator_values
                     ]
                 )
@@ -653,9 +648,6 @@ 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,
@@ -664,8 +656,6 @@ 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,
             )
@@ -691,8 +681,6 @@ 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/model_test.py

@@ -79,23 +79,10 @@ class TestHostScopedCredentials:
             headers={"Authorization": SecretStr("Bearer token")},
         )
 
-        # Non-standard ports require explicit port in credential host
+        assert creds.matches_url("http://localhost:8080/api/v1")
-        assert not creds.matches_url("http://localhost:8080/api/v1")
         assert creds.matches_url("https://localhost:443/secure/endpoint")
         assert creds.matches_url("http://localhost/simple")
 
-    def test_matches_url_with_explicit_port(self):
-        """Test URL matching with explicit port in credential host."""
-        creds = HostScopedCredentials(
-            provider="custom",
-            host="localhost:8080",
-            headers={"Authorization": SecretStr("Bearer token")},
-        )
-
-        assert creds.matches_url("http://localhost:8080/api/v1")
-        assert not creds.matches_url("http://localhost:3000/api/v1")
-        assert not creds.matches_url("http://localhost/simple")
-
     def test_empty_headers_dict(self):
         """Test HostScopedCredentials with empty headers."""
         creds = HostScopedCredentials(
@@ -141,20 +128,8 @@ class TestHostScopedCredentials:
             ("*.example.com", "https://sub.api.example.com/test", True),
             ("*.example.com", "https://example.com/test", True),
             ("*.example.com", "https://example.org/test", False),
-            # Non-standard ports require explicit port in credential host
+            ("localhost", "http://localhost:3000/test", True),
-            ("localhost", "http://localhost:3000/test", False),
-            ("localhost:3000", "http://localhost:3000/test", True),
             ("localhost", "http://127.0.0.1:3000/test", False),
-            # IPv6 addresses (frontend stores with brackets via URL.hostname)
-            ("[::1]", "http://[::1]/test", True),
-            ("[::1]", "http://[::1]:80/test", True),
-            ("[::1]", "https://[::1]:443/test", True),
-            ("[::1]", "http://[::1]:8080/test", False),  # Non-standard port
-            ("[::1]:8080", "http://[::1]:8080/test", True),
-            ("[::1]:8080", "http://[::1]:9090/test", False),
-            ("[2001:db8::1]", "http://[2001:db8::1]/path", True),
-            ("[2001:db8::1]", "https://[2001:db8::1]:443/path", True),
-            ("[2001:db8::1]", "http://[2001:db8::ff]/path", False),
         ],
     )
     def test_url_matching_parametrized(self, host: str, test_url: str, expected: bool):

autogpt_platform/backend/backend/executor/manager.py

@@ -172,81 +172,6 @@ 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,
@@ -346,14 +271,41 @@ async def execute_node(
         extra_exec_kwargs[field_name] = credentials
 
     # Handle auto-generated credentials (e.g., from GoogleDriveFileInput)
-    auto_extra_kwargs, auto_locks = await _acquire_auto_credentials(
+    for kwarg_name, info in input_model.get_auto_credentials_fields().items():
-        input_model=input_model,
+        field_name = info["field_name"]
-        input_data=input_data,
+        field_data = input_data.get(field_name)
-        creds_manager=creds_manager,
+        if field_data and isinstance(field_data, dict):
-        user_id=user_id,
+            # Check if _credentials_id key exists in the field data
-    )
+            if "_credentials_id" in field_data:
-    extra_exec_kwargs.update(auto_extra_kwargs)
+                cred_id = field_data["_credentials_id"]
-    creds_locks.extend(auto_locks)
+                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()}."
+                )
 
     output_size = 0
 

autogpt_platform/backend/backend/executor/manager_auto_credentials_test.py

@@ -1,320 +0,0 @@
-"""
-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

@@ -259,8 +259,7 @@ async def _validate_node_input_credentials(
 
         # Find any fields of type CredentialsMetaInput
         credentials_fields = block.input_schema.get_credentials_fields()
-        auto_credentials_fields = block.input_schema.get_auto_credentials_fields()
+        if not credentials_fields:
-        if not credentials_fields and not auto_credentials_fields:
             continue
 
         # Track if any credential field is missing for this node
@@ -340,47 +339,6 @@ 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 (
@@ -412,11 +370,10 @@ def make_node_credentials_input_map(
     """
     result: dict[str, dict[str, JsonValue]] = {}
 
-    # Only map regular credentials (not auto_credentials, which are resolved
+    # Get aggregated credentials fields for the graph
-    # at execution time from _credentials_id in file field data)
+    graph_cred_inputs = graph.aggregate_credentials_inputs()
-    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

@@ -907,335 +907,3 @@ 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

@@ -224,14 +224,6 @@ 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,
@@ -260,7 +252,6 @@ DEFAULT_CREDENTIALS = [
     v0_credentials,
     webshare_proxy_credentials,
     openweathermap_credentials,
-    elevenlabs_credentials,
 ]
 
 SYSTEM_CREDENTIAL_IDS = {cred.id for cred in DEFAULT_CREDENTIALS}
@@ -375,8 +366,6 @@ 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

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

autogpt_platform/backend/backend/util/file.py

@@ -8,8 +8,6 @@ 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
@@ -19,35 +17,6 @@ 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
@@ -214,20 +183,22 @@ async def store_media_file(
                 "This file type is only available in CoPilot sessions."
             )
 
-        # Parse workspace reference (strips #mimeType fragment from file ID)
+        # Parse workspace reference
-        ws = parse_workspace_uri(file)
+        # workspace://abc123 - by file ID
+        # workspace:///path/to/file.txt - by virtual path
+        file_ref = file[12:]  # Remove "workspace://"
 
-        if ws.is_path:
+        if file_ref.startswith("/"):
-            # Path reference: workspace:///path/to/file.txt
+            # Path reference
-            workspace_content = await workspace_manager.read_file(ws.file_ref)
+            workspace_content = await workspace_manager.read_file(file_ref)
-            file_info = await workspace_manager.get_file_info_by_path(ws.file_ref)
+            file_info = await workspace_manager.get_file_info_by_path(file_ref)
             filename = sanitize_filename(
                 file_info.name if file_info else f"{uuid.uuid4()}.bin"
             )
         else:
-            # ID reference: workspace://abc123 or workspace://abc123#video/mp4
+            # ID reference
-            workspace_content = await workspace_manager.read_file_by_id(ws.file_ref)
+            workspace_content = await workspace_manager.read_file_by_id(file_ref)
-            file_info = await workspace_manager.get_file_info(ws.file_ref)
+            file_info = await workspace_manager.get_file_info(file_ref)
             filename = sanitize_filename(
                 file_info.name if file_info else f"{uuid.uuid4()}.bin"
             )
@@ -363,21 +334,7 @@ async def store_media_file(
 
         # Don't re-save if input was already from workspace
         if is_from_workspace:
-            # Return original workspace reference, ensuring MIME type fragment
+            # Return original workspace reference
-            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
@@ -389,7 +346,7 @@ async def store_media_file(
             filename=filename,
             overwrite=True,
         )
-        return MediaFileType(f"workspace://{file_record.id}#{file_record.mimeType}")
+        return MediaFileType(f"workspace://{file_record.id}")
 
     else:
         raise ValueError(f"Invalid return_format: {return_format}")

autogpt_platform/backend/backend/util/request.py

@@ -157,7 +157,12 @@ async def validate_url(
         is_trusted: Boolean indicating if the hostname is in trusted_origins
         ip_addresses: List of IP addresses for the host; empty if the host is trusted
     """
-    parsed = parse_url(url)
+    # Canonicalize URL
+    url = url.strip("/ ").replace("\\", "/")
+    parsed = urlparse(url)
+    if not parsed.scheme:
+        url = f"http://{url}"
+        parsed = urlparse(url)
 
     # Check scheme
     if parsed.scheme not in ALLOWED_SCHEMES:
@@ -215,17 +220,6 @@ async def validate_url(
     )
 
 
-def parse_url(url: str) -> URL:
-    """Canonicalizes and parses a URL string."""
-    url = url.strip("/ ").replace("\\", "/")
-
-    # Ensure scheme is present for proper parsing
-    if not re.match(r"[a-z0-9+.\-]+://", url):
-        url = f"http://{url}"
-
-    return urlparse(url)
-
-
 def pin_url(url: URL, ip_addresses: Optional[list[str]] = None) -> URL:
     """
     Pins a URL to a specific IP address to prevent DNS rebinding attacks.

autogpt_platform/backend/backend/util/settings.py

@@ -656,7 +656,6 @@ 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

@@ -22,7 +22,6 @@ 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__)
@@ -188,9 +187,6 @@ class WorkspaceManager:
                 f"{Config().max_file_size_mb}MB limit"
             )
 
-        # Virus scan content before persisting (defense in depth)
-        await scan_content_safe(content, filename=filename)
-
         # Determine path with session scoping
         if path is None:
             path = f"/{filename}"

autogpt_platform/backend/poetry.lock

@@ -1169,29 +1169,6 @@ 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"
@@ -7384,28 +7361,6 @@ 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"
@@ -7557,4 +7512,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "8239323f9ae6713224dffd1fe8ba8b449fe88b6c3c7a90940294a74f43a0387a"
+content-hash = "ee5742dc1a9df50dfc06d4b26a1682cbb2b25cab6b79ce5625ec272f93e4f4bf"

autogpt_platform/backend/pyproject.toml

@@ -20,7 +20,6 @@ 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"
@@ -72,7 +71,6 @@ tweepy = "^4.16.0"
 uvicorn = { extras = ["standard"], version = "^0.35.0" }
 websockets = "^15.0"
 youtube-transcript-api = "^1.2.1"
-yt-dlp = "2025.12.08"
 zerobouncesdk = "^1.1.2"
 # NOTE: please insert new dependencies in their alphabetical location
 pytest-snapshot = "^0.9.0"

autogpt_platform/backend/snapshots/grph_single

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

autogpt_platform/backend/snapshots/grphs_all

@@ -1,14 +1,34 @@
 [
   {
-    "created_at": "2025-09-04T13:37:00",
+    "credentials_input_schema": {
+      "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 { GraphModel } from "@/app/api/__generated__/models/graphModel";
+import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
 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: GraphModel | null;
+  agent: GraphMeta | 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 { GraphModel } from "@/app/api/__generated__/models/graphModel";
+import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
 import { BlockIOCredentialsSubSchema } from "@/lib/autogpt-server-api/types";
 
 export function getCredentialFields(
-  agent: GraphModel | null,
+  agent: GraphMeta | 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 { GraphModel } from "@/app/api/__generated__/models/graphModel";
+import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
 
 export function computeInitialAgentInputs(
-  agent: GraphModel | null,
+  agent: GraphMeta | null,
   existingInputs?: InputValues | null,
 ): InputValues {
   const properties = agent?.input_schema?.properties || {};
@@ -29,7 +29,7 @@ export function computeInitialAgentInputs(
 }
 
 type IsRunDisabledParams = {
-  agent: GraphModel | null;
+  agent: GraphMeta | null;
   isRunning: boolean;
   agentInputs: InputValues | null | undefined;
 };

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

@@ -1,17 +1,6 @@
 import { OAuthPopupResultMessage } from "./types";
 import { NextResponse } from "next/server";
 
-/**
- * Safely encode a value as JSON for embedding in a script tag.
- * Escapes characters that could break out of the script context to prevent XSS.
- */
-function safeJsonStringify(value: unknown): string {
-  return JSON.stringify(value)
-    .replace(/</g, "\\u003c")
-    .replace(/>/g, "\\u003e")
-    .replace(/&/g, "\\u0026");
-}
-
 // This route is intended to be used as the callback for integration OAuth flows,
 // controlled by the CredentialsInput component. The CredentialsInput opens the login
 // page in a pop-up window, which then redirects to this route to close the loop.
@@ -34,13 +23,12 @@ export async function GET(request: Request) {
   console.debug("Sending message to opener:", message);
 
   // Return a response with the message as JSON and a script to close the window
-  // Use safeJsonStringify to prevent XSS by escaping <, >, and & characters
   return new NextResponse(
     `
     <html>
       <body>
         <script>
-          window.opener.postMessage(${safeJsonStringify(message)});
+          window.opener.postMessage(${JSON.stringify(message)});
           window.close();
         </script>
       </body>

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

@@ -30,8 +30,6 @@ 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;
@@ -109,8 +107,6 @@ 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 {
@@ -120,9 +116,8 @@ export function BlocksControl({
             `Ver.${flow.version}` +
             (flow.description ? ` | ${flow.description}` : ""),
           categories: [{ category: "AGENT", description: "" }],
-          // Empty schemas - will be populated when block is added
+          inputSchema: flow.input_schema,
-          inputSchema: { type: "object", properties: {} },
+          outputSchema: flow.output_schema,
-          outputSchema: { type: "object", properties: {} },
           staticOutput: false,
           uiType: BlockUIType.AGENT,
           costs: [],
@@ -130,7 +125,8 @@ export function BlocksControl({
           hardcodedValues: {
             graph_id: flow.id,
             graph_version: flow.version,
-            // Schemas will be fetched on-demand when block is added
+            input_schema: flow.input_schema,
+            output_schema: flow.output_schema,
           },
         };
       })
@@ -186,37 +182,6 @@ 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(
@@ -338,7 +303,10 @@ export function BlocksControl({
                       }),
                     );
                   }}
-                  onClick={() => handleAddBlock(block)}
+                  onClick={() =>
+                    !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, { useMemo, useState } from "react";
+import React, { useState } from "react";
 import { Button } from "../../../../../components/__legacy__/ui/button";
 import { ContentRenderer } from "../../../../../components/__legacy__/ui/render";
 import {
@@ -11,12 +11,6 @@ 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";
 
@@ -32,9 +26,6 @@ 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;
@@ -42,15 +33,6 @@ 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({
@@ -120,31 +102,15 @@ export default function DataTable({
                       <Clipboard size={18} />
                     </Button>
                   </div>
-                  {value.map((item, index) => {
+                  {value.map((item, index) => (
-                    const renderer = getItemRenderer?.(item);
+                    <React.Fragment key={index}>
-                    if (enableEnhancedOutputHandling && renderer) {
+                      <ContentRenderer
-                      const metadata: OutputMetadata = {};
+                        value={item}
-                      return (
+                        truncateLongData={truncateLongData}
-                        <React.Fragment key={index}>
+                      />
-                          <OutputItem
+                      {index < value.length - 1 && ", "}
-                            value={item}
+                    </React.Fragment>
-                            metadata={metadata}
+                  ))}
-                            renderer={renderer}
-                          />
-                          {index < value.length - 1 && ", "}
-                        </React.Fragment>
-                      );
-                    }
-                    return (
-                      <React.Fragment key={index}>
-                        <ContentRenderer
-                          value={item}
-                          truncateLongData={truncateLongData}
-                        />
-                        {index < value.length - 1 && ", "}
-                      </React.Fragment>
-                    );
-                  })}
                 </div>
               </TableCell>
             </TableRow>

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

@@ -29,17 +29,13 @@ 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";
@@ -691,94 +687,8 @@ 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(
-    async (
+    (blockId: string, nodeType: string, hardcodedValues: any = {}) => {
-      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}`);
@@ -797,42 +707,73 @@ const FlowEditor: React.FC<{
       // Alternative: We could also use D3 force, Intersection for this (React flow Pro examples)
 
       const { x, y } = getViewport();
-      const position =
+      const viewportCoordinates =
         nodeDimensions && Object.keys(nodeDimensions).length > 0
-          ? findNewlyAddedBlockCoordinates(
+          ? // we will get all the dimension of nodes, then store
+            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 = await createAndAddNode(
+      const newNode: CustomNode = {
-        blockId,
+        id: nodeId.toString(),
-        nodeType,
+        type: "custom",
-        hardcodedValues,
+        position: viewportCoordinates, // Set the position to the calculated viewport center
-        position,
+        data: {
-      );
+          blockType: nodeType,
-      if (!newNode) return;
+          blockCosts: nodeSchema.costs,
+          title: `${nodeType} ${nodeId}`,
+          description: nodeSchema.description,
+          categories: nodeSchema.categories,
+          inputSchema: nodeSchema.inputSchema,
+          outputSchema: nodeSchema.outputSchema,
+          hardcodedValues: hardcodedValues,
+          connections: [],
+          isOutputOpen: false,
+          block_id: blockId,
+          isOutputStatic: nodeSchema.staticOutput,
+          uiType: nodeSchema.uiType,
+        },
+      };
+
+      addNodes(newNode);
+      setNodeId((prevId) => prevId + 1);
+      clearNodesStatusAndOutput(); // Clear status and output when a new node is added
 
       setViewport(
         {
-          x: -position.x * 0.8 + (window.innerWidth - 0.0) / 2,
+          // Rough estimate of the dimension of the node is: 500x400px.
-          y: -position.y * 0.8 + (window.innerHeight - 400) / 2,
+          // Though we skip shifting the X, considering the block menu side-bar.
+          x: -viewportCoordinates.x * 0.8 + (window.innerWidth - 0.0) / 2,
+          y: -viewportCoordinates.y * 0.8 + (window.innerHeight - 400) / 2,
           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,
-      createAndAddNode,
+      deleteElements,
+      clearNodesStatusAndOutput,
     ],
   );
 
@@ -979,7 +920,7 @@ const FlowEditor: React.FC<{
   }, []);
 
   const onDrop = useCallback(
-    async (event: React.DragEvent) => {
+    (event: React.DragEvent) => {
       event.preventDefault();
 
       const blockData = event.dataTransfer.getData("application/reactflow");
@@ -994,17 +935,62 @@ const FlowEditor: React.FC<{
           y: event.clientY,
         });
 
-        await createAndAddNode(
+        // Find the block schema
-          blockId,
+        const nodeSchema = availableBlocks.find((node) => node.id === blockId);
-          blockName,
+        if (!nodeSchema) {
-          hardcodedValues || {},
+          console.error(`Schema not found for block ID: ${blockId}`);
+          return;
+        }
+
+        // Create the new node at the drop position
+        const newNode: CustomNode = {
+          id: nodeId.toString(),
+          type: "custom",
           position,
-        );
+          data: {
+            blockType: blockName,
+            blockCosts: nodeSchema.costs || [],
+            title: `${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

@@ -1,14 +1,8 @@
-import React, { useContext, useMemo, useState } from "react";
+import React, { useContext, 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";
 
@@ -27,9 +21,6 @@ export default function NodeOutputs({
   data,
 }: NodeOutputsProps) {
   const builderContext = useContext(BuilderContext);
-  const enableEnhancedOutputHandling = useGetFlag(
-    Flag.ENABLE_ENHANCED_OUTPUT_HANDLING,
-  );
 
   const [expandedDialog, setExpandedDialog] = useState<{
     isOpen: boolean;
@@ -46,15 +37,6 @@ 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);
@@ -105,31 +87,15 @@ 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);
+                <React.Fragment key={index}>
-                if (enableEnhancedOutputHandling && renderer) {
+                  <ContentRenderer
-                  const metadata: OutputMetadata = {};
+                    value={item}
-                  return (
+                    truncateLongData={truncateLongData}
-                    <React.Fragment key={index}>
+                  />
-                      <OutputItem
+                  {index < Math.min(dataArray.length, 10) - 1 && ", "}
-                        value={item}
+                </React.Fragment>
-                        metadata={metadata}
+              ))}
-                        renderer={renderer}
-                      />
-                      {index < Math.min(dataArray.length, 10) - 1 && ", "}
-                    </React.Fragment>
-                  );
-                }
-                return (
-                  <React.Fragment key={index}>
-                    <ContentRenderer
-                      value={item}
-                      truncateLongData={truncateLongData}
-                    />
-                    {index < Math.min(dataArray.length, 10) - 1 && ", "}
-                  </React.Fragment>
-                );
-              })}
               {dataArray.length > 10 && (
                 <span style={{ color: "#888" }}>
                   <br />

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

@@ -4,13 +4,13 @@ import { AgentRunDraftView } from "@/app/(platform)/library/agents/[id]/componen
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import type {
   CredentialsMetaInput,
-  Graph,
+  GraphMeta,
 } from "@/lib/autogpt-server-api/types";
 
 interface RunInputDialogProps {
   isOpen: boolean;
   doClose: () => void;
-  graph: Graph;
+  graph: GraphMeta;
   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,
-  Graph,
+  GraphMeta,
 } from "@/lib/autogpt-server-api/types";
 import RunnerOutputUI, { OutputNodeInfo } from "./RunnerOutputUI";
 import { RunnerInputDialog } from "./RunnerInputUI";
 
 interface RunnerUIWrapperProps {
-  graph: Graph;
+  graph: GraphMeta;
   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 { GraphLike, IncompatibilityInfo } from "./types";
+import { GraphMetaLike, 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: GraphLike,
+  latestSubGraphVersion: GraphMetaLike,
 ): Record<string, unknown> {
   return {
     ...currentInputs,

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

@@ -1,11 +1,7 @@
-import type {
+import type { GraphMeta as LegacyGraphMeta } from "@/lib/autogpt-server-api";
-  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 GraphLike = GraphLike> = {
+export type SubAgentUpdateInfo<T extends GraphMetaLike = GraphMetaLike> = {
   hasUpdate: boolean;
   currentVersion: number;
   latestVersion: number;
@@ -14,10 +10,7 @@ export type SubAgentUpdateInfo<T extends GraphLike = GraphLike> = {
   incompatibilities: IncompatibilityInfo | null;
 };
 
-// Union type for Graph (with schemas) that works with both legacy and new builder
+// Union type for GraphMeta 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

@@ -1,11 +1,5 @@
 import { useMemo } from "react";
-import type {
+import { GraphInputSchema, GraphOutputSchema } from "@/lib/autogpt-server-api";
-  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 {
@@ -17,38 +11,26 @@ import {
 /**
  * Checks if a newer version of a sub-agent is available and determines compatibility
  */
-export function useSubAgentUpdate(
+export function useSubAgentUpdate<T extends GraphMetaLike>(
   nodeID: string,
   graphID: string | undefined,
   graphVersion: number | undefined,
   currentInputSchema: GraphInputSchema | undefined,
   currentOutputSchema: GraphOutputSchema | undefined,
   connections: EdgeLike[],
-  availableGraphs: GraphMetaLike[],
+  availableGraphs: T[],
-): SubAgentUpdateInfo<GraphModel> {
+): SubAgentUpdateInfo<T> {
   // Find the latest version of the same graph
-  const latestGraphInfo = useMemo(() => {
+  const latestGraph = useMemo(() => {
     if (!graphID) return null;
     return availableGraphs.find((graph) => graph.id === graphID) || null;
   }, [graphID, availableGraphs]);
 
-  // Check if there's a newer version available
+  // Check if there's an update available
   const hasUpdate = useMemo(() => {
-    if (!latestGraphInfo || graphVersion === undefined) return false;
+    if (!latestGraph || graphVersion === undefined) return false;
-    return latestGraphInfo.version! > graphVersion;
+    return latestGraph.version! > graphVersion;
-  }, [latestGraphInfo, graphVersion]);
+  }, [latestGraph, 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(() => {
@@ -170,8 +152,8 @@ export function useSubAgentUpdate(
   return {
     hasUpdate,
     currentVersion: graphVersion || 0,
-    latestVersion: latestGraphInfo?.version || 0,
+    latestVersion: latestGraph?.version || 0,
-    latestGraph: latestGraph || null,
+    latestGraph,
     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 updated version detection
+  // Available graphs; used for sub-graph updates
   availableSubGraphs: GraphMeta[];
   setAvailableSubGraphs: (graphs: GraphMeta[]) => void;
 

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

@@ -26,20 +26,8 @@ export function buildCopilotChatUrl(prompt: string): string {
 
 export function getQuickActions(): string[] {
   return [
-    "I don't know where to start, just ask me stuff",
+    "Show me what I can automate",
-    "I do the same thing every week and it's killing me",
+    "Design a custom workflow",
-    "Help me find where I'm wasting my time",
+    "Help me with content creation",
   ];
 }
-
-export function getInputPlaceholder(width?: number) {
-  if (!width) return "What's your role and what eats up most of your day?";
-
-  if (width < 500) {
-    return "I'm a chef and I hate...";
-  }
-  if (width <= 1080) {
-    return "What's your role and what eats up most of your day?";
-  }
-  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
-}

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

@@ -6,9 +6,7 @@ import { Text } from "@/components/atoms/Text/Text";
 import { Chat } from "@/components/contextual/Chat/Chat";
 import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { useEffect, useState } from "react";
 import { useCopilotStore } from "./copilot-page-store";
-import { getInputPlaceholder } from "./helpers";
 import { useCopilotPage } from "./useCopilotPage";
 
 export default function CopilotPage() {
@@ -16,25 +14,8 @@ export default function CopilotPage() {
   const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
   const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
   const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
-
-  const [inputPlaceholder, setInputPlaceholder] = useState(
-    getInputPlaceholder(),
-  );
-
-  useEffect(() => {
-    const handleResize = () => {
-      setInputPlaceholder(getInputPlaceholder(window.innerWidth));
-    };
-
-    handleResize();
-
-    window.addEventListener("resize", handleResize);
-    return () => window.removeEventListener("resize", handleResize);
-  }, []);
-
   const { greetingName, quickActions, isLoading, hasSession, initialPrompt } =
     state;
-
   const {
     handleQuickAction,
     startChatWithPrompt,
@@ -92,7 +73,7 @@ export default function CopilotPage() {
   }
 
   return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-3 py-5 md:px-6 md:py-10">
+    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-6 py-10">
       <div className="w-full text-center">
         {isLoading ? (
           <div className="mx-auto max-w-2xl">
@@ -109,25 +90,25 @@ export default function CopilotPage() {
           </div>
         ) : (
           <>
-            <div className="mx-auto max-w-3xl">
+            <div className="mx-auto max-w-2xl">
               <Text
                 variant="h3"
-                className="mb-1 !text-[1.375rem] text-zinc-700"
+                className="mb-3 !text-[1.375rem] text-zinc-700"
               >
                 Hey, <span className="text-violet-600">{greetingName}</span>
               </Text>
               <Text variant="h3" className="mb-8 !font-normal">
-                Tell me about your work — I&apos;ll find what to automate.
+                What do you want to automate?
               </Text>
 
               <div className="mb-6">
                 <ChatInput
                   onSend={startChatWithPrompt}
-                  placeholder={inputPlaceholder}
+                  placeholder='You can search or just ask - e.g. "create a blog post outline"'
                 />
               </div>
             </div>
-            <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
+            <div className="flex flex-nowrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
               {quickActions.map((action) => (
                 <Button
                   key={action}
@@ -135,7 +116,7 @@ export default function CopilotPage() {
                   variant="outline"
                   size="small"
                   onClick={() => handleQuickAction(action)}
-                  className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
+                  className="h-auto shrink-0 border-zinc-600 !px-4 !py-2 text-[1rem] text-zinc-600"
                 >
                   {action}
                 </Button>

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: Graph;
+  graph: GraphMeta;
   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: Graph;
+  graph: GraphMeta;
   schedule: Schedule;
   agentActions: ButtonAction[];
   onForcedRun: (runID: GraphExecutionID) => void;

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

@@ -5629,9 +5629,7 @@
             "description": "Successful Response",
             "content": {
               "application/json": {
-                "schema": {
+                "schema": { "$ref": "#/components/schemas/GraphMeta" }
-                  "$ref": "#/components/schemas/GraphModelWithoutNodes"
-                }
               }
             }
           },
@@ -6497,6 +6495,18 @@
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Recommended Schedule Cron"
           },
+          "nodes": {
+            "items": { "$ref": "#/components/schemas/Node" },
+            "type": "array",
+            "title": "Nodes",
+            "default": []
+          },
+          "links": {
+            "items": { "$ref": "#/components/schemas/Link" },
+            "type": "array",
+            "title": "Links",
+            "default": []
+          },
           "forked_from_id": {
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Forked From Id"
@@ -6504,22 +6514,11 @@
           "forked_from_version": {
             "anyOf": [{ "type": "integer" }, { "type": "null" }],
             "title": "Forked From Version"
-          },
-          "nodes": {
-            "items": { "$ref": "#/components/schemas/Node" },
-            "type": "array",
-            "title": "Nodes"
-          },
-          "links": {
-            "items": { "$ref": "#/components/schemas/Link" },
-            "type": "array",
-            "title": "Links"
           }
         },
         "type": "object",
         "required": ["name", "description"],
-        "title": "BaseGraph",
+        "title": "BaseGraph"
-        "description": "Graph with nodes, links, and computed I/O schema fields.\n\nUsed to represent sub-graphs within a `Graph`. Contains the full graph\nstructure including nodes and links, plus computed fields for schemas\nand trigger info. Does NOT include user_id or created_at (see GraphModel)."
       },
       "BaseGraph-Output": {
         "properties": {
@@ -6540,6 +6539,18 @@
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Recommended Schedule Cron"
           },
+          "nodes": {
+            "items": { "$ref": "#/components/schemas/Node" },
+            "type": "array",
+            "title": "Nodes",
+            "default": []
+          },
+          "links": {
+            "items": { "$ref": "#/components/schemas/Link" },
+            "type": "array",
+            "title": "Links",
+            "default": []
+          },
           "forked_from_id": {
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Forked From Id"
@@ -6548,16 +6559,6 @@
             "anyOf": [{ "type": "integer" }, { "type": "null" }],
             "title": "Forked From Version"
           },
-          "nodes": {
-            "items": { "$ref": "#/components/schemas/Node" },
-            "type": "array",
-            "title": "Nodes"
-          },
-          "links": {
-            "items": { "$ref": "#/components/schemas/Link" },
-            "type": "array",
-            "title": "Links"
-          },
           "input_schema": {
             "additionalProperties": true,
             "type": "object",
@@ -6604,8 +6605,7 @@
           "has_sensitive_action",
           "trigger_setup_info"
         ],
-        "title": "BaseGraph",
+        "title": "BaseGraph"
-        "description": "Graph with nodes, links, and computed I/O schema fields.\n\nUsed to represent sub-graphs within a `Graph`. Contains the full graph\nstructure including nodes and links, plus computed fields for schemas\nand trigger info. Does NOT include user_id or created_at (see GraphModel)."
       },
       "BlockCategoryResponse": {
         "properties": {
@@ -7399,6 +7399,18 @@
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Recommended Schedule Cron"
           },
+          "nodes": {
+            "items": { "$ref": "#/components/schemas/Node" },
+            "type": "array",
+            "title": "Nodes",
+            "default": []
+          },
+          "links": {
+            "items": { "$ref": "#/components/schemas/Link" },
+            "type": "array",
+            "title": "Links",
+            "default": []
+          },
           "forked_from_id": {
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Forked From Id"
@@ -7407,26 +7419,16 @@
             "anyOf": [{ "type": "integer" }, { "type": "null" }],
             "title": "Forked From Version"
           },
-          "nodes": {
-            "items": { "$ref": "#/components/schemas/Node" },
-            "type": "array",
-            "title": "Nodes"
-          },
-          "links": {
-            "items": { "$ref": "#/components/schemas/Link" },
-            "type": "array",
-            "title": "Links"
-          },
           "sub_graphs": {
             "items": { "$ref": "#/components/schemas/BaseGraph-Input" },
             "type": "array",
-            "title": "Sub Graphs"
+            "title": "Sub Graphs",
+            "default": []
           }
         },
         "type": "object",
         "required": ["name", "description"],
-        "title": "Graph",
+        "title": "Graph"
-        "description": "Creatable graph model used in API create/update endpoints."
       },
       "GraphExecution": {
         "properties": {
@@ -7778,7 +7780,7 @@
       "GraphMeta": {
         "properties": {
           "id": { "type": "string", "title": "Id" },
-          "version": { "type": "integer", "title": "Version" },
+          "version": { "type": "integer", "title": "Version", "default": 1 },
           "is_active": {
             "type": "boolean",
             "title": "Is Active",
@@ -7802,24 +7804,68 @@
             "anyOf": [{ "type": "integer" }, { "type": "null" }],
             "title": "Forked From Version"
           },
+          "sub_graphs": {
+            "items": { "$ref": "#/components/schemas/BaseGraph-Output" },
+            "type": "array",
+            "title": "Sub Graphs",
+            "default": []
+          },
           "user_id": { "type": "string", "title": "User Id" },
-          "created_at": {
+          "input_schema": {
-            "type": "string",
+            "additionalProperties": true,
-            "format": "date-time",
+            "type": "object",
-            "title": "Created At"
+            "title": "Input Schema",
+            "readOnly": true
+          },
+          "output_schema": {
+            "additionalProperties": true,
+            "type": "object",
+            "title": "Output Schema",
+            "readOnly": true
+          },
+          "has_external_trigger": {
+            "type": "boolean",
+            "title": "Has External Trigger",
+            "readOnly": true
+          },
+          "has_human_in_the_loop": {
+            "type": "boolean",
+            "title": "Has Human In The Loop",
+            "readOnly": true
+          },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "readOnly": true
+          },
+          "trigger_setup_info": {
+            "anyOf": [
+              { "$ref": "#/components/schemas/GraphTriggerInfo" },
+              { "type": "null" }
+            ],
+            "readOnly": true
+          },
+          "credentials_input_schema": {
+            "additionalProperties": true,
+            "type": "object",
+            "title": "Credentials Input Schema",
+            "readOnly": true
           }
         },
         "type": "object",
         "required": [
-          "id",
-          "version",
           "name",
           "description",
           "user_id",
-          "created_at"
+          "input_schema",
+          "output_schema",
+          "has_external_trigger",
+          "has_human_in_the_loop",
+          "has_sensitive_action",
+          "trigger_setup_info",
+          "credentials_input_schema"
         ],
-        "title": "GraphMeta",
+        "title": "GraphMeta"
-        "description": "Lightweight graph metadata model representing an existing graph from the database,\nfor use in listings and summaries.\n\nLacks `GraphModel`'s nodes, links, and expensive computed fields.\nUse for list endpoints where full graph data is not needed and performance matters."
       },
       "GraphModel": {
         "properties": {
@@ -7840,111 +7886,17 @@
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Recommended Schedule Cron"
           },
-          "forked_from_id": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Forked From Id"
-          },
-          "forked_from_version": {
-            "anyOf": [{ "type": "integer" }, { "type": "null" }],
-            "title": "Forked From Version"
-          },
-          "user_id": { "type": "string", "title": "User Id" },
-          "created_at": {
-            "type": "string",
-            "format": "date-time",
-            "title": "Created At"
-          },
           "nodes": {
             "items": { "$ref": "#/components/schemas/NodeModel" },
             "type": "array",
-            "title": "Nodes"
+            "title": "Nodes",
+            "default": []
           },
           "links": {
             "items": { "$ref": "#/components/schemas/Link" },
             "type": "array",
-            "title": "Links"
+            "title": "Links",
-          },
+            "default": []
-          "sub_graphs": {
-            "items": { "$ref": "#/components/schemas/BaseGraph-Output" },
-            "type": "array",
-            "title": "Sub Graphs"
-          },
-          "input_schema": {
-            "additionalProperties": true,
-            "type": "object",
-            "title": "Input Schema",
-            "readOnly": true
-          },
-          "output_schema": {
-            "additionalProperties": true,
-            "type": "object",
-            "title": "Output Schema",
-            "readOnly": true
-          },
-          "has_external_trigger": {
-            "type": "boolean",
-            "title": "Has External Trigger",
-            "readOnly": true
-          },
-          "has_human_in_the_loop": {
-            "type": "boolean",
-            "title": "Has Human In The Loop",
-            "readOnly": true
-          },
-          "has_sensitive_action": {
-            "type": "boolean",
-            "title": "Has Sensitive Action",
-            "readOnly": true
-          },
-          "trigger_setup_info": {
-            "anyOf": [
-              { "$ref": "#/components/schemas/GraphTriggerInfo" },
-              { "type": "null" }
-            ],
-            "readOnly": true
-          },
-          "credentials_input_schema": {
-            "additionalProperties": true,
-            "type": "object",
-            "title": "Credentials Input Schema",
-            "readOnly": true
-          }
-        },
-        "type": "object",
-        "required": [
-          "name",
-          "description",
-          "user_id",
-          "created_at",
-          "input_schema",
-          "output_schema",
-          "has_external_trigger",
-          "has_human_in_the_loop",
-          "has_sensitive_action",
-          "trigger_setup_info",
-          "credentials_input_schema"
-        ],
-        "title": "GraphModel",
-        "description": "Full graph model representing an existing graph from the database.\n\nThis is the primary model for working with persisted graphs. Includes all\ngraph data (nodes, links, sub_graphs) plus user ownership and timestamps.\nProvides computed fields (input_schema, output_schema, etc.) used during\nset-up (frontend) and execution (backend).\n\nInherits from:\n- `Graph`: provides structure (nodes, links, sub_graphs) and computed schemas\n- `GraphMeta`: provides user_id, created_at for database records"
-      },
-      "GraphModelWithoutNodes": {
-        "properties": {
-          "id": { "type": "string", "title": "Id" },
-          "version": { "type": "integer", "title": "Version", "default": 1 },
-          "is_active": {
-            "type": "boolean",
-            "title": "Is Active",
-            "default": true
-          },
-          "name": { "type": "string", "title": "Name" },
-          "description": { "type": "string", "title": "Description" },
-          "instructions": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Instructions"
-          },
-          "recommended_schedule_cron": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Recommended Schedule Cron"
           },
           "forked_from_id": {
             "anyOf": [{ "type": "string" }, { "type": "null" }],
@@ -7954,6 +7906,12 @@
             "anyOf": [{ "type": "integer" }, { "type": "null" }],
             "title": "Forked From Version"
           },
+          "sub_graphs": {
+            "items": { "$ref": "#/components/schemas/BaseGraph-Output" },
+            "type": "array",
+            "title": "Sub Graphs",
+            "default": []
+          },
           "user_id": { "type": "string", "title": "User Id" },
           "created_at": {
             "type": "string",
@@ -8015,8 +7973,7 @@
           "trigger_setup_info",
           "credentials_input_schema"
         ],
-        "title": "GraphModelWithoutNodes",
+        "title": "GraphModel"
-        "description": "GraphModel variant that excludes nodes, links, and sub-graphs from serialization.\n\nUsed in contexts like the store where exposing internal graph structure\nis not desired. Inherits all computed fields from GraphModel but marks\nnodes and links as excluded from JSON output."
       },
       "GraphSettings": {
         "properties": {
@@ -8656,22 +8613,26 @@
           "input_default": {
             "additionalProperties": true,
             "type": "object",
-            "title": "Input Default"
+            "title": "Input Default",
+            "default": {}
           },
           "metadata": {
             "additionalProperties": true,
             "type": "object",
-            "title": "Metadata"
+            "title": "Metadata",
+            "default": {}
           },
           "input_links": {
             "items": { "$ref": "#/components/schemas/Link" },
             "type": "array",
-            "title": "Input Links"
+            "title": "Input Links",
+            "default": []
           },
           "output_links": {
             "items": { "$ref": "#/components/schemas/Link" },
             "type": "array",
-            "title": "Output Links"
+            "title": "Output Links",
+            "default": []
           }
         },
         "type": "object",
@@ -8751,22 +8712,26 @@
           "input_default": {
             "additionalProperties": true,
             "type": "object",
-            "title": "Input Default"
+            "title": "Input Default",
+            "default": {}
           },
           "metadata": {
             "additionalProperties": true,
             "type": "object",
-            "title": "Metadata"
+            "title": "Metadata",
+            "default": {}
           },
           "input_links": {
             "items": { "$ref": "#/components/schemas/Link" },
             "type": "array",
-            "title": "Input Links"
+            "title": "Input Links",
+            "default": []
           },
           "output_links": {
             "items": { "$ref": "#/components/schemas/Link" },
             "type": "array",
-            "title": "Output Links"
+            "title": "Output Links",
+            "default": []
           },
           "graph_id": { "type": "string", "title": "Graph Id" },
           "graph_version": { "type": "integer", "title": "Graph Version" },

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

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

@@ -2,6 +2,7 @@ import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessi
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { cn } from "@/lib/utils";
 import { GlobeHemisphereEastIcon } from "@phosphor-icons/react";
 import { useEffect } from "react";
@@ -55,6 +56,10 @@ export function ChatContainer({
     onStreamingChange?.(isStreaming);
   }, [isStreaming, onStreamingChange]);
 
+  const breakpoint = useBreakpoint();
+  const isMobile =
+    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
+
   return (
     <div
       className={cn(
@@ -122,7 +127,11 @@ export function ChatContainer({
           disabled={isStreaming || !sessionId}
           isStreaming={isStreaming}
           onStop={stopStreaming}
-          placeholder="What else can I help with?"
+          placeholder={
+            isMobile
+              ? "You can search or just ask"
+              : 'You can search or just ask — e.g. "create a blog post outline"'
+          }
         />
       </div>
     </div>

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

@@ -74,20 +74,19 @@ export function ChatInput({
             hasMultipleLines ? "rounded-xlarge" : "rounded-full",
           )}
         >
-          {!value && !isRecording && (
-            <div
-              className="pointer-events-none absolute inset-0 top-0.5 flex items-center justify-start pl-14 text-[1rem] text-zinc-400"
-              aria-hidden="true"
-            >
-              {isTranscribing ? "Transcribing..." : placeholder}
-            </div>
-          )}
           <textarea
             id={inputId}
             aria-label="Chat message input"
             value={value}
             onChange={handleChange}
             onKeyDown={handleKeyDown}
+            placeholder={
+              isTranscribing
+                ? "Transcribing..."
+                : isRecording
+                  ? ""
+                  : placeholder
+            }
             disabled={isInputDisabled}
             rows={1}
             className={cn(
@@ -123,14 +122,13 @@ export function ChatInput({
               size="icon"
               aria-label={isRecording ? "Stop recording" : "Start recording"}
               onClick={toggleRecording}
-              disabled={disabled || isTranscribing || isStreaming}
+              disabled={disabled || isTranscribing}
               className={cn(
                 isRecording
                   ? "animate-pulse border-red-500 bg-red-500 text-white hover:border-red-600 hover:bg-red-600"
                   : isTranscribing
                     ? "border-zinc-300 bg-zinc-100 text-zinc-400"
                     : "border-zinc-300 bg-white text-zinc-500 hover:border-zinc-400 hover:bg-zinc-50 hover:text-zinc-700",
-                isStreaming && "opacity-40",
               )}
             >
               {isTranscribing ? (

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/AudioWaveform.tsx

@@ -38,8 +38,8 @@ export function AudioWaveform({
     // Create audio context and analyser
     const audioContext = new AudioContext();
     const analyser = audioContext.createAnalyser();
-    analyser.fftSize = 256;
+    analyser.fftSize = 512;
-    analyser.smoothingTimeConstant = 0.3;
+    analyser.smoothingTimeConstant = 0.8;
 
     // Connect the stream to the analyser
     const source = audioContext.createMediaStreamSource(stream);
@@ -73,11 +73,10 @@ export function AudioWaveform({
           maxAmplitude = Math.max(maxAmplitude, amplitude);
         }
 
-        // Normalize amplitude (0-128 range) to 0-1
+        // Map amplitude (0-128) to bar height
-        const normalized = maxAmplitude / 128;
+        const normalized = (maxAmplitude / 128) * 255;
-        // Apply sensitivity boost (multiply by 4) and use sqrt curve to amplify quiet sounds
+        const height =
-        const boosted = Math.min(1, Math.sqrt(normalized) * 4);
+          minBarHeight + (normalized / 255) * (maxBarHeight - minBarHeight);
-        const height = minBarHeight + boosted * (maxBarHeight - minBarHeight);
         newBars.push(height);
       }
 

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts

@@ -224,7 +224,7 @@ export function useVoiceRecording({
     [value, isTranscribing, toggleRecording, baseHandleKeyDown],
   );
 
-  const showMicButton = isSupported;
+  const showMicButton = isSupported && !isStreaming;
   const isInputDisabled = disabled || isStreaming || isTranscribing;
 
   // Cleanup on unmount

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

@@ -102,6 +102,18 @@ 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;
@@ -150,22 +162,6 @@ 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}
@@ -350,7 +346,6 @@ 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, { useState } from "react";
+import React from "react";
 import ReactMarkdown from "react-markdown";
 import remarkGfm from "remark-gfm";
 
@@ -48,9 +48,7 @@ interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
  */
 function resolveWorkspaceUrl(src: string): string {
   if (src.startsWith("workspace://")) {
-    // Strip MIME type fragment if present (e.g., workspace://abc123#video/mp4 → abc123)
+    const fileId = src.replace("workspace://", "");
-    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)
@@ -67,49 +65,13 @@ 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);
 
@@ -122,18 +84,6 @@ 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 */}
@@ -142,9 +92,6 @@ 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

@@ -73,7 +73,6 @@ 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

@@ -5,13 +5,11 @@ 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;
 
@@ -23,7 +21,6 @@ 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

@@ -1,8 +1,6 @@
-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;
@@ -13,7 +11,6 @@ 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) {
@@ -52,18 +49,9 @@ 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">
+                <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">
-                  <div className="flex w-full max-w-[280px] flex-col gap-1.5">
+                  This could take a few minutes, grab a coffee ☕️
-                    <div className="flex items-center justify-between text-xs text-neutral-500">
+                </span>
-                      <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

@@ -1,50 +0,0 @@
-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

@@ -1,128 +0,0 @@
-"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

@@ -2,13 +2,11 @@ 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";
 
@@ -18,7 +16,6 @@ export interface ToolResponseMessageProps {
   result?: ToolResult;
   success?: boolean;
   className?: string;
-  onSendMessage?: (content: string) => void;
 }
 
 export function ToolResponseMessage({
@@ -27,7 +24,6 @@ export function ToolResponseMessage({
   result,
   success: _success,
   className,
-  onSendMessage,
 }: ToolResponseMessageProps) {
   if (isErrorResponse(result)) {
     const errorMessage = getErrorMessage(result);
@@ -47,18 +43,6 @@ 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

@@ -6,43 +6,6 @@ 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();
@@ -76,101 +39,69 @@ 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://");
 }
 
 /**
- * Extract MIME type from a workspace reference fragment.
+ * Check if a workspace reference appears to be an image based on common patterns.
- * e.g., "workspace://abc123#video/mp4" → "video/mp4"
+ * Since workspace refs don't have extensions, we check the context or assume image
- * Returns undefined if no fragment is present.
+ * for certain block types.
+ *
+ * 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 getWorkspaceMimeType(value: string): string | undefined {
+function isLikelyImageRef(value: string, outputKey?: string): boolean {
-  const hashIndex = value.indexOf("#");
+  if (!isWorkspaceRef(value)) return false;
-  if (hashIndex === -1) return undefined;
-  return value.slice(hashIndex + 1) || undefined;
-}
 
-/**
+  // Check output key name for video-related hints (these are NOT images)
- * Determine the media category of a workspace ref or data URI.
+  const videoKeywords = ["video", "mp4", "mov", "avi", "webm", "movie", "clip"];
- * Uses the MIME type fragment on workspace refs when available,
+  if (outputKey) {
- * falls back to output key keyword matching for older refs without it.
+    const lowerKey = outputKey.toLowerCase();
- */
+    if (videoKeywords.some((kw) => lowerKey.includes(kw))) {
-function getMediaCategory(
+      return false;
-  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();
-
-      const videoKeywords = [
-        "video",
-        "mp4",
-        "mov",
-        "avi",
-        "webm",
-        "movie",
-        "clip",
-      ];
-      if (videoKeywords.some((kw) => lowerKey.includes(kw))) return "video";
-
-      const imageKeywords = [
-        "image",
-        "img",
-        "photo",
-        "picture",
-        "thumbnail",
-        "avatar",
-        "icon",
-        "screenshot",
-      ];
-      if (imageKeywords.some((kw) => lowerKey.includes(kw))) return "image";
-    }
-
-    // Default to image for backward compatibility
-    return "image";
   }
 
-  return "unknown";
+  // Check output key name for image-related hints
+  const imageKeywords = [
+    "image",
+    "img",
+    "photo",
+    "picture",
+    "thumbnail",
+    "avatar",
+    "icon",
+    "screenshot",
+  ];
+  if (outputKey) {
+    const lowerKey = outputKey.toLowerCase();
+    if (imageKeywords.some((kw) => lowerKey.includes(kw))) {
+      return true;
+    }
+  }
+
+  // Default to treating workspace refs as potential images
+  // since that's the most common case for generated content
+  return true;
 }
 
 /**
- * Format a single output value, converting workspace refs to markdown images/videos.
+ * Format a single output value, converting workspace refs to markdown images.
- * 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)) {
+    // Format as markdown image
+    return `![${outputKey || "Generated image"}](${value})`;
+  }
+
   if (typeof value === "string") {
-    const category = getMediaCategory(value, outputKey);
+    // Check for data URIs (images)
-
+    if (value.startsWith("data:image/")) {
-    if (category === "video") {
-      // Format with "video:" prefix so MarkdownContent renders <video>
-      return `![video:${outputKey || "Video"}](${value})`;
-    }
-
-    if (category === "image") {
       return `![${outputKey || "Generated image"}](${value})`;
     }
-
-    // For audio, unknown workspace refs, data URIs, etc. - return as-is
     return value;
   }
 

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

@@ -41,17 +41,7 @@ export function HostScopedCredentialsModal({
   const currentHost = currentUrl ? getHostFromUrl(currentUrl) : "";
 
   const formSchema = z.object({
-    host: z
+    host: z.string().min(1, "Host is required"),
-      .string()
-      .min(1, "Host is required")
-      .refine((val) => !/^[a-zA-Z][a-zA-Z\d+\-.]*:\/\//.test(val), {
-        message: "Enter only the host (e.g. api.example.com), not a full URL",
-      })
-      .refine((val) => !val.includes("/"), {
-        message:
-          "Enter only the host (e.g. api.example.com), without a trailing path. " +
-          "You may specify a port (e.g. api.example.com:8080) if needed.",
-      }),
     title: z.string().optional(),
     headers: z.record(z.string()).optional(),
   });

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

@@ -26,7 +26,6 @@ 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

@@ -4,9 +4,7 @@ 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) {
@@ -29,9 +27,7 @@ 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: "Agents", href: "/library" }] : []),
+    ...(isChatEnabled === true ? [{ name: "Tasks", href: "/library" }] : []),
     ...loggedInLinks,
   ];
 

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

@@ -362,14 +362,25 @@ 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">;
 
@@ -436,22 +447,11 @@ 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

@@ -62,6 +62,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Get Store Agent Details](block-integrations/system/store_operations.md#get-store-agent-details) | Get detailed information about an agent from the store |
 | [Get Weather Information](block-integrations/basic.md#get-weather-information) | Retrieves weather information for a specified location using OpenWeatherMap API |
 | [Human In The Loop](block-integrations/basic.md#human-in-the-loop) | Pause execution and wait for human approval or modification of data |
+| [Linear Search Issues](block-integrations/linear/issues.md#linear-search-issues) | Searches for issues on Linear |
 | [List Is Empty](block-integrations/basic.md#list-is-empty) | Checks if a list is empty |
 | [List Library Agents](block-integrations/system/library_operations.md#list-library-agents) | List all agents in your personal library |
 | [Note](block-integrations/basic.md#note) | A visual annotation block that displays a sticky note in the workflow editor for documentation and organization purposes |
@@ -192,7 +193,6 @@ 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 |
@@ -233,7 +233,6 @@ 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
 
@@ -473,13 +472,9 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 
 | Block Name | Description |
 |------------|-------------|
-| [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 |
+| [Add Audio To Video](block-integrations/multimedia.md#add-audio-to-video) | Block to attach an audio file to a video file using moviepy |
-| [Loop Video](block-integrations/video/loop.md#loop-video) | Block to loop a video to a given duration or number of repeats |
+| [Loop Video](block-integrations/multimedia.md#loop-video) | Block to loop a video to a given duration or number of repeats |
-| [Media Duration](block-integrations/video/duration.md#media-duration) | Block to get the duration of a media file |
+| [Media Duration](block-integrations/multimedia.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
 
@@ -576,7 +571,6 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Linear Create Comment](block-integrations/linear/comment.md#linear-create-comment) | Creates a new comment on a Linear issue |
 | [Linear Create Issue](block-integrations/linear/issues.md#linear-create-issue) | Creates a new issue on Linear |
 | [Linear Get Project Issues](block-integrations/linear/issues.md#linear-get-project-issues) | Gets issues from a Linear project filtered by status and assignee |
-| [Linear Search Issues](block-integrations/linear/issues.md#linear-search-issues) | Searches for issues on Linear |
 | [Linear Search Projects](block-integrations/linear/projects.md#linear-search-projects) | Searches for projects on Linear |
 
 ## Hardware

docs/integrations/SUMMARY.md

@@ -85,6 +85,7 @@
 * [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)
@@ -128,13 +129,5 @@
 * [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)

docs/integrations/block-integrations/linear/issues.md

@@ -90,9 +90,9 @@ Searches for issues on Linear
 
 ### How it works
 <!-- MANUAL: how_it_works -->
-This block searches for issues in Linear using a text query. It searches across issue titles, descriptions, and other fields to find matching issues. You can limit the number of results returned using the `max_results` parameter (default: 10, max: 100) to control token consumption and response size.
+This block searches for issues in Linear using a text query. It searches across issue titles, descriptions, and other fields to find matching issues.
 
-Optionally filter results by team name to narrow searches to specific workspaces. If a team name is provided, the block resolves it to a team ID before searching. Returns matching issues with their state, creation date, project, and assignee information. If the search or team resolution fails, an error message is returned.
+Returns a list of issues matching the search term.
 <!-- END MANUAL -->
 
 ### Inputs
@@ -100,14 +100,12 @@ Optionally filter results by team name to narrow searches to specific workspaces
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | term | Term to search for issues | str | Yes |
-| max_results | Maximum number of results to return | int | No |
-| team_name | Optional team name to filter results (e.g., 'Internal', 'Open Source') | str | No |
 
 ### Outputs
 
 | Output | Description | Type |
 |--------|-------------|------|
-| error | Error message if the search failed | str |
+| error | Error message if the operation failed | str |
 | issues | List of issues | List[Issue] |
 
 ### Possible use case

docs/integrations/block-integrations/llm.md

@@ -65,7 +65,7 @@ The result routes data to yes_output or no_output, enabling intelligent branchin
 | condition | A plaintext English description of the condition to evaluate | str | Yes |
 | yes_value | (Optional) Value to output if the condition is true. If not provided, input_value will be used. | Yes Value | No |
 | no_value | (Optional) Value to output if the condition is false. If not provided, input_value will be used. | No Value | No |
-| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-opus-4-6" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 
 ### Outputs
 
@@ -103,7 +103,7 @@ The block sends the entire conversation history to the chosen LLM, including sys
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. | str | No |
 | messages | List of messages in the conversation. | List[Any] | Yes |
-| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-opus-4-6" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
 | ollama_host | Ollama host for local  models | str | No |
 
@@ -257,7 +257,7 @@ The block formulates a prompt based on the given focus or source data, sends it
 |-------|-------------|------|----------|
 | focus | The focus of the list to generate. | str | No |
 | source_data | The data to generate the list from. | str | No |
-| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-opus-4-6" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | max_retries | Maximum number of retries for generating a valid list. | int | No |
 | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
@@ -424,7 +424,7 @@ The block sends the input prompt to a chosen LLM, along with any system prompts
 | prompt | The prompt to send to the language model. | str | Yes |
 | expected_format | Expected format of the response. If provided, the response will be validated against this format. The keys should be the expected fields in the response, and the values should be the description of the field. | Dict[str, str] | Yes |
 | list_result | Whether the response should be a list of objects in the expected format. | bool | No |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-opus-4-6" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No |
@@ -464,7 +464,7 @@ The block sends the input prompt to a chosen LLM, processes the response, and re
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. You can use any of the {keys} from Prompt Values to fill in the prompt with values from the prompt values dictionary by putting them in curly braces. | str | Yes |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-opus-4-6" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | retry | Number of times to retry the LLM call if the response does not match the expected format. | int | No |
 | prompt_values | Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}. | Dict[str, str] | No |
@@ -501,7 +501,7 @@ The block splits the input text into smaller chunks, sends each chunk to an LLM
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | text | The text to summarize. | str | Yes |
-| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-opus-4-6" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | focus | The topic to focus on in the summary | str | No |
 | style | The style of the summary to generate. | "concise" \| "detailed" \| "bullet points" \| "numbered list" | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
@@ -763,7 +763,7 @@ Configure agent_mode_max_iterations to control loop behavior: 0 for single decis
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. | str | Yes |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-opus-4-6" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | multiple_tool_calls | Whether to allow multiple tool calls in a single response. | bool | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No |

docs/integrations/block-integrations/text.md

@@ -380,42 +380,6 @@ This is useful when working with data from APIs or files where escape sequences
 
 ---
 
-## Text Encoder
-
-### What it is
-Encodes a string by converting special characters into escape sequences
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The Text Encoder takes the input string and applies Python's `unicode_escape` encoding (equivalent to `codecs.encode(text, "unicode_escape").decode("utf-8")`) to transform special characters like newlines, tabs, and backslashes into their escaped forms.
-
-The block relies on the input schema to ensure the value is a string; non-string inputs are rejected by validation, and any encoding failures surface as block errors. Non-ASCII characters are emitted as `\uXXXX` sequences, which is useful for ASCII-only payloads.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| text | A string containing special characters to be encoded | str | Yes |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if encoding fails | str |
-| encoded_text | The encoded text with special characters converted to escape sequences | str |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-**JSON Payload Preparation**: Encode multiline or quoted text before embedding it in JSON string fields to ensure proper escaping.
-
-**Config/ENV Generation**: Convert template text into escaped strings for `.env` or YAML values that require special character handling.
-
-**Snapshot Fixtures**: Produce stable escaped strings for golden files or API tests where consistent text representation is needed.
-<!-- END MANUAL -->
-
----
-
 ## Text Replace
 
 ### What it is

docs/integrations/block-integrations/video/add_audio.md

@@ -1,39 +0,0 @@
-# Video Add Audio
-<!-- MANUAL: file_description -->
-This block allows you to attach a separate audio track to a video file, replacing or combining with the original audio.
-<!-- END MANUAL -->
-
-## Add Audio To Video
-
-### What it is
-Block to attach an audio file to a video file using moviepy.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy to combine video and audio files. It loads the video and audio inputs (which can be URLs, data URIs, or local paths), optionally scales the audio volume, then writes the combined result to a new video file using H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Video input (URL, data URI, or local path). | str (file) | Yes |
-| audio_in | Audio input (URL, data URI, or local path). | str (file) | Yes |
-| volume | Volume scale for the newly attached audio track (1.0 = original). | float | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Final video (with attached audio), as a path or data URI. | str (file) |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Adding background music to a silent screen recording
-- Replacing original audio with a voiceover or translated audio track
-- Combining AI-generated speech with stock footage
-- Adding sound effects to video content
-<!-- END MANUAL -->
-
----