Merge branch 'dev' into ntindle/systemallquietalerts

Replaces all usages of mock_client.discord_system_alert with mock_client.system_alert in manager_low_balance_test.py and retry_test.py. Updates assertions and related logic to reflect the new alerting mechanism, including checks for AllQuiet correlation IDs where applicable.

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

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

.github/workflows/claude-ci-failure-auto-fix.yml

@@ -42,7 +42,7 @@ jobs:
 
       - name: Get CI failure details
         id: failure_details
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const run = await github.rest.actions.getWorkflowRun({

.github/workflows/claude-dependabot.yml

@@ -41,7 +41,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -78,7 +78,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22"
 
@@ -91,7 +91,7 @@ jobs:
           echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
 
       - name: Cache frontend dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -124,7 +124,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes
@@ -309,7 +309,6 @@ jobs:
         uses: anthropics/claude-code-action@v1
         with:
           claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          allowed_bots: "dependabot[bot]"
           claude_args: |
             --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
           prompt: |

.github/workflows/claude.yml

@@ -57,7 +57,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -94,7 +94,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22"
 
@@ -107,7 +107,7 @@ jobs:
           echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
 
       - name: Cache frontend dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -140,7 +140,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes

.github/workflows/copilot-setup-steps.yml

@@ -39,7 +39,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -76,7 +76,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22"
 
@@ -89,7 +89,7 @@ jobs:
           echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
 
       - name: Cache frontend dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -132,7 +132,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes

.github/workflows/docs-block-sync.yml

@@ -33,7 +33,7 @@ jobs:
           python-version: "3.11"
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

.github/workflows/docs-claude-review.yml

@@ -33,7 +33,7 @@ jobs:
           python-version: "3.11"
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

.github/workflows/docs-enhance.yml

@@ -38,7 +38,7 @@ jobs:
           python-version: "3.11"
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

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

@@ -88,7 +88,7 @@ jobs:
         run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

.github/workflows/platform-dev-deploy-event-dispatcher.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Check comment permissions and deployment status
         id: check_status
         if: github.event_name == 'issue_comment' && github.event.issue.pull_request
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const commentBody = context.payload.comment.body.trim();
@@ -55,7 +55,7 @@ jobs:
 
       - name: Post permission denied comment
         if: steps.check_status.outputs.permission_denied == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({
@@ -68,7 +68,7 @@ jobs:
       - name: Get PR details for deployment
         id: pr_details
         if: steps.check_status.outputs.should_deploy == 'true' || steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const pr = await github.rest.pulls.get({
@@ -98,7 +98,7 @@ jobs:
 
       - name: Post deploy success comment
         if: steps.check_status.outputs.should_deploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({
@@ -126,7 +126,7 @@ jobs:
 
       - name: Post undeploy success comment
         if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({
@@ -139,7 +139,7 @@ jobs:
       - name: Check deployment status on PR close
         id: check_pr_close
         if: github.event_name == 'pull_request' && github.event.action == 'closed'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const comments = await github.rest.issues.listComments({
@@ -187,7 +187,7 @@ jobs:
           github.event_name == 'pull_request' &&
           github.event.action == 'closed' &&
           steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             await github.rest.issues.createComment({

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

@@ -27,22 +27,13 @@ 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@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -54,7 +45,7 @@ jobs:
         run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
 
       - name: Cache dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ steps.cache-key.outputs.key }}
@@ -74,7 +65,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -82,7 +73,7 @@ jobs:
         run: corepack enable
 
       - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}
@@ -99,11 +90,8 @@ jobs:
   chromatic:
     runs-on: ubuntu-latest
     needs: setup
-    # Disabled: to re-enable, remove 'false &&' from the condition below
-    if: >-
-      false
-      && (github.ref == 'refs/heads/dev' || github.base_ref == 'dev')
-      && needs.setup.outputs.components-changed == 'true'
+    # Only run on dev branch pushes or PRs targeting dev
+    if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
 
     steps:
       - name: Checkout repository
@@ -112,7 +100,7 @@ jobs:
           fetch-depth: 0
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -120,7 +108,7 @@ jobs:
         run: corepack enable
 
       - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}
@@ -153,7 +141,7 @@ jobs:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -176,7 +164,7 @@ jobs:
         uses: docker/setup-buildx-action@v3
 
       - name: Cache Docker layers
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: /tmp/.buildx-cache
           key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
@@ -231,7 +219,7 @@ jobs:
           fi
 
       - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}
@@ -282,7 +270,7 @@ jobs:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -290,7 +278,7 @@ jobs:
         run: corepack enable
 
       - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}

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

@@ -32,7 +32,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -44,7 +44,7 @@ jobs:
         run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
 
       - name: Cache dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ steps.cache-key.outputs.key }}
@@ -68,7 +68,7 @@ jobs:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -88,7 +88,7 @@ jobs:
           docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
 
       - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}

autogpt_platform/autogpt_libs/pyproject.toml

@@ -9,25 +9,25 @@ packages = [{ include = "autogpt_libs" }]
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 colorama = "^0.4.6"
-cryptography = "^46.0"
+cryptography = "^45.0"
 expiringdict = "^1.2.2"
-fastapi = "^0.128.0"
-google-cloud-logging = "^3.13.0"
-launchdarkly-server-sdk = "^9.14.1"
-pydantic = "^2.12.5"
-pydantic-settings = "^2.12.0"
-pyjwt = { version = "^2.11.0", extras = ["crypto"] }
+fastapi = "^0.116.1"
+google-cloud-logging = "^3.12.1"
+launchdarkly-server-sdk = "^9.12.0"
+pydantic = "^2.11.7"
+pydantic-settings = "^2.10.1"
+pyjwt = { version = "^2.10.1", extras = ["crypto"] }
 redis = "^6.2.0"
-supabase = "^2.27.2"
-uvicorn = "^0.40.0"
+supabase = "^2.16.0"
+uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-pyright = "^1.1.408"
+pyright = "^1.1.404"
 pytest = "^8.4.1"
-pytest-asyncio = "^1.3.0"
-pytest-mock = "^3.15.1"
+pytest-asyncio = "^1.1.0"
+pytest-mock = "^3.14.1"
 pytest-cov = "^6.2.1"
-ruff = "^0.15.0"
+ruff = "^0.12.11"
 
 [build-system]
 requires = ["poetry-core"]

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,
-                config.langfuse_prompt_name,
-                label=label,
-                cache_ttl_seconds=0,
+                langfuse.get_prompt, config.langfuse_prompt_name, 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)
-    return await library_db.create_graph_in_library(graph, user_id)
+        if graph.id:
+            existing_versions = await get_graph_all_versions(graph.id, user_id)
+            if existing_versions:
+                latest_version = max(v.version for v in existing_versions)
+                graph.version = latest_version + 1
+                _reassign_node_ids(graph)
+                logger.info(f"Updating agent {graph.id} to version {graph.version}")
+    else:
+        graph.id = str(uuid.uuid4())
+        graph.version = 1
+        _reassign_node_ids(graph)
+        logger.info(f"Creating new agent with ID {graph.id}")
+
+    created_graph = await create_graph(graph, user_id)
+
+    library_agents = await library_db.create_library_agent(
+        graph=created_graph,
+        user_id=user_id,
+        sensitive_action_safe_mode=True,
+        create_library_agents_for_sub_graphs=False,
+    )
+
+    return created_graph, library_agents[0]
 
 
 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/find_block.py

@@ -13,32 +13,10 @@ from backend.api.features.chat.tools.models import (
     NoResultsResponse,
 )
 from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.data.block import BlockType, get_block
+from backend.data.block import get_block
 
 logger = logging.getLogger(__name__)
 
-_TARGET_RESULTS = 10
-# Over-fetch to compensate for post-hoc filtering of graph-only blocks.
-# 40 is 2x current removed; speed of query 10 vs 40 is minimial
-_OVERFETCH_PAGE_SIZE = 40
-
-# Block types that only work within graphs and cannot run standalone in CoPilot.
-COPILOT_EXCLUDED_BLOCK_TYPES = {
-    BlockType.INPUT,  # Graph interface definition - data enters via chat, not graph inputs
-    BlockType.OUTPUT,  # Graph interface definition - data exits via chat, not graph outputs
-    BlockType.WEBHOOK,  # Wait for external events - would hang forever in CoPilot
-    BlockType.WEBHOOK_MANUAL,  # Same as WEBHOOK
-    BlockType.NOTE,  # Visual annotation only - no runtime behavior
-    BlockType.HUMAN_IN_THE_LOOP,  # Pauses for human approval - CoPilot IS human-in-the-loop
-    BlockType.AGENT,  # AgentExecutorBlock requires execution_context - use run_agent tool
-}
-
-# Specific block IDs excluded from CoPilot (STANDARD type but still require graph context)
-COPILOT_EXCLUDED_BLOCK_IDS = {
-    # SmartDecisionMakerBlock - dynamically discovers downstream blocks via graph topology
-    "3b191d9f-356f-482d-8238-ba04b6d18381",
-}
-
 
 class FindBlockTool(BaseTool):
     """Tool for searching available blocks."""
@@ -110,7 +88,7 @@ class FindBlockTool(BaseTool):
                 query=query,
                 content_types=[ContentType.BLOCK],
                 page=1,
-                page_size=_OVERFETCH_PAGE_SIZE,
+                page_size=10,
             )
 
             if not results:
@@ -130,90 +108,60 @@ class FindBlockTool(BaseTool):
                 block = get_block(block_id)
 
                 # Skip disabled blocks
-                if not block or block.disabled:
-                    continue
+                if block and not block.disabled:
+                    # Get input/output schemas
+                    input_schema = {}
+                    output_schema = {}
+                    try:
+                        input_schema = block.input_schema.jsonschema()
+                    except Exception:
+                        pass
+                    try:
+                        output_schema = block.output_schema.jsonschema()
+                    except Exception:
+                        pass
 
-                # Skip blocks excluded from CoPilot (graph-only blocks)
-                if (
-                    block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
-                    or block.id in COPILOT_EXCLUDED_BLOCK_IDS
-                ):
-                    continue
+                    # Get categories from block instance
+                    categories = []
+                    if hasattr(block, "categories") and block.categories:
+                        categories = [cat.value for cat in block.categories]
 
-                # Get input/output schemas
-                input_schema = {}
-                output_schema = {}
-                try:
-                    input_schema = block.input_schema.jsonschema()
-                except Exception as e:
-                    logger.debug(
-                        "Failed to generate input schema for block %s: %s",
-                        block_id,
-                        e,
-                    )
-                try:
-                    output_schema = block.output_schema.jsonschema()
-                except Exception as e:
-                    logger.debug(
-                        "Failed to generate output schema for block %s: %s",
-                        block_id,
-                        e,
-                    )
-
-                # Get categories from block instance
-                categories = []
-                if hasattr(block, "categories") and block.categories:
-                    categories = [cat.value for cat in block.categories]
-
-                # Extract required inputs for easier use
-                required_inputs: list[BlockInputFieldInfo] = []
-                if input_schema:
-                    properties = input_schema.get("properties", {})
-                    required_fields = set(input_schema.get("required", []))
-                    # Get credential field names to exclude from required inputs
-                    credentials_fields = set(
-                        block.input_schema.get_credentials_fields().keys()
-                    )
-
-                    for field_name, field_schema in properties.items():
-                        # Skip credential fields - they're handled separately
-                        if field_name in credentials_fields:
-                            continue
-
-                        required_inputs.append(
-                            BlockInputFieldInfo(
-                                name=field_name,
-                                type=field_schema.get("type", "string"),
-                                description=field_schema.get("description", ""),
-                                required=field_name in required_fields,
-                                default=field_schema.get("default"),
-                            )
+                    # Extract required inputs for easier use
+                    required_inputs: list[BlockInputFieldInfo] = []
+                    if input_schema:
+                        properties = input_schema.get("properties", {})
+                        required_fields = set(input_schema.get("required", []))
+                        # Get credential field names to exclude from required inputs
+                        credentials_fields = set(
+                            block.input_schema.get_credentials_fields().keys()
                         )
 
-                blocks.append(
-                    BlockInfoSummary(
-                        id=block_id,
-                        name=block.name,
-                        description=block.description or "",
-                        categories=categories,
-                        input_schema=input_schema,
-                        output_schema=output_schema,
-                        required_inputs=required_inputs,
+                        for field_name, field_schema in properties.items():
+                            # Skip credential fields - they're handled separately
+                            if field_name in credentials_fields:
+                                continue
+
+                            required_inputs.append(
+                                BlockInputFieldInfo(
+                                    name=field_name,
+                                    type=field_schema.get("type", "string"),
+                                    description=field_schema.get("description", ""),
+                                    required=field_name in required_fields,
+                                    default=field_schema.get("default"),
+                                )
+                            )
+
+                    blocks.append(
+                        BlockInfoSummary(
+                            id=block_id,
+                            name=block.name,
+                            description=block.description or "",
+                            categories=categories,
+                            input_schema=input_schema,
+                            output_schema=output_schema,
+                            required_inputs=required_inputs,
+                        )
                     )
-                )
-
-                if len(blocks) >= _TARGET_RESULTS:
-                    break
-
-            if blocks and len(blocks) < _TARGET_RESULTS:
-                logger.debug(
-                    "find_block returned %d/%d results for query '%s' "
-                    "(filtered %d excluded/disabled blocks)",
-                    len(blocks),
-                    _TARGET_RESULTS,
-                    query,
-                    len(results) - len(blocks),
-                )
 
             if not blocks:
                 return NoResultsResponse(

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

@@ -1,139 +0,0 @@
-"""Tests for block filtering in FindBlockTool."""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from backend.api.features.chat.tools.find_block import (
-    COPILOT_EXCLUDED_BLOCK_IDS,
-    COPILOT_EXCLUDED_BLOCK_TYPES,
-    FindBlockTool,
-)
-from backend.api.features.chat.tools.models import BlockListResponse
-from backend.data.block import BlockType
-
-from ._test_data import make_session
-
-_TEST_USER_ID = "test-user-find-block"
-
-
-def make_mock_block(
-    block_id: str, name: str, block_type: BlockType, disabled: bool = False
-):
-    """Create a mock block for testing."""
-    mock = MagicMock()
-    mock.id = block_id
-    mock.name = name
-    mock.description = f"{name} description"
-    mock.block_type = block_type
-    mock.disabled = disabled
-    mock.input_schema = MagicMock()
-    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
-    mock.input_schema.get_credentials_fields.return_value = {}
-    mock.output_schema = MagicMock()
-    mock.output_schema.jsonschema.return_value = {}
-    mock.categories = []
-    return mock
-
-
-class TestFindBlockFiltering:
-    """Tests for block filtering in FindBlockTool."""
-
-    def test_excluded_block_types_contains_expected_types(self):
-        """Verify COPILOT_EXCLUDED_BLOCK_TYPES contains all graph-only types."""
-        assert BlockType.INPUT in COPILOT_EXCLUDED_BLOCK_TYPES
-        assert BlockType.OUTPUT in COPILOT_EXCLUDED_BLOCK_TYPES
-        assert BlockType.WEBHOOK in COPILOT_EXCLUDED_BLOCK_TYPES
-        assert BlockType.WEBHOOK_MANUAL in COPILOT_EXCLUDED_BLOCK_TYPES
-        assert BlockType.NOTE in COPILOT_EXCLUDED_BLOCK_TYPES
-        assert BlockType.HUMAN_IN_THE_LOOP in COPILOT_EXCLUDED_BLOCK_TYPES
-        assert BlockType.AGENT in COPILOT_EXCLUDED_BLOCK_TYPES
-
-    def test_excluded_block_ids_contains_smart_decision_maker(self):
-        """Verify SmartDecisionMakerBlock is in COPILOT_EXCLUDED_BLOCK_IDS."""
-        assert "3b191d9f-356f-482d-8238-ba04b6d18381" in COPILOT_EXCLUDED_BLOCK_IDS
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_excluded_block_type_filtered_from_results(self):
-        """Verify blocks with excluded BlockTypes are filtered from search results."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        # Mock search returns an INPUT block (excluded) and a STANDARD block (included)
-        search_results = [
-            {"content_id": "input-block-id", "score": 0.9},
-            {"content_id": "standard-block-id", "score": 0.8},
-        ]
-
-        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
-        standard_block = make_mock_block(
-            "standard-block-id", "HTTP Request", BlockType.STANDARD
-        )
-
-        def mock_get_block(block_id):
-            return {
-                "input-block-id": input_block,
-                "standard-block-id": standard_block,
-            }.get(block_id)
-
-        with patch(
-            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
-            new_callable=AsyncMock,
-            return_value=(search_results, 2),
-        ):
-            with patch(
-                "backend.api.features.chat.tools.find_block.get_block",
-                side_effect=mock_get_block,
-            ):
-                tool = FindBlockTool()
-                response = await tool._execute(
-                    user_id=_TEST_USER_ID, session=session, query="test"
-                )
-
-        # Should only return the standard block, not the INPUT block
-        assert isinstance(response, BlockListResponse)
-        assert len(response.blocks) == 1
-        assert response.blocks[0].id == "standard-block-id"
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_excluded_block_id_filtered_from_results(self):
-        """Verify SmartDecisionMakerBlock is filtered from search results."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
-        search_results = [
-            {"content_id": smart_decision_id, "score": 0.9},
-            {"content_id": "normal-block-id", "score": 0.8},
-        ]
-
-        # SmartDecisionMakerBlock has STANDARD type but is excluded by ID
-        smart_block = make_mock_block(
-            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
-        )
-        normal_block = make_mock_block(
-            "normal-block-id", "Normal Block", BlockType.STANDARD
-        )
-
-        def mock_get_block(block_id):
-            return {
-                smart_decision_id: smart_block,
-                "normal-block-id": normal_block,
-            }.get(block_id)
-
-        with patch(
-            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
-            new_callable=AsyncMock,
-            return_value=(search_results, 2),
-        ):
-            with patch(
-                "backend.api.features.chat.tools.find_block.get_block",
-                side_effect=mock_get_block,
-            ):
-                tool = FindBlockTool()
-                response = await tool._execute(
-                    user_id=_TEST_USER_ID, session=session, query="decision"
-                )
-
-        # Should only return normal block, not SmartDecisionMakerBlock
-        assert isinstance(response, BlockListResponse)
-        assert len(response.blocks) == 1
-        assert response.blocks[0].id == "normal-block-id"

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

@@ -8,10 +8,6 @@ from typing import Any
 from pydantic_core import PydanticUndefined
 
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.find_block import (
-    COPILOT_EXCLUDED_BLOCK_IDS,
-    COPILOT_EXCLUDED_BLOCK_TYPES,
-)
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsMetaInput
@@ -216,19 +212,6 @@ class RunBlockTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if block is excluded from CoPilot (graph-only blocks)
-        if (
-            block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
-            or block.id in COPILOT_EXCLUDED_BLOCK_IDS
-        ):
-            return ErrorResponse(
-                message=(
-                    f"Block '{block.name}' cannot be run directly in CoPilot. "
-                    "This block is designed for use within graphs only."
-                ),
-                session_id=session_id,
-            )
-
         logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
         creds_manager = IntegrationCredentialsManager()

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

@@ -1,106 +0,0 @@
-"""Tests for block execution guards in RunBlockTool."""
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from backend.api.features.chat.tools.models import ErrorResponse
-from backend.api.features.chat.tools.run_block import RunBlockTool
-from backend.data.block import BlockType
-
-from ._test_data import make_session
-
-_TEST_USER_ID = "test-user-run-block"
-
-
-def make_mock_block(
-    block_id: str, name: str, block_type: BlockType, disabled: bool = False
-):
-    """Create a mock block for testing."""
-    mock = MagicMock()
-    mock.id = block_id
-    mock.name = name
-    mock.block_type = block_type
-    mock.disabled = disabled
-    mock.input_schema = MagicMock()
-    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
-    mock.input_schema.get_credentials_fields_info.return_value = []
-    return mock
-
-
-class TestRunBlockFiltering:
-    """Tests for block execution guards in RunBlockTool."""
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_excluded_block_type_returns_error(self):
-        """Attempting to execute a block with excluded BlockType returns error."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
-
-        with patch(
-            "backend.api.features.chat.tools.run_block.get_block",
-            return_value=input_block,
-        ):
-            tool = RunBlockTool()
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="input-block-id",
-                input_data={},
-            )
-
-        assert isinstance(response, ErrorResponse)
-        assert "cannot be run directly in CoPilot" in response.message
-        assert "designed for use within graphs only" in response.message
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_excluded_block_id_returns_error(self):
-        """Attempting to execute SmartDecisionMakerBlock returns error."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
-        smart_block = make_mock_block(
-            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
-        )
-
-        with patch(
-            "backend.api.features.chat.tools.run_block.get_block",
-            return_value=smart_block,
-        ):
-            tool = RunBlockTool()
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id=smart_decision_id,
-                input_data={},
-            )
-
-        assert isinstance(response, ErrorResponse)
-        assert "cannot be run directly in CoPilot" in response.message
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_non_excluded_block_passes_guard(self):
-        """Non-excluded blocks pass the filtering guard (may fail later for other reasons)."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        standard_block = make_mock_block(
-            "standard-id", "HTTP Request", BlockType.STANDARD
-        )
-
-        with patch(
-            "backend.api.features.chat.tools.run_block.get_block",
-            return_value=standard_block,
-        ):
-            tool = RunBlockTool()
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="standard-id",
-                input_data={},
-            )
-
-        # Should NOT be an ErrorResponse about CoPilot exclusion
-        # (may be other errors like missing credentials, but not the exclusion guard)
-        if isinstance(response, ErrorResponse):
-            assert "cannot be run directly in CoPilot" not in response.message

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

@@ -6,6 +6,7 @@ from typing import Any
 from backend.api.features.library import db as library_db
 from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
+from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
     CredentialsFieldInfo,
@@ -43,8 +44,14 @@ async def fetch_graph_from_store_slug(
         return None, None
 
     # Get the graph from store listing version
-    graph = await store_db.get_available_graph(
-        store_agent.store_listing_version_id, hide_nodes=False
+    graph_meta = await store_db.get_available_graph(
+        store_agent.store_listing_version_id
+    )
+    graph = await graph_db.get_graph(
+        graph_id=graph_meta.id,
+        version=graph_meta.version,
+        user_id=None,  # Public access
+        include_subgraphs=True,
     )
     return graph, store_agent
 
@@ -121,7 +128,7 @@ def build_missing_credentials_from_graph(
 
     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
     }
 
@@ -262,8 +269,7 @@ 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(

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 (
-    on_graph_activate,
-    on_graph_deactivate,
-)
+from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
 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,

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, 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:
+async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
     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(
-            store_listing_version.AgentGraph
-        )
+        return GraphModel.from_db(store_listing_version.AgentGraph).meta()
 
     except Exception as e:
         logger.error(f"Error getting agent: {e}")

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

@@ -8,7 +8,6 @@ Includes BM25 reranking for improved lexical relevance.
 
 import logging
 import re
-import time
 from dataclasses import dataclass
 from typing import Any, Literal
 
@@ -363,11 +362,7 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    try:
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -691,11 +686,7 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    try:
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
 
@@ -727,87 +718,6 @@ async def hybrid_search_simple(
     return await hybrid_search(query=query, page=page, page_size=page_size)
 
 
-# ============================================================================
-# Diagnostics
-# ============================================================================
-
-# Rate limit: only log vector error diagnostics once per this interval
-_VECTOR_DIAG_INTERVAL_SECONDS = 60
-_last_vector_diag_time: float = 0
-
-
-async def _log_vector_error_diagnostics(error: Exception) -> None:
-    """Log diagnostic info when 'type vector does not exist' error occurs.
-
-    Note: Diagnostic queries use query_raw_with_schema which may run on a different
-    pooled connection than the one that failed. Session-level search_path can differ,
-    so these diagnostics show cluster-wide state, not necessarily the failed session.
-
-    Includes rate limiting to avoid log spam - only logs once per minute.
-    Caller should re-raise the error after calling this function.
-    """
-    global _last_vector_diag_time
-
-    # Check if this is the vector type error
-    error_str = str(error).lower()
-    if not (
-        "type" in error_str and "vector" in error_str and "does not exist" in error_str
-    ):
-        return
-
-    # Rate limit: only log once per interval
-    now = time.time()
-    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
-        return
-    _last_vector_diag_time = now
-
-    try:
-        diagnostics: dict[str, object] = {}
-
-        try:
-            search_path_result = await query_raw_with_schema("SHOW search_path")
-            diagnostics["search_path"] = search_path_result
-        except Exception as e:
-            diagnostics["search_path"] = f"Error: {e}"
-
-        try:
-            schema_result = await query_raw_with_schema("SELECT current_schema()")
-            diagnostics["current_schema"] = schema_result
-        except Exception as e:
-            diagnostics["current_schema"] = f"Error: {e}"
-
-        try:
-            user_result = await query_raw_with_schema(
-                "SELECT current_user, session_user, current_database()"
-            )
-            diagnostics["user_info"] = user_result
-        except Exception as e:
-            diagnostics["user_info"] = f"Error: {e}"
-
-        try:
-            # Check pgvector extension installation (cluster-wide, stable info)
-            ext_result = await query_raw_with_schema(
-                "SELECT extname, extversion, nspname as schema "
-                "FROM pg_extension e "
-                "JOIN pg_namespace n ON e.extnamespace = n.oid "
-                "WHERE extname = 'vector'"
-            )
-            diagnostics["pgvector_extension"] = ext_result
-        except Exception as e:
-            diagnostics["pgvector_extension"] = f"Error: {e}"
-
-        logger.error(
-            f"Vector type error diagnostics:\n"
-            f"  Error: {error}\n"
-            f"  search_path: {diagnostics.get('search_path')}\n"
-            f"  current_schema: {diagnostics.get('current_schema')}\n"
-            f"  user_info: {diagnostics.get('user_info')}\n"
-            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
-        )
-    except Exception as diag_error:
-        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
-
-
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights

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'prominently featuring "{name}" in bold typography. The image clearly and '
-        f"literally depicts a {description}, along with recognizable objects directly "
-        f"associated with the primary function of a {name}. "
-        f"Ensure the imagery is concrete, intuitive, and immediately understandable, "
-        f"clearly conveying the purpose of a {name}. "
-        "Maintain vibrant, limited-palette colors, sharp vector lines, "
-        "geometric shapes, flat illustration techniques, and solid colors "
-        "without gradients or shading. Preserve a retro-futuristic aesthetic "
-        "influenced by mid-century futurism and 1960s psychedelia, "
-        "prioritizing clear visual storytelling and thematic clarity above all else."
+        f"Create a visually striking retro-futuristic vector pop art illustration prominently featuring "
+        f'"{name}" in bold typography. The image clearly and literally depicts a {description}, '
+        f"along with recognizable objects directly associated with the primary function of a {name}. "
+        f"Ensure the imagery is concrete, intuitive, and immediately understandable, clearly conveying the "
+        f"purpose of a {name}. Maintain vibrant, limited-palette colors, sharp vector lines, geometric "
+        f"shapes, flat illustration techniques, and solid colors without gradients or shading. Preserve a "
+        f"retro-futuristic aesthetic influenced by mid-century futurism and 1960s psychedelia, "
+        f"prioritizing clear visual storytelling and thematic clarity above all else."
     )
 
     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 = (
-            "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."
-        )
+        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."
 
         # 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(
-            user_id, new_graph_version
-        )
+        # Keep the library agent up to date with the new active version
+        await _update_library_agent_version_and_settings(user_id, new_graph_version)
+
+        # Handle activation of the new graph first to ensure continuity
         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(
-        user_id, new_active_graph
-    )
+    await _update_library_agent_version_and_settings(user_id, new_active_graph)
 
     if current_active_graph and current_active_graph.version != new_active_version:
         # Handle deactivation of the previously active version
         await on_graph_deactivate(current_active_graph, user_id=user_id)
 
 
+async def _update_library_agent_version_and_settings(
+    user_id: str, agent_graph: graph_db.GraphModel
+) -> library_model.LibraryAgent:
+    library = await library_db.update_agent_version_in_library(
+        user_id, agent_graph.id, agent_graph.version
+    )
+    updated_settings = GraphSettings.from_graph(
+        graph=agent_graph,
+        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
+        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
+    )
+    if updated_settings != library.settings:
+        library = await library_db.update_library_agent(
+            library_agent_id=library.id,
+            user_id=user_id,
+            settings=updated_settings,
+        )
+    return library
+
+
 @v1_router.patch(
     path="/graphs/{graph_id}/settings",
     summary="Update graph settings",

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

@@ -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/exa/websets.py

@@ -478,7 +478,7 @@ class ExaCreateOrFindWebsetBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         try:
-            webset = await aexa.websets.get(id=input_data.external_id)
+            webset = aexa.websets.get(id=input_data.external_id)
             webset_result = Webset.model_validate(webset.model_dump(by_alias=True))
 
             yield "webset", webset_result
@@ -494,7 +494,7 @@ class ExaCreateOrFindWebsetBlock(Block):
                         count=input_data.search_count,
                     )
 
-                webset = await aexa.websets.create(
+                webset = aexa.websets.create(
                     params=CreateWebsetParameters(
                         search=search_params,
                         external_id=input_data.external_id,
@@ -554,7 +554,7 @@ class ExaUpdateWebsetBlock(Block):
         if input_data.metadata is not None:
             payload["metadata"] = input_data.metadata
 
-        sdk_webset = await aexa.websets.update(id=input_data.webset_id, params=payload)
+        sdk_webset = aexa.websets.update(id=input_data.webset_id, params=payload)
 
         status_str = (
             sdk_webset.status.value
@@ -617,7 +617,7 @@ class ExaListWebsetsBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        response = await aexa.websets.list(
+        response = aexa.websets.list(
             cursor=input_data.cursor,
             limit=input_data.limit,
         )
@@ -678,7 +678,7 @@ class ExaGetWebsetBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_webset = await aexa.websets.get(id=input_data.webset_id)
+        sdk_webset = aexa.websets.get(id=input_data.webset_id)
 
         status_str = (
             sdk_webset.status.value
@@ -748,7 +748,7 @@ class ExaDeleteWebsetBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_webset = await aexa.websets.delete(id=input_data.webset_id)
+        deleted_webset = aexa.websets.delete(id=input_data.webset_id)
 
         status_str = (
             deleted_webset.status.value
@@ -798,7 +798,7 @@ class ExaCancelWebsetBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        canceled_webset = await aexa.websets.cancel(id=input_data.webset_id)
+        canceled_webset = aexa.websets.cancel(id=input_data.webset_id)
 
         status_str = (
             canceled_webset.status.value
@@ -968,7 +968,7 @@ class ExaPreviewWebsetBlock(Block):
                 entity["description"] = input_data.entity_description
             payload["entity"] = entity
 
-        sdk_preview = await aexa.websets.preview(params=payload)
+        sdk_preview = aexa.websets.preview(params=payload)
 
         preview = PreviewWebsetModel.from_sdk(sdk_preview)
 
@@ -1051,7 +1051,7 @@ class ExaWebsetStatusBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        webset = await aexa.websets.get(id=input_data.webset_id)
+        webset = aexa.websets.get(id=input_data.webset_id)
 
         status = (
             webset.status.value
@@ -1185,7 +1185,7 @@ class ExaWebsetSummaryBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        webset = await aexa.websets.get(id=input_data.webset_id)
+        webset = aexa.websets.get(id=input_data.webset_id)
 
         # Extract basic info
         webset_id = webset.id
@@ -1211,7 +1211,7 @@ class ExaWebsetSummaryBlock(Block):
         total_items = 0
 
         if input_data.include_sample_items and input_data.sample_size > 0:
-            items_response = await aexa.websets.items.list(
+            items_response = aexa.websets.items.list(
                 webset_id=input_data.webset_id, limit=input_data.sample_size
             )
             sample_items_data = [
@@ -1362,7 +1362,7 @@ class ExaWebsetReadyCheckBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         # Get webset details
-        webset = await aexa.websets.get(id=input_data.webset_id)
+        webset = aexa.websets.get(id=input_data.webset_id)
 
         status = (
             webset.status.value

autogpt_platform/backend/backend/blocks/exa/websets_enrichment.py

@@ -202,7 +202,7 @@ class ExaCreateEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_enrichment = await aexa.websets.enrichments.create(
+        sdk_enrichment = aexa.websets.enrichments.create(
             webset_id=input_data.webset_id, params=payload
         )
 
@@ -223,7 +223,7 @@ class ExaCreateEnrichmentBlock(Block):
             items_enriched = 0
 
             while time.time() - poll_start < input_data.polling_timeout:
-                current_enrich = await aexa.websets.enrichments.get(
+                current_enrich = aexa.websets.enrichments.get(
                     webset_id=input_data.webset_id, id=enrichment_id
                 )
                 current_status = (
@@ -234,7 +234,7 @@ class ExaCreateEnrichmentBlock(Block):
 
                 if current_status in ["completed", "failed", "cancelled"]:
                     # Estimate items from webset searches
-                    webset = await aexa.websets.get(id=input_data.webset_id)
+                    webset = aexa.websets.get(id=input_data.webset_id)
                     if webset.searches:
                         for search in webset.searches:
                             if search.progress:
@@ -329,7 +329,7 @@ class ExaGetEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_enrichment = await aexa.websets.enrichments.get(
+        sdk_enrichment = aexa.websets.enrichments.get(
             webset_id=input_data.webset_id, id=input_data.enrichment_id
         )
 
@@ -474,7 +474,7 @@ class ExaDeleteEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_enrichment = await aexa.websets.enrichments.delete(
+        deleted_enrichment = aexa.websets.enrichments.delete(
             webset_id=input_data.webset_id, id=input_data.enrichment_id
         )
 
@@ -525,13 +525,13 @@ class ExaCancelEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        canceled_enrichment = await aexa.websets.enrichments.cancel(
+        canceled_enrichment = aexa.websets.enrichments.cancel(
             webset_id=input_data.webset_id, id=input_data.enrichment_id
         )
 
         # Try to estimate how many items were enriched before cancellation
         items_enriched = 0
-        items_response = await aexa.websets.items.list(
+        items_response = aexa.websets.items.list(
             webset_id=input_data.webset_id, limit=100
         )
 

autogpt_platform/backend/backend/blocks/exa/websets_import_export.py

@@ -222,7 +222,7 @@ class ExaCreateImportBlock(Block):
     def _create_test_mock():
         """Create test mocks for the AsyncExa SDK."""
         from datetime import datetime
-        from unittest.mock import AsyncMock, MagicMock
+        from unittest.mock import MagicMock
 
         # Create mock SDK import object
         mock_import = MagicMock()
@@ -247,7 +247,7 @@ class ExaCreateImportBlock(Block):
         return {
             "_get_client": lambda *args, **kwargs: MagicMock(
                 websets=MagicMock(
-                    imports=MagicMock(create=AsyncMock(return_value=mock_import))
+                    imports=MagicMock(create=lambda *args, **kwargs: mock_import)
                 )
             )
         }
@@ -294,7 +294,7 @@ class ExaCreateImportBlock(Block):
         if input_data.metadata:
             payload["metadata"] = input_data.metadata
 
-        sdk_import = await aexa.websets.imports.create(
+        sdk_import = aexa.websets.imports.create(
             params=payload, csv_data=input_data.csv_data
         )
 
@@ -360,7 +360,7 @@ class ExaGetImportBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_import = await aexa.websets.imports.get(import_id=input_data.import_id)
+        sdk_import = aexa.websets.imports.get(import_id=input_data.import_id)
 
         import_obj = ImportModel.from_sdk(sdk_import)
 
@@ -426,7 +426,7 @@ class ExaListImportsBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        response = await aexa.websets.imports.list(
+        response = aexa.websets.imports.list(
             cursor=input_data.cursor,
             limit=input_data.limit,
         )
@@ -474,9 +474,7 @@ class ExaDeleteImportBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_import = await aexa.websets.imports.delete(
-            import_id=input_data.import_id
-        )
+        deleted_import = aexa.websets.imports.delete(import_id=input_data.import_id)
 
         yield "import_id", deleted_import.id
         yield "success", "true"
@@ -575,14 +573,14 @@ class ExaExportWebsetBlock(Block):
             }
         )
 
-        # Create async iterator for list_all
-        async def async_item_iterator(*args, **kwargs):
-            for item in [mock_item1, mock_item2]:
-                yield item
+        # Create mock iterator
+        mock_items = [mock_item1, mock_item2]
 
         return {
             "_get_client": lambda *args, **kwargs: MagicMock(
-                websets=MagicMock(items=MagicMock(list_all=async_item_iterator))
+                websets=MagicMock(
+                    items=MagicMock(list_all=lambda *args, **kwargs: iter(mock_items))
+                )
             )
         }
 
@@ -604,7 +602,7 @@ class ExaExportWebsetBlock(Block):
                 webset_id=input_data.webset_id, limit=input_data.max_items
             )
 
-            async for sdk_item in item_iterator:
+            for sdk_item in item_iterator:
                 if len(all_items) >= input_data.max_items:
                     break
 

autogpt_platform/backend/backend/blocks/exa/websets_items.py

@@ -178,7 +178,7 @@ class ExaGetWebsetItemBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_item = await aexa.websets.items.get(
+        sdk_item = aexa.websets.items.get(
             webset_id=input_data.webset_id, id=input_data.item_id
         )
 
@@ -269,7 +269,7 @@ class ExaListWebsetItemsBlock(Block):
             response = None
 
             while time.time() - start_time < input_data.wait_timeout:
-                response = await aexa.websets.items.list(
+                response = aexa.websets.items.list(
                     webset_id=input_data.webset_id,
                     cursor=input_data.cursor,
                     limit=input_data.limit,
@@ -282,13 +282,13 @@ class ExaListWebsetItemsBlock(Block):
                 interval = min(interval * 1.2, 10)
 
             if not response:
-                response = await aexa.websets.items.list(
+                response = aexa.websets.items.list(
                     webset_id=input_data.webset_id,
                     cursor=input_data.cursor,
                     limit=input_data.limit,
                 )
         else:
-            response = await aexa.websets.items.list(
+            response = aexa.websets.items.list(
                 webset_id=input_data.webset_id,
                 cursor=input_data.cursor,
                 limit=input_data.limit,
@@ -340,7 +340,7 @@ class ExaDeleteWebsetItemBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_item = await aexa.websets.items.delete(
+        deleted_item = aexa.websets.items.delete(
             webset_id=input_data.webset_id, id=input_data.item_id
         )
 
@@ -408,7 +408,7 @@ class ExaBulkWebsetItemsBlock(Block):
             webset_id=input_data.webset_id, limit=input_data.max_items
         )
 
-        async for sdk_item in item_iterator:
+        for sdk_item in item_iterator:
             if len(all_items) >= input_data.max_items:
                 break
 
@@ -475,7 +475,7 @@ class ExaWebsetItemsSummaryBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        webset = await aexa.websets.get(id=input_data.webset_id)
+        webset = aexa.websets.get(id=input_data.webset_id)
 
         entity_type = "unknown"
         if webset.searches:
@@ -495,7 +495,7 @@ class ExaWebsetItemsSummaryBlock(Block):
         # Get sample items if requested
         sample_items: List[WebsetItemModel] = []
         if input_data.sample_size > 0:
-            items_response = await aexa.websets.items.list(
+            items_response = aexa.websets.items.list(
                 webset_id=input_data.webset_id, limit=input_data.sample_size
             )
             # Convert to our stable models
@@ -569,7 +569,7 @@ class ExaGetNewItemsBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         # Get items starting from cursor
-        response = await aexa.websets.items.list(
+        response = aexa.websets.items.list(
             webset_id=input_data.webset_id,
             cursor=input_data.since_cursor,
             limit=input_data.max_items,

autogpt_platform/backend/backend/blocks/exa/websets_monitor.py

@@ -233,7 +233,7 @@ class ExaCreateMonitorBlock(Block):
     def _create_test_mock():
         """Create test mocks for the AsyncExa SDK."""
         from datetime import datetime
-        from unittest.mock import AsyncMock, MagicMock
+        from unittest.mock import MagicMock
 
         # Create mock SDK monitor object
         mock_monitor = MagicMock()
@@ -263,7 +263,7 @@ class ExaCreateMonitorBlock(Block):
         return {
             "_get_client": lambda *args, **kwargs: MagicMock(
                 websets=MagicMock(
-                    monitors=MagicMock(create=AsyncMock(return_value=mock_monitor))
+                    monitors=MagicMock(create=lambda *args, **kwargs: mock_monitor)
                 )
             )
         }
@@ -320,7 +320,7 @@ class ExaCreateMonitorBlock(Block):
         if input_data.metadata:
             payload["metadata"] = input_data.metadata
 
-        sdk_monitor = await aexa.websets.monitors.create(params=payload)
+        sdk_monitor = aexa.websets.monitors.create(params=payload)
 
         monitor = MonitorModel.from_sdk(sdk_monitor)
 
@@ -384,7 +384,7 @@ class ExaGetMonitorBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_monitor = await aexa.websets.monitors.get(monitor_id=input_data.monitor_id)
+        sdk_monitor = aexa.websets.monitors.get(monitor_id=input_data.monitor_id)
 
         monitor = MonitorModel.from_sdk(sdk_monitor)
 
@@ -476,7 +476,7 @@ class ExaUpdateMonitorBlock(Block):
         if input_data.metadata is not None:
             payload["metadata"] = input_data.metadata
 
-        sdk_monitor = await aexa.websets.monitors.update(
+        sdk_monitor = aexa.websets.monitors.update(
             monitor_id=input_data.monitor_id, params=payload
         )
 
@@ -522,9 +522,7 @@ class ExaDeleteMonitorBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_monitor = await aexa.websets.monitors.delete(
-            monitor_id=input_data.monitor_id
-        )
+        deleted_monitor = aexa.websets.monitors.delete(monitor_id=input_data.monitor_id)
 
         yield "monitor_id", deleted_monitor.id
         yield "success", "true"
@@ -581,7 +579,7 @@ class ExaListMonitorsBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        response = await aexa.websets.monitors.list(
+        response = aexa.websets.monitors.list(
             cursor=input_data.cursor,
             limit=input_data.limit,
             webset_id=input_data.webset_id,

autogpt_platform/backend/backend/blocks/exa/websets_polling.py

@@ -121,7 +121,7 @@ class ExaWaitForWebsetBlock(Block):
                 WebsetTargetStatus.IDLE,
                 WebsetTargetStatus.ANY_COMPLETE,
             ]:
-                final_webset = await aexa.websets.wait_until_idle(
+                final_webset = aexa.websets.wait_until_idle(
                     id=input_data.webset_id,
                     timeout=input_data.timeout,
                     poll_interval=input_data.check_interval,
@@ -164,7 +164,7 @@ class ExaWaitForWebsetBlock(Block):
                 interval = input_data.check_interval
                 while time.time() - start_time < input_data.timeout:
                     # Get current webset status
-                    webset = await aexa.websets.get(id=input_data.webset_id)
+                    webset = aexa.websets.get(id=input_data.webset_id)
                     current_status = (
                         webset.status.value
                         if hasattr(webset.status, "value")
@@ -209,7 +209,7 @@ class ExaWaitForWebsetBlock(Block):
 
                 # Timeout reached
                 elapsed = time.time() - start_time
-                webset = await aexa.websets.get(id=input_data.webset_id)
+                webset = aexa.websets.get(id=input_data.webset_id)
                 final_status = (
                     webset.status.value
                     if hasattr(webset.status, "value")
@@ -345,7 +345,7 @@ class ExaWaitForSearchBlock(Block):
         try:
             while time.time() - start_time < input_data.timeout:
                 # Get current search status using SDK
-                search = await aexa.websets.searches.get(
+                search = aexa.websets.searches.get(
                     webset_id=input_data.webset_id, id=input_data.search_id
                 )
 
@@ -401,7 +401,7 @@ class ExaWaitForSearchBlock(Block):
             elapsed = time.time() - start_time
 
             # Get last known status
-            search = await aexa.websets.searches.get(
+            search = aexa.websets.searches.get(
                 webset_id=input_data.webset_id, id=input_data.search_id
             )
             final_status = (
@@ -503,7 +503,7 @@ class ExaWaitForEnrichmentBlock(Block):
         try:
             while time.time() - start_time < input_data.timeout:
                 # Get current enrichment status using SDK
-                enrichment = await aexa.websets.enrichments.get(
+                enrichment = aexa.websets.enrichments.get(
                     webset_id=input_data.webset_id, id=input_data.enrichment_id
                 )
 
@@ -548,7 +548,7 @@ class ExaWaitForEnrichmentBlock(Block):
             elapsed = time.time() - start_time
 
             # Get last known status
-            enrichment = await aexa.websets.enrichments.get(
+            enrichment = aexa.websets.enrichments.get(
                 webset_id=input_data.webset_id, id=input_data.enrichment_id
             )
             final_status = (
@@ -575,7 +575,7 @@ class ExaWaitForEnrichmentBlock(Block):
     ) -> tuple[list[SampleEnrichmentModel], int]:
         """Get sample enriched data and count."""
         # Get a few items to see enrichment results using SDK
-        response = await aexa.websets.items.list(webset_id=webset_id, limit=5)
+        response = aexa.websets.items.list(webset_id=webset_id, limit=5)
 
         sample_data: list[SampleEnrichmentModel] = []
         enriched_count = 0

autogpt_platform/backend/backend/blocks/exa/websets_search.py

@@ -317,7 +317,7 @@ class ExaCreateWebsetSearchBlock(Block):
 
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_search = await aexa.websets.searches.create(
+        sdk_search = aexa.websets.searches.create(
             webset_id=input_data.webset_id, params=payload
         )
 
@@ -350,7 +350,7 @@ class ExaCreateWebsetSearchBlock(Block):
             poll_start = time.time()
 
             while time.time() - poll_start < input_data.polling_timeout:
-                current_search = await aexa.websets.searches.get(
+                current_search = aexa.websets.searches.get(
                     webset_id=input_data.webset_id, id=search_id
                 )
                 current_status = (
@@ -442,7 +442,7 @@ class ExaGetWebsetSearchBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_search = await aexa.websets.searches.get(
+        sdk_search = aexa.websets.searches.get(
             webset_id=input_data.webset_id, id=input_data.search_id
         )
 
@@ -523,7 +523,7 @@ class ExaCancelWebsetSearchBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        canceled_search = await aexa.websets.searches.cancel(
+        canceled_search = aexa.websets.searches.cancel(
             webset_id=input_data.webset_id, id=input_data.search_id
         )
 
@@ -604,7 +604,7 @@ class ExaFindOrCreateSearchBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         # Get webset to check existing searches
-        webset = await aexa.websets.get(id=input_data.webset_id)
+        webset = aexa.websets.get(id=input_data.webset_id)
 
         # Look for existing search with same query
         existing_search = None
@@ -636,7 +636,7 @@ class ExaFindOrCreateSearchBlock(Block):
             if input_data.entity_type != SearchEntityType.AUTO:
                 payload["entity"] = {"type": input_data.entity_type.value}
 
-            sdk_search = await aexa.websets.searches.create(
+            sdk_search = aexa.websets.searches.create(
                 webset_id=input_data.webset_id, params=payload
             )
 

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
@@ -531,12 +527,12 @@ class LLMResponse(BaseModel):
 
 def convert_openai_tool_fmt_to_anthropic(
     openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | anthropic.Omit:
+) -> Iterable[ToolParam] | anthropic.NotGiven:
     """
     Convert OpenAI tool format to Anthropic tool format.
     """
     if not openai_tools or len(openai_tools) == 0:
-        return anthropic.omit
+        return anthropic.NOT_GIVEN
 
     anthropic_tools = []
     for tool in openai_tools:
@@ -596,10 +592,10 @@ def extract_openai_tool_calls(response) -> list[ToolContentBlock] | None:
 
 def get_parallel_tool_calls_param(
     llm_model: LlmModel, parallel_tool_calls: bool | None
-) -> bool | openai.Omit:
+):
     """Get the appropriate parallel_tool_calls parameter for OpenAI-compatible APIs."""
     if llm_model.startswith("o") or parallel_tool_calls is None:
-        return openai.omit
+        return openai.NOT_GIVEN
     return parallel_tool_calls
 
 

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)
-            transcript = self.get_transcript(video_id, credentials)
-            transcript_text = self.format_transcript(transcript=transcript)
+        video_id = self.extract_video_id(input_data.youtube_url)
+        yield "video_id", video_id
 
-            # Only yield after all operations succeed
-            yield "video_id", video_id
-            yield "transcript", transcript_text
-        except Exception as e:
-            yield "error", str(e)
+        transcript = self.get_transcript(video_id, credentials)
+        transcript_text = self.format_transcript(transcript=transcript)
+
+        yield "transcript", transcript_text

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(
-                    cls.get_field_schema(field_name), field_name
-                )
+                credentials_fields[field_name].validate_credentials_field_schema(cls)
 
             elif field_name in credentials_fields:
                 raise KeyError(

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/execution.py

@@ -1,8 +1,9 @@
 import logging
-import queue
 from collections import defaultdict
 from datetime import datetime, timedelta, timezone
 from enum import Enum
+from multiprocessing import Manager
+from queue import Empty
 from typing import (
     TYPE_CHECKING,
     Annotated,
@@ -1199,16 +1200,12 @@ class NodeExecutionEntry(BaseModel):
 
 class ExecutionQueue(Generic[T]):
     """
-    Thread-safe queue for managing node execution within a single graph execution.
-
-    Note: Uses queue.Queue (not multiprocessing.Queue) since all access is from
-    threads within the same process. If migrating back to ProcessPoolExecutor,
-    replace with multiprocessing.Manager().Queue() for cross-process safety.
+    Queue for managing the execution of agents.
+    This will be shared between different processes
     """
 
     def __init__(self):
-        # Thread-safe queue (not multiprocessing) — see class docstring
-        self.queue: queue.Queue[T] = queue.Queue()
+        self.queue = Manager().Queue()
 
     def add(self, execution: T) -> T:
         self.queue.put(execution)
@@ -1223,7 +1220,7 @@ class ExecutionQueue(Generic[T]):
     def get_or_none(self) -> T | None:
         try:
             return self.queue.get_nowait()
-        except queue.Empty:
+        except Empty:
             return None
 
 

autogpt_platform/backend/backend/data/execution_queue_test.py

@@ -1,58 +0,0 @@
-"""Tests for ExecutionQueue thread-safety."""
-
-import queue
-import threading
-
-from backend.data.execution import ExecutionQueue
-
-
-def test_execution_queue_uses_stdlib_queue():
-    """Verify ExecutionQueue uses queue.Queue (not multiprocessing)."""
-    q = ExecutionQueue()
-    assert isinstance(q.queue, queue.Queue)
-
-
-def test_basic_operations():
-    """Test add, get, empty, and get_or_none."""
-    q = ExecutionQueue()
-
-    assert q.empty() is True
-    assert q.get_or_none() is None
-
-    result = q.add("item1")
-    assert result == "item1"
-    assert q.empty() is False
-
-    item = q.get()
-    assert item == "item1"
-    assert q.empty() is True
-
-
-def test_thread_safety():
-    """Test concurrent access from multiple threads."""
-    q = ExecutionQueue()
-    results = []
-    num_items = 100
-
-    def producer():
-        for i in range(num_items):
-            q.add(f"item_{i}")
-
-    def consumer():
-        count = 0
-        while count < num_items:
-            item = q.get_or_none()
-            if item is not None:
-                results.append(item)
-                count += 1
-
-    producer_thread = threading.Thread(target=producer)
-    consumer_thread = threading.Thread(target=consumer)
-
-    producer_thread.start()
-    consumer_thread.start()
-
-    producer_thread.join(timeout=5)
-    consumer_thread.join(timeout=5)
-
-    assert len(results) == num_items

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]
-        default_factory=dict
-    )
-    metadata: dict[str, Any] = Field(default_factory=dict)
-    input_links: list[Link] = Field(default_factory=list)
-    output_links: list[Link] = Field(default_factory=list)
+    input_default: BlockInput = {}  # dict[input_name, default_value]
+    metadata: dict[str, Any] = {}
+    input_links: list[Link] = []
+    output_links: list[Link] = []
 
     @property
     def credentials_optional(self) -> bool:
@@ -221,33 +221,18 @@ class NodeModel(Node):
         return result
 
 
-class GraphBaseMeta(BaseDbModel):
-    """
-    Shared base for `GraphMeta` and `BaseGraph`, with core graph metadata fields.
-    """
-
+class BaseGraph(BaseDbModel):
     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]:
@@ -376,79 +361,44 @@ class GraphTriggerInfo(BaseModel):
 
 
 class Graph(BaseGraph):
-    """Creatable graph model used in API create/update endpoints."""
-
-    sub_graphs: list[BaseGraph] = Field(default_factory=list)  # Flattened sub-graphs
-
-
-class GraphMeta(GraphBaseMeta):
-    """
-    Lightweight graph metadata model representing an existing graph from the database,
-    for use in listings and summaries.
-
-    Lacks `GraphModel`'s nodes, links, and expensive computed fields.
-    Use for list endpoints where full graph data is not needed and performance matters.
-    """
-
-    id: str  # type: ignore
-    version: int  # type: ignore
-    user_id: str
-    created_at: datetime
-
-    @classmethod
-    def from_db(cls, graph: "AgentGraph") -> Self:
-        return cls(
-            id=graph.id,
-            version=graph.version,
-            is_active=graph.isActive,
-            name=graph.name or "",
-            description=graph.description or "",
-            instructions=graph.instructions,
-            recommended_schedule_cron=graph.recommendedScheduleCron,
-            forked_from_id=graph.forkedFromId,
-            forked_from_version=graph.forkedFromVersion,
-            user_id=graph.userId,
-            created_at=graph.createdAt,
-        )
-
-
-class GraphModel(Graph, GraphMeta):
-    """
-    Full graph model representing an existing graph from the database.
-
-    This is the primary model for working with persisted graphs. Includes all
-    graph data (nodes, links, sub_graphs) plus user ownership and timestamps.
-    Provides computed fields (input_schema, output_schema, etc.) used during
-    set-up (frontend) and execution (backend).
-
-    Inherits from:
-    - `Graph`: provides structure (nodes, links, sub_graphs) and computed schemas
-    - `GraphMeta`: provides user_id, created_at for database records
-    """
-
-    nodes: list[NodeModel] = Field(default_factory=list)  # type: ignore
-
-    @property
-    def starting_nodes(self) -> list[NodeModel]:
-        outbound_nodes = {link.sink_id for link in self.links}
-        input_nodes = {
-            node.id for node in self.nodes if node.block.block_type == BlockType.INPUT
-        }
-        return [
-            node
-            for node in self.nodes
-            if node.id not in outbound_nodes or node.id in input_nodes
-        ]
-
-    @property
-    def webhook_input_node(self) -> NodeModel | None:  # type: ignore
-        return cast(NodeModel, super().webhook_input_node)
+    sub_graphs: list[BaseGraph] = []  # Flattened sub-graphs
 
     @computed_field
     @property
     def credentials_input_schema(self) -> dict[str, Any]:
-        graph_credentials_inputs = self.aggregate_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}"
@@ -456,8 +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 other_field, other_keys, _ in list(graph_cred_fields)[i + 1 :]:
+        for i, (field, keys) in enumerate(graph_cred_fields):
+            for other_field, other_keys in list(graph_cred_fields)[i + 1 :]:
                 if field.provider != other_field.provider:
                     continue
                 if ProviderName.HTTP in field.provider:
@@ -473,78 +423,31 @@ class GraphModel(Graph, GraphMeta):
                     f"keys: {keys} <> {other_keys}."
                 )
 
-        # Build JSON schema directly to avoid expensive create_model + validation overhead
-        properties = {}
-        required_fields = []
-
-        for agg_field_key, (
-            field_info,
-            _,
-            is_required,
-        ) in graph_credentials_inputs.items():
-            providers = list(field_info.provider)
-            cred_types = list(field_info.supported_types)
-
-            field_schema: dict[str, Any] = {
-                "credentials_provider": providers,
-                "credentials_types": cred_types,
-                "type": "object",
-                "properties": {
-                    "id": {"title": "Id", "type": "string"},
-                    "title": {
-                        "anyOf": [{"type": "string"}, {"type": "null"}],
-                        "default": None,
-                        "title": "Title",
-                    },
-                    "provider": {
-                        "title": "Provider",
-                        "type": "string",
-                        **(
-                            {"enum": providers}
-                            if len(providers) > 1
-                            else {"const": providers[0]}
-                        ),
-                    },
-                    "type": {
-                        "title": "Type",
-                        "type": "string",
-                        **(
-                            {"enum": cred_types}
-                            if len(cred_types) > 1
-                            else {"const": cred_types[0]}
-                        ),
-                    },
-                },
-                "required": ["id", "provider", "type"],
-            }
-
-            # Add other (optional) field info items
-            field_schema.update(
-                field_info.model_dump(
-                    by_alias=True,
-                    exclude_defaults=True,
-                    exclude={"provider", "supported_types"},  # already included above
-                )
+        fields: dict[str, tuple[type[CredentialsMetaInput], CredentialsMetaInput]] = {
+            agg_field_key: (
+                CredentialsMetaInput[
+                    Literal[tuple(field_info.provider)],  # type: ignore
+                    Literal[tuple(field_info.supported_types)],  # type: ignore
+                ],
+                CredentialsField(
+                    required_scopes=set(field_info.required_scopes or []),
+                    discriminator=field_info.discriminator,
+                    discriminator_mapping=field_info.discriminator_mapping,
+                    discriminator_values=field_info.discriminator_values,
+                ),
             )
-
-            # 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,
+            for agg_field_key, (field_info, _) in graph_credentials_inputs.items()
         }
 
+        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(
@@ -552,19 +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: list[tuple[CredentialsFieldInfo, tuple[str, str]]] = []
-        node_required_map: dict[str, bool] = {}  # node_id -> is_required
+        node_credential_data = []
 
         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,
@@ -588,21 +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
-        return {
-            key: (
-                field_info,
-                node_field_pairs,
-                any(
-                    node_required_map.get(node_id, True)
-                    for node_id, _ in node_field_pairs
-                ),
-            )
-            for key, (field_info, node_field_pairs) in combined.items()
+
+class GraphModel(Graph):
+    user_id: str
+    nodes: list[NodeModel] = []  # type: ignore
+
+    created_at: datetime
+
+    @property
+    def starting_nodes(self) -> list[NodeModel]:
+        outbound_nodes = {link.sink_id for link in self.links}
+        input_nodes = {
+            node.id for node in self.nodes if node.block.block_type == BlockType.INPUT
         }
+        return [
+            node
+            for node in self.nodes
+            if node.id not in outbound_nodes or node.id in input_nodes
+        ]
+
+    @property
+    def webhook_input_node(self) -> NodeModel | None:  # type: ignore
+        return cast(NodeModel, super().webhook_input_node)
+
+    def meta(self) -> "GraphMeta":
+        """
+        Returns a GraphMeta object with metadata about the graph.
+        This is used to return metadata about the graph without exposing nodes and links.
+        """
+        return GraphMeta.from_graph(self)
 
     def reassign_ids(self, user_id: str, reassign_graph_id: bool = False):
         """
@@ -886,14 +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
-    def from_db(  # type: ignore[reportIncompatibleMethodOverride]
-        cls,
+    @staticmethod
+    def from_db(
         graph: AgentGraph,
         for_export: bool = False,
         sub_graphs: list[AgentGraph] | None = None,
-    ) -> Self:
-        return cls(
+    ) -> "GraphModel":
+        return GraphModel(
             id=graph.id,
             user_id=graph.userId if not for_export else "",
             version=graph.version,
@@ -919,28 +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):
-    """
-    GraphModel variant that excludes nodes, links, and sub-graphs from serialization.
+    # Easy work-around to prevent exposing nodes and links in the API response
+    nodes: list[NodeModel] = Field(default=[], exclude=True)  # type: ignore
+    links: list[Link] = Field(default=[], exclude=True)
 
-    Used in contexts like the store where exposing internal graph structure
-    is not desired. Inherits all computed fields from GraphModel but marks
-    nodes and links as excluded from JSON output.
-    """
-
-    nodes: list[NodeModel] = Field(default_factory=list, exclude=True)
-    links: list[Link] = Field(default_factory=list, exclude=True)
-
-    sub_graphs: list[BaseGraph] = Field(default_factory=list, exclude=True)
+    @staticmethod
+    def from_graph(graph: GraphModel) -> "GraphMeta":
+        return GraphMeta(**graph.model_dump())
 
 
 class GraphsPaginated(BaseModel):
@@ -1011,11 +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/model.py

@@ -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__)
@@ -507,13 +508,15 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
     def allowed_cred_types(cls) -> tuple[CredentialsType, ...]:
         return get_args(cls.model_fields["type"].annotation)
 
-    @staticmethod
-    def validate_credentials_field_schema(
-        field_schema: dict[str, Any], field_name: str
-    ):
+    @classmethod
+    def validate_credentials_field_schema(cls, model: type["BlockSchema"]):
         """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 +526,11 @@ 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}' "

autogpt_platform/backend/backend/executor/manager.py

@@ -1385,8 +1385,23 @@ class ExecutionProcessor:
                 f"[View User Details]({base_url}/admin/spending?search={user_email})"
             )
 
-            get_notification_manager_client().discord_system_alert(
-                alert_message, DiscordChannel.PRODUCT
+            # Send both Discord and AllQuiet alerts
+            correlation_id = f"insufficient_funds_{user_id}"
+
+            get_notification_manager_client().system_alert(
+                content=alert_message,
+                channel=DiscordChannel.PRODUCT,
+                correlation_id=correlation_id,
+                severity="critical",
+                status="open",
+                extra_attributes={
+                    "user_id": user_id,
+                    "user_email": user_email or "unknown",
+                    "balance": f"${e.balance / 100:.2f}",
+                    "attempted_cost": f"${abs(e.amount) / 100:.2f}",
+                    "shortfall": f"${abs(shortfall) / 100:.2f}",
+                    "agent_name": metadata.name if metadata else "Unknown",
+                },
             )
         except Exception as alert_error:
             logger.error(
@@ -1433,8 +1448,22 @@ class ExecutionProcessor:
                     f"Transaction cost: ${transaction_cost / 100:.2f}\n"
                     f"[View User Details]({base_url}/admin/spending?search={user_email})"
                 )
-                get_notification_manager_client().discord_system_alert(
-                    alert_message, DiscordChannel.PRODUCT
+                # Send both Discord and AllQuiet alerts
+                correlation_id = f"low_balance_{user_id}"
+
+                get_notification_manager_client().system_alert(
+                    content=alert_message,
+                    channel=DiscordChannel.PRODUCT,
+                    correlation_id=correlation_id,
+                    severity="warning",
+                    status="open",
+                    extra_attributes={
+                        "user_id": user_id,
+                        "user_email": user_email or "unknown",
+                        "current_balance": f"${current_balance / 100:.2f}",
+                        "transaction_cost": f"${transaction_cost / 100:.2f}",
+                        "threshold": f"${LOW_BALANCE_THRESHOLD / 100:.2f}",
+                    },
                 )
             except Exception as e:
                 logger.error(f"Failed to send low balance Discord alert: {e}")

autogpt_platform/backend/backend/executor/manager_low_balance_test.py

@@ -54,13 +54,16 @@ async def test_handle_low_balance_threshold_crossing(server: SpinTestServer):
         assert isinstance(notification_call.data, LowBalanceData)
         assert notification_call.data.current_balance == current_balance
 
-        # Verify Discord alert was sent
-        mock_client.discord_system_alert.assert_called_once()
-        discord_message = mock_client.discord_system_alert.call_args[0][0]
+        # Verify system alert was sent (includes Discord and AllQuiet)
+        mock_client.system_alert.assert_called_once()
+        alert_call = mock_client.system_alert.call_args
+        discord_message = alert_call[1]["content"]
         assert "Low Balance Alert" in discord_message
         assert "test@example.com" in discord_message
         assert "$4.00" in discord_message
         assert "$6.00" in discord_message
+        # Verify AllQuiet correlation ID is set
+        assert alert_call[1]["correlation_id"] == f"low_balance_{user_id}"
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -103,7 +106,7 @@ async def test_handle_low_balance_no_notification_when_not_crossing(
 
         # Verify no notification was sent
         mock_queue_notif.assert_not_called()
-        mock_client.discord_system_alert.assert_not_called()
+        mock_client.system_alert.assert_not_called()
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -146,4 +149,4 @@ async def test_handle_low_balance_no_duplicate_when_already_below(
 
         # Verify no notification was sent (user was already below threshold)
         mock_queue_notif.assert_not_called()
-        mock_client.discord_system_alert.assert_not_called()
+        mock_client.system_alert.assert_not_called()

autogpt_platform/backend/backend/executor/utils.py

@@ -373,7 +373,7 @@ def make_node_credentials_input_map(
     # Get aggregated credentials fields for the graph
     graph_cred_inputs = graph.aggregate_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/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/monitoring/block_error_monitor.py

@@ -12,7 +12,7 @@ from backend.util.clients import (
     get_database_manager_client,
     get_notification_manager_client,
 )
-from backend.util.metrics import sentry_capture_error
+from backend.util.metrics import DiscordChannel, sentry_capture_error
 from backend.util.settings import Config
 
 logger = logging.getLogger(__name__)
@@ -75,7 +75,32 @@ class BlockErrorMonitor:
 
             if critical_alerts:
                 msg = "Block Error Rate Alert:\n\n" + "\n\n".join(critical_alerts)
-                self.notification_client.discord_system_alert(msg)
+
+                # Send alert with correlation ID for block errors
+                # We'll create a simple hash of the block IDs that have errors
+                blocks_with_errors = [
+                    stats.block_id
+                    for name, stats in block_stats.items()
+                    if stats.total_executions >= 10
+                    and stats.error_rate >= threshold * 100
+                ]
+                correlation_id = (
+                    f"block_errors_{len(blocks_with_errors)}_blocks_{end_time.date()}"
+                )
+
+                self.notification_client.system_alert(
+                    content=msg,
+                    channel=DiscordChannel.PLATFORM,
+                    correlation_id=correlation_id,
+                    severity="warning",
+                    status="open",
+                    extra_attributes={
+                        "blocks_affected": str(len(critical_alerts)),
+                        "date": end_time.date().isoformat(),
+                        "threshold": f"{threshold * 100}%",
+                    },
+                )
+
                 logger.info(
                     f"Sent block error rate alert for {len(critical_alerts)} blocks"
                 )
@@ -87,7 +112,22 @@ class BlockErrorMonitor:
                     block_stats, start_time, end_time
                 )
                 if top_blocks_msg:
-                    self.notification_client.discord_system_alert(top_blocks_msg)
+                    # Daily summary gets a date-based correlation ID
+                    correlation_id = f"block_error_daily_summary_{end_time.date()}"
+
+                    self.notification_client.system_alert(
+                        content=top_blocks_msg,
+                        channel=DiscordChannel.PLATFORM,
+                        correlation_id=correlation_id,
+                        severity="minor",
+                        status="open",
+                        extra_attributes={
+                            "type": "daily_summary",
+                            "date": end_time.date().isoformat(),
+                            "top_blocks_count": str(self.include_top_blocks),
+                        },
+                    )
+
                     logger.info("Sent top blocks summary")
                     return "Sent top blocks summary"
 
@@ -100,7 +140,22 @@ class BlockErrorMonitor:
             error = Exception(f"Error checking block error rates: {e}")
             msg = str(error)
             sentry_capture_error(error)
-            self.notification_client.discord_system_alert(msg)
+
+            # Send error alert with generic correlation ID
+            correlation_id = "block_error_monitoring_failure"
+
+            self.notification_client.system_alert(
+                content=msg,
+                channel=DiscordChannel.PLATFORM,
+                correlation_id=correlation_id,
+                severity="critical",
+                status="open",
+                extra_attributes={
+                    "error_type": type(e).__name__,
+                    "error_message": str(e)[:200],
+                },
+            )
+
             return msg
 
     def _get_block_stats_from_db(

autogpt_platform/backend/backend/monitoring/late_execution_monitor.py

@@ -8,7 +8,7 @@ from backend.util.clients import (
     get_database_manager_client,
     get_notification_manager_client,
 )
-from backend.util.metrics import sentry_capture_error
+from backend.util.metrics import DiscordChannel, sentry_capture_error
 from backend.util.settings import Config
 
 logger = logging.getLogger(__name__)
@@ -102,7 +102,28 @@ class LateExecutionMonitor:
         msg = str(error)
 
         sentry_capture_error(error)
-        self.notification_client.discord_system_alert(msg)
+
+        # Generate correlation ID based on the threshold and number of late executions
+        correlation_id = f"late_execution_{self.config.execution_late_notification_threshold_secs}s_{num_total_late}_execs_{num_users}_users"
+
+        # Send both Discord and AllQuiet alerts
+        self.notification_client.system_alert(
+            content=msg,
+            channel=DiscordChannel.PLATFORM,
+            correlation_id=correlation_id,
+            severity="critical",
+            status="open",
+            extra_attributes={
+                "total_late_executions": str(num_total_late),
+                "queued_executions": str(num_queued),
+                "running_executions": str(num_running),
+                "affected_users": str(num_users),
+                "threshold_seconds": str(
+                    self.config.execution_late_notification_threshold_secs
+                ),
+            },
+        )
+
         return msg
 
 

autogpt_platform/backend/backend/notifications/notifications.py

@@ -1,7 +1,7 @@
 import asyncio
 import logging
 from datetime import datetime, timedelta, timezone
-from typing import Awaitable, Callable
+from typing import Awaitable, Callable, Literal
 
 import aio_pika
 from prisma.enums import NotificationType
@@ -33,7 +33,12 @@ from backend.data.user import (
 from backend.notifications.email import EmailSender
 from backend.util.clients import get_database_manager_async_client
 from backend.util.logging import TruncatedLogger
-from backend.util.metrics import DiscordChannel, discord_send_alert
+from backend.util.metrics import (
+    AllQuietAlert,
+    DiscordChannel,
+    discord_send_alert,
+    send_allquiet_alert,
+)
 from backend.util.retry import continuous_retry
 from backend.util.service import (
     AppService,
@@ -417,6 +422,54 @@ class NotificationManager(AppService):
     ):
         await discord_send_alert(content, channel)
 
+    @expose
+    async def allquiet_system_alert(self, alert: AllQuietAlert):
+        await send_allquiet_alert(alert)
+
+    @expose
+    async def system_alert(
+        self,
+        content: str,
+        channel: DiscordChannel = DiscordChannel.PLATFORM,
+        correlation_id: str | None = None,
+        severity: Literal["warning", "critical", "minor"] = "warning",
+        status: Literal["resolved", "open"] = "open",
+        extra_attributes: dict[str, str] | None = None,
+    ):
+        """Send both Discord and AllQuiet alerts for system events."""
+        # Send Discord alert
+        await discord_send_alert(content, channel)
+
+        # Send AllQuiet alert if correlation_id is provided
+        if correlation_id:
+            # Extract title from content (first line or first sentence)
+            lines = content.split("\n")
+            title = lines[0] if lines else content[:100]
+            # Remove Discord formatting from title
+            title = (
+                title.replace("**", "")
+                .replace("🚨", "")
+                .replace("⚠️", "")
+                .replace("❌", "")
+                .replace("✅", "")
+                .replace("📊", "")
+                .strip()
+            )
+
+            alert = AllQuietAlert(
+                severity=severity,
+                status=status,
+                title=title[:100],  # Limit title length
+                description=content,
+                correlation_id=correlation_id,
+                channel=channel.value,  # Pass channel as first-class field
+                extra_attributes=extra_attributes or {},
+            )
+            try:
+                await send_allquiet_alert(alert)
+            except Exception as e:
+                logger.error(f"Failed to send AllQuiet alert: {e}")
+
     async def _queue_scheduled_notification(self, event: SummaryParamsEventModel):
         """Queue a scheduled notification - exposed method for other services to call"""
         try:
@@ -1106,3 +1159,5 @@ class NotificationManagerClient(AppServiceClient):
     )
     queue_weekly_summary = endpoint_to_sync(NotificationManager.queue_weekly_summary)
     discord_system_alert = endpoint_to_sync(NotificationManager.discord_system_alert)
+    allquiet_system_alert = endpoint_to_sync(NotificationManager.allquiet_system_alert)
+    system_alert = endpoint_to_sync(NotificationManager.system_alert)

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)
-        ws = parse_workspace_uri(file)
+        # Parse workspace reference
+        # workspace://abc123 - by file ID
+        # workspace:///path/to/file.txt - by virtual path
+        file_ref = file[12:]  # Remove "workspace://"
 
-        if ws.is_path:
-            # Path reference: workspace:///path/to/file.txt
-            workspace_content = await workspace_manager.read_file(ws.file_ref)
-            file_info = await workspace_manager.get_file_info_by_path(ws.file_ref)
+        if file_ref.startswith("/"):
+            # Path reference
+            workspace_content = await workspace_manager.read_file(file_ref)
+            file_info = await workspace_manager.get_file_info_by_path(file_ref)
             filename = sanitize_filename(
                 file_info.name if file_info else f"{uuid.uuid4()}.bin"
             )
         else:
-            # ID reference: workspace://abc123 or workspace://abc123#video/mp4
-            workspace_content = await workspace_manager.read_file_by_id(ws.file_ref)
-            file_info = await workspace_manager.get_file_info(ws.file_ref)
+            # ID reference
+            workspace_content = await workspace_manager.read_file_by_id(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"
             )
@@ -342,14 +313,6 @@ async def store_media_file(
         if not target_path.is_file():
             raise ValueError(f"Local file does not exist: {target_path}")
 
-        # Virus scan the local file before any further processing
-        local_content = target_path.read_bytes()
-        if len(local_content) > MAX_FILE_SIZE_BYTES:
-            raise ValueError(
-                f"File too large: {len(local_content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
-            )
-        await scan_content_safe(local_content, filename=sanitized_file)
-
     # Return based on requested format
     if return_format == "for_local_processing":
         # Use when processing files locally with tools like ffmpeg, MoviePy, PIL
@@ -371,21 +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
-            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 original workspace reference
             return MediaFileType(file)
 
         # Save new content to workspace
@@ -397,7 +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/file_test.py

@@ -247,100 +247,3 @@ class TestFileCloudIntegration:
                     execution_context=make_test_context(graph_exec_id=graph_exec_id),
                     return_format="for_local_processing",
                 )
-
-    @pytest.mark.asyncio
-    async def test_store_media_file_local_path_scanned(self):
-        """Test that local file paths are scanned for viruses."""
-        graph_exec_id = "test-exec-123"
-        local_file = "test_video.mp4"
-        file_content = b"fake video content"
-
-        with patch(
-            "backend.util.file.get_cloud_storage_handler"
-        ) as mock_handler_getter, patch(
-            "backend.util.file.scan_content_safe"
-        ) as mock_scan, patch(
-            "backend.util.file.Path"
-        ) as mock_path_class:
-
-            # Mock cloud storage handler - not a cloud path
-            mock_handler = MagicMock()
-            mock_handler.is_cloud_path.return_value = False
-            mock_handler_getter.return_value = mock_handler
-
-            # Mock virus scanner
-            mock_scan.return_value = None
-
-            # Mock file system operations
-            mock_base_path = MagicMock()
-            mock_target_path = MagicMock()
-            mock_resolved_path = MagicMock()
-
-            mock_path_class.return_value = mock_base_path
-            mock_base_path.mkdir = MagicMock()
-            mock_base_path.__truediv__ = MagicMock(return_value=mock_target_path)
-            mock_target_path.resolve.return_value = mock_resolved_path
-            mock_resolved_path.is_relative_to.return_value = True
-            mock_resolved_path.is_file.return_value = True
-            mock_resolved_path.read_bytes.return_value = file_content
-            mock_resolved_path.relative_to.return_value = Path(local_file)
-            mock_resolved_path.name = local_file
-
-            result = await store_media_file(
-                file=MediaFileType(local_file),
-                execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                return_format="for_local_processing",
-            )
-
-            # Verify virus scan was called for local file
-            mock_scan.assert_called_once_with(file_content, filename=local_file)
-
-            # Result should be the relative path
-            assert str(result) == local_file
-
-    @pytest.mark.asyncio
-    async def test_store_media_file_local_path_virus_detected(self):
-        """Test that infected local files raise VirusDetectedError."""
-        from backend.api.features.store.exceptions import VirusDetectedError
-
-        graph_exec_id = "test-exec-123"
-        local_file = "infected.exe"
-        file_content = b"malicious content"
-
-        with patch(
-            "backend.util.file.get_cloud_storage_handler"
-        ) as mock_handler_getter, patch(
-            "backend.util.file.scan_content_safe"
-        ) as mock_scan, patch(
-            "backend.util.file.Path"
-        ) as mock_path_class:
-
-            # Mock cloud storage handler - not a cloud path
-            mock_handler = MagicMock()
-            mock_handler.is_cloud_path.return_value = False
-            mock_handler_getter.return_value = mock_handler
-
-            # Mock virus scanner to detect virus
-            mock_scan.side_effect = VirusDetectedError(
-                "EICAR-Test-File", "File rejected due to virus detection"
-            )
-
-            # Mock file system operations
-            mock_base_path = MagicMock()
-            mock_target_path = MagicMock()
-            mock_resolved_path = MagicMock()
-
-            mock_path_class.return_value = mock_base_path
-            mock_base_path.mkdir = MagicMock()
-            mock_base_path.__truediv__ = MagicMock(return_value=mock_target_path)
-            mock_target_path.resolve.return_value = mock_resolved_path
-            mock_resolved_path.is_relative_to.return_value = True
-            mock_resolved_path.is_file.return_value = True
-            mock_resolved_path.read_bytes.return_value = file_content
-
-            with pytest.raises(VirusDetectedError):
-                await store_media_file(
-                    file=MediaFileType(local_file),
-                    execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                    return_format="for_local_processing",
-                )

autogpt_platform/backend/backend/util/metrics.py

@@ -1,8 +1,9 @@
 import logging
 from enum import Enum
+from typing import Literal
 
 import sentry_sdk
-from pydantic import SecretStr
+from pydantic import BaseModel, Field, SecretStr
 from sentry_sdk.integrations import DidNotEnable
 from sentry_sdk.integrations.anthropic import AnthropicIntegration
 from sentry_sdk.integrations.asyncio import AsyncioIntegration
@@ -51,6 +52,31 @@ def sentry_capture_error(error: BaseException):
     sentry_sdk.flush()
 
 
+class AllQuietAlert(BaseModel):
+    severity: Literal["warning"] | Literal["critical"] | Literal["minor"]
+    status: Literal["resolved"] | Literal["open"]
+    title: str | None = None
+    description: str | None = None
+    correlation_id: str | None = None
+    channel: str | None = None  # Discord channel (platform or product)
+    extra_attributes: dict[str, str] = Field(default_factory=dict)
+    environment: str = (
+        f"app:{settings.config.app_env.value}-behave:{settings.config.behave_as.value}"
+    )
+
+
+async def send_allquiet_alert(alert: AllQuietAlert):
+    hook_url = settings.secrets.allquiet_webhook_url
+
+    if not hook_url:
+        logging.warning("AllQuiet webhook URL not configured")
+        return
+
+    from backend.util.request import Requests
+
+    await Requests().post(hook_url, json=alert.model_dump())
+
+
 async def discord_send_alert(
     content: str, channel: DiscordChannel = DiscordChannel.PLATFORM
 ):

autogpt_platform/backend/backend/util/retry.py

@@ -4,6 +4,7 @@ import os
 import threading
 import time
 from functools import wraps
+from typing import Literal
 from uuid import uuid4
 
 from tenacity import (
@@ -43,7 +44,14 @@ def should_send_alert(func_name: str, exception: Exception, context: str = "") -
 
 
 def send_rate_limited_discord_alert(
-    func_name: str, exception: Exception, context: str, alert_msg: str, channel=None
+    func_name: str,
+    exception: Exception,
+    context: str,
+    alert_msg: str,
+    channel=None,
+    correlation_id: str | None = None,
+    severity: Literal["warning", "critical", "minor"] = "critical",
+    extra_attributes: dict | None = None,
 ) -> bool:
     """
     Send a Discord alert with rate limiting.
@@ -58,8 +66,13 @@ def send_rate_limited_discord_alert(
         from backend.util.metrics import DiscordChannel
 
         notification_client = get_notification_manager_client()
-        notification_client.discord_system_alert(
-            alert_msg, channel or DiscordChannel.PLATFORM
+        notification_client.system_alert(
+            content=alert_msg,
+            channel=channel or DiscordChannel.PLATFORM,
+            correlation_id=correlation_id,
+            severity=severity,
+            status="open",
+            extra_attributes=extra_attributes or {},
         )
         return True
 
@@ -74,14 +87,30 @@ def _send_critical_retry_alert(
     """Send alert when a function is approaching the retry failure threshold."""
 
     prefix = f"{context}: " if context else ""
+    error_type = type(exception).__name__
+
+    # Create correlation ID based on context, function name, and error type
+    correlation_id = f"retry_failure_{context}_{func_name}_{error_type}".replace(
+        " ", "_"
+    ).replace(":", "")
+
     if send_rate_limited_discord_alert(
         func_name,
         exception,
         context,
         f"🚨 CRITICAL: Operation Approaching Failure Threshold: {prefix}'{func_name}'\n\n"
         f"Current attempt: {attempt_number}/{EXCESSIVE_RETRY_THRESHOLD}\n"
-        f"Error: {type(exception).__name__}: {exception}\n\n"
+        f"Error: {error_type}: {exception}\n\n"
         f"This operation is about to fail permanently. Investigate immediately.",
+        correlation_id=correlation_id,
+        severity="critical",
+        extra_attributes={
+            "function_name": func_name,
+            "attempt_number": str(attempt_number),
+            "max_attempts": str(EXCESSIVE_RETRY_THRESHOLD),
+            "error_type": error_type,
+            "context": context or "none",
+        },
     ):
         logger.critical(
             f"CRITICAL ALERT SENT: Operation {func_name} at attempt {attempt_number}"
@@ -185,6 +214,14 @@ def conn_retry(
             logger.error(f"{prefix} {action_name} failed after retries: {exception}")
         else:
             if attempt_number == EXCESSIVE_RETRY_THRESHOLD:
+                error_type = type(exception).__name__
+                # Create correlation ID for infrastructure issues
+                correlation_id = (
+                    f"infrastructure_{resource_name}_{action_name}_{func_name}".replace(
+                        " ", "_"
+                    )
+                )
+
                 if send_rate_limited_discord_alert(
                     func_name,
                     exception,
@@ -194,8 +231,18 @@ def conn_retry(
                     f"Action: {action_name}\n"
                     f"Function: {func_name}\n"
                     f"Current attempt: {attempt_number}/{max_retry + 1}\n"
-                    f"Error: {type(exception).__name__}: {str(exception)[:200]}{'...' if len(str(exception)) > 200 else ''}\n\n"
+                    f"Error: {error_type}: {str(exception)[:200]}{'...' if len(str(exception)) > 200 else ''}\n\n"
                     f"Infrastructure component is approaching failure threshold. Investigate immediately.",
+                    correlation_id=correlation_id,
+                    severity="critical",
+                    extra_attributes={
+                        "resource_name": resource_name,
+                        "action_name": action_name,
+                        "function_name": func_name,
+                        "attempt_number": str(attempt_number),
+                        "max_attempts": str(max_retry + 1),
+                        "error_type": error_type,
+                    },
                 ):
                     logger.critical(
                         f"INFRASTRUCTURE ALERT SENT: {resource_name} at {attempt_number} attempts"

autogpt_platform/backend/backend/util/retry_test.py

@@ -166,16 +166,16 @@ class TestRetryRateLimiting:
 
         # First alert should be sent
         _send_critical_retry_alert("spend_credits", 50, exc, "Service communication")
-        assert mock_client.discord_system_alert.call_count == 1
+        assert mock_client.system_alert.call_count == 1
 
         # Second identical alert should be rate limited (not sent)
         _send_critical_retry_alert("spend_credits", 50, exc, "Service communication")
-        assert mock_client.discord_system_alert.call_count == 1  # Still 1, not 2
+        assert mock_client.system_alert.call_count == 1  # Still 1, not 2
 
         # Different error should be allowed
         exc2 = ValueError("different API error")
         _send_critical_retry_alert("spend_credits", 50, exc2, "Service communication")
-        assert mock_client.discord_system_alert.call_count == 2
+        assert mock_client.system_alert.call_count == 2
 
     @patch("backend.util.clients.get_notification_manager_client")
     def test_send_critical_retry_alert_handles_notification_failure(
@@ -183,7 +183,7 @@ class TestRetryRateLimiting:
     ):
         """Test that notification failures don't break the rate limiter."""
         mock_client = Mock()
-        mock_client.discord_system_alert.side_effect = Exception("Notification failed")
+        mock_client.system_alert.side_effect = Exception("Notification failed")
         mock_get_client.return_value = mock_client
 
         exc = ValueError("test error")
@@ -221,7 +221,7 @@ class TestRetryRateLimiting:
             _send_critical_retry_alert(
                 "_call_method_sync", 50, exc, "Service communication"
             )
-            assert mock_client.discord_system_alert.call_count == 1
+            assert mock_client.system_alert.call_count == 1
 
             # Next 950 failures should not send alerts (rate limited)
             for _ in range(950):
@@ -230,7 +230,7 @@ class TestRetryRateLimiting:
                 )
 
             # Still only 1 alert sent total
-            assert mock_client.discord_system_alert.call_count == 1
+            assert mock_client.system_alert.call_count == 1
 
     @patch("backend.util.clients.get_notification_manager_client")
     def test_retry_decorator_with_excessive_failures(self, mock_get_client):
@@ -248,4 +248,4 @@ class TestRetryRateLimiting:
             always_failing_function()
 
         # Should have sent exactly one alert at the threshold
-        assert mock_client.discord_system_alert.call_count == 1
+        assert mock_client.system_alert.call_count == 1

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")
@@ -683,6 +682,8 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
         description="The LaunchDarkly SDK key for feature flag management",
     )
 
+    allquiet_webhook_url: str = Field(default="", description="AllQuiet webhook URL")
+
     ayrshare_api_key: str = Field(default="", description="Ayrshare API Key")
     ayrshare_jwt_key: str = Field(default="", description="Ayrshare private Key")
 

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/pyproject.toml

@@ -12,16 +12,15 @@ python = ">=3.10,<3.14"
 aio-pika = "^9.5.5"
 aiohttp = "^3.10.0"
 aiodns = "^3.5.0"
-anthropic = "^0.79.0"
+anthropic = "^0.59.0"
 apscheduler = "^3.11.1"
 autogpt-libs = { path = "../autogpt_libs", develop = true }
 bleach = { extras = ["css"], version = "^6.2.0" }
 click = "^8.2.0"
-cryptography = "^46.0"
+cryptography = "^45.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
-elevenlabs = "^1.50.0"
-fastapi = "^0.128.5"
+fastapi = "^0.116.1"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
 google-api-python-client = "^2.177.0"
@@ -35,10 +34,10 @@ jinja2 = "^3.1.6"
 jsonref = "^1.1.0"
 jsonschema = "^4.25.0"
 langfuse = "^3.11.0"
-launchdarkly-server-sdk = "^9.14.1"
+launchdarkly-server-sdk = "^9.12.0"
 mem0ai = "^0.1.115"
 moviepy = "^2.1.2"
-ollama = "^0.6.1"
+ollama = "^0.5.1"
 openai = "^1.97.1"
 orjson = "^3.10.0"
 pika = "^1.3.2"
@@ -48,16 +47,16 @@ postmarker = "^1.0"
 praw = "~7.8.1"
 prisma = "^0.15.0"
 rank-bm25 = "^0.2.2"
-prometheus-client = "^0.24.1"
+prometheus-client = "^0.22.1"
 prometheus-fastapi-instrumentator = "^7.0.0"
 psutil = "^7.0.0"
 psycopg2-binary = "^2.9.10"
-pydantic = { extras = ["email"], version = "^2.12.5" }
-pydantic-settings = "^2.12.0"
+pydantic = { extras = ["email"], version = "^2.11.7" }
+pydantic-settings = "^2.10.1"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.1.0"
 python-dotenv = "^1.1.1"
-python-multipart = "^0.0.22"
+python-multipart = "^0.0.20"
 redis = "^6.2.0"
 regex = "^2025.9.18"
 replicate = "^1.0.6"
@@ -65,19 +64,18 @@ sentry-sdk = {extras = ["anthropic", "fastapi", "launchdarkly", "openai", "sqlal
 sqlalchemy = "^2.0.40"
 strenum = "^0.4.9"
 stripe = "^11.5.0"
-supabase = "2.27.3"
-tenacity = "^9.1.4"
+supabase = "2.17.0"
+tenacity = "^9.1.2"
 todoist-api-python = "^2.1.7"
 tweepy = "^4.16.0"
-uvicorn = { extras = ["standard"], version = "^0.40.0" }
+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"
 aiofiles = "^24.1.0"
-tiktoken = "^0.12.0"
+tiktoken = "^0.9.0"
 aioclamd = "^1.0.0"
 setuptools = "^80.9.0"
 gcloud-aio-storage = "^9.5.0"
@@ -95,13 +93,13 @@ black = "^24.10.0"
 faker = "^38.2.0"
 httpx = "^0.28.1"
 isort = "^5.13.2"
-poethepoet = "^0.41.0"
+poethepoet = "^0.37.0"
 pre-commit = "^4.4.0"
 pyright = "^1.1.407"
 pytest-mock = "^3.15.1"
-pytest-watcher = "^0.6.3"
+pytest-watcher = "^0.4.2"
 requests = "^2.32.5"
-ruff = "^0.15.0"
+ruff = "^0.14.5"
 # NOTE: please insert new dependencies in their alphabetical location
 
 [build-system]

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/package.json

@@ -102,7 +102,7 @@
     "react-markdown": "9.0.3",
     "react-modal": "3.16.3",
     "react-shepherd": "6.1.9",
-    "react-window": "2.2.0",
+    "react-window": "1.8.11",
     "recharts": "3.3.0",
     "rehype-autolink-headings": "7.1.0",
     "rehype-highlight": "7.0.2",
@@ -140,7 +140,7 @@
     "@types/react": "18.3.17",
     "@types/react-dom": "18.3.5",
     "@types/react-modal": "3.16.3",
-    "@types/react-window": "2.0.0",
+    "@types/react-window": "1.8.8",
     "@vitejs/plugin-react": "5.1.2",
     "axe-playwright": "2.2.2",
     "chromatic": "13.3.3",

autogpt_platform/frontend/pnpm-lock.yaml

@@ -228,8 +228,8 @@ importers:
         specifier: 6.1.9
         version: 6.1.9(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(typescript@5.9.3)
       react-window:
-        specifier: 2.2.0
-        version: 2.2.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+        specifier: 1.8.11
+        version: 1.8.11(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       recharts:
         specifier: 3.3.0
         version: 3.3.0(@types/react@18.3.17)(react-dom@18.3.1(react@18.3.1))(react-is@18.3.1)(react@18.3.1)(redux@5.0.1)
@@ -337,8 +337,8 @@ importers:
         specifier: 3.16.3
         version: 3.16.3
       '@types/react-window':
-        specifier: 2.0.0
-        version: 2.0.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+        specifier: 1.8.8
+        version: 1.8.8
       '@vitejs/plugin-react':
         specifier: 5.1.2
         version: 5.1.2(vite@7.3.1(@types/node@24.10.0)(jiti@2.6.1)(terser@5.44.1)(yaml@2.8.2))
@@ -3469,9 +3469,8 @@ packages:
   '@types/react-modal@3.16.3':
     resolution: {integrity: sha512-xXuGavyEGaFQDgBv4UVm8/ZsG+qxeQ7f77yNrW3n+1J6XAstUy5rYHeIHPh1KzsGc6IkCIdu6lQ2xWzu1jBTLg==}
 
-  '@types/react-window@2.0.0':
-    resolution: {integrity: sha512-E8hMDtImEpMk1SjswSvqoSmYvk7GEtyVaTa/GJV++FdDNuMVVEzpAClyJ0nqeKYBrMkGiyH6M1+rPLM0Nu1exQ==}
-    deprecated: This is a stub types definition. react-window provides its own type definitions, so you do not need this installed.
+  '@types/react-window@1.8.8':
+    resolution: {integrity: sha512-8Ls660bHR1AUA2kuRvVG9D/4XpRC6wjAaPT9dil7Ckc76eP9TKWZwwmgfq8Q1LANX3QNDnoU4Zp48A3w+zK69Q==}
 
   '@types/react@18.3.17':
     resolution: {integrity: sha512-opAQ5no6LqJNo9TqnxBKsgnkIYHozW9KSTlFVoSUJYh1Fl/sswkEoqIugRSm7tbh6pABtYjGAjW+GOS23j8qbw==}
@@ -5977,6 +5976,9 @@ packages:
     resolution: {integrity: sha512-UERzLsxzllchadvbPs5aolHh65ISpKpM+ccLbOJ8/vvpBKmAWf+la7dXFy7Mr0ySHbdHrFv5kGFCUHHe6GFEmw==}
     engines: {node: '>= 4.0.0'}
 
+  memoize-one@5.2.1:
+    resolution: {integrity: sha512-zYiwtZUcYyXKo/np96AGZAckk+FWWsUdJ3cHGGmld7+AhvcWmQyGCYUh1hc4Q/pkOhb65dQR/pqCyK0cOaHz4Q==}
+
   merge-stream@2.0.0:
     resolution: {integrity: sha512-abv/qOcuPfk3URPfDzmZU1LKmuw8kT+0nIHvKrKgFrwifol/doWcdA4ZqsWQ8ENrFKkd67Mfpo/LovbIUsbt3w==}
 
@@ -6889,11 +6891,12 @@ packages:
       '@types/react':
         optional: true
 
-  react-window@2.2.0:
-    resolution: {integrity: sha512-Y2L7yonHq6K1pQA2P98wT5QdIsEcjBTB7T8o6Mub12hH9eYppXoYu6vgClmcjlh3zfNcW2UrXiJJJqDxUY7GVw==}
+  react-window@1.8.11:
+    resolution: {integrity: sha512-+SRbUVT2scadgFSWx+R1P754xHPEqvcfSfVX10QYg6POOz+WNgkN48pS+BtZNIMGiL1HYrSEiCkwsMS15QogEQ==}
+    engines: {node: '>8.0.0'}
     peerDependencies:
-      react: ^18.0.0 || ^19.0.0
-      react-dom: ^18.0.0 || ^19.0.0
+      react: ^15.0.0 || ^16.0.0 || ^17.0.0 || ^18.0.0 || ^19.0.0
+      react-dom: ^15.0.0 || ^16.0.0 || ^17.0.0 || ^18.0.0 || ^19.0.0
 
   react@18.3.1:
     resolution: {integrity: sha512-wS+hAgJShR0KhEvPJArfuPVN1+Hz1t0Y6n5jLrGQbkb4urgPE/0Rve+1kMB1v/oWgHgm4WIcV+i7F2pTVj+2iQ==}
@@ -11600,12 +11603,9 @@ snapshots:
     dependencies:
       '@types/react': 18.3.17
 
-  '@types/react-window@2.0.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)':
+  '@types/react-window@1.8.8':
     dependencies:
-      react-window: 2.2.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
-    transitivePeerDependencies:
-      - react
-      - react-dom
+      '@types/react': 18.3.17
 
   '@types/react@18.3.17':
     dependencies:
@@ -14545,6 +14545,8 @@ snapshots:
     dependencies:
       fs-monkey: 1.1.0
 
+  memoize-one@5.2.1: {}
+
   merge-stream@2.0.0: {}
 
   merge2@1.4.1: {}
@@ -15590,8 +15592,10 @@ snapshots:
     optionalDependencies:
       '@types/react': 18.3.17
 
-  react-window@2.2.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
+  react-window@1.8.11(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
     dependencies:
+      '@babel/runtime': 7.28.4
+      memoize-one: 5.2.1
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
 

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)/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: { type: "object", properties: {} },
-          outputSchema: { type: "object", properties: {} },
+          inputSchema: flow.input_schema,
+          outputSchema: flow.output_schema,
           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) => {
-                    const renderer = getItemRenderer?.(item);
-                    if (enableEnhancedOutputHandling && renderer) {
-                      const metadata: OutputMetadata = {};
-                      return (
-                        <React.Fragment key={index}>
-                          <OutputItem
-                            value={item}
-                            metadata={metadata}
-                            renderer={renderer}
-                          />
-                          {index < value.length - 1 && ", "}
-                        </React.Fragment>
-                      );
-                    }
-                    return (
-                      <React.Fragment key={index}>
-                        <ContentRenderer
-                          value={item}
-                          truncateLongData={truncateLongData}
-                        />
-                        {index < value.length - 1 && ", "}
-                      </React.Fragment>
-                    );
-                  })}
+                  {value.map((item, index) => (
+                    <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: Record<string, any> = {},
-    ) => {
+    (blockId: string, nodeType: string, hardcodedValues: 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(
-        blockId,
-        nodeType,
-        hardcodedValues,
-        position,
-      );
-      if (!newNode) return;
+      const newNode: CustomNode = {
+        id: nodeId.toString(),
+        type: "custom",
+        position: viewportCoordinates, // Set the position to the calculated viewport center
+        data: {
+          blockType: nodeType,
+          blockCosts: nodeSchema.costs,
+          title: `${nodeType} ${nodeId}`,
+          description: nodeSchema.description,
+          categories: nodeSchema.categories,
+          inputSchema: nodeSchema.inputSchema,
+          outputSchema: nodeSchema.outputSchema,
+          hardcodedValues: hardcodedValues,
+          connections: [],
+          isOutputOpen: false,
+          block_id: blockId,
+          isOutputStatic: nodeSchema.staticOutput,
+          uiType: nodeSchema.uiType,
+        },
+      };
+
+      addNodes(newNode);
+      setNodeId((prevId) => prevId + 1);
+      clearNodesStatusAndOutput(); // Clear status and output when a new node is added
 
       setViewport(
         {
-          x: -position.x * 0.8 + (window.innerWidth - 0.0) / 2,
-          y: -position.y * 0.8 + (window.innerHeight - 400) / 2,
+          // Rough estimate of the dimension of the node is: 500x400px.
+          // Though we skip shifting the X, considering the block menu side-bar.
+          x: -viewportCoordinates.x * 0.8 + (window.innerWidth - 0.0) / 2,
+          y: -viewportCoordinates.y * 0.8 + (window.innerHeight - 400) / 2,
           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(
-          blockId,
-          blockName,
-          hardcodedValues || {},
+        // Find the block schema
+        const nodeSchema = availableBlocks.find((node) => node.id === blockId);
+        if (!nodeSchema) {
+          console.error(`Schema not found for block ID: ${blockId}`);
+          return;
+        }
+
+        // Create the new node at the drop position
+        const newNode: CustomNode = {
+          id: nodeId.toString(),
+          type: "custom",
           position,
-        );
+          data: {
+            blockType: blockName,
+            blockCosts: nodeSchema.costs || [],
+            title: `${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) => {
-                const renderer = getItemRenderer?.(item);
-                if (enableEnhancedOutputHandling && renderer) {
-                  const metadata: OutputMetadata = {};
-                  return (
-                    <React.Fragment key={index}>
-                      <OutputItem
-                        value={item}
-                        metadata={metadata}
-                        renderer={renderer}
-                      />
-                      {index < Math.min(dataArray.length, 10) - 1 && ", "}
-                    </React.Fragment>
-                  );
-                }
-                return (
-                  <React.Fragment key={index}>
-                    <ContentRenderer
-                      value={item}
-                      truncateLongData={truncateLongData}
-                    />
-                    {index < Math.min(dataArray.length, 10) - 1 && ", "}
-                  </React.Fragment>
-                );
-              })}
+              {dataArray.slice(0, 10).map((item, index) => (
+                <React.Fragment key={index}>
+                  <ContentRenderer
+                    value={item}
+                    truncateLongData={truncateLongData}
+                  />
+                  {index < Math.min(dataArray.length, 10) - 1 && ", "}
+                </React.Fragment>
+              ))}
               {dataArray.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 {
-  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 LegacyGraphMeta } from "@/lib/autogpt-server-api";
 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
-export type GraphLike = LegacyGraph | GeneratedGraph;
-
-// Union type for GraphMeta (without schemas) for version detection
+// Union type for GraphMeta that works with both legacy and new builder
 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 {
-  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 { GraphInputSchema, GraphOutputSchema } from "@/lib/autogpt-server-api";
 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[],
-): SubAgentUpdateInfo<GraphModel> {
+  availableGraphs: T[],
+): 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;
-    return latestGraphInfo.version! > graphVersion;
-  }, [latestGraphInfo, graphVersion]);
-
-  // Fetch full graph IF an update is detected
-  const { data: latestGraph } = useGetV1GetSpecificGraph(
-    graphID ?? "",
-    { version: latestGraphInfo?.version },
-    {
-      query: {
-        enabled: hasUpdate && !!graphID && !!latestGraphInfo?.version,
-        select: okData,
-      },
-    },
-  );
+    if (!latestGraph || graphVersion === undefined) return false;
+    return latestGraph.version! > graphVersion;
+  }, [latestGraph, graphVersion]);
 
   // 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,
-    latestGraph: latestGraph || null,
+    latestVersion: latestGraph?.version || 0,
+    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)/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": {
-                  "$ref": "#/components/schemas/GraphModelWithoutNodes"
-                }
+                "schema": { "$ref": "#/components/schemas/GraphMeta" }
               }
             }
           },
@@ -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",
-        "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)."
+        "title": "BaseGraph"
       },
       "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",
-        "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)."
+        "title": "BaseGraph"
       },
       "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": { "$ref": "#/components/schemas/BaseGraph-Input" },
             "type": "array",
-            "title": "Sub Graphs"
+            "title": "Sub Graphs",
+            "default": []
           }
         },
         "type": "object",
         "required": ["name", "description"],
-        "title": "Graph",
-        "description": "Creatable graph model used in API create/update endpoints."
+        "title": "Graph"
       },
       "GraphExecution": {
         "properties": {
@@ -7778,7 +7780,7 @@
       "GraphMeta": {
         "properties": {
           "id": { "type": "string", "title": "Id" },
-          "version": { "type": "integer", "title": "Version" },
+          "version": { "type": "integer", "title": "Version", "default": 1 },
           "is_active": {
             "type": "boolean",
             "title": "Is Active",
@@ -7802,24 +7804,68 @@
             "anyOf": [{ "type": "integer" }, { "type": "null" }],
             "title": "Forked From Version"
           },
+          "sub_graphs": {
+            "items": { "$ref": "#/components/schemas/BaseGraph-Output" },
+            "type": "array",
+            "title": "Sub Graphs",
+            "default": []
+          },
           "user_id": { "type": "string", "title": "User Id" },
-          "created_at": {
-            "type": "string",
-            "format": "date-time",
-            "title": "Created At"
+          "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": [
+              { "$ref": "#/components/schemas/GraphTriggerInfo" },
+              { "type": "null" }
+            ],
+            "readOnly": true
+          },
+          "credentials_input_schema": {
+            "additionalProperties": true,
+            "type": "object",
+            "title": "Credentials Input Schema",
+            "readOnly": true
           }
         },
         "type": "object",
         "required": [
-          "id",
-          "version",
           "name",
           "description",
           "user_id",
-          "created_at"
+          "input_schema",
+          "output_schema",
+          "has_external_trigger",
+          "has_human_in_the_loop",
+          "has_sensitive_action",
+          "trigger_setup_info",
+          "credentials_input_schema"
         ],
-        "title": "GraphMeta",
-        "description": "Lightweight graph metadata model representing an existing graph from the database,\nfor use in listings and summaries.\n\nLacks `GraphModel`'s nodes, links, and expensive computed fields.\nUse for list endpoints where full graph data is not needed and performance matters."
+        "title": "GraphMeta"
       },
       "GraphModel": {
         "properties": {
@@ -7840,111 +7886,17 @@
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Recommended Schedule Cron"
           },
-          "forked_from_id": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Forked From Id"
-          },
-          "forked_from_version": {
-            "anyOf": [{ "type": "integer" }, { "type": "null" }],
-            "title": "Forked From Version"
-          },
-          "user_id": { "type": "string", "title": "User Id" },
-          "created_at": {
-            "type": "string",
-            "format": "date-time",
-            "title": "Created At"
-          },
           "nodes": {
             "items": { "$ref": "#/components/schemas/NodeModel" },
             "type": "array",
-            "title": "Nodes"
+            "title": "Nodes",
+            "default": []
           },
           "links": {
             "items": { "$ref": "#/components/schemas/Link" },
             "type": "array",
-            "title": "Links"
-          },
-          "sub_graphs": {
-            "items": { "$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"
+            "title": "Links",
+            "default": []
           },
           "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",
-        "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."
+        "title": "GraphModel"
       },
       "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" },
@@ -12307,9 +12272,7 @@
             "title": "Location"
           },
           "msg": { "type": "string", "title": "Message" },
-          "type": { "type": "string", "title": "Error Type" },
-          "input": { "title": "Input" },
-          "ctx": { "type": "object", "title": "Context" }
+          "type": { "type": "string", "title": "Error Type" }
         },
         "type": "object",
         "required": ["loc", "msg", "type"],

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>
       )}