Merge branch 'dev' into otto/secrt-1887-auto-save-binary-outputs-v2

When CoPilot executes blocks that produce binary outputs (code execution
results with png/jpeg/pdf/svg fields), the data is now automatically saved
to the user's workspace and replaced with workspace:// references.

This reduces output token usage by ~97% for file generation tasks (observed:
17k -> 500 tokens for a PDF) and prevents data corruption from LLM
truncation/hallucination of base64 strings.

Detection is field-name based (png, jpeg, pdf, svg) which targets the
standard e2b CodeExecutionResult fields. Other image-producing blocks
already use store_media_file() and don't need this post-processing.

Features:
- Automatic detection and saving of binary content >1KB
- Content deduplication within single block execution via SHA-256 hash
- Graceful degradation (original value preserved on save failure)
- Support for both raw base64 and data URI formats

Closes SECRT-1887

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

@@ -27,20 +27,11 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       cache-key: ${{ steps.cache-key.outputs.key }}
-      components-changed: ${{ steps.filter.outputs.components }}
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
-      - name: Check for component changes
-        uses: dorny/paths-filter@v3
-        id: filter
-        with:
-          filters: |
-            components:
-              - 'autogpt_platform/frontend/src/components/**'
-
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
@@ -99,11 +90,8 @@ jobs:
   chromatic:
     runs-on: ubuntu-latest
     needs: setup
-    # Disabled: to re-enable, remove 'false &&' from the condition below
-    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

autogpt_platform/autogpt_libs/pyproject.toml

@@ -11,15 +11,15 @@ python = ">=3.10,<4.0"
 colorama = "^0.4.6"
 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 = "^1.1.404"

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/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/MCP_BLOCK_IMPLEMENTATION.md

@@ -1,76 +0,0 @@
-# MCP Block Implementation Plan
-
-## Overview
-
-Create a single **MCPBlock** that dynamically integrates with any MCP (Model Context Protocol)
-server. Users provide a server URL, the block discovers available tools, presents them as a
-dropdown, and dynamically adjusts input/output schema based on the selected tool — exactly like
-`AgentExecutorBlock` handles dynamic schemas.
-
-## Architecture
-
-```
-User provides MCP server URL + credentials
-         ↓
-MCPBlock fetches tools via MCP protocol (tools/list)
-         ↓
-User selects tool from dropdown (stored in constantInput)
-         ↓
-Input schema dynamically updates based on selected tool's inputSchema
-         ↓
-On execution: MCPBlock calls the tool via MCP protocol (tools/call)
-         ↓
-Result yielded as block output
-```
-
-## Design Decisions
-
-1. **Single block, not many blocks** — One `MCPBlock` handles all MCP servers/tools
-2. **Dynamic schema via AgentExecutorBlock pattern** — Override `get_input_schema()`,
-   `get_input_defaults()`, `get_missing_input()` on the Input class
-3. **Auth via API key or OAuth2 credentials** — Use existing `APIKeyCredentials` or
-   `OAuth2Credentials` with `ProviderName.MCP` provider. API keys are sent as Bearer tokens;
-   OAuth2 uses the access token.
-4. **HTTP-based MCP client** — Use `aiohttp` (already a dependency) to implement MCP Streamable
-   HTTP transport directly. No need for the `mcp` Python SDK — the protocol is simple JSON-RPC
-   over HTTP. Handles both JSON and SSE response formats.
-5. **No new DB tables** — Everything fits in existing `AgentBlock` + `AgentNode` tables
-
-## Implementation Files
-
-### New Files
-- `backend/blocks/mcp/` — MCP block package
-  - `__init__.py`
-  - `block.py` — MCPToolBlock implementation
-  - `client.py` — MCP HTTP client (list_tools, call_tool)
-  - `oauth.py` — MCP OAuth handler for dynamic endpoint discovery
-  - `test_mcp.py` — Unit tests
-  - `test_oauth.py` — OAuth handler tests
-  - `test_integration.py` — Integration tests with local test server
-  - `test_e2e.py` — E2E tests against real MCP servers
-
-### Modified Files
-- `backend/integrations/providers.py` — Add `MCP = "mcp"` to ProviderName
-
-## Dev Loop
-
-```bash
-cd autogpt_platform/backend
-poetry run pytest backend/blocks/mcp/test_mcp.py -xvs        # Unit tests
-poetry run pytest backend/blocks/mcp/test_oauth.py -xvs       # OAuth tests
-poetry run pytest backend/blocks/mcp/test_integration.py -xvs  # Integration tests
-poetry run pytest backend/blocks/mcp/ -xvs                     # All MCP tests
-```
-
-## Status
-
-- [x] Research & Design
-- [x] Add ProviderName.MCP
-- [x] Implement MCP client (client.py)
-- [x] Implement MCPToolBlock (block.py)
-- [x] Add OAuth2 support (oauth.py)
-- [x] Write unit tests
-- [x] Write integration tests
-- [x] Write E2E tests
-- [x] Run tests & fix issues
-- [x] Create PR

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/tools/binary_output_processor.py

@@ -0,0 +1,204 @@
+"""
+Save binary block outputs to workspace, return references instead of base64.
+
+This module post-processes block execution outputs to detect and save binary
+content (from code execution results) to the workspace, returning workspace://
+references instead of raw base64 data. This reduces LLM output token usage
+by ~97% for file generation tasks.
+
+Detection is field-name based, targeting the standard e2b CodeExecutionResult
+fields: png, jpeg, pdf, svg. Other image-producing blocks already use
+store_media_file() and don't need this post-processing.
+"""
+
+import base64
+import binascii
+import hashlib
+import logging
+import uuid
+from typing import Any
+
+from backend.util.file import sanitize_filename
+from backend.util.workspace import WorkspaceManager
+
+logger = logging.getLogger(__name__)
+
+# Field names that contain binary data (base64 encoded)
+BINARY_FIELDS = {"png", "jpeg", "pdf"}
+
+# Field names that contain large text data (not base64, save as-is)
+TEXT_FIELDS = {"svg"}
+
+# Combined set for quick lookup
+SAVEABLE_FIELDS = BINARY_FIELDS | TEXT_FIELDS
+
+# Only process content larger than this (string length, not decoded size)
+SIZE_THRESHOLD = 1024  # 1KB
+
+
+async def process_binary_outputs(
+    outputs: dict[str, list[Any]],
+    workspace_manager: WorkspaceManager,
+    block_name: str,
+) -> dict[str, list[Any]]:
+    """
+    Replace binary data in block outputs with workspace:// references.
+
+    Scans outputs for known binary field names (png, jpeg, pdf, svg) and saves
+    large content to the workspace. Returns processed outputs with base64 data
+    replaced by workspace:// references.
+
+    Deduplicates identical content within a single call using content hashing.
+
+    Args:
+        outputs: Block execution outputs (dict of output_name -> list of values)
+        workspace_manager: WorkspaceManager instance with session scoping
+        block_name: Name of the block (used in generated filenames)
+
+    Returns:
+        Processed outputs with binary data replaced by workspace references
+    """
+    cache: dict[str, str] = {}  # content_hash -> workspace_ref
+
+    processed: dict[str, list[Any]] = {}
+    for name, items in outputs.items():
+        processed_items: list[Any] = []
+        for item in items:
+            processed_items.append(
+                await _process_item(item, workspace_manager, block_name, cache)
+            )
+        processed[name] = processed_items
+    return processed
+
+
+async def _process_item(
+    item: Any,
+    wm: WorkspaceManager,
+    block: str,
+    cache: dict[str, str],
+) -> Any:
+    """Recursively process an item, handling dicts and lists."""
+    if isinstance(item, dict):
+        return await _process_dict(item, wm, block, cache)
+    if isinstance(item, list):
+        processed: list[Any] = []
+        for i in item:
+            processed.append(await _process_item(i, wm, block, cache))
+        return processed
+    return item
+
+
+async def _process_dict(
+    data: dict[str, Any],
+    wm: WorkspaceManager,
+    block: str,
+    cache: dict[str, str],
+) -> dict[str, Any]:
+    """Process a dict, saving binary fields and recursing into nested structures."""
+    result: dict[str, Any] = {}
+
+    for key, value in data.items():
+        if (
+            key in SAVEABLE_FIELDS
+            and isinstance(value, str)
+            and len(value) > SIZE_THRESHOLD
+        ):
+            # Determine content bytes based on field type
+            if key in BINARY_FIELDS:
+                content = _decode_base64(value)
+                if content is None:
+                    # Decode failed, keep original value
+                    result[key] = value
+                    continue
+            else:
+                # TEXT_FIELDS: encode as UTF-8
+                content = value.encode("utf-8")
+
+            # Hash decoded content for deduplication
+            content_hash = hashlib.sha256(content).hexdigest()
+
+            if content_hash in cache:
+                # Reuse existing workspace reference
+                result[key] = cache[content_hash]
+            elif ref := await _save_content(content, key, wm, block):
+                # Save succeeded, cache and use reference
+                cache[content_hash] = ref
+                result[key] = ref
+            else:
+                # Save failed, keep original value
+                result[key] = value
+
+        elif isinstance(value, dict):
+            result[key] = await _process_dict(value, wm, block, cache)
+        elif isinstance(value, list):
+            processed: list[Any] = []
+            for i in value:
+                processed.append(await _process_item(i, wm, block, cache))
+            result[key] = processed
+        else:
+            result[key] = value
+
+    return result
+
+
+async def _save_content(
+    content: bytes,
+    field: str,
+    wm: WorkspaceManager,
+    block: str,
+) -> str | None:
+    """
+    Save content to workspace, return workspace:// reference.
+
+    Args:
+        content: Decoded binary content to save
+        field: Field name (used for extension)
+        wm: WorkspaceManager instance
+        block: Block name (used in filename)
+
+    Returns:
+        workspace://file-id reference, or None if save failed
+    """
+    try:
+        # Map field name to file extension
+        ext = {"jpeg": "jpg"}.get(field, field)
+
+        # Sanitize block name for safe filename
+        safe_block = sanitize_filename(block.lower())[:20]
+        filename = f"{safe_block}_{field}_{uuid.uuid4().hex[:12]}.{ext}"
+
+        file = await wm.write_file(content=content, filename=filename)
+        return f"workspace://{file.id}"
+
+    except Exception as e:
+        logger.error(f"Failed to save {field} to workspace for block '{block}': {e}")
+        return None
+
+
+def _decode_base64(value: str) -> bytes | None:
+    """
+    Decode base64 string, handling both raw base64 and data URI formats.
+
+    Args:
+        value: Base64 string or data URI (data:<mime>;base64,<payload>)
+
+    Returns:
+        Decoded bytes, or None if decoding failed
+    """
+    try:
+        # Handle data URI format
+        if value.startswith("data:"):
+            if "," in value:
+                value = value.split(",", 1)[1]
+            else:
+                # Malformed data URI, no comma separator
+                return None
+
+        # Normalize padding (handle missing = chars)
+        padded = value + "=" * (-len(value) % 4)
+
+        # Strict validation to prevent corrupted data
+        return base64.b64decode(padded, validate=True)
+
+    except (binascii.Error, ValueError):
+        return None

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

@@ -8,12 +8,16 @@ from typing import Any
 from pydantic_core import PydanticUndefined
 
 from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools.binary_output_processor import (
+    process_binary_outputs,
+)
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
+from backend.util.workspace import WorkspaceManager
 
 from .base import BaseTool
 from .models import (
@@ -321,11 +325,20 @@ class RunBlockTool(BaseTool):
             ):
                 outputs[output_name].append(output_data)
 
+            # Save binary outputs to workspace to prevent context bloat
+            # (code execution results with png/jpeg/pdf/svg fields)
+            workspace_manager = WorkspaceManager(
+                user_id, workspace.id, session.session_id
+            )
+            processed_outputs = await process_binary_outputs(
+                dict(outputs), workspace_manager, block.name
+            )
+
             return BlockOutputResponse(
                 message=f"Block '{block.name}' executed successfully",
                 block_id=block_id,
                 block_name=block.name,
-                outputs=dict(outputs),
+                outputs=processed_outputs,
                 success=True,
                 session_id=session_id,
             )

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

@@ -0,0 +1,204 @@
+"""Unit tests for binary_output_processor module."""
+
+import base64
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from backend.api.features.chat.tools.binary_output_processor import (
+    _decode_base64,
+    process_binary_outputs,
+)
+
+
+@pytest.fixture
+def workspace_manager():
+    """Create a mock WorkspaceManager."""
+    mock = MagicMock()
+    mock_file = MagicMock()
+    mock_file.id = "file-123"
+    mock.write_file = AsyncMock(return_value=mock_file)
+    return mock
+
+
+class TestDecodeBase64:
+    """Tests for _decode_base64 function."""
+
+    def test_raw_base64(self):
+        """Decode raw base64 string."""
+        encoded = base64.b64encode(b"test content").decode()
+        result = _decode_base64(encoded)
+        assert result == b"test content"
+
+    def test_data_uri(self):
+        """Decode base64 from data URI format."""
+        content = b"test content"
+        encoded = base64.b64encode(content).decode()
+        data_uri = f"data:image/png;base64,{encoded}"
+        result = _decode_base64(data_uri)
+        assert result == content
+
+    def test_invalid_base64(self):
+        """Return None for invalid base64."""
+        result = _decode_base64("not valid base64!!!")
+        assert result is None
+
+    def test_missing_padding(self):
+        """Handle base64 with missing padding."""
+        # base64.b64encode(b"test") = "dGVzdA=="
+        # Remove padding
+        result = _decode_base64("dGVzdA")
+        assert result == b"test"
+
+    def test_malformed_data_uri(self):
+        """Return None for data URI without comma."""
+        result = _decode_base64("data:image/png;base64")
+        assert result is None
+
+
+class TestProcessBinaryOutputs:
+    """Tests for process_binary_outputs function."""
+
+    @pytest.mark.asyncio
+    async def test_saves_large_png(self, workspace_manager):
+        """Large PNG content should be saved to workspace."""
+        # Create content larger than SIZE_THRESHOLD (1KB)
+        large_content = b"x" * 2000
+        encoded = base64.b64encode(large_content).decode()
+        outputs = {"result": [{"png": encoded}]}
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        assert result["result"][0]["png"] == "workspace://file-123"
+        workspace_manager.write_file.assert_called_once()
+        call_kwargs = workspace_manager.write_file.call_args.kwargs
+        assert call_kwargs["content"] == large_content
+        assert "testblock_png_" in call_kwargs["filename"]
+        assert call_kwargs["filename"].endswith(".png")
+
+    @pytest.mark.asyncio
+    async def test_preserves_small_content(self, workspace_manager):
+        """Small content should be preserved as-is."""
+        small_content = base64.b64encode(b"tiny").decode()
+        outputs = {"result": [{"png": small_content}]}
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        assert result["result"][0]["png"] == small_content
+        workspace_manager.write_file.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_identical_content(self, workspace_manager):
+        """Identical content should only be saved once."""
+        large_content = b"x" * 2000
+        encoded = base64.b64encode(large_content).decode()
+        outputs = {
+            "main_result": [{"png": encoded}],
+            "results": [{"png": encoded}],
+        }
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        # Both should have the same workspace reference
+        assert result["main_result"][0]["png"] == "workspace://file-123"
+        assert result["results"][0]["png"] == "workspace://file-123"
+        # But write_file should only be called once
+        assert workspace_manager.write_file.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_graceful_degradation_on_save_failure(self, workspace_manager):
+        """Original content should be preserved if save fails."""
+        workspace_manager.write_file = AsyncMock(side_effect=Exception("Save failed"))
+        large_content = b"x" * 2000
+        encoded = base64.b64encode(large_content).decode()
+        outputs = {"result": [{"png": encoded}]}
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        # Original content should be preserved
+        assert result["result"][0]["png"] == encoded
+
+    @pytest.mark.asyncio
+    async def test_handles_nested_structures(self, workspace_manager):
+        """Should traverse nested dicts and lists."""
+        large_content = b"x" * 2000
+        encoded = base64.b64encode(large_content).decode()
+        outputs = {
+            "result": [
+                {
+                    "nested": {
+                        "deep": {
+                            "png": encoded,
+                        }
+                    }
+                }
+            ]
+        }
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        assert result["result"][0]["nested"]["deep"]["png"] == "workspace://file-123"
+
+    @pytest.mark.asyncio
+    async def test_handles_svg_as_text(self, workspace_manager):
+        """SVG should be saved as UTF-8 text, not base64 decoded."""
+        svg_content = "<svg>" + "x" * 2000 + "</svg>"
+        outputs = {"result": [{"svg": svg_content}]}
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        assert result["result"][0]["svg"] == "workspace://file-123"
+        call_kwargs = workspace_manager.write_file.call_args.kwargs
+        # SVG should be UTF-8 encoded, not base64 decoded
+        assert call_kwargs["content"] == svg_content.encode("utf-8")
+        assert call_kwargs["filename"].endswith(".svg")
+
+    @pytest.mark.asyncio
+    async def test_ignores_unknown_fields(self, workspace_manager):
+        """Fields not in SAVEABLE_FIELDS should be ignored."""
+        large_content = "x" * 2000  # Large text in an unknown field
+        outputs = {"result": [{"unknown_field": large_content}]}
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        assert result["result"][0]["unknown_field"] == large_content
+        workspace_manager.write_file.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_handles_jpeg_extension(self, workspace_manager):
+        """JPEG files should use .jpg extension."""
+        large_content = b"x" * 2000
+        encoded = base64.b64encode(large_content).decode()
+        outputs = {"result": [{"jpeg": encoded}]}
+
+        await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        call_kwargs = workspace_manager.write_file.call_args.kwargs
+        assert call_kwargs["filename"].endswith(".jpg")
+
+    @pytest.mark.asyncio
+    async def test_handles_data_uri_in_binary_field(self, workspace_manager):
+        """Data URI format in binary fields should be properly decoded."""
+        large_content = b"x" * 2000
+        encoded = base64.b64encode(large_content).decode()
+        data_uri = f"data:image/png;base64,{encoded}"
+        outputs = {"result": [{"png": data_uri}]}
+
+        result = await process_binary_outputs(outputs, workspace_manager, "TestBlock")
+
+        assert result["result"][0]["png"] == "workspace://file-123"
+        call_kwargs = workspace_manager.write_file.call_args.kwargs
+        assert call_kwargs["content"] == large_content
+
+    @pytest.mark.asyncio
+    async def test_invalid_base64_preserves_original(self, workspace_manager):
+        """Invalid base64 in a binary field should preserve the original value."""
+        invalid_content = "not valid base64!!!" + "x" * 2000
+        outputs = {"result": [{"png": invalid_content}]}
+
+        processed = await process_binary_outputs(
+            outputs, workspace_manager, "TestBlock"
+        )
+
+        assert processed["result"][0]["png"] == invalid_content
+        workspace_manager.write_file.assert_not_called()

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

@@ -374,7 +374,7 @@ 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]:

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/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/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
@@ -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/mcp/block.py

@@ -1,265 +0,0 @@
-"""
-MCP (Model Context Protocol) Tool Block.
-
-A single dynamic block that can connect to any MCP server, discover available tools,
-and execute them. Works like AgentExecutorBlock — the user selects a tool from a
-dropdown and the input/output schema adapts dynamically.
-"""
-
-import json
-import logging
-from typing import Any, Literal
-
-from pydantic import SecretStr
-
-from backend.blocks.mcp.client import MCPClient, MCPClientError
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockInput,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    BlockType,
-)
-from backend.data.model import (
-    APIKeyCredentials,
-    CredentialsField,
-    CredentialsMetaInput,
-    OAuth2Credentials,
-    SchemaField,
-)
-from backend.integrations.providers import ProviderName
-from backend.util.json import validate_with_jsonschema
-
-logger = logging.getLogger(__name__)
-
-MCPCredentials = APIKeyCredentials | OAuth2Credentials
-MCPCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.MCP], Literal["api_key", "oauth2"]
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="mcp",
-    api_key=SecretStr("test-mcp-token"),
-    title="Mock MCP Credentials",
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-class MCPToolBlock(Block):
-    """
-    A block that connects to an MCP server, lets the user pick a tool,
-    and executes it with dynamic input/output schema.
-
-    The flow:
-    1. User provides an MCP server URL (and optional credentials)
-    2. Frontend calls the backend to get tool list from that URL
-    3. User selects a tool from a dropdown (available_tools)
-    4. The block's input schema updates to reflect the selected tool's parameters
-    5. On execution, the block calls the MCP server to run the tool
-    """
-
-    class Input(BlockSchemaInput):
-        # -- Static fields (always shown) --
-        credentials: MCPCredentialsInput = CredentialsField(
-            description="Credentials for the MCP server. Use an API key for Bearer "
-            "token auth, or OAuth2 for servers that support it. For public "
-            "servers, create a credential with any placeholder value.",
-        )
-        server_url: str = SchemaField(
-            description="URL of the MCP server (Streamable HTTP endpoint)",
-            placeholder="https://mcp.example.com/mcp",
-        )
-        available_tools: dict[str, Any] = SchemaField(
-            description="Available tools on the MCP server. "
-            "This is populated automatically when a server URL is provided.",
-            default={},
-            hidden=True,
-        )
-        selected_tool: str = SchemaField(
-            description="The MCP tool to execute",
-            placeholder="Select a tool",
-            default="",
-        )
-        tool_input_schema: dict[str, Any] = SchemaField(
-            description="JSON Schema for the selected tool's input parameters. "
-            "Populated automatically when a tool is selected.",
-            default={},
-            hidden=True,
-        )
-
-        # -- Dynamic field: actual arguments for the selected tool --
-        tool_arguments: dict[str, Any] = SchemaField(
-            description="Arguments to pass to the selected MCP tool. "
-            "The fields here are defined by the tool's input schema.",
-            default={},
-        )
-
-        @classmethod
-        def get_input_schema(cls, data: BlockInput) -> dict[str, Any]:
-            """Return the tool's input schema so the builder UI renders dynamic fields."""
-            return data.get("tool_input_schema", {})
-
-        @classmethod
-        def get_input_defaults(cls, data: BlockInput) -> BlockInput:
-            """Return the current tool_arguments as defaults for the dynamic fields."""
-            return data.get("tool_arguments", {})
-
-        @classmethod
-        def get_missing_input(cls, data: BlockInput) -> set[str]:
-            """Check which required tool arguments are missing."""
-            required_fields = cls.get_input_schema(data).get("required", [])
-            tool_arguments = data.get("tool_arguments", {})
-            return set(required_fields) - set(tool_arguments)
-
-        @classmethod
-        def get_mismatch_error(cls, data: BlockInput) -> str | None:
-            """Validate tool_arguments against the tool's input schema."""
-            tool_schema = cls.get_input_schema(data)
-            if not tool_schema:
-                return None
-            tool_arguments = data.get("tool_arguments", {})
-            return validate_with_jsonschema(tool_schema, tool_arguments)
-
-    class Output(BlockSchemaOutput):
-        result: Any = SchemaField(description="The result returned by the MCP tool")
-        error: str = SchemaField(description="Error message if the tool call failed")
-
-    def __init__(self):
-        super().__init__(
-            id="a0a4b1c2-d3e4-4f56-a7b8-c9d0e1f2a3b4",
-            description="Connect to any MCP server and execute its tools. "
-            "Provide a server URL, select a tool, and pass arguments dynamically.",
-            categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=MCPToolBlock.Input,
-            output_schema=MCPToolBlock.Output,
-            block_type=BlockType.STANDARD,
-            test_input={
-                "server_url": "https://mcp.example.com/mcp",
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "selected_tool": "get_weather",
-                "tool_input_schema": {
-                    "type": "object",
-                    "properties": {"city": {"type": "string"}},
-                    "required": ["city"],
-                },
-                "tool_arguments": {"city": "London"},
-            },
-            test_output=[
-                (
-                    "result",
-                    {"weather": "sunny", "temperature": 20},
-                ),
-            ],
-            test_mock={
-                "_call_mcp_tool": lambda *a, **kw: {
-                    "weather": "sunny",
-                    "temperature": 20,
-                },
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def _call_mcp_tool(
-        self,
-        server_url: str,
-        tool_name: str,
-        arguments: dict[str, Any],
-        auth_token: str | None = None,
-    ) -> Any:
-        """Call a tool on the MCP server. Extracted for easy mocking in tests."""
-        # Trust the user-configured server URL to allow internal/localhost servers
-        client = MCPClient(
-            server_url,
-            auth_token=auth_token,
-            trusted_origins=[server_url],
-        )
-        await client.initialize()
-        result = await client.call_tool(tool_name, arguments)
-
-        if result.is_error:
-            error_text = ""
-            for item in result.content:
-                if item.get("type") == "text":
-                    error_text += item.get("text", "")
-            raise MCPClientError(
-                f"MCP tool '{tool_name}' returned an error: "
-                f"{error_text or 'Unknown error'}"
-            )
-
-        # Extract text content from the result
-        output_parts = []
-        for item in result.content:
-            if item.get("type") == "text":
-                text = item.get("text", "")
-                # Try to parse as JSON for structured output
-                try:
-                    output_parts.append(json.loads(text))
-                except (json.JSONDecodeError, ValueError):
-                    output_parts.append(text)
-            elif item.get("type") == "image":
-                output_parts.append(
-                    {
-                        "type": "image",
-                        "data": item.get("data"),
-                        "mimeType": item.get("mimeType"),
-                    }
-                )
-            elif item.get("type") == "resource":
-                output_parts.append(item.get("resource", {}))
-
-        # If single result, unwrap
-        if len(output_parts) == 1:
-            return output_parts[0]
-        return output_parts if output_parts else None
-
-    @staticmethod
-    def _extract_auth_token(credentials: MCPCredentials) -> str | None:
-        """Extract a Bearer token from either API key or OAuth2 credentials."""
-        if isinstance(credentials, OAuth2Credentials):
-            return credentials.access_token.get_secret_value()
-
-        if isinstance(credentials, APIKeyCredentials) and credentials.api_key:
-            token_value = credentials.api_key.get_secret_value()
-            if token_value:
-                return token_value
-
-        return None
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: MCPCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        if not input_data.server_url:
-            yield "error", "MCP server URL is required"
-            return
-
-        if not input_data.selected_tool:
-            yield "error", "No tool selected. Please select a tool from the dropdown."
-            return
-
-        auth_token = self._extract_auth_token(credentials)
-
-        try:
-            result = await self._call_mcp_tool(
-                server_url=input_data.server_url,
-                tool_name=input_data.selected_tool,
-                arguments=input_data.tool_arguments,
-                auth_token=auth_token,
-            )
-            yield "result", result
-        except MCPClientError as e:
-            yield "error", str(e)
-        except Exception as e:
-            logger.exception(f"MCP tool call failed: {e}")
-            yield "error", f"MCP tool call failed: {str(e)}"

autogpt_platform/backend/backend/blocks/mcp/client.py

@@ -1,316 +0,0 @@
-"""
-MCP (Model Context Protocol) HTTP client.
-
-Implements the MCP Streamable HTTP transport for listing tools and calling tools
-on remote MCP servers. Uses JSON-RPC 2.0 over HTTP POST.
-
-Handles both JSON and SSE (text/event-stream) response formats per the MCP spec.
-
-Reference: https://modelcontextprotocol.io/specification/2025-03-26/basic/transports
-"""
-
-import json
-import logging
-from dataclasses import dataclass, field
-from typing import Any
-
-from backend.util.request import Requests
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class MCPTool:
-    """Represents an MCP tool discovered from a server."""
-
-    name: str
-    description: str
-    input_schema: dict[str, Any]
-
-
-@dataclass
-class MCPCallResult:
-    """Result from calling an MCP tool."""
-
-    content: list[dict[str, Any]] = field(default_factory=list)
-    is_error: bool = False
-
-
-class MCPClientError(Exception):
-    """Raised when an MCP protocol error occurs."""
-
-    pass
-
-
-class MCPClient:
-    """
-    Async HTTP client for the MCP Streamable HTTP transport.
-
-    Communicates with MCP servers using JSON-RPC 2.0 over HTTP POST.
-    Supports optional Bearer token authentication.
-    """
-
-    def __init__(
-        self,
-        server_url: str,
-        auth_token: str | None = None,
-        trusted_origins: list[str] | None = None,
-    ):
-        self.server_url = server_url.rstrip("/")
-        self.auth_token = auth_token
-        self.trusted_origins = trusted_origins or []
-        self._request_id = 0
-
-    def _next_id(self) -> int:
-        self._request_id += 1
-        return self._request_id
-
-    def _build_headers(self) -> dict[str, str]:
-        headers = {
-            "Content-Type": "application/json",
-            "Accept": "application/json, text/event-stream",
-        }
-        if self.auth_token:
-            headers["Authorization"] = f"Bearer {self.auth_token}"
-        return headers
-
-    def _build_jsonrpc_request(
-        self, method: str, params: dict[str, Any] | None = None
-    ) -> dict[str, Any]:
-        req: dict[str, Any] = {
-            "jsonrpc": "2.0",
-            "method": method,
-            "id": self._next_id(),
-        }
-        if params is not None:
-            req["params"] = params
-        return req
-
-    @staticmethod
-    def _parse_sse_response(text: str) -> dict[str, Any]:
-        """Parse an SSE (text/event-stream) response body into JSON-RPC data.
-
-        MCP servers may return responses as SSE with format:
-            event: message
-            data: {"jsonrpc":"2.0","result":{...},"id":1}
-
-        We extract the last `data:` line that contains a JSON-RPC response
-        (i.e. has an "id" field), which is the reply to our request.
-        """
-        last_data: dict[str, Any] | None = None
-        for line in text.splitlines():
-            stripped = line.strip()
-            if stripped.startswith("data:"):
-                payload = stripped[len("data:") :].strip()
-                if not payload:
-                    continue
-                try:
-                    parsed = json.loads(payload)
-                    # Only keep JSON-RPC responses (have "id"), skip notifications
-                    if isinstance(parsed, dict) and "id" in parsed:
-                        last_data = parsed
-                except (json.JSONDecodeError, ValueError):
-                    continue
-        if last_data is None:
-            raise MCPClientError("No JSON-RPC response found in SSE stream")
-        return last_data
-
-    async def _send_request(
-        self, method: str, params: dict[str, Any] | None = None
-    ) -> Any:
-        """Send a JSON-RPC request to the MCP server and return the result.
-
-        Handles both ``application/json`` and ``text/event-stream`` responses
-        as required by the MCP Streamable HTTP transport specification.
-        """
-        payload = self._build_jsonrpc_request(method, params)
-        headers = self._build_headers()
-
-        requests = Requests(
-            raise_for_status=True,
-            extra_headers=headers,
-            trusted_origins=self.trusted_origins,
-        )
-        response = await requests.post(self.server_url, json=payload)
-
-        content_type = response.headers.get("content-type", "")
-        if "text/event-stream" in content_type:
-            body = self._parse_sse_response(response.text())
-        else:
-            try:
-                body = response.json()
-            except (ValueError, Exception) as e:
-                raise MCPClientError(
-                    f"MCP server returned non-JSON response: {e}"
-                ) from e
-
-        # Handle JSON-RPC error
-        if "error" in body:
-            error = body["error"]
-            if isinstance(error, dict):
-                raise MCPClientError(
-                    f"MCP server error [{error.get('code', '?')}]: "
-                    f"{error.get('message', 'Unknown error')}"
-                )
-            raise MCPClientError(f"MCP server error: {error}")
-
-        return body.get("result")
-
-    async def _send_notification(self, method: str) -> None:
-        """Send a JSON-RPC notification (no id, no response expected)."""
-        headers = self._build_headers()
-        notification = {"jsonrpc": "2.0", "method": method}
-        requests = Requests(
-            raise_for_status=False,
-            extra_headers=headers,
-            trusted_origins=self.trusted_origins,
-        )
-        await requests.post(self.server_url, json=notification)
-
-    async def discover_auth(self) -> dict[str, Any] | None:
-        """Probe the MCP server's OAuth metadata (RFC 9728 / MCP spec).
-
-        Returns ``None`` if the server doesn't require auth, otherwise returns
-        a dict with:
-          - ``authorization_servers``: list of authorization server URLs
-          - ``resource``: the resource indicator URL (usually the MCP endpoint)
-          - ``scopes_supported``: optional list of supported scopes
-
-        The caller can then fetch the authorization server metadata to get
-        ``authorization_endpoint``, ``token_endpoint``, etc.
-        """
-        from urllib.parse import urlparse
-
-        parsed = urlparse(self.server_url)
-        base = f"{parsed.scheme}://{parsed.netloc}"
-
-        # Build candidates for protected-resource metadata (per RFC 9728)
-        path = parsed.path.rstrip("/")
-        candidates = []
-        if path and path != "/":
-            candidates.append(f"{base}/.well-known/oauth-protected-resource{path}")
-        candidates.append(f"{base}/.well-known/oauth-protected-resource")
-
-        requests = Requests(
-            raise_for_status=False,
-            trusted_origins=self.trusted_origins,
-        )
-        for url in candidates:
-            try:
-                resp = await requests.get(url)
-                if resp.status == 200:
-                    data = resp.json()
-                    if isinstance(data, dict) and "authorization_servers" in data:
-                        return data
-            except Exception:
-                continue
-
-        return None
-
-    async def discover_auth_server_metadata(
-        self, auth_server_url: str
-    ) -> dict[str, Any] | None:
-        """Fetch the OAuth Authorization Server Metadata (RFC 8414).
-
-        Given an authorization server URL, returns a dict with:
-          - ``authorization_endpoint``
-          - ``token_endpoint``
-          - ``registration_endpoint`` (for dynamic client registration)
-          - ``scopes_supported``
-          - ``code_challenge_methods_supported``
-          - etc.
-        """
-        from urllib.parse import urlparse
-
-        parsed = urlparse(auth_server_url)
-        base = f"{parsed.scheme}://{parsed.netloc}"
-        path = parsed.path.rstrip("/")
-
-        # Try standard metadata endpoints (RFC 8414 and OpenID Connect)
-        candidates = []
-        if path and path != "/":
-            candidates.append(f"{base}/.well-known/oauth-authorization-server{path}")
-        candidates.append(f"{base}/.well-known/oauth-authorization-server")
-        candidates.append(f"{base}/.well-known/openid-configuration")
-
-        requests = Requests(
-            raise_for_status=False,
-            trusted_origins=self.trusted_origins,
-        )
-        for url in candidates:
-            try:
-                resp = await requests.get(url)
-                if resp.status == 200:
-                    data = resp.json()
-                    if isinstance(data, dict) and "authorization_endpoint" in data:
-                        return data
-            except Exception:
-                continue
-
-        return None
-
-    async def initialize(self) -> dict[str, Any]:
-        """
-        Send the MCP initialize request.
-
-        This is required by the MCP protocol before any other requests.
-        Returns the server's capabilities.
-        """
-        result = await self._send_request(
-            "initialize",
-            {
-                "protocolVersion": "2025-03-26",
-                "capabilities": {},
-                "clientInfo": {"name": "AutoGPT-Platform", "version": "1.0.0"},
-            },
-        )
-        # Send initialized notification (no response expected)
-        await self._send_notification("notifications/initialized")
-
-        return result or {}
-
-    async def list_tools(self) -> list[MCPTool]:
-        """
-        Discover available tools from the MCP server.
-
-        Returns a list of MCPTool objects with name, description, and input schema.
-        """
-        result = await self._send_request("tools/list")
-        if not result or "tools" not in result:
-            return []
-
-        tools = []
-        for tool_data in result["tools"]:
-            tools.append(
-                MCPTool(
-                    name=tool_data.get("name", ""),
-                    description=tool_data.get("description", ""),
-                    input_schema=tool_data.get("inputSchema", {}),
-                )
-            )
-        return tools
-
-    async def call_tool(
-        self, tool_name: str, arguments: dict[str, Any]
-    ) -> MCPCallResult:
-        """
-        Call a tool on the MCP server.
-
-        Args:
-            tool_name: The name of the tool to call.
-            arguments: The arguments to pass to the tool.
-
-        Returns:
-            MCPCallResult with the tool's response content.
-        """
-        result = await self._send_request(
-            "tools/call",
-            {"name": tool_name, "arguments": arguments},
-        )
-        if not result:
-            return MCPCallResult(is_error=True)
-
-        return MCPCallResult(
-            content=result.get("content", []),
-            is_error=result.get("isError", False),
-        )

autogpt_platform/backend/backend/blocks/mcp/conftest.py

@@ -1,21 +0,0 @@
-"""
-Conftest for MCP block tests.
-
-Override the session-scoped server and graph_cleanup fixtures from
-backend/conftest.py so that MCP integration tests don't spin up the
-full SpinTestServer infrastructure.
-"""
-
-import pytest
-
-
-@pytest.fixture(scope="session")
-def server():
-    """No-op override — MCP tests don't need the full platform server."""
-    yield None
-
-
-@pytest.fixture(scope="session", autouse=True)
-def graph_cleanup(server):
-    """No-op override — MCP tests don't create graphs."""
-    yield

autogpt_platform/backend/backend/blocks/mcp/oauth.py

@@ -1,198 +0,0 @@
-"""
-MCP OAuth handler for MCP servers that use OAuth 2.1 authorization.
-
-Unlike other OAuth handlers (GitHub, Google, etc.) where endpoints are fixed,
-MCP servers have dynamic endpoints discovered via RFC 9728 / RFC 8414 metadata.
-This handler accepts those endpoints at construction time.
-"""
-
-import logging
-import time
-import urllib.parse
-from typing import ClassVar, Optional
-
-from pydantic import SecretStr
-
-from backend.data.model import OAuth2Credentials
-from backend.integrations.oauth.base import BaseOAuthHandler
-from backend.integrations.providers import ProviderName
-from backend.util.request import Requests
-
-logger = logging.getLogger(__name__)
-
-
-class MCPOAuthHandler(BaseOAuthHandler):
-    """
-    OAuth handler for MCP servers with dynamically-discovered endpoints.
-
-    Construction requires the authorization and token endpoint URLs,
-    which are obtained via MCP OAuth metadata discovery
-    (``MCPClient.discover_auth`` + ``discover_auth_server_metadata``).
-    """
-
-    PROVIDER_NAME: ClassVar[ProviderName | str] = ProviderName.MCP
-    DEFAULT_SCOPES: ClassVar[list[str]] = []
-
-    def __init__(
-        self,
-        client_id: str,
-        client_secret: str,
-        redirect_uri: str,
-        *,
-        authorize_url: str,
-        token_url: str,
-        revoke_url: str | None = None,
-        resource_url: str | None = None,
-    ):
-        self.client_id = client_id
-        self.client_secret = client_secret
-        self.redirect_uri = redirect_uri
-        self.authorize_url = authorize_url
-        self.token_url = token_url
-        self.revoke_url = revoke_url
-        self.resource_url = resource_url
-
-    def get_login_url(
-        self,
-        scopes: list[str],
-        state: str,
-        code_challenge: Optional[str],
-    ) -> str:
-        scopes = self.handle_default_scopes(scopes)
-
-        params: dict[str, str] = {
-            "response_type": "code",
-            "client_id": self.client_id,
-            "redirect_uri": self.redirect_uri,
-            "state": state,
-        }
-        if scopes:
-            params["scope"] = " ".join(scopes)
-        # PKCE (S256) — included when the caller provides a code_challenge
-        if code_challenge:
-            params["code_challenge"] = code_challenge
-            params["code_challenge_method"] = "S256"
-        # MCP spec requires resource indicator (RFC 8707)
-        if self.resource_url:
-            params["resource"] = self.resource_url
-
-        return f"{self.authorize_url}?{urllib.parse.urlencode(params)}"
-
-    async def exchange_code_for_tokens(
-        self,
-        code: str,
-        scopes: list[str],
-        code_verifier: Optional[str],
-    ) -> OAuth2Credentials:
-        data: dict[str, str] = {
-            "grant_type": "authorization_code",
-            "code": code,
-            "redirect_uri": self.redirect_uri,
-            "client_id": self.client_id,
-        }
-        if self.client_secret:
-            data["client_secret"] = self.client_secret
-        if code_verifier:
-            data["code_verifier"] = code_verifier
-        if self.resource_url:
-            data["resource"] = self.resource_url
-
-        response = await Requests(raise_for_status=True).post(
-            self.token_url,
-            data=data,
-            headers={"Content-Type": "application/x-www-form-urlencoded"},
-        )
-        tokens = response.json()
-
-        if "error" in tokens:
-            raise RuntimeError(
-                f"Token exchange failed: {tokens.get('error_description', tokens['error'])}"
-            )
-
-        now = int(time.time())
-        expires_in = tokens.get("expires_in")
-
-        return OAuth2Credentials(
-            provider=str(self.PROVIDER_NAME),
-            title=None,
-            access_token=SecretStr(tokens["access_token"]),
-            refresh_token=(
-                SecretStr(tokens["refresh_token"])
-                if tokens.get("refresh_token")
-                else None
-            ),
-            access_token_expires_at=now + expires_in if expires_in else None,
-            refresh_token_expires_at=None,
-            scopes=scopes,
-            metadata={
-                "mcp_token_url": self.token_url,
-                "mcp_resource_url": self.resource_url,
-            },
-        )
-
-    async def _refresh_tokens(
-        self, credentials: OAuth2Credentials
-    ) -> OAuth2Credentials:
-        if not credentials.refresh_token:
-            raise ValueError("No refresh token available for MCP OAuth credentials")
-
-        data: dict[str, str] = {
-            "grant_type": "refresh_token",
-            "refresh_token": credentials.refresh_token.get_secret_value(),
-            "client_id": self.client_id,
-        }
-        if self.client_secret:
-            data["client_secret"] = self.client_secret
-        if self.resource_url:
-            data["resource"] = self.resource_url
-
-        response = await Requests(raise_for_status=True).post(
-            self.token_url,
-            data=data,
-            headers={"Content-Type": "application/x-www-form-urlencoded"},
-        )
-        tokens = response.json()
-
-        if "error" in tokens:
-            raise RuntimeError(
-                f"Token refresh failed: {tokens.get('error_description', tokens['error'])}"
-            )
-
-        now = int(time.time())
-        expires_in = tokens.get("expires_in")
-
-        return OAuth2Credentials(
-            id=credentials.id,
-            provider=str(self.PROVIDER_NAME),
-            title=credentials.title,
-            access_token=SecretStr(tokens["access_token"]),
-            refresh_token=(
-                SecretStr(str(tokens["refresh_token"]))
-                if tokens.get("refresh_token")
-                else credentials.refresh_token
-            ),
-            access_token_expires_at=now + expires_in if expires_in else None,
-            refresh_token_expires_at=credentials.refresh_token_expires_at,
-            scopes=credentials.scopes,
-            metadata=credentials.metadata,
-        )
-
-    async def revoke_tokens(self, credentials: OAuth2Credentials) -> bool:
-        if not self.revoke_url:
-            return False
-
-        try:
-            data = {
-                "token": credentials.access_token.get_secret_value(),
-                "token_type_hint": "access_token",
-                "client_id": self.client_id,
-            }
-            await Requests().post(
-                self.revoke_url,
-                data=data,
-                headers={"Content-Type": "application/x-www-form-urlencoded"},
-            )
-            return True
-        except Exception:
-            logger.warning("Failed to revoke MCP OAuth tokens", exc_info=True)
-            return False

autogpt_platform/backend/backend/blocks/mcp/test_e2e.py

@@ -1,104 +0,0 @@
-"""
-End-to-end tests against a real public MCP server.
-
-These tests hit the OpenAI docs MCP server (https://developers.openai.com/mcp)
-which is publicly accessible without authentication and returns SSE responses.
-
-Mark: These are tagged with ``@pytest.mark.e2e`` so they can be run/skipped
-independently of the rest of the test suite (they require network access).
-"""
-
-import json
-
-import pytest
-
-from backend.blocks.mcp.client import MCPClient
-
-# Public MCP server that requires no authentication
-OPENAI_DOCS_MCP_URL = "https://developers.openai.com/mcp"
-
-
-@pytest.mark.e2e
-class TestRealMCPServer:
-    """Tests against the live OpenAI docs MCP server."""
-
-    @pytest.mark.asyncio
-    async def test_initialize(self):
-        """Verify we can complete the MCP handshake with a real server."""
-        client = MCPClient(OPENAI_DOCS_MCP_URL)
-        result = await client.initialize()
-
-        assert result["protocolVersion"] == "2025-03-26"
-        assert "serverInfo" in result
-        assert result["serverInfo"]["name"] == "openai-docs-mcp"
-        assert "tools" in result.get("capabilities", {})
-
-    @pytest.mark.asyncio
-    async def test_list_tools(self):
-        """Verify we can discover tools from a real MCP server."""
-        client = MCPClient(OPENAI_DOCS_MCP_URL)
-        await client.initialize()
-        tools = await client.list_tools()
-
-        assert len(tools) >= 3  # server has at least 5 tools as of writing
-
-        tool_names = {t.name for t in tools}
-        # These tools are documented and should be stable
-        assert "search_openai_docs" in tool_names
-        assert "list_openai_docs" in tool_names
-        assert "fetch_openai_doc" in tool_names
-
-        # Verify schema structure
-        search_tool = next(t for t in tools if t.name == "search_openai_docs")
-        assert "query" in search_tool.input_schema.get("properties", {})
-        assert "query" in search_tool.input_schema.get("required", [])
-
-    @pytest.mark.asyncio
-    async def test_call_tool_list_api_endpoints(self):
-        """Call the list_api_endpoints tool and verify we get real data."""
-        client = MCPClient(OPENAI_DOCS_MCP_URL)
-        await client.initialize()
-        result = await client.call_tool("list_api_endpoints", {})
-
-        assert not result.is_error
-        assert len(result.content) >= 1
-        assert result.content[0]["type"] == "text"
-
-        data = json.loads(result.content[0]["text"])
-        assert "paths" in data or "urls" in data
-        # The OpenAI API should have many endpoints
-        total = data.get("total", len(data.get("paths", [])))
-        assert total > 50
-
-    @pytest.mark.asyncio
-    async def test_call_tool_search(self):
-        """Search for docs and verify we get results."""
-        client = MCPClient(OPENAI_DOCS_MCP_URL)
-        await client.initialize()
-        result = await client.call_tool(
-            "search_openai_docs", {"query": "chat completions", "limit": 3}
-        )
-
-        assert not result.is_error
-        assert len(result.content) >= 1
-
-    @pytest.mark.asyncio
-    async def test_sse_response_handling(self):
-        """Verify the client correctly handles SSE responses from a real server.
-
-        This is the key test — our local test server returns JSON,
-        but real MCP servers typically return SSE. This proves the
-        SSE parsing works end-to-end.
-        """
-        client = MCPClient(OPENAI_DOCS_MCP_URL)
-        # initialize() internally calls _send_request which must parse SSE
-        result = await client.initialize()
-
-        # If we got here without error, SSE parsing works
-        assert isinstance(result, dict)
-        assert "protocolVersion" in result
-
-        # Also verify list_tools works (another SSE response)
-        tools = await client.list_tools()
-        assert len(tools) > 0
-        assert all(hasattr(t, "name") for t in tools)

autogpt_platform/backend/backend/blocks/mcp/test_integration.py

@@ -1,367 +0,0 @@
-"""
-Integration tests for MCP client and MCPToolBlock against a real HTTP server.
-
-These tests spin up a local MCP test server and run the full client/block flow
-against it — no mocking, real HTTP requests.
-"""
-
-import asyncio
-import json
-import threading
-
-import pytest
-from aiohttp import web
-from pydantic import SecretStr
-
-from backend.blocks.mcp.block import MCPToolBlock
-from backend.blocks.mcp.client import MCPClient
-from backend.blocks.mcp.test_server import create_test_mcp_app
-from backend.data.model import APIKeyCredentials
-
-
-class _MCPTestServer:
-    """
-    Run an MCP test server in a background thread with its own event loop.
-    This avoids event loop conflicts with pytest-asyncio.
-    """
-
-    def __init__(self, auth_token: str | None = None):
-        self.auth_token = auth_token
-        self.url: str = ""
-        self._runner: web.AppRunner | None = None
-        self._loop: asyncio.AbstractEventLoop | None = None
-        self._thread: threading.Thread | None = None
-        self._started = threading.Event()
-
-    def _run(self):
-        self._loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(self._loop)
-        self._loop.run_until_complete(self._start())
-        self._started.set()
-        self._loop.run_forever()
-
-    async def _start(self):
-        app = create_test_mcp_app(auth_token=self.auth_token)
-        self._runner = web.AppRunner(app)
-        await self._runner.setup()
-        site = web.TCPSite(self._runner, "127.0.0.1", 0)
-        await site.start()
-        port = site._server.sockets[0].getsockname()[1]  # type: ignore[union-attr]
-        self.url = f"http://127.0.0.1:{port}/mcp"
-
-    def start(self):
-        self._thread = threading.Thread(target=self._run, daemon=True)
-        self._thread.start()
-        if not self._started.wait(timeout=5):
-            raise RuntimeError("MCP test server failed to start within 5 seconds")
-        return self
-
-    def stop(self):
-        if self._loop and self._runner:
-            asyncio.run_coroutine_threadsafe(self._runner.cleanup(), self._loop).result(
-                timeout=5
-            )
-            self._loop.call_soon_threadsafe(self._loop.stop)
-        if self._thread:
-            self._thread.join(timeout=5)
-
-
-@pytest.fixture(scope="module")
-def mcp_server():
-    """Start a local MCP test server in a background thread."""
-    server = _MCPTestServer()
-    server.start()
-    yield server.url
-    server.stop()
-
-
-@pytest.fixture(scope="module")
-def mcp_server_with_auth():
-    """Start a local MCP test server with auth in a background thread."""
-    server = _MCPTestServer(auth_token="test-secret-token")
-    server.start()
-    yield server.url, "test-secret-token"
-    server.stop()
-
-
-def _make_client(url: str, auth_token: str | None = None) -> MCPClient:
-    """Create an MCPClient with localhost trusted for integration tests."""
-    return MCPClient(url, auth_token=auth_token, trusted_origins=[url])
-
-
-def _make_fake_creds(api_key: str = "FAKE_API_KEY") -> APIKeyCredentials:
-    return APIKeyCredentials(
-        id="test-integration",
-        provider="mcp",
-        api_key=SecretStr(api_key),
-        title="test",
-    )
-
-
-# ── MCPClient integration tests ──────────────────────────────────────
-
-
-class TestMCPClientIntegration:
-    """Test MCPClient against a real local MCP server."""
-
-    @pytest.mark.asyncio
-    async def test_initialize(self, mcp_server):
-        client = _make_client(mcp_server)
-        result = await client.initialize()
-
-        assert result["protocolVersion"] == "2025-03-26"
-        assert result["serverInfo"]["name"] == "test-mcp-server"
-        assert "tools" in result["capabilities"]
-
-    @pytest.mark.asyncio
-    async def test_list_tools(self, mcp_server):
-        client = _make_client(mcp_server)
-        await client.initialize()
-        tools = await client.list_tools()
-
-        assert len(tools) == 3
-
-        tool_names = {t.name for t in tools}
-        assert tool_names == {"get_weather", "add_numbers", "echo"}
-
-        # Check get_weather schema
-        weather = next(t for t in tools if t.name == "get_weather")
-        assert weather.description == "Get current weather for a city"
-        assert "city" in weather.input_schema["properties"]
-        assert weather.input_schema["required"] == ["city"]
-
-        # Check add_numbers schema
-        add = next(t for t in tools if t.name == "add_numbers")
-        assert "a" in add.input_schema["properties"]
-        assert "b" in add.input_schema["properties"]
-
-    @pytest.mark.asyncio
-    async def test_call_tool_get_weather(self, mcp_server):
-        client = _make_client(mcp_server)
-        await client.initialize()
-        result = await client.call_tool("get_weather", {"city": "London"})
-
-        assert not result.is_error
-        assert len(result.content) == 1
-        assert result.content[0]["type"] == "text"
-
-        data = json.loads(result.content[0]["text"])
-        assert data["city"] == "London"
-        assert data["temperature"] == 22
-        assert data["condition"] == "sunny"
-
-    @pytest.mark.asyncio
-    async def test_call_tool_add_numbers(self, mcp_server):
-        client = _make_client(mcp_server)
-        await client.initialize()
-        result = await client.call_tool("add_numbers", {"a": 3, "b": 7})
-
-        assert not result.is_error
-        data = json.loads(result.content[0]["text"])
-        assert data["result"] == 10
-
-    @pytest.mark.asyncio
-    async def test_call_tool_echo(self, mcp_server):
-        client = _make_client(mcp_server)
-        await client.initialize()
-        result = await client.call_tool("echo", {"message": "Hello MCP!"})
-
-        assert not result.is_error
-        assert result.content[0]["text"] == "Hello MCP!"
-
-    @pytest.mark.asyncio
-    async def test_call_unknown_tool(self, mcp_server):
-        client = _make_client(mcp_server)
-        await client.initialize()
-        result = await client.call_tool("nonexistent_tool", {})
-
-        assert result.is_error
-        assert "Unknown tool" in result.content[0]["text"]
-
-    @pytest.mark.asyncio
-    async def test_auth_success(self, mcp_server_with_auth):
-        url, token = mcp_server_with_auth
-        client = _make_client(url, auth_token=token)
-        result = await client.initialize()
-
-        assert result["protocolVersion"] == "2025-03-26"
-
-        tools = await client.list_tools()
-        assert len(tools) == 3
-
-    @pytest.mark.asyncio
-    async def test_auth_failure(self, mcp_server_with_auth):
-        url, _ = mcp_server_with_auth
-        client = _make_client(url, auth_token="wrong-token")
-
-        with pytest.raises(Exception):
-            await client.initialize()
-
-    @pytest.mark.asyncio
-    async def test_auth_missing(self, mcp_server_with_auth):
-        url, _ = mcp_server_with_auth
-        client = _make_client(url)
-
-        with pytest.raises(Exception):
-            await client.initialize()
-
-
-# ── MCPToolBlock integration tests ───────────────────────────────────
-
-
-class TestMCPToolBlockIntegration:
-    """Test MCPToolBlock end-to-end against a real local MCP server."""
-
-    @pytest.mark.asyncio
-    async def test_full_flow_get_weather(self, mcp_server):
-        """Full flow: discover tools, select one, execute it."""
-        # Step 1: Discover tools (simulating what the frontend/API would do)
-        client = _make_client(mcp_server)
-        await client.initialize()
-        tools = await client.list_tools()
-        assert len(tools) == 3
-
-        # Step 2: User selects "get_weather" and we get its schema
-        weather_tool = next(t for t in tools if t.name == "get_weather")
-
-        # Step 3: Execute the block with the selected tool
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url=mcp_server,
-            selected_tool="get_weather",
-            tool_input_schema=weather_tool.input_schema,
-            tool_arguments={"city": "Paris"},
-            credentials={  # type: ignore
-                "provider": "mcp",
-                "id": "test",
-                "type": "api_key",
-                "title": "test",
-            },
-        )
-
-        outputs = []
-        async for name, data in block.run(input_data, credentials=_make_fake_creds()):
-            outputs.append((name, data))
-
-        assert len(outputs) == 1
-        assert outputs[0][0] == "result"
-        result = outputs[0][1]
-        assert result["city"] == "Paris"
-        assert result["temperature"] == 22
-        assert result["condition"] == "sunny"
-
-    @pytest.mark.asyncio
-    async def test_full_flow_add_numbers(self, mcp_server):
-        """Full flow for add_numbers tool."""
-        client = _make_client(mcp_server)
-        await client.initialize()
-        tools = await client.list_tools()
-        add_tool = next(t for t in tools if t.name == "add_numbers")
-
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url=mcp_server,
-            selected_tool="add_numbers",
-            tool_input_schema=add_tool.input_schema,
-            tool_arguments={"a": 42, "b": 58},
-            credentials={  # type: ignore
-                "provider": "mcp",
-                "id": "test",
-                "type": "api_key",
-                "title": "test",
-            },
-        )
-
-        outputs = []
-        async for name, data in block.run(input_data, credentials=_make_fake_creds()):
-            outputs.append((name, data))
-
-        assert len(outputs) == 1
-        assert outputs[0][0] == "result"
-        assert outputs[0][1]["result"] == 100
-
-    @pytest.mark.asyncio
-    async def test_full_flow_echo_plain_text(self, mcp_server):
-        """Verify plain text (non-JSON) responses work."""
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url=mcp_server,
-            selected_tool="echo",
-            tool_input_schema={
-                "type": "object",
-                "properties": {"message": {"type": "string"}},
-                "required": ["message"],
-            },
-            tool_arguments={"message": "Hello from AutoGPT!"},
-            credentials={  # type: ignore
-                "provider": "mcp",
-                "id": "test",
-                "type": "api_key",
-                "title": "test",
-            },
-        )
-
-        outputs = []
-        async for name, data in block.run(input_data, credentials=_make_fake_creds()):
-            outputs.append((name, data))
-
-        assert len(outputs) == 1
-        assert outputs[0][0] == "result"
-        assert outputs[0][1] == "Hello from AutoGPT!"
-
-    @pytest.mark.asyncio
-    async def test_full_flow_unknown_tool_yields_error(self, mcp_server):
-        """Calling an unknown tool should yield an error output."""
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url=mcp_server,
-            selected_tool="nonexistent_tool",
-            tool_arguments={},
-            credentials={  # type: ignore
-                "provider": "mcp",
-                "id": "test",
-                "type": "api_key",
-                "title": "test",
-            },
-        )
-
-        outputs = []
-        async for name, data in block.run(input_data, credentials=_make_fake_creds()):
-            outputs.append((name, data))
-
-        assert len(outputs) == 1
-        assert outputs[0][0] == "error"
-        assert "returned an error" in outputs[0][1]
-
-    @pytest.mark.asyncio
-    async def test_full_flow_with_auth(self, mcp_server_with_auth):
-        """Full flow with authentication."""
-        url, token = mcp_server_with_auth
-
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url=url,
-            selected_tool="echo",
-            tool_input_schema={
-                "type": "object",
-                "properties": {"message": {"type": "string"}},
-                "required": ["message"],
-            },
-            tool_arguments={"message": "Authenticated!"},
-            credentials={  # type: ignore
-                "provider": "mcp",
-                "id": "test",
-                "type": "api_key",
-                "title": "test",
-            },
-        )
-
-        outputs = []
-        async for name, data in block.run(
-            input_data, credentials=_make_fake_creds(api_key=token)
-        ):
-            outputs.append((name, data))
-
-        assert len(outputs) == 1
-        assert outputs[0][0] == "result"
-        assert outputs[0][1] == "Authenticated!"

autogpt_platform/backend/backend/blocks/mcp/test_mcp.py

@@ -1,667 +0,0 @@
-"""
-Tests for MCP client and MCPToolBlock.
-"""
-
-import json
-from unittest.mock import AsyncMock, patch
-
-import pytest
-from pydantic import SecretStr
-
-from backend.blocks.mcp.block import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    MCPToolBlock,
-)
-from backend.blocks.mcp.client import MCPCallResult, MCPClient, MCPClientError
-from backend.data.model import APIKeyCredentials, OAuth2Credentials
-from backend.util.test import execute_block_test
-
-# ── SSE parsing unit tests ───────────────────────────────────────────
-
-
-class TestSSEParsing:
-    """Tests for SSE (text/event-stream) response parsing."""
-
-    def test_parse_sse_simple(self):
-        sse = (
-            "event: message\n"
-            'data: {"jsonrpc":"2.0","result":{"tools":[]},"id":1}\n'
-            "\n"
-        )
-        body = MCPClient._parse_sse_response(sse)
-        assert body["result"] == {"tools": []}
-        assert body["id"] == 1
-
-    def test_parse_sse_with_notifications(self):
-        """SSE streams can contain notifications (no id) before the response."""
-        sse = (
-            "event: message\n"
-            'data: {"jsonrpc":"2.0","method":"some/notification"}\n'
-            "\n"
-            "event: message\n"
-            'data: {"jsonrpc":"2.0","result":{"ok":true},"id":2}\n'
-            "\n"
-        )
-        body = MCPClient._parse_sse_response(sse)
-        assert body["result"] == {"ok": True}
-        assert body["id"] == 2
-
-    def test_parse_sse_error_response(self):
-        sse = (
-            "event: message\n"
-            'data: {"jsonrpc":"2.0","error":{"code":-32600,"message":"Bad Request"},"id":1}\n'
-        )
-        body = MCPClient._parse_sse_response(sse)
-        assert "error" in body
-        assert body["error"]["code"] == -32600
-
-    def test_parse_sse_no_data_raises(self):
-        with pytest.raises(MCPClientError, match="No JSON-RPC response found"):
-            MCPClient._parse_sse_response("event: message\n\n")
-
-    def test_parse_sse_empty_raises(self):
-        with pytest.raises(MCPClientError, match="No JSON-RPC response found"):
-            MCPClient._parse_sse_response("")
-
-    def test_parse_sse_ignores_non_data_lines(self):
-        sse = (
-            ": comment line\n"
-            "event: message\n"
-            "id: 123\n"
-            'data: {"jsonrpc":"2.0","result":"ok","id":1}\n'
-            "\n"
-        )
-        body = MCPClient._parse_sse_response(sse)
-        assert body["result"] == "ok"
-
-    def test_parse_sse_uses_last_response(self):
-        """If multiple responses exist, use the last one."""
-        sse = (
-            'data: {"jsonrpc":"2.0","result":"first","id":1}\n'
-            "\n"
-            'data: {"jsonrpc":"2.0","result":"second","id":2}\n'
-            "\n"
-        )
-        body = MCPClient._parse_sse_response(sse)
-        assert body["result"] == "second"
-
-
-# ── MCPClient unit tests ─────────────────────────────────────────────
-
-
-class TestMCPClient:
-    """Tests for the MCP HTTP client."""
-
-    def test_build_headers_without_auth(self):
-        client = MCPClient("https://mcp.example.com")
-        headers = client._build_headers()
-        assert "Authorization" not in headers
-        assert headers["Content-Type"] == "application/json"
-
-    def test_build_headers_with_auth(self):
-        client = MCPClient("https://mcp.example.com", auth_token="my-token")
-        headers = client._build_headers()
-        assert headers["Authorization"] == "Bearer my-token"
-
-    def test_build_jsonrpc_request(self):
-        client = MCPClient("https://mcp.example.com")
-        req = client._build_jsonrpc_request("tools/list")
-        assert req["jsonrpc"] == "2.0"
-        assert req["method"] == "tools/list"
-        assert "id" in req
-        assert "params" not in req
-
-    def test_build_jsonrpc_request_with_params(self):
-        client = MCPClient("https://mcp.example.com")
-        req = client._build_jsonrpc_request(
-            "tools/call", {"name": "test", "arguments": {"x": 1}}
-        )
-        assert req["params"] == {"name": "test", "arguments": {"x": 1}}
-
-    def test_request_id_increments(self):
-        client = MCPClient("https://mcp.example.com")
-        req1 = client._build_jsonrpc_request("tools/list")
-        req2 = client._build_jsonrpc_request("tools/list")
-        assert req2["id"] > req1["id"]
-
-    def test_server_url_trailing_slash_stripped(self):
-        client = MCPClient("https://mcp.example.com/mcp/")
-        assert client.server_url == "https://mcp.example.com/mcp"
-
-    @pytest.mark.asyncio
-    async def test_send_request_success(self):
-        client = MCPClient("https://mcp.example.com")
-
-        mock_response = AsyncMock()
-        mock_response.json.return_value = {
-            "jsonrpc": "2.0",
-            "result": {"tools": []},
-            "id": 1,
-        }
-
-        with patch.object(client, "_send_request", return_value={"tools": []}):
-            result = await client._send_request("tools/list")
-            assert result == {"tools": []}
-
-    @pytest.mark.asyncio
-    async def test_send_request_error(self):
-        client = MCPClient("https://mcp.example.com")
-
-        async def mock_send(*args, **kwargs):
-            raise MCPClientError("MCP server error [-32600]: Invalid Request")
-
-        with patch.object(client, "_send_request", side_effect=mock_send):
-            with pytest.raises(MCPClientError, match="Invalid Request"):
-                await client._send_request("tools/list")
-
-    @pytest.mark.asyncio
-    async def test_list_tools(self):
-        client = MCPClient("https://mcp.example.com")
-
-        mock_result = {
-            "tools": [
-                {
-                    "name": "get_weather",
-                    "description": "Get current weather for a city",
-                    "inputSchema": {
-                        "type": "object",
-                        "properties": {"city": {"type": "string"}},
-                        "required": ["city"],
-                    },
-                },
-                {
-                    "name": "search",
-                    "description": "Search the web",
-                    "inputSchema": {
-                        "type": "object",
-                        "properties": {"query": {"type": "string"}},
-                        "required": ["query"],
-                    },
-                },
-            ]
-        }
-
-        with patch.object(client, "_send_request", return_value=mock_result):
-            tools = await client.list_tools()
-
-        assert len(tools) == 2
-        assert tools[0].name == "get_weather"
-        assert tools[0].description == "Get current weather for a city"
-        assert tools[0].input_schema["properties"]["city"]["type"] == "string"
-        assert tools[1].name == "search"
-
-    @pytest.mark.asyncio
-    async def test_list_tools_empty(self):
-        client = MCPClient("https://mcp.example.com")
-
-        with patch.object(client, "_send_request", return_value={"tools": []}):
-            tools = await client.list_tools()
-
-        assert tools == []
-
-    @pytest.mark.asyncio
-    async def test_list_tools_none_result(self):
-        client = MCPClient("https://mcp.example.com")
-
-        with patch.object(client, "_send_request", return_value=None):
-            tools = await client.list_tools()
-
-        assert tools == []
-
-    @pytest.mark.asyncio
-    async def test_call_tool_success(self):
-        client = MCPClient("https://mcp.example.com")
-
-        mock_result = {
-            "content": [
-                {"type": "text", "text": json.dumps({"temp": 20, "city": "London"})}
-            ],
-            "isError": False,
-        }
-
-        with patch.object(client, "_send_request", return_value=mock_result):
-            result = await client.call_tool("get_weather", {"city": "London"})
-
-        assert not result.is_error
-        assert len(result.content) == 1
-        assert result.content[0]["type"] == "text"
-
-    @pytest.mark.asyncio
-    async def test_call_tool_error(self):
-        client = MCPClient("https://mcp.example.com")
-
-        mock_result = {
-            "content": [{"type": "text", "text": "City not found"}],
-            "isError": True,
-        }
-
-        with patch.object(client, "_send_request", return_value=mock_result):
-            result = await client.call_tool("get_weather", {"city": "???"})
-
-        assert result.is_error
-
-    @pytest.mark.asyncio
-    async def test_call_tool_none_result(self):
-        client = MCPClient("https://mcp.example.com")
-
-        with patch.object(client, "_send_request", return_value=None):
-            result = await client.call_tool("get_weather", {"city": "London"})
-
-        assert result.is_error
-
-    @pytest.mark.asyncio
-    async def test_initialize(self):
-        client = MCPClient("https://mcp.example.com")
-
-        mock_result = {
-            "protocolVersion": "2025-03-26",
-            "capabilities": {"tools": {}},
-            "serverInfo": {"name": "test-server", "version": "1.0.0"},
-        }
-
-        with (
-            patch.object(client, "_send_request", return_value=mock_result) as mock_req,
-            patch.object(client, "_send_notification") as mock_notif,
-        ):
-            result = await client.initialize()
-
-        mock_req.assert_called_once()
-        mock_notif.assert_called_once_with("notifications/initialized")
-        assert result["protocolVersion"] == "2025-03-26"
-
-
-# ── MCPToolBlock unit tests ──────────────────────────────────────────
-
-
-class TestMCPToolBlock:
-    """Tests for the MCPToolBlock."""
-
-    def test_block_instantiation(self):
-        block = MCPToolBlock()
-        assert block.id == "a0a4b1c2-d3e4-4f56-a7b8-c9d0e1f2a3b4"
-        assert block.name == "MCPToolBlock"
-
-    def test_input_schema_has_required_fields(self):
-        block = MCPToolBlock()
-        schema = block.input_schema.jsonschema()
-        props = schema.get("properties", {})
-        assert "server_url" in props
-        assert "selected_tool" in props
-        assert "tool_arguments" in props
-        assert "credentials" in props
-
-    def test_output_schema(self):
-        block = MCPToolBlock()
-        schema = block.output_schema.jsonschema()
-        props = schema.get("properties", {})
-        assert "result" in props
-        assert "error" in props
-
-    def test_get_input_schema_with_tool_schema(self):
-        tool_schema = {
-            "type": "object",
-            "properties": {"query": {"type": "string"}},
-            "required": ["query"],
-        }
-        data = {"tool_input_schema": tool_schema}
-        result = MCPToolBlock.Input.get_input_schema(data)
-        assert result == tool_schema
-
-    def test_get_input_schema_without_tool_schema(self):
-        result = MCPToolBlock.Input.get_input_schema({})
-        assert result == {}
-
-    def test_get_input_defaults(self):
-        data = {"tool_arguments": {"city": "London"}}
-        result = MCPToolBlock.Input.get_input_defaults(data)
-        assert result == {"city": "London"}
-
-    def test_get_missing_input(self):
-        data = {
-            "tool_input_schema": {
-                "type": "object",
-                "properties": {
-                    "city": {"type": "string"},
-                    "units": {"type": "string"},
-                },
-                "required": ["city", "units"],
-            },
-            "tool_arguments": {"city": "London"},
-        }
-        missing = MCPToolBlock.Input.get_missing_input(data)
-        assert missing == {"units"}
-
-    def test_get_missing_input_all_present(self):
-        data = {
-            "tool_input_schema": {
-                "type": "object",
-                "properties": {"city": {"type": "string"}},
-                "required": ["city"],
-            },
-            "tool_arguments": {"city": "London"},
-        }
-        missing = MCPToolBlock.Input.get_missing_input(data)
-        assert missing == set()
-
-    @pytest.mark.asyncio
-    async def test_run_with_mock(self):
-        """Test the block using the built-in test infrastructure."""
-        block = MCPToolBlock()
-        await execute_block_test(block)
-
-    @pytest.mark.asyncio
-    async def test_run_missing_server_url(self):
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url="",
-            selected_tool="test",
-            credentials=TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-        outputs = []
-        async for name, data in block.run(input_data, credentials=TEST_CREDENTIALS):
-            outputs.append((name, data))
-        assert outputs == [("error", "MCP server URL is required")]
-
-    @pytest.mark.asyncio
-    async def test_run_missing_tool(self):
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url="https://mcp.example.com/mcp",
-            selected_tool="",
-            credentials=TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-        outputs = []
-        async for name, data in block.run(input_data, credentials=TEST_CREDENTIALS):
-            outputs.append((name, data))
-        assert outputs == [
-            ("error", "No tool selected. Please select a tool from the dropdown.")
-        ]
-
-    @pytest.mark.asyncio
-    async def test_run_success(self):
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url="https://mcp.example.com/mcp",
-            selected_tool="get_weather",
-            tool_input_schema={
-                "type": "object",
-                "properties": {"city": {"type": "string"}},
-            },
-            tool_arguments={"city": "London"},
-            credentials=TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        async def mock_call(*args, **kwargs):
-            return {"temp": 20, "city": "London"}
-
-        block._call_mcp_tool = mock_call  # type: ignore
-
-        outputs = []
-        async for name, data in block.run(input_data, credentials=TEST_CREDENTIALS):
-            outputs.append((name, data))
-
-        assert len(outputs) == 1
-        assert outputs[0][0] == "result"
-        assert outputs[0][1] == {"temp": 20, "city": "London"}
-
-    @pytest.mark.asyncio
-    async def test_run_mcp_error(self):
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url="https://mcp.example.com/mcp",
-            selected_tool="bad_tool",
-            credentials=TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        async def mock_call(*args, **kwargs):
-            raise MCPClientError("Tool not found")
-
-        block._call_mcp_tool = mock_call  # type: ignore
-
-        outputs = []
-        async for name, data in block.run(input_data, credentials=TEST_CREDENTIALS):
-            outputs.append((name, data))
-
-        assert outputs[0][0] == "error"
-        assert "Tool not found" in outputs[0][1]
-
-    @pytest.mark.asyncio
-    async def test_call_mcp_tool_parses_json_text(self):
-        block = MCPToolBlock()
-
-        mock_result = MCPCallResult(
-            content=[
-                {"type": "text", "text": '{"temp": 20}'},
-            ],
-            is_error=False,
-        )
-
-        async def mock_init(self):
-            return {}
-
-        async def mock_call(self, name, args):
-            return mock_result
-
-        with (
-            patch.object(MCPClient, "initialize", mock_init),
-            patch.object(MCPClient, "call_tool", mock_call),
-        ):
-            result = await block._call_mcp_tool(
-                "https://mcp.example.com", "test_tool", {}
-            )
-
-        assert result == {"temp": 20}
-
-    @pytest.mark.asyncio
-    async def test_call_mcp_tool_plain_text(self):
-        block = MCPToolBlock()
-
-        mock_result = MCPCallResult(
-            content=[
-                {"type": "text", "text": "Hello, world!"},
-            ],
-            is_error=False,
-        )
-
-        async def mock_init(self):
-            return {}
-
-        async def mock_call(self, name, args):
-            return mock_result
-
-        with (
-            patch.object(MCPClient, "initialize", mock_init),
-            patch.object(MCPClient, "call_tool", mock_call),
-        ):
-            result = await block._call_mcp_tool(
-                "https://mcp.example.com", "test_tool", {}
-            )
-
-        assert result == "Hello, world!"
-
-    @pytest.mark.asyncio
-    async def test_call_mcp_tool_multiple_content(self):
-        block = MCPToolBlock()
-
-        mock_result = MCPCallResult(
-            content=[
-                {"type": "text", "text": "Part 1"},
-                {"type": "text", "text": '{"part": 2}'},
-            ],
-            is_error=False,
-        )
-
-        async def mock_init(self):
-            return {}
-
-        async def mock_call(self, name, args):
-            return mock_result
-
-        with (
-            patch.object(MCPClient, "initialize", mock_init),
-            patch.object(MCPClient, "call_tool", mock_call),
-        ):
-            result = await block._call_mcp_tool(
-                "https://mcp.example.com", "test_tool", {}
-            )
-
-        assert result == ["Part 1", {"part": 2}]
-
-    @pytest.mark.asyncio
-    async def test_call_mcp_tool_error_result(self):
-        block = MCPToolBlock()
-
-        mock_result = MCPCallResult(
-            content=[{"type": "text", "text": "Something went wrong"}],
-            is_error=True,
-        )
-
-        async def mock_init(self):
-            return {}
-
-        async def mock_call(self, name, args):
-            return mock_result
-
-        with (
-            patch.object(MCPClient, "initialize", mock_init),
-            patch.object(MCPClient, "call_tool", mock_call),
-        ):
-            with pytest.raises(MCPClientError, match="returned an error"):
-                await block._call_mcp_tool("https://mcp.example.com", "test_tool", {})
-
-    @pytest.mark.asyncio
-    async def test_call_mcp_tool_image_content(self):
-        block = MCPToolBlock()
-
-        mock_result = MCPCallResult(
-            content=[
-                {
-                    "type": "image",
-                    "data": "base64data==",
-                    "mimeType": "image/png",
-                }
-            ],
-            is_error=False,
-        )
-
-        async def mock_init(self):
-            return {}
-
-        async def mock_call(self, name, args):
-            return mock_result
-
-        with (
-            patch.object(MCPClient, "initialize", mock_init),
-            patch.object(MCPClient, "call_tool", mock_call),
-        ):
-            result = await block._call_mcp_tool(
-                "https://mcp.example.com", "test_tool", {}
-            )
-
-        assert result == {
-            "type": "image",
-            "data": "base64data==",
-            "mimeType": "image/png",
-        }
-
-    @pytest.mark.asyncio
-    async def test_run_sends_api_key_credentials(self):
-        """Ensure non-empty API keys are sent to the MCP server."""
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url="https://mcp.example.com/mcp",
-            selected_tool="test_tool",
-            credentials=TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        creds = APIKeyCredentials(
-            id="test-id",
-            provider="mcp",
-            api_key=SecretStr("real-api-key"),
-            title="Real",
-        )
-
-        captured_tokens = []
-
-        async def mock_call(server_url, tool_name, arguments, auth_token=None):
-            captured_tokens.append(auth_token)
-            return "ok"
-
-        block._call_mcp_tool = mock_call  # type: ignore
-
-        async for _ in block.run(input_data, credentials=creds):
-            pass
-
-        assert captured_tokens == ["real-api-key"]
-
-
-# ── OAuth2 credential support tests ─────────────────────────────────
-
-
-class TestMCPOAuth2Support:
-    """Tests for OAuth2 credential support in MCPToolBlock."""
-
-    def test_extract_auth_token_from_api_key(self):
-        creds = APIKeyCredentials(
-            id="test",
-            provider="mcp",
-            api_key=SecretStr("my-api-key"),
-            title="test",
-        )
-        token = MCPToolBlock._extract_auth_token(creds)
-        assert token == "my-api-key"
-
-    def test_extract_auth_token_from_oauth2(self):
-        creds = OAuth2Credentials(
-            id="test",
-            provider="mcp",
-            access_token=SecretStr("oauth2-access-token"),
-            scopes=["read"],
-            title="test",
-        )
-        token = MCPToolBlock._extract_auth_token(creds)
-        assert token == "oauth2-access-token"
-
-    def test_extract_auth_token_empty_skipped(self):
-        creds = APIKeyCredentials(
-            id="test",
-            provider="mcp",
-            api_key=SecretStr(""),
-            title="test",
-        )
-        token = MCPToolBlock._extract_auth_token(creds)
-        assert token is None
-
-    @pytest.mark.asyncio
-    async def test_run_with_oauth2_credentials(self):
-        """Verify the block can run with OAuth2 credentials."""
-        block = MCPToolBlock()
-        input_data = MCPToolBlock.Input(
-            server_url="https://mcp.example.com/mcp",
-            selected_tool="test_tool",
-            credentials=TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        oauth2_creds = OAuth2Credentials(
-            id="test-id",
-            provider="mcp",
-            access_token=SecretStr("real-oauth2-token"),
-            scopes=["read", "write"],
-            title="MCP OAuth",
-        )
-
-        captured_tokens = []
-
-        async def mock_call(server_url, tool_name, arguments, auth_token=None):
-            captured_tokens.append(auth_token)
-            return {"status": "ok"}
-
-        block._call_mcp_tool = mock_call  # type: ignore
-
-        outputs = []
-        async for name, data in block.run(input_data, credentials=oauth2_creds):
-            outputs.append((name, data))
-
-        assert captured_tokens == ["real-oauth2-token"]
-        assert outputs == [("result", {"status": "ok"})]

autogpt_platform/backend/backend/blocks/mcp/test_oauth.py

@@ -1,242 +0,0 @@
-"""
-Tests for MCP OAuth handler.
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from pydantic import SecretStr
-
-from backend.blocks.mcp.client import MCPClient
-from backend.blocks.mcp.oauth import MCPOAuthHandler
-from backend.data.model import OAuth2Credentials
-
-
-def _mock_response(json_data: dict, status: int = 200) -> MagicMock:
-    """Create a mock Response with synchronous json() (matching Requests.Response)."""
-    resp = MagicMock()
-    resp.status = status
-    resp.ok = 200 <= status < 300
-    resp.json.return_value = json_data
-    return resp
-
-
-class TestMCPOAuthHandler:
-    """Tests for the MCPOAuthHandler."""
-
-    def _make_handler(self, **overrides) -> MCPOAuthHandler:
-        defaults = {
-            "client_id": "test-client-id",
-            "client_secret": "test-client-secret",
-            "redirect_uri": "https://app.example.com/callback",
-            "authorize_url": "https://auth.example.com/authorize",
-            "token_url": "https://auth.example.com/token",
-        }
-        defaults.update(overrides)
-        return MCPOAuthHandler(**defaults)
-
-    def test_get_login_url_basic(self):
-        handler = self._make_handler()
-        url = handler.get_login_url(
-            scopes=["read", "write"],
-            state="random-state-token",
-            code_challenge="S256-challenge-value",
-        )
-
-        assert "https://auth.example.com/authorize?" in url
-        assert "response_type=code" in url
-        assert "client_id=test-client-id" in url
-        assert "state=random-state-token" in url
-        assert "code_challenge=S256-challenge-value" in url
-        assert "code_challenge_method=S256" in url
-        assert "scope=read+write" in url
-
-    def test_get_login_url_with_resource(self):
-        handler = self._make_handler(resource_url="https://mcp.example.com/mcp")
-        url = handler.get_login_url(
-            scopes=[], state="state", code_challenge="challenge"
-        )
-
-        assert "resource=https" in url
-
-    def test_get_login_url_without_pkce(self):
-        handler = self._make_handler()
-        url = handler.get_login_url(scopes=["read"], state="state", code_challenge=None)
-
-        assert "code_challenge" not in url
-        assert "code_challenge_method" not in url
-
-    @pytest.mark.asyncio
-    async def test_exchange_code_for_tokens(self):
-        handler = self._make_handler()
-
-        resp = _mock_response(
-            {
-                "access_token": "new-access-token",
-                "refresh_token": "new-refresh-token",
-                "expires_in": 3600,
-                "token_type": "Bearer",
-            }
-        )
-
-        with patch("backend.blocks.mcp.oauth.Requests") as MockRequests:
-            instance = MockRequests.return_value
-            instance.post = AsyncMock(return_value=resp)
-
-            creds = await handler.exchange_code_for_tokens(
-                code="auth-code",
-                scopes=["read"],
-                code_verifier="pkce-verifier",
-            )
-
-        assert isinstance(creds, OAuth2Credentials)
-        assert creds.access_token.get_secret_value() == "new-access-token"
-        assert creds.refresh_token is not None
-        assert creds.refresh_token.get_secret_value() == "new-refresh-token"
-        assert creds.scopes == ["read"]
-        assert creds.access_token_expires_at is not None
-
-    @pytest.mark.asyncio
-    async def test_refresh_tokens(self):
-        handler = self._make_handler()
-
-        existing_creds = OAuth2Credentials(
-            id="existing-id",
-            provider="mcp",
-            access_token=SecretStr("old-token"),
-            refresh_token=SecretStr("old-refresh"),
-            scopes=["read"],
-            title="test",
-        )
-
-        resp = _mock_response(
-            {
-                "access_token": "refreshed-token",
-                "refresh_token": "new-refresh",
-                "expires_in": 3600,
-            }
-        )
-
-        with patch("backend.blocks.mcp.oauth.Requests") as MockRequests:
-            instance = MockRequests.return_value
-            instance.post = AsyncMock(return_value=resp)
-
-            refreshed = await handler._refresh_tokens(existing_creds)
-
-        assert refreshed.id == "existing-id"
-        assert refreshed.access_token.get_secret_value() == "refreshed-token"
-        assert refreshed.refresh_token is not None
-        assert refreshed.refresh_token.get_secret_value() == "new-refresh"
-
-    @pytest.mark.asyncio
-    async def test_refresh_tokens_no_refresh_token(self):
-        handler = self._make_handler()
-
-        creds = OAuth2Credentials(
-            provider="mcp",
-            access_token=SecretStr("token"),
-            scopes=["read"],
-            title="test",
-        )
-
-        with pytest.raises(ValueError, match="No refresh token"):
-            await handler._refresh_tokens(creds)
-
-    @pytest.mark.asyncio
-    async def test_revoke_tokens_no_url(self):
-        handler = self._make_handler(revoke_url=None)
-
-        creds = OAuth2Credentials(
-            provider="mcp",
-            access_token=SecretStr("token"),
-            scopes=[],
-            title="test",
-        )
-
-        result = await handler.revoke_tokens(creds)
-        assert result is False
-
-    @pytest.mark.asyncio
-    async def test_revoke_tokens_with_url(self):
-        handler = self._make_handler(revoke_url="https://auth.example.com/revoke")
-
-        creds = OAuth2Credentials(
-            provider="mcp",
-            access_token=SecretStr("token"),
-            scopes=[],
-            title="test",
-        )
-
-        resp = _mock_response({}, status=200)
-
-        with patch("backend.blocks.mcp.oauth.Requests") as MockRequests:
-            instance = MockRequests.return_value
-            instance.post = AsyncMock(return_value=resp)
-
-            result = await handler.revoke_tokens(creds)
-
-        assert result is True
-
-
-class TestMCPClientDiscovery:
-    """Tests for MCPClient OAuth metadata discovery."""
-
-    @pytest.mark.asyncio
-    async def test_discover_auth_found(self):
-        client = MCPClient("https://mcp.example.com/mcp")
-
-        metadata = {
-            "authorization_servers": ["https://auth.example.com"],
-            "resource": "https://mcp.example.com/mcp",
-        }
-
-        resp = _mock_response(metadata, status=200)
-
-        with patch("backend.blocks.mcp.client.Requests") as MockRequests:
-            instance = MockRequests.return_value
-            instance.get = AsyncMock(return_value=resp)
-
-            result = await client.discover_auth()
-
-        assert result is not None
-        assert result["authorization_servers"] == ["https://auth.example.com"]
-
-    @pytest.mark.asyncio
-    async def test_discover_auth_not_found(self):
-        client = MCPClient("https://mcp.example.com/mcp")
-
-        resp = _mock_response({}, status=404)
-
-        with patch("backend.blocks.mcp.client.Requests") as MockRequests:
-            instance = MockRequests.return_value
-            instance.get = AsyncMock(return_value=resp)
-
-            result = await client.discover_auth()
-
-        assert result is None
-
-    @pytest.mark.asyncio
-    async def test_discover_auth_server_metadata(self):
-        client = MCPClient("https://mcp.example.com/mcp")
-
-        server_metadata = {
-            "issuer": "https://auth.example.com",
-            "authorization_endpoint": "https://auth.example.com/authorize",
-            "token_endpoint": "https://auth.example.com/token",
-            "registration_endpoint": "https://auth.example.com/register",
-            "code_challenge_methods_supported": ["S256"],
-        }
-
-        resp = _mock_response(server_metadata, status=200)
-
-        with patch("backend.blocks.mcp.client.Requests") as MockRequests:
-            instance = MockRequests.return_value
-            instance.get = AsyncMock(return_value=resp)
-
-            result = await client.discover_auth_server_metadata(
-                "https://auth.example.com"
-            )
-
-        assert result is not None
-        assert result["authorization_endpoint"] == "https://auth.example.com/authorize"
-        assert result["token_endpoint"] == "https://auth.example.com/token"

autogpt_platform/backend/backend/blocks/mcp/test_server.py

@@ -1,162 +0,0 @@
-"""
-Minimal MCP server for integration testing.
-
-Implements the MCP Streamable HTTP transport (JSON-RPC 2.0 over HTTP POST)
-with a few sample tools. Runs on localhost with a random available port.
-"""
-
-import json
-import logging
-
-from aiohttp import web
-
-logger = logging.getLogger(__name__)
-
-# Sample tools this test server exposes
-TEST_TOOLS = [
-    {
-        "name": "get_weather",
-        "description": "Get current weather for a city",
-        "inputSchema": {
-            "type": "object",
-            "properties": {
-                "city": {
-                    "type": "string",
-                    "description": "City name",
-                },
-            },
-            "required": ["city"],
-        },
-    },
-    {
-        "name": "add_numbers",
-        "description": "Add two numbers together",
-        "inputSchema": {
-            "type": "object",
-            "properties": {
-                "a": {"type": "number", "description": "First number"},
-                "b": {"type": "number", "description": "Second number"},
-            },
-            "required": ["a", "b"],
-        },
-    },
-    {
-        "name": "echo",
-        "description": "Echo back the input message",
-        "inputSchema": {
-            "type": "object",
-            "properties": {
-                "message": {"type": "string", "description": "Message to echo"},
-            },
-            "required": ["message"],
-        },
-    },
-]
-
-
-def _handle_initialize(params: dict) -> dict:
-    return {
-        "protocolVersion": "2025-03-26",
-        "capabilities": {"tools": {"listChanged": False}},
-        "serverInfo": {"name": "test-mcp-server", "version": "1.0.0"},
-    }
-
-
-def _handle_tools_list(params: dict) -> dict:
-    return {"tools": TEST_TOOLS}
-
-
-def _handle_tools_call(params: dict) -> dict:
-    tool_name = params.get("name", "")
-    arguments = params.get("arguments", {})
-
-    if tool_name == "get_weather":
-        city = arguments.get("city", "Unknown")
-        return {
-            "content": [
-                {
-                    "type": "text",
-                    "text": json.dumps(
-                        {"city": city, "temperature": 22, "condition": "sunny"}
-                    ),
-                }
-            ],
-        }
-
-    elif tool_name == "add_numbers":
-        a = arguments.get("a", 0)
-        b = arguments.get("b", 0)
-        return {
-            "content": [{"type": "text", "text": json.dumps({"result": a + b})}],
-        }
-
-    elif tool_name == "echo":
-        message = arguments.get("message", "")
-        return {
-            "content": [{"type": "text", "text": message}],
-        }
-
-    else:
-        return {
-            "content": [{"type": "text", "text": f"Unknown tool: {tool_name}"}],
-            "isError": True,
-        }
-
-
-HANDLERS = {
-    "initialize": _handle_initialize,
-    "tools/list": _handle_tools_list,
-    "tools/call": _handle_tools_call,
-}
-
-
-async def handle_mcp_request(request: web.Request) -> web.Response:
-    """Handle incoming MCP JSON-RPC 2.0 requests."""
-    # Check auth if configured
-    expected_token = request.app.get("auth_token")
-    if expected_token:
-        auth_header = request.headers.get("Authorization", "")
-        if auth_header != f"Bearer {expected_token}":
-            return web.json_response(
-                {
-                    "jsonrpc": "2.0",
-                    "error": {"code": -32001, "message": "Unauthorized"},
-                    "id": None,
-                },
-                status=401,
-            )
-
-    body = await request.json()
-
-    # Handle notifications (no id field) — just acknowledge
-    if "id" not in body:
-        return web.Response(status=202)
-
-    method = body.get("method", "")
-    params = body.get("params", {})
-    request_id = body.get("id")
-
-    handler = HANDLERS.get(method)
-    if not handler:
-        return web.json_response(
-            {
-                "jsonrpc": "2.0",
-                "error": {
-                    "code": -32601,
-                    "message": f"Method not found: {method}",
-                },
-                "id": request_id,
-            }
-        )
-
-    result = handler(params)
-    return web.json_response({"jsonrpc": "2.0", "result": result, "id": request_id})
-
-
-def create_test_mcp_app(auth_token: str | None = None) -> web.Application:
-    """Create an aiohttp app that acts as an MCP server."""
-    app = web.Application()
-    app.router.add_post("/mcp", handle_mcp_request)
-    if auth_token:
-        app["auth_token"] = auth_token
-    return app

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/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/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/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"
@@ -30,7 +29,6 @@ class ProviderName(str, Enum):
     IDEOGRAM = "ideogram"
     JINA = "jina"
     LLAMA_API = "llama_api"
-    MCP = "mcp"
     MEDIUM = "medium"
     MEM0 = "mem0"
     NOTION = "notion"

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"
             )
@@ -363,21 +334,7 @@ async def store_media_file(
 
         # Don't re-save if input was already from workspace
         if is_from_workspace:
-            # Return original workspace reference, ensuring MIME type fragment
-            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
@@ -389,7 +346,7 @@ async def store_media_file(
             filename=filename,
             overwrite=True,
         )
-        return MediaFileType(f"workspace://{file_record.id}#{file_record.mimeType}")
+        return MediaFileType(f"workspace://{file_record.id}")
 
     else:
         raise ValueError(f"Invalid return_format: {return_format}")

autogpt_platform/backend/backend/util/settings.py

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

autogpt_platform/backend/backend/util/workspace.py

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

autogpt_platform/backend/pyproject.toml

@@ -20,8 +20,7 @@ click = "^8.2.0"
 cryptography = "^45.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
-elevenlabs = "^1.50.0"
-fastapi = "^0.128.0"
+fastapi = "^0.116.1"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
 google-api-python-client = "^2.177.0"
@@ -35,7 +34,7 @@ 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 = "^0.5.1"
@@ -52,8 +51,8 @@ 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"
@@ -65,14 +64,13 @@ sentry-sdk = {extras = ["anthropic", "fastapi", "launchdarkly", "openai", "sqlal
 sqlalchemy = "^2.0.40"
 strenum = "^0.4.9"
 stripe = "^11.5.0"
-supabase = "2.27.2"
+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"

autogpt_platform/backend/snapshots/grph_single

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

autogpt_platform/backend/snapshots/grphs_all

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

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

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

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

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

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

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

autogpt_platform/frontend/src/app/(platform)/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>
       )}

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

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

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

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

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

@@ -19,13 +19,13 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
     if (timerRef.current === null) {
       timerRef.current = setTimeout(() => {
         setShowSlowLoader(true);
-      }, 8000);
+      }, 3000);
     }
 
     if (coffeeTimerRef.current === null) {
       coffeeTimerRef.current = setTimeout(() => {
         setShowCoffeeMessage(true);
-      }, 10000);
+      }, 8000);
     }
 
     return () => {

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

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

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

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

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

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

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

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

docs/integrations/README.md

@@ -192,7 +192,6 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Get Current Time](block-integrations/text.md#get-current-time) | This block outputs the current time |
 | [Match Text Pattern](block-integrations/text.md#match-text-pattern) | Matches text against a regex pattern and forwards data to positive or negative output based on the match |
 | [Text Decoder](block-integrations/text.md#text-decoder) | Decodes a string containing escape sequences into actual text |
-| [Text Encoder](block-integrations/text.md#text-encoder) | Encodes a string by converting special characters into escape sequences |
 | [Text Replace](block-integrations/text.md#text-replace) | This block is used to replace a text with a new text |
 | [Text Split](block-integrations/text.md#text-split) | This block is used to split a text into a list of strings |
 | [Word Character Count](block-integrations/text.md#word-character-count) | Counts the number of words and characters in a given text |
@@ -233,7 +232,6 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Stagehand Extract](block-integrations/stagehand/blocks.md#stagehand-extract) | Extract structured data from a webpage |
 | [Stagehand Observe](block-integrations/stagehand/blocks.md#stagehand-observe) | Find suggested actions for your workflows |
 | [Unreal Text To Speech](block-integrations/llm.md#unreal-text-to-speech) | Converts text to speech using the Unreal Speech API |
-| [Video Narration](block-integrations/video/narration.md#video-narration) | Generate AI narration and add to video |
 
 ## Search and Information Retrieval
 
@@ -467,20 +465,15 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Github Update Comment](block-integrations/github/issues.md#github-update-comment) | A block that updates an existing comment on a GitHub issue or pull request |
 | [Github Update File](block-integrations/github/repo.md#github-update-file) | This block updates an existing file in a GitHub repository |
 | [Instantiate Code Sandbox](block-integrations/misc.md#instantiate-code-sandbox) | Instantiate a sandbox environment with internet access in which you can execute code with the Execute Code Step block |
-| [MCP Tool](block-integrations/mcp/block.md#mcp-tool) | Connect to any MCP server and execute its tools |
 | [Slant3D Order Webhook](block-integrations/slant3d/webhook.md#slant3d-order-webhook) | This block triggers on Slant3D order status updates and outputs the event details, including tracking information when orders are shipped |
 
 ## Media Generation
 
 | Block Name | Description |
 |------------|-------------|
-| [Add Audio To Video](block-integrations/video/add_audio.md#add-audio-to-video) | Block to attach an audio file to a video file using moviepy |
-| [Loop Video](block-integrations/video/loop.md#loop-video) | Block to loop a video to a given duration or number of repeats |
-| [Media Duration](block-integrations/video/duration.md#media-duration) | Block to get the duration of a media file |
-| [Video Clip](block-integrations/video/clip.md#video-clip) | Extract a time segment from a video |
-| [Video Concat](block-integrations/video/concat.md#video-concat) | Merge multiple video clips into one continuous video |
-| [Video Download](block-integrations/video/download.md#video-download) | Download video from URL (YouTube, Vimeo, news sites, direct links) |
-| [Video Text Overlay](block-integrations/video/text_overlay.md#video-text-overlay) | Add text overlay/caption to video |
+| [Add Audio To Video](block-integrations/multimedia.md#add-audio-to-video) | Block to attach an audio file to a video file using moviepy |
+| [Loop Video](block-integrations/multimedia.md#loop-video) | Block to loop a video to a given duration or number of repeats |
+| [Media Duration](block-integrations/multimedia.md#media-duration) | Block to get the duration of a media file |
 
 ## Productivity
 

docs/integrations/SUMMARY.md

@@ -84,8 +84,8 @@
 * [Linear Projects](block-integrations/linear/projects.md)
 * [LLM](block-integrations/llm.md)
 * [Logic](block-integrations/logic.md)
-* [Mcp Block](block-integrations/mcp/block.md)
 * [Misc](block-integrations/misc.md)
+* [Multimedia](block-integrations/multimedia.md)
 * [Notion Create Page](block-integrations/notion/create_page.md)
 * [Notion Read Database](block-integrations/notion/read_database.md)
 * [Notion Read Page](block-integrations/notion/read_page.md)
@@ -129,13 +129,5 @@
 * [Twitter Timeline](block-integrations/twitter/timeline.md)
 * [Twitter Tweet Lookup](block-integrations/twitter/tweet_lookup.md)
 * [Twitter User Lookup](block-integrations/twitter/user_lookup.md)
-* [Video Add Audio](block-integrations/video/add_audio.md)
-* [Video Clip](block-integrations/video/clip.md)
-* [Video Concat](block-integrations/video/concat.md)
-* [Video Download](block-integrations/video/download.md)
-* [Video Duration](block-integrations/video/duration.md)
-* [Video Loop](block-integrations/video/loop.md)
-* [Video Narration](block-integrations/video/narration.md)
-* [Video Text Overlay](block-integrations/video/text_overlay.md)
 * [Wolfram LLM API](block-integrations/wolfram/llm_api.md)
 * [Zerobounce Validate Emails](block-integrations/zerobounce/validate_emails.md)

docs/integrations/block-integrations/llm.md

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

docs/integrations/block-integrations/mcp/block.md

@@ -1,36 +0,0 @@
-# Mcp Block
-<!-- MANUAL: file_description -->
-_Add a description of this category of blocks._
-<!-- END MANUAL -->
-
-## MCP Tool
-
-### What it is
-Connect to any MCP server and execute its tools. Provide a server URL, select a tool, and pass arguments dynamically.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-_Add technical explanation here._
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| server_url | URL of the MCP server (Streamable HTTP endpoint) | str | Yes |
-| selected_tool | The MCP tool to execute | str | No |
-| tool_arguments | Arguments to pass to the selected MCP tool. The fields here are defined by the tool's input schema. | Dict[str, Any] | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the tool call failed | str |
-| result | The result returned by the MCP tool | Result |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-_Add practical use case examples here._
-<!-- END MANUAL -->
-
----

docs/integrations/block-integrations/text.md

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

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

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

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

@@ -1,41 +0,0 @@
-# Video Clip
-<!-- MANUAL: file_description -->
-This block extracts a specific time segment from a video file, allowing you to trim videos to precise start and end times.
-<!-- END MANUAL -->
-
-## Video Clip
-
-### What it is
-Extract a time segment from a video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's `subclipped` function to extract a portion of the video between specified start and end times. It validates that end time is greater than start time, then creates a new video file containing only the selected segment. The output is encoded with H.264 video codec and AAC audio codec, preserving both video and audio from the original clip.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Input video (URL, data URI, or local path) | str (file) | Yes |
-| start_time | Start time in seconds | float | Yes |
-| end_time | End time in seconds | float | Yes |
-| output_format | Output format | "mp4" \| "webm" \| "mkv" \| "mov" | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Clipped video file (path or data URI) | str (file) |
-| duration | Clip duration in seconds | float |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Extracting highlights from a longer video
-- Trimming intro/outro from recorded content
-- Creating short clips for social media from longer videos
-- Isolating specific segments for further processing in a workflow
-<!-- END MANUAL -->
-
----

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

@@ -1,41 +0,0 @@
-# Video Concat
-<!-- MANUAL: file_description -->
-This block merges multiple video clips into a single continuous video, with optional transitions between clips.
-<!-- END MANUAL -->
-
-## Video Concat
-
-### What it is
-Merge multiple video clips into one continuous video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's `concatenate_videoclips` function to join multiple videos in sequence. It supports three transition modes: **none** (direct concatenation), **crossfade** (smooth blending where clips overlap), and **fade_black** (each clip fades out to black and the next fades in). At least 2 videos are required. The output is encoded with H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| videos | List of video files to concatenate (in order) | List[str (file)] | Yes |
-| transition | Transition between clips | "none" \| "crossfade" \| "fade_black" | No |
-| transition_duration | Transition duration in seconds | int | No |
-| output_format | Output format | "mp4" \| "webm" \| "mkv" \| "mov" | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Concatenated video file (path or data URI) | str (file) |
-| total_duration | Total duration in seconds | float |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Combining multiple clips into a compilation video
-- Assembling intro, main content, and outro segments
-- Creating montages from multiple source videos
-- Building video playlists or slideshows with transitions
-<!-- END MANUAL -->
-
----

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

@@ -1,42 +0,0 @@
-# Video Download
-<!-- MANUAL: file_description -->
-This block downloads videos from URLs, supporting a wide range of video platforms and direct links.
-<!-- END MANUAL -->
-
-## Video Download
-
-### What it is
-Download video from URL (YouTube, Vimeo, news sites, direct links)
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses yt-dlp, a powerful video downloading library that supports over 1000 websites. It accepts a URL, quality preference, and output format, then downloads the video while merging the best available video and audio streams for the selected quality. Quality options: **best** (highest available), **1080p/720p/480p** (maximum resolution at that height), **audio_only** (extracts just the audio track).
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| url | URL of the video to download (YouTube, Vimeo, direct link, etc.) | str | Yes |
-| quality | Video quality preference | "best" \| "1080p" \| "720p" \| "480p" \| "audio_only" | No |
-| output_format | Output video format | "mp4" \| "webm" \| "mkv" | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_file | Downloaded video (path or data URI) | str (file) |
-| duration | Video duration in seconds | float |
-| title | Video title from source | str |
-| source_url | Original source URL | str |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Downloading source videos for editing or remixing
-- Archiving video content for offline processing
-- Extracting audio from videos for transcription or podcast creation
-- Gathering video content for automated content pipelines
-<!-- END MANUAL -->
-
----

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

@@ -1,38 +0,0 @@
-# Video Duration
-<!-- MANUAL: file_description -->
-This block retrieves the duration of video or audio files, useful for planning and conditional logic in media workflows.
-<!-- END MANUAL -->
-
-## Media Duration
-
-### What it is
-Block to get the duration of a media file.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy to load the media file and extract its duration property. It supports both video files (using VideoFileClip) and audio files (using AudioFileClip), determined by the `is_video` flag. The media can be provided as a URL, data URI, or local file path. The duration is returned in seconds as a floating-point number.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| media_in | Media input (URL, data URI, or local path). | str (file) | Yes |
-| is_video | Whether the media is a video (True) or audio (False). | bool | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| duration | Duration of the media file (in seconds). | float |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Checking video length before processing to avoid timeout issues
-- Calculating how many times to loop a video to reach a target duration
-- Validating that uploaded content meets length requirements
-- Building conditional workflows based on media duration
-<!-- END MANUAL -->
-
----

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

@@ -1,39 +0,0 @@
-# Video Loop
-<!-- MANUAL: file_description -->
-This block repeats a video to extend its duration, either to a specific length or a set number of repetitions.
-<!-- END MANUAL -->
-
-## Loop Video
-
-### What it is
-Block to loop a video to a given duration or number of repeats.
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's Loop effect to repeat a video clip. You can specify either a target duration (the video will repeat until reaching that length) or a number of loops (the video will repeat that many times). The Loop effect handles both video and audio looping automatically, maintaining sync. Either `duration` or `n_loops` must be provided. The output is encoded with H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | The input video (can be a URL, data URI, or local path). | str (file) | Yes |
-| duration | Target duration (in seconds) to loop the video to. Either duration or n_loops must be provided. | float | No |
-| n_loops | Number of times to repeat the video. Either n_loops or duration must be provided. | int | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Looped video returned either as a relative path or a data URI. | str (file) |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Extending a short background video to match the length of narration audio
-- Creating seamless looping content for digital signage
-- Repeating a product demo video multiple times for emphasis
-- Extending short clips to meet minimum duration requirements for platforms
-<!-- END MANUAL -->
-
----

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

@@ -1,44 +0,0 @@
-# Video Narration
-<!-- MANUAL: file_description -->
-This block generates AI voiceover narration using ElevenLabs and adds it to a video, with flexible audio mixing options.
-<!-- END MANUAL -->
-
-## Video Narration
-
-### What it is
-Generate AI narration and add to video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses ElevenLabs text-to-speech API to generate natural-sounding narration from your script. It then combines the narration with the video using MoviePy. Three audio mixing modes are available: **replace** (completely replaces original audio), **mix** (blends narration with original audio at configurable volumes), and **ducking** (similar to mix but applies stronger attenuation to original audio, making narration more prominent). The block outputs both the final video and the generated audio file separately.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Input video (URL, data URI, or local path) | str (file) | Yes |
-| script | Narration script text | str | Yes |
-| voice_id | ElevenLabs voice ID | str | No |
-| model_id | ElevenLabs TTS model | "eleven_multilingual_v2" \| "eleven_flash_v2_5" \| "eleven_turbo_v2_5" \| "eleven_turbo_v2" | No |
-| mix_mode | How to combine with original audio. 'ducking' applies stronger attenuation than 'mix'. | "replace" \| "mix" \| "ducking" | No |
-| narration_volume | Narration volume (0.0 to 2.0) | float | No |
-| original_volume | Original audio volume when mixing (0.0 to 1.0) | float | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Video with narration (path or data URI) | str (file) |
-| audio_file | Generated audio file (path or data URI) | str (file) |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Adding professional voiceover to product demos or tutorials
-- Creating narrated explainer videos from screen recordings
-- Generating multi-language versions of video content
-- Adding commentary to gameplay or walkthrough videos
-<!-- END MANUAL -->
-
----

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

@@ -1,44 +0,0 @@
-# Video Text Overlay
-<!-- MANUAL: file_description -->
-This block adds customizable text captions or titles to videos, with control over positioning, timing, and styling.
-<!-- END MANUAL -->
-
-## Video Text Overlay
-
-### What it is
-Add text overlay/caption to video
-
-### How it works
-<!-- MANUAL: how_it_works -->
-The block uses MoviePy's TextClip and CompositeVideoClip to render text onto video frames. The text is created as a separate clip with configurable font size, color, and optional background color, then composited over the video at the specified position. Timing can be controlled to show text only during specific portions of the video. Position options include center alignments (top, center, bottom) and corner positions (top-left, top-right, bottom-left, bottom-right). The output is encoded with H.264 video codec and AAC audio codec.
-<!-- END MANUAL -->
-
-### Inputs
-
-| Input | Description | Type | Required |
-|-------|-------------|------|----------|
-| video_in | Input video (URL, data URI, or local path) | str (file) | Yes |
-| text | Text to overlay on video | str | Yes |
-| position | Position of text on screen | "top" \| "center" \| "bottom" \| "top-left" \| "top-right" \| "bottom-left" \| "bottom-right" | No |
-| start_time | When to show text (seconds). None = entire video | float | No |
-| end_time | When to hide text (seconds). None = until end | float | No |
-| font_size | Font size | int | No |
-| font_color | Font color (hex or name) | str | No |
-| bg_color | Background color behind text (None for transparent) | str | No |
-
-### Outputs
-
-| Output | Description | Type |
-|--------|-------------|------|
-| error | Error message if the operation failed | str |
-| video_out | Video with text overlay (path or data URI) | str (file) |
-
-### Possible use case
-<!-- MANUAL: use_case -->
-- Adding titles or chapter headings to video content
-- Creating lower-thirds with speaker names or captions
-- Watermarking videos with branding text
-- Adding call-to-action text at specific moments in a video
-<!-- END MANUAL -->
-
----