fix(migration): only backfill endedAt for terminal executions

- Only set endedAt for COMPLETED, FAILED, TERMINATED statuses
- Leave endedAt as NULL for QUEUED, RUNNING, INCOMPLETE, REVIEW
- Add proper schema prefix "platform"
- Prevents incorrectly marking in-progress executions as ended

.branchlet.json

@@ -1,37 +0,0 @@
-{
-  "worktreeCopyPatterns": [
-    ".env*",
-    ".vscode/**",
-    ".auth/**",
-    ".claude/**",
-    "autogpt_platform/.env*",
-    "autogpt_platform/backend/.env*",
-    "autogpt_platform/frontend/.env*",
-    "autogpt_platform/frontend/.auth/**",
-    "autogpt_platform/db/docker/.env*"
-  ],
-  "worktreeCopyIgnores": [
-    "**/node_modules/**",
-    "**/dist/**",
-    "**/.git/**",
-    "**/Thumbs.db",
-    "**/.DS_Store",
-    "**/.next/**",
-    "**/__pycache__/**",
-    "**/.ruff_cache/**",
-    "**/.pytest_cache/**",
-    "**/*.pyc",
-    "**/playwright-report/**",
-    "**/logs/**",
-    "**/site/**"
-  ],
-  "worktreePathTemplate": "$BASE_PATH.worktree",
-  "postCreateCmd": [
-    "cd autogpt_platform/autogpt_libs && poetry install",
-    "cd autogpt_platform/backend && poetry install && poetry run prisma generate",
-    "cd autogpt_platform/frontend && pnpm install",
-    "cd docs && pip install -r requirements.txt"
-  ],
-  "terminalCommand": "code .",
-  "deleteBranchWithWorktree": false
-}

.dockerignore

@@ -16,7 +16,6 @@
 !autogpt_platform/backend/poetry.lock
 !autogpt_platform/backend/README.md
 !autogpt_platform/backend/.env
-!autogpt_platform/backend/gen_prisma_types_stub.py
 
 # Platform - Market
 !autogpt_platform/market/market/

.github/workflows/claude-dependabot.yml

@@ -74,7 +74,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js

.github/workflows/claude.yml

@@ -90,7 +90,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js

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

@@ -72,7 +72,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
@@ -108,16 +108,6 @@ jobs:
       #   run: pnpm playwright install --with-deps chromium
 
       # Docker setup for development environment
-      - name: Free up disk space
-        run: |
-          # Remove large unused tools to free disk space for Docker builds
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /opt/hostedtoolcache/CodeQL
-          sudo docker system prune -af
-          df -h
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 

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

@@ -134,7 +134,7 @@ jobs:
         run: poetry install
 
       - name: Generate Prisma Client
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       - id: supabase
         name: Start Supabase

autogpt_platform/.claude/settings.local.json

@@ -1,9 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(ls:*)",
-      "WebFetch(domain:langfuse.com)",
-      "Bash(poetry install:*)"
-    ]
-  }
-}

autogpt_platform/Makefile

@@ -6,14 +6,12 @@ start-core:
 
 # Stop core services
 stop-core:
-	docker compose stop 
+	docker compose stop deps
 
 reset-db:
-	docker compose stop db
 	rm -rf db/docker/volumes/db/data
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
-	cd backend && poetry run gen-prisma-stub
 	
 # View logs for core services
 logs-core:
@@ -35,7 +33,6 @@ init-env:
 migrate:
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
-	cd backend && poetry run gen-prisma-stub
 
 run-backend:
 	cd backend && poetry run app
@@ -61,4 +58,4 @@ help:
 	@echo "  run-backend - Run the backend FastAPI server"
 	@echo "  run-frontend - Run the frontend Next.js development server"
 	@echo "  test-data - Run the test data creator"
-	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
+	@echo "  load-store-agents - Load store agents from agents/ folder into test database"

autogpt_platform/backend/.env.default

@@ -58,13 +58,6 @@ V0_API_KEY=
 OPEN_ROUTER_API_KEY=
 NVIDIA_API_KEY=
 
-# Langfuse Prompt Management
-# Used for managing the CoPilot system prompt externally
-# Get credentials from https://cloud.langfuse.com or your self-hosted instance
-LANGFUSE_PUBLIC_KEY=
-LANGFUSE_SECRET_KEY=
-LANGFUSE_HOST=https://cloud.langfuse.com
-
 # OAuth Credentials
 # For the OAuth callback URL, use <your_frontend_url>/auth/integrations/oauth_callback,
 # e.g. http://localhost:3000/auth/integrations/oauth_callback

autogpt_platform/backend/Dockerfile

@@ -48,8 +48,7 @@ RUN poetry install --no-ansi --no-root
 # Generate Prisma client
 COPY autogpt_platform/backend/schema.prisma ./
 COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
-COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
-RUN poetry run prisma generate && poetry run gen-prisma-stub
+RUN poetry run prisma generate
 
 FROM debian:13-slim AS server_dependencies
 

autogpt_platform/backend/backend/api/features/admin/execution_analytics_routes.py

@@ -28,6 +28,7 @@ from backend.executor.manager import get_db_async_client
 from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
+settings = Settings()
 
 
 class ExecutionAnalyticsRequest(BaseModel):
@@ -63,6 +64,8 @@ class ExecutionAnalyticsResult(BaseModel):
     score: Optional[float]
     status: str  # "success", "failed", "skipped"
     error_message: Optional[str] = None
+    started_at: Optional[datetime] = None
+    ended_at: Optional[datetime] = None
 
 
 class ExecutionAnalyticsResponse(BaseModel):
@@ -224,11 +227,6 @@ async def generate_execution_analytics(
     )
 
     try:
-        # Validate model configuration
-        settings = Settings()
-        if not settings.secrets.openai_internal_api_key:
-            raise HTTPException(status_code=500, detail="OpenAI API key not configured")
-
         # Get database client
         db_client = get_db_async_client()
 
@@ -320,6 +318,8 @@ async def generate_execution_analytics(
                     ),
                     status="skipped",
                     error_message=None,  # Not an error - just already processed
+                    started_at=execution.started_at,
+                    ended_at=execution.ended_at,
                 )
             )
 
@@ -349,6 +349,9 @@ async def _process_batch(
 ) -> list[ExecutionAnalyticsResult]:
     """Process a batch of executions concurrently."""
 
+    if not settings.secrets.openai_internal_api_key:
+        raise HTTPException(status_code=500, detail="OpenAI API key not configured")
+
     async def process_single_execution(execution) -> ExecutionAnalyticsResult:
         try:
             # Generate activity status and score using the specified model
@@ -387,6 +390,8 @@ async def _process_batch(
                     score=None,
                     status="skipped",
                     error_message="Activity generation returned None",
+                    started_at=execution.started_at,
+                    ended_at=execution.ended_at,
                 )
 
             # Update the execution stats
@@ -416,6 +421,8 @@ async def _process_batch(
                 summary_text=activity_response["activity_status"],
                 score=activity_response["correctness_score"],
                 status="success",
+                started_at=execution.started_at,
+                ended_at=execution.ended_at,
             )
 
         except Exception as e:
@@ -429,6 +436,8 @@ async def _process_batch(
                 score=None,
                 status="failed",
                 error_message=str(e),
+                started_at=execution.started_at,
+                ended_at=execution.ended_at,
             )
 
     # Process all executions in the batch concurrently

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

@@ -1,6 +1,7 @@
 """Configuration management for chat system."""
 
 import os
+from pathlib import Path
 
 from pydantic import Field, field_validator
 from pydantic_settings import BaseSettings
@@ -11,11 +12,7 @@ class ChatConfig(BaseSettings):
 
     # OpenAI API Configuration
     model: str = Field(
-        default="anthropic/claude-opus-4.5", description="Default model to use"
-    )
-    title_model: str = Field(
-        default="openai/gpt-4o-mini",
-        description="Model to use for generating session titles (should be fast/cheap)",
+        default="qwen/qwen3-235b-a22b-2507", description="Default model to use"
     )
     api_key: str | None = Field(default=None, description="OpenAI API key")
     base_url: str | None = Field(
@@ -26,6 +23,12 @@ class ChatConfig(BaseSettings):
     # Session TTL Configuration - 12 hours
     session_ttl: int = Field(default=43200, description="Session TTL in seconds")
 
+    # System Prompt Configuration
+    system_prompt_path: str = Field(
+        default="prompts/chat_system.md",
+        description="Path to system prompt file relative to chat module",
+    )
+
     # Streaming Configuration
     max_context_messages: int = Field(
         default=50, ge=1, le=200, description="Maximum context messages"
@@ -38,13 +41,6 @@ class ChatConfig(BaseSettings):
         default=3, description="Maximum number of agent schedules"
     )
 
-    # Langfuse Prompt Management Configuration
-    # Note: Langfuse credentials are in Settings().secrets (settings.py)
-    langfuse_prompt_name: str = Field(
-        default="CoPilot Prompt",
-        description="Name of the prompt in Langfuse to fetch",
-    )
-
     @field_validator("api_key", mode="before")
     @classmethod
     def get_api_key(cls, v):
@@ -76,11 +72,43 @@ class ChatConfig(BaseSettings):
                 v = "https://openrouter.ai/api/v1"
         return v
 
-    # Prompt paths for different contexts
-    PROMPT_PATHS: dict[str, str] = {
-        "default": "prompts/chat_system.md",
-        "onboarding": "prompts/onboarding_system.md",
-    }
+    def get_system_prompt(self, **template_vars) -> str:
+        """Load and render the system prompt from file.
+
+        Args:
+            **template_vars: Variables to substitute in the template
+
+        Returns:
+            Rendered system prompt string
+
+        """
+        # Get the path relative to this module
+        module_dir = Path(__file__).parent
+        prompt_path = module_dir / self.system_prompt_path
+
+        # Check for .j2 extension first (Jinja2 template)
+        j2_path = Path(str(prompt_path) + ".j2")
+        if j2_path.exists():
+            try:
+                from jinja2 import Template
+
+                template = Template(j2_path.read_text())
+                return template.render(**template_vars)
+            except ImportError:
+                # Jinja2 not installed, fall back to reading as plain text
+                return j2_path.read_text()
+
+        # Check for markdown file
+        if prompt_path.exists():
+            content = prompt_path.read_text()
+
+            # Simple variable substitution if Jinja2 is not available
+            for key, value in template_vars.items():
+                placeholder = f"{{{key}}}"
+                content = content.replace(placeholder, str(value))
+
+            return content
+        raise FileNotFoundError(f"System prompt file not found: {prompt_path}")
 
     class Config:
         """Pydantic config."""

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

@@ -1,243 +0,0 @@
-"""Database operations for chat sessions."""
-
-import logging
-from datetime import UTC, datetime
-from typing import Any, cast
-
-from prisma.models import ChatMessage as PrismaChatMessage
-from prisma.models import ChatSession as PrismaChatSession
-from prisma.types import (
-    ChatMessageCreateInput,
-    ChatSessionCreateInput,
-    ChatSessionUpdateInput,
-)
-
-from backend.data.db import transaction
-from backend.util.json import SafeJson
-
-logger = logging.getLogger(__name__)
-
-
-async def get_chat_session(session_id: str) -> PrismaChatSession | None:
-    """Get a chat session by ID from the database."""
-    session = await PrismaChatSession.prisma().find_unique(
-        where={"id": session_id},
-        include={"Messages": True},
-    )
-    if session and session.Messages:
-        # Sort messages by sequence in Python since Prisma doesn't support order_by in include
-        session.Messages.sort(key=lambda m: m.sequence)
-    return session
-
-
-async def create_chat_session(
-    session_id: str,
-    user_id: str | None,
-) -> PrismaChatSession:
-    """Create a new chat session in the database."""
-    data = ChatSessionCreateInput(
-        id=session_id,
-        userId=user_id,
-        credentials=SafeJson({}),
-        successfulAgentRuns=SafeJson({}),
-        successfulAgentSchedules=SafeJson({}),
-    )
-    return await PrismaChatSession.prisma().create(
-        data=data,
-        include={"Messages": True},
-    )
-
-
-async def update_chat_session(
-    session_id: str,
-    credentials: dict[str, Any] | None = None,
-    successful_agent_runs: dict[str, Any] | None = None,
-    successful_agent_schedules: dict[str, Any] | None = None,
-    total_prompt_tokens: int | None = None,
-    total_completion_tokens: int | None = None,
-    title: str | None = None,
-) -> PrismaChatSession | None:
-    """Update a chat session's metadata."""
-    data: ChatSessionUpdateInput = {"updatedAt": datetime.now(UTC)}
-
-    if credentials is not None:
-        data["credentials"] = SafeJson(credentials)
-    if successful_agent_runs is not None:
-        data["successfulAgentRuns"] = SafeJson(successful_agent_runs)
-    if successful_agent_schedules is not None:
-        data["successfulAgentSchedules"] = SafeJson(successful_agent_schedules)
-    if total_prompt_tokens is not None:
-        data["totalPromptTokens"] = total_prompt_tokens
-    if total_completion_tokens is not None:
-        data["totalCompletionTokens"] = total_completion_tokens
-    if title is not None:
-        data["title"] = title
-
-    session = await PrismaChatSession.prisma().update(
-        where={"id": session_id},
-        data=data,
-        include={"Messages": True},
-    )
-    if session and session.Messages:
-        session.Messages.sort(key=lambda m: m.sequence)
-    return session
-
-
-async def add_chat_message(
-    session_id: str,
-    role: str,
-    sequence: int,
-    content: str | None = None,
-    name: str | None = None,
-    tool_call_id: str | None = None,
-    refusal: str | None = None,
-    tool_calls: list[dict[str, Any]] | None = None,
-    function_call: dict[str, Any] | None = None,
-) -> PrismaChatMessage:
-    """Add a message to a chat session."""
-    # Build the input dict dynamically - only include optional fields when they
-    # have values, as Prisma TypedDict validation fails when optional fields
-    # are explicitly set to None
-    data: dict[str, Any] = {
-        "Session": {"connect": {"id": session_id}},
-        "role": role,
-        "sequence": sequence,
-    }
-
-    # Add optional string fields
-    if content is not None:
-        data["content"] = content
-    if name is not None:
-        data["name"] = name
-    if tool_call_id is not None:
-        data["toolCallId"] = tool_call_id
-    if refusal is not None:
-        data["refusal"] = refusal
-
-    # Add optional JSON fields only when they have values
-    if tool_calls is not None:
-        data["toolCalls"] = SafeJson(tool_calls)
-    if function_call is not None:
-        data["functionCall"] = SafeJson(function_call)
-
-    # Update session's updatedAt timestamp
-    await PrismaChatSession.prisma().update(
-        where={"id": session_id},
-        data={"updatedAt": datetime.now(UTC)},
-    )
-
-    return await PrismaChatMessage.prisma().create(
-        data=cast(ChatMessageCreateInput, data)
-    )
-
-
-async def add_chat_messages_batch(
-    session_id: str,
-    messages: list[dict[str, Any]],
-    start_sequence: int,
-) -> list[PrismaChatMessage]:
-    """Add multiple messages to a chat session in a batch.
-
-    Uses a transaction for atomicity - if any message creation fails,
-    the entire batch is rolled back.
-    """
-    if not messages:
-        return []
-
-    created_messages = []
-
-    async with transaction() as tx:
-        for i, msg in enumerate(messages):
-            # Build the input dict dynamically - only include optional JSON fields
-            # when they have values, as Prisma TypedDict validation fails when
-            # optional fields are explicitly set to None
-            data: dict[str, Any] = {
-                "Session": {"connect": {"id": session_id}},
-                "role": msg["role"],
-                "sequence": start_sequence + i,
-            }
-
-            # Add optional string fields
-            if msg.get("content") is not None:
-                data["content"] = msg["content"]
-            if msg.get("name") is not None:
-                data["name"] = msg["name"]
-            if msg.get("tool_call_id") is not None:
-                data["toolCallId"] = msg["tool_call_id"]
-            if msg.get("refusal") is not None:
-                data["refusal"] = msg["refusal"]
-
-            # Add optional JSON fields only when they have values
-            if msg.get("tool_calls") is not None:
-                data["toolCalls"] = SafeJson(msg["tool_calls"])
-            if msg.get("function_call") is not None:
-                data["functionCall"] = SafeJson(msg["function_call"])
-
-            created = await PrismaChatMessage.prisma(tx).create(
-                data=cast(ChatMessageCreateInput, data)
-            )
-            created_messages.append(created)
-
-        # Update session's updatedAt timestamp within the same transaction
-        await PrismaChatSession.prisma(tx).update(
-            where={"id": session_id},
-            data={"updatedAt": datetime.now(UTC)},
-        )
-
-    return created_messages
-
-
-async def get_user_chat_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> list[PrismaChatSession]:
-    """Get chat sessions for a user, ordered by most recent."""
-    return await PrismaChatSession.prisma().find_many(
-        where={"userId": user_id},
-        order={"updatedAt": "desc"},
-        take=limit,
-        skip=offset,
-    )
-
-
-async def get_user_session_count(user_id: str) -> int:
-    """Get the total number of chat sessions for a user."""
-    return await PrismaChatSession.prisma().count(where={"userId": user_id})
-
-
-async def delete_chat_session(session_id: str, user_id: str | None = None) -> bool:
-    """Delete a chat session and all its messages.
-
-    Args:
-        session_id: The session ID to delete.
-        user_id: If provided, validates that the session belongs to this user
-            before deletion. This prevents unauthorized deletion of other
-            users' sessions.
-
-    Returns:
-        True if deleted successfully, False otherwise.
-    """
-    try:
-        # Build where clause with optional user_id validation
-        where_clause: dict[str, Any] = {"id": session_id}
-        if user_id is not None:
-            where_clause["userId"] = user_id
-
-        result = await PrismaChatSession.prisma().delete_many(where=where_clause)
-        if result == 0:
-            logger.warning(
-                f"No session deleted for {session_id} "
-                f"(user_id validation: {user_id is not None})"
-            )
-            return False
-        return True
-    except Exception as e:
-        logger.error(f"Failed to delete chat session {session_id}: {e}")
-        return False
-
-
-async def get_chat_session_message_count(session_id: str) -> int:
-    """Get the number of messages in a chat session."""
-    count = await PrismaChatMessage.prisma().count(where={"sessionId": session_id})
-    return count

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

@@ -1,4 +1,3 @@
-import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
@@ -17,32 +16,16 @@ from openai.types.chat.chat_completion_message_tool_call_param import (
     ChatCompletionMessageToolCallParam,
     Function,
 )
-from prisma.models import ChatMessage as PrismaChatMessage
-from prisma.models import ChatSession as PrismaChatSession
 from pydantic import BaseModel
 
 from backend.data.redis_client import get_redis_async
-from backend.util import json
-from backend.util.exceptions import DatabaseError, RedisError
+from backend.util.exceptions import RedisError
 
-from . import db as chat_db
 from .config import ChatConfig
 
 logger = logging.getLogger(__name__)
 config = ChatConfig()
 
-# Session-level locks to prevent race conditions during concurrent upserts
-_session_locks: dict[str, asyncio.Lock] = {}
-_session_locks_mutex = asyncio.Lock()
-
-
-async def _get_session_lock(session_id: str) -> asyncio.Lock:
-    """Get or create a lock for a specific session to prevent concurrent upserts."""
-    async with _session_locks_mutex:
-        if session_id not in _session_locks:
-            _session_locks[session_id] = asyncio.Lock()
-        return _session_locks[session_id]
-
 
 class ChatMessage(BaseModel):
     role: str
@@ -63,7 +46,6 @@ class Usage(BaseModel):
 class ChatSession(BaseModel):
     session_id: str
     user_id: str | None
-    title: str | None = None
     messages: list[ChatMessage]
     usage: list[Usage]
     credentials: dict[str, dict] = {}  # Map of provider -> credential metadata
@@ -77,7 +59,6 @@ class ChatSession(BaseModel):
         return ChatSession(
             session_id=str(uuid.uuid4()),
             user_id=user_id,
-            title=None,
             messages=[],
             usage=[],
             credentials={},
@@ -85,85 +66,6 @@ class ChatSession(BaseModel):
             updated_at=datetime.now(UTC),
         )
 
-    @staticmethod
-    def from_prisma(
-        prisma_session: PrismaChatSession,
-        prisma_messages: list[PrismaChatMessage] | None = None,
-    ) -> "ChatSession":
-        """Convert Prisma models to Pydantic ChatSession."""
-        messages = []
-        if prisma_messages:
-            for msg in prisma_messages:
-                tool_calls = None
-                if msg.toolCalls:
-                    tool_calls = (
-                        json.loads(msg.toolCalls)
-                        if isinstance(msg.toolCalls, str)
-                        else msg.toolCalls
-                    )
-
-                function_call = None
-                if msg.functionCall:
-                    function_call = (
-                        json.loads(msg.functionCall)
-                        if isinstance(msg.functionCall, str)
-                        else msg.functionCall
-                    )
-
-                messages.append(
-                    ChatMessage(
-                        role=msg.role,
-                        content=msg.content,
-                        name=msg.name,
-                        tool_call_id=msg.toolCallId,
-                        refusal=msg.refusal,
-                        tool_calls=tool_calls,
-                        function_call=function_call,
-                    )
-                )
-
-        # Parse JSON fields from Prisma
-        credentials = (
-            json.loads(prisma_session.credentials)
-            if isinstance(prisma_session.credentials, str)
-            else prisma_session.credentials or {}
-        )
-        successful_agent_runs = (
-            json.loads(prisma_session.successfulAgentRuns)
-            if isinstance(prisma_session.successfulAgentRuns, str)
-            else prisma_session.successfulAgentRuns or {}
-        )
-        successful_agent_schedules = (
-            json.loads(prisma_session.successfulAgentSchedules)
-            if isinstance(prisma_session.successfulAgentSchedules, str)
-            else prisma_session.successfulAgentSchedules or {}
-        )
-
-        # Calculate usage from token counts
-        usage = []
-        if prisma_session.totalPromptTokens or prisma_session.totalCompletionTokens:
-            usage.append(
-                Usage(
-                    prompt_tokens=prisma_session.totalPromptTokens or 0,
-                    completion_tokens=prisma_session.totalCompletionTokens or 0,
-                    total_tokens=(prisma_session.totalPromptTokens or 0)
-                    + (prisma_session.totalCompletionTokens or 0),
-                )
-            )
-
-        return ChatSession(
-            session_id=prisma_session.id,
-            user_id=prisma_session.userId,
-            title=prisma_session.title,
-            messages=messages,
-            usage=usage,
-            credentials=credentials,
-            started_at=prisma_session.createdAt,
-            updated_at=prisma_session.updatedAt,
-            successful_agent_runs=successful_agent_runs,
-            successful_agent_schedules=successful_agent_schedules,
-        )
-
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
         for message in self.messages:
@@ -253,308 +155,50 @@ class ChatSession(BaseModel):
         return messages
 
 
-async def _get_session_from_cache(session_id: str) -> ChatSession | None:
-    """Get a chat session from Redis cache."""
-    redis_key = f"chat:session:{session_id}"
-    async_redis = await get_redis_async()
-    raw_session: bytes | None = await async_redis.get(redis_key)
-
-    if raw_session is None:
-        return None
-
-    try:
-        session = ChatSession.model_validate_json(raw_session)
-        logger.info(
-            f"Loading session {session_id} from cache: "
-            f"message_count={len(session.messages)}, "
-            f"roles={[m.role for m in session.messages]}"
-        )
-        return session
-    except Exception as e:
-        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
-        raise RedisError(f"Corrupted session data for {session_id}") from e
-
-
-async def _cache_session(session: ChatSession) -> None:
-    """Cache a chat session in Redis."""
-    redis_key = f"chat:session:{session.session_id}"
-    async_redis = await get_redis_async()
-    await async_redis.setex(redis_key, config.session_ttl, session.model_dump_json())
-
-
-async def _get_session_from_db(session_id: str) -> ChatSession | None:
-    """Get a chat session from the database."""
-    prisma_session = await chat_db.get_chat_session(session_id)
-    if not prisma_session:
-        return None
-
-    messages = prisma_session.Messages
-    logger.info(
-        f"Loading session {session_id} from DB: "
-        f"has_messages={messages is not None}, "
-        f"message_count={len(messages) if messages else 0}, "
-        f"roles={[m.role for m in messages] if messages else []}"
-    )
-
-    return ChatSession.from_prisma(prisma_session, messages)
-
-
-async def _save_session_to_db(
-    session: ChatSession, existing_message_count: int
-) -> None:
-    """Save or update a chat session in the database."""
-    # Check if session exists in DB
-    existing = await chat_db.get_chat_session(session.session_id)
-
-    if not existing:
-        # Create new session
-        await chat_db.create_chat_session(
-            session_id=session.session_id,
-            user_id=session.user_id,
-        )
-        existing_message_count = 0
-
-    # Calculate total tokens from usage
-    total_prompt = sum(u.prompt_tokens for u in session.usage)
-    total_completion = sum(u.completion_tokens for u in session.usage)
-
-    # Update session metadata
-    await chat_db.update_chat_session(
-        session_id=session.session_id,
-        credentials=session.credentials,
-        successful_agent_runs=session.successful_agent_runs,
-        successful_agent_schedules=session.successful_agent_schedules,
-        total_prompt_tokens=total_prompt,
-        total_completion_tokens=total_completion,
-    )
-
-    # Add new messages (only those after existing count)
-    new_messages = session.messages[existing_message_count:]
-    if new_messages:
-        messages_data = []
-        for msg in new_messages:
-            messages_data.append(
-                {
-                    "role": msg.role,
-                    "content": msg.content,
-                    "name": msg.name,
-                    "tool_call_id": msg.tool_call_id,
-                    "refusal": msg.refusal,
-                    "tool_calls": msg.tool_calls,
-                    "function_call": msg.function_call,
-                }
-            )
-        logger.info(
-            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
-            f"roles={[m['role'] for m in messages_data]}, "
-            f"start_sequence={existing_message_count}"
-        )
-        await chat_db.add_chat_messages_batch(
-            session_id=session.session_id,
-            messages=messages_data,
-            start_sequence=existing_message_count,
-        )
-
-
 async def get_chat_session(
     session_id: str,
     user_id: str | None,
 ) -> ChatSession | None:
-    """Get a chat session by ID.
+    """Get a chat session by ID."""
+    redis_key = f"chat:session:{session_id}"
+    async_redis = await get_redis_async()
 
-    Checks Redis cache first, falls back to database if not found.
-    Caches database results back to Redis.
-    """
-    # Try cache first
-    try:
-        session = await _get_session_from_cache(session_id)
-        if session:
-            # Verify user ownership
-            if session.user_id is not None and session.user_id != user_id:
-                logger.warning(
-                    f"Session {session_id} user id mismatch: {session.user_id} != {user_id}"
-                )
-                return None
-            return session
-    except RedisError:
-        logger.warning(f"Cache error for session {session_id}, trying database")
-    except Exception as e:
-        logger.warning(f"Unexpected cache error for session {session_id}: {e}")
+    raw_session: bytes | None = await async_redis.get(redis_key)
 
-    # Fall back to database
-    logger.info(f"Session {session_id} not in cache, checking database")
-    session = await _get_session_from_db(session_id)
-
-    if session is None:
-        logger.warning(f"Session {session_id} not found in cache or database")
+    if raw_session is None:
+        logger.warning(f"Session {session_id} not found in Redis")
         return None
 
-    # Verify user ownership
+    try:
+        session = ChatSession.model_validate_json(raw_session)
+    except Exception as e:
+        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
+        raise RedisError(f"Corrupted session data for {session_id}") from e
+
     if session.user_id is not None and session.user_id != user_id:
         logger.warning(
             f"Session {session_id} user id mismatch: {session.user_id} != {user_id}"
         )
         return None
 
-    # Cache the session from DB
-    try:
-        await _cache_session(session)
-        logger.info(f"Cached session {session_id} from database")
-    except Exception as e:
-        logger.warning(f"Failed to cache session {session_id}: {e}")
-
     return session
 
 
 async def upsert_chat_session(
     session: ChatSession,
 ) -> ChatSession:
-    """Update a chat session in both cache and database.
+    """Update a chat session with the given messages."""
 
-    Uses session-level locking to prevent race conditions when concurrent
-    operations (e.g., background title update and main stream handler)
-    attempt to upsert the same session simultaneously.
+    redis_key = f"chat:session:{session.session_id}"
 
-    Raises:
-        DatabaseError: If the database write fails. The cache is still updated
-            as a best-effort optimization, but the error is propagated to ensure
-            callers are aware of the persistence failure.
-        RedisError: If the cache write fails (after successful DB write).
-    """
-    # Acquire session-specific lock to prevent concurrent upserts
-    lock = await _get_session_lock(session.session_id)
+    async_redis = await get_redis_async()
+    resp = await async_redis.setex(
+        redis_key, config.session_ttl, session.model_dump_json()
+    )
 
-    async with lock:
-        # Get existing message count from DB for incremental saves
-        existing_message_count = await chat_db.get_chat_session_message_count(
-            session.session_id
+    if not resp:
+        raise RedisError(
+            f"Failed to persist chat session {session.session_id} to Redis: {resp}"
         )
 
-        db_error: Exception | None = None
-
-        # Save to database (primary storage)
-        try:
-            await _save_session_to_db(session, existing_message_count)
-        except Exception as e:
-            logger.error(
-                f"Failed to save session {session.session_id} to database: {e}"
-            )
-            db_error = e
-
-        # Save to cache (best-effort, even if DB failed)
-        try:
-            await _cache_session(session)
-        except Exception as e:
-            # If DB succeeded but cache failed, raise cache error
-            if db_error is None:
-                raise RedisError(
-                    f"Failed to persist chat session {session.session_id} to Redis: {e}"
-                ) from e
-            # If both failed, log cache error but raise DB error (more critical)
-            logger.warning(
-                f"Cache write also failed for session {session.session_id}: {e}"
-            )
-
-        # Propagate DB error after attempting cache (prevents data loss)
-        if db_error is not None:
-            raise DatabaseError(
-                f"Failed to persist chat session {session.session_id} to database"
-            ) from db_error
-
-        return session
-
-
-async def create_chat_session(user_id: str | None) -> ChatSession:
-    """Create a new chat session and persist it."""
-    session = ChatSession.new(user_id)
-
-    # Create in database first
-    try:
-        await chat_db.create_chat_session(
-            session_id=session.session_id,
-            user_id=user_id,
-        )
-    except Exception as e:
-        logger.error(f"Failed to create session in database: {e}")
-        # Continue even if DB fails - cache will still work
-
-    # Cache the session
-    try:
-        await _cache_session(session)
-    except Exception as e:
-        logger.warning(f"Failed to cache new session: {e}")
-
     return session
-
-
-async def get_user_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> list[ChatSession]:
-    """Get all chat sessions for a user from the database."""
-    prisma_sessions = await chat_db.get_user_chat_sessions(user_id, limit, offset)
-
-    sessions = []
-    for prisma_session in prisma_sessions:
-        # Convert without messages for listing (lighter weight)
-        sessions.append(ChatSession.from_prisma(prisma_session, None))
-
-    return sessions
-
-
-async def delete_chat_session(session_id: str, user_id: str | None = None) -> bool:
-    """Delete a chat session from both cache and database.
-
-    Args:
-        session_id: The session ID to delete.
-        user_id: If provided, validates that the session belongs to this user
-            before deletion. This prevents unauthorized deletion.
-
-    Returns:
-        True if deleted successfully, False otherwise.
-    """
-    # Delete from cache (always attempt, regardless of ownership)
-    try:
-        redis_key = f"chat:session:{session_id}"
-        async_redis = await get_redis_async()
-        await async_redis.delete(redis_key)
-    except Exception as e:
-        logger.warning(f"Failed to delete session {session_id} from cache: {e}")
-
-    # Delete from database (with optional user_id validation)
-    return await chat_db.delete_chat_session(session_id, user_id)
-
-
-async def update_session_title(session_id: str, title: str) -> bool:
-    """Update only the title of a chat session.
-
-    This is a lightweight operation that doesn't touch messages, avoiding
-    race conditions with concurrent message updates. Use this for background
-    title generation instead of upsert_chat_session.
-
-    Args:
-        session_id: The session ID to update.
-        title: The new title to set.
-
-    Returns:
-        True if updated successfully, False otherwise.
-    """
-    try:
-        result = await chat_db.update_chat_session(session_id=session_id, title=title)
-        if result is None:
-            logger.warning(f"Session {session_id} not found for title update")
-            return False
-
-        # Invalidate cache so next fetch gets updated title
-        try:
-            redis_key = f"chat:session:{session_id}"
-            async_redis = await get_redis_async()
-            await async_redis.delete(redis_key)
-        except Exception as e:
-            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
-
-        return True
-    except Exception as e:
-        logger.error(f"Failed to update title for session {session_id}: {e}")
-        return False

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

@@ -68,50 +68,3 @@ async def test_chatsession_redis_storage_user_id_mismatch():
     s2 = await get_chat_session(s.session_id, None)
 
     assert s2 is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_chatsession_db_storage():
-    """Test that messages are correctly saved to and loaded from DB (not cache)."""
-    from backend.data.redis_client import get_redis_async
-
-    # Create session with messages including assistant message
-    s = ChatSession.new(user_id=None)
-    s.messages = messages  # Contains user, assistant, and tool messages
-    assert s.session_id is not None, "Session id is not set"
-    # Upsert to save to both cache and DB
-    s = await upsert_chat_session(s)
-
-    # Clear the Redis cache to force DB load
-    redis_key = f"chat:session:{s.session_id}"
-    async_redis = await get_redis_async()
-    await async_redis.delete(redis_key)
-
-    # Load from DB (cache was cleared)
-    s2 = await get_chat_session(
-        session_id=s.session_id,
-        user_id=s.user_id,
-    )
-
-    assert s2 is not None, "Session not found after loading from DB"
-    assert len(s2.messages) == len(
-        s.messages
-    ), f"Message count mismatch: expected {len(s.messages)}, got {len(s2.messages)}"
-
-    # Verify all roles are present
-    roles = [m.role for m in s2.messages]
-    assert "user" in roles, f"User message missing. Roles found: {roles}"
-    assert "assistant" in roles, f"Assistant message missing. Roles found: {roles}"
-    assert "tool" in roles, f"Tool message missing. Roles found: {roles}"
-
-    # Verify message content
-    for orig, loaded in zip(s.messages, s2.messages):
-        assert orig.role == loaded.role, f"Role mismatch: {orig.role} != {loaded.role}"
-        assert (
-            orig.content == loaded.content
-        ), f"Content mismatch for {orig.role}: {orig.content} != {loaded.content}"
-        if orig.tool_calls:
-            assert (
-                loaded.tool_calls is not None
-            ), f"Tool calls missing for {orig.role} message"
-            assert len(orig.tool_calls) == len(loaded.tool_calls)

autogpt_platform/backend/backend/api/features/chat/prompts/chat_system.md

@@ -0,0 +1,104 @@
+You are Otto, an AI Co-Pilot and Forward Deployed Engineer for AutoGPT, an AI Business Automation tool. Your mission is to help users quickly find and set up AutoGPT agents to solve their business problems.
+
+Here are the functions available to you:
+
+<functions>
+1. **find_agent** - Search for agents that solve the user's problem
+2. **run_agent** - Run or schedule an agent (automatically handles setup)
+</functions>
+
+## HOW run_agent WORKS
+
+The `run_agent` tool automatically handles the entire setup flow:
+
+1. **First call** (no inputs) → Returns available inputs so user can decide what values to use
+2. **Credentials check** → If missing, UI automatically prompts user to add them (you don't need to mention this)
+3. **Execution** → Runs when you provide `inputs` OR set `use_defaults=true`
+
+Parameters:
+- `username_agent_slug` (required): Agent identifier like "creator/agent-name"
+- `inputs`: Object with input values for the agent
+- `use_defaults`: Set to `true` to run with default values (only after user confirms)
+- `schedule_name` + `cron`: For scheduled execution
+
+## WORKFLOW
+
+1. **find_agent** - Search for agents that solve the user's problem
+2. **run_agent** (first call, no inputs) - Get available inputs for the agent
+3. **Ask user** what values they want to use OR if they want to use defaults
+4. **run_agent** (second call) - Either with `inputs={...}` or `use_defaults=true`
+
+## YOUR APPROACH
+
+**Step 1: Understand the Problem**
+- Ask maximum 1-2 targeted questions
+- Focus on: What business problem are they solving?
+- Move quickly to searching for solutions
+
+**Step 2: Find Agents**
+- Use `find_agent` immediately with relevant keywords
+- Suggest the best option from search results
+- Explain briefly how it solves their problem
+
+**Step 3: Get Agent Inputs**
+- Call `run_agent(username_agent_slug="creator/agent-name")` without inputs
+- This returns the available inputs (required and optional)
+- Present these to the user and ask what values they want
+
+**Step 4: Run with User's Choice**
+- If user provides values: `run_agent(username_agent_slug="...", inputs={...})`
+- If user says "use defaults": `run_agent(username_agent_slug="...", use_defaults=true)`
+- On success, share the agent link with the user
+
+**For Scheduled Execution:**
+- Add `schedule_name` and `cron` parameters
+- Example: `run_agent(username_agent_slug="...", inputs={...}, schedule_name="Daily Report", cron="0 9 * * *")`
+
+## FUNCTION CALL FORMAT
+
+To call a function, use this exact format:
+`<function_call>function_name(parameter="value")</function_call>`
+
+Examples:
+- `<function_call>find_agent(query="social media automation")</function_call>`
+- `<function_call>run_agent(username_agent_slug="creator/agent-name")</function_call>` (get inputs)
+- `<function_call>run_agent(username_agent_slug="creator/agent-name", inputs={"topic": "AI news"})</function_call>`
+- `<function_call>run_agent(username_agent_slug="creator/agent-name", use_defaults=true)</function_call>`
+
+## KEY RULES
+
+**What You DON'T Do:**
+- Don't help with login (frontend handles this)
+- Don't mention or explain credentials to the user (frontend handles this automatically)
+- Don't run agents without first showing available inputs to the user
+- Don't use `use_defaults=true` without user explicitly confirming
+- Don't write responses longer than 3 sentences
+
+**What You DO:**
+- Always call run_agent first without inputs to see what's available
+- Ask user what values they want OR if they want to use defaults
+- Keep all responses to maximum 3 sentences
+- Include the agent link in your response after successful execution
+
+**Error Handling:**
+- Authentication needed → "Please sign in via the interface"
+- Credentials missing → The UI handles this automatically. Focus on asking the user about input values instead.
+
+## RESPONSE STRUCTURE
+
+Before responding, wrap your analysis in <thinking> tags to systematically plan your approach:
+- Extract the key business problem or request from the user's message
+- Determine what function call (if any) you need to make next
+- Plan your response to stay under the 3-sentence maximum
+
+Example interaction:
+```
+User: "Run the AI news agent for me"
+Otto: <function_call>run_agent(username_agent_slug="autogpt/ai-news")</function_call>
+[Tool returns: Agent accepts inputs - Required: topic. Optional: num_articles (default: 5)]
+Otto: The AI News agent needs a topic. What topic would you like news about, or should I use the defaults?
+User: "Use defaults"
+Otto: <function_call>run_agent(username_agent_slug="autogpt/ai-news", use_defaults=true)</function_call>
+```
+
+KEEP ANSWERS TO 3 SENTENCES

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

@@ -1,10 +1,3 @@
-"""
-Response models for Vercel AI SDK UI Stream Protocol.
-
-This module implements the AI SDK UI Stream Protocol (v1) for streaming chat responses.
-See: https://ai-sdk.dev/docs/ai-sdk-ui/stream-protocol
-"""
-
 from enum import Enum
 from typing import Any
 
@@ -12,133 +5,97 @@ from pydantic import BaseModel, Field
 
 
 class ResponseType(str, Enum):
-    """Types of streaming responses following AI SDK protocol."""
+    """Types of streaming responses."""
 
-    # Message lifecycle
-    START = "start"
-    FINISH = "finish"
-
-    # Text streaming
-    TEXT_START = "text-start"
-    TEXT_DELTA = "text-delta"
-    TEXT_END = "text-end"
-
-    # Tool interaction
-    TOOL_INPUT_START = "tool-input-start"
-    TOOL_INPUT_AVAILABLE = "tool-input-available"
-    TOOL_OUTPUT_AVAILABLE = "tool-output-available"
-
-    # Other
+    TEXT_CHUNK = "text_chunk"
+    TEXT_ENDED = "text_ended"
+    TOOL_CALL = "tool_call"
+    TOOL_CALL_START = "tool_call_start"
+    TOOL_RESPONSE = "tool_response"
     ERROR = "error"
     USAGE = "usage"
+    STREAM_END = "stream_end"
 
 
 class StreamBaseResponse(BaseModel):
     """Base response model for all streaming responses."""
 
     type: ResponseType
+    timestamp: str | None = None
 
     def to_sse(self) -> str:
         """Convert to SSE format."""
         return f"data: {self.model_dump_json()}\n\n"
 
 
-# ========== Message Lifecycle ==========
+class StreamTextChunk(StreamBaseResponse):
+    """Streaming text content from the assistant."""
+
+    type: ResponseType = ResponseType.TEXT_CHUNK
+    content: str = Field(..., description="Text content chunk")
 
 
-class StreamStart(StreamBaseResponse):
-    """Start of a new message."""
-
-    type: ResponseType = ResponseType.START
-    messageId: str = Field(..., description="Unique message ID")
-
-
-class StreamFinish(StreamBaseResponse):
-    """End of message/stream."""
-
-    type: ResponseType = ResponseType.FINISH
-
-
-# ========== Text Streaming ==========
-
-
-class StreamTextStart(StreamBaseResponse):
-    """Start of a text block."""
-
-    type: ResponseType = ResponseType.TEXT_START
-    id: str = Field(..., description="Text block ID")
-
-
-class StreamTextDelta(StreamBaseResponse):
-    """Streaming text content delta."""
-
-    type: ResponseType = ResponseType.TEXT_DELTA
-    id: str = Field(..., description="Text block ID")
-    delta: str = Field(..., description="Text content delta")
-
-
-class StreamTextEnd(StreamBaseResponse):
-    """End of a text block."""
-
-    type: ResponseType = ResponseType.TEXT_END
-    id: str = Field(..., description="Text block ID")
-
-
-# ========== Tool Interaction ==========
-
-
-class StreamToolInputStart(StreamBaseResponse):
+class StreamToolCallStart(StreamBaseResponse):
     """Tool call started notification."""
 
-    type: ResponseType = ResponseType.TOOL_INPUT_START
-    toolCallId: str = Field(..., description="Unique tool call ID")
-    toolName: str = Field(..., description="Name of the tool being called")
+    type: ResponseType = ResponseType.TOOL_CALL_START
+    tool_name: str = Field(..., description="Name of the tool that was executed")
+    tool_id: str = Field(..., description="Unique tool call ID")
 
 
-class StreamToolInputAvailable(StreamBaseResponse):
-    """Tool input is ready for execution."""
+class StreamToolCall(StreamBaseResponse):
+    """Tool invocation notification."""
 
-    type: ResponseType = ResponseType.TOOL_INPUT_AVAILABLE
-    toolCallId: str = Field(..., description="Unique tool call ID")
-    toolName: str = Field(..., description="Name of the tool being called")
-    input: dict[str, Any] = Field(
-        default_factory=dict, description="Tool input arguments"
+    type: ResponseType = ResponseType.TOOL_CALL
+    tool_id: str = Field(..., description="Unique tool call ID")
+    tool_name: str = Field(..., description="Name of the tool being called")
+    arguments: dict[str, Any] = Field(
+        default_factory=dict, description="Tool arguments"
     )
 
 
-class StreamToolOutputAvailable(StreamBaseResponse):
+class StreamToolExecutionResult(StreamBaseResponse):
     """Tool execution result."""
 
-    type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
-    toolCallId: str = Field(..., description="Tool call ID this responds to")
-    output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Additional fields for internal use (not part of AI SDK spec but useful)
-    toolName: str | None = Field(
-        default=None, description="Name of the tool that was executed"
-    )
+    type: ResponseType = ResponseType.TOOL_RESPONSE
+    tool_id: str = Field(..., description="Tool call ID this responds to")
+    tool_name: str = Field(..., description="Name of the tool that was executed")
+    result: str | dict[str, Any] = Field(..., description="Tool execution result")
     success: bool = Field(
         default=True, description="Whether the tool execution succeeded"
     )
 
 
-# ========== Other ==========
-
-
 class StreamUsage(StreamBaseResponse):
     """Token usage statistics."""
 
     type: ResponseType = ResponseType.USAGE
-    promptTokens: int = Field(..., description="Number of prompt tokens")
-    completionTokens: int = Field(..., description="Number of completion tokens")
-    totalTokens: int = Field(..., description="Total number of tokens")
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
 
 
 class StreamError(StreamBaseResponse):
     """Error response."""
 
     type: ResponseType = ResponseType.ERROR
-    errorText: str = Field(..., description="Error message text")
+    message: str = Field(..., description="Error message")
     code: str | None = Field(default=None, description="Error code")
     details: dict[str, Any] | None = Field(
         default=None, description="Additional error details"
     )
+
+
+class StreamTextEnded(StreamBaseResponse):
+    """Text streaming completed marker."""
+
+    type: ResponseType = ResponseType.TEXT_ENDED
+
+
+class StreamEnd(StreamBaseResponse):
+    """End of stream marker."""
+
+    type: ResponseType = ResponseType.STREAM_END
+    summary: dict[str, Any] | None = Field(
+        default=None, description="Stream summary statistics"
+    )

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

@@ -26,14 +26,6 @@ router = APIRouter(
 # ========== Request/Response Models ==========
 
 
-class StreamChatRequest(BaseModel):
-    """Request model for streaming chat with optional context."""
-
-    message: str
-    is_user_message: bool = True
-    context: dict[str, str] | None = None  # {url: str, content: str}
-
-
 class CreateSessionResponse(BaseModel):
     """Response model containing information on a newly created chat session."""
 
@@ -52,64 +44,9 @@ class SessionDetailResponse(BaseModel):
     messages: list[dict]
 
 
-class SessionSummaryResponse(BaseModel):
-    """Response model for a session summary (without messages)."""
-
-    id: str
-    created_at: str
-    updated_at: str
-    title: str | None = None
-
-
-class ListSessionsResponse(BaseModel):
-    """Response model for listing chat sessions."""
-
-    sessions: list[SessionSummaryResponse]
-    total: int
-
-
 # ========== Routes ==========
 
 
-@router.get(
-    "/sessions",
-    dependencies=[Security(auth.requires_user)],
-)
-async def list_sessions(
-    user_id: Annotated[str, Security(auth.get_user_id)],
-    limit: int = Query(default=50, ge=1, le=100),
-    offset: int = Query(default=0, ge=0),
-) -> ListSessionsResponse:
-    """
-    List chat sessions for the authenticated user.
-
-    Returns a paginated list of chat sessions belonging to the current user,
-    ordered by most recently updated.
-
-    Args:
-        user_id: The authenticated user's ID.
-        limit: Maximum number of sessions to return (1-100).
-        offset: Number of sessions to skip for pagination.
-
-    Returns:
-        ListSessionsResponse: List of session summaries and total count.
-    """
-    sessions = await chat_service.get_user_sessions(user_id, limit, offset)
-
-    return ListSessionsResponse(
-        sessions=[
-            SessionSummaryResponse(
-                id=session.session_id,
-                created_at=session.started_at.isoformat(),
-                updated_at=session.updated_at.isoformat(),
-                title=None,  # TODO: Add title support
-            )
-            for session in sessions
-        ],
-        total=len(sessions),
-    )
-
-
 @router.post(
     "/sessions",
 )
@@ -165,92 +102,26 @@ async def get_session(
     session = await chat_service.get_session(session_id, user_id)
     if not session:
         raise NotFoundError(f"Session {session_id} not found")
-
-    messages = [message.model_dump() for message in session.messages]
-    logger.info(
-        f"Returning session {session_id}: "
-        f"message_count={len(messages)}, "
-        f"roles={[m.get('role') for m in messages]}"
-    )
-
     return SessionDetailResponse(
         id=session.session_id,
         created_at=session.started_at.isoformat(),
         updated_at=session.updated_at.isoformat(),
         user_id=session.user_id or None,
-        messages=messages,
-    )
-
-
-@router.post(
-    "/sessions/{session_id}/stream",
-)
-async def stream_chat_post(
-    session_id: str,
-    request: StreamChatRequest,
-    user_id: str | None = Depends(auth.get_user_id),
-):
-    """
-    Stream chat responses for a session (POST with context support).
-
-    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
-      - Text fragments as they are generated
-      - Tool call UI elements (if invoked)
-      - Tool execution results
-
-    Args:
-        session_id: The chat session identifier to associate with the streamed messages.
-        request: Request body containing message, is_user_message, and optional context.
-        user_id: Optional authenticated user ID.
-    Returns:
-        StreamingResponse: SSE-formatted response chunks.
-
-    """
-    # Validate session exists before starting the stream
-    # This prevents errors after the response has already started
-    session = await chat_service.get_session(session_id, user_id)
-
-    if not session:
-        raise NotFoundError(f"Session {session_id} not found. ")
-    if session.user_id is None and user_id is not None:
-        session = await chat_service.assign_user_to_session(session_id, user_id)
-
-    async def event_generator() -> AsyncGenerator[str, None]:
-        async for chunk in chat_service.stream_chat_completion(
-            session_id,
-            request.message,
-            is_user_message=request.is_user_message,
-            user_id=user_id,
-            session=session,  # Pass pre-fetched session to avoid double-fetch
-            context=request.context,
-        ):
-            yield chunk.to_sse()
-        # AI SDK protocol termination
-        yield "data: [DONE]\n\n"
-
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",  # Disable nginx buffering
-            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
-        },
+        messages=[message.model_dump() for message in session.messages],
     )
 
 
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def stream_chat_get(
+async def stream_chat(
     session_id: str,
     message: Annotated[str, Query(min_length=1, max_length=10000)],
     user_id: str | None = Depends(auth.get_user_id),
     is_user_message: bool = Query(default=True),
 ):
     """
-    Stream chat responses for a session (GET - legacy endpoint).
+    Stream chat responses for a session.
 
     Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
       - Text fragments as they are generated
@@ -284,8 +155,6 @@ async def stream_chat_get(
             session=session,  # Pass pre-fetched session to avoid double-fetch
         ):
             yield chunk.to_sse()
-        # AI SDK protocol termination
-        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -294,7 +163,6 @@ async def stream_chat_get(
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
             "X-Accel-Buffering": "no",  # Disable nginx buffering
-            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
         },
     )
 

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

@@ -1,35 +1,31 @@
 import logging
 from collections.abc import AsyncGenerator
+from datetime import UTC, datetime
 from typing import Any
 
 import orjson
-from langfuse import Langfuse
 from openai import AsyncOpenAI
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
-from backend.data.understanding import (
-    format_understanding_for_prompt,
-    get_business_understanding,
-)
 from backend.util.exceptions import NotFoundError
-from backend.util.settings import Settings
 
-from . import db as chat_db
 from .config import ChatConfig
-from .model import ChatMessage, ChatSession, Usage
-from .model import create_chat_session as model_create_chat_session
-from .model import get_chat_session, update_session_title, upsert_chat_session
+from .model import (
+    ChatMessage,
+    ChatSession,
+    Usage,
+    get_chat_session,
+    upsert_chat_session,
+)
 from .response_model import (
     StreamBaseResponse,
+    StreamEnd,
     StreamError,
-    StreamFinish,
-    StreamStart,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
+    StreamTextChunk,
+    StreamTextEnded,
+    StreamToolCall,
+    StreamToolCallStart,
+    StreamToolExecutionResult,
     StreamUsage,
 )
 from .tools import execute_tool, tools
@@ -37,146 +33,8 @@ from .tools import execute_tool, tools
 logger = logging.getLogger(__name__)
 
 config = ChatConfig()
-settings = Settings()
 client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-# Langfuse client (lazy initialization)
-_langfuse_client: Langfuse | None = None
-
-
-def _get_langfuse_client() -> Langfuse:
-    """Get or create the Langfuse client for prompt management and tracing."""
-    global _langfuse_client
-    if _langfuse_client is None:
-        if (
-            not settings.secrets.langfuse_public_key
-            or not settings.secrets.langfuse_secret_key
-        ):
-            raise ValueError(
-                "Langfuse credentials not configured. "
-                "Set LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
-            )
-        _langfuse_client = Langfuse(
-            public_key=settings.secrets.langfuse_public_key,
-            secret_key=settings.secrets.langfuse_secret_key,
-            host=settings.secrets.langfuse_host or "https://cloud.langfuse.com",
-        )
-    return _langfuse_client
-
-
-def _get_environment() -> str:
-    """Get the current environment name for Langfuse tagging."""
-    return settings.config.app_env.value
-
-
-def _get_langfuse_prompt() -> str:
-    """Fetch the latest production prompt from Langfuse.
-
-    Returns:
-        The compiled prompt text from Langfuse.
-
-    Raises:
-        Exception: If Langfuse is unavailable or prompt fetch fails.
-    """
-    try:
-        langfuse = _get_langfuse_client()
-        # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
-        prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
-        compiled = prompt.compile()
-        logger.info(
-            f"Fetched prompt '{config.langfuse_prompt_name}' from Langfuse "
-            f"(version: {prompt.version})"
-        )
-        return compiled
-    except Exception as e:
-        logger.error(f"Failed to fetch prompt from Langfuse: {e}")
-        raise
-
-
-async def _is_first_session(user_id: str) -> bool:
-    """Check if this is the user's first chat session.
-
-    Returns True if the user has 1 or fewer sessions (meaning this is their first).
-    """
-    try:
-        session_count = await chat_db.get_user_session_count(user_id)
-        return session_count <= 1
-    except Exception as e:
-        logger.warning(f"Failed to check session count for user {user_id}: {e}")
-        return False  # Default to non-onboarding if we can't check
-
-
-async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
-    """Build the full system prompt including business understanding if available.
-
-    Args:
-        user_id: The user ID for fetching business understanding
-                     If "default" and this is the user's first session, will use "onboarding" instead.
-
-    Returns:
-        Tuple of (compiled prompt string, Langfuse prompt object for tracing)
-    """
-
-    langfuse = _get_langfuse_client()
-
-    # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
-    prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
-
-    # If user is authenticated, try to fetch their business understanding
-    understanding = None
-    if user_id:
-        try:
-            understanding = await get_business_understanding(user_id)
-        except Exception as e:
-            logger.warning(f"Failed to fetch business understanding: {e}")
-            understanding = None
-    if understanding:
-        context = format_understanding_for_prompt(understanding)
-    else:
-        context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
-
-    compiled = prompt.compile(users_information=context)
-    return compiled, prompt
-
-
-async def _generate_session_title(message: str) -> str | None:
-    """Generate a concise title for a chat session based on the first message.
-
-    Args:
-        message: The first user message in the session
-
-    Returns:
-        A short title (3-6 words) or None if generation fails
-    """
-    try:
-        response = await client.chat.completions.create(
-            model=config.title_model,
-            messages=[
-                {
-                    "role": "system",
-                    "content": (
-                        "Generate a very short title (3-6 words) for a chat conversation "
-                        "based on the user's first message. The title should capture the "
-                        "main topic or intent. Return ONLY the title, no quotes or punctuation."
-                    ),
-                },
-                {"role": "user", "content": message[:500]},  # Limit input length
-            ],
-            max_tokens=20,
-        )
-        title = response.choices[0].message.content
-        if title:
-            # Clean up the title
-            title = title.strip().strip("\"'")
-            # Limit length
-            if len(title) > 50:
-                title = title[:47] + "..."
-            return title
-        return None
-    except Exception as e:
-        logger.warning(f"Failed to generate session title: {e}")
-        return None
-
 
 async def create_chat_session(
     user_id: str | None = None,
@@ -184,7 +42,9 @@ async def create_chat_session(
     """
     Create a new chat session and persist it to the database.
     """
-    return await model_create_chat_session(user_id)
+    session = ChatSession.new(user_id)
+    # Persist the session immediately so it can be used for streaming
+    return await upsert_chat_session(session)
 
 
 async def get_session(
@@ -197,19 +57,6 @@ async def get_session(
     return await get_chat_session(session_id, user_id)
 
 
-async def get_user_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> list[ChatSession]:
-    """
-    Get all chat sessions for a user.
-    """
-    from .model import get_user_sessions as model_get_user_sessions
-
-    return await model_get_user_sessions(user_id, limit, offset)
-
-
 async def assign_user_to_session(
     session_id: str,
     user_id: str,
@@ -231,7 +78,6 @@ async def stream_chat_completion(
     user_id: str | None = None,
     retry_count: int = 0,
     session: ChatSession | None = None,
-    context: dict[str, str] | None = None,  # {url: str, content: str}
 ) -> AsyncGenerator[StreamBaseResponse, None]:
     """Main entry point for streaming chat completions with database handling.
 
@@ -256,9 +102,6 @@ async def stream_chat_completion(
         f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
     )
 
-    # Langfuse trace will be created after session is loaded (need messages for input)
-    trace = None
-
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
         session = await get_chat_session(session_id, user_id)
@@ -278,18 +121,9 @@ async def stream_chat_completion(
         )
 
     if message:
-        # Build message content with context if provided
-        message_content = message
-        if context and context.get("url") and context.get("content"):
-            context_text = f"Page URL: {context['url']}\n\nPage Content:\n{context['content']}\n\n---\n\nUser Message: {message}"
-            message_content = context_text
-            logger.info(
-                f"Including page context: URL={context['url']}, content_length={len(context['content'])}"
-            )
-
         session.messages.append(
             ChatMessage(
-                role="user" if is_user_message else "assistant", content=message_content
+                role="user" if is_user_message else "assistant", content=message
             )
         )
         logger.info(
@@ -307,63 +141,6 @@ async def stream_chat_completion(
     session = await upsert_chat_session(session)
     assert session, "Session not found"
 
-    # Generate title for new sessions on first user message (non-blocking)
-    # Check: is_user_message, no title yet, and this is the first user message
-    if is_user_message and message and not session.title:
-        user_messages = [m for m in session.messages if m.role == "user"]
-        if len(user_messages) == 1:
-            # First user message - generate title in background
-            import asyncio
-
-            # Capture only the values we need (not the session object) to avoid
-            # stale data issues when the main flow modifies the session
-            captured_session_id = session_id
-            captured_message = message
-
-            async def _update_title():
-                try:
-                    title = await _generate_session_title(captured_message)
-                    if title:
-                        # Use dedicated title update function that doesn't
-                        # touch messages, avoiding race conditions
-                        await update_session_title(captured_session_id, title)
-                        logger.info(
-                            f"Generated title for session {captured_session_id}: {title}"
-                        )
-                except Exception as e:
-                    logger.warning(f"Failed to update session title: {e}")
-
-            # Fire and forget - don't block the chat response
-            asyncio.create_task(_update_title())
-
-    # Build system prompt with business understanding
-    system_prompt, langfuse_prompt = await _build_system_prompt(user_id)
-
-    # Create Langfuse trace for this LLM call (each call gets its own trace, grouped by session_id)
-    # Using v3 SDK: start_observation creates a root span, update_trace sets trace-level attributes
-    try:
-        langfuse = _get_langfuse_client()
-        env = _get_environment()
-        trace = langfuse.start_observation(
-            name="chat_completion",
-            input={"messages": [m.model_dump() for m in session.messages]},
-            metadata={
-                "environment": env,
-                "model": config.model,
-                "message_count": len(session.messages),
-                "prompt_name": langfuse_prompt.name if langfuse_prompt else None,
-                "prompt_version": langfuse_prompt.version if langfuse_prompt else None,
-            },
-        )
-        # Set trace-level attributes (session_id, user_id, tags)
-        trace.update_trace(
-            session_id=session_id,
-            user_id=user_id,
-            tags=[env, "copilot"],
-        )
-    except Exception as e:
-        logger.warning(f"Failed to create Langfuse trace: {e}")
-
     assistant_response = ChatMessage(
         role="assistant",
         content="",
@@ -378,91 +155,57 @@ async def stream_chat_completion(
     accumulated_tool_calls: list[dict[str, Any]] = []
     should_retry = False
 
-    # Generate unique IDs for AI SDK protocol
-    import uuid as uuid_module
-
-    message_id = str(uuid_module.uuid4())
-    text_block_id = str(uuid_module.uuid4())
-
-    # Yield message start
-    yield StreamStart(messageId=message_id)
-
-    # Create Langfuse generation for each LLM call, linked to the prompt
-    # Using v3 SDK: start_observation with as_type="generation"
-    generation = (
-        trace.start_observation(
-            as_type="generation",
-            name="llm_call",
-            model=config.model,
-            input={"messages": [m.model_dump() for m in session.messages]},
-            prompt=langfuse_prompt,
-        )
-        if trace
-        else None
-    )
-
     try:
         async for chunk in _stream_chat_chunks(
             session=session,
             tools=tools,
-            system_prompt=system_prompt,
-            text_block_id=text_block_id,
         ):
 
-            if isinstance(chunk, StreamTextStart):
-                # Emit text-start before first text delta
-                if not has_received_text:
-                    yield chunk
-            elif isinstance(chunk, StreamTextDelta):
-                delta = chunk.delta or ""
+            if isinstance(chunk, StreamTextChunk):
+                content = chunk.content or ""
                 assert assistant_response.content is not None
-                assistant_response.content += delta
+                assistant_response.content += content
                 has_received_text = True
                 yield chunk
-            elif isinstance(chunk, StreamTextEnd):
-                # Emit text-end after text completes
+            elif isinstance(chunk, StreamToolCallStart):
+                # Emit text_ended before first tool call, but only if we've received text
                 if has_received_text and not text_streaming_ended:
-                    text_streaming_ended = True
-                    yield chunk
-            elif isinstance(chunk, StreamToolInputStart):
-                # Emit text-end before first tool call, but only if we've received text
-                if has_received_text and not text_streaming_ended:
-                    yield StreamTextEnd(id=text_block_id)
+                    yield StreamTextEnded()
                     text_streaming_ended = True
                 yield chunk
-            elif isinstance(chunk, StreamToolInputAvailable):
+            elif isinstance(chunk, StreamToolCall):
                 # Accumulate tool calls in OpenAI format
                 accumulated_tool_calls.append(
                     {
-                        "id": chunk.toolCallId,
+                        "id": chunk.tool_id,
                         "type": "function",
                         "function": {
-                            "name": chunk.toolName,
-                            "arguments": orjson.dumps(chunk.input).decode("utf-8"),
+                            "name": chunk.tool_name,
+                            "arguments": orjson.dumps(chunk.arguments).decode("utf-8"),
                         },
                     }
                 )
-            elif isinstance(chunk, StreamToolOutputAvailable):
+            elif isinstance(chunk, StreamToolExecutionResult):
                 result_content = (
-                    chunk.output
-                    if isinstance(chunk.output, str)
-                    else orjson.dumps(chunk.output).decode("utf-8")
+                    chunk.result
+                    if isinstance(chunk.result, str)
+                    else orjson.dumps(chunk.result).decode("utf-8")
                 )
                 tool_response_messages.append(
                     ChatMessage(
                         role="tool",
                         content=result_content,
-                        tool_call_id=chunk.toolCallId,
+                        tool_call_id=chunk.tool_id,
                     )
                 )
                 has_done_tool_call = True
                 # Track if any tool execution failed
                 if not chunk.success:
                     logger.warning(
-                        f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
+                        f"Tool {chunk.tool_name} (ID: {chunk.tool_id}) execution failed"
                     )
                 yield chunk
-            elif isinstance(chunk, StreamFinish):
+            elif isinstance(chunk, StreamEnd):
                 if not has_done_tool_call:
                     has_yielded_end = True
                     yield chunk
@@ -471,9 +214,9 @@ async def stream_chat_completion(
             elif isinstance(chunk, StreamUsage):
                 session.usage.append(
                     Usage(
-                        prompt_tokens=chunk.promptTokens,
-                        completion_tokens=chunk.completionTokens,
-                        total_tokens=chunk.totalTokens,
+                        prompt_tokens=chunk.prompt_tokens,
+                        completion_tokens=chunk.completion_tokens,
+                        total_tokens=chunk.total_tokens,
                     )
                 )
             else:
@@ -515,10 +258,15 @@ async def stream_chat_completion(
                         f"Max retries ({config.max_retries}) exceeded: {error_message}"
                     )
 
-                error_response = StreamError(errorText=error_message)
+                error_response = StreamError(
+                    message=error_message,
+                    timestamp=datetime.now(UTC).isoformat(),
+                )
                 yield error_response
             if not has_yielded_end:
-                yield StreamFinish()
+                yield StreamEnd(
+                    timestamp=datetime.now(UTC).isoformat(),
+                )
             return
 
     # Handle retry outside of exception handler to avoid nesting
@@ -579,42 +327,10 @@ async def stream_chat_completion(
         ):
             yield chunk
 
-    # End Langfuse generation with output and usage
-    if generation:
-        latest_usage = session.usage[-1] if session.usage else None
-        generation.update(
-            model=config.model,
-            output={
-                "content": assistant_response.content,
-                "tool_calls": accumulated_tool_calls or None,
-            },
-            usage_details=(
-                {
-                    "input": latest_usage.prompt_tokens,
-                    "output": latest_usage.completion_tokens,
-                    "total": latest_usage.total_tokens,
-                }
-                if latest_usage
-                else None
-            ),
-        )
-        generation.end()
-
-    # Update trace with output and end the span
-    # Using v3 SDK: update_trace() for trace-level output, then end()
-    if trace:
-        if accumulated_tool_calls:
-            trace.update_trace(output={"tool_calls": accumulated_tool_calls})
-        else:
-            trace.update_trace(output={"response": assistant_response.content})
-        trace.end()
-
 
 async def _stream_chat_chunks(
     session: ChatSession,
     tools: list[ChatCompletionToolParam],
-    system_prompt: str | None = None,
-    text_block_id: str | None = None,
 ) -> AsyncGenerator[StreamBaseResponse, None]:
     """
     Pure streaming function for OpenAI chat completions with tool calling.
@@ -622,9 +338,9 @@ async def _stream_chat_chunks(
     This function is database-agnostic and focuses only on streaming logic.
 
     Args:
-        session: Chat session with conversation history
-        tools: Available tools for the model
-        system_prompt: System prompt to prepend to messages
+        messages: Conversation context as ChatCompletionMessageParam list
+        session_id: Session ID
+        user_id: User ID for tool execution
 
     Yields:
         SSE formatted JSON response objects
@@ -634,17 +350,6 @@ async def _stream_chat_chunks(
 
     logger.info("Starting pure chat stream")
 
-    # Build messages with system prompt prepended
-    messages = session.to_openai_messages()
-    if system_prompt:
-        from openai.types.chat import ChatCompletionSystemMessageParam
-
-        system_message = ChatCompletionSystemMessageParam(
-            role="system",
-            content=system_prompt,
-        )
-        messages = [system_message] + messages
-
     # Loop to handle tool calls and continue conversation
     while True:
         try:
@@ -653,11 +358,10 @@ async def _stream_chat_chunks(
             # Create the stream with proper types
             stream = await client.chat.completions.create(
                 model=model,
-                messages=messages,
+                messages=session.to_openai_messages(),
                 tools=tools,
                 tool_choice="auto",
                 stream=True,
-                stream_options={"include_usage": True},
             )
 
             # Variables to accumulate tool calls
@@ -667,17 +371,14 @@ async def _stream_chat_chunks(
             # Track which tool call indices have had their start event emitted
             emitted_start_for_idx: set[int] = set()
 
-            # Track if we've started the text block
-            text_started = False
-
             # Process the stream
             chunk: ChatCompletionChunk
             async for chunk in stream:
                 if chunk.usage:
                     yield StreamUsage(
-                        promptTokens=chunk.usage.prompt_tokens,
-                        completionTokens=chunk.usage.completion_tokens,
-                        totalTokens=chunk.usage.total_tokens,
+                        prompt_tokens=chunk.usage.prompt_tokens,
+                        completion_tokens=chunk.usage.completion_tokens,
+                        total_tokens=chunk.usage.total_tokens,
                     )
 
                 if chunk.choices:
@@ -691,14 +392,10 @@ async def _stream_chat_chunks(
 
                     # Handle content streaming
                     if delta.content:
-                        # Emit text-start on first text content
-                        if not text_started and text_block_id:
-                            yield StreamTextStart(id=text_block_id)
-                            text_started = True
-                        # Stream the text delta
-                        text_response = StreamTextDelta(
-                            id=text_block_id or "",
-                            delta=delta.content,
+                        # Stream the text chunk
+                        text_response = StreamTextChunk(
+                            content=delta.content,
+                            timestamp=datetime.now(UTC).isoformat(),
                         )
                         yield text_response
 
@@ -740,15 +437,16 @@ async def _stream_chat_chunks(
                                         "arguments"
                                     ] += tc_chunk.function.arguments
 
-                            # Emit StreamToolInputStart only after we have the tool call ID
+                            # Emit StreamToolCallStart only after we have the tool call ID
                             if (
                                 idx not in emitted_start_for_idx
                                 and tool_calls[idx]["id"]
                                 and tool_calls[idx]["function"]["name"]
                             ):
-                                yield StreamToolInputStart(
-                                    toolCallId=tool_calls[idx]["id"],
-                                    toolName=tool_calls[idx]["function"]["name"],
+                                yield StreamToolCallStart(
+                                    tool_id=tool_calls[idx]["id"],
+                                    tool_name=tool_calls[idx]["function"]["name"],
+                                    timestamp=datetime.now(UTC).isoformat(),
                                 )
                                 emitted_start_for_idx.add(idx)
             logger.info(f"Stream complete. Finish reason: {finish_reason}")
@@ -766,18 +464,26 @@ async def _stream_chat_chunks(
                         extra={"tool_call": tool_call},
                     )
                     yield StreamError(
-                        errorText=f"Invalid tool call arguments for tool {tool_call.get('function', {}).get('name', 'unknown')}: {e}",
+                        message=f"Invalid tool call arguments for tool {tool_call.get('function', {}).get('name', 'unknown')}: {e}",
+                        timestamp=datetime.now(UTC).isoformat(),
                     )
                     # Re-raise to trigger retry logic in the parent function
                     raise
 
-            yield StreamFinish()
+            yield StreamEnd(
+                timestamp=datetime.now(UTC).isoformat(),
+            )
             return
         except Exception as e:
             logger.error(f"Error in stream: {e!s}", exc_info=True)
-            error_response = StreamError(errorText=str(e))
+            error_response = StreamError(
+                message=str(e),
+                timestamp=datetime.now(UTC).isoformat(),
+            )
             yield error_response
-            yield StreamFinish()
+            yield StreamEnd(
+                timestamp=datetime.now(UTC).isoformat(),
+            )
             return
 
 
@@ -794,31 +500,25 @@ async def _yield_tool_call(
         KeyError: If expected tool call fields are missing
         TypeError: If tool call structure is invalid
     """
-    tool_name = tool_calls[yield_idx]["function"]["name"]
-    tool_call_id = tool_calls[yield_idx]["id"]
     logger.info(f"Yielding tool call: {tool_calls[yield_idx]}")
 
-    # Parse tool call arguments - handle empty arguments gracefully
-    raw_arguments = tool_calls[yield_idx]["function"]["arguments"]
-    if raw_arguments:
-        arguments = orjson.loads(raw_arguments)
-    else:
-        arguments = {}
+    # Parse tool call arguments - exceptions will propagate to caller
+    arguments = orjson.loads(tool_calls[yield_idx]["function"]["arguments"])
 
-    yield StreamToolInputAvailable(
-        toolCallId=tool_call_id,
-        toolName=tool_name,
-        input=arguments,
+    yield StreamToolCall(
+        tool_id=tool_calls[yield_idx]["id"],
+        tool_name=tool_calls[yield_idx]["function"]["name"],
+        arguments=arguments,
+        timestamp=datetime.now(UTC).isoformat(),
     )
 
-    tool_execution_response: StreamToolOutputAvailable = await execute_tool(
-        tool_name=tool_name,
+    tool_execution_response: StreamToolExecutionResult = await execute_tool(
+        tool_name=tool_calls[yield_idx]["function"]["name"],
         parameters=arguments,
-        tool_call_id=tool_call_id,
+        tool_call_id=tool_calls[yield_idx]["id"],
         user_id=session.user_id,
         session=session,
     )
-
     logger.info(f"Yielding Tool execution response: {tool_execution_response}")
     yield tool_execution_response
 

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

@@ -5,10 +5,10 @@ import pytest
 
 from . import service as chat_service
 from .response_model import (
+    StreamEnd,
     StreamError,
-    StreamFinish,
-    StreamTextDelta,
-    StreamToolOutputAvailable,
+    StreamTextChunk,
+    StreamToolExecutionResult,
 )
 
 logger = logging.getLogger(__name__)
@@ -34,9 +34,9 @@ async def test_stream_chat_completion():
         logger.info(chunk)
         if isinstance(chunk, StreamError):
             has_errors = True
-        if isinstance(chunk, StreamTextDelta):
-            assistant_message += chunk.delta
-        if isinstance(chunk, StreamFinish):
+        if isinstance(chunk, StreamTextChunk):
+            assistant_message += chunk.content
+        if isinstance(chunk, StreamEnd):
             has_ended = True
 
     assert has_ended, "Chat completion did not end"
@@ -68,9 +68,9 @@ async def test_stream_chat_completion_with_tool_calls():
         if isinstance(chunk, StreamError):
             has_errors = True
 
-        if isinstance(chunk, StreamFinish):
+        if isinstance(chunk, StreamEnd):
             has_ended = True
-        if isinstance(chunk, StreamToolOutputAvailable):
+        if isinstance(chunk, StreamToolExecutionResult):
             had_tool_calls = True
 
     assert has_ended, "Chat completion did not end"

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

@@ -4,30 +4,21 @@ from openai.types.chat import ChatCompletionToolParam
 
 from backend.api.features.chat.model import ChatSession
 
-from .add_understanding import AddUnderstandingTool
-from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .find_agent import FindAgentTool
-from .find_library_agent import FindLibraryAgentTool
 from .run_agent import RunAgentTool
 
 if TYPE_CHECKING:
-    from backend.api.features.chat.response_model import StreamToolOutputAvailable
+    from backend.api.features.chat.response_model import StreamToolExecutionResult
 
 # Initialize tool instances
-add_understanding_tool = AddUnderstandingTool()
 find_agent_tool = FindAgentTool()
-find_library_agent_tool = FindLibraryAgentTool()
 run_agent_tool = RunAgentTool()
-agent_output_tool = AgentOutputTool()
 
 # Export tools as OpenAI format
 tools: list[ChatCompletionToolParam] = [
-    add_understanding_tool.as_openai_tool(),
     find_agent_tool.as_openai_tool(),
-    find_library_agent_tool.as_openai_tool(),
     run_agent_tool.as_openai_tool(),
-    agent_output_tool.as_openai_tool(),
 ]
 
 
@@ -37,14 +28,11 @@ async def execute_tool(
     user_id: str | None,
     session: ChatSession,
     tool_call_id: str,
-) -> "StreamToolOutputAvailable":
+) -> "StreamToolExecutionResult":
 
     tool_map: dict[str, BaseTool] = {
-        "add_understanding": add_understanding_tool,
         "find_agent": find_agent_tool,
-        "find_library_agent": find_library_agent_tool,
         "run_agent": run_agent_tool,
-        "agent_output": agent_output_tool,
     }
     if tool_name not in tool_map:
         raise ValueError(f"Tool {tool_name} not found")

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

@@ -3,7 +3,6 @@ from datetime import UTC, datetime
 from os import getenv
 
 import pytest
-from prisma.types import ProfileCreateInput
 from pydantic import SecretStr
 
 from backend.api.features.chat.model import ChatSession
@@ -50,13 +49,13 @@ async def setup_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data=ProfileCreateInput(
-            userId=user.id,
-            username=username,
-            name=f"Test User {username}",
-            description="Test user profile",
-            links=[],  # Required field - empty array for test profiles
-        )
+        data={
+            "userId": user.id,
+            "username": username,
+            "name": f"Test User {username}",
+            "description": "Test user profile",
+            "links": [],  # Required field - empty array for test profiles
+        }
     )
 
     # 2. Create a test graph with agent input -> agent output
@@ -173,13 +172,13 @@ async def setup_llm_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data=ProfileCreateInput(
-            userId=user.id,
-            username=username,
-            name=f"Test User {username}",
-            description="Test user profile for LLM tests",
-            links=[],  # Required field - empty array for test profiles
-        )
+        data={
+            "userId": user.id,
+            "username": username,
+            "name": f"Test User {username}",
+            "description": "Test user profile for LLM tests",
+            "links": [],  # Required field - empty array for test profiles
+        }
     )
 
     # 2. Create test OpenAI credentials for the user
@@ -333,13 +332,13 @@ async def setup_firecrawl_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data=ProfileCreateInput(
-            userId=user.id,
-            username=username,
-            name=f"Test User {username}",
-            description="Test user profile for Firecrawl tests",
-            links=[],  # Required field - empty array for test profiles
-        )
+        data={
+            "userId": user.id,
+            "username": username,
+            "name": f"Test User {username}",
+            "description": "Test user profile for Firecrawl tests",
+            "links": [],  # Required field - empty array for test profiles
+        }
     )
 
     # NOTE: We deliberately do NOT create Firecrawl credentials for this user

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

@@ -1,119 +0,0 @@
-"""Tool for capturing user business understanding incrementally."""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.data.understanding import (
-    BusinessUnderstandingInput,
-    upsert_business_understanding,
-)
-
-from .base import BaseTool
-from .models import ErrorResponse, ToolResponseBase, UnderstandingUpdatedResponse
-
-logger = logging.getLogger(__name__)
-
-
-class AddUnderstandingTool(BaseTool):
-    """Tool for capturing user's business understanding incrementally."""
-
-    @property
-    def name(self) -> str:
-        return "add_understanding"
-
-    @property
-    def description(self) -> str:
-        return """Capture and store information about the user's business context,
-workflows, pain points, and automation goals. Call this tool whenever the user
-shares information about their business. Each call incrementally adds to the
-existing understanding - you don't need to provide all fields at once.
-
-Use this to build a comprehensive profile that helps recommend better agents
-and automations for the user's specific needs."""
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        # Auto-generate from Pydantic model schema
-        schema = BusinessUnderstandingInput.model_json_schema()
-        properties = {}
-        for field_name, field_schema in schema.get("properties", {}).items():
-            prop: dict[str, Any] = {"description": field_schema.get("description", "")}
-            # Handle anyOf for Optional types
-            if "anyOf" in field_schema:
-                for option in field_schema["anyOf"]:
-                    if option.get("type") != "null":
-                        prop["type"] = option.get("type", "string")
-                        if "items" in option:
-                            prop["items"] = option["items"]
-                        break
-            else:
-                prop["type"] = field_schema.get("type", "string")
-                if "items" in field_schema:
-                    prop["items"] = field_schema["items"]
-            properties[field_name] = prop
-        return {"type": "object", "properties": properties, "required": []}
-
-    @property
-    def requires_auth(self) -> bool:
-        """Requires authentication to store user-specific data."""
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """
-        Capture and store business understanding incrementally.
-
-        Each call merges new data with existing understanding:
-        - String fields are overwritten if provided
-        - List fields are appended (with deduplication)
-        """
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required to save business understanding.",
-                session_id=session_id,
-            )
-
-        # Check if any data was provided
-        if not any(v is not None for v in kwargs.values()):
-            return ErrorResponse(
-                message="Please provide at least one field to update.",
-                session_id=session_id,
-            )
-
-        # Build input model from kwargs (only include fields defined in the model)
-        valid_fields = set(BusinessUnderstandingInput.model_fields.keys())
-        input_data = BusinessUnderstandingInput(
-            **{k: v for k, v in kwargs.items() if k in valid_fields}
-        )
-
-        # Track which fields were updated
-        updated_fields = [
-            k for k, v in kwargs.items() if k in valid_fields and v is not None
-        ]
-
-        # Upsert with merge
-        understanding = await upsert_business_understanding(user_id, input_data)
-
-        # Build current understanding summary (filter out empty values)
-        current_understanding = {
-            k: v
-            for k, v in understanding.model_dump(
-                exclude={"id", "user_id", "created_at", "updated_at"}
-            ).items()
-            if v is not None and v != [] and v != ""
-        }
-
-        return UnderstandingUpdatedResponse(
-            message=f"Updated understanding with: {', '.join(updated_fields)}. "
-            "I now have a better picture of your business context.",
-            session_id=session_id,
-            updated_fields=updated_fields,
-            current_understanding=current_understanding,
-        )

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

@@ -1,446 +0,0 @@
-"""Tool for retrieving agent execution outputs from user's library."""
-
-import logging
-import re
-from datetime import datetime, timedelta, timezone
-from typing import Any
-
-from pydantic import BaseModel, field_validator
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.library import db as library_db
-from backend.api.features.library.model import LibraryAgent
-from backend.data import execution as execution_db
-from backend.data.execution import ExecutionStatus, GraphExecution, GraphExecutionMeta
-
-from .base import BaseTool
-from .models import (
-    AgentOutputResponse,
-    ErrorResponse,
-    ExecutionOutputInfo,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-from .utils import fetch_graph_from_store_slug
-
-logger = logging.getLogger(__name__)
-
-
-class AgentOutputInput(BaseModel):
-    """Input parameters for the agent_output tool."""
-
-    agent_name: str = ""
-    library_agent_id: str = ""
-    store_slug: str = ""
-    execution_id: str = ""
-    run_time: str = "latest"
-
-    @field_validator(
-        "agent_name",
-        "library_agent_id",
-        "store_slug",
-        "execution_id",
-        "run_time",
-        mode="before",
-    )
-    @classmethod
-    def strip_strings(cls, v: Any) -> Any:
-        """Strip whitespace from string fields."""
-        return v.strip() if isinstance(v, str) else v
-
-
-def parse_time_expression(
-    time_expr: str | None,
-) -> tuple[datetime | None, datetime | None]:
-    """
-    Parse time expression into datetime range (start, end).
-
-    Supports: "latest", "yesterday", "today", "last week", "last 7 days",
-    "last month", "last 30 days", ISO date "YYYY-MM-DD", ISO datetime.
-    """
-    if not time_expr or time_expr.lower() == "latest":
-        return None, None
-
-    now = datetime.now(timezone.utc)
-    today_start = now.replace(hour=0, minute=0, second=0, microsecond=0)
-    expr = time_expr.lower().strip()
-
-    # Relative time expressions lookup
-    relative_times: dict[str, tuple[datetime, datetime]] = {
-        "yesterday": (today_start - timedelta(days=1), today_start),
-        "today": (today_start, now),
-        "last week": (now - timedelta(days=7), now),
-        "last 7 days": (now - timedelta(days=7), now),
-        "last month": (now - timedelta(days=30), now),
-        "last 30 days": (now - timedelta(days=30), now),
-    }
-    if expr in relative_times:
-        return relative_times[expr]
-
-    # Try ISO date format (YYYY-MM-DD)
-    date_match = re.match(r"^(\d{4})-(\d{2})-(\d{2})$", expr)
-    if date_match:
-        try:
-            year, month, day = map(int, date_match.groups())
-            start = datetime(year, month, day, 0, 0, 0, tzinfo=timezone.utc)
-            return start, start + timedelta(days=1)
-        except ValueError:
-            # Invalid date components (e.g., month=13, day=32)
-            pass
-
-    # Try ISO datetime
-    try:
-        parsed = datetime.fromisoformat(expr.replace("Z", "+00:00"))
-        if parsed.tzinfo is None:
-            parsed = parsed.replace(tzinfo=timezone.utc)
-        return parsed - timedelta(hours=1), parsed + timedelta(hours=1)
-    except ValueError:
-        return None, None
-
-
-class AgentOutputTool(BaseTool):
-    """Tool for retrieving execution outputs from user's library agents."""
-
-    @property
-    def name(self) -> str:
-        return "agent_output"
-
-    @property
-    def description(self) -> str:
-        return """Retrieve execution outputs from agents in the user's library.
-
-        Identify the agent using one of:
-        - agent_name: Fuzzy search in user's library
-        - library_agent_id: Exact library agent ID
-        - store_slug: Marketplace format 'username/agent-name'
-
-        Select which run to retrieve using:
-        - execution_id: Specific execution ID
-        - run_time: 'latest' (default), 'yesterday', 'last week', or ISO date 'YYYY-MM-DD'
-        """
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "agent_name": {
-                    "type": "string",
-                    "description": "Agent name to search for in user's library (fuzzy match)",
-                },
-                "library_agent_id": {
-                    "type": "string",
-                    "description": "Exact library agent ID",
-                },
-                "store_slug": {
-                    "type": "string",
-                    "description": "Marketplace identifier: 'username/agent-slug'",
-                },
-                "execution_id": {
-                    "type": "string",
-                    "description": "Specific execution ID to retrieve",
-                },
-                "run_time": {
-                    "type": "string",
-                    "description": (
-                        "Time filter: 'latest', 'yesterday', 'last week', or 'YYYY-MM-DD'"
-                    ),
-                },
-            },
-            "required": [],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _resolve_agent(
-        self,
-        user_id: str,
-        agent_name: str | None,
-        library_agent_id: str | None,
-        store_slug: str | None,
-    ) -> tuple[LibraryAgent | None, str | None]:
-        """
-        Resolve agent from provided identifiers.
-        Returns (library_agent, error_message).
-        """
-        # Priority 1: Exact library agent ID
-        if library_agent_id:
-            try:
-                agent = await library_db.get_library_agent(library_agent_id, user_id)
-                return agent, None
-            except Exception as e:
-                logger.warning(f"Failed to get library agent by ID: {e}")
-                return None, f"Library agent '{library_agent_id}' not found"
-
-        # Priority 2: Store slug (username/agent-name)
-        if store_slug and "/" in store_slug:
-            username, agent_slug = store_slug.split("/", 1)
-            graph, _ = await fetch_graph_from_store_slug(username, agent_slug)
-            if not graph:
-                return None, f"Agent '{store_slug}' not found in marketplace"
-
-            # Find in user's library by graph_id
-            agent = await library_db.get_library_agent_by_graph_id(user_id, graph.id)
-            if not agent:
-                return (
-                    None,
-                    f"Agent '{store_slug}' is not in your library. "
-                    "Add it first to see outputs.",
-                )
-            return agent, None
-
-        # Priority 3: Fuzzy name search in library
-        if agent_name:
-            try:
-                response = await library_db.list_library_agents(
-                    user_id=user_id,
-                    search_term=agent_name,
-                    page_size=5,
-                )
-                if not response.agents:
-                    return (
-                        None,
-                        f"No agents matching '{agent_name}' found in your library",
-                    )
-
-                # Return best match (first result from search)
-                return response.agents[0], None
-            except Exception as e:
-                logger.error(f"Error searching library agents: {e}")
-                return None, f"Error searching for agent: {e}"
-
-        return (
-            None,
-            "Please specify an agent name, library_agent_id, or store_slug",
-        )
-
-    async def _get_execution(
-        self,
-        user_id: str,
-        graph_id: str,
-        execution_id: str | None,
-        time_start: datetime | None,
-        time_end: datetime | None,
-    ) -> tuple[GraphExecution | None, list[GraphExecutionMeta], str | None]:
-        """
-        Fetch execution(s) based on filters.
-        Returns (single_execution, available_executions_meta, error_message).
-        """
-        # If specific execution_id provided, fetch it directly
-        if execution_id:
-            execution = await execution_db.get_graph_execution(
-                user_id=user_id,
-                execution_id=execution_id,
-                include_node_executions=False,
-            )
-            if not execution:
-                return None, [], f"Execution '{execution_id}' not found"
-            return execution, [], None
-
-        # Get completed executions with time filters
-        executions = await execution_db.get_graph_executions(
-            graph_id=graph_id,
-            user_id=user_id,
-            statuses=[ExecutionStatus.COMPLETED],
-            created_time_gte=time_start,
-            created_time_lte=time_end,
-            limit=10,
-        )
-
-        if not executions:
-            return None, [], None  # No error, just no executions
-
-        # If only one execution, fetch full details
-        if len(executions) == 1:
-            full_execution = await execution_db.get_graph_execution(
-                user_id=user_id,
-                execution_id=executions[0].id,
-                include_node_executions=False,
-            )
-            return full_execution, [], None
-
-        # Multiple executions - return latest with full details, plus list of available
-        full_execution = await execution_db.get_graph_execution(
-            user_id=user_id,
-            execution_id=executions[0].id,
-            include_node_executions=False,
-        )
-        return full_execution, executions, None
-
-    def _build_response(
-        self,
-        agent: LibraryAgent,
-        execution: GraphExecution | None,
-        available_executions: list[GraphExecutionMeta],
-        session_id: str | None,
-    ) -> AgentOutputResponse:
-        """Build the response based on execution data."""
-        library_agent_link = f"/library/agents/{agent.id}"
-
-        if not execution:
-            return AgentOutputResponse(
-                message=f"No completed executions found for agent '{agent.name}'",
-                session_id=session_id,
-                agent_name=agent.name,
-                agent_id=agent.graph_id,
-                library_agent_id=agent.id,
-                library_agent_link=library_agent_link,
-                total_executions=0,
-            )
-
-        execution_info = ExecutionOutputInfo(
-            execution_id=execution.id,
-            status=execution.status.value,
-            started_at=execution.started_at,
-            ended_at=execution.ended_at,
-            outputs=dict(execution.outputs),
-            inputs_summary=execution.inputs if execution.inputs else None,
-        )
-
-        available_list = None
-        if len(available_executions) > 1:
-            available_list = [
-                {
-                    "id": e.id,
-                    "status": e.status.value,
-                    "started_at": e.started_at.isoformat() if e.started_at else None,
-                }
-                for e in available_executions[:5]
-            ]
-
-        message = f"Found execution outputs for agent '{agent.name}'"
-        if len(available_executions) > 1:
-            message += (
-                f". Showing latest of {len(available_executions)} matching executions."
-            )
-
-        return AgentOutputResponse(
-            message=message,
-            session_id=session_id,
-            agent_name=agent.name,
-            agent_id=agent.graph_id,
-            library_agent_id=agent.id,
-            library_agent_link=library_agent_link,
-            execution=execution_info,
-            available_executions=available_list,
-            total_executions=len(available_executions) if available_executions else 1,
-        )
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute the agent_output tool."""
-        session_id = session.session_id
-
-        # Parse and validate input
-        try:
-            input_data = AgentOutputInput(**kwargs)
-        except Exception as e:
-            logger.error(f"Invalid input: {e}")
-            return ErrorResponse(
-                message="Invalid input parameters",
-                error=str(e),
-                session_id=session_id,
-            )
-
-        # Ensure user_id is present (should be guaranteed by requires_auth)
-        if not user_id:
-            return ErrorResponse(
-                message="User authentication required",
-                session_id=session_id,
-            )
-
-        # Check if at least one identifier is provided
-        if not any(
-            [
-                input_data.agent_name,
-                input_data.library_agent_id,
-                input_data.store_slug,
-                input_data.execution_id,
-            ]
-        ):
-            return ErrorResponse(
-                message=(
-                    "Please specify at least one of: agent_name, "
-                    "library_agent_id, store_slug, or execution_id"
-                ),
-                session_id=session_id,
-            )
-
-        # If only execution_id provided, we need to find the agent differently
-        if (
-            input_data.execution_id
-            and not input_data.agent_name
-            and not input_data.library_agent_id
-            and not input_data.store_slug
-        ):
-            # Fetch execution directly to get graph_id
-            execution = await execution_db.get_graph_execution(
-                user_id=user_id,
-                execution_id=input_data.execution_id,
-                include_node_executions=False,
-            )
-            if not execution:
-                return ErrorResponse(
-                    message=f"Execution '{input_data.execution_id}' not found",
-                    session_id=session_id,
-                )
-
-            # Find library agent by graph_id
-            agent = await library_db.get_library_agent_by_graph_id(
-                user_id, execution.graph_id
-            )
-            if not agent:
-                return NoResultsResponse(
-                    message=(
-                        f"Execution found but agent not in your library. "
-                        f"Graph ID: {execution.graph_id}"
-                    ),
-                    session_id=session_id,
-                    suggestions=["Add the agent to your library to see more details"],
-                )
-
-            return self._build_response(agent, execution, [], session_id)
-
-        # Resolve agent from identifiers
-        agent, error = await self._resolve_agent(
-            user_id=user_id,
-            agent_name=input_data.agent_name or None,
-            library_agent_id=input_data.library_agent_id or None,
-            store_slug=input_data.store_slug or None,
-        )
-
-        if error or not agent:
-            return NoResultsResponse(
-                message=error or "Agent not found",
-                session_id=session_id,
-                suggestions=[
-                    "Check the agent name or ID",
-                    "Make sure the agent is in your library",
-                ],
-            )
-
-        # Parse time expression
-        time_start, time_end = parse_time_expression(input_data.run_time)
-
-        # Fetch execution(s)
-        execution, available_executions, exec_error = await self._get_execution(
-            user_id=user_id,
-            graph_id=agent.graph_id,
-            execution_id=input_data.execution_id or None,
-            time_start=time_start,
-            time_end=time_end,
-        )
-
-        if exec_error:
-            return ErrorResponse(
-                message=exec_error,
-                session_id=session_id,
-            )
-
-        return self._build_response(agent, execution, available_executions, session_id)

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

@@ -1,151 +0,0 @@
-"""Shared agent search functionality for find_agent and find_library_agent tools."""
-
-import logging
-from typing import Literal
-
-from backend.api.features.library import db as library_db
-from backend.api.features.store import db as store_db
-from backend.util.exceptions import DatabaseError, NotFoundError
-
-from .models import (
-    AgentInfo,
-    AgentsFoundResponse,
-    ErrorResponse,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-SearchSource = Literal["marketplace", "library"]
-
-
-async def search_agents(
-    query: str,
-    source: SearchSource,
-    session_id: str | None,
-    user_id: str | None = None,
-) -> ToolResponseBase:
-    """
-    Search for agents in marketplace or user library.
-
-    Args:
-        query: Search query string
-        source: "marketplace" or "library"
-        session_id: Chat session ID
-        user_id: User ID (required for library search)
-
-    Returns:
-        AgentsFoundResponse, NoResultsResponse, or ErrorResponse
-    """
-    if not query:
-        return ErrorResponse(
-            message="Please provide a search query", session_id=session_id
-        )
-
-    if source == "library" and not user_id:
-        return ErrorResponse(
-            message="User authentication required to search library",
-            session_id=session_id,
-        )
-
-    agents: list[AgentInfo] = []
-    try:
-        if source == "marketplace":
-            logger.info(f"Searching marketplace for: {query}")
-            results = await store_db.get_store_agents(search_query=query, page_size=5)
-            for agent in results.agents:
-                agents.append(
-                    AgentInfo(
-                        id=f"{agent.creator}/{agent.slug}",
-                        name=agent.agent_name,
-                        description=agent.description or "",
-                        source="marketplace",
-                        in_library=False,
-                        creator=agent.creator,
-                        category="general",
-                        rating=agent.rating,
-                        runs=agent.runs,
-                        is_featured=False,
-                    )
-                )
-        else:  # library
-            logger.info(f"Searching user library for: {query}")
-            results = await library_db.list_library_agents(
-                user_id=user_id,  # type: ignore[arg-type]
-                search_term=query,
-                page_size=10,
-            )
-            for agent in results.agents:
-                agents.append(
-                    AgentInfo(
-                        id=agent.id,
-                        name=agent.name,
-                        description=agent.description or "",
-                        source="library",
-                        in_library=True,
-                        creator=agent.creator_name,
-                        status=agent.status.value,
-                        can_access_graph=agent.can_access_graph,
-                        has_external_trigger=agent.has_external_trigger,
-                        new_output=agent.new_output,
-                        graph_id=agent.graph_id,
-                    )
-                )
-        logger.info(f"Found {len(agents)} agents in {source}")
-    except NotFoundError:
-        pass
-    except DatabaseError as e:
-        logger.error(f"Error searching {source}: {e}", exc_info=True)
-        return ErrorResponse(
-            message=f"Failed to search {source}. Please try again.",
-            error=str(e),
-            session_id=session_id,
-        )
-
-    if not agents:
-        suggestions = (
-            [
-                "Try more general terms",
-                "Browse categories in the marketplace",
-                "Check spelling",
-            ]
-            if source == "marketplace"
-            else [
-                "Try different keywords",
-                "Use find_agent to search the marketplace",
-                "Check your library at /library",
-            ]
-        )
-        no_results_msg = (
-            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
-            if source == "marketplace"
-            else f"No agents matching '{query}' found in your library."
-        )
-        return NoResultsResponse(
-            message=no_results_msg, session_id=session_id, suggestions=suggestions
-        )
-
-    title = f"Found {len(agents)} agent{'s' if len(agents) != 1 else ''} "
-    title += (
-        f"for '{query}'"
-        if source == "marketplace"
-        else f"in your library for '{query}'"
-    )
-
-    message = (
-        "Now you have found some options for the user to choose from. "
-        "You can add a link to a recommended agent at: /marketplace/agent/agent_id "
-        "Please ask the user if they would like to use any of these agents."
-        if source == "marketplace"
-        else "Found agents in the user's library. You can provide a link to view an agent at: "
-        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
-    )
-
-    return AgentsFoundResponse(
-        message=message,
-        title=title,
-        agents=agents,
-        count=len(agents),
-        session_id=session_id,
-    )

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

@@ -6,7 +6,7 @@ from typing import Any
 from openai.types.chat import ChatCompletionToolParam
 
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.response_model import StreamToolOutputAvailable
+from backend.api.features.chat.response_model import StreamToolExecutionResult
 
 from .models import ErrorResponse, NeedLoginResponse, ToolResponseBase
 
@@ -53,7 +53,7 @@ class BaseTool:
         session: ChatSession,
         tool_call_id: str,
         **kwargs,
-    ) -> StreamToolOutputAvailable:
+    ) -> StreamToolExecutionResult:
         """Execute the tool with authentication check.
 
         Args:
@@ -69,10 +69,10 @@ class BaseTool:
             logger.error(
                 f"Attempted tool call for {self.name} but user not authenticated"
             )
-            return StreamToolOutputAvailable(
-                toolCallId=tool_call_id,
-                toolName=self.name,
-                output=NeedLoginResponse(
+            return StreamToolExecutionResult(
+                tool_id=tool_call_id,
+                tool_name=self.name,
+                result=NeedLoginResponse(
                     message=f"Please sign in to use {self.name}",
                     session_id=session.session_id,
                 ).model_dump_json(),
@@ -81,17 +81,17 @@ class BaseTool:
 
         try:
             result = await self._execute(user_id, session, **kwargs)
-            return StreamToolOutputAvailable(
-                toolCallId=tool_call_id,
-                toolName=self.name,
-                output=result.model_dump_json(),
+            return StreamToolExecutionResult(
+                tool_id=tool_call_id,
+                tool_name=self.name,
+                result=result.model_dump_json(),
             )
         except Exception as e:
             logger.error(f"Error in {self.name}: {e}", exc_info=True)
-            return StreamToolOutputAvailable(
-                toolCallId=tool_call_id,
-                toolName=self.name,
-                output=ErrorResponse(
+            return StreamToolExecutionResult(
+                tool_id=tool_call_id,
+                tool_name=self.name,
+                result=ErrorResponse(
                     message=f"An error occurred while executing {self.name}",
                     error=str(e),
                     session_id=session.session_id,

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

@@ -1,16 +1,26 @@
-"""Tool for discovering agents from marketplace."""
+"""Tool for discovering agents from marketplace and user library."""
 
+import logging
 from typing import Any
 
 from backend.api.features.chat.model import ChatSession
+from backend.api.features.store import db as store_db
+from backend.util.exceptions import DatabaseError, NotFoundError
 
-from .agent_search import search_agents
 from .base import BaseTool
-from .models import ToolResponseBase
+from .models import (
+    AgentCarouselResponse,
+    AgentInfo,
+    ErrorResponse,
+    NoResultsResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
 
 
 class FindAgentTool(BaseTool):
-    """Tool for discovering agents from the marketplace."""
+    """Tool for discovering agents based on user needs."""
 
     @property
     def name(self) -> str:
@@ -36,11 +46,84 @@ class FindAgentTool(BaseTool):
         }
 
     async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
     ) -> ToolResponseBase:
-        return await search_agents(
-            query=kwargs.get("query", "").strip(),
-            source="marketplace",
-            session_id=session.session_id,
-            user_id=user_id,
+        """Search for agents in the marketplace.
+
+        Args:
+            user_id: User ID (may be anonymous)
+            session_id: Chat session ID
+            query: Search query
+
+        Returns:
+            AgentCarouselResponse: List of agents found in the marketplace
+            NoResultsResponse: No agents found in the marketplace
+            ErrorResponse: Error message
+        """
+        query = kwargs.get("query", "").strip()
+        session_id = session.session_id
+        if not query:
+            return ErrorResponse(
+                message="Please provide a search query",
+                session_id=session_id,
+            )
+        agents = []
+        try:
+            logger.info(f"Searching marketplace for: {query}")
+            store_results = await store_db.get_store_agents(
+                search_query=query,
+                page_size=5,
+            )
+
+            logger.info(f"Find agents tool found {len(store_results.agents)} agents")
+            for agent in store_results.agents:
+                agent_id = f"{agent.creator}/{agent.slug}"
+                logger.info(f"Building agent ID = {agent_id}")
+                agents.append(
+                    AgentInfo(
+                        id=agent_id,
+                        name=agent.agent_name,
+                        description=agent.description or "",
+                        source="marketplace",
+                        in_library=False,
+                        creator=agent.creator,
+                        category="general",
+                        rating=agent.rating,
+                        runs=agent.runs,
+                        is_featured=False,
+                    ),
+                )
+        except NotFoundError:
+            pass
+        except DatabaseError as e:
+            logger.error(f"Error searching agents: {e}", exc_info=True)
+            return ErrorResponse(
+                message="Failed to search for agents. Please try again.",
+                error=str(e),
+                session_id=session_id,
+            )
+        if not agents:
+            return NoResultsResponse(
+                message=f"No agents found matching '{query}'. Try different keywords or browse the marketplace. If you have 3 consecutive find_agent tool calls results and found no agents. Please stop trying and ask the user if there is anything else you can help with.",
+                session_id=session_id,
+                suggestions=[
+                    "Try more general terms",
+                    "Browse categories in the marketplace",
+                    "Check spelling",
+                ],
+            )
+
+        # Return formatted carousel
+        title = (
+            f"Found {len(agents)} agent{'s' if len(agents) != 1 else ''} for '{query}'"
+        )
+        return AgentCarouselResponse(
+            message="Now you have found some options for the user to choose from. You can add a link to a recommended agent at: /marketplace/agent/agent_id Please ask the user if they would like to use any of these agents. If they do, please call the get_agent_details tool for this agent.",
+            title=title,
+            agents=agents,
+            count=len(agents),
+            session_id=session_id,
         )

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

@@ -1,52 +0,0 @@
-"""Tool for searching agents in the user's library."""
-
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-
-from .agent_search import search_agents
-from .base import BaseTool
-from .models import ToolResponseBase
-
-
-class FindLibraryAgentTool(BaseTool):
-    """Tool for searching agents in the user's library."""
-
-    @property
-    def name(self) -> str:
-        return "find_library_agent"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Search for agents in the user's library. Use this to find agents "
-            "the user has already added to their library, including agents they "
-            "created or added from the marketplace."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": "Search query to find agents by name or description.",
-                },
-            },
-            "required": ["query"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs
-    ) -> ToolResponseBase:
-        return await search_agents(
-            query=kwargs.get("query", "").strip(),
-            source="library",
-            session_id=session.session_id,
-            user_id=user_id,
-        )

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

@@ -1,6 +1,5 @@
 """Pydantic models for tool responses."""
 
-from datetime import datetime
 from enum import Enum
 from typing import Any
 
@@ -12,15 +11,14 @@ from backend.data.model import CredentialsMetaInput
 class ResponseType(str, Enum):
     """Types of tool responses."""
 
-    AGENTS_FOUND = "agents_found"
+    AGENT_CAROUSEL = "agent_carousel"
     AGENT_DETAILS = "agent_details"
     SETUP_REQUIREMENTS = "setup_requirements"
     EXECUTION_STARTED = "execution_started"
     NEED_LOGIN = "need_login"
     ERROR = "error"
     NO_RESULTS = "no_results"
-    AGENT_OUTPUT = "agent_output"
-    UNDERSTANDING_UPDATED = "understanding_updated"
+    SUCCESS = "success"
 
 
 # Base response model
@@ -53,14 +51,14 @@ class AgentInfo(BaseModel):
     graph_id: str | None = None
 
 
-class AgentsFoundResponse(ToolResponseBase):
+class AgentCarouselResponse(ToolResponseBase):
     """Response for find_agent tool."""
 
-    type: ResponseType = ResponseType.AGENTS_FOUND
+    type: ResponseType = ResponseType.AGENT_CAROUSEL
     title: str = "Available Agents"
     agents: list[AgentInfo]
     count: int
-    name: str = "agents_found"
+    name: str = "agent_carousel"
 
 
 class NoResultsResponse(ToolResponseBase):
@@ -175,37 +173,3 @@ class ErrorResponse(ToolResponseBase):
     type: ResponseType = ResponseType.ERROR
     error: str | None = None
     details: dict[str, Any] | None = None
-
-
-# Agent output models
-class ExecutionOutputInfo(BaseModel):
-    """Summary of a single execution's outputs."""
-
-    execution_id: str
-    status: str
-    started_at: datetime | None = None
-    ended_at: datetime | None = None
-    outputs: dict[str, list[Any]]
-    inputs_summary: dict[str, Any] | None = None
-
-
-class AgentOutputResponse(ToolResponseBase):
-    """Response for agent_output tool."""
-
-    type: ResponseType = ResponseType.AGENT_OUTPUT
-    agent_name: str
-    agent_id: str
-    library_agent_id: str | None = None
-    library_agent_link: str | None = None
-    execution: ExecutionOutputInfo | None = None
-    available_executions: list[dict[str, Any]] | None = None
-    total_executions: int = 0
-
-
-# Business understanding models
-class UnderstandingUpdatedResponse(ToolResponseBase):
-    """Response for add_understanding tool."""
-
-    type: ResponseType = ResponseType.UNDERSTANDING_UPDATED
-    updated_fields: list[str] = Field(default_factory=list)
-    current_understanding: dict[str, Any] = Field(default_factory=dict)

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

@@ -7,7 +7,6 @@ from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.library import db as library_db
 from backend.data.graph import GraphModel
 from backend.data.model import CredentialsMetaInput
 from backend.data.user import get_user_by_id
@@ -58,7 +57,6 @@ class RunAgentInput(BaseModel):
     """Input parameters for the run_agent tool."""
 
     username_agent_slug: str = ""
-    library_agent_id: str = ""
     inputs: dict[str, Any] = Field(default_factory=dict)
     use_defaults: bool = False
     schedule_name: str = ""
@@ -66,12 +64,7 @@ class RunAgentInput(BaseModel):
     timezone: str = "UTC"
 
     @field_validator(
-        "username_agent_slug",
-        "library_agent_id",
-        "schedule_name",
-        "cron",
-        "timezone",
-        mode="before",
+        "username_agent_slug", "schedule_name", "cron", "timezone", mode="before"
     )
     @classmethod
     def strip_strings(cls, v: Any) -> Any:
@@ -97,7 +90,7 @@ class RunAgentTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return """Run or schedule an agent from the marketplace or user's library.
+        return """Run or schedule an agent from the marketplace.
 
         The tool automatically handles the setup flow:
         - Returns missing inputs if required fields are not provided
@@ -105,10 +98,6 @@ class RunAgentTool(BaseTool):
         - Executes immediately if all requirements are met
         - Schedules execution if cron expression is provided
 
-        Identify the agent using either:
-        - username_agent_slug: Marketplace format 'username/agent-name'
-        - library_agent_id: ID of an agent in the user's library
-
         For scheduled execution, provide: schedule_name, cron, and optionally timezone."""
 
     @property
@@ -120,10 +109,6 @@ class RunAgentTool(BaseTool):
                     "type": "string",
                     "description": "Agent identifier in format 'username/agent-name'",
                 },
-                "library_agent_id": {
-                    "type": "string",
-                    "description": "Library agent ID from user's library",
-                },
                 "inputs": {
                     "type": "object",
                     "description": "Input values for the agent",
@@ -146,7 +131,7 @@ class RunAgentTool(BaseTool):
                     "description": "IANA timezone for schedule (default: UTC)",
                 },
             },
-            "required": [],
+            "required": ["username_agent_slug"],
         }
 
     @property
@@ -164,16 +149,10 @@ class RunAgentTool(BaseTool):
         params = RunAgentInput(**kwargs)
         session_id = session.session_id
 
-        # Validate at least one identifier is provided
-        has_slug = params.username_agent_slug and "/" in params.username_agent_slug
-        has_library_id = bool(params.library_agent_id)
-
-        if not has_slug and not has_library_id:
+        # Validate agent slug format
+        if not params.username_agent_slug or "/" not in params.username_agent_slug:
             return ErrorResponse(
-                message=(
-                    "Please provide either a username_agent_slug "
-                    "(format 'username/agent-name') or a library_agent_id"
-                ),
+                message="Please provide an agent slug in format 'username/agent-name'",
                 session_id=session_id,
             )
 
@@ -188,41 +167,13 @@ class RunAgentTool(BaseTool):
         is_schedule = bool(params.schedule_name or params.cron)
 
         try:
-            # Step 1: Fetch agent details
-            graph: GraphModel | None = None
-            library_agent = None
-
-            # Priority: library_agent_id if provided
-            if has_library_id:
-                library_agent = await library_db.get_library_agent(
-                    params.library_agent_id, user_id
-                )
-                if not library_agent:
-                    return ErrorResponse(
-                        message=f"Library agent '{params.library_agent_id}' not found",
-                        session_id=session_id,
-                    )
-                # Get the graph from the library agent
-                from backend.data.graph import get_graph
-
-                graph = await get_graph(
-                    library_agent.graph_id,
-                    library_agent.graph_version,
-                    user_id=user_id,
-                )
-            else:
-                # Fetch from marketplace slug
-                username, agent_name = params.username_agent_slug.split("/", 1)
-                graph, _ = await fetch_graph_from_store_slug(username, agent_name)
+            # Step 1: Fetch agent details (always happens first)
+            username, agent_name = params.username_agent_slug.split("/", 1)
+            graph, store_agent = await fetch_graph_from_store_slug(username, agent_name)
 
             if not graph:
-                identifier = (
-                    params.library_agent_id
-                    if has_library_id
-                    else params.username_agent_slug
-                )
                 return ErrorResponse(
-                    message=f"Agent '{identifier}' not found",
+                    message=f"Agent '{params.username_agent_slug}' not found in marketplace",
                     session_id=session_id,
                 )
 

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

@@ -46,11 +46,11 @@ async def test_run_agent(setup_test_data):
 
     # Verify the response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
     # Parse the result JSON to verify the execution started
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
     assert "execution_id" in result_data
     assert "graph_id" in result_data
     assert result_data["graph_id"] == graph.id
@@ -86,11 +86,11 @@ async def test_run_agent_missing_inputs(setup_test_data):
 
     # Verify that we get an error response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
     # The tool should return an ErrorResponse when setup info indicates not ready
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
     assert "message" in result_data
 
 
@@ -118,10 +118,10 @@ async def test_run_agent_invalid_agent_id(setup_test_data):
 
     # Verify that we get an error response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
     assert "message" in result_data
     # Should get an error about failed setup or not found
     assert any(
@@ -158,12 +158,12 @@ async def test_run_agent_with_llm_credentials(setup_llm_test_data):
 
     # Verify the response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
 
     # Parse the result JSON to verify the execution started
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should successfully start execution since credentials are available
     assert "execution_id" in result_data
@@ -195,9 +195,9 @@ async def test_run_agent_shows_available_inputs_when_none_provided(setup_test_da
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return agent_details type showing available inputs
     assert result_data.get("type") == "agent_details"
@@ -230,9 +230,9 @@ async def test_run_agent_with_use_defaults(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should execute successfully
     assert "execution_id" in result_data
@@ -260,9 +260,9 @@ async def test_run_agent_missing_credentials(setup_firecrawl_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return setup_requirements type with missing credentials
     assert result_data.get("type") == "setup_requirements"
@@ -292,9 +292,9 @@ async def test_run_agent_invalid_slug_format(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return error
     assert result_data.get("type") == "error"
@@ -318,9 +318,9 @@ async def test_run_agent_unauthenticated():
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Base tool returns need_login type for unauthenticated users
     assert result_data.get("type") == "need_login"
@@ -350,9 +350,9 @@ async def test_run_agent_schedule_without_cron(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return error about missing cron
     assert result_data.get("type") == "error"
@@ -382,9 +382,9 @@ async def test_run_agent_schedule_without_name(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return error about missing schedule_name
     assert result_data.get("type") == "error"

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

@@ -489,7 +489,7 @@ async def update_agent_version_in_library(
     agent_graph_version: int,
 ) -> library_model.LibraryAgent:
     """
-    Updates the agent version in the library for any agent owned by the user.
+    Updates the agent version in the library if useGraphIsActiveVersion is True.
 
     Args:
         user_id: Owner of the LibraryAgent.
@@ -498,31 +498,20 @@ async def update_agent_version_in_library(
 
     Raises:
         DatabaseError: If there's an error with the update.
-        NotFoundError: If no library agent is found for this user and agent.
     """
     logger.debug(
         f"Updating agent version in library for user #{user_id}, "
         f"agent #{agent_graph_id} v{agent_graph_version}"
     )
-    async with transaction() as tx:
-        library_agent = await prisma.models.LibraryAgent.prisma(tx).find_first_or_raise(
+    try:
+        library_agent = await prisma.models.LibraryAgent.prisma().find_first_or_raise(
             where={
                 "userId": user_id,
                 "agentGraphId": agent_graph_id,
+                "useGraphIsActiveVersion": True,
             },
         )
-
-        # Delete any conflicting LibraryAgent for the target version
-        await prisma.models.LibraryAgent.prisma(tx).delete_many(
-            where={
-                "userId": user_id,
-                "agentGraphId": agent_graph_id,
-                "agentGraphVersion": agent_graph_version,
-                "id": {"not": library_agent.id},
-            }
-        )
-
-        lib = await prisma.models.LibraryAgent.prisma(tx).update(
+        lib = await prisma.models.LibraryAgent.prisma().update(
             where={"id": library_agent.id},
             data={
                 "AgentGraph": {
@@ -536,13 +525,13 @@ async def update_agent_version_in_library(
             },
             include={"AgentGraph": True},
         )
+        if lib is None:
+            raise NotFoundError(f"Library agent {library_agent.id} not found")
 
-    if lib is None:
-        raise NotFoundError(
-            f"Failed to update library agent for {agent_graph_id} v{agent_graph_version}"
-        )
-
-    return library_model.LibraryAgent.from_db(lib)
+        return library_model.LibraryAgent.from_db(lib)
+    except prisma.errors.PrismaError as e:
+        logger.error(f"Database error updating agent version in library: {e}")
+        raise DatabaseError("Failed to update agent version in library") from e
 
 
 async def update_library_agent(
@@ -836,7 +825,6 @@ async def add_store_agent_to_library(
                     }
                 },
                 "isCreatedByUser": False,
-                "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
                     _initialize_graph_settings(graph_model).model_dump()
                 ),

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

@@ -48,7 +48,6 @@ class LibraryAgent(pydantic.BaseModel):
     id: str
     graph_id: str
     graph_version: int
-    owner_user_id: str  # ID of user who owns/created this agent graph
 
     image_url: str | None
 
@@ -164,7 +163,6 @@ class LibraryAgent(pydantic.BaseModel):
             id=agent.id,
             graph_id=agent.agentGraphId,
             graph_version=agent.agentGraphVersion,
-            owner_user_id=agent.userId,
             image_url=agent.imageUrl,
             creator_name=creator_name,
             creator_image_url=creator_image_url,

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

@@ -42,7 +42,6 @@ async def test_get_library_agents_success(
                 id="test-agent-1",
                 graph_id="test-agent-1",
                 graph_version=1,
-                owner_user_id=test_user_id,
                 name="Test Agent 1",
                 description="Test Description 1",
                 image_url=None,
@@ -65,7 +64,6 @@ async def test_get_library_agents_success(
                 id="test-agent-2",
                 graph_id="test-agent-2",
                 graph_version=1,
-                owner_user_id=test_user_id,
                 name="Test Agent 2",
                 description="Test Description 2",
                 image_url=None,
@@ -140,7 +138,6 @@ async def test_get_favorite_library_agents_success(
                 id="test-agent-1",
                 graph_id="test-agent-1",
                 graph_version=1,
-                owner_user_id=test_user_id,
                 name="Favorite Agent 1",
                 description="Test Favorite Description 1",
                 image_url=None,
@@ -208,7 +205,6 @@ def test_add_agent_to_library_success(
         id="test-library-agent-id",
         graph_id="test-agent-1",
         graph_version=1,
-        owner_user_id=test_user_id,
         name="Test Agent 1",
         description="Test Description 1",
         image_url=None,

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

@@ -614,7 +614,6 @@ async def get_store_submissions(
         submission_models = []
         for sub in submissions:
             submission_model = store_model.StoreSubmission(
-                listing_id=sub.listing_id,
                 agent_id=sub.agent_id,
                 agent_version=sub.agent_version,
                 name=sub.name,
@@ -668,48 +667,35 @@ async def delete_store_submission(
     submission_id: str,
 ) -> bool:
     """
-    Delete a store submission version as the submitting user.
+    Delete a store listing submission as the submitting user.
 
     Args:
         user_id: ID of the authenticated user
-        submission_id: StoreListingVersion ID to delete
+        submission_id: ID of the submission to be deleted
 
     Returns:
-        bool: True if successfully deleted
+        bool: True if the submission was successfully deleted, False otherwise
     """
+    logger.debug(f"Deleting store submission {submission_id} for user {user_id}")
+
     try:
-        # Find the submission version with ownership check
-        version = await prisma.models.StoreListingVersion.prisma().find_first(
-            where={"id": submission_id}, include={"StoreListing": True}
+        # Verify the submission belongs to this user
+        submission = await prisma.models.StoreListing.prisma().find_first(
+            where={"agentGraphId": submission_id, "owningUserId": user_id}
         )
 
-        if (
-            not version
-            or not version.StoreListing
-            or version.StoreListing.owningUserId != user_id
-        ):
-            raise store_exceptions.SubmissionNotFoundError("Submission not found")
-
-        # Prevent deletion of approved submissions
-        if version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
-            raise store_exceptions.InvalidOperationError(
-                "Cannot delete approved submissions"
+        if not submission:
+            logger.warning(f"Submission not found for user {user_id}: {submission_id}")
+            raise store_exceptions.SubmissionNotFoundError(
+                f"Submission not found for this user. User ID: {user_id}, Submission ID: {submission_id}"
             )
 
-        # Delete the version
-        await prisma.models.StoreListingVersion.prisma().delete(
-            where={"id": version.id}
-        )
+        # Delete the submission
+        await prisma.models.StoreListing.prisma().delete(where={"id": submission.id})
 
-        # Clean up empty listing if this was the last version
-        remaining = await prisma.models.StoreListingVersion.prisma().count(
-            where={"storeListingId": version.storeListingId}
+        logger.debug(
+            f"Successfully deleted submission {submission_id} for user {user_id}"
         )
-        if remaining == 0:
-            await prisma.models.StoreListing.prisma().delete(
-                where={"id": version.storeListingId}
-            )
-
         return True
 
     except Exception as e:
@@ -773,15 +759,9 @@ async def create_store_submission(
             logger.warning(
                 f"Agent not found for user {user_id}: {agent_id} v{agent_version}"
             )
-            # Provide more user-friendly error message when agent_id is empty
-            if not agent_id or agent_id.strip() == "":
-                raise store_exceptions.AgentNotFoundError(
-                    "No agent selected. Please select an agent before submitting to the store."
-                )
-            else:
-                raise store_exceptions.AgentNotFoundError(
-                    f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
-                )
+            raise store_exceptions.AgentNotFoundError(
+                f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
+            )
 
         # Check if listing already exists for this agent
         existing_listing = await prisma.models.StoreListing.prisma().find_first(
@@ -853,7 +833,6 @@ async def create_store_submission(
         logger.debug(f"Created store listing for agent {agent_id}")
         # Return submission details
         return store_model.StoreSubmission(
-            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -965,56 +944,81 @@ async def edit_store_submission(
         # Currently we are not allowing user to update the agent associated with a submission
         # If we allow it in future, then we need a check here to verify the agent belongs to this user.
 
-        # Only allow editing of PENDING submissions
-        if current_version.submissionStatus != prisma.enums.SubmissionStatus.PENDING:
+        # Check if we can edit this submission
+        if current_version.submissionStatus == prisma.enums.SubmissionStatus.REJECTED:
             raise store_exceptions.InvalidOperationError(
-                f"Cannot edit a {current_version.submissionStatus.value.lower()} submission. Only pending submissions can be edited."
+                "Cannot edit a rejected submission"
+            )
+
+        # For APPROVED submissions, we need to create a new version
+        if current_version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
+            # Create a new version for the existing listing
+            return await create_store_version(
+                user_id=user_id,
+                agent_id=current_version.agentGraphId,
+                agent_version=current_version.agentGraphVersion,
+                store_listing_id=current_version.storeListingId,
+                name=name,
+                video_url=video_url,
+                agent_output_demo_url=agent_output_demo_url,
+                image_urls=image_urls,
+                description=description,
+                sub_heading=sub_heading,
+                categories=categories,
+                changes_summary=changes_summary,
+                recommended_schedule_cron=recommended_schedule_cron,
+                instructions=instructions,
             )
 
         # For PENDING submissions, we can update the existing version
-        # Update the existing version
-        updated_version = await prisma.models.StoreListingVersion.prisma().update(
-            where={"id": store_listing_version_id},
-            data=prisma.types.StoreListingVersionUpdateInput(
+        elif current_version.submissionStatus == prisma.enums.SubmissionStatus.PENDING:
+            # Update the existing version
+            updated_version = await prisma.models.StoreListingVersion.prisma().update(
+                where={"id": store_listing_version_id},
+                data=prisma.types.StoreListingVersionUpdateInput(
+                    name=name,
+                    videoUrl=video_url,
+                    agentOutputDemoUrl=agent_output_demo_url,
+                    imageUrls=image_urls,
+                    description=description,
+                    categories=categories,
+                    subHeading=sub_heading,
+                    changesSummary=changes_summary,
+                    recommendedScheduleCron=recommended_schedule_cron,
+                    instructions=instructions,
+                ),
+            )
+
+            logger.debug(
+                f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
+            )
+
+            if not updated_version:
+                raise DatabaseError("Failed to update store listing version")
+            return store_model.StoreSubmission(
+                agent_id=current_version.agentGraphId,
+                agent_version=current_version.agentGraphVersion,
                 name=name,
-                videoUrl=video_url,
-                agentOutputDemoUrl=agent_output_demo_url,
-                imageUrls=image_urls,
+                sub_heading=sub_heading,
+                slug=current_version.StoreListing.slug,
                 description=description,
-                categories=categories,
-                subHeading=sub_heading,
-                changesSummary=changes_summary,
-                recommendedScheduleCron=recommended_schedule_cron,
                 instructions=instructions,
-            ),
-        )
+                image_urls=image_urls,
+                date_submitted=updated_version.submittedAt or updated_version.createdAt,
+                status=updated_version.submissionStatus,
+                runs=0,
+                rating=0.0,
+                store_listing_version_id=updated_version.id,
+                changes_summary=changes_summary,
+                video_url=video_url,
+                categories=categories,
+                version=updated_version.version,
+            )
 
-        logger.debug(
-            f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
-        )
-
-        if not updated_version:
-            raise DatabaseError("Failed to update store listing version")
-        return store_model.StoreSubmission(
-            listing_id=current_version.StoreListing.id,
-            agent_id=current_version.agentGraphId,
-            agent_version=current_version.agentGraphVersion,
-            name=name,
-            sub_heading=sub_heading,
-            slug=current_version.StoreListing.slug,
-            description=description,
-            instructions=instructions,
-            image_urls=image_urls,
-            date_submitted=updated_version.submittedAt or updated_version.createdAt,
-            status=updated_version.submissionStatus,
-            runs=0,
-            rating=0.0,
-            store_listing_version_id=updated_version.id,
-            changes_summary=changes_summary,
-            video_url=video_url,
-            categories=categories,
-            version=updated_version.version,
-        )
+        else:
+            raise store_exceptions.InvalidOperationError(
+                f"Cannot edit submission with status: {current_version.submissionStatus}"
+            )
 
     except (
         store_exceptions.SubmissionNotFoundError,
@@ -1093,78 +1097,38 @@ async def create_store_version(
                 f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
             )
 
-        # Check if there's already a PENDING submission for this agent (any version)
-        existing_pending_submission = (
-            await prisma.models.StoreListingVersion.prisma().find_first(
-                where=prisma.types.StoreListingVersionWhereInput(
-                    storeListingId=store_listing_id,
-                    agentGraphId=agent_id,
-                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
-                    isDeleted=False,
-                )
+        # Get the latest version number
+        latest_version = listing.Versions[0] if listing.Versions else None
+
+        next_version = (latest_version.version + 1) if latest_version else 1
+
+        # Create a new version for the existing listing
+        new_version = await prisma.models.StoreListingVersion.prisma().create(
+            data=prisma.types.StoreListingVersionCreateInput(
+                version=next_version,
+                agentGraphId=agent_id,
+                agentGraphVersion=agent_version,
+                name=name,
+                videoUrl=video_url,
+                agentOutputDemoUrl=agent_output_demo_url,
+                imageUrls=image_urls,
+                description=description,
+                instructions=instructions,
+                categories=categories,
+                subHeading=sub_heading,
+                submissionStatus=prisma.enums.SubmissionStatus.PENDING,
+                submittedAt=datetime.now(),
+                changesSummary=changes_summary,
+                recommendedScheduleCron=recommended_schedule_cron,
+                storeListingId=store_listing_id,
             )
         )
 
-        # Handle existing pending submission and create new one atomically
-        async with transaction() as tx:
-            # Get the latest version number first
-            latest_listing = await prisma.models.StoreListing.prisma(tx).find_first(
-                where=prisma.types.StoreListingWhereInput(
-                    id=store_listing_id, owningUserId=user_id
-                ),
-                include={"Versions": {"order_by": {"version": "desc"}, "take": 1}},
-            )
-
-            if not latest_listing:
-                raise store_exceptions.ListingNotFoundError(
-                    f"Store listing not found. User ID: {user_id}, Listing ID: {store_listing_id}"
-                )
-
-            latest_version = (
-                latest_listing.Versions[0] if latest_listing.Versions else None
-            )
-            next_version = (latest_version.version + 1) if latest_version else 1
-
-            # If there's an existing pending submission, delete it atomically before creating new one
-            if existing_pending_submission:
-                logger.info(
-                    f"Found existing PENDING submission for agent {agent_id} (was v{existing_pending_submission.agentGraphVersion}, now v{agent_version}), replacing existing submission instead of creating duplicate"
-                )
-                await prisma.models.StoreListingVersion.prisma(tx).delete(
-                    where={"id": existing_pending_submission.id}
-                )
-                logger.debug(
-                    f"Deleted existing pending submission {existing_pending_submission.id}"
-                )
-
-            # Create a new version for the existing listing
-            new_version = await prisma.models.StoreListingVersion.prisma(tx).create(
-                data=prisma.types.StoreListingVersionCreateInput(
-                    version=next_version,
-                    agentGraphId=agent_id,
-                    agentGraphVersion=agent_version,
-                    name=name,
-                    videoUrl=video_url,
-                    agentOutputDemoUrl=agent_output_demo_url,
-                    imageUrls=image_urls,
-                    description=description,
-                    instructions=instructions,
-                    categories=categories,
-                    subHeading=sub_heading,
-                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
-                    submittedAt=datetime.now(),
-                    changesSummary=changes_summary,
-                    recommendedScheduleCron=recommended_schedule_cron,
-                    storeListingId=store_listing_id,
-                )
-            )
-
         logger.debug(
             f"Created new version for listing {store_listing_id} of agent {agent_id}"
         )
         # Return submission details
         return store_model.StoreSubmission(
-            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -1744,12 +1708,15 @@ async def review_store_submission(
 
         # Convert to Pydantic model for consistency
         return store_model.StoreSubmission(
-            listing_id=(submission.StoreListing.id if submission.StoreListing else ""),
             agent_id=submission.agentGraphId,
             agent_version=submission.agentGraphVersion,
             name=submission.name,
             sub_heading=submission.subHeading,
-            slug=(submission.StoreListing.slug if submission.StoreListing else ""),
+            slug=(
+                submission.StoreListing.slug
+                if hasattr(submission, "storeListing") and submission.StoreListing
+                else ""
+            ),
             description=submission.description,
             instructions=submission.instructions,
             image_urls=submission.imageUrls or [],
@@ -1851,7 +1818,9 @@ async def get_admin_listings_with_versions(
         where = prisma.types.StoreListingWhereInput(**where_dict)
         include = prisma.types.StoreListingInclude(
             Versions=prisma.types.FindManyStoreListingVersionArgsFromStoreListing(
-                order_by={"version": "desc"}
+                order_by=prisma.types._StoreListingVersion_version_OrderByInput(
+                    version="desc"
+                )
             ),
             OwningUser=True,
         )
@@ -1876,7 +1845,6 @@ async def get_admin_listings_with_versions(
             # If we have versions, turn them into StoreSubmission models
             for version in listing.Versions or []:
                 version_model = store_model.StoreSubmission(
-                    listing_id=listing.id,
                     agent_id=version.agentGraphId,
                     agent_version=version.agentGraphVersion,
                     name=version.name,

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

@@ -110,7 +110,6 @@ class Profile(pydantic.BaseModel):
 
 
 class StoreSubmission(pydantic.BaseModel):
-    listing_id: str
     agent_id: str
     agent_version: int
     name: str
@@ -165,12 +164,8 @@ class StoreListingsWithVersionsResponse(pydantic.BaseModel):
 
 
 class StoreSubmissionRequest(pydantic.BaseModel):
-    agent_id: str = pydantic.Field(
-        ..., min_length=1, description="Agent ID cannot be empty"
-    )
-    agent_version: int = pydantic.Field(
-        ..., gt=0, description="Agent version must be greater than 0"
-    )
+    agent_id: str
+    agent_version: int
     slug: str
     name: str
     sub_heading: str

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

@@ -138,7 +138,6 @@ def test_creator_details():
 
 def test_store_submission():
     submission = store_model.StoreSubmission(
-        listing_id="listing123",
         agent_id="agent123",
         agent_version=1,
         sub_heading="Test subheading",
@@ -160,7 +159,6 @@ def test_store_submissions_response():
     response = store_model.StoreSubmissionsResponse(
         submissions=[
             store_model.StoreSubmission(
-                listing_id="listing123",
                 agent_id="agent123",
                 agent_version=1,
                 sub_heading="Test subheading",

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

@@ -521,7 +521,6 @@ def test_get_submissions_success(
     mocked_value = store_model.StoreSubmissionsResponse(
         submissions=[
             store_model.StoreSubmission(
-                listing_id="test-listing-id",
                 name="Test Agent",
                 description="Test agent description",
                 image_urls=["test.jpg"],

autogpt_platform/backend/backend/blocks/airtable/_webhook.py

@@ -6,9 +6,6 @@ import hashlib
 import hmac
 import logging
 from enum import Enum
-from typing import cast
-
-from prisma.types import Serializable
 
 from backend.sdk import (
     BaseWebhooksManager,
@@ -87,9 +84,7 @@ class AirtableWebhookManager(BaseWebhooksManager):
         # update webhook config
         await update_webhook(
             webhook.id,
-            config=cast(
-                dict[str, Serializable], {"base_id": base_id, "cursor": response.cursor}
-            ),
+            config={"base_id": base_id, "cursor": response.cursor},
         )
 
         event_type = "notification"

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -1,184 +0,0 @@
-"""
-Shared helpers for Human-In-The-Loop (HITL) review functionality.
-Used by both the dedicated HumanInTheLoopBlock and blocks that require human review.
-"""
-
-import logging
-from typing import Any, Optional
-
-from prisma.enums import ReviewStatus
-from pydantic import BaseModel
-
-from backend.data.execution import ExecutionContext, ExecutionStatus
-from backend.data.human_review import ReviewResult
-from backend.executor.manager import async_update_node_execution_status
-from backend.util.clients import get_database_manager_async_client
-
-logger = logging.getLogger(__name__)
-
-
-class ReviewDecision(BaseModel):
-    """Result of a review decision."""
-
-    should_proceed: bool
-    message: str
-    review_result: ReviewResult
-
-
-class HITLReviewHelper:
-    """Helper class for Human-In-The-Loop review operations."""
-
-    @staticmethod
-    async def get_or_create_human_review(**kwargs) -> Optional[ReviewResult]:
-        """Create or retrieve a human review from the database."""
-        return await get_database_manager_async_client().get_or_create_human_review(
-            **kwargs
-        )
-
-    @staticmethod
-    async def update_node_execution_status(**kwargs) -> None:
-        """Update the execution status of a node."""
-        await async_update_node_execution_status(
-            db_client=get_database_manager_async_client(), **kwargs
-        )
-
-    @staticmethod
-    async def update_review_processed_status(
-        node_exec_id: str, processed: bool
-    ) -> None:
-        """Update the processed status of a review."""
-        return await get_database_manager_async_client().update_review_processed_status(
-            node_exec_id, processed
-        )
-
-    @staticmethod
-    async def _handle_review_request(
-        input_data: Any,
-        user_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        block_name: str = "Block",
-        editable: bool = False,
-    ) -> Optional[ReviewResult]:
-        """
-        Handle a review request for a block that requires human review.
-
-        Args:
-            input_data: The input data to be reviewed
-            user_id: ID of the user requesting the review
-            node_exec_id: ID of the node execution
-            graph_exec_id: ID of the graph execution
-            graph_id: ID of the graph
-            graph_version: Version of the graph
-            execution_context: Current execution context
-            block_name: Name of the block requesting review
-            editable: Whether the reviewer can edit the data
-
-        Returns:
-            ReviewResult if review is complete, None if waiting for human input
-
-        Raises:
-            Exception: If review creation or status update fails
-        """
-        # Skip review if safe mode is disabled - return auto-approved result
-        if not execution_context.safe_mode:
-            logger.info(
-                f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
-            )
-            return ReviewResult(
-                data=input_data,
-                status=ReviewStatus.APPROVED,
-                message="Auto-approved (safe mode disabled)",
-                processed=True,
-                node_exec_id=node_exec_id,
-            )
-
-        result = await HITLReviewHelper.get_or_create_human_review(
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            input_data=input_data,
-            message=f"Review required for {block_name} execution",
-            editable=editable,
-        )
-
-        if result is None:
-            logger.info(
-                f"Block {block_name} pausing execution for node {node_exec_id} - awaiting human review"
-            )
-            await HITLReviewHelper.update_node_execution_status(
-                exec_id=node_exec_id,
-                status=ExecutionStatus.REVIEW,
-            )
-            return None  # Signal that execution should pause
-
-        # Mark review as processed if not already done
-        if not result.processed:
-            await HITLReviewHelper.update_review_processed_status(
-                node_exec_id=node_exec_id, processed=True
-            )
-
-        return result
-
-    @staticmethod
-    async def handle_review_decision(
-        input_data: Any,
-        user_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        block_name: str = "Block",
-        editable: bool = False,
-    ) -> Optional[ReviewDecision]:
-        """
-        Handle a review request and return the decision in a single call.
-
-        Args:
-            input_data: The input data to be reviewed
-            user_id: ID of the user requesting the review
-            node_exec_id: ID of the node execution
-            graph_exec_id: ID of the graph execution
-            graph_id: ID of the graph
-            graph_version: Version of the graph
-            execution_context: Current execution context
-            block_name: Name of the block requesting review
-            editable: Whether the reviewer can edit the data
-
-        Returns:
-            ReviewDecision if review is complete (approved/rejected),
-            None if execution should pause (awaiting review)
-        """
-        review_result = await HITLReviewHelper._handle_review_request(
-            input_data=input_data,
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            execution_context=execution_context,
-            block_name=block_name,
-            editable=editable,
-        )
-
-        if review_result is None:
-            # Still awaiting review - return None to pause execution
-            return None
-
-        # Review is complete, determine outcome
-        should_proceed = review_result.status == ReviewStatus.APPROVED
-        message = review_result.message or (
-            "Execution approved by reviewer"
-            if should_proceed
-            else "Execution rejected by reviewer"
-        )
-
-        return ReviewDecision(
-            should_proceed=should_proceed, message=message, review_result=review_result
-        )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -3,7 +3,6 @@ from typing import Any
 
 from prisma.enums import ReviewStatus
 
-from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -12,9 +11,11 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext
+from backend.data.execution import ExecutionContext, ExecutionStatus
 from backend.data.human_review import ReviewResult
 from backend.data.model import SchemaField
+from backend.executor.manager import async_update_node_execution_status
+from backend.util.clients import get_database_manager_async_client
 
 logger = logging.getLogger(__name__)
 
@@ -71,26 +72,32 @@ class HumanInTheLoopBlock(Block):
                 ("approved_data", {"name": "John Doe", "age": 30}),
             ],
             test_mock={
-                "handle_review_decision": lambda **kwargs: type(
-                    "ReviewDecision",
-                    (),
-                    {
-                        "should_proceed": True,
-                        "message": "Test approval message",
-                        "review_result": ReviewResult(
-                            data={"name": "John Doe", "age": 30},
-                            status=ReviewStatus.APPROVED,
-                            message="",
-                            processed=False,
-                            node_exec_id="test-node-exec-id",
-                        ),
-                    },
-                )(),
+                "get_or_create_human_review": lambda *_args, **_kwargs: ReviewResult(
+                    data={"name": "John Doe", "age": 30},
+                    status=ReviewStatus.APPROVED,
+                    message="",
+                    processed=False,
+                    node_exec_id="test-node-exec-id",
+                ),
+                "update_node_execution_status": lambda *_args, **_kwargs: None,
+                "update_review_processed_status": lambda *_args, **_kwargs: None,
             },
         )
 
-    async def handle_review_decision(self, **kwargs):
-        return await HITLReviewHelper.handle_review_decision(**kwargs)
+    async def get_or_create_human_review(self, **kwargs):
+        return await get_database_manager_async_client().get_or_create_human_review(
+            **kwargs
+        )
+
+    async def update_node_execution_status(self, **kwargs):
+        return await async_update_node_execution_status(
+            db_client=get_database_manager_async_client(), **kwargs
+        )
+
+    async def update_review_processed_status(self, node_exec_id: str, processed: bool):
+        return await get_database_manager_async_client().update_review_processed_status(
+            node_exec_id, processed
+        )
 
     async def run(
         self,
@@ -102,7 +109,7 @@ class HumanInTheLoopBlock(Block):
         graph_id: str,
         graph_version: int,
         execution_context: ExecutionContext,
-        **_kwargs,
+        **kwargs,
     ) -> BlockOutput:
         if not execution_context.safe_mode:
             logger.info(
@@ -112,28 +119,48 @@ class HumanInTheLoopBlock(Block):
             yield "review_message", "Auto-approved (safe mode disabled)"
             return
 
-        decision = await self.handle_review_decision(
-            input_data=input_data.data,
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            execution_context=execution_context,
-            block_name=self.name,
-            editable=input_data.editable,
-        )
+        try:
+            result = await self.get_or_create_human_review(
+                user_id=user_id,
+                node_exec_id=node_exec_id,
+                graph_exec_id=graph_exec_id,
+                graph_id=graph_id,
+                graph_version=graph_version,
+                input_data=input_data.data,
+                message=input_data.name,
+                editable=input_data.editable,
+            )
+        except Exception as e:
+            logger.error(f"Error in HITL block for node {node_exec_id}: {str(e)}")
+            raise
 
-        if decision is None:
-            return
+        if result is None:
+            logger.info(
+                f"HITL block pausing execution for node {node_exec_id} - awaiting human review"
+            )
+            try:
+                await self.update_node_execution_status(
+                    exec_id=node_exec_id,
+                    status=ExecutionStatus.REVIEW,
+                )
+                return
+            except Exception as e:
+                logger.error(
+                    f"Failed to update node status for HITL block {node_exec_id}: {str(e)}"
+                )
+                raise
 
-        status = decision.review_result.status
-        if status == ReviewStatus.APPROVED:
-            yield "approved_data", decision.review_result.data
-        elif status == ReviewStatus.REJECTED:
-            yield "rejected_data", decision.review_result.data
-        else:
-            raise RuntimeError(f"Unexpected review status: {status}")
+        if not result.processed:
+            await self.update_review_processed_status(
+                node_exec_id=node_exec_id, processed=True
+            )
 
-        if decision.message:
-            yield "review_message", decision.message
+            if result.status == ReviewStatus.APPROVED:
+                yield "approved_data", result.data
+                if result.message:
+                    yield "review_message", result.message
+
+            elif result.status == ReviewStatus.REJECTED:
+                yield "rejected_data", result.data
+                if result.message:
+                    yield "review_message", result.message

autogpt_platform/backend/backend/blocks/search.py

@@ -18,7 +18,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.request import DEFAULT_USER_AGENT
 
 
 class GetWikipediaSummaryBlock(Block, GetRequest):
@@ -40,27 +39,17 @@ class GetWikipediaSummaryBlock(Block, GetRequest):
             output_schema=GetWikipediaSummaryBlock.Output,
             test_input={"topic": "Artificial Intelligence"},
             test_output=("summary", "summary content"),
-            test_mock={
-                "get_request": lambda url, headers, json: {"extract": "summary content"}
-            },
+            test_mock={"get_request": lambda url, json: {"extract": "summary content"}},
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         topic = input_data.topic
-        # URL-encode the topic to handle spaces and special characters
-        encoded_topic = quote(topic, safe="")
-        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{encoded_topic}"
-
-        # Set headers per Wikimedia robot policy (https://w.wiki/4wJS)
-        # - User-Agent: Required, must identify the bot
-        # - Accept-Encoding: gzip recommended to reduce bandwidth
-        headers = {
-            "User-Agent": DEFAULT_USER_AGENT,
-            "Accept-Encoding": "gzip, deflate",
-        }
+        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
 
+        # Note: User-Agent is now automatically set by the request library
+        # to comply with Wikimedia's robot policy (https://w.wiki/4wJS)
         try:
-            response = await self.get_request(url, headers=headers, json=True)
+            response = await self.get_request(url, json=True)
             if "extract" not in response:
                 raise ValueError(f"Unable to parse Wikipedia response: {response}")
             yield "summary", response["extract"]

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -391,12 +391,8 @@ class SmartDecisionMakerBlock(Block):
         """
         block = sink_node.block
 
-        # Use custom name from node metadata if set, otherwise fall back to block.name
-        custom_name = sink_node.metadata.get("customized_name")
-        tool_name = custom_name if custom_name else block.name
-
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(tool_name),
+            "name": SmartDecisionMakerBlock.cleanup(block.name),
             "description": block.description,
         }
         sink_block_input_schema = block.input_schema
@@ -493,12 +489,8 @@ class SmartDecisionMakerBlock(Block):
                 f"Sink graph metadata not found: {graph_id} {graph_version}"
             )
 
-        # Use custom name from node metadata if set, otherwise fall back to graph name
-        custom_name = sink_node.metadata.get("customized_name")
-        tool_name = custom_name if custom_name else sink_graph_meta.name
-
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(tool_name),
+            "name": SmartDecisionMakerBlock.cleanup(sink_graph_meta.name),
             "description": sink_graph_meta.description,
         }
 
@@ -989,28 +981,10 @@ class SmartDecisionMakerBlock(Block):
         graph_version: int,
         execution_context: ExecutionContext,
         execution_processor: "ExecutionProcessor",
-        nodes_to_skip: set[str] | None = None,
         **kwargs,
     ) -> BlockOutput:
 
         tool_functions = await self._create_tool_node_signatures(node_id)
-        original_tool_count = len(tool_functions)
-
-        # Filter out tools for nodes that should be skipped (e.g., missing optional credentials)
-        if nodes_to_skip:
-            tool_functions = [
-                tf
-                for tf in tool_functions
-                if tf.get("function", {}).get("_sink_node_id") not in nodes_to_skip
-            ]
-
-            # Only raise error if we had tools but they were all filtered out
-            if original_tool_count > 0 and not tool_functions:
-                raise ValueError(
-                    "No available tools to execute - all downstream nodes are unavailable "
-                    "(possibly due to missing optional credentials)"
-                )
-
         yield "tool_functions", json.dumps(tool_functions)
 
         conversation_history = input_data.conversation_history or []

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -1057,153 +1057,3 @@ async def test_smart_decision_maker_traditional_mode_default():
         )  # Should yield individual tool parameters
         assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
         assert "conversations" in outputs
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_uses_customized_name_for_blocks():
-    """Test that SmartDecisionMakerBlock uses customized_name from node metadata for tool names."""
-    from unittest.mock import MagicMock
-
-    from backend.blocks.basic import StoreValueBlock
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node with customized_name in metadata
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-node-id"
-    mock_node.block_id = StoreValueBlock().id
-    mock_node.metadata = {"customized_name": "My Custom Tool Name"}
-    mock_node.block = StoreValueBlock()
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "input"
-
-    # Call the function directly
-    result = await SmartDecisionMakerBlock._create_block_function_signature(
-        mock_node, [mock_link]
-    )
-
-    # Verify the tool name uses the customized name (cleaned up)
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "my_custom_tool_name"  # Cleaned version
-    assert result["function"]["_sink_node_id"] == "test-node-id"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_falls_back_to_block_name():
-    """Test that SmartDecisionMakerBlock falls back to block.name when no customized_name."""
-    from unittest.mock import MagicMock
-
-    from backend.blocks.basic import StoreValueBlock
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node without customized_name
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-node-id"
-    mock_node.block_id = StoreValueBlock().id
-    mock_node.metadata = {}  # No customized_name
-    mock_node.block = StoreValueBlock()
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "input"
-
-    # Call the function directly
-    result = await SmartDecisionMakerBlock._create_block_function_signature(
-        mock_node, [mock_link]
-    )
-
-    # Verify the tool name uses the block's default name
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "storevalueblock"  # Default block name cleaned
-    assert result["function"]["_sink_node_id"] == "test-node-id"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_uses_customized_name_for_agents():
-    """Test that SmartDecisionMakerBlock uses customized_name from metadata for agent nodes."""
-    from unittest.mock import AsyncMock, MagicMock, patch
-
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node with customized_name in metadata
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-agent-node-id"
-    mock_node.metadata = {"customized_name": "My Custom Agent"}
-    mock_node.input_default = {
-        "graph_id": "test-graph-id",
-        "graph_version": 1,
-        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
-    }
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "test_input"
-
-    # Mock the database client
-    mock_graph_meta = MagicMock()
-    mock_graph_meta.name = "Original Agent Name"
-    mock_graph_meta.description = "Agent description"
-
-    mock_db_client = AsyncMock()
-    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
-
-    with patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
-        return_value=mock_db_client,
-    ):
-        result = await SmartDecisionMakerBlock._create_agent_function_signature(
-            mock_node, [mock_link]
-        )
-
-    # Verify the tool name uses the customized name (cleaned up)
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "my_custom_agent"  # Cleaned version
-    assert result["function"]["_sink_node_id"] == "test-agent-node-id"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_agent_falls_back_to_graph_name():
-    """Test that agent node falls back to graph name when no customized_name."""
-    from unittest.mock import AsyncMock, MagicMock, patch
-
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node without customized_name
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-agent-node-id"
-    mock_node.metadata = {}  # No customized_name
-    mock_node.input_default = {
-        "graph_id": "test-graph-id",
-        "graph_version": 1,
-        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
-    }
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "test_input"
-
-    # Mock the database client
-    mock_graph_meta = MagicMock()
-    mock_graph_meta.name = "Original Agent Name"
-    mock_graph_meta.description = "Agent description"
-
-    mock_db_client = AsyncMock()
-    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
-
-    with patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
-        return_value=mock_db_client,
-    ):
-        result = await SmartDecisionMakerBlock._create_agent_function_signature(
-            mock_node, [mock_link]
-        )
-
-    # Verify the tool name uses the graph's default name
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "original_agent_name"  # Graph name cleaned
-    assert result["function"]["_sink_node_id"] == "test-agent-node-id"

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dict.py

@@ -15,7 +15,6 @@ async def test_smart_decision_maker_handles_dynamic_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields
     mock_links = [
@@ -78,7 +77,6 @@ async def test_smart_decision_maker_handles_dynamic_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -44,7 +44,6 @@ async def test_create_block_function_signature_with_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields (source sanitized, sink original)
     mock_links = [
@@ -107,7 +106,6 @@ async def test_create_block_function_signature_with_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [
@@ -161,7 +159,6 @@ async def test_create_block_function_signature_with_object_fields():
     mock_node.block = MatchTextPatternBlock()
     mock_node.block_id = MatchTextPatternBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic object fields
     mock_links = [
@@ -211,13 +208,11 @@ async def test_create_tool_node_signatures():
         mock_dict_node.block = CreateDictionaryBlock()
         mock_dict_node.block_id = CreateDictionaryBlock().id
         mock_dict_node.input_default = {}
-        mock_dict_node.metadata = {}
 
         mock_list_node = Mock()
         mock_list_node.block = AddToListBlock()
         mock_list_node.block_id = AddToListBlock().id
         mock_list_node.input_default = {}
-        mock_list_node.metadata = {}
 
         # Mock links with dynamic fields
         dict_link1 = Mock(
@@ -428,7 +423,6 @@ async def test_mixed_regular_and_dynamic_fields():
     mock_node.block.name = "TestBlock"
     mock_node.block.description = "A test block"
     mock_node.block.input_schema = Mock()
-    mock_node.metadata = {}
 
     # Mock the get_field_schema to return a proper schema for regular fields
     def get_field_schema(field_name):

autogpt_platform/backend/backend/blocks/wordpress/__init__.py

@@ -1,3 +1,3 @@
-from .blog import WordPressCreatePostBlock, WordPressGetAllPostsBlock
+from .blog import WordPressCreatePostBlock
 
-__all__ = ["WordPressCreatePostBlock", "WordPressGetAllPostsBlock"]
+__all__ = ["WordPressCreatePostBlock"]

autogpt_platform/backend/backend/blocks/wordpress/_api.py

@@ -161,7 +161,7 @@ async def oauth_exchange_code_for_tokens(
         grant_type="authorization_code",
     ).model_dump(exclude_none=True)
 
-    response = await Requests(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -205,7 +205,7 @@ async def oauth_refresh_tokens(
         grant_type="refresh_token",
     ).model_dump(exclude_none=True)
 
-    response = await Requests(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -252,7 +252,7 @@ async def validate_token(
         "token": token,
     }
 
-    response = await Requests(raise_for_status=False).get(
+    response = await Requests().get(
         f"{WORDPRESS_BASE_URL}oauth2/token-info",
         params=params,
     )
@@ -296,7 +296,7 @@ async def make_api_request(
 
     url = f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}"
 
-    request_method = getattr(Requests(raise_for_status=False), method.lower())
+    request_method = getattr(Requests(), method.lower())
     response = await request_method(
         url,
         headers=headers,
@@ -476,7 +476,6 @@ async def create_post(
         data["tags"] = ",".join(str(t) for t in data["tags"])
 
     # Make the API request
-    site = normalize_site(site)
     endpoint = f"/rest/v1.1/sites/{site}/posts/new"
 
     headers = {
@@ -484,7 +483,7 @@ async def create_post(
         "Content-Type": "application/x-www-form-urlencoded",
     }
 
-    response = await Requests(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
         headers=headers,
         data=data,
@@ -500,132 +499,3 @@ async def create_post(
     )
     error_message = error_data.get("message", response.text)
     raise ValueError(f"Failed to create post: {response.status} - {error_message}")
-
-
-class Post(BaseModel):
-    """Response model for individual posts in a posts list response.
-
-    This is a simplified version compared to PostResponse, as the list endpoint
-    returns less detailed information than the create/get single post endpoints.
-    """
-
-    ID: int
-    site_ID: int
-    author: PostAuthor
-    date: datetime
-    modified: datetime
-    title: str
-    URL: str
-    short_URL: str
-    content: str | None = None
-    excerpt: str | None = None
-    slug: str
-    guid: str
-    status: str
-    sticky: bool
-    password: str | None = ""
-    parent: Union[Dict[str, Any], bool, None] = None
-    type: str
-    discussion: Dict[str, Union[str, bool, int]] | None = None
-    likes_enabled: bool | None = None
-    sharing_enabled: bool | None = None
-    like_count: int | None = None
-    i_like: bool | None = None
-    is_reblogged: bool | None = None
-    is_following: bool | None = None
-    global_ID: str | None = None
-    featured_image: str | None = None
-    post_thumbnail: Dict[str, Any] | None = None
-    format: str | None = None
-    geo: Union[Dict[str, Any], bool, None] = None
-    menu_order: int | None = None
-    page_template: str | None = None
-    publicize_URLs: List[str] | None = None
-    terms: Dict[str, Dict[str, Any]] | None = None
-    tags: Dict[str, Dict[str, Any]] | None = None
-    categories: Dict[str, Dict[str, Any]] | None = None
-    attachments: Dict[str, Dict[str, Any]] | None = None
-    attachment_count: int | None = None
-    metadata: List[Dict[str, Any]] | None = None
-    meta: Dict[str, Any] | None = None
-    capabilities: Dict[str, bool] | None = None
-    revisions: List[int] | None = None
-    other_URLs: Dict[str, Any] | None = None
-
-
-class PostsResponse(BaseModel):
-    """Response model for WordPress posts list."""
-
-    found: int
-    posts: List[Post]
-    meta: Dict[str, Any]
-
-
-def normalize_site(site: str) -> str:
-    """
-    Normalize a site identifier by stripping protocol and trailing slashes.
-
-    Args:
-        site: Site URL, domain, or ID (e.g., "https://myblog.wordpress.com/", "myblog.wordpress.com", "123456789")
-
-    Returns:
-        Normalized site identifier (domain or ID only)
-    """
-    site = site.strip()
-    if site.startswith("https://"):
-        site = site[8:]
-    elif site.startswith("http://"):
-        site = site[7:]
-    return site.rstrip("/")
-
-
-async def get_posts(
-    credentials: Credentials,
-    site: str,
-    status: PostStatus | None = None,
-    number: int = 100,
-    offset: int = 0,
-) -> PostsResponse:
-    """
-    Get posts from a WordPress site.
-
-    Args:
-        credentials: OAuth credentials
-        site: Site ID or domain (e.g., "myblog.wordpress.com" or "123456789")
-        status: Filter by post status using PostStatus enum, or None for all
-        number: Number of posts to retrieve (max 100)
-        offset: Number of posts to skip (for pagination)
-
-    Returns:
-        PostsResponse with the list of posts
-    """
-    site = normalize_site(site)
-    endpoint = f"/rest/v1.1/sites/{site}/posts"
-
-    headers = {
-        "Authorization": credentials.auth_header(),
-    }
-
-    params: Dict[str, Any] = {
-        "number": max(1, min(number, 100)),  # 1–100 posts per request
-        "offset": offset,
-    }
-
-    if status:
-        params["status"] = status.value
-    response = await Requests(raise_for_status=False).get(
-        f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
-        headers=headers,
-        params=params,
-    )
-
-    if response.ok:
-        return PostsResponse.model_validate(response.json())
-
-    error_data = (
-        response.json()
-        if response.headers.get("content-type", "").startswith("application/json")
-        else {}
-    )
-    error_message = error_data.get("message", response.text)
-    raise ValueError(f"Failed to get posts: {response.status} - {error_message}")

autogpt_platform/backend/backend/blocks/wordpress/blog.py

@@ -9,15 +9,7 @@ from backend.sdk import (
     SchemaField,
 )
 
-from ._api import (
-    CreatePostRequest,
-    Post,
-    PostResponse,
-    PostsResponse,
-    PostStatus,
-    create_post,
-    get_posts,
-)
+from ._api import CreatePostRequest, PostResponse, PostStatus, create_post
 from ._config import wordpress
 
 
@@ -57,15 +49,8 @@ class WordPressCreatePostBlock(Block):
         media_urls: list[str] = SchemaField(
             description="URLs of images to sideload and attach to the post", default=[]
         )
-        publish_as_draft: bool = SchemaField(
-            description="If True, publishes the post as a draft. If False, publishes it publicly.",
-            default=False,
-        )
 
     class Output(BlockSchemaOutput):
-        site: str = SchemaField(
-            description="The site ID or domain (pass-through for chaining with other blocks)"
-        )
         post_id: int = SchemaField(description="The ID of the created post")
         post_url: str = SchemaField(description="The full URL of the created post")
         short_url: str = SchemaField(description="The shortened wp.me URL")
@@ -93,9 +78,7 @@ class WordPressCreatePostBlock(Block):
             tags=input_data.tags,
             featured_image=input_data.featured_image,
             media_urls=input_data.media_urls,
-            status=(
-                PostStatus.DRAFT if input_data.publish_as_draft else PostStatus.PUBLISH
-            ),
+            status=PostStatus.PUBLISH,
         )
 
         post_response: PostResponse = await create_post(
@@ -104,69 +87,7 @@ class WordPressCreatePostBlock(Block):
             post_data=post_request,
         )
 
-        yield "site", input_data.site
         yield "post_id", post_response.ID
         yield "post_url", post_response.URL
         yield "short_url", post_response.short_URL
         yield "post_data", post_response.model_dump()
-
-
-class WordPressGetAllPostsBlock(Block):
-    """
-    Fetches all posts from a WordPress.com site or Jetpack-enabled site.
-    Supports filtering by status and pagination.
-    """
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = wordpress.credentials_field()
-        site: str = SchemaField(
-            description="Site ID or domain (e.g., 'myblog.wordpress.com' or '123456789')"
-        )
-        status: PostStatus | None = SchemaField(
-            description="Filter by post status, or None for all",
-            default=None,
-        )
-        number: int = SchemaField(
-            description="Number of posts to retrieve (max 100 per request)", default=20
-        )
-        offset: int = SchemaField(
-            description="Number of posts to skip (for pagination)", default=0
-        )
-
-    class Output(BlockSchemaOutput):
-        site: str = SchemaField(
-            description="The site ID or domain (pass-through for chaining with other blocks)"
-        )
-        found: int = SchemaField(description="Total number of posts found")
-        posts: list[Post] = SchemaField(
-            description="List of post objects with their details"
-        )
-        post: Post = SchemaField(
-            description="Individual post object (yielded for each post)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="97728fa7-7f6f-4789-ba0c-f2c114119536",
-            description="Fetch all posts from WordPress.com or Jetpack sites",
-            categories={BlockCategory.SOCIAL},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: Credentials, **kwargs
-    ) -> BlockOutput:
-        posts_response: PostsResponse = await get_posts(
-            credentials=credentials,
-            site=input_data.site,
-            status=input_data.status,
-            number=input_data.number,
-            offset=input_data.offset,
-        )
-
-        yield "site", input_data.site
-        yield "found", posts_response.found
-        yield "posts", posts_response.posts
-        for post in posts_response.posts:
-            yield "post", post

autogpt_platform/backend/backend/data/analytics.py

@@ -104,7 +104,7 @@ async def get_accuracy_trends_and_alerts(
             AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
             {user_filter}
         GROUP BY DATE(e."createdAt")
-        HAVING COUNT(*) >= 3  -- Need at least 3 executions per day
+        HAVING COUNT(*) >= 1  -- Include all days with at least 1 execution
     ),
     trends AS (
         SELECT 

autogpt_platform/backend/backend/data/block.py

@@ -50,8 +50,6 @@ from .model import (
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
-    from backend.data.execution import ExecutionContext
-
     from .graph import Link
 
 app_config = Config()
@@ -474,7 +472,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.block_type = block_type
         self.webhook_config = webhook_config
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
-        self.requires_human_review: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -617,77 +614,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                     block_id=self.id,
                 ) from ex
 
-    async def is_block_exec_need_review(
-        self,
-        input_data: BlockInput,
-        *,
-        user_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: "ExecutionContext",
-        **kwargs,
-    ) -> tuple[bool, BlockInput]:
-        """
-        Check if this block execution needs human review and handle the review process.
-
-        Returns:
-            Tuple of (should_pause, input_data_to_use)
-            - should_pause: True if execution should be paused for review
-            - input_data_to_use: The input data to use (may be modified by reviewer)
-        """
-        # Skip review if not required or safe mode is disabled
-        if not self.requires_human_review or not execution_context.safe_mode:
-            return False, input_data
-
-        from backend.blocks.helpers.review import HITLReviewHelper
-
-        # Handle the review request and get decision
-        decision = await HITLReviewHelper.handle_review_decision(
-            input_data=input_data,
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            execution_context=execution_context,
-            block_name=self.name,
-            editable=True,
-        )
-
-        if decision is None:
-            # We're awaiting review - pause execution
-            return True, input_data
-
-        if not decision.should_proceed:
-            # Review was rejected, raise an error to stop execution
-            raise BlockExecutionError(
-                message=f"Block execution rejected by reviewer: {decision.message}",
-                block_name=self.name,
-                block_id=self.id,
-            )
-
-        # Review was approved - use the potentially modified data
-        # ReviewResult.data must be a dict for block inputs
-        reviewed_data = decision.review_result.data
-        if not isinstance(reviewed_data, dict):
-            raise BlockExecutionError(
-                message=f"Review data must be a dict for block input, got {type(reviewed_data).__name__}",
-                block_name=self.name,
-                block_id=self.id,
-            )
-        return False, reviewed_data
-
     async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
-        # Check for review requirement and get potentially modified input data
-        should_pause, input_data = await self.is_block_exec_need_review(
-            input_data, **kwargs
-        )
-        if should_pause:
-            return
-
-        # Validate the input data (original or reviewer-modified) once
         if error := self.input_schema.validate_data(input_data):
             raise BlockInputError(
                 message=f"Unable to execute block with invalid input data: {error}",
@@ -695,7 +622,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                 block_id=self.id,
             )
 
-        # Use the validated input data
         async for output_name, output_data in self.run(
             self.input_schema(**{k: v for k, v in input_data.items() if v is not None}),
             **kwargs,

autogpt_platform/backend/backend/data/execution.py

@@ -153,8 +153,14 @@ class GraphExecutionMeta(BaseDbModel):
     nodes_input_masks: Optional[dict[str, BlockInput]]
     preset_id: Optional[str]
     status: ExecutionStatus
-    started_at: datetime
-    ended_at: datetime
+    started_at: Optional[datetime] = Field(
+        None,
+        description="When execution started running. Null if not yet started (QUEUED).",
+    )
+    ended_at: Optional[datetime] = Field(
+        None,
+        description="When execution finished. Null if not yet completed (QUEUED, RUNNING, INCOMPLETE, REVIEW).",
+    )
     is_shared: bool = False
     share_token: Optional[str] = None
 
@@ -229,10 +235,8 @@ class GraphExecutionMeta(BaseDbModel):
 
     @staticmethod
     def from_db(_graph_exec: AgentGraphExecution):
-        now = datetime.now(timezone.utc)
-        # TODO: make started_at and ended_at optional
-        start_time = _graph_exec.startedAt or _graph_exec.createdAt
-        end_time = _graph_exec.updatedAt or now
+        start_time = _graph_exec.startedAt
+        end_time = _graph_exec.endedAt
 
         try:
             stats = GraphExecutionStats.model_validate(_graph_exec.stats)
@@ -383,7 +387,6 @@ class GraphExecutionWithNodes(GraphExecution):
         self,
         execution_context: ExecutionContext,
         compiled_nodes_input_masks: Optional[NodesInputMasks] = None,
-        nodes_to_skip: Optional[set[str]] = None,
     ):
         return GraphExecutionEntry(
             user_id=self.user_id,
@@ -391,7 +394,6 @@ class GraphExecutionWithNodes(GraphExecution):
             graph_version=self.graph_version or 0,
             graph_exec_id=self.id,
             nodes_input_masks=compiled_nodes_input_masks,
-            nodes_to_skip=nodes_to_skip or set(),
             execution_context=execution_context,
         )
 
@@ -902,6 +904,14 @@ async def update_graph_execution_stats(
 
     if status:
         update_data["executionStatus"] = status
+        # Set endedAt when execution reaches a terminal status
+        terminal_statuses = [
+            ExecutionStatus.COMPLETED,
+            ExecutionStatus.FAILED,
+            ExecutionStatus.TERMINATED,
+        ]
+        if status in terminal_statuses:
+            update_data["endedAt"] = datetime.now(tz=timezone.utc)
 
     where_clause: AgentGraphExecutionWhereInput = {"id": graph_exec_id}
 
@@ -1147,8 +1157,6 @@ class GraphExecutionEntry(BaseModel):
     graph_id: str
     graph_version: int
     nodes_input_masks: Optional[NodesInputMasks] = None
-    nodes_to_skip: set[str] = Field(default_factory=set)
-    """Node IDs that should be skipped due to optional credentials not being configured."""
     execution_context: ExecutionContext = Field(default_factory=ExecutionContext)
 
 

autogpt_platform/backend/backend/data/graph.py

@@ -94,15 +94,6 @@ class Node(BaseDbModel):
     input_links: list[Link] = []
     output_links: list[Link] = []
 
-    @property
-    def credentials_optional(self) -> bool:
-        """
-        Whether credentials are optional for this node.
-        When True and credentials are not configured, the node will be skipped
-        during execution rather than causing a validation error.
-        """
-        return self.metadata.get("credentials_optional", False)
-
     @property
     def block(self) -> AnyBlockSchema | "_UnknownBlockBase":
         """Get the block for this node. Returns UnknownBlock if block is deleted/missing."""
@@ -244,10 +235,7 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if (
-                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-                or node.block.requires_human_review
-            )
+            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
         )
 
     @property
@@ -338,35 +326,7 @@ class Graph(BaseGraph):
     @computed_field
     @property
     def credentials_input_schema(self) -> dict[str, Any]:
-        schema = self._credentials_input_schema.jsonschema()
-
-        # Determine which credential fields are required based on credentials_optional metadata
-        graph_credentials_inputs = self.aggregate_credentials_inputs()
-        required_fields = []
-
-        # Build a map of node_id -> node for quick lookup
-        all_nodes = {node.id: node for node in self.nodes}
-        for sub_graph in self.sub_graphs:
-            for node in sub_graph.nodes:
-                all_nodes[node.id] = node
-
-        for field_key, (
-            _field_info,
-            node_field_pairs,
-        ) in graph_credentials_inputs.items():
-            # A field is required if ANY node using it has credentials_optional=False
-            is_required = False
-            for node_id, _field_name in node_field_pairs:
-                node = all_nodes.get(node_id)
-                if node and not node.credentials_optional:
-                    is_required = True
-                    break
-
-            if is_required:
-                required_fields.append(field_key)
-
-        schema["required"] = required_fields
-        return schema
+        return self._credentials_input_schema.jsonschema()
 
     @property
     def _credentials_input_schema(self) -> type[BlockSchema]:

autogpt_platform/backend/backend/data/graph_test.py

@@ -396,58 +396,3 @@ async def test_access_store_listing_graph(server: SpinTestServer):
         created_graph.id, created_graph.version, "3e53486c-cf57-477e-ba2a-cb02dc828e1b"
     )
     assert got_graph is not None
-
-
-# ============================================================================
-# Tests for Optional Credentials Feature
-# ============================================================================
-
-
-def test_node_credentials_optional_default():
-    """Test that credentials_optional defaults to False when not set in metadata."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={},
-    )
-    assert node.credentials_optional is False
-
-
-def test_node_credentials_optional_true():
-    """Test that credentials_optional returns True when explicitly set."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={"credentials_optional": True},
-    )
-    assert node.credentials_optional is True
-
-
-def test_node_credentials_optional_false():
-    """Test that credentials_optional returns False when explicitly set to False."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={"credentials_optional": False},
-    )
-    assert node.credentials_optional is False
-
-
-def test_node_credentials_optional_with_other_metadata():
-    """Test that credentials_optional works correctly with other metadata present."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={
-            "position": {"x": 100, "y": 200},
-            "customized_name": "My Custom Node",
-            "credentials_optional": True,
-        },
-    )
-    assert node.credentials_optional is True
-    assert node.metadata["position"] == {"x": 100, "y": 200}
-    assert node.metadata["customized_name"] == "My Custom Node"

autogpt_platform/backend/backend/data/understanding.py

@@ -1,396 +0,0 @@
-"""Data models and access layer for user business understanding."""
-
-import logging
-from datetime import datetime
-from typing import Any, Optional, cast
-
-import pydantic
-from prisma.models import UserBusinessUnderstanding
-from prisma.types import (
-    UserBusinessUnderstandingCreateInput,
-    UserBusinessUnderstandingUpdateInput,
-)
-
-from backend.data.redis_client import get_redis_async
-from backend.util.json import SafeJson
-
-logger = logging.getLogger(__name__)
-
-# Cache configuration
-CACHE_KEY_PREFIX = "understanding"
-CACHE_TTL_SECONDS = 48 * 60 * 60  # 48 hours
-
-
-def _cache_key(user_id: str) -> str:
-    """Generate cache key for user business understanding."""
-    return f"{CACHE_KEY_PREFIX}:{user_id}"
-
-
-def _json_to_list(value: Any) -> list[str]:
-    """Convert Json field to list[str], handling None."""
-    if value is None:
-        return []
-    if isinstance(value, list):
-        return cast(list[str], value)
-    return []
-
-
-class BusinessUnderstandingInput(pydantic.BaseModel):
-    """Input model for updating business understanding - all fields optional for incremental updates."""
-
-    # User info
-    user_name: Optional[str] = pydantic.Field(None, description="The user's name")
-    job_title: Optional[str] = pydantic.Field(None, description="The user's job title")
-
-    # Business basics
-    business_name: Optional[str] = pydantic.Field(
-        None, description="Name of the user's business"
-    )
-    industry: Optional[str] = pydantic.Field(None, description="Industry or sector")
-    business_size: Optional[str] = pydantic.Field(
-        None, description="Company size (e.g., '1-10', '11-50')"
-    )
-    user_role: Optional[str] = pydantic.Field(
-        None,
-        description="User's role in the organization (e.g., 'decision maker', 'implementer')",
-    )
-
-    # Processes & activities
-    key_workflows: Optional[list[str]] = pydantic.Field(
-        None, description="Key business workflows"
-    )
-    daily_activities: Optional[list[str]] = pydantic.Field(
-        None, description="Daily activities performed"
-    )
-
-    # Pain points & goals
-    pain_points: Optional[list[str]] = pydantic.Field(
-        None, description="Current pain points"
-    )
-    bottlenecks: Optional[list[str]] = pydantic.Field(
-        None, description="Process bottlenecks"
-    )
-    manual_tasks: Optional[list[str]] = pydantic.Field(
-        None, description="Manual/repetitive tasks"
-    )
-    automation_goals: Optional[list[str]] = pydantic.Field(
-        None, description="Desired automation goals"
-    )
-
-    # Current tools
-    current_software: Optional[list[str]] = pydantic.Field(
-        None, description="Software/tools currently used"
-    )
-    existing_automation: Optional[list[str]] = pydantic.Field(
-        None, description="Existing automations"
-    )
-
-    # Additional context
-    additional_notes: Optional[str] = pydantic.Field(
-        None, description="Any additional context"
-    )
-
-
-class BusinessUnderstanding(pydantic.BaseModel):
-    """Full business understanding model returned from database."""
-
-    id: str
-    user_id: str
-    created_at: datetime
-    updated_at: datetime
-
-    # User info
-    user_name: Optional[str] = None
-    job_title: Optional[str] = None
-
-    # Business basics
-    business_name: Optional[str] = None
-    industry: Optional[str] = None
-    business_size: Optional[str] = None
-    user_role: Optional[str] = None
-
-    # Processes & activities
-    key_workflows: list[str] = pydantic.Field(default_factory=list)
-    daily_activities: list[str] = pydantic.Field(default_factory=list)
-
-    # Pain points & goals
-    pain_points: list[str] = pydantic.Field(default_factory=list)
-    bottlenecks: list[str] = pydantic.Field(default_factory=list)
-    manual_tasks: list[str] = pydantic.Field(default_factory=list)
-    automation_goals: list[str] = pydantic.Field(default_factory=list)
-
-    # Current tools
-    current_software: list[str] = pydantic.Field(default_factory=list)
-    existing_automation: list[str] = pydantic.Field(default_factory=list)
-
-    # Additional context
-    additional_notes: Optional[str] = None
-
-    @classmethod
-    def from_db(cls, db_record: UserBusinessUnderstanding) -> "BusinessUnderstanding":
-        """Convert database record to Pydantic model."""
-        return cls(
-            id=db_record.id,
-            user_id=db_record.userId,
-            created_at=db_record.createdAt,
-            updated_at=db_record.updatedAt,
-            user_name=db_record.usersName,
-            job_title=db_record.jobTitle,
-            business_name=db_record.businessName,
-            industry=db_record.industry,
-            business_size=db_record.businessSize,
-            user_role=db_record.userRole,
-            key_workflows=_json_to_list(db_record.keyWorkflows),
-            daily_activities=_json_to_list(db_record.dailyActivities),
-            pain_points=_json_to_list(db_record.painPoints),
-            bottlenecks=_json_to_list(db_record.bottlenecks),
-            manual_tasks=_json_to_list(db_record.manualTasks),
-            automation_goals=_json_to_list(db_record.automationGoals),
-            current_software=_json_to_list(db_record.currentSoftware),
-            existing_automation=_json_to_list(db_record.existingAutomation),
-            additional_notes=db_record.additionalNotes,
-        )
-
-
-def _merge_lists(existing: list | None, new: list | None) -> list | None:
-    """Merge two lists, removing duplicates while preserving order."""
-    if new is None:
-        return existing
-    if existing is None:
-        return new
-    # Preserve order, add new items that don't exist
-    merged = list(existing)
-    for item in new:
-        if item not in merged:
-            merged.append(item)
-    return merged
-
-
-async def _get_from_cache(user_id: str) -> Optional[BusinessUnderstanding]:
-    """Get business understanding from Redis cache."""
-    try:
-        redis = await get_redis_async()
-        cached_data = await redis.get(_cache_key(user_id))
-        if cached_data:
-            return BusinessUnderstanding.model_validate_json(cached_data)
-    except Exception as e:
-        logger.warning(f"Failed to get understanding from cache: {e}")
-    return None
-
-
-async def _set_cache(user_id: str, understanding: BusinessUnderstanding) -> None:
-    """Set business understanding in Redis cache with TTL."""
-    try:
-        redis = await get_redis_async()
-        await redis.setex(
-            _cache_key(user_id),
-            CACHE_TTL_SECONDS,
-            understanding.model_dump_json(),
-        )
-    except Exception as e:
-        logger.warning(f"Failed to set understanding in cache: {e}")
-
-
-async def _delete_cache(user_id: str) -> None:
-    """Delete business understanding from Redis cache."""
-    try:
-        redis = await get_redis_async()
-        await redis.delete(_cache_key(user_id))
-    except Exception as e:
-        logger.warning(f"Failed to delete understanding from cache: {e}")
-
-
-async def get_business_understanding(
-    user_id: str,
-) -> Optional[BusinessUnderstanding]:
-    """Get the business understanding for a user.
-
-    Checks cache first, falls back to database if not cached.
-    Results are cached for 48 hours.
-    """
-    # Try cache first
-    cached = await _get_from_cache(user_id)
-    if cached:
-        logger.debug(f"Business understanding cache hit for user {user_id}")
-        return cached
-
-    # Cache miss - load from database
-    logger.debug(f"Business understanding cache miss for user {user_id}")
-    record = await UserBusinessUnderstanding.prisma().find_unique(
-        where={"userId": user_id}
-    )
-    if record is None:
-        return None
-
-    understanding = BusinessUnderstanding.from_db(record)
-
-    # Store in cache for next time
-    await _set_cache(user_id, understanding)
-
-    return understanding
-
-
-async def upsert_business_understanding(
-    user_id: str,
-    data: BusinessUnderstandingInput,
-) -> BusinessUnderstanding:
-    """
-    Create or update business understanding with incremental merge strategy.
-
-    - String fields: new value overwrites if provided (not None)
-    - List fields: new items are appended to existing (deduplicated)
-    """
-    # Get existing record for merge
-    existing = await UserBusinessUnderstanding.prisma().find_unique(
-        where={"userId": user_id}
-    )
-
-    # Build update data with merge strategy
-    update_data: UserBusinessUnderstandingUpdateInput = {}
-    create_data: dict[str, Any] = {"userId": user_id}
-
-    # Field mappings: (pydantic_field, db_field)
-    string_fields = [
-        ("user_name", "usersName"),
-        ("job_title", "jobTitle"),
-        ("business_name", "businessName"),
-        ("industry", "industry"),
-        ("business_size", "businessSize"),
-        ("user_role", "userRole"),
-        ("additional_notes", "additionalNotes"),
-    ]
-    list_fields = [
-        ("key_workflows", "keyWorkflows"),
-        ("daily_activities", "dailyActivities"),
-        ("pain_points", "painPoints"),
-        ("bottlenecks", "bottlenecks"),
-        ("manual_tasks", "manualTasks"),
-        ("automation_goals", "automationGoals"),
-        ("current_software", "currentSoftware"),
-        ("existing_automation", "existingAutomation"),
-    ]
-
-    # String fields - overwrite if provided
-    for pydantic_field, db_field in string_fields:
-        value = getattr(data, pydantic_field)
-        if value is not None:
-            update_data[db_field] = value  # type: ignore[literal-required]
-            create_data[db_field] = value
-
-    # List fields - merge with existing
-    for pydantic_field, db_field in list_fields:
-        value = getattr(data, pydantic_field)
-        if value is not None:
-            existing_list = (
-                _json_to_list(getattr(existing, db_field)) if existing else None
-            )
-            merged = _merge_lists(existing_list, value)
-            update_data[db_field] = SafeJson(merged)  # type: ignore[literal-required]
-            create_data[db_field] = SafeJson(merged)
-
-    # Upsert
-    record = await UserBusinessUnderstanding.prisma().upsert(
-        where={"userId": user_id},
-        data={
-            "create": UserBusinessUnderstandingCreateInput(**create_data),
-            "update": update_data,
-        },
-    )
-
-    understanding = BusinessUnderstanding.from_db(record)
-
-    # Update cache with new understanding
-    await _set_cache(user_id, understanding)
-
-    return understanding
-
-
-async def clear_business_understanding(user_id: str) -> bool:
-    """Clear/delete business understanding for a user from both DB and cache."""
-    # Delete from cache first
-    await _delete_cache(user_id)
-
-    try:
-        await UserBusinessUnderstanding.prisma().delete(where={"userId": user_id})
-        return True
-    except Exception:
-        # Record might not exist
-        return False
-
-
-def format_understanding_for_prompt(understanding: BusinessUnderstanding) -> str:
-    """Format business understanding as text for system prompt injection."""
-    sections = []
-
-    # User info section
-    user_info = []
-    if understanding.user_name:
-        user_info.append(f"Name: {understanding.user_name}")
-    if understanding.job_title:
-        user_info.append(f"Job Title: {understanding.job_title}")
-    if user_info:
-        sections.append("## User\n" + "\n".join(user_info))
-
-    # Business section
-    business_info = []
-    if understanding.business_name:
-        business_info.append(f"Company: {understanding.business_name}")
-    if understanding.industry:
-        business_info.append(f"Industry: {understanding.industry}")
-    if understanding.business_size:
-        business_info.append(f"Size: {understanding.business_size}")
-    if understanding.user_role:
-        business_info.append(f"Role Context: {understanding.user_role}")
-    if business_info:
-        sections.append("## Business\n" + "\n".join(business_info))
-
-    # Processes section
-    processes = []
-    if understanding.key_workflows:
-        processes.append(f"Key Workflows: {', '.join(understanding.key_workflows)}")
-    if understanding.daily_activities:
-        processes.append(
-            f"Daily Activities: {', '.join(understanding.daily_activities)}"
-        )
-    if processes:
-        sections.append("## Processes\n" + "\n".join(processes))
-
-    # Pain points section
-    pain_points = []
-    if understanding.pain_points:
-        pain_points.append(f"Pain Points: {', '.join(understanding.pain_points)}")
-    if understanding.bottlenecks:
-        pain_points.append(f"Bottlenecks: {', '.join(understanding.bottlenecks)}")
-    if understanding.manual_tasks:
-        pain_points.append(f"Manual Tasks: {', '.join(understanding.manual_tasks)}")
-    if pain_points:
-        sections.append("## Pain Points\n" + "\n".join(pain_points))
-
-    # Goals section
-    if understanding.automation_goals:
-        sections.append(
-            "## Automation Goals\n"
-            + "\n".join(f"- {goal}" for goal in understanding.automation_goals)
-        )
-
-    # Current tools section
-    tools_info = []
-    if understanding.current_software:
-        tools_info.append(
-            f"Current Software: {', '.join(understanding.current_software)}"
-        )
-    if understanding.existing_automation:
-        tools_info.append(
-            f"Existing Automation: {', '.join(understanding.existing_automation)}"
-        )
-    if tools_info:
-        sections.append("## Current Tools\n" + "\n".join(tools_info))
-
-    # Additional notes
-    if understanding.additional_notes:
-        sections.append(f"## Additional Context\n{understanding.additional_notes}")
-
-    if not sections:
-        return ""
-
-    return "# User Business Context\n\n" + "\n\n".join(sections)

autogpt_platform/backend/backend/executor/manager.py

@@ -178,7 +178,6 @@ async def execute_node(
     execution_processor: "ExecutionProcessor",
     execution_stats: NodeExecutionStats | None = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-    nodes_to_skip: Optional[set[str]] = None,
 ) -> BlockOutput:
     """
     Execute a node in the graph. This will trigger a block execution on a node,
@@ -246,7 +245,6 @@ async def execute_node(
         "user_id": user_id,
         "execution_context": execution_context,
         "execution_processor": execution_processor,
-        "nodes_to_skip": nodes_to_skip or set(),
     }
 
     # Last-minute fetch credentials + acquire a system-wide read-write lock to prevent
@@ -544,7 +542,6 @@ class ExecutionProcessor:
         node_exec_progress: NodeExecutionProgress,
         nodes_input_masks: Optional[NodesInputMasks],
         graph_stats_pair: tuple[GraphExecutionStats, threading.Lock],
-        nodes_to_skip: Optional[set[str]] = None,
     ) -> NodeExecutionStats:
         log_metadata = LogMetadata(
             logger=_logger,
@@ -567,7 +564,6 @@ class ExecutionProcessor:
             db_client=db_client,
             log_metadata=log_metadata,
             nodes_input_masks=nodes_input_masks,
-            nodes_to_skip=nodes_to_skip,
         )
         if isinstance(status, BaseException):
             raise status
@@ -613,7 +609,6 @@ class ExecutionProcessor:
         db_client: "DatabaseManagerAsyncClient",
         log_metadata: LogMetadata,
         nodes_input_masks: Optional[NodesInputMasks] = None,
-        nodes_to_skip: Optional[set[str]] = None,
     ) -> ExecutionStatus:
         status = ExecutionStatus.RUNNING
 
@@ -650,7 +645,6 @@ class ExecutionProcessor:
                 execution_processor=self,
                 execution_stats=stats,
                 nodes_input_masks=nodes_input_masks,
-                nodes_to_skip=nodes_to_skip,
             ):
                 await persist_output(output_name, output_data)
 
@@ -962,21 +956,6 @@ class ExecutionProcessor:
 
                 queued_node_exec = execution_queue.get()
 
-                # Check if this node should be skipped due to optional credentials
-                if queued_node_exec.node_id in graph_exec.nodes_to_skip:
-                    log_metadata.info(
-                        f"Skipping node execution {queued_node_exec.node_exec_id} "
-                        f"for node {queued_node_exec.node_id} - optional credentials not configured"
-                    )
-                    # Mark the node as completed without executing
-                    # No outputs will be produced, so downstream nodes won't trigger
-                    update_node_execution_status(
-                        db_client=db_client,
-                        exec_id=queued_node_exec.node_exec_id,
-                        status=ExecutionStatus.COMPLETED,
-                    )
-                    continue
-
                 log_metadata.debug(
                     f"Dispatching node execution {queued_node_exec.node_exec_id} "
                     f"for node {queued_node_exec.node_id}",
@@ -1037,7 +1016,6 @@ class ExecutionProcessor:
                             execution_stats,
                             execution_stats_lock,
                         ),
-                        nodes_to_skip=graph_exec.nodes_to_skip,
                     ),
                     self.node_execution_loop,
                 )

autogpt_platform/backend/backend/executor/utils.py

@@ -239,19 +239,14 @@ async def _validate_node_input_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[dict[str, dict[str, str]], set[str]]:
+) -> dict[str, dict[str, str]]:
     """
-    Checks all credentials for all nodes of the graph and returns structured errors
-    and a set of nodes that should be skipped due to optional missing credentials.
+    Checks all credentials for all nodes of the graph and returns structured errors.
 
     Returns:
-        tuple[
-            dict[node_id, dict[field_name, error_message]]: Credential validation errors per node,
-            set[node_id]: Nodes that should be skipped (optional credentials not configured)
-        ]
+        dict[node_id, dict[field_name, error_message]]: Credential validation errors per node
     """
     credential_errors: dict[str, dict[str, str]] = defaultdict(dict)
-    nodes_to_skip: set[str] = set()
 
     for node in graph.nodes:
         block = node.block
@@ -261,46 +256,27 @@ async def _validate_node_input_credentials(
         if not credentials_fields:
             continue
 
-        # Track if any credential field is missing for this node
-        has_missing_credentials = False
-
         for field_name, credentials_meta_type in credentials_fields.items():
             try:
-                # Check nodes_input_masks first, then input_default
-                field_value = None
                 if (
                     nodes_input_masks
                     and (node_input_mask := nodes_input_masks.get(node.id))
                     and field_name in node_input_mask
                 ):
-                    field_value = node_input_mask[field_name]
+                    credentials_meta = credentials_meta_type.model_validate(
+                        node_input_mask[field_name]
+                    )
                 elif field_name in node.input_default:
-                    # For optional credentials, don't use input_default - treat as missing
-                    # This prevents stale credential IDs from failing validation
-                    if node.credentials_optional:
-                        field_value = None
-                    else:
-                        field_value = node.input_default[field_name]
-
-                # Check if credentials are missing (None, empty, or not present)
-                if field_value is None or (
-                    isinstance(field_value, dict) and not field_value.get("id")
-                ):
-                    has_missing_credentials = True
-                    # If node has credentials_optional flag, mark for skipping instead of error
-                    if node.credentials_optional:
-                        continue  # Don't add error, will be marked for skip after loop
-                    else:
-                        credential_errors[node.id][
-                            field_name
-                        ] = "These credentials are required"
-                        continue
-
-                credentials_meta = credentials_meta_type.model_validate(field_value)
-
+                    credentials_meta = credentials_meta_type.model_validate(
+                        node.input_default[field_name]
+                    )
+                else:
+                    # Missing credentials
+                    credential_errors[node.id][
+                        field_name
+                    ] = "These credentials are required"
+                    continue
             except ValidationError as e:
-                # Validation error means credentials were provided but invalid
-                # This should always be an error, even if optional
                 credential_errors[node.id][field_name] = f"Invalid credentials: {e}"
                 continue
 
@@ -311,7 +287,6 @@ async def _validate_node_input_credentials(
                 )
             except Exception as e:
                 # Handle any errors fetching credentials
-                # If credentials were explicitly configured but unavailable, it's an error
                 credential_errors[node.id][
                     field_name
                 ] = f"Credentials not available: {e}"
@@ -338,19 +313,7 @@ async def _validate_node_input_credentials(
                 ] = "Invalid credentials: type/provider mismatch"
                 continue
 
-        # If node has optional credentials and any are missing, mark for skipping
-        # But only if there are no other errors for this node
-        if (
-            has_missing_credentials
-            and node.credentials_optional
-            and node.id not in credential_errors
-        ):
-            nodes_to_skip.add(node.id)
-            logger.info(
-                f"Node #{node.id} will be skipped: optional credentials not configured"
-            )
-
-    return credential_errors, nodes_to_skip
+    return credential_errors
 
 
 def make_node_credentials_input_map(
@@ -392,25 +355,21 @@ async def validate_graph_with_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[Mapping[str, Mapping[str, str]], set[str]]:
+) -> Mapping[str, Mapping[str, str]]:
     """
-    Validate graph including credentials and return structured errors per node,
-    along with a set of nodes that should be skipped due to optional missing credentials.
+    Validate graph including credentials and return structured errors per node.
 
     Returns:
-        tuple[
-            dict[node_id, dict[field_name, error_message]]: Validation errors per node,
-            set[node_id]: Nodes that should be skipped (optional credentials not configured)
-        ]
+        dict[node_id, dict[field_name, error_message]]: Validation errors per node
     """
     # Get input validation errors
     node_input_errors = GraphModel.validate_graph_get_errors(
         graph, for_run=True, nodes_input_masks=nodes_input_masks
     )
 
-    # Get credential input/availability/validation errors and nodes to skip
-    node_credential_input_errors, nodes_to_skip = (
-        await _validate_node_input_credentials(graph, user_id, nodes_input_masks)
+    # Get credential input/availability/validation errors
+    node_credential_input_errors = await _validate_node_input_credentials(
+        graph, user_id, nodes_input_masks
     )
 
     # Merge credential errors with structural errors
@@ -419,7 +378,7 @@ async def validate_graph_with_credentials(
             node_input_errors[node_id] = {}
         node_input_errors[node_id].update(field_errors)
 
-    return node_input_errors, nodes_to_skip
+    return node_input_errors
 
 
 async def _construct_starting_node_execution_input(
@@ -427,7 +386,7 @@ async def _construct_starting_node_execution_input(
     user_id: str,
     graph_inputs: BlockInput,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[list[tuple[str, BlockInput]], set[str]]:
+) -> list[tuple[str, BlockInput]]:
     """
     Validates and prepares the input data for executing a graph.
     This function checks the graph for starting nodes, validates the input data
@@ -441,14 +400,11 @@ async def _construct_starting_node_execution_input(
         node_credentials_map: `dict[node_id, dict[input_name, CredentialsMetaInput]]`
 
     Returns:
-        tuple[
-            list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID
-                and the corresponding input data for that node.
-            set[str]: Node IDs that should be skipped (optional credentials not configured)
-        ]
+        list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID and
+            the corresponding input data for that node.
     """
     # Use new validation function that includes credentials
-    validation_errors, nodes_to_skip = await validate_graph_with_credentials(
+    validation_errors = await validate_graph_with_credentials(
         graph, user_id, nodes_input_masks
     )
     n_error_nodes = len(validation_errors)
@@ -489,7 +445,7 @@ async def _construct_starting_node_execution_input(
             "No starting nodes found for the graph, make sure an AgentInput or blocks with no inbound links are present as starting nodes."
         )
 
-    return nodes_input, nodes_to_skip
+    return nodes_input
 
 
 async def validate_and_construct_node_execution_input(
@@ -500,7 +456,7 @@ async def validate_and_construct_node_execution_input(
     graph_credentials_inputs: Optional[Mapping[str, CredentialsMetaInput]] = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
     is_sub_graph: bool = False,
-) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks, set[str]]:
+) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks]:
     """
     Public wrapper that handles graph fetching, credential mapping, and validation+construction.
     This centralizes the logic used by both scheduler validation and actual execution.
@@ -517,7 +473,6 @@ async def validate_and_construct_node_execution_input(
         GraphModel: Full graph object for the given `graph_id`.
         list[tuple[node_id, BlockInput]]: Starting node IDs with corresponding inputs.
         dict[str, BlockInput]: Node input masks including all passed-in credentials.
-        set[str]: Node IDs that should be skipped (optional credentials not configured).
 
     Raises:
         NotFoundError: If the graph is not found.
@@ -559,16 +514,14 @@ async def validate_and_construct_node_execution_input(
         nodes_input_masks or {},
     )
 
-    starting_nodes_input, nodes_to_skip = (
-        await _construct_starting_node_execution_input(
-            graph=graph,
-            user_id=user_id,
-            graph_inputs=graph_inputs,
-            nodes_input_masks=nodes_input_masks,
-        )
+    starting_nodes_input = await _construct_starting_node_execution_input(
+        graph=graph,
+        user_id=user_id,
+        graph_inputs=graph_inputs,
+        nodes_input_masks=nodes_input_masks,
     )
 
-    return graph, starting_nodes_input, nodes_input_masks, nodes_to_skip
+    return graph, starting_nodes_input, nodes_input_masks
 
 
 def _merge_nodes_input_masks(
@@ -826,9 +779,6 @@ async def add_graph_execution(
 
         # Use existing execution's compiled input masks
         compiled_nodes_input_masks = graph_exec.nodes_input_masks or {}
-        # For resumed executions, nodes_to_skip was already determined at creation time
-        # TODO: Consider storing nodes_to_skip in DB if we need to preserve it across resumes
-        nodes_to_skip: set[str] = set()
 
         logger.info(f"Resuming graph execution #{graph_exec.id} for graph #{graph_id}")
     else:
@@ -837,7 +787,7 @@ async def add_graph_execution(
         )
 
         # Create new execution
-        graph, starting_nodes_input, compiled_nodes_input_masks, nodes_to_skip = (
+        graph, starting_nodes_input, compiled_nodes_input_masks = (
             await validate_and_construct_node_execution_input(
                 graph_id=graph_id,
                 user_id=user_id,
@@ -886,7 +836,6 @@ async def add_graph_execution(
     try:
         graph_exec_entry = graph_exec.to_graph_execution_entry(
             compiled_nodes_input_masks=compiled_nodes_input_masks,
-            nodes_to_skip=nodes_to_skip,
             execution_context=execution_context,
         )
         logger.info(f"Publishing execution {graph_exec.id} to execution queue")

autogpt_platform/backend/backend/executor/utils_test.py

@@ -367,13 +367,10 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     )
 
     # Setup mock returns
-    # The function returns (graph, starting_nodes_input, compiled_nodes_input_masks, nodes_to_skip)
-    nodes_to_skip: set[str] = set()
     mock_validate.return_value = (
         mock_graph,
         starting_nodes_input,
         compiled_nodes_input_masks,
-        nodes_to_skip,
     )
     mock_prisma.is_connected.return_value = True
     mock_edb.create_graph_execution = mocker.AsyncMock(return_value=mock_graph_exec)
@@ -459,212 +456,3 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     # Both executions should succeed (though they create different objects)
     assert result1 == mock_graph_exec
     assert result2 == mock_graph_exec_2
-
-
-# ============================================================================
-# Tests for Optional Credentials Feature
-# ============================================================================
-
-
-@pytest.mark.asyncio
-async def test_validate_node_input_credentials_returns_nodes_to_skip(
-    mocker: MockerFixture,
-):
-    """
-    Test that _validate_node_input_credentials returns nodes_to_skip set
-    for nodes with credentials_optional=True and missing credentials.
-    """
-    from backend.executor.utils import _validate_node_input_credentials
-
-    # Create a mock node with credentials_optional=True
-    mock_node = mocker.MagicMock()
-    mock_node.id = "node-with-optional-creds"
-    mock_node.credentials_optional = True
-    mock_node.input_default = {}  # No credentials configured
-
-    # Create a mock block with credentials field
-    mock_block = mocker.MagicMock()
-    mock_credentials_field_type = mocker.MagicMock()
-    mock_block.input_schema.get_credentials_fields.return_value = {
-        "credentials": mock_credentials_field_type
-    }
-    mock_node.block = mock_block
-
-    # Create mock graph
-    mock_graph = mocker.MagicMock()
-    mock_graph.nodes = [mock_node]
-
-    # Call the function
-    errors, nodes_to_skip = await _validate_node_input_credentials(
-        graph=mock_graph,
-        user_id="test-user-id",
-        nodes_input_masks=None,
-    )
-
-    # Node should be in nodes_to_skip, not in errors
-    assert mock_node.id in nodes_to_skip
-    assert mock_node.id not in errors
-
-
-@pytest.mark.asyncio
-async def test_validate_node_input_credentials_required_missing_creds_error(
-    mocker: MockerFixture,
-):
-    """
-    Test that _validate_node_input_credentials returns errors
-    for nodes with credentials_optional=False and missing credentials.
-    """
-    from backend.executor.utils import _validate_node_input_credentials
-
-    # Create a mock node with credentials_optional=False (required)
-    mock_node = mocker.MagicMock()
-    mock_node.id = "node-with-required-creds"
-    mock_node.credentials_optional = False
-    mock_node.input_default = {}  # No credentials configured
-
-    # Create a mock block with credentials field
-    mock_block = mocker.MagicMock()
-    mock_credentials_field_type = mocker.MagicMock()
-    mock_block.input_schema.get_credentials_fields.return_value = {
-        "credentials": mock_credentials_field_type
-    }
-    mock_node.block = mock_block
-
-    # Create mock graph
-    mock_graph = mocker.MagicMock()
-    mock_graph.nodes = [mock_node]
-
-    # Call the function
-    errors, nodes_to_skip = await _validate_node_input_credentials(
-        graph=mock_graph,
-        user_id="test-user-id",
-        nodes_input_masks=None,
-    )
-
-    # Node should be in errors, not in nodes_to_skip
-    assert mock_node.id in errors
-    assert "credentials" in errors[mock_node.id]
-    assert "required" in errors[mock_node.id]["credentials"].lower()
-    assert mock_node.id not in nodes_to_skip
-
-
-@pytest.mark.asyncio
-async def test_validate_graph_with_credentials_returns_nodes_to_skip(
-    mocker: MockerFixture,
-):
-    """
-    Test that validate_graph_with_credentials returns nodes_to_skip set
-    from _validate_node_input_credentials.
-    """
-    from backend.executor.utils import validate_graph_with_credentials
-
-    # Mock _validate_node_input_credentials to return specific values
-    mock_validate = mocker.patch(
-        "backend.executor.utils._validate_node_input_credentials"
-    )
-    expected_errors = {"node1": {"field": "error"}}
-    expected_nodes_to_skip = {"node2", "node3"}
-    mock_validate.return_value = (expected_errors, expected_nodes_to_skip)
-
-    # Mock GraphModel with validate_graph_get_errors method
-    mock_graph = mocker.MagicMock()
-    mock_graph.validate_graph_get_errors.return_value = {}
-
-    # Call the function
-    errors, nodes_to_skip = await validate_graph_with_credentials(
-        graph=mock_graph,
-        user_id="test-user-id",
-        nodes_input_masks=None,
-    )
-
-    # Verify nodes_to_skip is passed through
-    assert nodes_to_skip == expected_nodes_to_skip
-    assert "node1" in errors
-
-
-@pytest.mark.asyncio
-async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
-    """
-    Test that add_graph_execution properly passes nodes_to_skip
-    to the graph execution entry.
-    """
-    from backend.data.execution import GraphExecutionWithNodes
-    from backend.executor.utils import add_graph_execution
-
-    # Mock data
-    graph_id = "test-graph-id"
-    user_id = "test-user-id"
-    inputs = {"test_input": "test_value"}
-    graph_version = 1
-
-    # Mock the graph object
-    mock_graph = mocker.MagicMock()
-    mock_graph.version = graph_version
-
-    # Starting nodes and masks
-    starting_nodes_input = [("node1", {"input1": "value1"})]
-    compiled_nodes_input_masks = {}
-    nodes_to_skip = {"skipped-node-1", "skipped-node-2"}
-
-    # Mock the graph execution object
-    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
-    mock_graph_exec.id = "execution-id-123"
-    mock_graph_exec.node_executions = []
-
-    # Track what's passed to to_graph_execution_entry
-    captured_kwargs = {}
-
-    def capture_to_entry(**kwargs):
-        captured_kwargs.update(kwargs)
-        return mocker.MagicMock()
-
-    mock_graph_exec.to_graph_execution_entry.side_effect = capture_to_entry
-
-    # Setup mocks
-    mock_validate = mocker.patch(
-        "backend.executor.utils.validate_and_construct_node_execution_input"
-    )
-    mock_edb = mocker.patch("backend.executor.utils.execution_db")
-    mock_prisma = mocker.patch("backend.executor.utils.prisma")
-    mock_udb = mocker.patch("backend.executor.utils.user_db")
-    mock_gdb = mocker.patch("backend.executor.utils.graph_db")
-    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
-    mock_get_event_bus = mocker.patch(
-        "backend.executor.utils.get_async_execution_event_bus"
-    )
-
-    # Setup returns - include nodes_to_skip in the tuple
-    mock_validate.return_value = (
-        mock_graph,
-        starting_nodes_input,
-        compiled_nodes_input_masks,
-        nodes_to_skip,  # This should be passed through
-    )
-    mock_prisma.is_connected.return_value = True
-    mock_edb.create_graph_execution = mocker.AsyncMock(return_value=mock_graph_exec)
-    mock_edb.update_graph_execution_stats = mocker.AsyncMock(
-        return_value=mock_graph_exec
-    )
-    mock_edb.update_node_execution_status_batch = mocker.AsyncMock()
-
-    mock_user = mocker.MagicMock()
-    mock_user.timezone = "UTC"
-    mock_settings = mocker.MagicMock()
-    mock_settings.human_in_the_loop_safe_mode = True
-
-    mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
-    mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)
-    mock_get_queue.return_value = mocker.AsyncMock()
-    mock_get_event_bus.return_value = mocker.MagicMock(publish=mocker.AsyncMock())
-
-    # Call the function
-    await add_graph_execution(
-        graph_id=graph_id,
-        user_id=user_id,
-        inputs=inputs,
-        graph_version=graph_version,
-    )
-
-    # Verify nodes_to_skip was passed to to_graph_execution_entry
-    assert "nodes_to_skip" in captured_kwargs
-    assert captured_kwargs["nodes_to_skip"] == nodes_to_skip

autogpt_platform/backend/backend/integrations/oauth/__init__.py

@@ -8,7 +8,6 @@ from .discord import DiscordOAuthHandler
 from .github import GitHubOAuthHandler
 from .google import GoogleOAuthHandler
 from .notion import NotionOAuthHandler
-from .reddit import RedditOAuthHandler
 from .twitter import TwitterOAuthHandler
 
 if TYPE_CHECKING:
@@ -21,7 +20,6 @@ _ORIGINAL_HANDLERS = [
     GitHubOAuthHandler,
     GoogleOAuthHandler,
     NotionOAuthHandler,
-    RedditOAuthHandler,
     TwitterOAuthHandler,
     TodoistOAuthHandler,
 ]

autogpt_platform/backend/backend/integrations/oauth/reddit.py

@@ -1,208 +0,0 @@
-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
-from backend.util.settings import Settings
-
-settings = Settings()
-
-
-class RedditOAuthHandler(BaseOAuthHandler):
-    """
-    Reddit OAuth 2.0 handler.
-
-    Based on the documentation at:
-    - https://github.com/reddit-archive/reddit/wiki/OAuth2
-
-    Notes:
-    - Reddit requires `duration=permanent` to get refresh tokens
-    - Access tokens expire after 1 hour (3600 seconds)
-    - Reddit requires HTTP Basic Auth for token requests
-    - Reddit requires a unique User-Agent header
-    """
-
-    PROVIDER_NAME = ProviderName.REDDIT
-    DEFAULT_SCOPES: ClassVar[list[str]] = [
-        "identity",  # Get username, verify auth
-        "read",  # Access posts and comments
-        "submit",  # Submit new posts and comments
-        "edit",  # Edit own posts and comments
-        "history",  # Access user's post history
-        "privatemessages",  # Access inbox and send private messages
-        "flair",  # Access and set flair on posts/subreddits
-    ]
-
-    AUTHORIZE_URL = "https://www.reddit.com/api/v1/authorize"
-    TOKEN_URL = "https://www.reddit.com/api/v1/access_token"
-    USERNAME_URL = "https://oauth.reddit.com/api/v1/me"
-    REVOKE_URL = "https://www.reddit.com/api/v1/revoke_token"
-
-    def __init__(self, client_id: str, client_secret: str, redirect_uri: str):
-        self.client_id = client_id
-        self.client_secret = client_secret
-        self.redirect_uri = redirect_uri
-
-    def get_login_url(
-        self, scopes: list[str], state: str, code_challenge: Optional[str]
-    ) -> str:
-        """Generate Reddit OAuth 2.0 authorization URL"""
-        scopes = self.handle_default_scopes(scopes)
-
-        params = {
-            "response_type": "code",
-            "client_id": self.client_id,
-            "redirect_uri": self.redirect_uri,
-            "scope": " ".join(scopes),
-            "state": state,
-            "duration": "permanent",  # Required for refresh tokens
-        }
-
-        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:
-        """Exchange authorization code for access tokens"""
-        scopes = self.handle_default_scopes(scopes)
-
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        data = {
-            "grant_type": "authorization_code",
-            "code": code,
-            "redirect_uri": self.redirect_uri,
-        }
-
-        # Reddit requires HTTP Basic Auth for token requests
-        auth = (self.client_id, self.client_secret)
-
-        response = await Requests().post(
-            self.TOKEN_URL, headers=headers, data=data, auth=auth
-        )
-
-        if not response.ok:
-            error_text = response.text()
-            raise ValueError(
-                f"Reddit token exchange failed: {response.status} - {error_text}"
-            )
-
-        tokens = response.json()
-
-        if "error" in tokens:
-            raise ValueError(f"Reddit OAuth error: {tokens.get('error')}")
-
-        username = await self._get_username(tokens["access_token"])
-
-        return OAuth2Credentials(
-            provider=self.PROVIDER_NAME,
-            title=None,
-            username=username,
-            access_token=tokens["access_token"],
-            refresh_token=tokens.get("refresh_token"),
-            access_token_expires_at=int(time.time()) + tokens.get("expires_in", 3600),
-            refresh_token_expires_at=None,  # Reddit refresh tokens don't expire
-            scopes=scopes,
-        )
-
-    async def _get_username(self, access_token: str) -> str:
-        """Get the username from the access token"""
-        headers = {
-            "Authorization": f"Bearer {access_token}",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        response = await Requests().get(self.USERNAME_URL, headers=headers)
-
-        if not response.ok:
-            raise ValueError(f"Failed to get Reddit username: {response.status}")
-
-        data = response.json()
-        return data.get("name", "unknown")
-
-    async def _refresh_tokens(
-        self, credentials: OAuth2Credentials
-    ) -> OAuth2Credentials:
-        """Refresh access tokens using refresh token"""
-        if not credentials.refresh_token:
-            raise ValueError("No refresh token available")
-
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        data = {
-            "grant_type": "refresh_token",
-            "refresh_token": credentials.refresh_token.get_secret_value(),
-        }
-
-        auth = (self.client_id, self.client_secret)
-
-        response = await Requests().post(
-            self.TOKEN_URL, headers=headers, data=data, auth=auth
-        )
-
-        if not response.ok:
-            error_text = response.text()
-            raise ValueError(
-                f"Reddit token refresh failed: {response.status} - {error_text}"
-            )
-
-        tokens = response.json()
-
-        if "error" in tokens:
-            raise ValueError(f"Reddit OAuth error: {tokens.get('error')}")
-
-        username = await self._get_username(tokens["access_token"])
-
-        # Reddit may or may not return a new refresh token
-        new_refresh_token = tokens.get("refresh_token")
-        if new_refresh_token:
-            refresh_token: SecretStr | None = SecretStr(new_refresh_token)
-        elif credentials.refresh_token:
-            # Keep the existing refresh token
-            refresh_token = credentials.refresh_token
-        else:
-            refresh_token = None
-
-        return OAuth2Credentials(
-            id=credentials.id,
-            provider=self.PROVIDER_NAME,
-            title=credentials.title,
-            username=username,
-            access_token=tokens["access_token"],
-            refresh_token=refresh_token,
-            access_token_expires_at=int(time.time()) + tokens.get("expires_in", 3600),
-            refresh_token_expires_at=None,
-            scopes=credentials.scopes,
-        )
-
-    async def revoke_tokens(self, credentials: OAuth2Credentials) -> bool:
-        """Revoke the access token"""
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        data = {
-            "token": credentials.access_token.get_secret_value(),
-            "token_type_hint": "access_token",
-        }
-
-        auth = (self.client_id, self.client_secret)
-
-        response = await Requests().post(
-            self.REVOKE_URL, headers=headers, data=data, auth=auth
-        )
-
-        # Reddit returns 204 No Content on successful revocation
-        return response.ok

autogpt_platform/backend/backend/monitoring/late_execution_monitor.py

@@ -60,8 +60,10 @@ class LateExecutionMonitor:
         if not all_late_executions:
             return "No late executions detected."
 
-        # Sort by created time (oldest first)
-        all_late_executions.sort(key=lambda x: x.started_at)
+        # Sort by started time (oldest first), with None values (unstarted) first
+        all_late_executions.sort(
+            key=lambda x: x.started_at or datetime.min.replace(tzinfo=timezone.utc)
+        )
 
         num_total_late = len(all_late_executions)
         num_queued = len(queued_late_executions)
@@ -74,7 +76,7 @@ class LateExecutionMonitor:
         was_truncated = num_total_late > tuncate_size
 
         late_execution_details = [
-            f"* `Execution ID: {exec.id}, Graph ID: {exec.graph_id}v{exec.graph_version}, User ID: {exec.user_id}, Status: {exec.status}, Created At: {exec.started_at.isoformat()}`"
+            f"* `Execution ID: {exec.id}, Graph ID: {exec.graph_id}v{exec.graph_version}, User ID: {exec.user_id}, Status: {exec.status}, Started At: {exec.started_at.isoformat() if exec.started_at else 'Not started'}`"
             for exec in truncated_executions
         ]
 

autogpt_platform/backend/backend/util/settings.py

@@ -264,7 +264,7 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
     )
 
     reddit_user_agent: str = Field(
-        default="web:AutoGPT:v0.6.0 (by /u/autogpt)",
+        default="AutoGPT:1.0 (by /u/autogpt)",
         description="The user agent for the Reddit API",
     )
 
@@ -658,14 +658,6 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
 
     ayrshare_api_key: str = Field(default="", description="Ayrshare API Key")
     ayrshare_jwt_key: str = Field(default="", description="Ayrshare private Key")
-
-    # Langfuse prompt management
-    langfuse_public_key: str = Field(default="", description="Langfuse public key")
-    langfuse_secret_key: str = Field(default="", description="Langfuse secret key")
-    langfuse_host: str = Field(
-        default="https://cloud.langfuse.com", description="Langfuse host URL"
-    )
-
     # Add more secret fields as needed
     model_config = SettingsConfigDict(
         env_file=".env",

autogpt_platform/backend/gen_prisma_types_stub.py

@@ -1,227 +0,0 @@
-#!/usr/bin/env python3
-"""
-Generate a lightweight stub for prisma/types.py that collapses all exported
-symbols to Any. This prevents Pyright from spending time/budget on Prisma's
-query DSL types while keeping runtime behavior unchanged.
-
-Usage:
-    poetry run gen-prisma-stub
-
-This script automatically finds the prisma package location and generates
-the types.pyi stub file in the same directory as types.py.
-"""
-
-from __future__ import annotations
-
-import ast
-import importlib.util
-import sys
-from pathlib import Path
-from typing import Iterable, Set
-
-
-def _iter_assigned_names(target: ast.expr) -> Iterable[str]:
-    """Extract names from assignment targets (handles tuple unpacking)."""
-    if isinstance(target, ast.Name):
-        yield target.id
-    elif isinstance(target, (ast.Tuple, ast.List)):
-        for elt in target.elts:
-            yield from _iter_assigned_names(elt)
-
-
-def _is_private(name: str) -> bool:
-    """Check if a name is private (starts with _ but not __)."""
-    return name.startswith("_") and not name.startswith("__")
-
-
-def _is_safe_type_alias(node: ast.Assign) -> bool:
-    """Check if an assignment is a safe type alias that shouldn't be stubbed.
-
-    Safe types are:
-    - Literal types (don't cause type budget issues)
-    - Simple type references (SortMode, SortOrder, etc.)
-    - TypeVar definitions
-    """
-    if not node.value:
-        return False
-
-    # Check if it's a Subscript (like Literal[...], Union[...], TypeVar[...])
-    if isinstance(node.value, ast.Subscript):
-        # Get the base type name
-        if isinstance(node.value.value, ast.Name):
-            base_name = node.value.value.id
-            # Literal types are safe
-            if base_name == "Literal":
-                return True
-            # TypeVar is safe
-            if base_name == "TypeVar":
-                return True
-        elif isinstance(node.value.value, ast.Attribute):
-            # Handle typing_extensions.Literal etc.
-            if node.value.value.attr == "Literal":
-                return True
-
-    # Check if it's a simple Name reference (like SortMode = _types.SortMode)
-    if isinstance(node.value, ast.Attribute):
-        return True
-
-    # Check if it's a Call (like TypeVar(...))
-    if isinstance(node.value, ast.Call):
-        if isinstance(node.value.func, ast.Name):
-            if node.value.func.id == "TypeVar":
-                return True
-
-    return False
-
-
-def collect_top_level_symbols(
-    tree: ast.Module, source_lines: list[str]
-) -> tuple[Set[str], Set[str], list[str], Set[str]]:
-    """Collect all top-level symbols from an AST module.
-
-    Returns:
-        Tuple of (class_names, function_names, safe_variable_sources, unsafe_variable_names)
-        safe_variable_sources contains the actual source code lines for safe variables
-    """
-    classes: Set[str] = set()
-    functions: Set[str] = set()
-    safe_variable_sources: list[str] = []
-    unsafe_variables: Set[str] = set()
-
-    for node in tree.body:
-        if isinstance(node, ast.ClassDef):
-            if not _is_private(node.name):
-                classes.add(node.name)
-        elif isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
-            if not _is_private(node.name):
-                functions.add(node.name)
-        elif isinstance(node, ast.Assign):
-            is_safe = _is_safe_type_alias(node)
-            names = []
-            for t in node.targets:
-                for n in _iter_assigned_names(t):
-                    if not _is_private(n):
-                        names.append(n)
-            if names:
-                if is_safe:
-                    # Extract the source code for this assignment
-                    start_line = node.lineno - 1  # 0-indexed
-                    end_line = node.end_lineno if node.end_lineno else node.lineno
-                    source = "\n".join(source_lines[start_line:end_line])
-                    safe_variable_sources.append(source)
-                else:
-                    unsafe_variables.update(names)
-        elif isinstance(node, ast.AnnAssign) and node.target:
-            # Annotated assignments are always stubbed
-            for n in _iter_assigned_names(node.target):
-                if not _is_private(n):
-                    unsafe_variables.add(n)
-
-    return classes, functions, safe_variable_sources, unsafe_variables
-
-
-def find_prisma_types_path() -> Path:
-    """Find the prisma types.py file in the installed package."""
-    spec = importlib.util.find_spec("prisma")
-    if spec is None or spec.origin is None:
-        raise RuntimeError("Could not find prisma package. Is it installed?")
-
-    prisma_dir = Path(spec.origin).parent
-    types_path = prisma_dir / "types.py"
-
-    if not types_path.exists():
-        raise RuntimeError(f"prisma/types.py not found at {types_path}")
-
-    return types_path
-
-
-def generate_stub(src_path: Path, stub_path: Path) -> int:
-    """Generate the .pyi stub file from the source types.py."""
-    code = src_path.read_text(encoding="utf-8", errors="ignore")
-    source_lines = code.splitlines()
-    tree = ast.parse(code, filename=str(src_path))
-    classes, functions, safe_variable_sources, unsafe_variables = (
-        collect_top_level_symbols(tree, source_lines)
-    )
-
-    header = """\
-# -*- coding: utf-8 -*-
-# Auto-generated stub file - DO NOT EDIT
-# Generated by gen_prisma_types_stub.py
-#
-# This stub intentionally collapses complex Prisma query DSL types to Any.
-# Prisma's generated types can explode Pyright's type inference budgets
-# on large schemas. We collapse them to Any so the rest of the codebase
-# can remain strongly typed while keeping runtime behavior unchanged.
-#
-# Safe types (Literal, TypeVar, simple references) are preserved from the
-# original types.py to maintain proper type checking where possible.
-
-from __future__ import annotations
-from typing import Any
-from typing_extensions import Literal
-
-# Re-export commonly used typing constructs that may be imported from this module
-from typing import TYPE_CHECKING, TypeVar, Generic, Union, Optional, List, Dict
-
-# Base type alias for stubbed Prisma types - allows any dict structure
-_PrismaDict = dict[str, Any]
-
-"""
-
-    lines = [header]
-
-    # Include safe variable definitions (Literal types, TypeVars, etc.)
-    lines.append("# Safe type definitions preserved from original types.py")
-    for source in safe_variable_sources:
-        lines.append(source)
-    lines.append("")
-
-    # Stub all classes and unsafe variables uniformly as dict[str, Any] aliases
-    # This allows:
-    # 1. Use in type annotations: x: SomeType
-    # 2. Constructor calls: SomeType(...)
-    # 3. Dict literal assignments: x: SomeType = {...}
-    lines.append(
-        "# Stubbed types (collapsed to dict[str, Any] to prevent type budget exhaustion)"
-    )
-    all_stubbed = sorted(classes | unsafe_variables)
-    for name in all_stubbed:
-        lines.append(f"{name} = _PrismaDict")
-
-    lines.append("")
-
-    # Stub functions
-    for name in sorted(functions):
-        lines.append(f"def {name}(*args: Any, **kwargs: Any) -> Any: ...")
-
-    lines.append("")
-
-    stub_path.write_text("\n".join(lines), encoding="utf-8")
-    return (
-        len(classes)
-        + len(functions)
-        + len(safe_variable_sources)
-        + len(unsafe_variables)
-    )
-
-
-def main() -> None:
-    """Main entry point."""
-    try:
-        types_path = find_prisma_types_path()
-        stub_path = types_path.with_suffix(".pyi")
-
-        print(f"Found prisma types.py at: {types_path}")
-        print(f"Generating stub at: {stub_path}")
-
-        num_symbols = generate_stub(types_path, stub_path)
-        print(f"Generated {stub_path.name} with {num_symbols} Any-typed symbols")
-
-    except Exception as e:
-        print(f"Error: {e}", file=sys.stderr)
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

autogpt_platform/backend/linter.py

@@ -25,9 +25,6 @@ def run(*command: str) -> None:
 
 
 def lint():
-    # Generate Prisma types stub before running pyright to prevent type budget exhaustion
-    run("gen-prisma-stub")
-
     lint_step_args: list[list[str]] = [
         ["ruff", "check", *TARGET_DIRS, "--exit-zero"],
         ["ruff", "format", "--diff", "--check", LIBS_DIR],
@@ -52,6 +49,4 @@ def format():
     run("ruff", "format", LIBS_DIR)
     run("isort", "--profile", "black", BACKEND_DIR)
     run("black", BACKEND_DIR)
-    # Generate Prisma types stub before running pyright to prevent type budget exhaustion
-    run("gen-prisma-stub")
     run("pyright", *TARGET_DIRS)

autogpt_platform/backend/migrations/20260113080512_add_chat_tables/migration.sql

@@ -1,81 +0,0 @@
--- DropIndex
-DROP INDEX "StoreListingVersion_storeListingId_version_key";
-
--- CreateTable
-CREATE TABLE "UserBusinessUnderstanding" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "userId" TEXT NOT NULL,
-    "usersName" TEXT,
-    "jobTitle" TEXT,
-    "businessName" TEXT,
-    "industry" TEXT,
-    "businessSize" TEXT,
-    "userRole" TEXT,
-    "keyWorkflows" JSONB,
-    "dailyActivities" JSONB,
-    "painPoints" JSONB,
-    "bottlenecks" JSONB,
-    "manualTasks" JSONB,
-    "automationGoals" JSONB,
-    "currentSoftware" JSONB,
-    "existingAutomation" JSONB,
-    "additionalNotes" TEXT,
-
-    CONSTRAINT "UserBusinessUnderstanding_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "ChatSession" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "userId" TEXT,
-    "title" TEXT,
-    "credentials" JSONB NOT NULL DEFAULT '{}',
-    "successfulAgentRuns" JSONB NOT NULL DEFAULT '{}',
-    "successfulAgentSchedules" JSONB NOT NULL DEFAULT '{}',
-    "totalPromptTokens" INTEGER NOT NULL DEFAULT 0,
-    "totalCompletionTokens" INTEGER NOT NULL DEFAULT 0,
-
-    CONSTRAINT "ChatSession_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "ChatMessage" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "sessionId" TEXT NOT NULL,
-    "role" TEXT NOT NULL,
-    "content" TEXT,
-    "name" TEXT,
-    "toolCallId" TEXT,
-    "refusal" TEXT,
-    "toolCalls" JSONB,
-    "functionCall" JSONB,
-    "sequence" INTEGER NOT NULL,
-
-    CONSTRAINT "ChatMessage_pkey" PRIMARY KEY ("id")
-);
-
--- CreateIndex
-CREATE UNIQUE INDEX "UserBusinessUnderstanding_userId_key" ON "UserBusinessUnderstanding"("userId");
-
--- CreateIndex
-CREATE INDEX "UserBusinessUnderstanding_userId_idx" ON "UserBusinessUnderstanding"("userId");
-
--- CreateIndex
-CREATE INDEX "ChatSession_userId_updatedAt_idx" ON "ChatSession"("userId", "updatedAt");
-
--- CreateIndex
-CREATE INDEX "ChatMessage_sessionId_sequence_idx" ON "ChatMessage"("sessionId", "sequence");
-
--- CreateIndex
-CREATE UNIQUE INDEX "ChatMessage_sessionId_sequence_key" ON "ChatMessage"("sessionId", "sequence");
-
--- AddForeignKey
-ALTER TABLE "UserBusinessUnderstanding" ADD CONSTRAINT "UserBusinessUnderstanding_userId_fkey" FOREIGN KEY ("userId") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "ChatMessage" ADD CONSTRAINT "ChatMessage_sessionId_fkey" FOREIGN KEY ("sessionId") REFERENCES "ChatSession"("id") ON DELETE CASCADE ON UPDATE CASCADE;

autogpt_platform/backend/migrations/20260113170839_add_ended_at_to_agent_graph_execution/migration.sql

@@ -0,0 +1,8 @@
+-- AlterTable
+ALTER TABLE "AgentGraphExecution" ADD COLUMN "endedAt" TIMESTAMP(3);
+
+-- Set endedAt to updatedAt for existing records with terminal status only
+UPDATE "AgentGraphExecution"
+SET "endedAt" = "updatedAt"
+WHERE "endedAt" IS NULL
+  AND "executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED');

autogpt_platform/backend/poetry.lock

@@ -2777,30 +2777,6 @@ enabler = ["pytest-enabler (>=2.2)"]
 test = ["pyfakefs", "pytest (>=6,!=8.1.*)"]
 type = ["pygobject-stubs", "pytest-mypy", "shtab", "types-pywin32"]
 
-[[package]]
-name = "langfuse"
-version = "3.11.2"
-description = "A client library for accessing langfuse"
-optional = false
-python-versions = "<4.0,>=3.10"
-groups = ["main"]
-files = [
-    {file = "langfuse-3.11.2-py3-none-any.whl", hash = "sha256:84faea9f909694023cc7f0eb45696be190248c8790424f22af57ca4cd7a29f2d"},
-    {file = "langfuse-3.11.2.tar.gz", hash = "sha256:ab5f296a8056815b7288c7f25bc308a5e79f82a8634467b25daffdde99276e09"},
-]
-
-[package.dependencies]
-backoff = ">=1.10.0"
-httpx = ">=0.15.4,<1.0"
-openai = ">=0.27.8"
-opentelemetry-api = ">=1.33.1,<2.0.0"
-opentelemetry-exporter-otlp-proto-http = ">=1.33.1,<2.0.0"
-opentelemetry-sdk = ">=1.33.1,<2.0.0"
-packaging = ">=23.2,<26.0"
-pydantic = ">=1.10.7,<3.0"
-requests = ">=2,<3"
-wrapt = ">=1.14,<2.0"
-
 [[package]]
 name = "launchdarkly-eventsource"
 version = "1.3.0"
@@ -3492,90 +3468,6 @@ files = [
 importlib-metadata = ">=6.0,<8.8.0"
 typing-extensions = ">=4.5.0"
 
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.35.0"
-description = "OpenTelemetry Protobuf encoding"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.35.0-py3-none-any.whl", hash = "sha256:863465de697ae81279ede660f3918680b4480ef5f69dcdac04f30722ed7b74cc"},
-    {file = "opentelemetry_exporter_otlp_proto_common-1.35.0.tar.gz", hash = "sha256:6f6d8c39f629b9fa5c79ce19a2829dbd93034f8ac51243cdf40ed2196f00d7eb"},
-]
-
-[package.dependencies]
-opentelemetry-proto = "1.35.0"
-
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-http"
-version = "1.35.0"
-description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_http-1.35.0-py3-none-any.whl", hash = "sha256:9a001e3df3c7f160fb31056a28ed7faa2de7df68877ae909516102ae36a54e1d"},
-    {file = "opentelemetry_exporter_otlp_proto_http-1.35.0.tar.gz", hash = "sha256:cf940147f91b450ef5f66e9980d40eb187582eed399fa851f4a7a45bb880de79"},
-]
-
-[package.dependencies]
-googleapis-common-protos = ">=1.52,<2.0"
-opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.35.0"
-opentelemetry-proto = "1.35.0"
-opentelemetry-sdk = ">=1.35.0,<1.36.0"
-requests = ">=2.7,<3.0"
-typing-extensions = ">=4.5.0"
-
-[[package]]
-name = "opentelemetry-proto"
-version = "1.35.0"
-description = "OpenTelemetry Python Proto"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_proto-1.35.0-py3-none-any.whl", hash = "sha256:98fffa803164499f562718384e703be8d7dfbe680192279a0429cb150a2f8809"},
-    {file = "opentelemetry_proto-1.35.0.tar.gz", hash = "sha256:532497341bd3e1c074def7c5b00172601b28bb83b48afc41a4b779f26eb4ee05"},
-]
-
-[package.dependencies]
-protobuf = ">=5.0,<7.0"
-
-[[package]]
-name = "opentelemetry-sdk"
-version = "1.35.0"
-description = "OpenTelemetry Python SDK"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_sdk-1.35.0-py3-none-any.whl", hash = "sha256:223d9e5f5678518f4842311bb73966e0b6db5d1e0b74e35074c052cd2487f800"},
-    {file = "opentelemetry_sdk-1.35.0.tar.gz", hash = "sha256:2a400b415ab68aaa6f04e8a6a9f6552908fb3090ae2ff78d6ae0c597ac581954"},
-]
-
-[package.dependencies]
-opentelemetry-api = "1.35.0"
-opentelemetry-semantic-conventions = "0.56b0"
-typing-extensions = ">=4.5.0"
-
-[[package]]
-name = "opentelemetry-semantic-conventions"
-version = "0.56b0"
-description = "OpenTelemetry Semantic Conventions"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_semantic_conventions-0.56b0-py3-none-any.whl", hash = "sha256:df44492868fd6b482511cc43a942e7194be64e94945f572db24df2e279a001a2"},
-    {file = "opentelemetry_semantic_conventions-0.56b0.tar.gz", hash = "sha256:c114c2eacc8ff6d3908cb328c811eaf64e6d68623840be9224dc829c4fd6c2ea"},
-]
-
-[package.dependencies]
-opentelemetry-api = "1.35.0"
-typing-extensions = ">=4.5.0"
-
 [[package]]
 name = "orjson"
 version = "3.11.3"
@@ -7030,97 +6922,6 @@ files = [
     {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
 ]
 
-[[package]]
-name = "wrapt"
-version = "1.17.3"
-description = "Module for decorators, wrappers and monkey patching."
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-files = [
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-]
-
 [[package]]
 name = "xattr"
 version = "1.2.0"
@@ -7494,4 +7295,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "86838b5ae40d606d6e01a14dad8a56c389d890d7a6a0c274a6602cca80f0df84"
+content-hash = "a93ba0cea3b465cb6ec3e3f258b383b09f84ea352ccfdbfa112902cde5653fc6"

autogpt_platform/backend/pyproject.toml

@@ -33,7 +33,6 @@ html2text = "^2024.2.26"
 jinja2 = "^3.1.6"
 jsonref = "^1.1.0"
 jsonschema = "^4.25.0"
-langfuse = "^3.11.0"
 launchdarkly-server-sdk = "^9.12.0"
 mem0ai = "^0.1.115"
 moviepy = "^2.1.2"
@@ -118,7 +117,6 @@ lint = "linter:lint"
 test = "run_tests:test"
 load-store-agents = "test.load_store_agents:run"
 export-api-schema = "backend.cli.generate_openapi_json:main"
-gen-prisma-stub = "gen_prisma_types_stub:main"
 oauth-tool = "backend.cli.oauth_tool:cli"
 
 [tool.isort]
@@ -136,9 +134,6 @@ ignore_patterns = []
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "session"
-# Disable syrupy plugin to avoid conflict with pytest-snapshot
-# Both provide --snapshot-update argument causing ArgumentError
-addopts = "-p no:syrupy"
 filterwarnings = [
     "ignore:'audioop' is deprecated:DeprecationWarning:discord.player",
     "ignore:invalid escape sequence:DeprecationWarning:tweepy.api",

autogpt_platform/backend/schema.prisma

@@ -53,7 +53,6 @@ model User {
 
   Profile               Profile[]
   UserOnboarding        UserOnboarding?
-  BusinessUnderstanding UserBusinessUnderstanding?
   BuilderSearchHistory  BuilderSearchHistory[]
   StoreListings         StoreListing[]
   StoreListingReviews   StoreListingReview[]
@@ -122,109 +121,19 @@ model UserOnboarding {
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 }
 
-model UserBusinessUnderstanding {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  userId String @unique
-  User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
-
-  // User info
-  usersName String?
-  jobTitle  String?
-
-  // Business basics (string columns)
-  businessName String?
-  industry     String?
-  businessSize String? // "1-10", "11-50", "51-200", "201-1000", "1000+"
-  userRole     String? // Role in organization context (e.g., "decision maker", "implementer")
-
-  // Processes & activities (JSON arrays)
-  keyWorkflows    Json?
-  dailyActivities Json?
-
-  // Pain points & goals (JSON arrays)
-  painPoints      Json?
-  bottlenecks     Json?
-  manualTasks     Json?
-  automationGoals Json?
-
-  // Current tools (JSON arrays)
-  currentSoftware    Json?
-  existingAutomation Json?
-
-  additionalNotes String?
-
-  @@index([userId])
-}
-
 model BuilderSearchHistory {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())
   updatedAt DateTime @default(now()) @updatedAt
 
   searchQuery String
-  filter      String[] @default([])
-  byCreator   String[] @default([])
+  filter      String[]  @default([])
+  byCreator   String[]  @default([])
 
   userId String
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 }
 
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-////////////////   CHAT SESSION TABLES   ///////////////////
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-
-model ChatSession {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  userId String?
-
-  // Session metadata
-  title       String?
-  credentials Json    @default("{}") // Map of provider -> credential metadata
-
-  // Rate limiting counters (stored as JSON maps)
-  successfulAgentRuns      Json @default("{}") // Map of graph_id -> count
-  successfulAgentSchedules Json @default("{}") // Map of graph_id -> count
-
-  // Usage tracking
-  totalPromptTokens     Int @default(0)
-  totalCompletionTokens Int @default(0)
-
-  Messages ChatMessage[]
-
-  @@index([userId, updatedAt])
-}
-
-model ChatMessage {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-
-  sessionId String
-  Session   ChatSession @relation(fields: [sessionId], references: [id], onDelete: Cascade)
-
-  // Message content
-  role         String // "user", "assistant", "system", "tool", "function"
-  content      String?
-  name         String?
-  toolCallId   String?
-  refusal      String?
-  toolCalls    Json? // List of tool calls for assistant messages
-  functionCall Json? // Deprecated but kept for compatibility
-
-  // Ordering within session
-  sequence Int
-
-  @@unique([sessionId, sequence])
-  @@index([sessionId, sequence])
-}
-
 // This model describes the Agent Graph/Flow (Multi Agent System).
 model AgentGraph {
   id        String    @default(uuid())
@@ -474,6 +383,7 @@ model AgentGraphExecution {
   createdAt DateTime  @default(now())
   updatedAt DateTime? @updatedAt
   startedAt DateTime?
+  endedAt   DateTime?
 
   isDeleted Boolean @default(false)
 
@@ -812,26 +722,26 @@ view StoreAgent {
   storeListingVersionId String
   updated_at            DateTime
 
-  slug              String
-  agent_name        String
-  agent_video       String?
-  agent_output_demo String?
-  agent_image       String[]
+  slug                String
+  agent_name          String
+  agent_video         String?
+  agent_output_demo   String?
+  agent_image         String[]
 
-  featured           Boolean                  @default(false)
-  creator_username   String?
-  creator_avatar     String?
-  sub_heading        String
-  description        String
-  categories         String[]
-  search             Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
-  runs               Int
-  rating             Float
-  versions           String[]
-  agentGraphVersions String[]
-  agentGraphId       String
-  is_available       Boolean                  @default(true)
-  useForOnboarding   Boolean                  @default(false)
+  featured         Boolean                  @default(false)
+  creator_username String?
+  creator_avatar   String?
+  sub_heading      String
+  description      String
+  categories       String[]
+  search           Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
+  runs                Int
+  rating              Float
+  versions            String[]
+  agentGraphVersions  String[]
+  agentGraphId        String
+  is_available        Boolean                  @default(true)
+  useForOnboarding    Boolean                  @default(false)
 
   // Materialized views used (refreshed every 15 minutes via pg_cron):
   // - mv_agent_run_counts - Pre-aggregated agent execution counts by agentGraphId
@@ -947,14 +857,14 @@ model StoreListingVersion {
   AgentGraph        AgentGraph @relation(fields: [agentGraphId, agentGraphVersion], references: [id, version])
 
   // Content fields
-  name               String
-  subHeading         String
-  videoUrl           String?
-  agentOutputDemoUrl String?
-  imageUrls          String[]
-  description        String
-  instructions       String?
-  categories         String[]
+  name                String
+  subHeading          String
+  videoUrl            String?
+  agentOutputDemoUrl  String?
+  imageUrls           String[]
+  description         String
+  instructions        String?
+  categories          String[]
 
   isFeatured Boolean @default(false)
 
@@ -990,6 +900,7 @@ model StoreListingVersion {
   // Reviews for this specific version
   Reviews StoreListingReview[]
 
+  @@unique([storeListingId, version])
   @@index([storeListingId, submissionStatus, isAvailable])
   @@index([submissionStatus])
   @@index([reviewerId])
@@ -1088,16 +999,16 @@ model OAuthApplication {
   updatedAt DateTime @updatedAt
 
   // Application metadata
-  name             String
-  description      String?
-  logoUrl          String? // URL to app logo stored in GCS
-  clientId         String  @unique
-  clientSecret     String // Hashed with Scrypt (same as API keys)
-  clientSecretSalt String // Salt for Scrypt hashing
+  name               String
+  description        String?
+  logoUrl            String? // URL to app logo stored in GCS
+  clientId           String @unique
+  clientSecret       String // Hashed with Scrypt (same as API keys)
+  clientSecretSalt   String // Salt for Scrypt hashing
 
   // OAuth configuration
   redirectUris String[] // Allowed callback URLs
-  grantTypes   String[]           @default(["authorization_code", "refresh_token"])
+  grantTypes   String[] @default(["authorization_code", "refresh_token"])
   scopes       APIKeyPermission[] // Which permissions the app can request
 
   // Application management

autogpt_platform/backend/snapshots/grph_single

@@ -2,7 +2,6 @@
   "created_at": "2025-09-04T13:37:00",
   "credentials_input_schema": {
     "properties": {},
-    "required": [],
     "title": "TestGraphCredentialsInputSchema",
     "type": "object"
   },

autogpt_platform/backend/snapshots/grphs_all

@@ -2,7 +2,6 @@
   {
     "credentials_input_schema": {
       "properties": {},
-      "required": [],
       "title": "TestGraphCredentialsInputSchema",
       "type": "object"
     },

autogpt_platform/backend/snapshots/lib_agts_search

@@ -4,7 +4,6 @@
       "id": "test-agent-1",
       "graph_id": "test-agent-1",
       "graph_version": 1,
-      "owner_user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
       "image_url": null,
       "creator_name": "Test Creator",
       "creator_image_url": "",
@@ -42,7 +41,6 @@
       "id": "test-agent-2",
       "graph_id": "test-agent-2",
       "graph_version": 1,
-      "owner_user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
       "image_url": null,
       "creator_name": "Test Creator",
       "creator_image_url": "",

autogpt_platform/backend/snapshots/sub_success

@@ -1,7 +1,6 @@
 {
   "submissions": [
     {
-      "listing_id": "test-listing-id",
       "agent_id": "test-agent-id",
       "agent_version": 1,
       "name": "Test Agent",

autogpt_platform/docker-compose.platform.yml

@@ -37,7 +37,7 @@ services:
       context: ../
       dockerfile: autogpt_platform/backend/Dockerfile
       target: migrate
-    command: ["sh", "-c", "poetry run prisma generate && poetry run gen-prisma-stub && poetry run prisma migrate deploy"]
+    command: ["sh", "-c", "poetry run prisma generate && poetry run prisma migrate deploy"]
     develop:
       watch:
         - path: ./

autogpt_platform/frontend/package.json

@@ -92,6 +92,7 @@
     "react-currency-input-field": "4.0.3",
     "react-day-picker": "9.11.1",
     "react-dom": "18.3.1",
+    "react-drag-drop-files": "2.4.0",
     "react-hook-form": "7.66.0",
     "react-icons": "5.5.0",
     "react-markdown": "9.0.3",

autogpt_platform/frontend/pnpm-lock.yaml

@@ -200,6 +200,9 @@ importers:
       react-dom:
         specifier: 18.3.1
         version: 18.3.1(react@18.3.1)
+      react-drag-drop-files:
+        specifier: 2.4.0
+        version: 2.4.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       react-hook-form:
         specifier: 7.66.0
         version: 7.66.0(react@18.3.1)
@@ -1001,6 +1004,9 @@ packages:
   '@emotion/memoize@0.8.1':
     resolution: {integrity: sha512-W2P2c/VRW1/1tLox0mVUalvnWXxavmv/Oum2aPsRcoDJuob75FC3Y8FbpfLwUegRcxINtGUMPq0tFCvYNTBXNA==}
 
+  '@emotion/unitless@0.8.1':
+    resolution: {integrity: sha512-KOEGMu6dmJZtpadb476IsZBclKvILjopjUii3V+7MnXIQCYh8W3NgNcgwo21n9LXZX6EDIKvqfjYxXebDwxKmQ==}
+
   '@epic-web/invariant@1.0.0':
     resolution: {integrity: sha512-lrTPqgvfFQtR/eY/qkIzp98OGdNJu0m5ji3q/nJI8v3SXkRKEnWiOxMmbvcSoAIzv/cGiuvRy57k4suKQSAdwA==}
 
@@ -3116,6 +3122,9 @@ packages:
   '@types/statuses@2.0.6':
     resolution: {integrity: sha512-xMAgYwceFhRA2zY+XbEA7mxYbA093wdiW8Vu6gZPGWy9cmOyU9XesH1tNcEWsKFd5Vzrqx5T3D38PWx1FIIXkA==}
 
+  '@types/stylis@4.2.7':
+    resolution: {integrity: sha512-VgDNokpBoKF+wrdvhAAfS55OMQpL6QRglwTwNC3kIgBrzZxA4WsFj+2eLfEA/uMUDzBcEhYmjSbwQakn/i3ajA==}
+
   '@types/tedious@4.0.14':
     resolution: {integrity: sha512-KHPsfX/FoVbUGbyYvk1q9MMQHLPeRZhRJZdO45Q4YjvFkv4hMNghCWTvy7rdKessBsmtz4euWCWAB6/tVpI1Iw==}
 
@@ -3772,6 +3781,9 @@ packages:
     resolution: {integrity: sha512-QOSvevhslijgYwRx6Rv7zKdMF8lbRmx+uQGx2+vDc+KI/eBnsy9kit5aj23AgGu3pa4t9AgwbnXWqS+iOY+2aA==}
     engines: {node: '>= 6'}
 
+  camelize@1.0.1:
+    resolution: {integrity: sha512-dU+Tx2fsypxTgtLoE36npi3UqcjSSMNYfkqgmoEhtZrraP5VWq0K7FkWVTYa8eMPtnU/G2txVsfdCJTn9uzpuQ==}
+
   caniuse-lite@1.0.30001762:
     resolution: {integrity: sha512-PxZwGNvH7Ak8WX5iXzoK1KPZttBXNPuaOvI2ZYU7NrlM+d9Ov+TUvlLOBNGzVXAntMSMMlJPd+jY6ovrVjSmUw==}
 
@@ -3985,6 +3997,10 @@ packages:
     resolution: {integrity: sha512-r4ESw/IlusD17lgQi1O20Fa3qNnsckR126TdUuBgAu7GBYSIPvdNyONd3Zrxh0xCwA4+6w/TDArBPsMvhur+KQ==}
     engines: {node: '>= 0.10'}
 
+  css-color-keywords@1.0.0:
+    resolution: {integrity: sha512-FyyrDHZKEjXDpNJYvVsV960FiqQyXc/LlYmsxl2BcdMb2WPx0OGRVgTg55rPSyLSNMqP52R9r8geSp7apN3Ofg==}
+    engines: {node: '>=4'}
+
   css-loader@6.11.0:
     resolution: {integrity: sha512-CTJ+AEQJjq5NzLga5pE39qdiSV56F8ywCIsqNIRF0r7BDgWsN25aazToqAFg7ZrtA/U016xudB3ffgweORxX7g==}
     engines: {node: '>= 12.13.0'}
@@ -4000,6 +4016,9 @@ packages:
   css-select@4.3.0:
     resolution: {integrity: sha512-wPpOYtnsVontu2mODhA19JrqWxNsfdatRKd64kmpRbQgh1KtItko5sTnEpPdpSaJszTOhEMlF/RPz28qj4HqhQ==}
 
+  css-to-react-native@3.2.0:
+    resolution: {integrity: sha512-e8RKaLXMOFii+02mOlqwjbD00KSEKqblnpO9e++1aXS1fPQOpS1YoqdVHBqPjHNoxeF2mimzVqawm2KCbEdtHQ==}
+
   css-what@6.2.2:
     resolution: {integrity: sha512-u/O3vwbptzhMs3L1fQE82ZSLHQQfto5gyZzwteVIEyeaY5Fc7R4dapF/BvRoSYFeqfBk4m0V1Vafq5Pjv25wvA==}
     engines: {node: '>= 6'}
@@ -6112,6 +6131,10 @@ packages:
     resolution: {integrity: sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==}
     engines: {node: ^10 || ^12 || >=14}
 
+  postcss@8.4.49:
+    resolution: {integrity: sha512-OCVPnIObs4N29kxTjzLfUryOkvZEq+pf8jTF0lg8E7uETuWHA+v7j3c/xJmiqpX450191LlmZfUKkXxkTry7nA==}
+    engines: {node: ^10 || ^12 || >=14}
+
   postcss@8.5.6:
     resolution: {integrity: sha512-3Ybi1tAuwAP9s0r1UQ2J4n5Y0G05bJkpUIO0/bI9MhwmD70S5aTWbXGBwxHrelT+XM1k6dM0pk+SwNkpTRN7Pg==}
     engines: {node: ^10 || ^12 || >=14}
@@ -6283,6 +6306,12 @@ packages:
     peerDependencies:
       react: ^18.3.1
 
+  react-drag-drop-files@2.4.0:
+    resolution: {integrity: sha512-MGPV3HVVnwXEXq3gQfLtSU3jz5j5jrabvGedokpiSEMoONrDHgYl/NpIOlfsqGQ4zBv1bzzv7qbKURZNOX32PA==}
+    peerDependencies:
+      react: ^18.0.0
+      react-dom: ^18.0.0
+
   react-hook-form@7.66.0:
     resolution: {integrity: sha512-xXBqsWGKrY46ZqaHDo+ZUYiMUgi8suYu5kdrS20EG8KiL7VRQitEbNjm+UcrDYrNi1YLyfpmAeGjCZYXLT9YBw==}
     engines: {node: '>=18.0.0'}
@@ -6649,6 +6678,9 @@ packages:
     engines: {node: '>= 0.10'}
     hasBin: true
 
+  shallowequal@1.1.0:
+    resolution: {integrity: sha512-y0m1JoUZSlPAjXVtPPW70aZWfIL/dSP7AFkRnniLCrK/8MDKog3TySTBmckD+RObVxH0v4Tox67+F14PdED2oQ==}
+
   sharp@0.34.5:
     resolution: {integrity: sha512-Ou9I5Ft9WNcCbXrU9cMgPBcCK8LiwLqcbywW3t4oDV37n1pzpuNLsYiAV8eODnjbtQlSDwZ2cUEeQz4E54Hltg==}
     engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0}
@@ -6862,6 +6894,13 @@ packages:
   style-to-object@1.0.14:
     resolution: {integrity: sha512-LIN7rULI0jBscWQYaSswptyderlarFkjQ+t79nzty8tcIAceVomEVlLzH5VP4Cmsv6MtKhs7qaAiwlcp+Mgaxw==}
 
+  styled-components@6.2.0:
+    resolution: {integrity: sha512-ryFCkETE++8jlrBmC+BoGPUN96ld1/Yp0s7t5bcXDobrs4XoXroY1tN+JbFi09hV6a5h3MzbcVi8/BGDP0eCgQ==}
+    engines: {node: '>= 16'}
+    peerDependencies:
+      react: '>= 16.8.0'
+      react-dom: '>= 16.8.0'
+
   styled-jsx@5.1.6:
     resolution: {integrity: sha512-qSVyDTeMotdvQYoHWLNGwRFJHC+i+ZvdBRYosOFgC+Wg1vx4frN2/RG/NA7SYqqvKNLf39P2LSRA2pu6n0XYZA==}
     engines: {node: '>= 12.0.0'}
@@ -6888,6 +6927,9 @@ packages:
       babel-plugin-macros:
         optional: true
 
+  stylis@4.3.6:
+    resolution: {integrity: sha512-yQ3rwFWRfwNUY7H5vpU0wfdkNSnvnJinhF9830Swlaxl03zsOjCfmX0ugac+3LtK0lYSgwL/KXc8oYL3mG4YFQ==}
+
   sucrase@3.35.1:
     resolution: {integrity: sha512-DhuTmvZWux4H1UOnWMB3sk0sbaCVOoQZjv8u1rDoTV0HTdGem9hkAZtl4JZy8P2z4Bg0nT+YMeOFyVr4zcG5Tw==}
     engines: {node: '>=16 || 14 >=14.17'}
@@ -7054,6 +7096,9 @@ packages:
   tslib@1.14.1:
     resolution: {integrity: sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==}
 
+  tslib@2.6.2:
+    resolution: {integrity: sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==}
+
   tslib@2.8.1:
     resolution: {integrity: sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==}
 
@@ -8290,10 +8335,10 @@ snapshots:
   '@emotion/is-prop-valid@1.2.2':
     dependencies:
       '@emotion/memoize': 0.8.1
-    optional: true
 
-  '@emotion/memoize@0.8.1':
-    optional: true
+  '@emotion/memoize@0.8.1': {}
+
+  '@emotion/unitless@0.8.1': {}
 
   '@epic-web/invariant@1.0.0': {}
 
@@ -10689,6 +10734,8 @@ snapshots:
 
   '@types/statuses@2.0.6': {}
 
+  '@types/stylis@4.2.7': {}
+
   '@types/tedious@4.0.14':
     dependencies:
       '@types/node': 24.10.0
@@ -11385,6 +11432,8 @@ snapshots:
 
   camelcase-css@2.0.1: {}
 
+  camelize@1.0.1: {}
+
   caniuse-lite@1.0.30001762: {}
 
   case-sensitive-paths-webpack-plugin@2.4.0: {}
@@ -11596,6 +11645,8 @@ snapshots:
       randombytes: 2.1.0
       randomfill: 1.0.4
 
+  css-color-keywords@1.0.0: {}
+
   css-loader@6.11.0(webpack@5.104.1(esbuild@0.25.12)):
     dependencies:
       icss-utils: 5.1.0(postcss@8.5.6)
@@ -11617,6 +11668,12 @@ snapshots:
       domutils: 2.8.0
       nth-check: 2.1.1
 
+  css-to-react-native@3.2.0:
+    dependencies:
+      camelize: 1.0.1
+      css-color-keywords: 1.0.0
+      postcss-value-parser: 4.2.0
+
   css-what@6.2.2: {}
 
   css.escape@1.5.1: {}
@@ -12070,8 +12127,8 @@ snapshots:
       '@typescript-eslint/parser': 8.52.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
       eslint-plugin-jsx-a11y: 6.10.2(eslint@8.57.1)
       eslint-plugin-react: 7.37.5(eslint@8.57.1)
       eslint-plugin-react-hooks: 5.2.0(eslint@8.57.1)
@@ -12090,7 +12147,7 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1):
+  eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1):
     dependencies:
       '@nolyfill/is-core-module': 1.0.39
       debug: 4.4.3
@@ -12101,22 +12158,22 @@ snapshots:
       tinyglobby: 0.2.15
       unrs-resolver: 1.11.1
     optionalDependencies:
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
+  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1):
     dependencies:
       debug: 3.2.7
     optionalDependencies:
       '@typescript-eslint/parser': 8.52.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
+      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
+  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1):
     dependencies:
       '@rtsao/scc': 1.1.0
       array-includes: 3.1.9
@@ -12127,7 +12184,7 @@ snapshots:
       doctrine: 2.1.0
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
       hasown: 2.0.2
       is-core-module: 2.16.1
       is-glob: 4.0.3
@@ -14202,6 +14259,12 @@ snapshots:
       picocolors: 1.1.1
       source-map-js: 1.2.1
 
+  postcss@8.4.49:
+    dependencies:
+      nanoid: 3.3.11
+      picocolors: 1.1.1
+      source-map-js: 1.2.1
+
   postcss@8.5.6:
     dependencies:
       nanoid: 3.3.11
@@ -14323,6 +14386,13 @@ snapshots:
       react: 18.3.1
       scheduler: 0.23.2
 
+  react-drag-drop-files@2.4.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
+    dependencies:
+      prop-types: 15.8.1
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+      styled-components: 6.2.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+
   react-hook-form@7.66.0(react@18.3.1):
     dependencies:
       react: 18.3.1
@@ -14816,6 +14886,8 @@ snapshots:
       safe-buffer: 5.2.1
       to-buffer: 1.2.2
 
+  shallowequal@1.1.0: {}
+
   sharp@0.34.5:
     dependencies:
       '@img/colour': 1.0.0
@@ -15106,6 +15178,20 @@ snapshots:
     dependencies:
       inline-style-parser: 0.2.7
 
+  styled-components@6.2.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
+    dependencies:
+      '@emotion/is-prop-valid': 1.2.2
+      '@emotion/unitless': 0.8.1
+      '@types/stylis': 4.2.7
+      css-to-react-native: 3.2.0
+      csstype: 3.2.3
+      postcss: 8.4.49
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+      shallowequal: 1.1.0
+      stylis: 4.3.6
+      tslib: 2.6.2
+
   styled-jsx@5.1.6(@babel/core@7.28.5)(react@18.3.1):
     dependencies:
       client-only: 0.0.1
@@ -15120,6 +15206,8 @@ snapshots:
     optionalDependencies:
       '@babel/core': 7.28.5
 
+  stylis@4.3.6: {}
+
   sucrase@3.35.1:
     dependencies:
       '@jridgewell/gen-mapping': 0.3.13
@@ -15302,6 +15390,8 @@ snapshots:
 
   tslib@1.14.1: {}
 
+  tslib@2.6.2: {}
+
   tslib@2.8.1: {}
 
   tty-browserify@0.0.1: {}

autogpt_platform/frontend/src/app/(platform)/admin/execution-analytics/components/AnalyticsResultsTable.tsx

@@ -51,6 +51,8 @@ export function AnalyticsResultsTable({ results }: Props) {
       "Execution ID",
       "Status",
       "Score",
+      "Started At",
+      "Ended At",
       "Summary Text",
       "Error Message",
     ];
@@ -62,6 +64,8 @@ export function AnalyticsResultsTable({ results }: Props) {
       result.exec_id,
       result.status,
       result.score?.toString() || "",
+      result.started_at ? new Date(result.started_at).toLocaleString() : "",
+      result.ended_at ? new Date(result.ended_at).toLocaleString() : "",
       `"${(result.summary_text || "").replace(/"/g, '""')}"`, // Escape quotes in summary
       `"${(result.error_message || "").replace(/"/g, '""')}"`, // Escape quotes in error
     ]);
@@ -248,15 +252,13 @@ export function AnalyticsResultsTable({ results }: Props) {
                         )}
                       </td>
                       <td className="px-4 py-3">
-                        {(result.summary_text || result.error_message) && (
-                          <Button
-                            variant="ghost"
-                            size="small"
-                            onClick={() => toggleRowExpansion(result.exec_id)}
-                          >
-                            <EyeIcon size={16} />
-                          </Button>
-                        )}
+                        <Button
+                          variant="ghost"
+                          size="small"
+                          onClick={() => toggleRowExpansion(result.exec_id)}
+                        >
+                          <EyeIcon size={16} />
+                        </Button>
                       </td>
                     </tr>
 
@@ -264,6 +266,44 @@ export function AnalyticsResultsTable({ results }: Props) {
                       <tr>
                         <td colSpan={7} className="bg-gray-50 px-4 py-3">
                           <div className="space-y-3">
+                            {/* Timestamps section */}
+                            <div className="grid grid-cols-2 gap-4 border-b border-gray-200 pb-3">
+                              <div>
+                                <Text
+                                  variant="body"
+                                  className="text-xs font-medium text-gray-600"
+                                >
+                                  Started At:
+                                </Text>
+                                <Text
+                                  variant="body"
+                                  className="text-sm text-gray-700"
+                                >
+                                  {result.started_at
+                                    ? new Date(
+                                        result.started_at,
+                                      ).toLocaleString()
+                                    : "—"}
+                                </Text>
+                              </div>
+                              <div>
+                                <Text
+                                  variant="body"
+                                  className="text-xs font-medium text-gray-600"
+                                >
+                                  Ended At:
+                                </Text>
+                                <Text
+                                  variant="body"
+                                  className="text-sm text-gray-700"
+                                >
+                                  {result.ended_at
+                                    ? new Date(result.ended_at).toLocaleString()
+                                    : "—"}
+                                </Text>
+                              </div>
+                            </div>
+
                             {result.summary_text && (
                               <div>
                                 <Text

autogpt_platform/frontend/src/app/(platform)/admin/execution-analytics/components/ExecutionAnalyticsForm.tsx

@@ -541,7 +541,19 @@ export function ExecutionAnalyticsForm() {
       {/* Accuracy Trends Display */}
       {trendsData && (
         <div className="space-y-4">
-          <h3 className="text-lg font-semibold">Execution Accuracy Trends</h3>
+          <div className="flex items-start justify-between">
+            <h3 className="text-lg font-semibold">Execution Accuracy Trends</h3>
+            <div className="rounded-md bg-blue-50 px-3 py-2 text-xs text-blue-700">
+              <p className="font-medium">
+                Chart Filters (matches monitoring system):
+              </p>
+              <ul className="mt-1 list-inside list-disc space-y-1">
+                <li>Only days with ≥1 execution with correctness score</li>
+                <li>Last 30 days</li>
+                <li>Averages calculated from scored executions only</li>
+              </ul>
+            </div>
+          </div>
 
           {/* Alert Section */}
           {trendsData.alert && (

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/RunInputDialog.tsx

@@ -66,7 +66,6 @@ export const RunInputDialog = ({
                     formContext={{
                       showHandles: false,
                       size: "large",
-                      showOptionalToggle: false,
                     }}
                   />
                 </div>

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/useRunInputDialog.ts

@@ -66,7 +66,7 @@ export const useRunInputDialog = ({
         if (isCredentialFieldSchema(fieldSchema)) {
           dynamicUiSchema[fieldName] = {
             ...dynamicUiSchema[fieldName],
-            "ui:field": "custom/credential_field",
+            "ui:field": "credentials",
           };
         }
       });
@@ -76,18 +76,12 @@ export const useRunInputDialog = ({
   }, [credentialsSchema]);
 
   const handleManualRun = async () => {
-    // Filter out incomplete credentials (those without a valid id)
-    // RJSF auto-populates const values (provider, type) but not id field
-    const validCredentials = Object.fromEntries(
-      Object.entries(credentialValues).filter(([_, cred]) => cred && cred.id),
-    );
-
     await executeGraph({
       graphId: flowID ?? "",
       graphVersion: flowVersion || null,
       data: {
         inputs: inputValues,
-        credentials_inputs: validCredentials,
+        credentials_inputs: credentialValues,
         source: "builder",
       },
     });

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/Flow.tsx

@@ -97,9 +97,6 @@ export const Flow = () => {
           onConnect={onConnect}
           onEdgesChange={onEdgesChange}
           onNodeDragStop={onNodeDragStop}
-          onNodeContextMenu={(event) => {
-            event.preventDefault();
-          }}
           maxZoom={2}
           minZoom={0.1}
           onDragOver={onDragOver}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/CustomNode.tsx

@@ -1,25 +1,24 @@
-import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
-import { BlockCost } from "@/app/api/__generated__/models/blockCost";
-import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
-import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
-import { NodeModelMetadata } from "@/app/api/__generated__/models/nodeModelMetadata";
-import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
-import { cn } from "@/lib/utils";
-import { RJSFSchema } from "@rjsf/utils";
-import { NodeProps, Node as XYNode } from "@xyflow/react";
 import React from "react";
+import { Node as XYNode, NodeProps } from "@xyflow/react";
+import { RJSFSchema } from "@rjsf/utils";
 import { BlockUIType } from "../../../types";
-import { FormCreator } from "../FormCreator";
-import { OutputHandler } from "../OutputHandler";
-import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
-import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
-import { NodeContainer } from "./components/NodeContainer";
-import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
-import { NodeHeader } from "./components/NodeHeader";
-import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
-import { NodeRightClickMenu } from "./components/NodeRightClickMenu";
 import { StickyNoteBlock } from "./components/StickyNoteBlock";
+import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
+import { BlockCost } from "@/app/api/__generated__/models/blockCost";
+import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
+import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
+import { NodeContainer } from "./components/NodeContainer";
+import { NodeHeader } from "./components/NodeHeader";
+import { FormCreator } from "../FormCreator";
+import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
+import { OutputHandler } from "../OutputHandler";
+import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
+import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
+import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
+import { cn } from "@/lib/utils";
 import { WebhookDisclaimer } from "./components/WebhookDisclaimer";
+import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
+import { NodeModelMetadata } from "@/app/api/__generated__/models/nodeModelMetadata";
 
 export type CustomNodeData = {
   hardcodedValues: {
@@ -89,7 +88,7 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
 
     // Currently all blockTypes design are similar - that's why i am using the same component for all of them
     // If in future - if we need some drastic change in some blockTypes design - we can create separate components for them
-    const node = (
+    return (
       <NodeContainer selected={selected} nodeId={nodeId} hasErrors={hasErrors}>
         <div className="rounded-xlarge bg-white">
           <NodeHeader data={data} nodeId={nodeId} />
@@ -118,15 +117,6 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
         <NodeExecutionBadge nodeId={nodeId} />
       </NodeContainer>
     );
-
-    return (
-      <NodeRightClickMenu
-        nodeId={nodeId}
-        subGraphID={data.hardcodedValues?.graph_id}
-      >
-        {node}
-      </NodeRightClickMenu>
-    );
   },
 );
 

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeContextMenu.tsx

@@ -1,31 +1,26 @@
-import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { Separator } from "@/components/__legacy__/ui/separator";
 import {
   DropdownMenu,
+  DropdownMenuContent,
+  DropdownMenuItem,
   DropdownMenuTrigger,
 } from "@/components/molecules/DropdownMenu/DropdownMenu";
-import {
-  SecondaryDropdownMenuContent,
-  SecondaryDropdownMenuItem,
-  SecondaryDropdownMenuSeparator,
-} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
-import {
-  ArrowSquareOutIcon,
-  CopyIcon,
-  DotsThreeOutlineVerticalIcon,
-  TrashIcon,
-} from "@phosphor-icons/react";
+import { DotsThreeOutlineVerticalIcon } from "@phosphor-icons/react";
+import { Copy, Trash2, ExternalLink } from "lucide-react";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
 import { useReactFlow } from "@xyflow/react";
 
-type Props = {
+export const NodeContextMenu = ({
+  nodeId,
+  subGraphID,
+}: {
   nodeId: string;
   subGraphID?: string;
-};
-
-export const NodeContextMenu = ({ nodeId, subGraphID }: Props) => {
+}) => {
   const { deleteElements } = useReactFlow();
 
-  function handleCopy() {
+  const handleCopy = () => {
     useNodeStore.setState((state) => ({
       nodes: state.nodes.map((node) => ({
         ...node,
@@ -35,47 +30,47 @@ export const NodeContextMenu = ({ nodeId, subGraphID }: Props) => {
 
     useCopyPasteStore.getState().copySelectedNodes();
     useCopyPasteStore.getState().pasteNodes();
-  }
+  };
 
-  function handleDelete() {
+  const handleDelete = () => {
     deleteElements({ nodes: [{ id: nodeId }] });
-  }
+  };
 
   return (
     <DropdownMenu>
       <DropdownMenuTrigger className="py-2">
         <DotsThreeOutlineVerticalIcon size={16} weight="fill" />
       </DropdownMenuTrigger>
-      <SecondaryDropdownMenuContent side="right" align="start">
-        <SecondaryDropdownMenuItem onClick={handleCopy}>
-          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
-          <span className="dark:text-gray-100">Copy</span>
-        </SecondaryDropdownMenuItem>
-        <SecondaryDropdownMenuSeparator />
+      <DropdownMenuContent
+        side="right"
+        align="start"
+        className="rounded-xlarge"
+      >
+        <DropdownMenuItem onClick={handleCopy} className="hover:rounded-xlarge">
+          <Copy className="mr-2 h-4 w-4" />
+          Copy Node
+        </DropdownMenuItem>
 
         {subGraphID && (
-          <>
-            <SecondaryDropdownMenuItem
-              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
-            >
-              <ArrowSquareOutIcon
-                size={20}
-                className="mr-2 dark:text-gray-100"
-              />
-              <span className="dark:text-gray-100">Open agent</span>
-            </SecondaryDropdownMenuItem>
-            <SecondaryDropdownMenuSeparator />
-          </>
+          <DropdownMenuItem
+            onClick={() => window.open(`/build?flowID=${subGraphID}`)}
+            className="hover:rounded-xlarge"
+          >
+            <ExternalLink className="mr-2 h-4 w-4" />
+            Open Agent
+          </DropdownMenuItem>
         )}
 
-        <SecondaryDropdownMenuItem variant="destructive" onClick={handleDelete}>
-          <TrashIcon
-            size={20}
-            className="mr-2 text-red-500 dark:text-red-400"
-          />
-          <span className="dark:text-red-400">Delete</span>
-        </SecondaryDropdownMenuItem>
-      </SecondaryDropdownMenuContent>
+        <Separator className="my-2" />
+
+        <DropdownMenuItem
+          onClick={handleDelete}
+          className="text-red-600 hover:rounded-xlarge"
+        >
+          <Trash2 className="mr-2 h-4 w-4" />
+          Delete
+        </DropdownMenuItem>
+      </DropdownMenuContent>
     </DropdownMenu>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeHeader.tsx

@@ -1,24 +1,25 @@
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { Text } from "@/components/atoms/Text/Text";
+import { beautifyString, cn } from "@/lib/utils";
+import { NodeCost } from "./NodeCost";
+import { NodeBadges } from "./NodeBadges";
+import { NodeContextMenu } from "./NodeContextMenu";
+import { CustomNodeData } from "../CustomNode";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { useState } from "react";
 import {
   Tooltip,
   TooltipContent,
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
-import { beautifyString, cn } from "@/lib/utils";
-import { useState } from "react";
-import { CustomNodeData } from "../CustomNode";
-import { NodeBadges } from "./NodeBadges";
-import { NodeContextMenu } from "./NodeContextMenu";
-import { NodeCost } from "./NodeCost";
 
-type Props = {
+export const NodeHeader = ({
+  data,
+  nodeId,
+}: {
   data: CustomNodeData;
   nodeId: string;
-};
-
-export const NodeHeader = ({ data, nodeId }: Props) => {
+}) => {
   const updateNodeData = useNodeStore((state) => state.updateNodeData);
   const title = (data.metadata?.customized_name as string) || data.title;
   const [isEditingTitle, setIsEditingTitle] = useState(false);
@@ -68,10 +69,7 @@ export const NodeHeader = ({ data, nodeId }: Props) => {
                 <Tooltip>
                   <TooltipTrigger asChild>
                     <div>
-                      <Text
-                        variant="large-semibold"
-                        className="line-clamp-1 hover:cursor-text"
-                      >
+                      <Text variant="large-semibold" className="line-clamp-1">
                         {beautifyString(title).replace("Block", "").trim()}
                       </Text>
                     </div>

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeOutput/components/NodeDataViewer/NodeDataViewer.tsx

@@ -151,7 +151,7 @@ export const NodeDataViewer: FC<NodeDataViewerProps> = ({
         </div>
 
         <div className="flex justify-end pt-4">
-          {outputItems.length > 1 && (
+          {outputItems.length > 0 && (
             <OutputActions
               items={outputItems.map((item) => ({
                 value: item.value,

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeRightClickMenu.tsx

@@ -1,104 +0,0 @@
-import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import {
-  SecondaryMenuContent,
-  SecondaryMenuItem,
-  SecondaryMenuSeparator,
-} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
-import { ArrowSquareOutIcon, CopyIcon, TrashIcon } from "@phosphor-icons/react";
-import * as ContextMenu from "@radix-ui/react-context-menu";
-import { useReactFlow } from "@xyflow/react";
-import { useEffect, useRef } from "react";
-import { CustomNode } from "../CustomNode";
-
-type Props = {
-  nodeId: string;
-  subGraphID?: string;
-  children: React.ReactNode;
-};
-
-const DOUBLE_CLICK_TIMEOUT = 300;
-
-export function NodeRightClickMenu({ nodeId, subGraphID, children }: Props) {
-  const { deleteElements } = useReactFlow<CustomNode>();
-  const lastRightClickTime = useRef<number>(0);
-  const containerRef = useRef<HTMLDivElement>(null);
-
-  function copyNode() {
-    useNodeStore.setState((state) => ({
-      nodes: state.nodes.map((node) => ({
-        ...node,
-        selected: node.id === nodeId,
-      })),
-    }));
-
-    useCopyPasteStore.getState().copySelectedNodes();
-    useCopyPasteStore.getState().pasteNodes();
-  }
-
-  function deleteNode() {
-    deleteElements({ nodes: [{ id: nodeId }] });
-  }
-
-  useEffect(() => {
-    const container = containerRef.current;
-    if (!container) return;
-
-    function handleContextMenu(e: MouseEvent) {
-      const now = Date.now();
-      const timeSinceLastClick = now - lastRightClickTime.current;
-
-      if (timeSinceLastClick < DOUBLE_CLICK_TIMEOUT) {
-        e.stopImmediatePropagation();
-        lastRightClickTime.current = 0;
-        return;
-      }
-
-      lastRightClickTime.current = now;
-    }
-
-    container.addEventListener("contextmenu", handleContextMenu, true);
-
-    return () => {
-      container.removeEventListener("contextmenu", handleContextMenu, true);
-    };
-  }, []);
-
-  return (
-    <ContextMenu.Root>
-      <ContextMenu.Trigger asChild>
-        <div ref={containerRef}>{children}</div>
-      </ContextMenu.Trigger>
-      <SecondaryMenuContent>
-        <SecondaryMenuItem onSelect={copyNode}>
-          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
-          <span className="dark:text-gray-100">Copy</span>
-        </SecondaryMenuItem>
-        <SecondaryMenuSeparator />
-
-        {subGraphID && (
-          <>
-            <SecondaryMenuItem
-              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
-            >
-              <ArrowSquareOutIcon
-                size={20}
-                className="mr-2 dark:text-gray-100"
-              />
-              <span className="dark:text-gray-100">Open agent</span>
-            </SecondaryMenuItem>
-            <SecondaryMenuSeparator />
-          </>
-        )}
-
-        <SecondaryMenuItem variant="destructive" onSelect={deleteNode}>
-          <TrashIcon
-            size={20}
-            className="mr-2 text-red-500 dark:text-red-400"
-          />
-          <span className="dark:text-red-400">Delete</span>
-        </SecondaryMenuItem>
-      </SecondaryMenuContent>
-    </ContextMenu.Root>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/helpers.ts

@@ -89,18 +89,6 @@ export function extractOptions(
 
 // get display type and color for schema types [need for type display next to field name]
 export const getTypeDisplayInfo = (schema: any) => {
-  if (
-    schema?.type === "array" &&
-    "format" in schema &&
-    schema.format === "table"
-  ) {
-    return {
-      displayType: "table",
-      colorClass: "!text-indigo-500",
-      hexColor: "#6366f1",
-    };
-  }
-
   if (schema?.type === "string" && schema?.format) {
     const formatMap: Record<
       string,

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/uiSchema.ts

@@ -1,6 +1,6 @@
 export const uiSchema = {
   credentials: {
-    "ui:field": "custom/credential_field",
+    "ui:field": "credentials",
     provider: { "ui:widget": "hidden" },
     type: { "ui:widget": "hidden" },
     id: { "ui:autofocus": true },

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/BlockMenuFilters.tsx

@@ -1,57 +0,0 @@
-import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
-import { FilterChip } from "../FilterChip";
-import { categories } from "./constants";
-import { FilterSheet } from "../FilterSheet/FilterSheet";
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-
-export const BlockMenuFilters = () => {
-  const {
-    filters,
-    addFilter,
-    removeFilter,
-    categoryCounts,
-    creators,
-    addCreator,
-    removeCreator,
-  } = useBlockMenuStore();
-
-  const handleFilterClick = (filter: GetV2BuilderSearchFilterAnyOfItem) => {
-    if (filters.includes(filter)) {
-      removeFilter(filter);
-    } else {
-      addFilter(filter);
-    }
-  };
-
-  const handleCreatorClick = (creator: string) => {
-    if (creators.includes(creator)) {
-      removeCreator(creator);
-    } else {
-      addCreator(creator);
-    }
-  };
-
-  return (
-    <div className="flex flex-wrap gap-2">
-      <FilterSheet categories={categories} />
-      {creators.length > 0 &&
-        creators.map((creator) => (
-          <FilterChip
-            key={creator}
-            name={"Created by " + creator.slice(0, 10) + "..."}
-            selected={creators.includes(creator)}
-            onClick={() => handleCreatorClick(creator)}
-          />
-        ))}
-      {categories.map((category) => (
-        <FilterChip
-          key={category.key}
-          name={category.name}
-          selected={filters.includes(category.key)}
-          onClick={() => handleFilterClick(category.key)}
-          number={categoryCounts[category.key] ?? 0}
-        />
-      ))}
-    </div>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/constants.ts

@@ -1,15 +0,0 @@
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-import { CategoryKey } from "./types";
-
-export const categories: Array<{ key: CategoryKey; name: string }> = [
-  { key: GetV2BuilderSearchFilterAnyOfItem.blocks, name: "Blocks" },
-  {
-    key: GetV2BuilderSearchFilterAnyOfItem.integrations,
-    name: "Integrations",
-  },
-  {
-    key: GetV2BuilderSearchFilterAnyOfItem.marketplace_agents,
-    name: "Marketplace agents",
-  },
-  { key: GetV2BuilderSearchFilterAnyOfItem.my_agents, name: "My agents" },
-];

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/types.ts

@@ -1,26 +0,0 @@
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-
-export type DefaultStateType =
-  | "suggestion"
-  | "all_blocks"
-  | "input_blocks"
-  | "action_blocks"
-  | "output_blocks"
-  | "integrations"
-  | "marketplace_agents"
-  | "my_agents";
-
-export type CategoryKey = GetV2BuilderSearchFilterAnyOfItem;
-
-export interface Filters {
-  categories: {
-    blocks: boolean;
-    integrations: boolean;
-    marketplace_agents: boolean;
-    my_agents: boolean;
-    providers: boolean;
-  };
-  createdBy: string[];
-}
-
-export type CategoryCounts = Record<CategoryKey, number>;

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearch/BlockMenuSearch.tsx

@@ -1,14 +1,111 @@
 import { Text } from "@/components/atoms/Text/Text";
+import { useBlockMenuSearch } from "./useBlockMenuSearch";
+import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
+import { LoadingSpinner } from "@/components/__legacy__/ui/loading";
+import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
+import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
+import { Block } from "../Block";
+import { UGCAgentBlock } from "../UGCAgentBlock";
+import { getSearchItemType } from "./helper";
+import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { blockMenuContainerStyle } from "../style";
-import { BlockMenuFilters } from "../BlockMenuFilters/BlockMenuFilters";
-import { BlockMenuSearchContent } from "../BlockMenuSearchContent/BlockMenuSearchContent";
+import { cn } from "@/lib/utils";
+import { NoSearchResult } from "../NoSearchResult";
 
 export const BlockMenuSearch = () => {
+  const {
+    searchResults,
+    isFetchingNextPage,
+    fetchNextPage,
+    hasNextPage,
+    searchLoading,
+    handleAddLibraryAgent,
+    handleAddMarketplaceAgent,
+    addingLibraryAgentId,
+    addingMarketplaceAgentSlug,
+  } = useBlockMenuSearch();
+  const { searchQuery } = useBlockMenuStore();
+
+  if (searchLoading) {
+    return (
+      <div
+        className={cn(
+          blockMenuContainerStyle,
+          "flex items-center justify-center",
+        )}
+      >
+        <LoadingSpinner className="size-13" />
+      </div>
+    );
+  }
+
+  if (searchResults.length === 0) {
+    return <NoSearchResult />;
+  }
+
   return (
     <div className={blockMenuContainerStyle}>
-      <BlockMenuFilters />
       <Text variant="body-medium">Search results</Text>
-      <BlockMenuSearchContent />
+      <InfiniteScroll
+        isFetchingNextPage={isFetchingNextPage}
+        fetchNextPage={fetchNextPage}
+        hasNextPage={hasNextPage}
+        loader={<LoadingSpinner className="size-13" />}
+        className="space-y-2.5"
+      >
+        {searchResults.map((item: SearchResponseItemsItem, index: number) => {
+          const { type, data } = getSearchItemType(item);
+          // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
+          switch (type) {
+            case "store_agent":
+              return (
+                <MarketplaceAgentBlock
+                  key={index}
+                  slug={data.slug}
+                  highlightedText={searchQuery}
+                  title={data.agent_name}
+                  image_url={data.agent_image}
+                  creator_name={data.creator}
+                  number_of_runs={data.runs}
+                  loading={addingMarketplaceAgentSlug === data.slug}
+                  onClick={() =>
+                    handleAddMarketplaceAgent({
+                      creator_name: data.creator,
+                      slug: data.slug,
+                    })
+                  }
+                />
+              );
+            case "block":
+              return (
+                <Block
+                  key={index}
+                  title={data.name}
+                  highlightedText={searchQuery}
+                  description={data.description}
+                  blockData={data}
+                />
+              );
+
+            case "library_agent":
+              return (
+                <UGCAgentBlock
+                  key={index}
+                  title={data.name}
+                  highlightedText={searchQuery}
+                  image_url={data.image_url}
+                  version={data.graph_version}
+                  edited_time={data.updated_at}
+                  isLoading={addingLibraryAgentId === data.id}
+                  onClick={() => handleAddLibraryAgent(data)}
+                />
+              );
+
+            default:
+              return null;
+          }
+        })}
+      </InfiniteScroll>
     </div>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearch/useBlockMenuSearch.ts

@@ -23,19 +23,9 @@ import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { getQueryClient } from "@/lib/react-query/queryClient";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import * as Sentry from "@sentry/nextjs";
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-
-export const useBlockMenuSearchContent = () => {
-  const {
-    searchQuery,
-    searchId,
-    setSearchId,
-    filters,
-    setCreatorsList,
-    creators,
-    setCategoryCounts,
-  } = useBlockMenuStore();
 
+export const useBlockMenuSearch = () => {
+  const { searchQuery, searchId, setSearchId } = useBlockMenuStore();
   const { toast } = useToast();
   const { addAgentToBuilder, addLibraryAgentToBuilder } =
     useAddAgentToBuilder();
@@ -67,8 +57,6 @@ export const useBlockMenuSearchContent = () => {
       page_size: 8,
       search_query: searchQuery,
       search_id: searchId,
-      filter: filters.length > 0 ? filters : undefined,
-      by_creator: creators.length > 0 ? creators : undefined,
     },
     {
       query: { getNextPageParam: getPaginationNextPageNumber },
@@ -110,26 +98,6 @@ export const useBlockMenuSearchContent = () => {
     }
   }, [searchQueryData, searchId, setSearchId]);
 
-  // from all the results, we need to get all the unique creators
-  useEffect(() => {
-    if (!searchQueryData?.pages?.length) {
-      return;
-    }
-    const latestData = okData(searchQueryData.pages.at(-1));
-    setCategoryCounts(
-      (latestData?.total_items as Record<
-        GetV2BuilderSearchFilterAnyOfItem,
-        number
-      >) || {
-        blocks: 0,
-        integrations: 0,
-        marketplace_agents: 0,
-        my_agents: 0,
-      },
-    );
-    setCreatorsList(latestData?.items || []);
-  }, [searchQueryData]);
-
   useEffect(() => {
     if (searchId && !searchQuery) {
       resetSearchSession();

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/BlockMenuSearchContent.tsx

@@ -1,108 +0,0 @@
-import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
-import { getSearchItemType } from "./helper";
-import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
-import { Block } from "../Block";
-import { UGCAgentBlock } from "../UGCAgentBlock";
-import { useBlockMenuSearchContent } from "./useBlockMenuSearchContent";
-import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
-import { cn } from "@/lib/utils";
-import { blockMenuContainerStyle } from "../style";
-import { NoSearchResult } from "../NoSearchResult";
-
-export const BlockMenuSearchContent = () => {
-  const {
-    searchResults,
-    isFetchingNextPage,
-    fetchNextPage,
-    hasNextPage,
-    searchLoading,
-    handleAddLibraryAgent,
-    handleAddMarketplaceAgent,
-    addingLibraryAgentId,
-    addingMarketplaceAgentSlug,
-  } = useBlockMenuSearchContent();
-
-  const { searchQuery } = useBlockMenuStore();
-
-  if (searchLoading) {
-    return (
-      <div
-        className={cn(
-          blockMenuContainerStyle,
-          "flex items-center justify-center",
-        )}
-      >
-        <LoadingSpinner className="size-13" />
-      </div>
-    );
-  }
-
-  if (searchResults.length === 0) {
-    return <NoSearchResult />;
-  }
-
-  return (
-    <InfiniteScroll
-      isFetchingNextPage={isFetchingNextPage}
-      fetchNextPage={fetchNextPage}
-      hasNextPage={hasNextPage}
-      loader={<LoadingSpinner className="size-13" />}
-      className="space-y-2.5"
-    >
-      {searchResults.map((item: SearchResponseItemsItem, index: number) => {
-        const { type, data } = getSearchItemType(item);
-        // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
-        switch (type) {
-          case "store_agent":
-            return (
-              <MarketplaceAgentBlock
-                key={index}
-                slug={data.slug}
-                highlightedText={searchQuery}
-                title={data.agent_name}
-                image_url={data.agent_image}
-                creator_name={data.creator}
-                number_of_runs={data.runs}
-                loading={addingMarketplaceAgentSlug === data.slug}
-                onClick={() =>
-                  handleAddMarketplaceAgent({
-                    creator_name: data.creator,
-                    slug: data.slug,
-                  })
-                }
-              />
-            );
-          case "block":
-            return (
-              <Block
-                key={index}
-                title={data.name}
-                highlightedText={searchQuery}
-                description={data.description}
-                blockData={data}
-              />
-            );
-
-          case "library_agent":
-            return (
-              <UGCAgentBlock
-                key={index}
-                title={data.name}
-                highlightedText={searchQuery}
-                image_url={data.image_url}
-                version={data.graph_version}
-                edited_time={data.updated_at}
-                isLoading={addingLibraryAgentId === data.id}
-                onClick={() => handleAddLibraryAgent(data)}
-              />
-            );
-
-          default:
-            return null;
-        }
-      })}
-    </InfiniteScroll>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterChip.tsx

@@ -1,9 +1,7 @@
 import { Button } from "@/components/__legacy__/ui/button";
 import { cn } from "@/lib/utils";
-import { XIcon } from "@phosphor-icons/react";
-import { AnimatePresence, motion } from "framer-motion";
-
-import React, { ButtonHTMLAttributes, useState } from "react";
+import { X } from "lucide-react";
+import React, { ButtonHTMLAttributes } from "react";
 
 interface Props extends ButtonHTMLAttributes<HTMLButtonElement> {
   selected?: boolean;
@@ -18,51 +16,39 @@ export const FilterChip: React.FC<Props> = ({
   className,
   ...rest
 }) => {
-  const [isHovered, setIsHovered] = useState(false);
   return (
-    <AnimatePresence mode="wait">
-      <Button
-        onMouseEnter={() => setIsHovered(true)}
-        onMouseLeave={() => setIsHovered(false)}
+    <Button
+      className={cn(
+        "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none transition-transform duration-300 ease-in-out",
+        "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
+        selected && "border-0 bg-violet-700 hover:border",
+        className,
+      )}
+      {...rest}
+    >
+      <span
         className={cn(
-          "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none",
-          "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
-          selected && "border-0 bg-violet-700 hover:border",
-          className,
+          "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
+          selected && "text-zinc-50",
         )}
-        {...rest}
       >
-        <span
-          className={cn(
-            "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
-            selected && "text-zinc-50",
+        {name}
+      </span>
+      {selected && (
+        <>
+          <span className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50 transition-all duration-300 ease-in-out group-hover:hidden">
+            <X
+              className="h-3 w-3 rounded-full text-violet-700"
+              strokeWidth={2}
+            />
+          </span>
+          {number !== undefined && (
+            <span className="hidden h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50 transition-all duration-300 ease-in-out animate-in fade-in zoom-in group-hover:flex">
+              {number > 100 ? "100+" : number}
+            </span>
           )}
-        >
-          {name}
-        </span>
-        {selected && !isHovered && (
-          <motion.span
-            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
-            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
-            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
-            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
-            className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50"
-          >
-            <XIcon size={12} weight="bold" className="text-violet-700" />
-          </motion.span>
-        )}
-        {number !== undefined && isHovered && (
-          <motion.span
-            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
-            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
-            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
-            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
-            className="flex h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50"
-          >
-            {number > 100 ? "100+" : number}
-          </motion.span>
-        )}
-      </Button>
-    </AnimatePresence>
+        </>
+      )}
+    </Button>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/FilterSheet.tsx

@@ -1,156 +0,0 @@
-import { FilterChip } from "../FilterChip";
-import { cn } from "@/lib/utils";
-import { CategoryKey } from "../BlockMenuFilters/types";
-import { AnimatePresence, motion } from "framer-motion";
-import { XIcon } from "@phosphor-icons/react";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { Separator } from "@/components/__legacy__/ui/separator";
-import { Checkbox } from "@/components/__legacy__/ui/checkbox";
-import { useFilterSheet } from "./useFilterSheet";
-import { INITIAL_CREATORS_TO_SHOW } from "./constant";
-
-export function FilterSheet({
-  categories,
-}: {
-  categories: Array<{ key: CategoryKey; name: string }>;
-}) {
-  const {
-    isOpen,
-    localCategories,
-    localCreators,
-    displayedCreatorsCount,
-    handleLocalCategoryChange,
-    handleToggleShowMoreCreators,
-    handleLocalCreatorChange,
-    handleClearFilters,
-    handleCloseButton,
-    handleApplyFilters,
-    hasLocalActiveFilters,
-    visibleCreators,
-    creators,
-    handleOpenFilters,
-    hasActiveFilters,
-  } = useFilterSheet();
-
-  return (
-    <div className="m-0 inline w-fit p-0">
-      <FilterChip
-        name={hasActiveFilters() ? "Edit filters" : "All filters"}
-        onClick={handleOpenFilters}
-      />
-
-      <AnimatePresence>
-        {isOpen && (
-          <motion.div
-            className={cn(
-              "absolute bottom-2 left-2 top-2 z-20 w-3/4 max-w-[22.5rem] space-y-4 overflow-hidden rounded-[0.75rem] bg-white pb-4 shadow-[0_4px_12px_2px_rgba(0,0,0,0.1)]",
-            )}
-            initial={{ x: "-100%", filter: "blur(10px)" }}
-            animate={{ x: 0, filter: "blur(0px)" }}
-            exit={{ x: "-110%", filter: "blur(10px)" }}
-            transition={{ duration: 0.4, type: "spring", bounce: 0.2 }}
-          >
-            {/* Top section */}
-            <div className="flex items-center justify-between px-5 pt-4">
-              <Text variant="body">Filters</Text>
-              <Button
-                className="p-0"
-                variant="ghost"
-                size="icon"
-                onClick={handleCloseButton}
-              >
-                <XIcon size={20} />
-              </Button>
-            </div>
-
-            <Separator className="h-[1px] w-full text-zinc-300" />
-
-            {/* Category section */}
-            <div className="space-y-4 px-5">
-              <Text variant="large">Categories</Text>
-              <div className="space-y-2">
-                {categories.map((category) => (
-                  <div
-                    key={category.key}
-                    className="flex items-center space-x-2"
-                  >
-                    <Checkbox
-                      id={category.key}
-                      checked={localCategories.includes(category.key)}
-                      onCheckedChange={() =>
-                        handleLocalCategoryChange(category.key)
-                      }
-                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
-                    />
-                    <label
-                      htmlFor={category.key}
-                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
-                    >
-                      {category.name}
-                    </label>
-                  </div>
-                ))}
-              </div>
-            </div>
-
-            {/* Created by section */}
-            <div className="space-y-4 px-5">
-              <p className="font-sans text-base font-medium text-zinc-800">
-                Created by
-              </p>
-              <div className="space-y-2">
-                {visibleCreators.map((creator, i) => (
-                  <div key={i} className="flex items-center space-x-2">
-                    <Checkbox
-                      id={`creator-${creator}`}
-                      checked={localCreators.includes(creator)}
-                      onCheckedChange={() => handleLocalCreatorChange(creator)}
-                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
-                    />
-                    <label
-                      htmlFor={`creator-${creator}`}
-                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
-                    >
-                      {creator}
-                    </label>
-                  </div>
-                ))}
-              </div>
-              {creators.length > INITIAL_CREATORS_TO_SHOW && (
-                <Button
-                  variant={"link"}
-                  className="m-0 p-0 font-sans text-sm font-medium leading-[1.375rem] text-zinc-800 underline hover:text-zinc-600"
-                  onClick={handleToggleShowMoreCreators}
-                >
-                  {displayedCreatorsCount < creators.length ? "More" : "Less"}
-                </Button>
-              )}
-            </div>
-
-            {/* Footer section */}
-            <div className="fixed bottom-0 flex w-full justify-between gap-3 border-t border-zinc-200 bg-white px-5 py-3">
-              <Button
-                size="small"
-                variant={"outline"}
-                onClick={handleClearFilters}
-                className="rounded-[8px] px-2 py-1.5"
-              >
-                Clear
-              </Button>
-
-              <Button
-                size="small"
-                onClick={handleApplyFilters}
-                disabled={!hasLocalActiveFilters()}
-                className="rounded-[8px] px-2 py-1.5"
-              >
-                Apply filters
-              </Button>
-            </div>
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </div>
-  );
-}