added ability to disable cors at the backend

.github/workflows/platform-autgpt-deploy-prod.yml

@@ -3,7 +3,6 @@ name: AutoGPT Platform - Deploy Prod Environment
 on:
   release:
     types: [published]
-  workflow_dispatch:
 
 permissions:
   contents: 'read'
@@ -18,8 +17,6 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-        with:
-          ref: ${{ github.ref_name || 'master' }}
 
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -39,7 +36,7 @@ jobs:
           DATABASE_URL: ${{ secrets.BACKEND_DATABASE_URL }}
           DIRECT_URL: ${{ secrets.BACKEND_DATABASE_URL }}
 
-
+          
   trigger:
     needs: migrate
     runs-on: ubuntu-latest
@@ -50,5 +47,4 @@ jobs:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
           event-type: build_deploy_prod
-          client-payload: |
-            {"ref": "${{ github.ref_name || 'master' }}", "repository": "${{ github.repository }}"}
+          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

.github/workflows/platform-autogpt-deploy-dev.yaml

@@ -5,13 +5,6 @@ on:
     branches: [ dev ]
     paths:
       - 'autogpt_platform/**'
-  workflow_dispatch:
-    inputs:
-      git_ref:
-        description: 'Git ref (branch/tag) of AutoGPT to deploy'
-        required: true
-        default: 'master'
-        type: string
 
 permissions:
   contents: 'read'
@@ -26,8 +19,6 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.git_ref || github.ref_name }}
 
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -57,4 +48,4 @@ jobs:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
           event-type: build_deploy_dev
-          client-payload: '{"ref": "${{ github.event.inputs.git_ref || github.ref }}", "repository": "${{ github.repository }}"}'
+          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

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

@@ -37,7 +37,9 @@ jobs:
 
     services:
       redis:
-        image: redis:latest
+        image: bitnami/redis:6.2
+        env:
+          REDIS_PASSWORD: testpassword
         ports:
           - 6379:6379
       rabbitmq:
@@ -202,6 +204,7 @@ jobs:
           JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
           REDIS_HOST: "localhost"
           REDIS_PORT: "6379"
+          REDIS_PASSWORD: "testpassword"
           ENCRYPTION_KEY: "dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=" # DO NOT USE IN PRODUCTION!!
 
     env:

.pr_agent.toml

@@ -1,3 +1,6 @@
+[pr_reviewer]
+num_code_suggestions=0
+
 [pr_code_suggestions]
 commitable_code_suggestions=false
 num_code_suggestions=0

autogpt_platform/Makefile

@@ -1,47 +0,0 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend
-
-# Run just Supabase + Redis + RabbitMQ
-start-core:
-	docker compose up -d deps
-
-# Stop core services
-stop-core:
-	docker compose stop deps
-
-# View logs for core services
-logs-core:
-	docker compose logs -f deps
-
-# Run formatting and linting for backend and frontend
-format:
-	cd backend && poetry run format
-	cd frontend && pnpm format
-	cd frontend && pnpm lint
-
-init-env:
-	cp -n .env.default .env || true
-	cd backend && cp -n .env.default .env || true
-	cd frontend && cp -n .env.default .env || true
-
-
-# Run migrations for backend
-migrate:
-	cd backend && poetry run prisma migrate deploy
-	cd backend && poetry run prisma generate
-
-run-backend:
-	cd backend && poetry run app
-
-run-frontend:
-	cd frontend && pnpm dev
-
-help:
-	@echo "Usage: make <target>"
-	@echo "Targets:"
-	@echo "  start-core - Start just the core services (Supabase, Redis, RabbitMQ) in background"
-	@echo "  stop-core - Stop the core services"
-	@echo "  logs-core - Tail the logs for core services"
-	@echo "  format - Format & lint backend (Python) and frontend (TypeScript) code"
-	@echo "  migrate - Run backend database migrations"
-	@echo "  run-backend - Run the backend FastAPI server"
-	@echo "  run-frontend - Run the frontend Next.js development server"

autogpt_platform/README.md

@@ -38,37 +38,6 @@ To run the AutoGPT Platform, follow these steps:
 
 4. After all the services are in ready state, open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
 
-### Running Just Core services
-
-You can now run the following to enable just the core services.
-
-```
-# For help
-make help
-
-# Run just Supabase + Redis + RabbitMQ
-make start-core
-
-# Stop core services
-make stop-core
-
-# View logs from core services 
-make logs-core
-
-# Run formatting and linting for backend and frontend
-make format
-
-# Run migrations for backend database
-make migrate
-
-# Run backend server
-make run-backend
-
-# Run frontend development server
-make run-frontend
-
-```
-
 ### Docker Compose Commands
 
 Here are some useful Docker Compose commands for managing your AutoGPT Platform:

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py

@@ -10,7 +10,7 @@ from .jwt_utils import get_jwt_payload, verify_user
 from .models import User
 
 
-async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
+def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
     FastAPI dependency that requires a valid authenticated user.
 
@@ -20,9 +20,7 @@ async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -
     return verify_user(jwt_payload, admin_only=False)
 
 
-async def requires_admin_user(
-    jwt_payload: dict = fastapi.Security(get_jwt_payload),
-) -> User:
+def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
     FastAPI dependency that requires a valid admin user.
 
@@ -32,7 +30,7 @@ async def requires_admin_user(
     return verify_user(jwt_payload, admin_only=True)
 
 
-async def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
+def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
     """
     FastAPI dependency that returns the ID of the authenticated user.
 

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -45,7 +45,7 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
-    async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
+    def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user with valid JWT payload."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
 
@@ -53,12 +53,12 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_user(jwt_payload)
+        user = requires_user(jwt_payload)
         assert isinstance(user, User)
         assert user.user_id == "user-123"
         assert user.role == "user"
 
-    async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
+    def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user accepts admin users."""
         jwt_payload = {
             "sub": "admin-456",
@@ -69,28 +69,28 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_user(jwt_payload)
+        user = requires_user(jwt_payload)
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
-    async def test_requires_user_missing_sub(self):
+    def test_requires_user_missing_sub(self):
         """Test requires_user with missing user ID."""
         jwt_payload = {"role": "user", "email": "user@example.com"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_user(jwt_payload)
+            requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    async def test_requires_user_empty_sub(self):
+    def test_requires_user_empty_sub(self):
         """Test requires_user with empty user ID."""
         jwt_payload = {"sub": "", "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_user(jwt_payload)
+            requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
 
-    async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
+    def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
         """Test requires_admin_user with admin role."""
         jwt_payload = {
             "sub": "admin-789",
@@ -101,51 +101,51 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_admin_user(jwt_payload)
+        user = requires_admin_user(jwt_payload)
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
-    async def test_requires_admin_user_with_regular_user(self):
+    def test_requires_admin_user_with_regular_user(self):
         """Test requires_admin_user rejects regular users."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_admin_user(jwt_payload)
+            requires_admin_user(jwt_payload)
         assert exc_info.value.status_code == 403
         assert "Admin access required" in exc_info.value.detail
 
-    async def test_requires_admin_user_missing_role(self):
+    def test_requires_admin_user_missing_role(self):
         """Test requires_admin_user with missing role."""
         jwt_payload = {"sub": "user-123", "email": "user@example.com"}
 
         with pytest.raises(KeyError):
-            await requires_admin_user(jwt_payload)
+            requires_admin_user(jwt_payload)
 
-    async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
+    def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
         """Test get_user_id extracts user ID correctly."""
         jwt_payload = {"sub": "user-id-xyz", "role": "user"}
 
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user_id = await get_user_id(jwt_payload)
+        user_id = get_user_id(jwt_payload)
         assert user_id == "user-id-xyz"
 
-    async def test_get_user_id_missing_sub(self):
+    def test_get_user_id_missing_sub(self):
         """Test get_user_id with missing user ID."""
         jwt_payload = {"role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(jwt_payload)
+            get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    async def test_get_user_id_none_sub(self):
+    def test_get_user_id_none_sub(self):
         """Test get_user_id with None user ID."""
         jwt_payload = {"sub": None, "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(jwt_payload)
+            get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -170,7 +170,7 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
-    async def test_endpoint_auth_enabled_no_token(self):
+    def test_endpoint_auth_enabled_no_token(self):
         """Test endpoints require token when auth is enabled."""
         app = FastAPI()
 
@@ -184,7 +184,7 @@ class TestAuthDependenciesIntegration:
         response = client.get("/test")
         assert response.status_code == 401
 
-    async def test_endpoint_with_valid_token(self, create_token):
+    def test_endpoint_with_valid_token(self, create_token):
         """Test endpoint with valid JWT token."""
         app = FastAPI()
 
@@ -203,7 +203,7 @@ class TestAuthDependenciesIntegration:
         assert response.status_code == 200
         assert response.json()["user_id"] == "test-user"
 
-    async def test_admin_endpoint_requires_admin_role(self, create_token):
+    def test_admin_endpoint_requires_admin_role(self, create_token):
         """Test admin endpoint rejects non-admin users."""
         app = FastAPI()
 
@@ -240,7 +240,7 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
     """Edge case tests for authentication dependencies."""
 
-    async def test_dependency_with_complex_payload(self):
+    def test_dependency_with_complex_payload(self):
         """Test dependencies handle complex JWT payloads."""
         complex_payload = {
             "sub": "user-123",
@@ -256,14 +256,14 @@ class TestAuthDependenciesEdgeCases:
             "exp": 9999999999,
         }
 
-        user = await requires_user(complex_payload)
+        user = requires_user(complex_payload)
         assert user.user_id == "user-123"
         assert user.email == "test@example.com"
 
-        admin = await requires_admin_user(complex_payload)
+        admin = requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
-    async def test_dependency_with_unicode_in_payload(self):
+    def test_dependency_with_unicode_in_payload(self):
         """Test dependencies handle unicode in JWT payloads."""
         unicode_payload = {
             "sub": "user-😀-123",
@@ -272,11 +272,11 @@ class TestAuthDependenciesEdgeCases:
             "name": "日本語",
         }
 
-        user = await requires_user(unicode_payload)
+        user = requires_user(unicode_payload)
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
-    async def test_dependency_with_null_values(self):
+    def test_dependency_with_null_values(self):
         """Test dependencies handle null values in payload."""
         null_payload = {
             "sub": "user-123",
@@ -286,18 +286,18 @@ class TestAuthDependenciesEdgeCases:
             "metadata": None,
         }
 
-        user = await requires_user(null_payload)
+        user = requires_user(null_payload)
         assert user.user_id == "user-123"
         assert user.email is None
 
-    async def test_concurrent_requests_isolation(self):
+    def test_concurrent_requests_isolation(self):
         """Test that concurrent requests don't interfere with each other."""
         payload1 = {"sub": "user-1", "role": "user"}
         payload2 = {"sub": "user-2", "role": "admin"}
 
         # Simulate concurrent processing
-        user1 = await requires_user(payload1)
-        user2 = await requires_admin_user(payload2)
+        user1 = requires_user(payload1)
+        user2 = requires_admin_user(payload2)
 
         assert user1.user_id == "user-1"
         assert user2.user_id == "user-2"
@@ -314,7 +314,7 @@ class TestAuthDependenciesEdgeCases:
             ({"sub": "user", "role": "user"}, "Admin access required", True),
         ],
     )
-    async def test_dependency_error_cases(
+    def test_dependency_error_cases(
         self, payload, expected_error: str, admin_only: bool
     ):
         """Test that errors propagate correctly through dependencies."""
@@ -325,7 +325,7 @@ class TestAuthDependenciesEdgeCases:
             verify_user(payload, admin_only=admin_only)
         assert expected_error in exc_info.value.detail
 
-    async def test_dependency_valid_user(self):
+    def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
         from autogpt_libs.auth.jwt_utils import verify_user

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py

@@ -16,7 +16,7 @@ bearer_jwt_auth = HTTPBearer(
 )
 
 
-async def get_jwt_payload(
+def get_jwt_payload(
     credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
 ) -> dict[str, Any]:
     """

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils_test.py

@@ -116,32 +116,32 @@ def test_parse_jwt_token_missing_audience():
     assert "Invalid token" in str(exc_info.value)
 
 
-async def test_get_jwt_payload_with_valid_token():
+def test_get_jwt_payload_with_valid_token():
     """Test extracting JWT payload with valid bearer token."""
     token = create_token(TEST_USER_PAYLOAD)
     credentials = HTTPAuthorizationCredentials(scheme="Bearer", credentials=token)
 
-    result = await jwt_utils.get_jwt_payload(credentials)
+    result = jwt_utils.get_jwt_payload(credentials)
     assert result["sub"] == "test-user-id"
     assert result["role"] == "user"
 
 
-async def test_get_jwt_payload_no_credentials():
+def test_get_jwt_payload_no_credentials():
     """Test JWT payload when no credentials provided."""
     with pytest.raises(HTTPException) as exc_info:
-        await jwt_utils.get_jwt_payload(None)
+        jwt_utils.get_jwt_payload(None)
     assert exc_info.value.status_code == 401
     assert "Authorization header is missing" in exc_info.value.detail
 
 
-async def test_get_jwt_payload_invalid_token():
+def test_get_jwt_payload_invalid_token():
     """Test JWT payload extraction with invalid token."""
     credentials = HTTPAuthorizationCredentials(
         scheme="Bearer", credentials="invalid.token.here"
     )
 
     with pytest.raises(HTTPException) as exc_info:
-        await jwt_utils.get_jwt_payload(credentials)
+        jwt_utils.get_jwt_payload(credentials)
     assert exc_info.value.status_code == 401
     assert "Invalid token" in exc_info.value.detail
 

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -4,7 +4,6 @@ import logging
 import os
 import socket
 import sys
-from logging.handlers import RotatingFileHandler
 from pathlib import Path
 
 from pydantic import Field, field_validator
@@ -140,13 +139,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
         print(f"Log directory: {config.log_dir}")
 
         # Activity log handler (INFO and above)
-        # Security fix: Use RotatingFileHandler with size limits to prevent disk exhaustion
-        activity_log_handler = RotatingFileHandler(
-            config.log_dir / LOG_FILE,
-            mode="a",
-            encoding="utf-8",
-            maxBytes=10 * 1024 * 1024,  # 10MB per file
-            backupCount=3,  # Keep 3 backup files (40MB total)
+        activity_log_handler = logging.FileHandler(
+            config.log_dir / LOG_FILE, "a", "utf-8"
         )
         activity_log_handler.setLevel(config.level)
         activity_log_handler.setFormatter(
@@ -156,13 +150,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
 
         if config.level == logging.DEBUG:
             # Debug log handler (all levels)
-            # Security fix: Use RotatingFileHandler with size limits
-            debug_log_handler = RotatingFileHandler(
-                config.log_dir / DEBUG_LOG_FILE,
-                mode="a",
-                encoding="utf-8",
-                maxBytes=10 * 1024 * 1024,  # 10MB per file
-                backupCount=3,  # Keep 3 backup files (40MB total)
+            debug_log_handler = logging.FileHandler(
+                config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
             )
             debug_log_handler.setLevel(logging.DEBUG)
             debug_log_handler.setFormatter(
@@ -171,13 +160,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
             log_handlers.append(debug_log_handler)
 
         # Error log handler (ERROR and above)
-        # Security fix: Use RotatingFileHandler with size limits
-        error_log_handler = RotatingFileHandler(
-            config.log_dir / ERROR_LOG_FILE,
-            mode="a",
-            encoding="utf-8",
-            maxBytes=10 * 1024 * 1024,  # 10MB per file
-            backupCount=3,  # Keep 3 backup files (40MB total)
+        error_log_handler = logging.FileHandler(
+            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
         )
         error_log_handler.setLevel(logging.ERROR)
         error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/config.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from pydantic import Field
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -15,8 +13,8 @@ class RateLimitSettings(BaseSettings):
         default="6379", description="Redis port", validation_alias="REDIS_PORT"
     )
 
-    redis_password: Optional[str] = Field(
-        default=None,
+    redis_password: str = Field(
+        default="password",
         description="Redis password",
         validation_alias="REDIS_PASSWORD",
     )

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/limiter.py

@@ -11,7 +11,7 @@ class RateLimiter:
         self,
         redis_host: str = RATE_LIMIT_SETTINGS.redis_host,
         redis_port: str = RATE_LIMIT_SETTINGS.redis_port,
-        redis_password: str | None = RATE_LIMIT_SETTINGS.redis_password,
+        redis_password: str = RATE_LIMIT_SETTINGS.redis_password,
         requests_per_minute: int = RATE_LIMIT_SETTINGS.requests_per_minute,
     ):
         self.redis = Redis(

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -1,68 +1,90 @@
-import asyncio
 import inspect
 import logging
 import threading
 import time
 from functools import wraps
 from typing import (
-    Any,
+    Awaitable,
     Callable,
     ParamSpec,
     Protocol,
+    Tuple,
     TypeVar,
     cast,
+    overload,
     runtime_checkable,
 )
 
 P = ParamSpec("P")
 R = TypeVar("R")
-R_co = TypeVar("R_co", covariant=True)
 
 logger = logging.getLogger(__name__)
 
 
-def _make_hashable_key(
-    args: tuple[Any, ...], kwargs: dict[str, Any]
-) -> tuple[Any, ...]:
-    """
-    Convert args and kwargs into a hashable cache key.
+@overload
+def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    pass
 
-    Handles unhashable types like dict, list, set by converting them to
-    their sorted string representations.
-    """
 
-    def make_hashable(obj: Any) -> Any:
-        """Recursively convert an object to a hashable representation."""
-        if isinstance(obj, dict):
-            # Sort dict items to ensure consistent ordering
-            return (
-                "__dict__",
-                tuple(sorted((k, make_hashable(v)) for k, v in obj.items())),
-            )
-        elif isinstance(obj, (list, tuple)):
-            return ("__list__", tuple(make_hashable(item) for item in obj))
-        elif isinstance(obj, set):
-            return ("__set__", tuple(sorted(make_hashable(item) for item in obj)))
-        elif hasattr(obj, "__dict__"):
-            # Handle objects with __dict__ attribute
-            return ("__obj__", obj.__class__.__name__, make_hashable(obj.__dict__))
-        else:
-            # For basic hashable types (str, int, bool, None, etc.)
-            try:
-                hash(obj)
-                return obj
-            except TypeError:
-                # Fallback: convert to string representation
-                return ("__str__", str(obj))
+@overload
+def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
+    pass
 
-    hashable_args = tuple(make_hashable(arg) for arg in args)
-    hashable_kwargs = tuple(sorted((k, make_hashable(v)) for k, v in kwargs.items()))
-    return (hashable_args, hashable_kwargs)
+
+def thread_cached(
+    func: Callable[P, R] | Callable[P, Awaitable[R]],
+) -> Callable[P, R] | Callable[P, Awaitable[R]]:
+    thread_local = threading.local()
+
+    def _clear():
+        if hasattr(thread_local, "cache"):
+            del thread_local.cache
+
+    if inspect.iscoroutinefunction(func):
+
+        async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = (args, tuple(sorted(kwargs.items())))
+            if key not in cache:
+                cache[key] = await cast(Callable[P, Awaitable[R]], func)(
+                    *args, **kwargs
+                )
+            return cache[key]
+
+        setattr(async_wrapper, "clear_cache", _clear)
+        return async_wrapper
+
+    else:
+
+        def sync_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = (args, tuple(sorted(kwargs.items())))
+            if key not in cache:
+                cache[key] = func(*args, **kwargs)
+            return cache[key]
+
+        setattr(sync_wrapper, "clear_cache", _clear)
+        return sync_wrapper
+
+
+def clear_thread_cache(func: Callable) -> None:
+    if clear := getattr(func, "clear_cache", None):
+        clear()
+
+
+FuncT = TypeVar("FuncT")
+
+
+R_co = TypeVar("R_co", covariant=True)
 
 
 @runtime_checkable
-class CachedFunction(Protocol[P, R_co]):
-    """Protocol for cached functions with cache management methods."""
+class AsyncCachedFunction(Protocol[P, R_co]):
+    """Protocol for async functions with cache management methods."""
 
     def cache_clear(self) -> None:
         """Clear all cached entries."""
@@ -72,180 +94,101 @@ class CachedFunction(Protocol[P, R_co]):
         """Get cache statistics."""
         return {}
 
-    def cache_delete(self, *args: P.args, **kwargs: P.kwargs) -> bool:
-        """Delete a specific cache entry by its arguments. Returns True if entry existed."""
-        return False
-
-    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
+    async def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
         """Call the cached function."""
         return None  # type: ignore
 
 
-def cached(
-    *,
-    maxsize: int = 128,
-    ttl_seconds: int | None = None,
-) -> Callable[[Callable], CachedFunction]:
+def async_ttl_cache(
+    maxsize: int = 128, ttl_seconds: int | None = None
+) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
     """
-    Thundering herd safe cache decorator for both sync and async functions.
+    TTL (Time To Live) cache decorator for async functions.
 
-    Uses double-checked locking to prevent multiple threads/coroutines from
-    executing the expensive operation simultaneously during cache misses.
+    Similar to functools.lru_cache but works with async functions and includes optional TTL.
 
     Args:
-        func: The function to cache (when used without parentheses)
         maxsize: Maximum number of cached entries
-        ttl_seconds: Time to live in seconds. If None, entries never expire
+        ttl_seconds: Time to live in seconds. If None, entries never expire (like lru_cache)
 
     Returns:
-        Decorated function or decorator
+        Decorator function
 
     Example:
-        @cache()  # Default: maxsize=128, no TTL
-        def expensive_sync_operation(param: str) -> dict:
+        # With TTL
+        @async_ttl_cache(maxsize=1000, ttl_seconds=300)
+        async def api_call(param: str) -> dict:
             return {"result": param}
 
-        @cache()  # Works with async too
-        async def expensive_async_operation(param: str) -> dict:
-            return {"result": param}
-
-        @cache(maxsize=1000, ttl_seconds=300)  # Custom maxsize and TTL
-        def another_operation(param: str) -> dict:
+        # Without TTL (permanent cache like lru_cache)
+        @async_ttl_cache(maxsize=1000)
+        async def expensive_computation(param: str) -> dict:
             return {"result": param}
     """
 
-    def decorator(target_func):
-        # Cache storage and per-event-loop locks
-        cache_storage = {}
-        _event_loop_locks = {}  # Maps event loop to its asyncio.Lock
+    def decorator(
+        async_func: Callable[P, Awaitable[R]],
+    ) -> AsyncCachedFunction[P, R]:
+        # Cache storage - use union type to handle both cases
+        cache_storage: dict[tuple, R | Tuple[R, float]] = {}
 
-        if inspect.iscoroutinefunction(target_func):
+        @wraps(async_func)
+        async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            # Create cache key from arguments
+            key = (args, tuple(sorted(kwargs.items())))
+            current_time = time.time()
 
-            def _get_cache_lock():
-                """Get or create an asyncio.Lock for the current event loop."""
-                try:
-                    loop = asyncio.get_running_loop()
-                except RuntimeError:
-                    # No event loop, use None as default key
-                    loop = None
-
-                if loop not in _event_loop_locks:
-                    return _event_loop_locks.setdefault(loop, asyncio.Lock())
-                return _event_loop_locks[loop]
-
-            @wraps(target_func)
-            async def async_wrapper(*args: P.args, **kwargs: P.kwargs):
-                key = _make_hashable_key(args, kwargs)
-                current_time = time.time()
-
-                # Fast path: check cache without lock
-                if key in cache_storage:
-                    if ttl_seconds is None:
-                        logger.debug(f"Cache hit for {target_func.__name__}")
-                        return cache_storage[key]
-                    else:
-                        cached_data = cache_storage[key]
-                        if isinstance(cached_data, tuple):
-                            result, timestamp = cached_data
-                            if current_time - timestamp < ttl_seconds:
-                                logger.debug(f"Cache hit for {target_func.__name__}")
-                                return result
-
-                # Slow path: acquire lock for cache miss/expiry
-                async with _get_cache_lock():
-                    # Double-check: another coroutine might have populated cache
-                    if key in cache_storage:
-                        if ttl_seconds is None:
-                            return cache_storage[key]
+            # Check if we have a valid cached entry
+            if key in cache_storage:
+                if ttl_seconds is None:
+                    # No TTL - return cached result directly
+                    logger.debug(
+                        f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
+                    )
+                    return cast(R, cache_storage[key])
+                else:
+                    # With TTL - check expiration
+                    cached_data = cache_storage[key]
+                    if isinstance(cached_data, tuple):
+                        result, timestamp = cached_data
+                        if current_time - timestamp < ttl_seconds:
+                            logger.debug(
+                                f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
+                            )
+                            return cast(R, result)
                         else:
-                            cached_data = cache_storage[key]
-                            if isinstance(cached_data, tuple):
-                                result, timestamp = cached_data
-                                if current_time - timestamp < ttl_seconds:
-                                    return result
+                            # Expired entry
+                            del cache_storage[key]
+                            logger.debug(
+                                f"Cache entry expired for {async_func.__name__}"
+                            )
 
-                    # Cache miss - execute function
-                    logger.debug(f"Cache miss for {target_func.__name__}")
-                    result = await target_func(*args, **kwargs)
+            # Cache miss or expired - fetch fresh data
+            logger.debug(
+                f"Cache miss for {async_func.__name__} with key: {str(key)[:50]}"
+            )
+            result = await async_func(*args, **kwargs)
 
-                    # Store result
-                    if ttl_seconds is None:
-                        cache_storage[key] = result
-                    else:
-                        cache_storage[key] = (result, current_time)
+            # Store in cache
+            if ttl_seconds is None:
+                cache_storage[key] = result
+            else:
+                cache_storage[key] = (result, current_time)
 
-                    # Cleanup if needed
-                    if len(cache_storage) > maxsize:
-                        cutoff = maxsize // 2
-                        oldest_keys = (
-                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
-                        )
-                        for old_key in oldest_keys:
-                            cache_storage.pop(old_key, None)
+            # Simple cleanup when cache gets too large
+            if len(cache_storage) > maxsize:
+                # Remove oldest entries (simple FIFO cleanup)
+                cutoff = maxsize // 2
+                oldest_keys = list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
+                for old_key in oldest_keys:
+                    cache_storage.pop(old_key, None)
+                logger.debug(
+                    f"Cache cleanup: removed {len(oldest_keys)} entries for {async_func.__name__}"
+                )
 
-                    return result
+            return result
 
-            wrapper = async_wrapper
-
-        else:
-            # Sync function with threading.Lock
-            cache_lock = threading.Lock()
-
-            @wraps(target_func)
-            def sync_wrapper(*args: P.args, **kwargs: P.kwargs):
-                key = _make_hashable_key(args, kwargs)
-                current_time = time.time()
-
-                # Fast path: check cache without lock
-                if key in cache_storage:
-                    if ttl_seconds is None:
-                        logger.debug(f"Cache hit for {target_func.__name__}")
-                        return cache_storage[key]
-                    else:
-                        cached_data = cache_storage[key]
-                        if isinstance(cached_data, tuple):
-                            result, timestamp = cached_data
-                            if current_time - timestamp < ttl_seconds:
-                                logger.debug(f"Cache hit for {target_func.__name__}")
-                                return result
-
-                # Slow path: acquire lock for cache miss/expiry
-                with cache_lock:
-                    # Double-check: another thread might have populated cache
-                    if key in cache_storage:
-                        if ttl_seconds is None:
-                            return cache_storage[key]
-                        else:
-                            cached_data = cache_storage[key]
-                            if isinstance(cached_data, tuple):
-                                result, timestamp = cached_data
-                                if current_time - timestamp < ttl_seconds:
-                                    return result
-
-                    # Cache miss - execute function
-                    logger.debug(f"Cache miss for {target_func.__name__}")
-                    result = target_func(*args, **kwargs)
-
-                    # Store result
-                    if ttl_seconds is None:
-                        cache_storage[key] = result
-                    else:
-                        cache_storage[key] = (result, current_time)
-
-                    # Cleanup if needed
-                    if len(cache_storage) > maxsize:
-                        cutoff = maxsize // 2
-                        oldest_keys = (
-                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
-                        )
-                        for old_key in oldest_keys:
-                            cache_storage.pop(old_key, None)
-
-                    return result
-
-            wrapper = sync_wrapper
-
-        # Add cache management methods
+        # Add cache management methods (similar to functools.lru_cache)
         def cache_clear() -> None:
             cache_storage.clear()
 
@@ -256,84 +199,68 @@ def cached(
                 "ttl_seconds": ttl_seconds,
             }
 
-        def cache_delete(*args, **kwargs) -> bool:
-            """Delete a specific cache entry. Returns True if entry existed."""
-            key = _make_hashable_key(args, kwargs)
-            if key in cache_storage:
-                del cache_storage[key]
-                return True
-            return False
-
+        # Attach methods to wrapper
         setattr(wrapper, "cache_clear", cache_clear)
         setattr(wrapper, "cache_info", cache_info)
-        setattr(wrapper, "cache_delete", cache_delete)
 
-        return cast(CachedFunction, wrapper)
+        return cast(AsyncCachedFunction[P, R], wrapper)
 
     return decorator
 
 
-def thread_cached(func):
-    """
-    Thread-local cache decorator for both sync and async functions.
+@overload
+def async_cache(
+    func: Callable[P, Awaitable[R]],
+) -> AsyncCachedFunction[P, R]:
+    pass
 
-    Each thread gets its own cache, which is useful for request-scoped caching
-    in web applications where you want to cache within a single request but
-    not across requests.
+
+@overload
+def async_cache(
+    func: None = None,
+    *,
+    maxsize: int = 128,
+) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
+    pass
+
+
+def async_cache(
+    func: Callable[P, Awaitable[R]] | None = None,
+    *,
+    maxsize: int = 128,
+) -> (
+    AsyncCachedFunction[P, R]
+    | Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]
+):
+    """
+    Process-level cache decorator for async functions (no TTL).
+
+    Similar to functools.lru_cache but works with async functions.
+    This is a convenience wrapper around async_ttl_cache with ttl_seconds=None.
 
     Args:
-        func: The function to cache
+        func: The async function to cache (when used without parentheses)
+        maxsize: Maximum number of cached entries
 
     Returns:
-        Decorated function with thread-local caching
+        Decorated function or decorator
 
     Example:
-        @thread_cached
-        def expensive_operation(param: str) -> dict:
+        # Without parentheses (uses default maxsize=128)
+        @async_cache
+        async def get_data(param: str) -> dict:
             return {"result": param}
 
-        @thread_cached  # Works with async too
-        async def expensive_async_operation(param: str) -> dict:
+        # With parentheses and custom maxsize
+        @async_cache(maxsize=1000)
+        async def expensive_computation(param: str) -> dict:
+            # Expensive computation here
             return {"result": param}
     """
-    thread_local = threading.local()
-
-    def _clear():
-        if hasattr(thread_local, "cache"):
-            del thread_local.cache
-
-    if inspect.iscoroutinefunction(func):
-
-        @wraps(func)
-        async def async_wrapper(*args, **kwargs):
-            cache = getattr(thread_local, "cache", None)
-            if cache is None:
-                cache = thread_local.cache = {}
-            key = _make_hashable_key(args, kwargs)
-            if key not in cache:
-                cache[key] = await func(*args, **kwargs)
-            return cache[key]
-
-        setattr(async_wrapper, "clear_cache", _clear)
-        return async_wrapper
-
+    if func is None:
+        # Called with parentheses @async_cache() or @async_cache(maxsize=...)
+        return async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
     else:
-
-        @wraps(func)
-        def sync_wrapper(*args, **kwargs):
-            cache = getattr(thread_local, "cache", None)
-            if cache is None:
-                cache = thread_local.cache = {}
-            key = _make_hashable_key(args, kwargs)
-            if key not in cache:
-                cache[key] = func(*args, **kwargs)
-            return cache[key]
-
-        setattr(sync_wrapper, "clear_cache", _clear)
-        return sync_wrapper
-
-
-def clear_thread_cache(func: Callable) -> None:
-    """Clear thread-local cache for a function."""
-    if clear := getattr(func, "clear_cache", None):
-        clear()
+        # Called without parentheses @async_cache
+        decorator = async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
+        return decorator(func)

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py

@@ -16,7 +16,12 @@ from unittest.mock import Mock
 
 import pytest
 
-from autogpt_libs.utils.cache import cached, clear_thread_cache, thread_cached
+from autogpt_libs.utils.cache import (
+    async_cache,
+    async_ttl_cache,
+    clear_thread_cache,
+    thread_cached,
+)
 
 
 class TestThreadCached:
@@ -325,202 +330,102 @@ class TestThreadCached:
         assert mock.call_count == 2
 
 
-class TestCache:
-    """Tests for the unified @cache decorator (works for both sync and async)."""
-
-    def test_basic_sync_caching(self):
-        """Test basic sync caching functionality."""
-        call_count = 0
-
-        @cached()
-        def expensive_sync_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x + y
-
-        # First call
-        result1 = expensive_sync_function(1, 2)
-        assert result1 == 3
-        assert call_count == 1
-
-        # Second call with same args - should use cache
-        result2 = expensive_sync_function(1, 2)
-        assert result2 == 3
-        assert call_count == 1
-
-        # Different args - should call function again
-        result3 = expensive_sync_function(2, 3)
-        assert result3 == 5
-        assert call_count == 2
+class TestAsyncTTLCache:
+    """Tests for the @async_ttl_cache decorator."""
 
     @pytest.mark.asyncio
-    async def test_basic_async_caching(self):
-        """Test basic async caching functionality."""
+    async def test_basic_caching(self):
+        """Test basic caching functionality."""
         call_count = 0
 
-        @cached()
-        async def expensive_async_function(x: int, y: int = 0) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def cached_function(x: int, y: int = 0) -> int:
             nonlocal call_count
             call_count += 1
             await asyncio.sleep(0.01)  # Simulate async work
             return x + y
 
         # First call
-        result1 = await expensive_async_function(1, 2)
+        result1 = await cached_function(1, 2)
         assert result1 == 3
         assert call_count == 1
 
         # Second call with same args - should use cache
-        result2 = await expensive_async_function(1, 2)
+        result2 = await cached_function(1, 2)
         assert result2 == 3
-        assert call_count == 1
+        assert call_count == 1  # No additional call
 
         # Different args - should call function again
-        result3 = await expensive_async_function(2, 3)
+        result3 = await cached_function(2, 3)
         assert result3 == 5
         assert call_count == 2
 
-    def test_sync_thundering_herd_protection(self):
-        """Test that concurrent sync calls don't cause thundering herd."""
-        call_count = 0
-        results = []
-
-        @cached()
-        def slow_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            time.sleep(0.1)  # Simulate expensive operation
-            return x * x
-
-        def worker():
-            result = slow_function(5)
-            results.append(result)
-
-        # Launch multiple concurrent threads
-        with ThreadPoolExecutor(max_workers=5) as executor:
-            futures = [executor.submit(worker) for _ in range(5)]
-            for future in futures:
-                future.result()
-
-        # All results should be the same
-        assert all(result == 25 for result in results)
-        # Only one thread should have executed the expensive operation
-        assert call_count == 1
-
     @pytest.mark.asyncio
-    async def test_async_thundering_herd_protection(self):
-        """Test that concurrent async calls don't cause thundering herd."""
+    async def test_ttl_expiration(self):
+        """Test that cache entries expire after TTL."""
         call_count = 0
 
-        @cached()
-        async def slow_async_function(x: int) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
+        async def short_lived_cache(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.1)  # Simulate expensive operation
-            return x * x
-
-        # Launch concurrent coroutines
-        tasks = [slow_async_function(7) for _ in range(5)]
-        results = await asyncio.gather(*tasks)
-
-        # All results should be the same
-        assert all(result == 49 for result in results)
-        # Only one coroutine should have executed the expensive operation
-        assert call_count == 1
-
-    def test_ttl_functionality(self):
-        """Test TTL functionality with sync function."""
-        call_count = 0
-
-        @cached(maxsize=10, ttl_seconds=1)  # Short TTL
-        def ttl_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 3
+            return x * 2
 
         # First call
-        result1 = ttl_function(3)
-        assert result1 == 9
+        result1 = await short_lived_cache(5)
+        assert result1 == 10
         assert call_count == 1
 
         # Second call immediately - should use cache
-        result2 = ttl_function(3)
-        assert result2 == 9
-        assert call_count == 1
-
-        # Wait for TTL to expire
-        time.sleep(1.1)
-
-        # Third call after expiration - should call function again
-        result3 = ttl_function(3)
-        assert result3 == 9
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_async_ttl_functionality(self):
-        """Test TTL functionality with async function."""
-        call_count = 0
-
-        @cached(maxsize=10, ttl_seconds=1)  # Short TTL
-        async def async_ttl_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 4
-
-        # First call
-        result1 = await async_ttl_function(3)
-        assert result1 == 12
-        assert call_count == 1
-
-        # Second call immediately - should use cache
-        result2 = await async_ttl_function(3)
-        assert result2 == 12
+        result2 = await short_lived_cache(5)
+        assert result2 == 10
         assert call_count == 1
 
         # Wait for TTL to expire
         await asyncio.sleep(1.1)
 
         # Third call after expiration - should call function again
-        result3 = await async_ttl_function(3)
-        assert result3 == 12
+        result3 = await short_lived_cache(5)
+        assert result3 == 10
         assert call_count == 2
 
-    def test_cache_info(self):
+    @pytest.mark.asyncio
+    async def test_cache_info(self):
         """Test cache info functionality."""
 
-        @cached(maxsize=10, ttl_seconds=60)
-        def info_test_function(x: int) -> int:
+        @async_ttl_cache(maxsize=5, ttl_seconds=300)
+        async def info_test_function(x: int) -> int:
             return x * 3
 
         # Check initial cache info
         info = info_test_function.cache_info()
         assert info["size"] == 0
-        assert info["maxsize"] == 10
-        assert info["ttl_seconds"] == 60
+        assert info["maxsize"] == 5
+        assert info["ttl_seconds"] == 300
 
         # Add an entry
-        info_test_function(1)
+        await info_test_function(1)
         info = info_test_function.cache_info()
         assert info["size"] == 1
 
-    def test_cache_clear(self):
+    @pytest.mark.asyncio
+    async def test_cache_clear(self):
         """Test cache clearing functionality."""
         call_count = 0
 
-        @cached()
-        def clearable_function(x: int) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def clearable_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
             return x * 4
 
         # First call
-        result1 = clearable_function(2)
+        result1 = await clearable_function(2)
         assert result1 == 8
         assert call_count == 1
 
         # Second call - should use cache
-        result2 = clearable_function(2)
+        result2 = await clearable_function(2)
         assert result2 == 8
         assert call_count == 1
 
@@ -528,149 +433,273 @@ class TestCache:
         clearable_function.cache_clear()
 
         # Third call after clear - should call function again
-        result3 = clearable_function(2)
+        result3 = await clearable_function(2)
         assert result3 == 8
         assert call_count == 2
 
     @pytest.mark.asyncio
-    async def test_async_cache_clear(self):
-        """Test cache clearing functionality with async function."""
+    async def test_maxsize_cleanup(self):
+        """Test that cache cleans up when maxsize is exceeded."""
         call_count = 0
 
-        @cached()
-        async def async_clearable_function(x: int) -> int:
+        @async_ttl_cache(maxsize=3, ttl_seconds=60)
+        async def size_limited_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 5
+            return x**2
 
-        # First call
-        result1 = await async_clearable_function(2)
+        # Fill cache to maxsize
+        await size_limited_function(1)  # call_count: 1
+        await size_limited_function(2)  # call_count: 2
+        await size_limited_function(3)  # call_count: 3
+
+        info = size_limited_function.cache_info()
+        assert info["size"] == 3
+
+        # Add one more entry - should trigger cleanup
+        await size_limited_function(4)  # call_count: 4
+
+        # Cache size should be reduced (cleanup removes oldest entries)
+        info = size_limited_function.cache_info()
+        assert info["size"] is not None and info["size"] <= 3  # Should be cleaned up
+
+    @pytest.mark.asyncio
+    async def test_argument_variations(self):
+        """Test caching with different argument patterns."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def arg_test_function(a: int, b: str = "default", *, c: int = 100) -> str:
+            nonlocal call_count
+            call_count += 1
+            return f"{a}-{b}-{c}"
+
+        # Different ways to call with same logical arguments
+        result1 = await arg_test_function(1, "test", c=200)
+        assert call_count == 1
+
+        # Same arguments, same order - should use cache
+        result2 = await arg_test_function(1, "test", c=200)
+        assert call_count == 1
+        assert result1 == result2
+
+        # Different arguments - should call function
+        result3 = await arg_test_function(2, "test", c=200)
+        assert call_count == 2
+        assert result1 != result3
+
+    @pytest.mark.asyncio
+    async def test_exception_handling(self):
+        """Test that exceptions are not cached."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def exception_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            if x < 0:
+                raise ValueError("Negative value not allowed")
+            return x * 2
+
+        # Successful call - should be cached
+        result1 = await exception_function(5)
         assert result1 == 10
         assert call_count == 1
 
-        # Second call - should use cache
-        result2 = await async_clearable_function(2)
+        # Same successful call - should use cache
+        result2 = await exception_function(5)
         assert result2 == 10
         assert call_count == 1
 
-        # Clear cache
-        async_clearable_function.cache_clear()
-
-        # Third call after clear - should call function again
-        result3 = await async_clearable_function(2)
-        assert result3 == 10
+        # Exception call - should not be cached
+        with pytest.raises(ValueError):
+            await exception_function(-1)
         assert call_count == 2
 
+        # Same exception call - should call again (not cached)
+        with pytest.raises(ValueError):
+            await exception_function(-1)
+        assert call_count == 3
+
     @pytest.mark.asyncio
-    async def test_async_function_returns_results_not_coroutines(self):
-        """Test that cached async functions return actual results, not coroutines."""
+    async def test_concurrent_calls(self):
+        """Test caching behavior with concurrent calls."""
         call_count = 0
 
-        @cached()
-        async def async_result_function(x: int) -> str:
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def concurrent_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.01)
-            return f"result_{x}"
+            await asyncio.sleep(0.05)  # Simulate work
+            return x * x
+
+        # Launch concurrent calls with same arguments
+        tasks = [concurrent_function(3) for _ in range(5)]
+        results = await asyncio.gather(*tasks)
+
+        # All results should be the same
+        assert all(result == 9 for result in results)
+
+        # Note: Due to race conditions, call_count might be up to 5 for concurrent calls
+        # This tests that the cache doesn't break under concurrent access
+        assert 1 <= call_count <= 5
+
+
+class TestAsyncCache:
+    """Tests for the @async_cache decorator (no TTL)."""
+
+    @pytest.mark.asyncio
+    async def test_basic_caching_no_ttl(self):
+        """Test basic caching functionality without TTL."""
+        call_count = 0
+
+        @async_cache(maxsize=10)
+        async def cached_function(x: int, y: int = 0) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)  # Simulate async work
+            return x + y
 
         # First call
-        result1 = await async_result_function(1)
-        assert result1 == "result_1"
-        assert isinstance(result1, str)  # Should be string, not coroutine
+        result1 = await cached_function(1, 2)
+        assert result1 == 3
         assert call_count == 1
 
-        # Second call - should return cached result (string), not coroutine
-        result2 = await async_result_function(1)
-        assert result2 == "result_1"
-        assert isinstance(result2, str)  # Should be string, not coroutine
-        assert call_count == 1  # Function should not be called again
+        # Second call with same args - should use cache
+        result2 = await cached_function(1, 2)
+        assert result2 == 3
+        assert call_count == 1  # No additional call
 
-        # Verify results are identical
-        assert result1 is result2  # Should be same cached object
+        # Third call after some time - should still use cache (no TTL)
+        await asyncio.sleep(0.05)
+        result3 = await cached_function(1, 2)
+        assert result3 == 3
+        assert call_count == 1  # Still no additional call
 
-    def test_cache_delete(self):
-        """Test selective cache deletion functionality."""
-        call_count = 0
-
-        @cached()
-        def deletable_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 6
-
-        # First call for x=1
-        result1 = deletable_function(1)
-        assert result1 == 6
-        assert call_count == 1
-
-        # First call for x=2
-        result2 = deletable_function(2)
-        assert result2 == 12
+        # Different args - should call function again
+        result4 = await cached_function(2, 3)
+        assert result4 == 5
         assert call_count == 2
 
-        # Second calls - should use cache
-        assert deletable_function(1) == 6
-        assert deletable_function(2) == 12
-        assert call_count == 2
-
-        # Delete specific entry for x=1
-        was_deleted = deletable_function.cache_delete(1)
-        assert was_deleted is True
-
-        # Call with x=1 should execute function again
-        result3 = deletable_function(1)
-        assert result3 == 6
-        assert call_count == 3
-
-        # Call with x=2 should still use cache
-        assert deletable_function(2) == 12
-        assert call_count == 3
-
-        # Try to delete non-existent entry
-        was_deleted = deletable_function.cache_delete(99)
-        assert was_deleted is False
-
     @pytest.mark.asyncio
-    async def test_async_cache_delete(self):
-        """Test selective cache deletion functionality with async function."""
+    async def test_no_ttl_vs_ttl_behavior(self):
+        """Test the difference between TTL and no-TTL caching."""
+        ttl_call_count = 0
+        no_ttl_call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
+        async def ttl_function(x: int) -> int:
+            nonlocal ttl_call_count
+            ttl_call_count += 1
+            return x * 2
+
+        @async_cache(maxsize=10)  # No TTL
+        async def no_ttl_function(x: int) -> int:
+            nonlocal no_ttl_call_count
+            no_ttl_call_count += 1
+            return x * 2
+
+        # First calls
+        await ttl_function(5)
+        await no_ttl_function(5)
+        assert ttl_call_count == 1
+        assert no_ttl_call_count == 1
+
+        # Wait for TTL to expire
+        await asyncio.sleep(1.1)
+
+        # Second calls after TTL expiry
+        await ttl_function(5)  # Should call function again (TTL expired)
+        await no_ttl_function(5)  # Should use cache (no TTL)
+        assert ttl_call_count == 2  # TTL function called again
+        assert no_ttl_call_count == 1  # No-TTL function still cached
+
+    @pytest.mark.asyncio
+    async def test_async_cache_info(self):
+        """Test cache info for no-TTL cache."""
+
+        @async_cache(maxsize=5)
+        async def info_test_function(x: int) -> int:
+            return x * 3
+
+        # Check initial cache info
+        info = info_test_function.cache_info()
+        assert info["size"] == 0
+        assert info["maxsize"] == 5
+        assert info["ttl_seconds"] is None  # No TTL
+
+        # Add an entry
+        await info_test_function(1)
+        info = info_test_function.cache_info()
+        assert info["size"] == 1
+
+
+class TestTTLOptional:
+    """Tests for optional TTL functionality."""
+
+    @pytest.mark.asyncio
+    async def test_ttl_none_behavior(self):
+        """Test that ttl_seconds=None works like no TTL."""
         call_count = 0
 
-        @cached()
-        async def async_deletable_function(x: int) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=None)
+        async def no_ttl_via_none(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 7
+            return x**2
 
-        # First call for x=1
-        result1 = await async_deletable_function(1)
-        assert result1 == 7
+        # First call
+        result1 = await no_ttl_via_none(3)
+        assert result1 == 9
         assert call_count == 1
 
-        # First call for x=2
-        result2 = await async_deletable_function(2)
-        assert result2 == 14
-        assert call_count == 2
+        # Wait (would expire if there was TTL)
+        await asyncio.sleep(0.1)
 
-        # Second calls - should use cache
-        assert await async_deletable_function(1) == 7
-        assert await async_deletable_function(2) == 14
-        assert call_count == 2
+        # Second call - should still use cache
+        result2 = await no_ttl_via_none(3)
+        assert result2 == 9
+        assert call_count == 1  # No additional call
 
-        # Delete specific entry for x=1
-        was_deleted = async_deletable_function.cache_delete(1)
-        assert was_deleted is True
+        # Check cache info
+        info = no_ttl_via_none.cache_info()
+        assert info["ttl_seconds"] is None
 
-        # Call with x=1 should execute function again
-        result3 = await async_deletable_function(1)
-        assert result3 == 7
-        assert call_count == 3
+    @pytest.mark.asyncio
+    async def test_cache_options_comparison(self):
+        """Test different cache options work as expected."""
+        ttl_calls = 0
+        no_ttl_calls = 0
 
-        # Call with x=2 should still use cache
-        assert await async_deletable_function(2) == 14
-        assert call_count == 3
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # With TTL
+        async def ttl_function(x: int) -> int:
+            nonlocal ttl_calls
+            ttl_calls += 1
+            return x * 10
 
-        # Try to delete non-existent entry
-        was_deleted = async_deletable_function.cache_delete(99)
-        assert was_deleted is False
+        @async_cache(maxsize=10)  # Process-level cache (no TTL)
+        async def process_function(x: int) -> int:
+            nonlocal no_ttl_calls
+            no_ttl_calls += 1
+            return x * 10
+
+        # Both should cache initially
+        await ttl_function(3)
+        await process_function(3)
+        assert ttl_calls == 1
+        assert no_ttl_calls == 1
+
+        # Immediate second calls - both should use cache
+        await ttl_function(3)
+        await process_function(3)
+        assert ttl_calls == 1
+        assert no_ttl_calls == 1
+
+        # Wait for TTL to expire
+        await asyncio.sleep(1.1)
+
+        # After TTL expiry
+        await ttl_function(3)  # Should call function again
+        await process_function(3)  # Should still use cache
+        assert ttl_calls == 2  # TTL cache expired, called again
+        assert no_ttl_calls == 1  # Process cache never expires

autogpt_platform/backend/.env.default

@@ -21,7 +21,7 @@ PRISMA_SCHEMA="postgres/schema.prisma"
 # Redis Configuration
 REDIS_HOST=localhost
 REDIS_PORT=6379
-# REDIS_PASSWORD=
+REDIS_PASSWORD=password
 
 # RabbitMQ Credentials
 RABBITMQ_DEFAULT_USER=rabbitmq_user_default
@@ -66,11 +66,6 @@ NVIDIA_API_KEY=
 GITHUB_CLIENT_ID=
 GITHUB_CLIENT_SECRET=
 
-# Notion OAuth App server credentials - https://developers.notion.com/docs/authorization
-# Configure a public integration
-NOTION_CLIENT_ID=
-NOTION_CLIENT_SECRET=
-
 # Google OAuth App server credentials - https://console.cloud.google.com/apis/credentials, and enable gmail api and set scopes
 # https://console.cloud.google.com/apis/credentials/consent ?project=<your_project_id>
 # You'll need to add/enable the following scopes (minimum):

autogpt_platform/backend/.gitignore

@@ -9,12 +9,4 @@ secrets/*
 !secrets/.gitkeep
 
 *.ignore.*
-*.ign.*
-
-# Load test results and reports
-load-tests/*_RESULTS.md
-load-tests/*_REPORT.md
-load-tests/results/
-load-tests/*.json
-load-tests/*.log
-load-tests/node_modules/*
+*.ign.*

autogpt_platform/backend/Dockerfile

@@ -9,15 +9,8 @@ WORKDIR /app
 
 RUN echo 'Acquire::http::Pipeline-Depth 0;\nAcquire::http::No-Cache true;\nAcquire::BrokenProxy true;\n' > /etc/apt/apt.conf.d/99fixbadproxy
 
-# Install Node.js repository key and setup
+# Update package list and install Python and build dependencies
 RUN apt-get update --allow-releaseinfo-change --fix-missing \
-    && apt-get install -y curl ca-certificates gnupg \
-    && mkdir -p /etc/apt/keyrings \
-    && curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \
-    && echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" | tee /etc/apt/sources.list.d/nodesource.list
-
-# Update package list and install Python, Node.js, and build dependencies
-RUN apt-get update \
     && apt-get install -y \
     python3.13 \
     python3.13-dev \
@@ -27,9 +20,7 @@ RUN apt-get update \
     libpq5 \
     libz-dev \
     libssl-dev \
-    postgresql-client \
-    nodejs \
-    && rm -rf /var/lib/apt/lists/*
+    postgresql-client
 
 ENV POETRY_HOME=/opt/poetry
 ENV POETRY_NO_INTERACTION=1
@@ -63,18 +54,13 @@ ENV PATH=/opt/poetry/bin:$PATH
 # Install Python without upgrading system-managed packages
 RUN apt-get update && apt-get install -y \
     python3.13 \
-    python3-pip \
-    && rm -rf /var/lib/apt/lists/*
+    python3-pip
 
 # Copy only necessary files from builder
 COPY --from=builder /app /app
 COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
 COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
-# Copy Node.js installation for Prisma
-COPY --from=builder /usr/bin/node /usr/bin/node
-COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
-COPY --from=builder /usr/bin/npm /usr/bin/npm
-COPY --from=builder /usr/bin/npx /usr/bin/npx
+# Copy Prisma binaries
 COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
 
 ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"

autogpt_platform/backend/backend/blocks/__init__.py

@@ -1,3 +1,4 @@
+import functools
 import importlib
 import logging
 import os
@@ -5,8 +6,6 @@ import re
 from pathlib import Path
 from typing import TYPE_CHECKING, TypeVar
 
-from autogpt_libs.utils.cache import cached
-
 logger = logging.getLogger(__name__)
 
 
@@ -16,7 +15,7 @@ if TYPE_CHECKING:
 T = TypeVar("T")
 
 
-@cached()
+@functools.cache
 def load_all_blocks() -> dict[str, type["Block"]]:
     from backend.data.block import Block
     from backend.util.settings import Config

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -1,214 +0,0 @@
-from typing import Any
-
-from backend.blocks.llm import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    AIBlockBase,
-    AICredentials,
-    AICredentialsField,
-    LlmModel,
-    LLMResponse,
-    llm_call,
-)
-from backend.data.block import BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
-
-
-class AIConditionBlock(AIBlockBase):
-    """
-    An AI-powered condition block that uses natural language to evaluate conditions.
-
-    This block allows users to define conditions in plain English (e.g., "the input is an email address",
-    "the input is a city in the USA") and uses AI to determine if the input satisfies the condition.
-    It provides the same yes/no data pass-through functionality as the standard ConditionBlock.
-    """
-
-    class Input(BlockSchema):
-        input_value: Any = SchemaField(
-            description="The input value to evaluate with the AI condition",
-            placeholder="Enter the value to be evaluated (text, number, or any data)",
-        )
-        condition: str = SchemaField(
-            description="A plaintext English description of the condition to evaluate",
-            placeholder="E.g., 'the input is the body of an email', 'the input is a City in the USA', 'the input is an error or a refusal'",
-        )
-        yes_value: Any = SchemaField(
-            description="(Optional) Value to output if the condition is true. If not provided, input_value will be used.",
-            placeholder="Leave empty to use input_value, or enter a specific value",
-            default=None,
-        )
-        no_value: Any = SchemaField(
-            description="(Optional) Value to output if the condition is false. If not provided, input_value will be used.",
-            placeholder="Leave empty to use input_value, or enter a specific value",
-            default=None,
-        )
-        model: LlmModel = SchemaField(
-            title="LLM Model",
-            default=LlmModel.GPT4O,
-            description="The language model to use for evaluating the condition.",
-            advanced=False,
-        )
-        credentials: AICredentials = AICredentialsField()
-
-    class Output(BlockSchema):
-        result: bool = SchemaField(
-            description="The result of the AI condition evaluation (True or False)"
-        )
-        yes_output: Any = SchemaField(
-            description="The output value if the condition is true"
-        )
-        no_output: Any = SchemaField(
-            description="The output value if the condition is false"
-        )
-        error: str = SchemaField(
-            description="Error message if the AI evaluation is uncertain or fails"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="553ec5b8-6c45-4299-8d75-b394d05f72ff",
-            input_schema=AIConditionBlock.Input,
-            output_schema=AIConditionBlock.Output,
-            description="Uses AI to evaluate natural language conditions and provide conditional outputs",
-            categories={BlockCategory.AI, BlockCategory.LOGIC},
-            test_input={
-                "input_value": "john@example.com",
-                "condition": "the input is an email address",
-                "yes_value": "Valid email",
-                "no_value": "Not an email",
-                "model": LlmModel.GPT4O,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("result", True),
-                ("yes_output", "Valid email"),
-            ],
-            test_mock={
-                "llm_call": lambda *args, **kwargs: LLMResponse(
-                    raw_response="",
-                    prompt=[],
-                    response="true",
-                    tool_calls=None,
-                    prompt_tokens=50,
-                    completion_tokens=10,
-                    reasoning=None,
-                )
-            },
-        )
-
-    async def llm_call(
-        self,
-        credentials: APIKeyCredentials,
-        llm_model: LlmModel,
-        prompt: list,
-        max_tokens: int,
-    ) -> LLMResponse:
-        """Wrapper method for llm_call to enable mocking in tests."""
-        return await llm_call(
-            credentials=credentials,
-            llm_model=llm_model,
-            prompt=prompt,
-            force_json_output=False,
-            max_tokens=max_tokens,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        """
-        Evaluate the AI condition and return appropriate outputs.
-        """
-        # Prepare the yes and no values, using input_value as default
-        yes_value = (
-            input_data.yes_value
-            if input_data.yes_value is not None
-            else input_data.input_value
-        )
-        no_value = (
-            input_data.no_value
-            if input_data.no_value is not None
-            else input_data.input_value
-        )
-
-        # Convert input_value to string for AI evaluation
-        input_str = str(input_data.input_value)
-
-        # Create the prompt for AI evaluation
-        prompt = [
-            {
-                "role": "system",
-                "content": (
-                    "You are an AI assistant that evaluates conditions based on input data. "
-                    "You must respond with only 'true' or 'false' (lowercase) to indicate whether "
-                    "the given condition is met by the input value. Be accurate and consider the "
-                    "context and meaning of both the input and the condition."
-                ),
-            },
-            {
-                "role": "user",
-                "content": (
-                    f"Input value: {input_str}\n"
-                    f"Condition to evaluate: {input_data.condition}\n\n"
-                    f"Does the input value satisfy the condition? Respond with only 'true' or 'false'."
-                ),
-            },
-        ]
-
-        # Call the LLM
-        try:
-            response = await self.llm_call(
-                credentials=credentials,
-                llm_model=input_data.model,
-                prompt=prompt,
-                max_tokens=10,  # We only expect a true/false response
-            )
-
-            # Extract the boolean result from the response
-            response_text = response.response.strip().lower()
-            if response_text == "true":
-                result = True
-            elif response_text == "false":
-                result = False
-            else:
-                # If the response is not clear, try to interpret it using word boundaries
-                import re
-
-                # Use word boundaries to avoid false positives like 'untrue' or '10'
-                tokens = set(re.findall(r"\b(true|false|yes|no|1|0)\b", response_text))
-
-                if tokens == {"true"} or tokens == {"yes"} or tokens == {"1"}:
-                    result = True
-                elif tokens == {"false"} or tokens == {"no"} or tokens == {"0"}:
-                    result = False
-                else:
-                    # Unclear or conflicting response - default to False and yield error
-                    result = False
-                    yield "error", f"Unclear AI response: '{response.response}'"
-
-            # Update internal stats
-            self.merge_stats(
-                NodeExecutionStats(
-                    input_token_count=response.prompt_tokens,
-                    output_token_count=response.completion_tokens,
-                )
-            )
-            self.prompt = response.prompt
-
-        except Exception as e:
-            # In case of any error, default to False to be safe
-            result = False
-            # Log the error but don't fail the block execution
-            import logging
-
-            logger = logging.getLogger(__name__)
-            logger.error(f"AI condition evaluation failed: {str(e)}")
-            yield "error", f"AI evaluation failed: {str(e)}"
-
-        # Yield results
-        yield "result", result
-
-        if result:
-            yield "yes_output", yes_value
-        else:
-            yield "no_output", no_value

autogpt_platform/backend/backend/blocks/airtable/records.py

@@ -241,7 +241,6 @@ class AirtableCreateRecordsBlock(Block):
 
     class Output(BlockSchema):
         records: list[dict] = SchemaField(description="Array of created record objects")
-        details: dict = SchemaField(description="Details of the created records")
 
     def __init__(self):
         super().__init__(
@@ -280,9 +279,6 @@ class AirtableCreateRecordsBlock(Block):
             result_records = normalized_data["records"]
 
         yield "records", result_records
-        details = data.get("details", None)
-        if details:
-            yield "details", details
 
 
 class AirtableUpdateRecordsBlock(Block):

autogpt_platform/backend/backend/blocks/code_executor.py

@@ -1,10 +1,8 @@
 from enum import Enum
-from typing import Any, Literal, Optional
+from typing import Literal
 
 from e2b_code_interpreter import AsyncSandbox
-from e2b_code_interpreter import Result as E2BExecutionResult
-from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
-from pydantic import BaseModel, JsonValue, SecretStr
+from pydantic import SecretStr
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
@@ -38,135 +36,14 @@ class ProgrammingLanguage(Enum):
     JAVA = "java"
 
 
-class MainCodeExecutionResult(BaseModel):
-    """
-    *Pydantic model mirroring `e2b_code_interpreter.Result`*
-
-    Represents the data to be displayed as a result of executing a cell in a Jupyter notebook.
-    The result is similar to the structure returned by ipython kernel: https://ipython.readthedocs.io/en/stable/development/execution.html#execution-semantics
-
-    The result can contain multiple types of data, such as text, images, plots, etc. Each type of data is represented
-    as a string, and the result can contain multiple types of data. The display calls don't have to have text representation,
-    for the actual result the representation is always present for the result, the other representations are always optional.
-    """  # noqa
-
-    class Chart(BaseModel, E2BExecutionResultChart):
-        pass
-
-    text: Optional[str] = None
-    html: Optional[str] = None
-    markdown: Optional[str] = None
-    svg: Optional[str] = None
-    png: Optional[str] = None
-    jpeg: Optional[str] = None
-    pdf: Optional[str] = None
-    latex: Optional[str] = None
-    json: Optional[JsonValue] = None  # type: ignore (reportIncompatibleMethodOverride)
-    javascript: Optional[str] = None
-    data: Optional[dict] = None
-    chart: Optional[Chart] = None
-    extra: Optional[dict] = None
-    """Extra data that can be included. Not part of the standard types."""
-
-
-class CodeExecutionResult(MainCodeExecutionResult):
-    __doc__ = MainCodeExecutionResult.__doc__
-
-    is_main_result: bool = False
-    """Whether this data is the main result of the cell. Data can be produced by display calls of which can be multiple in a cell."""  # noqa
-
-
-class BaseE2BExecutorMixin:
-    """Shared implementation methods for E2B executor blocks."""
-
-    async def execute_code(
-        self,
-        api_key: str,
-        code: str,
-        language: ProgrammingLanguage,
-        template_id: str = "",
-        setup_commands: Optional[list[str]] = None,
-        timeout: Optional[int] = None,
-        sandbox_id: Optional[str] = None,
-        dispose_sandbox: bool = False,
-    ):
-        """
-        Unified code execution method that handles all three use cases:
-        1. Create new sandbox and execute (ExecuteCodeBlock)
-        2. Create new sandbox, execute, and return sandbox_id (InstantiateCodeSandboxBlock)
-        3. Connect to existing sandbox and execute (ExecuteCodeStepBlock)
-        """  # noqa
-        sandbox = None
-        try:
-            if sandbox_id:
-                # Connect to existing sandbox (ExecuteCodeStepBlock case)
-                sandbox = await AsyncSandbox.connect(
-                    sandbox_id=sandbox_id, api_key=api_key
-                )
-            else:
-                # Create new sandbox (ExecuteCodeBlock/InstantiateCodeSandboxBlock case)
-                sandbox = await AsyncSandbox.create(
-                    api_key=api_key, template=template_id, timeout=timeout
-                )
-                if setup_commands:
-                    for cmd in setup_commands:
-                        await sandbox.commands.run(cmd)
-
-            # Execute the code
-            execution = await sandbox.run_code(
-                code,
-                language=language.value,
-                on_error=lambda e: sandbox.kill(),  # Kill the sandbox on error
-            )
-
-            if execution.error:
-                raise Exception(execution.error)
-
-            results = execution.results
-            text_output = execution.text
-            stdout_logs = "".join(execution.logs.stdout)
-            stderr_logs = "".join(execution.logs.stderr)
-
-            return results, text_output, stdout_logs, stderr_logs, sandbox.sandbox_id
-        finally:
-            # Dispose of sandbox if requested to reduce usage costs
-            if dispose_sandbox and sandbox:
-                await sandbox.kill()
-
-    def process_execution_results(
-        self, results: list[E2BExecutionResult]
-    ) -> tuple[dict[str, Any] | None, list[dict[str, Any]]]:
-        """Process and filter execution results."""
-        # Filter out empty formats and convert to dicts
-        processed_results = [
-            {
-                f: value
-                for f in [*r.formats(), "extra", "is_main_result"]
-                if (value := getattr(r, f, None)) is not None
-            }
-            for r in results
-        ]
-        if main_result := next(
-            (r for r in processed_results if r.get("is_main_result")), None
-        ):
-            # Make main_result a copy we can modify & remove is_main_result
-            (main_result := {**main_result}).pop("is_main_result")
-
-        return main_result, processed_results
-
-
-class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
+class CodeExecutionBlock(Block):
     # TODO : Add support to upload and download files
-    # NOTE: Currently, you can only customize the CPU and Memory
-    #       by creating a pre customized sandbox template
+    # Currently, You can customized the CPU and Memory, only by creating a pre customized sandbox template
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description=(
-                "Enter your API key for the E2B platform. "
-                "You can get it in here - https://e2b.dev/docs"
-            ),
+            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
         )
 
         # Todo : Option to run commond in background
@@ -199,14 +76,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
             description="Execution timeout in seconds", default=300
         )
 
-        dispose_sandbox: bool = SchemaField(
-            description=(
-                "Whether to dispose of the sandbox immediately after execution. "
-                "If disabled, the sandbox will run until its timeout expires."
-            ),
-            default=True,
-        )
-
         template_id: str = SchemaField(
             description=(
                 "You can use an E2B sandbox template by entering its ID here. "
@@ -218,16 +87,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
         )
 
     class Output(BlockSchema):
-        main_result: MainCodeExecutionResult = SchemaField(
-            title="Main Result", description="The main result from the code execution"
-        )
-        results: list[CodeExecutionResult] = SchemaField(
-            description="List of results from the code execution"
-        )
-        response: str = SchemaField(
-            title="Main Text Output",
-            description="Text output (if any) of the main execution result",
-        )
+        response: str = SchemaField(description="Response from code execution")
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -237,10 +97,10 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
     def __init__(self):
         super().__init__(
             id="0b02b072-abe7-11ef-8372-fb5d162dd712",
-            description="Executes code in a sandbox environment with internet access.",
+            description="Executes code in an isolated sandbox environment with internet access.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=ExecuteCodeBlock.Input,
-            output_schema=ExecuteCodeBlock.Output,
+            input_schema=CodeExecutionBlock.Input,
+            output_schema=CodeExecutionBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -251,59 +111,91 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 "template_id": "",
             },
             test_output=[
-                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox: (  # noqa
-                    [],  # results
-                    "Hello World",  # text_output
-                    "Hello World\n",  # stdout_logs
-                    "",  # stderr_logs
-                    "sandbox_id",  # sandbox_id
+                "execute_code": lambda code, language, setup_commands, timeout, api_key, template_id: (
+                    "Hello World",
+                    "Hello World\n",
+                    "",
                 ),
             },
         )
 
+    async def execute_code(
+        self,
+        code: str,
+        language: ProgrammingLanguage,
+        setup_commands: list[str],
+        timeout: int,
+        api_key: str,
+        template_id: str,
+    ):
+        try:
+            sandbox = None
+            if template_id:
+                sandbox = await AsyncSandbox.create(
+                    template=template_id, api_key=api_key, timeout=timeout
+                )
+            else:
+                sandbox = await AsyncSandbox.create(api_key=api_key, timeout=timeout)
+
+            if not sandbox:
+                raise Exception("Sandbox not created")
+
+            # Running setup commands
+            for cmd in setup_commands:
+                await sandbox.commands.run(cmd)
+
+            # Executing the code
+            execution = await sandbox.run_code(
+                code,
+                language=language.value,
+                on_error=lambda e: sandbox.kill(),  # Kill the sandbox if there is an error
+            )
+
+            if execution.error:
+                raise Exception(execution.error)
+
+            response = execution.text
+            stdout_logs = "".join(execution.logs.stdout)
+            stderr_logs = "".join(execution.logs.stderr)
+
+            return response, stdout_logs, stderr_logs
+
+        except Exception as e:
+            raise e
+
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            results, text_output, stdout, stderr, _ = await self.execute_code(
-                api_key=credentials.api_key.get_secret_value(),
-                code=input_data.code,
-                language=input_data.language,
-                template_id=input_data.template_id,
-                setup_commands=input_data.setup_commands,
-                timeout=input_data.timeout,
-                dispose_sandbox=input_data.dispose_sandbox,
+            response, stdout_logs, stderr_logs = await self.execute_code(
+                input_data.code,
+                input_data.language,
+                input_data.setup_commands,
+                input_data.timeout,
+                credentials.api_key.get_secret_value(),
+                input_data.template_id,
             )
 
-            # Determine result object shape & filter out empty formats
-            main_result, results = self.process_execution_results(results)
-            if main_result:
-                yield "main_result", main_result
-            yield "results", results
-            if text_output:
-                yield "response", text_output
-            if stdout:
-                yield "stdout_logs", stdout
-            if stderr:
-                yield "stderr_logs", stderr
+            if response:
+                yield "response", response
+            if stdout_logs:
+                yield "stdout_logs", stdout_logs
+            if stderr_logs:
+                yield "stderr_logs", stderr_logs
         except Exception as e:
             yield "error", str(e)
 
 
-class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
+class InstantiationBlock(Block):
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description=(
-                "Enter your API key for the E2B platform. "
-                "You can get it in here - https://e2b.dev/docs"
-            )
+            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
         )
 
         # Todo : Option to run commond in background
@@ -348,10 +240,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
 
     class Output(BlockSchema):
         sandbox_id: str = SchemaField(description="ID of the sandbox instance")
-        response: str = SchemaField(
-            title="Text Result",
-            description="Text result (if any) of the setup code execution",
-        )
+        response: str = SchemaField(description="Response from code execution")
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -361,13 +250,10 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
     def __init__(self):
         super().__init__(
             id="ff0861c9-1726-4aec-9e5b-bf53f3622112",
-            description=(
-                "Instantiate a sandbox environment with internet access "
-                "in which you can execute code with the Execute Code Step block."
-            ),
+            description="Instantiate an isolated sandbox environment with internet access where to execute code in.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=InstantiateCodeSandboxBlock.Input,
-            output_schema=InstantiateCodeSandboxBlock.Output,
+            input_schema=InstantiationBlock.Input,
+            output_schema=InstantiationBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -383,12 +269,11 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout: (  # noqa
-                    [],  # results
-                    "Hello World",  # text_output
-                    "Hello World\n",  # stdout_logs
-                    "",  # stderr_logs
-                    "sandbox_id",  # sandbox_id
+                "execute_code": lambda setup_code, language, setup_commands, timeout, api_key, template_id: (
+                    "sandbox_id",
+                    "Hello World",
+                    "Hello World\n",
+                    "",
                 ),
             },
         )
@@ -397,38 +282,78 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            _, text_output, stdout, stderr, sandbox_id = await self.execute_code(
-                api_key=credentials.api_key.get_secret_value(),
-                code=input_data.setup_code,
-                language=input_data.language,
-                template_id=input_data.template_id,
-                setup_commands=input_data.setup_commands,
-                timeout=input_data.timeout,
+            sandbox_id, response, stdout_logs, stderr_logs = await self.execute_code(
+                input_data.setup_code,
+                input_data.language,
+                input_data.setup_commands,
+                input_data.timeout,
+                credentials.api_key.get_secret_value(),
+                input_data.template_id,
             )
             if sandbox_id:
                 yield "sandbox_id", sandbox_id
             else:
                 yield "error", "Sandbox ID not found"
-
-            if text_output:
-                yield "response", text_output
-            if stdout:
-                yield "stdout_logs", stdout
-            if stderr:
-                yield "stderr_logs", stderr
+            if response:
+                yield "response", response
+            if stdout_logs:
+                yield "stdout_logs", stdout_logs
+            if stderr_logs:
+                yield "stderr_logs", stderr_logs
         except Exception as e:
             yield "error", str(e)
 
+    async def execute_code(
+        self,
+        code: str,
+        language: ProgrammingLanguage,
+        setup_commands: list[str],
+        timeout: int,
+        api_key: str,
+        template_id: str,
+    ):
+        try:
+            sandbox = None
+            if template_id:
+                sandbox = await AsyncSandbox.create(
+                    template=template_id, api_key=api_key, timeout=timeout
+                )
+            else:
+                sandbox = await AsyncSandbox.create(api_key=api_key, timeout=timeout)
 
-class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
+            if not sandbox:
+                raise Exception("Sandbox not created")
+
+            # Running setup commands
+            for cmd in setup_commands:
+                await sandbox.commands.run(cmd)
+
+            # Executing the code
+            execution = await sandbox.run_code(
+                code,
+                language=language.value,
+                on_error=lambda e: sandbox.kill(),  # Kill the sandbox if there is an error
+            )
+
+            if execution.error:
+                raise Exception(execution.error)
+
+            response = execution.text
+            stdout_logs = "".join(execution.logs.stdout)
+            stderr_logs = "".join(execution.logs.stderr)
+
+            return sandbox.sandbox_id, response, stdout_logs, stderr_logs
+
+        except Exception as e:
+            raise e
+
+
+class StepExecutionBlock(Block):
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description=(
-                "Enter your API key for the E2B platform. "
-                "You can get it in here - https://e2b.dev/docs"
-            ),
+            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
         )
 
         sandbox_id: str = SchemaField(
@@ -449,22 +374,8 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
             advanced=False,
         )
 
-        dispose_sandbox: bool = SchemaField(
-            description="Whether to dispose of the sandbox after executing this code.",
-            default=False,
-        )
-
     class Output(BlockSchema):
-        main_result: MainCodeExecutionResult = SchemaField(
-            title="Main Result", description="The main result from the code execution"
-        )
-        results: list[CodeExecutionResult] = SchemaField(
-            description="List of results from the code execution"
-        )
-        response: str = SchemaField(
-            title="Main Text Output",
-            description="Text output (if any) of the main execution result",
-        )
+        response: str = SchemaField(description="Response from code execution")
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -474,10 +385,10 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
     def __init__(self):
         super().__init__(
             id="82b59b8e-ea10-4d57-9161-8b169b0adba6",
-            description="Execute code in a previously instantiated sandbox.",
+            description="Execute code in a previously instantiated sandbox environment.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=ExecuteCodeStepBlock.Input,
-            output_schema=ExecuteCodeStepBlock.Output,
+            input_schema=StepExecutionBlock.Input,
+            output_schema=StepExecutionBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -486,43 +397,61 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
                 "language": ProgrammingLanguage.PYTHON.value,
             },
             test_output=[
-                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda api_key, code, language, sandbox_id, dispose_sandbox: (  # noqa
-                    [],  # results
-                    "Hello World",  # text_output
-                    "Hello World\n",  # stdout_logs
-                    "",  # stderr_logs
-                    sandbox_id,  # sandbox_id
+                "execute_step_code": lambda sandbox_id, step_code, language, api_key: (
+                    "Hello World",
+                    "Hello World\n",
+                    "",
                 ),
             },
         )
 
+    async def execute_step_code(
+        self,
+        sandbox_id: str,
+        code: str,
+        language: ProgrammingLanguage,
+        api_key: str,
+    ):
+        try:
+            sandbox = await AsyncSandbox.connect(sandbox_id=sandbox_id, api_key=api_key)
+            if not sandbox:
+                raise Exception("Sandbox not found")
+
+            # Executing the code
+            execution = await sandbox.run_code(code, language=language.value)
+
+            if execution.error:
+                raise Exception(execution.error)
+
+            response = execution.text
+            stdout_logs = "".join(execution.logs.stdout)
+            stderr_logs = "".join(execution.logs.stderr)
+
+            return response, stdout_logs, stderr_logs
+
+        except Exception as e:
+            raise e
+
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            results, text_output, stdout, stderr, _ = await self.execute_code(
-                api_key=credentials.api_key.get_secret_value(),
-                code=input_data.step_code,
-                language=input_data.language,
-                sandbox_id=input_data.sandbox_id,
-                dispose_sandbox=input_data.dispose_sandbox,
+            response, stdout_logs, stderr_logs = await self.execute_step_code(
+                input_data.sandbox_id,
+                input_data.step_code,
+                input_data.language,
+                credentials.api_key.get_secret_value(),
             )
 
-            # Determine result object shape & filter out empty formats
-            main_result, results = self.process_execution_results(results)
-            if main_result:
-                yield "main_result", main_result
-            yield "results", results
-            if text_output:
-                yield "response", text_output
-            if stdout:
-                yield "stdout_logs", stdout
-            if stderr:
-                yield "stderr_logs", stderr
+            if response:
+                yield "response", response
+            if stdout_logs:
+                yield "stdout_logs", stdout_logs
+            if stderr_logs:
+                yield "stderr_logs", stderr_logs
         except Exception as e:
             yield "error", str(e)

autogpt_platform/backend/backend/blocks/code_extraction_block.py

@@ -90,7 +90,7 @@ class CodeExtractionBlock(Block):
                 for aliases in language_aliases.values()
                 for alias in aliases
             )
-            + r")[ \t]*\n[\s\S]*?```"
+            + r")\s+[\s\S]*?```"
         )
 
         remaining_text = re.sub(pattern, "", input_data.text).strip()
@@ -103,9 +103,7 @@ class CodeExtractionBlock(Block):
         # Escape special regex characters in the language string
         language = re.escape(language)
         # Extract all code blocks enclosed in ```language``` blocks
-        pattern = re.compile(
-            rf"```{language}[ \t]*\n(.*?)\n```", re.DOTALL | re.IGNORECASE
-        )
+        pattern = re.compile(rf"```{language}\s+(.*?)```", re.DOTALL | re.IGNORECASE)
         matches = pattern.finditer(text)
         # Combine all code blocks for this language with newlines between them
         code_blocks = [match.group(1).strip() for match in matches]

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -66,7 +66,6 @@ class AddToDictionaryBlock(Block):
         dictionary: dict[Any, Any] = SchemaField(
             default_factory=dict,
             description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
-            advanced=False,
         )
         key: str = SchemaField(
             default="",

autogpt_platform/backend/backend/blocks/dataforseo/_api.py

@@ -113,7 +113,6 @@ class DataForSeoClient:
         include_serp_info: bool = False,
         include_clickstream_data: bool = False,
         limit: int = 100,
-        depth: Optional[int] = None,
     ) -> List[Dict[str, Any]]:
         """
         Get related keywords from DataForSEO Labs.
@@ -126,7 +125,6 @@ class DataForSeoClient:
             include_serp_info: Include SERP data
             include_clickstream_data: Include clickstream metrics
             limit: Maximum number of results (up to 3000)
-            depth: Keyword search depth (0-4), controls number of returned keywords
 
         Returns:
             API response with related keywords
@@ -150,8 +148,6 @@ class DataForSeoClient:
             task_data["include_clickstream_data"] = include_clickstream_data
         if limit is not None:
             task_data["limit"] = limit
-        if depth is not None:
-            task_data["depth"] = depth
 
         payload = [task_data]
 

autogpt_platform/backend/backend/blocks/dataforseo/keyword_suggestions.py

@@ -90,7 +90,6 @@ class DataForSeoKeywordSuggestionsBlock(Block):
         seed_keyword: str = SchemaField(
             description="The seed keyword used for the query"
         )
-        error: str = SchemaField(description="Error message if the API call failed")
 
     def __init__(self):
         super().__init__(
@@ -162,52 +161,43 @@ class DataForSeoKeywordSuggestionsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the keyword suggestions query."""
-        try:
-            client = DataForSeoClient(credentials)
+        client = DataForSeoClient(credentials)
 
-            results = await self._fetch_keyword_suggestions(client, input_data)
+        results = await self._fetch_keyword_suggestions(client, input_data)
 
-            # Process and format the results
-            suggestions = []
-            if results and len(results) > 0:
-                # results is a list, get the first element
-                first_result = results[0] if isinstance(results, list) else results
-                items = (
-                    first_result.get("items", [])
-                    if isinstance(first_result, dict)
-                    else []
+        # Process and format the results
+        suggestions = []
+        if results and len(results) > 0:
+            # results is a list, get the first element
+            first_result = results[0] if isinstance(results, list) else results
+            items = (
+                first_result.get("items", []) if isinstance(first_result, dict) else []
+            )
+            for item in items:
+                # Create the KeywordSuggestion object
+                suggestion = KeywordSuggestion(
+                    keyword=item.get("keyword", ""),
+                    search_volume=item.get("keyword_info", {}).get("search_volume"),
+                    competition=item.get("keyword_info", {}).get("competition"),
+                    cpc=item.get("keyword_info", {}).get("cpc"),
+                    keyword_difficulty=item.get("keyword_properties", {}).get(
+                        "keyword_difficulty"
+                    ),
+                    serp_info=(
+                        item.get("serp_info") if input_data.include_serp_info else None
+                    ),
+                    clickstream_data=(
+                        item.get("clickstream_keyword_info")
+                        if input_data.include_clickstream_data
+                        else None
+                    ),
                 )
-                if items is None:
-                    items = []
-                for item in items:
-                    # Create the KeywordSuggestion object
-                    suggestion = KeywordSuggestion(
-                        keyword=item.get("keyword", ""),
-                        search_volume=item.get("keyword_info", {}).get("search_volume"),
-                        competition=item.get("keyword_info", {}).get("competition"),
-                        cpc=item.get("keyword_info", {}).get("cpc"),
-                        keyword_difficulty=item.get("keyword_properties", {}).get(
-                            "keyword_difficulty"
-                        ),
-                        serp_info=(
-                            item.get("serp_info")
-                            if input_data.include_serp_info
-                            else None
-                        ),
-                        clickstream_data=(
-                            item.get("clickstream_keyword_info")
-                            if input_data.include_clickstream_data
-                            else None
-                        ),
-                    )
-                    yield "suggestion", suggestion
-                    suggestions.append(suggestion)
+                yield "suggestion", suggestion
+                suggestions.append(suggestion)
 
-            yield "suggestions", suggestions
-            yield "total_count", len(suggestions)
-            yield "seed_keyword", input_data.keyword
-        except Exception as e:
-            yield "error", f"Failed to fetch keyword suggestions: {str(e)}"
+        yield "suggestions", suggestions
+        yield "total_count", len(suggestions)
+        yield "seed_keyword", input_data.keyword
 
 
 class KeywordSuggestionExtractorBlock(Block):

autogpt_platform/backend/backend/blocks/dataforseo/related_keywords.py

@@ -78,12 +78,6 @@ class DataForSeoRelatedKeywordsBlock(Block):
             ge=1,
             le=3000,
         )
-        depth: int = SchemaField(
-            description="Keyword search depth (0-4). Controls the number of returned keywords: 0=1 keyword, 1=~8 keywords, 2=~72 keywords, 3=~584 keywords, 4=~4680 keywords",
-            default=1,
-            ge=0,
-            le=4,
-        )
 
     class Output(BlockSchema):
         related_keywords: List[RelatedKeyword] = SchemaField(
@@ -98,7 +92,6 @@ class DataForSeoRelatedKeywordsBlock(Block):
         seed_keyword: str = SchemaField(
             description="The seed keyword used for the query"
         )
-        error: str = SchemaField(description="Error message if the API call failed")
 
     def __init__(self):
         super().__init__(
@@ -161,7 +154,6 @@ class DataForSeoRelatedKeywordsBlock(Block):
             include_serp_info=input_data.include_serp_info,
             include_clickstream_data=input_data.include_clickstream_data,
             limit=input_data.limit,
-            depth=input_data.depth,
         )
 
     async def run(
@@ -172,60 +164,50 @@ class DataForSeoRelatedKeywordsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the related keywords query."""
-        try:
-            client = DataForSeoClient(credentials)
+        client = DataForSeoClient(credentials)
 
-            results = await self._fetch_related_keywords(client, input_data)
+        results = await self._fetch_related_keywords(client, input_data)
 
-            # Process and format the results
-            related_keywords = []
-            if results and len(results) > 0:
-                # results is a list, get the first element
-                first_result = results[0] if isinstance(results, list) else results
-                items = (
-                    first_result.get("items", [])
-                    if isinstance(first_result, dict)
-                    else []
+        # Process and format the results
+        related_keywords = []
+        if results and len(results) > 0:
+            # results is a list, get the first element
+            first_result = results[0] if isinstance(results, list) else results
+            items = (
+                first_result.get("items", []) if isinstance(first_result, dict) else []
+            )
+            for item in items:
+                # Extract keyword_data from the item
+                keyword_data = item.get("keyword_data", {})
+
+                # Create the RelatedKeyword object
+                keyword = RelatedKeyword(
+                    keyword=keyword_data.get("keyword", ""),
+                    search_volume=keyword_data.get("keyword_info", {}).get(
+                        "search_volume"
+                    ),
+                    competition=keyword_data.get("keyword_info", {}).get("competition"),
+                    cpc=keyword_data.get("keyword_info", {}).get("cpc"),
+                    keyword_difficulty=keyword_data.get("keyword_properties", {}).get(
+                        "keyword_difficulty"
+                    ),
+                    serp_info=(
+                        keyword_data.get("serp_info")
+                        if input_data.include_serp_info
+                        else None
+                    ),
+                    clickstream_data=(
+                        keyword_data.get("clickstream_keyword_info")
+                        if input_data.include_clickstream_data
+                        else None
+                    ),
                 )
-                # Ensure items is never None
-                if items is None:
-                    items = []
-                for item in items:
-                    # Extract keyword_data from the item
-                    keyword_data = item.get("keyword_data", {})
+                yield "related_keyword", keyword
+                related_keywords.append(keyword)
 
-                    # Create the RelatedKeyword object
-                    keyword = RelatedKeyword(
-                        keyword=keyword_data.get("keyword", ""),
-                        search_volume=keyword_data.get("keyword_info", {}).get(
-                            "search_volume"
-                        ),
-                        competition=keyword_data.get("keyword_info", {}).get(
-                            "competition"
-                        ),
-                        cpc=keyword_data.get("keyword_info", {}).get("cpc"),
-                        keyword_difficulty=keyword_data.get(
-                            "keyword_properties", {}
-                        ).get("keyword_difficulty"),
-                        serp_info=(
-                            keyword_data.get("serp_info")
-                            if input_data.include_serp_info
-                            else None
-                        ),
-                        clickstream_data=(
-                            keyword_data.get("clickstream_keyword_info")
-                            if input_data.include_clickstream_data
-                            else None
-                        ),
-                    )
-                    yield "related_keyword", keyword
-                    related_keywords.append(keyword)
-
-            yield "related_keywords", related_keywords
-            yield "total_count", len(related_keywords)
-            yield "seed_keyword", input_data.keyword
-        except Exception as e:
-            yield "error", f"Failed to fetch related keywords: {str(e)}"
+        yield "related_keywords", related_keywords
+        yield "total_count", len(related_keywords)
+        yield "seed_keyword", input_data.keyword
 
 
 class RelatedKeywordExtractorBlock(Block):

autogpt_platform/backend/backend/blocks/discord/bot_blocks.py

@@ -171,11 +171,11 @@ class SendDiscordMessageBlock(Block):
             description="The content of the message to send"
         )
         channel_name: str = SchemaField(
-            description="Channel ID or channel name to send the message to"
+            description="The name of the channel the message will be sent to"
         )
         server_name: str = SchemaField(
-            description="Server name (only needed if using channel name)",
-            advanced=True,
+            description="The name of the server where the channel is located",
+            advanced=True,  # Optional field for server name
             default="",
         )
 
@@ -231,49 +231,25 @@ class SendDiscordMessageBlock(Block):
         @client.event
         async def on_ready():
             print(f"Logged in as {client.user}")
-            channel = None
+            for guild in client.guilds:
+                if server_name and guild.name != server_name:
+                    continue
+                for channel in guild.text_channels:
+                    if channel.name == channel_name:
+                        # Split message into chunks if it exceeds 2000 characters
+                        chunks = self.chunk_message(message_content)
+                        last_message = None
+                        for chunk in chunks:
+                            last_message = await channel.send(chunk)
+                        result["status"] = "Message sent"
+                        result["message_id"] = (
+                            str(last_message.id) if last_message else ""
+                        )
+                        result["channel_id"] = str(channel.id)
+                        await client.close()
+                        return
 
-            # Try to parse as channel ID first
-            try:
-                channel_id = int(channel_name)
-                channel = client.get_channel(channel_id)
-            except ValueError:
-                # Not a valid ID, will try name lookup
-                pass
-
-            # If not found by ID (or not an ID), try name lookup
-            if not channel:
-                for guild in client.guilds:
-                    if server_name and guild.name != server_name:
-                        continue
-                    for ch in guild.text_channels:
-                        if ch.name == channel_name:
-                            channel = ch
-                            break
-                    if channel:
-                        break
-
-            if not channel:
-                result["status"] = f"Channel not found: {channel_name}"
-                await client.close()
-                return
-
-            # Type check - ensure it's a text channel that can send messages
-            if not hasattr(channel, "send"):
-                result["status"] = (
-                    f"Channel {channel_name} cannot receive messages (not a text channel)"
-                )
-                await client.close()
-                return
-
-            # Split message into chunks if it exceeds 2000 characters
-            chunks = self.chunk_message(message_content)
-            last_message = None
-            for chunk in chunks:
-                last_message = await channel.send(chunk)  # type: ignore
-            result["status"] = "Message sent"
-            result["message_id"] = str(last_message.id) if last_message else ""
-            result["channel_id"] = str(channel.id)
+            result["status"] = "Channel not found"
             await client.close()
 
         await client.start(token)

autogpt_platform/backend/backend/blocks/firecrawl/_api.py

@@ -1,12 +0,0 @@
-from enum import Enum
-
-
-class ScrapeFormat(Enum):
-    MARKDOWN = "markdown"
-    HTML = "html"
-    RAW_HTML = "rawHtml"
-    LINKS = "links"
-    SCREENSHOT = "screenshot"
-    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
-    JSON = "json"
-    CHANGE_TRACKING = "changeTracking"

autogpt_platform/backend/backend/blocks/firecrawl/_format_utils.py

@@ -1,28 +0,0 @@
-"""Utility functions for converting between our ScrapeFormat enum and firecrawl FormatOption types."""
-
-from typing import List
-
-from firecrawl.v2.types import FormatOption, ScreenshotFormat
-
-from backend.blocks.firecrawl._api import ScrapeFormat
-
-
-def convert_to_format_options(
-    formats: List[ScrapeFormat],
-) -> List[FormatOption]:
-    """Convert our ScrapeFormat enum values to firecrawl FormatOption types.
-
-    Handles special cases like screenshot@fullPage which needs to be converted
-    to a ScreenshotFormat object.
-    """
-    result: List[FormatOption] = []
-
-    for format_enum in formats:
-        if format_enum.value == "screenshot@fullPage":
-            # Special case: convert to ScreenshotFormat with full_page=True
-            result.append(ScreenshotFormat(type="screenshot", full_page=True))
-        else:
-            # Regular string literals
-            result.append(format_enum.value)
-
-    return result

autogpt_platform/backend/backend/blocks/firecrawl/crawl.py

@@ -1,9 +1,8 @@
+from enum import Enum
 from typing import Any
 
-from firecrawl import FirecrawlApp
-from firecrawl.v2.types import ScrapeOptions
+from firecrawl import FirecrawlApp, ScrapeOptions
 
-from backend.blocks.firecrawl._api import ScrapeFormat
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -15,10 +14,21 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-from ._format_utils import convert_to_format_options
+
+
+class ScrapeFormat(Enum):
+    MARKDOWN = "markdown"
+    HTML = "html"
+    RAW_HTML = "rawHtml"
+    LINKS = "links"
+    SCREENSHOT = "screenshot"
+    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
+    JSON = "json"
+    CHANGE_TRACKING = "changeTracking"
 
 
 class FirecrawlCrawlBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
@@ -68,17 +78,18 @@ class FirecrawlCrawlBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         # Sync call
-        crawl_result = app.crawl(
+        crawl_result = app.crawl_url(
             input_data.url,
             limit=input_data.limit,
             scrape_options=ScrapeOptions(
-                formats=convert_to_format_options(input_data.formats),
-                only_main_content=input_data.only_main_content,
-                max_age=input_data.max_age,
-                wait_for=input_data.wait_for,
+                formats=[format.value for format in input_data.formats],
+                onlyMainContent=input_data.only_main_content,
+                maxAge=input_data.max_age,
+                waitFor=input_data.wait_for,
             ),
         )
         yield "data", crawl_result.data
@@ -90,7 +101,7 @@ class FirecrawlCrawlBlock(Block):
                 elif f == ScrapeFormat.HTML:
                     yield "html", data.html
                 elif f == ScrapeFormat.RAW_HTML:
-                    yield "raw_html", data.raw_html
+                    yield "raw_html", data.rawHtml
                 elif f == ScrapeFormat.LINKS:
                     yield "links", data.links
                 elif f == ScrapeFormat.SCREENSHOT:
@@ -98,6 +109,6 @@ class FirecrawlCrawlBlock(Block):
                 elif f == ScrapeFormat.SCREENSHOT_FULL_PAGE:
                     yield "screenshot_full_page", data.screenshot
                 elif f == ScrapeFormat.CHANGE_TRACKING:
-                    yield "change_tracking", data.change_tracking
+                    yield "change_tracking", data.changeTracking
                 elif f == ScrapeFormat.JSON:
                     yield "json", data.json

autogpt_platform/backend/backend/blocks/firecrawl/extract.py

@@ -20,6 +20,7 @@ from ._config import firecrawl
 
 @cost(BlockCost(2, BlockCostType.RUN))
 class FirecrawlExtractBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         urls: list[str] = SchemaField(
@@ -52,6 +53,7 @@ class FirecrawlExtractBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         extract_result = app.extract(

autogpt_platform/backend/backend/blocks/firecrawl/map.py

@@ -1,5 +1,3 @@
-from typing import Any
-
 from firecrawl import FirecrawlApp
 
 from backend.sdk import (
@@ -16,16 +14,14 @@ from ._config import firecrawl
 
 
 class FirecrawlMapWebsiteBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
 
         url: str = SchemaField(description="The website url to map")
 
     class Output(BlockSchema):
-        links: list[str] = SchemaField(description="List of URLs found on the website")
-        results: list[dict[str, Any]] = SchemaField(
-            description="List of search results with url, title, and description"
-        )
+        links: list[str] = SchemaField(description="The links of the website")
 
     def __init__(self):
         super().__init__(
@@ -39,22 +35,12 @@ class FirecrawlMapWebsiteBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         # Sync call
-        map_result = app.map(
+        map_result = app.map_url(
             url=input_data.url,
         )
 
-        # Convert SearchResult objects to dicts
-        results_data = [
-            {
-                "url": link.url,
-                "title": link.title,
-                "description": link.description,
-            }
-            for link in map_result.links
-        ]
-
-        yield "links", [link.url for link in map_result.links]
-        yield "results", results_data
+        yield "links", map_result.links

autogpt_platform/backend/backend/blocks/firecrawl/scrape.py

@@ -1,8 +1,8 @@
+from enum import Enum
 from typing import Any
 
 from firecrawl import FirecrawlApp
 
-from backend.blocks.firecrawl._api import ScrapeFormat
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -14,10 +14,21 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-from ._format_utils import convert_to_format_options
+
+
+class ScrapeFormat(Enum):
+    MARKDOWN = "markdown"
+    HTML = "html"
+    RAW_HTML = "rawHtml"
+    LINKS = "links"
+    SCREENSHOT = "screenshot"
+    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
+    JSON = "json"
+    CHANGE_TRACKING = "changeTracking"
 
 
 class FirecrawlScrapeBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
@@ -67,11 +78,12 @@ class FirecrawlScrapeBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
-        scrape_result = app.scrape(
+        scrape_result = app.scrape_url(
             input_data.url,
-            formats=convert_to_format_options(input_data.formats),
+            formats=[format.value for format in input_data.formats],
             only_main_content=input_data.only_main_content,
             max_age=input_data.max_age,
             wait_for=input_data.wait_for,
@@ -84,7 +96,7 @@ class FirecrawlScrapeBlock(Block):
             elif f == ScrapeFormat.HTML:
                 yield "html", scrape_result.html
             elif f == ScrapeFormat.RAW_HTML:
-                yield "raw_html", scrape_result.raw_html
+                yield "raw_html", scrape_result.rawHtml
             elif f == ScrapeFormat.LINKS:
                 yield "links", scrape_result.links
             elif f == ScrapeFormat.SCREENSHOT:
@@ -92,6 +104,6 @@ class FirecrawlScrapeBlock(Block):
             elif f == ScrapeFormat.SCREENSHOT_FULL_PAGE:
                 yield "screenshot_full_page", scrape_result.screenshot
             elif f == ScrapeFormat.CHANGE_TRACKING:
-                yield "change_tracking", scrape_result.change_tracking
+                yield "change_tracking", scrape_result.changeTracking
             elif f == ScrapeFormat.JSON:
                 yield "json", scrape_result.json

autogpt_platform/backend/backend/blocks/firecrawl/search.py

@@ -1,9 +1,8 @@
+from enum import Enum
 from typing import Any
 
-from firecrawl import FirecrawlApp
-from firecrawl.v2.types import ScrapeOptions
+from firecrawl import FirecrawlApp, ScrapeOptions
 
-from backend.blocks.firecrawl._api import ScrapeFormat
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -15,10 +14,21 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-from ._format_utils import convert_to_format_options
+
+
+class ScrapeFormat(Enum):
+    MARKDOWN = "markdown"
+    HTML = "html"
+    RAW_HTML = "rawHtml"
+    LINKS = "links"
+    SCREENSHOT = "screenshot"
+    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
+    JSON = "json"
+    CHANGE_TRACKING = "changeTracking"
 
 
 class FirecrawlSearchBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         query: str = SchemaField(description="The query to search for")
@@ -51,6 +61,7 @@ class FirecrawlSearchBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         # Sync call
@@ -58,12 +69,11 @@ class FirecrawlSearchBlock(Block):
             input_data.query,
             limit=input_data.limit,
             scrape_options=ScrapeOptions(
-                formats=convert_to_format_options(input_data.formats) or None,
-                max_age=input_data.max_age,
-                wait_for=input_data.wait_for,
+                formats=[format.value for format in input_data.formats],
+                maxAge=input_data.max_age,
+                waitFor=input_data.wait_for,
             ),
         )
         yield "data", scrape_result
-        if hasattr(scrape_result, "web") and scrape_result.web:
-            for site in scrape_result.web:
-                yield "site", site
+        for site in scrape_result.data:
+            yield "site", site

autogpt_platform/backend/backend/blocks/io.py

@@ -10,6 +10,7 @@ from backend.util.settings import Config
 from backend.util.text import TextFormatter
 from backend.util.type import LongTextType, MediaFileType, ShortTextType
 
+formatter = TextFormatter()
 config = Config()
 
 
@@ -131,11 +132,6 @@ class AgentOutputBlock(Block):
             default="",
             advanced=True,
         )
-        escape_html: bool = SchemaField(
-            default=False,
-            advanced=True,
-            description="Whether to escape special characters in the inserted values to be HTML-safe. Enable for HTML output, disable for plain text.",
-        )
         advanced: bool = SchemaField(
             description="Whether to treat the output as advanced.",
             default=False,
@@ -197,7 +193,6 @@ class AgentOutputBlock(Block):
         """
         if input_data.format:
             try:
-                formatter = TextFormatter(autoescape=input_data.escape_html)
                 yield "output", formatter.format_string(
                     input_data.format, {input_data.name: input_data.value}
                 )
@@ -554,89 +549,6 @@ class AgentToggleInputBlock(AgentInputBlock):
         )
 
 
-class AgentTableInputBlock(AgentInputBlock):
-    """
-    This block allows users to input data in a table format.
-
-    Configure the table columns at build time, then users can input
-    rows of data at runtime. Each row is output as a dictionary
-    with column names as keys.
-    """
-
-    class Input(AgentInputBlock.Input):
-        value: Optional[list[dict[str, Any]]] = SchemaField(
-            description="The table data as a list of dictionaries.",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-        column_headers: list[str] = SchemaField(
-            description="Column headers for the table.",
-            default_factory=lambda: ["Column 1", "Column 2", "Column 3"],
-            advanced=False,
-            title="Column Headers",
-        )
-
-        def generate_schema(self):
-            """Generate schema for the value field with table format."""
-            schema = super().generate_schema()
-            schema["type"] = "array"
-            schema["format"] = "table"
-            schema["items"] = {
-                "type": "object",
-                "properties": {
-                    header: {"type": "string"}
-                    for header in (
-                        self.column_headers or ["Column 1", "Column 2", "Column 3"]
-                    )
-                },
-            }
-            if self.value is not None:
-                schema["default"] = self.value
-            return schema
-
-    class Output(AgentInputBlock.Output):
-        result: list[dict[str, Any]] = SchemaField(
-            description="The table data as a list of dictionaries with headers as keys."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="5603b273-f41e-4020-af7d-fbc9c6a8d928",
-            description="Block for table data input with customizable headers.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentTableInputBlock.Input,
-            output_schema=AgentTableInputBlock.Output,
-            test_input=[
-                {
-                    "name": "test_table",
-                    "column_headers": ["Name", "Age", "City"],
-                    "value": [
-                        {"Name": "John", "Age": "30", "City": "New York"},
-                        {"Name": "Jane", "Age": "25", "City": "London"},
-                    ],
-                    "description": "Example table input",
-                }
-            ],
-            test_output=[
-                (
-                    "result",
-                    [
-                        {"Name": "John", "Age": "30", "City": "New York"},
-                        {"Name": "Jane", "Age": "25", "City": "London"},
-                    ],
-                )
-            ],
-        )
-
-    async def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
-        """
-        Yields the table data as a list of dictionaries.
-        """
-        # Pass through the value, defaulting to empty list if None
-        yield "result", input_data.value if input_data.value is not None else []
-
-
 IO_BLOCK_IDs = [
     AgentInputBlock().id,
     AgentOutputBlock().id,
@@ -648,5 +560,4 @@ IO_BLOCK_IDs = [
     AgentFileInputBlock().id,
     AgentDropdownInputBlock().id,
     AgentToggleInputBlock().id,
-    AgentTableInputBlock().id,
 ]

autogpt_platform/backend/backend/blocks/iteration.py

@@ -54,43 +54,20 @@ class StepThroughItemsBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Security fix: Add limits to prevent DoS from large iterations
-        MAX_ITEMS = 10000  # Maximum items to iterate
-        MAX_ITEM_SIZE = 1024 * 1024  # 1MB per item
-
         for data in [input_data.items, input_data.items_object, input_data.items_str]:
             if not data:
                 continue
-
-            # Limit string size before parsing
             if isinstance(data, str):
-                if len(data) > MAX_ITEM_SIZE:
-                    raise ValueError(
-                        f"Input too large: {len(data)} bytes > {MAX_ITEM_SIZE} bytes"
-                    )
                 items = json.loads(data)
             else:
                 items = data
-
-            # Check total item count
-            if isinstance(items, (list, dict)):
-                if len(items) > MAX_ITEMS:
-                    raise ValueError(f"Too many items: {len(items)} > {MAX_ITEMS}")
-
-            iteration_count = 0
             if isinstance(items, dict):
                 # If items is a dictionary, iterate over its values
-                for key, value in items.items():
-                    if iteration_count >= MAX_ITEMS:
-                        break
-                    yield "item", value
-                    yield "key", key  # Fixed: should yield key, not item
-                    iteration_count += 1
+                for item in items.values():
+                    yield "item", item
+                    yield "key", item
             else:
                 # If items is a list, iterate over the list
                 for index, item in enumerate(items):
-                    if iteration_count >= MAX_ITEMS:
-                        break
                     yield "item", item
                     yield "key", index
-                    iteration_count += 1

autogpt_platform/backend/backend/blocks/jina/fact_checker.py

@@ -1,8 +1,5 @@
-from typing import List
 from urllib.parse import quote
 
-from typing_extensions import TypedDict
-
 from backend.blocks.jina._auth import (
     JinaCredentials,
     JinaCredentialsField,
@@ -13,12 +10,6 @@ from backend.data.model import SchemaField
 from backend.util.request import Requests
 
 
-class Reference(TypedDict):
-    url: str
-    keyQuote: str
-    isSupportive: bool
-
-
 class FactCheckerBlock(Block):
     class Input(BlockSchema):
         statement: str = SchemaField(
@@ -32,10 +23,6 @@ class FactCheckerBlock(Block):
         )
         result: bool = SchemaField(description="The result of the factuality check")
         reason: str = SchemaField(description="The reason for the factuality result")
-        references: List[Reference] = SchemaField(
-            description="List of references supporting or contradicting the statement",
-            default=[],
-        )
         error: str = SchemaField(description="Error message if the check fails")
 
     def __init__(self):
@@ -66,11 +53,5 @@ class FactCheckerBlock(Block):
             yield "factuality", data["factuality"]
             yield "result", data["result"]
             yield "reason", data["reason"]
-
-            # Yield references if present in the response
-            if "references" in data:
-                yield "references", data["references"]
-            else:
-                yield "references", []
         else:
             raise RuntimeError(f"Expected 'data' key not found in response: {data}")

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

@@ -37,5 +37,5 @@ class Project(BaseModel):
     name: str
     description: str
     priority: int
-    progress: float
-    content: str | None
+    progress: int
+    content: str

autogpt_platform/backend/backend/blocks/llm.py

@@ -1,9 +1,5 @@
-# This file contains a lot of prompt block strings that would trigger "line too long"
-# flake8: noqa: E501
 import ast
 import logging
-import re
-import secrets
 from abc import ABC
 from enum import Enum, EnumMeta
 from json import JSONDecodeError
@@ -31,7 +27,7 @@ from backend.util.prompt import compress_prompt, estimate_token_count
 from backend.util.text import TextFormatter
 
 logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]")
-fmt = TextFormatter(autoescape=False)
+fmt = TextFormatter()
 
 LLMProviderName = Literal[
     ProviderName.AIML_API,
@@ -101,8 +97,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_1_OPUS = "claude-opus-4-1-20250805"
     CLAUDE_4_OPUS = "claude-opus-4-20250514"
     CLAUDE_4_SONNET = "claude-sonnet-4-20250514"
-    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
-    CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
     CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
     CLAUDE_3_5_SONNET = "claude-3-5-sonnet-latest"
     CLAUDE_3_5_HAIKU = "claude-3-5-haiku-latest"
@@ -210,19 +204,13 @@ MODEL_METADATA = {
         "anthropic", 200000, 32000
     ),  # claude-opus-4-1-20250805
     LlmModel.CLAUDE_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000
+        "anthropic", 200000, 8192
     ),  # claude-4-opus-20250514
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 8192
     ),  # claude-4-sonnet-20250514
-    LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
-    ),  # claude-sonnet-4-5-20250929
-    LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
-        "anthropic", 200000, 64000
-    ),  # claude-haiku-4-5-20251001
     LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 8192
     ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_5_SONNET: ModelMetadata(
         "anthropic", 200000, 8192
@@ -394,9 +382,7 @@ def extract_openai_tool_calls(response) -> list[ToolContentBlock] | None:
     return None
 
 
-def get_parallel_tool_calls_param(
-    llm_model: LlmModel, parallel_tool_calls: bool | None
-):
+def get_parallel_tool_calls_param(llm_model: LlmModel, parallel_tool_calls):
     """Get the appropriate parallel_tool_calls parameter for OpenAI-compatible APIs."""
     if llm_model.startswith("o") or parallel_tool_calls is None:
         return openai.NOT_GIVEN
@@ -407,8 +393,8 @@ async def llm_call(
     credentials: APIKeyCredentials,
     llm_model: LlmModel,
     prompt: list[dict],
+    json_format: bool,
     max_tokens: int | None,
-    force_json_output: bool = False,
     tools: list[dict] | None = None,
     ollama_host: str = "localhost:11434",
     parallel_tool_calls=None,
@@ -421,7 +407,7 @@ async def llm_call(
         credentials: The API key credentials to use.
         llm_model: The LLM model to use.
         prompt: The prompt to send to the LLM.
-        force_json_output: Whether the response should be in JSON format.
+        json_format: Whether the response should be in JSON format.
         max_tokens: The maximum number of tokens to generate in the chat completion.
         tools: The tools to use in the chat completion.
         ollama_host: The host for ollama to use.
@@ -460,7 +446,7 @@ async def llm_call(
             llm_model, parallel_tool_calls
         )
 
-        if force_json_output:
+        if json_format:
             response_format = {"type": "json_object"}
 
         response = await oai_client.chat.completions.create(
@@ -573,7 +559,7 @@ async def llm_call(
             raise ValueError("Groq does not support tools.")
 
         client = AsyncGroq(api_key=credentials.api_key.get_secret_value())
-        response_format = {"type": "json_object"} if force_json_output else None
+        response_format = {"type": "json_object"} if json_format else None
         response = await client.chat.completions.create(
             model=llm_model.value,
             messages=prompt,  # type: ignore
@@ -731,7 +717,7 @@ async def llm_call(
         )
 
         response_format = None
-        if force_json_output:
+        if json_format:
             response_format = {"type": "json_object"}
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
@@ -794,17 +780,6 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
-        force_json_output: bool = SchemaField(
-            title="Restrict LLM to pure JSON output",
-            default=False,
-            description=(
-                "Whether to force the LLM to produce a JSON-only response. "
-                "This can increase the block's reliability, "
-                "but may also reduce the quality of the response "
-                "because it prohibits the LLM from reasoning "
-                "before providing its JSON response."
-            ),
-        )
         credentials: AICredentials = AICredentialsField()
         sys_prompt: str = SchemaField(
             title="System Prompt",
@@ -873,18 +848,17 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 "llm_call": lambda *args, **kwargs: LLMResponse(
                     raw_response="",
                     prompt=[""],
-                    response=(
-                        '<json_output id="test123456">{\n'
-                        '  "key1": "key1Value",\n'
-                        '  "key2": "key2Value"\n'
-                        "}</json_output>"
+                    response=json.dumps(
+                        {
+                            "key1": "key1Value",
+                            "key2": "key2Value",
+                        }
                     ),
                     tool_calls=None,
                     prompt_tokens=0,
                     completion_tokens=0,
                     reasoning=None,
-                ),
-                "get_collision_proof_output_tag_id": lambda *args: "test123456",
+                )
             },
         )
 
@@ -893,9 +867,9 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         credentials: APIKeyCredentials,
         llm_model: LlmModel,
         prompt: list[dict],
+        json_format: bool,
+        compress_prompt_to_fit: bool,
         max_tokens: int | None,
-        force_json_output: bool = False,
-        compress_prompt_to_fit: bool = True,
         tools: list[dict] | None = None,
         ollama_host: str = "localhost:11434",
     ) -> LLMResponse:
@@ -908,8 +882,8 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             credentials=credentials,
             llm_model=llm_model,
             prompt=prompt,
+            json_format=json_format,
             max_tokens=max_tokens,
-            force_json_output=force_json_output,
             tools=tools,
             ollama_host=ollama_host,
             compress_prompt_to_fit=compress_prompt_to_fit,
@@ -921,6 +895,10 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         logger.debug(f"Calling LLM with input data: {input_data}")
         prompt = [json.to_dict(p) for p in input_data.conversation_history]
 
+        def trim_prompt(s: str) -> str:
+            lines = s.strip().split("\n")
+            return "\n".join([line.strip().lstrip("|") for line in lines])
+
         values = input_data.prompt_values
         if values:
             input_data.prompt = fmt.format_string(input_data.prompt, values)
@@ -929,15 +907,27 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         if input_data.sys_prompt:
             prompt.append({"role": "system", "content": input_data.sys_prompt})
 
-        # Use a one-time unique tag to prevent collisions with user/LLM content
-        output_tag_id = self.get_collision_proof_output_tag_id()
-        output_tag_start = f'<json_output id="{output_tag_id}">'
         if input_data.expected_format:
-            sys_prompt = self.response_format_instructions(
-                input_data.expected_format,
-                list_mode=input_data.list_result,
-                pure_json_mode=input_data.force_json_output,
-                output_tag_start=output_tag_start,
+            expected_format = [
+                f'"{k}": "{v}"' for k, v in input_data.expected_format.items()
+            ]
+            if input_data.list_result:
+                format_prompt = (
+                    f'"results": [\n  {{\n  {", ".join(expected_format)}\n  }}\n]'
+                )
+            else:
+                format_prompt = "\n  ".join(expected_format)
+
+            sys_prompt = trim_prompt(
+                f"""
+                  |Reply strictly only in the following JSON format:
+                  |{{
+                  |  {format_prompt}
+                  |}}
+                  |
+                  |Ensure the response is valid JSON. Do not include any additional text outside of the JSON.
+                  |If you cannot provide all the keys, provide an empty string for the values you cannot answer.
+                """
             )
             prompt.append({"role": "system", "content": sys_prompt})
 
@@ -955,21 +945,18 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             except JSONDecodeError as e:
                 return f"JSON decode error: {e}"
 
-        error_feedback_message = ""
+        logger.debug(f"LLM request: {prompt}")
+        retry_prompt = ""
         llm_model = input_data.model
 
         for retry_count in range(input_data.retry):
-            logger.debug(f"LLM request: {prompt}")
             try:
                 llm_response = await self.llm_call(
                     credentials=credentials,
                     llm_model=llm_model,
                     prompt=prompt,
                     compress_prompt_to_fit=input_data.compress_prompt_to_fit,
-                    force_json_output=(
-                        input_data.force_json_output
-                        and bool(input_data.expected_format)
-                    ),
+                    json_format=bool(input_data.expected_format),
                     ollama_host=input_data.ollama_host,
                     max_tokens=input_data.max_tokens,
                 )
@@ -983,55 +970,16 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 logger.debug(f"LLM attempt-{retry_count} response: {response_text}")
 
                 if input_data.expected_format:
-                    try:
-                        response_obj = self.get_json_from_response(
-                            response_text,
-                            pure_json_mode=input_data.force_json_output,
-                            output_tag_start=output_tag_start,
-                        )
-                    except (ValueError, JSONDecodeError) as parse_error:
-                        censored_response = re.sub(r"[A-Za-z0-9]", "*", response_text)
-                        response_snippet = (
-                            f"{censored_response[:50]}...{censored_response[-30:]}"
-                        )
-                        logger.warning(
-                            f"Error getting JSON from LLM response: {parse_error}\n\n"
-                            f"Response start+end: `{response_snippet}`"
-                        )
-                        prompt.append({"role": "assistant", "content": response_text})
 
-                        error_feedback_message = self.invalid_response_feedback(
-                            parse_error,
-                            was_parseable=False,
-                            list_mode=input_data.list_result,
-                            pure_json_mode=input_data.force_json_output,
-                            output_tag_start=output_tag_start,
-                        )
-                        prompt.append(
-                            {"role": "user", "content": error_feedback_message}
-                        )
-                        continue
+                    response_obj = json.loads(response_text)
 
-                    # Handle object response for `force_json_output`+`list_result`
                     if input_data.list_result and isinstance(response_obj, dict):
-                        if "results" in response_obj and isinstance(
-                            response_obj["results"], list
-                        ):
-                            response_obj = response_obj["results"]
-                        else:
-                            error_feedback_message = (
-                                "Expected an array of objects in the 'results' key, "
-                                f"but got: {response_obj}"
-                            )
-                            prompt.append(
-                                {"role": "assistant", "content": response_text}
-                            )
-                            prompt.append(
-                                {"role": "user", "content": error_feedback_message}
-                            )
-                            continue
+                        if "results" in response_obj:
+                            response_obj = response_obj.get("results", [])
+                        elif len(response_obj) == 1:
+                            response_obj = list(response_obj.values())
 
-                    validation_errors = "\n".join(
+                    response_error = "\n".join(
                         [
                             validation_error
                             for response_item in (
@@ -1043,7 +991,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                         ]
                     )
 
-                    if not validation_errors:
+                    if not response_error:
                         self.merge_stats(
                             NodeExecutionStats(
                                 llm_call_count=retry_count + 1,
@@ -1053,16 +1001,6 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                         yield "response", response_obj
                         yield "prompt", self.prompt
                         return
-
-                    prompt.append({"role": "assistant", "content": response_text})
-                    error_feedback_message = self.invalid_response_feedback(
-                        validation_errors,
-                        was_parseable=True,
-                        list_mode=input_data.list_result,
-                        pure_json_mode=input_data.force_json_output,
-                        output_tag_start=output_tag_start,
-                    )
-                    prompt.append({"role": "user", "content": error_feedback_message})
                 else:
                     self.merge_stats(
                         NodeExecutionStats(
@@ -1073,6 +1011,21 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                     yield "response", {"response": response_text}
                     yield "prompt", self.prompt
                     return
+
+                retry_prompt = trim_prompt(
+                    f"""
+                  |This is your previous error response:
+                  |--
+                  |{response_text}
+                  |--
+                  |
+                  |And this is the error:
+                  |--
+                  |{response_error}
+                  |--
+                """
+                )
+                prompt.append({"role": "user", "content": retry_prompt})
             except Exception as e:
                 logger.exception(f"Error calling LLM: {e}")
                 if (
@@ -1085,133 +1038,9 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                     logger.debug(
                         f"Reducing max_tokens to {input_data.max_tokens} for next attempt"
                     )
-                    # Don't add retry prompt for token limit errors,
-                    # just retry with lower maximum output tokens
+                retry_prompt = f"Error calling LLM: {e}"
 
-                error_feedback_message = f"Error calling LLM: {e}"
-
-        raise RuntimeError(error_feedback_message)
-
-    def response_format_instructions(
-        self,
-        expected_object_format: dict[str, str],
-        *,
-        list_mode: bool,
-        pure_json_mode: bool,
-        output_tag_start: str,
-    ) -> str:
-        expected_output_format = json.dumps(expected_object_format, indent=2)
-        output_type = "object" if not list_mode else "array"
-        outer_output_type = "object" if pure_json_mode else output_type
-
-        if output_type == "array":
-            indented_obj_format = expected_output_format.replace("\n", "\n  ")
-            expected_output_format = f"[\n  {indented_obj_format},\n  ...\n]"
-            if pure_json_mode:
-                indented_list_format = expected_output_format.replace("\n", "\n  ")
-                expected_output_format = (
-                    "{\n"
-                    '  "reasoning": "... (optional)",\n'  # for better performance
-                    f'  "results": {indented_list_format}\n'
-                    "}"
-                )
-
-        # Preserve indentation in prompt
-        expected_output_format = expected_output_format.replace("\n", "\n|")
-
-        # Prepare prompt
-        if not pure_json_mode:
-            expected_output_format = (
-                f"{output_tag_start}\n{expected_output_format}\n</json_output>"
-            )
-
-        instructions = f"""
-        |In your response you MUST include a valid JSON {outer_output_type} strictly following this format:
-        |{expected_output_format}
-        |
-        |If you cannot provide all the keys, you MUST provide an empty string for the values you cannot answer.
-        """.strip()
-
-        if not pure_json_mode:
-            instructions += f"""
-            |
-            |You MUST enclose your final JSON answer in {output_tag_start}...</json_output> tags, even if the user specifies a different tag.
-            |There MUST be exactly ONE {output_tag_start}...</json_output> block in your response, which MUST ONLY contain the JSON {outer_output_type} and nothing else. Other text outside this block is allowed.
-            """.strip()
-
-        return trim_prompt(instructions)
-
-    def invalid_response_feedback(
-        self,
-        error,
-        *,
-        was_parseable: bool,
-        list_mode: bool,
-        pure_json_mode: bool,
-        output_tag_start: str,
-    ) -> str:
-        outer_output_type = "object" if not list_mode or pure_json_mode else "array"
-
-        if was_parseable:
-            complaint = f"Your previous response did not match the expected {outer_output_type} format."
-        else:
-            complaint = f"Your previous response did not contain a parseable JSON {outer_output_type}."
-
-        indented_parse_error = str(error).replace("\n", "\n|")
-
-        instruction = (
-            f"Please provide a {output_tag_start}...</json_output> block containing a"
-            if not pure_json_mode
-            else "Please provide a"
-        ) + f" valid JSON {outer_output_type} that matches the expected format."
-
-        return trim_prompt(
-            f"""
-            |{complaint}
-            |
-            |{indented_parse_error}
-            |
-            |{instruction}
-        """
-        )
-
-    def get_json_from_response(
-        self, response_text: str, *, pure_json_mode: bool, output_tag_start: str
-    ) -> dict[str, Any] | list[dict[str, Any]]:
-        if pure_json_mode:
-            # Handle pure JSON responses
-            try:
-                return json.loads(response_text)
-            except JSONDecodeError as first_parse_error:
-                # If that didn't work, try finding the { and } to deal with possible ```json fences etc.
-                json_start = response_text.find("{")
-                json_end = response_text.rfind("}")
-                try:
-                    return json.loads(response_text[json_start : json_end + 1])
-                except JSONDecodeError:
-                    # Raise the original error, as it's more likely to be relevant
-                    raise first_parse_error from None
-
-        if output_tag_start not in response_text:
-            raise ValueError(
-                "Response does not contain the expected "
-                f"{output_tag_start}...</json_output> block."
-            )
-        json_output = (
-            response_text.split(output_tag_start, 1)[1]
-            .rsplit("</json_output>", 1)[0]
-            .strip()
-        )
-        return json.loads(json_output)
-
-    def get_collision_proof_output_tag_id(self) -> str:
-        return secrets.token_hex(8)
-
-
-def trim_prompt(s: str) -> str:
-    """Removes indentation up to and including `|` from a multi-line prompt."""
-    lines = s.strip().split("\n")
-    return "\n".join([line.strip().lstrip("|") for line in lines])
+        raise RuntimeError(retry_prompt)
 
 
 class AITextGeneratorBlock(AIBlockBase):
@@ -1408,27 +1237,11 @@ class AITextSummarizerBlock(AIBlockBase):
 
     @staticmethod
     def _split_text(text: str, max_tokens: int, overlap: int) -> list[str]:
-        # Security fix: Add validation to prevent DoS attacks
-        # Limit text size to prevent memory exhaustion
-        MAX_TEXT_LENGTH = 1_000_000  # 1MB character limit
-        MAX_CHUNKS = 100  # Maximum number of chunks to prevent excessive memory use
-
-        if len(text) > MAX_TEXT_LENGTH:
-            text = text[:MAX_TEXT_LENGTH]
-
-        # Ensure chunk_size is at least 1 to prevent infinite loops
-        chunk_size = max(1, max_tokens - overlap)
-
-        # Ensure overlap is less than max_tokens to prevent invalid configurations
-        if overlap >= max_tokens:
-            overlap = max(0, max_tokens - 1)
-
         words = text.split()
         chunks = []
+        chunk_size = max_tokens - overlap
 
         for i in range(0, len(words), chunk_size):
-            if len(chunks) >= MAX_CHUNKS:
-                break  # Limit the number of chunks to prevent memory exhaustion
             chunk = " ".join(words[i : i + max_tokens])
             chunks.append(chunk)
 

autogpt_platform/backend/backend/blocks/notion/_api.py

@@ -1,536 +0,0 @@
-"""
-Notion API helper functions and client for making authenticated requests.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from backend.data.model import OAuth2Credentials
-from backend.util.request import Requests
-
-NOTION_VERSION = "2022-06-28"
-
-
-class NotionAPIException(Exception):
-    """Exception raised for Notion API errors."""
-
-    def __init__(self, message: str, status_code: int):
-        super().__init__(message)
-        self.status_code = status_code
-
-
-class NotionClient:
-    """Client for interacting with the Notion API."""
-
-    def __init__(self, credentials: OAuth2Credentials):
-        self.credentials = credentials
-        self.headers = {
-            "Authorization": credentials.auth_header(),
-            "Notion-Version": NOTION_VERSION,
-            "Content-Type": "application/json",
-        }
-        self.requests = Requests()
-
-    async def get_page(self, page_id: str) -> dict:
-        """
-        Fetch a page by ID.
-
-        Args:
-            page_id: The ID of the page to fetch.
-
-        Returns:
-            The page object from Notion API.
-        """
-        url = f"https://api.notion.com/v1/pages/{page_id}"
-        response = await self.requests.get(url, headers=self.headers)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to fetch page: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def get_blocks(self, block_id: str, recursive: bool = True) -> List[dict]:
-        """
-        Fetch all blocks from a page or block.
-
-        Args:
-            block_id: The ID of the page or block to fetch children from.
-            recursive: Whether to fetch nested blocks recursively.
-
-        Returns:
-            List of block objects.
-        """
-        blocks = []
-        cursor = None
-
-        while True:
-            url = f"https://api.notion.com/v1/blocks/{block_id}/children"
-            params = {"page_size": 100}
-            if cursor:
-                params["start_cursor"] = cursor
-
-            response = await self.requests.get(url, headers=self.headers, params=params)
-
-            if not response.ok:
-                raise NotionAPIException(
-                    f"Failed to fetch blocks: {response.status} - {response.text()}",
-                    response.status,
-                )
-
-            data = response.json()
-            current_blocks = data.get("results", [])
-
-            # If recursive, fetch children for blocks that have them
-            if recursive:
-                for block in current_blocks:
-                    if block.get("has_children"):
-                        block["children"] = await self.get_blocks(
-                            block["id"], recursive=True
-                        )
-
-            blocks.extend(current_blocks)
-
-            if not data.get("has_more"):
-                break
-            cursor = data.get("next_cursor")
-
-        return blocks
-
-    async def query_database(
-        self,
-        database_id: str,
-        filter_obj: Optional[dict] = None,
-        sorts: Optional[List[dict]] = None,
-        page_size: int = 100,
-    ) -> dict:
-        """
-        Query a database with optional filters and sorts.
-
-        Args:
-            database_id: The ID of the database to query.
-            filter_obj: Optional filter object for the query.
-            sorts: Optional list of sort objects.
-            page_size: Number of results per page.
-
-        Returns:
-            Query results including pages and pagination info.
-        """
-        url = f"https://api.notion.com/v1/databases/{database_id}/query"
-
-        payload: Dict[str, Any] = {"page_size": page_size}
-        if filter_obj:
-            payload["filter"] = filter_obj
-        if sorts:
-            payload["sorts"] = sorts
-
-        response = await self.requests.post(url, headers=self.headers, json=payload)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to query database: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def create_page(
-        self,
-        parent: dict,
-        properties: dict,
-        children: Optional[List[dict]] = None,
-        icon: Optional[dict] = None,
-        cover: Optional[dict] = None,
-    ) -> dict:
-        """
-        Create a new page.
-
-        Args:
-            parent: Parent object (page_id or database_id).
-            properties: Page properties.
-            children: Optional list of block children.
-            icon: Optional icon object.
-            cover: Optional cover object.
-
-        Returns:
-            The created page object.
-        """
-        url = "https://api.notion.com/v1/pages"
-
-        payload: Dict[str, Any] = {"parent": parent, "properties": properties}
-
-        if children:
-            payload["children"] = children
-        if icon:
-            payload["icon"] = icon
-        if cover:
-            payload["cover"] = cover
-
-        response = await self.requests.post(url, headers=self.headers, json=payload)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to create page: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def update_page(self, page_id: str, properties: dict) -> dict:
-        """
-        Update a page's properties.
-
-        Args:
-            page_id: The ID of the page to update.
-            properties: Properties to update.
-
-        Returns:
-            The updated page object.
-        """
-        url = f"https://api.notion.com/v1/pages/{page_id}"
-
-        response = await self.requests.patch(
-            url, headers=self.headers, json={"properties": properties}
-        )
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to update page: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def append_blocks(self, block_id: str, children: List[dict]) -> dict:
-        """
-        Append blocks to a page or block.
-
-        Args:
-            block_id: The ID of the page or block to append to.
-            children: List of block objects to append.
-
-        Returns:
-            Response with the created blocks.
-        """
-        url = f"https://api.notion.com/v1/blocks/{block_id}/children"
-
-        response = await self.requests.patch(
-            url, headers=self.headers, json={"children": children}
-        )
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to append blocks: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def search(
-        self,
-        query: str = "",
-        filter_obj: Optional[dict] = None,
-        sort: Optional[dict] = None,
-        page_size: int = 100,
-    ) -> dict:
-        """
-        Search for pages and databases.
-
-        Args:
-            query: Search query text.
-            filter_obj: Optional filter object.
-            sort: Optional sort object.
-            page_size: Number of results per page.
-
-        Returns:
-            Search results.
-        """
-        url = "https://api.notion.com/v1/search"
-
-        payload: Dict[str, Any] = {"page_size": page_size}
-        if query:
-            payload["query"] = query
-        if filter_obj:
-            payload["filter"] = filter_obj
-        if sort:
-            payload["sort"] = sort
-
-        response = await self.requests.post(url, headers=self.headers, json=payload)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Search failed: {response.status} - {response.text()}", response.status
-            )
-
-        return response.json()
-
-
-# Conversion helper functions
-
-
-def parse_rich_text(rich_text_array: List[dict]) -> str:
-    """
-    Extract plain text from a Notion rich text array.
-
-    Args:
-        rich_text_array: Array of rich text objects from Notion.
-
-    Returns:
-        Plain text string.
-    """
-    if not rich_text_array:
-        return ""
-
-    text_parts = []
-    for text_obj in rich_text_array:
-        if "plain_text" in text_obj:
-            text_parts.append(text_obj["plain_text"])
-
-    return "".join(text_parts)
-
-
-def rich_text_to_markdown(rich_text_array: List[dict]) -> str:
-    """
-    Convert Notion rich text array to markdown with formatting.
-
-    Args:
-        rich_text_array: Array of rich text objects from Notion.
-
-    Returns:
-        Markdown formatted string.
-    """
-    if not rich_text_array:
-        return ""
-
-    markdown_parts = []
-
-    for text_obj in rich_text_array:
-        text = text_obj.get("plain_text", "")
-        annotations = text_obj.get("annotations", {})
-
-        # Apply formatting based on annotations
-        if annotations.get("code"):
-            text = f"`{text}`"
-        else:
-            if annotations.get("bold"):
-                text = f"**{text}**"
-            if annotations.get("italic"):
-                text = f"*{text}*"
-            if annotations.get("strikethrough"):
-                text = f"~~{text}~~"
-            if annotations.get("underline"):
-                text = f"<u>{text}</u>"
-
-        # Handle links
-        if text_obj.get("href"):
-            text = f"[{text}]({text_obj['href']})"
-
-        markdown_parts.append(text)
-
-    return "".join(markdown_parts)
-
-
-def block_to_markdown(block: dict, indent_level: int = 0) -> str:
-    """
-    Convert a single Notion block to markdown.
-
-    Args:
-        block: Block object from Notion API.
-        indent_level: Current indentation level for nested blocks.
-
-    Returns:
-        Markdown string representation of the block.
-    """
-    block_type = block.get("type")
-    indent = "  " * indent_level
-    markdown_lines = []
-
-    # Handle different block types
-    if block_type == "paragraph":
-        text = rich_text_to_markdown(block["paragraph"].get("rich_text", []))
-        if text:
-            markdown_lines.append(f"{indent}{text}")
-
-    elif block_type == "heading_1":
-        text = parse_rich_text(block["heading_1"].get("rich_text", []))
-        markdown_lines.append(f"{indent}# {text}")
-
-    elif block_type == "heading_2":
-        text = parse_rich_text(block["heading_2"].get("rich_text", []))
-        markdown_lines.append(f"{indent}## {text}")
-
-    elif block_type == "heading_3":
-        text = parse_rich_text(block["heading_3"].get("rich_text", []))
-        markdown_lines.append(f"{indent}### {text}")
-
-    elif block_type == "bulleted_list_item":
-        text = rich_text_to_markdown(block["bulleted_list_item"].get("rich_text", []))
-        markdown_lines.append(f"{indent}- {text}")
-
-    elif block_type == "numbered_list_item":
-        text = rich_text_to_markdown(block["numbered_list_item"].get("rich_text", []))
-        # Note: This is simplified - proper numbering would need context
-        markdown_lines.append(f"{indent}1. {text}")
-
-    elif block_type == "to_do":
-        text = rich_text_to_markdown(block["to_do"].get("rich_text", []))
-        checked = "x" if block["to_do"].get("checked") else " "
-        markdown_lines.append(f"{indent}- [{checked}] {text}")
-
-    elif block_type == "toggle":
-        text = rich_text_to_markdown(block["toggle"].get("rich_text", []))
-        markdown_lines.append(f"{indent}<details>")
-        markdown_lines.append(f"{indent}<summary>{text}</summary>")
-        markdown_lines.append(f"{indent}")
-        # Process children if they exist
-        if block.get("children"):
-            for child in block["children"]:
-                child_markdown = block_to_markdown(child, indent_level + 1)
-                if child_markdown:
-                    markdown_lines.append(child_markdown)
-        markdown_lines.append(f"{indent}</details>")
-
-    elif block_type == "code":
-        code = parse_rich_text(block["code"].get("rich_text", []))
-        language = block["code"].get("language", "")
-        markdown_lines.append(f"{indent}```{language}")
-        markdown_lines.append(f"{indent}{code}")
-        markdown_lines.append(f"{indent}```")
-
-    elif block_type == "quote":
-        text = rich_text_to_markdown(block["quote"].get("rich_text", []))
-        markdown_lines.append(f"{indent}> {text}")
-
-    elif block_type == "divider":
-        markdown_lines.append(f"{indent}---")
-
-    elif block_type == "image":
-        image = block["image"]
-        url = image.get("external", {}).get("url") or image.get("file", {}).get(
-            "url", ""
-        )
-        caption = parse_rich_text(image.get("caption", []))
-        alt_text = caption if caption else "Image"
-        markdown_lines.append(f"{indent}![{alt_text}]({url})")
-        if caption:
-            markdown_lines.append(f"{indent}*{caption}*")
-
-    elif block_type == "video":
-        video = block["video"]
-        url = video.get("external", {}).get("url") or video.get("file", {}).get(
-            "url", ""
-        )
-        caption = parse_rich_text(video.get("caption", []))
-        markdown_lines.append(f"{indent}[Video]({url})")
-        if caption:
-            markdown_lines.append(f"{indent}*{caption}*")
-
-    elif block_type == "file":
-        file = block["file"]
-        url = file.get("external", {}).get("url") or file.get("file", {}).get("url", "")
-        caption = parse_rich_text(file.get("caption", []))
-        name = caption if caption else "File"
-        markdown_lines.append(f"{indent}[{name}]({url})")
-
-    elif block_type == "bookmark":
-        url = block["bookmark"].get("url", "")
-        caption = parse_rich_text(block["bookmark"].get("caption", []))
-        markdown_lines.append(f"{indent}[{caption if caption else url}]({url})")
-
-    elif block_type == "equation":
-        expression = block["equation"].get("expression", "")
-        markdown_lines.append(f"{indent}$${expression}$$")
-
-    elif block_type == "callout":
-        text = rich_text_to_markdown(block["callout"].get("rich_text", []))
-        icon = block["callout"].get("icon", {})
-        if icon.get("emoji"):
-            markdown_lines.append(f"{indent}> {icon['emoji']} {text}")
-        else:
-            markdown_lines.append(f"{indent}> ℹ️ {text}")
-
-    elif block_type == "child_page":
-        title = block["child_page"].get("title", "Untitled")
-        markdown_lines.append(f"{indent}📄 [{title}](notion://page/{block['id']})")
-
-    elif block_type == "child_database":
-        title = block["child_database"].get("title", "Untitled Database")
-        markdown_lines.append(f"{indent}🗂️ [{title}](notion://database/{block['id']})")
-
-    elif block_type == "table":
-        # Tables are complex - for now just indicate there's a table
-        markdown_lines.append(
-            f"{indent}[Table with {block['table'].get('table_width', 0)} columns]"
-        )
-
-    elif block_type == "column_list":
-        # Process columns
-        if block.get("children"):
-            markdown_lines.append(f"{indent}<div style='display: flex'>")
-            for column in block["children"]:
-                markdown_lines.append(f"{indent}<div style='flex: 1'>")
-                if column.get("children"):
-                    for child in column["children"]:
-                        child_markdown = block_to_markdown(child, indent_level + 1)
-                        if child_markdown:
-                            markdown_lines.append(child_markdown)
-                markdown_lines.append(f"{indent}</div>")
-            markdown_lines.append(f"{indent}</div>")
-
-    # Handle children for blocks that haven't been processed yet
-    elif block.get("children") and block_type not in ["toggle", "column_list"]:
-        for child in block["children"]:
-            child_markdown = block_to_markdown(child, indent_level)
-            if child_markdown:
-                markdown_lines.append(child_markdown)
-
-    return "\n".join(markdown_lines) if markdown_lines else ""
-
-
-def blocks_to_markdown(blocks: List[dict]) -> str:
-    """
-    Convert a list of Notion blocks to a markdown document.
-
-    Args:
-        blocks: List of block objects from Notion API.
-
-    Returns:
-        Complete markdown document as a string.
-    """
-    markdown_parts = []
-
-    for i, block in enumerate(blocks):
-        markdown = block_to_markdown(block)
-        if markdown:
-            markdown_parts.append(markdown)
-            # Add spacing between top-level blocks (except lists)
-            if i < len(blocks) - 1:
-                next_type = blocks[i + 1].get("type", "")
-                current_type = block.get("type", "")
-                # Don't add extra spacing between list items
-                list_types = {"bulleted_list_item", "numbered_list_item", "to_do"}
-                if not (current_type in list_types and next_type in list_types):
-                    markdown_parts.append("")
-
-    return "\n".join(markdown_parts)
-
-
-def extract_page_title(page: dict) -> str:
-    """
-    Extract the title from a Notion page object.
-
-    Args:
-        page: Page object from Notion API.
-
-    Returns:
-        Page title as a string.
-    """
-    properties = page.get("properties", {})
-
-    # Find the title property (it has type "title")
-    for prop_name, prop_value in properties.items():
-        if prop_value.get("type") == "title":
-            return parse_rich_text(prop_value.get("title", []))
-
-    return "Untitled"

autogpt_platform/backend/backend/blocks/notion/_auth.py

@@ -1,42 +0,0 @@
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import CredentialsField, CredentialsMetaInput, OAuth2Credentials
-from backend.integrations.providers import ProviderName
-from backend.util.settings import Secrets
-
-secrets = Secrets()
-NOTION_OAUTH_IS_CONFIGURED = bool(
-    secrets.notion_client_id and secrets.notion_client_secret
-)
-
-NotionCredentials = OAuth2Credentials
-NotionCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.NOTION], Literal["oauth2"]
-]
-
-
-def NotionCredentialsField() -> NotionCredentialsInput:
-    """Creates a Notion OAuth2 credentials field."""
-    return CredentialsField(
-        description="Connect your Notion account. Ensure the pages/databases are shared with the integration."
-    )
-
-
-# Test credentials for Notion OAuth2
-TEST_CREDENTIALS = OAuth2Credentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="notion",
-    access_token=SecretStr("test_access_token"),
-    title="Mock Notion OAuth",
-    scopes=["read_content", "insert_content", "update_content"],
-    username="testuser",
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}

autogpt_platform/backend/backend/blocks/notion/create_page.py

@@ -1,360 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional
-
-from pydantic import model_validator
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionCreatePageBlock(Block):
-    """Create a new page in Notion with content."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        parent_page_id: Optional[str] = SchemaField(
-            description="Parent page ID to create the page under. Either this OR parent_database_id is required.",
-            default=None,
-        )
-        parent_database_id: Optional[str] = SchemaField(
-            description="Parent database ID to create the page in. Either this OR parent_page_id is required.",
-            default=None,
-        )
-        title: str = SchemaField(
-            description="Title of the new page",
-        )
-        content: Optional[str] = SchemaField(
-            description="Content for the page. Can be plain text or markdown - will be converted to Notion blocks.",
-            default=None,
-        )
-        properties: Optional[Dict[str, Any]] = SchemaField(
-            description="Additional properties for database pages (e.g., {'Status': 'In Progress', 'Priority': 'High'})",
-            default=None,
-        )
-        icon_emoji: Optional[str] = SchemaField(
-            description="Emoji to use as the page icon (e.g., '📄', '🚀')", default=None
-        )
-
-        @model_validator(mode="after")
-        def validate_parent(self):
-            """Ensure either parent_page_id or parent_database_id is provided."""
-            if not self.parent_page_id and not self.parent_database_id:
-                raise ValueError(
-                    "Either parent_page_id or parent_database_id must be provided"
-                )
-            if self.parent_page_id and self.parent_database_id:
-                raise ValueError(
-                    "Only one of parent_page_id or parent_database_id should be provided, not both"
-                )
-            return self
-
-    class Output(BlockSchema):
-        page_id: str = SchemaField(description="ID of the created page.")
-        page_url: str = SchemaField(description="URL of the created page.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="c15febe0-66ce-4c6f-aebd-5ab351653804",
-            description="Create a new page in Notion. Requires EITHER a parent_page_id OR parent_database_id. Supports markdown content.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionCreatePageBlock.Input,
-            output_schema=NotionCreatePageBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "parent_page_id": "00000000-0000-0000-0000-000000000000",
-                "title": "Test Page",
-                "content": "This is test content.",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("page_id", "12345678-1234-1234-1234-123456789012"),
-                (
-                    "page_url",
-                    "https://notion.so/Test-Page-12345678123412341234123456789012",
-                ),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "create_page": lambda *args, **kwargs: (
-                    "12345678-1234-1234-1234-123456789012",
-                    "https://notion.so/Test-Page-12345678123412341234123456789012",
-                )
-            },
-        )
-
-    @staticmethod
-    def _markdown_to_blocks(content: str) -> List[dict]:
-        """Convert markdown content to Notion block objects."""
-        if not content:
-            return []
-
-        blocks = []
-        lines = content.split("\n")
-        i = 0
-
-        while i < len(lines):
-            line = lines[i]
-
-            # Skip empty lines
-            if not line.strip():
-                i += 1
-                continue
-
-            # Headings
-            if line.startswith("### "):
-                blocks.append(
-                    {
-                        "type": "heading_3",
-                        "heading_3": {
-                            "rich_text": [
-                                {"type": "text", "text": {"content": line[4:].strip()}}
-                            ]
-                        },
-                    }
-                )
-            elif line.startswith("## "):
-                blocks.append(
-                    {
-                        "type": "heading_2",
-                        "heading_2": {
-                            "rich_text": [
-                                {"type": "text", "text": {"content": line[3:].strip()}}
-                            ]
-                        },
-                    }
-                )
-            elif line.startswith("# "):
-                blocks.append(
-                    {
-                        "type": "heading_1",
-                        "heading_1": {
-                            "rich_text": [
-                                {"type": "text", "text": {"content": line[2:].strip()}}
-                            ]
-                        },
-                    }
-                )
-            # Bullet points
-            elif line.strip().startswith("- "):
-                blocks.append(
-                    {
-                        "type": "bulleted_list_item",
-                        "bulleted_list_item": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": line.strip()[2:].strip()},
-                                }
-                            ]
-                        },
-                    }
-                )
-            # Numbered list
-            elif line.strip() and line.strip()[0].isdigit() and ". " in line:
-                content_start = line.find(". ") + 2
-                blocks.append(
-                    {
-                        "type": "numbered_list_item",
-                        "numbered_list_item": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": line[content_start:].strip()},
-                                }
-                            ]
-                        },
-                    }
-                )
-            # Code block
-            elif line.strip().startswith("```"):
-                code_lines = []
-                language = line[3:].strip() or "plain text"
-                i += 1
-                while i < len(lines) and not lines[i].strip().startswith("```"):
-                    code_lines.append(lines[i])
-                    i += 1
-                blocks.append(
-                    {
-                        "type": "code",
-                        "code": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": "\n".join(code_lines)},
-                                }
-                            ],
-                            "language": language,
-                        },
-                    }
-                )
-            # Quote
-            elif line.strip().startswith("> "):
-                blocks.append(
-                    {
-                        "type": "quote",
-                        "quote": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": line.strip()[2:].strip()},
-                                }
-                            ]
-                        },
-                    }
-                )
-            # Horizontal rule
-            elif line.strip() in ["---", "***", "___"]:
-                blocks.append({"type": "divider", "divider": {}})
-            # Regular paragraph
-            else:
-                # Parse for basic markdown formatting
-                text_content = line.strip()
-                rich_text = []
-
-                # Simple bold/italic parsing (this is simplified)
-                if "**" in text_content or "*" in text_content:
-                    # For now, just pass as plain text
-                    # A full implementation would parse and create proper annotations
-                    rich_text = [{"type": "text", "text": {"content": text_content}}]
-                else:
-                    rich_text = [{"type": "text", "text": {"content": text_content}}]
-
-                blocks.append(
-                    {"type": "paragraph", "paragraph": {"rich_text": rich_text}}
-                )
-
-            i += 1
-
-        return blocks
-
-    @staticmethod
-    def _build_properties(
-        title: str, additional_properties: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
-        """Build properties object for page creation."""
-        properties: Dict[str, Any] = {
-            "title": {"title": [{"type": "text", "text": {"content": title}}]}
-        }
-
-        if additional_properties:
-            for key, value in additional_properties.items():
-                if key.lower() == "title":
-                    continue  # Skip title as we already have it
-
-                # Try to intelligently map property types
-                if isinstance(value, bool):
-                    properties[key] = {"checkbox": value}
-                elif isinstance(value, (int, float)):
-                    properties[key] = {"number": value}
-                elif isinstance(value, list):
-                    # Assume multi-select
-                    properties[key] = {
-                        "multi_select": [{"name": str(item)} for item in value]
-                    }
-                elif isinstance(value, str):
-                    # Could be select, rich_text, or other types
-                    # For simplicity, try common patterns
-                    if key.lower() in ["status", "priority", "type", "category"]:
-                        properties[key] = {"select": {"name": value}}
-                    elif key.lower() in ["url", "link"]:
-                        properties[key] = {"url": value}
-                    elif key.lower() in ["email"]:
-                        properties[key] = {"email": value}
-                    else:
-                        properties[key] = {
-                            "rich_text": [{"type": "text", "text": {"content": value}}]
-                        }
-
-        return properties
-
-    @staticmethod
-    async def create_page(
-        credentials: OAuth2Credentials,
-        title: str,
-        parent_page_id: Optional[str] = None,
-        parent_database_id: Optional[str] = None,
-        content: Optional[str] = None,
-        properties: Optional[Dict[str, Any]] = None,
-        icon_emoji: Optional[str] = None,
-    ) -> tuple[str, str]:
-        """
-        Create a new Notion page.
-
-        Returns:
-            Tuple of (page_id, page_url)
-        """
-        if not parent_page_id and not parent_database_id:
-            raise ValueError(
-                "Either parent_page_id or parent_database_id must be provided"
-            )
-        if parent_page_id and parent_database_id:
-            raise ValueError(
-                "Only one of parent_page_id or parent_database_id should be provided, not both"
-            )
-
-        client = NotionClient(credentials)
-
-        # Build parent object
-        if parent_page_id:
-            parent = {"type": "page_id", "page_id": parent_page_id}
-        else:
-            parent = {"type": "database_id", "database_id": parent_database_id}
-
-        # Build properties
-        page_properties = NotionCreatePageBlock._build_properties(title, properties)
-
-        # Convert content to blocks if provided
-        children = None
-        if content:
-            children = NotionCreatePageBlock._markdown_to_blocks(content)
-
-        # Build icon if provided
-        icon = None
-        if icon_emoji:
-            icon = {"type": "emoji", "emoji": icon_emoji}
-
-        # Create the page
-        result = await client.create_page(
-            parent=parent, properties=page_properties, children=children, icon=icon
-        )
-
-        page_id = result.get("id", "")
-        page_url = result.get("url", "")
-
-        if not page_id or not page_url:
-            raise ValueError("Failed to get page ID or URL from Notion response")
-
-        return page_id, page_url
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            page_id, page_url = await self.create_page(
-                credentials,
-                input_data.title,
-                input_data.parent_page_id,
-                input_data.parent_database_id,
-                input_data.content,
-                input_data.properties,
-                input_data.icon_emoji,
-            )
-            yield "page_id", page_id
-            yield "page_url", page_url
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/read_database.py

@@ -1,285 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient, parse_rich_text
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionReadDatabaseBlock(Block):
-    """Query a Notion database and retrieve entries with their properties."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        database_id: str = SchemaField(
-            description="Notion database ID. Must be accessible by the connected integration.",
-        )
-        filter_property: Optional[str] = SchemaField(
-            description="Property name to filter by (e.g., 'Status', 'Priority')",
-            default=None,
-        )
-        filter_value: Optional[str] = SchemaField(
-            description="Value to filter for in the specified property", default=None
-        )
-        sort_property: Optional[str] = SchemaField(
-            description="Property name to sort by", default=None
-        )
-        sort_direction: Optional[str] = SchemaField(
-            description="Sort direction: 'ascending' or 'descending'",
-            default="ascending",
-        )
-        limit: int = SchemaField(
-            description="Maximum number of entries to retrieve",
-            default=100,
-            ge=1,
-            le=100,
-        )
-
-    class Output(BlockSchema):
-        entries: List[Dict[str, Any]] = SchemaField(
-            description="List of database entries with their properties."
-        )
-        entry: Dict[str, Any] = SchemaField(
-            description="Individual database entry (yields one per entry found)."
-        )
-        entry_ids: List[str] = SchemaField(
-            description="List of entry IDs for batch operations."
-        )
-        entry_id: str = SchemaField(
-            description="Individual entry ID (yields one per entry found)."
-        )
-        count: int = SchemaField(description="Number of entries retrieved.")
-        database_title: str = SchemaField(description="Title of the database.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="fcd53135-88c9-4ba3-be50-cc6936286e6c",
-            description="Query a Notion database with optional filtering and sorting, returning structured entries.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionReadDatabaseBlock.Input,
-            output_schema=NotionReadDatabaseBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "database_id": "00000000-0000-0000-0000-000000000000",
-                "limit": 10,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "entries",
-                    [{"Name": "Test Entry", "Status": "Active", "_id": "test-123"}],
-                ),
-                ("entry_ids", ["test-123"]),
-                (
-                    "entry",
-                    {"Name": "Test Entry", "Status": "Active", "_id": "test-123"},
-                ),
-                ("entry_id", "test-123"),
-                ("count", 1),
-                ("database_title", "Test Database"),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "query_database": lambda *args, **kwargs: (
-                    [{"Name": "Test Entry", "Status": "Active", "_id": "test-123"}],
-                    1,
-                    "Test Database",
-                )
-            },
-        )
-
-    @staticmethod
-    def _parse_property_value(prop: dict) -> Any:
-        """Parse a Notion property value into a simple Python type."""
-        prop_type = prop.get("type")
-
-        if prop_type == "title":
-            return parse_rich_text(prop.get("title", []))
-        elif prop_type == "rich_text":
-            return parse_rich_text(prop.get("rich_text", []))
-        elif prop_type == "number":
-            return prop.get("number")
-        elif prop_type == "select":
-            select = prop.get("select")
-            return select.get("name") if select else None
-        elif prop_type == "multi_select":
-            return [item.get("name") for item in prop.get("multi_select", [])]
-        elif prop_type == "date":
-            date = prop.get("date")
-            if date:
-                return date.get("start")
-            return None
-        elif prop_type == "checkbox":
-            return prop.get("checkbox", False)
-        elif prop_type == "url":
-            return prop.get("url")
-        elif prop_type == "email":
-            return prop.get("email")
-        elif prop_type == "phone_number":
-            return prop.get("phone_number")
-        elif prop_type == "people":
-            return [
-                person.get("name", person.get("id"))
-                for person in prop.get("people", [])
-            ]
-        elif prop_type == "files":
-            files = prop.get("files", [])
-            return [
-                f.get(
-                    "name",
-                    f.get("external", {}).get("url", f.get("file", {}).get("url")),
-                )
-                for f in files
-            ]
-        elif prop_type == "relation":
-            return [rel.get("id") for rel in prop.get("relation", [])]
-        elif prop_type == "formula":
-            formula = prop.get("formula", {})
-            return formula.get(formula.get("type"))
-        elif prop_type == "rollup":
-            rollup = prop.get("rollup", {})
-            return rollup.get(rollup.get("type"))
-        elif prop_type == "created_time":
-            return prop.get("created_time")
-        elif prop_type == "created_by":
-            return prop.get("created_by", {}).get(
-                "name", prop.get("created_by", {}).get("id")
-            )
-        elif prop_type == "last_edited_time":
-            return prop.get("last_edited_time")
-        elif prop_type == "last_edited_by":
-            return prop.get("last_edited_by", {}).get(
-                "name", prop.get("last_edited_by", {}).get("id")
-            )
-        else:
-            # Return the raw value for unknown types
-            return prop
-
-    @staticmethod
-    def _build_filter(property_name: str, value: str) -> dict:
-        """Build a simple filter object for a property."""
-        # This is a simplified filter - in reality, you'd need to know the property type
-        # For now, we'll try common filter types
-        return {
-            "or": [
-                {"property": property_name, "rich_text": {"contains": value}},
-                {"property": property_name, "title": {"contains": value}},
-                {"property": property_name, "select": {"equals": value}},
-                {"property": property_name, "multi_select": {"contains": value}},
-            ]
-        }
-
-    @staticmethod
-    async def query_database(
-        credentials: OAuth2Credentials,
-        database_id: str,
-        filter_property: Optional[str] = None,
-        filter_value: Optional[str] = None,
-        sort_property: Optional[str] = None,
-        sort_direction: str = "ascending",
-        limit: int = 100,
-    ) -> tuple[List[Dict[str, Any]], int, str]:
-        """
-        Query a Notion database and parse the results.
-
-        Returns:
-            Tuple of (entries_list, count, database_title)
-        """
-        client = NotionClient(credentials)
-
-        # Build filter if specified
-        filter_obj = None
-        if filter_property and filter_value:
-            filter_obj = NotionReadDatabaseBlock._build_filter(
-                filter_property, filter_value
-            )
-
-        # Build sorts if specified
-        sorts = None
-        if sort_property:
-            sorts = [{"property": sort_property, "direction": sort_direction}]
-
-        # Query the database
-        result = await client.query_database(
-            database_id, filter_obj=filter_obj, sorts=sorts, page_size=limit
-        )
-
-        # Parse the entries
-        entries = []
-        for page in result.get("results", []):
-            entry = {}
-            properties = page.get("properties", {})
-
-            for prop_name, prop_value in properties.items():
-                entry[prop_name] = NotionReadDatabaseBlock._parse_property_value(
-                    prop_value
-                )
-
-            # Add metadata
-            entry["_id"] = page.get("id")
-            entry["_url"] = page.get("url")
-            entry["_created_time"] = page.get("created_time")
-            entry["_last_edited_time"] = page.get("last_edited_time")
-
-            entries.append(entry)
-
-        # Get database title (we need to make a separate call for this)
-        try:
-            database_url = f"https://api.notion.com/v1/databases/{database_id}"
-            db_response = await client.requests.get(
-                database_url, headers=client.headers
-            )
-            if db_response.ok:
-                db_data = db_response.json()
-                db_title = parse_rich_text(db_data.get("title", []))
-            else:
-                db_title = "Unknown Database"
-        except Exception:
-            db_title = "Unknown Database"
-
-        return entries, len(entries), db_title
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            entries, count, db_title = await self.query_database(
-                credentials,
-                input_data.database_id,
-                input_data.filter_property,
-                input_data.filter_value,
-                input_data.sort_property,
-                input_data.sort_direction or "ascending",
-                input_data.limit,
-            )
-            # Yield the complete list for batch operations
-            yield "entries", entries
-
-            # Extract and yield IDs as a list for batch operations
-            entry_ids = [entry["_id"] for entry in entries if "_id" in entry]
-            yield "entry_ids", entry_ids
-
-            # Yield each individual entry and its ID for single connections
-            for entry in entries:
-                yield "entry", entry
-                if "_id" in entry:
-                    yield "entry_id", entry["_id"]
-
-            yield "count", count
-            yield "database_title", db_title
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/read_page.py

@@ -1,64 +0,0 @@
-from __future__ import annotations
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionReadPageBlock(Block):
-    """Read a Notion page by ID and return its raw JSON."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        page_id: str = SchemaField(
-            description="Notion page ID. Must be accessible by the connected integration. You can get this from the page URL notion.so/A-Page-586edd711467478da59fe3ce29a1ffab would be 586edd711467478da59fe35e29a1ffab",
-        )
-
-    class Output(BlockSchema):
-        page: dict = SchemaField(description="Raw Notion page JSON.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="5246cc1d-34b7-452b-8fc5-3fb25fd8f542",
-            description="Read a Notion page by its ID and return its raw JSON.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionReadPageBlock.Input,
-            output_schema=NotionReadPageBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "page_id": "00000000-0000-0000-0000-000000000000",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[("page", dict)],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "get_page": lambda *args, **kwargs: {"object": "page", "id": "mocked"}
-            },
-        )
-
-    @staticmethod
-    async def get_page(credentials: OAuth2Credentials, page_id: str) -> dict:
-        client = NotionClient(credentials)
-        return await client.get_page(page_id)
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            page = await self.get_page(credentials, input_data.page_id)
-            yield "page", page
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/read_page_markdown.py

@@ -1,109 +0,0 @@
-from __future__ import annotations
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient, blocks_to_markdown, extract_page_title
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionReadPageMarkdownBlock(Block):
-    """Read a Notion page and convert it to clean Markdown format."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        page_id: str = SchemaField(
-            description="Notion page ID. Must be accessible by the connected integration. You can get this from the page URL notion.so/A-Page-586edd711467478da59fe35e29a1ffab would be 586edd711467478da59fe35e29a1ffab",
-        )
-        include_title: bool = SchemaField(
-            description="Whether to include the page title as a header in the markdown",
-            default=True,
-        )
-
-    class Output(BlockSchema):
-        markdown: str = SchemaField(description="Page content in Markdown format.")
-        title: str = SchemaField(description="Page title.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="d1312c4d-fae2-4e70-893d-f4d07cce1d4e",
-            description="Read a Notion page and convert it to Markdown format with proper formatting for headings, lists, links, and rich text.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionReadPageMarkdownBlock.Input,
-            output_schema=NotionReadPageMarkdownBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "page_id": "00000000-0000-0000-0000-000000000000",
-                "include_title": True,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("markdown", "# Test Page\n\nThis is test content."),
-                ("title", "Test Page"),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "get_page_markdown": lambda *args, **kwargs: (
-                    "# Test Page\n\nThis is test content.",
-                    "Test Page",
-                )
-            },
-        )
-
-    @staticmethod
-    async def get_page_markdown(
-        credentials: OAuth2Credentials, page_id: str, include_title: bool = True
-    ) -> tuple[str, str]:
-        """
-        Get a Notion page and convert it to markdown.
-
-        Args:
-            credentials: OAuth2 credentials for Notion.
-            page_id: The ID of the page to fetch.
-            include_title: Whether to include the page title in the markdown.
-
-        Returns:
-            Tuple of (markdown_content, title)
-        """
-        client = NotionClient(credentials)
-
-        # Get page metadata
-        page = await client.get_page(page_id)
-        title = extract_page_title(page)
-
-        # Get all blocks from the page
-        blocks = await client.get_blocks(page_id, recursive=True)
-
-        # Convert blocks to markdown
-        content_markdown = blocks_to_markdown(blocks)
-
-        # Combine title and content if requested
-        if include_title and title:
-            full_markdown = f"# {title}\n\n{content_markdown}"
-        else:
-            full_markdown = content_markdown
-
-        return full_markdown, title
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            markdown, title = await self.get_page_markdown(
-                credentials, input_data.page_id, input_data.include_title
-            )
-            yield "markdown", markdown
-            yield "title", title
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/search.py

@@ -1,225 +0,0 @@
-from __future__ import annotations
-
-from typing import List, Optional
-
-from pydantic import BaseModel
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient, extract_page_title, parse_rich_text
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionSearchResult(BaseModel):
-    """Typed model for Notion search results."""
-
-    id: str
-    type: str  # 'page' or 'database'
-    title: str
-    url: str
-    created_time: Optional[str] = None
-    last_edited_time: Optional[str] = None
-    parent_type: Optional[str] = None  # 'page', 'database', or 'workspace'
-    parent_id: Optional[str] = None
-    icon: Optional[str] = None  # emoji icon if present
-    is_inline: Optional[bool] = None  # for databases only
-
-
-class NotionSearchBlock(Block):
-    """Search across your Notion workspace for pages and databases."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        query: str = SchemaField(
-            description="Search query text. Leave empty to get all accessible pages/databases.",
-            default="",
-        )
-        filter_type: Optional[str] = SchemaField(
-            description="Filter results by type: 'page' or 'database'. Leave empty for both.",
-            default=None,
-        )
-        limit: int = SchemaField(
-            description="Maximum number of results to return", default=20, ge=1, le=100
-        )
-
-    class Output(BlockSchema):
-        results: List[NotionSearchResult] = SchemaField(
-            description="List of search results with title, type, URL, and metadata."
-        )
-        result: NotionSearchResult = SchemaField(
-            description="Individual search result (yields one per result found)."
-        )
-        result_ids: List[str] = SchemaField(
-            description="List of IDs from search results for batch operations."
-        )
-        count: int = SchemaField(description="Number of results found.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="313515dd-9848-46ea-9cd6-3c627c892c56",
-            description="Search your Notion workspace for pages and databases by text query.",
-            categories={BlockCategory.PRODUCTIVITY, BlockCategory.SEARCH},
-            input_schema=NotionSearchBlock.Input,
-            output_schema=NotionSearchBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "query": "project",
-                "limit": 5,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "results",
-                    [
-                        NotionSearchResult(
-                            id="123",
-                            type="page",
-                            title="Project Plan",
-                            url="https://notion.so/Project-Plan-123",
-                        )
-                    ],
-                ),
-                ("result_ids", ["123"]),
-                (
-                    "result",
-                    NotionSearchResult(
-                        id="123",
-                        type="page",
-                        title="Project Plan",
-                        url="https://notion.so/Project-Plan-123",
-                    ),
-                ),
-                ("count", 1),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "search_workspace": lambda *args, **kwargs: (
-                    [
-                        NotionSearchResult(
-                            id="123",
-                            type="page",
-                            title="Project Plan",
-                            url="https://notion.so/Project-Plan-123",
-                        )
-                    ],
-                    1,
-                )
-            },
-        )
-
-    @staticmethod
-    async def search_workspace(
-        credentials: OAuth2Credentials,
-        query: str = "",
-        filter_type: Optional[str] = None,
-        limit: int = 20,
-    ) -> tuple[List[NotionSearchResult], int]:
-        """
-        Search the Notion workspace.
-
-        Returns:
-            Tuple of (results_list, count)
-        """
-        client = NotionClient(credentials)
-
-        # Build filter if type is specified
-        filter_obj = None
-        if filter_type:
-            filter_obj = {"property": "object", "value": filter_type}
-
-        # Execute search
-        response = await client.search(
-            query=query, filter_obj=filter_obj, page_size=limit
-        )
-
-        # Parse results
-        results = []
-        for item in response.get("results", []):
-            result_data = {
-                "id": item.get("id", ""),
-                "type": item.get("object", ""),
-                "url": item.get("url", ""),
-                "created_time": item.get("created_time"),
-                "last_edited_time": item.get("last_edited_time"),
-                "title": "",  # Will be set below
-            }
-
-            # Extract title based on type
-            if item.get("object") == "page":
-                # For pages, get the title from properties
-                result_data["title"] = extract_page_title(item)
-
-                # Add parent info
-                parent = item.get("parent", {})
-                if parent.get("type") == "page_id":
-                    result_data["parent_type"] = "page"
-                    result_data["parent_id"] = parent.get("page_id")
-                elif parent.get("type") == "database_id":
-                    result_data["parent_type"] = "database"
-                    result_data["parent_id"] = parent.get("database_id")
-                elif parent.get("type") == "workspace":
-                    result_data["parent_type"] = "workspace"
-
-                # Add icon if present
-                icon = item.get("icon")
-                if icon and icon.get("type") == "emoji":
-                    result_data["icon"] = icon.get("emoji")
-
-            elif item.get("object") == "database":
-                # For databases, get title from the title array
-                result_data["title"] = parse_rich_text(item.get("title", []))
-
-                # Add database-specific metadata
-                result_data["is_inline"] = item.get("is_inline", False)
-
-                # Add parent info
-                parent = item.get("parent", {})
-                if parent.get("type") == "page_id":
-                    result_data["parent_type"] = "page"
-                    result_data["parent_id"] = parent.get("page_id")
-                elif parent.get("type") == "workspace":
-                    result_data["parent_type"] = "workspace"
-
-                # Add icon if present
-                icon = item.get("icon")
-                if icon and icon.get("type") == "emoji":
-                    result_data["icon"] = icon.get("emoji")
-
-            results.append(NotionSearchResult(**result_data))
-
-        return results, len(results)
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            results, count = await self.search_workspace(
-                credentials, input_data.query, input_data.filter_type, input_data.limit
-            )
-
-            # Yield the complete list for batch operations
-            yield "results", results
-
-            # Extract and yield IDs as a list for batch operations
-            result_ids = [r.id for r in results]
-            yield "result_ids", result_ids
-
-            # Yield each individual result for single connections
-            for result in results:
-                yield "result", result
-
-            yield "count", count
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/perplexity.py

@@ -1,226 +0,0 @@
-# flake8: noqa: E501
-import logging
-from enum import Enum
-from typing import Any, Literal
-
-import openai
-from pydantic import SecretStr
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import (
-    APIKeyCredentials,
-    CredentialsField,
-    CredentialsMetaInput,
-    NodeExecutionStats,
-    SchemaField,
-)
-from backend.integrations.providers import ProviderName
-from backend.util.logging import TruncatedLogger
-
-logger = TruncatedLogger(logging.getLogger(__name__), "[Perplexity-Block]")
-
-
-class PerplexityModel(str, Enum):
-    """Perplexity sonar models available via OpenRouter"""
-
-    SONAR = "perplexity/sonar"
-    SONAR_PRO = "perplexity/sonar-pro"
-    SONAR_DEEP_RESEARCH = "perplexity/sonar-deep-research"
-
-
-PerplexityCredentials = CredentialsMetaInput[
-    Literal[ProviderName.OPEN_ROUTER], Literal["api_key"]
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="test-perplexity-creds",
-    provider="open_router",
-    api_key=SecretStr("mock-openrouter-api-key"),
-    title="Mock OpenRouter API key",
-    expires_at=None,
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-def PerplexityCredentialsField() -> PerplexityCredentials:
-    return CredentialsField(
-        description="OpenRouter API key for accessing Perplexity models.",
-    )
-
-
-class PerplexityBlock(Block):
-    class Input(BlockSchema):
-        prompt: str = SchemaField(
-            description="The query to send to the Perplexity model.",
-            placeholder="Enter your query here...",
-        )
-        model: PerplexityModel = SchemaField(
-            title="Perplexity Model",
-            default=PerplexityModel.SONAR,
-            description="The Perplexity sonar model to use.",
-            advanced=False,
-        )
-        credentials: PerplexityCredentials = PerplexityCredentialsField()
-        system_prompt: str = SchemaField(
-            title="System Prompt",
-            default="",
-            description="Optional system prompt to provide context to the model.",
-            advanced=True,
-        )
-        max_tokens: int | None = SchemaField(
-            advanced=True,
-            default=None,
-            description="The maximum number of tokens to generate.",
-        )
-
-    class Output(BlockSchema):
-        response: str = SchemaField(
-            description="The response from the Perplexity model."
-        )
-        annotations: list[dict[str, Any]] = SchemaField(
-            description="List of URL citations and annotations from the response."
-        )
-        error: str = SchemaField(description="Error message if the API call failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="c8a5f2e9-8b3d-4a7e-9f6c-1d5e3c9b7a4f",
-            description="Query Perplexity's sonar models with real-time web search capabilities and receive annotated responses with source citations.",
-            categories={BlockCategory.AI, BlockCategory.SEARCH},
-            input_schema=PerplexityBlock.Input,
-            output_schema=PerplexityBlock.Output,
-            test_input={
-                "prompt": "What is the weather today?",
-                "model": PerplexityModel.SONAR,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("response", "The weather varies by location..."),
-                ("annotations", list),
-            ],
-            test_mock={
-                "call_perplexity": lambda *args, **kwargs: {
-                    "response": "The weather varies by location...",
-                    "annotations": [
-                        {
-                            "type": "url_citation",
-                            "url_citation": {
-                                "title": "weather.com",
-                                "url": "https://weather.com",
-                            },
-                        }
-                    ],
-                }
-            },
-        )
-        self.execution_stats = NodeExecutionStats()
-
-    async def call_perplexity(
-        self,
-        credentials: APIKeyCredentials,
-        model: PerplexityModel,
-        prompt: str,
-        system_prompt: str = "",
-        max_tokens: int | None = None,
-    ) -> dict[str, Any]:
-        """Call Perplexity via OpenRouter and extract annotations."""
-        client = openai.AsyncOpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=credentials.api_key.get_secret_value(),
-        )
-
-        messages = []
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-        messages.append({"role": "user", "content": prompt})
-
-        try:
-            response = await client.chat.completions.create(
-                extra_headers={
-                    "HTTP-Referer": "https://agpt.co",
-                    "X-Title": "AutoGPT",
-                },
-                model=model.value,
-                messages=messages,
-                max_tokens=max_tokens,
-            )
-
-            if not response.choices:
-                raise ValueError("No response from Perplexity via OpenRouter.")
-
-            # Extract the response content
-            response_content = response.choices[0].message.content or ""
-
-            # Extract annotations if present in the message
-            annotations = []
-            if hasattr(response.choices[0].message, "annotations"):
-                # If annotations are directly available
-                annotations = response.choices[0].message.annotations
-            else:
-                # Check if there's a raw response with annotations
-                raw = getattr(response.choices[0].message, "_raw_response", None)
-                if isinstance(raw, dict) and "annotations" in raw:
-                    annotations = raw["annotations"]
-
-            if not annotations and hasattr(response, "model_extra"):
-                # Check model_extra for annotations
-                model_extra = response.model_extra
-                if isinstance(model_extra, dict):
-                    # Check in choices
-                    if "choices" in model_extra and len(model_extra["choices"]) > 0:
-                        choice = model_extra["choices"][0]
-                        if "message" in choice and "annotations" in choice["message"]:
-                            annotations = choice["message"]["annotations"]
-
-            # Also check the raw response object for annotations
-            if not annotations:
-                raw = getattr(response, "_raw_response", None)
-                if isinstance(raw, dict):
-                    # Check various possible locations for annotations
-                    if "annotations" in raw:
-                        annotations = raw["annotations"]
-                    elif "choices" in raw and len(raw["choices"]) > 0:
-                        choice = raw["choices"][0]
-                        if "message" in choice and "annotations" in choice["message"]:
-                            annotations = choice["message"]["annotations"]
-
-            # Update execution stats
-            if response.usage:
-                self.execution_stats.input_token_count = response.usage.prompt_tokens
-                self.execution_stats.output_token_count = (
-                    response.usage.completion_tokens
-                )
-
-            return {"response": response_content, "annotations": annotations or []}
-
-        except Exception as e:
-            logger.error(f"Error calling Perplexity: {e}")
-            raise
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        logger.debug(f"Running Perplexity block with model: {input_data.model}")
-
-        try:
-            result = await self.call_perplexity(
-                credentials=credentials,
-                model=input_data.model,
-                prompt=input_data.prompt,
-                system_prompt=input_data.system_prompt,
-                max_tokens=input_data.max_tokens,
-            )
-
-            yield "response", result["response"]
-            yield "annotations", result["annotations"]
-
-        except Exception as e:
-            error_msg = f"Error calling Perplexity: {str(e)}"
-            logger.error(error_msg)
-            yield "error", error_msg

autogpt_platform/backend/backend/blocks/rss.py

@@ -1,7 +1,4 @@
 import asyncio
-import logging
-import urllib.parse
-import urllib.request
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
@@ -104,38 +101,7 @@ class ReadRSSFeedBlock(Block):
 
     @staticmethod
     def parse_feed(url: str) -> dict[str, Any]:
-        # Security fix: Add protection against memory exhaustion attacks
-        MAX_FEED_SIZE = 10 * 1024 * 1024  # 10MB limit for RSS feeds
-
-        # Validate URL
-        parsed_url = urllib.parse.urlparse(url)
-        if parsed_url.scheme not in ("http", "https"):
-            raise ValueError(f"Invalid URL scheme: {parsed_url.scheme}")
-
-        # Download with size limit
-        try:
-            with urllib.request.urlopen(url, timeout=30) as response:
-                # Check content length if available
-                content_length = response.headers.get("Content-Length")
-                if content_length and int(content_length) > MAX_FEED_SIZE:
-                    raise ValueError(
-                        f"Feed too large: {content_length} bytes exceeds {MAX_FEED_SIZE} limit"
-                    )
-
-                # Read with size limit
-                content = response.read(MAX_FEED_SIZE + 1)
-                if len(content) > MAX_FEED_SIZE:
-                    raise ValueError(
-                        f"Feed too large: exceeds {MAX_FEED_SIZE} byte limit"
-                    )
-
-                # Parse with feedparser using the validated content
-                # feedparser has built-in protection against XML attacks
-                return feedparser.parse(content)  # type: ignore
-        except Exception as e:
-            # Log error and return empty feed
-            logging.warning(f"Failed to parse RSS feed from {url}: {e}")
-            return {"entries": []}
+        return feedparser.parse(url)  # type: ignore
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         keep_going = True

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -13,11 +13,6 @@ from backend.data.block import (
     BlockSchema,
     BlockType,
 )
-from backend.data.dynamic_fields import (
-    extract_base_field_name,
-    get_dynamic_field_description,
-    is_dynamic_field,
-)
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util import json
 from backend.util.clients import get_database_manager_async_client
@@ -103,22 +98,6 @@ def _create_tool_response(call_id: str, output: Any) -> dict[str, Any]:
     return {"role": "tool", "tool_call_id": call_id, "content": content}
 
 
-def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
-    """
-    Safely convert raw_response to dictionary format for conversation history.
-    Handles different response types from different LLM providers.
-    """
-    if isinstance(raw_response, str):
-        # Ollama returns a string, convert to dict format
-        return {"role": "assistant", "content": raw_response}
-    elif isinstance(raw_response, dict):
-        # Already a dict (from tests or some providers)
-        return raw_response
-    else:
-        # OpenAI/Anthropic return objects, convert with json.to_dict
-        return json.to_dict(raw_response)
-
-
 def get_pending_tool_calls(conversation_history: list[Any]) -> dict[str, int]:
     """
     All the tool calls entry in the conversation history requires a response.
@@ -282,7 +261,6 @@ class SmartDecisionMakerBlock(Block):
 
     @staticmethod
     def cleanup(s: str):
-        """Clean up block names for use as tool function names."""
         return re.sub(r"[^a-zA-Z0-9_-]", "_", s).lower()
 
     @staticmethod
@@ -310,66 +288,41 @@ class SmartDecisionMakerBlock(Block):
         }
         sink_block_input_schema = block.input_schema
         properties = {}
-        field_mapping = {}  # clean_name -> original_name
 
         for link in links:
-            field_name = link.sink_name
-            is_dynamic = is_dynamic_field(field_name)
-            # Clean property key to ensure Anthropic API compatibility for ALL fields
-            clean_field_name = SmartDecisionMakerBlock.cleanup(field_name)
-            field_mapping[clean_field_name] = field_name
+            sink_name = SmartDecisionMakerBlock.cleanup(link.sink_name)
 
-            if is_dynamic:
-                # For dynamic fields, use cleaned name but preserve original in description
-                properties[clean_field_name] = {
+            # Handle dynamic fields (e.g., values_#_*, items_$_*, etc.)
+            # These are fields that get merged by the executor into their base field
+            if (
+                "_#_" in link.sink_name
+                or "_$_" in link.sink_name
+                or "_@_" in link.sink_name
+            ):
+                # For dynamic fields, provide a generic string schema
+                # The executor will handle merging these into the appropriate structure
+                properties[sink_name] = {
                     "type": "string",
-                    "description": get_dynamic_field_description(field_name),
+                    "description": f"Dynamic value for {link.sink_name}",
                 }
             else:
-                # For regular fields, use the block's schema directly
+                # For regular fields, use the block's schema
                 try:
-                    properties[clean_field_name] = (
-                        sink_block_input_schema.get_field_schema(field_name)
+                    properties[sink_name] = sink_block_input_schema.get_field_schema(
+                        link.sink_name
                     )
                 except (KeyError, AttributeError):
-                    # If field doesn't exist in schema, provide a generic one
-                    properties[clean_field_name] = {
+                    # If the field doesn't exist in the schema, provide a generic schema
+                    properties[sink_name] = {
                         "type": "string",
-                        "description": f"Value for {field_name}",
+                        "description": f"Value for {link.sink_name}",
                     }
 
-        # Build the parameters schema using a single unified path
-        base_schema = block.input_schema.jsonschema()
-        base_required = set(base_schema.get("required", []))
-
-        # Compute required fields at the leaf level:
-        # - If a linked field is dynamic and its base is required in the block schema, require the leaf
-        # - If a linked field is regular and is required in the block schema, require the leaf
-        required_fields: set[str] = set()
-        for link in links:
-            field_name = link.sink_name
-            is_dynamic = is_dynamic_field(field_name)
-            # Always use cleaned field name for property key (Anthropic API compliance)
-            clean_field_name = SmartDecisionMakerBlock.cleanup(field_name)
-
-            if is_dynamic:
-                base_name = extract_base_field_name(field_name)
-                if base_name in base_required:
-                    required_fields.add(clean_field_name)
-            else:
-                if field_name in base_required:
-                    required_fields.add(clean_field_name)
-
         tool_function["parameters"] = {
-            "type": "object",
+            **block.input_schema.jsonschema(),
             "properties": properties,
-            "additionalProperties": False,
-            "required": sorted(required_fields),
         }
 
-        # Store field mapping for later use in output processing
-        tool_function["_field_mapping"] = field_mapping
-
         return {"type": "function", "function": tool_function}
 
     @staticmethod
@@ -413,12 +366,13 @@ class SmartDecisionMakerBlock(Block):
             sink_block_properties = sink_block_input_schema.get("properties", {}).get(
                 link.sink_name, {}
             )
+            sink_name = SmartDecisionMakerBlock.cleanup(link.sink_name)
             description = (
                 sink_block_properties["description"]
                 if "description" in sink_block_properties
                 else f"The {link.sink_name} of the tool"
             )
-            properties[link.sink_name] = {
+            properties[sink_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -434,17 +388,24 @@ class SmartDecisionMakerBlock(Block):
         return {"type": "function", "function": tool_function}
 
     @staticmethod
-    async def _create_function_signature(
-        node_id: str,
-    ) -> list[dict[str, Any]]:
+    async def _create_function_signature(node_id: str) -> list[dict[str, Any]]:
         """
-        Creates function signatures for connected tools.
+        Creates function signatures for tools linked to a specified node within a graph.
+
+        This method filters the graph links to identify those that are tools and are
+        connected to the given node_id. It then constructs function signatures for each
+        tool based on the metadata and input schema of the linked nodes.
 
         Args:
             node_id: The node_id for which to create function signatures.
 
         Returns:
-            List of function signatures for tools
+            list[dict[str, Any]]: A list of dictionaries, each representing a function signature
+                                  for a tool, including its name, description, and parameters.
+
+        Raises:
+            ValueError: If no tool links are found for the specified node_id, or if a sink node
+                        or its metadata cannot be found.
         """
         db_client = get_database_manager_async_client()
         tools = [
@@ -469,116 +430,20 @@ class SmartDecisionMakerBlock(Block):
                 raise ValueError(f"Sink node not found: {links[0].sink_id}")
 
             if sink_node.block_id == AgentExecutorBlock().id:
-                tool_func = (
+                return_tool_functions.append(
                     await SmartDecisionMakerBlock._create_agent_function_signature(
                         sink_node, links
                     )
                 )
-                return_tool_functions.append(tool_func)
             else:
-                tool_func = (
+                return_tool_functions.append(
                     await SmartDecisionMakerBlock._create_block_function_signature(
                         sink_node, links
                     )
                 )
-                return_tool_functions.append(tool_func)
 
         return return_tool_functions
 
-    async def _attempt_llm_call_with_validation(
-        self,
-        credentials: llm.APIKeyCredentials,
-        input_data: Input,
-        current_prompt: list[dict],
-        tool_functions: list[dict[str, Any]],
-    ):
-        """
-        Attempt a single LLM call with tool validation.
-
-        Returns the response if successful, raises ValueError if validation fails.
-        """
-        resp = await llm.llm_call(
-            credentials=credentials,
-            llm_model=input_data.model,
-            prompt=current_prompt,
-            max_tokens=input_data.max_tokens,
-            tools=tool_functions,
-            ollama_host=input_data.ollama_host,
-            parallel_tool_calls=input_data.multiple_tool_calls,
-        )
-
-        # Track LLM usage stats per call
-        self.merge_stats(
-            NodeExecutionStats(
-                input_token_count=resp.prompt_tokens,
-                output_token_count=resp.completion_tokens,
-                llm_call_count=1,
-            )
-        )
-
-        if not resp.tool_calls:
-            return resp
-        validation_errors_list: list[str] = []
-        for tool_call in resp.tool_calls:
-            tool_name = tool_call.function.name
-            try:
-                tool_args = json.loads(tool_call.function.arguments)
-            except Exception as e:
-                validation_errors_list.append(
-                    f"Tool call '{tool_name}' has invalid JSON arguments: {e}"
-                )
-                continue
-
-            # Find the tool definition to get the expected arguments
-            tool_def = next(
-                (
-                    tool
-                    for tool in tool_functions
-                    if tool["function"]["name"] == tool_name
-                ),
-                None,
-            )
-            if tool_def is None and len(tool_functions) == 1:
-                tool_def = tool_functions[0]
-
-            # Get parameters schema from tool definition
-            if (
-                tool_def
-                and "function" in tool_def
-                and "parameters" in tool_def["function"]
-            ):
-                parameters = tool_def["function"]["parameters"]
-                expected_args = parameters.get("properties", {})
-                required_params = set(parameters.get("required", []))
-            else:
-                expected_args = {arg: {} for arg in tool_args.keys()}
-                required_params = set()
-
-            # Validate tool call arguments
-            provided_args = set(tool_args.keys())
-            expected_args_set = set(expected_args.keys())
-
-            # Check for unexpected arguments (typos)
-            unexpected_args = provided_args - expected_args_set
-            # Only check for missing REQUIRED parameters
-            missing_required_args = required_params - provided_args
-
-            if unexpected_args or missing_required_args:
-                error_msg = f"Tool call '{tool_name}' has parameter errors:"
-                if unexpected_args:
-                    error_msg += f" Unknown parameters: {sorted(unexpected_args)}."
-                if missing_required_args:
-                    error_msg += f" Missing required parameters: {sorted(missing_required_args)}."
-                error_msg += f" Expected parameters: {sorted(expected_args_set)}."
-                if required_params:
-                    error_msg += f" Required parameters: {sorted(required_params)}."
-                validation_errors_list.append(error_msg)
-
-        if validation_errors_list:
-            raise ValueError("; ".join(validation_errors_list))
-
-        return resp
-
     async def run(
         self,
         input_data: Input,
@@ -601,19 +466,27 @@ class SmartDecisionMakerBlock(Block):
         if pending_tool_calls and input_data.last_tool_output is None:
             raise ValueError(f"Tool call requires an output for {pending_tool_calls}")
 
+        # Only assign the last tool output to the first pending tool call
         tool_output = []
         if pending_tool_calls and input_data.last_tool_output is not None:
+            # Get the first pending tool call ID
             first_call_id = next(iter(pending_tool_calls.keys()))
             tool_output.append(
                 _create_tool_response(first_call_id, input_data.last_tool_output)
             )
 
+            # Add tool output to prompt right away
             prompt.extend(tool_output)
+
+            # Check if there are still pending tool calls after handling the first one
             remaining_pending_calls = get_pending_tool_calls(prompt)
 
+            # If there are still pending tool calls, yield the conversation and return early
             if remaining_pending_calls:
                 yield "conversations", prompt
                 return
+
+        # Fallback on adding tool output in the conversation history as user prompt.
         elif input_data.last_tool_output:
             logger.error(
                 f"[SmartDecisionMakerBlock-node_exec_id={node_exec_id}] "
@@ -646,33 +519,25 @@ class SmartDecisionMakerBlock(Block):
         ):
             prompt.append({"role": "user", "content": prefix + input_data.prompt})
 
-        current_prompt = list(prompt)
-        max_attempts = max(1, int(input_data.retry))
-        response = None
+        response = await llm.llm_call(
+            credentials=credentials,
+            llm_model=input_data.model,
+            prompt=prompt,
+            json_format=False,
+            max_tokens=input_data.max_tokens,
+            tools=tool_functions,
+            ollama_host=input_data.ollama_host,
+            parallel_tool_calls=input_data.multiple_tool_calls,
+        )
 
-        last_error = None
-        for attempt in range(max_attempts):
-            try:
-                response = await self._attempt_llm_call_with_validation(
-                    credentials, input_data, current_prompt, tool_functions
-                )
-                break
-
-            except ValueError as e:
-                last_error = e
-                error_feedback = (
-                    "Your tool call had parameter errors. Please fix the following issues and try again:\n"
-                    + f"- {str(e)}\n"
-                    + "\nPlease make sure to use the exact parameter names as specified in the function schema."
-                )
-                current_prompt = list(current_prompt) + [
-                    {"role": "user", "content": error_feedback}
-                ]
-
-        if response is None:
-            raise last_error or ValueError(
-                "Failed to get valid response after all retry attempts"
+        # Track LLM usage stats
+        self.merge_stats(
+            NodeExecutionStats(
+                input_token_count=response.prompt_tokens,
+                output_token_count=response.completion_tokens,
+                llm_call_count=1,
             )
+        )
 
         if not response.tool_calls:
             yield "finished", response.response
@@ -682,6 +547,7 @@ class SmartDecisionMakerBlock(Block):
             tool_name = tool_call.function.name
             tool_args = json.loads(tool_call.function.arguments)
 
+            # Find the tool definition to get the expected arguments
             tool_def = next(
                 (
                     tool
@@ -690,6 +556,7 @@ class SmartDecisionMakerBlock(Block):
                 ),
                 None,
             )
+
             if (
                 tool_def
                 and "function" in tool_def
@@ -697,38 +564,20 @@ class SmartDecisionMakerBlock(Block):
             ):
                 expected_args = tool_def["function"]["parameters"].get("properties", {})
             else:
-                expected_args = {arg: {} for arg in tool_args.keys()}
+                expected_args = tool_args.keys()
 
-            # Get field mapping from tool definition
-            field_mapping = (
-                tool_def.get("function", {}).get("_field_mapping", {})
-                if tool_def
-                else {}
-            )
-
-            for clean_arg_name in expected_args:
-                # arg_name is now always the cleaned field name (for Anthropic API compliance)
-                # Get the original field name from field mapping for proper emit key generation
-                original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
-                arg_value = tool_args.get(clean_arg_name)
-
-                sanitized_tool_name = self.cleanup(tool_name)
-                sanitized_arg_name = self.cleanup(original_field_name)
-                emit_key = f"tools_^_{sanitized_tool_name}_~_{sanitized_arg_name}"
-
-                logger.debug(
-                    "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",
-                    graph_exec_id,
-                    node_exec_id,
-                    emit_key,
-                )
-                yield emit_key, arg_value
+            # Yield provided arguments and None for missing ones
+            for arg_name in expected_args:
+                if arg_name in tool_args:
+                    yield f"tools_^_{tool_name}_~_{arg_name}", tool_args[arg_name]
+                else:
+                    yield f"tools_^_{tool_name}_~_{arg_name}", None
 
+        # Add reasoning to conversation history if available
         if response.reasoning:
             prompt.append(
                 {"role": "assistant", "content": f"[Reasoning]: {response.reasoning}"}
             )
 
-        prompt.append(_convert_raw_response_to_dict(response.raw_response))
-
+        prompt.append(response.raw_response)
         yield "conversations", prompt

autogpt_platform/backend/backend/blocks/test/test_block.py

@@ -19,7 +19,7 @@ async def test_block_ids_valid(block: Type[Block]):
     # Skip list for blocks with known invalid UUIDs
     skip_blocks = {
         "GetWeatherInformationBlock",
-        "ExecuteCodeBlock",
+        "CodeExecutionBlock",
         "CountdownTimerBlock",
         "TwitterGetListTweetsBlock",
         "TwitterRemoveListMemberBlock",

autogpt_platform/backend/backend/blocks/test/test_blocks_dos_vulnerability.py

@@ -1,269 +0,0 @@
-"""
-Test security fixes for various DoS vulnerabilities.
-"""
-
-import asyncio
-from unittest.mock import patch
-
-import pytest
-
-from backend.blocks.code_extraction_block import CodeExtractionBlock
-from backend.blocks.iteration import StepThroughItemsBlock
-from backend.blocks.llm import AITextSummarizerBlock
-from backend.blocks.text import ExtractTextInformationBlock
-from backend.blocks.xml_parser import XMLParserBlock
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
-
-
-class TestCodeExtractionBlockSecurity:
-    """Test ReDoS fixes in CodeExtractionBlock."""
-
-    async def test_redos_protection(self):
-        """Test that the regex patterns don't cause ReDoS."""
-        block = CodeExtractionBlock()
-
-        # Test with input that would previously cause ReDoS
-        malicious_input = "```python" + " " * 10000  # Large spaces
-
-        result = []
-        async for output_name, output_data in block.run(
-            CodeExtractionBlock.Input(text=malicious_input)
-        ):
-            result.append((output_name, output_data))
-
-        # Should complete without hanging
-        assert len(result) >= 1
-        assert any(name == "remaining_text" for name, _ in result)
-
-
-class TestAITextSummarizerBlockSecurity:
-    """Test memory exhaustion fixes in AITextSummarizerBlock."""
-
-    def test_split_text_limits(self):
-        """Test that _split_text has proper limits."""
-        # Test text size limit
-        large_text = "a" * 2_000_000  # 2MB text
-        result = AITextSummarizerBlock._split_text(large_text, 1000, 100)
-
-        # Should be truncated to 1MB
-        total_chars = sum(len(chunk) for chunk in result)
-        assert total_chars <= 1_000_000 + 1000  # Allow for chunk boundary
-
-        # Test chunk count limit
-        result = AITextSummarizerBlock._split_text("word " * 10000, 10, 9)
-        assert len(result) <= 100  # MAX_CHUNKS limit
-
-        # Test parameter validation
-        result = AITextSummarizerBlock._split_text(
-            "test", 10, 15
-        )  # overlap > max_tokens
-        assert len(result) >= 1  # Should still work
-
-
-class TestExtractTextInformationBlockSecurity:
-    """Test ReDoS and memory exhaustion fixes in ExtractTextInformationBlock."""
-
-    async def test_text_size_limits(self):
-        """Test text size limits."""
-        block = ExtractTextInformationBlock()
-
-        # Test with large input
-        large_text = "a" * 2_000_000  # 2MB
-
-        results = []
-        async for output_name, output_data in block.run(
-            ExtractTextInformationBlock.Input(
-                text=large_text, pattern=r"a+", find_all=True, group=0
-            )
-        ):
-            results.append((output_name, output_data))
-
-        # Should complete and have limits applied
-        matched_results = [r for name, r in results if name == "matched_results"]
-        if matched_results:
-            assert len(matched_results[0]) <= 1000  # MAX_MATCHES limit
-
-    async def test_dangerous_pattern_timeout(self):
-        """Test timeout protection for dangerous patterns."""
-        block = ExtractTextInformationBlock()
-
-        # Test with potentially dangerous lookahead pattern
-        test_input = "a" * 1000
-
-        # This should complete quickly due to timeout protection
-        start_time = asyncio.get_event_loop().time()
-        results = []
-        async for output_name, output_data in block.run(
-            ExtractTextInformationBlock.Input(
-                text=test_input, pattern=r"(?=.+)", find_all=True, group=0
-            )
-        ):
-            results.append((output_name, output_data))
-
-        end_time = asyncio.get_event_loop().time()
-        # Should complete within reasonable time (much less than 5s timeout)
-        assert (end_time - start_time) < 10
-
-    async def test_redos_catastrophic_backtracking(self):
-        """Test that ReDoS patterns with catastrophic backtracking are handled."""
-        block = ExtractTextInformationBlock()
-
-        # Pattern that causes catastrophic backtracking: (a+)+b
-        # With input "aaaaaaaaaaaaaaaaaaaaaaaaaaaa" (no 'b'), this causes exponential time
-        dangerous_pattern = r"(a+)+b"
-        test_input = "a" * 30  # 30 'a's without a 'b' at the end
-
-        # This should be handled by timeout protection or pattern detection
-        start_time = asyncio.get_event_loop().time()
-        results = []
-
-        async for output_name, output_data in block.run(
-            ExtractTextInformationBlock.Input(
-                text=test_input, pattern=dangerous_pattern, find_all=True, group=0
-            )
-        ):
-            results.append((output_name, output_data))
-
-        end_time = asyncio.get_event_loop().time()
-        elapsed = end_time - start_time
-
-        # Should complete within timeout (6 seconds to be safe)
-        # The current threading.Timer approach doesn't work, so this will likely fail
-        # demonstrating the need for a fix
-        assert elapsed < 6, f"Regex took {elapsed}s, timeout mechanism failed"
-
-        # Should return empty results on timeout or no match
-        matched_results = [r for name, r in results if name == "matched_results"]
-        assert matched_results[0] == []  # No matches expected
-
-
-class TestStepThroughItemsBlockSecurity:
-    """Test iteration limits in StepThroughItemsBlock."""
-
-    async def test_item_count_limits(self):
-        """Test maximum item count limits."""
-        block = StepThroughItemsBlock()
-
-        # Test with too many items
-        large_list = list(range(20000))  # Exceeds MAX_ITEMS (10000)
-
-        with pytest.raises(ValueError, match="Too many items"):
-            async for _ in block.run(StepThroughItemsBlock.Input(items=large_list)):
-                pass
-
-    async def test_string_size_limits(self):
-        """Test string input size limits."""
-        block = StepThroughItemsBlock()
-
-        # Test with large JSON string
-        large_string = '["item"]' * 200000  # Large JSON string
-
-        with pytest.raises(ValueError, match="Input too large"):
-            async for _ in block.run(
-                StepThroughItemsBlock.Input(items_str=large_string)
-            ):
-                pass
-
-    async def test_normal_iteration_works(self):
-        """Test that normal iteration still works."""
-        block = StepThroughItemsBlock()
-
-        results = []
-        async for output_name, output_data in block.run(
-            StepThroughItemsBlock.Input(items=[1, 2, 3])
-        ):
-            results.append((output_name, output_data))
-
-        # Should have 6 outputs (item, key for each of 3 items)
-        assert len(results) == 6
-        items = [data for name, data in results if name == "item"]
-        assert items == [1, 2, 3]
-
-
-class TestXMLParserBlockSecurity:
-    """Test XML size limits in XMLParserBlock."""
-
-    async def test_xml_size_limits(self):
-        """Test XML input size limits."""
-        block = XMLParserBlock()
-
-        # Test with large XML - need to exceed 10MB limit
-        # Each "<item>data</item>" is 17 chars, need ~620K items for >10MB
-        large_xml = "<root>" + "<item>data</item>" * 620000 + "</root>"
-
-        with pytest.raises(ValueError, match="XML too large"):
-            async for _ in block.run(XMLParserBlock.Input(input_xml=large_xml)):
-                pass
-
-
-class TestStoreMediaFileSecurity:
-    """Test file storage security limits."""
-
-    @patch("backend.util.file.scan_content_safe")
-    @patch("backend.util.file.get_cloud_storage_handler")
-    async def test_file_size_limits(self, mock_cloud_storage, mock_scan):
-        """Test file size limits."""
-        # Mock cloud storage handler - get_cloud_storage_handler is async
-        # but is_cloud_path and parse_cloud_path are sync methods
-        from unittest.mock import MagicMock
-
-        mock_handler = MagicMock()
-        mock_handler.is_cloud_path.return_value = False
-
-        # Make get_cloud_storage_handler an async function that returns the mock handler
-        async def async_get_handler():
-            return mock_handler
-
-        mock_cloud_storage.side_effect = async_get_handler
-        mock_scan.return_value = None
-
-        # Test with large base64 content
-        large_content = "a" * (200 * 1024 * 1024)  # 200MB
-        large_data_uri = f"data:text/plain;base64,{large_content}"
-
-        with pytest.raises(ValueError, match="File too large"):
-            await store_media_file(
-                graph_exec_id="test",
-                file=MediaFileType(large_data_uri),
-                user_id="test_user",
-            )
-
-    @patch("backend.util.file.Path")
-    @patch("backend.util.file.scan_content_safe")
-    @patch("backend.util.file.get_cloud_storage_handler")
-    async def test_directory_size_limits(self, mock_cloud_storage, mock_scan, MockPath):
-        """Test directory size limits."""
-        from unittest.mock import MagicMock
-
-        mock_handler = MagicMock()
-        mock_handler.is_cloud_path.return_value = False
-
-        async def async_get_handler():
-            return mock_handler
-
-        mock_cloud_storage.side_effect = async_get_handler
-        mock_scan.return_value = None
-
-        # Create mock path instance for the execution directory
-        mock_path_instance = MagicMock()
-        mock_path_instance.exists.return_value = True
-
-        # Mock glob to return files that total > 1GB
-        mock_file = MagicMock()
-        mock_file.is_file.return_value = True
-        mock_file.stat.return_value.st_size = 2 * 1024 * 1024 * 1024  # 2GB
-        mock_path_instance.glob.return_value = [mock_file]
-
-        # Make Path() return our mock
-        MockPath.return_value = mock_path_instance
-
-        # Should raise an error when directory size exceeds limit
-        with pytest.raises(ValueError, match="Disk usage limit exceeded"):
-            await store_media_file(
-                graph_exec_id="test",
-                file=MediaFileType(
-                    "data:text/plain;base64,dGVzdA=="
-                ),  # Small test file
-                user_id="test_user",
-            )

autogpt_platform/backend/backend/blocks/test/test_llm.py

@@ -30,6 +30,7 @@ class TestLLMStatsTracking:
                 credentials=llm.TEST_CREDENTIALS,
                 llm_model=llm.LlmModel.GPT4O,
                 prompt=[{"role": "user", "content": "Hello"}],
+                json_format=False,
                 max_tokens=100,
             )
 
@@ -41,8 +42,6 @@ class TestLLMStatsTracking:
     @pytest.mark.asyncio
     async def test_ai_structured_response_block_tracks_stats(self):
         """Test that AIStructuredResponseGeneratorBlock correctly tracks stats."""
-        from unittest.mock import patch
-
         import backend.blocks.llm as llm
 
         block = llm.AIStructuredResponseGeneratorBlock()
@@ -52,7 +51,7 @@ class TestLLMStatsTracking:
             return llm.LLMResponse(
                 raw_response="",
                 prompt=[],
-                response='<json_output id="test123456">{"key1": "value1", "key2": "value2"}</json_output>',
+                response='{"key1": "value1", "key2": "value2"}',
                 tool_calls=None,
                 prompt_tokens=15,
                 completion_tokens=25,
@@ -70,12 +69,10 @@ class TestLLMStatsTracking:
         )
 
         outputs = {}
-        # Mock secrets.token_hex to return consistent ID
-        with patch("secrets.token_hex", return_value="test123456"):
-            async for output_name, output_data in block.run(
-                input_data, credentials=llm.TEST_CREDENTIALS
-            ):
-                outputs[output_name] = output_data
+        async for output_name, output_data in block.run(
+            input_data, credentials=llm.TEST_CREDENTIALS
+        ):
+            outputs[output_name] = output_data
 
         # Check stats
         assert block.execution_stats.input_token_count == 15
@@ -146,7 +143,7 @@ class TestLLMStatsTracking:
                 return llm.LLMResponse(
                     raw_response="",
                     prompt=[],
-                    response='<json_output id="test123456">{"wrong": "format"}</json_output>',
+                    response='{"wrong": "format"}',
                     tool_calls=None,
                     prompt_tokens=10,
                     completion_tokens=15,
@@ -157,7 +154,7 @@ class TestLLMStatsTracking:
                 return llm.LLMResponse(
                     raw_response="",
                     prompt=[],
-                    response='<json_output id="test123456">{"key1": "value1", "key2": "value2"}</json_output>',
+                    response='{"key1": "value1", "key2": "value2"}',
                     tool_calls=None,
                     prompt_tokens=20,
                     completion_tokens=25,
@@ -176,12 +173,10 @@ class TestLLMStatsTracking:
         )
 
         outputs = {}
-        # Mock secrets.token_hex to return consistent ID
-        with patch("secrets.token_hex", return_value="test123456"):
-            async for output_name, output_data in block.run(
-                input_data, credentials=llm.TEST_CREDENTIALS
-            ):
-                outputs[output_name] = output_data
+        async for output_name, output_data in block.run(
+            input_data, credentials=llm.TEST_CREDENTIALS
+        ):
+            outputs[output_name] = output_data
 
         # Check stats - should accumulate both calls
         # For 2 attempts: attempt 1 (failed) + attempt 2 (success) = 2 total
@@ -274,8 +269,7 @@ class TestLLMStatsTracking:
                 mock_response.choices = [
                     MagicMock(
                         message=MagicMock(
-                            content='<json_output id="test123456">{"summary": "Test chunk summary"}</json_output>',
-                            tool_calls=None,
+                            content='{"summary": "Test chunk summary"}', tool_calls=None
                         )
                     )
                 ]
@@ -283,7 +277,7 @@ class TestLLMStatsTracking:
                 mock_response.choices = [
                     MagicMock(
                         message=MagicMock(
-                            content='<json_output id="test123456">{"final_summary": "Test final summary"}</json_output>',
+                            content='{"final_summary": "Test final summary"}',
                             tool_calls=None,
                         )
                     )
@@ -304,13 +298,11 @@ class TestLLMStatsTracking:
                 max_tokens=1000,  # Large enough to avoid chunking
             )
 
-            # Mock secrets.token_hex to return consistent ID
-            with patch("secrets.token_hex", return_value="test123456"):
-                outputs = {}
-                async for output_name, output_data in block.run(
-                    input_data, credentials=llm.TEST_CREDENTIALS
-                ):
-                    outputs[output_name] = output_data
+            outputs = {}
+            async for output_name, output_data in block.run(
+                input_data, credentials=llm.TEST_CREDENTIALS
+            ):
+                outputs[output_name] = output_data
 
             print(f"Actual calls made: {call_count}")
             print(f"Block stats: {block.execution_stats}")
@@ -465,7 +457,7 @@ class TestLLMStatsTracking:
             return llm.LLMResponse(
                 raw_response="",
                 prompt=[],
-                response='<json_output id="test123456">{"result": "test"}</json_output>',
+                response='{"result": "test"}',
                 tool_calls=None,
                 prompt_tokens=10,
                 completion_tokens=20,
@@ -484,12 +476,10 @@ class TestLLMStatsTracking:
 
         # Run the block
         outputs = {}
-        # Mock secrets.token_hex to return consistent ID
-        with patch("secrets.token_hex", return_value="test123456"):
-            async for output_name, output_data in block.run(
-                input_data, credentials=llm.TEST_CREDENTIALS
-            ):
-                outputs[output_name] = output_data
+        async for output_name, output_data in block.run(
+            input_data, credentials=llm.TEST_CREDENTIALS
+        ):
+            outputs[output_name] = output_data
 
         # Block finished - now grab and assert stats
         assert block.execution_stats is not None

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

@@ -216,17 +216,8 @@ async def test_smart_decision_maker_tracks_llm_stats():
     }
 
     # Mock the _create_function_signature method to avoid database calls
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=[],
+    with patch("backend.blocks.llm.llm_call", return_value=mock_response), patch.object(
+        SmartDecisionMakerBlock, "_create_function_signature", return_value=[]
     ):
 
         # Create test input
@@ -258,471 +249,3 @@ async def test_smart_decision_maker_tracks_llm_stats():
         # Verify outputs
         assert "finished" in outputs  # Should have finished since no tool calls
         assert outputs["finished"] == "I need to think about this."
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_parameter_validation():
-    """Test that SmartDecisionMakerBlock correctly validates tool call parameters."""
-    from unittest.mock import MagicMock, patch
-
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool functions with specific parameter schema
-    mock_tool_functions = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "description": "Search for keywords with difficulty filtering",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {"type": "string", "description": "Search query"},
-                        "max_keyword_difficulty": {
-                            "type": "integer",
-                            "description": "Maximum keyword difficulty (required)",
-                        },
-                        "optional_param": {
-                            "type": "string",
-                            "description": "Optional parameter with default",
-                            "default": "default_value",
-                        },
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    # Test case 1: Tool call with TYPO in parameter name (should retry and eventually fail)
-    mock_tool_call_with_typo = MagicMock()
-    mock_tool_call_with_typo.function.name = "search_keywords"
-    mock_tool_call_with_typo.function.arguments = '{"query": "test", "maximum_keyword_difficulty": 50}'  # TYPO: maximum instead of max
-
-    mock_response_with_typo = MagicMock()
-    mock_response_with_typo.response = None
-    mock_response_with_typo.tool_calls = [mock_tool_call_with_typo]
-    mock_response_with_typo.prompt_tokens = 50
-    mock_response_with_typo.completion_tokens = 25
-    mock_response_with_typo.reasoning = None
-    mock_response_with_typo.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_with_typo,
-    ) as mock_llm_call, patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            retry=2,  # Set retry to 2 for testing
-        )
-
-        # Should raise ValueError after retries due to typo'd parameter name
-        with pytest.raises(ValueError) as exc_info:
-            outputs = {}
-            async for output_name, output_data in block.run(
-                input_data,
-                credentials=llm_module.TEST_CREDENTIALS,
-                graph_id="test-graph-id",
-                node_id="test-node-id",
-                graph_exec_id="test-exec-id",
-                node_exec_id="test-node-exec-id",
-                user_id="test-user-id",
-            ):
-                outputs[output_name] = output_data
-
-        # Verify error message contains details about the typo
-        error_msg = str(exc_info.value)
-        assert "Tool call 'search_keywords' has parameter errors" in error_msg
-        assert "Unknown parameters: ['maximum_keyword_difficulty']" in error_msg
-
-        # Verify that LLM was called the expected number of times (retries)
-        assert mock_llm_call.call_count == 2  # Should retry based on input_data.retry
-
-    # Test case 2: Tool call missing REQUIRED parameter (should raise ValueError)
-    mock_tool_call_missing_required = MagicMock()
-    mock_tool_call_missing_required.function.name = "search_keywords"
-    mock_tool_call_missing_required.function.arguments = (
-        '{"query": "test"}'  # Missing required max_keyword_difficulty
-    )
-
-    mock_response_missing_required = MagicMock()
-    mock_response_missing_required.response = None
-    mock_response_missing_required.tool_calls = [mock_tool_call_missing_required]
-    mock_response_missing_required.prompt_tokens = 50
-    mock_response_missing_required.completion_tokens = 25
-    mock_response_missing_required.reasoning = None
-    mock_response_missing_required.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_missing_required,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        # Should raise ValueError due to missing required parameter
-        with pytest.raises(ValueError) as exc_info:
-            outputs = {}
-            async for output_name, output_data in block.run(
-                input_data,
-                credentials=llm_module.TEST_CREDENTIALS,
-                graph_id="test-graph-id",
-                node_id="test-node-id",
-                graph_exec_id="test-exec-id",
-                node_exec_id="test-node-exec-id",
-                user_id="test-user-id",
-            ):
-                outputs[output_name] = output_data
-
-        error_msg = str(exc_info.value)
-        assert "Tool call 'search_keywords' has parameter errors" in error_msg
-        assert "Missing required parameters: ['max_keyword_difficulty']" in error_msg
-
-    # Test case 3: Valid tool call with OPTIONAL parameter missing (should succeed)
-    mock_tool_call_valid = MagicMock()
-    mock_tool_call_valid.function.name = "search_keywords"
-    mock_tool_call_valid.function.arguments = '{"query": "test", "max_keyword_difficulty": 50}'  # optional_param missing, but that's OK
-
-    mock_response_valid = MagicMock()
-    mock_response_valid.response = None
-    mock_response_valid.tool_calls = [mock_tool_call_valid]
-    mock_response_valid.prompt_tokens = 50
-    mock_response_valid.completion_tokens = 25
-    mock_response_valid.reasoning = None
-    mock_response_valid.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_valid,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        # Should succeed - optional parameter missing is OK
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Verify tool outputs were generated correctly
-        assert "tools_^_search_keywords_~_query" in outputs
-        assert outputs["tools_^_search_keywords_~_query"] == "test"
-        assert "tools_^_search_keywords_~_max_keyword_difficulty" in outputs
-        assert outputs["tools_^_search_keywords_~_max_keyword_difficulty"] == 50
-        # Optional parameter should be None when not provided
-        assert "tools_^_search_keywords_~_optional_param" in outputs
-        assert outputs["tools_^_search_keywords_~_optional_param"] is None
-
-    # Test case 4: Valid tool call with ALL parameters (should succeed)
-    mock_tool_call_all_params = MagicMock()
-    mock_tool_call_all_params.function.name = "search_keywords"
-    mock_tool_call_all_params.function.arguments = '{"query": "test", "max_keyword_difficulty": 50, "optional_param": "custom_value"}'
-
-    mock_response_all_params = MagicMock()
-    mock_response_all_params.response = None
-    mock_response_all_params.tool_calls = [mock_tool_call_all_params]
-    mock_response_all_params.prompt_tokens = 50
-    mock_response_all_params.completion_tokens = 25
-    mock_response_all_params.reasoning = None
-    mock_response_all_params.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_all_params,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        # Should succeed with all parameters
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Verify all tool outputs were generated correctly
-        assert outputs["tools_^_search_keywords_~_query"] == "test"
-        assert outputs["tools_^_search_keywords_~_max_keyword_difficulty"] == 50
-        assert outputs["tools_^_search_keywords_~_optional_param"] == "custom_value"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_raw_response_conversion():
-    """Test that SmartDecisionMaker correctly handles different raw_response types with retry mechanism."""
-    from unittest.mock import MagicMock, patch
-
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool functions
-    mock_tool_functions = [
-        {
-            "type": "function",
-            "function": {
-                "name": "test_tool",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"param": {"type": "string"}},
-                    "required": ["param"],
-                },
-            },
-        }
-    ]
-
-    # Test case 1: Simulate ChatCompletionMessage raw_response that caused the original error
-    class MockChatCompletionMessage:
-        """Simulate OpenAI's ChatCompletionMessage object that lacks .get() method"""
-
-        def __init__(self, role, content, tool_calls=None):
-            self.role = role
-            self.content = content
-            self.tool_calls = tool_calls or []
-
-        # This is what caused the error - no .get() method
-        # def get(self, key, default=None):  # Intentionally missing
-
-    # First response: has invalid parameter name (triggers retry)
-    mock_tool_call_invalid = MagicMock()
-    mock_tool_call_invalid.function.name = "test_tool"
-    mock_tool_call_invalid.function.arguments = (
-        '{"wrong_param": "test_value"}'  # Invalid parameter name
-    )
-
-    mock_response_retry = MagicMock()
-    mock_response_retry.response = None
-    mock_response_retry.tool_calls = [mock_tool_call_invalid]
-    mock_response_retry.prompt_tokens = 50
-    mock_response_retry.completion_tokens = 25
-    mock_response_retry.reasoning = None
-    # This would cause the original error without our fix
-    mock_response_retry.raw_response = MockChatCompletionMessage(
-        role="assistant", content=None, tool_calls=[mock_tool_call_invalid]
-    )
-
-    # Second response: successful (correct parameter name)
-    mock_tool_call_valid = MagicMock()
-    mock_tool_call_valid.function.name = "test_tool"
-    mock_tool_call_valid.function.arguments = (
-        '{"param": "test_value"}'  # Correct parameter name
-    )
-
-    mock_response_success = MagicMock()
-    mock_response_success.response = None
-    mock_response_success.tool_calls = [mock_tool_call_valid]
-    mock_response_success.prompt_tokens = 50
-    mock_response_success.completion_tokens = 25
-    mock_response_success.reasoning = None
-    mock_response_success.raw_response = MockChatCompletionMessage(
-        role="assistant", content=None, tool_calls=[mock_tool_call_valid]
-    )
-
-    # Mock llm_call to return different responses on different calls
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call", new_callable=AsyncMock
-    ) as mock_llm_call, patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-        # First call returns response that will trigger retry due to validation error
-        # Second call returns successful response
-        mock_llm_call.side_effect = [mock_response_retry, mock_response_success]
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Test prompt",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            retry=2,
-        )
-
-        # Should succeed after retry, demonstrating our helper function works
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Verify the tool output was generated successfully
-        assert "tools_^_test_tool_~_param" in outputs
-        assert outputs["tools_^_test_tool_~_param"] == "test_value"
-
-        # Verify conversation history was properly maintained
-        assert "conversations" in outputs
-        conversations = outputs["conversations"]
-        assert len(conversations) > 0
-
-        # The conversations should contain properly converted raw_response objects as dicts
-        # This would have failed with the original bug due to ChatCompletionMessage.get() error
-        for msg in conversations:
-            assert isinstance(msg, dict), f"Expected dict, got {type(msg)}"
-            if msg.get("role") == "assistant":
-                # Should have been converted from ChatCompletionMessage to dict
-                assert "role" in msg
-
-        # Verify LLM was called twice (initial + 1 retry)
-        assert mock_llm_call.call_count == 2
-
-    # Test case 2: Test with different raw_response types (Ollama string, dict)
-    # Test Ollama string response
-    mock_response_ollama = MagicMock()
-    mock_response_ollama.response = "I'll help you with that."
-    mock_response_ollama.tool_calls = None
-    mock_response_ollama.prompt_tokens = 30
-    mock_response_ollama.completion_tokens = 15
-    mock_response_ollama.reasoning = None
-    mock_response_ollama.raw_response = (
-        "I'll help you with that."  # Ollama returns string
-    )
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_ollama,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=[],  # No tools for this test
-    ):
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Simple prompt",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Should finish since no tool calls
-        assert "finished" in outputs
-        assert outputs["finished"] == "I'll help you with that."
-
-    # Test case 3: Test with dict raw_response (some providers/tests)
-    mock_response_dict = MagicMock()
-    mock_response_dict.response = "Test response"
-    mock_response_dict.tool_calls = None
-    mock_response_dict.prompt_tokens = 25
-    mock_response_dict.completion_tokens = 10
-    mock_response_dict.reasoning = None
-    mock_response_dict.raw_response = {
-        "role": "assistant",
-        "content": "Test response",
-    }  # Dict format
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_dict,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=[],
-    ):
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Another test",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        assert "finished" in outputs
-        assert outputs["finished"] == "Test response"

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

@@ -48,24 +48,16 @@ async def test_smart_decision_maker_handles_dynamic_dict_fields():
     assert "parameters" in signature["function"]
     assert "properties" in signature["function"]["parameters"]
 
-    # Check that dynamic fields are handled with original names
+    # Check that dynamic fields are handled
     properties = signature["function"]["parameters"]["properties"]
     assert len(properties) == 3  # Should have all three fields
 
-    # Check that field names are cleaned (for Anthropic API compatibility)
-    assert "values___name" in properties
-    assert "values___age" in properties
-    assert "values___city" in properties
-
-    # Each dynamic field should have proper schema with descriptive text
-    for field_name, prop_value in properties.items():
+    # Each dynamic field should have proper schema
+    for prop_value in properties.values():
         assert "type" in prop_value
         assert prop_value["type"] == "string"  # Dynamic fields get string type
         assert "description" in prop_value
-        # Check that descriptions properly explain the dynamic field
-        if field_name == "values___name":
-            assert "Dictionary field 'name'" in prop_value["description"]
-            assert "values['name']" in prop_value["description"]
+        assert "Dynamic value for" in prop_value["description"]
 
 
 @pytest.mark.asyncio
@@ -104,18 +96,10 @@ async def test_smart_decision_maker_handles_dynamic_list_fields():
     properties = signature["function"]["parameters"]["properties"]
     assert len(properties) == 2  # Should have both list items
 
-    # Check that field names are cleaned (for Anthropic API compatibility)
-    assert "entries___0" in properties
-    assert "entries___1" in properties
-
-    # Each dynamic field should have proper schema with descriptive text
-    for field_name, prop_value in properties.items():
+    # Each dynamic field should have proper schema
+    for prop_value in properties.values():
         assert prop_value["type"] == "string"
-        assert "description" in prop_value
-        # Check that descriptions properly explain the list field
-        if field_name == "entries___0":
-            assert "List item 0" in prop_value["description"]
-            assert "entries[0]" in prop_value["description"]
+        assert "Dynamic value for" in prop_value["description"]
 
 
 @pytest.mark.asyncio

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

@@ -1,553 +0,0 @@
-"""Comprehensive tests for SmartDecisionMakerBlock dynamic field handling."""
-
-import json
-from unittest.mock import AsyncMock, Mock, patch
-
-import pytest
-
-from backend.blocks.data_manipulation import AddToListBlock, CreateDictionaryBlock
-from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-from backend.blocks.text import MatchTextPatternBlock
-from backend.data.dynamic_fields import get_dynamic_field_description
-
-
-@pytest.mark.asyncio
-async def test_dynamic_field_description_generation():
-    """Test that dynamic field descriptions are generated correctly."""
-    # Test dictionary field description
-    desc = get_dynamic_field_description("values_#_name")
-    assert "Dictionary field 'name' for base field 'values'" in desc
-    assert "values['name']" in desc
-
-    # Test list field description
-    desc = get_dynamic_field_description("items_$_0")
-    assert "List item 0 for base field 'items'" in desc
-    assert "items[0]" in desc
-
-    # Test object field description
-    desc = get_dynamic_field_description("user_@_email")
-    assert "Object attribute 'email' for base field 'user'" in desc
-    assert "user.email" in desc
-
-    # Test regular field fallback
-    desc = get_dynamic_field_description("regular_field")
-    assert desc == "Value for regular_field"
-
-
-@pytest.mark.asyncio
-async def test_create_block_function_signature_with_dict_fields():
-    """Test that function signatures are created correctly for dictionary dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node for CreateDictionaryBlock
-    mock_node = Mock()
-    mock_node.block = CreateDictionaryBlock()
-    mock_node.block_id = CreateDictionaryBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic dictionary fields (source sanitized, sink original)
-    mock_links = [
-        Mock(
-            source_name="tools_^_create_dict_~_values___name",  # Sanitized source
-            sink_name="values_#_name",  # Original sink
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_create_dict_~_values___age",  # Sanitized source
-            sink_name="values_#_age",  # Original sink
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_create_dict_~_values___email",  # Sanitized source
-            sink_name="values_#_email",  # Original sink
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Verify the signature structure
-    assert signature["type"] == "function"
-    assert "function" in signature
-    assert "parameters" in signature["function"]
-    assert "properties" in signature["function"]["parameters"]
-
-    # Check that dynamic fields are handled with original names
-    properties = signature["function"]["parameters"]["properties"]
-    assert len(properties) == 3
-
-    # Check cleaned field names (for Anthropic API compatibility)
-    assert "values___name" in properties
-    assert "values___age" in properties
-    assert "values___email" in properties
-
-    # Check descriptions mention they are dictionary fields
-    assert "Dictionary field" in properties["values___name"]["description"]
-    assert "values['name']" in properties["values___name"]["description"]
-
-    assert "Dictionary field" in properties["values___age"]["description"]
-    assert "values['age']" in properties["values___age"]["description"]
-
-    assert "Dictionary field" in properties["values___email"]["description"]
-    assert "values['email']" in properties["values___email"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_create_block_function_signature_with_list_fields():
-    """Test that function signatures are created correctly for list dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node for AddToListBlock
-    mock_node = Mock()
-    mock_node.block = AddToListBlock()
-    mock_node.block_id = AddToListBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic list fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_add_list_~_0",
-            sink_name="entries_$_0",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_add_list_~_1",
-            sink_name="entries_$_1",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_add_list_~_2",
-            sink_name="entries_$_2",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Verify the signature structure
-    assert signature["type"] == "function"
-    properties = signature["function"]["parameters"]["properties"]
-
-    # Check cleaned field names (for Anthropic API compatibility)
-    assert "entries___0" in properties
-    assert "entries___1" in properties
-    assert "entries___2" in properties
-
-    # Check descriptions mention they are list items
-    assert "List item 0" in properties["entries___0"]["description"]
-    assert "entries[0]" in properties["entries___0"]["description"]
-
-    assert "List item 1" in properties["entries___1"]["description"]
-    assert "entries[1]" in properties["entries___1"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_create_block_function_signature_with_object_fields():
-    """Test that function signatures are created correctly for object dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node for MatchTextPatternBlock (simulating object fields)
-    mock_node = Mock()
-    mock_node.block = MatchTextPatternBlock()
-    mock_node.block_id = MatchTextPatternBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic object fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_extract_~_user_name",
-            sink_name="data_@_user_name",  # Dynamic object field
-            sink_id="extract_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_extract_~_user_email",
-            sink_name="data_@_user_email",  # Dynamic object field
-            sink_id="extract_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Verify the signature structure
-    properties = signature["function"]["parameters"]["properties"]
-
-    # Check cleaned field names (for Anthropic API compatibility)
-    assert "data___user_name" in properties
-    assert "data___user_email" in properties
-
-    # Check descriptions mention they are object attributes
-    assert "Object attribute" in properties["data___user_name"]["description"]
-    assert "data.user_name" in properties["data___user_name"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_create_function_signature():
-    """Test that the mapping between sanitized and original field names is built correctly."""
-    block = SmartDecisionMakerBlock()
-
-    # Mock the database client and connected nodes
-    with patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-    ) as mock_db:
-        mock_client = AsyncMock()
-        mock_db.return_value = mock_client
-
-        # Create mock nodes and links
-        mock_dict_node = Mock()
-        mock_dict_node.block = CreateDictionaryBlock()
-        mock_dict_node.block_id = CreateDictionaryBlock().id
-        mock_dict_node.input_default = {}
-
-        mock_list_node = Mock()
-        mock_list_node.block = AddToListBlock()
-        mock_list_node.block_id = AddToListBlock().id
-        mock_list_node.input_default = {}
-
-        # Mock links with dynamic fields
-        dict_link1 = Mock(
-            source_name="tools_^_create_dictionary_~_name",
-            sink_name="values_#_name",
-            sink_id="dict_node_id",
-            source_id="test_node_id",
-        )
-        dict_link2 = Mock(
-            source_name="tools_^_create_dictionary_~_age",
-            sink_name="values_#_age",
-            sink_id="dict_node_id",
-            source_id="test_node_id",
-        )
-        list_link = Mock(
-            source_name="tools_^_add_to_list_~_0",
-            sink_name="entries_$_0",
-            sink_id="list_node_id",
-            source_id="test_node_id",
-        )
-
-        mock_client.get_connected_output_nodes.return_value = [
-            (dict_link1, mock_dict_node),
-            (dict_link2, mock_dict_node),
-            (list_link, mock_list_node),
-        ]
-
-        # Call the method that builds signatures
-        tool_functions = await block._create_function_signature("test_node_id")
-
-        # Verify we got 2 tool functions (one for dict, one for list)
-        assert len(tool_functions) == 2
-
-        # Verify the tool functions contain the dynamic field names
-        dict_tool = next(
-            (
-                tool
-                for tool in tool_functions
-                if tool["function"]["name"] == "createdictionaryblock"
-            ),
-            None,
-        )
-        assert dict_tool is not None
-        dict_properties = dict_tool["function"]["parameters"]["properties"]
-        assert "values___name" in dict_properties
-        assert "values___age" in dict_properties
-
-        list_tool = next(
-            (
-                tool
-                for tool in tool_functions
-                if tool["function"]["name"] == "addtolistblock"
-            ),
-            None,
-        )
-        assert list_tool is not None
-        list_properties = list_tool["function"]["parameters"]["properties"]
-        assert "entries___0" in list_properties
-
-
-@pytest.mark.asyncio
-async def test_output_yielding_with_dynamic_fields():
-    """Test that outputs are yielded correctly with dynamic field names mapped back."""
-    block = SmartDecisionMakerBlock()
-
-    # No more sanitized mapping needed since we removed sanitization
-
-    # Mock LLM response with tool calls
-    mock_response = Mock()
-    mock_response.tool_calls = [
-        Mock(
-            function=Mock(
-                arguments=json.dumps(
-                    {
-                        "values___name": "Alice",
-                        "values___age": 30,
-                        "values___email": "alice@example.com",
-                    }
-                ),
-            )
-        )
-    ]
-    # Ensure function name is a real string, not a Mock name
-    mock_response.tool_calls[0].function.name = "createdictionaryblock"
-    mock_response.reasoning = "Creating a dictionary with user information"
-    mock_response.raw_response = {"role": "assistant", "content": "test"}
-    mock_response.prompt_tokens = 100
-    mock_response.completion_tokens = 50
-
-    # Mock the LLM call
-    with patch(
-        "backend.blocks.smart_decision_maker.llm.llm_call", new_callable=AsyncMock
-    ) as mock_llm:
-        mock_llm.return_value = mock_response
-
-        # Mock the function signature creation
-        with patch.object(
-            block, "_create_function_signature", new_callable=AsyncMock
-        ) as mock_sig:
-            mock_sig.return_value = [
-                {
-                    "type": "function",
-                    "function": {
-                        "name": "createdictionaryblock",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "values___name": {"type": "string"},
-                                "values___age": {"type": "number"},
-                                "values___email": {"type": "string"},
-                            },
-                        },
-                    },
-                }
-            ]
-
-            # Create input data
-            from backend.blocks import llm
-
-            input_data = block.input_schema(
-                prompt="Create a user dictionary",
-                credentials=llm.TEST_CREDENTIALS_INPUT,
-                model=llm.LlmModel.GPT4O,
-            )
-
-            # Run the block
-            outputs = {}
-            async for output_name, output_value in block.run(
-                input_data,
-                credentials=llm.TEST_CREDENTIALS,
-                graph_id="test_graph",
-                node_id="test_node",
-                graph_exec_id="test_exec",
-                node_exec_id="test_node_exec",
-                user_id="test_user",
-            ):
-                outputs[output_name] = output_value
-
-            # Verify the outputs use sanitized field names (matching frontend normalizeToolName)
-            assert "tools_^_createdictionaryblock_~_values___name" in outputs
-            assert outputs["tools_^_createdictionaryblock_~_values___name"] == "Alice"
-
-            assert "tools_^_createdictionaryblock_~_values___age" in outputs
-            assert outputs["tools_^_createdictionaryblock_~_values___age"] == 30
-
-            assert "tools_^_createdictionaryblock_~_values___email" in outputs
-            assert (
-                outputs["tools_^_createdictionaryblock_~_values___email"]
-                == "alice@example.com"
-            )
-
-
-@pytest.mark.asyncio
-async def test_mixed_regular_and_dynamic_fields():
-    """Test handling of blocks with both regular and dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node
-    mock_node = Mock()
-    mock_node.block = Mock()
-    mock_node.block.name = "TestBlock"
-    mock_node.block.description = "A test block"
-    mock_node.block.input_schema = Mock()
-
-    # Mock the get_field_schema to return a proper schema for regular fields
-    def get_field_schema(field_name):
-        if field_name == "regular_field":
-            return {"type": "string", "description": "A regular field"}
-        elif field_name == "values":
-            return {"type": "object", "description": "A dictionary field"}
-        else:
-            raise KeyError(f"Field {field_name} not found")
-
-    mock_node.block.input_schema.get_field_schema = get_field_schema
-    mock_node.block.input_schema.jsonschema = Mock(
-        return_value={"properties": {}, "required": []}
-    )
-
-    # Create links with both regular and dynamic fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_test_~_regular",
-            sink_name="regular_field",  # Regular field
-            sink_id="test_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_test_~_dict_key",
-            sink_name="values_#_key1",  # Dynamic dict field
-            sink_id="test_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_test_~_dict_key2",
-            sink_name="values_#_key2",  # Dynamic dict field
-            sink_id="test_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Check properties
-    properties = signature["function"]["parameters"]["properties"]
-    assert len(properties) == 3
-
-    # Regular field should have its original schema
-    assert "regular_field" in properties
-    assert properties["regular_field"]["description"] == "A regular field"
-
-    # Dynamic fields should have generated descriptions
-    assert "values___key1" in properties
-    assert "Dictionary field" in properties["values___key1"]["description"]
-
-    assert "values___key2" in properties
-    assert "Dictionary field" in properties["values___key2"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_validation_errors_dont_pollute_conversation():
-    """Test that validation errors are only used during retries and don't pollute the conversation."""
-    block = SmartDecisionMakerBlock()
-
-    # Track conversation history changes
-    conversation_snapshots = []
-
-    # Mock response with invalid tool call (missing required parameter)
-    invalid_response = Mock()
-    invalid_response.tool_calls = [
-        Mock(
-            function=Mock(
-                arguments=json.dumps({"wrong_param": "value"}),  # Wrong parameter name
-            )
-        )
-    ]
-    # Ensure function name is a real string, not a Mock name
-    invalid_response.tool_calls[0].function.name = "test_tool"
-    invalid_response.reasoning = None
-    invalid_response.raw_response = {"role": "assistant", "content": "invalid"}
-    invalid_response.prompt_tokens = 100
-    invalid_response.completion_tokens = 50
-
-    # Mock valid response after retry
-    valid_response = Mock()
-    valid_response.tool_calls = [
-        Mock(function=Mock(arguments=json.dumps({"correct_param": "value"})))
-    ]
-    # Ensure function name is a real string, not a Mock name
-    valid_response.tool_calls[0].function.name = "test_tool"
-    valid_response.reasoning = None
-    valid_response.raw_response = {"role": "assistant", "content": "valid"}
-    valid_response.prompt_tokens = 100
-    valid_response.completion_tokens = 50
-
-    call_count = 0
-
-    async def mock_llm_call(**kwargs):
-        nonlocal call_count
-        # Capture conversation state
-        conversation_snapshots.append(kwargs.get("prompt", []).copy())
-        call_count += 1
-        if call_count == 1:
-            return invalid_response
-        else:
-            return valid_response
-
-    # Mock the LLM call
-    with patch(
-        "backend.blocks.smart_decision_maker.llm.llm_call", new_callable=AsyncMock
-    ) as mock_llm:
-        mock_llm.side_effect = mock_llm_call
-
-        # Mock the function signature creation
-        with patch.object(
-            block, "_create_function_signature", new_callable=AsyncMock
-        ) as mock_sig:
-            mock_sig.return_value = [
-                {
-                    "type": "function",
-                    "function": {
-                        "name": "test_tool",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "correct_param": {
-                                    "type": "string",
-                                    "description": "The correct parameter",
-                                }
-                            },
-                            "required": ["correct_param"],
-                        },
-                    },
-                }
-            ]
-
-            # Create input data
-            from backend.blocks import llm
-
-            input_data = block.input_schema(
-                prompt="Test prompt",
-                credentials=llm.TEST_CREDENTIALS_INPUT,
-                model=llm.LlmModel.GPT4O,
-                retry=3,  # Allow retries
-            )
-
-            # Run the block
-            outputs = {}
-            async for output_name, output_value in block.run(
-                input_data,
-                credentials=llm.TEST_CREDENTIALS,
-                graph_id="test_graph",
-                node_id="test_node",
-                graph_exec_id="test_exec",
-                node_exec_id="test_node_exec",
-                user_id="test_user",
-            ):
-                outputs[output_name] = output_value
-
-            # Verify we had 2 LLM calls (initial + retry)
-            assert call_count == 2
-
-            # Check the final conversation output
-            final_conversation = outputs.get("conversations", [])
-
-            # The final conversation should NOT contain the validation error message
-            error_messages = [
-                msg
-                for msg in final_conversation
-                if msg.get("role") == "user"
-                and "parameter errors" in msg.get("content", "")
-            ]
-            assert (
-                len(error_messages) == 0
-            ), "Validation error leaked into final conversation"
-
-            # The final conversation should only have the successful response
-            assert final_conversation[-1]["content"] == "valid"

autogpt_platform/backend/backend/blocks/test/test_table_input.py

@@ -1,131 +0,0 @@
-import pytest
-
-from backend.blocks.io import AgentTableInputBlock
-from backend.util.test import execute_block_test
-
-
-@pytest.mark.asyncio
-async def test_table_input_block():
-    """Test the AgentTableInputBlock with basic input/output."""
-    block = AgentTableInputBlock()
-    await execute_block_test(block)
-
-
-@pytest.mark.asyncio
-async def test_table_input_with_data():
-    """Test AgentTableInputBlock with actual table data."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="test_table",
-        column_headers=["Name", "Age", "City"],
-        value=[
-            {"Name": "John", "Age": "30", "City": "New York"},
-            {"Name": "Jane", "Age": "25", "City": "London"},
-            {"Name": "Bob", "Age": "35", "City": "Paris"},
-        ],
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-
-    result = output_data[0][1]
-    assert len(result) == 3
-    assert result[0]["Name"] == "John"
-    assert result[1]["Age"] == "25"
-    assert result[2]["City"] == "Paris"
-
-
-@pytest.mark.asyncio
-async def test_table_input_empty_data():
-    """Test AgentTableInputBlock with empty data."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="empty_table", column_headers=["Col1", "Col2"], value=[]
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-    assert output_data[0][1] == []
-
-
-@pytest.mark.asyncio
-async def test_table_input_with_missing_columns():
-    """Test AgentTableInputBlock passes through data with missing columns as-is."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="partial_table",
-        column_headers=["Name", "Age", "City"],
-        value=[
-            {"Name": "John", "Age": "30"},  # Missing City
-            {"Name": "Jane", "City": "London"},  # Missing Age
-            {"Age": "35", "City": "Paris"},  # Missing Name
-        ],
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    result = output_data[0][1]
-    assert len(result) == 3
-
-    # Check data is passed through as-is
-    assert result[0] == {"Name": "John", "Age": "30"}
-    assert result[1] == {"Name": "Jane", "City": "London"}
-    assert result[2] == {"Age": "35", "City": "Paris"}
-
-
-@pytest.mark.asyncio
-async def test_table_input_none_value():
-    """Test AgentTableInputBlock with None value returns empty list."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="none_table", column_headers=["Name", "Age"], value=None
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-    assert output_data[0][1] == []
-
-
-@pytest.mark.asyncio
-async def test_table_input_with_default_headers():
-    """Test AgentTableInputBlock with default column headers."""
-    block = AgentTableInputBlock()
-
-    # Don't specify column_headers, should use defaults
-    input_data = block.Input(
-        name="default_headers_table",
-        value=[
-            {"Column 1": "A", "Column 2": "B", "Column 3": "C"},
-            {"Column 1": "D", "Column 2": "E", "Column 3": "F"},
-        ],
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-
-    result = output_data[0][1]
-    assert len(result) == 2
-    assert result[0]["Column 1"] == "A"
-    assert result[1]["Column 3"] == "F"

autogpt_platform/backend/backend/blocks/text.py

@@ -2,8 +2,6 @@ import re
 from pathlib import Path
 from typing import Any
 
-import regex  # Has built-in timeout support
-
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
 from backend.util import json, text
@@ -139,11 +137,6 @@ class ExtractTextInformationBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Security fix: Add limits to prevent ReDoS and memory exhaustion
-        MAX_TEXT_LENGTH = 1_000_000  # 1MB character limit
-        MAX_MATCHES = 1000  # Maximum number of matches to prevent memory exhaustion
-        MAX_MATCH_LENGTH = 10_000  # Maximum length per match
-
         flags = 0
         if not input_data.case_sensitive:
             flags = flags | re.IGNORECASE
@@ -155,85 +148,20 @@ class ExtractTextInformationBlock(Block):
         else:
             txt = json.dumps(input_data.text)
 
-        # Limit text size to prevent DoS
-        if len(txt) > MAX_TEXT_LENGTH:
-            txt = txt[:MAX_TEXT_LENGTH]
-
-        # Validate regex pattern to prevent dangerous patterns
-        dangerous_patterns = [
-            r".*\+.*\+",  # Nested quantifiers
-            r".*\*.*\*",  # Nested quantifiers
-            r"(?=.*\+)",  # Lookahead with quantifier
-            r"(?=.*\*)",  # Lookahead with quantifier
-            r"\(.+\)\+",  # Group with nested quantifier
-            r"\(.+\)\*",  # Group with nested quantifier
-            r"\([^)]+\+\)\+",  # Nested quantifiers like (a+)+
-            r"\([^)]+\*\)\*",  # Nested quantifiers like (a*)*
+        matches = [
+            match.group(input_data.group)
+            for match in re.finditer(input_data.pattern, txt, flags)
+            if input_data.group <= len(match.groups())
         ]
-
-        # Check if pattern is potentially dangerous
-        is_dangerous = any(
-            re.search(dangerous, input_data.pattern) for dangerous in dangerous_patterns
-        )
-
-        # Use regex module with timeout for dangerous patterns
-        # For safe patterns, use standard re module for compatibility
-        try:
-            matches = []
-            match_count = 0
-
-            if is_dangerous:
-                # Use regex module with timeout (5 seconds) for dangerous patterns
-                # The regex module supports timeout parameter in finditer
-                try:
-                    for match in regex.finditer(
-                        input_data.pattern, txt, flags=flags, timeout=5.0
-                    ):
-                        if match_count >= MAX_MATCHES:
-                            break
-                        if input_data.group <= len(match.groups()):
-                            match_text = match.group(input_data.group)
-                            # Limit match length to prevent memory exhaustion
-                            if len(match_text) > MAX_MATCH_LENGTH:
-                                match_text = match_text[:MAX_MATCH_LENGTH]
-                            matches.append(match_text)
-                            match_count += 1
-                except regex.error as e:
-                    # Timeout occurred or regex error
-                    if "timeout" in str(e).lower():
-                        # Timeout - return empty results
-                        pass
-                    else:
-                        # Other regex error
-                        raise
-            else:
-                # Use standard re module for non-dangerous patterns
-                for match in re.finditer(input_data.pattern, txt, flags):
-                    if match_count >= MAX_MATCHES:
-                        break
-                    if input_data.group <= len(match.groups()):
-                        match_text = match.group(input_data.group)
-                        # Limit match length to prevent memory exhaustion
-                        if len(match_text) > MAX_MATCH_LENGTH:
-                            match_text = match_text[:MAX_MATCH_LENGTH]
-                        matches.append(match_text)
-                        match_count += 1
-
-            if not input_data.find_all:
-                matches = matches[:1]
-
-            for match in matches:
-                yield "positive", match
-            if not matches:
-                yield "negative", input_data.text
-
-            yield "matched_results", matches
-            yield "matched_count", len(matches)
-        except Exception:
-            # Return empty results on any regex error
+        if not input_data.find_all:
+            matches = matches[:1]
+        for match in matches:
+            yield "positive", match
+        if not matches:
             yield "negative", input_data.text
-            yield "matched_results", []
-            yield "matched_count", 0
+
+        yield "matched_results", matches
+        yield "matched_count", len(matches)
 
 
 class FillTextTemplateBlock(Block):
@@ -244,11 +172,6 @@ class FillTextTemplateBlock(Block):
         format: str = SchemaField(
             description="Template to format the text using `values`. Use Jinja2 syntax."
         )
-        escape_html: bool = SchemaField(
-            default=False,
-            advanced=True,
-            description="Whether to escape special characters in the inserted values to be HTML-safe. Enable for HTML output, disable for plain text.",
-        )
 
     class Output(BlockSchema):
         output: str = SchemaField(description="Formatted text")
@@ -282,7 +205,6 @@ class FillTextTemplateBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        formatter = text.TextFormatter(autoescape=input_data.escape_html)
         yield "output", formatter.format_string(input_data.format, input_data.values)
 
 

autogpt_platform/backend/backend/blocks/time_blocks.py

@@ -270,17 +270,13 @@ class GetCurrentDateBlock(Block):
             test_output=[
                 (
                     "date",
-                    lambda t: abs(
-                        datetime.now().date() - datetime.strptime(t, "%Y-%m-%d").date()
-                    )
-                    <= timedelta(days=8),  # 7 days difference + 1 day error margin.
+                    lambda t: abs(datetime.now() - datetime.strptime(t, "%Y-%m-%d"))
+                    < timedelta(days=8),  # 7 days difference + 1 day error margin.
                 ),
                 (
                     "date",
-                    lambda t: abs(
-                        datetime.now().date() - datetime.strptime(t, "%m/%d/%Y").date()
-                    )
-                    <= timedelta(days=8),
+                    lambda t: abs(datetime.now() - datetime.strptime(t, "%m/%d/%Y"))
+                    < timedelta(days=8),
                     # 7 days difference + 1 day error margin.
                 ),
                 (
@@ -386,7 +382,7 @@ class GetCurrentDateAndTimeBlock(Block):
                     lambda t: abs(
                         datetime.now().date() - datetime.strptime(t, "%Y/%m/%d").date()
                     )
-                    <= timedelta(days=1),  # Date format only, no time component
+                    < timedelta(days=1),  # Date format only, no time component
                 ),
                 (
                     "date_time",

autogpt_platform/backend/backend/blocks/xml_parser.py

@@ -26,14 +26,6 @@ class XMLParserBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Security fix: Add size limits to prevent XML bomb attacks
-        MAX_XML_SIZE = 10 * 1024 * 1024  # 10MB limit for XML input
-
-        if len(input_data.input_xml) > MAX_XML_SIZE:
-            raise ValueError(
-                f"XML too large: {len(input_data.input_xml)} bytes > {MAX_XML_SIZE} bytes"
-            )
-
         try:
             tokens = tokenize(input_data.input_xml)
             parser = Parser(tokens)

autogpt_platform/backend/backend/data/api_key.py

@@ -9,7 +9,6 @@ from prisma.models import APIKey as PrismaAPIKey
 from prisma.types import APIKeyWhereUniqueInput
 from pydantic import BaseModel, Field
 
-from backend.data.includes import MAX_USER_API_KEYS_FETCH
 from backend.util.exceptions import NotAuthorizedError, NotFoundError
 
 logger = logging.getLogger(__name__)
@@ -179,13 +178,9 @@ async def revoke_api_key(key_id: str, user_id: str) -> APIKeyInfo:
     return APIKeyInfo.from_db(updated_api_key)
 
 
-async def list_user_api_keys(
-    user_id: str, limit: int = MAX_USER_API_KEYS_FETCH
-) -> list[APIKeyInfo]:
+async def list_user_api_keys(user_id: str) -> list[APIKeyInfo]:
     api_keys = await PrismaAPIKey.prisma().find_many(
-        where={"userId": user_id},
-        order={"createdAt": "desc"},
-        take=limit,
+        where={"userId": user_id}, order={"createdAt": "desc"}
     )
 
     return [APIKeyInfo.from_db(key) for key in api_keys]

autogpt_platform/backend/backend/data/block.py

@@ -1,3 +1,4 @@
+import functools
 import inspect
 import logging
 import os
@@ -20,7 +21,6 @@ from typing import (
 
 import jsonref
 import jsonschema
-from autogpt_libs.utils.cache import cached
 from prisma.models import AgentBlock
 from prisma.types import AgentBlockCreateInput
 from pydantic import BaseModel
@@ -722,7 +722,7 @@ def get_block(block_id: str) -> Block[BlockSchema, BlockSchema] | None:
     return cls() if cls else None
 
 
-@cached()
+@functools.cache
 def get_webhook_block_ids() -> Sequence[str]:
     return [
         id
@@ -731,7 +731,7 @@ def get_webhook_block_ids() -> Sequence[str]:
     ]
 
 
-@cached()
+@functools.cache
 def get_io_block_ids() -> Sequence[str]:
     return [
         id

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -69,8 +69,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_1_OPUS: 21,
     LlmModel.CLAUDE_4_OPUS: 21,
     LlmModel.CLAUDE_4_SONNET: 5,
-    LlmModel.CLAUDE_4_5_HAIKU: 4,
-    LlmModel.CLAUDE_4_5_SONNET: 9,
     LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_5_SONNET: 4,
     LlmModel.CLAUDE_3_5_HAIKU: 1,  # $0.80 / $4.00

autogpt_platform/backend/backend/data/credit.py

@@ -23,7 +23,6 @@ from pydantic import BaseModel
 
 from backend.data import db
 from backend.data.block_cost_config import BLOCK_COSTS
-from backend.data.includes import MAX_CREDIT_REFUND_REQUESTS_FETCH
 from backend.data.model import (
     AutoTopUpConfig,
     RefundRequest,
@@ -906,9 +905,7 @@ class UserCredit(UserCreditBase):
             ),
         )
 
-    async def get_refund_requests(
-        self, user_id: str, limit: int = MAX_CREDIT_REFUND_REQUESTS_FETCH
-    ) -> list[RefundRequest]:
+    async def get_refund_requests(self, user_id: str) -> list[RefundRequest]:
         return [
             RefundRequest(
                 id=r.id,
@@ -924,7 +921,6 @@ class UserCredit(UserCreditBase):
             for r in await CreditRefundRequest.prisma().find_many(
                 where={"userId": user_id},
                 order={"createdAt": "desc"},
-                take=limit,
             )
         ]
 

autogpt_platform/backend/backend/data/db.py

@@ -83,7 +83,7 @@ async def disconnect():
 
 
 # Transaction timeout constant (in milliseconds)
-TRANSACTION_TIMEOUT = 30000  # 30 seconds - Increased from 15s to prevent timeout errors during graph creation under load
+TRANSACTION_TIMEOUT = 15000  # 15 seconds - Increased from 5s to prevent timeout errors
 
 
 @asynccontextmanager

autogpt_platform/backend/backend/data/dynamic_fields.py

@@ -1,284 +0,0 @@
-"""
-Utilities for handling dynamic field names with special delimiters.
-
-Dynamic fields allow graphs to connect complex data structures using special delimiters:
-- _#_ for dictionary keys (e.g., "values_#_name" → values["name"])
-- _$_ for list indices (e.g., "items_$_0" → items[0])
-- _@_ for object attributes (e.g., "obj_@_attr" → obj.attr)
-"""
-
-from typing import Any
-
-from backend.util.mock import MockObject
-
-# Dynamic field delimiters
-LIST_SPLIT = "_$_"
-DICT_SPLIT = "_#_"
-OBJC_SPLIT = "_@_"
-
-DYNAMIC_DELIMITERS = (LIST_SPLIT, DICT_SPLIT, OBJC_SPLIT)
-
-
-def extract_base_field_name(field_name: str) -> str:
-    """
-    Extract the base field name from a dynamic field name by removing all dynamic suffixes.
-
-    Examples:
-        extract_base_field_name("values_#_name") → "values"
-        extract_base_field_name("items_$_0") → "items"
-        extract_base_field_name("obj_@_attr") → "obj"
-        extract_base_field_name("regular_field") → "regular_field"
-
-    Args:
-        field_name: The field name that may contain dynamic delimiters
-
-    Returns:
-        The base field name without any dynamic suffixes
-    """
-    base_name = field_name
-    for delimiter in DYNAMIC_DELIMITERS:
-        if delimiter in base_name:
-            base_name = base_name.split(delimiter)[0]
-    return base_name
-
-
-def is_dynamic_field(field_name: str) -> bool:
-    """
-    Check if a field name contains dynamic delimiters.
-
-    Args:
-        field_name: The field name to check
-
-    Returns:
-        True if the field contains any dynamic delimiters, False otherwise
-    """
-    return any(delimiter in field_name for delimiter in DYNAMIC_DELIMITERS)
-
-
-def get_dynamic_field_description(field_name: str) -> str:
-    """
-    Generate a description for a dynamic field based on its structure.
-
-    Args:
-        field_name: The full dynamic field name (e.g., "values_#_name")
-
-    Returns:
-        A descriptive string explaining what this dynamic field represents
-    """
-    base_name = extract_base_field_name(field_name)
-
-    if DICT_SPLIT in field_name:
-        # Extract the key part after _#_
-        parts = field_name.split(DICT_SPLIT)
-        if len(parts) > 1:
-            key = parts[1].split("_")[0] if "_" in parts[1] else parts[1]
-            return f"Dictionary field '{key}' for base field '{base_name}' ({base_name}['{key}'])"
-    elif LIST_SPLIT in field_name:
-        # Extract the index part after _$_
-        parts = field_name.split(LIST_SPLIT)
-        if len(parts) > 1:
-            index = parts[1].split("_")[0] if "_" in parts[1] else parts[1]
-            return (
-                f"List item {index} for base field '{base_name}' ({base_name}[{index}])"
-            )
-    elif OBJC_SPLIT in field_name:
-        # Extract the attribute part after _@_
-        parts = field_name.split(OBJC_SPLIT)
-        if len(parts) > 1:
-            # Get the full attribute name (everything after _@_)
-            attr = parts[1]
-            return f"Object attribute '{attr}' for base field '{base_name}' ({base_name}.{attr})"
-
-    return f"Value for {field_name}"
-
-
-# --------------------------------------------------------------------------- #
-#  Dynamic field parsing and merging utilities
-# --------------------------------------------------------------------------- #
-
-
-def _next_delim(s: str) -> tuple[str | None, int]:
-    """
-    Return the *earliest* delimiter appearing in `s` and its index.
-
-    If none present → (None, -1).
-    """
-    first: str | None = None
-    pos = len(s)  # sentinel: larger than any real index
-    for d in DYNAMIC_DELIMITERS:
-        i = s.find(d)
-        if 0 <= i < pos:
-            first, pos = d, i
-    return first, (pos if first else -1)
-
-
-def _tokenise(path: str) -> list[tuple[str, str]] | None:
-    """
-    Convert the raw path string (starting with a delimiter) into
-    [ (delimiter, identifier), … ] or None if the syntax is malformed.
-    """
-    tokens: list[tuple[str, str]] = []
-    while path:
-        # 1. Which delimiter starts this chunk?
-        delim = next((d for d in DYNAMIC_DELIMITERS if path.startswith(d)), None)
-        if delim is None:
-            return None  # invalid syntax
-
-        # 2. Slice off the delimiter, then up to the next delimiter (or EOS)
-        path = path[len(delim) :]
-        nxt_delim, pos = _next_delim(path)
-        token, path = (
-            path[: pos if pos != -1 else len(path)],
-            path[pos if pos != -1 else len(path) :],
-        )
-        if token == "":
-            return None  # empty identifier is invalid
-        tokens.append((delim, token))
-    return tokens
-
-
-def parse_execution_output(output: tuple[str, Any], name: str) -> Any:
-    """
-    Retrieve a nested value out of `output` using the flattened *name*.
-
-    On any failure (wrong name, wrong type, out-of-range, bad path)
-    returns **None**.
-
-    Args:
-        output: Tuple of (base_name, data) representing a block output entry
-        name: The flattened field name to extract from the output data
-
-    Returns:
-        The value at the specified path, or None if not found/invalid
-    """
-    base_name, data = output
-
-    # Exact match → whole object
-    if name == base_name:
-        return data
-
-    # Must start with the expected name
-    if not name.startswith(base_name):
-        return None
-    path = name[len(base_name) :]
-    if not path:
-        return None  # nothing left to parse
-
-    tokens = _tokenise(path)
-    if tokens is None:
-        return None
-
-    cur: Any = data
-    for delim, ident in tokens:
-        if delim == LIST_SPLIT:
-            # list[index]
-            try:
-                idx = int(ident)
-            except ValueError:
-                return None
-            if not isinstance(cur, list) or idx >= len(cur):
-                return None
-            cur = cur[idx]
-
-        elif delim == DICT_SPLIT:
-            if not isinstance(cur, dict) or ident not in cur:
-                return None
-            cur = cur[ident]
-
-        elif delim == OBJC_SPLIT:
-            if not hasattr(cur, ident):
-                return None
-            cur = getattr(cur, ident)
-
-        else:
-            return None  # unreachable
-
-    return cur
-
-
-def _assign(container: Any, tokens: list[tuple[str, str]], value: Any) -> Any:
-    """
-    Recursive helper that *returns* the (possibly new) container with
-    `value` assigned along the remaining `tokens` path.
-    """
-    if not tokens:
-        return value  # leaf reached
-
-    delim, ident = tokens[0]
-    rest = tokens[1:]
-
-    # ---------- list ----------
-    if delim == LIST_SPLIT:
-        try:
-            idx = int(ident)
-        except ValueError:
-            raise ValueError("index must be an integer")
-
-        if container is None:
-            container = []
-        elif not isinstance(container, list):
-            container = list(container) if hasattr(container, "__iter__") else []
-
-        while len(container) <= idx:
-            container.append(None)
-        container[idx] = _assign(container[idx], rest, value)
-        return container
-
-    # ---------- dict ----------
-    if delim == DICT_SPLIT:
-        if container is None:
-            container = {}
-        elif not isinstance(container, dict):
-            container = dict(container) if hasattr(container, "items") else {}
-        container[ident] = _assign(container.get(ident), rest, value)
-        return container
-
-    # ---------- object ----------
-    if delim == OBJC_SPLIT:
-        if container is None:
-            container = MockObject()
-        elif not hasattr(container, "__dict__"):
-            # If it's not an object, create a new one
-            container = MockObject()
-        setattr(
-            container,
-            ident,
-            _assign(getattr(container, ident, None), rest, value),
-        )
-        return container
-
-    return value  # unreachable
-
-
-def merge_execution_input(data: dict[str, Any]) -> dict[str, Any]:
-    """
-    Reconstruct nested objects from a *flattened* dict of key → value.
-
-    Raises ValueError on syntactically invalid list indices.
-
-    Args:
-        data: Dictionary with potentially flattened dynamic field keys
-
-    Returns:
-        Dictionary with nested objects reconstructed from flattened keys
-    """
-    merged: dict[str, Any] = {}
-
-    for key, value in data.items():
-        # Split off the base name (before the first delimiter, if any)
-        delim, pos = _next_delim(key)
-        if delim is None:
-            merged[key] = value
-            continue
-
-        base, path = key[:pos], key[pos:]
-        tokens = _tokenise(path)
-        if tokens is None:
-            # Invalid key; treat as scalar under the raw name
-            merged[key] = value
-            continue
-
-        merged[base] = _assign(merged.get(base), tokens, value)
-
-    data.update(merged)
-    return data

autogpt_platform/backend/backend/data/execution.py

@@ -38,8 +38,8 @@ from prisma.types import (
 from pydantic import BaseModel, ConfigDict, JsonValue, ValidationError
 from pydantic.fields import Field
 
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util import type as type_utils
-from backend.util.exceptions import DatabaseError
 from backend.util.json import SafeJson
 from backend.util.models import Pagination
 from backend.util.retry import func_retry
@@ -92,31 +92,6 @@ ExecutionStatus = AgentExecutionStatus
 NodeInputMask = Mapping[str, JsonValue]
 NodesInputMasks = Mapping[str, NodeInputMask]
 
-# dest: source
-VALID_STATUS_TRANSITIONS = {
-    ExecutionStatus.QUEUED: [
-        ExecutionStatus.INCOMPLETE,
-    ],
-    ExecutionStatus.RUNNING: [
-        ExecutionStatus.INCOMPLETE,
-        ExecutionStatus.QUEUED,
-        ExecutionStatus.TERMINATED,  # For resuming halted execution
-    ],
-    ExecutionStatus.COMPLETED: [
-        ExecutionStatus.RUNNING,
-    ],
-    ExecutionStatus.FAILED: [
-        ExecutionStatus.INCOMPLETE,
-        ExecutionStatus.QUEUED,
-        ExecutionStatus.RUNNING,
-    ],
-    ExecutionStatus.TERMINATED: [
-        ExecutionStatus.INCOMPLETE,
-        ExecutionStatus.QUEUED,
-        ExecutionStatus.RUNNING,
-    ],
-}
-
 
 class GraphExecutionMeta(BaseDbModel):
     id: str  # type: ignore # Override base class to make this required
@@ -130,8 +105,6 @@ class GraphExecutionMeta(BaseDbModel):
     status: ExecutionStatus
     started_at: datetime
     ended_at: datetime
-    is_shared: bool = False
-    share_token: Optional[str] = None
 
     class Stats(BaseModel):
         model_config = ConfigDict(
@@ -248,8 +221,6 @@ class GraphExecutionMeta(BaseDbModel):
                 if stats
                 else None
             ),
-            is_shared=_graph_exec.isShared,
-            share_token=_graph_exec.shareToken,
         )
 
 
@@ -478,48 +449,6 @@ async def get_graph_executions(
     return [GraphExecutionMeta.from_db(execution) for execution in executions]
 
 
-async def get_graph_executions_count(
-    user_id: Optional[str] = None,
-    graph_id: Optional[str] = None,
-    statuses: Optional[list[ExecutionStatus]] = None,
-    created_time_gte: Optional[datetime] = None,
-    created_time_lte: Optional[datetime] = None,
-) -> int:
-    """
-    Get count of graph executions with optional filters.
-
-    Args:
-        user_id: Optional user ID to filter by
-        graph_id: Optional graph ID to filter by
-        statuses: Optional list of execution statuses to filter by
-        created_time_gte: Optional minimum creation time
-        created_time_lte: Optional maximum creation time
-
-    Returns:
-        Count of matching graph executions
-    """
-    where_filter: AgentGraphExecutionWhereInput = {
-        "isDeleted": False,
-    }
-
-    if user_id:
-        where_filter["userId"] = user_id
-
-    if graph_id:
-        where_filter["agentGraphId"] = graph_id
-
-    if created_time_gte or created_time_lte:
-        where_filter["createdAt"] = {
-            "gte": created_time_gte or datetime.min.replace(tzinfo=timezone.utc),
-            "lte": created_time_lte or datetime.max.replace(tzinfo=timezone.utc),
-        }
-    if statuses:
-        where_filter["OR"] = [{"executionStatus": status} for status in statuses]
-
-    count = await AgentGraphExecution.prisma().count(where=where_filter)
-    return count
-
-
 class GraphExecutionsPaginated(BaseModel):
     """Response schema for paginated graph executions."""
 
@@ -651,7 +580,7 @@ async def create_graph_execution(
         data={
             "agentGraphId": graph_id,
             "agentGraphVersion": graph_version,
-            "executionStatus": ExecutionStatus.INCOMPLETE,
+            "executionStatus": ExecutionStatus.QUEUED,
             "inputs": SafeJson(inputs),
             "credentialInputs": (
                 SafeJson(credential_inputs) if credential_inputs else Json({})
@@ -798,11 +727,6 @@ async def update_graph_execution_stats(
     status: ExecutionStatus | None = None,
     stats: GraphExecutionStats | None = None,
 ) -> GraphExecution | None:
-    if not status and not stats:
-        raise ValueError(
-            f"Must provide either status or stats to update for execution {graph_exec_id}"
-        )
-
     update_data: AgentGraphExecutionUpdateManyMutationInput = {}
 
     if stats:
@@ -814,25 +738,20 @@ async def update_graph_execution_stats(
     if status:
         update_data["executionStatus"] = status
 
-    where_clause: AgentGraphExecutionWhereInput = {"id": graph_exec_id}
-
-    if status:
-        if allowed_from := VALID_STATUS_TRANSITIONS.get(status, []):
-            # Add OR clause to check if current status is one of the allowed source statuses
-            where_clause["AND"] = [
-                {"id": graph_exec_id},
-                {"OR": [{"executionStatus": s} for s in allowed_from]},
-            ]
-        else:
-            raise ValueError(
-                f"Status {status} cannot be set via update for execution {graph_exec_id}. "
-                f"This status can only be set at creation or is not a valid target status."
-            )
-
-    await AgentGraphExecution.prisma().update_many(
-        where=where_clause,
+    updated_count = await AgentGraphExecution.prisma().update_many(
+        where={
+            "id": graph_exec_id,
+            "OR": [
+                {"executionStatus": ExecutionStatus.RUNNING},
+                {"executionStatus": ExecutionStatus.QUEUED},
+                # Terminated graph can be resumed.
+                {"executionStatus": ExecutionStatus.TERMINATED},
+            ],
+        },
         data=update_data,
     )
+    if updated_count == 0:
+        return None
 
     graph_exec = await AgentGraphExecution.prisma().find_unique_or_raise(
         where={"id": graph_exec_id},
@@ -840,7 +759,6 @@ async def update_graph_execution_stats(
             [*get_io_block_ids(), *get_webhook_block_ids()]
         ),
     )
-
     return GraphExecution.from_db(graph_exec)
 
 
@@ -1067,18 +985,6 @@ class NodeExecutionEvent(NodeExecutionResult):
     )
 
 
-class SharedExecutionResponse(BaseModel):
-    """Public-safe response for shared executions"""
-
-    id: str
-    graph_name: str
-    graph_description: Optional[str]
-    status: ExecutionStatus
-    created_at: datetime
-    outputs: CompletedBlockOutput  # Only the final outputs, no intermediate data
-    # Deliberately exclude: user_id, inputs, credentials, node details
-
-
 ExecutionEvent = Annotated[
     GraphExecutionEvent | NodeExecutionEvent, Field(discriminator="event_type")
 ]
@@ -1256,98 +1162,3 @@ async def get_block_error_stats(
         )
         for row in result
     ]
-
-
-async def update_graph_execution_share_status(
-    execution_id: str,
-    user_id: str,
-    is_shared: bool,
-    share_token: str | None,
-    shared_at: datetime | None,
-) -> None:
-    """Update the sharing status of a graph execution."""
-    await AgentGraphExecution.prisma().update(
-        where={"id": execution_id},
-        data={
-            "isShared": is_shared,
-            "shareToken": share_token,
-            "sharedAt": shared_at,
-        },
-    )
-
-
-async def get_graph_execution_by_share_token(
-    share_token: str,
-) -> SharedExecutionResponse | None:
-    """Get a shared execution with limited public-safe data."""
-    execution = await AgentGraphExecution.prisma().find_first(
-        where={
-            "shareToken": share_token,
-            "isShared": True,
-            "isDeleted": False,
-        },
-        include={
-            "AgentGraph": True,
-            "NodeExecutions": {
-                "include": {
-                    "Output": True,
-                    "Node": {
-                        "include": {
-                            "AgentBlock": True,
-                        }
-                    },
-                },
-            },
-        },
-    )
-
-    if not execution:
-        return None
-
-    # Extract outputs from OUTPUT blocks only (consistent with GraphExecution.from_db)
-    outputs: CompletedBlockOutput = defaultdict(list)
-    if execution.NodeExecutions:
-        for node_exec in execution.NodeExecutions:
-            if node_exec.Node and node_exec.Node.agentBlockId:
-                # Get the block definition to check its type
-                block = get_block(node_exec.Node.agentBlockId)
-
-                if block and block.block_type == BlockType.OUTPUT:
-                    # For OUTPUT blocks, the data is stored in executionData or Input
-                    # The executionData contains the structured input with 'name' and 'value' fields
-                    if hasattr(node_exec, "executionData") and node_exec.executionData:
-                        exec_data = type_utils.convert(
-                            node_exec.executionData, dict[str, Any]
-                        )
-                        if "name" in exec_data:
-                            name = exec_data["name"]
-                            value = exec_data.get("value")
-                            outputs[name].append(value)
-                    elif node_exec.Input:
-                        # Build input_data from Input relation
-                        input_data = {}
-                        for data in node_exec.Input:
-                            if data.name and data.data is not None:
-                                input_data[data.name] = type_utils.convert(
-                                    data.data, JsonValue
-                                )
-
-                        if "name" in input_data:
-                            name = input_data["name"]
-                            value = input_data.get("value")
-                            outputs[name].append(value)
-
-    return SharedExecutionResponse(
-        id=execution.id,
-        graph_name=(
-            execution.AgentGraph.name
-            if (execution.AgentGraph and execution.AgentGraph.name)
-            else "Untitled Agent"
-        ),
-        graph_description=(
-            execution.AgentGraph.description if execution.AgentGraph else None
-        ),
-        status=ExecutionStatus(execution.executionStatus),
-        created_at=execution.createdAt,
-        outputs=outputs,
-    )

autogpt_platform/backend/backend/data/generate_data.py

@@ -7,7 +7,7 @@ from prisma.enums import AgentExecutionStatus
 from backend.data.execution import get_graph_executions
 from backend.data.graph import get_graph_metadata
 from backend.data.model import UserExecutionSummaryStats
-from backend.util.exceptions import DatabaseError
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.logging import TruncatedLogger
 
 logger = TruncatedLogger(logging.getLogger(__name__), prefix="[SummaryData]")

autogpt_platform/backend/backend/data/graph.py

@@ -1,7 +1,6 @@
 import logging
 import uuid
 from collections import defaultdict
-from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any, Literal, Optional, cast
 
 from prisma.enums import SubmissionStatus
@@ -20,8 +19,6 @@ from backend.blocks.agent import AgentExecutorBlock
 from backend.blocks.io import AgentInputBlock, AgentOutputBlock
 from backend.blocks.llm import LlmModel
 from backend.data.db import prisma as db
-from backend.data.dynamic_fields import extract_base_field_name
-from backend.data.includes import MAX_GRAPH_VERSIONS_FETCH
 from backend.data.model import (
     CredentialsField,
     CredentialsFieldInfo,
@@ -31,17 +28,8 @@ from backend.data.model import (
 from backend.integrations.providers import ProviderName
 from backend.util import type as type_utils
 from backend.util.json import SafeJson
-from backend.util.models import Pagination
 
-from .block import (
-    Block,
-    BlockInput,
-    BlockSchema,
-    BlockType,
-    EmptySchema,
-    get_block,
-    get_blocks,
-)
+from .block import Block, BlockInput, BlockSchema, BlockType, get_block, get_blocks
 from .db import BaseDbModel, query_raw_with_schema, transaction
 from .includes import AGENT_GRAPH_INCLUDE, AGENT_NODE_INCLUDE
 
@@ -82,15 +70,12 @@ class Node(BaseDbModel):
     output_links: list[Link] = []
 
     @property
-    def block(self) -> "Block[BlockSchema, BlockSchema] | _UnknownBlockBase":
-        """Get the block for this node. Returns UnknownBlock if block is deleted/missing."""
+    def block(self) -> Block[BlockSchema, BlockSchema]:
         block = get_block(self.block_id)
         if not block:
-            # Log warning but don't raise exception - return a placeholder block for deleted blocks
-            logger.warning(
-                f"Block #{self.block_id} does not exist for Node #{self.id} (deleted/missing block), using UnknownBlock"
+            raise ValueError(
+                f"Block #{self.block_id} does not exist -> Node #{self.id} is invalid"
             )
-            return _UnknownBlockBase(self.block_id)
         return block
 
 
@@ -129,20 +114,17 @@ class NodeModel(Node):
         Returns a copy of the node model, stripped of any non-transferable properties
         """
         stripped_node = self.model_copy(deep=True)
-
-        # Remove credentials and other (possible) secrets from node input
+        # Remove credentials from node input
         if stripped_node.input_default:
             stripped_node.input_default = NodeModel._filter_secrets_from_node_input(
                 stripped_node.input_default, self.block.input_schema.jsonschema()
             )
 
-        # Remove default secret value from secret input nodes
         if (
             stripped_node.block.block_type == BlockType.INPUT
-            and stripped_node.input_default.get("secret", False) is True
             and "value" in stripped_node.input_default
         ):
-            del stripped_node.input_default["value"]
+            stripped_node.input_default["value"] = ""
 
         # Remove webhook info
         stripped_node.webhook_id = None
@@ -159,10 +141,8 @@ class NodeModel(Node):
         result = {}
         for key, value in input_data.items():
             field_schema: dict | None = field_schemas.get(key)
-            if (field_schema and field_schema.get("secret", False)) or (
-                any(sensitive_key in key.lower() for sensitive_key in sensitive_keys)
-                # Prevent removing `secret` flag on input nodes
-                and type(value) is not bool
+            if (field_schema and field_schema.get("secret", False)) or any(
+                sensitive_key in key.lower() for sensitive_key in sensitive_keys
             ):
                 # This is a secret value -> filter this key-value pair out
                 continue
@@ -180,7 +160,6 @@ class BaseGraph(BaseDbModel):
     is_active: bool = True
     name: str
     description: str
-    instructions: str | None = None
     recommended_schedule_cron: str | None = None
     nodes: list[Node] = []
     links: list[Link] = []
@@ -402,8 +381,6 @@ class GraphModel(Graph):
     user_id: str
     nodes: list[NodeModel] = []  # type: ignore
 
-    created_at: datetime
-
     @property
     def starting_nodes(self) -> list[NodeModel]:
         outbound_nodes = {link.sink_id for link in self.links}
@@ -416,10 +393,6 @@ class GraphModel(Graph):
             if node.id not in outbound_nodes or node.id in input_nodes
         ]
 
-    @property
-    def webhook_input_node(self) -> NodeModel | None:  # type: ignore
-        return cast(NodeModel, super().webhook_input_node)
-
     def meta(self) -> "GraphMeta":
         """
         Returns a GraphMeta object with metadata about the graph.
@@ -721,11 +694,9 @@ class GraphModel(Graph):
             version=graph.version,
             forked_from_id=graph.forkedFromId,
             forked_from_version=graph.forkedFromVersion,
-            created_at=graph.createdAt,
             is_active=graph.isActive,
             name=graph.name or "",
             description=graph.description or "",
-            instructions=graph.instructions,
             recommended_schedule_cron=graph.recommendedScheduleCron,
             nodes=[NodeModel.from_db(node, for_export) for node in graph.Nodes or []],
             links=list(
@@ -747,7 +718,7 @@ def _is_tool_pin(name: str) -> bool:
 
 
 def _sanitize_pin_name(name: str) -> str:
-    sanitized_name = extract_base_field_name(name)
+    sanitized_name = name.split("_#_")[0].split("_@_")[0].split("_$_")[0]
     if _is_tool_pin(sanitized_name):
         return "tools"
     return sanitized_name
@@ -765,13 +736,6 @@ class GraphMeta(Graph):
         return GraphMeta(**graph.model_dump())
 
 
-class GraphsPaginated(BaseModel):
-    """Response schema for paginated graphs."""
-
-    graphs: list[GraphMeta]
-    pagination: Pagination
-
-
 # --------------------- CRUD functions --------------------- #
 
 
@@ -800,42 +764,31 @@ async def set_node_webhook(node_id: str, webhook_id: str | None) -> NodeModel:
     return NodeModel.from_db(node)
 
 
-async def list_graphs_paginated(
+async def list_graphs(
     user_id: str,
-    page: int = 1,
-    page_size: int = 25,
     filter_by: Literal["active"] | None = "active",
-) -> GraphsPaginated:
+) -> list[GraphMeta]:
     """
-    Retrieves paginated graph metadata objects.
+    Retrieves graph metadata objects.
+    Default behaviour is to get all currently active graphs.
 
     Args:
-        user_id: The ID of the user that owns the graphs.
-        page: Page number (1-based).
-        page_size: Number of graphs per page.
         filter_by: An optional filter to either select graphs.
+        user_id: The ID of the user that owns the graph.
 
     Returns:
-        GraphsPaginated: Paginated list of graph metadata.
+        list[GraphMeta]: A list of objects representing the retrieved graphs.
     """
     where_clause: AgentGraphWhereInput = {"userId": user_id}
 
     if filter_by == "active":
         where_clause["isActive"] = True
 
-    # Get total count
-    total_count = await AgentGraph.prisma().count(where=where_clause)
-    total_pages = (total_count + page_size - 1) // page_size
-
-    # Get paginated results
-    offset = (page - 1) * page_size
     graphs = await AgentGraph.prisma().find_many(
         where=where_clause,
         distinct=["id"],
         order={"version": "desc"},
         include=AGENT_GRAPH_INCLUDE,
-        skip=offset,
-        take=page_size,
     )
 
     graph_models: list[GraphMeta] = []
@@ -849,15 +802,7 @@ async def list_graphs_paginated(
             logger.error(f"Error processing graph {graph.id}: {e}")
             continue
 
-    return GraphsPaginated(
-        graphs=graph_models,
-        pagination=Pagination(
-            total_items=total_count,
-            total_pages=total_pages,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
+    return graph_models
 
 
 async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph | None:
@@ -1077,14 +1022,11 @@ async def set_graph_active_version(graph_id: str, version: int, user_id: str) ->
     )
 
 
-async def get_graph_all_versions(
-    graph_id: str, user_id: str, limit: int = MAX_GRAPH_VERSIONS_FETCH
-) -> list[GraphModel]:
+async def get_graph_all_versions(graph_id: str, user_id: str) -> list[GraphModel]:
     graph_versions = await AgentGraph.prisma().find_many(
         where={"id": graph_id, "userId": user_id},
         order={"version": "desc"},
         include=AGENT_GRAPH_INCLUDE,
-        take=limit,
     )
 
     if not graph_versions:
@@ -1202,7 +1144,6 @@ def make_graph_model(creatable_graph: Graph, user_id: str) -> GraphModel:
     return GraphModel(
         **creatable_graph.model_dump(exclude={"nodes"}),
         user_id=user_id,
-        created_at=datetime.now(tz=timezone.utc),
         nodes=[
             NodeModel(
                 **creatable_node.model_dump(),
@@ -1333,34 +1274,3 @@ async def migrate_llm_models(migrate_to: LlmModel):
             id,
             path,
         )
-
-
-# Simple placeholder class for deleted/missing blocks
-class _UnknownBlockBase(Block):
-    """
-    Placeholder for deleted/missing blocks that inherits from Block
-    but uses a name that doesn't end with 'Block' to avoid auto-discovery.
-    """
-
-    def __init__(self, block_id: str = "00000000-0000-0000-0000-000000000000"):
-        # Initialize with minimal valid Block parameters
-        super().__init__(
-            id=block_id,
-            description=f"Unknown or deleted block (original ID: {block_id})",
-            disabled=True,
-            input_schema=EmptySchema,
-            output_schema=EmptySchema,
-            categories=set(),
-            contributors=[],
-            static_output=False,
-            block_type=BlockType.STANDARD,
-            webhook_config=None,
-        )
-
-    @property
-    def name(self):
-        return "UnknownBlock"
-
-    async def run(self, input_data, **kwargs):
-        """Always yield an error for missing blocks."""
-        yield "error", f"Block {self.id} no longer exists"

autogpt_platform/backend/backend/data/graph_test.py

@@ -201,56 +201,25 @@ async def test_get_input_schema(server: SpinTestServer, snapshot: Snapshot):
 @pytest.mark.asyncio(loop_scope="session")
 async def test_clean_graph(server: SpinTestServer):
     """
-    Test the stripped_for_export function that:
-    1. Removes sensitive/secret fields from node inputs
-    2. Removes webhook information
-    3. Preserves non-sensitive data including input block values
+    Test the clean_graph function that:
+    1. Clears input block values
+    2. Removes credentials from nodes
     """
-    # Create a graph with input blocks containing both sensitive and normal data
+    # Create a graph with input blocks and credentials
     graph = Graph(
         id="test_clean_graph",
         name="Test Clean Graph",
         description="Test graph cleaning",
         nodes=[
             Node(
+                id="input_node",
                 block_id=AgentInputBlock().id,
                 input_default={
-                    "_test_id": "input_node",
                     "name": "test_input",
-                    "value": "test value",  # This should be preserved
+                    "value": "test value",
                     "description": "Test input description",
                 },
             ),
-            Node(
-                block_id=AgentInputBlock().id,
-                input_default={
-                    "_test_id": "input_node_secret",
-                    "name": "secret_input",
-                    "value": "another value",
-                    "secret": True,  # This makes the input secret
-                },
-            ),
-            Node(
-                block_id=StoreValueBlock().id,
-                input_default={
-                    "_test_id": "node_with_secrets",
-                    "input": "normal_value",
-                    "control_test_input": "should be preserved",
-                    "api_key": "secret_api_key_123",  # Should be filtered
-                    "password": "secret_password_456",  # Should be filtered
-                    "token": "secret_token_789",  # Should be filtered
-                    "credentials": {  # Should be filtered
-                        "id": "fake-github-credentials-id",
-                        "provider": "github",
-                        "type": "api_key",
-                    },
-                    "anthropic_credentials": {  # Should be filtered
-                        "id": "fake-anthropic-credentials-id",
-                        "provider": "anthropic",
-                        "type": "api_key",
-                    },
-                },
-            ),
         ],
         links=[],
     )
@@ -262,54 +231,15 @@ async def test_clean_graph(server: SpinTestServer):
     )
 
     # Clean the graph
-    cleaned_graph = await server.agent_server.test_get_graph(
+    created_graph = await server.agent_server.test_get_graph(
         created_graph.id, created_graph.version, DEFAULT_USER_ID, for_export=True
     )
 
-    # Verify sensitive fields are removed but normal fields are preserved
+    # # Verify input block value is cleared
     input_node = next(
-        n for n in cleaned_graph.nodes if n.input_default["_test_id"] == "input_node"
+        n for n in created_graph.nodes if n.block_id == AgentInputBlock().id
     )
-
-    # Non-sensitive fields should be preserved
-    assert input_node.input_default["name"] == "test_input"
-    assert input_node.input_default["value"] == "test value"  # Should be preserved now
-    assert input_node.input_default["description"] == "Test input description"
-
-    # Sensitive fields should be filtered out
-    assert "api_key" not in input_node.input_default
-    assert "password" not in input_node.input_default
-
-    # Verify secret input node preserves non-sensitive fields but removes secret value
-    secret_node = next(
-        n
-        for n in cleaned_graph.nodes
-        if n.input_default["_test_id"] == "input_node_secret"
-    )
-    assert secret_node.input_default["name"] == "secret_input"
-    assert "value" not in secret_node.input_default  # Secret default should be removed
-    assert secret_node.input_default["secret"] is True
-
-    # Verify sensitive fields are filtered from nodes with secrets
-    secrets_node = next(
-        n
-        for n in cleaned_graph.nodes
-        if n.input_default["_test_id"] == "node_with_secrets"
-    )
-    # Normal fields should be preserved
-    assert secrets_node.input_default["input"] == "normal_value"
-    assert secrets_node.input_default["control_test_input"] == "should be preserved"
-    # Sensitive fields should be filtered out
-    assert "api_key" not in secrets_node.input_default
-    assert "password" not in secrets_node.input_default
-    assert "token" not in secrets_node.input_default
-    assert "credentials" not in secrets_node.input_default
-    assert "anthropic_credentials" not in secrets_node.input_default
-
-    # Verify webhook info is removed (if any nodes had it)
-    for node in cleaned_graph.nodes:
-        assert node.webhook_id is None
-        assert node.webhook is None
+    assert input_node.input_default["value"] == ""
 
 
 @pytest.mark.asyncio(loop_scope="session")

autogpt_platform/backend/backend/data/includes.py

@@ -14,7 +14,6 @@ AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
     "Nodes": {"include": AGENT_NODE_INCLUDE}
 }
 
-
 EXECUTION_RESULT_ORDER: list[prisma.types.AgentNodeExecutionOrderByInput] = [
     {"queuedTime": "desc"},
     # Fallback: Incomplete execs has no queuedTime.
@@ -29,13 +28,6 @@ EXECUTION_RESULT_INCLUDE: prisma.types.AgentNodeExecutionInclude = {
 }
 
 MAX_NODE_EXECUTIONS_FETCH = 1000
-MAX_LIBRARY_AGENT_EXECUTIONS_FETCH = 10
-
-# Default limits for potentially large result sets
-MAX_CREDIT_REFUND_REQUESTS_FETCH = 100
-MAX_INTEGRATION_WEBHOOKS_FETCH = 100
-MAX_USER_API_KEYS_FETCH = 500
-MAX_GRAPH_VERSIONS_FETCH = 50
 
 GRAPH_EXECUTION_INCLUDE_WITH_NODES: prisma.types.AgentGraphExecutionInclude = {
     "NodeExecutions": {
@@ -79,56 +71,13 @@ INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
 }
 
 
-def library_agent_include(
-    user_id: str,
-    include_nodes: bool = True,
-    include_executions: bool = True,
-    execution_limit: int = MAX_LIBRARY_AGENT_EXECUTIONS_FETCH,
-) -> prisma.types.LibraryAgentInclude:
-    """
-    Fully configurable includes for library agent queries with performance optimization.
-
-    Args:
-        user_id: User ID for filtering user-specific data
-        include_nodes: Whether to include graph nodes (default: True, needed for get_sub_graphs)
-        include_executions: Whether to include executions (default: True, safe with execution_limit)
-        execution_limit: Limit on executions to fetch (default: MAX_LIBRARY_AGENT_EXECUTIONS_FETCH)
-
-    Defaults maintain backward compatibility and safety - includes everything needed for all functionality.
-    For performance optimization, explicitly set include_nodes=False and include_executions=False
-    for listing views where frontend fetches data separately.
-
-    Performance impact:
-    - Default (full nodes + limited executions): Original performance, works everywhere
-    - Listing optimization (no nodes/executions): ~2s for 15 agents vs potential timeouts
-    - Unlimited executions: varies by user (thousands of executions = timeouts)
-    """
-    result: prisma.types.LibraryAgentInclude = {
-        "Creator": True,  # Always needed for creator info
-    }
-
-    # Build AgentGraph include based on requested options
-    if include_nodes or include_executions:
-        agent_graph_include = {}
-
-        # Add nodes if requested (always full nodes)
-        if include_nodes:
-            agent_graph_include.update(AGENT_GRAPH_INCLUDE)  # Full nodes
-
-        # Add executions if requested
-        if include_executions:
-            agent_graph_include["Executions"] = {
-                "where": {"userId": user_id},
-                "order_by": {"createdAt": "desc"},
-                "take": execution_limit,
+def library_agent_include(user_id: str) -> prisma.types.LibraryAgentInclude:
+    return {
+        "AgentGraph": {
+            "include": {
+                **AGENT_GRAPH_INCLUDE,
+                "Executions": {"where": {"userId": user_id}},
             }
-
-        result["AgentGraph"] = cast(
-            prisma.types.AgentGraphArgsFromLibraryAgent,
-            {"include": agent_graph_include},
-        )
-    else:
-        # Default: Basic metadata only (fast - recommended for most use cases)
-        result["AgentGraph"] = True  # Basic graph metadata (name, description, id)
-
-    return result
+        },
+        "Creator": True,
+    }

autogpt_platform/backend/backend/data/integrations.py

@@ -11,10 +11,7 @@ from prisma.types import (
 from pydantic import Field, computed_field
 
 from backend.data.event_bus import AsyncRedisEventBus
-from backend.data.includes import (
-    INTEGRATION_WEBHOOK_INCLUDE,
-    MAX_INTEGRATION_WEBHOOKS_FETCH,
-)
+from backend.data.includes import INTEGRATION_WEBHOOK_INCLUDE
 from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks.utils import webhook_ingress_url
 from backend.server.v2.library.model import LibraryAgentPreset
@@ -131,36 +128,22 @@ async def get_webhook(
 
 @overload
 async def get_all_webhooks_by_creds(
-    user_id: str,
-    credentials_id: str,
-    *,
-    include_relations: Literal[True],
-    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
+    user_id: str, credentials_id: str, *, include_relations: Literal[True]
 ) -> list[WebhookWithRelations]: ...
 @overload
 async def get_all_webhooks_by_creds(
-    user_id: str,
-    credentials_id: str,
-    *,
-    include_relations: Literal[False] = False,
-    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
+    user_id: str, credentials_id: str, *, include_relations: Literal[False] = False
 ) -> list[Webhook]: ...
 
 
 async def get_all_webhooks_by_creds(
-    user_id: str,
-    credentials_id: str,
-    *,
-    include_relations: bool = False,
-    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
+    user_id: str, credentials_id: str, *, include_relations: bool = False
 ) -> list[Webhook] | list[WebhookWithRelations]:
     if not credentials_id:
         raise ValueError("credentials_id must not be empty")
     webhooks = await IntegrationWebhook.prisma().find_many(
         where={"userId": user_id, "credentialsId": credentials_id},
         include=INTEGRATION_WEBHOOK_INCLUDE if include_relations else None,
-        order={"createdAt": "desc"},
-        take=limit,
     )
     return [
         (WebhookWithRelations if include_relations else Webhook).from_db(webhook)

autogpt_platform/backend/backend/data/model.py

@@ -270,7 +270,6 @@ def SchemaField(
     min_length: Optional[int] = None,
     max_length: Optional[int] = None,
     discriminator: Optional[str] = None,
-    format: Optional[str] = None,
     json_schema_extra: Optional[dict[str, Any]] = None,
 ) -> T:
     if default is PydanticUndefined and default_factory is None:
@@ -286,7 +285,6 @@ def SchemaField(
             "advanced": advanced,
             "hidden": hidden,
             "depends_on": depends_on,
-            "format": format,
             **(json_schema_extra or {}),
         }.items()
         if v is not None

autogpt_platform/backend/backend/data/notifications.py

@@ -15,7 +15,7 @@ from prisma.types import (
 # from backend.notifications.models import NotificationEvent
 from pydantic import BaseModel, ConfigDict, EmailStr, Field, field_validator
 
-from backend.util.exceptions import DatabaseError
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.json import SafeJson
 from backend.util.logging import TruncatedLogger
 
@@ -235,7 +235,6 @@ class BaseEventModel(BaseModel):
 
 
 class NotificationEventModel(BaseEventModel, Generic[NotificationDataType_co]):
-    id: Optional[str] = None  # None when creating, populated when reading from DB
     data: NotificationDataType_co
 
     @property
@@ -379,7 +378,6 @@ class NotificationPreference(BaseModel):
 
 
 class UserNotificationEventDTO(BaseModel):
-    id: str  # Added to track notifications for removal
     type: NotificationType
     data: dict
     created_at: datetime
@@ -388,7 +386,6 @@ class UserNotificationEventDTO(BaseModel):
     @staticmethod
     def from_db(model: NotificationEvent) -> "UserNotificationEventDTO":
         return UserNotificationEventDTO(
-            id=model.id,
             type=model.type,
             data=dict(model.data),
             created_at=model.createdAt,
@@ -544,79 +541,6 @@ async def empty_user_notification_batch(
         ) from e
 
 
-async def clear_all_user_notification_batches(user_id: str) -> None:
-    """Clear ALL notification batches for a user across all types.
-
-    Used when user's email is bounced/inactive and we should stop
-    trying to send them ANY emails.
-    """
-    try:
-        # Delete all notification events for this user
-        await NotificationEvent.prisma().delete_many(
-            where={"UserNotificationBatch": {"is": {"userId": user_id}}}
-        )
-
-        # Delete all batches for this user
-        await UserNotificationBatch.prisma().delete_many(where={"userId": user_id})
-
-        logger.info(f"Cleared all notification batches for user {user_id}")
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to clear all notification batches for user {user_id}: {e}"
-        ) from e
-
-
-async def remove_notifications_from_batch(
-    user_id: str, notification_type: NotificationType, notification_ids: list[str]
-) -> None:
-    """Remove specific notifications from a user's batch by their IDs.
-
-    This is used after successful sending to remove only the
-    sent notifications, preventing duplicates on retry.
-    """
-    if not notification_ids:
-        return
-
-    try:
-        # Delete the specific notification events
-        deleted_count = await NotificationEvent.prisma().delete_many(
-            where={
-                "id": {"in": notification_ids},
-                "UserNotificationBatch": {
-                    "is": {"userId": user_id, "type": notification_type}
-                },
-            }
-        )
-
-        logger.info(
-            f"Removed {deleted_count} notifications from batch for user {user_id}"
-        )
-
-        # Check if batch is now empty and delete it if so
-        remaining = await NotificationEvent.prisma().count(
-            where={
-                "UserNotificationBatch": {
-                    "is": {"userId": user_id, "type": notification_type}
-                }
-            }
-        )
-
-        if remaining == 0:
-            await UserNotificationBatch.prisma().delete_many(
-                where=UserNotificationBatchWhereInput(
-                    userId=user_id,
-                    type=notification_type,
-                )
-            )
-            logger.info(
-                f"Deleted empty batch for user {user_id} and type {notification_type}"
-            )
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to remove notifications from batch for user {user_id} and type {notification_type}: {e}"
-        ) from e
-
-
 async def get_user_notification_batch(
     user_id: str,
     notification_type: NotificationType,

autogpt_platform/backend/backend/data/onboarding.py

@@ -1,16 +1,15 @@
 import re
-from datetime import datetime
 from typing import Any, Optional
 
 import prisma
 import pydantic
-from autogpt_libs.utils.cache import cached
 from prisma.enums import OnboardingStep
 from prisma.models import UserOnboarding
 from prisma.types import UserOnboardingCreateInput, UserOnboardingUpdateInput
 
 from backend.data.block import get_blocks
 from backend.data.credit import get_user_credit_model
+from backend.data.graph import GraphModel
 from backend.data.model import CredentialsMetaInput
 from backend.server.v2.store.model import StoreAgentDetails
 from backend.util.json import SafeJson
@@ -31,7 +30,7 @@ user_credit = get_user_credit_model()
 
 class UserOnboardingUpdate(pydantic.BaseModel):
     completedSteps: Optional[list[OnboardingStep]] = None
-    walletShown: Optional[bool] = None
+    notificationDot: Optional[bool] = None
     notified: Optional[list[OnboardingStep]] = None
     usageReason: Optional[str] = None
     integrations: Optional[list[str]] = None
@@ -40,8 +39,6 @@ class UserOnboardingUpdate(pydantic.BaseModel):
     agentInput: Optional[dict[str, Any]] = None
     onboardingAgentExecutionId: Optional[str] = None
     agentRuns: Optional[int] = None
-    lastRunAt: Optional[datetime] = None
-    consecutiveRunDays: Optional[int] = None
 
 
 async def get_user_onboarding(user_id: str):
@@ -60,22 +57,16 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
         update["completedSteps"] = list(set(data.completedSteps))
         for step in (
             OnboardingStep.AGENT_NEW_RUN,
-            OnboardingStep.MARKETPLACE_VISIT,
+            OnboardingStep.RUN_AGENTS,
             OnboardingStep.MARKETPLACE_ADD_AGENT,
             OnboardingStep.MARKETPLACE_RUN_AGENT,
             OnboardingStep.BUILDER_SAVE_AGENT,
-            OnboardingStep.RE_RUN_AGENT,
-            OnboardingStep.SCHEDULE_AGENT,
-            OnboardingStep.RUN_AGENTS,
-            OnboardingStep.RUN_3_DAYS,
-            OnboardingStep.TRIGGER_WEBHOOK,
-            OnboardingStep.RUN_14_DAYS,
-            OnboardingStep.RUN_AGENTS_100,
+            OnboardingStep.BUILDER_RUN_AGENT,
         ):
             if step in data.completedSteps:
                 await reward_user(user_id, step)
-    if data.walletShown is not None:
-        update["walletShown"] = data.walletShown
+    if data.notificationDot is not None:
+        update["notificationDot"] = data.notificationDot
     if data.notified is not None:
         update["notified"] = list(set(data.notified))
     if data.usageReason is not None:
@@ -92,10 +83,6 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
         update["onboardingAgentExecutionId"] = data.onboardingAgentExecutionId
     if data.agentRuns is not None:
         update["agentRuns"] = data.agentRuns
-    if data.lastRunAt is not None:
-        update["lastRunAt"] = data.lastRunAt
-    if data.consecutiveRunDays is not None:
-        update["consecutiveRunDays"] = data.consecutiveRunDays
 
     return await UserOnboarding.prisma().upsert(
         where={"userId": user_id},
@@ -114,28 +101,16 @@ async def reward_user(user_id: str, step: OnboardingStep):
         # This is seen as a reward for the GET_RESULTS step in the wallet
         case OnboardingStep.AGENT_NEW_RUN:
             reward = 300
-        case OnboardingStep.MARKETPLACE_VISIT:
-            reward = 100
+        case OnboardingStep.RUN_AGENTS:
+            reward = 300
         case OnboardingStep.MARKETPLACE_ADD_AGENT:
             reward = 100
         case OnboardingStep.MARKETPLACE_RUN_AGENT:
             reward = 100
         case OnboardingStep.BUILDER_SAVE_AGENT:
             reward = 100
-        case OnboardingStep.RE_RUN_AGENT:
+        case OnboardingStep.BUILDER_RUN_AGENT:
             reward = 100
-        case OnboardingStep.SCHEDULE_AGENT:
-            reward = 100
-        case OnboardingStep.RUN_AGENTS:
-            reward = 300
-        case OnboardingStep.RUN_3_DAYS:
-            reward = 100
-        case OnboardingStep.TRIGGER_WEBHOOK:
-            reward = 100
-        case OnboardingStep.RUN_14_DAYS:
-            reward = 300
-        case OnboardingStep.RUN_AGENTS_100:
-            reward = 300
 
     if reward == 0:
         return
@@ -157,22 +132,6 @@ async def reward_user(user_id: str, step: OnboardingStep):
     )
 
 
-async def complete_webhook_trigger_step(user_id: str):
-    """
-    Completes the TRIGGER_WEBHOOK onboarding step for the user if not already completed.
-    """
-
-    onboarding = await get_user_onboarding(user_id)
-    if OnboardingStep.TRIGGER_WEBHOOK not in onboarding.completedSteps:
-        await update_user_onboarding(
-            user_id,
-            UserOnboardingUpdate(
-                completedSteps=onboarding.completedSteps
-                + [OnboardingStep.TRIGGER_WEBHOOK]
-            ),
-        )
-
-
 def clean_and_split(text: str) -> list[str]:
     """
     Removes all special characters from a string, truncates it to 100 characters,
@@ -277,14 +236,8 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
         for word in user_onboarding.integrations
     ]
 
-    where_clause["is_available"] = True
-
-    # Try to take only agents that are available and allowed for onboarding
     storeAgents = await prisma.models.StoreAgent.prisma().find_many(
-        where={
-            "is_available": True,
-            "useForOnboarding": True,
-        },
+        where=prisma.types.StoreAgentWhereInput(**where_clause),
         order=[
             {"featured": "desc"},
             {"runs": "desc"},
@@ -293,16 +246,59 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
         take=100,
     )
 
-    # If not enough agents found, relax the useForOnboarding filter
+    agentListings = await prisma.models.StoreListingVersion.prisma().find_many(
+        where={
+            "id": {"in": [agent.storeListingVersionId for agent in storeAgents]},
+        },
+        include={"AgentGraph": True},
+    )
+
+    for listing in agentListings:
+        agent = listing.AgentGraph
+        if agent is None:
+            continue
+        graph = GraphModel.from_db(agent)
+        # Remove agents with empty input schema
+        if not graph.input_schema:
+            storeAgents = [
+                a for a in storeAgents if a.storeListingVersionId != listing.id
+            ]
+            continue
+
+        # Remove agents with empty credentials
+        # Get nodes from this agent that have credentials
+        nodes = await prisma.models.AgentNode.prisma().find_many(
+            where={
+                "agentGraphId": agent.id,
+                "agentBlockId": {"in": list(CREDENTIALS_FIELDS.keys())},
+            },
+        )
+        for node in nodes:
+            block_id = node.agentBlockId
+            field_name = CREDENTIALS_FIELDS[block_id]
+            # If there are no credentials or they are empty, remove the agent
+            # FIXME ignores default values
+            if (
+                field_name not in node.constantInput
+                or node.constantInput[field_name] is None
+            ):
+                storeAgents = [
+                    a for a in storeAgents if a.storeListingVersionId != listing.id
+                ]
+                break
+
+    # If there are less than 2 agents, add more agents to the list
     if len(storeAgents) < 2:
-        storeAgents = await prisma.models.StoreAgent.prisma().find_many(
-            where=prisma.types.StoreAgentWhereInput(**where_clause),
+        storeAgents += await prisma.models.StoreAgent.prisma().find_many(
+            where={
+                "listing_id": {"not_in": [agent.listing_id for agent in storeAgents]},
+            },
             order=[
                 {"featured": "desc"},
                 {"runs": "desc"},
                 {"rating": "desc"},
             ],
-            take=100,
+            take=2 - len(storeAgents),
         )
 
     # Calculate points for the first X agents and choose the top 2
@@ -337,13 +333,8 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
     ]
 
 
-@cached(maxsize=1, ttl_seconds=300)  # Cache for 5 minutes since this rarely changes
 async def onboarding_enabled() -> bool:
-    """
-    Check if onboarding should be enabled based on store agent count.
-    Cached to prevent repeated slow database queries.
-    """
-    # Use a more efficient query that stops counting after finding enough agents
     count = await prisma.models.StoreAgent.prisma().count(take=MIN_AGENT_COUNT + 1)
-    # Onboarding is enabled if there are at least 2 agents in the store
+
+    # Onboading is enabled if there are at least 2 agents in the store
     return count >= MIN_AGENT_COUNT

autogpt_platform/backend/backend/data/redis_client.py

@@ -1,7 +1,8 @@
 import logging
 import os
+from functools import cache
 
-from autogpt_libs.utils.cache import cached, thread_cached
+from autogpt_libs.utils.cache import thread_cached
 from dotenv import load_dotenv
 from redis import Redis
 from redis.asyncio import Redis as AsyncRedis
@@ -12,7 +13,7 @@ load_dotenv()
 
 HOST = os.getenv("REDIS_HOST", "localhost")
 PORT = int(os.getenv("REDIS_PORT", "6379"))
-PASSWORD = os.getenv("REDIS_PASSWORD", None)
+PASSWORD = os.getenv("REDIS_PASSWORD", "password")
 
 logger = logging.getLogger(__name__)
 
@@ -34,7 +35,7 @@ def disconnect():
     get_redis().close()
 
 
-@cached()
+@cache
 def get_redis() -> Redis:
     return connect()
 

autogpt_platform/backend/backend/data/user.py

@@ -7,7 +7,6 @@ from typing import Optional, cast
 from urllib.parse import quote_plus
 
 from autogpt_libs.auth.models import DEFAULT_USER_ID
-from autogpt_libs.utils.cache import cached
 from fastapi import HTTPException
 from prisma.enums import NotificationType
 from prisma.models import User as PrismaUser
@@ -16,19 +15,15 @@ from prisma.types import JsonFilter, UserCreateInput, UserUpdateInput
 from backend.data.db import prisma
 from backend.data.model import User, UserIntegrations, UserMetadata
 from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.encryption import JSONCryptor
-from backend.util.exceptions import DatabaseError
 from backend.util.json import SafeJson
 from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
 settings = Settings()
 
-# Cache decorator alias for consistent user lookup caching
-cache_user_lookup = cached(maxsize=1000, ttl_seconds=300)
 
-
-@cache_user_lookup
 async def get_or_create_user(user_data: dict) -> User:
     try:
         user_id = user_data.get("sub")
@@ -54,7 +49,6 @@ async def get_or_create_user(user_data: dict) -> User:
         raise DatabaseError(f"Failed to get or create user {user_data}: {e}") from e
 
 
-@cache_user_lookup
 async def get_user_by_id(user_id: str) -> User:
     user = await prisma.user.find_unique(where={"id": user_id})
     if not user:
@@ -70,7 +64,6 @@ async def get_user_email_by_id(user_id: str) -> Optional[str]:
         raise DatabaseError(f"Failed to get user email for user {user_id}: {e}") from e
 
 
-@cache_user_lookup
 async def get_user_by_email(email: str) -> Optional[User]:
     try:
         user = await prisma.user.find_unique(where={"email": email})
@@ -81,17 +74,7 @@ async def get_user_by_email(email: str) -> Optional[User]:
 
 async def update_user_email(user_id: str, email: str):
     try:
-        # Get old email first for cache invalidation
-        old_user = await prisma.user.find_unique(where={"id": user_id})
-        old_email = old_user.email if old_user else None
-
         await prisma.user.update(where={"id": user_id}, data={"email": email})
-
-        # Selectively invalidate only the specific user entries
-        get_user_by_id.cache_delete(user_id)
-        if old_email:
-            get_user_by_email.cache_delete(old_email)
-        get_user_by_email.cache_delete(email)
     except Exception as e:
         raise DatabaseError(
             f"Failed to update user email for user {user_id}: {e}"
@@ -131,8 +114,6 @@ async def update_user_integrations(user_id: str, data: UserIntegrations):
         where={"id": user_id},
         data={"integrations": encrypted_data},
     )
-    # Invalidate cache for this user
-    get_user_by_id.cache_delete(user_id)
 
 
 async def migrate_and_encrypt_user_integrations():
@@ -304,10 +285,6 @@ async def update_user_notification_preference(
         )
         if not user:
             raise ValueError(f"User not found with ID: {user_id}")
-
-        # Invalidate cache for this user since notification preferences are part of user data
-        get_user_by_id.cache_delete(user_id)
-
         preferences: dict[NotificationType, bool] = {
             NotificationType.AGENT_RUN: user.notifyOnAgentRun or True,
             NotificationType.ZERO_BALANCE: user.notifyOnZeroBalance or True,
@@ -346,44 +323,12 @@ async def set_user_email_verification(user_id: str, verified: bool) -> None:
             where={"id": user_id},
             data={"emailVerified": verified},
         )
-        # Invalidate cache for this user
-        get_user_by_id.cache_delete(user_id)
     except Exception as e:
         raise DatabaseError(
             f"Failed to set email verification status for user {user_id}: {e}"
         ) from e
 
 
-async def disable_all_user_notifications(user_id: str) -> None:
-    """Disable all notification preferences for a user.
-
-    Used when user's email bounces/is inactive to prevent any future notifications.
-    """
-    try:
-        await PrismaUser.prisma().update(
-            where={"id": user_id},
-            data={
-                "notifyOnAgentRun": False,
-                "notifyOnZeroBalance": False,
-                "notifyOnLowBalance": False,
-                "notifyOnBlockExecutionFailed": False,
-                "notifyOnContinuousAgentError": False,
-                "notifyOnDailySummary": False,
-                "notifyOnWeeklySummary": False,
-                "notifyOnMonthlySummary": False,
-                "notifyOnAgentApproved": False,
-                "notifyOnAgentRejected": False,
-            },
-        )
-        # Invalidate cache for this user
-        get_user_by_id.cache_delete(user_id)
-        logger.info(f"Disabled all notification preferences for user {user_id}")
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to disable notifications for user {user_id}: {e}"
-        ) from e
-
-
 async def get_user_email_verification(user_id: str) -> bool:
     """Get the email verification status for a user."""
     try:
@@ -462,10 +407,6 @@ async def update_user_timezone(user_id: str, timezone: str) -> User:
         )
         if not user:
             raise ValueError(f"User not found with ID: {user_id}")
-
-        # Invalidate cache for this user
-        get_user_by_id.cache_delete(user_id)
-
         return User.from_db(user)
     except Exception as e:
         raise DatabaseError(f"Failed to update timezone for user {user_id}: {e}") from e

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -4,12 +4,7 @@ Module for generating AI-based activity status for graph executions.
 
 import json
 import logging
-from typing import TYPE_CHECKING, Any, TypedDict
-
-try:
-    from typing import NotRequired
-except ImportError:
-    from typing_extensions import NotRequired
+from typing import TYPE_CHECKING, Any, NotRequired, TypedDict
 
 from pydantic import SecretStr
 
@@ -112,7 +107,7 @@ async def generate_activity_status_for_execution(
     # Check if we have OpenAI API key
     try:
         settings = Settings()
-        if not settings.secrets.openai_internal_api_key:
+        if not settings.secrets.openai_api_key:
             logger.debug(
                 "OpenAI API key not configured, skipping activity status generation"
             )
@@ -151,35 +146,17 @@ async def generate_activity_status_for_execution(
                     "Focus on the ACTUAL TASK the user wanted done, not the internal workflow steps. "
                     "Avoid technical terms like 'workflow', 'execution', 'components', 'nodes', 'processing', etc. "
                     "Keep it to 3 sentences maximum. Be conversational and human-friendly.\n\n"
-                    "UNDERSTAND THE INTENDED PURPOSE:\n"
-                    "- FIRST: Read the graph description carefully to understand what the user wanted to accomplish\n"
-                    "- The graph name and description tell you the main goal/intention of this automation\n"
-                    "- Use this intended purpose as your PRIMARY criteria for success/failure evaluation\n"
-                    "- Ask yourself: 'Did this execution actually accomplish what the graph was designed to do?'\n\n"
-                    "CRITICAL OUTPUT ANALYSIS:\n"
-                    "- Check if blocks that should produce user-facing results actually produced outputs\n"
-                    "- Blocks with names containing 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' are usually meant to produce final results\n"
-                    "- If these critical blocks have NO outputs (empty recent_outputs), the task likely FAILED even if status shows 'completed'\n"
-                    "- Sub-agents (AgentExecutorBlock) that produce no outputs usually indicate failed sub-tasks\n"
-                    "- Most importantly: Does the execution result match what the graph description promised to deliver?\n\n"
-                    "SUCCESS EVALUATION BASED ON INTENTION:\n"
-                    "- If the graph is meant to 'create blog posts' → check if blog content was actually created\n"
-                    "- If the graph is meant to 'send emails' → check if emails were actually sent\n"
-                    "- If the graph is meant to 'analyze data' → check if analysis results were produced\n"
-                    "- If the graph is meant to 'generate reports' → check if reports were generated\n"
-                    "- Technical completion ≠ goal achievement. Focus on whether the USER'S INTENDED OUTCOME was delivered\n\n"
                     "IMPORTANT: Be HONEST about what actually happened:\n"
                     "- If the input was invalid/nonsensical, say so directly\n"
                     "- If the task failed, explain what went wrong in simple terms\n"
                     "- If errors occurred, focus on what the user needs to know\n"
-                    "- Only claim success if the INTENDED PURPOSE was genuinely accomplished AND produced expected outputs\n"
-                    "- Don't sugar-coat failures or present them as helpful feedback\n"
-                    "- ESPECIALLY: If the graph's main purpose wasn't achieved, this is a failure regardless of 'completed' status\n\n"
+                    "- Only claim success if the task was genuinely completed\n"
+                    "- Don't sugar-coat failures or present them as helpful feedback\n\n"
                     "Understanding Errors:\n"
                     "- Node errors: Individual steps may fail but the overall task might still complete (e.g., one data source fails but others work)\n"
                     "- Graph error (in overall_status.graph_error): This means the entire execution failed and nothing was accomplished\n"
-                    "- Missing outputs from critical blocks: Even if no errors, this means the task failed to produce expected results\n"
-                    "- Focus on whether the graph's intended purpose was fulfilled, not whether technical steps completed"
+                    "- Even if execution shows 'completed', check if critical nodes failed that would prevent the desired outcome\n"
+                    "- Focus on the end result the user wanted, not whether technical steps completed"
                 ),
             },
             {
@@ -188,28 +165,15 @@ async def generate_activity_status_for_execution(
                     f"A user ran '{graph_name}' to accomplish something. Based on this execution data, "
                     f"write what they achieved in simple, user-friendly terms:\n\n"
                     f"{json.dumps(execution_data, indent=2)}\n\n"
-                    "ANALYSIS CHECKLIST:\n"
-                    "1. READ graph_info.description FIRST - this tells you what the user intended to accomplish\n"
-                    "2. Check overall_status.graph_error - if present, the entire execution failed\n"
-                    "3. Look for nodes with 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' in their block_name\n"
-                    "4. Check if these critical blocks have empty recent_outputs arrays - this indicates failure\n"
-                    "5. Look for AgentExecutorBlock (sub-agents) with no outputs - this suggests sub-task failures\n"
-                    "6. Count how many nodes produced outputs vs total nodes - low ratio suggests problems\n"
-                    "7. MOST IMPORTANT: Does the execution outcome match what graph_info.description promised?\n\n"
-                    "INTENTION-BASED EVALUATION:\n"
-                    "- If description mentions 'blog writing' → did it create blog content?\n"
-                    "- If description mentions 'email automation' → were emails actually sent?\n"
-                    "- If description mentions 'data analysis' → were analysis results produced?\n"
-                    "- If description mentions 'content generation' → was content actually generated?\n"
-                    "- If description mentions 'social media posting' → were posts actually made?\n"
-                    "- Match the outputs to the stated intention, not just technical completion\n\n"
+                    "CRITICAL: Check overall_status.graph_error FIRST - if present, the entire execution failed.\n"
+                    "Then check individual node errors to understand partial failures.\n\n"
                     "Write 1-3 sentences about what the user accomplished, such as:\n"
                     "- 'I analyzed your resume and provided detailed feedback for the IT industry.'\n"
-                    "- 'I couldn't complete the task because critical steps failed to produce any results.'\n"
-                    "- 'I failed to generate the content you requested due to missing API access.'\n"
+                    "- 'I couldn't analyze your resume because the input was just nonsensical text.'\n"
+                    "- 'I failed to complete the task due to missing API access.'\n"
                     "- 'I extracted key information from your documents and organized it into a summary.'\n"
-                    "- 'The task failed because the blog post creation step didn't produce any output.'\n\n"
-                    "BE CRITICAL: If the graph's intended purpose (from description) wasn't achieved, report this as a failure even if status is 'completed'."
+                    "- 'The task failed to run due to system configuration issues.'\n\n"
+                    "Focus on what ACTUALLY happened, not what was attempted."
                 ),
             },
         ]
@@ -223,7 +187,7 @@ async def generate_activity_status_for_execution(
         credentials = APIKeyCredentials(
             id="openai",
             provider="openai",
-            api_key=SecretStr(settings.secrets.openai_internal_api_key),
+            api_key=SecretStr(settings.secrets.openai_api_key),
             title="System OpenAI",
         )
 
@@ -233,7 +197,6 @@ async def generate_activity_status_for_execution(
         logger.debug(
             f"Generated activity status for {graph_exec_id}: {activity_status}"
         )
-
         return activity_status
 
     except Exception as e:
@@ -460,6 +423,7 @@ async def _call_llm_direct(
         credentials=credentials,
         llm_model=LlmModel.GPT4O_MINI,
         prompt=prompt,
+        json_format=False,
         max_tokens=150,
         compress_prompt_to_fit=True,
     )

autogpt_platform/backend/backend/executor/activity_status_generator_test.py

@@ -468,7 +468,7 @@ class TestGenerateActivityStatusForExecution:
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
             mock_llm.return_value = (
                 "I analyzed your data and provided the requested insights."
             )
@@ -520,7 +520,7 @@ class TestGenerateActivityStatusForExecution:
             "backend.executor.activity_status_generator.is_feature_enabled",
             return_value=True,
         ):
-            mock_settings.return_value.secrets.openai_internal_api_key = ""
+            mock_settings.return_value.secrets.openai_api_key = ""
 
             result = await generate_activity_status_for_execution(
                 graph_exec_id="test_exec",
@@ -546,7 +546,7 @@ class TestGenerateActivityStatusForExecution:
             "backend.executor.activity_status_generator.is_feature_enabled",
             return_value=True,
         ):
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
 
             result = await generate_activity_status_for_execution(
                 graph_exec_id="test_exec",
@@ -581,7 +581,7 @@ class TestGenerateActivityStatusForExecution:
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
             mock_llm.return_value = "Agent completed execution."
 
             result = await generate_activity_status_for_execution(
@@ -633,7 +633,7 @@ class TestIntegration:
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
 
             mock_response = LLMResponse(
                 raw_response={},

autogpt_platform/backend/backend/executor/cluster_lock.py

@@ -1,115 +0,0 @@
-"""Redis-based distributed locking for cluster coordination."""
-
-import logging
-import time
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from redis import Redis
-
-logger = logging.getLogger(__name__)
-
-
-class ClusterLock:
-    """Simple Redis-based distributed lock for preventing duplicate execution."""
-
-    def __init__(self, redis: "Redis", key: str, owner_id: str, timeout: int = 300):
-        self.redis = redis
-        self.key = key
-        self.owner_id = owner_id
-        self.timeout = timeout
-        self._last_refresh = 0.0
-
-    def try_acquire(self) -> str | None:
-        """Try to acquire the lock.
-
-        Returns:
-            - owner_id (self.owner_id) if successfully acquired
-            - different owner_id if someone else holds the lock
-            - None if Redis is unavailable or other error
-        """
-        try:
-            success = self.redis.set(self.key, self.owner_id, nx=True, ex=self.timeout)
-            if success:
-                self._last_refresh = time.time()
-                return self.owner_id  # Successfully acquired
-
-            # Failed to acquire, get current owner
-            current_value = self.redis.get(self.key)
-            if current_value:
-                current_owner = (
-                    current_value.decode("utf-8")
-                    if isinstance(current_value, bytes)
-                    else str(current_value)
-                )
-                return current_owner
-
-            # Key doesn't exist but we failed to set it - race condition or Redis issue
-            return None
-
-        except Exception as e:
-            logger.error(f"ClusterLock.try_acquire failed for key {self.key}: {e}")
-            return None
-
-    def refresh(self) -> bool:
-        """Refresh lock TTL if we still own it.
-
-        Rate limited to at most once every timeout/10 seconds (minimum 1 second).
-        During rate limiting, still verifies lock existence but skips TTL extension.
-        Setting _last_refresh to 0 bypasses rate limiting for testing.
-        """
-        # Calculate refresh interval: max(timeout // 10, 1)
-        refresh_interval = max(self.timeout // 10, 1)
-        current_time = time.time()
-
-        # Check if we're within the rate limit period
-        # _last_refresh == 0 forces a refresh (bypasses rate limiting for testing)
-        is_rate_limited = (
-            self._last_refresh > 0
-            and (current_time - self._last_refresh) < refresh_interval
-        )
-
-        try:
-            # Always verify lock existence, even during rate limiting
-            current_value = self.redis.get(self.key)
-            if not current_value:
-                self._last_refresh = 0
-                return False
-
-            stored_owner = (
-                current_value.decode("utf-8")
-                if isinstance(current_value, bytes)
-                else str(current_value)
-            )
-            if stored_owner != self.owner_id:
-                self._last_refresh = 0
-                return False
-
-            # If rate limited, return True but don't update TTL or timestamp
-            if is_rate_limited:
-                return True
-
-            # Perform actual refresh
-            if self.redis.expire(self.key, self.timeout):
-                self._last_refresh = current_time
-                return True
-
-            self._last_refresh = 0
-            return False
-
-        except Exception as e:
-            logger.error(f"ClusterLock.refresh failed for key {self.key}: {e}")
-            self._last_refresh = 0
-            return False
-
-    def release(self):
-        """Release the lock."""
-        if self._last_refresh == 0:
-            return
-
-        try:
-            self.redis.delete(self.key)
-        except Exception:
-            pass
-
-        self._last_refresh = 0.0

autogpt_platform/backend/backend/executor/cluster_lock_test.py

@@ -1,507 +0,0 @@
-"""
-Integration tests for ClusterLock - Redis-based distributed locking.
-
-Tests the complete lock lifecycle without mocking Redis to ensure
-real-world behavior is correct. Covers acquisition, refresh, expiry,
-contention, and error scenarios.
-"""
-
-import logging
-import time
-import uuid
-from threading import Thread
-
-import pytest
-import redis
-
-from .cluster_lock import ClusterLock
-
-logger = logging.getLogger(__name__)
-
-
-@pytest.fixture
-def redis_client():
-    """Get Redis client for testing using same config as backend."""
-    from backend.data.redis_client import HOST, PASSWORD, PORT
-
-    # Use same config as backend but without decode_responses since ClusterLock needs raw bytes
-    client = redis.Redis(
-        host=HOST,
-        port=PORT,
-        password=PASSWORD,
-        decode_responses=False,  # ClusterLock needs raw bytes for ownership verification
-    )
-
-    # Clean up any existing test keys
-    try:
-        for key in client.scan_iter(match="test_lock:*"):
-            client.delete(key)
-    except Exception:
-        pass  # Ignore cleanup errors
-
-    return client
-
-
-@pytest.fixture
-def lock_key():
-    """Generate unique lock key for each test."""
-    return f"test_lock:{uuid.uuid4()}"
-
-
-@pytest.fixture
-def owner_id():
-    """Generate unique owner ID for each test."""
-    return str(uuid.uuid4())
-
-
-class TestClusterLockBasic:
-    """Basic lock acquisition and release functionality."""
-
-    def test_lock_acquisition_success(self, redis_client, lock_key, owner_id):
-        """Test basic lock acquisition succeeds."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-
-        # Lock should be acquired successfully
-        result = lock.try_acquire()
-        assert result == owner_id  # Returns our owner_id when successfully acquired
-        assert lock._last_refresh > 0
-
-        # Lock key should exist in Redis
-        assert redis_client.exists(lock_key) == 1
-        assert redis_client.get(lock_key).decode("utf-8") == owner_id
-
-    def test_lock_acquisition_contention(self, redis_client, lock_key):
-        """Test second acquisition fails when lock is held."""
-        owner1 = str(uuid.uuid4())
-        owner2 = str(uuid.uuid4())
-
-        lock1 = ClusterLock(redis_client, lock_key, owner1, timeout=60)
-        lock2 = ClusterLock(redis_client, lock_key, owner2, timeout=60)
-
-        # First lock should succeed
-        result1 = lock1.try_acquire()
-        assert result1 == owner1  # Successfully acquired, returns our owner_id
-
-        # Second lock should fail and return the first owner
-        result2 = lock2.try_acquire()
-        assert result2 == owner1  # Returns the current owner (first owner)
-        assert lock2._last_refresh == 0
-
-    def test_lock_release_deletes_redis_key(self, redis_client, lock_key, owner_id):
-        """Test lock release deletes Redis key and marks locally as released."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-
-        lock.try_acquire()
-        assert lock._last_refresh > 0
-        assert redis_client.exists(lock_key) == 1
-
-        # Release should delete Redis key and mark locally as released
-        lock.release()
-        assert lock._last_refresh == 0
-        assert lock._last_refresh == 0.0
-
-        # Redis key should be deleted for immediate release
-        assert redis_client.exists(lock_key) == 0
-
-        # Another lock should be able to acquire immediately
-        new_owner_id = str(uuid.uuid4())
-        new_lock = ClusterLock(redis_client, lock_key, new_owner_id, timeout=60)
-        assert new_lock.try_acquire() == new_owner_id
-
-
-class TestClusterLockRefresh:
-    """Lock refresh and TTL management."""
-
-    def test_lock_refresh_success(self, redis_client, lock_key, owner_id):
-        """Test lock refresh extends TTL."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-
-        lock.try_acquire()
-        original_ttl = redis_client.ttl(lock_key)
-
-        # Wait a bit then refresh
-        time.sleep(1)
-        lock._last_refresh = 0  # Force refresh past rate limit
-        assert lock.refresh() is True
-
-        # TTL should be reset to full timeout (allow for small timing differences)
-        new_ttl = redis_client.ttl(lock_key)
-        assert new_ttl >= original_ttl or new_ttl >= 58  # Allow for timing variance
-
-    def test_lock_refresh_rate_limiting(self, redis_client, lock_key, owner_id):
-        """Test refresh is rate-limited to timeout/10."""
-        lock = ClusterLock(
-            redis_client, lock_key, owner_id, timeout=100
-        )  # 100s timeout
-
-        lock.try_acquire()
-
-        # First refresh should work
-        assert lock.refresh() is True
-        first_refresh_time = lock._last_refresh
-
-        # Immediate second refresh should be skipped (rate limited) but verify key exists
-        assert lock.refresh() is True  # Returns True but skips actual refresh
-        assert lock._last_refresh == first_refresh_time  # Time unchanged
-
-    def test_lock_refresh_verifies_existence_during_rate_limit(
-        self, redis_client, lock_key, owner_id
-    ):
-        """Test refresh verifies lock existence even during rate limiting."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=100)
-
-        lock.try_acquire()
-
-        # Manually delete the key (simulates expiry or external deletion)
-        redis_client.delete(lock_key)
-
-        # Refresh should detect missing key even during rate limit period
-        assert lock.refresh() is False
-        assert lock._last_refresh == 0
-
-    def test_lock_refresh_ownership_lost(self, redis_client, lock_key, owner_id):
-        """Test refresh fails when ownership is lost."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-
-        lock.try_acquire()
-
-        # Simulate another process taking the lock
-        different_owner = str(uuid.uuid4())
-        redis_client.set(lock_key, different_owner, ex=60)
-
-        # Force refresh past rate limit and verify it fails
-        lock._last_refresh = 0  # Force refresh past rate limit
-        assert lock.refresh() is False
-        assert lock._last_refresh == 0
-
-    def test_lock_refresh_when_not_acquired(self, redis_client, lock_key, owner_id):
-        """Test refresh fails when lock was never acquired."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-
-        # Refresh without acquiring should fail
-        assert lock.refresh() is False
-
-
-class TestClusterLockExpiry:
-    """Lock expiry and timeout behavior."""
-
-    def test_lock_natural_expiry(self, redis_client, lock_key, owner_id):
-        """Test lock expires naturally via Redis TTL."""
-        lock = ClusterLock(
-            redis_client, lock_key, owner_id, timeout=2
-        )  # 2 second timeout
-
-        lock.try_acquire()
-        assert redis_client.exists(lock_key) == 1
-
-        # Wait for expiry
-        time.sleep(3)
-        assert redis_client.exists(lock_key) == 0
-
-        # New lock with same key should succeed
-        new_lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-        assert new_lock.try_acquire() == owner_id
-
-    def test_lock_refresh_prevents_expiry(self, redis_client, lock_key, owner_id):
-        """Test refreshing prevents lock from expiring."""
-        lock = ClusterLock(
-            redis_client, lock_key, owner_id, timeout=3
-        )  # 3 second timeout
-
-        lock.try_acquire()
-
-        # Wait and refresh before expiry
-        time.sleep(1)
-        lock._last_refresh = 0  # Force refresh past rate limit
-        assert lock.refresh() is True
-
-        # Wait beyond original timeout
-        time.sleep(2.5)
-        assert redis_client.exists(lock_key) == 1  # Should still exist
-
-
-class TestClusterLockConcurrency:
-    """Concurrent access patterns."""
-
-    def test_multiple_threads_contention(self, redis_client, lock_key):
-        """Test multiple threads competing for same lock."""
-        num_threads = 5
-        successful_acquisitions = []
-
-        def try_acquire_lock(thread_id):
-            owner_id = f"thread_{thread_id}"
-            lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-            if lock.try_acquire() == owner_id:
-                successful_acquisitions.append(thread_id)
-                time.sleep(0.1)  # Hold lock briefly
-                lock.release()
-
-        threads = []
-        for i in range(num_threads):
-            thread = Thread(target=try_acquire_lock, args=(i,))
-            threads.append(thread)
-            thread.start()
-
-        for thread in threads:
-            thread.join()
-
-        # Only one thread should have acquired the lock
-        assert len(successful_acquisitions) == 1
-
-    def test_sequential_lock_reuse(self, redis_client, lock_key):
-        """Test lock can be reused after natural expiry."""
-        owners = [str(uuid.uuid4()) for _ in range(3)]
-
-        for i, owner_id in enumerate(owners):
-            lock = ClusterLock(redis_client, lock_key, owner_id, timeout=1)  # 1 second
-
-            assert lock.try_acquire() == owner_id
-            time.sleep(1.5)  # Wait for expiry
-
-            # Verify lock expired
-            assert redis_client.exists(lock_key) == 0
-
-    def test_refresh_during_concurrent_access(self, redis_client, lock_key):
-        """Test lock refresh works correctly during concurrent access attempts."""
-        owner1 = str(uuid.uuid4())
-        owner2 = str(uuid.uuid4())
-
-        lock1 = ClusterLock(redis_client, lock_key, owner1, timeout=5)
-        lock2 = ClusterLock(redis_client, lock_key, owner2, timeout=5)
-
-        # Thread 1 holds lock and refreshes
-        assert lock1.try_acquire() == owner1
-
-        def refresh_continuously():
-            for _ in range(10):
-                lock1._last_refresh = 0  # Force refresh
-                lock1.refresh()
-                time.sleep(0.1)
-
-        def try_acquire_continuously():
-            attempts = 0
-            while attempts < 20:
-                if lock2.try_acquire() == owner2:
-                    return True
-                time.sleep(0.1)
-                attempts += 1
-            return False
-
-        refresh_thread = Thread(target=refresh_continuously)
-        acquire_thread = Thread(target=try_acquire_continuously)
-
-        refresh_thread.start()
-        acquire_thread.start()
-
-        refresh_thread.join()
-        acquire_thread.join()
-
-        # Lock1 should still own the lock due to refreshes
-        assert lock1._last_refresh > 0
-        assert lock2._last_refresh == 0
-
-
-class TestClusterLockErrorHandling:
-    """Error handling and edge cases."""
-
-    def test_redis_connection_failure_on_acquire(self, lock_key, owner_id):
-        """Test graceful handling when Redis is unavailable during acquisition."""
-        # Use invalid Redis connection
-        bad_redis = redis.Redis(
-            host="invalid_host", port=1234, socket_connect_timeout=1
-        )
-        lock = ClusterLock(bad_redis, lock_key, owner_id, timeout=60)
-
-        # Should return None for Redis connection failures
-        result = lock.try_acquire()
-        assert result is None  # Returns None when Redis fails
-        assert lock._last_refresh == 0
-
-    def test_redis_connection_failure_on_refresh(
-        self, redis_client, lock_key, owner_id
-    ):
-        """Test graceful handling when Redis fails during refresh."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-
-        # Acquire normally
-        assert lock.try_acquire() == owner_id
-
-        # Replace Redis client with failing one
-        lock.redis = redis.Redis(
-            host="invalid_host", port=1234, socket_connect_timeout=1
-        )
-
-        # Refresh should fail gracefully
-        lock._last_refresh = 0  # Force refresh
-        assert lock.refresh() is False
-        assert lock._last_refresh == 0
-
-    def test_invalid_lock_parameters(self, redis_client):
-        """Test validation of lock parameters."""
-        owner_id = str(uuid.uuid4())
-
-        # All parameters are now simple - no validation needed
-        # Just test basic construction works
-        lock = ClusterLock(redis_client, "test_key", owner_id, timeout=60)
-        assert lock.key == "test_key"
-        assert lock.owner_id == owner_id
-        assert lock.timeout == 60
-
-    def test_refresh_after_redis_key_deleted(self, redis_client, lock_key, owner_id):
-        """Test refresh behavior when Redis key is manually deleted."""
-        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-
-        lock.try_acquire()
-
-        # Manually delete the key (simulates external deletion)
-        redis_client.delete(lock_key)
-
-        # Refresh should fail and mark as not acquired
-        lock._last_refresh = 0  # Force refresh
-        assert lock.refresh() is False
-        assert lock._last_refresh == 0
-
-
-class TestClusterLockDynamicRefreshInterval:
-    """Dynamic refresh interval based on timeout."""
-
-    def test_refresh_interval_calculation(self, redis_client, lock_key, owner_id):
-        """Test refresh interval is calculated as max(timeout/10, 1)."""
-        test_cases = [
-            (5, 1),  # 5/10 = 0, but minimum is 1
-            (10, 1),  # 10/10 = 1
-            (30, 3),  # 30/10 = 3
-            (100, 10),  # 100/10 = 10
-            (200, 20),  # 200/10 = 20
-            (1000, 100),  # 1000/10 = 100
-        ]
-
-        for timeout, expected_interval in test_cases:
-            lock = ClusterLock(
-                redis_client, f"{lock_key}_{timeout}", owner_id, timeout=timeout
-            )
-            lock.try_acquire()
-
-            # Calculate expected interval using same logic as implementation
-            refresh_interval = max(timeout // 10, 1)
-            assert refresh_interval == expected_interval
-
-            # Test rate limiting works with calculated interval
-            assert lock.refresh() is True
-            first_refresh_time = lock._last_refresh
-
-            # Sleep less than interval - should be rate limited
-            time.sleep(0.1)
-            assert lock.refresh() is True
-            assert lock._last_refresh == first_refresh_time  # No actual refresh
-
-
-class TestClusterLockRealWorldScenarios:
-    """Real-world usage patterns."""
-
-    def test_execution_coordination_simulation(self, redis_client):
-        """Simulate graph execution coordination across multiple pods."""
-        graph_exec_id = str(uuid.uuid4())
-        lock_key = f"execution:{graph_exec_id}"
-
-        # Simulate 3 pods trying to execute same graph
-        pods = [f"pod_{i}" for i in range(3)]
-        execution_results = {}
-
-        def execute_graph(pod_id):
-            """Simulate graph execution with cluster lock."""
-            lock = ClusterLock(redis_client, lock_key, pod_id, timeout=300)
-
-            if lock.try_acquire() == pod_id:
-                # Simulate execution work
-                execution_results[pod_id] = "executed"
-                time.sleep(0.1)
-                lock.release()
-            else:
-                execution_results[pod_id] = "rejected"
-
-        threads = []
-        for pod_id in pods:
-            thread = Thread(target=execute_graph, args=(pod_id,))
-            threads.append(thread)
-            thread.start()
-
-        for thread in threads:
-            thread.join()
-
-        # Only one pod should have executed
-        executed_count = sum(
-            1 for result in execution_results.values() if result == "executed"
-        )
-        rejected_count = sum(
-            1 for result in execution_results.values() if result == "rejected"
-        )
-
-        assert executed_count == 1
-        assert rejected_count == 2
-
-    def test_long_running_execution_with_refresh(
-        self, redis_client, lock_key, owner_id
-    ):
-        """Test lock maintains ownership during long execution with periodic refresh."""
-        lock = ClusterLock(
-            redis_client, lock_key, owner_id, timeout=30
-        )  # 30 second timeout, refresh interval = max(30//10, 1) = 3 seconds
-
-        def long_execution_with_refresh():
-            """Simulate long-running execution with periodic refresh."""
-            assert lock.try_acquire() == owner_id
-
-            # Simulate 10 seconds of work with refreshes every 2 seconds
-            # This respects rate limiting - actual refreshes will happen at 0s, 3s, 6s, 9s
-            try:
-                for i in range(5):  # 5 iterations * 2 seconds = 10 seconds total
-                    time.sleep(2)
-                    refresh_success = lock.refresh()
-                    assert refresh_success is True, f"Refresh failed at iteration {i}"
-                return "completed"
-            finally:
-                lock.release()
-
-        # Should complete successfully without losing lock
-        result = long_execution_with_refresh()
-        assert result == "completed"
-
-    def test_graceful_degradation_pattern(self, redis_client, lock_key):
-        """Test graceful degradation when Redis becomes unavailable."""
-        owner_id = str(uuid.uuid4())
-        lock = ClusterLock(
-            redis_client, lock_key, owner_id, timeout=3
-        )  # Use shorter timeout
-
-        # Normal operation
-        assert lock.try_acquire() == owner_id
-        lock._last_refresh = 0  # Force refresh past rate limit
-        assert lock.refresh() is True
-
-        # Simulate Redis becoming unavailable
-        original_redis = lock.redis
-        lock.redis = redis.Redis(
-            host="invalid_host",
-            port=1234,
-            socket_connect_timeout=1,
-            decode_responses=False,
-        )
-
-        # Should degrade gracefully
-        lock._last_refresh = 0  # Force refresh past rate limit
-        assert lock.refresh() is False
-        assert lock._last_refresh == 0
-
-        # Restore Redis and verify can acquire again
-        lock.redis = original_redis
-        # Wait for original lock to expire (use longer wait for 3s timeout)
-        time.sleep(4)
-
-        new_lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
-        assert new_lock.try_acquire() == owner_id
-
-
-if __name__ == "__main__":
-    # Run specific test for quick validation
-    pytest.main([__file__, "-v"])

autogpt_platform/backend/backend/executor/database.py

@@ -9,7 +9,6 @@ from backend.data.execution import (
     get_execution_kv_data,
     get_graph_execution_meta,
     get_graph_executions,
-    get_graph_executions_count,
     get_latest_node_execution,
     get_node_execution,
     get_node_executions,
@@ -29,13 +28,11 @@ from backend.data.graph import (
     get_node,
 )
 from backend.data.notifications import (
-    clear_all_user_notification_batches,
     create_or_add_to_user_notification_batch,
     empty_user_notification_batch,
     get_all_batches_by_type,
     get_user_notification_batch,
     get_user_notification_oldest_message_in_batch,
-    remove_notifications_from_batch,
 )
 from backend.data.user import (
     get_active_user_ids_in_timerange,
@@ -74,6 +71,7 @@ async def _get_credits(user_id: str) -> int:
 
 
 class DatabaseManager(AppService):
+
     def run_service(self) -> None:
         logger.info(f"[{self.service_name}] ⏳ Connecting to Database...")
         self.run_and_wait(db.connect())
@@ -87,16 +85,6 @@ class DatabaseManager(AppService):
     async def health_check(self) -> str:
         if not db.is_connected():
             raise UnhealthyServiceError("Database is not connected")
-
-        try:
-            # Test actual database connectivity by executing a simple query
-            # This will fail if Prisma query engine is not responding
-            result = await db.query_raw_with_schema("SELECT 1 as health_check")
-            if not result or result[0].get("health_check") != 1:
-                raise UnhealthyServiceError("Database query test failed")
-        except Exception as e:
-            raise UnhealthyServiceError(f"Database health check failed: {e}")
-
         return await super().health_check()
 
     @classmethod
@@ -113,7 +101,6 @@ class DatabaseManager(AppService):
 
     # Executions
     get_graph_executions = _(get_graph_executions)
-    get_graph_executions_count = _(get_graph_executions_count)
     get_graph_execution_meta = _(get_graph_execution_meta)
     create_graph_execution = _(create_graph_execution)
     get_node_execution = _(get_node_execution)
@@ -150,12 +137,10 @@ class DatabaseManager(AppService):
     get_user_notification_preference = _(get_user_notification_preference)
 
     # Notifications - async
-    clear_all_user_notification_batches = _(clear_all_user_notification_batches)
     create_or_add_to_user_notification_batch = _(
         create_or_add_to_user_notification_batch
     )
     empty_user_notification_batch = _(empty_user_notification_batch)
-    remove_notifications_from_batch = _(remove_notifications_from_batch)
     get_all_batches_by_type = _(get_all_batches_by_type)
     get_user_notification_batch = _(get_user_notification_batch)
     get_user_notification_oldest_message_in_batch = _(
@@ -184,7 +169,6 @@ class DatabaseManagerClient(AppServiceClient):
 
     # Executions
     get_graph_executions = _(d.get_graph_executions)
-    get_graph_executions_count = _(d.get_graph_executions_count)
     get_graph_execution_meta = _(d.get_graph_execution_meta)
     get_node_executions = _(d.get_node_executions)
     update_node_execution_status = _(d.update_node_execution_status)
@@ -247,12 +231,10 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     get_user_notification_preference = d.get_user_notification_preference
 
     # Notifications
-    clear_all_user_notification_batches = d.clear_all_user_notification_batches
     create_or_add_to_user_notification_batch = (
         d.create_or_add_to_user_notification_batch
     )
     empty_user_notification_batch = d.empty_user_notification_batch
-    remove_notifications_from_batch = d.remove_notifications_from_batch
     get_all_batches_by_type = d.get_all_batches_by_type
     get_user_notification_batch = d.get_user_notification_batch
     get_user_notification_oldest_message_in_batch = (

autogpt_platform/backend/backend/executor/manager.py

@@ -3,42 +3,16 @@ import logging
 import os
 import threading
 import time
-import uuid
 from collections import defaultdict
 from concurrent.futures import Future, ThreadPoolExecutor
 from contextlib import asynccontextmanager
-from datetime import datetime, timedelta, timezone
 from typing import TYPE_CHECKING, Any, Optional, TypeVar, cast
 
-import sentry_sdk
 from pika.adapters.blocking_connection import BlockingChannel
 from pika.spec import Basic, BasicProperties
-from prometheus_client import Gauge, start_http_server
-from redis.asyncio.lock import Lock as AsyncRedisLock
+from redis.asyncio.lock import Lock as RedisLock
 
-from backend.blocks.agent import AgentExecutorBlock
 from backend.blocks.io import AgentOutputBlock
-from backend.data import redis_client as redis
-from backend.data.block import (
-    BlockInput,
-    BlockOutput,
-    BlockOutputEntry,
-    BlockSchema,
-    get_block,
-)
-from backend.data.credit import UsageTransactionMetadata
-from backend.data.dynamic_fields import parse_execution_output
-from backend.data.execution import (
-    ExecutionQueue,
-    ExecutionStatus,
-    GraphExecution,
-    GraphExecutionEntry,
-    NodeExecutionEntry,
-    NodeExecutionResult,
-    NodesInputMasks,
-    UserContext,
-)
-from backend.data.graph import Link, Node
 from backend.data.model import GraphExecutionStats, NodeExecutionStats
 from backend.data.notifications import (
     AgentRunData,
@@ -51,21 +25,50 @@ from backend.data.rabbitmq import SyncRabbitMQ
 from backend.executor.activity_status_generator import (
     generate_activity_status_for_execution,
 )
+from backend.executor.utils import LogMetadata
+from backend.notifications.notifications import queue_notification
+from backend.util.exceptions import InsufficientBalanceError, ModerationError
+
+if TYPE_CHECKING:
+    from backend.executor import DatabaseManagerClient, DatabaseManagerAsyncClient
+
+from prometheus_client import Gauge, start_http_server
+
+from backend.blocks.agent import AgentExecutorBlock
+from backend.data import redis_client as redis
+from backend.data.block import (
+    BlockInput,
+    BlockOutput,
+    BlockOutputEntry,
+    BlockSchema,
+    get_block,
+)
+from backend.data.credit import UsageTransactionMetadata
+from backend.data.execution import (
+    ExecutionQueue,
+    ExecutionStatus,
+    GraphExecution,
+    GraphExecutionEntry,
+    NodeExecutionEntry,
+    NodeExecutionResult,
+    NodesInputMasks,
+    UserContext,
+)
+from backend.data.graph import Link, Node
 from backend.executor.utils import (
     GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS,
     GRAPH_EXECUTION_CANCEL_QUEUE_NAME,
     GRAPH_EXECUTION_QUEUE_NAME,
     CancelExecutionEvent,
     ExecutionOutputEntry,
-    LogMetadata,
     NodeExecutionProgress,
     block_usage_cost,
     create_execution_queue_config,
     execution_usage_cost,
+    parse_execution_output,
     validate_exec,
 )
 from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.notifications.notifications import queue_notification
 from backend.server.v2.AutoMod.manager import automod_manager
 from backend.util import json
 from backend.util.clients import (
@@ -81,24 +84,13 @@ from backend.util.decorator import (
     error_logged,
     time_measured,
 )
-from backend.util.exceptions import InsufficientBalanceError, ModerationError
 from backend.util.file import clean_exec_files
 from backend.util.logging import TruncatedLogger, configure_logging
 from backend.util.metrics import DiscordChannel
 from backend.util.process import AppProcess, set_service_name
-from backend.util.retry import (
-    continuous_retry,
-    func_retry,
-    send_rate_limited_discord_alert,
-)
+from backend.util.retry import continuous_retry, func_retry
 from backend.util.settings import Settings
 
-from .cluster_lock import ClusterLock
-
-if TYPE_CHECKING:
-    from backend.executor import DatabaseManagerAsyncClient, DatabaseManagerClient
-
-
 _logger = logging.getLogger(__name__)
 logger = TruncatedLogger(_logger, prefix="[GraphExecutor]")
 settings = Settings()
@@ -114,7 +106,6 @@ utilization_gauge = Gauge(
     "Ratio of active graph runs to max graph workers",
 )
 
-
 # Thread-local storage for ExecutionProcessor instances
 _tls = threading.local()
 
@@ -126,14 +117,10 @@ def init_worker():
 
 
 def execute_graph(
-    graph_exec_entry: "GraphExecutionEntry",
-    cancel_event: threading.Event,
-    cluster_lock: ClusterLock,
+    graph_exec_entry: "GraphExecutionEntry", cancel_event: threading.Event
 ):
     """Execute graph using thread-local ExecutionProcessor instance"""
-    return _tls.processor.on_graph_execution(
-        graph_exec_entry, cancel_event, cluster_lock
-    )
+    return _tls.processor.on_graph_execution(graph_exec_entry, cancel_event)
 
 
 T = TypeVar("T")
@@ -190,7 +177,6 @@ async def execute_node(
         _input_data.inputs = input_data
         if nodes_input_masks:
             _input_data.nodes_input_masks = nodes_input_masks
-        _input_data.user_id = user_id
         input_data = _input_data.model_dump()
     data.inputs = input_data
 
@@ -225,23 +211,6 @@ async def execute_node(
         extra_exec_kwargs[field_name] = credentials
 
     output_size = 0
-
-    # sentry tracking nonsense to get user counts for blocks because isolation scopes don't work :(
-    scope = sentry_sdk.get_current_scope()
-
-    # save the tags
-    original_user = scope._user
-    original_tags = dict(scope._tags) if scope._tags else {}
-    # Set user ID for error tracking
-    scope.set_user({"id": user_id})
-
-    scope.set_tag("graph_id", graph_id)
-    scope.set_tag("node_id", node_id)
-    scope.set_tag("block_name", node_block.name)
-    scope.set_tag("block_id", node_block.id)
-    for k, v in (data.user_context or UserContext(timezone="UTC")).model_dump().items():
-        scope.set_tag(f"user_context.{k}", v)
-
     try:
         async for output_name, output_data in node_block.execute(
             input_data, **extra_exec_kwargs
@@ -250,12 +219,6 @@ async def execute_node(
             output_size += len(json.dumps(output_data))
             log_metadata.debug("Node produced output", **{output_name: output_data})
             yield output_name, output_data
-    except Exception:
-        # Capture exception WITH context still set before restoring scope
-        sentry_sdk.capture_exception(scope=scope)
-        sentry_sdk.flush()  # Ensure it's sent before we restore scope
-        # Re-raise to maintain normal error flow
-        raise
     finally:
         # Ensure credentials are released even if execution fails
         if creds_lock and (await creds_lock.locked()) and (await creds_lock.owned()):
@@ -270,10 +233,6 @@ async def execute_node(
             execution_stats.input_size = input_size
             execution_stats.output_size = output_size
 
-        # Restore scope AFTER error has been captured
-        scope._user = original_user
-        scope._tags = original_tags
-
 
 async def _enqueue_next_nodes(
     db_client: "DatabaseManagerAsyncClient",
@@ -470,7 +429,7 @@ class ExecutionProcessor:
             graph_id=node_exec.graph_id,
             node_eid=node_exec.node_exec_id,
             node_id=node_exec.node_id,
-            block_name=b.name if (b := get_block(node_exec.block_id)) else "-",
+            block_name="-",
         )
         db_client = get_db_async_client()
         node = await db_client.get_node(node_exec.node_id)
@@ -598,6 +557,7 @@ class ExecutionProcessor:
                 await persist_output(
                     "error", str(stats.error) or type(stats.error).__name__
                 )
+
         return status
 
     @func_retry
@@ -623,7 +583,6 @@ class ExecutionProcessor:
         self,
         graph_exec: GraphExecutionEntry,
         cancel: threading.Event,
-        cluster_lock: ClusterLock,
     ):
         log_metadata = LogMetadata(
             logger=_logger,
@@ -646,7 +605,7 @@ class ExecutionProcessor:
             )
             return
 
-        if exec_meta.status in [ExecutionStatus.QUEUED, ExecutionStatus.INCOMPLETE]:
+        if exec_meta.status == ExecutionStatus.QUEUED:
             log_metadata.info(f"⚙️ Starting graph execution #{graph_exec.graph_exec_id}")
             exec_meta.status = ExecutionStatus.RUNNING
             send_execution_update(
@@ -682,7 +641,6 @@ class ExecutionProcessor:
             cancel=cancel,
             log_metadata=log_metadata,
             execution_stats=exec_stats,
-            cluster_lock=cluster_lock,
         )
         exec_stats.walltime += timing_info.wall_time
         exec_stats.cputime += timing_info.cpu_time
@@ -784,7 +742,6 @@ class ExecutionProcessor:
         cancel: threading.Event,
         log_metadata: LogMetadata,
         execution_stats: GraphExecutionStats,
-        cluster_lock: ClusterLock,
     ) -> ExecutionStatus:
         """
         Returns:
@@ -970,7 +927,7 @@ class ExecutionProcessor:
                         and execution_queue.empty()
                         and (running_node_execution or running_node_evaluation)
                     ):
-                        cluster_lock.refresh()
+                        # There is nothing to execute, and no output to process, let's relax for a while.
                         time.sleep(0.1)
 
             # loop done --------------------------------------------------
@@ -1012,31 +969,16 @@ class ExecutionProcessor:
                 if isinstance(e, Exception)
                 else Exception(f"{e.__class__.__name__}: {e}")
             )
-            if not execution_stats.error:
-                execution_stats.error = str(error)
 
             known_errors = (InsufficientBalanceError, ModerationError)
             if isinstance(error, known_errors):
+                execution_stats.error = str(error)
                 return ExecutionStatus.FAILED
 
             execution_status = ExecutionStatus.FAILED
             log_metadata.exception(
                 f"Failed graph execution {graph_exec.graph_exec_id}: {error}"
             )
-
-            # Send rate-limited Discord alert for unknown/unexpected errors
-            send_rate_limited_discord_alert(
-                "graph_execution",
-                error,
-                "unknown_error",
-                f"🚨 **Unknown Graph Execution Error**\n"
-                f"User: {graph_exec.user_id}\n"
-                f"Graph ID: {graph_exec.graph_id}\n"
-                f"Execution ID: {graph_exec.graph_exec_id}\n"
-                f"Error Type: {type(error).__name__}\n"
-                f"Error: {str(error)[:200]}{'...' if len(str(error)) > 200 else ''}\n",
-            )
-
             raise
 
         finally:
@@ -1211,9 +1153,9 @@ class ExecutionProcessor:
                 f"❌ **Insufficient Funds Alert**\n"
                 f"User: {user_email or user_id}\n"
                 f"Agent: {metadata.name if metadata else 'Unknown Agent'}\n"
-                f"Current balance: ${e.balance / 100:.2f}\n"
-                f"Attempted cost: ${abs(e.amount) / 100:.2f}\n"
-                f"Shortfall: ${abs(shortfall) / 100:.2f}\n"
+                f"Current balance: ${e.balance/100:.2f}\n"
+                f"Attempted cost: ${abs(e.amount)/100:.2f}\n"
+                f"Shortfall: ${abs(shortfall)/100:.2f}\n"
                 f"[View User Details]({base_url}/admin/spending?search={user_email})"
             )
 
@@ -1260,9 +1202,9 @@ class ExecutionProcessor:
                 alert_message = (
                     f"⚠️ **Low Balance Alert**\n"
                     f"User: {user_email or user_id}\n"
-                    f"Balance dropped below ${LOW_BALANCE_THRESHOLD / 100:.2f}\n"
-                    f"Current balance: ${current_balance / 100:.2f}\n"
-                    f"Transaction cost: ${transaction_cost / 100:.2f}\n"
+                    f"Balance dropped below ${LOW_BALANCE_THRESHOLD/100:.2f}\n"
+                    f"Current balance: ${current_balance/100:.2f}\n"
+                    f"Transaction cost: ${transaction_cost/100:.2f}\n"
                     f"[View User Details]({base_url}/admin/spending?search={user_email})"
                 )
                 get_notification_manager_client().discord_system_alert(
@@ -1277,7 +1219,6 @@ class ExecutionManager(AppProcess):
         super().__init__()
         self.pool_size = settings.config.num_graph_workers
         self.active_graph_runs: dict[str, tuple[Future, threading.Event]] = {}
-        self.executor_id = str(uuid.uuid4())
 
         self._executor = None
         self._stop_consuming = None
@@ -1287,8 +1228,6 @@ class ExecutionManager(AppProcess):
         self._run_thread = None
         self._run_client = None
 
-        self._execution_locks = {}
-
     @property
     def cancel_thread(self) -> threading.Thread:
         if self._cancel_thread is None:
@@ -1493,78 +1432,20 @@ class ExecutionManager(AppProcess):
             return
 
         graph_exec_id = graph_exec_entry.graph_exec_id
-        user_id = graph_exec_entry.user_id
-        graph_id = graph_exec_entry.graph_id
         logger.info(
-            f"[{self.service_name}] Received RUN for graph_exec_id={graph_exec_id}, user_id={user_id}"
+            f"[{self.service_name}] Received RUN for graph_exec_id={graph_exec_id}"
         )
-
-        # Check user rate limit before processing
-        try:
-            # Only check executions from the last 24 hours for performance
-            current_running_count = get_db_client().get_graph_executions_count(
-                user_id=user_id,
-                graph_id=graph_id,
-                statuses=[ExecutionStatus.RUNNING],
-                created_time_gte=datetime.now(timezone.utc) - timedelta(hours=24),
-            )
-
-            if (
-                current_running_count
-                >= settings.config.max_concurrent_graph_executions_per_user
-            ):
-                logger.warning(
-                    f"[{self.service_name}] Rate limit exceeded for user {user_id} on graph {graph_id}: "
-                    f"{current_running_count}/{settings.config.max_concurrent_graph_executions_per_user} running executions"
-                )
-                _ack_message(reject=True, requeue=True)
-                return
-
-        except Exception as e:
-            logger.error(
-                f"[{self.service_name}] Failed to check rate limit for user {user_id}: {e}, proceeding with execution"
-            )
-            # If rate limit check fails, proceed to avoid blocking executions
-
-        # Check for local duplicate execution first
         if graph_exec_id in self.active_graph_runs:
-            logger.warning(
-                f"[{self.service_name}] Graph {graph_exec_id} already running locally; rejecting duplicate."
+            # TODO: Make this check cluster-wide, prevent duplicate runs across executor pods.
+            logger.error(
+                f"[{self.service_name}] Graph {graph_exec_id} already running; rejecting duplicate run."
             )
-            _ack_message(reject=True, requeue=True)
+            _ack_message(reject=True, requeue=False)
             return
 
-        # Try to acquire cluster-wide execution lock
-        cluster_lock = ClusterLock(
-            redis=redis.get_redis(),
-            key=f"exec_lock:{graph_exec_id}",
-            owner_id=self.executor_id,
-            timeout=settings.config.cluster_lock_timeout,
-        )
-        current_owner = cluster_lock.try_acquire()
-        if current_owner != self.executor_id:
-            # Either someone else has it or Redis is unavailable
-            if current_owner is not None:
-                logger.warning(
-                    f"[{self.service_name}] Graph {graph_exec_id} already running on pod {current_owner}"
-                )
-            else:
-                logger.warning(
-                    f"[{self.service_name}] Could not acquire lock for {graph_exec_id} - Redis unavailable"
-                )
-            _ack_message(reject=True, requeue=True)
-            return
-        self._execution_locks[graph_exec_id] = cluster_lock
-
-        logger.info(
-            f"[{self.service_name}] Acquired cluster lock for {graph_exec_id} with executor {self.executor_id}"
-        )
-
         cancel_event = threading.Event()
 
-        future = self.executor.submit(
-            execute_graph, graph_exec_entry, cancel_event, cluster_lock
-        )
+        future = self.executor.submit(execute_graph, graph_exec_entry, cancel_event)
         self.active_graph_runs[graph_exec_id] = (future, cancel_event)
         self._update_prompt_metrics()
 
@@ -1583,10 +1464,6 @@ class ExecutionManager(AppProcess):
                     f"[{self.service_name}] Error in run completion callback: {e}"
                 )
             finally:
-                # Release the cluster-wide execution lock
-                if graph_exec_id in self._execution_locks:
-                    self._execution_locks[graph_exec_id].release()
-                    del self._execution_locks[graph_exec_id]
                 self._cleanup_completed_runs()
 
         future.add_done_callback(_on_run_done)
@@ -1669,10 +1546,6 @@ class ExecutionManager(AppProcess):
                         f"{prefix} ⏳ Still waiting for {len(self.active_graph_runs)} executions: {ids}"
                     )
 
-                    for graph_exec_id in self.active_graph_runs:
-                        if lock := self._execution_locks.get(graph_exec_id):
-                            lock.refresh()
-
                 time.sleep(wait_interval)
                 waited += wait_interval
 
@@ -1690,15 +1563,6 @@ class ExecutionManager(AppProcess):
         except Exception as e:
             logger.error(f"{prefix} ⚠️ Error during executor shutdown: {type(e)} {e}")
 
-        # Release remaining execution locks
-        try:
-            for lock in self._execution_locks.values():
-                lock.release()
-            self._execution_locks.clear()
-            logger.info(f"{prefix} ✅ Released execution locks")
-        except Exception as e:
-            logger.warning(f"{prefix} ⚠️ Failed to release all locks: {e}")
-
         # Disconnect the run execution consumer
         self._stop_message_consumers(
             self.run_thread,
@@ -1804,18 +1668,15 @@ def update_graph_execution_state(
 
 
 @asynccontextmanager
-async def synchronized(key: str, timeout: int = settings.config.cluster_lock_timeout):
+async def synchronized(key: str, timeout: int = 60):
     r = await redis.get_redis_async()
-    lock: AsyncRedisLock = r.lock(f"lock:{key}", timeout=timeout)
+    lock: RedisLock = r.lock(f"lock:{key}", timeout=timeout)
     try:
         await lock.acquire()
         yield
     finally:
         if await lock.locked() and await lock.owned():
-            try:
-                await lock.release()
-            except Exception as e:
-                logger.warning(f"Failed to release lock for key {key}: {e}")
+            await lock.release()
 
 
 def increment_execution_count(user_id: str) -> int:

autogpt_platform/backend/backend/executor/scheduler.py

@@ -191,22 +191,15 @@ class GraphExecutionJobInfo(GraphExecutionJobArgs):
     id: str
     name: str
     next_run_time: str
-    timezone: str = Field(default="UTC", description="Timezone used for scheduling")
 
     @staticmethod
     def from_db(
         job_args: GraphExecutionJobArgs, job_obj: JobObj
     ) -> "GraphExecutionJobInfo":
-        # Extract timezone from the trigger if it's a CronTrigger
-        timezone_str = "UTC"
-        if hasattr(job_obj.trigger, "timezone"):
-            timezone_str = str(job_obj.trigger.timezone)
-
         return GraphExecutionJobInfo(
             id=job_obj.id,
             name=job_obj.name,
             next_run_time=job_obj.next_run_time.isoformat(),
-            timezone=timezone_str,
             **job_args.model_dump(),
         )
 
@@ -402,7 +395,6 @@ class Scheduler(AppService):
         input_data: BlockInput,
         input_credentials: dict[str, CredentialsMetaInput],
         name: Optional[str] = None,
-        user_timezone: str | None = None,
     ) -> GraphExecutionJobInfo:
         # Validate the graph before scheduling to prevent runtime failures
         # We don't need the return value, just want the validation to run
@@ -416,18 +408,7 @@ class Scheduler(AppService):
             )
         )
 
-        # Use provided timezone or default to UTC
-        # Note: Timezone should be passed from the client to avoid database lookups
-        if not user_timezone:
-            user_timezone = "UTC"
-            logger.warning(
-                f"No timezone provided for user {user_id}, using UTC for scheduling. "
-                f"Client should pass user's timezone for correct scheduling."
-            )
-
-        logger.info(
-            f"Scheduling job for user {user_id} with timezone {user_timezone} (cron: {cron})"
-        )
+        logger.info(f"Scheduling job for user {user_id} in UTC (cron: {cron})")
 
         job_args = GraphExecutionJobArgs(
             user_id=user_id,
@@ -441,12 +422,12 @@ class Scheduler(AppService):
             execute_graph,
             kwargs=job_args.model_dump(),
             name=name,
-            trigger=CronTrigger.from_crontab(cron, timezone=user_timezone),
+            trigger=CronTrigger.from_crontab(cron, timezone="UTC"),
             jobstore=Jobstores.EXECUTION.value,
             replace_existing=True,
         )
         logger.info(
-            f"Added job {job.id} with cron schedule '{cron}' in timezone {user_timezone}, input data: {input_data}"
+            f"Added job {job.id} with cron schedule '{cron}' in UTC, input data: {input_data}"
         )
         return GraphExecutionJobInfo.from_db(job_args, job)
 

autogpt_platform/backend/backend/executor/utils.py

@@ -4,7 +4,7 @@ import threading
 import time
 from collections import defaultdict
 from concurrent.futures import Future
-from typing import Mapping, Optional, cast
+from typing import Any, Mapping, Optional, cast
 
 from pydantic import BaseModel, JsonValue, ValidationError
 
@@ -20,9 +20,6 @@ from backend.data.block import (
 )
 from backend.data.block_cost_config import BLOCK_COSTS
 from backend.data.db import prisma
-
-# Import dynamic field utilities from centralized location
-from backend.data.dynamic_fields import merge_execution_input
 from backend.data.execution import (
     ExecutionStatus,
     GraphExecutionStats,
@@ -42,6 +39,7 @@ from backend.util.clients import (
 )
 from backend.util.exceptions import GraphValidationError, NotFoundError
 from backend.util.logging import TruncatedLogger
+from backend.util.mock import MockObject
 from backend.util.settings import Config
 from backend.util.type import convert
 
@@ -188,7 +186,195 @@ def _is_cost_filter_match(cost_filter: BlockInput, input_data: BlockInput) -> bo
 
 # ============ Execution Input Helpers ============ #
 
-# Dynamic field utilities are now imported from backend.data.dynamic_fields
+# --------------------------------------------------------------------------- #
+#  Delimiters
+# --------------------------------------------------------------------------- #
+
+LIST_SPLIT = "_$_"
+DICT_SPLIT = "_#_"
+OBJC_SPLIT = "_@_"
+
+_DELIMS = (LIST_SPLIT, DICT_SPLIT, OBJC_SPLIT)
+
+# --------------------------------------------------------------------------- #
+#  Tokenisation utilities
+# --------------------------------------------------------------------------- #
+
+
+def _next_delim(s: str) -> tuple[str | None, int]:
+    """
+    Return the *earliest* delimiter appearing in `s` and its index.
+
+    If none present → (None, -1).
+    """
+    first: str | None = None
+    pos = len(s)  # sentinel: larger than any real index
+    for d in _DELIMS:
+        i = s.find(d)
+        if 0 <= i < pos:
+            first, pos = d, i
+    return first, (pos if first else -1)
+
+
+def _tokenise(path: str) -> list[tuple[str, str]] | None:
+    """
+    Convert the raw path string (starting with a delimiter) into
+    [ (delimiter, identifier), … ] or None if the syntax is malformed.
+    """
+    tokens: list[tuple[str, str]] = []
+    while path:
+        # 1. Which delimiter starts this chunk?
+        delim = next((d for d in _DELIMS if path.startswith(d)), None)
+        if delim is None:
+            return None  # invalid syntax
+
+        # 2. Slice off the delimiter, then up to the next delimiter (or EOS)
+        path = path[len(delim) :]
+        nxt_delim, pos = _next_delim(path)
+        token, path = (
+            path[: pos if pos != -1 else len(path)],
+            path[pos if pos != -1 else len(path) :],
+        )
+        if token == "":
+            return None  # empty identifier is invalid
+        tokens.append((delim, token))
+    return tokens
+
+
+# --------------------------------------------------------------------------- #
+#  Public API – parsing (flattened ➜ concrete)
+# --------------------------------------------------------------------------- #
+
+
+def parse_execution_output(output: BlockOutputEntry, name: str) -> JsonValue | None:
+    """
+    Retrieve a nested value out of `output` using the flattened *name*.
+
+    On any failure (wrong name, wrong type, out-of-range, bad path)
+    returns **None**.
+    """
+    base_name, data = output
+
+    # Exact match → whole object
+    if name == base_name:
+        return data
+
+    # Must start with the expected name
+    if not name.startswith(base_name):
+        return None
+    path = name[len(base_name) :]
+    if not path:
+        return None  # nothing left to parse
+
+    tokens = _tokenise(path)
+    if tokens is None:
+        return None
+
+    cur: JsonValue = data
+    for delim, ident in tokens:
+        if delim == LIST_SPLIT:
+            # list[index]
+            try:
+                idx = int(ident)
+            except ValueError:
+                return None
+            if not isinstance(cur, list) or idx >= len(cur):
+                return None
+            cur = cur[idx]
+
+        elif delim == DICT_SPLIT:
+            if not isinstance(cur, dict) or ident not in cur:
+                return None
+            cur = cur[ident]
+
+        elif delim == OBJC_SPLIT:
+            if not hasattr(cur, ident):
+                return None
+            cur = getattr(cur, ident)
+
+        else:
+            return None  # unreachable
+
+    return cur
+
+
+def _assign(container: Any, tokens: list[tuple[str, str]], value: Any) -> Any:
+    """
+    Recursive helper that *returns* the (possibly new) container with
+    `value` assigned along the remaining `tokens` path.
+    """
+    if not tokens:
+        return value  # leaf reached
+
+    delim, ident = tokens[0]
+    rest = tokens[1:]
+
+    # ---------- list ----------
+    if delim == LIST_SPLIT:
+        try:
+            idx = int(ident)
+        except ValueError:
+            raise ValueError("index must be an integer")
+
+        if container is None:
+            container = []
+        elif not isinstance(container, list):
+            container = list(container) if hasattr(container, "__iter__") else []
+
+        while len(container) <= idx:
+            container.append(None)
+        container[idx] = _assign(container[idx], rest, value)
+        return container
+
+    # ---------- dict ----------
+    if delim == DICT_SPLIT:
+        if container is None:
+            container = {}
+        elif not isinstance(container, dict):
+            container = dict(container) if hasattr(container, "items") else {}
+        container[ident] = _assign(container.get(ident), rest, value)
+        return container
+
+    # ---------- object ----------
+    if delim == OBJC_SPLIT:
+        if container is None or not isinstance(container, MockObject):
+            container = MockObject()
+        setattr(
+            container,
+            ident,
+            _assign(getattr(container, ident, None), rest, value),
+        )
+        return container
+
+    return value  # unreachable
+
+
+def merge_execution_input(data: BlockInput) -> BlockInput:
+    """
+    Reconstruct nested objects from a *flattened* dict of key → value.
+
+    Raises ValueError on syntactically invalid list indices.
+    """
+    merged: BlockInput = {}
+
+    for key, value in data.items():
+        # Split off the base name (before the first delimiter, if any)
+        delim, pos = _next_delim(key)
+        if delim is None:
+            merged[key] = value
+            continue
+
+        base, path = key[:pos], key[pos:]
+        tokens = _tokenise(path)
+        if tokens is None:
+            # Invalid key; treat as scalar under the raw name
+            merged[key] = value
+            continue
+
+        merged[base] = _assign(merged.get(base), tokens, value)
+
+    data.update(merged)
+    return data
 
 
 def validate_exec(
@@ -728,30 +914,29 @@ async def add_graph_execution(
             preset_id=preset_id,
         )
 
+        # Fetch user context for the graph execution
+        user_context = await get_user_context(user_id)
+
+        queue = await get_async_execution_queue()
         graph_exec_entry = graph_exec.to_graph_execution_entry(
-            user_context=await get_user_context(user_id),
-            compiled_nodes_input_masks=compiled_nodes_input_masks,
+            user_context, compiled_nodes_input_masks
         )
+
         logger.info(
             f"Created graph execution #{graph_exec.id} for graph "
             f"#{graph_id} with {len(starting_nodes_input)} starting nodes. "
             f"Now publishing to execution queue."
         )
 
-        exec_queue = await get_async_execution_queue()
-        await exec_queue.publish_message(
+        await queue.publish_message(
             routing_key=GRAPH_EXECUTION_ROUTING_KEY,
             message=graph_exec_entry.model_dump_json(),
             exchange=GRAPH_EXECUTION_EXCHANGE,
         )
         logger.info(f"Published execution {graph_exec.id} to RabbitMQ queue")
 
-        graph_exec.status = ExecutionStatus.QUEUED
-        await edb.update_graph_execution_stats(
-            graph_exec_id=graph_exec.id,
-            status=graph_exec.status,
-        )
-        await get_async_execution_event_bus().publish(graph_exec)
+        bus = get_async_execution_event_bus()
+        await bus.publish(graph_exec)
 
         return graph_exec
     except BaseException as e:

autogpt_platform/backend/backend/executor/utils_test.py

@@ -3,7 +3,7 @@ from typing import cast
 import pytest
 from pytest_mock import MockerFixture
 
-from backend.data.dynamic_fields import merge_execution_input, parse_execution_output
+from backend.executor.utils import merge_execution_input, parse_execution_output
 from backend.util.mock import MockObject
 
 
@@ -316,7 +316,6 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     # Mock the graph execution object
     mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
     mock_graph_exec.id = "execution-id-123"
-    mock_graph_exec.node_executions = []  # Add this to avoid AttributeError
     mock_graph_exec.to_graph_execution_entry.return_value = mocker.MagicMock()
 
     # Mock user context
@@ -347,10 +346,6 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     )
     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_get_user_context.return_value = mock_user_context
     mock_get_queue.return_value = mock_queue
     mock_get_event_bus.return_value = mock_event_bus

autogpt_platform/backend/backend/integrations/creds_manager.py

@@ -151,10 +151,7 @@ class IntegrationCredentialsManager:
                 fresh_credentials = await oauth_handler.refresh_tokens(credentials)
                 await self.store.update_creds(user_id, fresh_credentials)
                 if _lock and (await _lock.locked()) and (await _lock.owned()):
-                    try:
-                        await _lock.release()
-                    except Exception as e:
-                        logger.warning(f"Failed to release OAuth refresh lock: {e}")
+                    await _lock.release()
 
                 credentials = fresh_credentials
         return credentials
@@ -187,10 +184,7 @@ class IntegrationCredentialsManager:
             yield
         finally:
             if (await lock.locked()) and (await lock.owned()):
-                try:
-                    await lock.release()
-                except Exception as e:
-                    logger.warning(f"Failed to release credentials lock: {e}")
+                await lock.release()
 
     async def release_all_locks(self):
         """Call this on process termination to ensure all locks are released"""

autogpt_platform/backend/backend/integrations/webhooks/__init__.py

@@ -1,14 +1,13 @@
+import functools
 from typing import TYPE_CHECKING
 
-from autogpt_libs.utils.cache import cached
-
 if TYPE_CHECKING:
     from ..providers import ProviderName
     from ._base import BaseWebhooksManager
 
 
 # --8<-- [start:load_webhook_managers]
-@cached()
+@functools.cache
 def load_webhook_managers() -> dict["ProviderName", type["BaseWebhooksManager"]]:
     webhook_managers = {}
 

autogpt_platform/backend/backend/integrations/webhooks/graph_lifecycle_hooks.py

@@ -7,9 +7,10 @@ from backend.data.graph import set_node_webhook
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 
 from . import get_webhook_manager, supports_webhooks
+from .utils import setup_webhook_for_block
 
 if TYPE_CHECKING:
-    from backend.data.graph import BaseGraph, GraphModel, NodeModel
+    from backend.data.graph import BaseGraph, GraphModel, Node, NodeModel
     from backend.data.model import Credentials
 
     from ._base import BaseWebhooksManager
@@ -42,19 +43,32 @@ async def _on_graph_activate(graph: "BaseGraph", user_id: str) -> "BaseGraph": .
 
 async def _on_graph_activate(graph: "BaseGraph | GraphModel", user_id: str):
     get_credentials = credentials_manager.cached_getter(user_id)
+    updated_nodes = []
     for new_node in graph.nodes:
         block_input_schema = cast(BlockSchema, new_node.block.input_schema)
 
-        for creds_field_name in block_input_schema.get_credentials_fields().keys():
-            # Prevent saving graph with non-existent credentials
-            if (
-                creds_meta := new_node.input_default.get(creds_field_name)
-            ) and not await get_credentials(creds_meta["id"]):
-                raise ValueError(
-                    f"Node #{new_node.id} input '{creds_field_name}' updated with "
-                    f"non-existent credentials #{creds_meta['id']}"
+        node_credentials = None
+        if (
+            # Webhook-triggered blocks are only allowed to have 1 credentials input
+            (
+                creds_field_name := next(
+                    iter(block_input_schema.get_credentials_fields()), None
                 )
+            )
+            and (creds_meta := new_node.input_default.get(creds_field_name))
+            and not (node_credentials := await get_credentials(creds_meta["id"]))
+        ):
+            raise ValueError(
+                f"Node #{new_node.id} input '{creds_field_name}' updated with "
+                f"non-existent credentials #{creds_meta['id']}"
+            )
 
+        updated_node = await on_node_activate(
+            user_id, graph.id, new_node, credentials=node_credentials
+        )
+        updated_nodes.append(updated_node)
+
+    graph.nodes = updated_nodes
     return graph
 
 
@@ -71,14 +85,20 @@ async def on_graph_deactivate(graph: "GraphModel", user_id: str):
         block_input_schema = cast(BlockSchema, node.block.input_schema)
 
         node_credentials = None
-        for creds_field_name in block_input_schema.get_credentials_fields().keys():
-            if (creds_meta := node.input_default.get(creds_field_name)) and not (
-                node_credentials := await get_credentials(creds_meta["id"])
-            ):
-                logger.warning(
-                    f"Node #{node.id} input '{creds_field_name}' referenced "
-                    f"non-existent credentials #{creds_meta['id']}"
+        if (
+            # Webhook-triggered blocks are only allowed to have 1 credentials input
+            (
+                creds_field_name := next(
+                    iter(block_input_schema.get_credentials_fields()), None
                 )
+            )
+            and (creds_meta := node.input_default.get(creds_field_name))
+            and not (node_credentials := await get_credentials(creds_meta["id"]))
+        ):
+            logger.error(
+                f"Node #{node.id} input '{creds_field_name}' referenced non-existent "
+                f"credentials #{creds_meta['id']}"
+            )
 
         updated_node = await on_node_deactivate(
             user_id, node, credentials=node_credentials
@@ -89,6 +109,32 @@ async def on_graph_deactivate(graph: "GraphModel", user_id: str):
     return graph
 
 
+async def on_node_activate(
+    user_id: str,
+    graph_id: str,
+    node: "Node",
+    *,
+    credentials: Optional["Credentials"] = None,
+) -> "Node":
+    """Hook to be called when the node is activated/created"""
+
+    if node.block.webhook_config:
+        new_webhook, feedback = await setup_webhook_for_block(
+            user_id=user_id,
+            trigger_block=node.block,
+            trigger_config=node.input_default,
+            for_graph_id=graph_id,
+        )
+        if new_webhook:
+            node = await set_node_webhook(node.id, new_webhook.id)
+        else:
+            logger.debug(
+                f"Node #{node.id} does not have everything for a webhook: {feedback}"
+            )
+
+    return node
+
+
 async def on_node_deactivate(
     user_id: str,
     node: "NodeModel",

autogpt_platform/backend/backend/integrations/webhooks/utils.py

@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Optional, cast
 from pydantic import JsonValue
 
 from backend.integrations.creds_manager import IntegrationCredentialsManager
+from backend.integrations.providers import ProviderName
 from backend.util.settings import Config
 
 from . import get_webhook_manager, supports_webhooks
@@ -12,7 +13,6 @@ if TYPE_CHECKING:
     from backend.data.block import Block, BlockSchema
     from backend.data.integrations import Webhook
     from backend.data.model import Credentials
-    from backend.integrations.providers import ProviderName
 
 logger = logging.getLogger(__name__)
 app_config = Config()
@@ -20,7 +20,7 @@ credentials_manager = IntegrationCredentialsManager()
 
 
 # TODO: add test to assert this matches the actual API route
-def webhook_ingress_url(provider_name: "ProviderName", webhook_id: str) -> str:
+def webhook_ingress_url(provider_name: ProviderName, webhook_id: str) -> str:
     return (
         f"{app_config.platform_base_url}/api/integrations/{provider_name.value}"
         f"/webhooks/{webhook_id}/ingress"
@@ -144,69 +144,3 @@ async def setup_webhook_for_block(
         )
     logger.debug(f"Acquired webhook: {webhook}")
     return webhook, None
-
-
-async def migrate_legacy_triggered_graphs():
-    from prisma.models import AgentGraph
-
-    from backend.data.graph import AGENT_GRAPH_INCLUDE, GraphModel, set_node_webhook
-    from backend.data.model import is_credentials_field_name
-    from backend.server.v2.library.db import create_preset
-    from backend.server.v2.library.model import LibraryAgentPresetCreatable
-
-    triggered_graphs = [
-        GraphModel.from_db(_graph)
-        for _graph in await AgentGraph.prisma().find_many(
-            where={
-                "isActive": True,
-                "Nodes": {"some": {"NOT": [{"webhookId": None}]}},
-            },
-            include=AGENT_GRAPH_INCLUDE,
-        )
-    ]
-
-    n_migrated_webhooks = 0
-
-    for graph in triggered_graphs:
-        try:
-            if not (
-                (trigger_node := graph.webhook_input_node) and trigger_node.webhook_id
-            ):
-                continue
-
-            # Use trigger node's inputs for the preset
-            preset_credentials = {
-                field_name: creds_meta
-                for field_name, creds_meta in trigger_node.input_default.items()
-                if is_credentials_field_name(field_name)
-            }
-            preset_inputs = {
-                field_name: value
-                for field_name, value in trigger_node.input_default.items()
-                if not is_credentials_field_name(field_name)
-            }
-
-            # Create a triggered preset for the graph
-            await create_preset(
-                graph.user_id,
-                LibraryAgentPresetCreatable(
-                    graph_id=graph.id,
-                    graph_version=graph.version,
-                    inputs=preset_inputs,
-                    credentials=preset_credentials,
-                    name=graph.name,
-                    description=graph.description,
-                    webhook_id=trigger_node.webhook_id,
-                    is_active=True,
-                ),
-            )
-
-            # Detach webhook from the graph node
-            await set_node_webhook(trigger_node.id, None)
-
-            n_migrated_webhooks += 1
-        except Exception as e:
-            logger.error(f"Failed to migrate graph #{graph.id} trigger to preset: {e}")
-            continue
-
-    logger.info(f"Migrated {n_migrated_webhooks} node triggers to triggered presets")

autogpt_platform/backend/backend/monitoring/instrumentation.py

@@ -1,287 +0,0 @@
-"""
-Prometheus instrumentation for FastAPI services.
-
-This module provides centralized metrics collection and instrumentation
-for all FastAPI services in the AutoGPT platform.
-"""
-
-import logging
-from typing import Optional
-
-from fastapi import FastAPI
-from prometheus_client import Counter, Gauge, Histogram, Info
-from prometheus_fastapi_instrumentator import Instrumentator, metrics
-
-logger = logging.getLogger(__name__)
-
-# Custom business metrics with controlled cardinality
-GRAPH_EXECUTIONS = Counter(
-    "autogpt_graph_executions_total",
-    "Total number of graph executions",
-    labelnames=[
-        "status"
-    ],  # Removed graph_id and user_id to prevent cardinality explosion
-)
-
-GRAPH_EXECUTIONS_BY_USER = Counter(
-    "autogpt_graph_executions_by_user_total",
-    "Total number of graph executions by user (sampled)",
-    labelnames=["status"],  # Only status, user_id tracked separately when needed
-)
-
-BLOCK_EXECUTIONS = Counter(
-    "autogpt_block_executions_total",
-    "Total number of block executions",
-    labelnames=["block_type", "status"],  # block_type is bounded
-)
-
-BLOCK_DURATION = Histogram(
-    "autogpt_block_duration_seconds",
-    "Duration of block executions in seconds",
-    labelnames=["block_type"],
-    buckets=[0.1, 0.25, 0.5, 1, 2.5, 5, 10, 30, 60],
-)
-
-WEBSOCKET_CONNECTIONS = Gauge(
-    "autogpt_websocket_connections_total",
-    "Total number of active WebSocket connections",
-    # Removed user_id label - track total only to prevent cardinality explosion
-)
-
-SCHEDULER_JOBS = Gauge(
-    "autogpt_scheduler_jobs",
-    "Current number of scheduled jobs",
-    labelnames=["job_type", "status"],
-)
-
-DATABASE_QUERIES = Histogram(
-    "autogpt_database_query_duration_seconds",
-    "Duration of database queries in seconds",
-    labelnames=["operation", "table"],
-    buckets=[0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
-)
-
-RABBITMQ_MESSAGES = Counter(
-    "autogpt_rabbitmq_messages_total",
-    "Total number of RabbitMQ messages",
-    labelnames=["queue", "status"],
-)
-
-AUTHENTICATION_ATTEMPTS = Counter(
-    "autogpt_auth_attempts_total",
-    "Total number of authentication attempts",
-    labelnames=["method", "status"],
-)
-
-API_KEY_USAGE = Counter(
-    "autogpt_api_key_usage_total",
-    "API key usage by provider",
-    labelnames=["provider", "block_type", "status"],
-)
-
-# Function/operation level metrics with controlled cardinality
-GRAPH_OPERATIONS = Counter(
-    "autogpt_graph_operations_total",
-    "Graph operations by type",
-    labelnames=["operation", "status"],  # create, update, delete, execute, etc.
-)
-
-USER_OPERATIONS = Counter(
-    "autogpt_user_operations_total",
-    "User operations by type",
-    labelnames=["operation", "status"],  # login, register, update_profile, etc.
-)
-
-RATE_LIMIT_HITS = Counter(
-    "autogpt_rate_limit_hits_total",
-    "Number of rate limit hits",
-    labelnames=["endpoint"],  # Removed user_id to prevent cardinality explosion
-)
-
-SERVICE_INFO = Info(
-    "autogpt_service",
-    "Service information",
-)
-
-
-def instrument_fastapi(
-    app: FastAPI,
-    service_name: str,
-    expose_endpoint: bool = True,
-    endpoint: str = "/metrics",
-    include_in_schema: bool = False,
-    excluded_handlers: Optional[list] = None,
-) -> Instrumentator:
-    """
-    Instrument a FastAPI application with Prometheus metrics.
-
-    Args:
-        app: FastAPI application instance
-        service_name: Name of the service for metrics labeling
-        expose_endpoint: Whether to expose /metrics endpoint
-        endpoint: Path for metrics endpoint
-        include_in_schema: Whether to include metrics endpoint in OpenAPI schema
-        excluded_handlers: List of paths to exclude from metrics
-
-    Returns:
-        Configured Instrumentator instance
-    """
-
-    # Set service info
-    try:
-        from importlib.metadata import version
-
-        service_version = version("autogpt-platform-backend")
-    except Exception:
-        service_version = "unknown"
-
-    SERVICE_INFO.info(
-        {
-            "service": service_name,
-            "version": service_version,
-        }
-    )
-
-    # Create instrumentator with default metrics
-    instrumentator = Instrumentator(
-        should_group_status_codes=True,
-        should_ignore_untemplated=True,
-        should_respect_env_var=True,
-        should_instrument_requests_inprogress=True,
-        excluded_handlers=excluded_handlers or ["/health", "/readiness"],
-        env_var_name="ENABLE_METRICS",
-        inprogress_name="autogpt_http_requests_inprogress",
-        inprogress_labels=True,
-    )
-
-    # Add default HTTP metrics
-    instrumentator.add(
-        metrics.default(
-            metric_namespace="autogpt",
-            metric_subsystem=service_name.replace("-", "_"),
-        )
-    )
-
-    # Add request size metrics
-    instrumentator.add(
-        metrics.request_size(
-            metric_namespace="autogpt",
-            metric_subsystem=service_name.replace("-", "_"),
-        )
-    )
-
-    # Add response size metrics
-    instrumentator.add(
-        metrics.response_size(
-            metric_namespace="autogpt",
-            metric_subsystem=service_name.replace("-", "_"),
-        )
-    )
-
-    # Add latency metrics with custom buckets for better granularity
-    instrumentator.add(
-        metrics.latency(
-            metric_namespace="autogpt",
-            metric_subsystem=service_name.replace("-", "_"),
-            buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10, 30, 60],
-        )
-    )
-
-    # Add combined metrics (requests by method and status)
-    instrumentator.add(
-        metrics.combined_size(
-            metric_namespace="autogpt",
-            metric_subsystem=service_name.replace("-", "_"),
-        )
-    )
-
-    # Instrument the app
-    instrumentator.instrument(app)
-
-    # Expose metrics endpoint if requested
-    if expose_endpoint:
-        instrumentator.expose(
-            app,
-            endpoint=endpoint,
-            include_in_schema=include_in_schema,
-            tags=["monitoring"] if include_in_schema else None,
-        )
-        logger.info(f"Metrics endpoint exposed at {endpoint} for {service_name}")
-
-    return instrumentator
-
-
-def record_graph_execution(graph_id: str, status: str, user_id: str):
-    """Record a graph execution event.
-
-    Args:
-        graph_id: Graph identifier (kept for future sampling/debugging)
-        status: Execution status (success/error/validation_error)
-        user_id: User identifier (kept for future sampling/debugging)
-    """
-    # Track overall executions without high-cardinality labels
-    GRAPH_EXECUTIONS.labels(status=status).inc()
-
-    # Optionally track per-user executions (implement sampling if needed)
-    # For now, just track status to avoid cardinality explosion
-    GRAPH_EXECUTIONS_BY_USER.labels(status=status).inc()
-
-
-def record_block_execution(block_type: str, status: str, duration: float):
-    """Record a block execution event with duration."""
-    BLOCK_EXECUTIONS.labels(block_type=block_type, status=status).inc()
-    BLOCK_DURATION.labels(block_type=block_type).observe(duration)
-
-
-def update_websocket_connections(user_id: str, delta: int):
-    """Update the number of active WebSocket connections.
-
-    Args:
-        user_id: User identifier (kept for future sampling/debugging)
-        delta: Change in connection count (+1 for connect, -1 for disconnect)
-    """
-    # Track total connections without user_id to prevent cardinality explosion
-    if delta > 0:
-        WEBSOCKET_CONNECTIONS.inc(delta)
-    else:
-        WEBSOCKET_CONNECTIONS.dec(abs(delta))
-
-
-def record_database_query(operation: str, table: str, duration: float):
-    """Record a database query with duration."""
-    DATABASE_QUERIES.labels(operation=operation, table=table).observe(duration)
-
-
-def record_rabbitmq_message(queue: str, status: str):
-    """Record a RabbitMQ message event."""
-    RABBITMQ_MESSAGES.labels(queue=queue, status=status).inc()
-
-
-def record_authentication_attempt(method: str, status: str):
-    """Record an authentication attempt."""
-    AUTHENTICATION_ATTEMPTS.labels(method=method, status=status).inc()
-
-
-def record_api_key_usage(provider: str, block_type: str, status: str):
-    """Record API key usage by provider and block."""
-    API_KEY_USAGE.labels(provider=provider, block_type=block_type, status=status).inc()
-
-
-def record_rate_limit_hit(endpoint: str, user_id: str):
-    """Record a rate limit hit.
-
-    Args:
-        endpoint: API endpoint that was rate limited
-        user_id: User identifier (kept for future sampling/debugging)
-    """
-    RATE_LIMIT_HITS.labels(endpoint=endpoint).inc()
-
-
-def record_graph_operation(operation: str, status: str):
-    """Record a graph operation (create, update, delete, execute, etc.)."""
-    GRAPH_OPERATIONS.labels(operation=operation, status=status).inc()
-
-
-def record_user_operation(operation: str, status: str):
-    """Record a user operation (login, register, etc.)."""
-    USER_OPERATIONS.labels(operation=operation, status=status).inc()

autogpt_platform/backend/backend/notifications/notifications.py

@@ -25,11 +25,7 @@ from backend.data.notifications import (
     get_summary_params_type,
 )
 from backend.data.rabbitmq import Exchange, ExchangeType, Queue, RabbitMQConfig
-from backend.data.user import (
-    disable_all_user_notifications,
-    generate_unsubscribe_link,
-    set_user_email_verification,
-)
+from backend.data.user import generate_unsubscribe_link
 from backend.notifications.email import EmailSender
 from backend.util.clients import get_database_manager_async_client
 from backend.util.logging import TruncatedLogger
@@ -42,7 +38,7 @@ from backend.util.service import (
     endpoint_to_sync,
     expose,
 )
-from backend.util.settings import AppEnvironment, Settings
+from backend.util.settings import Settings
 
 logger = TruncatedLogger(logging.getLogger(__name__), "[NotificationManager]")
 settings = Settings()
@@ -128,12 +124,6 @@ def get_routing_key(event_type: NotificationType) -> str:
 
 def queue_notification(event: NotificationEventModel) -> NotificationResult:
     """Queue a notification - exposed method for other services to call"""
-    # Disable in production
-    if settings.config.app_env == AppEnvironment.PRODUCTION:
-        return NotificationResult(
-            success=True,
-            message="Queueing notifications is disabled in production",
-        )
     try:
         logger.debug(f"Received Request to queue {event=}")
 
@@ -161,12 +151,6 @@ def queue_notification(event: NotificationEventModel) -> NotificationResult:
 
 async def queue_notification_async(event: NotificationEventModel) -> NotificationResult:
     """Queue a notification - exposed method for other services to call"""
-    # Disable in production
-    if settings.config.app_env == AppEnvironment.PRODUCTION:
-        return NotificationResult(
-            success=True,
-            message="Queueing notifications is disabled in production",
-        )
     try:
         logger.debug(f"Received Request to queue {event=}")
 
@@ -229,9 +213,6 @@ class NotificationManager(AppService):
 
     @expose
     async def queue_weekly_summary(self):
-        # disable in prod
-        if settings.config.app_env == AppEnvironment.PRODUCTION:
-            return
         # Use the existing event loop instead of creating a new one with asyncio.run()
         asyncio.create_task(self._queue_weekly_summary())
 
@@ -245,9 +226,7 @@ class NotificationManager(AppService):
             logger.info(
                 f"Querying for active users between {start_time} and {current_time}"
             )
-            users = await get_database_manager_async_client(
-                should_retry=False
-            ).get_active_user_ids_in_timerange(
+            users = await get_database_manager_async_client().get_active_user_ids_in_timerange(
                 end_time=current_time.isoformat(),
                 start_time=start_time.isoformat(),
             )
@@ -274,9 +253,6 @@ class NotificationManager(AppService):
     async def process_existing_batches(
         self, notification_types: list[NotificationType]
     ):
-        # disable in prod
-        if settings.config.app_env == AppEnvironment.PRODUCTION:
-            return
         # Use the existing event loop instead of creating a new process
         asyncio.create_task(self._process_existing_batches(notification_types))
 
@@ -290,15 +266,15 @@ class NotificationManager(AppService):
 
             for notification_type in notification_types:
                 # Get all batches for this notification type
-                batches = await get_database_manager_async_client(
-                    should_retry=False
-                ).get_all_batches_by_type(notification_type)
+                batches = (
+                    await get_database_manager_async_client().get_all_batches_by_type(
+                        notification_type
+                    )
+                )
 
                 for batch in batches:
                     # Check if batch has aged out
-                    oldest_message = await get_database_manager_async_client(
-                        should_retry=False
-                    ).get_user_notification_oldest_message_in_batch(
+                    oldest_message = await get_database_manager_async_client().get_user_notification_oldest_message_in_batch(
                         batch.user_id, notification_type
                     )
 
@@ -313,9 +289,9 @@ class NotificationManager(AppService):
 
                     # If batch has aged out, process it
                     if oldest_message.created_at + max_delay < current_time:
-                        recipient_email = await get_database_manager_async_client(
-                            should_retry=False
-                        ).get_user_email_by_id(batch.user_id)
+                        recipient_email = await get_database_manager_async_client().get_user_email_by_id(
+                            batch.user_id
+                        )
 
                         if not recipient_email:
                             logger.error(
@@ -332,25 +308,21 @@ class NotificationManager(AppService):
                                 f"User {batch.user_id} does not want to receive {notification_type} notifications"
                             )
                             # Clear the batch
-                            await get_database_manager_async_client(
-                                should_retry=False
-                            ).empty_user_notification_batch(
+                            await get_database_manager_async_client().empty_user_notification_batch(
                                 batch.user_id, notification_type
                             )
                             continue
 
-                        batch_data = await get_database_manager_async_client(
-                            should_retry=False
-                        ).get_user_notification_batch(batch.user_id, notification_type)
+                        batch_data = await get_database_manager_async_client().get_user_notification_batch(
+                            batch.user_id, notification_type
+                        )
 
                         if not batch_data or not batch_data.notifications:
                             logger.error(
                                 f"Batch data not found for user {batch.user_id}"
                             )
                             # Clear the batch
-                            await get_database_manager_async_client(
-                                should_retry=False
-                            ).empty_user_notification_batch(
+                            await get_database_manager_async_client().empty_user_notification_batch(
                                 batch.user_id, notification_type
                             )
                             continue
@@ -386,9 +358,7 @@ class NotificationManager(AppService):
                         )
 
                         # Clear the batch
-                        await get_database_manager_async_client(
-                            should_retry=False
-                        ).empty_user_notification_batch(
+                        await get_database_manager_async_client().empty_user_notification_batch(
                             batch.user_id, notification_type
                         )
 
@@ -443,13 +413,15 @@ class NotificationManager(AppService):
         self, user_id: str, event_type: NotificationType
     ) -> bool:
         """Check if a user wants to receive a notification based on their preferences and email verification status"""
-        validated_email = await get_database_manager_async_client(
-            should_retry=False
-        ).get_user_email_verification(user_id)
+        validated_email = (
+            await get_database_manager_async_client().get_user_email_verification(
+                user_id
+            )
+        )
         preference = (
-            await get_database_manager_async_client(
-                should_retry=False
-            ).get_user_notification_preference(user_id)
+            await get_database_manager_async_client().get_user_notification_preference(
+                user_id
+            )
         ).preferences.get(event_type, True)
         # only if both are true, should we email this person
         return validated_email and preference
@@ -465,9 +437,7 @@ class NotificationManager(AppService):
 
         try:
             # Get summary data from the database
-            summary_data = await get_database_manager_async_client(
-                should_retry=False
-            ).get_user_execution_summary_data(
+            summary_data = await get_database_manager_async_client().get_user_execution_summary_data(
                 user_id=user_id,
                 start_time=params.start_date,
                 end_time=params.end_date,
@@ -554,13 +524,13 @@ class NotificationManager(AppService):
         self, user_id: str, event_type: NotificationType, event: NotificationEventModel
     ) -> bool:
 
-        await get_database_manager_async_client(
-            should_retry=False
-        ).create_or_add_to_user_notification_batch(user_id, event_type, event)
+        await get_database_manager_async_client().create_or_add_to_user_notification_batch(
+            user_id, event_type, event
+        )
 
-        oldest_message = await get_database_manager_async_client(
-            should_retry=False
-        ).get_user_notification_oldest_message_in_batch(user_id, event_type)
+        oldest_message = await get_database_manager_async_client().get_user_notification_oldest_message_in_batch(
+            user_id, event_type
+        )
         if not oldest_message:
             logger.error(
                 f"Batch for user {user_id} and type {event_type} has no oldest message whichshould never happen!!!!!!!!!!!!!!!!"
@@ -610,9 +580,11 @@ class NotificationManager(AppService):
                 return False
             logger.debug(f"Processing immediate notification: {event}")
 
-            recipient_email = await get_database_manager_async_client(
-                should_retry=False
-            ).get_user_email_by_id(event.user_id)
+            recipient_email = (
+                await get_database_manager_async_client().get_user_email_by_id(
+                    event.user_id
+                )
+            )
             if not recipient_email:
                 logger.error(f"User email not found for user {event.user_id}")
                 return False
@@ -647,9 +619,11 @@ class NotificationManager(AppService):
                 return False
             logger.info(f"Processing batch notification: {event}")
 
-            recipient_email = await get_database_manager_async_client(
-                should_retry=False
-            ).get_user_email_by_id(event.user_id)
+            recipient_email = (
+                await get_database_manager_async_client().get_user_email_by_id(
+                    event.user_id
+                )
+            )
             if not recipient_email:
                 logger.error(f"User email not found for user {event.user_id}")
                 return False
@@ -668,9 +642,11 @@ class NotificationManager(AppService):
             if not should_send:
                 logger.info("Batch not old enough to send")
                 return False
-            batch = await get_database_manager_async_client(
-                should_retry=False
-            ).get_user_notification_batch(event.user_id, event.type)
+            batch = (
+                await get_database_manager_async_client().get_user_notification_batch(
+                    event.user_id, event.type
+                )
+            )
             if not batch or not batch.notifications:
                 logger.error(f"Batch not found for user {event.user_id}")
                 return False
@@ -681,7 +657,6 @@ class NotificationManager(AppService):
                     get_notif_data_type(db_event.type)
                 ].model_validate(
                     {
-                        "id": db_event.id,  # Include ID from database
                         "user_id": event.user_id,
                         "type": db_event.type,
                         "data": db_event.data,
@@ -704,9 +679,6 @@ class NotificationManager(AppService):
                 chunk_sent = False
                 for attempt_size in [chunk_size, 50, 25, 10, 5, 1]:
                     chunk = batch_messages[i : i + attempt_size]
-                    chunk_ids = [
-                        msg.id for msg in chunk if msg.id
-                    ]  # Extract IDs for removal
 
                     try:
                         # Try to render the email to check its size
@@ -733,23 +705,6 @@ class NotificationManager(AppService):
                                 user_unsub_link=unsub_link,
                             )
 
-                            # Remove successfully sent notifications immediately
-                            if chunk_ids:
-                                try:
-                                    await get_database_manager_async_client(
-                                        should_retry=False
-                                    ).remove_notifications_from_batch(
-                                        event.user_id, event.type, chunk_ids
-                                    )
-                                    logger.info(
-                                        f"Removed {len(chunk_ids)} sent notifications from batch"
-                                    )
-                                except Exception as e:
-                                    logger.error(
-                                        f"Failed to remove sent notifications: {e}"
-                                    )
-                                    # Continue anyway - better to risk duplicates than lose emails
-
                             # Track successful sends
                             successfully_sent_count += len(chunk)
 
@@ -767,137 +722,13 @@ class NotificationManager(AppService):
                             i += len(chunk)
                             chunk_sent = True
                             break
-                        else:
-                            # Message is too large even after size reduction
-                            if attempt_size == 1:
-                                logger.error(
-                                    f"Failed to send notification at index {i}: "
-                                    f"Single notification exceeds email size limit "
-                                    f"({len(test_message):,} chars > {MAX_EMAIL_SIZE:,} chars). "
-                                    f"Removing permanently from batch - will not retry."
-                                )
-
-                                # Remove the oversized notification permanently - it will NEVER fit
-                                if chunk_ids:
-                                    try:
-                                        await get_database_manager_async_client(
-                                            should_retry=False
-                                        ).remove_notifications_from_batch(
-                                            event.user_id, event.type, chunk_ids
-                                        )
-                                        logger.info(
-                                            f"Removed oversized notification {chunk_ids[0]} from batch permanently"
-                                        )
-                                    except Exception as e:
-                                        logger.error(
-                                            f"Failed to remove oversized notification: {e}"
-                                        )
-
-                                failed_indices.append(i)
-                                i += 1
-                                chunk_sent = True
-                                break
-                            # Try smaller chunk size
-                            continue
                     except Exception as e:
-                        # Check if it's a Postmark API error
                         if attempt_size == 1:
-                            # Single notification failed - determine the actual cause
-                            error_message = str(e).lower()
-                            error_type = type(e).__name__
-
-                            # Check for HTTP 406 - Inactive recipient (common in Postmark errors)
-                            if "406" in error_message or "inactive" in error_message:
-                                logger.warning(
-                                    f"Failed to send notification at index {i}: "
-                                    f"Recipient marked as inactive by Postmark. "
-                                    f"Error: {e}. Disabling ALL notifications for this user."
-                                )
-
-                                # 1. Mark email as unverified
-                                try:
-                                    await set_user_email_verification(
-                                        event.user_id, False
-                                    )
-                                    logger.info(
-                                        f"Set email verification to false for user {event.user_id}"
-                                    )
-                                except Exception as deactivation_error:
-                                    logger.error(
-                                        f"Failed to deactivate email for user {event.user_id}: "
-                                        f"{deactivation_error}"
-                                    )
-
-                                # 2. Disable all notification preferences
-                                try:
-                                    await disable_all_user_notifications(event.user_id)
-                                    logger.info(
-                                        f"Disabled all notification preferences for user {event.user_id}"
-                                    )
-                                except Exception as disable_error:
-                                    logger.error(
-                                        f"Failed to disable notification preferences: {disable_error}"
-                                    )
-
-                                # 3. Clear ALL notification batches for this user
-                                try:
-                                    await get_database_manager_async_client(
-                                        should_retry=False
-                                    ).clear_all_user_notification_batches(event.user_id)
-                                    logger.info(
-                                        f"Cleared ALL notification batches for user {event.user_id}"
-                                    )
-                                except Exception as remove_error:
-                                    logger.error(
-                                        f"Failed to clear batches for inactive recipient: {remove_error}"
-                                    )
-
-                                # Stop processing - we've nuked everything for this user
-                                return True
-                            # Check for HTTP 422 - Malformed data
-                            elif (
-                                "422" in error_message
-                                or "unprocessable" in error_message
-                            ):
-                                logger.error(
-                                    f"Failed to send notification at index {i}: "
-                                    f"Malformed notification data rejected by Postmark. "
-                                    f"Error: {e}. Removing from batch permanently."
-                                )
-
-                                # Remove from batch - 422 means bad data that won't fix itself
-                                if chunk_ids:
-                                    try:
-                                        await get_database_manager_async_client(
-                                            should_retry=False
-                                        ).remove_notifications_from_batch(
-                                            event.user_id, event.type, chunk_ids
-                                        )
-                                        logger.info(
-                                            "Removed malformed notification from batch permanently"
-                                        )
-                                    except Exception as remove_error:
-                                        logger.error(
-                                            f"Failed to remove malformed notification: {remove_error}"
-                                        )
-                            # Check if it's a ValueError for size limit
-                            elif (
-                                isinstance(e, ValueError)
-                                and "too large" in error_message
-                            ):
-                                logger.error(
-                                    f"Failed to send notification at index {i}: "
-                                    f"Notification size exceeds email limit. "
-                                    f"Error: {e}. Skipping this notification."
-                                )
-                            # Other API errors
-                            else:
-                                logger.error(
-                                    f"Failed to send notification at index {i}: "
-                                    f"Email API error ({error_type}): {e}. "
-                                    f"Skipping this notification."
-                                )
-
+                            # Even single notification is too large
+                            logger.error(
+                                f"Single notification too large to send: {e}. "
+                                f"Skipping notification at index {i}"
+                            )
                             failed_indices.append(i)
                             i += 1
                             chunk_sent = True
@@ -911,20 +742,18 @@ class NotificationManager(AppService):
                     failed_indices.append(i)
                     i += 1
 
-            # Check what remains in the batch (notifications are removed as sent)
-            remaining_batch = await get_database_manager_async_client(
-                should_retry=False
-            ).get_user_notification_batch(event.user_id, event.type)
-
-            if not remaining_batch or not remaining_batch.notifications:
+            # Only empty the batch if ALL notifications were sent successfully
+            if successfully_sent_count == len(batch_messages):
                 logger.info(
-                    f"All {successfully_sent_count} notifications sent and removed from batch"
+                    f"Successfully sent all {successfully_sent_count} notifications, clearing batch"
+                )
+                await get_database_manager_async_client().empty_user_notification_batch(
+                    event.user_id, event.type
                 )
             else:
-                remaining_count = len(remaining_batch.notifications)
                 logger.warning(
-                    f"Sent {successfully_sent_count} notifications. "
-                    f"{remaining_count} remain in batch for retry due to errors."
+                    f"Only sent {successfully_sent_count} of {len(batch_messages)} notifications. "
+                    f"Failed indices: {failed_indices}. Batch will be retained for retry."
                 )
             return True
         except Exception as e:
@@ -942,9 +771,11 @@ class NotificationManager(AppService):
 
             logger.info(f"Processing summary notification: {model}")
 
-            recipient_email = await get_database_manager_async_client(
-                should_retry=False
-            ).get_user_email_by_id(event.user_id)
+            recipient_email = (
+                await get_database_manager_async_client().get_user_email_by_id(
+                    event.user_id
+                )
+            )
             if not recipient_email:
                 logger.error(f"User email not found for user {event.user_id}")
                 return False

autogpt_platform/backend/backend/notifications/test_notifications.py

@@ -1,598 +0,0 @@
-"""Tests for notification error handling in NotificationManager."""
-
-from datetime import datetime, timezone
-from unittest.mock import AsyncMock, MagicMock, Mock, patch
-
-import pytest
-from prisma.enums import NotificationType
-
-from backend.data.notifications import AgentRunData, NotificationEventModel
-from backend.notifications.notifications import NotificationManager
-
-
-class TestNotificationErrorHandling:
-    """Test cases for notification error handling in NotificationManager."""
-
-    @pytest.fixture
-    def notification_manager(self):
-        """Create a NotificationManager instance for testing."""
-        with patch("backend.notifications.notifications.AppService.__init__"):
-            manager = NotificationManager()
-            manager.email_sender = MagicMock()
-            # Mock the _get_template method used by _process_batch
-            template_mock = Mock()
-            template_mock.base_template = "base"
-            template_mock.subject_template = "subject"
-            template_mock.body_template = "body"
-            manager.email_sender._get_template = Mock(return_value=template_mock)
-            # Mock the formatter
-            manager.email_sender.formatter = Mock()
-            manager.email_sender.formatter.format_email = Mock(
-                return_value=("subject", "body content")
-            )
-            manager.email_sender.formatter.env = Mock()
-            manager.email_sender.formatter.env.globals = {
-                "base_url": "http://example.com"
-            }
-            return manager
-
-    @pytest.fixture
-    def sample_batch_event(self):
-        """Create a sample batch event for testing."""
-        return NotificationEventModel(
-            type=NotificationType.AGENT_RUN,
-            user_id="user_1",
-            created_at=datetime.now(timezone.utc),
-            data=AgentRunData(
-                agent_name="Test Agent",
-                credits_used=10.0,
-                execution_time=5.0,
-                node_count=3,
-                graph_id="graph_1",
-                outputs=[],
-            ),
-        )
-
-    @pytest.fixture
-    def sample_batch_notifications(self):
-        """Create sample batch notifications for testing."""
-        notifications = []
-        for i in range(3):
-            notification = Mock()
-            notification.type = NotificationType.AGENT_RUN
-            notification.data = {
-                "agent_name": f"Test Agent {i}",
-                "credits_used": 10.0 * (i + 1),
-                "execution_time": 5.0 * (i + 1),
-                "node_count": 3 + i,
-                "graph_id": f"graph_{i}",
-                "outputs": [],
-            }
-            notification.created_at = datetime.now(timezone.utc)
-            notifications.append(notification)
-        return notifications
-
-    @pytest.mark.asyncio
-    async def test_406_stops_all_processing_for_user(
-        self, notification_manager, sample_batch_event
-    ):
-        """Test that 406 inactive recipient error stops ALL processing for that user."""
-        with patch("backend.notifications.notifications.logger"), patch(
-            "backend.notifications.notifications.set_user_email_verification",
-            new_callable=AsyncMock,
-        ) as mock_set_verification, patch(
-            "backend.notifications.notifications.disable_all_user_notifications",
-            new_callable=AsyncMock,
-        ) as mock_disable_all, patch(
-            "backend.notifications.notifications.get_database_manager_async_client"
-        ) as mock_db_client, patch(
-            "backend.notifications.notifications.generate_unsubscribe_link"
-        ) as mock_unsub_link:
-
-            # Create batch of 5 notifications
-            notifications = []
-            for i in range(5):
-                notification = Mock()
-                notification.id = f"notif_{i}"
-                notification.type = NotificationType.AGENT_RUN
-                notification.data = {
-                    "agent_name": f"Test Agent {i}",
-                    "credits_used": 10.0 * (i + 1),
-                    "execution_time": 5.0 * (i + 1),
-                    "node_count": 3 + i,
-                    "graph_id": f"graph_{i}",
-                    "outputs": [],
-                }
-                notification.created_at = datetime.now(timezone.utc)
-                notifications.append(notification)
-
-            # Setup mocks
-            mock_db = mock_db_client.return_value
-            mock_db.get_user_email_by_id = AsyncMock(return_value="test@example.com")
-            mock_db.get_user_notification_batch = AsyncMock(
-                return_value=Mock(notifications=notifications)
-            )
-            mock_db.clear_all_user_notification_batches = AsyncMock()
-            mock_db.remove_notifications_from_batch = AsyncMock()
-            mock_unsub_link.return_value = "http://example.com/unsub"
-
-            # Mock internal methods
-            notification_manager._should_email_user_based_on_preference = AsyncMock(
-                return_value=True
-            )
-            notification_manager._should_batch = AsyncMock(return_value=True)
-            notification_manager._parse_message = Mock(return_value=sample_batch_event)
-
-            # Track calls
-            call_count = [0]
-
-            def send_side_effect(*args, **kwargs):
-                data = kwargs.get("data", [])
-                if isinstance(data, list) and len(data) == 1:
-                    current_call = call_count[0]
-                    call_count[0] += 1
-
-                    # First two succeed, third hits 406
-                    if current_call < 2:
-                        return None
-                    else:
-                        raise Exception("Recipient marked as inactive (406)")
-                # Force single processing
-                raise Exception("Force single processing")
-
-            notification_manager.email_sender.send_templated.side_effect = (
-                send_side_effect
-            )
-
-            # Act
-            result = await notification_manager._process_batch(
-                sample_batch_event.model_dump_json()
-            )
-
-            # Assert
-            assert result is True
-
-            # Only 3 calls should have been made (2 successful, 1 failed with 406)
-            assert call_count[0] == 3
-
-            # User should be deactivated
-            mock_set_verification.assert_called_once_with("user_1", False)
-            mock_disable_all.assert_called_once_with("user_1")
-            mock_db.clear_all_user_notification_batches.assert_called_once_with(
-                "user_1"
-            )
-
-            # No further processing should occur after 406
-
-    @pytest.mark.asyncio
-    async def test_422_permanently_removes_malformed_notification(
-        self, notification_manager, sample_batch_event
-    ):
-        """Test that 422 error permanently removes the malformed notification from batch and continues with others."""
-        with patch("backend.notifications.notifications.logger") as mock_logger, patch(
-            "backend.notifications.notifications.get_database_manager_async_client"
-        ) as mock_db_client, patch(
-            "backend.notifications.notifications.generate_unsubscribe_link"
-        ) as mock_unsub_link:
-
-            # Create batch of 5 notifications
-            notifications = []
-            for i in range(5):
-                notification = Mock()
-                notification.id = f"notif_{i}"
-                notification.type = NotificationType.AGENT_RUN
-                notification.data = {
-                    "agent_name": f"Test Agent {i}",
-                    "credits_used": 10.0 * (i + 1),
-                    "execution_time": 5.0 * (i + 1),
-                    "node_count": 3 + i,
-                    "graph_id": f"graph_{i}",
-                    "outputs": [],
-                }
-                notification.created_at = datetime.now(timezone.utc)
-                notifications.append(notification)
-
-            # Setup mocks
-            mock_db = mock_db_client.return_value
-            mock_db.get_user_email_by_id = AsyncMock(return_value="test@example.com")
-            mock_db.get_user_notification_batch = AsyncMock(
-                side_effect=[
-                    Mock(notifications=notifications),
-                    Mock(notifications=[]),  # Empty after processing
-                ]
-            )
-            mock_db.remove_notifications_from_batch = AsyncMock()
-            mock_unsub_link.return_value = "http://example.com/unsub"
-
-            # Mock internal methods
-            notification_manager._should_email_user_based_on_preference = AsyncMock(
-                return_value=True
-            )
-            notification_manager._should_batch = AsyncMock(return_value=True)
-            notification_manager._parse_message = Mock(return_value=sample_batch_event)
-
-            # Track calls
-            call_count = [0]
-            successful_indices = []
-            removed_notification_ids = []
-
-            # Capture what gets removed
-            def remove_side_effect(user_id, notif_type, notif_ids):
-                removed_notification_ids.extend(notif_ids)
-                return None
-
-            mock_db.remove_notifications_from_batch.side_effect = remove_side_effect
-
-            def send_side_effect(*args, **kwargs):
-                data = kwargs.get("data", [])
-                if isinstance(data, list) and len(data) == 1:
-                    current_call = call_count[0]
-                    call_count[0] += 1
-
-                    # Index 2 has malformed data (422)
-                    if current_call == 2:
-                        raise Exception(
-                            "Unprocessable entity (422): Malformed email data"
-                        )
-                    else:
-                        successful_indices.append(current_call)
-                        return None
-                # Force single processing
-                raise Exception("Force single processing")
-
-            notification_manager.email_sender.send_templated.side_effect = (
-                send_side_effect
-            )
-
-            # Act
-            result = await notification_manager._process_batch(
-                sample_batch_event.model_dump_json()
-            )
-
-            # Assert
-            assert result is True
-            assert call_count[0] == 5  # All 5 attempted
-            assert len(successful_indices) == 4  # 4 succeeded (all except index 2)
-            assert 2 not in successful_indices  # Index 2 failed
-
-            # Verify 422 error was logged
-            error_calls = [call[0][0] for call in mock_logger.error.call_args_list]
-            assert any(
-                "422" in call or "malformed" in call.lower() for call in error_calls
-            )
-
-            # Verify all notifications were removed (4 successful + 1 malformed)
-            assert mock_db.remove_notifications_from_batch.call_count == 5
-            assert (
-                "notif_2" in removed_notification_ids
-            )  # Malformed one was removed permanently
-
-    @pytest.mark.asyncio
-    async def test_oversized_notification_permanently_removed(
-        self, notification_manager, sample_batch_event
-    ):
-        """Test that oversized notifications are permanently removed from batch but others continue."""
-        with patch("backend.notifications.notifications.logger") as mock_logger, patch(
-            "backend.notifications.notifications.get_database_manager_async_client"
-        ) as mock_db_client, patch(
-            "backend.notifications.notifications.generate_unsubscribe_link"
-        ) as mock_unsub_link:
-
-            # Create batch of 5 notifications
-            notifications = []
-            for i in range(5):
-                notification = Mock()
-                notification.id = f"notif_{i}"
-                notification.type = NotificationType.AGENT_RUN
-                notification.data = {
-                    "agent_name": f"Test Agent {i}",
-                    "credits_used": 10.0 * (i + 1),
-                    "execution_time": 5.0 * (i + 1),
-                    "node_count": 3 + i,
-                    "graph_id": f"graph_{i}",
-                    "outputs": [],
-                }
-                notification.created_at = datetime.now(timezone.utc)
-                notifications.append(notification)
-
-            # Setup mocks
-            mock_db = mock_db_client.return_value
-            mock_db.get_user_email_by_id = AsyncMock(return_value="test@example.com")
-            mock_db.get_user_notification_batch = AsyncMock(
-                side_effect=[
-                    Mock(notifications=notifications),
-                    Mock(notifications=[]),  # Empty after processing
-                ]
-            )
-            mock_db.remove_notifications_from_batch = AsyncMock()
-            mock_unsub_link.return_value = "http://example.com/unsub"
-
-            # Mock internal methods
-            notification_manager._should_email_user_based_on_preference = AsyncMock(
-                return_value=True
-            )
-            notification_manager._should_batch = AsyncMock(return_value=True)
-            notification_manager._parse_message = Mock(return_value=sample_batch_event)
-
-            # Override formatter to simulate oversized on index 3
-            # original_format = notification_manager.email_sender.formatter.format_email
-
-            def format_side_effect(*args, **kwargs):
-                # Check if we're formatting index 3
-                data = kwargs.get("data", {}).get("notifications", [])
-                if data and len(data) == 1:
-                    # Check notification content to identify index 3
-                    if any(
-                        "Test Agent 3" in str(n.data)
-                        for n in data
-                        if hasattr(n, "data")
-                    ):
-                        # Return oversized message for index 3
-                        return ("subject", "x" * 5_000_000)  # Over 4.5MB limit
-                return ("subject", "normal sized content")
-
-            notification_manager.email_sender.formatter.format_email = Mock(
-                side_effect=format_side_effect
-            )
-
-            # Track calls
-            successful_indices = []
-
-            def send_side_effect(*args, **kwargs):
-                data = kwargs.get("data", [])
-                if isinstance(data, list) and len(data) == 1:
-                    # Track which notification was sent based on content
-                    for i, notif in enumerate(notifications):
-                        if any(
-                            f"Test Agent {i}" in str(n.data)
-                            for n in data
-                            if hasattr(n, "data")
-                        ):
-                            successful_indices.append(i)
-                            return None
-                    return None
-                # Force single processing
-                raise Exception("Force single processing")
-
-            notification_manager.email_sender.send_templated.side_effect = (
-                send_side_effect
-            )
-
-            # Act
-            result = await notification_manager._process_batch(
-                sample_batch_event.model_dump_json()
-            )
-
-            # Assert
-            assert result is True
-            assert (
-                len(successful_indices) == 4
-            )  # Only 4 sent (index 3 skipped due to size)
-            assert 3 not in successful_indices  # Index 3 was not sent
-
-            # Verify oversized error was logged
-            error_calls = [call[0][0] for call in mock_logger.error.call_args_list]
-            assert any(
-                "exceeds email size limit" in call or "oversized" in call.lower()
-                for call in error_calls
-            )
-
-    @pytest.mark.asyncio
-    async def test_generic_api_error_keeps_notification_for_retry(
-        self, notification_manager, sample_batch_event
-    ):
-        """Test that generic API errors keep notifications in batch for retry while others continue."""
-        with patch("backend.notifications.notifications.logger") as mock_logger, patch(
-            "backend.notifications.notifications.get_database_manager_async_client"
-        ) as mock_db_client, patch(
-            "backend.notifications.notifications.generate_unsubscribe_link"
-        ) as mock_unsub_link:
-
-            # Create batch of 5 notifications
-            notifications = []
-            for i in range(5):
-                notification = Mock()
-                notification.id = f"notif_{i}"
-                notification.type = NotificationType.AGENT_RUN
-                notification.data = {
-                    "agent_name": f"Test Agent {i}",
-                    "credits_used": 10.0 * (i + 1),
-                    "execution_time": 5.0 * (i + 1),
-                    "node_count": 3 + i,
-                    "graph_id": f"graph_{i}",
-                    "outputs": [],
-                }
-                notification.created_at = datetime.now(timezone.utc)
-                notifications.append(notification)
-
-            # Notification that failed with generic error
-            failed_notifications = [notifications[1]]  # Only index 1 remains for retry
-
-            # Setup mocks
-            mock_db = mock_db_client.return_value
-            mock_db.get_user_email_by_id = AsyncMock(return_value="test@example.com")
-            mock_db.get_user_notification_batch = AsyncMock(
-                side_effect=[
-                    Mock(notifications=notifications),
-                    Mock(
-                        notifications=failed_notifications
-                    ),  # Failed ones remain for retry
-                ]
-            )
-            mock_db.remove_notifications_from_batch = AsyncMock()
-            mock_unsub_link.return_value = "http://example.com/unsub"
-
-            # Mock internal methods
-            notification_manager._should_email_user_based_on_preference = AsyncMock(
-                return_value=True
-            )
-            notification_manager._should_batch = AsyncMock(return_value=True)
-            notification_manager._parse_message = Mock(return_value=sample_batch_event)
-
-            # Track calls
-            successful_indices = []
-            failed_indices = []
-            removed_notification_ids = []
-
-            # Capture what gets removed
-            def remove_side_effect(user_id, notif_type, notif_ids):
-                removed_notification_ids.extend(notif_ids)
-                return None
-
-            mock_db.remove_notifications_from_batch.side_effect = remove_side_effect
-
-            def send_side_effect(*args, **kwargs):
-                data = kwargs.get("data", [])
-                if isinstance(data, list) and len(data) == 1:
-                    # Track which notification based on content
-                    for i, notif in enumerate(notifications):
-                        if any(
-                            f"Test Agent {i}" in str(n.data)
-                            for n in data
-                            if hasattr(n, "data")
-                        ):
-                            # Index 1 has generic API error
-                            if i == 1:
-                                failed_indices.append(i)
-                                raise Exception("Network timeout - temporary failure")
-                            else:
-                                successful_indices.append(i)
-                                return None
-                    return None
-                # Force single processing
-                raise Exception("Force single processing")
-
-            notification_manager.email_sender.send_templated.side_effect = (
-                send_side_effect
-            )
-
-            # Act
-            result = await notification_manager._process_batch(
-                sample_batch_event.model_dump_json()
-            )
-
-            # Assert
-            assert result is True
-            assert len(successful_indices) == 4  # 4 succeeded (0, 2, 3, 4)
-            assert len(failed_indices) == 1  # 1 failed
-            assert 1 in failed_indices  # Index 1 failed
-
-            # Verify generic error was logged
-            error_calls = [call[0][0] for call in mock_logger.error.call_args_list]
-            assert any(
-                "api error" in call.lower() or "skipping" in call.lower()
-                for call in error_calls
-            )
-
-            # Only successful ones should be removed from batch (failed one stays for retry)
-            assert mock_db.remove_notifications_from_batch.call_count == 4
-            assert (
-                "notif_1" not in removed_notification_ids
-            )  # Failed one NOT removed (stays for retry)
-            assert "notif_0" in removed_notification_ids  # Successful one removed
-            assert "notif_2" in removed_notification_ids  # Successful one removed
-            assert "notif_3" in removed_notification_ids  # Successful one removed
-            assert "notif_4" in removed_notification_ids  # Successful one removed
-
-    @pytest.mark.asyncio
-    async def test_batch_all_notifications_sent_successfully(
-        self, notification_manager, sample_batch_event
-    ):
-        """Test successful batch processing where all notifications are sent without errors."""
-        with patch("backend.notifications.notifications.logger") as mock_logger, patch(
-            "backend.notifications.notifications.get_database_manager_async_client"
-        ) as mock_db_client, patch(
-            "backend.notifications.notifications.generate_unsubscribe_link"
-        ) as mock_unsub_link:
-
-            # Create batch of 5 notifications
-            notifications = []
-            for i in range(5):
-                notification = Mock()
-                notification.id = f"notif_{i}"
-                notification.type = NotificationType.AGENT_RUN
-                notification.data = {
-                    "agent_name": f"Test Agent {i}",
-                    "credits_used": 10.0 * (i + 1),
-                    "execution_time": 5.0 * (i + 1),
-                    "node_count": 3 + i,
-                    "graph_id": f"graph_{i}",
-                    "outputs": [],
-                }
-                notification.created_at = datetime.now(timezone.utc)
-                notifications.append(notification)
-
-            # Setup mocks
-            mock_db = mock_db_client.return_value
-            mock_db.get_user_email_by_id = AsyncMock(return_value="test@example.com")
-            mock_db.get_user_notification_batch = AsyncMock(
-                side_effect=[
-                    Mock(notifications=notifications),
-                    Mock(notifications=[]),  # Empty after all sent successfully
-                ]
-            )
-            mock_db.remove_notifications_from_batch = AsyncMock()
-            mock_unsub_link.return_value = "http://example.com/unsub"
-
-            # Mock internal methods
-            notification_manager._should_email_user_based_on_preference = AsyncMock(
-                return_value=True
-            )
-            notification_manager._should_batch = AsyncMock(return_value=True)
-            notification_manager._parse_message = Mock(return_value=sample_batch_event)
-
-            # Track successful sends
-            successful_indices = []
-            removed_notification_ids = []
-
-            # Capture what gets removed
-            def remove_side_effect(user_id, notif_type, notif_ids):
-                removed_notification_ids.extend(notif_ids)
-                return None
-
-            mock_db.remove_notifications_from_batch.side_effect = remove_side_effect
-
-            def send_side_effect(*args, **kwargs):
-                data = kwargs.get("data", [])
-                if isinstance(data, list) and len(data) == 1:
-                    # Track which notification was sent
-                    for i, notif in enumerate(notifications):
-                        if any(
-                            f"Test Agent {i}" in str(n.data)
-                            for n in data
-                            if hasattr(n, "data")
-                        ):
-                            successful_indices.append(i)
-                            return None
-                    return None  # Success
-                # Force single processing
-                raise Exception("Force single processing")
-
-            notification_manager.email_sender.send_templated.side_effect = (
-                send_side_effect
-            )
-
-            # Act
-            result = await notification_manager._process_batch(
-                sample_batch_event.model_dump_json()
-            )
-
-            # Assert
-            assert result is True
-
-            # All 5 notifications should be sent successfully
-            assert len(successful_indices) == 5
-            assert successful_indices == [0, 1, 2, 3, 4]
-
-            # All notifications should be removed from batch
-            assert mock_db.remove_notifications_from_batch.call_count == 5
-            assert len(removed_notification_ids) == 5
-            for i in range(5):
-                assert f"notif_{i}" in removed_notification_ids
-
-            # No errors should be logged
-            assert mock_logger.error.call_count == 0
-
-            # Info message about successful sends should be logged
-            info_calls = [call[0][0] for call in mock_logger.info.call_args_list]
-            assert any("sent and removed" in call.lower() for call in info_calls)

autogpt_platform/backend/backend/sdk/registry.py

@@ -6,10 +6,10 @@ import logging
 import threading
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type
 
-from pydantic import BaseModel
+from pydantic import BaseModel, SecretStr
 
 from backend.blocks.basic import Block
-from backend.data.model import Credentials
+from backend.data.model import APIKeyCredentials, Credentials
 from backend.integrations.oauth.base import BaseOAuthHandler
 from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks._base import BaseWebhooksManager
@@ -17,8 +17,6 @@ from backend.integrations.webhooks._base import BaseWebhooksManager
 if TYPE_CHECKING:
     from backend.sdk.provider import Provider
 
-logger = logging.getLogger(__name__)
-
 
 class SDKOAuthCredentials(BaseModel):
     """OAuth credentials configuration for SDK providers."""
@@ -104,8 +102,21 @@ class AutoRegistry:
         """Register an environment variable as an API key for a provider."""
         with cls._lock:
             cls._api_key_mappings[provider] = env_var_name
-            # Note: The credential itself is created by ProviderBuilder.with_api_key()
-            # We only store the mapping here to avoid duplication
+
+            # Dynamically check if the env var exists and create credential
+            import os
+
+            api_key = os.getenv(env_var_name)
+            if api_key:
+                credential = APIKeyCredentials(
+                    id=f"{provider}-default",
+                    provider=provider,
+                    api_key=SecretStr(api_key),
+                    title=f"Default {provider} credentials",
+                )
+                # Check if credential already exists to avoid duplicates
+                if not any(c.id == credential.id for c in cls._default_credentials):
+                    cls._default_credentials.append(credential)
 
     @classmethod
     def get_all_credentials(cls) -> List[Credentials]:
@@ -199,43 +210,3 @@ class AutoRegistry:
                 webhooks.load_webhook_managers = patched_load
         except Exception as e:
             logging.warning(f"Failed to patch webhook managers: {e}")
-
-        # Patch credentials store to include SDK-registered credentials
-        try:
-            import sys
-            from typing import Any
-
-            # Get the module from sys.modules to respect mocking
-            if "backend.integrations.credentials_store" in sys.modules:
-                creds_store: Any = sys.modules["backend.integrations.credentials_store"]
-            else:
-                import backend.integrations.credentials_store
-
-                creds_store: Any = backend.integrations.credentials_store
-
-            if hasattr(creds_store, "IntegrationCredentialsStore"):
-                store_class = creds_store.IntegrationCredentialsStore
-                if hasattr(store_class, "get_all_creds"):
-                    original_get_all_creds = store_class.get_all_creds
-
-                    async def patched_get_all_creds(self, user_id: str):
-                        # Get original credentials
-                        original_creds = await original_get_all_creds(self, user_id)
-
-                        # Add SDK-registered credentials
-                        sdk_creds = cls.get_all_credentials()
-
-                        # Combine credentials, avoiding duplicates by ID
-                        existing_ids = {c.id for c in original_creds}
-                        for cred in sdk_creds:
-                            if cred.id not in existing_ids:
-                                original_creds.append(cred)
-
-                        return original_creds
-
-                    store_class.get_all_creds = patched_get_all_creds
-                    logger.info(
-                        "Successfully patched IntegrationCredentialsStore.get_all_creds"
-                    )
-        except Exception as e:
-            logging.warning(f"Failed to patch credentials store: {e}")

autogpt_platform/backend/backend/server/external/api.py

@@ -1,6 +1,5 @@
 from fastapi import FastAPI
 
-from backend.monitoring.instrumentation import instrument_fastapi
 from backend.server.middleware.security import SecurityHeadersMiddleware
 
 from .routes.v1 import v1_router
@@ -14,12 +13,3 @@ external_app = FastAPI(
 
 external_app.add_middleware(SecurityHeadersMiddleware)
 external_app.include_router(v1_router, prefix="/v1")
-
-# Add Prometheus instrumentation
-instrument_fastapi(
-    external_app,
-    service_name="external-api",
-    expose_endpoint=True,
-    endpoint="/metrics",
-    include_in_schema=True,
-)

autogpt_platform/backend/backend/server/external/routes/v1.py

@@ -49,7 +49,7 @@ class GraphExecutionResult(TypedDict):
     tags=["blocks"],
     dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
 )
-async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
+def get_graph_blocks() -> Sequence[dict[Any, Any]]:
     blocks = [block() for block in backend.data.block.get_blocks().values()]
     return [b.to_dict() for b in blocks if not b.disabled]
 

autogpt_platform/backend/backend/server/integrations/router.py

@@ -32,7 +32,6 @@ from backend.data.model import (
     OAuth2Credentials,
     UserIntegrations,
 )
-from backend.data.onboarding import complete_webhook_trigger_step
 from backend.data.user import get_user_integrations
 from backend.executor.utils import add_graph_execution
 from backend.integrations.ayrshare import AyrshareClient, SocialPlatform
@@ -64,7 +63,7 @@ class LoginResponse(BaseModel):
     state_token: str
 
 
-@router.get("/{provider}/login", summary="Initiate OAuth flow")
+@router.get("/{provider}/login")
 async def login(
     provider: Annotated[
         ProviderName, Path(title="The provider to initiate an OAuth flow for")
@@ -102,7 +101,7 @@ class CredentialsMetaResponse(BaseModel):
     )
 
 
-@router.post("/{provider}/callback", summary="Exchange OAuth code for tokens")
+@router.post("/{provider}/callback")
 async def callback(
     provider: Annotated[
         ProviderName, Path(title="The target provider for this OAuth exchange")
@@ -180,7 +179,7 @@ async def callback(
     )
 
 
-@router.get("/credentials", summary="List Credentials")
+@router.get("/credentials")
 async def list_credentials(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> list[CredentialsMetaResponse]:
@@ -221,9 +220,7 @@ async def list_credentials_by_provider(
     ]
 
 
-@router.get(
-    "/{provider}/credentials/{cred_id}", summary="Get Specific Credential By ID"
-)
+@router.get("/{provider}/credentials/{cred_id}")
 async def get_credential(
     provider: Annotated[
         ProviderName, Path(title="The provider to retrieve credentials for")
@@ -244,7 +241,7 @@ async def get_credential(
     return credential
 
 
-@router.post("/{provider}/credentials", status_code=201, summary="Create Credentials")
+@router.post("/{provider}/credentials", status_code=201)
 async def create_credentials(
     user_id: Annotated[str, Security(get_user_id)],
     provider: Annotated[
@@ -370,8 +367,6 @@ async def webhook_ingress_generic(
         return
 
     executions: list[Awaitable] = []
-    await complete_webhook_trigger_step(user_id)
-
     for node in webhook.triggered_nodes:
         logger.debug(f"Webhook-attached node: {node}")
         if not node.is_triggered_by_event_type(event_type):

autogpt_platform/backend/backend/server/middleware/security.py

@@ -1,10 +1,12 @@
 import re
 from typing import Set
 
-from starlette.types import ASGIApp, Message, Receive, Scope, Send
+from fastapi import Request, Response
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.types import ASGIApp
 
 
-class SecurityHeadersMiddleware:
+class SecurityHeadersMiddleware(BaseHTTPMiddleware):
     """
     Middleware to add security headers to responses, with cache control
     disabled by default for all endpoints except those explicitly allowed.
@@ -23,8 +25,6 @@ class SecurityHeadersMiddleware:
         "/api/health",
         "/api/v1/health",
         "/api/status",
-        "/api/blocks",
-        "/api/v1/blocks",
         # Public store/marketplace pages (read-only)
         "/api/store/agents",
         "/api/v1/store/agents",
@@ -49,7 +49,7 @@ class SecurityHeadersMiddleware:
     }
 
     def __init__(self, app: ASGIApp):
-        self.app = app
+        super().__init__(app)
         # Compile regex patterns for wildcard matching
         self.cacheable_patterns = [
             re.compile(pattern.replace("*", "[^/]+"))
@@ -72,42 +72,22 @@ class SecurityHeadersMiddleware:
 
         return False
 
-    async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
-        """Pure ASGI middleware implementation for better performance than BaseHTTPMiddleware."""
-        if scope["type"] != "http":
-            await self.app(scope, receive, send)
-            return
+    async def dispatch(self, request: Request, call_next):
+        response: Response = await call_next(request)
 
-        # Extract path from scope
-        path = scope["path"]
+        # Add general security headers
+        response.headers["X-Content-Type-Options"] = "nosniff"
+        response.headers["X-Frame-Options"] = "DENY"
+        response.headers["X-XSS-Protection"] = "1; mode=block"
+        response.headers["Referrer-Policy"] = "strict-origin-when-cross-origin"
 
-        async def send_wrapper(message: Message) -> None:
-            if message["type"] == "http.response.start":
-                # Add security headers to the response
-                headers = dict(message.get("headers", []))
+        # Default: Disable caching for all endpoints
+        # Only allow caching for explicitly permitted paths
+        if not self.is_cacheable_path(request.url.path):
+            response.headers["Cache-Control"] = (
+                "no-store, no-cache, must-revalidate, private"
+            )
+            response.headers["Pragma"] = "no-cache"
+            response.headers["Expires"] = "0"
 
-                # Add general security headers (HTTP spec requires proper capitalization)
-                headers[b"X-Content-Type-Options"] = b"nosniff"
-                headers[b"X-Frame-Options"] = b"DENY"
-                headers[b"X-XSS-Protection"] = b"1; mode=block"
-                headers[b"Referrer-Policy"] = b"strict-origin-when-cross-origin"
-
-                # Add noindex header for shared execution pages
-                if "/public/shared" in path:
-                    headers[b"X-Robots-Tag"] = b"noindex, nofollow"
-
-                # Default: Disable caching for all endpoints
-                # Only allow caching for explicitly permitted paths
-                if not self.is_cacheable_path(path):
-                    headers[b"Cache-Control"] = (
-                        b"no-store, no-cache, must-revalidate, private"
-                    )
-                    headers[b"Pragma"] = b"no-cache"
-                    headers[b"Expires"] = b"0"
-
-                # Convert headers back to list format
-                message["headers"] = list(headers.items())
-
-            await send(message)
-
-        await self.app(scope, receive, send_wrapper)
+        return response

autogpt_platform/backend/backend/server/rest_api.py

@@ -1,6 +1,5 @@
 import contextlib
 import logging
-import platform
 from enum import Enum
 from typing import Any, Optional
 
@@ -12,7 +11,6 @@ import uvicorn
 from autogpt_libs.auth import add_auth_responses_to_openapi
 from autogpt_libs.auth import verify_settings as verify_auth_settings
 from fastapi.exceptions import RequestValidationError
-from fastapi.middleware.gzip import GZipMiddleware
 from fastapi.routing import APIRoute
 from prisma.errors import PrismaError
 
@@ -20,7 +18,6 @@ import backend.data.block
 import backend.data.db
 import backend.data.graph
 import backend.data.user
-import backend.integrations.webhooks.utils
 import backend.server.routers.postmark.postmark
 import backend.server.routers.v1
 import backend.server.v2.admin.credit_admin_routes
@@ -39,7 +36,6 @@ import backend.util.settings
 from backend.blocks.llm import LlmModel
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
-from backend.monitoring.instrumentation import instrument_fastapi
 from backend.server.external.api import external_app
 from backend.server.middleware.security import SecurityHeadersMiddleware
 from backend.util import json
@@ -72,26 +68,6 @@ async def lifespan_context(app: fastapi.FastAPI):
 
     await backend.data.db.connect()
 
-    # Configure thread pool for FastAPI sync operation performance
-    # CRITICAL: FastAPI automatically runs ALL sync functions in this thread pool:
-    # - Any endpoint defined with 'def' (not async def)
-    # - Any dependency function defined with 'def' (not async def)
-    # - Manual run_in_threadpool() calls (like JWT decoding)
-    # Default pool size is only 40 threads, causing bottlenecks under high concurrency
-    config = backend.util.settings.Config()
-    try:
-        import anyio.to_thread
-
-        anyio.to_thread.current_default_thread_limiter().total_tokens = (
-            config.fastapi_thread_pool_size
-        )
-        logger.info(
-            f"Thread pool size set to {config.fastapi_thread_pool_size} for sync endpoint/dependency performance"
-        )
-    except (ImportError, AttributeError) as e:
-        logger.warning(f"Could not configure thread pool size: {e}")
-        # Continue without thread pool configuration
-
     # Ensure SDK auto-registration is patched before initializing blocks
     from backend.sdk.registry import AutoRegistry
 
@@ -102,8 +78,6 @@ async def lifespan_context(app: fastapi.FastAPI):
     await backend.data.user.migrate_and_encrypt_user_integrations()
     await backend.data.graph.fix_llm_provider_credentials()
     await backend.data.graph.migrate_llm_models(LlmModel.GPT4O)
-    await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
-
     with launch_darkly_context():
         yield
 
@@ -162,22 +136,9 @@ app = fastapi.FastAPI(
 
 app.add_middleware(SecurityHeadersMiddleware)
 
-# Add GZip compression middleware for large responses (like /api/blocks)
-app.add_middleware(GZipMiddleware, minimum_size=50_000)  # 50KB threshold
-
 # Add 401 responses to authenticated endpoints in OpenAPI spec
 add_auth_responses_to_openapi(app)
 
-# Add Prometheus instrumentation
-instrument_fastapi(
-    app,
-    service_name="rest-api",
-    expose_endpoint=True,
-    endpoint="/metrics",
-    include_in_schema=settings.config.app_env
-    == backend.util.settings.AppEnvironment.LOCAL,
-)
-
 
 def handle_internal_http_error(status_code: int = 500, log_error: bool = True):
     def handler(request: fastapi.Request, exc: Exception):
@@ -291,35 +252,25 @@ async def health():
 
 class AgentServer(backend.util.service.AppProcess):
     def run(self):
-        server_app = starlette.middleware.cors.CORSMiddleware(
-            app=app,
-            allow_origins=settings.config.backend_cors_allow_origins,
-            allow_credentials=True,
-            allow_methods=["*"],  # Allows all methods
-            allow_headers=["*"],  # Allows all headers
+
+        if settings.config.enable_cors_all_origins:
+            server_app = starlette.middleware.cors.CORSMiddleware(
+                app=app,
+                allow_origins=settings.config.backend_cors_allow_origins,
+                allow_credentials=True,
+                allow_methods=["*"],  # Allows all methods
+                allow_headers=["*"],  # Allows all headers
+            )
+        else:
+            logger.info("CORS is disabled")
+            server_app = app
+
+        uvicorn.run(
+            server_app,
+            host=backend.util.settings.Config().agent_api_host,
+            port=backend.util.settings.Config().agent_api_port,
+            log_config=None,
         )
-        config = backend.util.settings.Config()
-
-        # Configure uvicorn with performance optimizations from Kludex FastAPI tips
-        uvicorn_config = {
-            "app": server_app,
-            "host": config.agent_api_host,
-            "port": config.agent_api_port,
-            "log_config": None,
-            # Use httptools for HTTP parsing (if available)
-            "http": "httptools",
-            # Only use uvloop on Unix-like systems (not supported on Windows)
-            "loop": "uvloop" if platform.system() != "Windows" else "auto",
-        }
-
-        # Only add debug in local environment (not supported in all uvicorn versions)
-        if config.app_env == backend.util.settings.AppEnvironment.LOCAL:
-            import os
-
-            # Enable asyncio debug mode via environment variable
-            os.environ["PYTHONASYNCIODEBUG"] = "1"
-
-        uvicorn.run(**uvicorn_config)
 
     def cleanup(self):
         super().cleanup()

autogpt_platform/backend/backend/server/routers/v1.py

@@ -1,17 +1,14 @@
 import asyncio
 import base64
 import logging
-import time
-import uuid
 from collections import defaultdict
-from datetime import datetime, timezone
+from datetime import datetime
 from typing import Annotated, Any, Sequence
 
 import pydantic
 import stripe
 from autogpt_libs.auth import get_user_id, requires_user
 from autogpt_libs.auth.jwt_utils import get_jwt_payload
-from autogpt_libs.utils.cache import cached
 from fastapi import (
     APIRouter,
     Body,
@@ -24,8 +21,6 @@ from fastapi import (
     Security,
     UploadFile,
 )
-from fastapi.concurrency import run_in_threadpool
-from pydantic import BaseModel
 from starlette.status import HTTP_204_NO_CONTENT, HTTP_404_NOT_FOUND
 from typing_extensions import Optional, TypedDict
 
@@ -41,10 +36,10 @@ from backend.data.credit import (
     RefundRequest,
     TransactionHistory,
     get_auto_top_up,
+    get_block_costs,
     get_user_credit_model,
     set_auto_top_up,
 )
-from backend.data.execution import UserContext
 from backend.data.model import CredentialsMetaInput
 from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
 from backend.data.onboarding import (
@@ -68,11 +63,6 @@ from backend.integrations.webhooks.graph_lifecycle_hooks import (
     on_graph_activate,
     on_graph_deactivate,
 )
-from backend.monitoring.instrumentation import (
-    record_block_execution,
-    record_graph_execution,
-    record_graph_operation,
-)
 from backend.server.model import (
     CreateAPIKeyRequest,
     CreateAPIKeyResponse,
@@ -87,9 +77,9 @@ from backend.server.model import (
 from backend.util.clients import get_scheduler_client
 from backend.util.cloud_storage import get_cloud_storage_handler
 from backend.util.exceptions import GraphValidationError, NotFoundError
-from backend.util.json import dumps
 from backend.util.settings import Settings
 from backend.util.timezone_utils import (
+    convert_cron_to_utc,
     convert_utc_time_to_user_timezone,
     get_user_timezone_or_utc,
 )
@@ -107,7 +97,6 @@ def _create_file_size_error(size_bytes: int, max_size_mb: int) -> HTTPException:
 settings = Settings()
 logger = logging.getLogger(__name__)
 
-
 _user_credit_model = get_user_credit_model()
 
 # Define the API routes
@@ -266,69 +255,18 @@ async def is_onboarding_enabled():
 ########################################################
 
 
-def _compute_blocks_sync() -> str:
-    """
-    Synchronous function to compute blocks data.
-    This does the heavy lifting: instantiate 226+ blocks, compute costs, serialize.
-    """
-    from backend.data.credit import get_block_cost
-
-    block_classes = get_blocks()
-    result = []
-
-    for block_class in block_classes.values():
-        block_instance = block_class()
-        if not block_instance.disabled:
-            costs = get_block_cost(block_instance)
-            # Convert BlockCost BaseModel objects to dictionaries for JSON serialization
-            costs_dict = [
-                cost.model_dump() if isinstance(cost, BaseModel) else cost
-                for cost in costs
-            ]
-            result.append({**block_instance.to_dict(), "costs": costs_dict})
-
-    # Use our JSON utility which properly handles complex types through to_dict conversion
-    return dumps(result)
-
-
-@cached()
-async def _get_cached_blocks() -> str:
-    """
-    Async cached function with thundering herd protection.
-    On cache miss: runs heavy work in thread pool
-    On cache hit: returns cached string immediately (no thread pool needed)
-    """
-    # Only run in thread pool on cache miss - cache hits return immediately
-    return await run_in_threadpool(_compute_blocks_sync)
-
-
 @v1_router.get(
     path="/blocks",
     summary="List available blocks",
     tags=["blocks"],
     dependencies=[Security(requires_user)],
-    responses={
-        200: {
-            "description": "Successful Response",
-            "content": {
-                "application/json": {
-                    "schema": {
-                        "items": {"additionalProperties": True, "type": "object"},
-                        "type": "array",
-                        "title": "Response Getv1List Available Blocks",
-                    }
-                }
-            },
-        }
-    },
 )
-async def get_graph_blocks() -> Response:
-    # Cache hit: returns immediately, Cache miss: runs in thread pool
-    content = await _get_cached_blocks()
-    return Response(
-        content=content,
-        media_type="application/json",
-    )
+def get_graph_blocks() -> Sequence[dict[Any, Any]]:
+    blocks = [block() for block in get_blocks().values()]
+    costs = get_block_costs()
+    return [
+        {**b.to_dict(), "costs": costs.get(b.id, [])} for b in blocks if not b.disabled
+    ]
 
 
 @v1_router.post(
@@ -337,45 +275,15 @@ async def get_graph_blocks() -> Response:
     tags=["blocks"],
     dependencies=[Security(requires_user)],
 )
-async def execute_graph_block(
-    block_id: str, data: BlockInput, user_id: Annotated[str, Security(get_user_id)]
-) -> CompletedBlockOutput:
+async def execute_graph_block(block_id: str, data: BlockInput) -> CompletedBlockOutput:
     obj = get_block(block_id)
     if not obj:
         raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
 
-    # Get user context for block execution
-    user = await get_user_by_id(user_id)
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found.")
-
-    user_context = UserContext(timezone=user.timezone)
-
-    start_time = time.time()
-    try:
-        output = defaultdict(list)
-        async for name, data in obj.execute(
-            data,
-            user_context=user_context,
-            user_id=user_id,
-            # Note: graph_exec_id and graph_id are not available for direct block execution
-        ):
-            output[name].append(data)
-
-        # Record successful block execution with duration
-        duration = time.time() - start_time
-        block_type = obj.__class__.__name__
-        record_block_execution(
-            block_type=block_type, status="success", duration=duration
-        )
-
-        return output
-    except Exception:
-        # Record failed block execution
-        duration = time.time() - start_time
-        block_type = obj.__class__.__name__
-        record_block_execution(block_type=block_type, status="error", duration=duration)
-        raise
+    output = defaultdict(list)
+    async for name, data in obj.execute(data):
+        output[name].append(data)
+    return output
 
 
 @v1_router.post(
@@ -668,13 +576,7 @@ class DeleteGraphResponse(TypedDict):
 async def list_graphs(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> Sequence[graph_db.GraphMeta]:
-    paginated_result = await graph_db.list_graphs_paginated(
-        user_id=user_id,
-        page=1,
-        page_size=250,
-        filter_by="active",
-    )
-    return paginated_result.graphs
+    return await graph_db.list_graphs(filter_by="active", user_id=user_id)
 
 
 @v1_router.get(
@@ -877,7 +779,7 @@ async def execute_graph(
         )
 
     try:
-        result = await execution_utils.add_graph_execution(
+        return await execution_utils.add_graph_execution(
             graph_id=graph_id,
             user_id=user_id,
             inputs=inputs,
@@ -885,16 +787,7 @@ async def execute_graph(
             graph_version=graph_version,
             graph_credentials_inputs=credentials_inputs,
         )
-        # Record successful graph execution
-        record_graph_execution(graph_id=graph_id, status="success", user_id=user_id)
-        record_graph_operation(operation="execute", status="success")
-        return result
     except GraphValidationError as e:
-        # Record failed graph execution
-        record_graph_execution(
-            graph_id=graph_id, status="validation_error", user_id=user_id
-        )
-        record_graph_operation(operation="execute", status="validation_error")
         # Return structured validation errors that the frontend can parse
         raise HTTPException(
             status_code=400,
@@ -905,11 +798,6 @@ async def execute_graph(
                 "node_errors": e.node_errors,
             },
         )
-    except Exception:
-        # Record any other failures
-        record_graph_execution(graph_id=graph_id, status="error", user_id=user_id)
-        record_graph_operation(operation="execute", status="error")
-        raise
 
 
 @v1_router.post(
@@ -963,12 +851,7 @@ async def _stop_graph_run(
 async def list_graphs_executions(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> list[execution_db.GraphExecutionMeta]:
-    paginated_result = await execution_db.get_graph_executions_paginated(
-        user_id=user_id,
-        page=1,
-        page_size=250,
-    )
-    return paginated_result.executions
+    return await execution_db.get_graph_executions(user_id=user_id)
 
 
 @v1_router.get(
@@ -1039,99 +922,6 @@ async def delete_graph_execution(
     )
 
 
-class ShareRequest(pydantic.BaseModel):
-    """Optional request body for share endpoint."""
-
-    pass  # Empty body is fine
-
-
-class ShareResponse(pydantic.BaseModel):
-    """Response from share endpoints."""
-
-    share_url: str
-    share_token: str
-
-
-@v1_router.post(
-    "/graphs/{graph_id}/executions/{graph_exec_id}/share",
-    dependencies=[Security(requires_user)],
-)
-async def enable_execution_sharing(
-    graph_id: Annotated[str, Path],
-    graph_exec_id: Annotated[str, Path],
-    user_id: Annotated[str, Security(get_user_id)],
-    _body: ShareRequest = Body(default=ShareRequest()),
-) -> ShareResponse:
-    """Enable sharing for a graph execution."""
-    # Verify the execution belongs to the user
-    execution = await execution_db.get_graph_execution(
-        user_id=user_id, execution_id=graph_exec_id
-    )
-    if not execution:
-        raise HTTPException(status_code=404, detail="Execution not found")
-
-    # Generate a unique share token
-    share_token = str(uuid.uuid4())
-
-    # Update the execution with share info
-    await execution_db.update_graph_execution_share_status(
-        execution_id=graph_exec_id,
-        user_id=user_id,
-        is_shared=True,
-        share_token=share_token,
-        shared_at=datetime.now(timezone.utc),
-    )
-
-    # Return the share URL
-    frontend_url = Settings().config.frontend_base_url or "http://localhost:3000"
-    share_url = f"{frontend_url}/share/{share_token}"
-
-    return ShareResponse(share_url=share_url, share_token=share_token)
-
-
-@v1_router.delete(
-    "/graphs/{graph_id}/executions/{graph_exec_id}/share",
-    status_code=HTTP_204_NO_CONTENT,
-    dependencies=[Security(requires_user)],
-)
-async def disable_execution_sharing(
-    graph_id: Annotated[str, Path],
-    graph_exec_id: Annotated[str, Path],
-    user_id: Annotated[str, Security(get_user_id)],
-) -> None:
-    """Disable sharing for a graph execution."""
-    # Verify the execution belongs to the user
-    execution = await execution_db.get_graph_execution(
-        user_id=user_id, execution_id=graph_exec_id
-    )
-    if not execution:
-        raise HTTPException(status_code=404, detail="Execution not found")
-
-    # Remove share info
-    await execution_db.update_graph_execution_share_status(
-        execution_id=graph_exec_id,
-        user_id=user_id,
-        is_shared=False,
-        share_token=None,
-        shared_at=None,
-    )
-
-
-@v1_router.get("/public/shared/{share_token}")
-async def get_shared_execution(
-    share_token: Annotated[
-        str,
-        Path(regex=r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$"),
-    ],
-) -> execution_db.SharedExecutionResponse:
-    """Get a shared graph execution by share token (no auth required)."""
-    execution = await execution_db.get_graph_execution_by_share_token(share_token)
-    if not execution:
-        raise HTTPException(status_code=404, detail="Shared execution not found")
-
-    return execution
-
-
 ########################################################
 ##################### Schedules ########################
 ########################################################
@@ -1143,10 +933,6 @@ class ScheduleCreationRequest(pydantic.BaseModel):
     cron: str
     inputs: dict[str, Any]
     credentials: dict[str, CredentialsMetaInput] = pydantic.Field(default_factory=dict)
-    timezone: Optional[str] = pydantic.Field(
-        default=None,
-        description="User's timezone for scheduling (e.g., 'America/New_York'). If not provided, will use user's saved timezone or UTC.",
-    )
 
 
 @v1_router.post(
@@ -1171,22 +957,26 @@ async def create_graph_execution_schedule(
             detail=f"Graph #{graph_id} v{schedule_params.graph_version} not found.",
         )
 
-    # Use timezone from request if provided, otherwise fetch from user profile
-    if schedule_params.timezone:
-        user_timezone = schedule_params.timezone
-    else:
-        user = await get_user_by_id(user_id)
-        user_timezone = get_user_timezone_or_utc(user.timezone if user else None)
+    user = await get_user_by_id(user_id)
+    user_timezone = get_user_timezone_or_utc(user.timezone if user else None)
+
+    # Convert cron expression from user timezone to UTC
+    try:
+        utc_cron = convert_cron_to_utc(schedule_params.cron, user_timezone)
+    except ValueError as e:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid cron expression for timezone {user_timezone}: {e}",
+        )
 
     result = await get_scheduler_client().add_execution_schedule(
         user_id=user_id,
         graph_id=graph_id,
         graph_version=graph.version,
         name=schedule_params.name,
-        cron=schedule_params.cron,
+        cron=utc_cron,  # Send UTC cron to scheduler
         input_data=schedule_params.inputs,
         input_credentials=schedule_params.credentials,
-        user_timezone=user_timezone,
     )
 
     # Convert the next_run_time back to user timezone for display
@@ -1208,11 +998,24 @@ async def list_graph_execution_schedules(
     user_id: Annotated[str, Security(get_user_id)],
     graph_id: str = Path(),
 ) -> list[scheduler.GraphExecutionJobInfo]:
-    return await get_scheduler_client().get_execution_schedules(
+    schedules = await get_scheduler_client().get_execution_schedules(
         user_id=user_id,
         graph_id=graph_id,
     )
 
+    # Get user timezone for conversion
+    user = await get_user_by_id(user_id)
+    user_timezone = get_user_timezone_or_utc(user.timezone if user else None)
+
+    # Convert next_run_time to user timezone for display
+    for schedule in schedules:
+        if schedule.next_run_time:
+            schedule.next_run_time = convert_utc_time_to_user_timezone(
+                schedule.next_run_time, user_timezone
+            )
+
+    return schedules
+
 
 @v1_router.get(
     path="/schedules",
@@ -1223,7 +1026,20 @@ async def list_graph_execution_schedules(
 async def list_all_graphs_execution_schedules(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> list[scheduler.GraphExecutionJobInfo]:
-    return await get_scheduler_client().get_execution_schedules(user_id=user_id)
+    schedules = await get_scheduler_client().get_execution_schedules(user_id=user_id)
+
+    # Get user timezone for conversion
+    user = await get_user_by_id(user_id)
+    user_timezone = get_user_timezone_or_utc(user.timezone if user else None)
+
+    # Convert UTC next_run_time to user timezone for display
+    for schedule in schedules:
+        if schedule.next_run_time:
+            schedule.next_run_time = convert_utc_time_to_user_timezone(
+                schedule.next_run_time, user_timezone
+            )
+
+    return schedules
 
 
 @v1_router.delete(