Merge branch 'dev' into ci-chromatic

Navbar sometimes disappears outside `/onboarding`.

### Changes 🏗️

This PR solves the problem of disappearing Navbar outside `/onboarding`
by introducing `app/(platform)` route group.

- Move all routes requiring Navbar to `app/(platform)`
- Move `<Navbar>` to `app/(platform)/layout.tsx`
- Move `/onboarding` to `app/(no-navbar/`
- Remove pathname injection to header from middleware and stop relying
on it to hide the navbar

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Common routes work properly

.github/dependabot.yml

@@ -129,30 +129,6 @@ updates:
         - "minor"
         - "patch"
 
-
-  # Submodules
-  - package-ecosystem: "gitsubmodule"
-    directory: "autogpt_platform/supabase"
-    schedule:
-      interval: "weekly"
-    open-pull-requests-limit: 1
-    target-branch: "dev"
-    commit-message:
-      prefix: "chore(platform/deps)"
-      prefix-development: "chore(platform/deps-dev)"
-    groups:
-      production-dependencies:
-        dependency-type: "production"
-        update-types:
-        - "minor"
-        - "patch"
-      development-dependencies:
-        dependency-type: "development"
-        update-types:
-        - "minor"
-        - "patch"
-
-
   # Docs
   - package-ecosystem: 'pip'
     directory: "docs/"

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

@@ -34,6 +34,7 @@ jobs:
           python -m prisma migrate deploy
         env:
           DATABASE_URL: ${{ secrets.BACKEND_DATABASE_URL }}
+          DIRECT_URL: ${{ secrets.BACKEND_DATABASE_URL }}
 
           
   trigger:

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

@@ -36,6 +36,7 @@ jobs:
           python -m prisma migrate deploy
         env:
           DATABASE_URL: ${{ secrets.BACKEND_DATABASE_URL }}
+          DIRECT_URL: ${{ secrets.BACKEND_DATABASE_URL }}
 
   trigger:
     needs: migrate

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

@@ -80,18 +80,35 @@ jobs:
 
       - name: Install Poetry (Unix)
         run: |
-          curl -sSL https://install.python-poetry.org | python3 -
+          # Extract Poetry version from backend/poetry.lock
+          HEAD_POETRY_VERSION=$(head -n 1 poetry.lock | grep -oP '(?<=Poetry )[0-9]+\.[0-9]+\.[0-9]+')
+          echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
+
+          if [ -n "$BASE_REF" ]; then
+            BASE_BRANCH=${BASE_REF/refs\/heads\//}
+            BASE_POETRY_VERSION=$((git show "origin/$BASE_BRANCH":./poetry.lock; true) | head -n 1 | grep -oP '(?<=Poetry )[0-9]+\.[0-9]+\.[0-9]+')
+            echo "Found Poetry version ${BASE_POETRY_VERSION} in backend/poetry.lock on ${BASE_REF}"
+            POETRY_VERSION=$(printf '%s\n' "$HEAD_POETRY_VERSION" "$BASE_POETRY_VERSION" | sort -V | tail -n1)
+          else
+            POETRY_VERSION=$HEAD_POETRY_VERSION
+          fi
+          echo "Using Poetry version ${POETRY_VERSION}"
+
+          # Install Poetry
+          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$POETRY_VERSION python3 -
 
           if [ "${{ runner.os }}" = "macOS" ]; then
             PATH="$HOME/.local/bin:$PATH"
             echo "$HOME/.local/bin" >> $GITHUB_PATH
           fi
+        env:
+          BASE_REF: ${{ github.base_ref || github.event.merge_group.base_ref }}
 
       - name: Check poetry.lock
         run: |
           poetry lock
 
-          if ! git diff --quiet poetry.lock; then
+          if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
             echo "Error: poetry.lock not up to date."
             echo
             git diff poetry.lock
@@ -118,6 +135,7 @@ jobs:
         run: poetry run prisma migrate dev --name updates
         env:
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
+          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
 
       - id: lint
         name: Run Linter
@@ -134,12 +152,13 @@ jobs:
         env:
           LOG_LEVEL: ${{ runner.debug && 'DEBUG' || 'INFO' }}
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
+          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
           SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
           SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
           SUPABASE_JWT_SECRET: ${{ steps.supabase.outputs.JWT_SECRET }}
-          REDIS_HOST: 'localhost'
-          REDIS_PORT: '6379'
-          REDIS_PASSWORD: 'testpassword'
+          REDIS_HOST: "localhost"
+          REDIS_PORT: "6379"
+          REDIS_PASSWORD: "testpassword"
 
     env:
       CI: true
@@ -152,8 +171,8 @@ jobs:
       # If you want to replace this, you can do so by making our entire system generate
       # new credentials for each local user and update the environment variables in
       # the backend service, docker composes, and examples
-      RABBITMQ_DEFAULT_USER: 'rabbitmq_user_default'
-      RABBITMQ_DEFAULT_PASS: 'k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7'
+      RABBITMQ_DEFAULT_USER: "rabbitmq_user_default"
+      RABBITMQ_DEFAULT_PASS: "k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7"
 
       # - name: Upload coverage reports to Codecov
       #   uses: codecov/codecov-action@v4

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

@@ -56,6 +56,30 @@ jobs:
         run: |
           yarn type-check
 
+  design:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "21"
+
+      - name: Install dependencies
+        run: |
+          yarn install --frozen-lockfile
+
+      - name: Run Chromatic
+        uses: chromaui/action@latest
+        with:
+          # ⚠️ Make sure to configure a `CHROMATIC_PROJECT_TOKEN` repository secret
+          projectToken: ${{ secrets.CHROMATIC_PROJECT_TOKEN }}
+          workingDir: autogpt_platform/frontend
+
   test:
     runs-on: ubuntu-latest
     strategy:
@@ -82,7 +106,7 @@ jobs:
 
       - name: Copy default supabase .env
         run: |
-          cp ../supabase/docker/.env.example ../.env
+          cp ../.env.example ../.env
 
       - name: Copy backend .env
         run: |

.gitmodules

@@ -1,6 +1,3 @@
 [submodule "classic/forge/tests/vcr_cassettes"]
 	path = classic/forge/tests/vcr_cassettes
 	url = https://github.com/Significant-Gravitas/Auto-GPT-test-cassettes
-[submodule "autogpt_platform/supabase"]
-	path = autogpt_platform/supabase
-	url = https://github.com/supabase/supabase.git

.pre-commit-config.yaml

@@ -140,7 +140,7 @@ repos:
         language: system
 
   - repo: https://github.com/psf/black
-    rev: 23.12.1
+    rev: 24.10.0
     # Black has sensible defaults, doesn't need package context, and ignores
     # everything in .gitignore, so it works fine without any config or arguments.
     hooks:

CONTRIBUTING.md

@@ -2,9 +2,6 @@
 If you are reading this, you are probably looking for the full **[contribution guide]**,
 which is part of our [wiki].
 
-Also check out our [🚀 Roadmap][roadmap] for information about our priorities and associated tasks.
-<!-- You can find our immediate priorities and their progress on our public [kanban board]. -->
-
 [contribution guide]: https://github.com/Significant-Gravitas/AutoGPT/wiki/Contributing
 [wiki]: https://github.com/Significant-Gravitas/AutoGPT/wiki
 [roadmap]: https://github.com/Significant-Gravitas/AutoGPT/discussions/6971

README.md

@@ -15,7 +15,11 @@
 > Setting up and hosting the AutoGPT Platform yourself is a technical process. 
 > If you'd rather something that just works, we recommend [joining the waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta.
 
-https://github.com/user-attachments/assets/d04273a5-b36a-4a37-818e-f631ce72d603
+### Updated Setup Instructions:
+We’ve moved to a fully maintained and regularly updated documentation site.
+
+👉 [Follow the official self-hosting guide here](https://docs.agpt.co/platform/getting-started/)
+
 
 This tutorial assumes you have Docker, VSCode, git and npm installed.
 

autogpt_platform/.env.example

@@ -0,0 +1,123 @@
+############
+# Secrets
+# YOU MUST CHANGE THESE BEFORE GOING INTO PRODUCTION
+############
+
+POSTGRES_PASSWORD=your-super-secret-and-long-postgres-password
+JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
+ANON_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
+SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
+DASHBOARD_USERNAME=supabase
+DASHBOARD_PASSWORD=this_password_is_insecure_and_should_be_updated
+SECRET_KEY_BASE=UpNVntn3cDxHJpq99YMc1T1AQgQpc8kfYTuRgBiYa15BLrx8etQoXz3gZv1/u2oq
+VAULT_ENC_KEY=your-encryption-key-32-chars-min
+
+
+############
+# Database - You can change these to any PostgreSQL database that has logical replication enabled.
+############
+
+POSTGRES_HOST=db
+POSTGRES_DB=postgres
+POSTGRES_PORT=5432
+# default user is postgres
+
+
+############
+# Supavisor -- Database pooler
+############
+POOLER_PROXY_PORT_TRANSACTION=6543
+POOLER_DEFAULT_POOL_SIZE=20
+POOLER_MAX_CLIENT_CONN=100
+POOLER_TENANT_ID=your-tenant-id
+
+
+############
+# API Proxy - Configuration for the Kong Reverse proxy.
+############
+
+KONG_HTTP_PORT=8000
+KONG_HTTPS_PORT=8443
+
+
+############
+# API - Configuration for PostgREST.
+############
+
+PGRST_DB_SCHEMAS=public,storage,graphql_public
+
+
+############
+# Auth - Configuration for the GoTrue authentication server.
+############
+
+## General
+SITE_URL=http://localhost:3000
+ADDITIONAL_REDIRECT_URLS=
+JWT_EXPIRY=3600
+DISABLE_SIGNUP=false
+API_EXTERNAL_URL=http://localhost:8000
+
+## Mailer Config
+MAILER_URLPATHS_CONFIRMATION="/auth/v1/verify"
+MAILER_URLPATHS_INVITE="/auth/v1/verify"
+MAILER_URLPATHS_RECOVERY="/auth/v1/verify"
+MAILER_URLPATHS_EMAIL_CHANGE="/auth/v1/verify"
+
+## Email auth
+ENABLE_EMAIL_SIGNUP=true
+ENABLE_EMAIL_AUTOCONFIRM=false
+SMTP_ADMIN_EMAIL=admin@example.com
+SMTP_HOST=supabase-mail
+SMTP_PORT=2500
+SMTP_USER=fake_mail_user
+SMTP_PASS=fake_mail_password
+SMTP_SENDER_NAME=fake_sender
+ENABLE_ANONYMOUS_USERS=false
+
+## Phone auth
+ENABLE_PHONE_SIGNUP=true
+ENABLE_PHONE_AUTOCONFIRM=true
+
+
+############
+# Studio - Configuration for the Dashboard
+############
+
+STUDIO_DEFAULT_ORGANIZATION=Default Organization
+STUDIO_DEFAULT_PROJECT=Default Project
+
+STUDIO_PORT=3000
+# replace if you intend to use Studio outside of localhost
+SUPABASE_PUBLIC_URL=http://localhost:8000
+
+# Enable webp support
+IMGPROXY_ENABLE_WEBP_DETECTION=true
+
+# Add your OpenAI API key to enable SQL Editor Assistant
+OPENAI_API_KEY=
+
+
+############
+# Functions - Configuration for Functions
+############
+# NOTE: VERIFY_JWT applies to all functions. Per-function VERIFY_JWT is not supported yet.
+FUNCTIONS_VERIFY_JWT=false
+
+
+############
+# Logs - Configuration for Logflare
+# Please refer to https://supabase.com/docs/reference/self-hosting-analytics/introduction
+############
+
+LOGFLARE_LOGGER_BACKEND_API_KEY=your-super-secret-and-long-logflare-key
+
+# Change vector.toml sinks to reflect this change
+LOGFLARE_API_KEY=your-super-secret-and-long-logflare-key
+
+# Docker socket location - this value will differ depending on your OS
+DOCKER_SOCKET_LOCATION=/var/run/docker.sock
+
+# Google Cloud Project details
+GOOGLE_PROJECT_ID=GOOGLE_PROJECT_ID
+GOOGLE_PROJECT_NUMBER=GOOGLE_PROJECT_NUMBER

autogpt_platform/README.md

@@ -22,35 +22,29 @@ To run the AutoGPT Platform, follow these steps:
 
 2. Run the following command:
    ```
-   git submodule update --init --recursive --progress
+   cp .env.example .env
    ```
-   This command will initialize and update the submodules in the repository. The `supabase` folder will be cloned to the root directory.
+   This command will copy the `.env.example` file to `.env`. You can modify the `.env` file to add your own environment variables.
 
 3. Run the following command:
-   ```
-   cp supabase/docker/.env.example .env
-   ```
-   This command will copy the `.env.example` file to `.env` in the `supabase/docker` directory. You can modify the `.env` file to add your own environment variables.
-
-4. Run the following command:
    ```
    docker compose up -d
    ```
    This command will start all the necessary backend services defined in the `docker-compose.yml` file in detached mode.
 
-5. Navigate to `frontend` within the `autogpt_platform` directory:
+4. Navigate to `frontend` within the `autogpt_platform` directory:
    ```
    cd frontend
    ```
    You will need to run your frontend application separately on your local machine.
 
-6. Run the following command: 
+5. Run the following command: 
    ```
    cp .env.example .env.local
    ```
    This command will copy the `.env.example` file to `.env.local` in the `frontend` directory. You can modify the `.env.local` within this folder to add your own environment variables for the frontend application.
 
-7. Run the following command:
+6. Run the following command:
    ```
    npm install
    npm run dev
@@ -61,7 +55,7 @@ To run the AutoGPT Platform, follow these steps:
    yarn install && yarn dev
    ```
 
-8. Open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
+7. Open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
 
 ### Docker Compose Commands
 

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -1,11 +1,9 @@
-from .config import Settings
 from .depends import requires_admin_user, requires_user
 from .jwt_utils import parse_jwt_token
 from .middleware import APIKeyValidator, auth_middleware
 from .models import User
 
 __all__ = [
-    "Settings",
     "parse_jwt_token",
     "requires_user",
     "requires_admin_user",

autogpt_platform/autogpt_libs/autogpt_libs/auth/config.py

@@ -1,14 +1,11 @@
 import os
 
-from dotenv import load_dotenv
-
-load_dotenv()
-
 
 class Settings:
-    JWT_SECRET_KEY: str = os.getenv("SUPABASE_JWT_SECRET", "")
-    ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
-    JWT_ALGORITHM: str = "HS256"
+    def __init__(self):
+        self.JWT_SECRET_KEY: str = os.getenv("SUPABASE_JWT_SECRET", "")
+        self.ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
+        self.JWT_ALGORITHM: str = "HS256"
 
     @property
     def is_configured(self) -> bool:

autogpt_platform/autogpt_libs/autogpt_libs/auth/depends.py

@@ -1,6 +1,6 @@
 import fastapi
 
-from .config import Settings
+from .config import settings
 from .middleware import auth_middleware
 from .models import DEFAULT_USER_ID, User
 
@@ -17,7 +17,7 @@ def requires_admin_user(
 
 def verify_user(payload: dict | None, admin_only: bool) -> User:
     if not payload:
-        if Settings.ENABLE_AUTH:
+        if settings.ENABLE_AUTH:
             raise fastapi.HTTPException(
                 status_code=401, detail="Authorization header is missing"
             )

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -8,7 +8,7 @@ from pydantic import Field, field_validator
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 from .filters import BelowLevelFilter
-from .formatters import AGPTFormatter, StructuredLoggingFormatter
+from .formatters import AGPTFormatter
 
 LOG_DIR = Path(__file__).parent.parent.parent.parent / "logs"
 LOG_FILE = "activity.log"
@@ -81,9 +81,26 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
     """
 
     config = LoggingConfig()
-
     log_handlers: list[logging.Handler] = []
 
+    # Console output handlers
+    stdout = logging.StreamHandler(stream=sys.stdout)
+    stdout.setLevel(config.level)
+    stdout.addFilter(BelowLevelFilter(logging.WARNING))
+    if config.level == logging.DEBUG:
+        stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+    else:
+        stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+
+    stderr = logging.StreamHandler()
+    stderr.setLevel(logging.WARNING)
+    if config.level == logging.DEBUG:
+        stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+    else:
+        stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+
+    log_handlers += [stdout, stderr]
+
     # Cloud logging setup
     if config.enable_cloud_logging or force_cloud_logging:
         import google.cloud.logging
@@ -97,28 +114,7 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
             transport=SyncTransport,
         )
         cloud_handler.setLevel(config.level)
-        cloud_handler.setFormatter(StructuredLoggingFormatter())
         log_handlers.append(cloud_handler)
-        print("Cloud logging enabled")
-    else:
-        # Console output handlers
-        stdout = logging.StreamHandler(stream=sys.stdout)
-        stdout.setLevel(config.level)
-        stdout.addFilter(BelowLevelFilter(logging.WARNING))
-        if config.level == logging.DEBUG:
-            stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
-
-        stderr = logging.StreamHandler()
-        stderr.setLevel(logging.WARNING)
-        if config.level == logging.DEBUG:
-            stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
-
-        log_handlers += [stdout, stderr]
-        print("Console logging enabled")
 
     # File logging setup
     if config.enable_file_logging:
@@ -156,7 +152,6 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
         error_log_handler.setLevel(logging.ERROR)
         error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))
         log_handlers.append(error_log_handler)
-        print("File logging enabled")
 
     # Configure the root logger
     logging.basicConfig(

autogpt_platform/autogpt_libs/autogpt_libs/logging/formatters.py

@@ -1,7 +1,6 @@
 import logging
 
 from colorama import Fore, Style
-from google.cloud.logging_v2.handlers import CloudLoggingFilter, StructuredLogHandler
 
 from .utils import remove_color_codes
 
@@ -80,16 +79,3 @@ class AGPTFormatter(FancyConsoleFormatter):
             return remove_color_codes(super().format(record))
         else:
             return super().format(record)
-
-
-class StructuredLoggingFormatter(StructuredLogHandler, logging.Formatter):
-    def __init__(self):
-        # Set up CloudLoggingFilter to add diagnostic info to the log records
-        self.cloud_logging_filter = CloudLoggingFilter()
-
-        # Init StructuredLogHandler
-        super().__init__()
-
-    def format(self, record: logging.LogRecord) -> str:
-        self.cloud_logging_filter.filter(record)
-        return super().format(record)

autogpt_platform/autogpt_libs/autogpt_libs/logging/utils.py

@@ -2,6 +2,7 @@ import logging
 import re
 from typing import Any
 
+import uvicorn.config
 from colorama import Fore
 
 
@@ -25,3 +26,14 @@ def print_attribute(
             "color": value_color,
         },
     )
+
+
+def generate_uvicorn_config():
+    """
+    Generates a uvicorn logging config that silences uvicorn's default logging and tells it to use the native logging module.
+    """
+    log_config = dict(uvicorn.config.LOGGING_CONFIG)
+    log_config["loggers"]["uvicorn"] = {"handlers": []}
+    log_config["loggers"]["uvicorn.error"] = {"handlers": []}
+    log_config["loggers"]["uvicorn.access"] = {"handlers": []}
+    return log_config

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -1,20 +1,59 @@
+import inspect
 import threading
-from typing import Callable, ParamSpec, TypeVar
+from typing import Awaitable, Callable, ParamSpec, TypeVar, cast, overload
 
 P = ParamSpec("P")
 R = TypeVar("R")
 
 
-def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
+@overload
+def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
+
+
+@overload
+def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...
+
+
+def thread_cached(
+    func: Callable[P, R] | Callable[P, Awaitable[R]],
+) -> Callable[P, R] | Callable[P, Awaitable[R]]:
     thread_local = threading.local()
 
-    def 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]
+    def _clear():
+        if hasattr(thread_local, "cache"):
+            del thread_local.cache
 
-    return wrapper
+    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()

autogpt_platform/autogpt_libs/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -14,113 +14,104 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.10.5"
+version = "3.11.15"
 description = "Async http client/server framework (asyncio)"
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 ]
 
 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
 
 [package.extras]
 speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
@@ -186,7 +177,7 @@ files = [
     {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
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 ]
-markers = {main = "python_version < \"3.11\"", dev = "python_full_version < \"3.11.3\""}
+markers = {main = "python_version == \"3.10\"", dev = "python_full_version < \"3.11.3\""}
 
 [[package]]
 name = "attrs"
@@ -384,7 +375,7 @@ description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
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@@ -1071,6 +1062,114 @@ httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
 pydantic = ">=1.9,<3.0"
 strenum = {version = ">=0.4.9,<0.5.0", markers = "python_version < \"3.11\""}
 
+[[package]]
+name = "propcache"
+version = "0.3.1"
+description = "Accelerated property cache"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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 typing-extensions = ">=4.12.2"
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 [package.dependencies]
@@ -1273,14 +1372,14 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydantic-settings"
-version = "2.7.1"
+version = "2.8.1"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 [package.dependencies]
@@ -1335,14 +1434,14 @@ dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments
 
 [[package]]
 name = "pytest-asyncio"
-version = "0.25.3"
+version = "0.26.0"
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 [package.dependencies]
@@ -1402,21 +1501,21 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "realtime"
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+version = "2.4.2"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.9"
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 python-dateutil = ">=2.8.1,<3.0.0"
 typing-extensions = ">=4.12.2,<5.0.0"
-websockets = ">=11,<13"
+websockets = ">=11,<15"
 
 [[package]]
 name = "redis"
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 [[package]]
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 ]
 
 [[package]]
@@ -1561,21 +1660,21 @@ test = ["pylint", "pytest", "pytest-black", "pytest-cov", "pytest-pylint"]
 
 [[package]]
 name = "supabase"
-version = "2.13.0"
+version = "2.15.0"
 description = "Supabase client for Python."
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
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 httpx = ">=0.26,<0.29"
-postgrest = ">=0.19,<0.20"
-realtime = ">=2.0.0,<3.0.0"
+postgrest = ">0.19,<1.1"
+realtime = ">=2.4.0,<2.5.0"
 storage3 = ">=0.10,<0.12"
 supafunc = ">=0.9,<0.10"
 
@@ -1602,7 +1701,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
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@@ -1620,6 +1719,21 @@ files = [
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 ]
 
+[[package]]
+name = "typing-inspection"
+version = "0.4.0"
+description = "Runtime typing introspection tools"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
 [[package]]
 name = "urllib3"
 version = "2.2.2"
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 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [[package]]
 name = "zipp"
@@ -1929,4 +2034,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "f5cd0d1dafeb2b5c97d0ef27bef8a2235d4a1f54e3c60583d05ef582ac49c0e6"
+content-hash = "c8e23c0609cae0717447f575849b658bee9203b784ec7270b62629cddbbbd9ca"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -10,18 +10,17 @@ packages = [{ include = "autogpt_libs" }]
 colorama = "^0.4.6"
 expiringdict = "^1.2.2"
 google-cloud-logging = "^3.11.4"
-pydantic = "^2.10.6"
-pydantic-settings = "^2.7.1"
+pydantic = "^2.11.1"
+pydantic-settings = "^2.8.1"
 pyjwt = "^2.10.1"
-pytest-asyncio = "^0.25.3"
+pytest-asyncio = "^0.26.0"
 pytest-mock = "^3.14.0"
 python = ">=3.10,<4.0"
-python-dotenv = "^1.0.1"
-supabase = "^2.13.0"
+supabase = "^2.15.0"
 
 [tool.poetry.group.dev.dependencies]
 redis = "^5.2.1"
-ruff = "^0.9.6"
+ruff = "^0.11.0"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.example

@@ -8,6 +8,7 @@ DB_CONNECT_TIMEOUT=60
 DB_POOL_TIMEOUT=300
 DB_SCHEMA=platform
 DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
+DIRECT_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
 PRISMA_SCHEMA="postgres/schema.prisma"
 
 # EXECUTOR
@@ -188,6 +189,8 @@ SMARTLEAD_API_KEY=
 # ZeroBounce
 ZEROBOUNCE_API_KEY=
 
+## ===== OPTIONAL API KEYS END ===== ##
+
 # Logging Configuration
 LOG_LEVEL=INFO
 ENABLE_CLOUD_LOGGING=false

autogpt_platform/backend/Dockerfile

@@ -73,7 +73,6 @@ FROM server_dependencies AS server
 COPY autogpt_platform/backend /app/autogpt_platform/backend
 RUN poetry install --no-ansi --only-root
 
-ENV DATABASE_URL=""
 ENV PORT=8000
 
 CMD ["poetry", "run", "rest"]

autogpt_platform/backend/backend/blocks/__init__.py

@@ -2,88 +2,103 @@ import importlib
 import os
 import re
 from pathlib import Path
-from typing import Type, TypeVar
-
-from backend.data.block import Block
-
-# Dynamically load all modules under backend.blocks
-AVAILABLE_MODULES = []
-current_dir = Path(__file__).parent
-modules = [
-    str(f.relative_to(current_dir))[:-3].replace(os.path.sep, ".")
-    for f in current_dir.rglob("*.py")
-    if f.is_file() and f.name != "__init__.py"
-]
-for module in modules:
-    if not re.match("^[a-z0-9_.]+$", module):
-        raise ValueError(
-            f"Block module {module} error: module name must be lowercase, "
-            "and contain only alphanumeric characters and underscores."
-        )
-
-    importlib.import_module(f".{module}", package=__name__)
-    AVAILABLE_MODULES.append(module)
-
-# Load all Block instances from the available modules
-AVAILABLE_BLOCKS: dict[str, Type[Block]] = {}
+from typing import TYPE_CHECKING, TypeVar
 
+if TYPE_CHECKING:
+    from backend.data.block import Block
 
 T = TypeVar("T")
 
 
-def all_subclasses(cls: Type[T]) -> list[Type[T]]:
+_AVAILABLE_BLOCKS: dict[str, type["Block"]] = {}
+
+
+def load_all_blocks() -> dict[str, type["Block"]]:
+    from backend.data.block import Block
+
+    if _AVAILABLE_BLOCKS:
+        return _AVAILABLE_BLOCKS
+
+    # Dynamically load all modules under backend.blocks
+    AVAILABLE_MODULES = []
+    current_dir = Path(__file__).parent
+    modules = [
+        str(f.relative_to(current_dir))[:-3].replace(os.path.sep, ".")
+        for f in current_dir.rglob("*.py")
+        if f.is_file() and f.name != "__init__.py"
+    ]
+    for module in modules:
+        if not re.match("^[a-z0-9_.]+$", module):
+            raise ValueError(
+                f"Block module {module} error: module name must be lowercase, "
+                "and contain only alphanumeric characters and underscores."
+            )
+
+        importlib.import_module(f".{module}", package=__name__)
+        AVAILABLE_MODULES.append(module)
+
+    # Load all Block instances from the available modules
+    for block_cls in all_subclasses(Block):
+        class_name = block_cls.__name__
+
+        if class_name.endswith("Base"):
+            continue
+
+        if not class_name.endswith("Block"):
+            raise ValueError(
+                f"Block class {class_name} does not end with 'Block'. "
+                "If you are creating an abstract class, "
+                "please name the class with 'Base' at the end"
+            )
+
+        block = block_cls.create()
+
+        if not isinstance(block.id, str) or len(block.id) != 36:
+            raise ValueError(
+                f"Block ID {block.name} error: {block.id} is not a valid UUID"
+            )
+
+        if block.id in _AVAILABLE_BLOCKS:
+            raise ValueError(
+                f"Block ID {block.name} error: {block.id} is already in use"
+            )
+
+        input_schema = block.input_schema.model_fields
+        output_schema = block.output_schema.model_fields
+
+        # Make sure `error` field is a string in the output schema
+        if "error" in output_schema and output_schema["error"].annotation is not str:
+            raise ValueError(
+                f"{block.name} `error` field in output_schema must be a string"
+            )
+
+        # Ensure all fields in input_schema and output_schema are annotated SchemaFields
+        for field_name, field in [*input_schema.items(), *output_schema.items()]:
+            if field.annotation is None:
+                raise ValueError(
+                    f"{block.name} has a field {field_name} that is not annotated"
+                )
+            if field.json_schema_extra is None:
+                raise ValueError(
+                    f"{block.name} has a field {field_name} not defined as SchemaField"
+                )
+
+        for field in block.input_schema.model_fields.values():
+            if field.annotation is bool and field.default not in (True, False):
+                raise ValueError(
+                    f"{block.name} has a boolean field with no default value"
+                )
+
+        _AVAILABLE_BLOCKS[block.id] = block_cls
+
+    return _AVAILABLE_BLOCKS
+
+
+__all__ = ["load_all_blocks"]
+
+
+def all_subclasses(cls: type[T]) -> list[type[T]]:
     subclasses = cls.__subclasses__()
     for subclass in subclasses:
         subclasses += all_subclasses(subclass)
     return subclasses
-
-
-for block_cls in all_subclasses(Block):
-    name = block_cls.__name__
-
-    if block_cls.__name__.endswith("Base"):
-        continue
-
-    if not block_cls.__name__.endswith("Block"):
-        raise ValueError(
-            f"Block class {block_cls.__name__} does not end with 'Block', If you are creating an abstract class, please name the class with 'Base' at the end"
-        )
-
-    block = block_cls.create()
-
-    if not isinstance(block.id, str) or len(block.id) != 36:
-        raise ValueError(f"Block ID {block.name} error: {block.id} is not a valid UUID")
-
-    if block.id in AVAILABLE_BLOCKS:
-        raise ValueError(f"Block ID {block.name} error: {block.id} is already in use")
-
-    input_schema = block.input_schema.model_fields
-    output_schema = block.output_schema.model_fields
-
-    # Make sure `error` field is a string in the output schema
-    if "error" in output_schema and output_schema["error"].annotation is not str:
-        raise ValueError(
-            f"{block.name} `error` field in output_schema must be a string"
-        )
-
-    # Make sure all fields in input_schema and output_schema are annotated and has a value
-    for field_name, field in [*input_schema.items(), *output_schema.items()]:
-        if field.annotation is None:
-            raise ValueError(
-                f"{block.name} has a field {field_name} that is not annotated"
-            )
-        if field.json_schema_extra is None:
-            raise ValueError(
-                f"{block.name} has a field {field_name} not defined as SchemaField"
-            )
-
-    for field in block.input_schema.model_fields.values():
-        if field.annotation is bool and field.default not in (True, False):
-            raise ValueError(f"{block.name} has a boolean field with no default value")
-
-    if block.disabled:
-        continue
-
-    AVAILABLE_BLOCKS[block.id] = block_cls
-
-__all__ = ["AVAILABLE_MODULES", "AVAILABLE_BLOCKS"]

autogpt_platform/backend/backend/blocks/agent.py

@@ -1,8 +1,6 @@
 import logging
 from typing import Any
 
-from autogpt_libs.utils.cache import thread_cached
-
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -19,21 +17,6 @@ from backend.util import json
 logger = logging.getLogger(__name__)
 
 
-@thread_cached
-def get_executor_manager_client():
-    from backend.executor import ExecutionManager
-    from backend.util.service import get_service_client
-
-    return get_service_client(ExecutionManager)
-
-
-@thread_cached
-def get_event_bus():
-    from backend.data.execution import RedisExecutionEventBus
-
-    return RedisExecutionEventBus()
-
-
 class AgentExecutorBlock(Block):
     class Input(BlockSchema):
         user_id: str = SchemaField(description="User ID")
@@ -75,26 +58,26 @@ class AgentExecutorBlock(Block):
         )
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        executor_manager = get_executor_manager_client()
-        event_bus = get_event_bus()
+        from backend.data.execution import ExecutionEventType
+        from backend.executor import utils as execution_utils
 
-        graph_exec = executor_manager.add_execution(
+        event_bus = execution_utils.get_execution_event_bus()
+
+        graph_exec = execution_utils.add_graph_execution(
             graph_id=input_data.graph_id,
             graph_version=input_data.graph_version,
             user_id=input_data.user_id,
-            data=input_data.data,
+            inputs=input_data.data,
         )
-        log_id = f"Graph #{input_data.graph_id}-V{input_data.graph_version}, exec-id: {graph_exec.graph_exec_id}"
+        log_id = f"Graph #{input_data.graph_id}-V{input_data.graph_version}, exec-id: {graph_exec.id}"
         logger.info(f"Starting execution of {log_id}")
 
         for event in event_bus.listen(
-            graph_id=graph_exec.graph_id, graph_exec_id=graph_exec.graph_exec_id
+            user_id=graph_exec.user_id,
+            graph_id=graph_exec.graph_id,
+            graph_exec_id=graph_exec.id,
         ):
-            logger.info(
-                f"Execution {log_id} produced input {event.input_data} output {event.output_data}"
-            )
-
-            if not event.node_id:
+            if event.event_type == ExecutionEventType.GRAPH_EXEC_UPDATE:
                 if event.status in [
                     ExecutionStatus.COMPLETED,
                     ExecutionStatus.TERMINATED,
@@ -105,6 +88,10 @@ class AgentExecutorBlock(Block):
                 else:
                     continue
 
+            logger.debug(
+                f"Execution {log_id} produced input {event.input_data} output {event.output_data}"
+            )
+
             if not event.block_id:
                 logger.warning(f"{log_id} received event without block_id {event}")
                 continue
@@ -119,5 +106,7 @@ class AgentExecutorBlock(Block):
                 continue
 
             for output_data in event.output_data.get("output", []):
-                logger.info(f"Execution {log_id} produced {output_name}: {output_data}")
+                logger.debug(
+                    f"Execution {log_id} produced {output_name}: {output_data}"
+                )
                 yield output_name, output_data

autogpt_platform/backend/backend/blocks/apollo/models.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any, Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 from backend.data.model import SchemaField
 
@@ -143,11 +143,12 @@ class ContactEmail(BaseModel):
 class EmploymentHistory(BaseModel):
     """An employment history in Apollo"""
 
-    class Config:
-        extra = "allow"
-        arbitrary_types_allowed = True
-        from_attributes = True
-        populate_by_name = True
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+        from_attributes=True,
+        populate_by_name=True,
+    )
 
     _id: Optional[str] = None
     created_at: Optional[str] = None
@@ -188,11 +189,12 @@ class TypedCustomField(BaseModel):
 class Pagination(BaseModel):
     """Pagination in Apollo"""
 
-    class Config:
-        extra = "allow"  # Allow extra fields
-        arbitrary_types_allowed = True  # Allow any type
-        from_attributes = True  # Allow from_orm
-        populate_by_name = True  # Allow field aliases to work both ways
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+        from_attributes=True,
+        populate_by_name=True,
+    )
 
     page: int = 0
     per_page: int = 0
@@ -230,11 +232,12 @@ class PhoneNumber(BaseModel):
 class Organization(BaseModel):
     """An organization in Apollo"""
 
-    class Config:
-        extra = "allow"
-        arbitrary_types_allowed = True
-        from_attributes = True
-        populate_by_name = True
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+        from_attributes=True,
+        populate_by_name=True,
+    )
 
     id: Optional[str] = "N/A"
     name: Optional[str] = "N/A"
@@ -268,11 +271,12 @@ class Organization(BaseModel):
 class Contact(BaseModel):
     """A contact in Apollo"""
 
-    class Config:
-        extra = "allow"
-        arbitrary_types_allowed = True
-        from_attributes = True
-        populate_by_name = True
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+        from_attributes=True,
+        populate_by_name=True,
+    )
 
     contact_roles: list[Any] = []
     id: Optional[str] = None
@@ -369,14 +373,14 @@ If a company has several office locations, results are still based on the headqu
 
 To exclude companies based on location, use the organization_not_locations parameter.
 """,
-        default=[],
+        default_factory=list,
     )
     organizations_not_locations: list[str] = SchemaField(
         description="""Exclude companies from search results based on the location of the company headquarters. You can use cities, US states, and countries as locations to exclude.
 
 This parameter is useful for ensuring you do not prospect in an undesirable territory. For example, if you use ireland as a value, no Ireland-based companies will appear in your search results.
 """,
-        default=[],
+        default_factory=list,
     )
     q_organization_keyword_tags: list[str] = SchemaField(
         description="""Filter search results based on keywords associated with companies. For example, you can enter mining as a value to return only companies that have an association with the mining industry."""
@@ -390,7 +394,7 @@ If the value you enter for this parameter does not match with a company's name,
         description="""The Apollo IDs for the companies you want to include in your search results. Each company in the Apollo database is assigned a unique ID.
 
 To find IDs, identify the values for organization_id when you call this endpoint.""",
-        default=[],
+        default_factory=list,
     )
     max_results: int = SchemaField(
         description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
@@ -443,14 +447,14 @@ Results also include job titles with the same terms, even if they are not exact
 
 Use this parameter in combination with the person_seniorities[] parameter to find people based on specific job functions and seniority levels.
 """,
-        default=[],
+        default_factory=list,
         placeholder="marketing manager",
     )
     person_locations: list[str] = SchemaField(
         description="""The location where people live. You can search across cities, US states, and countries.
 
 To find people based on the headquarters locations of their current employer, use the organization_locations parameter.""",
-        default=[],
+        default_factory=list,
     )
     person_seniorities: list[SenorityLevels] = SchemaField(
         description="""The job seniority that people hold within their current employer. This enables you to find people that currently hold positions at certain reporting levels, such as Director level or senior IC level.
@@ -460,7 +464,7 @@ For a person to be included in search results, they only need to match 1 of the
 Searches only return results based on their current job title, so searching for Director-level employees only returns people that currently hold a Director-level title. If someone was previously a Director, but is currently a VP, they would not be included in your search results.
 
 Use this parameter in combination with the person_titles[] parameter to find people based on specific job functions and seniority levels.""",
-        default=[],
+        default_factory=list,
     )
     organization_locations: list[str] = SchemaField(
         description="""The location of the company headquarters for a person's current employer. You can search across cities, US states, and countries.
@@ -468,7 +472,7 @@ Use this parameter in combination with the person_titles[] parameter to find peo
 If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, people that work for the Boston-based company will not appear in your results, even if they match other parameters.
 
 To find people based on their personal location, use the person_locations parameter.""",
-        default=[],
+        default_factory=list,
     )
     q_organization_domains: list[str] = SchemaField(
         description="""The domain name for the person's employer. This can be the current employer or a previous employer. Do not include www., the @ symbol, or similar.
@@ -476,23 +480,23 @@ To find people based on their personal location, use the person_locations parame
 You can add multiple domains to search across companies.
 
   Examples: apollo.io and microsoft.com""",
-        default=[],
+        default_factory=list,
     )
     contact_email_statuses: list[ContactEmailStatuses] = SchemaField(
         description="""The email statuses for the people you want to find. You can add multiple statuses to expand your search.""",
-        default=[],
+        default_factory=list,
     )
     organization_ids: list[str] = SchemaField(
         description="""The Apollo IDs for the companies (employers) you want to include in your search results. Each company in the Apollo database is assigned a unique ID.
 
 To find IDs, call the Organization Search endpoint and identify the values for organization_id.""",
-        default=[],
+        default_factory=list,
     )
     organization_num_empoloyees_range: list[int] = SchemaField(
         description="""The number range of employees working for the company. This enables you to find companies based on headcount. You can add multiple ranges to expand your search results.
 
 Each range you add needs to be a string, with the upper and lower numbers of the range separated only by a comma.""",
-        default=[],
+        default_factory=list,
     )
     q_keywords: str = SchemaField(
         description="""A string of words over which we want to filter the results""",
@@ -522,11 +526,12 @@ Use the page parameter to search the different pages of data.""",
 class SearchPeopleResponse(BaseModel):
     """Response from Apollo's search people API"""
 
-    class Config:
-        extra = "allow"  # Allow extra fields
-        arbitrary_types_allowed = True  # Allow any type
-        from_attributes = True  # Allow from_orm
-        populate_by_name = True  # Allow field aliases to work both ways
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+        from_attributes=True,
+        populate_by_name=True,
+    )
 
     breadcrumbs: list[Breadcrumb] = []
     partial_results_only: bool = True

autogpt_platform/backend/backend/blocks/apollo/organization.py

@@ -32,18 +32,18 @@ If a company has several office locations, results are still based on the headqu
 
 To exclude companies based on location, use the organization_not_locations parameter.
 """,
-            default=[],
+            default_factory=list,
         )
         organizations_not_locations: list[str] = SchemaField(
             description="""Exclude companies from search results based on the location of the company headquarters. You can use cities, US states, and countries as locations to exclude.
 
 This parameter is useful for ensuring you do not prospect in an undesirable territory. For example, if you use ireland as a value, no Ireland-based companies will appear in your search results.
 """,
-            default=[],
+            default_factory=list,
         )
         q_organization_keyword_tags: list[str] = SchemaField(
             description="""Filter search results based on keywords associated with companies. For example, you can enter mining as a value to return only companies that have an association with the mining industry.""",
-            default=[],
+            default_factory=list,
         )
         q_organization_name: str = SchemaField(
             description="""Filter search results to include a specific company name.
@@ -56,7 +56,7 @@ If the value you enter for this parameter does not match with a company's name,
             description="""The Apollo IDs for the companies you want to include in your search results. Each company in the Apollo database is assigned a unique ID.
 
 To find IDs, identify the values for organization_id when you call this endpoint.""",
-            default=[],
+            default_factory=list,
         )
         max_results: int = SchemaField(
             description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
@@ -72,7 +72,7 @@ To find IDs, identify the values for organization_id when you call this endpoint
     class Output(BlockSchema):
         organizations: list[Organization] = SchemaField(
             description="List of organizations found",
-            default=[],
+            default_factory=list,
         )
         organization: Organization = SchemaField(
             description="Each found organization, one at a time",

autogpt_platform/backend/backend/blocks/apollo/people.py

@@ -26,14 +26,14 @@ class SearchPeopleBlock(Block):
 
         Use this parameter in combination with the person_seniorities[] parameter to find people based on specific job functions and seniority levels.
         """,
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         person_locations: list[str] = SchemaField(
             description="""The location where people live. You can search across cities, US states, and countries.
 
         To find people based on the headquarters locations of their current employer, use the organization_locations parameter.""",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         person_seniorities: list[SenorityLevels] = SchemaField(
@@ -44,7 +44,7 @@ class SearchPeopleBlock(Block):
         Searches only return results based on their current job title, so searching for Director-level employees only returns people that currently hold a Director-level title. If someone was previously a Director, but is currently a VP, they would not be included in your search results.
 
         Use this parameter in combination with the person_titles[] parameter to find people based on specific job functions and seniority levels.""",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         organization_locations: list[str] = SchemaField(
@@ -53,7 +53,7 @@ class SearchPeopleBlock(Block):
         If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, people that work for the Boston-based company will not appear in your results, even if they match other parameters.
 
         To find people based on their personal location, use the person_locations parameter.""",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         q_organization_domains: list[str] = SchemaField(
@@ -62,26 +62,26 @@ class SearchPeopleBlock(Block):
         You can add multiple domains to search across companies.
 
           Examples: apollo.io and microsoft.com""",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         contact_email_statuses: list[ContactEmailStatuses] = SchemaField(
             description="""The email statuses for the people you want to find. You can add multiple statuses to expand your search.""",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         organization_ids: list[str] = SchemaField(
             description="""The Apollo IDs for the companies (employers) you want to include in your search results. Each company in the Apollo database is assigned a unique ID.
 
         To find IDs, call the Organization Search endpoint and identify the values for organization_id.""",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         organization_num_empoloyees_range: list[int] = SchemaField(
             description="""The number range of employees working for the company. This enables you to find companies based on headcount. You can add multiple ranges to expand your search results.
 
         Each range you add needs to be a string, with the upper and lower numbers of the range separated only by a comma.""",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         q_keywords: str = SchemaField(
@@ -104,7 +104,7 @@ class SearchPeopleBlock(Block):
     class Output(BlockSchema):
         people: list[Contact] = SchemaField(
             description="List of people found",
-            default=[],
+            default_factory=list,
         )
         person: Contact = SchemaField(
             description="Each found person, one at a time",

autogpt_platform/backend/backend/blocks/basic.py

@@ -1,35 +1,22 @@
 import enum
-from typing import TYPE_CHECKING, Any, List
+from typing import Any, List
 
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockInput,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
 from backend.data.model import SchemaField
 from backend.util import json
-from backend.util.file import MediaFile, store_media_file
+from backend.util.file import store_media_file
 from backend.util.mock import MockObject
-from backend.util.text import TextFormatter
-from backend.util.type import convert
-
-if TYPE_CHECKING:
-    from backend.data.graph import Link
-
-formatter = TextFormatter()
+from backend.util.type import MediaFileType, convert
 
 
 class FileStoreBlock(Block):
     class Input(BlockSchema):
-        file_in: MediaFile = SchemaField(
+        file_in: MediaFileType = SchemaField(
             description="The file to store in the temporary directory, it can be a URL, data URI, or local path."
         )
 
     class Output(BlockSchema):
-        file_out: MediaFile = SchemaField(
+        file_out: MediaFileType = SchemaField(
             description="The relative path to the stored file in the temporary directory."
         )
 
@@ -101,29 +88,6 @@ class StoreValueBlock(Block):
         yield "output", input_data.data or input_data.input
 
 
-class PrintToConsoleBlock(Block):
-    class Input(BlockSchema):
-        text: str = SchemaField(description="The text to print to the console.")
-
-    class Output(BlockSchema):
-        status: str = SchemaField(description="The status of the print operation.")
-
-    def __init__(self):
-        super().__init__(
-            id="f3b1c1b2-4c4f-4f0d-8d2f-4c4f0d8d2f4c",
-            description="Print the given text to the console, this is used for a debugging purpose.",
-            categories={BlockCategory.BASIC},
-            input_schema=PrintToConsoleBlock.Input,
-            output_schema=PrintToConsoleBlock.Output,
-            test_input={"text": "Hello, World!"},
-            test_output=("status", "printed"),
-        )
-
-    def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        print(">>>>> Print: ", input_data.text)
-        yield "status", "printed"
-
-
 class FindInDictionaryBlock(Block):
     class Input(BlockSchema):
         input: Any = SchemaField(description="Dictionary to lookup from")
@@ -184,192 +148,10 @@ class FindInDictionaryBlock(Block):
             yield "missing", input_data.input
 
 
-class AgentInputBlock(Block):
-    """
-    This block is used to provide input to the graph.
-
-    It takes in a value, name, description, default values list and bool to limit selection to default values.
-
-    It Outputs the value passed as input.
-    """
-
-    class Input(BlockSchema):
-        name: str = SchemaField(description="The name of the input.")
-        value: Any = SchemaField(
-            description="The value to be passed as input.",
-            default=None,
-        )
-        title: str | None = SchemaField(
-            description="The title of the input.", default=None, advanced=True
-        )
-        description: str | None = SchemaField(
-            description="The description of the input.",
-            default=None,
-            advanced=True,
-        )
-        placeholder_values: List[Any] = SchemaField(
-            description="The placeholder values to be passed as input.",
-            default=[],
-            advanced=True,
-        )
-        limit_to_placeholder_values: bool = SchemaField(
-            description="Whether to limit the selection to placeholder values.",
-            default=False,
-            advanced=True,
-        )
-        advanced: bool = SchemaField(
-            description="Whether to show the input in the advanced section, if the field is not required.",
-            default=False,
-            advanced=True,
-        )
-        secret: bool = SchemaField(
-            description="Whether the input should be treated as a secret.",
-            default=False,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        result: Any = SchemaField(description="The value passed as input.")
-
-    def __init__(self):
-        super().__init__(
-            id="c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-            description="This block is used to provide input to the graph.",
-            input_schema=AgentInputBlock.Input,
-            output_schema=AgentInputBlock.Output,
-            test_input=[
-                {
-                    "value": "Hello, World!",
-                    "name": "input_1",
-                    "description": "This is a test input.",
-                    "placeholder_values": [],
-                    "limit_to_placeholder_values": False,
-                },
-                {
-                    "value": "Hello, World!",
-                    "name": "input_2",
-                    "description": "This is a test input.",
-                    "placeholder_values": ["Hello, World!"],
-                    "limit_to_placeholder_values": True,
-                },
-            ],
-            test_output=[
-                ("result", "Hello, World!"),
-                ("result", "Hello, World!"),
-            ],
-            categories={BlockCategory.INPUT, BlockCategory.BASIC},
-            block_type=BlockType.INPUT,
-            static_output=True,
-        )
-
-    def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        yield "result", input_data.value
-
-
-class AgentOutputBlock(Block):
-    """
-    Records the output of the graph for users to see.
-
-    Behavior:
-        If `format` is provided and the `value` is of a type that can be formatted,
-        the block attempts to format the recorded_value using the `format`.
-        If formatting fails or no `format` is provided, the raw `value` is output.
-    """
-
-    class Input(BlockSchema):
-        value: Any = SchemaField(
-            description="The value to be recorded as output.",
-            default=None,
-            advanced=False,
-        )
-        name: str = SchemaField(description="The name of the output.")
-        title: str | None = SchemaField(
-            description="The title of the output.",
-            default=None,
-            advanced=True,
-        )
-        description: str | None = SchemaField(
-            description="The description of the output.",
-            default=None,
-            advanced=True,
-        )
-        format: str = SchemaField(
-            description="The format string to be used to format the recorded_value. Use Jinja2 syntax.",
-            default="",
-            advanced=True,
-        )
-        advanced: bool = SchemaField(
-            description="Whether to treat the output as advanced.",
-            default=False,
-            advanced=True,
-        )
-        secret: bool = SchemaField(
-            description="Whether the output should be treated as a secret.",
-            default=False,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        output: Any = SchemaField(description="The value recorded as output.")
-        name: Any = SchemaField(description="The name of the value recorded as output.")
-
-    def __init__(self):
-        super().__init__(
-            id="363ae599-353e-4804-937e-b2ee3cef3da4",
-            description="Stores the output of the graph for users to see.",
-            input_schema=AgentOutputBlock.Input,
-            output_schema=AgentOutputBlock.Output,
-            test_input=[
-                {
-                    "value": "Hello, World!",
-                    "name": "output_1",
-                    "description": "This is a test output.",
-                    "format": "{{ output_1 }}!!",
-                },
-                {
-                    "value": "42",
-                    "name": "output_2",
-                    "description": "This is another test output.",
-                    "format": "{{ output_2 }}",
-                },
-                {
-                    "value": MockObject(value="!!", key="key"),
-                    "name": "output_3",
-                    "description": "This is a test output with a mock object.",
-                    "format": "{{ output_3 }}",
-                },
-            ],
-            test_output=[
-                ("output", "Hello, World!!!"),
-                ("output", "42"),
-                ("output", MockObject(value="!!", key="key")),
-            ],
-            categories={BlockCategory.OUTPUT, BlockCategory.BASIC},
-            block_type=BlockType.OUTPUT,
-            static_output=True,
-        )
-
-    def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        """
-        Attempts to format the recorded_value using the fmt_string if provided.
-        If formatting fails or no fmt_string is given, returns the original recorded_value.
-        """
-        if input_data.format:
-            try:
-                yield "output", formatter.format_string(
-                    input_data.format, {input_data.name: input_data.value}
-                )
-            except Exception as e:
-                yield "output", f"Error: {e}, {input_data.value}"
-        else:
-            yield "output", input_data.value
-            yield "name", input_data.name
-
-
 class AddToDictionaryBlock(Block):
     class Input(BlockSchema):
         dictionary: dict[Any, Any] = SchemaField(
-            default={},
+            default_factory=dict,
             description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
         )
         key: str = SchemaField(
@@ -385,7 +167,7 @@ class AddToDictionaryBlock(Block):
             advanced=False,
         )
         entries: dict[Any, Any] = SchemaField(
-            default={},
+            default_factory=dict,
             description="The entries to add to the dictionary. This is the batch version of the `key` and `value` fields.",
             advanced=True,
         )
@@ -447,7 +229,7 @@ class AddToDictionaryBlock(Block):
 class AddToListBlock(Block):
     class Input(BlockSchema):
         list: List[Any] = SchemaField(
-            default=[],
+            default_factory=list,
             advanced=False,
             description="The list to add the entry to. If not provided, a new list will be created.",
         )
@@ -457,7 +239,7 @@ class AddToListBlock(Block):
             default=None,
         )
         entries: List[Any] = SchemaField(
-            default=[],
+            default_factory=lambda: list(),
             description="The entries to add to the list. This is the batch version of the `entry` field.",
             advanced=True,
         )
@@ -466,17 +248,6 @@ class AddToListBlock(Block):
             description="The position to insert the new entry. If not provided, the entry will be appended to the end of the list.",
         )
 
-        @classmethod
-        def get_missing_links(cls, data: BlockInput, links: List["Link"]) -> set[str]:
-            return super().get_missing_links(
-                data,
-                [
-                    link
-                    for link in links
-                    if link.sink_name != "list" or link.sink_id != link.source_id
-                ],
-            )
-
     class Output(BlockSchema):
         updated_list: List[Any] = SchemaField(
             description="The list with the new entry added."

autogpt_platform/backend/backend/blocks/code_executor.py

@@ -55,7 +55,7 @@ class CodeExecutionBlock(Block):
                 "These commands are executed with `sh`, in the foreground."
             ),
             placeholder="pip install cowsay",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
 
@@ -207,7 +207,7 @@ class InstantiationBlock(Block):
                 "These commands are executed with `sh`, in the foreground."
             ),
             placeholder="pip install cowsay",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
 

autogpt_platform/backend/backend/blocks/compass/triggers.py

@@ -8,6 +8,7 @@ from backend.data.block import (
     BlockSchema,
 )
 from backend.data.model import SchemaField
+from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks.compass import CompassWebhookType
 
 
@@ -42,7 +43,7 @@ class CompassAITriggerBlock(Block):
             input_schema=CompassAITriggerBlock.Input,
             output_schema=CompassAITriggerBlock.Output,
             webhook_config=BlockManualWebhookConfig(
-                provider="compass",
+                provider=ProviderName.COMPASS,
                 webhook_type=CompassWebhookType.TRANSCRIPTION,
             ),
             test_input=[

autogpt_platform/backend/backend/blocks/csv.py

@@ -34,7 +34,7 @@ class ReadCsvBlock(Block):
         )
         skip_columns: list[str] = SchemaField(
             description="The columns to skip from the start of the row",
-            default=[],
+            default_factory=list,
         )
 
     class Output(BlockSchema):

autogpt_platform/backend/backend/blocks/exa/contents.py

@@ -49,7 +49,7 @@ class ExaContentsBlock(Block):
     class Output(BlockSchema):
         results: list = SchemaField(
             description="List of document contents",
-            default=[],
+            default_factory=list,
         )
         error: str = SchemaField(description="Error message if the request failed")
 

autogpt_platform/backend/backend/blocks/exa/search.py

@@ -38,11 +38,11 @@ class ExaSearchBlock(Block):
         )
         include_domains: List[str] = SchemaField(
             description="Domains to include in search",
-            default=[],
+            default_factory=list,
         )
         exclude_domains: List[str] = SchemaField(
             description="Domains to exclude from search",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         start_crawl_date: datetime = SchemaField(
@@ -59,12 +59,12 @@ class ExaSearchBlock(Block):
         )
         include_text: List[str] = SchemaField(
             description="Text patterns to include",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         exclude_text: List[str] = SchemaField(
             description="Text patterns to exclude",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         contents: ContentSettings = SchemaField(
@@ -76,7 +76,7 @@ class ExaSearchBlock(Block):
     class Output(BlockSchema):
         results: list = SchemaField(
             description="List of search results",
-            default=[],
+            default_factory=list,
         )
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/exa/similar.py

@@ -26,12 +26,12 @@ class ExaFindSimilarBlock(Block):
         )
         include_domains: List[str] = SchemaField(
             description="Domains to include in search",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         exclude_domains: List[str] = SchemaField(
             description="Domains to exclude from search",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         start_crawl_date: datetime = SchemaField(
@@ -48,12 +48,12 @@ class ExaFindSimilarBlock(Block):
         )
         include_text: List[str] = SchemaField(
             description="Text patterns to include (max 1 string, up to 5 words)",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         exclude_text: List[str] = SchemaField(
             description="Text patterns to exclude (max 1 string, up to 5 words)",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         contents: ContentSettings = SchemaField(
@@ -65,7 +65,7 @@ class ExaFindSimilarBlock(Block):
     class Output(BlockSchema):
         results: List[Any] = SchemaField(
             description="List of similar documents with title, URL, published date, author, and score",
-            default=[],
+            default_factory=list,
         )
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -42,7 +42,7 @@ class AIVideoGeneratorBlock(Block):
             description="Error message if video generation failed."
         )
         logs: list[str] = SchemaField(
-            description="Generation progress logs.", optional=True
+            description="Generation progress logs.",
         )
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/generic_webhook/triggers.py

@@ -0,0 +1,51 @@
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockManualWebhookConfig,
+    BlockOutput,
+    BlockSchema,
+)
+from backend.data.model import SchemaField
+from backend.integrations.providers import ProviderName
+from backend.integrations.webhooks.generic import GenericWebhookType
+
+
+class GenericWebhookTriggerBlock(Block):
+    class Input(BlockSchema):
+        payload: dict = SchemaField(hidden=True, default_factory=dict)
+        constants: dict = SchemaField(
+            description="The constants to be set when the block is put on the graph",
+            default_factory=dict,
+        )
+
+    class Output(BlockSchema):
+        payload: dict = SchemaField(
+            description="The complete webhook payload that was received from the generic webhook."
+        )
+        constants: dict = SchemaField(
+            description="The constants to be set when the block is put on the graph"
+        )
+
+    example_payload = {"message": "Hello, World!"}
+
+    def __init__(self):
+        super().__init__(
+            id="8fa8c167-2002-47ce-aba8-97572fc5d387",
+            description="This block will output the contents of the generic input for the webhook.",
+            categories={BlockCategory.INPUT},
+            input_schema=GenericWebhookTriggerBlock.Input,
+            output_schema=GenericWebhookTriggerBlock.Output,
+            webhook_config=BlockManualWebhookConfig(
+                provider=ProviderName.GENERIC_WEBHOOK,
+                webhook_type=GenericWebhookType.PLAIN,
+            ),
+            test_input={"constants": {"key": "value"}, "payload": self.example_payload},
+            test_output=[
+                ("constants", {"key": "value"}),
+                ("payload", self.example_payload),
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        yield "constants", input_data.constants
+        yield "payload", input_data.payload

autogpt_platform/backend/backend/blocks/github/triggers.py

@@ -12,6 +12,7 @@ from backend.data.block import (
     BlockWebhookConfig,
 )
 from backend.data.model import SchemaField
+from backend.integrations.providers import ProviderName
 
 from ._auth import (
     TEST_CREDENTIALS,
@@ -36,7 +37,7 @@ class GitHubTriggerBase:
             placeholder="{owner}/{repo}",
         )
         # --8<-- [start:example-payload-field]
-        payload: dict = SchemaField(hidden=True, default={})
+        payload: dict = SchemaField(hidden=True, default_factory=dict)
         # --8<-- [end:example-payload-field]
 
     class Output(BlockSchema):
@@ -123,7 +124,7 @@ class GithubPullRequestTriggerBlock(GitHubTriggerBase, Block):
             output_schema=GithubPullRequestTriggerBlock.Output,
             # --8<-- [start:example-webhook_config]
             webhook_config=BlockWebhookConfig(
-                provider="github",
+                provider=ProviderName.GITHUB,
                 webhook_type=GithubWebhookType.REPO,
                 resource_format="{repo}",
                 event_filter_input="events",

autogpt_platform/backend/backend/blocks/http.py

@@ -1,11 +1,16 @@
 import json
+import logging
 from enum import Enum
 from typing import Any
 
+from requests.exceptions import HTTPError, RequestException
+
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
 from backend.util.request import requests
 
+logger = logging.getLogger(name=__name__)
+
 
 class HttpMethod(Enum):
     GET = "GET"
@@ -29,7 +34,7 @@ class SendWebRequestBlock(Block):
         )
         headers: dict[str, str] = SchemaField(
             description="The headers to include in the request",
-            default={},
+            default_factory=dict,
         )
         json_format: bool = SchemaField(
             title="JSON format",
@@ -43,8 +48,9 @@ class SendWebRequestBlock(Block):
 
     class Output(BlockSchema):
         response: object = SchemaField(description="The response from the server")
-        client_error: object = SchemaField(description="The error on 4xx status codes")
-        server_error: object = SchemaField(description="The error on 5xx status codes")
+        client_error: object = SchemaField(description="Errors on 4xx status codes")
+        server_error: object = SchemaField(description="Errors on 5xx status codes")
+        error: str = SchemaField(description="Errors for all other exceptions")
 
     def __init__(self):
         super().__init__(
@@ -68,20 +74,40 @@ class SendWebRequestBlock(Block):
                     # we should send it as plain text instead
                     input_data.json_format = False
 
-        response = requests.request(
-            input_data.method.value,
-            input_data.url,
-            headers=input_data.headers,
-            json=body if input_data.json_format else None,
-            data=body if not input_data.json_format else None,
-        )
-        result = response.json() if input_data.json_format else response.text
-
-        if response.status_code // 100 == 2:
+        try:
+            response = requests.request(
+                input_data.method.value,
+                input_data.url,
+                headers=input_data.headers,
+                json=body if input_data.json_format else None,
+                data=body if not input_data.json_format else None,
+            )
+            result = response.json() if input_data.json_format else response.text
             yield "response", result
-        elif response.status_code // 100 == 4:
-            yield "client_error", result
-        elif response.status_code // 100 == 5:
-            yield "server_error", result
-        else:
-            raise ValueError(f"Unexpected status code: {response.status_code}")
+
+        except HTTPError as e:
+            # Handle error responses
+            try:
+                result = e.response.json() if input_data.json_format else str(e)
+            except json.JSONDecodeError:
+                result = str(e)
+
+            if 400 <= e.response.status_code < 500:
+                yield "client_error", result
+            elif 500 <= e.response.status_code < 600:
+                yield "server_error", result
+            else:
+                error_msg = (
+                    "Unexpected status code "
+                    f"{e.response.status_code} '{e.response.reason}'"
+                )
+                logger.warning(error_msg)
+                yield "error", error_msg
+
+        except RequestException as e:
+            # Handle other request-related exceptions
+            yield "error", str(e)
+
+        except Exception as e:
+            # Catch any other unexpected exceptions
+            yield "error", str(e)

autogpt_platform/backend/backend/blocks/hubspot/company.py

@@ -15,7 +15,8 @@ class HubSpotCompanyBlock(Block):
             description="Operation to perform (create, update, get)", default="get"
         )
         company_data: dict = SchemaField(
-            description="Company data for create/update operations", default={}
+            description="Company data for create/update operations",
+            default_factory=dict,
         )
         domain: str = SchemaField(
             description="Company domain for get/update operations", default=""

autogpt_platform/backend/backend/blocks/hubspot/contact.py

@@ -15,7 +15,8 @@ class HubSpotContactBlock(Block):
             description="Operation to perform (create, update, get)", default="get"
         )
         contact_data: dict = SchemaField(
-            description="Contact data for create/update operations", default={}
+            description="Contact data for create/update operations",
+            default_factory=dict,
         )
         email: str = SchemaField(
             description="Email address for get/update operations", default=""

autogpt_platform/backend/backend/blocks/hubspot/engagement.py

@@ -19,7 +19,7 @@ class HubSpotEngagementBlock(Block):
         )
         email_data: dict = SchemaField(
             description="Email data including recipient, subject, content",
-            default={},
+            default_factory=dict,
         )
         contact_id: str = SchemaField(
             description="Contact ID for engagement tracking", default=""
@@ -27,7 +27,6 @@ class HubSpotEngagementBlock(Block):
         timeframe_days: int = SchemaField(
             description="Number of days to look back for engagement",
             default=30,
-            optional=True,
         )
 
     class Output(BlockSchema):

autogpt_platform/backend/backend/blocks/io.py

@@ -0,0 +1,556 @@
+import copy
+from datetime import date, time
+from typing import Any, Optional
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
+from backend.data.model import SchemaField
+from backend.util.file import store_media_file
+from backend.util.mock import MockObject
+from backend.util.settings import Config
+from backend.util.text import TextFormatter
+from backend.util.type import LongTextType, MediaFileType, ShortTextType
+
+formatter = TextFormatter()
+config = Config()
+
+
+class AgentInputBlock(Block):
+    """
+    This block is used to provide input to the graph.
+
+    It takes in a value, name, description, default values list and bool to limit selection to default values.
+
+    It Outputs the value passed as input.
+    """
+
+    class Input(BlockSchema):
+        name: str = SchemaField(description="The name of the input.")
+        value: Any = SchemaField(
+            description="The value to be passed as input.",
+            default=None,
+        )
+        title: str | None = SchemaField(
+            description="The title of the input.", default=None, advanced=True
+        )
+        description: str | None = SchemaField(
+            description="The description of the input.",
+            default=None,
+            advanced=True,
+        )
+        placeholder_values: list = SchemaField(
+            description="The placeholder values to be passed as input.",
+            default_factory=list,
+            advanced=True,
+            hidden=True,
+        )
+        advanced: bool = SchemaField(
+            description="Whether to show the input in the advanced section, if the field is not required.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the input should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
+
+        def generate_schema(self):
+            schema = copy.deepcopy(self.get_field_schema("value"))
+            if possible_values := self.placeholder_values:
+                schema["enum"] = possible_values
+            return schema
+
+    class Output(BlockSchema):
+        result: Any = SchemaField(description="The value passed as input.")
+
+    def __init__(self, **kwargs):
+        super().__init__(
+            **{
+                "id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
+                "description": "Base block for user inputs.",
+                "input_schema": AgentInputBlock.Input,
+                "output_schema": AgentInputBlock.Output,
+                "test_input": [
+                    {
+                        "value": "Hello, World!",
+                        "name": "input_1",
+                        "description": "Example test input.",
+                        "placeholder_values": [],
+                    },
+                    {
+                        "value": "Hello, World!",
+                        "name": "input_2",
+                        "description": "Example test input with placeholders.",
+                        "placeholder_values": ["Hello, World!"],
+                    },
+                ],
+                "test_output": [
+                    ("result", "Hello, World!"),
+                    ("result", "Hello, World!"),
+                ],
+                "categories": {BlockCategory.INPUT, BlockCategory.BASIC},
+                "block_type": BlockType.INPUT,
+                "static_output": True,
+                **kwargs,
+            }
+        )
+
+    def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
+        if input_data.value is not None:
+            yield "result", input_data.value
+
+
+class AgentOutputBlock(Block):
+    """
+    Records the output of the graph for users to see.
+
+    Behavior:
+        If `format` is provided and the `value` is of a type that can be formatted,
+        the block attempts to format the recorded_value using the `format`.
+        If formatting fails or no `format` is provided, the raw `value` is output.
+    """
+
+    class Input(BlockSchema):
+        value: Any = SchemaField(
+            description="The value to be recorded as output.",
+            default=None,
+            advanced=False,
+        )
+        name: str = SchemaField(description="The name of the output.")
+        title: str | None = SchemaField(
+            description="The title of the output.",
+            default=None,
+            advanced=True,
+        )
+        description: str | None = SchemaField(
+            description="The description of the output.",
+            default=None,
+            advanced=True,
+        )
+        format: str = SchemaField(
+            description="The format string to be used to format the recorded_value. Use Jinja2 syntax.",
+            default="",
+            advanced=True,
+        )
+        advanced: bool = SchemaField(
+            description="Whether to treat the output as advanced.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the output should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
+
+        def generate_schema(self):
+            return self.get_field_schema("value")
+
+    class Output(BlockSchema):
+        output: Any = SchemaField(description="The value recorded as output.")
+        name: Any = SchemaField(description="The name of the value recorded as output.")
+
+    def __init__(self):
+        super().__init__(
+            id="363ae599-353e-4804-937e-b2ee3cef3da4",
+            description="Stores the output of the graph for users to see.",
+            input_schema=AgentOutputBlock.Input,
+            output_schema=AgentOutputBlock.Output,
+            test_input=[
+                {
+                    "value": "Hello, World!",
+                    "name": "output_1",
+                    "description": "This is a test output.",
+                    "format": "{{ output_1 }}!!",
+                },
+                {
+                    "value": "42",
+                    "name": "output_2",
+                    "description": "This is another test output.",
+                    "format": "{{ output_2 }}",
+                },
+                {
+                    "value": MockObject(value="!!", key="key"),
+                    "name": "output_3",
+                    "description": "This is a test output with a mock object.",
+                    "format": "{{ output_3 }}",
+                },
+            ],
+            test_output=[
+                ("output", "Hello, World!!!"),
+                ("output", "42"),
+                ("output", MockObject(value="!!", key="key")),
+            ],
+            categories={BlockCategory.OUTPUT, BlockCategory.BASIC},
+            block_type=BlockType.OUTPUT,
+            static_output=True,
+        )
+
+    def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
+        """
+        Attempts to format the recorded_value using the fmt_string if provided.
+        If formatting fails or no fmt_string is given, returns the original recorded_value.
+        """
+        if input_data.format:
+            try:
+                yield "output", formatter.format_string(
+                    input_data.format, {input_data.name: input_data.value}
+                )
+            except Exception as e:
+                yield "output", f"Error: {e}, {input_data.value}"
+        else:
+            yield "output", input_data.value
+            yield "name", input_data.name
+
+
+class AgentShortTextInputBlock(AgentInputBlock):
+    class Input(AgentInputBlock.Input):
+        value: Optional[ShortTextType] = SchemaField(
+            description="Short text input.",
+            default=None,
+            advanced=False,
+            title="Default Value",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: str = SchemaField(description="Short text result.")
+
+    def __init__(self):
+        super().__init__(
+            id="7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
+            description="Block for short text input (single-line).",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentShortTextInputBlock.Input,
+            output_schema=AgentShortTextInputBlock.Output,
+            test_input=[
+                {
+                    "value": "Hello",
+                    "name": "short_text_1",
+                    "description": "Short text example 1",
+                    "placeholder_values": [],
+                },
+                {
+                    "value": "Quick test",
+                    "name": "short_text_2",
+                    "description": "Short text example 2",
+                    "placeholder_values": ["Quick test", "Another option"],
+                },
+            ],
+            test_output=[
+                ("result", "Hello"),
+                ("result", "Quick test"),
+            ],
+        )
+
+
+class AgentLongTextInputBlock(AgentInputBlock):
+    class Input(AgentInputBlock.Input):
+        value: Optional[LongTextType] = SchemaField(
+            description="Long text input (potentially multi-line).",
+            default=None,
+            advanced=False,
+            title="Default Value",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: str = SchemaField(description="Long text result.")
+
+    def __init__(self):
+        super().__init__(
+            id="90a56ffb-7024-4b2b-ab50-e26c5e5ab8ba",
+            description="Block for long text input (multi-line).",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentLongTextInputBlock.Input,
+            output_schema=AgentLongTextInputBlock.Output,
+            test_input=[
+                {
+                    "value": "Lorem ipsum dolor sit amet...",
+                    "name": "long_text_1",
+                    "description": "Long text example 1",
+                    "placeholder_values": [],
+                },
+                {
+                    "value": "Another multiline text input.",
+                    "name": "long_text_2",
+                    "description": "Long text example 2",
+                    "placeholder_values": ["Another multiline text input."],
+                },
+            ],
+            test_output=[
+                ("result", "Lorem ipsum dolor sit amet..."),
+                ("result", "Another multiline text input."),
+            ],
+        )
+
+
+class AgentNumberInputBlock(AgentInputBlock):
+    class Input(AgentInputBlock.Input):
+        value: Optional[int] = SchemaField(
+            description="Number input.",
+            default=None,
+            advanced=False,
+            title="Default Value",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: int = SchemaField(description="Number result.")
+
+    def __init__(self):
+        super().__init__(
+            id="96dae2bb-97a2-41c2-bd2f-13a3b5a8ea98",
+            description="Block for number input.",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentNumberInputBlock.Input,
+            output_schema=AgentNumberInputBlock.Output,
+            test_input=[
+                {
+                    "value": 42,
+                    "name": "number_input_1",
+                    "description": "Number example 1",
+                    "placeholder_values": [],
+                },
+                {
+                    "value": 314,
+                    "name": "number_input_2",
+                    "description": "Number example 2",
+                    "placeholder_values": [314, 2718],
+                },
+            ],
+            test_output=[
+                ("result", 42),
+                ("result", 314),
+            ],
+        )
+
+
+class AgentDateInputBlock(AgentInputBlock):
+    class Input(AgentInputBlock.Input):
+        value: Optional[date] = SchemaField(
+            description="Date input (YYYY-MM-DD).",
+            default=None,
+            advanced=False,
+            title="Default Value",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: date = SchemaField(description="Date result.")
+
+    def __init__(self):
+        super().__init__(
+            id="7e198b09-4994-47db-8b4d-952d98241817",
+            description="Block for date input.",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentDateInputBlock.Input,
+            output_schema=AgentDateInputBlock.Output,
+            test_input=[
+                {
+                    # If your system can parse JSON date strings to date objects
+                    "value": str(date(2025, 3, 19)),
+                    "name": "date_input_1",
+                    "description": "Example date input 1",
+                },
+                {
+                    "value": str(date(2023, 12, 31)),
+                    "name": "date_input_2",
+                    "description": "Example date input 2",
+                },
+            ],
+            test_output=[
+                ("result", date(2025, 3, 19)),
+                ("result", date(2023, 12, 31)),
+            ],
+        )
+
+
+class AgentTimeInputBlock(AgentInputBlock):
+    class Input(AgentInputBlock.Input):
+        value: Optional[time] = SchemaField(
+            description="Time input (HH:MM:SS).",
+            default=None,
+            advanced=False,
+            title="Default Value",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: time = SchemaField(description="Time result.")
+
+    def __init__(self):
+        super().__init__(
+            id="2a1c757e-86cf-4c7e-aacf-060dc382e434",
+            description="Block for time input.",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentTimeInputBlock.Input,
+            output_schema=AgentTimeInputBlock.Output,
+            test_input=[
+                {
+                    "value": str(time(9, 30, 0)),
+                    "name": "time_input_1",
+                    "description": "Time example 1",
+                },
+                {
+                    "value": str(time(23, 59, 59)),
+                    "name": "time_input_2",
+                    "description": "Time example 2",
+                },
+            ],
+            test_output=[
+                ("result", time(9, 30, 0)),
+                ("result", time(23, 59, 59)),
+            ],
+        )
+
+
+class AgentFileInputBlock(AgentInputBlock):
+    """
+    A simplified file-upload block. In real usage, you might have a custom
+    file type or handle binary data. Here, we'll store a string path as the example.
+    """
+
+    class Input(AgentInputBlock.Input):
+        value: Optional[MediaFileType] = SchemaField(
+            description="Path or reference to an uploaded file.",
+            default=None,
+            advanced=False,
+            title="Default Value",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: str = SchemaField(description="File reference/path result.")
+
+    def __init__(self):
+        super().__init__(
+            id="95ead23f-8283-4654-aef3-10c053b74a31",
+            description="Block for file upload input (string path for example).",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentFileInputBlock.Input,
+            output_schema=AgentFileInputBlock.Output,
+            test_input=[
+                {
+                    "value": "data:image/png;base64,MQ==",
+                    "name": "file_upload_1",
+                    "description": "Example file upload 1",
+                },
+            ],
+            test_output=[
+                ("result", str),
+            ],
+        )
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        graph_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        if not input_data.value:
+            return
+
+        file_path = store_media_file(
+            graph_exec_id=graph_exec_id,
+            file=input_data.value,
+            return_content=False,
+        )
+        yield "result", file_path
+
+
+class AgentDropdownInputBlock(AgentInputBlock):
+    """
+    A specialized text input block that relies on placeholder_values to present a dropdown.
+    """
+
+    class Input(AgentInputBlock.Input):
+        value: Optional[str] = SchemaField(
+            description="Text selected from a dropdown.",
+            default=None,
+            advanced=False,
+            title="Default Value",
+        )
+        placeholder_values: list = SchemaField(
+            description="Possible values for the dropdown.",
+            default_factory=list,
+            advanced=False,
+            title="Dropdown Options",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: str = SchemaField(description="Selected dropdown value.")
+
+    def __init__(self):
+        super().__init__(
+            id="655d6fdf-a334-421c-b733-520549c07cd1",
+            description="Block for dropdown text selection.",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentDropdownInputBlock.Input,
+            output_schema=AgentDropdownInputBlock.Output,
+            test_input=[
+                {
+                    "value": "Option A",
+                    "name": "dropdown_1",
+                    "placeholder_values": ["Option A", "Option B", "Option C"],
+                    "description": "Dropdown example 1",
+                },
+                {
+                    "value": "Option C",
+                    "name": "dropdown_2",
+                    "placeholder_values": ["Option A", "Option B", "Option C"],
+                    "description": "Dropdown example 2",
+                },
+            ],
+            test_output=[
+                ("result", "Option A"),
+                ("result", "Option C"),
+            ],
+        )
+
+
+class AgentToggleInputBlock(AgentInputBlock):
+    class Input(AgentInputBlock.Input):
+        value: bool = SchemaField(
+            description="Boolean toggle input.",
+            default=False,
+            advanced=False,
+            title="Default Value",
+        )
+
+    class Output(AgentInputBlock.Output):
+        result: bool = SchemaField(description="Boolean toggle result.")
+
+    def __init__(self):
+        super().__init__(
+            id="cbf36ab5-df4a-43b6-8a7f-f7ed8652116e",
+            description="Block for boolean toggle input.",
+            disabled=not config.enable_agent_input_subtype_blocks,
+            input_schema=AgentToggleInputBlock.Input,
+            output_schema=AgentToggleInputBlock.Output,
+            test_input=[
+                {
+                    "value": True,
+                    "name": "toggle_1",
+                    "description": "Toggle example 1",
+                },
+                {
+                    "value": False,
+                    "name": "toggle_2",
+                    "description": "Toggle example 2",
+                },
+            ],
+            test_output=[
+                ("result", True),
+                ("result", False),
+            ],
+        )
+
+
+IO_BLOCK_IDs = [
+    AgentInputBlock().id,
+    AgentOutputBlock().id,
+    AgentShortTextInputBlock().id,
+    AgentLongTextInputBlock().id,
+    AgentNumberInputBlock().id,
+    AgentDateInputBlock().id,
+    AgentTimeInputBlock().id,
+    AgentFileInputBlock().id,
+    AgentDropdownInputBlock().id,
+    AgentToggleInputBlock().id,
+]

autogpt_platform/backend/backend/blocks/iteration.py

@@ -11,13 +11,13 @@ class StepThroughItemsBlock(Block):
             advanced=False,
             description="The list or dictionary of items to iterate over",
             placeholder="[1, 2, 3, 4, 5] or {'key1': 'value1', 'key2': 'value2'}",
-            default=[],
+            default_factory=list,
         )
         items_object: dict = SchemaField(
             advanced=False,
             description="The list or dictionary of items to iterate over",
             placeholder="[1, 2, 3, 4, 5] or {'key1': 'value1', 'key2': 'value2'}",
-            default={},
+            default_factory=dict,
         )
         items_str: str = SchemaField(
             advanced=False,

autogpt_platform/backend/backend/blocks/jina/chunking.py

@@ -23,7 +23,7 @@ class JinaChunkingBlock(Block):
     class Output(BlockSchema):
         chunks: list = SchemaField(description="List of chunked texts")
         tokens: list = SchemaField(
-            description="List of token information for each chunk", optional=True
+            description="List of token information for each chunk",
         )
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/jina/search.py

@@ -1,4 +1,4 @@
-from groq._utils._utils import quote
+from urllib.parse import quote
 
 from backend.blocks.jina._auth import (
     TEST_CREDENTIALS,

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

@@ -28,8 +28,8 @@ class LinearCreateIssueBlock(Block):
         priority: int | None = SchemaField(
             description="Priority of the issue",
             default=None,
-            minimum=0,
-            maximum=4,
+            ge=0,
+            le=4,
         )
         project_name: str | None = SchemaField(
             description="Name of the project to create the issue on",

autogpt_platform/backend/backend/blocks/llm.py

@@ -4,30 +4,24 @@ from abc import ABC
 from enum import Enum, EnumMeta
 from json import JSONDecodeError
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Iterable, List, Literal, NamedTuple, Optional
-
-from pydantic import BaseModel, SecretStr
-
-from backend.data.model import NodeExecutionStats
-from backend.integrations.providers import ProviderName
-
-if TYPE_CHECKING:
-    from enum import _EnumMemberT
+from typing import Any, Iterable, List, Literal, NamedTuple, Optional
 
 import anthropic
 import ollama
 import openai
-from anthropic._types import NotGiven
 from anthropic.types import ToolParam
 from groq import Groq
+from pydantic import BaseModel, 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 import json
 from backend.util.settings import BehaveAs, Settings
 from backend.util.text import TextFormatter
@@ -77,12 +71,10 @@ class ModelMetadata(NamedTuple):
 
 class LlmModelMeta(EnumMeta):
     @property
-    def __members__(
-        self: type["_EnumMemberT"],
-    ) -> MappingProxyType[str, "_EnumMemberT"]:
+    def __members__(self) -> MappingProxyType:
         if Settings().config.behave_as == BehaveAs.LOCAL:
             members = super().__members__
-            return members
+            return MappingProxyType(members)
         else:
             removed_providers = ["ollama"]
             existing_members = super().__members__
@@ -97,14 +89,17 @@ class LlmModelMeta(EnumMeta):
 class LlmModel(str, Enum, metaclass=LlmModelMeta):
     # OpenAI models
     O3_MINI = "o3-mini"
+    O3 = "o3-2025-04-16"
     O1 = "o1"
     O1_PREVIEW = "o1-preview"
     O1_MINI = "o1-mini"
+    GPT41 = "gpt-4.1-2025-04-14"
     GPT4O_MINI = "gpt-4o-mini"
     GPT4O = "gpt-4o"
     GPT4_TURBO = "gpt-4-turbo"
     GPT3_5_TURBO = "gpt-3.5-turbo"
     # Anthropic models
+    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"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
@@ -125,6 +120,7 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     OLLAMA_DOLPHIN = "dolphin-mistral:latest"
     # OpenRouter models
     GEMINI_FLASH_1_5 = "google/gemini-flash-1.5"
+    GEMINI_2_5_PRO = "google/gemini-2.5-pro-preview-03-25"
     GROK_BETA = "x-ai/grok-beta"
     MISTRAL_NEMO = "mistralai/mistral-nemo"
     COHERE_COMMAND_R_08_2024 = "cohere/command-r-08-2024"
@@ -142,6 +138,8 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     AMAZON_NOVA_PRO_V1 = "amazon/nova-pro-v1"
     MICROSOFT_WIZARDLM_2_8X22B = "microsoft/wizardlm-2-8x22b"
     GRYPHE_MYTHOMAX_L2_13B = "gryphe/mythomax-l2-13b"
+    META_LLAMA_4_SCOUT = "meta-llama/llama-4-scout"
+    META_LLAMA_4_MAVERICK = "meta-llama/llama-4-maverick"
 
     @property
     def metadata(self) -> ModelMetadata:
@@ -162,12 +160,14 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
 
 MODEL_METADATA = {
     # https://platform.openai.com/docs/models
+    LlmModel.O3: ModelMetadata("openai", 200000, 100000),
     LlmModel.O3_MINI: ModelMetadata("openai", 200000, 100000),  # o3-mini-2025-01-31
     LlmModel.O1: ModelMetadata("openai", 200000, 100000),  # o1-2024-12-17
     LlmModel.O1_PREVIEW: ModelMetadata(
         "openai", 128000, 32768
     ),  # o1-preview-2024-09-12
     LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
+    LlmModel.GPT41: ModelMetadata("openai", 1047576, 32768),
     LlmModel.GPT4O_MINI: ModelMetadata(
         "openai", 128000, 16384
     ),  # gpt-4o-mini-2024-07-18
@@ -177,6 +177,9 @@ MODEL_METADATA = {
     ),  # gpt-4-turbo-2024-04-09
     LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, 4096),  # gpt-3.5-turbo-0125
     # https://docs.anthropic.com/en/docs/about-claude/models
+    LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
+        "anthropic", 200000, 8192
+    ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_5_SONNET: ModelMetadata(
         "anthropic", 200000, 8192
     ),  # claude-3-5-sonnet-20241022
@@ -202,6 +205,7 @@ MODEL_METADATA = {
     LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
     # https://openrouter.ai/models
     LlmModel.GEMINI_FLASH_1_5: ModelMetadata("open_router", 1000000, 8192),
+    LlmModel.GEMINI_2_5_PRO: ModelMetadata("open_router", 1050000, 8192),
     LlmModel.GROK_BETA: ModelMetadata("open_router", 131072, 131072),
     LlmModel.MISTRAL_NEMO: ModelMetadata("open_router", 128000, 4096),
     LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata("open_router", 128000, 4096),
@@ -223,6 +227,8 @@ MODEL_METADATA = {
     LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata("open_router", 300000, 5120),
     LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata("open_router", 65536, 4096),
     LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata("open_router", 4096, 4096),
+    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata("open_router", 131072, 131072),
+    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata("open_router", 1048576, 1000000),
 }
 
 for model in LlmModel:
@@ -252,7 +258,7 @@ class LLMResponse(BaseModel):
 
 def convert_openai_tool_fmt_to_anthropic(
     openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | NotGiven:
+) -> Iterable[ToolParam] | anthropic.NotGiven:
     """
     Convert OpenAI tool format to Anthropic tool format.
     """
@@ -290,6 +296,7 @@ def llm_call(
     max_tokens: int | None,
     tools: list[dict] | None = None,
     ollama_host: str = "localhost:11434",
+    parallel_tool_calls: bool | None = None,
 ) -> LLMResponse:
     """
     Make a call to a language model.
@@ -335,6 +342,9 @@ def llm_call(
             response_format=response_format,  # type: ignore
             max_completion_tokens=max_tokens,
             tools=tools_param,  # type: ignore
+            parallel_tool_calls=(
+                openai.NOT_GIVEN if parallel_tool_calls is None else parallel_tool_calls
+            ),
         )
 
         if response.choices[0].message.tool_calls:
@@ -424,7 +434,7 @@ def llm_call(
                 response=(
                     resp.content[0].name
                     if isinstance(resp.content[0], anthropic.types.ToolUseBlock)
-                    else resp.content[0].text
+                    else getattr(resp.content[0], "text", "")
                 ),
                 tool_calls=tool_calls,
                 prompt_tokens=resp.usage.input_tokens,
@@ -490,6 +500,9 @@ def llm_call(
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
+            parallel_tool_calls=(
+                openai.NOT_GIVEN if parallel_tool_calls is None else parallel_tool_calls
+            ),
         )
 
         # If there's no response, raise an error
@@ -528,7 +541,7 @@ def llm_call(
 class AIBlockBase(Block, ABC):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.prompt = ""
+        self.prompt = []
 
     def merge_llm_stats(self, block: "AIBlockBase"):
         self.merge_stats(block.execution_stats)
@@ -558,7 +571,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             description="The system prompt to provide additional context to the model.",
         )
         conversation_history: list[dict] = SchemaField(
-            default=[],
+            default_factory=list,
             description="The conversation history to provide context for the prompt.",
         )
         retry: int = SchemaField(
@@ -568,7 +581,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         )
         prompt_values: dict[str, str] = SchemaField(
             advanced=False,
-            default={},
+            default_factory=dict,
             description="Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.",
         )
         max_tokens: int | None = SchemaField(
@@ -587,7 +600,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         response: dict[str, Any] = SchemaField(
             description="The response object generated by the language model."
         )
-        prompt: str = SchemaField(description="The prompt sent to the language model.")
+        prompt: list = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -609,7 +622,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             test_credentials=TEST_CREDENTIALS,
             test_output=[
                 ("response", {"key1": "key1Value", "key2": "key2Value"}),
-                ("prompt", str),
+                ("prompt", list),
             ],
             test_mock={
                 "llm_call": lambda *args, **kwargs: LLMResponse(
@@ -642,6 +655,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         Test mocks work only on class functions, this wraps the llm_call function
         so that it can be mocked withing the block testing framework.
         """
+        self.prompt = prompt
         return llm_call(
             credentials=credentials,
             llm_model=llm_model,
@@ -796,7 +810,7 @@ class AITextGeneratorBlock(AIBlockBase):
         )
         prompt_values: dict[str, str] = SchemaField(
             advanced=False,
-            default={},
+            default_factory=dict,
             description="Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.",
         )
         ollama_host: str = SchemaField(
@@ -814,7 +828,7 @@ class AITextGeneratorBlock(AIBlockBase):
         response: str = SchemaField(
             description="The response generated by the language model."
         )
-        prompt: str = SchemaField(description="The prompt sent to the language model.")
+        prompt: list = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -831,7 +845,7 @@ class AITextGeneratorBlock(AIBlockBase):
             test_credentials=TEST_CREDENTIALS,
             test_output=[
                 ("response", "Response text"),
-                ("prompt", str),
+                ("prompt", list),
             ],
             test_mock={"llm_call": lambda *args, **kwargs: "Response text"},
         )
@@ -850,7 +864,10 @@ class AITextGeneratorBlock(AIBlockBase):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         object_input_data = AIStructuredResponseGeneratorBlock.Input(
-            **{attr: getattr(input_data, attr) for attr in input_data.model_fields},
+            **{
+                attr: getattr(input_data, attr)
+                for attr in AITextGeneratorBlock.Input.model_fields
+            },
             expected_format={},
         )
         yield "response", self.llm_call(object_input_data, credentials)
@@ -907,7 +924,7 @@ class AITextSummarizerBlock(AIBlockBase):
 
     class Output(BlockSchema):
         summary: str = SchemaField(description="The final summary of the text.")
-        prompt: str = SchemaField(description="The prompt sent to the language model.")
+        prompt: list = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -924,7 +941,7 @@ class AITextSummarizerBlock(AIBlockBase):
             test_credentials=TEST_CREDENTIALS,
             test_output=[
                 ("summary", "Final summary of a long text"),
-                ("prompt", str),
+                ("prompt", list),
             ],
             test_mock={
                 "llm_call": lambda input_data, credentials: (
@@ -1033,8 +1050,14 @@ class AITextSummarizerBlock(AIBlockBase):
 
 class AIConversationBlock(AIBlockBase):
     class Input(BlockSchema):
+        prompt: str = SchemaField(
+            description="The prompt to send to the language model.",
+            placeholder="Enter your prompt here...",
+            default="",
+            advanced=False,
+        )
         messages: List[Any] = SchemaField(
-            description="List of messages in the conversation.", min_length=1
+            description="List of messages in the conversation.",
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
@@ -1057,7 +1080,7 @@ class AIConversationBlock(AIBlockBase):
         response: str = SchemaField(
             description="The model's response to the conversation."
         )
-        prompt: str = SchemaField(description="The prompt sent to the language model.")
+        prompt: list = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -1086,7 +1109,7 @@ class AIConversationBlock(AIBlockBase):
                     "response",
                     "The 2020 World Series was played at Globe Life Field in Arlington, Texas.",
                 ),
-                ("prompt", str),
+                ("prompt", list),
             ],
             test_mock={
                 "llm_call": lambda *args, **kwargs: "The 2020 World Series was played at Globe Life Field in Arlington, Texas."
@@ -1108,7 +1131,7 @@ class AIConversationBlock(AIBlockBase):
     ) -> BlockOutput:
         response = self.llm_call(
             AIStructuredResponseGeneratorBlock.Input(
-                prompt="",
+                prompt=input_data.prompt,
                 credentials=input_data.credentials,
                 model=input_data.model,
                 conversation_history=input_data.messages,
@@ -1166,7 +1189,7 @@ class AIListGeneratorBlock(AIBlockBase):
         list_item: str = SchemaField(
             description="Each individual item in the list.",
         )
-        prompt: str = SchemaField(description="The prompt sent to the language model.")
+        prompt: list = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(
             description="Error message if the list generation failed."
         )
@@ -1198,7 +1221,7 @@ class AIListGeneratorBlock(AIBlockBase):
                     "generated_list",
                     ["Zylora Prime", "Kharon-9", "Vortexia", "Oceara", "Draknos"],
                 ),
-                ("prompt", str),
+                ("prompt", list),
                 ("list_item", "Zylora Prime"),
                 ("list_item", "Kharon-9"),
                 ("list_item", "Vortexia"),

autogpt_platform/backend/backend/blocks/media.py

@@ -8,13 +8,13 @@ from moviepy.video.io.VideoFileClip import VideoFileClip
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
-from backend.util.file import MediaFile, get_exec_file_path, store_media_file
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 
 
 class MediaDurationBlock(Block):
 
     class Input(BlockSchema):
-        media_in: MediaFile = SchemaField(
+        media_in: MediaFileType = SchemaField(
             description="Media input (URL, data URI, or local path)."
         )
         is_video: bool = SchemaField(
@@ -69,7 +69,7 @@ class LoopVideoBlock(Block):
     """
 
     class Input(BlockSchema):
-        video_in: MediaFile = SchemaField(
+        video_in: MediaFileType = SchemaField(
             description="The input video (can be a URL, data URI, or local path)."
         )
         # Provide EITHER a `duration` or `n_loops` or both. We'll demonstrate `duration`.
@@ -137,7 +137,7 @@ class LoopVideoBlock(Block):
         assert isinstance(looped_clip, VideoFileClip)
 
         # 4) Save the looped output
-        output_filename = MediaFile(
+        output_filename = MediaFileType(
             f"{node_exec_id}_looped_{os.path.basename(local_video_path)}"
         )
         output_abspath = get_exec_file_path(graph_exec_id, output_filename)
@@ -162,10 +162,10 @@ class AddAudioToVideoBlock(Block):
     """
 
     class Input(BlockSchema):
-        video_in: MediaFile = SchemaField(
+        video_in: MediaFileType = SchemaField(
             description="Video input (URL, data URI, or local path)."
         )
-        audio_in: MediaFile = SchemaField(
+        audio_in: MediaFileType = SchemaField(
             description="Audio input (URL, data URI, or local path)."
         )
         volume: float = SchemaField(
@@ -178,7 +178,7 @@ class AddAudioToVideoBlock(Block):
         )
 
     class Output(BlockSchema):
-        video_out: MediaFile = SchemaField(
+        video_out: MediaFileType = SchemaField(
             description="Final video (with attached audio), as a path or data URI."
         )
         error: str = SchemaField(
@@ -229,7 +229,7 @@ class AddAudioToVideoBlock(Block):
         final_clip = video_clip.with_audio(audio_clip)
 
         # 4) Write to output file
-        output_filename = MediaFile(
+        output_filename = MediaFileType(
             f"{node_exec_id}_audio_attached_{os.path.basename(local_video_path)}"
         )
         output_abspath = os.path.join(abs_temp_dir, output_filename)

autogpt_platform/backend/backend/blocks/mem0.py

@@ -65,7 +65,7 @@ class AddMemoryBlock(Block, Mem0Base):
             default=Content(discriminator="content", content="I'm a vegetarian"),
         )
         metadata: dict[str, Any] = SchemaField(
-            description="Optional metadata for the memory", default={}
+            description="Optional metadata for the memory", default_factory=dict
         )
 
         limit_memory_to_run: bool = SchemaField(
@@ -173,7 +173,7 @@ class SearchMemoryBlock(Block, Mem0Base):
         )
         categories_filter: list[str] = SchemaField(
             description="Categories to filter by",
-            default=[],
+            default_factory=list,
             advanced=True,
         )
         limit_memory_to_run: bool = SchemaField(

autogpt_platform/backend/backend/blocks/nvidia/deepfake.py

@@ -6,13 +6,14 @@ from backend.blocks.nvidia._auth import (
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
 from backend.util.request import requests
+from backend.util.type import MediaFileType
 
 
 class NvidiaDeepfakeDetectBlock(Block):
     class Input(BlockSchema):
         credentials: NvidiaCredentialsInput = NvidiaCredentialsField()
-        image_base64: str = SchemaField(
-            description="Image to analyze for deepfakes", image_upload=True
+        image_base64: MediaFileType = SchemaField(
+            description="Image to analyze for deepfakes",
         )
         return_image: bool = SchemaField(
             description="Whether to return the processed image with markings",
@@ -22,16 +23,12 @@ class NvidiaDeepfakeDetectBlock(Block):
     class Output(BlockSchema):
         status: str = SchemaField(
             description="Detection status (SUCCESS, ERROR, CONTENT_FILTERED)",
-            default="",
         )
-        image: str = SchemaField(
+        image: MediaFileType = SchemaField(
             description="Processed image with detection markings (if return_image=True)",
-            default="",
-            image_output=True,
         )
         is_deepfake: float = SchemaField(
             description="Probability that the image is a deepfake (0-1)",
-            default=0.0,
         )
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/pinecone.py

@@ -177,7 +177,8 @@ class PineconeInsertBlock(Block):
             description="Namespace to use in Pinecone", default=""
         )
         metadata: dict = SchemaField(
-            description="Additional metadata to store with each vector", default={}
+            description="Additional metadata to store with each vector",
+            default_factory=dict,
         )
 
     class Output(BlockSchema):

autogpt_platform/backend/backend/blocks/screenshotone.py

@@ -12,7 +12,7 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import MediaFile, store_media_file
+from backend.util.file import MediaFileType, store_media_file
 from backend.util.request import Requests
 
 
@@ -57,7 +57,7 @@ class ScreenshotWebPageBlock(Block):
         )
 
     class Output(BlockSchema):
-        image: MediaFile = SchemaField(description="The screenshot image data")
+        image: MediaFileType = SchemaField(description="The screenshot image data")
         error: str = SchemaField(description="Error message if the screenshot failed")
 
     def __init__(self):
@@ -142,7 +142,9 @@ class ScreenshotWebPageBlock(Block):
         return {
             "image": store_media_file(
                 graph_exec_id=graph_exec_id,
-                file=f"data:image/{format.value};base64,{b64encode(response.content).decode('utf-8')}",
+                file=MediaFileType(
+                    f"data:image/{format.value};base64,{b64encode(response.content).decode('utf-8')}"
+                ),
                 return_content=True,
             )
         }

autogpt_platform/backend/backend/blocks/slant3d/webhook.py

@@ -8,6 +8,7 @@ from backend.data.block import (
     BlockWebhookConfig,
 )
 from backend.data.model import SchemaField
+from backend.integrations.providers import ProviderName
 from backend.util import settings
 from backend.util.settings import AppEnvironment, BehaveAs
 
@@ -25,7 +26,7 @@ class Slant3DTriggerBase:
     class Input(BlockSchema):
         credentials: Slant3DCredentialsInput = Slant3DCredentialsField()
         # Webhook URL is handled by the webhook system
-        payload: dict = SchemaField(hidden=True, default={})
+        payload: dict = SchemaField(hidden=True, default_factory=dict)
 
     class Output(BlockSchema):
         payload: dict = SchemaField(
@@ -82,7 +83,7 @@ class Slant3DOrderWebhookBlock(Slant3DTriggerBase, Block):
             input_schema=self.Input,
             output_schema=self.Output,
             webhook_config=BlockWebhookConfig(
-                provider="slant3d",
+                provider=ProviderName.SLANT3D,
                 webhook_type="orders",  # Only one type for now
                 resource_format="",  # No resource format needed
                 event_filter_input="events",

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -14,7 +14,6 @@ from backend.data.block import (
     BlockOutput,
     BlockSchema,
     BlockType,
-    get_block,
 )
 from backend.data.model import SchemaField
 from backend.util import json
@@ -155,7 +154,7 @@ class SmartDecisionMakerBlock(Block):
             description="The system prompt to provide additional context to the model.",
         )
         conversation_history: list[dict] = SchemaField(
-            default=[],
+            default_factory=list,
             description="The conversation history to provide context for the prompt.",
         )
         last_tool_output: Any = SchemaField(
@@ -169,7 +168,7 @@ class SmartDecisionMakerBlock(Block):
         )
         prompt_values: dict[str, str] = SchemaField(
             advanced=False,
-            default={},
+            default_factory=dict,
             description="Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.",
         )
         max_tokens: int | None = SchemaField(
@@ -264,9 +263,7 @@ class SmartDecisionMakerBlock(Block):
         Raises:
             ValueError: If the block specified by sink_node.block_id is not found.
         """
-        block = get_block(sink_node.block_id)
-        if not block:
-            raise ValueError(f"Block not found: {sink_node.block_id}")
+        block = sink_node.block
 
         tool_function: dict[str, Any] = {
             "name": re.sub(r"[^a-zA-Z0-9_-]", "_", block.name).lower(),
@@ -494,6 +491,7 @@ class SmartDecisionMakerBlock(Block):
             max_tokens=input_data.max_tokens,
             tools=tool_functions,
             ollama_host=input_data.ollama_host,
+            parallel_tool_calls=False,
         )
 
         if not response.tool_calls:

autogpt_platform/backend/backend/blocks/smartlead/campaign.py

@@ -112,7 +112,7 @@ class AddLeadToCampaignBlock(Block):
         lead_list: list[LeadInput] = SchemaField(
             description="An array of JSON objects, each representing a lead's details. Can hold max 100 leads.",
             max_length=100,
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         settings: LeadUploadSettings = SchemaField(
@@ -248,7 +248,7 @@ class SaveCampaignSequencesBlock(Block):
         )
         sequences: list[Sequence] = SchemaField(
             description="The sequences to save",
-            default=[],
+            default_factory=list,
             advanced=False,
         )
         credentials: SmartLeadCredentialsInput = SchemaField(

autogpt_platform/backend/backend/blocks/smartlead/models.py

@@ -39,7 +39,7 @@ class LeadCustomFields(BaseModel):
     fields: dict[str, str] = SchemaField(
         description="Custom fields for a lead (max 20 fields)",
         max_length=20,
-        default={},
+        default_factory=dict,
     )
 
 
@@ -85,7 +85,7 @@ class AddLeadsRequest(BaseModel):
     lead_list: list[LeadInput] = SchemaField(
         description="List of leads to add to the campaign",
         max_length=100,
-        default=[],
+        default_factory=list,
     )
     settings: LeadUploadSettings
     campaign_id: int

autogpt_platform/backend/backend/blocks/twitter/direct_message/manage_direct_message.py

@@ -156,7 +156,7 @@
 #         participant_ids: list[str] = SchemaField(
 #             description="Array of User IDs to create conversation with (max 50)",
 #             placeholder="Enter participant user IDs",
-#             default=[],
+#             default_factory=list,
 #             advanced=False
 #         )
 

autogpt_platform/backend/backend/blocks/twitter/lists/list_lookup.py

@@ -39,7 +39,6 @@ class TwitterGetListBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to lookup",
             placeholder="Enter list ID",
-            required=True,
         )
 
     class Output(BlockSchema):
@@ -184,7 +183,6 @@ class TwitterGetOwnedListsBlock(Block):
         user_id: str = SchemaField(
             description="The user ID whose owned Lists to retrieve",
             placeholder="Enter user ID",
-            required=True,
         )
 
         max_results: int | None = SchemaField(

autogpt_platform/backend/backend/blocks/twitter/lists/list_members.py

@@ -45,13 +45,11 @@ class TwitterRemoveListMemberBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to remove the member from",
             placeholder="Enter list ID",
-            required=True,
         )
 
         user_id: str = SchemaField(
             description="The ID of the user to remove from the List",
             placeholder="Enter user ID to remove",
-            required=True,
         )
 
     class Output(BlockSchema):
@@ -120,13 +118,11 @@ class TwitterAddListMemberBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to add the member to",
             placeholder="Enter list ID",
-            required=True,
         )
 
         user_id: str = SchemaField(
             description="The ID of the user to add to the List",
             placeholder="Enter user ID to add",
-            required=True,
         )
 
     class Output(BlockSchema):
@@ -195,7 +191,6 @@ class TwitterGetListMembersBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to get members from",
             placeholder="Enter list ID",
-            required=True,
         )
 
         max_results: int | None = SchemaField(
@@ -376,7 +371,6 @@ class TwitterGetListMembershipsBlock(Block):
         user_id: str = SchemaField(
             description="The ID of the user whose List memberships to retrieve",
             placeholder="Enter user ID",
-            required=True,
         )
 
         max_results: int | None = SchemaField(

autogpt_platform/backend/backend/blocks/twitter/lists/list_tweets_lookup.py

@@ -42,7 +42,6 @@ class TwitterGetListTweetsBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List whose Tweets you would like to retrieve",
             placeholder="Enter list ID",
-            required=True,
         )
 
         max_results: int | None = SchemaField(

autogpt_platform/backend/backend/blocks/twitter/lists/manage_lists.py

@@ -28,7 +28,6 @@ class TwitterDeleteListBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to be deleted",
             placeholder="Enter list ID",
-            required=True,
         )
 
     class Output(BlockSchema):

autogpt_platform/backend/backend/blocks/twitter/lists/pinned_lists.py

@@ -39,7 +39,6 @@ class TwitterUnpinListBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to unpin",
             placeholder="Enter list ID",
-            required=True,
         )
 
     class Output(BlockSchema):
@@ -103,7 +102,6 @@ class TwitterPinListBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to pin",
             placeholder="Enter list ID",
-            required=True,
         )
 
     class Output(BlockSchema):

autogpt_platform/backend/backend/blocks/twitter/spaces/spaces_lookup.py

@@ -44,7 +44,7 @@ class SpaceList(BaseModel):
     space_ids: list[str] = SchemaField(
         description="List of Space IDs to lookup (up to 100)",
         placeholder="Enter Space IDs",
-        default=[],
+        default_factory=list,
         advanced=False,
     )
 
@@ -54,7 +54,7 @@ class UserList(BaseModel):
     user_ids: list[str] = SchemaField(
         description="List of user IDs to lookup their Spaces (up to 100)",
         placeholder="Enter user IDs",
-        default=[],
+        default_factory=list,
         advanced=False,
     )
 
@@ -227,7 +227,6 @@ class TwitterGetSpaceByIdBlock(Block):
         space_id: str = SchemaField(
             description="Space ID to lookup",
             placeholder="Enter Space ID",
-            required=True,
         )
 
     class Output(BlockSchema):
@@ -389,7 +388,6 @@ class TwitterGetSpaceBuyersBlock(Block):
         space_id: str = SchemaField(
             description="Space ID to lookup buyers for",
             placeholder="Enter Space ID",
-            required=True,
         )
 
     class Output(BlockSchema):
@@ -517,7 +515,6 @@ class TwitterGetSpaceTweetsBlock(Block):
         space_id: str = SchemaField(
             description="Space ID to lookup tweets for",
             placeholder="Enter Space ID",
-            required=True,
         )
 
     class Output(BlockSchema):

autogpt_platform/backend/backend/blocks/twitter/users/user_lookup.py

@@ -200,7 +200,7 @@ class UserIdList(BaseModel):
     user_ids: list[str] = SchemaField(
         description="List of user IDs to lookup (max 100)",
         placeholder="Enter user IDs",
-        default=[],
+        default_factory=list,
         advanced=False,
     )
 
@@ -210,7 +210,7 @@ class UsernameList(BaseModel):
     usernames: list[str] = SchemaField(
         description="List of Twitter usernames/handles to lookup (max 100)",
         placeholder="Enter usernames",
-        default=[],
+        default_factory=list,
         advanced=False,
     )
 

autogpt_platform/backend/backend/cli.py

@@ -8,7 +8,6 @@ import pathlib
 import click
 import psutil
 
-from backend import app
 from backend.util.process import AppProcess
 
 
@@ -42,8 +41,13 @@ def write_pid(pid: int):
 
 class MainApp(AppProcess):
     def run(self):
+        from backend import app
+
         app.main(silent=True)
 
+    def cleanup(self):
+        pass
+
 
 @click.group()
 def main():
@@ -220,9 +224,8 @@ def event():
 
 @test.command()
 @click.argument("server_address")
-@click.argument("graph_id")
-@click.argument("graph_version")
-def websocket(server_address: str, graph_id: str, graph_version: int):
+@click.argument("graph_exec_id")
+def websocket(server_address: str, graph_exec_id: str):
     """
     Tests the websocket connection.
     """
@@ -230,16 +233,20 @@ def websocket(server_address: str, graph_id: str, graph_version: int):
 
     import websockets.asyncio.client
 
-    from backend.server.ws_api import ExecutionSubscription, Methods, WsMessage
+    from backend.server.ws_api import (
+        WSMessage,
+        WSMethod,
+        WSSubscribeGraphExecutionRequest,
+    )
 
     async def send_message(server_address: str):
         uri = f"ws://{server_address}"
         async with websockets.asyncio.client.connect(uri) as websocket:
             try:
-                msg = WsMessage(
-                    method=Methods.SUBSCRIBE,
-                    data=ExecutionSubscription(
-                        graph_id=graph_id, graph_version=graph_version
+                msg = WSMessage(
+                    method=WSMethod.SUBSCRIBE_GRAPH_EXEC,
+                    data=WSSubscribeGraphExecutionRequest(
+                        graph_exec_id=graph_exec_id,
                     ).model_dump(),
                 ).model_dump_json()
                 await websocket.send(msg)

autogpt_platform/backend/backend/data/analytics.py

@@ -12,12 +12,12 @@ async def log_raw_analytics(
     data_index: str,
 ):
     details = await prisma.models.AnalyticsDetails.prisma().create(
-        data={
-            "userId": user_id,
-            "type": type,
-            "data": prisma.Json(data),
-            "dataIndex": data_index,
-        }
+        data=prisma.types.AnalyticsDetailsCreateInput(
+            userId=user_id,
+            type=type,
+            data=prisma.Json(data),
+            dataIndex=data_index,
+        )
     )
     return details
 
@@ -32,12 +32,12 @@ async def log_raw_metric(
         raise ValueError("metric_value must be non-negative")
 
     result = await prisma.models.AnalyticsMetrics.prisma().create(
-        data={
-            "value": metric_value,
-            "analyticMetric": metric_name,
-            "userId": user_id,
-            "dataString": data_string,
-        },
+        data=prisma.types.AnalyticsMetricsCreateInput(
+            value=metric_value,
+            analyticMetric=metric_name,
+            userId=user_id,
+            dataString=data_string,
+        )
     )
 
     return result

autogpt_platform/backend/backend/data/block.py

@@ -17,15 +17,18 @@ from typing import (
 import jsonref
 import jsonschema
 from prisma.models import AgentBlock
+from prisma.types import AgentBlockCreateInput
 from pydantic import BaseModel
 
 from backend.data.model import NodeExecutionStats
+from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.settings import Config
 
 from .model import (
     ContributorDetails,
     Credentials,
+    CredentialsFieldInfo,
     CredentialsMetaInput,
     is_credentials_field_name,
 )
@@ -119,21 +122,26 @@ class BlockSchema(BaseModel):
     def get_mismatch_error(cls, data: BlockInput) -> str | None:
         return cls.validate_data(data)
 
+    @classmethod
+    def get_field_schema(cls, field_name: str) -> dict[str, Any]:
+        model_schema = cls.jsonschema().get("properties", {})
+        if not model_schema:
+            raise ValueError(f"Invalid model schema {cls}")
+
+        property_schema = model_schema.get(field_name)
+        if not property_schema:
+            raise ValueError(f"Invalid property name {field_name}")
+
+        return property_schema
+
     @classmethod
     def validate_field(cls, field_name: str, data: BlockInput) -> str | None:
         """
         Validate the data against a specific property (one of the input/output name).
         Returns the validation error message if the data does not match the schema.
         """
-        model_schema = cls.jsonschema().get("properties", {})
-        if not model_schema:
-            return f"Invalid model schema {cls}"
-
-        property_schema = model_schema.get(field_name)
-        if not property_schema:
-            return f"Invalid property name {field_name}"
-
         try:
+            property_schema = cls.get_field_schema(field_name)
             jsonschema.validate(json.to_dict(data), property_schema)
             return None
         except jsonschema.ValidationError as e:
@@ -196,6 +204,15 @@ class BlockSchema(BaseModel):
             )
         }
 
+    @classmethod
+    def get_credentials_fields_info(cls) -> dict[str, CredentialsFieldInfo]:
+        return {
+            field_name: CredentialsFieldInfo.model_validate(
+                cls.get_field_schema(field_name), by_alias=True
+            )
+            for field_name in cls.get_credentials_fields().keys()
+        }
+
     @classmethod
     def get_input_defaults(cls, data: BlockInput) -> BlockInput:
         return data  # Return as is, by default.
@@ -225,7 +242,7 @@ class BlockManualWebhookConfig(BaseModel):
     the user has to manually set up the webhook at the provider.
     """
 
-    provider: str
+    provider: ProviderName
     """The service provider that the webhook connects to"""
 
     webhook_type: str
@@ -461,9 +478,9 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
 
 
 def get_blocks() -> dict[str, Type[Block]]:
-    from backend.blocks import AVAILABLE_BLOCKS  # noqa: E402
+    from backend.blocks import load_all_blocks
 
-    return AVAILABLE_BLOCKS
+    return load_all_blocks()
 
 
 async def initialize_blocks() -> None:
@@ -474,12 +491,12 @@ async def initialize_blocks() -> None:
         )
         if not existing_block:
             await AgentBlock.prisma().create(
-                data={
-                    "id": block.id,
-                    "name": block.name,
-                    "inputSchema": json.dumps(block.input_schema.jsonschema()),
-                    "outputSchema": json.dumps(block.output_schema.jsonschema()),
-                }
+                data=AgentBlockCreateInput(
+                    id=block.id,
+                    name=block.name,
+                    inputSchema=json.dumps(block.input_schema.jsonschema()),
+                    outputSchema=json.dumps(block.output_schema.jsonschema()),
+                )
             )
             continue
 
@@ -502,6 +519,7 @@ async def initialize_blocks() -> None:
             )
 
 
-def get_block(block_id: str) -> Block | None:
+# Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
+def get_block(block_id: str) -> Block[BlockSchema, BlockSchema] | None:
     cls = get_blocks().get(block_id)
     return cls() if cls else None

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -36,14 +36,17 @@ from backend.integrations.credentials_store import (
 # =============== Configure the cost for each LLM Model call =============== #
 
 MODEL_COST: dict[LlmModel, int] = {
+    LlmModel.O3: 7,
     LlmModel.O3_MINI: 2,  # $1.10 / $4.40
     LlmModel.O1: 16,  # $15 / $60
     LlmModel.O1_PREVIEW: 16,
     LlmModel.O1_MINI: 4,
+    LlmModel.GPT41: 2,
     LlmModel.GPT4O_MINI: 1,
     LlmModel.GPT4O: 3,
     LlmModel.GPT4_TURBO: 10,
     LlmModel.GPT3_5_TURBO: 1,
+    LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_5_SONNET: 4,
     LlmModel.CLAUDE_3_5_HAIKU: 1,  # $0.80 / $4.00
     LlmModel.CLAUDE_3_HAIKU: 1,
@@ -60,6 +63,7 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.DEEPSEEK_LLAMA_70B: 1,  # ? / ?
     LlmModel.OLLAMA_DOLPHIN: 1,
     LlmModel.GEMINI_FLASH_1_5: 1,
+    LlmModel.GEMINI_2_5_PRO: 4,
     LlmModel.GROK_BETA: 5,
     LlmModel.MISTRAL_NEMO: 1,
     LlmModel.COHERE_COMMAND_R_08_2024: 1,
@@ -75,6 +79,8 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.AMAZON_NOVA_PRO_V1: 1,
     LlmModel.MICROSOFT_WIZARDLM_2_8X22B: 1,
     LlmModel.GRYPHE_MYTHOMAX_L2_13B: 1,
+    LlmModel.META_LLAMA_4_SCOUT: 1,
+    LlmModel.META_LLAMA_4_MAVERICK: 1,
 }
 
 for model in LlmModel:

autogpt_platform/backend/backend/data/credit.py

@@ -11,18 +11,20 @@ from prisma.enums import (
     CreditRefundRequestStatus,
     CreditTransactionType,
     NotificationType,
+    OnboardingStep,
 )
 from prisma.errors import UniqueViolationError
 from prisma.models import CreditRefundRequest, CreditTransaction, User
-from prisma.types import CreditTransactionCreateInput, CreditTransactionWhereInput
-from pydantic import BaseModel
+from prisma.types import (
+    CreditRefundRequestCreateInput,
+    CreditTransactionCreateInput,
+    CreditTransactionWhereInput,
+)
 from tenacity import retry, stop_after_attempt, wait_exponential
 
 from backend.data import db
-from backend.data.block import Block, BlockInput, get_block
 from backend.data.block_cost_config import BLOCK_COSTS
-from backend.data.cost import BlockCost, BlockCostType
-from backend.data.execution import NodeExecutionEntry
+from backend.data.cost import BlockCost
 from backend.data.model import (
     AutoTopUpConfig,
     RefundRequest,
@@ -31,6 +33,7 @@ from backend.data.model import (
 )
 from backend.data.notifications import NotificationEventDTO, RefundRequestData
 from backend.data.user import get_user_by_id
+from backend.executor.utils import UsageTransactionMetadata
 from backend.notifications import NotificationManager
 from backend.util.exceptions import InsufficientBalanceError
 from backend.util.service import get_service_client
@@ -39,6 +42,7 @@ from backend.util.settings import Settings
 settings = Settings()
 stripe.api_key = settings.secrets.stripe_api_key
 logger = logging.getLogger(__name__)
+base_url = settings.config.frontend_base_url or settings.config.platform_base_url
 
 
 class UserCreditBase(ABC):
@@ -90,20 +94,20 @@ class UserCreditBase(ABC):
     @abstractmethod
     async def spend_credits(
         self,
-        entry: NodeExecutionEntry,
-        data_size: float,
-        run_time: float,
+        user_id: str,
+        cost: int,
+        metadata: UsageTransactionMetadata,
     ) -> int:
         """
-        Spend the credits for the user based on the block usage.
+        Spend the credits for the user based on the cost.
 
         Args:
-            entry (NodeExecutionEntry): The node execution identifiers & data.
-            data_size (float): The size of the data being processed.
-            run_time (float): The time taken to run the block.
+            user_id (str): The user ID.
+            cost (int): The cost to spend.
+            metadata (UsageTransactionMetadata): The metadata of the transaction.
 
         Returns:
-            int: amount of credit spent
+            int: The remaining balance.
         """
         pass
 
@@ -118,6 +122,18 @@ class UserCreditBase(ABC):
         """
         pass
 
+    @abstractmethod
+    async def onboarding_reward(self, user_id: str, credits: int, step: OnboardingStep):
+        """
+        Reward the user with credits for completing an onboarding step.
+        Won't reward if the user has already received credits for the step.
+
+        Args:
+            user_id (str): The user ID.
+            step (OnboardingStep): The onboarding step.
+        """
+        pass
+
     @abstractmethod
     async def top_up_intent(self, user_id: str, amount: int) -> str:
         """
@@ -185,6 +201,14 @@ class UserCreditBase(ABC):
         """
         pass
 
+    @staticmethod
+    async def create_billing_portal_session(user_id: str) -> str:
+        session = stripe.billing_portal.Session.create(
+            customer=await get_stripe_customer_id(user_id),
+            return_url=base_url + "/profile/credits",
+        )
+        return session.url
+
     @staticmethod
     def time_now() -> datetime:
         return datetime.now(timezone.utc)
@@ -202,7 +226,7 @@ class UserCreditBase(ABC):
                 "userId": user_id,
                 "createdAt": {"lte": top_time},
                 "isActive": True,
-                "runningBalance": {"not": None},  # type: ignore
+                "NOT": [{"runningBalance": None}],
             },
             order={"createdAt": "desc"},
         )
@@ -324,31 +348,21 @@ class UserCreditBase(ABC):
                 amount = min(-user_balance, 0)
 
             # Create the transaction
-            transaction_data: CreditTransactionCreateInput = {
-                "userId": user_id,
-                "amount": amount,
-                "runningBalance": user_balance + amount,
-                "type": transaction_type,
-                "metadata": metadata,
-                "isActive": is_active,
-                "createdAt": self.time_now(),
-            }
+            transaction_data = CreditTransactionCreateInput(
+                userId=user_id,
+                amount=amount,
+                runningBalance=user_balance + amount,
+                type=transaction_type,
+                metadata=metadata,
+                isActive=is_active,
+                createdAt=self.time_now(),
+            )
             if transaction_key:
                 transaction_data["transactionKey"] = transaction_key
             tx = await CreditTransaction.prisma().create(data=transaction_data)
             return user_balance + amount, tx.transactionKey
 
 
-class UsageTransactionMetadata(BaseModel):
-    graph_exec_id: str | None = None
-    graph_id: str | None = None
-    node_id: str | None = None
-    node_exec_id: str | None = None
-    block_id: str | None = None
-    block: str | None = None
-    input: BlockInput | None = None
-
-
 class UserCredit(UserCreditBase):
     @thread_cached
     def notification_client(self) -> NotificationManager:
@@ -369,89 +383,21 @@ class UserCredit(UserCreditBase):
             )
         )
 
-    def _block_usage_cost(
-        self,
-        block: Block,
-        input_data: BlockInput,
-        data_size: float,
-        run_time: float,
-    ) -> tuple[int, BlockInput]:
-        block_costs = BLOCK_COSTS.get(type(block))
-        if not block_costs:
-            return 0, {}
-
-        for block_cost in block_costs:
-            if not self._is_cost_filter_match(block_cost.cost_filter, input_data):
-                continue
-
-            if block_cost.cost_type == BlockCostType.RUN:
-                return block_cost.cost_amount, block_cost.cost_filter
-
-            if block_cost.cost_type == BlockCostType.SECOND:
-                return (
-                    int(run_time * block_cost.cost_amount),
-                    block_cost.cost_filter,
-                )
-
-            if block_cost.cost_type == BlockCostType.BYTE:
-                return (
-                    int(data_size * block_cost.cost_amount),
-                    block_cost.cost_filter,
-                )
-
-        return 0, {}
-
-    def _is_cost_filter_match(
-        self, cost_filter: BlockInput, input_data: BlockInput
-    ) -> bool:
-        """
-        Filter rules:
-          - If cost_filter is an object, then check if cost_filter is the subset of input_data
-          - Otherwise, check if cost_filter is equal to input_data.
-          - Undefined, null, and empty string are considered as equal.
-        """
-        if not isinstance(cost_filter, dict) or not isinstance(input_data, dict):
-            return cost_filter == input_data
-
-        return all(
-            (not input_data.get(k) and not v)
-            or (input_data.get(k) and self._is_cost_filter_match(v, input_data[k]))
-            for k, v in cost_filter.items()
-        )
-
     async def spend_credits(
         self,
-        entry: NodeExecutionEntry,
-        data_size: float,
-        run_time: float,
+        user_id: str,
+        cost: int,
+        metadata: UsageTransactionMetadata,
     ) -> int:
-        block = get_block(entry.block_id)
-        if not block:
-            raise ValueError(f"Block not found: {entry.block_id}")
-
-        cost, matching_filter = self._block_usage_cost(
-            block=block, input_data=entry.data, data_size=data_size, run_time=run_time
-        )
         if cost == 0:
             return 0
 
         balance, _ = await self._add_transaction(
-            user_id=entry.user_id,
+            user_id=user_id,
             amount=-cost,
             transaction_type=CreditTransactionType.USAGE,
-            metadata=Json(
-                UsageTransactionMetadata(
-                    graph_exec_id=entry.graph_exec_id,
-                    graph_id=entry.graph_id,
-                    node_id=entry.node_id,
-                    node_exec_id=entry.node_exec_id,
-                    block_id=entry.block_id,
-                    block=block.name,
-                    input=matching_filter,
-                ).model_dump()
-            ),
+            metadata=Json(metadata.model_dump()),
         )
-        user_id = entry.user_id
 
         # Auto top-up if balance is below threshold.
         auto_top_up = await get_auto_top_up(user_id)
@@ -461,7 +407,7 @@ class UserCredit(UserCreditBase):
                     user_id=user_id,
                     amount=auto_top_up.amount,
                     # Avoid multiple auto top-ups within the same graph execution.
-                    key=f"AUTO-TOP-UP-{user_id}-{entry.graph_exec_id}",
+                    key=f"AUTO-TOP-UP-{user_id}-{metadata.graph_exec_id}",
                     ceiling_balance=auto_top_up.threshold,
                 )
             except Exception as e:
@@ -470,11 +416,29 @@ class UserCredit(UserCreditBase):
                     f"Auto top-up failed for user {user_id}, balance: {balance}, amount: {auto_top_up.amount}, error: {e}"
                 )
 
-        return cost
+        return balance
 
     async def top_up_credits(self, user_id: str, amount: int):
         await self._top_up_credits(user_id, amount)
 
+    async def onboarding_reward(self, user_id: str, credits: int, step: OnboardingStep):
+        key = f"REWARD-{user_id}-{step.value}"
+        if not await CreditTransaction.prisma().find_first(
+            where={
+                "userId": user_id,
+                "transactionKey": key,
+            }
+        ):
+            await self._add_transaction(
+                user_id=user_id,
+                amount=credits,
+                transaction_type=CreditTransactionType.GRANT,
+                transaction_key=key,
+                metadata=Json(
+                    {"reason": f"Reward for completing {step.value} onboarding step."}
+                ),
+            )
+
     async def top_up_refund(
         self, user_id: str, transaction_key: str, metadata: dict[str, str]
     ) -> int:
@@ -493,15 +457,15 @@ class UserCredit(UserCreditBase):
 
         try:
             refund_request = await CreditRefundRequest.prisma().create(
-                data={
-                    "id": refund_key,
-                    "transactionKey": transaction_key,
-                    "userId": user_id,
-                    "amount": amount,
-                    "reason": metadata.get("reason", ""),
-                    "status": CreditRefundRequestStatus.PENDING,
-                    "result": "The refund request is under review.",
-                }
+                data=CreditRefundRequestCreateInput(
+                    id=refund_key,
+                    transactionKey=transaction_key,
+                    userId=user_id,
+                    amount=amount,
+                    reason=metadata.get("reason", ""),
+                    status=CreditRefundRequestStatus.PENDING,
+                    result="The refund request is under review.",
+                )
             )
         except UniqueViolationError:
             raise ValueError(
@@ -765,10 +729,8 @@ class UserCredit(UserCreditBase):
             ui_mode="hosted",
             payment_intent_data={"setup_future_usage": "off_session"},
             saved_payment_method_options={"payment_method_save": "enabled"},
-            success_url=settings.config.frontend_base_url
-            + "/profile/credits?topup=success",
-            cancel_url=settings.config.frontend_base_url
-            + "/profile/credits?topup=cancel",
+            success_url=base_url + "/profile/credits?topup=success",
+            cancel_url=base_url + "/profile/credits?topup=cancel",
             allow_promotion_codes=True,
         )
 
@@ -964,6 +926,9 @@ class DisabledUserCredit(UserCreditBase):
     async def top_up_credits(self, *args, **kwargs):
         pass
 
+    async def onboarding_reward(self, *args, **kwargs):
+        pass
+
     async def top_up_intent(self, *args, **kwargs) -> str:
         return ""
 

autogpt_platform/backend/backend/data/db.py

@@ -62,10 +62,10 @@ async def connect():
 
     # Connection acquired from a pool like Supabase somehow still possibly allows
     # the db client obtains a connection but still reject query connection afterward.
-    try:
-        await prisma.execute_raw("SELECT 1")
-    except Exception as e:
-        raise ConnectionError("Failed to connect to Prisma.") from e
+    # try:
+    #     await prisma.execute_raw("SELECT 1")
+    # except Exception as e:
+    #     raise ConnectionError("Failed to connect to Prisma.") from e
 
 
 @conn_retry("Prisma", "Releasing connection")
@@ -89,7 +89,7 @@ async def transaction():
 async def locked_transaction(key: str):
     lock_key = zlib.crc32(key.encode("utf-8"))
     async with transaction() as tx:
-        await tx.execute_raw(f"SELECT pg_advisory_xact_lock({lock_key})")
+        await tx.execute_raw("SELECT pg_advisory_xact_lock($1)", lock_key)
         yield tx
 
 

autogpt_platform/backend/backend/data/includes.py

@@ -1,4 +1,9 @@
-import prisma
+from typing import cast
+
+import prisma.enums
+import prisma.types
+
+from backend.blocks.io import IO_BLOCK_IDs
 
 AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
     "Input": True,
@@ -8,38 +13,60 @@ AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
 }
 
 AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
-    "AgentNodes": {"include": AGENT_NODE_INCLUDE}  # type: ignore
+    "Nodes": {"include": AGENT_NODE_INCLUDE}
 }
 
 EXECUTION_RESULT_INCLUDE: prisma.types.AgentNodeExecutionInclude = {
     "Input": True,
     "Output": True,
-    "AgentNode": True,
-    "AgentGraphExecution": True,
+    "Node": True,
+    "GraphExecution": True,
 }
 
-GRAPH_EXECUTION_INCLUDE: prisma.types.AgentGraphExecutionInclude = {
-    "AgentNodeExecutions": {
+MAX_NODE_EXECUTIONS_FETCH = 1000
+
+GRAPH_EXECUTION_INCLUDE_WITH_NODES: prisma.types.AgentGraphExecutionInclude = {
+    "NodeExecutions": {
         "include": {
             "Input": True,
             "Output": True,
-            "AgentNode": True,
-            "AgentGraphExecution": True,
-        }
+            "Node": True,
+            "GraphExecution": True,
+        },
+        "order_by": [
+            {"queuedTime": "desc"},
+            # Fallback: Incomplete execs has no queuedTime.
+            {"addedTime": "desc"},
+        ],
+        "take": MAX_NODE_EXECUTIONS_FETCH,  # Avoid loading excessive node executions.
     }
 }
 
+GRAPH_EXECUTION_INCLUDE: prisma.types.AgentGraphExecutionInclude = {
+    "NodeExecutions": {
+        **cast(
+            prisma.types.FindManyAgentNodeExecutionArgsFromAgentGraphExecution,
+            GRAPH_EXECUTION_INCLUDE_WITH_NODES["NodeExecutions"],
+        ),
+        "where": {
+            "Node": {"is": {"AgentBlock": {"is": {"id": {"in": IO_BLOCK_IDs}}}}},
+            "NOT": [{"executionStatus": prisma.enums.AgentExecutionStatus.INCOMPLETE}],
+        },
+    }
+}
+
+
 INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
-    "AgentNodes": {"include": AGENT_NODE_INCLUDE}  # type: ignore
+    "AgentNodes": {"include": AGENT_NODE_INCLUDE}
 }
 
 
 def library_agent_include(user_id: str) -> prisma.types.LibraryAgentInclude:
     return {
-        "Agent": {
+        "AgentGraph": {
             "include": {
                 **AGENT_GRAPH_INCLUDE,
-                "AgentGraphExecution": {"where": {"userId": user_id}},
+                "Executions": {"where": {"userId": user_id}},
             }
         },
         "Creator": True,

autogpt_platform/backend/backend/data/integrations.py

@@ -3,12 +3,14 @@ from typing import TYPE_CHECKING, AsyncGenerator, Optional
 
 from prisma import Json
 from prisma.models import IntegrationWebhook
+from prisma.types import IntegrationWebhookCreateInput
 from pydantic import Field, computed_field
 
 from backend.data.includes import INTEGRATION_WEBHOOK_INCLUDE
 from backend.data.queue import AsyncRedisEventBus
 from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks.utils import webhook_ingress_url
+from backend.util.exceptions import NotFoundError
 
 from .db import BaseDbModel
 
@@ -65,28 +67,35 @@ class Webhook(BaseDbModel):
 
 async def create_webhook(webhook: Webhook) -> Webhook:
     created_webhook = await IntegrationWebhook.prisma().create(
-        data={
-            "id": webhook.id,
-            "userId": webhook.user_id,
-            "provider": webhook.provider.value,
-            "credentialsId": webhook.credentials_id,
-            "webhookType": webhook.webhook_type,
-            "resource": webhook.resource,
-            "events": webhook.events,
-            "config": Json(webhook.config),
-            "secret": webhook.secret,
-            "providerWebhookId": webhook.provider_webhook_id,
-        }
+        data=IntegrationWebhookCreateInput(
+            id=webhook.id,
+            userId=webhook.user_id,
+            provider=webhook.provider.value,
+            credentialsId=webhook.credentials_id,
+            webhookType=webhook.webhook_type,
+            resource=webhook.resource,
+            events=webhook.events,
+            config=Json(webhook.config),
+            secret=webhook.secret,
+            providerWebhookId=webhook.provider_webhook_id,
+        )
     )
     return Webhook.from_db(created_webhook)
 
 
 async def get_webhook(webhook_id: str) -> Webhook:
-    """⚠️ No `user_id` check: DO NOT USE without check in user-facing endpoints."""
-    webhook = await IntegrationWebhook.prisma().find_unique_or_raise(
+    """
+    ⚠️ No `user_id` check: DO NOT USE without check in user-facing endpoints.
+
+    Raises:
+        NotFoundError: if no record with the given ID exists
+    """
+    webhook = await IntegrationWebhook.prisma().find_unique(
         where={"id": webhook_id},
         include=INTEGRATION_WEBHOOK_INCLUDE,
     )
+    if not webhook:
+        raise NotFoundError(f"Webhook #{webhook_id} not found")
     return Webhook.from_db(webhook)
 
 

autogpt_platform/backend/backend/data/model.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import base64
 import logging
+from collections import defaultdict
 from datetime import datetime, timezone
 from typing import (
     TYPE_CHECKING,
@@ -12,6 +13,7 @@ from typing import (
     Generic,
     Literal,
     Optional,
+    Sequence,
     TypedDict,
     TypeVar,
     get_args,
@@ -141,17 +143,20 @@ def SchemaField(
     secret: bool = False,
     exclude: bool = False,
     hidden: Optional[bool] = None,
-    depends_on: list[str] | None = None,
-    image_upload: Optional[bool] = None,
-    image_output: Optional[bool] = None,
-    **kwargs,
+    depends_on: Optional[list[str]] = None,
+    ge: Optional[float] = None,
+    le: Optional[float] = None,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    discriminator: Optional[str] = None,
+    json_schema_extra: Optional[dict[str, Any]] = None,
 ) -> T:
     if default is PydanticUndefined and default_factory is None:
         advanced = False
     elif advanced is None:
         advanced = True
 
-    json_extra = {
+    json_schema_extra = {
         k: v
         for k, v in {
             "placeholder": placeholder,
@@ -159,8 +164,7 @@ def SchemaField(
             "advanced": advanced,
             "hidden": hidden,
             "depends_on": depends_on,
-            "image_upload": image_upload,
-            "image_output": image_output,
+            **(json_schema_extra or {}),
         }.items()
         if v is not None
     }
@@ -172,8 +176,12 @@ def SchemaField(
         title=title,
         description=description,
         exclude=exclude,
-        json_schema_extra=json_extra,
-        **kwargs,
+        ge=ge,
+        le=le,
+        min_length=min_length,
+        max_length=max_length,
+        discriminator=discriminator,
+        json_schema_extra=json_schema_extra,
     )  # type: ignore
 
 
@@ -294,9 +302,7 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
         )
         field_schema = model.jsonschema()["properties"][field_name]
         try:
-            schema_extra = _CredentialsFieldSchemaExtra[CP, CT].model_validate(
-                field_schema
-            )
+            schema_extra = CredentialsFieldInfo[CP, CT].model_validate(field_schema)
         except ValidationError as e:
             if "Field required [type=missing" not in str(e):
                 raise
@@ -322,14 +328,90 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
     )
 
 
-class _CredentialsFieldSchemaExtra(BaseModel, Generic[CP, CT]):
+class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
     # TODO: move discrimination mechanism out of CredentialsField (frontend + backend)
-    credentials_provider: list[CP]
-    credentials_scopes: Optional[list[str]] = None
-    credentials_types: list[CT]
+    provider: frozenset[CP] = Field(..., alias="credentials_provider")
+    supported_types: frozenset[CT] = Field(..., alias="credentials_types")
+    required_scopes: Optional[frozenset[str]] = Field(None, alias="credentials_scopes")
     discriminator: Optional[str] = None
     discriminator_mapping: Optional[dict[str, CP]] = None
 
+    @classmethod
+    def combine(
+        cls, *fields: tuple[CredentialsFieldInfo[CP, CT], T]
+    ) -> Sequence[tuple[CredentialsFieldInfo[CP, CT], set[T]]]:
+        """
+        Combines multiple CredentialsFieldInfo objects into as few as possible.
+
+        Rules:
+        - Items can only be combined if they have the same supported credentials types
+          and the same supported providers.
+        - When combining items, the `required_scopes` of the result is a join
+          of the `required_scopes` of the original items.
+
+        Params:
+            *fields: (CredentialsFieldInfo, key) objects to group and combine
+
+        Returns:
+            A sequence of tuples containing combined CredentialsFieldInfo objects and
+            the set of keys of the respective original items that were grouped together.
+        """
+        if not fields:
+            return []
+
+        # Group fields by their provider and supported_types
+        grouped_fields: defaultdict[
+            tuple[frozenset[CP], frozenset[CT]],
+            list[tuple[T, CredentialsFieldInfo[CP, CT]]],
+        ] = defaultdict(list)
+
+        for field, key in fields:
+            group_key = (frozenset(field.provider), frozenset(field.supported_types))
+            grouped_fields[group_key].append((key, field))
+
+        # Combine fields within each group
+        result: list[tuple[CredentialsFieldInfo[CP, CT], set[T]]] = []
+
+        for group in grouped_fields.values():
+            # Start with the first field in the group
+            _, combined = group[0]
+
+            # Track the keys that were combined
+            combined_keys = {key for key, _ in group}
+
+            # Combine required_scopes from all fields in the group
+            all_scopes = set()
+            for _, field in group:
+                if field.required_scopes:
+                    all_scopes.update(field.required_scopes)
+
+            # Create a new combined field
+            result.append(
+                (
+                    CredentialsFieldInfo[CP, CT](
+                        credentials_provider=combined.provider,
+                        credentials_types=combined.supported_types,
+                        credentials_scopes=frozenset(all_scopes) or None,
+                        discriminator=combined.discriminator,
+                        discriminator_mapping=combined.discriminator_mapping,
+                    ),
+                    combined_keys,
+                )
+            )
+
+        return result
+
+    def discriminate(self, discriminator_value: Any) -> CredentialsFieldInfo:
+        if not (self.discriminator and self.discriminator_mapping):
+            return self
+
+        discriminator_value = self.discriminator_mapping[discriminator_value]
+        return CredentialsFieldInfo(
+            credentials_provider=frozenset([discriminator_value]),
+            credentials_types=self.supported_types,
+            credentials_scopes=self.required_scopes,
+        )
+
 
 def CredentialsField(
     required_scopes: set[str] = set(),
@@ -407,13 +489,14 @@ class RefundRequest(BaseModel):
 class NodeExecutionStats(BaseModel):
     """Execution statistics for a node execution."""
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+    )
 
     error: Optional[Exception | str] = None
     walltime: float = 0
     cputime: float = 0
-    cost: float = 0
     input_size: int = 0
     output_size: int = 0
     llm_call_count: int = 0
@@ -425,14 +508,22 @@ class NodeExecutionStats(BaseModel):
 class GraphExecutionStats(BaseModel):
     """Execution statistics for a graph execution."""
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+    )
 
     error: Optional[Exception | str] = None
-    walltime: float = 0
+    walltime: float = Field(
+        default=0, description="Time between start and end of run (seconds)"
+    )
     cputime: float = 0
-    nodes_walltime: float = 0
+    nodes_walltime: float = Field(
+        default=0, description="Total node execution time (seconds)"
+    )
     nodes_cputime: float = 0
-    node_count: int = 0
-    node_error_count: int = 0
-    cost: float = 0
+    node_count: int = Field(default=0, description="Total number of node executions")
+    node_error_count: int = Field(
+        default=0, description="Total number of errors generated"
+    )
+    cost: int = Field(default=0, description="Total execution cost (cents)")

autogpt_platform/backend/backend/data/notifications.py

@@ -6,10 +6,14 @@ from typing import Annotated, Any, Generic, Optional, TypeVar, Union
 from prisma import Json
 from prisma.enums import NotificationType
 from prisma.models import NotificationEvent, UserNotificationBatch
-from prisma.types import UserNotificationBatchWhereInput
+from prisma.types import (
+    NotificationEventCreateInput,
+    UserNotificationBatchCreateInput,
+    UserNotificationBatchWhereInput,
+)
 
 # from backend.notifications.models import NotificationEvent
-from pydantic import BaseModel, EmailStr, Field, field_validator
+from pydantic import BaseModel, ConfigDict, EmailStr, Field, field_validator
 
 from backend.server.v2.store.exceptions import DatabaseError
 
@@ -35,7 +39,7 @@ class QueueType(Enum):
 
 
 class BaseNotificationData(BaseModel):
-    pass
+    model_config = ConfigDict(extra="allow")
 
 
 class AgentRunData(BaseNotificationData):
@@ -341,9 +345,46 @@ class NotificationPreference(BaseModel):
     )
 
 
+class UserNotificationEventDTO(BaseModel):
+    type: NotificationType
+    data: dict
+    created_at: datetime
+    updated_at: datetime
+
+    @staticmethod
+    def from_db(model: NotificationEvent) -> "UserNotificationEventDTO":
+        return UserNotificationEventDTO(
+            type=model.type,
+            data=dict(model.data),
+            created_at=model.createdAt,
+            updated_at=model.updatedAt,
+        )
+
+
+class UserNotificationBatchDTO(BaseModel):
+    user_id: str
+    type: NotificationType
+    notifications: list[UserNotificationEventDTO]
+    created_at: datetime
+    updated_at: datetime
+
+    @staticmethod
+    def from_db(model: UserNotificationBatch) -> "UserNotificationBatchDTO":
+        return UserNotificationBatchDTO(
+            user_id=model.userId,
+            type=model.type,
+            notifications=[
+                UserNotificationEventDTO.from_db(notification)
+                for notification in model.Notifications or []
+            ],
+            created_at=model.createdAt,
+            updated_at=model.updatedAt,
+        )
+
+
 def get_batch_delay(notification_type: NotificationType) -> timedelta:
     return {
-        NotificationType.AGENT_RUN: timedelta(minutes=1),
+        NotificationType.AGENT_RUN: timedelta(minutes=60),
         NotificationType.ZERO_BALANCE: timedelta(minutes=60),
         NotificationType.LOW_BALANCE: timedelta(minutes=60),
         NotificationType.BLOCK_EXECUTION_FAILED: timedelta(minutes=60),
@@ -355,11 +396,13 @@ async def create_or_add_to_user_notification_batch(
     user_id: str,
     notification_type: NotificationType,
     notification_data: NotificationEventModel,
-) -> UserNotificationBatch:
+) -> UserNotificationBatchDTO:
     try:
         logger.info(
             f"Creating or adding to notification batch for {user_id} with type {notification_type} and data {notification_data}"
         )
+        if not notification_data.data:
+            raise ValueError("Notification data must be provided")
 
         # Serialize the data
         json_data: Json = Json(notification_data.data.model_dump())
@@ -372,50 +415,50 @@ async def create_or_add_to_user_notification_batch(
                     "type": notification_type,
                 }
             },
-            include={"notifications": True},
+            include={"Notifications": True},
         )
 
         if not existing_batch:
             async with transaction() as tx:
                 notification_event = await tx.notificationevent.create(
-                    data={
-                        "type": notification_type,
-                        "data": json_data,
-                    }
+                    data=NotificationEventCreateInput(
+                        type=notification_type,
+                        data=json_data,
+                    )
                 )
 
                 # Create new batch
                 resp = await tx.usernotificationbatch.create(
-                    data={
-                        "userId": user_id,
-                        "type": notification_type,
-                        "notifications": {"connect": [{"id": notification_event.id}]},
-                    },
-                    include={"notifications": True},
+                    data=UserNotificationBatchCreateInput(
+                        userId=user_id,
+                        type=notification_type,
+                        Notifications={"connect": [{"id": notification_event.id}]},
+                    ),
+                    include={"Notifications": True},
                 )
-                return resp
+                return UserNotificationBatchDTO.from_db(resp)
         else:
             async with transaction() as tx:
                 notification_event = await tx.notificationevent.create(
-                    data={
-                        "type": notification_type,
-                        "data": json_data,
-                        "UserNotificationBatch": {"connect": {"id": existing_batch.id}},
-                    }
+                    data=NotificationEventCreateInput(
+                        type=notification_type,
+                        data=json_data,
+                        UserNotificationBatch={"connect": {"id": existing_batch.id}},
+                    )
                 )
                 # Add to existing batch
                 resp = await tx.usernotificationbatch.update(
                     where={"id": existing_batch.id},
                     data={
-                        "notifications": {"connect": [{"id": notification_event.id}]}
+                        "Notifications": {"connect": [{"id": notification_event.id}]}
                     },
-                    include={"notifications": True},
+                    include={"Notifications": True},
                 )
             if not resp:
                 raise DatabaseError(
                     f"Failed to add notification event {notification_event.id} to existing batch {existing_batch.id}"
                 )
-            return resp
+            return UserNotificationBatchDTO.from_db(resp)
     except Exception as e:
         raise DatabaseError(
             f"Failed to create or add to notification batch for user {user_id} and type {notification_type}: {e}"
@@ -425,18 +468,23 @@ async def create_or_add_to_user_notification_batch(
 async def get_user_notification_oldest_message_in_batch(
     user_id: str,
     notification_type: NotificationType,
-) -> NotificationEvent | None:
+) -> UserNotificationEventDTO | None:
     try:
         batch = await UserNotificationBatch.prisma().find_first(
             where={"userId": user_id, "type": notification_type},
-            include={"notifications": True},
+            include={"Notifications": True},
         )
         if not batch:
             return None
-        if not batch.notifications:
+        if not batch.Notifications:
             return None
-        sorted_notifications = sorted(batch.notifications, key=lambda x: x.createdAt)
-        return sorted_notifications[0]
+        sorted_notifications = sorted(batch.Notifications, key=lambda x: x.createdAt)
+
+        return (
+            UserNotificationEventDTO.from_db(sorted_notifications[0])
+            if sorted_notifications
+            else None
+        )
     except Exception as e:
         raise DatabaseError(
             f"Failed to get user notification last message in batch for user {user_id} and type {notification_type}: {e}"
@@ -471,12 +519,13 @@ async def empty_user_notification_batch(
 async def get_user_notification_batch(
     user_id: str,
     notification_type: NotificationType,
-) -> UserNotificationBatch | None:
+) -> UserNotificationBatchDTO | None:
     try:
-        return await UserNotificationBatch.prisma().find_first(
+        batch = await UserNotificationBatch.prisma().find_first(
             where={"userId": user_id, "type": notification_type},
-            include={"notifications": True},
+            include={"Notifications": True},
         )
+        return UserNotificationBatchDTO.from_db(batch) if batch else None
     except Exception as e:
         raise DatabaseError(
             f"Failed to get user notification batch for user {user_id} and type {notification_type}: {e}"
@@ -485,17 +534,18 @@ async def get_user_notification_batch(
 
 async def get_all_batches_by_type(
     notification_type: NotificationType,
-) -> list[UserNotificationBatch]:
+) -> list[UserNotificationBatchDTO]:
     try:
-        return await UserNotificationBatch.prisma().find_many(
+        batches = await UserNotificationBatch.prisma().find_many(
             where={
                 "type": notification_type,
-                "notifications": {
+                "Notifications": {
                     "some": {}  # Only return batches with at least one notification
                 },
             },
-            include={"notifications": True},
+            include={"Notifications": True},
         )
+        return [UserNotificationBatchDTO.from_db(batch) for batch in batches]
     except Exception as e:
         raise DatabaseError(
             f"Failed to get all batches by type {notification_type}: {e}"

autogpt_platform/backend/backend/data/onboarding.py

@@ -4,16 +4,15 @@ from typing import Any, Optional
 import prisma
 import pydantic
 from prisma import Json
-from prisma.models import (
-    AgentGraph,
-    AgentGraphExecution,
-    StoreListingVersion,
-    UserOnboarding,
-)
-from prisma.types import UserOnboardingUpdateInput
+from prisma.enums import OnboardingStep
+from prisma.models import UserOnboarding
+from prisma.types import UserOnboardingCreateInput, UserOnboardingUpdateInput
 
-from backend.server.v2.library.db import set_is_deleted_for_library_agent
-from backend.server.v2.store.db import get_store_agent_details
+from backend.data import db
+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
 
 # Mapping from user reason id to categories to search for when choosing agent to show
@@ -24,85 +23,113 @@ REASON_MAPPING: dict[str, list[str]] = {
     "ai_innovation": ["development", "research"],
     "personal_productivity": ["personal", "productivity"],
 }
+POINTS_AGENT_COUNT = 50  # Number of agents to calculate points for
+MIN_AGENT_COUNT = 2  # Minimum number of marketplace agents to enable onboarding
+
+user_credit = get_user_credit_model()
 
 
 class UserOnboardingUpdate(pydantic.BaseModel):
-    step: int
+    completedSteps: Optional[list[OnboardingStep]] = None
+    notificationDot: Optional[bool] = None
+    notified: Optional[list[OnboardingStep]] = None
     usageReason: Optional[str] = None
-    integrations: list[str] = pydantic.Field(default_factory=list)
+    integrations: Optional[list[str]] = None
     otherIntegrations: Optional[str] = None
-    selectedAgentCreator: Optional[str] = None
-    selectedAgentSlug: Optional[str] = None
+    selectedStoreListingVersionId: Optional[str] = None
     agentInput: Optional[dict[str, Any]] = None
-    isCompleted: bool = False
+    onboardingAgentExecutionId: Optional[str] = None
 
 
 async def get_user_onboarding(user_id: str):
     return await UserOnboarding.prisma().upsert(
         where={"userId": user_id},
         data={
-            "create": {"userId": user_id},  # type: ignore
+            "create": UserOnboardingCreateInput(userId=user_id),
             "update": {},
         },
     )
 
 
 async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
-    # Get the user onboarding data
-    user_onboarding = await get_user_onboarding(user_id)
-    update: UserOnboardingUpdateInput = {
-        "step": data.step,
-        "isCompleted": data.isCompleted,
-    }
-    if data.usageReason:
-        update["usageReason"] = data.usageReason
-    if data.integrations:
-        update["integrations"] = data.integrations
-    if data.otherIntegrations:
-        update["otherIntegrations"] = data.otherIntegrations
-    if data.selectedAgentSlug and data.selectedAgentCreator:
-        update["selectedAgentSlug"] = data.selectedAgentSlug
-        update["selectedAgentCreator"] = data.selectedAgentCreator
-        # Check if slug changes
-        if (
-            user_onboarding.selectedAgentCreator
-            and user_onboarding.selectedAgentSlug
-            and user_onboarding.selectedAgentSlug != data.selectedAgentSlug
+    update: UserOnboardingUpdateInput = {}
+    if data.completedSteps is not None:
+        update["completedSteps"] = list(set(data.completedSteps))
+        for step in (
+            OnboardingStep.AGENT_NEW_RUN,
+            OnboardingStep.GET_RESULTS,
+            OnboardingStep.MARKETPLACE_ADD_AGENT,
+            OnboardingStep.MARKETPLACE_RUN_AGENT,
+            OnboardingStep.BUILDER_SAVE_AGENT,
+            OnboardingStep.BUILDER_RUN_AGENT,
         ):
-            store_agent = await get_store_agent_details(
-                user_onboarding.selectedAgentCreator, user_onboarding.selectedAgentSlug
-            )
-            store_listing = await StoreListingVersion.prisma().find_unique_or_raise(
-                where={"id": store_agent.store_listing_version_id}
-            )
-            agent_graph = await AgentGraph.prisma().find_first(
-                where={"id": store_listing.agentId, "version": store_listing.version}
-            )
-            execution_count = await AgentGraphExecution.prisma().count(
-                where={
-                    "userId": user_id,
-                    "agentGraphId": store_listing.agentId,
-                    "agentGraphVersion": store_listing.version,
-                }
-            )
-            # If there was no execution and graph doesn't belong to the user,
-            # mark the agent as deleted
-            if execution_count == 0 and agent_graph and agent_graph.userId != user_id:
-                await set_is_deleted_for_library_agent(
-                    user_id, store_listing.agentId, store_listing.agentVersion, True
-                )
-    if data.agentInput:
+            if step in data.completedSteps:
+                await reward_user(user_id, step)
+    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:
+        update["usageReason"] = data.usageReason
+    if data.integrations is not None:
+        update["integrations"] = data.integrations
+    if data.otherIntegrations is not None:
+        update["otherIntegrations"] = data.otherIntegrations
+    if data.selectedStoreListingVersionId is not None:
+        update["selectedStoreListingVersionId"] = data.selectedStoreListingVersionId
+    if data.agentInput is not None:
         update["agentInput"] = Json(data.agentInput)
+    if data.onboardingAgentExecutionId is not None:
+        update["onboardingAgentExecutionId"] = data.onboardingAgentExecutionId
 
     return await UserOnboarding.prisma().upsert(
         where={"userId": user_id},
         data={
-            "create": {"userId": user_id, **update},  # type: ignore
+            "create": {"userId": user_id, **update},
             "update": update,
         },
     )
 
 
+async def reward_user(user_id: str, step: OnboardingStep):
+    async with db.locked_transaction(f"usr_trx_{user_id}-reward"):
+        reward = 0
+        match step:
+            # Reward user when they clicked New Run during onboarding
+            # This is because they need credits before scheduling a run (next step)
+            case OnboardingStep.AGENT_NEW_RUN:
+                reward = 300
+            case OnboardingStep.GET_RESULTS:
+                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.BUILDER_RUN_AGENT:
+                reward = 100
+
+        if reward == 0:
+            return
+
+        onboarding = await get_user_onboarding(user_id)
+
+        # Skip if already rewarded
+        if step in onboarding.rewardedFor:
+            return
+
+        onboarding.rewardedFor.append(step)
+        await user_credit.onboarding_reward(user_id, reward, step)
+        await UserOnboarding.prisma().update(
+            where={"userId": user_id},
+            data={
+                "completedSteps": list(set(onboarding.completedSteps + [step])),
+                "rewardedFor": onboarding.rewardedFor,
+            },
+        )
+
+
 def clean_and_split(text: str) -> list[str]:
     """
     Removes all special characters from a string, truncates it to 100 characters,
@@ -170,6 +197,20 @@ def calculate_points(
     return int(points)
 
 
+def get_credentials_blocks() -> dict[str, str]:
+    # Returns a dictionary of block id to credentials field name
+    creds: dict[str, str] = {}
+    blocks = get_blocks()
+    for id, block in blocks.items():
+        for field_name, field_info in block().input_schema.model_fields.items():
+            if field_info.annotation == CredentialsMetaInput:
+                creds[id] = field_name
+    return creds
+
+
+CREDENTIALS_FIELDS: dict[str, str] = get_credentials_blocks()
+
+
 async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
     user_onboarding = await get_user_onboarding(user_id)
     categories = REASON_MAPPING.get(user_onboarding.usageReason or "", [])
@@ -193,31 +234,74 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
         for word in user_onboarding.integrations
     ]
 
-    agents = await prisma.models.StoreAgent.prisma().find_many(
+    storeAgents = await prisma.models.StoreAgent.prisma().find_many(
         where=prisma.types.StoreAgentWhereInput(**where_clause),
         order=[
             {"featured": "desc"},
             {"runs": "desc"},
             {"rating": "desc"},
         ],
+        take=100,
     )
 
-    if len(agents) < 2:
-        agents += await prisma.models.StoreAgent.prisma().find_many(
+    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={
-                "listing_id": {"not_in": [agent.listing_id for agent in agents]},
+                "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={
+                "listing_id": {"not_in": [agent.listing_id for agent in storeAgents]},
             },
             order=[
                 {"featured": "desc"},
                 {"runs": "desc"},
                 {"rating": "desc"},
             ],
-            take=2 - len(agents),
+            take=2 - len(storeAgents),
         )
 
-    # Calculate points for the first 30 agents and choose the top 2
+    # Calculate points for the first X agents and choose the top 2
     agent_points = []
-    for agent in agents[:50]:
+    for agent in storeAgents[:POINTS_AGENT_COUNT]:
         points = calculate_points(
             agent, categories, custom, user_onboarding.integrations
         )
@@ -245,3 +329,10 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
         )
         for agent in recommended_agents
     ]
+
+
+async def onboarding_enabled() -> bool:
+    count = await prisma.models.StoreAgent.prisma().count(take=MIN_AGENT_COUNT + 1)
+
+    # Onboading is enabled if there are at least 2 agents in the store
+    return count >= MIN_AGENT_COUNT

autogpt_platform/backend/backend/data/queue.py

@@ -1,8 +1,6 @@
 import asyncio
-import json
 import logging
 from abc import ABC, abstractmethod
-from datetime import datetime
 from typing import Any, AsyncGenerator, Generator, Generic, Optional, TypeVar
 
 from pydantic import BaseModel
@@ -14,13 +12,6 @@ from backend.data import redis
 logger = logging.getLogger(__name__)
 
 
-class DateTimeEncoder(json.JSONEncoder):
-    def default(self, o):
-        if isinstance(o, datetime):
-            return o.isoformat()
-        return super().default(o)
-
-
 M = TypeVar("M", bound=BaseModel)
 
 
@@ -32,8 +23,12 @@ class BaseRedisEventBus(Generic[M], ABC):
     def event_bus_name(self) -> str:
         pass
 
+    @property
+    def Message(self) -> type["_EventPayloadWrapper[M]"]:
+        return _EventPayloadWrapper[self.Model]
+
     def _serialize_message(self, item: M, channel_key: str) -> tuple[str, str]:
-        message = json.dumps(item.model_dump(), cls=DateTimeEncoder)
+        message = self.Message(payload=item).model_dump_json()
         channel_name = f"{self.event_bus_name}/{channel_key}"
         logger.debug(f"[{channel_name}] Publishing an event to Redis {message}")
         return message, channel_name
@@ -43,9 +38,8 @@ class BaseRedisEventBus(Generic[M], ABC):
         if msg["type"] != message_type:
             return None
         try:
-            data = json.loads(msg["data"])
-            logger.debug(f"Consuming an event from Redis {data}")
-            return self.Model(**data)
+            logger.debug(f"[{channel_key}] Consuming an event from Redis {msg['data']}")
+            return self.Message.model_validate_json(msg["data"]).payload
         except Exception as e:
             logger.error(f"Failed to parse event result from Redis {msg} {e}")
 
@@ -57,9 +51,16 @@ class BaseRedisEventBus(Generic[M], ABC):
         return pubsub, full_channel_name
 
 
-class RedisEventBus(BaseRedisEventBus[M], ABC):
-    Model: type[M]
+class _EventPayloadWrapper(BaseModel, Generic[M]):
+    """
+    Wrapper model to allow `RedisEventBus.Model` to be a discriminated union
+    of multiple event types.
+    """
 
+    payload: M
+
+
+class RedisEventBus(BaseRedisEventBus[M], ABC):
     @property
     def connection(self) -> redis.Redis:
         return redis.get_redis()
@@ -85,8 +86,6 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
 
 
 class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
-    Model: type[M]
-
     @property
     async def connection(self) -> redis.AsyncRedis:
         return await redis.get_redis_async()

autogpt_platform/backend/backend/data/rabbitmq.py

@@ -4,10 +4,18 @@ from enum import Enum
 from typing import Awaitable, Optional
 
 import aio_pika
+import aio_pika.exceptions as aio_ex
 import pika
 import pika.adapters.blocking_connection
+from pika.exceptions import AMQPError
 from pika.spec import BasicProperties
 from pydantic import BaseModel
+from tenacity import (
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_random_exponential,
+)
 
 from backend.util.retry import conn_retry
 from backend.util.settings import Settings
@@ -161,6 +169,12 @@ class SyncRabbitMQ(RabbitMQBase):
                     routing_key=queue.routing_key or queue.name,
                 )
 
+    @retry(
+        retry=retry_if_exception_type((AMQPError, ConnectionError)),
+        wait=wait_random_exponential(multiplier=1, max=5),
+        stop=stop_after_attempt(5),
+        reraise=True,
+    )
     def publish_message(
         self,
         routing_key: str,
@@ -258,6 +272,12 @@ class AsyncRabbitMQ(RabbitMQBase):
                     exchange, routing_key=queue.routing_key or queue.name
                 )
 
+    @retry(
+        retry=retry_if_exception_type((aio_ex.AMQPError, ConnectionError)),
+        wait=wait_random_exponential(multiplier=1, max=5),
+        stop=stop_after_attempt(5),
+        reraise=True,
+    )
     async def publish_message(
         self,
         routing_key: str,

autogpt_platform/backend/backend/data/user.py

@@ -11,7 +11,7 @@ from fastapi import HTTPException
 from prisma import Json
 from prisma.enums import NotificationType
 from prisma.models import User
-from prisma.types import UserUpdateInput
+from prisma.types import JsonFilter, UserCreateInput, UserUpdateInput
 
 from backend.data.db import prisma
 from backend.data.model import UserIntegrations, UserMetadata, UserMetadataRaw
@@ -36,11 +36,11 @@ async def get_or_create_user(user_data: dict) -> User:
         user = await prisma.user.find_unique(where={"id": user_id})
         if not user:
             user = await prisma.user.create(
-                data={
-                    "id": user_id,
-                    "email": user_email,
-                    "name": user_data.get("user_metadata", {}).get("name"),
-                }
+                data=UserCreateInput(
+                    id=user_id,
+                    email=user_email,
+                    name=user_data.get("user_metadata", {}).get("name"),
+                )
             )
 
         return User.model_validate(user)
@@ -84,11 +84,11 @@ async def create_default_user() -> Optional[User]:
     user = await prisma.user.find_unique(where={"id": DEFAULT_USER_ID})
     if not user:
         user = await prisma.user.create(
-            data={
-                "id": DEFAULT_USER_ID,
-                "email": "default@example.com",
-                "name": "Default User",
-            }
+            data=UserCreateInput(
+                id=DEFAULT_USER_ID,
+                email="default@example.com",
+                name="Default User",
+            )
         )
     return User.model_validate(user)
 
@@ -135,16 +135,21 @@ async def migrate_and_encrypt_user_integrations():
     """Migrate integration credentials and OAuth states from metadata to integrations column."""
     users = await User.prisma().find_many(
         where={
-            "metadata": {
-                "path": ["integration_credentials"],
-                "not": Json({"a": "yolo"}),  # bogus value works to check if key exists
-            }  # type: ignore
+            "metadata": cast(
+                JsonFilter,
+                {
+                    "path": ["integration_credentials"],
+                    "not": Json(
+                        {"a": "yolo"}
+                    ),  # bogus value works to check if key exists
+                },
+            )
         }
     )
     logger.info(f"Migrating integration credentials for {len(users)} users")
 
     for user in users:
-        raw_metadata = cast(UserMetadataRaw, user.metadata)
+        raw_metadata = cast(dict, user.metadata)
         metadata = UserMetadata.model_validate(raw_metadata)
 
         # Get existing integrations data
@@ -160,7 +165,6 @@ async def migrate_and_encrypt_user_integrations():
         await update_user_integrations(user_id=user.id, data=integrations)
 
         # Remove from metadata
-        raw_metadata = dict(raw_metadata)
         raw_metadata.pop("integration_credentials", None)
         raw_metadata.pop("integration_oauth_states", None)
 

autogpt_platform/backend/backend/exec.py

@@ -1,15 +1,12 @@
 from backend.app import run_processes
-from backend.executor import DatabaseManager, ExecutionManager
+from backend.executor import ExecutionManager
 
 
 def main():
     """
     Run all the processes required for the AutoGPT-server REST API.
     """
-    run_processes(
-        DatabaseManager(),
-        ExecutionManager(),
-    )
+    run_processes(ExecutionManager())
 
 
 if __name__ == "__main__":

autogpt_platform/backend/backend/executor/database.py

@@ -1,16 +1,18 @@
-from backend.data.credit import get_user_credit_model
+import logging
+
+from backend.data import db
+from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
 from backend.data.execution import (
-    ExecutionResult,
-    NodeExecutionEntry,
-    RedisExecutionEventBus,
     create_graph_execution,
-    get_execution_results,
-    get_incomplete_executions,
-    get_latest_execution,
-    update_execution_status,
+    get_graph_execution,
+    get_incomplete_node_executions,
+    get_latest_node_execution,
+    get_node_execution_results,
     update_graph_execution_start_time,
     update_graph_execution_stats,
     update_node_execution_stats,
+    update_node_execution_status,
+    update_node_execution_status_batch,
     upsert_execution_input,
     upsert_execution_output,
 )
@@ -20,44 +22,65 @@ from backend.data.graph import (
     get_graph_metadata,
     get_node,
 )
+from backend.data.notifications import (
+    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,
+)
 from backend.data.user import (
+    get_active_user_ids_in_timerange,
+    get_user_email_by_id,
+    get_user_email_verification,
     get_user_integrations,
     get_user_metadata,
+    get_user_notification_preference,
     update_user_integrations,
     update_user_metadata,
 )
-from backend.util.service import AppService, expose, exposed_run_and_wait
+from backend.util.service import AppService, exposed_run_and_wait
 from backend.util.settings import Config
 
 config = Config()
 _user_credit_model = get_user_credit_model()
+logger = logging.getLogger(__name__)
 
 
-async def _spend_credits(entry: NodeExecutionEntry) -> int:
-    return await _user_credit_model.spend_credits(entry, 0, 0)
+async def _spend_credits(
+    user_id: str, cost: int, metadata: UsageTransactionMetadata
+) -> int:
+    return await _user_credit_model.spend_credits(user_id, cost, metadata)
 
 
 class DatabaseManager(AppService):
-    def __init__(self):
-        super().__init__()
-        self.use_db = True
-        self.use_redis = True
-        self.event_queue = RedisExecutionEventBus()
+
+    def run_service(self) -> None:
+        logger.info(f"[{self.service_name}] ⏳ Connecting to Database...")
+        self.run_and_wait(db.connect())
+        super().run_service()
+
+    def cleanup(self):
+        super().cleanup()
+        logger.info(f"[{self.service_name}] ⏳ Disconnecting Database...")
+        self.run_and_wait(db.disconnect())
 
     @classmethod
     def get_port(cls) -> int:
         return config.database_api_port
 
-    @expose
-    def send_execution_update(self, execution_result: ExecutionResult):
-        self.event_queue.publish(execution_result)
-
     # Executions
+    get_graph_execution = exposed_run_and_wait(get_graph_execution)
     create_graph_execution = exposed_run_and_wait(create_graph_execution)
-    get_execution_results = exposed_run_and_wait(get_execution_results)
-    get_incomplete_executions = exposed_run_and_wait(get_incomplete_executions)
-    get_latest_execution = exposed_run_and_wait(get_latest_execution)
-    update_execution_status = exposed_run_and_wait(update_execution_status)
+    get_node_execution_results = exposed_run_and_wait(get_node_execution_results)
+    get_incomplete_node_executions = exposed_run_and_wait(
+        get_incomplete_node_executions
+    )
+    get_latest_node_execution = exposed_run_and_wait(get_latest_node_execution)
+    update_node_execution_status = exposed_run_and_wait(update_node_execution_status)
+    update_node_execution_status_batch = exposed_run_and_wait(
+        update_node_execution_status_batch
+    )
     update_graph_execution_start_time = exposed_run_and_wait(
         update_graph_execution_start_time
     )
@@ -80,3 +103,24 @@ class DatabaseManager(AppService):
     update_user_metadata = exposed_run_and_wait(update_user_metadata)
     get_user_integrations = exposed_run_and_wait(get_user_integrations)
     update_user_integrations = exposed_run_and_wait(update_user_integrations)
+
+    # User Comms - async
+    get_active_user_ids_in_timerange = exposed_run_and_wait(
+        get_active_user_ids_in_timerange
+    )
+    get_user_email_by_id = exposed_run_and_wait(get_user_email_by_id)
+    get_user_email_verification = exposed_run_and_wait(get_user_email_verification)
+    get_user_notification_preference = exposed_run_and_wait(
+        get_user_notification_preference
+    )
+
+    # Notifications - async
+    create_or_add_to_user_notification_batch = exposed_run_and_wait(
+        create_or_add_to_user_notification_batch
+    )
+    empty_user_notification_batch = exposed_run_and_wait(empty_user_notification_batch)
+    get_all_batches_by_type = exposed_run_and_wait(get_all_batches_by_type)
+    get_user_notification_batch = exposed_run_and_wait(get_user_notification_batch)
+    get_user_notification_oldest_message_in_batch = exposed_run_and_wait(
+        get_user_notification_oldest_message_in_batch
+    )

autogpt_platform/backend/backend/executor/manager.py

@@ -5,14 +5,17 @@ import os
 import signal
 import sys
 import threading
+import time
 from concurrent.futures import Future, ProcessPoolExecutor
 from contextlib import contextmanager
 from multiprocessing.pool import AsyncResult, Pool
-from typing import TYPE_CHECKING, Any, Generator, Optional, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Generator, TypeVar, cast
 
+from pika.adapters.blocking_connection import BlockingChannel
+from pika.spec import Basic, BasicProperties
 from redis.lock import Lock as RedisLock
 
-from backend.blocks.basic import AgentOutputBlock
+from backend.blocks.io import AgentOutputBlock
 from backend.data.model import GraphExecutionStats, NodeExecutionStats
 from backend.data.notifications import (
     AgentRunData,
@@ -26,42 +29,40 @@ if TYPE_CHECKING:
     from backend.executor import DatabaseManager
     from backend.notifications.notifications import NotificationManager
 
-from autogpt_libs.utils.cache import thread_cached
+from autogpt_libs.utils.cache import clear_thread_cache, thread_cached
 
 from backend.blocks.agent import AgentExecutorBlock
 from backend.data import redis
-from backend.data.block import (
-    Block,
-    BlockData,
-    BlockInput,
-    BlockSchema,
-    BlockType,
-    get_block,
-)
+from backend.data.block import BlockData, BlockInput, BlockSchema, get_block
 from backend.data.execution import (
     ExecutionQueue,
-    ExecutionResult,
     ExecutionStatus,
+    GraphExecution,
     GraphExecutionEntry,
     NodeExecutionEntry,
-    merge_execution_input,
-    parse_execution_output,
+    NodeExecutionResult,
+)
+from backend.data.graph import Link, Node
+from backend.executor.utils import (
+    GRAPH_EXECUTION_CANCEL_QUEUE_NAME,
+    GRAPH_EXECUTION_QUEUE_NAME,
+    CancelExecutionEvent,
+    UsageTransactionMetadata,
+    block_usage_cost,
+    execution_usage_cost,
+    get_execution_event_bus,
+    get_execution_queue,
+    parse_execution_output,
+    validate_exec,
 )
-from backend.data.graph import GraphModel, Link, Node
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util import json
 from backend.util.decorator import error_logged, time_measured
 from backend.util.file import clean_exec_files
 from backend.util.logging import configure_logging
-from backend.util.process import set_service_name
-from backend.util.service import (
-    AppService,
-    close_service_client,
-    expose,
-    get_service_client,
-)
+from backend.util.process import AppProcess, set_service_name
+from backend.util.service import close_service_client, get_service_client
 from backend.util.settings import Settings
-from backend.util.type import convert
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -86,7 +87,7 @@ class LogMetadata:
             "node_id": node_id,
             "block_name": block_name,
         }
-        self.prefix = f"[ExecutionManager|uid:{user_id}|gid:{graph_id}|nid:{node_id}]|geid:{graph_eid}|nid:{node_eid}|{block_name}]"
+        self.prefix = f"[ExecutionManager|uid:{user_id}|gid:{graph_id}|nid:{node_id}]|geid:{graph_eid}|neid:{node_eid}|{block_name}]"
 
     def info(self, msg: str, **extra):
         msg = self._wrap(msg, **extra)
@@ -144,17 +145,22 @@ def execute_node(
     node_exec_id = data.node_exec_id
     node_id = data.node_id
 
-    def update_execution(status: ExecutionStatus) -> ExecutionResult:
-        exec_update = db_client.update_execution_status(node_exec_id, status)
-        db_client.send_execution_update(exec_update)
+    def update_execution_status(status: ExecutionStatus) -> NodeExecutionResult:
+        """Sets status and fetches+broadcasts the latest state of the node execution"""
+        exec_update = db_client.update_node_execution_status(node_exec_id, status)
+        send_execution_update(exec_update)
         return exec_update
 
     node = db_client.get_node(node_id)
 
-    node_block = get_block(node.block_id)
-    if not node_block:
-        logger.error(f"Block {node.block_id} not found.")
-        return
+    node_block = node.block
+
+    def push_output(output_name: str, output_data: Any) -> None:
+        db_client.upsert_execution_output(
+            node_exec_id=node_exec_id,
+            output_name=output_name,
+            output_data=output_data,
+        )
 
     log_metadata = LogMetadata(
         user_id=user_id,
@@ -169,8 +175,8 @@ def execute_node(
     input_data, error = validate_exec(node, data.data, resolve_input=False)
     if input_data is None:
         log_metadata.error(f"Skip execution, input validation error: {error}")
-        db_client.upsert_execution_output(node_exec_id, "error", error)
-        update_execution(ExecutionStatus.FAILED)
+        push_output("error", error)
+        update_execution_status(ExecutionStatus.FAILED)
         return
 
     # Re-shape the input data for agent block.
@@ -182,8 +188,8 @@ def execute_node(
     # Execute the node
     input_data_str = json.dumps(input_data)
     input_size = len(input_data_str)
-    log_metadata.info("Executed node with input", input=input_data_str)
-    update_execution(ExecutionStatus.RUNNING)
+    log_metadata.debug("Executed node with input", input=input_data_str)
+    update_execution_status(ExecutionStatus.RUNNING)
 
     # Inject extra execution arguments for the blocks via kwargs
     extra_exec_kwargs: dict = {
@@ -206,19 +212,15 @@ def execute_node(
         extra_exec_kwargs[field_name] = credentials
 
     output_size = 0
-    cost = 0
     try:
-        # Charge the user for the execution before running the block.
-        cost = db_client.spend_credits(data)
-
         outputs: dict[str, Any] = {}
         for output_name, output_data in node_block.execute(
             input_data, **extra_exec_kwargs
         ):
             output_data = json.convert_pydantic_to_json(output_data)
             output_size += len(json.dumps(output_data))
-            log_metadata.info("Node produced output", **{output_name: output_data})
-            db_client.upsert_execution_output(node_exec_id, output_name, output_data)
+            log_metadata.debug("Node produced output", **{output_name: output_data})
+            push_output(output_name, output_data)
             outputs[output_name] = output_data
             for execution in _enqueue_next_nodes(
                 db_client=db_client,
@@ -231,13 +233,12 @@ def execute_node(
             ):
                 yield execution
 
-        # Update execution status and spend credits
-        update_execution(ExecutionStatus.COMPLETED)
+        update_execution_status(ExecutionStatus.COMPLETED)
 
     except Exception as e:
         error_msg = str(e)
-        db_client.upsert_execution_output(node_exec_id, "error", error_msg)
-        update_execution(ExecutionStatus.FAILED)
+        push_output("error", error_msg)
+        update_execution_status(ExecutionStatus.FAILED)
 
         for execution in _enqueue_next_nodes(
             db_client=db_client,
@@ -266,7 +267,6 @@ def execute_node(
             )
             execution_stats.input_size = input_size
             execution_stats.output_size = output_size
-            execution_stats.cost = cost
 
 
 def _enqueue_next_nodes(
@@ -281,10 +281,10 @@ def _enqueue_next_nodes(
     def add_enqueued_execution(
         node_exec_id: str, node_id: str, block_id: str, data: BlockInput
     ) -> NodeExecutionEntry:
-        exec_update = db_client.update_execution_status(
+        exec_update = db_client.update_node_execution_status(
             node_exec_id, ExecutionStatus.QUEUED, data
         )
-        db_client.send_execution_update(exec_update)
+        send_execution_update(exec_update)
         return NodeExecutionEntry(
             user_id=user_id,
             graph_exec_id=graph_exec_id,
@@ -326,7 +326,7 @@ def _enqueue_next_nodes(
                 if link.is_static and link.sink_name not in next_node_input
             }
             if static_link_names and (
-                latest_execution := db_client.get_latest_execution(
+                latest_execution := db_client.get_latest_node_execution(
                     next_node_id, graph_exec_id
                 )
             ):
@@ -359,7 +359,7 @@ def _enqueue_next_nodes(
 
             # If link is static, there could be some incomplete executions waiting for it.
             # Load and complete the input missing input data, and try to re-enqueue them.
-            for iexec in db_client.get_incomplete_executions(
+            for iexec in db_client.get_incomplete_node_executions(
                 next_node_id, graph_exec_id
             ):
                 idata = iexec.input_data
@@ -396,60 +396,6 @@ def _enqueue_next_nodes(
     ]
 
 
-def validate_exec(
-    node: Node,
-    data: BlockInput,
-    resolve_input: bool = True,
-) -> tuple[BlockInput | None, str]:
-    """
-    Validate the input data for a node execution.
-
-    Args:
-        node: The node to execute.
-        data: The input data for the node execution.
-        resolve_input: Whether to resolve dynamic pins into dict/list/object.
-
-    Returns:
-        A tuple of the validated data and the block name.
-        If the data is invalid, the first element will be None, and the second element
-        will be an error message.
-        If the data is valid, the first element will be the resolved input data, and
-        the second element will be the block name.
-    """
-    node_block: Block | None = get_block(node.block_id)
-    if not node_block:
-        return None, f"Block for {node.block_id} not found."
-    schema = node_block.input_schema
-
-    # Convert non-matching data types to the expected input schema.
-    for name, data_type in schema.__annotations__.items():
-        if (value := data.get(name)) and (type(value) is not data_type):
-            data[name] = convert(value, data_type)
-
-    # Input data (without default values) should contain all required fields.
-    error_prefix = f"Input data missing or mismatch for `{node_block.name}`:"
-    if missing_links := schema.get_missing_links(data, node.input_links):
-        return None, f"{error_prefix} unpopulated links {missing_links}"
-
-    # Merge input data with default values and resolve dynamic dict/list/object pins.
-    input_default = schema.get_input_defaults(node.input_default)
-    data = {**input_default, **data}
-    if resolve_input:
-        data = merge_execution_input(data)
-
-    # Input data post-merge should contain all required fields from the schema.
-    if missing_input := schema.get_missing_input(data):
-        return None, f"{error_prefix} missing input {missing_input}"
-
-    # Last validation: Validate the input values against the schema.
-    if error := schema.get_mismatch_error(data):
-        error_message = f"{error_prefix} {error}"
-        logger.error(error_message)
-        return None, error_message
-
-    return data, node_block.name
-
-
 class Executor:
     """
     This class contains event handlers for the process pool executor events.
@@ -626,25 +572,79 @@ class Executor:
             node_eid="*",
             block_name="-",
         )
-        cls.db_client.update_graph_execution_start_time(graph_exec.graph_exec_id)
+        exec_meta = cls.db_client.update_graph_execution_start_time(
+            graph_exec.graph_exec_id
+        )
+        if exec_meta is None:
+            logger.warning(
+                f"Skipped graph execution {graph_exec.graph_exec_id}, the graph execution is not found or not currently in the QUEUED state."
+            )
+            return
+
+        send_execution_update(exec_meta)
         timing_info, (exec_stats, status, error) = cls._on_graph_execution(
             graph_exec, cancel, log_metadata
         )
         exec_stats.walltime = timing_info.wall_time
         exec_stats.cputime = timing_info.cpu_time
-        exec_stats.error = error
+        exec_stats.error = str(error)
 
-        if isinstance(exec_stats.error, Exception):
-            exec_stats.error = str(exec_stats.error)
-        result = cls.db_client.update_graph_execution_stats(
+        if graph_exec_result := cls.db_client.update_graph_execution_stats(
             graph_exec_id=graph_exec.graph_exec_id,
             status=status,
             stats=exec_stats,
-        )
-        cls.db_client.send_execution_update(result)
+        ):
+            send_execution_update(graph_exec_result)
 
         cls._handle_agent_run_notif(graph_exec, exec_stats)
 
+    @classmethod
+    def _charge_usage(
+        cls,
+        node_exec: NodeExecutionEntry,
+        execution_count: int,
+        execution_stats: GraphExecutionStats,
+    ) -> int:
+        block = get_block(node_exec.block_id)
+        if not block:
+            logger.error(f"Block {node_exec.block_id} not found.")
+            return execution_count
+
+        cost, matching_filter = block_usage_cost(block=block, input_data=node_exec.data)
+        if cost > 0:
+            cls.db_client.spend_credits(
+                user_id=node_exec.user_id,
+                cost=cost,
+                metadata=UsageTransactionMetadata(
+                    graph_exec_id=node_exec.graph_exec_id,
+                    graph_id=node_exec.graph_id,
+                    node_exec_id=node_exec.node_exec_id,
+                    node_id=node_exec.node_id,
+                    block_id=node_exec.block_id,
+                    block=block.name,
+                    input=matching_filter,
+                ),
+            )
+            execution_stats.cost += cost
+
+        cost, execution_count = execution_usage_cost(execution_count)
+        if cost > 0:
+            cls.db_client.spend_credits(
+                user_id=node_exec.user_id,
+                cost=cost,
+                metadata=UsageTransactionMetadata(
+                    graph_exec_id=node_exec.graph_exec_id,
+                    graph_id=node_exec.graph_id,
+                    input={
+                        "execution_count": execution_count,
+                        "charge": "Execution Cost",
+                    },
+                ),
+            )
+            execution_stats.cost += cost
+
+        return execution_count
+
     @classmethod
     @time_measured
     def _on_graph_execution(
@@ -660,15 +660,19 @@ class Executor:
             Exception | None: The error that occurred during the execution, if any.
         """
         log_metadata.info(f"Start graph execution {graph_exec.graph_exec_id}")
-        exec_stats = GraphExecutionStats()
+        execution_stats = GraphExecutionStats()
+        execution_status = ExecutionStatus.RUNNING
         error = None
         finished = False
 
         def cancel_handler():
+            nonlocal execution_status
+
             while not cancel.is_set():
                 cancel.wait(1)
             if finished:
                 return
+            execution_status = ExecutionStatus.TERMINATED
             cls.executor.terminate()
             log_metadata.info(f"Terminated graph execution {graph_exec.graph_exec_id}")
             cls._init_node_executor_pool()
@@ -679,44 +683,51 @@ class Executor:
         try:
             queue = ExecutionQueue[NodeExecutionEntry]()
             for node_exec in graph_exec.start_node_execs:
-                exec_update = cls.db_client.update_execution_status(
-                    node_exec.node_exec_id, ExecutionStatus.QUEUED, node_exec.data
-                )
-                cls.db_client.send_execution_update(exec_update)
                 queue.add(node_exec)
 
+            exec_cost_counter = 0
             running_executions: dict[str, AsyncResult] = {}
-            low_balance_error: Optional[InsufficientBalanceError] = None
 
             def make_exec_callback(exec_data: NodeExecutionEntry):
-
                 def callback(result: object):
                     running_executions.pop(exec_data.node_id)
 
                     if not isinstance(result, NodeExecutionStats):
                         return
 
-                    nonlocal exec_stats, low_balance_error
-                    exec_stats.node_count += 1
-                    exec_stats.nodes_cputime += result.cputime
-                    exec_stats.nodes_walltime += result.walltime
-                    exec_stats.cost += result.cost
+                    nonlocal execution_stats
+                    execution_stats.node_count += 1
+                    execution_stats.nodes_cputime += result.cputime
+                    execution_stats.nodes_walltime += result.walltime
                     if (err := result.error) and isinstance(err, Exception):
-                        exec_stats.node_error_count += 1
+                        execution_stats.node_error_count += 1
 
-                        if isinstance(err, InsufficientBalanceError):
-                            low_balance_error = err
+                    if _graph_exec := cls.db_client.update_graph_execution_stats(
+                        graph_exec_id=exec_data.graph_exec_id,
+                        status=execution_status,
+                        stats=execution_stats,
+                    ):
+                        send_execution_update(_graph_exec)
+                    else:
+                        logger.error(
+                            "Callback for "
+                            f"finished node execution #{exec_data.node_exec_id} "
+                            "could not update execution stats "
+                            f"for graph execution #{exec_data.graph_exec_id}; "
+                            f"triggered while graph exec status = {execution_status}"
+                        )
 
                 return callback
 
             while not queue.empty():
                 if cancel.is_set():
-                    return exec_stats, ExecutionStatus.TERMINATED, error
+                    execution_status = ExecutionStatus.TERMINATED
+                    return execution_stats, execution_status, error
 
-                exec_data = queue.get()
+                queued_node_exec = queue.get()
 
                 # Avoid parallel execution of the same node.
-                execution = running_executions.get(exec_data.node_id)
+                execution = running_executions.get(queued_node_exec.node_id)
                 if execution and not execution.ready():
                     # TODO (performance improvement):
                     #   Wait for the completion of the same node execution is blocking.
@@ -725,13 +736,55 @@ class Executor:
                     execution.wait()
 
                 log_metadata.debug(
-                    f"Dispatching node execution {exec_data.node_exec_id} "
-                    f"for node {exec_data.node_id}",
+                    f"Dispatching node execution {queued_node_exec.node_exec_id} "
+                    f"for node {queued_node_exec.node_id}",
                 )
-                running_executions[exec_data.node_id] = cls.executor.apply_async(
+
+                try:
+                    exec_cost_counter = cls._charge_usage(
+                        node_exec=queued_node_exec,
+                        execution_count=exec_cost_counter + 1,
+                        execution_stats=execution_stats,
+                    )
+                except InsufficientBalanceError as error:
+                    node_exec_id = queued_node_exec.node_exec_id
+                    cls.db_client.upsert_execution_output(
+                        node_exec_id=node_exec_id,
+                        output_name="error",
+                        output_data=str(error),
+                    )
+
+                    execution_status = ExecutionStatus.FAILED
+                    exec_update = cls.db_client.update_node_execution_status(
+                        node_exec_id, execution_status
+                    )
+                    send_execution_update(exec_update)
+
+                    cls._handle_low_balance_notif(
+                        graph_exec.user_id,
+                        graph_exec.graph_id,
+                        execution_stats,
+                        error,
+                    )
+                    raise
+
+                # Add credentials input overrides
+                node_id = queued_node_exec.node_id
+                if (node_creds_map := graph_exec.node_credentials_input_map) and (
+                    node_field_creds_map := node_creds_map.get(node_id)
+                ):
+                    queued_node_exec.data.update(
+                        {
+                            field_name: creds_meta.model_dump()
+                            for field_name, creds_meta in node_field_creds_map.items()
+                        }
+                    )
+
+                # Initiate node execution
+                running_executions[queued_node_exec.node_id] = cls.executor.apply_async(
                     cls.on_node_execution,
-                    (queue, exec_data),
-                    callback=make_exec_callback(exec_data),
+                    (queue, queued_node_exec),
+                    callback=make_exec_callback(queued_node_exec),
                 )
 
                 # Avoid terminating graph execution when some nodes are still running.
@@ -741,7 +794,8 @@ class Executor:
                     )
                     for node_id, execution in list(running_executions.items()):
                         if cancel.is_set():
-                            return exec_stats, ExecutionStatus.TERMINATED, error
+                            execution_status = ExecutionStatus.TERMINATED
+                            return execution_stats, execution_status, error
 
                         if not queue.empty():
                             break  # yield to parent loop to execute new queue items
@@ -751,32 +805,24 @@ class Executor:
 
             log_metadata.info(f"Finished graph execution {graph_exec.graph_exec_id}")
 
-            if isinstance(low_balance_error, InsufficientBalanceError):
-                cls._handle_low_balance_notif(
-                    graph_exec.user_id,
-                    graph_exec.graph_id,
-                    exec_stats,
-                    low_balance_error,
-                )
-                raise low_balance_error
-
         except Exception as e:
-            log_metadata.exception(
-                f"Failed graph execution {graph_exec.graph_exec_id}: {e}"
-            )
             error = e
         finally:
+            if error:
+                log_metadata.error(
+                    f"Failed graph execution {graph_exec.graph_exec_id}: {error}"
+                )
+                execution_status = ExecutionStatus.FAILED
+            else:
+                execution_status = ExecutionStatus.COMPLETED
+
             if not cancel.is_set():
                 finished = True
                 cancel.set()
             cancel_thread.join()
             clean_exec_files(graph_exec.graph_exec_id)
 
-        return (
-            exec_stats,
-            ExecutionStatus.FAILED if error else ExecutionStatus.COMPLETED,
-            error,
-        )
+            return execution_stats, execution_status, error
 
     @classmethod
     def _handle_agent_run_notif(
@@ -787,7 +833,10 @@ class Executor:
         metadata = cls.db_client.get_graph_metadata(
             graph_exec.graph_id, graph_exec.graph_version
         )
-        outputs = cls.db_client.get_execution_results(graph_exec.graph_exec_id)
+        outputs = cls.db_client.get_node_execution_results(
+            graph_exec.graph_exec_id,
+            block_ids=[AgentOutputBlock().id],
+        )
 
         named_outputs = [
             {
@@ -795,7 +844,6 @@ class Executor:
                 for key, value in output.output_data.items()
             }
             for output in outputs
-            if output.block_id == AgentOutputBlock().id
         ]
 
         event = NotificationEventDTO(
@@ -840,222 +888,178 @@ class Executor:
         )
 
 
-class ExecutionManager(AppService):
+class ExecutionManager(AppProcess):
     def __init__(self):
         super().__init__()
-        self.use_redis = True
-        self.use_supabase = True
         self.pool_size = settings.config.num_graph_workers
-        self.queue = ExecutionQueue[GraphExecutionEntry]()
+        self.running = True
         self.active_graph_runs: dict[str, tuple[Future, threading.Event]] = {}
 
     @classmethod
     def get_port(cls) -> int:
         return settings.config.execution_manager_port
 
-    def run_service(self):
-        from backend.integrations.credentials_store import IntegrationCredentialsStore
+    def run(self):
+        retry_count_max = settings.config.execution_manager_loop_max_retry
+        retry_count = 0
 
-        self.credentials_store = IntegrationCredentialsStore()
+        for retry_count in range(retry_count_max):
+            try:
+                self._run()
+            except Exception as e:
+                if not self.running:
+                    break
+                logger.exception(
+                    f"[{self.service_name}] Error in execution manager: {e}"
+                )
+
+            if retry_count >= retry_count_max:
+                logger.error(
+                    f"[{self.service_name}] Max retries reached ({retry_count_max}), exiting..."
+                )
+                break
+            else:
+                logger.info(
+                    f"[{self.service_name}] Retrying execution loop in {retry_count} seconds..."
+                )
+                time.sleep(retry_count)
+
+    def _run(self):
+        logger.info(f"[{self.service_name}] ⏳ Spawn max-{self.pool_size} workers...")
         self.executor = ProcessPoolExecutor(
             max_workers=self.pool_size,
             initializer=Executor.on_graph_executor_start,
         )
-        sync_manager = multiprocessing.Manager()
-        logger.info(
-            f"[{self.service_name}] Started with max-{self.pool_size} graph workers"
+
+        logger.info(f"[{self.service_name}] ⏳ Connecting to Redis...")
+        redis.connect()
+
+        # Consume Cancel & Run execution requests.
+        clear_thread_cache(get_execution_queue)
+        channel = get_execution_queue().get_channel()
+        channel.basic_qos(prefetch_count=self.pool_size)
+        channel.basic_consume(
+            queue=GRAPH_EXECUTION_CANCEL_QUEUE_NAME,
+            on_message_callback=self._handle_cancel_message,
+            auto_ack=True,
         )
-        while True:
-            graph_exec_data = self.queue.get()
-            graph_exec_id = graph_exec_data.graph_exec_id
-            logger.debug(
-                f"[ExecutionManager] Dispatching graph execution {graph_exec_id}"
-            )
-            cancel_event = sync_manager.Event()
-            future = self.executor.submit(
-                Executor.on_graph_execution, graph_exec_data, cancel_event
-            )
-            self.active_graph_runs[graph_exec_id] = (future, cancel_event)
-            future.add_done_callback(
-                lambda _: self.active_graph_runs.pop(graph_exec_id, None)
+        channel.basic_consume(
+            queue=GRAPH_EXECUTION_QUEUE_NAME,
+            on_message_callback=self._handle_run_message,
+            auto_ack=False,
+        )
+
+        logger.info(f"[{self.service_name}] Ready to consume messages...")
+        channel.start_consuming()
+
+    def _handle_cancel_message(
+        self,
+        channel: BlockingChannel,
+        method: Basic.Deliver,
+        properties: BasicProperties,
+        body: bytes,
+    ):
+        """
+        Called whenever we receive a CANCEL message from the queue.
+        (With auto_ack=True, message is considered 'acked' automatically.)
+        """
+        try:
+            request = CancelExecutionEvent.model_validate_json(body)
+            graph_exec_id = request.graph_exec_id
+            if not graph_exec_id:
+                logger.warning(
+                    f"[{self.service_name}] Cancel message missing 'graph_exec_id'"
+                )
+                return
+            if graph_exec_id not in self.active_graph_runs:
+                logger.debug(
+                    f"[{self.service_name}] Cancel received for {graph_exec_id} but not active."
+                )
+                return
+
+            _, cancel_event = self.active_graph_runs[graph_exec_id]
+            logger.info(f"[{self.service_name}] Received cancel for {graph_exec_id}")
+            if not cancel_event.is_set():
+                cancel_event.set()
+            else:
+                logger.debug(
+                    f"[{self.service_name}] Cancel already set for {graph_exec_id}"
+                )
+
+        except Exception as e:
+            logger.exception(f"Error handling cancel message: {e}")
+
+    def _handle_run_message(
+        self,
+        channel: BlockingChannel,
+        method: Basic.Deliver,
+        properties: BasicProperties,
+        body: bytes,
+    ):
+        delivery_tag = method.delivery_tag
+        try:
+            graph_exec_entry = GraphExecutionEntry.model_validate_json(body)
+        except Exception as e:
+            logger.error(f"[{self.service_name}] Could not parse run message: {e}")
+            channel.basic_nack(delivery_tag, requeue=False)
+            return
+
+        graph_exec_id = graph_exec_entry.graph_exec_id
+        logger.info(
+            f"[{self.service_name}] Received RUN for graph_exec_id={graph_exec_id}"
+        )
+        if graph_exec_id in self.active_graph_runs:
+            logger.warning(
+                f"[{self.service_name}] Graph {graph_exec_id} already running; rejecting duplicate run."
             )
+            channel.basic_nack(delivery_tag, requeue=False)
+            return
+
+        cancel_event = multiprocessing.Manager().Event()
+        future = self.executor.submit(
+            Executor.on_graph_execution, graph_exec_entry, cancel_event
+        )
+        self.active_graph_runs[graph_exec_id] = (future, cancel_event)
+
+        def _on_run_done(f: Future):
+            logger.info(f"[{self.service_name}] Run completed for {graph_exec_id}")
+            try:
+                self.active_graph_runs.pop(graph_exec_id, None)
+                if f.exception():
+                    logger.error(
+                        f"[{self.service_name}] Execution for {graph_exec_id} failed: {f.exception()}"
+                    )
+                    channel.connection.add_callback_threadsafe(
+                        lambda: channel.basic_nack(delivery_tag, requeue=False)
+                    )
+                else:
+                    channel.connection.add_callback_threadsafe(
+                        lambda: channel.basic_ack(delivery_tag)
+                    )
+            except Exception as e:
+                logger.error(f"[{self.service_name}] Error acknowledging message: {e}")
+
+        future.add_done_callback(_on_run_done)
 
     def cleanup(self):
-        logger.info(f"[{__class__.__name__}] ⏳ Shutting down graph executor pool...")
+        super().cleanup()
+
+        logger.info(f"[{self.service_name}] ⏳ Shutting down service loop...")
+        self.running = False
+
+        logger.info(f"[{self.service_name}] ⏳ Shutting down RabbitMQ channel...")
+        get_execution_queue().get_channel().stop_consuming()
+
+        logger.info(f"[{self.service_name}] ⏳ Shutting down graph executor pool...")
         self.executor.shutdown(cancel_futures=True)
 
-        super().cleanup()
+        logger.info(f"[{self.service_name}] ⏳ Disconnecting Redis...")
+        redis.disconnect()
 
     @property
     def db_client(self) -> "DatabaseManager":
         return get_db_client()
 
-    @expose
-    def add_execution(
-        self,
-        graph_id: str,
-        data: BlockInput,
-        user_id: str,
-        graph_version: Optional[int] = None,
-        preset_id: str | None = None,
-    ) -> GraphExecutionEntry:
-        graph: GraphModel | None = self.db_client.get_graph(
-            graph_id=graph_id, user_id=user_id, version=graph_version
-        )
-        if not graph:
-            raise ValueError(f"Graph #{graph_id} not found.")
-
-        graph.validate_graph(for_run=True)
-        self._validate_node_input_credentials(graph, user_id)
-
-        nodes_input = []
-        for node in graph.starting_nodes:
-            input_data = {}
-            block = get_block(node.block_id)
-
-            # Invalid block & Note block should never be executed.
-            if not block or block.block_type == BlockType.NOTE:
-                continue
-
-            # Extract request input data, and assign it to the input pin.
-            if block.block_type == BlockType.INPUT:
-                input_name = node.input_default.get("name")
-                if input_name and input_name in data:
-                    input_data = {"value": data[input_name]}
-
-            # Extract webhook payload, and assign it to the input pin
-            webhook_payload_key = f"webhook_{node.webhook_id}_payload"
-            if (
-                block.block_type in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
-                and node.webhook_id
-            ):
-                if webhook_payload_key not in data:
-                    raise ValueError(
-                        f"Node {block.name} #{node.id} webhook payload is missing"
-                    )
-                input_data = {"payload": data[webhook_payload_key]}
-
-            input_data, error = validate_exec(node, input_data)
-            if input_data is None:
-                raise ValueError(error)
-            else:
-                nodes_input.append((node.id, input_data))
-
-        if not nodes_input:
-            raise ValueError(
-                "No starting nodes found for the graph, make sure an AgentInput or blocks with no inbound links are present as starting nodes."
-            )
-
-        graph_exec_id, node_execs = self.db_client.create_graph_execution(
-            graph_id=graph_id,
-            graph_version=graph.version,
-            nodes_input=nodes_input,
-            user_id=user_id,
-            preset_id=preset_id,
-        )
-
-        starting_node_execs = []
-        for node_exec in node_execs:
-            starting_node_execs.append(
-                NodeExecutionEntry(
-                    user_id=user_id,
-                    graph_exec_id=node_exec.graph_exec_id,
-                    graph_id=node_exec.graph_id,
-                    node_exec_id=node_exec.node_exec_id,
-                    node_id=node_exec.node_id,
-                    block_id=node_exec.block_id,
-                    data=node_exec.input_data,
-                )
-            )
-
-        graph_exec = GraphExecutionEntry(
-            user_id=user_id,
-            graph_id=graph_id,
-            graph_version=graph_version or 0,
-            graph_exec_id=graph_exec_id,
-            start_node_execs=starting_node_execs,
-        )
-        self.queue.add(graph_exec)
-
-        return graph_exec
-
-    @expose
-    def cancel_execution(self, graph_exec_id: str) -> None:
-        """
-        Mechanism:
-        1. Set the cancel event
-        2. Graph executor's cancel handler thread detects the event, terminates workers,
-           reinitializes worker pool, and returns.
-        3. Update execution statuses in DB and set `error` outputs to `"TERMINATED"`.
-        """
-        if graph_exec_id not in self.active_graph_runs:
-            raise Exception(
-                f"Graph execution #{graph_exec_id} not active/running: "
-                "possibly already completed/cancelled."
-            )
-
-        future, cancel_event = self.active_graph_runs[graph_exec_id]
-        if cancel_event.is_set():
-            return
-
-        cancel_event.set()
-        future.result()
-
-        # Update the status of the unfinished node executions
-        node_execs = self.db_client.get_execution_results(graph_exec_id)
-        for node_exec in node_execs:
-            if node_exec.status not in (
-                ExecutionStatus.COMPLETED,
-                ExecutionStatus.FAILED,
-            ):
-                exec_update = self.db_client.update_execution_status(
-                    node_exec.node_exec_id, ExecutionStatus.TERMINATED
-                )
-                self.db_client.send_execution_update(exec_update)
-
-    def _validate_node_input_credentials(self, graph: GraphModel, user_id: str):
-        """Checks all credentials for all nodes of the graph"""
-
-        for node in graph.nodes:
-            block = get_block(node.block_id)
-            if not block:
-                raise ValueError(f"Unknown block {node.block_id} for node #{node.id}")
-
-            # Find any fields of type CredentialsMetaInput
-            credentials_fields = cast(
-                type[BlockSchema], block.input_schema
-            ).get_credentials_fields()
-            if not credentials_fields:
-                continue
-
-            for field_name, credentials_meta_type in credentials_fields.items():
-                credentials_meta = credentials_meta_type.model_validate(
-                    node.input_default[field_name]
-                )
-                # Fetch the corresponding Credentials and perform sanity checks
-                credentials = self.credentials_store.get_creds_by_id(
-                    user_id, credentials_meta.id
-                )
-                if not credentials:
-                    raise ValueError(
-                        f"Unknown credentials #{credentials_meta.id} "
-                        f"for node #{node.id} input '{field_name}'"
-                    )
-                if (
-                    credentials.provider != credentials_meta.provider
-                    or credentials.type != credentials_meta.type
-                ):
-                    logger.warning(
-                        f"Invalid credentials #{credentials.id} for node #{node.id}: "
-                        "type/provider mismatch: "
-                        f"{credentials_meta.type}<>{credentials.type};"
-                        f"{credentials_meta.provider}<>{credentials.provider}"
-                    )
-                    raise ValueError(
-                        f"Invalid credentials #{credentials.id} for node #{node.id}: "
-                        "type/provider mismatch"
-                    )
-
 
 # ------- UTILITIES ------- #
 
@@ -1074,6 +1078,10 @@ def get_notification_service() -> "NotificationManager":
     return get_service_client(NotificationManager)
 
 
+def send_execution_update(entry: GraphExecution | NodeExecutionResult):
+    return get_execution_event_bus().publish(entry)
+
+
 @contextmanager
 def synchronized(key: str, timeout: int = 60):
     lock: RedisLock = redis.get_redis().lock(f"lock:{key}", timeout=timeout)

autogpt_platform/backend/backend/executor/scheduler.py

@@ -16,7 +16,7 @@ from pydantic import BaseModel
 from sqlalchemy import MetaData, create_engine
 
 from backend.data.block import BlockInput
-from backend.executor.manager import ExecutionManager
+from backend.executor import utils as execution_utils
 from backend.notifications.notifications import NotificationManager
 from backend.util.service import AppService, expose, get_service_client
 from backend.util.settings import Config
@@ -57,11 +57,6 @@ def job_listener(event):
         log(f"Job {event.job_id} completed successfully.")
 
 
-@thread_cached
-def get_execution_client() -> ExecutionManager:
-    return get_service_client(ExecutionManager)
-
-
 @thread_cached
 def get_notification_client():
     from backend.notifications import NotificationManager
@@ -73,9 +68,9 @@ def execute_graph(**kwargs):
     args = ExecutionJobArgs(**kwargs)
     try:
         log(f"Executing recurring job for graph #{args.graph_id}")
-        get_execution_client().add_execution(
+        execution_utils.add_graph_execution(
             graph_id=args.graph_id,
-            data=args.input_data,
+            inputs=args.input_data,
             user_id=args.user_id,
             graph_version=args.graph_version,
         )
@@ -164,11 +159,6 @@ class Scheduler(AppService):
     def db_pool_size(cls) -> int:
         return config.scheduler_db_pool_size
 
-    @property
-    @thread_cached
-    def execution_client(self) -> ExecutionManager:
-        return get_service_client(ExecutionManager)
-
     @property
     @thread_cached
     def notification_client(self) -> NotificationManager:
@@ -176,7 +166,7 @@ class Scheduler(AppService):
 
     def run_service(self):
         load_dotenv()
-        db_schema, db_url = _extract_schema_from_url(os.getenv("DATABASE_URL"))
+        db_schema, db_url = _extract_schema_from_url(os.getenv("DIRECT_URL"))
         self.scheduler = BlockingScheduler(
             jobstores={
                 Jobstores.EXECUTION.value: SQLAlchemyJobStore(
@@ -206,6 +196,12 @@ class Scheduler(AppService):
         self.scheduler.add_listener(job_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
         self.scheduler.start()
 
+    def cleanup(self):
+        super().cleanup()
+        logger.info(f"[{self.service_name}] ⏳ Shutting down scheduler...")
+        if self.scheduler:
+            self.scheduler.shutdown(wait=False)
+
     @expose
     def add_execution_schedule(
         self,

autogpt_platform/backend/backend/executor/utils.py

@@ -0,0 +1,748 @@
+import logging
+from typing import TYPE_CHECKING, Any, Optional, cast
+
+from autogpt_libs.utils.cache import thread_cached
+from pydantic import BaseModel
+
+from backend.data.block import (
+    Block,
+    BlockData,
+    BlockInput,
+    BlockSchema,
+    BlockType,
+    get_block,
+)
+from backend.data.block_cost_config import BLOCK_COSTS
+from backend.data.cost import BlockCostType
+from backend.data.execution import (
+    AsyncRedisExecutionEventBus,
+    ExecutionStatus,
+    GraphExecutionStats,
+    GraphExecutionWithNodes,
+    RedisExecutionEventBus,
+    create_graph_execution,
+    update_graph_execution_stats,
+    update_node_execution_status_batch,
+)
+from backend.data.graph import GraphModel, Node, get_graph
+from backend.data.model import CredentialsMetaInput
+from backend.data.rabbitmq import (
+    AsyncRabbitMQ,
+    Exchange,
+    ExchangeType,
+    Queue,
+    RabbitMQConfig,
+    SyncRabbitMQ,
+)
+from backend.util.exceptions import NotFoundError
+from backend.util.mock import MockObject
+from backend.util.service import get_service_client
+from backend.util.settings import Config
+from backend.util.type import convert
+
+if TYPE_CHECKING:
+    from backend.executor import DatabaseManager
+    from backend.integrations.credentials_store import IntegrationCredentialsStore
+
+config = Config()
+logger = logging.getLogger(__name__)
+
+# ============ Resource Helpers ============ #
+
+
+@thread_cached
+def get_execution_event_bus() -> RedisExecutionEventBus:
+    return RedisExecutionEventBus()
+
+
+@thread_cached
+def get_async_execution_event_bus() -> AsyncRedisExecutionEventBus:
+    return AsyncRedisExecutionEventBus()
+
+
+@thread_cached
+def get_execution_queue() -> SyncRabbitMQ:
+    client = SyncRabbitMQ(create_execution_queue_config())
+    client.connect()
+    return client
+
+
+@thread_cached
+async def get_async_execution_queue() -> AsyncRabbitMQ:
+    client = AsyncRabbitMQ(create_execution_queue_config())
+    await client.connect()
+    return client
+
+
+@thread_cached
+def get_integration_credentials_store() -> "IntegrationCredentialsStore":
+    from backend.integrations.credentials_store import IntegrationCredentialsStore
+
+    return IntegrationCredentialsStore()
+
+
+@thread_cached
+def get_db_client() -> "DatabaseManager":
+    from backend.executor import DatabaseManager
+
+    return get_service_client(DatabaseManager)
+
+
+# ============ Execution Cost Helpers ============ #
+
+
+class UsageTransactionMetadata(BaseModel):
+    graph_exec_id: str | None = None
+    graph_id: str | None = None
+    node_id: str | None = None
+    node_exec_id: str | None = None
+    block_id: str | None = None
+    block: str | None = None
+    input: BlockInput | None = None
+
+
+def execution_usage_cost(execution_count: int) -> tuple[int, int]:
+    """
+    Calculate the cost of executing a graph based on the number of executions.
+
+    Args:
+        execution_count: Number of executions
+
+    Returns:
+        Tuple of cost amount and remaining execution count
+    """
+    return (
+        execution_count
+        // config.execution_cost_count_threshold
+        * config.execution_cost_per_threshold,
+        execution_count % config.execution_cost_count_threshold,
+    )
+
+
+def block_usage_cost(
+    block: Block,
+    input_data: BlockInput,
+    data_size: float = 0,
+    run_time: float = 0,
+) -> tuple[int, BlockInput]:
+    """
+    Calculate the cost of using a block based on the input data and the block type.
+
+    Args:
+        block: Block object
+        input_data: Input data for the block
+        data_size: Size of the input data in bytes
+        run_time: Execution time of the block in seconds
+
+    Returns:
+        Tuple of cost amount and cost filter
+    """
+    block_costs = BLOCK_COSTS.get(type(block))
+    if not block_costs:
+        return 0, {}
+
+    for block_cost in block_costs:
+        if not _is_cost_filter_match(block_cost.cost_filter, input_data):
+            continue
+
+        if block_cost.cost_type == BlockCostType.RUN:
+            return block_cost.cost_amount, block_cost.cost_filter
+
+        if block_cost.cost_type == BlockCostType.SECOND:
+            return (
+                int(run_time * block_cost.cost_amount),
+                block_cost.cost_filter,
+            )
+
+        if block_cost.cost_type == BlockCostType.BYTE:
+            return (
+                int(data_size * block_cost.cost_amount),
+                block_cost.cost_filter,
+            )
+
+    return 0, {}
+
+
+def _is_cost_filter_match(cost_filter: BlockInput, input_data: BlockInput) -> bool:
+    """
+    Filter rules:
+      - If cost_filter is an object, then check if cost_filter is the subset of input_data
+      - Otherwise, check if cost_filter is equal to input_data.
+      - Undefined, null, and empty string are considered as equal.
+    """
+    if not isinstance(cost_filter, dict) or not isinstance(input_data, dict):
+        return cost_filter == input_data
+
+    return all(
+        (not input_data.get(k) and not v)
+        or (input_data.get(k) and _is_cost_filter_match(v, input_data[k]))
+        for k, v in cost_filter.items()
+    )
+
+
+# ============ Execution Input Helpers ============ #
+
+LIST_SPLIT = "_$_"
+DICT_SPLIT = "_#_"
+OBJC_SPLIT = "_@_"
+
+
+def parse_execution_output(output: BlockData, name: str) -> Any | None:
+    """
+    Extracts partial output data by name from a given BlockData.
+
+    The function supports extracting data from lists, dictionaries, and objects
+    using specific naming conventions:
+    - For lists: <output_name>_$_<index>
+    - For dictionaries: <output_name>_#_<key>
+    - For objects: <output_name>_@_<attribute>
+
+    Args:
+        output (BlockData): A tuple containing the output name and data.
+        name (str): The name used to extract specific data from the output.
+
+    Returns:
+        Any | None: The extracted data if found, otherwise None.
+
+    Examples:
+        >>> output = ("result", [10, 20, 30])
+        >>> parse_execution_output(output, "result_$_1")
+        20
+
+        >>> output = ("config", {"key1": "value1", "key2": "value2"})
+        >>> parse_execution_output(output, "config_#_key1")
+        'value1'
+
+        >>> class Sample:
+        ...     attr1 = "value1"
+        ...     attr2 = "value2"
+        >>> output = ("object", Sample())
+        >>> parse_execution_output(output, "object_@_attr1")
+        'value1'
+    """
+    output_name, output_data = output
+
+    if name == output_name:
+        return output_data
+
+    if name.startswith(f"{output_name}{LIST_SPLIT}"):
+        index = int(name.split(LIST_SPLIT)[1])
+        if not isinstance(output_data, list) or len(output_data) <= index:
+            return None
+        return output_data[int(name.split(LIST_SPLIT)[1])]
+
+    if name.startswith(f"{output_name}{DICT_SPLIT}"):
+        index = name.split(DICT_SPLIT)[1]
+        if not isinstance(output_data, dict) or index not in output_data:
+            return None
+        return output_data[index]
+
+    if name.startswith(f"{output_name}{OBJC_SPLIT}"):
+        index = name.split(OBJC_SPLIT)[1]
+        if isinstance(output_data, object) and hasattr(output_data, index):
+            return getattr(output_data, index)
+        return None
+
+    return None
+
+
+def validate_exec(
+    node: Node,
+    data: BlockInput,
+    resolve_input: bool = True,
+) -> tuple[BlockInput | None, str]:
+    """
+    Validate the input data for a node execution.
+
+    Args:
+        node: The node to execute.
+        data: The input data for the node execution.
+        resolve_input: Whether to resolve dynamic pins into dict/list/object.
+
+    Returns:
+        A tuple of the validated data and the block name.
+        If the data is invalid, the first element will be None, and the second element
+        will be an error message.
+        If the data is valid, the first element will be the resolved input data, and
+        the second element will be the block name.
+    """
+    node_block: Block | None = get_block(node.block_id)
+    if not node_block:
+        return None, f"Block for {node.block_id} not found."
+    schema = node_block.input_schema
+
+    # Convert non-matching data types to the expected input schema.
+    for name, data_type in schema.__annotations__.items():
+        if (value := data.get(name)) and (type(value) is not data_type):
+            data[name] = convert(value, data_type)
+
+    # Input data (without default values) should contain all required fields.
+    error_prefix = f"Input data missing or mismatch for `{node_block.name}`:"
+    if missing_links := schema.get_missing_links(data, node.input_links):
+        return None, f"{error_prefix} unpopulated links {missing_links}"
+
+    # Merge input data with default values and resolve dynamic dict/list/object pins.
+    input_default = schema.get_input_defaults(node.input_default)
+    data = {**input_default, **data}
+    if resolve_input:
+        data = merge_execution_input(data)
+
+    # Input data post-merge should contain all required fields from the schema.
+    if missing_input := schema.get_missing_input(data):
+        return None, f"{error_prefix} missing input {missing_input}"
+
+    # Last validation: Validate the input values against the schema.
+    if error := schema.get_mismatch_error(data):
+        error_message = f"{error_prefix} {error}"
+        logger.error(error_message)
+        return None, error_message
+
+    return data, node_block.name
+
+
+def merge_execution_input(data: BlockInput) -> BlockInput:
+    """
+    Merges dynamic input pins into a single list, dictionary, or object based on naming patterns.
+
+    This function processes input keys that follow specific patterns to merge them into a unified structure:
+    - `<input_name>_$_<index>` for list inputs.
+    - `<input_name>_#_<index>` for dictionary inputs.
+    - `<input_name>_@_<index>` for object inputs.
+
+    Args:
+        data (BlockInput): A dictionary containing input keys and their corresponding values.
+
+    Returns:
+        BlockInput: A dictionary with merged inputs.
+
+    Raises:
+        ValueError: If a list index is not an integer.
+
+    Examples:
+        >>> data = {
+        ...     "list_$_0": "a",
+        ...     "list_$_1": "b",
+        ...     "dict_#_key1": "value1",
+        ...     "dict_#_key2": "value2",
+        ...     "object_@_attr1": "value1",
+        ...     "object_@_attr2": "value2"
+        ... }
+        >>> merge_execution_input(data)
+        {
+            "list": ["a", "b"],
+            "dict": {"key1": "value1", "key2": "value2"},
+            "object": <MockObject attr1="value1" attr2="value2">
+        }
+    """
+
+    # Merge all input with <input_name>_$_<index> into a single list.
+    items = list(data.items())
+
+    for key, value in items:
+        if LIST_SPLIT not in key:
+            continue
+        name, index = key.split(LIST_SPLIT)
+        if not index.isdigit():
+            raise ValueError(f"Invalid key: {key}, #{index} index must be an integer.")
+
+        data[name] = data.get(name, [])
+        if int(index) >= len(data[name]):
+            # Pad list with empty string on missing indices.
+            data[name].extend([""] * (int(index) - len(data[name]) + 1))
+        data[name][int(index)] = value
+
+    # Merge all input with <input_name>_#_<index> into a single dict.
+    for key, value in items:
+        if DICT_SPLIT not in key:
+            continue
+        name, index = key.split(DICT_SPLIT)
+        data[name] = data.get(name, {})
+        data[name][index] = value
+
+    # Merge all input with <input_name>_@_<index> into a single object.
+    for key, value in items:
+        if OBJC_SPLIT not in key:
+            continue
+        name, index = key.split(OBJC_SPLIT)
+        if name not in data or not isinstance(data[name], object):
+            data[name] = MockObject()
+        setattr(data[name], index, value)
+
+    return data
+
+
+def _validate_node_input_credentials(
+    graph: GraphModel,
+    user_id: str,
+    node_credentials_input_map: Optional[
+        dict[str, dict[str, CredentialsMetaInput]]
+    ] = None,
+):
+    """Checks all credentials for all nodes of the graph"""
+
+    for node in graph.nodes:
+        block = node.block
+
+        # Find any fields of type CredentialsMetaInput
+        credentials_fields = cast(
+            type[BlockSchema], block.input_schema
+        ).get_credentials_fields()
+        if not credentials_fields:
+            continue
+
+        for field_name, credentials_meta_type in credentials_fields.items():
+            if (
+                node_credentials_input_map
+                and (node_credentials_inputs := node_credentials_input_map.get(node.id))
+                and field_name in node_credentials_inputs
+            ):
+                credentials_meta = node_credentials_input_map[node.id][field_name]
+            elif field_name in node.input_default:
+                credentials_meta = credentials_meta_type.model_validate(
+                    node.input_default[field_name]
+                )
+            else:
+                raise ValueError(
+                    f"Credentials absent for {block.name} node #{node.id} "
+                    f"input '{field_name}'"
+                )
+
+            # Fetch the corresponding Credentials and perform sanity checks
+            credentials = get_integration_credentials_store().get_creds_by_id(
+                user_id, credentials_meta.id
+            )
+            if not credentials:
+                raise ValueError(
+                    f"Unknown credentials #{credentials_meta.id} "
+                    f"for node #{node.id} input '{field_name}'"
+                )
+            if (
+                credentials.provider != credentials_meta.provider
+                or credentials.type != credentials_meta.type
+            ):
+                logger.warning(
+                    f"Invalid credentials #{credentials.id} for node #{node.id}: "
+                    "type/provider mismatch: "
+                    f"{credentials_meta.type}<>{credentials.type};"
+                    f"{credentials_meta.provider}<>{credentials.provider}"
+                )
+                raise ValueError(
+                    f"Invalid credentials #{credentials.id} for node #{node.id}: "
+                    "type/provider mismatch"
+                )
+
+
+def make_node_credentials_input_map(
+    graph: GraphModel,
+    graph_credentials_input: dict[str, CredentialsMetaInput],
+) -> dict[str, dict[str, CredentialsMetaInput]]:
+    """
+    Maps credentials for an execution to the correct nodes.
+
+    Params:
+        graph: The graph to be executed.
+        graph_credentials_input: A (graph_input_name, credentials_meta) map.
+
+    Returns:
+        dict[node_id, dict[field_name, CredentialsMetaInput]]: Node credentials input map.
+    """
+    result: dict[str, dict[str, CredentialsMetaInput]] = {}
+
+    # Get aggregated credentials fields for the graph
+    graph_cred_inputs = graph.aggregate_credentials_inputs()
+
+    for graph_input_name, (_, compatible_node_fields) in graph_cred_inputs.items():
+        # Best-effort map: skip missing items
+        if graph_input_name not in graph_credentials_input:
+            continue
+
+        # Use passed-in credentials for all compatible node input fields
+        for node_id, node_field_name in compatible_node_fields:
+            if node_id not in result:
+                result[node_id] = {}
+            result[node_id][node_field_name] = graph_credentials_input[graph_input_name]
+
+    return result
+
+
+def construct_node_execution_input(
+    graph: GraphModel,
+    user_id: str,
+    graph_inputs: BlockInput,
+    node_credentials_input_map: Optional[
+        dict[str, dict[str, CredentialsMetaInput]]
+    ] = None,
+) -> list[tuple[str, BlockInput]]:
+    """
+    Validates and prepares the input data for executing a graph.
+    This function checks the graph for starting nodes, validates the input data
+    against the schema, and resolves dynamic input pins into a single list,
+    dictionary, or object.
+
+    Args:
+        graph (GraphModel): The graph model to execute.
+        user_id (str): The ID of the user executing the graph.
+        data (BlockInput): The input data for the graph execution.
+        node_credentials_map: `dict[node_id, dict[input_name, CredentialsMetaInput]]`
+
+    Returns:
+        list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID and
+            the corresponding input data for that node.
+    """
+    graph.validate_graph(for_run=True)
+    _validate_node_input_credentials(graph, user_id, node_credentials_input_map)
+
+    nodes_input = []
+    for node in graph.starting_nodes:
+        input_data = {}
+        block = node.block
+
+        # Note block should never be executed.
+        if block.block_type == BlockType.NOTE:
+            continue
+
+        # Extract request input data, and assign it to the input pin.
+        if block.block_type == BlockType.INPUT:
+            input_name = node.input_default.get("name")
+            if input_name and input_name in graph_inputs:
+                input_data = {"value": graph_inputs[input_name]}
+
+        # Extract webhook payload, and assign it to the input pin
+        webhook_payload_key = f"webhook_{node.webhook_id}_payload"
+        if (
+            block.block_type in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
+            and node.webhook_id
+        ):
+            if webhook_payload_key not in graph_inputs:
+                raise ValueError(
+                    f"Node {block.name} #{node.id} webhook payload is missing"
+                )
+            input_data = {"payload": graph_inputs[webhook_payload_key]}
+
+        # Apply node credentials overrides
+        if node_credentials_input_map and (
+            node_credentials := node_credentials_input_map.get(node.id)
+        ):
+            input_data.update({k: v.model_dump() for k, v in node_credentials.items()})
+
+        input_data, error = validate_exec(node, input_data)
+        if input_data is None:
+            raise ValueError(error)
+        else:
+            nodes_input.append((node.id, input_data))
+
+    if not nodes_input:
+        raise ValueError(
+            "No starting nodes found for the graph, make sure an AgentInput or blocks with no inbound links are present as starting nodes."
+        )
+
+    return nodes_input
+
+
+# ============ Execution Queue Helpers ============ #
+
+
+class CancelExecutionEvent(BaseModel):
+    graph_exec_id: str
+
+
+GRAPH_EXECUTION_EXCHANGE = Exchange(
+    name="graph_execution",
+    type=ExchangeType.DIRECT,
+    durable=True,
+    auto_delete=False,
+)
+GRAPH_EXECUTION_QUEUE_NAME = "graph_execution_queue"
+GRAPH_EXECUTION_ROUTING_KEY = "graph_execution.run"
+
+GRAPH_EXECUTION_CANCEL_EXCHANGE = Exchange(
+    name="graph_execution_cancel",
+    type=ExchangeType.FANOUT,
+    durable=True,
+    auto_delete=True,
+)
+GRAPH_EXECUTION_CANCEL_QUEUE_NAME = "graph_execution_cancel_queue"
+
+
+def create_execution_queue_config() -> RabbitMQConfig:
+    """
+    Define two exchanges and queues:
+    - 'graph_execution' (DIRECT) for run tasks.
+    - 'graph_execution_cancel' (FANOUT) for cancel requests.
+    """
+    run_queue = Queue(
+        name=GRAPH_EXECUTION_QUEUE_NAME,
+        exchange=GRAPH_EXECUTION_EXCHANGE,
+        routing_key=GRAPH_EXECUTION_ROUTING_KEY,
+        durable=True,
+        auto_delete=False,
+    )
+    cancel_queue = Queue(
+        name=GRAPH_EXECUTION_CANCEL_QUEUE_NAME,
+        exchange=GRAPH_EXECUTION_CANCEL_EXCHANGE,
+        routing_key="",  # not used for FANOUT
+        durable=True,
+        auto_delete=False,
+    )
+    return RabbitMQConfig(
+        vhost="/",
+        exchanges=[GRAPH_EXECUTION_EXCHANGE, GRAPH_EXECUTION_CANCEL_EXCHANGE],
+        queues=[run_queue, cancel_queue],
+    )
+
+
+async def add_graph_execution_async(
+    graph_id: str,
+    user_id: str,
+    inputs: BlockInput,
+    preset_id: Optional[str] = None,
+    graph_version: Optional[int] = None,
+    graph_credentials_inputs: Optional[dict[str, CredentialsMetaInput]] = None,
+) -> GraphExecutionWithNodes:
+    """
+    Adds a graph execution to the queue and returns the execution entry.
+
+    Args:
+        graph_id: The ID of the graph to execute.
+        user_id: The ID of the user executing the graph.
+        inputs: The input data for the graph execution.
+        preset_id: The ID of the preset to use.
+        graph_version: The version of the graph to execute.
+        graph_credentials_inputs: Credentials inputs to use in the execution.
+            Keys should map to the keys generated by `GraphModel.aggregate_credentials_inputs`.
+    Returns:
+        GraphExecutionEntry: The entry for the graph execution.
+    Raises:
+        ValueError: If the graph is not found or if there are validation errors.
+    """  # noqa
+    graph: GraphModel | None = await get_graph(
+        graph_id=graph_id, user_id=user_id, version=graph_version
+    )
+    if not graph:
+        raise NotFoundError(f"Graph #{graph_id} not found.")
+
+    node_credentials_input_map = (
+        make_node_credentials_input_map(graph, graph_credentials_inputs)
+        if graph_credentials_inputs
+        else None
+    )
+
+    graph_exec = await create_graph_execution(
+        user_id=user_id,
+        graph_id=graph_id,
+        graph_version=graph.version,
+        starting_nodes_input=construct_node_execution_input(
+            graph=graph,
+            user_id=user_id,
+            graph_inputs=inputs,
+            node_credentials_input_map=node_credentials_input_map,
+        ),
+        preset_id=preset_id,
+    )
+    try:
+        queue = await get_async_execution_queue()
+        graph_exec_entry = graph_exec.to_graph_execution_entry()
+        if node_credentials_input_map:
+            graph_exec_entry.node_credentials_input_map = node_credentials_input_map
+        await queue.publish_message(
+            routing_key=GRAPH_EXECUTION_ROUTING_KEY,
+            message=graph_exec_entry.model_dump_json(),
+            exchange=GRAPH_EXECUTION_EXCHANGE,
+        )
+
+        bus = get_async_execution_event_bus()
+        await bus.publish(graph_exec)
+
+        return graph_exec
+    except Exception as e:
+        logger.error(f"Unable to publish graph #{graph_id} exec #{graph_exec.id}: {e}")
+
+        await update_node_execution_status_batch(
+            [node_exec.node_exec_id for node_exec in graph_exec.node_executions],
+            ExecutionStatus.FAILED,
+        )
+        await update_graph_execution_stats(
+            graph_exec_id=graph_exec.id,
+            status=ExecutionStatus.FAILED,
+            stats=GraphExecutionStats(error=str(e)),
+        )
+        raise
+
+
+def add_graph_execution(
+    graph_id: str,
+    user_id: str,
+    inputs: BlockInput,
+    preset_id: Optional[str] = None,
+    graph_version: Optional[int] = None,
+    graph_credentials_inputs: Optional[dict[str, CredentialsMetaInput]] = None,
+) -> GraphExecutionWithNodes:
+    """
+    Adds a graph execution to the queue and returns the execution entry.
+
+    Args:
+        graph_id: The ID of the graph to execute.
+        user_id: The ID of the user executing the graph.
+        inputs: The input data for the graph execution.
+        preset_id: The ID of the preset to use.
+        graph_version: The version of the graph to execute.
+        graph_credentials_inputs: Credentials inputs to use in the execution.
+            Keys should map to the keys generated by `GraphModel.aggregate_credentials_inputs`.
+    Returns:
+        GraphExecutionEntry: The entry for the graph execution.
+    Raises:
+        ValueError: If the graph is not found or if there are validation errors.
+    """
+    db = get_db_client()
+    graph: GraphModel | None = db.get_graph(
+        graph_id=graph_id, user_id=user_id, version=graph_version
+    )
+    if not graph:
+        raise NotFoundError(f"Graph #{graph_id} not found.")
+
+    node_credentials_input_map = (
+        make_node_credentials_input_map(graph, graph_credentials_inputs)
+        if graph_credentials_inputs
+        else None
+    )
+
+    graph_exec = db.create_graph_execution(
+        user_id=user_id,
+        graph_id=graph_id,
+        graph_version=graph.version,
+        starting_nodes_input=construct_node_execution_input(
+            graph=graph,
+            user_id=user_id,
+            graph_inputs=inputs,
+            node_credentials_input_map=node_credentials_input_map,
+        ),
+        preset_id=preset_id,
+    )
+    try:
+        queue = get_execution_queue()
+        graph_exec_entry = graph_exec.to_graph_execution_entry()
+        if node_credentials_input_map:
+            graph_exec_entry.node_credentials_input_map = node_credentials_input_map
+        queue.publish_message(
+            routing_key=GRAPH_EXECUTION_ROUTING_KEY,
+            message=graph_exec_entry.model_dump_json(),
+            exchange=GRAPH_EXECUTION_EXCHANGE,
+        )
+
+        bus = get_execution_event_bus()
+        bus.publish(graph_exec)
+
+        return graph_exec
+    except Exception as e:
+        logger.error(f"Unable to publish graph #{graph_id} exec #{graph_exec.id}: {e}")
+
+        db.update_node_execution_status_batch(
+            [node_exec.node_exec_id for node_exec in graph_exec.node_executions],
+            ExecutionStatus.FAILED,
+        )
+        db.update_graph_execution_stats(
+            graph_exec_id=graph_exec.id,
+            status=ExecutionStatus.FAILED,
+            stats=GraphExecutionStats(error=str(e)),
+        )
+        raise

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -161,6 +161,14 @@ smartlead_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
+google_maps_credentials = APIKeyCredentials(
+    id="9aa1bde0-4947-4a70-a20c-84daa3850d52",
+    provider="google_maps",
+    api_key=SecretStr(settings.secrets.google_maps_api_key),
+    title="Use Credits for Google Maps",
+    expires_at=None,
+)
+
 zerobounce_credentials = APIKeyCredentials(
     id="63a6e279-2dc2-448e-bf57-85776f7176dc",
     provider="zerobounce",
@@ -190,6 +198,7 @@ DEFAULT_CREDENTIALS = [
     apollo_credentials,
     smartlead_credentials,
     zerobounce_credentials,
+    google_maps_credentials,
 ]
 
 
@@ -263,6 +272,8 @@ class IntegrationCredentialsStore:
             all_credentials.append(smartlead_credentials)
         if settings.secrets.zerobounce_api_key:
             all_credentials.append(zerobounce_credentials)
+        if settings.secrets.google_maps_api_key:
+            all_credentials.append(google_maps_credentials)
         return all_credentials
 
     def get_creds_by_id(self, user_id: str, credentials_id: str) -> Credentials | None:

autogpt_platform/backend/backend/integrations/creds_manager.py

@@ -10,6 +10,7 @@ from backend.data import redis
 from backend.data.model import Credentials
 from backend.integrations.credentials_store import IntegrationCredentialsStore
 from backend.integrations.oauth import HANDLERS_BY_NAME
+from backend.integrations.providers import ProviderName
 from backend.util.exceptions import MissingConfigError
 from backend.util.settings import Settings
 
@@ -153,12 +154,13 @@ class IntegrationCredentialsManager:
         self.store.locks.release_all_locks()
 
 
-def _get_provider_oauth_handler(provider_name: str) -> "BaseOAuthHandler":
+def _get_provider_oauth_handler(provider_name_str: str) -> "BaseOAuthHandler":
+    provider_name = ProviderName(provider_name_str)
     if provider_name not in HANDLERS_BY_NAME:
         raise KeyError(f"Unknown provider '{provider_name}'")
 
-    client_id = getattr(settings.secrets, f"{provider_name}_client_id")
-    client_secret = getattr(settings.secrets, f"{provider_name}_client_secret")
+    client_id = getattr(settings.secrets, f"{provider_name.value}_client_id")
+    client_secret = getattr(settings.secrets, f"{provider_name.value}_client_secret")
     if not (client_id and client_secret):
         raise MissingConfigError(
             f"Integration with provider '{provider_name}' is not configured",

autogpt_platform/backend/backend/integrations/providers.py

@@ -11,6 +11,7 @@ class ProviderName(str, Enum):
     E2B = "e2b"
     EXA = "exa"
     FAL = "fal"
+    GENERIC_WEBHOOK = "generic_webhook"
     GITHUB = "github"
     GOOGLE = "google"
     GOOGLE_MAPS = "google_maps"

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

@@ -1,22 +1,45 @@
 from typing import TYPE_CHECKING
 
-from .compass import CompassWebhookManager
-from .github import GithubWebhooksManager
-from .slant3d import Slant3DWebhooksManager
-
 if TYPE_CHECKING:
     from ..providers import ProviderName
     from ._base import BaseWebhooksManager
 
-# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
-WEBHOOK_MANAGERS_BY_NAME: dict["ProviderName", type["BaseWebhooksManager"]] = {
-    handler.PROVIDER_NAME: handler
-    for handler in [
-        CompassWebhookManager,
-        GithubWebhooksManager,
-        Slant3DWebhooksManager,
-    ]
-}
-# --8<-- [end:WEBHOOK_MANAGERS_BY_NAME]
+_WEBHOOK_MANAGERS: dict["ProviderName", type["BaseWebhooksManager"]] = {}
 
-__all__ = ["WEBHOOK_MANAGERS_BY_NAME"]
+
+# --8<-- [start:load_webhook_managers]
+def load_webhook_managers() -> dict["ProviderName", type["BaseWebhooksManager"]]:
+    if _WEBHOOK_MANAGERS:
+        return _WEBHOOK_MANAGERS
+
+    from .compass import CompassWebhookManager
+    from .generic import GenericWebhooksManager
+    from .github import GithubWebhooksManager
+    from .slant3d import Slant3DWebhooksManager
+
+    _WEBHOOK_MANAGERS.update(
+        {
+            handler.PROVIDER_NAME: handler
+            for handler in [
+                CompassWebhookManager,
+                GithubWebhooksManager,
+                Slant3DWebhooksManager,
+                GenericWebhooksManager,
+            ]
+        }
+    )
+    return _WEBHOOK_MANAGERS
+
+
+# --8<-- [end:load_webhook_managers]
+
+
+def get_webhook_manager(provider_name: "ProviderName") -> "BaseWebhooksManager":
+    return load_webhook_managers()[provider_name]()
+
+
+def supports_webhooks(provider_name: "ProviderName") -> bool:
+    return provider_name in load_webhook_managers()
+
+
+__all__ = ["get_webhook_manager", "supports_webhooks"]

autogpt_platform/backend/backend/integrations/webhooks/generic.py

@@ -0,0 +1,29 @@
+import logging
+
+from fastapi import Request
+from strenum import StrEnum
+
+from backend.data import integrations
+from backend.integrations.providers import ProviderName
+
+from ._manual_base import ManualWebhookManagerBase
+
+logger = logging.getLogger(__name__)
+
+
+class GenericWebhookType(StrEnum):
+    PLAIN = "plain"
+
+
+class GenericWebhooksManager(ManualWebhookManagerBase):
+    PROVIDER_NAME = ProviderName.GENERIC_WEBHOOK
+    WebhookType = GenericWebhookType
+
+    @classmethod
+    async def validate_payload(
+        cls, webhook: integrations.Webhook, request: Request
+    ) -> tuple[dict, str]:
+        payload = await request.json()
+        event_type = GenericWebhookType.PLAIN
+
+        return payload, event_type

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

@@ -1,9 +1,9 @@
 import logging
 from typing import TYPE_CHECKING, Callable, Optional, cast
 
-from backend.data.block import BlockSchema, BlockWebhookConfig, get_block
+from backend.data.block import BlockSchema, BlockWebhookConfig
 from backend.data.graph import set_node_webhook
-from backend.integrations.webhooks import WEBHOOK_MANAGERS_BY_NAME
+from backend.integrations.webhooks import get_webhook_manager, supports_webhooks
 
 if TYPE_CHECKING:
     from backend.data.graph import GraphModel, NodeModel
@@ -29,12 +29,7 @@ async def on_graph_activate(
     # Compare nodes in new_graph_version with previous_graph_version
     updated_nodes = []
     for new_node in graph.nodes:
-        block = get_block(new_node.block_id)
-        if not block:
-            raise ValueError(
-                f"Node #{new_node.id} is instance of unknown block #{new_node.block_id}"
-            )
-        block_input_schema = cast(BlockSchema, block.input_schema)
+        block_input_schema = cast(BlockSchema, new_node.block.input_schema)
 
         node_credentials = None
         if (
@@ -75,12 +70,7 @@ async def on_graph_deactivate(
     """
     updated_nodes = []
     for node in graph.nodes:
-        block = get_block(node.block_id)
-        if not block:
-            raise ValueError(
-                f"Node #{node.id} is instance of unknown block #{node.block_id}"
-            )
-        block_input_schema = cast(BlockSchema, block.input_schema)
+        block_input_schema = cast(BlockSchema, node.block.input_schema)
 
         node_credentials = None
         if (
@@ -113,17 +103,13 @@ async def on_node_activate(
 ) -> "NodeModel":
     """Hook to be called when the node is activated/created"""
 
-    block = get_block(node.block_id)
-    if not block:
-        raise ValueError(
-            f"Node #{node.id} is instance of unknown block #{node.block_id}"
-        )
+    block = node.block
 
     if not block.webhook_config:
         return node
 
     provider = block.webhook_config.provider
-    if provider not in WEBHOOK_MANAGERS_BY_NAME:
+    if not supports_webhooks(provider):
         raise ValueError(
             f"Block #{block.id} has webhook_config for provider {provider} "
             "which does not support webhooks"
@@ -133,7 +119,7 @@ async def on_node_activate(
         f"Activating webhook node #{node.id} with config {block.webhook_config}"
     )
 
-    webhooks_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
+    webhooks_manager = get_webhook_manager(provider)
 
     if auto_setup_webhook := isinstance(block.webhook_config, BlockWebhookConfig):
         try:
@@ -224,23 +210,19 @@ async def on_node_deactivate(
     """Hook to be called when node is deactivated/deleted"""
 
     logger.debug(f"Deactivating node #{node.id}")
-    block = get_block(node.block_id)
-    if not block:
-        raise ValueError(
-            f"Node #{node.id} is instance of unknown block #{node.block_id}"
-        )
+    block = node.block
 
     if not block.webhook_config:
         return node
 
     provider = block.webhook_config.provider
-    if provider not in WEBHOOK_MANAGERS_BY_NAME:
+    if not supports_webhooks(provider):
         raise ValueError(
             f"Block #{block.id} has webhook_config for provider {provider} "
             "which does not support webhooks"
         )
 
-    webhooks_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
+    webhooks_manager = get_webhook_manager(provider)
 
     if node.webhook_id:
         logger.debug(f"Node #{node.id} has webhook_id {node.webhook_id}")

autogpt_platform/backend/backend/notifications/notifications.py

@@ -9,6 +9,7 @@ from autogpt_libs.utils.cache import thread_cached
 from prisma.enums import NotificationType
 from pydantic import BaseModel
 
+from backend.data import rabbitmq
 from backend.data.notifications import (
     BaseSummaryData,
     BaseSummaryParams,
@@ -23,23 +24,12 @@ from backend.data.notifications import (
     SummaryParamsEventModel,
     WeeklySummaryData,
     WeeklySummaryParams,
-    create_or_add_to_user_notification_batch,
-    empty_user_notification_batch,
-    get_all_batches_by_type,
     get_batch_delay,
     get_notif_data_type,
     get_summary_params_type,
-    get_user_notification_batch,
-    get_user_notification_oldest_message_in_batch,
 )
 from backend.data.rabbitmq import Exchange, ExchangeType, Queue, RabbitMQConfig
-from backend.data.user import (
-    generate_unsubscribe_link,
-    get_active_user_ids_in_timerange,
-    get_user_email_by_id,
-    get_user_email_verification,
-    get_user_notification_preference,
-)
+from backend.data.user import generate_unsubscribe_link
 from backend.notifications.email import EmailSender
 from backend.util.service import AppService, expose, get_service_client
 from backend.util.settings import Settings
@@ -123,16 +113,36 @@ def get_scheduler():
     return get_service_client(Scheduler)
 
 
+@thread_cached
+def get_db():
+    from backend.executor.database import DatabaseManager
+
+    return get_service_client(DatabaseManager)
+
+
 class NotificationManager(AppService):
     """Service for handling notifications with batching support"""
 
     def __init__(self):
         super().__init__()
-        self.use_db = True
         self.rabbitmq_config = create_notification_config()
         self.running = True
         self.email_sender = EmailSender()
 
+    @property
+    def rabbit(self) -> rabbitmq.AsyncRabbitMQ:
+        """Access the RabbitMQ service. Will raise if not configured."""
+        if not self.rabbitmq_service:
+            raise RuntimeError("RabbitMQ not configured for this service")
+        return self.rabbitmq_service
+
+    @property
+    def rabbit_config(self) -> rabbitmq.RabbitMQConfig:
+        """Access the RabbitMQ config. Will raise if not configured."""
+        if not self.rabbitmq_config:
+            raise RuntimeError("RabbitMQ not configured for this service")
+        return self.rabbitmq_config
+
     @classmethod
     def get_port(cls) -> int:
         return settings.config.notification_service_port
@@ -160,11 +170,9 @@ class NotificationManager(AppService):
             processed_count = 0
             current_time = datetime.now(tz=timezone.utc)
             start_time = current_time - timedelta(days=7)
-            users = self.run_and_wait(
-                get_active_user_ids_in_timerange(
-                    end_time=current_time.isoformat(),
-                    start_time=start_time.isoformat(),
-                )
+            users = get_db().get_active_user_ids_in_timerange(
+                end_time=current_time.isoformat(),
+                start_time=start_time.isoformat(),
             )
             for user in users:
 
@@ -194,84 +202,84 @@ class NotificationManager(AppService):
 
             for notification_type in notification_types:
                 # Get all batches for this notification type
-                batches = self.run_and_wait(get_all_batches_by_type(notification_type))
+                batches = get_db().get_all_batches_by_type(notification_type)
 
                 for batch in batches:
                     # Check if batch has aged out
-                    oldest_message = self.run_and_wait(
-                        get_user_notification_oldest_message_in_batch(
-                            batch.userId, notification_type
+                    oldest_message = (
+                        get_db().get_user_notification_oldest_message_in_batch(
+                            batch.user_id, notification_type
                         )
                     )
 
                     if not oldest_message:
                         # this should never happen
                         logger.error(
-                            f"Batch for user {batch.userId} and type {notification_type} has no oldest message whichshould never happen!!!!!!!!!!!!!!!!"
+                            f"Batch for user {batch.user_id} and type {notification_type} has no oldest message whichshould never happen!!!!!!!!!!!!!!!!"
                         )
                         continue
 
                     max_delay = get_batch_delay(notification_type)
 
                     # If batch has aged out, process it
-                    if oldest_message.createdAt + max_delay < current_time:
-                        recipient_email = self.run_and_wait(
-                            get_user_email_by_id(batch.userId)
-                        )
+                    if oldest_message.created_at + max_delay < current_time:
+                        recipient_email = get_db().get_user_email_by_id(batch.user_id)
 
                         if not recipient_email:
                             logger.error(
-                                f"User email not found for user {batch.userId}"
+                                f"User email not found for user {batch.user_id}"
                             )
                             continue
 
                         should_send = self._should_email_user_based_on_preference(
-                            batch.userId, notification_type
+                            batch.user_id, notification_type
                         )
 
                         if not should_send:
                             logger.debug(
-                                f"User {batch.userId} does not want to receive {notification_type} notifications"
+                                f"User {batch.user_id} does not want to receive {notification_type} notifications"
                             )
                             # Clear the batch
-                            self.run_and_wait(
-                                empty_user_notification_batch(
-                                    batch.userId, notification_type
-                                )
+                            get_db().empty_user_notification_batch(
+                                batch.user_id, notification_type
                             )
                             continue
 
-                        batch_data = self.run_and_wait(
-                            get_user_notification_batch(batch.userId, notification_type)
+                        batch_data = get_db().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.userId}"
+                                f"Batch data not found for user {batch.user_id}"
                             )
                             # Clear the batch
-                            self.run_and_wait(
-                                empty_user_notification_batch(
-                                    batch.userId, notification_type
-                                )
+                            get_db().empty_user_notification_batch(
+                                batch.user_id, notification_type
                             )
                             continue
 
-                        unsub_link = generate_unsubscribe_link(batch.userId)
-
-                        events = [
-                            NotificationEventModel[
-                                get_notif_data_type(db_event.type)
-                            ].model_validate(
-                                {
-                                    "user_id": batch.userId,
-                                    "type": db_event.type,
-                                    "data": db_event.data,
-                                    "created_at": db_event.createdAt,
-                                }
-                            )
-                            for db_event in batch_data.notifications
-                        ]
+                        unsub_link = generate_unsubscribe_link(batch.user_id)
+                        events = []
+                        for db_event in batch_data.notifications:
+                            try:
+                                events.append(
+                                    NotificationEventModel[
+                                        get_notif_data_type(db_event.type)
+                                    ].model_validate(
+                                        {
+                                            "user_id": batch.user_id,
+                                            "type": db_event.type,
+                                            "data": db_event.data,
+                                            "created_at": db_event.created_at,
+                                        }
+                                    )
+                                )
+                            except Exception as e:
+                                logger.error(
+                                    f"Error parsing notification event: {e=}, {db_event=}"
+                                )
+                                continue
                         logger.info(f"{events=}")
 
                         self.email_sender.send_templated(
@@ -282,10 +290,8 @@ class NotificationManager(AppService):
                         )
 
                         # Clear the batch
-                        self.run_and_wait(
-                            empty_user_notification_batch(
-                                batch.userId, notification_type
-                            )
+                        get_db().empty_user_notification_batch(
+                            batch.user_id, notification_type
                         )
 
                         processed_count += 1
@@ -377,14 +383,16 @@ 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 = self.run_and_wait(get_user_email_verification(user_id))
-        preference = self.run_and_wait(
-            get_user_notification_preference(user_id)
-        ).preferences.get(event_type, True)
+        validated_email = get_db().get_user_email_verification(user_id)
+        preference = (
+            get_db()
+            .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
 
-    async def _gather_summary_data(
+    def _gather_summary_data(
         self, user_id: str, event_type: NotificationType, params: BaseSummaryParams
     ) -> BaseSummaryData:
         """Gathers the data to build a summary notification"""
@@ -464,13 +472,13 @@ class NotificationManager(AppService):
         else:
             raise ValueError("Invalid event type or params")
 
-    async def _should_batch(
+    def _should_batch(
         self, user_id: str, event_type: NotificationType, event: NotificationEventModel
     ) -> bool:
 
-        await create_or_add_to_user_notification_batch(user_id, event_type, event)
+        get_db().create_or_add_to_user_notification_batch(user_id, event_type, event)
 
-        oldest_message = await get_user_notification_oldest_message_in_batch(
+        oldest_message = get_db().get_user_notification_oldest_message_in_batch(
             user_id, event_type
         )
         if not oldest_message:
@@ -478,7 +486,7 @@ class NotificationManager(AppService):
                 f"Batch for user {user_id} and type {event_type} has no oldest message whichshould never happen!!!!!!!!!!!!!!!!"
             )
             return False
-        oldest_age = oldest_message.createdAt
+        oldest_age = oldest_message.created_at
 
         max_delay = get_batch_delay(event_type)
 
@@ -527,7 +535,7 @@ class NotificationManager(AppService):
             model = parsed.model
             logger.debug(f"Processing immediate notification: {model}")
 
-            recipient_email = self.run_and_wait(get_user_email_by_id(event.user_id))
+            recipient_email = get_db().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
@@ -564,7 +572,7 @@ class NotificationManager(AppService):
             model = parsed.model
             logger.info(f"Processing batch notification: {model}")
 
-            recipient_email = self.run_and_wait(get_user_email_by_id(event.user_id))
+            recipient_email = get_db().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
@@ -578,16 +586,12 @@ class NotificationManager(AppService):
                 )
                 return True
 
-            should_send = self.run_and_wait(
-                self._should_batch(event.user_id, event.type, model)
-            )
+            should_send = self._should_batch(event.user_id, event.type, model)
 
             if not should_send:
                 logger.info("Batch not old enough to send")
                 return False
-            batch = self.run_and_wait(
-                get_user_notification_batch(event.user_id, event.type)
-            )
+            batch = get_db().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
@@ -601,7 +605,7 @@ class NotificationManager(AppService):
                         "user_id": event.user_id,
                         "type": db_event.type,
                         "data": db_event.data,
-                        "created_at": db_event.createdAt,
+                        "created_at": db_event.created_at,
                     }
                 )
                 for db_event in batch.notifications
@@ -614,7 +618,7 @@ class NotificationManager(AppService):
                 user_unsub_link=unsub_link,
             )
             # only empty the batch if we sent the email successfully
-            self.run_and_wait(empty_user_notification_batch(event.user_id, event.type))
+            get_db().empty_user_notification_batch(event.user_id, event.type)
             return True
         except Exception as e:
             logger.exception(f"Error processing notification for batch queue: {e}")
@@ -631,7 +635,7 @@ class NotificationManager(AppService):
 
             logger.info(f"Processing summary notification: {model}")
 
-            recipient_email = self.run_and_wait(get_user_email_by_id(event.user_id))
+            recipient_email = get_db().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
@@ -644,8 +648,8 @@ class NotificationManager(AppService):
                 )
                 return True
 
-            summary_data = self.run_and_wait(
-                self._gather_summary_data(event.user_id, event.type, model.data)
+            summary_data = self._gather_summary_data(
+                event.user_id, event.type, model.data
             )
 
             unsub_link = generate_unsubscribe_link(event.user_id)
@@ -685,6 +689,8 @@ class NotificationManager(AppService):
 
         except QueueEmpty:
             logger.debug(f"Queue {error_queue_name} empty")
+        except TimeoutError:
+            logger.debug(f"Queue {error_queue_name} timed out")
         except Exception as e:
             if message:
                 logger.error(
@@ -692,15 +698,19 @@ class NotificationManager(AppService):
                 )
                 self.run_and_wait(message.reject(requeue=False))
             else:
-                logger.error(
-                    f"Error in notification service loop, message unable to be rejected, and will have to be manually removed to free space in the queue: {e}"
+                logger.exception(
+                    f"Error in notification service loop, message unable to be rejected, and will have to be manually removed to free space in the queue: {e=}"
                 )
 
     def run_service(self):
+        logger.info(f"[{self.service_name}] ⏳ Configuring RabbitMQ...")
+        self.rabbitmq_service = rabbitmq.AsyncRabbitMQ(self.rabbitmq_config)
+        self.run_and_wait(self.rabbitmq_service.connect())
+
         logger.info(f"[{self.service_name}] Started notification service")
 
         # Set up scheduler for batch processing of all notification types
-        # this can be changed later to spawn differnt cleanups on different schedules
+        # this can be changed later to spawn different cleanups on different schedules
         try:
             get_scheduler().add_batched_notification_schedule(
                 notification_types=list(NotificationType),
@@ -762,3 +772,5 @@ class NotificationManager(AppService):
         """Cleanup service resources"""
         self.running = False
         super().cleanup()
+        logger.info(f"[{self.service_name}] ⏳ Disconnecting RabbitMQ...")
+        self.run_and_wait(self.rabbitmq_service.disconnect())

autogpt_platform/backend/backend/server/conn_manager.py

@@ -2,8 +2,17 @@ from typing import Dict, Set
 
 from fastapi import WebSocket
 
-from backend.data import execution
-from backend.server.model import Methods, WsMessage
+from backend.data.execution import (
+    ExecutionEventType,
+    GraphExecutionEvent,
+    NodeExecutionEvent,
+)
+from backend.server.model import WSMessage, WSMethod
+
+_EVENT_TYPE_TO_METHOD_MAP: dict[ExecutionEventType, WSMethod] = {
+    ExecutionEventType.GRAPH_EXEC_UPDATE: WSMethod.GRAPH_EXECUTION_EVENT,
+    ExecutionEventType.NODE_EXEC_UPDATE: WSMethod.NODE_EXECUTION_EVENT,
+}
 
 
 class ConnectionManager:
@@ -11,37 +20,96 @@ class ConnectionManager:
         self.active_connections: Set[WebSocket] = set()
         self.subscriptions: Dict[str, Set[WebSocket]] = {}
 
-    async def connect(self, websocket: WebSocket):
+    async def connect_socket(self, websocket: WebSocket):
         await websocket.accept()
         self.active_connections.add(websocket)
 
-    def disconnect(self, websocket: WebSocket):
+    def disconnect_socket(self, websocket: WebSocket):
         self.active_connections.remove(websocket)
         for subscribers in self.subscriptions.values():
             subscribers.discard(websocket)
 
-    async def subscribe(self, graph_id: str, graph_version: int, websocket: WebSocket):
-        key = f"{graph_id}_{graph_version}"
-        if key not in self.subscriptions:
-            self.subscriptions[key] = set()
-        self.subscriptions[key].add(websocket)
+    async def subscribe_graph_exec(
+        self, *, user_id: str, graph_exec_id: str, websocket: WebSocket
+    ) -> str:
+        return await self._subscribe(
+            _graph_exec_channel_key(user_id, graph_exec_id=graph_exec_id), websocket
+        )
 
-    async def unsubscribe(
-        self, graph_id: str, graph_version: int, websocket: WebSocket
-    ):
-        key = f"{graph_id}_{graph_version}"
-        if key in self.subscriptions:
-            self.subscriptions[key].discard(websocket)
-            if not self.subscriptions[key]:
-                del self.subscriptions[key]
+    async def subscribe_graph_execs(
+        self, *, user_id: str, graph_id: str, websocket: WebSocket
+    ) -> str:
+        return await self._subscribe(
+            _graph_execs_channel_key(user_id, graph_id=graph_id), websocket
+        )
 
-    async def send_execution_result(self, result: execution.ExecutionResult):
-        key = f"{result.graph_id}_{result.graph_version}"
-        if key in self.subscriptions:
-            message = WsMessage(
-                method=Methods.EXECUTION_EVENT,
-                channel=key,
-                data=result.model_dump(),
+    async def unsubscribe_graph_exec(
+        self, *, user_id: str, graph_exec_id: str, websocket: WebSocket
+    ) -> str | None:
+        return await self._unsubscribe(
+            _graph_exec_channel_key(user_id, graph_exec_id=graph_exec_id), websocket
+        )
+
+    async def unsubscribe_graph_execs(
+        self, *, user_id: str, graph_id: str, websocket: WebSocket
+    ) -> str | None:
+        return await self._unsubscribe(
+            _graph_execs_channel_key(user_id, graph_id=graph_id), websocket
+        )
+
+    async def send_execution_update(
+        self, exec_event: GraphExecutionEvent | NodeExecutionEvent
+    ) -> int:
+        graph_exec_id = (
+            exec_event.id
+            if isinstance(exec_event, GraphExecutionEvent)
+            else exec_event.graph_exec_id
+        )
+
+        n_sent = 0
+
+        channels: set[str] = {
+            # Send update to listeners for this graph execution
+            _graph_exec_channel_key(exec_event.user_id, graph_exec_id=graph_exec_id)
+        }
+        if isinstance(exec_event, GraphExecutionEvent):
+            # Send update to listeners for all executions of this graph
+            channels.add(
+                _graph_execs_channel_key(
+                    exec_event.user_id, graph_id=exec_event.graph_id
+                )
+            )
+
+        for channel in channels.intersection(self.subscriptions.keys()):
+            message = WSMessage(
+                method=_EVENT_TYPE_TO_METHOD_MAP[exec_event.event_type],
+                channel=channel,
+                data=exec_event.model_dump(),
             ).model_dump_json()
-            for connection in self.subscriptions[key]:
+            for connection in self.subscriptions[channel]:
                 await connection.send_text(message)
+                n_sent += 1
+
+        return n_sent
+
+    async def _subscribe(self, channel_key: str, websocket: WebSocket) -> str:
+        if channel_key not in self.subscriptions:
+            self.subscriptions[channel_key] = set()
+        self.subscriptions[channel_key].add(websocket)
+        return channel_key
+
+    async def _unsubscribe(self, channel_key: str, websocket: WebSocket) -> str | None:
+        if channel_key in self.subscriptions:
+            self.subscriptions[channel_key].discard(websocket)
+            if not self.subscriptions[channel_key]:
+                del self.subscriptions[channel_key]
+            return channel_key
+        return None
+
+
+def _graph_exec_channel_key(user_id: str, *, graph_exec_id: str) -> str:
+    return f"{user_id}|graph_exec#{graph_exec_id}"
+
+
+def _graph_execs_channel_key(user_id: str, *, graph_id: str) -> str:
+    return f"{user_id}|graph#{graph_id}|executions"

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

@@ -2,7 +2,6 @@ import logging
 from collections import defaultdict
 from typing import Annotated, Any, Dict, List, Optional, Sequence
 
-from autogpt_libs.utils.cache import thread_cached
 from fastapi import APIRouter, Body, Depends, HTTPException
 from prisma.enums import AgentExecutionStatus, APIKeyPermission
 from typing_extensions import TypedDict
@@ -12,18 +11,11 @@ from backend.data import execution as execution_db
 from backend.data import graph as graph_db
 from backend.data.api_key import APIKey
 from backend.data.block import BlockInput, CompletedBlockOutput
-from backend.data.execution import ExecutionResult
-from backend.executor import ExecutionManager
+from backend.data.execution import NodeExecutionResult
+from backend.executor.utils import add_graph_execution_async
 from backend.server.external.middleware import require_permission
-from backend.util.service import get_service_client
 from backend.util.settings import Settings
 
-
-@thread_cached
-def execution_manager_client() -> ExecutionManager:
-    return get_service_client(ExecutionManager)
-
-
 settings = Settings()
 logger = logging.getLogger(__name__)
 
@@ -53,7 +45,7 @@ class GraphExecutionResult(TypedDict):
     output: Optional[List[Dict[str, str]]]
 
 
-def get_outputs_with_names(results: List[ExecutionResult]) -> List[Dict[str, str]]:
+def get_outputs_with_names(results: list[NodeExecutionResult]) -> list[dict[str, str]]:
     outputs = []
     for result in results:
         if "output" in result.output_data:
@@ -71,7 +63,7 @@ def get_outputs_with_names(results: List[ExecutionResult]) -> List[Dict[str, str
 )
 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]
+    return [b.to_dict() for b in blocks if not b.disabled]
 
 
 @v1_router.post(
@@ -98,20 +90,20 @@ def execute_graph_block(
     path="/graphs/{graph_id}/execute/{graph_version}",
     tags=["graphs"],
 )
-def execute_graph(
+async def execute_graph(
     graph_id: str,
     graph_version: int,
     node_input: Annotated[dict[str, Any], Body(..., embed=True, default_factory=dict)],
     api_key: APIKey = Depends(require_permission(APIKeyPermission.EXECUTE_GRAPH)),
 ) -> dict[str, Any]:
     try:
-        graph_exec = execution_manager_client().add_execution(
-            graph_id,
-            graph_version=graph_version,
-            data=node_input,
+        graph_exec = await add_graph_execution_async(
+            graph_id=graph_id,
             user_id=api_key.user_id,
+            inputs=node_input,
+            graph_version=graph_version,
         )
-        return {"id": graph_exec.graph_exec_id}
+        return {"id": graph_exec.id}
     except Exception as e:
         msg = str(e).encode().decode("unicode_escape")
         raise HTTPException(status_code=400, detail=msg)
@@ -130,7 +122,7 @@ async def get_graph_execution_results(
     if not graph:
         raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")
 
-    results = await execution_db.get_execution_results(graph_exec_id)
+    results = await execution_db.get_node_execution_results(graph_exec_id)
     last_result = results[-1] if results else None
     execution_status = (
         last_result.status if last_result else AgentExecutionStatus.INCOMPLETE

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

@@ -1,8 +1,10 @@
+import asyncio
 import logging
-from typing import TYPE_CHECKING, Annotated, Literal
+from typing import TYPE_CHECKING, Annotated, Awaitable, Literal
 
 from fastapi import APIRouter, Body, Depends, HTTPException, Path, Query, Request
 from pydantic import BaseModel, Field
+from starlette.status import HTTP_404_NOT_FOUND
 
 from backend.data.graph import set_node_webhook
 from backend.data.integrations import (
@@ -13,13 +15,12 @@ from backend.data.integrations import (
     wait_for_webhook_event,
 )
 from backend.data.model import Credentials, CredentialsType, OAuth2Credentials
-from backend.executor.manager import ExecutionManager
+from backend.executor.utils import add_graph_execution_async
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.oauth import HANDLERS_BY_NAME
 from backend.integrations.providers import ProviderName
-from backend.integrations.webhooks import WEBHOOK_MANAGERS_BY_NAME
-from backend.util.exceptions import NeedConfirmation
-from backend.util.service import get_service_client
+from backend.integrations.webhooks import get_webhook_manager
+from backend.util.exceptions import NeedConfirmation, NotFoundError
 from backend.util.settings import Settings
 
 if TYPE_CHECKING:
@@ -281,8 +282,14 @@ async def webhook_ingress_generic(
     webhook_id: Annotated[str, Path(title="Our ID for the webhook")],
 ):
     logger.debug(f"Received {provider.value} webhook ingress for ID {webhook_id}")
-    webhook_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
-    webhook = await get_webhook(webhook_id)
+    webhook_manager = get_webhook_manager(provider)
+    try:
+        webhook = await get_webhook(webhook_id)
+    except NotFoundError as e:
+        logger.warning(f"Webhook payload received for unknown webhook: {e}")
+        raise HTTPException(
+            status_code=HTTP_404_NOT_FOUND, detail=f"Webhook #{webhook_id} not found"
+        ) from e
     logger.debug(f"Webhook #{webhook_id}: {webhook}")
     payload, event_type = await webhook_manager.validate_payload(webhook, request)
     logger.debug(
@@ -302,19 +309,22 @@ async def webhook_ingress_generic(
     if not webhook.attached_nodes:
         return
 
-    executor = get_service_client(ExecutionManager)
+    executions: list[Awaitable] = []
     for node in webhook.attached_nodes:
         logger.debug(f"Webhook-attached node: {node}")
         if not node.is_triggered_by_event_type(event_type):
             logger.debug(f"Node #{node.id} doesn't trigger on event {event_type}")
             continue
         logger.debug(f"Executing graph #{node.graph_id} node #{node.id}")
-        executor.add_execution(
-            graph_id=node.graph_id,
-            graph_version=node.graph_version,
-            data={f"webhook_{webhook_id}_payload": payload},
-            user_id=webhook.user_id,
+        executions.append(
+            add_graph_execution_async(
+                user_id=webhook.user_id,
+                graph_id=node.graph_id,
+                graph_version=node.graph_version,
+                inputs={f"webhook_{webhook_id}_payload": payload},
+            )
         )
+    asyncio.gather(*executions)
 
 
 @router.post("/webhooks/{webhook_id}/ping")
@@ -323,7 +333,7 @@ async def webhook_ping(
     user_id: Annotated[str, Depends(get_user_id)],  # require auth
 ):
     webhook = await get_webhook(webhook_id)
-    webhook_manager = WEBHOOK_MANAGERS_BY_NAME[webhook.provider]()
+    webhook_manager = get_webhook_manager(webhook.provider)
 
     credentials = (
         creds_manager.get(user_id, webhook.credentials_id)
@@ -358,14 +368,6 @@ async def remove_all_webhooks_for_credentials(
         NeedConfirmation: If any of the webhooks are still in use and `force` is `False`
     """
     webhooks = await get_all_webhooks_by_creds(credentials.id)
-    if credentials.provider not in WEBHOOK_MANAGERS_BY_NAME:
-        if webhooks:
-            logger.error(
-                f"Credentials #{credentials.id} for provider {credentials.provider} "
-                f"are attached to {len(webhooks)} webhooks, "
-                f"but there is no available WebhooksHandler for {credentials.provider}"
-            )
-        return
     if any(w.attached_nodes for w in webhooks) and not force:
         raise NeedConfirmation(
             "Some webhooks linked to these credentials are still in use by an agent"
@@ -376,7 +378,7 @@ async def remove_all_webhooks_for_credentials(
             await set_node_webhook(node.id, None)
 
         # Prune the webhook
-        webhook_manager = WEBHOOK_MANAGERS_BY_NAME[credentials.provider]()
+        webhook_manager = get_webhook_manager(ProviderName(credentials.provider))
         success = await webhook_manager.prune_webhook_if_dangling(
             webhook.id, credentials
         )

autogpt_platform/backend/backend/server/model.py

@@ -1,31 +1,36 @@
 import enum
-from typing import Any, List, Optional, Union
+from typing import Any, Optional
 
 import pydantic
 
-import backend.data.graph
 from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
+from backend.data.graph import Graph
 
 
-class Methods(enum.Enum):
-    SUBSCRIBE = "subscribe"
+class WSMethod(enum.Enum):
+    SUBSCRIBE_GRAPH_EXEC = "subscribe_graph_execution"
+    SUBSCRIBE_GRAPH_EXECS = "subscribe_graph_executions"
     UNSUBSCRIBE = "unsubscribe"
-    EXECUTION_EVENT = "execution_event"
+    GRAPH_EXECUTION_EVENT = "graph_execution_event"
+    NODE_EXECUTION_EVENT = "node_execution_event"
     ERROR = "error"
     HEARTBEAT = "heartbeat"
 
 
-class WsMessage(pydantic.BaseModel):
-    method: Methods
-    data: Optional[Union[dict[str, Any], list[Any], str]] = None
+class WSMessage(pydantic.BaseModel):
+    method: WSMethod
+    data: Optional[dict[str, Any] | list[Any] | str] = None
     success: bool | None = None
     channel: str | None = None
     error: str | None = None
 
 
-class ExecutionSubscription(pydantic.BaseModel):
+class WSSubscribeGraphExecutionRequest(pydantic.BaseModel):
+    graph_exec_id: str
+
+
+class WSSubscribeGraphExecutionsRequest(pydantic.BaseModel):
     graph_id: str
-    graph_version: int
 
 
 class ExecuteGraphResponse(pydantic.BaseModel):
@@ -33,12 +38,12 @@ class ExecuteGraphResponse(pydantic.BaseModel):
 
 
 class CreateGraph(pydantic.BaseModel):
-    graph: backend.data.graph.Graph
+    graph: Graph
 
 
 class CreateAPIKeyRequest(pydantic.BaseModel):
     name: str
-    permissions: List[APIKeyPermission]
+    permissions: list[APIKeyPermission]
     description: Optional[str] = None
 
 
@@ -52,7 +57,7 @@ class SetGraphActiveVersion(pydantic.BaseModel):
 
 
 class UpdatePermissionsRequest(pydantic.BaseModel):
-    permissions: List[APIKeyPermission]
+    permissions: list[APIKeyPermission]
 
 
 class Pagination(pydantic.BaseModel):

autogpt_platform/backend/backend/server/rest_api.py

@@ -11,22 +11,24 @@ from autogpt_libs.feature_flag.client import (
     initialize_launchdarkly,
     shutdown_launchdarkly,
 )
+from autogpt_libs.logging.utils import generate_uvicorn_config
 
 import backend.data.block
 import backend.data.db
 import backend.data.graph
 import backend.data.user
-import backend.server.integrations.router
+import backend.server.routers.postmark.postmark
 import backend.server.routers.v1
+import backend.server.v2.admin.store_admin_routes
 import backend.server.v2.library.db
 import backend.server.v2.library.model
 import backend.server.v2.library.routes
 import backend.server.v2.otto.routes
-import backend.server.v2.postmark.postmark
 import backend.server.v2.store.model
 import backend.server.v2.store.routes
 import backend.util.service
 import backend.util.settings
+from backend.blocks.llm import LlmModel
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
 from backend.server.external.api import external_app
@@ -55,6 +57,7 @@ async def lifespan_context(app: fastapi.FastAPI):
     await backend.data.block.initialize_blocks()
     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)
     with launch_darkly_context():
         yield
     await backend.data.db.disconnect()
@@ -99,6 +102,11 @@ app.include_router(backend.server.routers.v1.v1_router, tags=["v1"], prefix="/ap
 app.include_router(
     backend.server.v2.store.routes.router, tags=["v2"], prefix="/api/store"
 )
+app.include_router(
+    backend.server.v2.admin.store_admin_routes.router,
+    tags=["v2", "admin"],
+    prefix="/api/store",
+)
 app.include_router(
     backend.server.v2.library.routes.router, tags=["v2"], prefix="/api/library"
 )
@@ -107,8 +115,8 @@ app.include_router(
 )
 
 app.include_router(
-    backend.server.v2.postmark.postmark.router,
-    tags=["v2", "email"],
+    backend.server.routers.postmark.postmark.router,
+    tags=["v1", "email"],
     prefix="/api/email",
 )
 
@@ -133,8 +141,13 @@ class AgentServer(backend.util.service.AppProcess):
             server_app,
             host=backend.util.settings.Config().agent_api_host,
             port=backend.util.settings.Config().agent_api_port,
+            log_config=generate_uvicorn_config(),
         )
 
+    def cleanup(self):
+        super().cleanup()
+        logger.info(f"[{self.service_name}] ⏳ Shutting down Agent Server...")
+
     @staticmethod
     async def test_execute_graph(
         graph_id: str,
@@ -142,11 +155,12 @@ class AgentServer(backend.util.service.AppProcess):
         graph_version: Optional[int] = None,
         node_input: Optional[dict[str, Any]] = None,
     ):
-        return backend.server.routers.v1.execute_graph(
+        return await backend.server.routers.v1.execute_graph(
             user_id=user_id,
             graph_id=graph_id,
             graph_version=graph_version,
-            node_input=node_input or {},
+            inputs=node_input or {},
+            credentials_inputs={},
         )
 
     @staticmethod
@@ -154,9 +168,10 @@ class AgentServer(backend.util.service.AppProcess):
         graph_id: str,
         graph_version: int,
         user_id: str,
+        for_export: bool = False,
     ):
         return await backend.server.routers.v1.get_graph(
-            graph_id, user_id, graph_version
+            graph_id, user_id, graph_version, for_export
         )
 
     @staticmethod
@@ -168,21 +183,15 @@ class AgentServer(backend.util.service.AppProcess):
 
     @staticmethod
     async def test_get_graph_run_status(graph_exec_id: str, user_id: str):
-        execution = await backend.data.graph.get_execution_meta(
+        from backend.data.execution import get_graph_execution_meta
+
+        execution = await get_graph_execution_meta(
             user_id=user_id, execution_id=graph_exec_id
         )
         if not execution:
             raise ValueError(f"Execution {graph_exec_id} not found")
         return execution.status
 
-    @staticmethod
-    async def test_get_graph_run_results(
-        graph_id: str, graph_exec_id: str, user_id: str
-    ):
-        return await backend.server.routers.v1.get_graph_execution(
-            graph_id, graph_exec_id, user_id
-        )
-
     @staticmethod
     async def test_delete_graph(graph_id: str, user_id: str):
         await backend.server.v2.library.db.delete_library_agent_by_graph_id(
@@ -249,12 +258,16 @@ class AgentServer(backend.util.service.AppProcess):
     ):
         return await backend.server.v2.store.routes.create_submission(request, user_id)
 
+    ### ADMIN ###
+
     @staticmethod
     async def test_review_store_listing(
         request: backend.server.v2.store.model.ReviewSubmissionRequest,
         user: autogpt_libs.auth.models.User,
     ):
-        return await backend.server.v2.store.routes.review_submission(request, user)
+        return await backend.server.v2.admin.store_admin_routes.review_submission(
+            request.store_listing_version_id, request, user
+        )
 
     @staticmethod
     def test_create_credentials(
@@ -262,7 +275,9 @@ class AgentServer(backend.util.service.AppProcess):
         provider: ProviderName,
         credentials: Credentials,
     ) -> Credentials:
-        return backend.server.integrations.router.create_credentials(
+        from backend.server.integrations.router import create_credentials
+
+        return create_credentials(
             user_id=user_id, provider=provider, credentials=credentials
         )