Merge branch 'dev' into pwuts/open-2330-implement-agent-presets-functionality

.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-backend-ci.yml

@@ -66,7 +66,7 @@ jobs:
       - name: Setup Supabase
         uses: supabase/setup-cli@v1
         with:
-          version: latest
+          version: 1.178.1
 
       - id: get_date
         name: Get date
@@ -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

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

@@ -82,7 +82,7 @@ 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.
 

SECURITY.md

@@ -20,6 +20,7 @@ Instead, please report them via:
 - Please provide detailed reports with reproducible steps
 - Include the version/commit hash where you discovered the vulnerability
 - Allow us a 90-day security fix window before any public disclosure
+- After patch is released, allow 30 days for users to update before public disclosure (for a total of 120 days max between update time and fix time)
 - Share any potential mitigations or workarounds if known
 
 ## Supported Versions

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/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)"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:18a01eba2574fb9edd5f6e5fb25f66e6ce061da5dab5db75e13fe1558142e0a3"},
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:94fac7c6e77ccb1ca91e9eb4cb0ac0270b9fb9b289738654120ba8cebb1189c6"},
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2f1f1c75c395991ce9c94d3e4aa96e5c59c8356a15b1c9231e783865e2772699"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f7acae3cf1a2a2361ec4c8e787eaaa86a94171d2417aae53c0cca6ca3118ff6"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:94c4381ffba9cc508b37d2e536b418d5ea9cfdc2848b9a7fea6aebad4ec6aac1"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c31ad0c0c507894e3eaa843415841995bf8de4d6b2d24c6e33099f4bc9fc0d4f"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0912b8a8fadeb32ff67a3ed44249448c20148397c1ed905d5dac185b4ca547bb"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0d93400c18596b7dc4794d48a63fb361b01a0d8eb39f28800dc900c8fbdaca91"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d00f3c5e0d764a5c9aa5a62d99728c56d455310bcc288a79cab10157b3af426f"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:d742c36ed44f2798c8d3f4bc511f479b9ceef2b93f348671184139e7d708042c"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:814375093edae5f1cb31e3407997cf3eacefb9010f96df10d64829362ae2df69"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:8224f98be68a84b19f48e0bdc14224b5a71339aff3a27df69989fa47d01296f3"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d9a487ef090aea982d748b1b0d74fe7c3950b109df967630a20584f9a99c0683"},
-    {file = "aiohttp-3.10.5-cp310-cp310-win32.whl", hash = "sha256:d9ef084e3dc690ad50137cc05831c52b6ca428096e6deb3c43e95827f531d5ef"},
-    {file = "aiohttp-3.10.5-cp310-cp310-win_amd64.whl", hash = "sha256:66bf9234e08fe561dccd62083bf67400bdbf1c67ba9efdc3dac03650e97c6088"},
-    {file = "aiohttp-3.10.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:8c6a4e5e40156d72a40241a25cc226051c0a8d816610097a8e8f517aeacd59a2"},
-    {file = "aiohttp-3.10.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2c634a3207a5445be65536d38c13791904fda0748b9eabf908d3fe86a52941cf"},
-    {file = "aiohttp-3.10.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4aff049b5e629ef9b3e9e617fa6e2dfeda1bf87e01bcfecaf3949af9e210105e"},
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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 = [
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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"]
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 [[package]]
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+version = "2.11.1"
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 typing-extensions = ">=4.12.2"
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 [package.extras]
 email = ["email-validator (>=2.0.0)"]
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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"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1402,21 +1501,21 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "realtime"
-version = "2.0.2"
+version = "2.4.2"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
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 [package.dependencies]
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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"
@@ -1476,30 +1575,30 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.9.6"
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 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
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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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 [package.dependencies]
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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"
@@ -1802,109 +1916,100 @@ files = [
 
 [[package]]
 name = "yarl"
-version = "1.11.1"
+version = "1.18.3"
 description = "Yet another URL library"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
 ]
 
 [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

@@ -2,13 +2,23 @@ DB_USER=postgres
 DB_PASS=your-super-secret-and-long-postgres-password
 DB_NAME=postgres
 DB_PORT=5432
-DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@localhost:${DB_PORT}/${DB_NAME}?connect_timeout=60&schema=platform"
+DB_HOST=localhost
+DB_CONNECTION_LIMIT=12
+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}"
 PRISMA_SCHEMA="postgres/schema.prisma"
 
+# EXECUTOR
+NUM_GRAPH_WORKERS=10
+NUM_NODE_WORKERS=3
+
 BACKEND_CORS_ALLOW_ORIGINS=["http://localhost:3000"]
 
 # generate using `from cryptography.fernet import Fernet;Fernet.generate_key().decode()`
 ENCRYPTION_KEY='dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw='
+UNSUBSCRIBE_SECRET_KEY = 'HlP8ivStJjmbf6NKi78m_3FnOogut0t5ckzjsIqeaio='
 
 REDIS_HOST=localhost
 REDIS_PORT=6379
@@ -178,6 +188,8 @@ SMARTLEAD_API_KEY=
 # ZeroBounce
 ZEROBOUNCE_API_KEY=
 
+## ===== OPTIONAL API KEYS END ===== ##
+
 # Logging Configuration
 LOG_LEVEL=INFO
 ENABLE_CLOUD_LOGGING=false

autogpt_platform/backend/README.advanced.md

@@ -1,75 +1 @@
-# AutoGPT Agent Server Advanced set up
-
-This guide walks you through a dockerized set up, with an external DB (postgres)
-
-## Setup
-
-We use the Poetry to manage the dependencies. To set up the project, follow these steps inside this directory:
-
-0. Install Poetry
-    ```sh
-    pip install poetry
-    ```
-    
-1. Configure Poetry to use .venv in your project directory
-    ```sh
-    poetry config virtualenvs.in-project true
-    ```
-
-2. Enter the poetry shell
-
-   ```sh
-   poetry shell
-   ```
-   
-3. Install dependencies
-
-   ```sh
-   poetry install
-   ```
-   
-4. Copy .env.example to .env
-
-   ```sh
-   cp .env.example .env
-   ```
-
-5. Generate the Prisma client
-
-   ```sh
-   poetry run prisma generate
-   ```
-   
-
-   > In case Prisma generates the client for the global Python installation instead of the virtual environment, the current mitigation is to just uninstall the global Prisma package:
-   >
-   > ```sh
-   > pip uninstall prisma
-   > ```
-   >
-   > Then run the generation again. The path *should* look something like this:  
-   > `<some path>/pypoetry/virtualenvs/backend-TQIRSwR6-py3.12/bin/prisma`
-
-6. Run the postgres database from the /rnd folder
-
-   ```sh
-   cd autogpt_platform/
-   docker compose up -d
-   ```
-
-7. Run the migrations (from the backend folder)
-
-   ```sh
-   cd ../backend
-   prisma migrate deploy
-   ```
-
-## Running The Server
-
-### Starting the server directly
-
-Run the following command:
-
-```sh
-poetry run app
-```
+[Advanced Setup (Dev Branch)](https://dev-docs.agpt.co/platform/advanced_setup/#autogpt_agent_server_advanced_set_up)

autogpt_platform/backend/README.md

@@ -1,210 +1 @@
-# AutoGPT Agent Server 
-
-This is an initial project for creating the next generation of agent execution, which is an AutoGPT agent server.
-The agent server will enable the creation of composite multi-agent systems that utilize AutoGPT agents and other non-agent components as its primitives.
-
-## Docs
-
-You can access the docs for the [AutoGPT Agent Server here](https://docs.agpt.co/server/setup).
-
-## Setup
-
-We use the Poetry to manage the dependencies. To set up the project, follow these steps inside this directory:
-
-0. Install Poetry
-    ```sh
-    pip install poetry
-    ```
-    
-1. Configure Poetry to use .venv in your project directory
-    ```sh
-    poetry config virtualenvs.in-project true
-    ```
-
-2. Enter the poetry shell
-
-   ```sh
-   poetry shell
-   ```
-   
-3. Install dependencies
-
-   ```sh
-   poetry install
-   ```
-
-4. Copy .env.example to .env
-
-   ```sh
-   cp .env.example .env
-   ```
-   
-5. Generate the Prisma client
-
-   ```sh
-   poetry run prisma generate
-   ```
-   
-
-   > In case Prisma generates the client for the global Python installation instead of the virtual environment, the current mitigation is to just uninstall the global Prisma package:
-   >
-   > ```sh
-   > pip uninstall prisma
-   > ```
-   >
-   > Then run the generation again. The path *should* look something like this:  
-   > `<some path>/pypoetry/virtualenvs/backend-TQIRSwR6-py3.12/bin/prisma`
-
-6. Migrate the database. Be careful because this deletes current data in the database.
-
-   ```sh
-   docker compose up db -d
-   poetry run prisma migrate deploy
-   ```
-
-## Running The Server
-
-### Starting the server without Docker
-
-To run the server locally, start in the autogpt_platform folder:
-
-```sh
-cd ..
-```
-
-Run the following command to run database in docker but the application locally:
-
-```sh
-docker compose --profile local up deps --build --detach
-cd backend
-poetry run app
-```
-
-### Starting the server with Docker
-
-Run the following command to build the dockerfiles:
-
-```sh
-docker compose build
-```
-
-Run the following command to run the app:
-
-```sh
-docker compose up
-```
-
-Run the following to automatically rebuild when code changes, in another terminal:
-
-```sh
-docker compose watch
-```
-
-Run the following command to shut down:
-
-```sh
-docker compose down
-```
-
-If you run into issues with dangling orphans, try:
-
-```sh
-docker compose down --volumes --remove-orphans && docker-compose up --force-recreate --renew-anon-volumes --remove-orphans  
-```
-
-## Testing
-
-To run the tests:
-
-```sh
-poetry run test
-```
-
-## Development
-
-### Formatting & Linting
-Auto formatter and linter are set up in the project. To run them:
-
-Install:
-```sh
-poetry install --with dev
-```
-
-Format the code:
-```sh
-poetry run format
-```
-
-Lint the code:
-```sh
-poetry run lint
-```
-
-## Project Outline
-
-The current project has the following main modules:
-
-### **blocks**
-
-This module stores all the Agent Blocks, which are reusable components to build a graph that represents the agent's behavior.
-
-### **data**
-
-This module stores the logical model that is persisted in the database.
-It abstracts the database operations into functions that can be called by the service layer.
-Any code that interacts with Prisma objects or the database should reside in this module.
-The main models are:
-* `block`: anything related to the block used in the graph
-* `execution`: anything related to the execution graph execution
-* `graph`: anything related to the graph, node, and its relations
-
-### **execution**
-
-This module stores the business logic of executing the graph.
-It currently has the following main modules:
-* `manager`: A service that consumes the queue of the graph execution and executes the graph. It contains both pieces of logic.
-* `scheduler`: A service that triggers scheduled graph execution based on a cron expression. It pushes an execution request to the manager.
-
-### **server**
-
-This module stores the logic for the server API.
-It contains all the logic used for the API that allows the client to create, execute, and monitor the graph and its execution.
-This API service interacts with other services like those defined in `manager` and `scheduler`.
-
-### **utils**
-
-This module stores utility functions that are used across the project.
-Currently, it has two main modules:
-* `process`: A module that contains the logic to spawn a new process.
-* `service`: A module that serves as a parent class for all the services in the project.
-
-## Service Communication
-
-Currently, there are only 3 active services:
-
-- AgentServer (the API, defined in `server.py`)
-- ExecutionManager (the executor, defined in `manager.py`)
-- ExecutionScheduler (the scheduler, defined in `scheduler.py`)
-
-The services run in independent Python processes and communicate through an IPC.
-A communication layer (`service.py`) is created to decouple the communication library from the implementation.
-
-Currently, the IPC is done using Pyro5 and abstracted in a way that allows a function decorated with `@expose` to be called from a different process.
-
-
-By default the daemons run on the following ports: 
-
-Execution Manager Daemon: 8002
-Execution Scheduler Daemon: 8003
-Rest Server Daemon: 8004
-
-## Adding a New Agent Block
-
-To add a new agent block, you need to create a new class that inherits from `Block` and provides the following information:
-* All the block code should live in the `blocks` (`backend.blocks`) module.
-* `input_schema`: the schema of the input data, represented by a Pydantic object.
-* `output_schema`: the schema of the output data, represented by a Pydantic object.
-* `run` method: the main logic of the block.
-* `test_input` & `test_output`: the sample input and output data for the block, which will be used to auto-test the block.
-* You can mock the functions declared in the block using the `test_mock` field for your unit tests.
-* Once you finish creating the block, you can test it by running `poetry run pytest -s test/block/test_block.py`.
+[Getting Started (Released)](https://docs.agpt.co/platform/getting-started/#autogpt_agent_server)

autogpt_platform/backend/backend/app.py

@@ -1,22 +1,30 @@
+import logging
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
     from backend.util.process import AppProcess
 
+logger = logging.getLogger(__name__)
+
 
 def run_processes(*processes: "AppProcess", **kwargs):
     """
     Execute all processes in the app. The last process is run in the foreground.
+    Includes enhanced error handling and process lifecycle management.
     """
     try:
+        # Run all processes except the last one in the background.
         for process in processes[:-1]:
             process.start(background=True, **kwargs)
 
-        # Run the last process in the foreground
+        # Run the last process in the foreground.
         processes[-1].start(background=False, **kwargs)
     finally:
         for process in processes:
-            process.stop()
+            try:
+                process.stop()
+            except Exception as e:
+                logger.exception(f"[{process.service_name}] unable to stop: {e}")
 
 
 def main(**kwargs):
@@ -24,7 +32,7 @@ def main(**kwargs):
     Run all the processes required for the AutoGPT-server (REST and WebSocket APIs).
     """
 
-    from backend.executor import DatabaseManager, ExecutionManager, ExecutionScheduler
+    from backend.executor import DatabaseManager, ExecutionManager, Scheduler
     from backend.notifications import NotificationManager
     from backend.server.rest_api import AgentServer
     from backend.server.ws_api import WebsocketServer
@@ -32,7 +40,7 @@ def main(**kwargs):
     run_processes(
         DatabaseManager(),
         ExecutionManager(),
-        ExecutionScheduler(),
+        Scheduler(),
         NotificationManager(),
         WebsocketServer(),
         AgentServer(),

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

@@ -1,4 +1,5 @@
 import logging
+from typing import Any
 
 from autogpt_libs.utils.cache import thread_cached
 
@@ -13,6 +14,7 @@ from backend.data.block import (
 )
 from backend.data.execution import ExecutionStatus
 from backend.data.model import SchemaField
+from backend.util import json
 
 logger = logging.getLogger(__name__)
 
@@ -42,6 +44,23 @@ class AgentExecutorBlock(Block):
         input_schema: dict = SchemaField(description="Input schema for the graph")
         output_schema: dict = SchemaField(description="Output schema for the graph")
 
+        @classmethod
+        def get_input_schema(cls, data: BlockInput) -> dict[str, Any]:
+            return data.get("input_schema", {})
+
+        @classmethod
+        def get_input_defaults(cls, data: BlockInput) -> BlockInput:
+            return data.get("data", {})
+
+        @classmethod
+        def get_missing_input(cls, data: BlockInput) -> set[str]:
+            required_fields = cls.get_input_schema(data).get("required", [])
+            return set(required_fields) - set(data)
+
+        @classmethod
+        def get_mismatch_error(cls, data: BlockInput) -> str | None:
+            return json.validate_with_jsonschema(cls.get_input_schema(data), data)
+
     class Output(BlockSchema):
         pass
 
@@ -56,6 +75,8 @@ class AgentExecutorBlock(Block):
         )
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        from backend.data.execution import ExecutionEventType
+
         executor_manager = get_executor_manager_client()
         event_bus = get_event_bus()
 
@@ -69,13 +90,11 @@ class AgentExecutorBlock(Block):
         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.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,
@@ -86,6 +105,10 @@ class AgentExecutorBlock(Block):
                 else:
                     continue
 
+            logger.info(
+                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

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
@@ -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/basic.py

@@ -3,22 +3,20 @@ from typing import Any, List
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
 from backend.data.model import SchemaField
-from backend.util.file import MediaFile, store_media_file
+from backend.util import json
+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
-
-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."
         )
 
@@ -90,29 +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")
@@ -153,6 +128,9 @@ class FindInDictionaryBlock(Block):
         obj = input_data.input
         key = input_data.key
 
+        if isinstance(obj, str):
+            obj = json.loads(obj)
+
         if isinstance(obj, dict) and key in obj:
             yield "output", obj[key]
         elif isinstance(obj, list) and isinstance(key, int) and 0 <= key < len(obj):
@@ -170,188 +148,6 @@ 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(

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/exa/contents.py

@@ -51,6 +51,7 @@ class ExaContentsBlock(Block):
             description="List of document contents",
             default=[],
         )
+        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(

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={})
+        constants: dict = SchemaField(
+            description="The constants to be set when the block is put on the graph",
+            default={},
+        )
+
+    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/_api.py

@@ -38,6 +38,59 @@ def _get_headers(credentials: GithubCredentials) -> dict[str, str]:
     }
 
 
+def convert_comment_url_to_api_endpoint(comment_url: str) -> str:
+    """
+    Converts a GitHub comment URL (web interface) to the appropriate API endpoint URL.
+
+    Handles:
+    1. Issue/PR comments: #issuecomment-{id}
+    2. PR review comments: #discussion_r{id}
+
+    Returns the appropriate API endpoint path for the comment.
+    """
+    # First, check if this is already an API URL
+    parsed_url = urlparse(comment_url)
+    if parsed_url.hostname == "api.github.com":
+        return comment_url
+
+    # Replace pull with issues for comment endpoints
+    if "/pull/" in comment_url:
+        comment_url = comment_url.replace("/pull/", "/issues/")
+
+    # Handle issue/PR comments (#issuecomment-xxx)
+    if "#issuecomment-" in comment_url:
+        base_url, comment_part = comment_url.split("#issuecomment-")
+        comment_id = comment_part
+
+        # Extract repo information from base URL
+        parsed_url = urlparse(base_url)
+        path_parts = parsed_url.path.strip("/").split("/")
+        owner, repo = path_parts[0], path_parts[1]
+
+        # Construct API URL for issue comments
+        return (
+            f"https://api.github.com/repos/{owner}/{repo}/issues/comments/{comment_id}"
+        )
+
+    # Handle PR review comments (#discussion_r)
+    elif "#discussion_r" in comment_url:
+        base_url, comment_part = comment_url.split("#discussion_r")
+        comment_id = comment_part
+
+        # Extract repo information from base URL
+        parsed_url = urlparse(base_url)
+        path_parts = parsed_url.path.strip("/").split("/")
+        owner, repo = path_parts[0], path_parts[1]
+
+        # Construct API URL for PR review comments
+        return (
+            f"https://api.github.com/repos/{owner}/{repo}/pulls/comments/{comment_id}"
+        )
+
+    # If no specific comment identifiers are found, use the general URL conversion
+    return _convert_to_api_url(comment_url)
+
+
 def get_api(
     credentials: GithubCredentials | GithubFineGrainedAPICredentials,
     convert_urls: bool = True,

autogpt_platform/backend/backend/blocks/github/checks.py

@@ -172,7 +172,9 @@ class GithubCreateCheckRunBlock(Block):
             data.output = output_data
 
         check_runs_url = f"{repo_url}/check-runs"
-        response = api.post(check_runs_url)
+        response = api.post(
+            check_runs_url, data=data.model_dump_json(exclude_none=True)
+        )
         result = response.json()
 
         return {
@@ -323,7 +325,9 @@ class GithubUpdateCheckRunBlock(Block):
             data.output = output_data
 
         check_run_url = f"{repo_url}/check-runs/{check_run_id}"
-        response = api.patch(check_run_url)
+        response = api.patch(
+            check_run_url, data=data.model_dump_json(exclude_none=True)
+        )
         result = response.json()
 
         return {

autogpt_platform/backend/backend/blocks/github/issues.py

@@ -1,3 +1,4 @@
+import logging
 from urllib.parse import urlparse
 
 from typing_extensions import TypedDict
@@ -5,7 +6,7 @@ from typing_extensions import TypedDict
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
 
-from ._api import get_api
+from ._api import convert_comment_url_to_api_endpoint, get_api
 from ._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
@@ -14,6 +15,8 @@ from ._auth import (
     GithubCredentialsInput,
 )
 
+logger = logging.getLogger(__name__)
+
 
 def is_github_url(url: str) -> bool:
     return urlparse(url).netloc == "github.com"
@@ -108,6 +111,228 @@ class GithubCommentBlock(Block):
 # --8<-- [end:GithubCommentBlockExample]
 
 
+class GithubUpdateCommentBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        comment_url: str = SchemaField(
+            description="URL of the GitHub comment",
+            placeholder="https://github.com/owner/repo/issues/1#issuecomment-123456789",
+            default="",
+            advanced=False,
+        )
+        issue_url: str = SchemaField(
+            description="URL of the GitHub issue or pull request",
+            placeholder="https://github.com/owner/repo/issues/1",
+            default="",
+        )
+        comment_id: str = SchemaField(
+            description="ID of the GitHub comment",
+            placeholder="123456789",
+            default="",
+        )
+        comment: str = SchemaField(
+            description="Comment to update",
+            placeholder="Enter your comment",
+        )
+
+    class Output(BlockSchema):
+        id: int = SchemaField(description="ID of the updated comment")
+        url: str = SchemaField(description="URL to the comment on GitHub")
+        error: str = SchemaField(
+            description="Error message if the comment update failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="b3f4d747-10e3-4e69-8c51-f2be1d99c9a7",
+            description="This block updates a comment on a specified GitHub issue or pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubUpdateCommentBlock.Input,
+            output_schema=GithubUpdateCommentBlock.Output,
+            test_input={
+                "comment_url": "https://github.com/owner/repo/issues/1#issuecomment-123456789",
+                "comment": "This is an updated comment.",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("id", 123456789),
+                (
+                    "url",
+                    "https://github.com/owner/repo/issues/1#issuecomment-123456789",
+                ),
+            ],
+            test_mock={
+                "update_comment": lambda *args, **kwargs: (
+                    123456789,
+                    "https://github.com/owner/repo/issues/1#issuecomment-123456789",
+                )
+            },
+        )
+
+    @staticmethod
+    def update_comment(
+        credentials: GithubCredentials, comment_url: str, body_text: str
+    ) -> tuple[int, str]:
+        api = get_api(credentials, convert_urls=False)
+        data = {"body": body_text}
+        url = convert_comment_url_to_api_endpoint(comment_url)
+
+        logger.info(url)
+        response = api.patch(url, json=data)
+        comment = response.json()
+        return comment["id"], comment["html_url"]
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        if (
+            not input_data.comment_url
+            and input_data.comment_id
+            and input_data.issue_url
+        ):
+            parsed_url = urlparse(input_data.issue_url)
+            path_parts = parsed_url.path.strip("/").split("/")
+            owner, repo = path_parts[0], path_parts[1]
+
+            input_data.comment_url = f"https://api.github.com/repos/{owner}/{repo}/issues/comments/{input_data.comment_id}"
+
+        elif (
+            not input_data.comment_url
+            and not input_data.comment_id
+            and input_data.issue_url
+        ):
+            raise ValueError(
+                "Must provide either comment_url or comment_id and issue_url"
+            )
+        id, url = self.update_comment(
+            credentials,
+            input_data.comment_url,
+            input_data.comment,
+        )
+        yield "id", id
+        yield "url", url
+
+
+class GithubListCommentsBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        issue_url: str = SchemaField(
+            description="URL of the GitHub issue or pull request",
+            placeholder="https://github.com/owner/repo/issues/1",
+        )
+
+    class Output(BlockSchema):
+        class CommentItem(TypedDict):
+            id: int
+            body: str
+            user: str
+            url: str
+
+        comment: CommentItem = SchemaField(
+            title="Comment", description="Comments with their ID, body, user, and URL"
+        )
+        comments: list[CommentItem] = SchemaField(
+            description="List of comments with their ID, body, user, and URL"
+        )
+        error: str = SchemaField(description="Error message if listing comments failed")
+
+    def __init__(self):
+        super().__init__(
+            id="c4b5fb63-0005-4a11-b35a-0c2467bd6b59",
+            description="This block lists all comments for a specified GitHub issue or pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListCommentsBlock.Input,
+            output_schema=GithubListCommentsBlock.Output,
+            test_input={
+                "issue_url": "https://github.com/owner/repo/issues/1",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "comment",
+                    {
+                        "id": 123456789,
+                        "body": "This is a test comment.",
+                        "user": "test_user",
+                        "url": "https://github.com/owner/repo/issues/1#issuecomment-123456789",
+                    },
+                ),
+                (
+                    "comments",
+                    [
+                        {
+                            "id": 123456789,
+                            "body": "This is a test comment.",
+                            "user": "test_user",
+                            "url": "https://github.com/owner/repo/issues/1#issuecomment-123456789",
+                        }
+                    ],
+                ),
+            ],
+            test_mock={
+                "list_comments": lambda *args, **kwargs: [
+                    {
+                        "id": 123456789,
+                        "body": "This is a test comment.",
+                        "user": "test_user",
+                        "url": "https://github.com/owner/repo/issues/1#issuecomment-123456789",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_comments(
+        credentials: GithubCredentials, issue_url: str
+    ) -> list[Output.CommentItem]:
+        parsed_url = urlparse(issue_url)
+        path_parts = parsed_url.path.strip("/").split("/")
+
+        owner = path_parts[0]
+        repo = path_parts[1]
+
+        # GitHub API uses 'issues' for both issues and pull requests when it comes to comments
+        issue_number = path_parts[3]  # Whether 'issues/123' or 'pull/123'
+
+        # Construct the proper API URL directly
+        api_url = f"https://api.github.com/repos/{owner}/{repo}/issues/{issue_number}/comments"
+
+        # Set convert_urls=False since we're already providing an API URL
+        api = get_api(credentials, convert_urls=False)
+        response = api.get(api_url)
+        comments = response.json()
+        parsed_comments: list[GithubListCommentsBlock.Output.CommentItem] = [
+            {
+                "id": comment["id"],
+                "body": comment["body"],
+                "user": comment["user"]["login"],
+                "url": comment["html_url"],
+            }
+            for comment in comments
+        ]
+        return parsed_comments
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        comments = self.list_comments(
+            credentials,
+            input_data.issue_url,
+        )
+        yield from (("comment", comment) for comment in comments)
+        yield "comments", comments
+
+
 class GithubMakeIssueBlock(Block):
     class Input(BlockSchema):
         credentials: GithubCredentialsInput = GithubCredentialsField("repo")

autogpt_platform/backend/backend/blocks/github/statuses.py

@@ -144,7 +144,7 @@ class GithubCreateStatusBlock(Block):
             data.description = description
 
         status_url = f"{repo_url}/statuses/{sha}"
-        response = api.post(status_url, json=data)
+        response = api.post(status_url, data=data.model_dump_json(exclude_none=True))
         result = response.json()
 
         return {

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,
@@ -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/google/gmail.py

@@ -8,6 +8,7 @@ from pydantic import BaseModel
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
+from backend.util.settings import Settings
 
 from ._auth import (
     GOOGLE_OAUTH_IS_CONFIGURED,
@@ -150,8 +151,8 @@ class GmailReadBlock(Block):
                 else None
             ),
             token_uri="https://oauth2.googleapis.com/token",
-            client_id=kwargs.get("client_id"),
-            client_secret=kwargs.get("client_secret"),
+            client_id=Settings().secrets.google_client_id,
+            client_secret=Settings().secrets.google_client_secret,
             scopes=credentials.scopes,
         )
         return build("gmail", "v1", credentials=creds)

autogpt_platform/backend/backend/blocks/google/sheets.py

@@ -3,6 +3,7 @@ from googleapiclient.discovery import build
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
+from backend.util.settings import Settings
 
 from ._auth import (
     GOOGLE_OAUTH_IS_CONFIGURED,
@@ -86,8 +87,8 @@ class GoogleSheetsReadBlock(Block):
                 else None
             ),
             token_uri="https://oauth2.googleapis.com/token",
-            client_id=kwargs.get("client_id"),
-            client_secret=kwargs.get("client_secret"),
+            client_id=Settings().secrets.google_client_id,
+            client_secret=Settings().secrets.google_client_secret,
             scopes=credentials.scopes,
         )
         return build("sheets", "v4", credentials=creds)

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"
@@ -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/engagement.py

@@ -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/ideogram.py

@@ -142,6 +142,16 @@ class IdeogramModelBlock(Block):
             title="Color Palette Preset",
             advanced=True,
         )
+        custom_color_palette: Optional[list[str]] = SchemaField(
+            description=(
+                "Only available for model version V_2 or V_2_TURBO. Provide one or more color hex codes "
+                "(e.g., ['#000030', '#1C0C47', '#9900FF', '#4285F4', '#FFFFFF']) to define a custom color "
+                "palette. Only used if 'color_palette_name' is 'NONE'."
+            ),
+            default=None,
+            title="Custom Color Palette",
+            advanced=True,
+        )
 
     class Output(BlockSchema):
         result: str = SchemaField(description="Generated image URL")
@@ -164,6 +174,13 @@ class IdeogramModelBlock(Block):
                 "style_type": StyleType.AUTO,
                 "negative_prompt": None,
                 "color_palette_name": ColorPalettePreset.NONE,
+                "custom_color_palette": [
+                    "#000030",
+                    "#1C0C47",
+                    "#9900FF",
+                    "#4285F4",
+                    "#FFFFFF",
+                ],
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_output=[
@@ -173,7 +190,7 @@ class IdeogramModelBlock(Block):
                 ),
             ],
             test_mock={
-                "run_model": lambda api_key, model_name, prompt, seed, aspect_ratio, magic_prompt_option, style_type, negative_prompt, color_palette_name: "https://ideogram.ai/api/images/test-generated-image-url.png",
+                "run_model": lambda api_key, model_name, prompt, seed, aspect_ratio, magic_prompt_option, style_type, negative_prompt, color_palette_name, custom_colors: "https://ideogram.ai/api/images/test-generated-image-url.png",
                 "upscale_image": lambda api_key, image_url: "https://ideogram.ai/api/images/test-upscaled-image-url.png",
             },
             test_credentials=TEST_CREDENTIALS,
@@ -195,6 +212,7 @@ class IdeogramModelBlock(Block):
             style_type=input_data.style_type.value,
             negative_prompt=input_data.negative_prompt,
             color_palette_name=input_data.color_palette_name.value,
+            custom_colors=input_data.custom_color_palette,
         )
 
         # Step 2: Upscale the image if requested
@@ -217,6 +235,7 @@ class IdeogramModelBlock(Block):
         style_type: str,
         negative_prompt: Optional[str],
         color_palette_name: str,
+        custom_colors: Optional[list[str]],
     ):
         url = "https://api.ideogram.ai/generate"
         headers = {
@@ -241,7 +260,11 @@ class IdeogramModelBlock(Block):
             data["image_request"]["negative_prompt"] = negative_prompt
 
         if color_palette_name != "NONE":
-            data["image_request"]["color_palette"] = {"name": color_palette_name}
+            data["color_palette"] = {"name": color_palette_name}
+        elif custom_colors:
+            data["color_palette"] = {
+                "members": [{"color_hex": color} for color in custom_colors]
+            }
 
         try:
             response = requests.post(url, json=data, headers=headers)
@@ -267,9 +290,7 @@ class IdeogramModelBlock(Block):
             response = requests.post(
                 url,
                 headers=headers,
-                data={
-                    "image_request": "{}",  # Empty JSON object
-                },
+                data={"image_request": "{}"},
                 files=files,
             )
 

autogpt_platform/backend/backend/blocks/io.py

@@ -0,0 +1,555 @@
+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=[],
+            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 = 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=[],
+            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/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

@@ -4,29 +4,25 @@ 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.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 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
@@ -76,12 +72,10 @@ 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__
@@ -229,17 +223,6 @@ for model in LlmModel:
         raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
 
 
-class MessageRole(str, Enum):
-    SYSTEM = "system"
-    USER = "user"
-    ASSISTANT = "assistant"
-
-
-class Message(BlockSchema):
-    role: MessageRole
-    content: str
-
-
 class ToolCall(BaseModel):
     name: str
     arguments: str
@@ -252,7 +235,8 @@ class ToolContentBlock(BaseModel):
 
 
 class LLMResponse(BaseModel):
-    prompt: str
+    raw_response: Any
+    prompt: List[Any]
     response: str
     tool_calls: Optional[List[ToolContentBlock]] | None
     prompt_tokens: int
@@ -362,7 +346,8 @@ def llm_call(
             tool_calls = None
 
         return LLMResponse(
-            prompt=json.dumps(prompt),
+            raw_response=response.choices[0].message,
+            prompt=prompt,
             response=response.choices[0].message.content or "",
             tool_calls=tool_calls,
             prompt_tokens=response.usage.prompt_tokens if response.usage else 0,
@@ -379,12 +364,16 @@ def llm_call(
         last_role = None
         for p in prompt:
             if p["role"] in ["user", "assistant"]:
-                if p["role"] != last_role:
+                if (
+                    p["role"] == last_role
+                    and isinstance(messages[-1]["content"], str)
+                    and isinstance(p["content"], str)
+                ):
+                    # If the role is the same as the last one, combine the content
+                    messages[-1]["content"] += p["content"]
+                else:
                     messages.append({"role": p["role"], "content": p["content"]})
                     last_role = p["role"]
-                else:
-                    # If the role is the same as the last one, combine the content
-                    messages[-1]["content"] += "\n" + p["content"]
 
         client = anthropic.Anthropic(api_key=credentials.api_key.get_secret_value())
         try:
@@ -423,11 +412,12 @@ def llm_call(
                 )
 
             return LLMResponse(
-                prompt=json.dumps(prompt),
+                raw_response=resp,
+                prompt=prompt,
                 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,
@@ -450,7 +440,8 @@ def llm_call(
             max_tokens=max_tokens,
         )
         return LLMResponse(
-            prompt=json.dumps(prompt),
+            raw_response=response.choices[0].message,
+            prompt=prompt,
             response=response.choices[0].message.content or "",
             tool_calls=None,
             prompt_tokens=response.usage.prompt_tokens if response.usage else 0,
@@ -469,7 +460,8 @@ def llm_call(
             stream=False,
         )
         return LLMResponse(
-            prompt=json.dumps(prompt),
+            raw_response=response.get("response") or "",
+            prompt=prompt,
             response=response.get("response") or "",
             tool_calls=None,
             prompt_tokens=response.get("prompt_eval_count") or 0,
@@ -515,7 +507,8 @@ def llm_call(
             tool_calls = None
 
         return LLMResponse(
-            prompt=json.dumps(prompt),
+            raw_response=response.choices[0].message,
+            prompt=prompt,
             response=response.choices[0].message.content or "",
             tool_calls=tool_calls,
             prompt_tokens=response.usage.prompt_tokens if response.usage else 0,
@@ -528,7 +521,7 @@ 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)
@@ -557,7 +550,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             default="",
             description="The system prompt to provide additional context to the model.",
         )
-        conversation_history: list[Message] = SchemaField(
+        conversation_history: list[dict] = SchemaField(
             default=[],
             description="The conversation history to provide context for the prompt.",
         )
@@ -587,7 +580,7 @@ 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):
@@ -609,11 +602,12 @@ 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(
-                    prompt="",
+                    raw_response="",
+                    prompt=[""],
                     response=json.dumps(
                         {
                             "key1": "key1Value",
@@ -641,6 +635,7 @@ 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,
@@ -655,7 +650,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         logger.debug(f"Calling LLM with input data: {input_data}")
-        prompt = [p.model_dump() for p in input_data.conversation_history]
+        prompt = [json.to_dict(p) for p in input_data.conversation_history]
 
         def trim_prompt(s: str) -> str:
             lines = s.strip().split("\n")
@@ -715,10 +710,10 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 )
                 response_text = llm_response.response
                 self.merge_stats(
-                    {
-                        "input_token_count": llm_response.prompt_tokens,
-                        "output_token_count": llm_response.completion_tokens,
-                    }
+                    NodeExecutionStats(
+                        input_token_count=llm_response.prompt_tokens,
+                        output_token_count=llm_response.completion_tokens,
+                    )
                 )
                 logger.info(f"LLM attempt-{retry_count} response: {response_text}")
 
@@ -761,10 +756,10 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 retry_prompt = f"Error calling LLM: {e}"
             finally:
                 self.merge_stats(
-                    {
-                        "llm_call_count": retry_count + 1,
-                        "llm_retry_count": retry_count,
-                    }
+                    NodeExecutionStats(
+                        llm_call_count=retry_count + 1,
+                        llm_retry_count=retry_count,
+                    )
                 )
 
         raise RuntimeError(retry_prompt)
@@ -813,7 +808,7 @@ 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):
@@ -830,7 +825,7 @@ 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"},
         )
@@ -849,7 +844,10 @@ 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)
@@ -906,7 +904,7 @@ 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):
@@ -923,7 +921,7 @@ 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: (
@@ -1032,8 +1030,14 @@ class AITextSummarizerBlock(AIBlockBase):
 
 class AIConversationBlock(AIBlockBase):
     class Input(BlockSchema):
-        messages: List[Message] = SchemaField(
-            description="List of messages in the conversation.", min_length=1
+        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.",
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
@@ -1056,7 +1060,7 @@ 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):
@@ -1085,7 +1089,7 @@ 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."
@@ -1107,7 +1111,7 @@ 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,
@@ -1165,7 +1169,7 @@ 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."
         )
@@ -1197,7 +1201,7 @@ 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/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/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
 
@@ -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

@@ -1,7 +1,7 @@
-import json
 import logging
 import re
-from typing import TYPE_CHECKING, Any, List
+from collections import Counter
+from typing import TYPE_CHECKING, Any
 
 from autogpt_libs.utils.cache import thread_cached
 
@@ -10,15 +10,16 @@ from backend.blocks.agent import AgentExecutorBlock
 from backend.data.block import (
     Block,
     BlockCategory,
+    BlockInput,
     BlockOutput,
     BlockSchema,
     BlockType,
-    get_blocks,
 )
 from backend.data.model import SchemaField
+from backend.util import json
 
 if TYPE_CHECKING:
-    from backend.data.graph import Graph, Link, Node
+    from backend.data.graph import Link, Node
 
 logger = logging.getLogger(__name__)
 
@@ -31,6 +32,99 @@ def get_database_manager_client():
     return get_service_client(DatabaseManager)
 
 
+def _get_tool_requests(entry: dict[str, Any]) -> list[str]:
+    """
+    Return a list of tool_call_ids if the entry is a tool request.
+    Supports both OpenAI and Anthropics formats.
+    """
+    tool_call_ids = []
+    if entry.get("role") != "assistant":
+        return tool_call_ids
+
+    # OpenAI: check for tool_calls in the entry.
+    calls = entry.get("tool_calls")
+    if isinstance(calls, list):
+        for call in calls:
+            if tool_id := call.get("id"):
+                tool_call_ids.append(tool_id)
+
+    # Anthropics: check content items for tool_use type.
+    content = entry.get("content")
+    if isinstance(content, list):
+        for item in content:
+            if item.get("type") != "tool_use":
+                continue
+            if tool_id := item.get("id"):
+                tool_call_ids.append(tool_id)
+
+    return tool_call_ids
+
+
+def _get_tool_responses(entry: dict[str, Any]) -> list[str]:
+    """
+    Return a list of tool_call_ids if the entry is a tool response.
+    Supports both OpenAI and Anthropics formats.
+    """
+    tool_call_ids: list[str] = []
+
+    # OpenAI: a tool response message with role "tool" and key "tool_call_id".
+    if entry.get("role") == "tool":
+        if tool_call_id := entry.get("tool_call_id"):
+            tool_call_ids.append(str(tool_call_id))
+
+    # Anthropics: check content items for tool_result type.
+    if entry.get("role") == "user":
+        content = entry.get("content")
+        if isinstance(content, list):
+            for item in content:
+                if item.get("type") != "tool_result":
+                    continue
+                if tool_call_id := item.get("tool_use_id"):
+                    tool_call_ids.append(tool_call_id)
+
+    return tool_call_ids
+
+
+def _create_tool_response(call_id: str, output: dict[str, Any]) -> dict[str, Any]:
+    """
+    Create a tool response message for either OpenAI or Anthropics,
+    based on the tool_id format.
+    """
+    content = output if isinstance(output, str) else json.dumps(output)
+
+    # Anthropics format: tool IDs typically start with "toolu_"
+    if call_id.startswith("toolu_"):
+        return {
+            "role": "user",
+            "type": "message",
+            "content": [
+                {"tool_use_id": call_id, "type": "tool_result", "content": content}
+            ],
+        }
+
+    # OpenAI format: tool IDs typically start with "call_".
+    # Or default fallback (if the tool_id doesn't match any known prefix)
+    return {"role": "tool", "tool_call_id": call_id, "content": content}
+
+
+def get_pending_tool_calls(conversation_history: list[Any]) -> dict[str, int]:
+    """
+    All the tool calls entry in the conversation history requires a response.
+    This function returns the pending tool calls that has not generated an output yet.
+
+    Return: dict[str, int] - A dictionary of pending tool call IDs with their count.
+    """
+    pending_calls = Counter()
+    for history in conversation_history:
+        for call_id in _get_tool_requests(history):
+            pending_calls[call_id] += 1
+
+        for call_id in _get_tool_responses(history):
+            pending_calls[call_id] -= 1
+
+    return {call_id: count for call_id, count in pending_calls.items() if count > 0}
+
+
 class SmartDecisionMakerBlock(Block):
     """
     A block that uses a language model to make smart decisions based on a given prompt.
@@ -50,13 +144,23 @@ class SmartDecisionMakerBlock(Block):
         credentials: llm.AICredentials = llm.AICredentialsField()
         sys_prompt: str = SchemaField(
             title="System Prompt",
-            default="Thinking carefully step by step decide which function to call. Always choose a function call from the list of function signatures.",
+            default="Thinking carefully step by step decide which function to call. "
+            "Always choose a function call from the list of function signatures, "
+            "and always provide the complete argument provided with the type "
+            "matching the required jsonschema signature, no missing argument is allowed. "
+            "If you have already completed the task objective, you can end the task "
+            "by providing the end result of your work as a finish message. "
+            "Only provide EXACTLY one function call, multiple tool calls is strictly prohibited.",
             description="The system prompt to provide additional context to the model.",
         )
-        conversation_history: list[llm.Message] = SchemaField(
+        conversation_history: list[dict] = SchemaField(
             default=[],
             description="The conversation history to provide context for the prompt.",
         )
+        last_tool_output: Any = SchemaField(
+            default=None,
+            description="The output of the last tool that was called.",
+        )
         retry: int = SchemaField(
             title="Retry Count",
             default=3,
@@ -78,17 +182,53 @@ class SmartDecisionMakerBlock(Block):
             description="Ollama host for local  models",
         )
 
-    class Output(BlockSchema):
+        @classmethod
+        def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
+            # conversation_history & last_tool_output validation is handled differently
+            missing_links = super().get_missing_links(
+                data,
+                [
+                    link
+                    for link in links
+                    if link.sink_name
+                    not in ["conversation_history", "last_tool_output"]
+                ],
+            )
 
-        prompt: str = SchemaField(description="The prompt sent to the language model.")
+            # Avoid executing the block if the last_tool_output is connected to a static
+            # link, like StoreValueBlock or AgentInputBlock.
+            if any(link.sink_name == "conversation_history" for link in links) and any(
+                link.sink_name == "last_tool_output" and link.is_static
+                for link in links
+            ):
+                raise ValueError(
+                    "Last Tool Output can't be connected to a static (dashed line) "
+                    "link like the output of `StoreValue` or `AgentInput` block"
+                )
+
+            return missing_links
+
+        @classmethod
+        def get_missing_input(cls, data: BlockInput) -> set[str]:
+            if missing_input := super().get_missing_input(data):
+                return missing_input
+
+            conversation_history = data.get("conversation_history", [])
+            pending_tool_calls = get_pending_tool_calls(conversation_history)
+            last_tool_output = data.get("last_tool_output")
+            if not last_tool_output and pending_tool_calls:
+                return {"last_tool_output"}
+            return set()
+
+    class Output(BlockSchema):
         error: str = SchemaField(description="Error message if the API call failed.")
-        function_signatures: list[dict[str, Any]] = SchemaField(
-            description="The function signatures that are sent to the language model."
-        )
         tools: Any = SchemaField(description="The tools that are available to use.")
         finished: str = SchemaField(
             description="The finished message to display to the user."
         )
+        conversations: list[Any] = SchemaField(
+            description="The conversation history to provide context for the prompt."
+        )
 
     def __init__(self):
         super().__init__(
@@ -106,41 +246,6 @@ class SmartDecisionMakerBlock(Block):
             test_credentials=llm.TEST_CREDENTIALS,
         )
 
-    # If I import Graph here, it will break with a circular import.
-    def _get_tool_graph_metadata(self, node_id: str, graph: "Graph") -> List["Graph"]:
-        """
-        Retrieves metadata for tool graphs linked to a specified node within a graph.
-
-        This method identifies the tool links connected to the given node_id and fetches
-        the metadata for each linked tool graph from the database.
-
-        Args:
-            node_id (str): The ID of the node for which tool graph metadata is to be retrieved.
-            graph (Any): The graph object containing nodes and links.
-
-        Returns:
-            List[Any]: A list of metadata for the tool graphs linked to the specified node.
-        """
-        db_client = get_database_manager_client()
-        graph_meta = []
-
-        tool_links = {
-            link.sink_id
-            for link in graph.links
-            if link.source_name.startswith("tools_^_") and link.source_id == node_id
-        }
-
-        for link_id in tool_links:
-            node = next((node for node in graph.nodes if node.id == link_id), None)
-            if node and node.block_id == AgentExecutorBlock().id:
-                node_graph_meta = db_client.get_graph_metadata(
-                    node.input_default["graph_id"], node.input_default["graph_version"]
-                )
-                if node_graph_meta:
-                    graph_meta.append(node_graph_meta)
-
-        return graph_meta
-
     @staticmethod
     def _create_block_function_signature(
         sink_node: "Node", links: list["Link"]
@@ -158,20 +263,18 @@ class SmartDecisionMakerBlock(Block):
         Raises:
             ValueError: If the block specified by sink_node.block_id is not found.
         """
-        block = get_blocks()[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(),
-            "description": block().description,
+            "name": re.sub(r"[^a-zA-Z0-9_-]", "_", block.name).lower(),
+            "description": block.description,
         }
 
         properties = {}
         required = []
 
         for link in links:
-            sink_block_input_schema = block().input_schema
+            sink_block_input_schema = block.input_schema
             description = (
                 sink_block_input_schema.model_fields[link.sink_name].description
                 if link.sink_name in sink_block_input_schema.model_fields
@@ -195,7 +298,7 @@ class SmartDecisionMakerBlock(Block):
 
     @staticmethod
     def _create_agent_function_signature(
-        sink_node: "Node", links: list["Link"], tool_graph_metadata: list["Graph"]
+        sink_node: "Node", links: list["Link"]
     ) -> dict[str, Any]:
         """
         Creates a function signature for an agent node.
@@ -203,7 +306,6 @@ class SmartDecisionMakerBlock(Block):
         Args:
             sink_node: The agent node for which to create a function signature.
             links: The list of links connected to the sink node.
-            tool_graph_metadata: List of metadata for available tool graphs.
 
         Returns:
             A dictionary representing the function signature in the format expected by LLM tools.
@@ -211,18 +313,13 @@ class SmartDecisionMakerBlock(Block):
         Raises:
             ValueError: If the graph metadata for the specified graph_id and graph_version is not found.
         """
-        graph_id = sink_node.input_default["graph_id"]
-        graph_version = sink_node.input_default["graph_version"]
-
-        sink_graph_meta = next(
-            (
-                meta
-                for meta in tool_graph_metadata
-                if meta.id == graph_id and meta.version == graph_version
-            ),
-            None,
-        )
+        graph_id = sink_node.input_default.get("graph_id")
+        graph_version = sink_node.input_default.get("graph_version")
+        if not graph_id or not graph_version:
+            raise ValueError("Graph ID or Graph Version not found in sink node.")
 
+        db_client = get_database_manager_client()
+        sink_graph_meta = db_client.get_graph_metadata(graph_id, graph_version)
         if not sink_graph_meta:
             raise ValueError(
                 f"Sink graph metadata not found: {graph_id} {graph_version}"
@@ -260,12 +357,7 @@ class SmartDecisionMakerBlock(Block):
         return {"type": "function", "function": tool_function}
 
     @staticmethod
-    def _create_function_signature(
-        # If I import Graph here, it will break with a circular import.
-        node_id: str,
-        graph: "Graph",
-        tool_graph_metadata: List["Graph"],
-    ) -> list[dict[str, Any]]:
+    def _create_function_signature(node_id: str) -> list[dict[str, Any]]:
         """
         Creates function signatures for tools linked to a specified node within a graph.
 
@@ -274,10 +366,7 @@ class SmartDecisionMakerBlock(Block):
         tool based on the metadata and input schema of the linked nodes.
 
         Args:
-            node_id (str): The ID of the node for which tool function signatures are to be created.
-            graph (Any): The graph object containing nodes and links.
-            tool_graph_metadata (List[Any]): Metadata for the tool graphs, used to retrieve
-                                             names and descriptions for the tools.
+            node_id: The node_id for which to create function signatures.
 
         Returns:
             list[dict[str, Any]]: A list of dictionaries, each representing a function signature
@@ -287,44 +376,32 @@ class SmartDecisionMakerBlock(Block):
             ValueError: If no tool links are found for the specified node_id, or if a sink node
                         or its metadata cannot be found.
         """
-        # Filter the graph links to find those that are tools and are linked to the specified node_id
-        tool_links = [
-            link
-            for link in graph.links
-            # NOTE: Maybe we can do a specific database call to only get relevant nodes
-            # async def get_connected_output_nodes(source_node_id: str) -> list[Node]:
-            #     links = await AgentNodeLink.prisma().find_many(
-            #         where={"agentNodeSourceId": source_node_id},
-            #         include={"AgentNode": {"include": AGENT_NODE_INCLUDE}},
-            #     )
-            #     return [NodeModel.from_db(link.AgentNodeSink) for link in links]
+        db_client = get_database_manager_client()
+        tools = [
+            (link, node)
+            for link, node in db_client.get_connected_output_nodes(node_id)
             if link.source_name.startswith("tools_^_") and link.source_id == node_id
         ]
-
-        if not tool_links:
-            raise ValueError(
-                f"Expected at least one tool link in the graph. Node ID: {node_id}. Graph: {graph.links}"
-            )
+        if not tools:
+            raise ValueError("There is no next node to execute.")
 
         return_tool_functions = []
 
-        grouped_tool_links = {}
-
-        for link in tool_links:
-            grouped_tool_links.setdefault(link.sink_id, []).append(link)
-
-        for _, links in grouped_tool_links.items():
-            sink_node = next(
-                (node for node in graph.nodes if node.id == links[0].sink_id), None
-            )
+        grouped_tool_links: dict[str, tuple["Node", list["Link"]]] = {}
+        for link, node in tools:
+            if link.sink_id not in grouped_tool_links:
+                grouped_tool_links[link.sink_id] = (node, [link])
+            else:
+                grouped_tool_links[link.sink_id][1].append(link)
 
+        for sink_node, links in grouped_tool_links.values():
             if not sink_node:
                 raise ValueError(f"Sink node not found: {links[0].sink_id}")
 
             if sink_node.block_id == AgentExecutorBlock().id:
                 return_tool_functions.append(
                     SmartDecisionMakerBlock._create_agent_function_signature(
-                        sink_node, links, tool_graph_metadata
+                        sink_node, links
                     )
                 )
             else:
@@ -348,34 +425,63 @@ class SmartDecisionMakerBlock(Block):
         user_id: str,
         **kwargs,
     ) -> BlockOutput:
-        db_client = get_database_manager_client()
+        tool_functions = self._create_function_signature(node_id)
 
-        # Retrieve the current graph and node details
-        graph = db_client.get_graph(graph_id=graph_id, user_id=user_id)
+        input_data.conversation_history = input_data.conversation_history or []
+        prompt = [json.to_dict(p) for p in input_data.conversation_history if p]
 
-        if not graph:
-            raise ValueError(
-                f"The currently running graph that is executing this node is not found {graph_id}"
+        pending_tool_calls = get_pending_tool_calls(input_data.conversation_history)
+        if pending_tool_calls and not input_data.last_tool_output:
+            raise ValueError(f"Tool call requires an output for {pending_tool_calls}")
+
+        # Prefill all missing tool calls with the last tool output/
+        # TODO: we need a better way to handle this.
+        tool_output = [
+            _create_tool_response(pending_call_id, input_data.last_tool_output)
+            for pending_call_id, count in pending_tool_calls.items()
+            for _ in range(count)
+        ]
+
+        # If the SDM block only calls 1 tool at a time, this should not happen.
+        if len(tool_output) > 1:
+            logger.warning(
+                f"[SmartDecisionMakerBlock-node_exec_id={node_exec_id}] "
+                f"Multiple pending tool calls are prefilled using a single output. "
+                f"Execution may not be accurate."
             )
 
-        tool_graph_metadata = self._get_tool_graph_metadata(node_id, graph)
+        # Fallback on adding tool output in the conversation history as user prompt.
+        if len(tool_output) == 0 and input_data.last_tool_output:
+            logger.warning(
+                f"[SmartDecisionMakerBlock-node_exec_id={node_exec_id}] "
+                f"No pending tool calls found. This may indicate an issue with the "
+                f"conversation history, or an LLM calling two tools at the same time."
+            )
+            tool_output.append(
+                {
+                    "role": "user",
+                    "content": f"Last tool output: {json.dumps(input_data.last_tool_output)}",
+                }
+            )
 
-        tool_functions = self._create_function_signature(
-            node_id, graph, tool_graph_metadata
-        )
-
-        prompt = [p.model_dump() for p in input_data.conversation_history]
+        prompt.extend(tool_output)
 
         values = input_data.prompt_values
         if values:
             input_data.prompt = llm.fmt.format_string(input_data.prompt, values)
             input_data.sys_prompt = llm.fmt.format_string(input_data.sys_prompt, values)
 
-        if input_data.sys_prompt:
-            prompt.append({"role": "system", "content": input_data.sys_prompt})
+        prefix = "[Main Objective Prompt]: "
 
-        if input_data.prompt:
-            prompt.append({"role": "user", "content": input_data.prompt})
+        if input_data.sys_prompt and not any(
+            p["role"] == "system" and p["content"].startswith(prefix) for p in prompt
+        ):
+            prompt.append({"role": "system", "content": prefix + input_data.sys_prompt})
+
+        if input_data.prompt and not any(
+            p["role"] == "user" and p["content"].startswith(prefix) for p in prompt
+        ):
+            prompt.append({"role": "user", "content": prefix + input_data.prompt})
 
         response = llm.llm_call(
             credentials=credentials,
@@ -388,13 +494,15 @@ class SmartDecisionMakerBlock(Block):
         )
 
         if not response.tool_calls:
+            yield "finished", response.response
+            return
 
-            yield "finished", f"No Decision Made finishing task: {response.response}"
+        for tool_call in response.tool_calls:
+            tool_name = tool_call.function.name
+            tool_args = json.loads(tool_call.function.arguments)
 
-        if response.tool_calls:
-            for tool_call in response.tool_calls:
-                tool_name = tool_call.function.name
-                tool_args = json.loads(tool_call.function.arguments)
+            for arg_name, arg_value in tool_args.items():
+                yield f"tools_^_{tool_name}_{arg_name}".lower(), arg_value
 
-                for arg_name, arg_value in tool_args.items():
-                    yield f"tools_^_{tool_name}_{arg_name}".lower(), arg_value
+        response.prompt.append(response.raw_response)
+        yield "conversations", response.prompt

autogpt_platform/backend/backend/blocks/time_blocks.py

@@ -156,6 +156,10 @@ class CountdownTimerBlock(Block):
         days: Union[int, str] = SchemaField(
             advanced=False, description="Duration in days", default=0
         )
+        repeat: int = SchemaField(
+            description="Number of times to repeat the timer",
+            default=1,
+        )
 
     class Output(BlockSchema):
         output_message: Any = SchemaField(
@@ -187,5 +191,6 @@ class CountdownTimerBlock(Block):
 
         total_seconds = seconds + minutes * 60 + hours * 3600 + days * 86400
 
-        time.sleep(total_seconds)
-        yield "output_message", input_data.input_message
+        for _ in range(input_data.repeat):
+            time.sleep(total_seconds)
+            yield "output_message", input_data.input_message

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

@@ -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/cli.py

@@ -220,9 +220,8 @@ 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 +229,20 @@ 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

@@ -2,6 +2,7 @@ import inspect
 from abc import ABC, abstractmethod
 from enum import Enum
 from typing import (
+    TYPE_CHECKING,
     Any,
     ClassVar,
     Generator,
@@ -16,8 +17,11 @@ 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
 
@@ -28,6 +32,9 @@ from .model import (
     is_credentials_field_name,
 )
 
+if TYPE_CHECKING:
+    from .graph import Link
+
 app_config = Config()
 
 BlockData = tuple[str, Any]  # Input & Output data should be a tuple of (name, data).
@@ -110,21 +117,30 @@ class BlockSchema(BaseModel):
     def validate_data(cls, data: BlockInput) -> str | None:
         return json.validate_with_jsonschema(schema=cls.jsonschema(), data=data)
 
+    @classmethod
+    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:
@@ -187,6 +203,19 @@ class BlockSchema(BaseModel):
             )
         }
 
+    @classmethod
+    def get_input_defaults(cls, data: BlockInput) -> BlockInput:
+        return data  # Return as is, by default.
+
+    @classmethod
+    def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
+        input_fields_from_nodes = {link.sink_name for link in links}
+        return input_fields_from_nodes - set(data)
+
+    @classmethod
+    def get_missing_input(cls, data: BlockInput) -> set[str]:
+        return cls.get_required_fields() - set(data)
+
 
 BlockSchemaInputType = TypeVar("BlockSchemaInputType", bound=BlockSchema)
 BlockSchemaOutputType = TypeVar("BlockSchemaOutputType", bound=BlockSchema)
@@ -203,7 +232,7 @@ 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
@@ -295,7 +324,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.static_output = static_output
         self.block_type = block_type
         self.webhook_config = webhook_config
-        self.execution_stats = {}
+        self.execution_stats: NodeExecutionStats = NodeExecutionStats()
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -373,18 +402,29 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                 return data
         raise ValueError(f"{self.name} did not produce any output for {output}")
 
-    def merge_stats(self, stats: dict[str, Any]) -> dict[str, Any]:
-        for key, value in stats.items():
-            if isinstance(value, dict):
-                self.execution_stats.setdefault(key, {}).update(value)
-            elif isinstance(value, (int, float)):
-                self.execution_stats.setdefault(key, 0)
-                self.execution_stats[key] += value
-            elif isinstance(value, list):
-                self.execution_stats.setdefault(key, [])
-                self.execution_stats[key].extend(value)
+    def merge_stats(self, stats: NodeExecutionStats) -> NodeExecutionStats:
+        stats_dict = stats.model_dump()
+        current_stats = self.execution_stats.model_dump()
+
+        for key, value in stats_dict.items():
+            if key not in current_stats:
+                # Field doesn't exist yet, just set it, but this will probably
+                # not happen, just in case though so we throw for invalid when
+                # converting back in
+                current_stats[key] = value
+            elif isinstance(value, dict) and isinstance(current_stats[key], dict):
+                current_stats[key].update(value)
+            elif isinstance(value, (int, float)) and isinstance(
+                current_stats[key], (int, float)
+            ):
+                current_stats[key] += value
+            elif isinstance(value, list) and isinstance(current_stats[key], list):
+                current_stats[key].extend(value)
             else:
-                self.execution_stats[key] = value
+                current_stats[key] = value
+
+        self.execution_stats = NodeExecutionStats(**current_stats)
+
         return self.execution_stats
 
     @property
@@ -407,7 +447,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         }
 
     def execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
-        # Merge the input data with the extra execution arguments, preferring the args for security
         if error := self.input_schema.validate_data(input_data):
             raise ValueError(
                 f"Unable to execute block with invalid input data: {error}"
@@ -429,9 +468,9 @@ 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:
@@ -442,12 +481,12 @@ 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
 

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -15,6 +15,7 @@ from backend.blocks.llm import (
     LlmModel,
 )
 from backend.blocks.replicate_flux_advanced import ReplicateFluxAdvancedModelBlock
+from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
 from backend.blocks.text_to_speech_block import UnrealTextToSpeechBlock
 from backend.data.block import Block
@@ -265,4 +266,5 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
             },
         )
     ],
+    SmartDecisionMakerBlock: LLM_COST,
 }

autogpt_platform/backend/backend/data/credit.py

@@ -14,15 +14,16 @@ from prisma.enums import (
 )
 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,
@@ -31,13 +32,16 @@ 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
 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):
@@ -89,20 +93,20 @@ 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
 
@@ -184,6 +188,14 @@ 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)
@@ -249,7 +261,6 @@ class UserCreditBase(ABC):
         metadata: Json,
         new_transaction_key: str | None = None,
     ):
-
         transaction = await CreditTransaction.prisma().find_first_or_raise(
             where={"transactionKey": transaction_key, "userId": user_id}
         )
@@ -314,39 +325,32 @@ class UserCreditBase(ABC):
 
             if amount < 0 and user_balance + amount < 0:
                 if fail_insufficient_credits:
-                    raise ValueError(
-                        f"Insufficient balance of ${user_balance/100}, where this will cost ${abs(amount)/100}"
+                    raise InsufficientBalanceError(
+                        message=f"Insufficient balance of ${user_balance/100}, where this will cost ${abs(amount)/100}",
+                        user_id=user_id,
+                        balance=user_balance,
+                        amount=amount,
                     )
+
                 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:
         return get_service_client(NotificationManager)
@@ -366,89 +370,21 @@ 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)
@@ -458,7 +394,7 @@ 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:
@@ -467,7 +403,7 @@ 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)
@@ -490,15 +426,15 @@ 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(
@@ -762,10 +698,8 @@ 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,
         )
 
@@ -840,7 +774,6 @@ class UserCredit(UserCreditBase):
         transaction_time_ceiling: datetime | None = None,
         transaction_type: str | None = None,
     ) -> TransactionHistory:
-
         transactions_filter: CreditTransactionWhereInput = {
             "userId": user_id,
             "isActive": True,

autogpt_platform/backend/backend/data/db.py

@@ -2,6 +2,7 @@ import logging
 import os
 import zlib
 from contextlib import asynccontextmanager
+from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
 from uuid import uuid4
 
 from dotenv import load_dotenv
@@ -15,7 +16,36 @@ load_dotenv()
 PRISMA_SCHEMA = os.getenv("PRISMA_SCHEMA", "schema.prisma")
 os.environ["PRISMA_SCHEMA_PATH"] = PRISMA_SCHEMA
 
-prisma = Prisma(auto_register=True)
+
+def add_param(url: str, key: str, value: str) -> str:
+    p = urlparse(url)
+    qs = dict(parse_qsl(p.query))
+    qs[key] = value
+    return urlunparse(p._replace(query=urlencode(qs)))
+
+
+DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://localhost:5432")
+
+CONN_LIMIT = os.getenv("DB_CONNECTION_LIMIT")
+if CONN_LIMIT:
+    DATABASE_URL = add_param(DATABASE_URL, "connection_limit", CONN_LIMIT)
+
+CONN_TIMEOUT = os.getenv("DB_CONNECT_TIMEOUT")
+if CONN_TIMEOUT:
+    DATABASE_URL = add_param(DATABASE_URL, "connect_timeout", CONN_TIMEOUT)
+
+POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
+if POOL_TIMEOUT:
+    DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
+
+HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
+
+prisma = Prisma(
+    auto_register=True,
+    http={"timeout": HTTP_TIMEOUT},
+    datasource={"url": DATABASE_URL},
+)
+
 
 logger = logging.getLogger(__name__)
 

autogpt_platform/backend/backend/data/graph.py

@@ -1,37 +1,33 @@
-import asyncio
 import logging
 import uuid
 from collections import defaultdict
-from datetime import datetime, timezone
 from typing import Any, Literal, Optional, Type
 
 import prisma
 from prisma import Json
-from prisma.models import (
-    AgentGraph,
-    AgentGraphExecution,
-    AgentNode,
-    AgentNodeLink,
-    StoreListingVersion,
+from prisma.enums import SubmissionStatus
+from prisma.models import AgentGraph, AgentNode, AgentNodeLink, StoreListingVersion
+from prisma.types import (
+    AgentGraphCreateInput,
+    AgentGraphWhereInput,
+    AgentNodeCreateInput,
+    AgentNodeLinkCreateInput,
 )
-from prisma.types import AgentGraphWhereInput
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
-from backend.blocks.basic import AgentInputBlock, AgentOutputBlock
-from backend.util import type
+from backend.blocks.io import AgentInputBlock, AgentOutputBlock
+from backend.blocks.llm import LlmModel
+from backend.data.db import prisma as db
+from backend.util import type as type_utils
 
-from .block import BlockInput, BlockType, get_block, get_blocks
+from .block import Block, BlockInput, BlockSchema, BlockType, get_block, get_blocks
 from .db import BaseDbModel, transaction
-from .execution import ExecutionResult, ExecutionStatus
 from .includes import AGENT_GRAPH_INCLUDE, AGENT_NODE_INCLUDE
 from .integrations import Webhook
 
 logger = logging.getLogger(__name__)
 
-_INPUT_BLOCK_ID = AgentInputBlock().id
-_OUTPUT_BLOCK_ID = AgentOutputBlock().id
-
 
 class Link(BaseDbModel):
     source_id: str
@@ -62,22 +58,30 @@ class Node(BaseDbModel):
     input_links: list[Link] = []
     output_links: list[Link] = []
 
-    webhook_id: Optional[str] = None
+    @property
+    def block(self) -> Block[BlockSchema, BlockSchema]:
+        block = get_block(self.block_id)
+        if not block:
+            raise ValueError(
+                f"Block #{self.block_id} does not exist -> Node #{self.id} is invalid"
+            )
+        return block
 
 
 class NodeModel(Node):
     graph_id: str
     graph_version: int
 
+    webhook_id: Optional[str] = None
     webhook: Optional[Webhook] = None
 
     @staticmethod
-    def from_db(node: AgentNode):
+    def from_db(node: AgentNode, for_export: bool = False) -> "NodeModel":
         obj = NodeModel(
             id=node.id,
             block_id=node.agentBlockId,
-            input_default=type.convert(node.constantInput, dict[str, Any]),
-            metadata=type.convert(node.metadata, dict[str, Any]),
+            input_default=type_utils.convert(node.constantInput, dict[str, Any]),
+            metadata=type_utils.convert(node.metadata, dict[str, Any]),
             graph_id=node.agentGraphId,
             graph_version=node.agentGraphVersion,
             webhook_id=node.webhookId,
@@ -85,11 +89,12 @@ class NodeModel(Node):
         )
         obj.input_links = [Link.from_db(link) for link in node.Input or []]
         obj.output_links = [Link.from_db(link) for link in node.Output or []]
+        if for_export:
+            return obj.stripped_for_export()
         return obj
 
     def is_triggered_by_event_type(self, event_type: str) -> bool:
-        if not (block := get_block(self.block_id)):
-            raise ValueError(f"Block #{self.block_id} not found for node #{self.id}")
+        block = self.block
         if not block.webhook_config:
             raise TypeError("This method can't be used on non-webhook blocks")
         if not block.webhook_config.event_filter_input:
@@ -103,104 +108,59 @@ class NodeModel(Node):
             if event_filter[k] is True
         ]
 
+    def stripped_for_export(self) -> "NodeModel":
+        """
+        Returns a copy of the node model, stripped of any non-transferable properties
+        """
+        stripped_node = self.model_copy(deep=True)
+        # Remove credentials from node input
+        if stripped_node.input_default:
+            stripped_node.input_default = NodeModel._filter_secrets_from_node_input(
+                stripped_node.input_default, self.block.input_schema.jsonschema()
+            )
+
+        if (
+            stripped_node.block.block_type == BlockType.INPUT
+            and "value" in stripped_node.input_default
+        ):
+            stripped_node.input_default["value"] = ""
+
+        # Remove webhook info
+        stripped_node.webhook_id = None
+        stripped_node.webhook = None
+
+        return stripped_node
+
+    @staticmethod
+    def _filter_secrets_from_node_input(
+        input_data: dict[str, Any], schema: dict[str, Any] | None
+    ) -> dict[str, Any]:
+        sensitive_keys = ["credentials", "api_key", "password", "token", "secret"]
+        field_schemas = schema.get("properties", {}) if schema else {}
+        result = {}
+        for key, value in input_data.items():
+            field_schema: dict | None = field_schemas.get(key)
+            if (field_schema and field_schema.get("secret", False)) or any(
+                sensitive_key in key.lower() for sensitive_key in sensitive_keys
+            ):
+                # This is a secret value -> filter this key-value pair out
+                continue
+            elif isinstance(value, dict):
+                result[key] = NodeModel._filter_secrets_from_node_input(
+                    value, field_schema
+                )
+            else:
+                result[key] = value
+        return result
+
 
 # Fix 2-way reference Node <-> Webhook
 Webhook.model_rebuild()
 
 
-class GraphExecutionMeta(BaseDbModel):
-    execution_id: str
-    started_at: datetime
-    ended_at: datetime
-    duration: float
-    total_run_time: float
-    status: ExecutionStatus
-    graph_id: str
-    graph_version: int
-    preset_id: Optional[str]
-
-    @staticmethod
-    def from_db(_graph_exec: AgentGraphExecution):
-        now = datetime.now(timezone.utc)
-        start_time = _graph_exec.startedAt or _graph_exec.createdAt
-        end_time = _graph_exec.updatedAt or now
-        duration = (end_time - start_time).total_seconds()
-        total_run_time = duration
-
-        try:
-            stats = type.convert(_graph_exec.stats or {}, dict[str, Any])
-        except ValueError:
-            stats = {}
-
-        duration = stats.get("walltime", duration)
-        total_run_time = stats.get("nodes_walltime", total_run_time)
-
-        return GraphExecutionMeta(
-            id=_graph_exec.id,
-            execution_id=_graph_exec.id,
-            started_at=start_time,
-            ended_at=end_time,
-            duration=duration,
-            total_run_time=total_run_time,
-            status=ExecutionStatus(_graph_exec.executionStatus),
-            graph_id=_graph_exec.agentGraphId,
-            graph_version=_graph_exec.agentGraphVersion,
-            preset_id=_graph_exec.agentPresetId,
-        )
-
-
-class GraphExecution(GraphExecutionMeta):
-    inputs: dict[str, Any]
-    outputs: dict[str, list[Any]]
-    node_executions: list[ExecutionResult]
-
-    @staticmethod
-    def from_db(_graph_exec: AgentGraphExecution):
-        if _graph_exec.AgentNodeExecutions is None:
-            raise ValueError("Node executions must be included in query")
-
-        graph_exec = GraphExecutionMeta.from_db(_graph_exec)
-
-        node_executions = [
-            ExecutionResult.from_db(ne) for ne in _graph_exec.AgentNodeExecutions
-        ]
-
-        inputs = {
-            **{
-                # inputs from Agent Input Blocks
-                exec.input_data["name"]: exec.input_data["value"]
-                for exec in node_executions
-                if exec.block_id == _INPUT_BLOCK_ID
-            },
-            **{
-                # input from webhook-triggered block
-                "payload": exec.input_data["payload"]
-                for exec in node_executions
-                if (block := get_block(exec.block_id))
-                and block.block_type in [BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL]
-            },
-        }
-
-        outputs: dict[str, list] = defaultdict(list)
-        for exec in node_executions:
-            if exec.block_id == _OUTPUT_BLOCK_ID:
-                outputs[exec.input_data["name"]].append(exec.input_data["value"])
-
-        return GraphExecution(
-            **{
-                field_name: getattr(graph_exec, field_name)
-                for field_name in graph_exec.model_fields
-            },
-            inputs=inputs,
-            outputs=outputs,
-            node_executions=node_executions,
-        )
-
-
-class Graph(BaseDbModel):
+class BaseGraph(BaseDbModel):
     version: int = 1
     is_active: bool = True
-    is_template: bool = False
     name: str
     description: str
     nodes: list[Node] = []
@@ -210,46 +170,46 @@ class Graph(BaseDbModel):
     @property
     def input_schema(self) -> dict[str, Any]:
         return self._generate_schema(
-            AgentInputBlock.Input,
-            [
-                node.input_default
+            *(
+                (block.input_schema, node.input_default)
                 for node in self.nodes
-                if (b := get_block(node.block_id))
-                and b.block_type == BlockType.INPUT
-                and "name" in node.input_default
-            ],
+                if (block := node.block).block_type == BlockType.INPUT
+                and issubclass(block.input_schema, AgentInputBlock.Input)
+            )
         )
 
     @computed_field
     @property
     def output_schema(self) -> dict[str, Any]:
         return self._generate_schema(
-            AgentOutputBlock.Input,
-            [
-                node.input_default
+            *(
+                (block.input_schema, node.input_default)
                 for node in self.nodes
-                if (b := get_block(node.block_id))
-                and b.block_type == BlockType.OUTPUT
-                and "name" in node.input_default
-            ],
+                if (block := node.block).block_type == BlockType.OUTPUT
+                and issubclass(block.input_schema, AgentOutputBlock.Input)
+            )
         )
 
     @staticmethod
     def _generate_schema(
-        type_class: Type[AgentInputBlock.Input] | Type[AgentOutputBlock.Input],
-        data: list[dict],
+        *props: tuple[Type[AgentInputBlock.Input] | Type[AgentOutputBlock.Input], dict],
     ) -> dict[str, Any]:
-        props = []
-        for p in data:
+        schema = []
+        for type_class, input_default in props:
             try:
-                props.append(type_class(**p))
+                schema.append(type_class(**input_default))
             except Exception as e:
-                logger.warning(f"Invalid {type_class}: {p}, {e}")
+                logger.warning(f"Invalid {type_class}: {input_default}, {e}")
 
         return {
             "type": "object",
             "properties": {
                 p.name: {
+                    **{
+                        k: v
+                        for k, v in p.generate_schema().items()
+                        if k not in ["description", "default"]
+                    },
                     "secret": p.secret,
                     # Default value has to be set for advanced fields.
                     "advanced": p.advanced and p.value is not None,
@@ -257,23 +217,30 @@ class Graph(BaseDbModel):
                     **({"description": p.description} if p.description else {}),
                     **({"default": p.value} if p.value is not None else {}),
                 }
-                for p in props
+                for p in schema
             },
-            "required": [p.name for p in props if p.value is None],
+            "required": [p.name for p in schema if p.value is None],
         }
 
 
+class Graph(BaseGraph):
+    sub_graphs: list[BaseGraph] = []  # Flattened sub-graphs, only used in export
+
+
 class GraphModel(Graph):
     user_id: str
     nodes: list[NodeModel] = []  # type: ignore
 
+    @computed_field
     @property
-    def starting_nodes(self) -> list[Node]:
+    def has_webhook_trigger(self) -> bool:
+        return self.webhook_input_node is not None
+
+    @property
+    def starting_nodes(self) -> list[NodeModel]:
         outbound_nodes = {link.sink_id for link in self.links}
         input_nodes = {
-            v.id
-            for v in self.nodes
-            if (b := get_block(v.block_id)) and b.block_type == BlockType.INPUT
+            node.id for node in self.nodes if node.block.block_type == BlockType.INPUT
         }
         return [
             node
@@ -281,36 +248,71 @@ class GraphModel(Graph):
             if node.id not in outbound_nodes or node.id in input_nodes
         ]
 
+    @property
+    def webhook_input_node(self) -> NodeModel | None:
+        return next(
+            (
+                node
+                for node in self.nodes
+                if node.block.block_type
+                in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
+            ),
+            None,
+        )
+
     def reassign_ids(self, user_id: str, reassign_graph_id: bool = False):
         """
         Reassigns all IDs in the graph to new UUIDs.
         This method can be used before storing a new graph to the database.
         """
-
-        # Reassign Graph ID
-        id_map = {node.id: str(uuid.uuid4()) for node in self.nodes}
         if reassign_graph_id:
-            self.id = str(uuid.uuid4())
+            graph_id_map = {
+                self.id: str(uuid.uuid4()),
+                **{sub_graph.id: str(uuid.uuid4()) for sub_graph in self.sub_graphs},
+            }
+        else:
+            graph_id_map = {}
+
+        self._reassign_ids(self, user_id, graph_id_map)
+        for sub_graph in self.sub_graphs:
+            self._reassign_ids(sub_graph, user_id, graph_id_map)
+
+    @staticmethod
+    def _reassign_ids(
+        graph: BaseGraph,
+        user_id: str,
+        graph_id_map: dict[str, str],
+    ):
+        # Reassign Graph ID
+        if graph.id in graph_id_map:
+            graph.id = graph_id_map[graph.id]
 
         # Reassign Node IDs
-        for node in self.nodes:
+        id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
+        for node in graph.nodes:
             node.id = id_map[node.id]
 
         # Reassign Link IDs
-        for link in self.links:
+        for link in graph.links:
             link.source_id = id_map[link.source_id]
             link.sink_id = id_map[link.sink_id]
 
         # Reassign User IDs for agent blocks
-        for node in self.nodes:
+        for node in graph.nodes:
             if node.block_id != AgentExecutorBlock().id:
                 continue
             node.input_default["user_id"] = user_id
             node.input_default.setdefault("data", {})
-
-        self.validate_graph()
+            if (graph_id := node.input_default.get("graph_id")) in graph_id_map:
+                node.input_default["graph_id"] = graph_id_map[graph_id]
 
     def validate_graph(self, for_run: bool = False):
+        self._validate_graph(self, for_run)
+        for sub_graph in self.sub_graphs:
+            self._validate_graph(sub_graph, for_run)
+
+    @staticmethod
+    def _validate_graph(graph: BaseGraph, for_run: bool = False):
         def sanitize(name):
             sanitized_name = name.split("_#_")[0].split("_@_")[0].split("_$_")[0]
             if sanitized_name.startswith("tools_^_"):
@@ -322,11 +324,11 @@ class GraphModel(Graph):
         agent_nodes = set()
         nodes_block = {
             node.id: block
-            for node in self.nodes
+            for node in graph.nodes
             if (block := get_block(node.block_id)) is not None
         }
 
-        for node in self.nodes:
+        for node in graph.nodes:
             if (block := nodes_block.get(node.id)) is None:
                 raise ValueError(f"Invalid block {node.block_id} for node #{node.id}")
 
@@ -339,11 +341,11 @@ class GraphModel(Graph):
 
         input_links = defaultdict(list)
 
-        for link in self.links:
+        for link in graph.links:
             input_links[link.sink_id].append(link)
 
         # Nodes: required fields are filled or connected and dependencies are satisfied
-        for node in self.nodes:
+        for node in graph.nodes:
             if (block := nodes_block.get(node.id)) is None:
                 raise ValueError(f"Invalid block {node.block_id} for node #{node.id}")
 
@@ -361,10 +363,12 @@ class GraphModel(Graph):
                     )
                     and (
                         for_run  # Skip input completion validation, unless when executing.
-                        or block.block_type == BlockType.INPUT
-                        or block.block_type == BlockType.OUTPUT
-                        or block.block_type == BlockType.AGENT
-                        or block.block_type == BlockType.AI
+                        or block.block_type
+                        in [
+                            BlockType.INPUT,
+                            BlockType.OUTPUT,
+                            BlockType.AGENT,
+                        ]
                     )
                 ):
                     raise ValueError(
@@ -402,31 +406,29 @@ class GraphModel(Graph):
                         f"Node {block.name} #{node.id}: Field `{field_name}` requires [{', '.join(missing_deps)}] to be set"
                     )
 
-        node_map = {v.id: v for v in self.nodes}
+        node_map = {v.id: v for v in graph.nodes}
 
         def is_static_output_block(nid: str) -> bool:
-            bid = node_map[nid].block_id
-            b = get_block(bid)
-            return b.static_output if b else False
+            return node_map[nid].block.static_output
 
         # Links: links are connected and the connected pin data type are compatible.
-        for link in self.links:
+        for link in graph.links:
             source = (link.source_id, link.source_name)
             sink = (link.sink_id, link.sink_name)
-            suffix = f"Link {source} <-> {sink}"
+            prefix = f"Link {source} <-> {sink}"
 
             for i, (node_id, name) in enumerate([source, sink]):
                 node = node_map.get(node_id)
                 if not node:
                     raise ValueError(
-                        f"{suffix}, {node_id} is invalid node id, available nodes: {node_map.keys()}"
+                        f"{prefix}, {node_id} is invalid node id, available nodes: {node_map.keys()}"
                     )
 
                 block = get_block(node.block_id)
                 if not block:
                     blocks = {v().id: v().name for v in get_blocks().values()}
                     raise ValueError(
-                        f"{suffix}, {node.block_id} is invalid block id, available blocks: {blocks}"
+                        f"{prefix}, {node.block_id} is invalid block id, available blocks: {blocks}"
                     )
 
                 sanitized_name = sanitize(name)
@@ -445,24 +447,26 @@ class GraphModel(Graph):
                     )
                 if sanitized_name not in fields and not name.startswith("tools_^_"):
                     fields_msg = f"Allowed fields: {fields}"
-                    raise ValueError(f"{suffix}, `{name}` invalid, {fields_msg}")
+                    raise ValueError(f"{prefix}, `{name}` invalid, {fields_msg}")
 
             if is_static_output_block(link.source_id):
                 link.is_static = True  # Each value block output should be static.
 
     @staticmethod
-    def from_db(graph: AgentGraph, for_export: bool = False):
+    def from_db(
+        graph: AgentGraph,
+        for_export: bool = False,
+        sub_graphs: list[AgentGraph] | None = None,
+    ):
         return GraphModel(
             id=graph.id,
-            user_id=graph.userId,
+            user_id=graph.userId if not for_export else "",
             version=graph.version,
             is_active=graph.isActive,
-            is_template=graph.isTemplate,
             name=graph.name or "",
             description=graph.description or "",
             nodes=[
-                NodeModel.from_db(GraphModel._process_node(node, for_export))
-                for node in graph.AgentNodes or []
+                NodeModel.from_db(node, for_export) for node in graph.AgentNodes or []
             ],
             links=list(
                 {
@@ -471,59 +475,12 @@ class GraphModel(Graph):
                     for link in (node.Input or []) + (node.Output or [])
                 }
             ),
+            sub_graphs=[
+                GraphModel.from_db(sub_graph, for_export)
+                for sub_graph in sub_graphs or []
+            ],
         )
 
-    @staticmethod
-    def _process_node(node: AgentNode, for_export: bool) -> AgentNode:
-        if for_export:
-            # Remove credentials from node input
-            if node.constantInput:
-                constant_input = type.convert(node.constantInput, dict[str, Any])
-                constant_input = GraphModel._hide_node_input_credentials(constant_input)
-                node.constantInput = Json(constant_input)
-
-            # Remove webhook info
-            node.webhookId = None
-            node.Webhook = None
-
-        return node
-
-    @staticmethod
-    def _hide_node_input_credentials(input_data: dict[str, Any]) -> dict[str, Any]:
-        sensitive_keys = ["credentials", "api_key", "password", "token", "secret"]
-        result = {}
-        for key, value in input_data.items():
-            if isinstance(value, dict):
-                result[key] = GraphModel._hide_node_input_credentials(value)
-            elif isinstance(value, str) and any(
-                sensitive_key in key.lower() for sensitive_key in sensitive_keys
-            ):
-                # Skip this key-value pair in the result
-                continue
-            else:
-                result[key] = value
-        return result
-
-    def clean_graph(self):
-        blocks = [block() for block in get_blocks().values()]
-
-        input_blocks = [
-            node
-            for node in self.nodes
-            if next(
-                (
-                    b
-                    for b in blocks
-                    if b.id == node.block_id and b.block_type == BlockType.INPUT
-                ),
-                None,
-            )
-        ]
-
-        for node in self.nodes:
-            if any(input_block.id == node.id for input_block in input_blocks):
-                node.input_default["value"] = ""
-
 
 # --------------------- CRUD functions --------------------- #
 
@@ -553,14 +510,14 @@ async def set_node_webhook(node_id: str, webhook_id: str | None) -> NodeModel:
 
 async def get_graphs(
     user_id: str,
-    filter_by: Literal["active", "template"] | None = "active",
+    filter_by: Literal["active"] | None = "active",
 ) -> list[GraphModel]:
     """
     Retrieves graph metadata objects.
     Default behaviour is to get all currently active graphs.
 
     Args:
-        filter_by: An optional filter to either select templates or active graphs.
+        filter_by: An optional filter to either select graphs.
         user_id: The ID of the user that owns the graph.
 
     Returns:
@@ -570,8 +527,6 @@ async def get_graphs(
 
     if filter_by == "active":
         where_clause["isActive"] = True
-    elif filter_by == "template":
-        where_clause["isTemplate"] = True
 
     graphs = await AgentGraph.prisma().find_many(
         where=where_clause,
@@ -591,50 +546,6 @@ async def get_graphs(
     return graph_models
 
 
-# TODO: move execution stuff to .execution
-async def get_graphs_executions(user_id: str) -> list[GraphExecutionMeta]:
-    executions = await AgentGraphExecution.prisma().find_many(
-        where={"userId": user_id},
-        order={"createdAt": "desc"},
-    )
-    return [GraphExecutionMeta.from_db(execution) for execution in executions]
-
-
-async def get_graph_executions(graph_id: str, user_id: str) -> list[GraphExecutionMeta]:
-    executions = await AgentGraphExecution.prisma().find_many(
-        where={"agentGraphId": graph_id, "userId": user_id},
-        order={"createdAt": "desc"},
-    )
-    return [GraphExecutionMeta.from_db(execution) for execution in executions]
-
-
-async def get_execution_meta(
-    user_id: str, execution_id: str
-) -> GraphExecutionMeta | None:
-    execution = await AgentGraphExecution.prisma().find_first(
-        where={"id": execution_id, "userId": user_id}
-    )
-    return GraphExecutionMeta.from_db(execution) if execution else None
-
-
-async def get_execution(user_id: str, execution_id: str) -> GraphExecution | None:
-    execution = await AgentGraphExecution.prisma().find_first(
-        where={"id": execution_id, "userId": user_id},
-        include={
-            "AgentNodeExecutions": {
-                "include": {"AgentNode": True, "Input": True, "Output": True},
-                "order_by": [
-                    {"queuedTime": "asc"},
-                    {  # Fallback: Incomplete execs has no queuedTime.
-                        "addedTime": "asc"
-                    },
-                ],
-            },
-        },
-    )
-    return GraphExecution.from_db(execution) if execution else None
-
-
 async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph | None:
     where_clause: AgentGraphWhereInput = {
         "id": graph_id,
@@ -645,7 +556,6 @@ async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph
 
     graph = await AgentGraph.prisma().find_first(
         where=where_clause,
-        include=AGENT_GRAPH_INCLUDE,
         order={"version": "desc"},
     )
 
@@ -658,21 +568,18 @@ async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph
         description=graph.description or "",
         version=graph.version,
         is_active=graph.isActive,
-        is_template=graph.isTemplate,
     )
 
 
 async def get_graph(
     graph_id: str,
     version: int | None = None,
-    template: bool = False,  # note: currently not in use; TODO: remove from DB entirely
     user_id: str | None = None,
     for_export: bool = False,
 ) -> GraphModel | None:
     """
     Retrieves a graph from the DB.
-    Defaults to the version with `is_active` if `version` is not passed,
-    or the latest version with `is_template` if `template=True`.
+    Defaults to the version with `is_active` if `version` is not passed.
 
     Returns `None` if the record is not found.
     """
@@ -682,8 +589,6 @@ async def get_graph(
 
     if version is not None:
         where_clause["version"] = version
-    elif not template:
-        where_clause["isActive"] = True
 
     graph = await AgentGraph.prisma().find_first(
         where=where_clause,
@@ -700,16 +605,81 @@ async def get_graph(
                     "agentId": graph_id,
                     "agentVersion": version or graph.version,
                     "isDeleted": False,
-                    "StoreListing": {"is": {"isApproved": True}},
+                    "submissionStatus": SubmissionStatus.APPROVED,
                 }
             )
         )
     ):
         return None
 
+    if for_export:
+        sub_graphs = await get_sub_graphs(graph)
+        return GraphModel.from_db(
+            graph=graph,
+            sub_graphs=sub_graphs,
+            for_export=for_export,
+        )
+
     return GraphModel.from_db(graph, for_export)
 
 
+async def get_sub_graphs(graph: AgentGraph) -> list[AgentGraph]:
+    """
+    Iteratively fetches all sub-graphs of a given graph, and flattens them into a list.
+    This call involves a DB fetch in batch, breadth-first, per-level of graph depth.
+    On each DB fetch we will only fetch the sub-graphs that are not already in the list.
+    """
+    sub_graphs = {graph.id: graph}
+    search_graphs = [graph]
+    agent_block_id = AgentExecutorBlock().id
+
+    while search_graphs:
+        sub_graph_ids = [
+            (graph_id, graph_version)
+            for graph in search_graphs
+            for node in graph.AgentNodes or []
+            if (
+                node.AgentBlock
+                and node.AgentBlock.id == agent_block_id
+                and (graph_id := dict(node.constantInput).get("graph_id"))
+                and (graph_version := dict(node.constantInput).get("graph_version"))
+            )
+        ]
+        if not sub_graph_ids:
+            break
+
+        graphs = await AgentGraph.prisma().find_many(
+            where={
+                "OR": [
+                    {
+                        "id": graph_id,
+                        "version": graph_version,
+                        "userId": graph.userId,  # Ensure the sub-graph is owned by the same user
+                    }
+                    for graph_id, graph_version in sub_graph_ids
+                ]  # type: ignore
+            },
+            include=AGENT_GRAPH_INCLUDE,
+        )
+
+        search_graphs = [graph for graph in graphs if graph.id not in sub_graphs]
+        sub_graphs.update({graph.id: graph for graph in search_graphs})
+
+    return [g for g in sub_graphs.values() if g.id != graph.id]
+
+
+async def get_connected_output_nodes(node_id: str) -> list[tuple[Link, Node]]:
+    links = await AgentNodeLink.prisma().find_many(
+        where={"agentNodeSourceId": node_id},
+        include={"AgentNodeSink": {"include": AGENT_NODE_INCLUDE}},  # type: ignore
+    )
+    return [
+        (Link.from_db(link), NodeModel.from_db(link.AgentNodeSink))
+        for link in links
+        if link.AgentNodeSink
+    ]
+
+
 async def set_graph_active_version(graph_id: str, version: int, user_id: str) -> None:
     # Activate the requested version if it exists and is owned by the user.
     updated_count = await AgentGraph.prisma().update_many(
@@ -761,50 +731,55 @@ async def create_graph(graph: Graph, user_id: str) -> GraphModel:
     async with transaction() as tx:
         await __create_graph(tx, graph, user_id)
 
-    if created_graph := await get_graph(
-        graph.id, graph.version, template=graph.is_template, user_id=user_id
-    ):
+    if created_graph := await get_graph(graph.id, graph.version, user_id=user_id):
         return created_graph
 
     raise ValueError(f"Created graph {graph.id} v{graph.version} is not in DB")
 
 
 async def __create_graph(tx, graph: Graph, user_id: str):
-    await AgentGraph.prisma(tx).create(
-        data={
-            "id": graph.id,
-            "version": graph.version,
-            "name": graph.name,
-            "description": graph.description,
-            "isTemplate": graph.is_template,
-            "isActive": graph.is_active,
-            "userId": user_id,
-            "AgentNodes": {
-                "create": [
-                    {
-                        "id": node.id,
-                        "agentBlockId": node.block_id,
-                        "constantInput": Json(node.input_default),
-                        "metadata": Json(node.metadata),
-                    }
-                    for node in graph.nodes
-                ]
-            },
-        }
+    graphs = [graph] + graph.sub_graphs
+
+    await AgentGraph.prisma(tx).create_many(
+        data=[
+            AgentGraphCreateInput(
+                id=graph.id,
+                version=graph.version,
+                name=graph.name,
+                description=graph.description,
+                isActive=graph.is_active,
+                userId=user_id,
+            )
+            for graph in graphs
+        ]
     )
 
-    await asyncio.gather(
-        *[
-            AgentNodeLink.prisma(tx).create(
-                {
-                    "id": str(uuid.uuid4()),
-                    "sourceName": link.source_name,
-                    "sinkName": link.sink_name,
-                    "agentNodeSourceId": link.source_id,
-                    "agentNodeSinkId": link.sink_id,
-                    "isStatic": link.is_static,
-                }
+    await AgentNode.prisma(tx).create_many(
+        data=[
+            AgentNodeCreateInput(
+                id=node.id,
+                agentGraphId=graph.id,
+                agentGraphVersion=graph.version,
+                agentBlockId=node.block_id,
+                constantInput=Json(node.input_default),
+                metadata=Json(node.metadata),
             )
+            for graph in graphs
+            for node in graph.nodes
+        ]
+    )
+
+    await AgentNodeLink.prisma(tx).create_many(
+        data=[
+            AgentNodeLinkCreateInput(
+                id=str(uuid.uuid4()),
+                sourceName=link.source_name,
+                sinkName=link.sink_name,
+                agentNodeSourceId=link.source_id,
+                agentNodeSinkId=link.sink_id,
+                isStatic=link.is_static,
+            )
+            for graph in graphs
             for link in graph.links
         ]
     )
@@ -914,3 +889,40 @@ async def fix_llm_provider_credentials():
             where={"id": node_id},
             data={"constantInput": Json(node_preset_input)},
         )
+
+
+async def migrate_llm_models(migrate_to: LlmModel):
+    """
+    Update all LLM models in all AI blocks that don't exist in the enum.
+    Note: Only updates top level LlmModel SchemaFields of blocks (won't update nested fields).
+    """
+    logger.info("Migrating LLM models")
+    # Scan all blocks and search for LlmModel fields
+    llm_model_fields: dict[str, str] = {}  # {block_id: field_name}
+
+    # Search for all LlmModel fields
+    for block_type in get_blocks().values():
+        block = block_type()
+        from pydantic.fields import FieldInfo
+
+        fields: dict[str, FieldInfo] = block.input_schema.model_fields
+
+        # Collect top-level LlmModel fields
+        for field_name, field in fields.items():
+            if field.annotation == LlmModel:
+                llm_model_fields[block.id] = field_name
+
+    # Update each block
+    for id, path in llm_model_fields.items():
+        # Convert enum values to a list of strings for the SQL query
+        enum_values = [v.value for v in LlmModel.__members__.values()]
+
+        query = f"""
+            UPDATE "AgentNode"
+            SET "constantInput" = jsonb_set("constantInput", '{{{path}}}', '"{migrate_to.value}"', true)
+            WHERE "agentBlockId" = '{id}'
+            AND "constantInput" ? '{path}'
+            AND "constantInput"->>'{path}' NOT IN ({','.join(f"'{value}'" for value in enum_values)})
+            """
+
+        await db.execute_raw(query)

autogpt_platform/backend/backend/data/includes.py

@@ -1,4 +1,7 @@
-import prisma
+import prisma.enums
+import prisma.types
+
+from backend.blocks.io import IO_BLOCK_IDs
 
 AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
     "Input": True,
@@ -18,17 +21,52 @@ EXECUTION_RESULT_INCLUDE: prisma.types.AgentNodeExecutionInclude = {
     "AgentGraphExecution": True,
 }
 
-GRAPH_EXECUTION_INCLUDE: prisma.types.AgentGraphExecutionInclude = {
+MAX_NODE_EXECUTIONS_FETCH = 1000
+
+GRAPH_EXECUTION_INCLUDE_WITH_NODES: prisma.types.AgentGraphExecutionInclude = {
     "AgentNodeExecutions": {
         "include": {
             "Input": True,
             "Output": True,
             "AgentNode": True,
             "AgentGraphExecution": 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 = {
+    "AgentNodeExecutions": {
+        **GRAPH_EXECUTION_INCLUDE_WITH_NODES["AgentNodeExecutions"],  # type: ignore
+        "where": {
+            "AgentNode": {
+                "AgentBlock": {"id": {"in": IO_BLOCK_IDs}},  # type: ignore
+            },
+            "NOT": {
+                "executionStatus": prisma.enums.AgentExecutionStatus.INCOMPLETE,
+            },
+        },
+    }
+}
+
+
 INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
     "AgentNodes": {"include": AGENT_NODE_INCLUDE}  # type: ignore
 }
+
+
+def library_agent_include(user_id: str) -> prisma.types.LibraryAgentInclude:
+    return {
+        "AgentGraph": {
+            "include": {
+                **AGENT_GRAPH_INCLUDE,
+                "AgentGraphExecutions": {"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

@@ -141,17 +141,20 @@ 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 +162,7 @@ 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 +174,12 @@ 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
 
 
@@ -402,3 +408,46 @@ class RefundRequest(BaseModel):
     status: str
     created_at: datetime
     updated_at: datetime
+
+
+class NodeExecutionStats(BaseModel):
+    """Execution statistics for a node execution."""
+
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+    )
+
+    error: Optional[Exception | str] = None
+    walltime: float = 0
+    cputime: float = 0
+    input_size: int = 0
+    output_size: int = 0
+    llm_call_count: int = 0
+    llm_retry_count: int = 0
+    input_token_count: int = 0
+    output_token_count: int = 0
+
+
+class GraphExecutionStats(BaseModel):
+    """Execution statistics for a graph execution."""
+
+    model_config = ConfigDict(
+        extra="allow",
+        arbitrary_types_allowed=True,
+    )
+
+    error: Optional[Exception | str] = None
+    walltime: float = Field(
+        default=0, description="Time between start and end of run (seconds)"
+    )
+    cputime: float = 0
+    nodes_walltime: float = Field(
+        default=0, description="Total node execution time (seconds)"
+    )
+    nodes_cputime: 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

@@ -1,15 +1,19 @@
 import logging
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
 from enum import Enum
 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
 
@@ -18,19 +22,24 @@ from .db import transaction
 logger = logging.getLogger(__name__)
 
 
-T_co = TypeVar("T_co", bound="BaseNotificationData", covariant=True)
+NotificationDataType_co = TypeVar(
+    "NotificationDataType_co", bound="BaseNotificationData", covariant=True
+)
+SummaryParamsType_co = TypeVar(
+    "SummaryParamsType_co", bound="BaseSummaryParams", covariant=True
+)
 
 
 class QueueType(Enum):
     IMMEDIATE = "immediate"  # Send right away (errors, critical notifications)
-    HOURLY = "hourly"  # Batch for up to an hour (usage reports)
-    DAILY = "daily"  # Daily digest (summary notifications)
+    BATCH = "batch"  # Batch for up to an hour (usage reports)
+    SUMMARY = "summary"  # Daily digest (summary notifications)
     BACKOFF = "backoff"  # Backoff strategy (exponential backoff)
     ADMIN = "admin"  # Admin notifications (errors, critical notifications)
 
 
 class BaseNotificationData(BaseModel):
-    pass
+    model_config = ConfigDict(extra="allow")
 
 
 class AgentRunData(BaseNotificationData):
@@ -39,7 +48,7 @@ class AgentRunData(BaseNotificationData):
     execution_time: float
     node_count: int = Field(..., description="Number of nodes executed")
     graph_id: str
-    outputs: dict[str, Any] = Field(..., description="Outputs of the agent")
+    outputs: list[dict[str, Any]] = Field(..., description="Outputs of the agent")
 
 
 class ZeroBalanceData(BaseNotificationData):
@@ -47,12 +56,21 @@ class ZeroBalanceData(BaseNotificationData):
     last_transaction_time: datetime
     top_up_link: str
 
+    @field_validator("last_transaction_time")
+    @classmethod
+    def validate_timezone(cls, value: datetime):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
+
 
 class LowBalanceData(BaseNotificationData):
-    current_balance: float
-    threshold_amount: float
-    top_up_link: str
-    recent_usage: float = Field(..., description="Usage in the last 24 hours")
+    agent_name: str = Field(..., description="Name of the agent")
+    current_balance: float = Field(
+        ..., description="Current balance in credits (100 = $1)"
+    )
+    billing_page_link: str = Field(..., description="Link to billing page")
+    shortfall: float = Field(..., description="Amount of credits needed to continue")
 
 
 class BlockExecutionFailedData(BaseNotificationData):
@@ -73,6 +91,13 @@ class ContinuousAgentErrorData(BaseNotificationData):
     error_time: datetime
     attempts: int = Field(..., description="Number of retry attempts made")
 
+    @field_validator("start_time", "error_time")
+    @classmethod
+    def validate_timezone(cls, value: datetime):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
+
 
 class BaseSummaryData(BaseNotificationData):
     total_credits_used: float
@@ -85,18 +110,53 @@ class BaseSummaryData(BaseNotificationData):
     cost_breakdown: dict[str, float]
 
 
+class BaseSummaryParams(BaseModel):
+    pass
+
+
+class DailySummaryParams(BaseSummaryParams):
+    date: datetime
+
+    @field_validator("date")
+    def validate_timezone(cls, value):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
+
+
+class WeeklySummaryParams(BaseSummaryParams):
+    start_date: datetime
+    end_date: datetime
+
+    @field_validator("start_date", "end_date")
+    def validate_timezone(cls, value):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
+
+
 class DailySummaryData(BaseSummaryData):
     date: datetime
 
+    @field_validator("date")
+    def validate_timezone(cls, value):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
+
 
 class WeeklySummaryData(BaseSummaryData):
     start_date: datetime
     end_date: datetime
-    week_number: int
-    year: int
+
+    @field_validator("start_date", "end_date")
+    def validate_timezone(cls, value):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
 
 
-class MonthlySummaryData(BaseSummaryData):
+class MonthlySummaryData(BaseNotificationData):
     month: int
     year: int
 
@@ -123,6 +183,7 @@ NotificationData = Annotated[
         WeeklySummaryData,
         DailySummaryData,
         RefundRequestData,
+        BaseSummaryData,
     ],
     Field(discriminator="type"),
 ]
@@ -132,15 +193,22 @@ class NotificationEventDTO(BaseModel):
     user_id: str
     type: NotificationType
     data: dict
-    created_at: datetime = Field(default_factory=datetime.now)
+    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
     retry_count: int = 0
 
 
-class NotificationEventModel(BaseModel, Generic[T_co]):
+class SummaryParamsEventDTO(BaseModel):
     user_id: str
     type: NotificationType
-    data: T_co
-    created_at: datetime = Field(default_factory=datetime.now)
+    data: dict
+    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
+
+
+class NotificationEventModel(BaseModel, Generic[NotificationDataType_co]):
+    user_id: str
+    type: NotificationType
+    data: NotificationDataType_co
+    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
 
     @property
     def strategy(self) -> QueueType:
@@ -157,7 +225,14 @@ class NotificationEventModel(BaseModel, Generic[T_co]):
         return NotificationTypeOverride(self.type).template
 
 
-def get_data_type(
+class SummaryParamsEventModel(BaseModel, Generic[SummaryParamsType_co]):
+    user_id: str
+    type: NotificationType
+    data: SummaryParamsType_co
+    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
+
+
+def get_notif_data_type(
     notification_type: NotificationType,
 ) -> type[BaseNotificationData]:
     return {
@@ -174,11 +249,20 @@ def get_data_type(
     }[notification_type]
 
 
+def get_summary_params_type(
+    notification_type: NotificationType,
+) -> type[BaseSummaryParams]:
+    return {
+        NotificationType.DAILY_SUMMARY: DailySummaryParams,
+        NotificationType.WEEKLY_SUMMARY: WeeklySummaryParams,
+    }[notification_type]
+
+
 class NotificationBatch(BaseModel):
     user_id: str
     events: list[NotificationEvent]
     strategy: QueueType
-    last_update: datetime = datetime.now()
+    last_update: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
 
 
 class NotificationResult(BaseModel):
@@ -194,15 +278,15 @@ class NotificationTypeOverride:
     def strategy(self) -> QueueType:
         BATCHING_RULES = {
             # These are batched by the notification service
-            NotificationType.AGENT_RUN: QueueType.IMMEDIATE,
+            NotificationType.AGENT_RUN: QueueType.BATCH,
             # These are batched by the notification service, but with a backoff strategy
             NotificationType.ZERO_BALANCE: QueueType.BACKOFF,
-            NotificationType.LOW_BALANCE: QueueType.BACKOFF,
+            NotificationType.LOW_BALANCE: QueueType.IMMEDIATE,
             NotificationType.BLOCK_EXECUTION_FAILED: QueueType.BACKOFF,
             NotificationType.CONTINUOUS_AGENT_ERROR: QueueType.BACKOFF,
-            NotificationType.DAILY_SUMMARY: QueueType.DAILY,
-            NotificationType.WEEKLY_SUMMARY: QueueType.DAILY,
-            NotificationType.MONTHLY_SUMMARY: QueueType.DAILY,
+            NotificationType.DAILY_SUMMARY: QueueType.SUMMARY,
+            NotificationType.WEEKLY_SUMMARY: QueueType.SUMMARY,
+            NotificationType.MONTHLY_SUMMARY: QueueType.SUMMARY,
             NotificationType.REFUND_REQUEST: QueueType.ADMIN,
             NotificationType.REFUND_PROCESSED: QueueType.ADMIN,
         }
@@ -256,12 +340,51 @@ class NotificationPreference(BaseModel):
     )
     daily_limit: int = 10  # Max emails per day
     emails_sent_today: int = 0
-    last_reset_date: datetime = Field(default_factory=datetime.now)
+    last_reset_date: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc)
+    )
+
+
+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(seconds=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),
@@ -272,19 +395,17 @@ def get_batch_delay(notification_type: NotificationType) -> timedelta:
 async def create_or_add_to_user_notification_batch(
     user_id: str,
     notification_type: NotificationType,
-    data: str,  # type: 'NotificationEventModel'
-) -> dict:
+    notification_data: NotificationEventModel,
+) -> UserNotificationBatchDTO:
     try:
         logger.info(
-            f"Creating or adding to notification batch for {user_id} with type {notification_type} and data {data}"
+            f"Creating or adding to notification batch for {user_id} with type {notification_type} and data {notification_data}"
         )
-
-        notification_data = NotificationEventModel[
-            get_data_type(notification_type)
-        ].model_validate_json(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_json())
+        json_data: Json = Json(notification_data.data.model_dump())
 
         # First try to find existing batch
         existing_batch = await UserNotificationBatch.prisma().find_unique(
@@ -294,70 +415,76 @@ 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.model_dump()
+                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.model_dump()
+            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}"
         ) from e
 
 
-async def get_user_notification_last_message_in_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},
-            order={"createdAt": "desc"},
+            include={"Notifications": True},
         )
         if not batch:
             return None
-        if not batch.notifications:
+        if not batch.Notifications:
             return None
-        return batch.notifications[-1]
+        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}"
@@ -392,13 +519,34 @@ 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}"
         ) from e
+
+
+async def get_all_batches_by_type(
+    notification_type: NotificationType,
+) -> list[UserNotificationBatchDTO]:
+    try:
+        batches = await UserNotificationBatch.prisma().find_many(
+            where={
+                "type": notification_type,
+                "Notifications": {
+                    "some": {}  # Only return batches with at least one notification
+                },
+            },
+            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}"
+        ) from e

autogpt_platform/backend/backend/data/onboarding.py

@@ -0,0 +1,276 @@
+import re
+from typing import Any, Optional
+
+import prisma
+import pydantic
+from prisma import Json
+from prisma.enums import OnboardingStep
+from prisma.models import UserOnboarding
+from prisma.types import UserOnboardingUpdateInput
+
+from backend.data.block import get_blocks
+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
+REASON_MAPPING: dict[str, list[str]] = {
+    "content_marketing": ["writing", "marketing", "creative"],
+    "business_workflow_automation": ["business", "productivity"],
+    "data_research": ["data", "research"],
+    "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
+
+
+class UserOnboardingUpdate(pydantic.BaseModel):
+    completedSteps: Optional[list[OnboardingStep]] = None
+    usageReason: Optional[str] = None
+    integrations: Optional[list[str]] = None
+    otherIntegrations: Optional[str] = None
+    selectedStoreListingVersionId: Optional[str] = None
+    agentInput: Optional[dict[str, Any]] = 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
+            "update": {},
+        },
+    )
+
+
+async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
+    update: UserOnboardingUpdateInput = {}
+    if data.completedSteps is not None:
+        update["completedSteps"] = list(set(data.completedSteps))
+    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)
+
+    return await UserOnboarding.prisma().upsert(
+        where={"userId": user_id},
+        data={
+            "create": {"userId": user_id, **update},  # type: ignore
+            "update": update,
+        },
+    )
+
+
+def clean_and_split(text: str) -> list[str]:
+    """
+    Removes all special characters from a string, truncates it to 100 characters,
+    and splits it by whitespace and commas.
+
+    Args:
+        text (str): The input string.
+
+    Returns:
+        list[str]: A list of cleaned words.
+    """
+    # Remove all special characters (keep only alphanumeric and whitespace)
+    cleaned_text = re.sub(r"[^a-zA-Z0-9\s,]", "", text.strip()[:100])
+
+    # Split by whitespace and commas
+    words = re.split(r"[\s,]+", cleaned_text)
+
+    # Remove empty strings from the list
+    words = [word.lower() for word in words if word]
+
+    return words
+
+
+def calculate_points(
+    agent, categories: list[str], custom: list[str], integrations: list[str]
+) -> int:
+    """
+    Calculates the total points for an agent based on the specified criteria.
+
+    Args:
+        agent: The agent object.
+        categories (list[str]): List of categories to match.
+        words (list[str]): List of words to match in the description.
+
+    Returns:
+        int: Total points for the agent.
+    """
+    points = 0
+
+    # 1. Category Matches
+    matched_categories = sum(
+        1 for category in categories if category in agent.categories
+    )
+    points += matched_categories * 100
+
+    # 2. Description Word Matches
+    description_words = agent.description.split()  # Split description into words
+    matched_words = sum(1 for word in custom if word in description_words)
+    points += matched_words * 100
+
+    matched_words = sum(1 for word in integrations if word in description_words)
+    points += matched_words * 50
+
+    # 3. Featured Bonus
+    if agent.featured:
+        points += 50
+
+    # 4. Rating Bonus
+    points += agent.rating * 10
+
+    # 5. Runs Bonus
+    runs_points = min(agent.runs / 1000 * 100, 100)  # Cap at 100 points
+    points += runs_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 "", [])
+
+    where_clause: dict[str, Any] = {}
+
+    custom = clean_and_split((user_onboarding.usageReason or "").lower())
+
+    if categories:
+        where_clause["OR"] = [
+            {"categories": {"has": category}} for category in categories
+        ]
+    else:
+        where_clause["OR"] = [
+            {"description": {"contains": word, "mode": "insensitive"}}
+            for word in custom
+        ]
+
+    where_clause["OR"] += [
+        {"description": {"contains": word, "mode": "insensitive"}}
+        for word in user_onboarding.integrations
+    ]
+
+    storeAgents = await prisma.models.StoreAgent.prisma().find_many(
+        where=prisma.types.StoreAgentWhereInput(**where_clause),
+        order=[
+            {"featured": "desc"},
+            {"runs": "desc"},
+            {"rating": "desc"},
+        ],
+        take=100,
+    )
+
+    agentListings = await prisma.models.StoreListingVersion.prisma().find_many(
+        where={
+            "id": {"in": [agent.storeListingVersionId for agent in storeAgents]},
+        },
+        include={"Agent": True},
+    )
+
+    for listing in agentListings:
+        agent = listing.Agent
+        if agent is None:
+            continue
+        graph = GraphModel.from_db(agent)
+        # Remove agents with empty input schema
+        if not graph.input_schema:
+            storeAgents = [
+                a for a in storeAgents if a.storeListingVersionId != listing.id
+            ]
+            continue
+
+        # Remove agents with empty credentials
+        # Get nodes from this agent that have credentials
+        nodes = await prisma.models.AgentNode.prisma().find_many(
+            where={
+                "agentGraphId": agent.id,
+                "agentBlockId": {"in": list(CREDENTIALS_FIELDS.keys())},
+            },
+        )
+        for node in nodes:
+            block_id = node.agentBlockId
+            field_name = CREDENTIALS_FIELDS[block_id]
+            # If there are no credentials or they are empty, remove the agent
+            # FIXME ignores default values
+            if (
+                field_name not in node.constantInput
+                or node.constantInput[field_name] is None
+            ):
+                storeAgents = [
+                    a for a in storeAgents if a.storeListingVersionId != listing.id
+                ]
+                break
+
+    # If there are less than 2 agents, add more agents to the list
+    if len(storeAgents) < 2:
+        storeAgents += await prisma.models.StoreAgent.prisma().find_many(
+            where={
+                "listing_id": {"not_in": [agent.listing_id for agent in storeAgents]},
+            },
+            order=[
+                {"featured": "desc"},
+                {"runs": "desc"},
+                {"rating": "desc"},
+            ],
+            take=2 - len(storeAgents),
+        )
+
+    # Calculate points for the first X agents and choose the top 2
+    agent_points = []
+    for agent in storeAgents[:POINTS_AGENT_COUNT]:
+        points = calculate_points(
+            agent, categories, custom, user_onboarding.integrations
+        )
+        agent_points.append((agent, points))
+
+    agent_points.sort(key=lambda x: x[1], reverse=True)
+    recommended_agents = [agent for agent, _ in agent_points[:2]]
+
+    return [
+        StoreAgentDetails(
+            store_listing_version_id=agent.storeListingVersionId,
+            slug=agent.slug,
+            agent_name=agent.agent_name,
+            agent_video=agent.agent_video or "",
+            agent_image=agent.agent_image,
+            creator=agent.creator_username,
+            creator_avatar=agent.creator_avatar,
+            sub_heading=agent.sub_heading,
+            description=agent.description,
+            categories=agent.categories,
+            runs=agent.runs,
+            rating=agent.rating,
+            versions=agent.versions,
+            last_updated=agent.updated_at,
+        )
+        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)
 
 
@@ -32,10 +23,14 @@ 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.info(f"[{channel_name}] Publishing an event to Redis {message}")
+        logger.debug(f"[{channel_name}] Publishing an event to Redis {message}")
         return message, channel_name
 
     def _deserialize_message(self, msg: Any, channel_key: str) -> M | None:
@@ -43,9 +38,8 @@ class BaseRedisEventBus(Generic[M], ABC):
         if msg["type"] != message_type:
             return None
         try:
-            data = json.loads(msg["data"])
-            logger.info(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/user.py

@@ -1,19 +1,24 @@
+import base64
+import hashlib
+import hmac
 import logging
 from datetime import datetime, timedelta
 from typing import Optional, cast
+from urllib.parse import quote_plus
 
 from autogpt_libs.auth.models import DEFAULT_USER_ID
 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 UserCreateInput, UserUpdateInput
 
 from backend.data.db import prisma
 from backend.data.model import UserIntegrations, UserMetadata, UserMetadataRaw
 from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
 from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.encryption import JSONCryptor
+from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
 
@@ -31,11 +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)
@@ -79,11 +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)
 
@@ -334,3 +339,59 @@ async def get_user_email_verification(user_id: str) -> bool:
         raise DatabaseError(
             f"Failed to get email verification status for user {user_id}: {e}"
         ) from e
+
+
+def generate_unsubscribe_link(user_id: str) -> str:
+    """Generate a link to unsubscribe from all notifications"""
+    # Create an HMAC using a secret key
+    secret_key = Settings().secrets.unsubscribe_secret_key
+    signature = hmac.new(
+        secret_key.encode("utf-8"), user_id.encode("utf-8"), hashlib.sha256
+    ).digest()
+
+    # Create a token that combines the user_id and signature
+    token = base64.urlsafe_b64encode(
+        f"{user_id}:{signature.hex()}".encode("utf-8")
+    ).decode("utf-8")
+    logger.info(f"Generating unsubscribe link for user {user_id}")
+
+    base_url = Settings().config.platform_base_url
+    return f"{base_url}/api/email/unsubscribe?token={quote_plus(token)}"
+
+
+async def unsubscribe_user_by_token(token: str) -> None:
+    """Unsubscribe a user from all notifications using the token"""
+    try:
+        # Decode the token
+        decoded = base64.urlsafe_b64decode(token).decode("utf-8")
+        user_id, received_signature_hex = decoded.split(":", 1)
+
+        # Verify the signature
+        secret_key = Settings().secrets.unsubscribe_secret_key
+        expected_signature = hmac.new(
+            secret_key.encode("utf-8"), user_id.encode("utf-8"), hashlib.sha256
+        ).digest()
+
+        if not hmac.compare_digest(expected_signature.hex(), received_signature_hex):
+            raise ValueError("Invalid token signature")
+
+        user = await get_user_by_id(user_id)
+        await update_user_notification_preference(
+            user.id,
+            NotificationPreferenceDTO(
+                email=user.email,
+                daily_limit=0,
+                preferences={
+                    NotificationType.AGENT_RUN: False,
+                    NotificationType.ZERO_BALANCE: False,
+                    NotificationType.LOW_BALANCE: False,
+                    NotificationType.BLOCK_EXECUTION_FAILED: False,
+                    NotificationType.CONTINUOUS_AGENT_ERROR: False,
+                    NotificationType.DAILY_SUMMARY: False,
+                    NotificationType.WEEKLY_SUMMARY: False,
+                    NotificationType.MONTHLY_SUMMARY: False,
+                },
+            ),
+        )
+    except Exception as e:
+        raise DatabaseError(f"Failed to unsubscribe user by token {token}: {e}") from e

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/__init__.py

@@ -1,9 +1,9 @@
 from .database import DatabaseManager
 from .manager import ExecutionManager
-from .scheduler import ExecutionScheduler
+from .scheduler import Scheduler
 
 __all__ = [
     "DatabaseManager",
     "ExecutionManager",
-    "ExecutionScheduler",
+    "Scheduler",
 ]

autogpt_platform/backend/backend/executor/database.py

@@ -1,22 +1,41 @@
-from backend.data.credit import get_user_credit_model
+from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
 from backend.data.execution import (
-    ExecutionResult,
-    NodeExecutionEntry,
+    GraphExecution,
+    NodeExecutionResult,
     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,
 )
-from backend.data.graph import get_graph, get_graph_metadata, get_node
+from backend.data.graph import (
+    get_connected_output_nodes,
+    get_graph,
+    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,
 )
@@ -27,8 +46,10 @@ config = Config()
 _user_credit_model = get_user_credit_model()
 
 
-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):
@@ -36,22 +57,33 @@ class DatabaseManager(AppService):
         super().__init__()
         self.use_db = True
         self.use_redis = True
-        self.event_queue = RedisExecutionEventBus()
+        self.execution_event_bus = RedisExecutionEventBus()
 
     @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)
+    def send_execution_update(
+        self, execution_result: GraphExecution | NodeExecutionResult
+    ):
+        self.execution_event_bus.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
+    )
     update_graph_execution_stats = exposed_run_and_wait(update_graph_execution_stats)
     update_node_execution_stats = exposed_run_and_wait(update_node_execution_stats)
     upsert_execution_input = exposed_run_and_wait(upsert_execution_input)
@@ -60,6 +92,7 @@ class DatabaseManager(AppService):
     # Graphs
     get_node = exposed_run_and_wait(get_node)
     get_graph = exposed_run_and_wait(get_graph)
+    get_connected_output_nodes = exposed_run_and_wait(get_connected_output_nodes)
     get_graph_metadata = exposed_run_and_wait(get_graph_metadata)
 
     # Credits
@@ -70,3 +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

@@ -12,11 +12,15 @@ from typing import TYPE_CHECKING, Any, Generator, Optional, TypeVar, cast
 
 from redis.lock import Lock as RedisLock
 
+from backend.blocks.io import AgentOutputBlock
+from backend.data.model import GraphExecutionStats, NodeExecutionStats
 from backend.data.notifications import (
     AgentRunData,
+    LowBalanceData,
     NotificationEventDTO,
     NotificationType,
 )
+from backend.util.exceptions import InsufficientBalanceError
 
 if TYPE_CHECKING:
     from backend.executor import DatabaseManager
@@ -36,14 +40,19 @@ from backend.data.block import (
 )
 from backend.data.execution import (
     ExecutionQueue,
-    ExecutionResult,
     ExecutionStatus,
     GraphExecutionEntry,
     NodeExecutionEntry,
+    NodeExecutionResult,
     merge_execution_input,
     parse_execution_output,
 )
 from backend.data.graph import GraphModel, Link, Node
+from backend.executor.utils import (
+    UsageTransactionMetadata,
+    block_usage_cost,
+    execution_usage_cost,
+)
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util import json
 from backend.util.decorator import error_logged, time_measured
@@ -105,7 +114,10 @@ class LogMetadata:
         logger.exception(msg, extra={"json_fields": {**self.metadata, **extra}})
 
     def _wrap(self, msg: str, **extra):
-        return f"{self.prefix} {msg} {extra}"
+        extra_msg = str(extra or "")
+        if len(extra_msg) > 1000:
+            extra_msg = extra_msg[:1000] + "..."
+        return f"{self.prefix} {msg} {extra_msg}"
 
 
 T = TypeVar("T")
@@ -115,9 +127,8 @@ ExecutionStream = Generator[NodeExecutionEntry, None, None]
 def execute_node(
     db_client: "DatabaseManager",
     creds_manager: IntegrationCredentialsManager,
-    notification_service: "NotificationManager",
     data: NodeExecutionEntry,
-    execution_stats: dict[str, Any] | None = None,
+    execution_stats: NodeExecutionStats | None = None,
 ) -> ExecutionStream:
     """
     Execute a node in the graph. This will trigger a block execution on a node,
@@ -126,7 +137,6 @@ def execute_node(
     Args:
         db_client: The client to send execution updates to the server.
         creds_manager: The manager to acquire and release credentials.
-        notification_service: The service to send notifications.
         data: The execution data for executing the current node.
         execution_stats: The execution statistics to be updated.
 
@@ -139,17 +149,22 @@ 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)
+    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)
         db_client.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,
@@ -164,8 +179,8 @@ 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.
@@ -178,7 +193,7 @@ def execute_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)
+    update_execution_status(ExecutionStatus.RUNNING)
 
     # Inject extra execution arguments for the blocks via kwargs
     extra_exec_kwargs: dict = {
@@ -202,9 +217,6 @@ def execute_node(
 
     output_size = 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
@@ -212,7 +224,7 @@ def execute_node(
             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)
+            push_output(output_name, output_data)
             outputs[output_name] = output_data
             for execution in _enqueue_next_nodes(
                 db_client=db_client,
@@ -225,28 +237,12 @@ def execute_node(
             ):
                 yield execution
 
-        # Update execution status and spend credits
-        update_execution(ExecutionStatus.COMPLETED)
-        event = NotificationEventDTO(
-            user_id=user_id,
-            type=NotificationType.AGENT_RUN,
-            data=AgentRunData(
-                outputs=outputs,
-                agent_name=node_block.name,
-                credits_used=cost,
-                execution_time=0,
-                graph_id=graph_id,
-                node_count=1,
-            ).model_dump(),
-        )
-
-        logger.info(f"Sending notification for {event}")
-        notification_service.queue_notification(event)
+        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,
@@ -270,9 +266,11 @@ def execute_node(
 
         # Update execution stats
         if execution_stats is not None:
-            execution_stats.update(node_block.execution_stats)
-            execution_stats["input_size"] = input_size
-            execution_stats["output_size"] = output_size
+            execution_stats = execution_stats.model_copy(
+                update=node_block.execution_stats.model_dump()
+            )
+            execution_stats.input_size = input_size
+            execution_stats.output_size = output_size
 
 
 def _enqueue_next_nodes(
@@ -287,7 +285,7 @@ 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)
@@ -332,7 +330,7 @@ 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
                 )
             ):
@@ -365,7 +363,7 @@ 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
@@ -425,46 +423,30 @@ def validate_exec(
     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
 
-    if isinstance(node_block, AgentExecutorBlock):
-        # Validate the execution metadata for the agent executor block.
-        try:
-            exec_data = AgentExecutorBlock.Input(**node.input_default)
-        except Exception as e:
-            return None, f"Input data doesn't match {node_block.name}: {str(e)}"
-
-        # Validation input
-        input_schema = exec_data.input_schema
-        required_fields = set(input_schema["required"])
-        input_default = exec_data.data
-    else:
-        # Convert non-matching data types to the expected input schema.
-        for name, data_type in node_block.input_schema.__annotations__.items():
-            if (value := data.get(name)) and (type(value) is not data_type):
-                data[name] = convert(value, data_type)
-
-        # Validation input
-        input_schema = node_block.input_schema.jsonschema()
-        required_fields = node_block.input_schema.get_required_fields()
-        input_default = node.input_default
+    # 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}`:"
-    input_fields_from_nodes = {link.sink_name for link in node.input_links}
-    if not input_fields_from_nodes.issubset(data):
-        return None, f"{error_prefix} {input_fields_from_nodes - set(data)}"
+    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 not required_fields.issubset(data):
-        return None, f"{error_prefix} {required_fields - set(data)}"
+    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 := json.validate_with_jsonschema(schema=input_schema, data=data):
+    if error := schema.get_mismatch_error(data):
         error_message = f"{error_prefix} {error}"
         logger.error(error_message)
         return None, error_message
@@ -505,7 +487,6 @@ class Executor:
         cls.pid = os.getpid()
         cls.db_client = get_db_client()
         cls.creds_manager = IntegrationCredentialsManager()
-        cls.notification_service = get_notification_service()
 
         # Set up shutdown handlers
         cls.shutdown_lock = threading.Lock()
@@ -546,7 +527,7 @@ class Executor:
         cls,
         q: ExecutionQueue[NodeExecutionEntry],
         node_exec: NodeExecutionEntry,
-    ) -> dict[str, Any]:
+    ) -> NodeExecutionStats:
         log_metadata = LogMetadata(
             user_id=node_exec.user_id,
             graph_eid=node_exec.graph_exec_id,
@@ -556,13 +537,15 @@ class Executor:
             block_name="-",
         )
 
-        execution_stats = {}
+        execution_stats = NodeExecutionStats()
         timing_info, _ = cls._on_node_execution(
             q, node_exec, log_metadata, execution_stats
         )
-        execution_stats["walltime"] = timing_info.wall_time
-        execution_stats["cputime"] = timing_info.cpu_time
+        execution_stats.walltime = timing_info.wall_time
+        execution_stats.cputime = timing_info.cpu_time
 
+        if isinstance(execution_stats.error, Exception):
+            execution_stats.error = str(execution_stats.error)
         cls.db_client.update_node_execution_stats(
             node_exec.node_exec_id, execution_stats
         )
@@ -575,14 +558,13 @@ class Executor:
         q: ExecutionQueue[NodeExecutionEntry],
         node_exec: NodeExecutionEntry,
         log_metadata: LogMetadata,
-        stats: dict[str, Any] | None = None,
+        stats: NodeExecutionStats | None = None,
     ):
         try:
             log_metadata.info(f"Start node execution {node_exec.node_exec_id}")
             for execution in execute_node(
                 db_client=cls.db_client,
                 creds_manager=cls.creds_manager,
-                notification_service=cls.notification_service,
                 data=node_exec,
                 execution_stats=stats,
             ):
@@ -599,6 +581,9 @@ class Executor:
                     f"Failed node execution {node_exec.node_exec_id}: {e}"
                 )
 
+            if stats is not None:
+                stats.error = e
+
     @classmethod
     def on_graph_executor_start(cls):
         configure_logging()
@@ -607,6 +592,7 @@ class Executor:
         cls.db_client = get_db_client()
         cls.pool_size = settings.config.num_node_workers
         cls.pid = os.getpid()
+        cls.notification_service = get_notification_service()
         cls._init_node_executor_pool()
         logger.info(
             f"Graph executor {cls.pid} started with {cls.pool_size} node workers"
@@ -644,18 +630,72 @@ class Executor:
             node_eid="*",
             block_name="-",
         )
+        exec_meta = cls.db_client.update_graph_execution_start_time(
+            graph_exec.graph_exec_id
+        )
+        cls.db_client.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"] = str(error) if error else None
-        result = cls.db_client.update_graph_execution_stats(
+        exec_stats.walltime = timing_info.wall_time
+        exec_stats.cputime = timing_info.cpu_time
+        exec_stats.error = str(error)
+
+        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)
+        ):
+            cls.db_client.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
@@ -664,7 +704,7 @@ class Executor:
         graph_exec: GraphExecutionEntry,
         cancel: threading.Event,
         log_metadata: LogMetadata,
-    ) -> tuple[dict[str, Any], ExecutionStatus, Exception | None]:
+    ) -> tuple[GraphExecutionStats, ExecutionStatus, Exception | None]:
         """
         Returns:
             dict: The execution statistics of the graph execution.
@@ -672,19 +712,19 @@ 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 = {
-            "nodes_walltime": 0,
-            "nodes_cputime": 0,
-            "node_count": 0,
-        }
+        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()
@@ -695,30 +735,46 @@ 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] = {}
 
             def make_exec_callback(exec_data: NodeExecutionEntry):
-                node_id = exec_data.node_id
-
                 def callback(result: object):
-                    running_executions.pop(node_id)
-                    nonlocal exec_stats
-                    if isinstance(result, dict):
-                        exec_stats["node_count"] += 1
-                        exec_stats["nodes_cputime"] += result.get("cputime", 0)
-                        exec_stats["nodes_walltime"] += result.get("walltime", 0)
+                    running_executions.pop(exec_data.node_id)
+
+                    if not isinstance(result, NodeExecutionStats):
+                        return
+
+                    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):
+                        execution_stats.node_error_count += 1
+
+                    if _graph_exec := cls.db_client.update_graph_execution_stats(
+                        graph_exec_id=exec_data.graph_exec_id,
+                        status=execution_status,
+                        stats=execution_stats,
+                    ):
+                        cls.db_client.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()
 
@@ -735,6 +791,35 @@ class Executor:
                     f"Dispatching node execution {exec_data.node_exec_id} "
                     f"for node {exec_data.node_id}",
                 )
+
+                try:
+                    exec_cost_counter = cls._charge_usage(
+                        node_exec=exec_data,
+                        execution_count=exec_cost_counter + 1,
+                        execution_stats=execution_stats,
+                    )
+                except InsufficientBalanceError as error:
+                    node_exec_id = exec_data.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
+                    )
+                    cls.db_client.send_execution_update(exec_update)
+
+                    cls._handle_low_balance_notif(
+                        graph_exec.user_id,
+                        graph_exec.graph_id,
+                        execution_stats,
+                        error,
+                    )
+                    raise
+
                 running_executions[exec_data.node_id] = cls.executor.apply_async(
                     cls.on_node_execution,
                     (queue, exec_data),
@@ -748,7 +833,8 @@ 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
@@ -757,22 +843,87 @@ class Executor:
                         execution.wait(3)
 
             log_metadata.info(f"Finished graph execution {graph_exec.graph_exec_id}")
+
         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(
+        cls,
+        graph_exec: GraphExecutionEntry,
+        exec_stats: GraphExecutionStats,
+    ):
+        metadata = cls.db_client.get_graph_metadata(
+            graph_exec.graph_id, graph_exec.graph_version
+        )
+        outputs = cls.db_client.get_node_execution_results(
+            graph_exec.graph_exec_id,
+            block_ids=[AgentOutputBlock().id],
+        )
+
+        named_outputs = [
+            {
+                key: value[0] if key == "name" else value
+                for key, value in output.output_data.items()
+            }
+            for output in outputs
+        ]
+
+        event = NotificationEventDTO(
+            user_id=graph_exec.user_id,
+            type=NotificationType.AGENT_RUN,
+            data=AgentRunData(
+                outputs=named_outputs,
+                agent_name=metadata.name if metadata else "Unknown Agent",
+                credits_used=exec_stats.cost,
+                execution_time=exec_stats.walltime,
+                graph_id=graph_exec.graph_id,
+                node_count=exec_stats.node_count,
+            ).model_dump(),
+        )
+
+        cls.notification_service.queue_notification(event)
+
+    @classmethod
+    def _handle_low_balance_notif(
+        cls,
+        user_id: str,
+        graph_id: str,
+        exec_stats: GraphExecutionStats,
+        e: InsufficientBalanceError,
+    ):
+        shortfall = e.balance - e.amount
+        metadata = cls.db_client.get_graph_metadata(graph_id)
+        base_url = (
+            settings.config.frontend_base_url or settings.config.platform_base_url
+        )
+        cls.notification_service.queue_notification(
+            NotificationEventDTO(
+                user_id=user_id,
+                type=NotificationType.LOW_BALANCE,
+                data=LowBalanceData(
+                    current_balance=exec_stats.cost,
+                    billing_page_link=f"{base_url}/profile/credits",
+                    shortfall=shortfall,
+                    agent_name=metadata.name if metadata else "Unknown Agent",
+                ).model_dump(),
+            )
         )
 
 
@@ -847,10 +998,10 @@ class ExecutionManager(AppService):
         nodes_input = []
         for node in graph.starting_nodes:
             input_data = {}
-            block = get_block(node.block_id)
+            block = node.block
 
-            # Invalid block & Note block should never be executed.
-            if not block or block.block_type == BlockType.NOTE:
+            # Note block should never be executed.
+            if block.block_type == BlockType.NOTE:
                 continue
 
             # Extract request input data, and assign it to the input pin.
@@ -877,17 +1028,26 @@ class ExecutionManager(AppService):
             else:
                 nodes_input.append((node.id, input_data))
 
-        graph_exec_id, node_execs = self.db_client.create_graph_execution(
+        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 = 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,
         )
+        self.db_client.send_execution_update(graph_exec)
 
-        starting_node_execs = []
-        for node_exec in node_execs:
-            starting_node_execs.append(
+        graph_exec_entry = GraphExecutionEntry(
+            user_id=user_id,
+            graph_id=graph_id,
+            graph_version=graph_version or 0,
+            graph_exec_id=graph_exec.id,
+            start_node_execs=[
                 NodeExecutionEntry(
                     user_id=user_id,
                     graph_exec_id=node_exec.graph_exec_id,
@@ -897,18 +1057,12 @@ class ExecutionManager(AppService):
                     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,
+                for node_exec in graph_exec.node_executions
+            ],
         )
-        self.queue.add(graph_exec)
+        self.queue.add(graph_exec_entry)
 
-        return graph_exec
+        return graph_exec_entry
 
     @expose
     def cancel_execution(self, graph_exec_id: str) -> None:
@@ -920,37 +1074,42 @@ class ExecutionManager(AppService):
         3. Update execution statuses in DB and set `error` outputs to `"TERMINATED"`.
         """
         if graph_exec_id not in self.active_graph_runs:
-            raise Exception(
+            logger.warning(
                 f"Graph execution #{graph_exec_id} not active/running: "
                 "possibly already completed/cancelled."
             )
+        else:
+            future, cancel_event = self.active_graph_runs[graph_exec_id]
+            if not cancel_event.is_set():
+                cancel_event.set()
+                future.result()
 
-        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)
+        # Update the status of the graph & node executions
+        self.db_client.update_graph_execution_stats(
+            graph_exec_id,
+            ExecutionStatus.TERMINATED,
+        )
+        node_execs = self.db_client.get_node_execution_results(
+            graph_exec_id=graph_exec_id,
+            statuses=[
+                ExecutionStatus.QUEUED,
+                ExecutionStatus.RUNNING,
+                ExecutionStatus.INCOMPLETE,
+            ],
+        )
+        self.db_client.update_node_execution_status_batch(
+            [node_exec.node_exec_id for node_exec in node_execs],
+            ExecutionStatus.TERMINATED,
+        )
         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)
+            node_exec.status = ExecutionStatus.TERMINATED
+            self.db_client.send_execution_update(node_exec)
 
     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}")
+            block = node.block
 
             # Find any fields of type CredentialsMetaInput
             credentials_fields = cast(

autogpt_platform/backend/backend/executor/scheduler.py

@@ -1,19 +1,23 @@
 import logging
 import os
+from enum import Enum
 from urllib.parse import parse_qs, urlencode, urlparse, urlunparse
 
 from apscheduler.events import EVENT_JOB_ERROR, EVENT_JOB_EXECUTED
 from apscheduler.job import Job as JobObj
+from apscheduler.jobstores.memory import MemoryJobStore
 from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore
 from apscheduler.schedulers.blocking import BlockingScheduler
 from apscheduler.triggers.cron import CronTrigger
 from autogpt_libs.utils.cache import thread_cached
 from dotenv import load_dotenv
+from prisma.enums import NotificationType
 from pydantic import BaseModel
 from sqlalchemy import MetaData, create_engine
 
 from backend.data.block import BlockInput
 from backend.executor.manager import ExecutionManager
+from backend.notifications.notifications import NotificationManager
 from backend.util.service import AppService, expose, get_service_client
 from backend.util.settings import Config
 
@@ -42,7 +46,7 @@ config = Config()
 
 
 def log(msg, **kwargs):
-    logger.info("[ExecutionScheduler] " + msg, **kwargs)
+    logger.info("[Scheduler] " + msg, **kwargs)
 
 
 def job_listener(event):
@@ -58,8 +62,15 @@ def get_execution_client() -> ExecutionManager:
     return get_service_client(ExecutionManager)
 
 
+@thread_cached
+def get_notification_client():
+    from backend.notifications import NotificationManager
+
+    return get_service_client(NotificationManager)
+
+
 def execute_graph(**kwargs):
-    args = JobArgs(**kwargs)
+    args = ExecutionJobArgs(**kwargs)
     try:
         log(f"Executing recurring job for graph #{args.graph_id}")
         get_execution_client().add_execution(
@@ -72,7 +83,32 @@ def execute_graph(**kwargs):
         logger.exception(f"Error executing graph {args.graph_id}: {e}")
 
 
-class JobArgs(BaseModel):
+def process_existing_batches(**kwargs):
+    args = NotificationJobArgs(**kwargs)
+    try:
+        log(
+            f"Processing existing batches for notification type {args.notification_types}"
+        )
+        get_notification_client().process_existing_batches(args.notification_types)
+    except Exception as e:
+        logger.exception(f"Error processing existing batches: {e}")
+
+
+def process_weekly_summary(**kwargs):
+    try:
+        log("Processing weekly summary")
+        get_notification_client().queue_weekly_summary()
+    except Exception as e:
+        logger.exception(f"Error processing weekly summary: {e}")
+
+
+class Jobstores(Enum):
+    EXECUTION = "execution"
+    BATCHED_NOTIFICATIONS = "batched_notifications"
+    WEEKLY_NOTIFICATIONS = "weekly_notifications"
+
+
+class ExecutionJobArgs(BaseModel):
     graph_id: str
     input_data: BlockInput
     user_id: str
@@ -80,14 +116,14 @@ class JobArgs(BaseModel):
     cron: str
 
 
-class JobInfo(JobArgs):
+class ExecutionJobInfo(ExecutionJobArgs):
     id: str
     name: str
     next_run_time: str
 
     @staticmethod
-    def from_db(job_args: JobArgs, job_obj: JobObj) -> "JobInfo":
-        return JobInfo(
+    def from_db(job_args: ExecutionJobArgs, job_obj: JobObj) -> "ExecutionJobInfo":
+        return ExecutionJobInfo(
             id=job_obj.id,
             name=job_obj.name,
             next_run_time=job_obj.next_run_time.isoformat(),
@@ -95,7 +131,29 @@ class JobInfo(JobArgs):
         )
 
 
-class ExecutionScheduler(AppService):
+class NotificationJobArgs(BaseModel):
+    notification_types: list[NotificationType]
+    cron: str
+
+
+class NotificationJobInfo(NotificationJobArgs):
+    id: str
+    name: str
+    next_run_time: str
+
+    @staticmethod
+    def from_db(
+        job_args: NotificationJobArgs, job_obj: JobObj
+    ) -> "NotificationJobInfo":
+        return NotificationJobInfo(
+            id=job_obj.id,
+            name=job_obj.name,
+            next_run_time=job_obj.next_run_time.isoformat(),
+            **job_args.model_dump(),
+        )
+
+
+class Scheduler(AppService):
     scheduler: BlockingScheduler
 
     @classmethod
@@ -111,19 +169,38 @@ class ExecutionScheduler(AppService):
     def execution_client(self) -> ExecutionManager:
         return get_service_client(ExecutionManager)
 
+    @property
+    @thread_cached
+    def notification_client(self) -> NotificationManager:
+        return get_service_client(NotificationManager)
+
     def run_service(self):
         load_dotenv()
         db_schema, db_url = _extract_schema_from_url(os.getenv("DATABASE_URL"))
         self.scheduler = BlockingScheduler(
             jobstores={
-                "default": SQLAlchemyJobStore(
+                Jobstores.EXECUTION.value: SQLAlchemyJobStore(
                     engine=create_engine(
                         url=db_url,
                         pool_size=self.db_pool_size(),
                         max_overflow=0,
                     ),
                     metadata=MetaData(schema=db_schema),
-                )
+                    # this one is pre-existing so it keeps the
+                    # default table name.
+                    tablename="apscheduler_jobs",
+                ),
+                Jobstores.BATCHED_NOTIFICATIONS.value: SQLAlchemyJobStore(
+                    engine=create_engine(
+                        url=db_url,
+                        pool_size=self.db_pool_size(),
+                        max_overflow=0,
+                    ),
+                    metadata=MetaData(schema=db_schema),
+                    tablename="apscheduler_jobs_batched_notifications",
+                ),
+                # These don't really need persistence
+                Jobstores.WEEKLY_NOTIFICATIONS.value: MemoryJobStore(),
             }
         )
         self.scheduler.add_listener(job_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
@@ -137,8 +214,8 @@ class ExecutionScheduler(AppService):
         cron: str,
         input_data: BlockInput,
         user_id: str,
-    ) -> JobInfo:
-        job_args = JobArgs(
+    ) -> ExecutionJobInfo:
+        job_args = ExecutionJobArgs(
             graph_id=graph_id,
             input_data=input_data,
             user_id=user_id,
@@ -150,37 +227,80 @@ class ExecutionScheduler(AppService):
             CronTrigger.from_crontab(cron),
             kwargs=job_args.model_dump(),
             replace_existing=True,
+            jobstore=Jobstores.EXECUTION.value,
         )
         log(f"Added job {job.id} with cron schedule '{cron}' input data: {input_data}")
-        return JobInfo.from_db(job_args, job)
+        return ExecutionJobInfo.from_db(job_args, job)
 
     @expose
-    def delete_schedule(self, schedule_id: str, user_id: str) -> JobInfo:
-        job = self.scheduler.get_job(schedule_id)
+    def delete_schedule(self, schedule_id: str, user_id: str) -> ExecutionJobInfo:
+        job = self.scheduler.get_job(schedule_id, jobstore=Jobstores.EXECUTION.value)
         if not job:
             log(f"Job {schedule_id} not found.")
             raise ValueError(f"Job #{schedule_id} not found.")
 
-        job_args = JobArgs(**job.kwargs)
+        job_args = ExecutionJobArgs(**job.kwargs)
         if job_args.user_id != user_id:
             raise ValueError("User ID does not match the job's user ID.")
 
         log(f"Deleting job {schedule_id}")
         job.remove()
 
-        return JobInfo.from_db(job_args, job)
+        return ExecutionJobInfo.from_db(job_args, job)
 
     @expose
     def get_execution_schedules(
         self, graph_id: str | None = None, user_id: str | None = None
-    ) -> list[JobInfo]:
+    ) -> list[ExecutionJobInfo]:
         schedules = []
-        for job in self.scheduler.get_jobs():
-            job_args = JobArgs(**job.kwargs)
+        for job in self.scheduler.get_jobs(jobstore=Jobstores.EXECUTION.value):
+            logger.info(
+                f"Found job {job.id} with cron schedule {job.trigger} and args {job.kwargs}"
+            )
+            job_args = ExecutionJobArgs(**job.kwargs)
             if (
                 job.next_run_time is not None
                 and (graph_id is None or job_args.graph_id == graph_id)
                 and (user_id is None or job_args.user_id == user_id)
             ):
-                schedules.append(JobInfo.from_db(job_args, job))
+                schedules.append(ExecutionJobInfo.from_db(job_args, job))
         return schedules
+
+    @expose
+    def add_batched_notification_schedule(
+        self,
+        notification_types: list[NotificationType],
+        data: dict,
+        cron: str,
+    ) -> NotificationJobInfo:
+        job_args = NotificationJobArgs(
+            notification_types=notification_types,
+            cron=cron,
+        )
+        job = self.scheduler.add_job(
+            process_existing_batches,
+            CronTrigger.from_crontab(cron),
+            kwargs=job_args.model_dump(),
+            replace_existing=True,
+            jobstore=Jobstores.BATCHED_NOTIFICATIONS.value,
+        )
+        log(f"Added job {job.id} with cron schedule '{cron}' input data: {data}")
+        return NotificationJobInfo.from_db(job_args, job)
+
+    @expose
+    def add_weekly_notification_schedule(self, cron: str) -> NotificationJobInfo:
+
+        job = self.scheduler.add_job(
+            process_weekly_summary,
+            CronTrigger.from_crontab(cron),
+            kwargs={},
+            replace_existing=True,
+            jobstore=Jobstores.WEEKLY_NOTIFICATIONS.value,
+        )
+        log(f"Added job {job.id} with cron schedule '{cron}'")
+        return NotificationJobInfo.from_db(
+            NotificationJobArgs(
+                cron=cron, notification_types=[NotificationType.WEEKLY_SUMMARY]
+            ),
+            job,
+        )

autogpt_platform/backend/backend/executor/utils.py

@@ -0,0 +1,97 @@
+from pydantic import BaseModel
+
+from backend.data.block import Block, BlockInput
+from backend.data.block_cost_config import BLOCK_COSTS
+from backend.data.cost import BlockCostType
+from backend.util.settings import Config
+
+config = Config()
+
+
+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()
+    )

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
 
@@ -153,7 +154,8 @@ 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}'")
 

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/email.py

@@ -7,9 +7,9 @@ from prisma.enums import NotificationType
 from pydantic import BaseModel
 
 from backend.data.notifications import (
+    NotificationDataType_co,
     NotificationEventModel,
     NotificationTypeOverride,
-    T_co,
 )
 from backend.util.settings import Settings
 from backend.util.text import TextFormatter
@@ -48,7 +48,11 @@ class EmailSender:
         self,
         notification: NotificationType,
         user_email: str,
-        data: NotificationEventModel[T_co] | list[NotificationEventModel[T_co]],
+        data: (
+            NotificationEventModel[NotificationDataType_co]
+            | list[NotificationEventModel[NotificationDataType_co]]
+        ),
+        user_unsub_link: str | None = None,
     ):
         """Send an email to a user using a template pulled from the notification type"""
         if not self.postmark:
@@ -56,20 +60,34 @@ class EmailSender:
             return
         template = self._get_template(notification)
 
+        base_url = (
+            settings.config.frontend_base_url or settings.config.platform_base_url
+        )
+
+        # Handle the case when data is a list
+        template_data = data
+        if isinstance(data, list):
+            # Create a dictionary with a 'notifications' key containing the list
+            template_data = {"notifications": data}
+
         try:
             subject, full_message = self.formatter.format_email(
                 base_template=template.base_template,
                 subject_template=template.subject_template,
                 content_template=template.body_template,
-                data=data,
-                unsubscribe_link="https://platform.agpt.co/profile/settings",
+                data=template_data,
+                unsubscribe_link=f"{base_url}/profile/settings",
             )
-
         except Exception as e:
             logger.error(f"Error formatting full message: {e}")
             raise e
 
-        self._send_email(user_email, subject, full_message)
+        self._send_email(
+            user_email=user_email,
+            user_unsubscribe_link=user_unsub_link,
+            subject=subject,
+            body=full_message,
+        )
 
     def _get_template(self, notification: NotificationType):
         # convert the notification type to a notification type override
@@ -90,7 +108,13 @@ class EmailSender:
             base_template=base_template,
         )
 
-    def _send_email(self, user_email: str, subject: str, body: str):
+    def _send_email(
+        self,
+        user_email: str,
+        subject: str,
+        body: str,
+        user_unsubscribe_link: str | None = None,
+    ):
         if not self.postmark:
             logger.warning("Email tried to send without postmark configured")
             return
@@ -100,4 +124,13 @@ class EmailSender:
             To=user_email,
             Subject=subject,
             HtmlBody=body,
+            # Headers default to None internally so this is fine
+            Headers=(
+                {
+                    "List-Unsubscribe-Post": "List-Unsubscribe=One-Click",
+                    "List-Unsubscribe": f"<{user_unsubscribe_link}>",
+                }
+                if user_unsubscribe_link
+                else None
+            ),
         )

autogpt_platform/backend/backend/notifications/notifications.py

@@ -1,27 +1,36 @@
 import logging
 import time
+from datetime import datetime, timedelta, timezone
 from typing import Callable
 
 import aio_pika
 from aio_pika.exceptions import QueueEmpty
+from autogpt_libs.utils.cache import thread_cached
 from prisma.enums import NotificationType
 from pydantic import BaseModel
 
 from backend.data.notifications import (
+    BaseSummaryData,
+    BaseSummaryParams,
+    DailySummaryData,
+    DailySummaryParams,
     NotificationEventDTO,
     NotificationEventModel,
     NotificationResult,
+    NotificationTypeOverride,
     QueueType,
-    get_data_type,
+    SummaryParamsEventDTO,
+    SummaryParamsEventModel,
+    WeeklySummaryData,
+    WeeklySummaryParams,
+    get_batch_delay,
+    get_notif_data_type,
+    get_summary_params_type,
 )
 from backend.data.rabbitmq import Exchange, ExchangeType, Queue, RabbitMQConfig
-from backend.data.user import (
-    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
+from backend.util.service import AppService, expose, get_service_client
 from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
@@ -59,6 +68,26 @@ def create_notification_config() -> RabbitMQConfig:
                 "x-dead-letter-routing-key": "failed.admin",
             },
         ),
+        # Summary notification queues
+        Queue(
+            name="summary_notifications",
+            exchange=notification_exchange,
+            routing_key="notification.summary.#",
+            arguments={
+                "x-dead-letter-exchange": dead_letter_exchange.name,
+                "x-dead-letter-routing-key": "failed.summary",
+            },
+        ),
+        # Batch Queue
+        Queue(
+            name="batch_notifications",
+            exchange=notification_exchange,
+            routing_key="notification.batch.#",
+            arguments={
+                "x-dead-letter-exchange": dead_letter_exchange.name,
+                "x-dead-letter-routing-key": "failed.batch",
+            },
+        ),
         # Failed notifications queue
         Queue(
             name="failed_notifications",
@@ -76,12 +105,25 @@ def create_notification_config() -> RabbitMQConfig:
     )
 
 
+@thread_cached
+def get_scheduler():
+    from backend.executor import 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()
@@ -90,19 +132,171 @@ class NotificationManager(AppService):
     def get_port(cls) -> int:
         return settings.config.notification_service_port
 
-    def get_routing_key(self, event: NotificationEventModel) -> str:
+    def get_routing_key(self, event_type: NotificationType) -> str:
+        strategy = NotificationTypeOverride(event_type).strategy
         """Get the appropriate routing key for an event"""
-        if event.strategy == QueueType.IMMEDIATE:
-            return f"notification.immediate.{event.type.value}"
-        elif event.strategy == QueueType.BACKOFF:
-            return f"notification.backoff.{event.type.value}"
-        elif event.strategy == QueueType.ADMIN:
-            return f"notification.admin.{event.type.value}"
-        elif event.strategy == QueueType.HOURLY:
-            return f"notification.hourly.{event.type.value}"
-        elif event.strategy == QueueType.DAILY:
-            return f"notification.daily.{event.type.value}"
-        return f"notification.{event.type.value}"
+        if strategy == QueueType.IMMEDIATE:
+            return f"notification.immediate.{event_type.value}"
+        elif strategy == QueueType.BACKOFF:
+            return f"notification.backoff.{event_type.value}"
+        elif strategy == QueueType.ADMIN:
+            return f"notification.admin.{event_type.value}"
+        elif strategy == QueueType.BATCH:
+            return f"notification.batch.{event_type.value}"
+        elif strategy == QueueType.SUMMARY:
+            return f"notification.summary.{event_type.value}"
+        return f"notification.{event_type.value}"
+
+    @expose
+    def queue_weekly_summary(self):
+        """Process weekly summary for specified notification types"""
+        try:
+            logger.info("Processing weekly summary queuing operation")
+            processed_count = 0
+            current_time = datetime.now(tz=timezone.utc)
+            start_time = current_time - timedelta(days=7)
+            users = get_db().get_active_user_ids_in_timerange(
+                end_time=current_time.isoformat(),
+                start_time=start_time.isoformat(),
+            )
+            for user in users:
+
+                self._queue_scheduled_notification(
+                    SummaryParamsEventDTO(
+                        user_id=user,
+                        type=NotificationType.WEEKLY_SUMMARY,
+                        data=WeeklySummaryParams(
+                            start_date=start_time,
+                            end_date=current_time,
+                        ).model_dump(),
+                    ),
+                )
+                processed_count += 1
+
+            logger.info(f"Processed {processed_count} weekly summaries into queue")
+
+        except Exception as e:
+            logger.exception(f"Error processing weekly summary: {e}")
+
+    @expose
+    def process_existing_batches(self, notification_types: list[NotificationType]):
+        """Process existing batches for specified notification types"""
+        try:
+            processed_count = 0
+            current_time = datetime.now(tz=timezone.utc)
+
+            for notification_type in notification_types:
+                # Get all batches for this notification type
+                batches = get_db().get_all_batches_by_type(notification_type)
+
+                for batch in batches:
+                    # Check if batch has aged out
+                    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.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.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.user_id}"
+                            )
+                            continue
+
+                        should_send = self._should_email_user_based_on_preference(
+                            batch.user_id, notification_type
+                        )
+
+                        if not should_send:
+                            logger.debug(
+                                f"User {batch.user_id} does not want to receive {notification_type} notifications"
+                            )
+                            # Clear the batch
+                            get_db().empty_user_notification_batch(
+                                batch.user_id, notification_type
+                            )
+                            continue
+
+                        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.user_id}"
+                            )
+                            # Clear the batch
+                            get_db().empty_user_notification_batch(
+                                batch.user_id, notification_type
+                            )
+                            continue
+
+                        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(
+                            notification=notification_type,
+                            user_email=recipient_email,
+                            data=events,
+                            user_unsub_link=unsub_link,
+                        )
+
+                        # Clear the batch
+                        get_db().empty_user_notification_batch(
+                            batch.user_id, notification_type
+                        )
+
+                        processed_count += 1
+
+            logger.info(f"Processed {processed_count} aged batches")
+            return {
+                "success": True,
+                "processed_count": processed_count,
+                "notification_types": [nt.value for nt in notification_types],
+                "timestamp": current_time.isoformat(),
+            }
+
+        except Exception as e:
+            logger.exception(f"Error processing batches: {e}")
+            return {
+                "success": False,
+                "error": str(e),
+                "notification_types": [nt.value for nt in notification_types],
+                "timestamp": datetime.now(tz=timezone.utc).isoformat(),
+            }
 
     @expose
     def queue_notification(self, event: NotificationEventDTO) -> NotificationResult:
@@ -111,9 +305,9 @@ class NotificationManager(AppService):
             logger.info(f"Received Request to queue {event=}")
             # Workaround for not being able to serialize generics over the expose bus
             parsed_event = NotificationEventModel[
-                get_data_type(event.type)
+                get_notif_data_type(event.type)
             ].model_validate(event.model_dump())
-            routing_key = self.get_routing_key(parsed_event)
+            routing_key = self.get_routing_key(parsed_event.type)
             message = parsed_event.model_dump_json()
 
             logger.info(f"Received Request to queue {message=}")
@@ -140,22 +334,160 @@ class NotificationManager(AppService):
             logger.exception(f"Error queueing notification: {e}")
             return NotificationResult(success=False, message=str(e))
 
+    def _queue_scheduled_notification(self, event: SummaryParamsEventDTO):
+        """Queue a scheduled notification - exposed method for other services to call"""
+        try:
+            logger.info(f"Received Request to queue scheduled notification {event=}")
+
+            parsed_event = SummaryParamsEventModel[
+                get_summary_params_type(event.type)
+            ].model_validate(event.model_dump())
+
+            routing_key = self.get_routing_key(event.type)
+            message = parsed_event.model_dump_json()
+
+            logger.info(f"Received Request to queue {message=}")
+
+            exchange = "notifications"
+
+            # Publish to RabbitMQ
+            self.run_and_wait(
+                self.rabbit.publish_message(
+                    routing_key=routing_key,
+                    message=message,
+                    exchange=next(
+                        ex for ex in self.rabbit_config.exchanges if ex.name == exchange
+                    ),
+                )
+            )
+
+        except Exception as e:
+            logger.exception(f"Error queueing notification: {e}")
+
     def _should_email_user_based_on_preference(
         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
 
+    def _gather_summary_data(
+        self, user_id: str, event_type: NotificationType, params: BaseSummaryParams
+    ) -> BaseSummaryData:
+        """Gathers the data to build a summary notification"""
+
+        logger.info(
+            f"Gathering summary data for {user_id} and {event_type} wiht {params=}"
+        )
+
+        # total_credits_used = self.run_and_wait(
+        #     get_total_credits_used(user_id, start_time, end_time)
+        # )
+
+        # total_executions = self.run_and_wait(
+        #     get_total_executions(user_id, start_time, end_time)
+        # )
+
+        # most_used_agent = self.run_and_wait(
+        #     get_most_used_agent(user_id, start_time, end_time)
+        # )
+
+        # execution_times = self.run_and_wait(
+        #     get_execution_time(user_id, start_time, end_time)
+        # )
+
+        # runs = self.run_and_wait(
+        #     get_runs(user_id, start_time, end_time)
+        # )
+        total_credits_used = 3.0
+        total_executions = 2
+        most_used_agent = {"name": "Some"}
+        execution_times = [1, 2, 3]
+        runs = [{"status": "COMPLETED"}, {"status": "FAILED"}]
+
+        successful_runs = len([run for run in runs if run["status"] == "COMPLETED"])
+        failed_runs = len([run for run in runs if run["status"] != "COMPLETED"])
+        average_execution_time = (
+            sum(execution_times) / len(execution_times) if execution_times else 0
+        )
+        # cost_breakdown = self.run_and_wait(
+        #     get_cost_breakdown(user_id, start_time, end_time)
+        # )
+
+        cost_breakdown = {
+            "agent1": 1.0,
+            "agent2": 2.0,
+        }
+
+        if event_type == NotificationType.DAILY_SUMMARY and isinstance(
+            params, DailySummaryParams
+        ):
+            return DailySummaryData(
+                total_credits_used=total_credits_used,
+                total_executions=total_executions,
+                most_used_agent=most_used_agent["name"],
+                total_execution_time=sum(execution_times),
+                successful_runs=successful_runs,
+                failed_runs=failed_runs,
+                average_execution_time=average_execution_time,
+                cost_breakdown=cost_breakdown,
+                date=params.date,
+            )
+        elif event_type == NotificationType.WEEKLY_SUMMARY and isinstance(
+            params, WeeklySummaryParams
+        ):
+            return WeeklySummaryData(
+                total_credits_used=total_credits_used,
+                total_executions=total_executions,
+                most_used_agent=most_used_agent["name"],
+                total_execution_time=sum(execution_times),
+                successful_runs=successful_runs,
+                failed_runs=failed_runs,
+                average_execution_time=average_execution_time,
+                cost_breakdown=cost_breakdown,
+                start_date=params.start_date,
+                end_date=params.end_date,
+            )
+        else:
+            raise ValueError("Invalid event type or params")
+
+    def _should_batch(
+        self, user_id: str, event_type: NotificationType, event: NotificationEventModel
+    ) -> bool:
+
+        get_db().create_or_add_to_user_notification_batch(user_id, event_type, event)
+
+        oldest_message = get_db().get_user_notification_oldest_message_in_batch(
+            user_id, event_type
+        )
+        if not oldest_message:
+            logger.error(
+                f"Batch for user {user_id} and type {event_type} has no oldest message whichshould never happen!!!!!!!!!!!!!!!!"
+            )
+            return False
+        oldest_age = oldest_message.created_at
+
+        max_delay = get_batch_delay(event_type)
+
+        if oldest_age + max_delay < datetime.now(tz=timezone.utc):
+            logger.info(f"Batch for user {user_id} and type {event_type} is old enough")
+            return True
+        logger.info(
+            f"Batch for user {user_id} and type {event_type} is not old enough: {oldest_age + max_delay} < {datetime.now(tz=timezone.utc)} max_delay={max_delay}"
+        )
+        return False
+
     def _parse_message(self, message: str) -> NotificationEvent | None:
         try:
             event = NotificationEventDTO.model_validate_json(message)
             model = NotificationEventModel[
-                get_data_type(event.type)
+                get_notif_data_type(event.type)
             ].model_validate_json(message)
             return NotificationEvent(event=event, model=model)
         except Exception as e:
@@ -175,7 +507,7 @@ class NotificationManager(AppService):
             self.email_sender.send_templated(event.type, recipient_email, model)
             return True
         except Exception as e:
-            logger.exception(f"Error processing notification: {e}")
+            logger.exception(f"Error processing notification for admin queue: {e}")
             return False
 
     def _process_immediate(self, message: str) -> bool:
@@ -188,7 +520,7 @@ 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
@@ -202,10 +534,126 @@ class NotificationManager(AppService):
                 )
                 return True
 
-            self.email_sender.send_templated(event.type, recipient_email, model)
+            unsub_link = generate_unsubscribe_link(event.user_id)
+
+            self.email_sender.send_templated(
+                notification=event.type,
+                user_email=recipient_email,
+                data=model,
+                user_unsub_link=unsub_link,
+            )
             return True
         except Exception as e:
-            logger.exception(f"Error processing notification: {e}")
+            logger.exception(f"Error processing notification for immediate queue: {e}")
+            return False
+
+    def _process_batch(self, message: str) -> bool:
+        """Process a single notification with a batching strategy, returning whether to put into the failed queue"""
+        try:
+            parsed = self._parse_message(message)
+            if not parsed:
+                return False
+            event = parsed.event
+            model = parsed.model
+            logger.info(f"Processing batch notification: {model}")
+
+            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
+
+            should_send = self._should_email_user_based_on_preference(
+                event.user_id, event.type
+            )
+            if not should_send:
+                logger.info(
+                    f"User {event.user_id} does not want to receive {event.type} notifications"
+                )
+                return True
+
+            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 = 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
+            unsub_link = generate_unsubscribe_link(event.user_id)
+
+            batch_messages = [
+                NotificationEventModel[
+                    get_notif_data_type(db_event.type)
+                ].model_validate(
+                    {
+                        "user_id": event.user_id,
+                        "type": db_event.type,
+                        "data": db_event.data,
+                        "created_at": db_event.created_at,
+                    }
+                )
+                for db_event in batch.notifications
+            ]
+
+            self.email_sender.send_templated(
+                notification=event.type,
+                user_email=recipient_email,
+                data=batch_messages,
+                user_unsub_link=unsub_link,
+            )
+            # only empty the batch if we sent the email successfully
+            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}")
+            return False
+
+    def _process_summary(self, message: str) -> bool:
+        """Process a single notification with a summary strategy, returning whether to put into the failed queue"""
+        try:
+            logger.info(f"Processing summary notification: {message}")
+            event = SummaryParamsEventDTO.model_validate_json(message)
+            model = SummaryParamsEventModel[
+                get_summary_params_type(event.type)
+            ].model_validate_json(message)
+
+            logger.info(f"Processing summary notification: {model}")
+
+            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
+            should_send = self._should_email_user_based_on_preference(
+                event.user_id, event.type
+            )
+            if not should_send:
+                logger.info(
+                    f"User {event.user_id} does not want to receive {event.type} notifications"
+                )
+                return True
+
+            summary_data = self._gather_summary_data(
+                event.user_id, event.type, model.data
+            )
+
+            unsub_link = generate_unsubscribe_link(event.user_id)
+
+            data = NotificationEventModel(
+                user_id=event.user_id,
+                type=event.type,
+                data=summary_data,
+            )
+
+            self.email_sender.send_templated(
+                notification=event.type,
+                user_email=recipient_email,
+                data=data,
+                user_unsub_link=unsub_link,
+            )
+            return True
+        except Exception as e:
+            logger.exception(f"Error processing notification for summary queue: {e}")
             return False
 
     def _run_queue(
@@ -226,6 +674,8 @@ 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(
@@ -233,22 +683,41 @@ 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}] 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
+        try:
+            get_scheduler().add_batched_notification_schedule(
+                notification_types=list(NotificationType),
+                data={},
+                cron="0 * * * *",
+            )
+            # get_scheduler().add_weekly_notification_schedule(
+            #     # weekly on Friday at 12pm
+            #     cron="0 12 * * 5",
+            # )
+            logger.info("Scheduled notification cleanup")
+        except Exception as e:
+            logger.error(f"Error scheduling notification cleanup: {e}")
+
         # Set up queue consumers
         channel = self.run_and_wait(self.rabbit.get_channel())
 
         immediate_queue = self.run_and_wait(
             channel.get_queue("immediate_notifications")
         )
+        batch_queue = self.run_and_wait(channel.get_queue("batch_notifications"))
 
         admin_queue = self.run_and_wait(channel.get_queue("admin_notifications"))
 
+        summary_queue = self.run_and_wait(channel.get_queue("summary_notifications"))
+
         while self.running:
             try:
                 self._run_queue(
@@ -261,6 +730,17 @@ class NotificationManager(AppService):
                     process_func=self._process_admin_message,
                     error_queue_name="admin_notifications",
                 )
+                self._run_queue(
+                    queue=batch_queue,
+                    process_func=self._process_batch,
+                    error_queue_name="batch_notifications",
+                )
+
+                self._run_queue(
+                    queue=summary_queue,
+                    process_func=self._process_summary,
+                    error_queue_name="summary_notifications",
+                )
 
                 time.sleep(0.1)
 

autogpt_platform/backend/backend/notifications/templates/agent_run.html.jinja2

@@ -1,75 +1,142 @@
 {# Agent Run #}
 {# Template variables:
-data.name: the name of the agent
+notification.data: the stuff below but a list of them
+data.agent_name: the name of the agent
 data.credits_used: the number of credits used by the agent
 data.node_count: the number of nodes the agent ran on
 data.execution_time: the time it took to run the agent
 data.graph_id: the id of the graph the agent ran on
-data.outputs: the dict[str, Any] of outputs of the agent
+data.outputs: the list of outputs of the agent
 #}
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-  Hi,
-</p>
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-  We've run your agent {{ data.name }} and it took {{ data.execution_time }} seconds to complete.
-</p>
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-  It ran on {{ data.node_count }} nodes and used {{ data.credits_used }} credits.
-</p>
-<ul style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-  It output the following:
-  {# jinja2 list iteration thorugh data.outputs #}
-  {% for key, value in data.outputs.items() %}
-  <li>{{ key }}: {{ value }}</li>
-  {% endfor %}
-</ul>
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-  Your feedback has been instrumental in shaping AutoGPT, and we couldn't have
-  done it without you. We look forward to continuing this journey together as we
-  bring AI-powered automation to the world.
-</p>
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 0;
-  ">
-  Thank you again for your time and support.
-</p>
+{% if notifications is defined %}
+  {# BATCH MODE #}
+  <div style="font-family: 'Poppins', sans-serif; color: #070629;">
+    <h2 style="color: #5D23BB; margin-bottom: 15px;">Agent Run Summary</h2>
+    <p style="font-size: 16px; line-height: 165%; margin-top: 0; margin-bottom: 15px;">
+      <strong>{{ notifications|length }}</strong> agent runs have completed!
+    </p>
+    
+    {# Calculate summary stats #}
+    {% set total_time = 0 %}
+    {% set total_nodes = 0 %}
+    {% set total_credits = 0 %}
+    {% set agent_names = [] %}
+    
+    {% for notification in notifications %}
+      {% set total_time = total_time + notification.data.execution_time %}
+      {% set total_nodes = total_nodes + notification.data.node_count %}
+      {% set total_credits = total_credits + notification.data.credits_used %}
+      {% if notification.data.agent_name not in agent_names %}
+        {% set agent_names = agent_names + [notification.data.agent_name] %}
+      {% endif %}
+    {% endfor %}
+    
+    <div style="background-color: #f8f7ff; border-radius: 8px; padding: 15px; margin-bottom: 25px;">
+      <h3 style="margin-top: 0; margin-bottom: 10px; color: #5D23BB;">Summary</h3>
+      <p style="margin: 5px 0;"><strong>Agents:</strong> {{ agent_names|join(", ") }}</p>
+      <p style="margin: 5px 0;"><strong>Total Time:</strong> {{ total_time | int }} seconds</p>
+      <p style="margin: 5px 0;"><strong>Total Nodes:</strong> {{ total_nodes }}</p>
+      <p style="margin: 5px 0;"><strong>Total Cost:</strong> ${{ "{:.2f}".format((total_credits|float)/100) }}</p>
+    </div>
+    
+    <h3 style="margin-top: 25px; margin-bottom: 15px; color: #5D23BB;">Individual Runs</h3>
+    
+    {% for notification in notifications %}
+      <div style="margin-bottom: 30px; border-left: 3px solid #5D23BB; padding-left: 15px;">
+        <p style="font-size: 16px; font-weight: 600; margin-top: 0; margin-bottom: 10px;">
+          Agent: <strong>{{ notification.data.agent_name }}</strong>
+        </p>
+        
+        <div style="margin-left: 10px;">
+          <p style="margin: 5px 0;"><strong>Time:</strong> {{ notification.data.execution_time | int }} seconds</p>
+          <p style="margin: 5px 0;"><strong>Nodes:</strong> {{ notification.data.node_count }}</p>
+          <p style="margin: 5px 0;"><strong>Cost:</strong> ${{ "{:.2f}".format((notification.data.credits_used|float)/100) }}</p>
+        </div>
+        
+        {% if notification.data.outputs and notification.data.outputs|length > 0 %}
+        <div style="margin-left: 10px; margin-top: 15px;">
+          <p style="font-weight: 600; margin-bottom: 10px;">Results:</p>
+          
+          {% for output in notification.data.outputs %}
+          <div style="margin-left: 10px; margin-bottom: 12px;">
+            <p style="color: #5D23BB; font-weight: 500; margin-top: 0; margin-bottom: 5px;">
+              {{ output.name }}
+            </p>
+            
+            {% for key, value in output.items() %}
+              {% if key != 'name' %}
+              <div style="margin-left: 10px; background-color: #f5f5ff; padding: 8px 12px; border-radius: 4px; 
+                          font-family: 'Roboto Mono', monospace; white-space: pre-wrap; word-break: break-word; 
+                          overflow-wrap: break-word; max-width: 100%; overflow-x: auto; margin-top: 3px; 
+                          margin-bottom: 8px; line-height: 1.4;">
+              {% if value is iterable and value is not string %}
+                {% if value|length == 1 %}
+                  {{ value[0] }}
+                {% else %}
+                  [{% for item in value %}{{ item }}{% if not loop.last %}, {% endif %}{% endfor %}]
+                {% endif %}
+              {% else %}
+                {{ value }}
+              {% endif %}
+              </div>
+              {% endif %}
+            {% endfor %}
+          </div>
+          {% endfor %}
+        </div>
+        {% endif %}
+      </div>
+    {% endfor %}
+  </div>
+
+{% else %}
+  {# SINGLE NOTIFICATION MODE - Original template #}
+  <p style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 165%; 
+            margin-top: 0; margin-bottom: 10px;">
+    Your agent, <strong>{{ data.agent_name }}</strong>, has completed its run!
+  </p>
+  
+  <p style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 165%; 
+            margin-top: 0; margin-bottom: 20px; padding-left: 20px;">
+    <p style="margin-bottom: 10px;"><strong>Time Taken:</strong> {{ data.execution_time | int }} seconds</p>
+    <p style="margin-bottom: 10px;"><strong>Nodes Used:</strong> {{ data.node_count }}</p>
+    <p style="margin-bottom: 10px;"><strong>Cost:</strong> ${{ "{:.2f}".format((data.credits_used|float)/100) }}</p>
+  </p>
+  
+  {% if data.outputs and data.outputs|length > 0 %}
+  <div style="margin-left: 15px; margin-bottom: 20px;">
+    <p style="font-family: 'Poppins', sans-serif; color: #070629; font-weight: 600; 
+              font-size: 16px; margin-bottom: 10px;">
+      Results:
+    </p>
+  
+    {% for output in data.outputs %}
+    <div style="margin-left: 15px; margin-bottom: 15px;">
+      <p style="font-family: 'Poppins', sans-serif; color: #5D23BB; font-weight: 500; 
+                font-size: 16px; margin-top: 0; margin-bottom: 8px;">
+        {{ output.name }}
+      </p>
+      
+      {% for key, value in output.items() %}
+        {% if key != 'name' %}
+        <div style="margin-left: 15px; background-color: #f5f5ff; padding: 8px 12px; border-radius: 4px; 
+                    font-family: 'Roboto Mono', monospace; white-space: pre-wrap; word-break: break-word; 
+                    overflow-wrap: break-word; max-width: 100%; overflow-x: auto; margin-top: 5px; 
+                    margin-bottom: 10px; line-height: 1.4;">
+        {% if value is iterable and value is not string %}
+          {% if value|length == 1 %}
+            {{ value[0] }}
+          {% else %}
+            [{% for item in value %}{{ item }}{% if not loop.last %}, {% endif %}{% endfor %}]
+          {% endif %}
+        {% else %}
+          {{ value }}
+        {% endif %}
+        </div>
+        {% endif %}
+      {% endfor %}
+    </div>
+    {% endfor %}
+  </div>
+  {% endif %}
+{% endif %}

autogpt_platform/backend/backend/notifications/templates/base.html.jinja2

@@ -227,19 +227,10 @@
                       <table class="ml-8 wrapper" border="0" cellspacing="0" cellpadding="0"
                         style="color: #070629; text-align: left;">
                         <tr>
-                          <td class="col mobile-center" align="center" width="80">
-                            <img
-                              src="https://storage.mlcdn.com/account_image/597379/68W8w94Zwl52yQyrKdFERRquu2CivAcn17ST22HF.jpg"
-                              border="0" alt="" width="80" class="avatar"
-                              style="display: inline-block; max-width: 80px; border-radius: 80px;">
-                          </td>
-                          <td class="col" width="30" height="30" style="line-height: 30px;"></td>
                           <td class="col center mobile-center" align>
                             <p
                               style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 165%; margin-top: 0; margin-bottom: 0;">
-                              John Ababseh<br>Product Manager<br>
-                              <a href="mailto:john.ababseh@agpt.co" target="_blank"
-                                style="color: #4285F4; font-weight: normal; font-style: normal; text-decoration: underline;">john.ababseh@agpt.co</a>
+                              Thank you for being a part of the AutoGPT community! Join the conversation on our Discord <a href="https://discord.gg/autogpt" style="color: #4285F4; text-decoration: underline;">here</a> and share your thoughts with us anytime.
                             </p>
                           </td>
                         </tr>

autogpt_platform/backend/backend/notifications/templates/low_balance.html.jinja2

@@ -0,0 +1,114 @@
+{# Low Balance Notification Email Template #}
+{# Template variables:
+data.agent_name: the name of the agent
+data.current_balance: the current balance of the user
+data.billing_page_link: the link to the billing page
+data.shortfall: the shortfall amount
+#}
+
+<p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    line-height: 165%;
+    margin-top: 0;
+    margin-bottom: 10px;
+  ">
+  <strong>Low Balance Warning</strong>
+</p>
+
+<p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    line-height: 165%;
+    margin-top: 0;
+    margin-bottom: 20px;
+  ">
+  Your agent "<strong>{{ data.agent_name }}</strong>" has been stopped due to low balance.
+</p>
+
+<div style="
+    margin-left: 15px;
+    margin-bottom: 20px;
+    padding: 15px;
+    border-left: 4px solid #5D23BB;
+    background-color: #f8f8ff;
+  ">
+  <p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    margin-top: 0;
+    margin-bottom: 10px;
+  ">
+    <strong>Current Balance:</strong> ${{ "{:.2f}".format((data.current_balance|float)/100) }}
+  </p>
+  <p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    margin-top: 0;
+    margin-bottom: 10px;
+  ">
+    <strong>Shortfall:</strong> ${{ "{:.2f}".format((data.shortfall|float)/100) }}
+  </p>
+</div>
+
+
+<div style="
+    margin-left: 15px;
+    margin-bottom: 20px;
+    padding: 15px;
+    border-left: 4px solid #FF6B6B;
+    background-color: #FFF0F0;
+  ">
+  <p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    margin-top: 0;
+    margin-bottom: 10px;
+  ">
+    <strong>Low Balance:</strong>
+  </p>
+  <p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    margin-top: 0;
+    margin-bottom: 5px;
+  ">
+    Your agent "<strong>{{ data.agent_name }}</strong>" requires additional credits to continue running. The current operation has been canceled until your balance is replenished.
+  </p>
+</div>
+
+<div style="
+    text-align: center;
+    margin: 30px 0;
+  ">
+  <a href="{{ data.billing_page_link }}" style="
+    font-family: 'Poppins', sans-serif;
+    background-color: #5D23BB;
+    color: white;
+    padding: 12px 24px;
+    text-decoration: none;
+    border-radius: 4px;
+    font-weight: 500;
+    display: inline-block;
+  ">
+    Manage Billing
+  </a>
+</div>
+
+<p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    line-height: 150%;
+    margin-top: 30px;
+    margin-bottom: 10px;
+    font-style: italic;
+  ">
+  This is an automated notification. Your agent is stopped and will need manually restarted unless set to trigger automatically.
+</p>

autogpt_platform/backend/backend/notifications/templates/weekly_summary.html.jinja2

@@ -0,0 +1,27 @@
+{# Weekly Summary #}
+{# Template variables:
+data: the stuff below
+data.start_date: the start date of the summary
+data.end_date: the end date of the summary
+data.total_credits_used: the total credits used during the summary
+data.total_executions: the total number of executions during the summary
+data.most_used_agent: the most used agent's nameduring the summary
+data.total_execution_time: the total execution time during the summary
+data.successful_runs: the total number of successful runs during the summary
+data.failed_runs: the total number of failed runs during the summary
+data.average_execution_time: the average execution time during the summary
+data.cost_breakdown: the cost breakdown during the summary
+#}
+
+<h1>Weekly Summary</h1>
+
+<p>Start Date: {{ data.start_date }}</p>
+<p>End Date: {{ data.end_date }}</p>
+<p>Total Credits Used: {{ data.total_credits_used }}</p>
+<p>Total Executions: {{ data.total_executions }}</p>
+<p>Most Used Agent: {{ data.most_used_agent }}</p>
+<p>Total Execution Time: {{ data.total_execution_time }}</p>
+<p>Successful Runs: {{ data.successful_runs }}</p>
+<p>Failed Runs: {{ data.failed_runs }}</p>
+<p>Average Execution Time: {{ data.average_execution_time }}</p>
+<p>Cost Breakdown: {{ data.cost_breakdown }}</p>

autogpt_platform/backend/backend/rest.py

@@ -1,5 +1,5 @@
 from backend.app import run_processes
-from backend.executor import DatabaseManager, ExecutionScheduler
+from backend.executor import DatabaseManager, Scheduler
 from backend.notifications.notifications import NotificationManager
 from backend.server.rest_api import AgentServer
 
@@ -11,7 +11,7 @@ def main():
     run_processes(
         NotificationManager(),
         DatabaseManager(),
-        ExecutionScheduler(),
+        Scheduler(),
         AgentServer(),
     )
 

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

@@ -12,7 +12,7 @@ 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.data.execution import NodeExecutionResult
 from backend.executor import ExecutionManager
 from backend.server.external.middleware import require_permission
 from backend.util.service import get_service_client
@@ -53,7 +53,7 @@ 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 +71,7 @@ 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(
@@ -130,7 +130,7 @@ 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

@@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Annotated, 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 (
@@ -17,8 +18,8 @@ from backend.executor.manager import ExecutionManager
 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.integrations.webhooks import get_webhook_manager
+from backend.util.exceptions import NeedConfirmation, NotFoundError
 from backend.util.service import get_service_client
 from backend.util.settings import Settings
 
@@ -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(
@@ -323,7 +330,7 @@ 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 +365,6 @@ 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 +375,7 @@ 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

@@ -11,17 +11,20 @@ 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.postmark.postmark
+import backend.server.v2.otto.routes
 import backend.server.v2.store.model
 import backend.server.v2.store.routes
 import backend.util.service
@@ -54,6 +57,8 @@ 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()
+    # FIXME ERROR: operator does not exist: text ? unknown
+    # await backend.data.graph.migrate_llm_models(LlmModel.GPT4O)
     with launch_darkly_context():
         yield
     await backend.data.db.disconnect()
@@ -68,8 +73,7 @@ docs_url = (
 app = fastapi.FastAPI(
     title="AutoGPT Agent Server",
     description=(
-        "This server is used to execute agents that are created by the "
-        "AutoGPT system."
+        "This server is used to execute agents that are created by the AutoGPT system."
     ),
     summary="AutoGPT Agent Server",
     version="0.1",
@@ -99,12 +103,21 @@ 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"
 )
 app.include_router(
-    backend.server.v2.postmark.postmark.router,
-    tags=["v2", "email"],
+    backend.server.v2.otto.routes.router, tags=["v2"], prefix="/api/otto"
+)
+
+app.include_router(
+    backend.server.routers.postmark.postmark.router,
+    tags=["v1", "email"],
     prefix="/api/email",
 )
 
@@ -129,6 +142,7 @@ 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(),
         )
 
     @staticmethod
@@ -150,9 +164,10 @@ 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
@@ -164,21 +179,15 @@ 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(
@@ -245,12 +254,16 @@ 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(

autogpt_platform/backend/backend/server/routers/postmark/postmark.py

@@ -2,10 +2,15 @@ import logging
 from typing import Annotated
 
 from autogpt_libs.auth.middleware import APIKeyValidator
-from fastapi import APIRouter, Body, Depends
+from fastapi import APIRouter, Body, Depends, Query
+from fastapi.responses import JSONResponse
 
-from backend.data.user import get_user_by_email, set_user_email_verification
-from backend.server.v2.postmark.models import (
+from backend.data.user import (
+    get_user_by_email,
+    set_user_email_verification,
+    unsubscribe_user_by_token,
+)
+from backend.server.routers.postmark.models import (
     PostmarkBounceEnum,
     PostmarkBounceWebhook,
     PostmarkClickWebhook,
@@ -23,13 +28,24 @@ postmark_validator = APIKeyValidator(
     settings.secrets.postmark_webhook_token,
 )
 
-router = APIRouter(dependencies=[Depends(postmark_validator.get_dependency())])
+router = APIRouter()
 
 
 logger = logging.getLogger(__name__)
 
 
-@router.post("/")
+@router.post("/unsubscribe")
+async def unsubscribe_via_one_click(token: Annotated[str, Query()]):
+    logger.info(f"Received unsubscribe request from One Click Unsubscribe: {token}")
+    try:
+        await unsubscribe_user_by_token(token)
+    except Exception as e:
+        logger.error(f"Failed to unsubscribe user by token {token}: {e}")
+        raise e
+    return JSONResponse(status_code=200, content={"status": "ok"})
+
+
+@router.post("/", dependencies=[Depends(postmark_validator.get_dependency())])
 async def postmark_webhook_handler(
     webhook: Annotated[
         PostmarkWebhook,

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

@@ -10,7 +10,7 @@ from autogpt_libs.auth.middleware import auth_middleware
 from autogpt_libs.feature_flag.client import feature_flag
 from autogpt_libs.utils.cache import thread_cached
 from fastapi import APIRouter, Body, Depends, HTTPException, Request, Response
-from starlette.status import HTTP_204_NO_CONTENT
+from starlette.status import HTTP_204_NO_CONTENT, HTTP_404_NOT_FOUND
 from typing_extensions import Optional, TypedDict
 
 import backend.data.block
@@ -38,18 +38,24 @@ from backend.data.credit import (
     TransactionHistory,
     get_auto_top_up,
     get_block_costs,
-    get_stripe_customer_id,
     get_user_credit_model,
     set_auto_top_up,
 )
 from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
+from backend.data.onboarding import (
+    UserOnboardingUpdate,
+    get_recommended_agents,
+    get_user_onboarding,
+    onboarding_enabled,
+    update_user_onboarding,
+)
 from backend.data.user import (
     get_or_create_user,
     get_user_notification_preference,
     update_user_email,
     update_user_notification_preference,
 )
-from backend.executor import ExecutionManager, ExecutionScheduler, scheduler
+from backend.executor import ExecutionManager, Scheduler, scheduler
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks.graph_lifecycle_hooks import (
     on_graph_activate,
@@ -78,8 +84,8 @@ def execution_manager_client() -> ExecutionManager:
 
 
 @thread_cached
-def execution_scheduler_client() -> ExecutionScheduler:
-    return get_service_client(ExecutionScheduler)
+def execution_scheduler_client() -> Scheduler:
+    return get_service_client(Scheduler)
 
 
 settings = Settings()
@@ -152,6 +158,47 @@ async def update_preferences(
     return output
 
 
+########################################################
+##################### Onboarding #######################
+########################################################
+
+
+@v1_router.get(
+    "/onboarding", tags=["onboarding"], dependencies=[Depends(auth_middleware)]
+)
+async def get_onboarding(user_id: Annotated[str, Depends(get_user_id)]):
+    return await get_user_onboarding(user_id)
+
+
+@v1_router.patch(
+    "/onboarding", tags=["onboarding"], dependencies=[Depends(auth_middleware)]
+)
+async def update_onboarding(
+    user_id: Annotated[str, Depends(get_user_id)], data: UserOnboardingUpdate
+):
+    return await update_user_onboarding(user_id, data)
+
+
+@v1_router.get(
+    "/onboarding/agents",
+    tags=["onboarding"],
+    dependencies=[Depends(auth_middleware)],
+)
+async def get_onboarding_agents(
+    user_id: Annotated[str, Depends(get_user_id)],
+):
+    return await get_recommended_agents(user_id)
+
+
+@v1_router.get(
+    "/onboarding/enabled",
+    tags=["onboarding", "public"],
+    dependencies=[Depends(auth_middleware)],
+)
+async def is_onboarding_enabled():
+    return await onboarding_enabled()
+
+
 ########################################################
 ##################### Blocks ###########################
 ########################################################
@@ -161,7 +208,9 @@ async def update_preferences(
 def get_graph_blocks() -> Sequence[dict[Any, Any]]:
     blocks = [block() for block in backend.data.block.get_blocks().values()]
     costs = get_block_costs()
-    return [{**b.to_dict(), "costs": costs.get(b.id, [])} for b in blocks]
+    return [
+        {**b.to_dict(), "costs": costs.get(b.id, [])} for b in blocks if not b.disabled
+    ]
 
 
 @v1_router.post(
@@ -303,15 +352,7 @@ async def stripe_webhook(request: Request):
 async def manage_payment_method(
     user_id: Annotated[str, Depends(get_user_id)],
 ) -> dict[str, str]:
-    session = stripe.billing_portal.Session.create(
-        customer=await get_stripe_customer_id(user_id),
-        return_url=settings.config.frontend_base_url + "/profile/credits",
-    )
-    if not session:
-        raise HTTPException(
-            status_code=400, detail="Failed to create billing portal session"
-        )
-    return {"url": session.url}
+    return {"url": await _user_credit_model.create_billing_portal_session(user_id)}
 
 
 @v1_router.get(path="/credits/transactions", dependencies=[Depends(auth_middleware)])
@@ -367,10 +408,10 @@ async def get_graph(
     graph_id: str,
     user_id: Annotated[str, Depends(get_user_id)],
     version: int | None = None,
-    hide_credentials: bool = False,
+    for_export: bool = False,
 ) -> graph_db.GraphModel:
     graph = await graph_db.get_graph(
-        graph_id, version, user_id=user_id, for_export=hide_credentials
+        graph_id, version, user_id=user_id, for_export=for_export
     )
     if not graph:
         raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")
@@ -395,18 +436,19 @@ async def get_graph_all_versions(
     path="/graphs", tags=["graphs"], dependencies=[Depends(auth_middleware)]
 )
 async def create_new_graph(
-    create_graph: CreateGraph, user_id: Annotated[str, Depends(get_user_id)]
+    create_graph: CreateGraph,
+    user_id: Annotated[str, Depends(get_user_id)],
 ) -> graph_db.GraphModel:
     graph = graph_db.make_graph_model(create_graph.graph, user_id)
     graph.reassign_ids(user_id=user_id, reassign_graph_id=True)
+    graph.validate_graph(for_run=False)
 
     graph = await graph_db.create_graph(graph, user_id=user_id)
 
     # Create a library agent for the new graph
-    await library_db.create_library_agent(
-        graph.id,
-        graph.version,
-        user_id,
+    library_agent = await library_db.create_library_agent(graph, user_id)
+    _ = asyncio.create_task(
+        library_db.add_generated_agent_image(graph, library_agent.id)
     )
 
     graph = await on_graph_activate(
@@ -451,17 +493,10 @@ async def update_graph(
     latest_version_number = max(g.version for g in existing_versions)
     graph.version = latest_version_number + 1
 
-    latest_version_graph = next(
-        v for v in existing_versions if v.version == latest_version_number
-    )
     current_active_version = next((v for v in existing_versions if v.is_active), None)
-    if latest_version_graph.is_template != graph.is_template:
-        raise HTTPException(
-            400, detail="Changing is_template on an existing graph is forbidden"
-        )
-    graph.is_active = not graph.is_template
     graph = graph_db.make_graph_model(graph, user_id)
-    graph.reassign_ids(user_id=user_id)
+    graph.reassign_ids(user_id=user_id, reassign_graph_id=False)
+    graph.validate_graph(for_run=False)
 
     new_graph_version = await graph_db.create_graph(graph, user_id=user_id)
 
@@ -551,14 +586,10 @@ def execute_graph(
     user_id: Annotated[str, Depends(get_user_id)],
     graph_version: Optional[int] = None,
 ) -> ExecuteGraphResponse:
-    try:
-        graph_exec = execution_manager_client().add_execution(
-            graph_id, node_input, user_id=user_id, graph_version=graph_version
-        )
-        return ExecuteGraphResponse(graph_exec_id=graph_exec.graph_exec_id)
-    except Exception as e:
-        msg = str(e).encode().decode("unicode_escape")
-        raise HTTPException(status_code=400, detail=msg)
+    graph_exec = execution_manager_client().add_execution(
+        graph_id, node_input, user_id=user_id, graph_version=graph_version
+    )
+    return ExecuteGraphResponse(graph_exec_id=graph_exec.graph_exec_id)
 
 
 @v1_router.post(
@@ -568,8 +599,8 @@ def execute_graph(
 )
 async def stop_graph_run(
     graph_exec_id: str, user_id: Annotated[str, Depends(get_user_id)]
-) -> graph_db.GraphExecution:
-    if not await graph_db.get_execution_meta(
+) -> execution_db.GraphExecution:
+    if not await execution_db.get_graph_execution_meta(
         user_id=user_id, execution_id=graph_exec_id
     ):
         raise HTTPException(404, detail=f"Agent execution #{graph_exec_id} not found")
@@ -579,7 +610,9 @@ async def stop_graph_run(
     )
 
     # Retrieve & return canceled graph execution in its final state
-    result = await graph_db.get_execution(execution_id=graph_exec_id, user_id=user_id)
+    result = await execution_db.get_graph_execution(
+        execution_id=graph_exec_id, user_id=user_id
+    )
     if not result:
         raise HTTPException(
             500,
@@ -595,8 +628,8 @@ async def stop_graph_run(
 )
 async def get_graphs_executions(
     user_id: Annotated[str, Depends(get_user_id)],
-) -> list[graph_db.GraphExecutionMeta]:
-    return await graph_db.get_graphs_executions(user_id=user_id)
+) -> list[execution_db.GraphExecutionMeta]:
+    return await execution_db.get_graph_executions(user_id=user_id)
 
 
 @v1_router.get(
@@ -607,8 +640,8 @@ async def get_graphs_executions(
 async def get_graph_executions(
     graph_id: str,
     user_id: Annotated[str, Depends(get_user_id)],
-) -> list[graph_db.GraphExecutionMeta]:
-    return await graph_db.get_graph_executions(graph_id=graph_id, user_id=user_id)
+) -> list[execution_db.GraphExecutionMeta]:
+    return await execution_db.get_graph_executions(graph_id=graph_id, user_id=user_id)
 
 
 @v1_router.get(
@@ -620,14 +653,22 @@ async def get_graph_execution(
     graph_id: str,
     graph_exec_id: str,
     user_id: Annotated[str, Depends(get_user_id)],
-) -> graph_db.GraphExecution:
-    graph = await graph_db.get_graph(graph_id, user_id=user_id)
+) -> execution_db.GraphExecution | execution_db.GraphExecutionWithNodes:
+    graph = await graph_db.get_graph(graph_id=graph_id, user_id=user_id)
     if not graph:
-        raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")
+        raise HTTPException(
+            status_code=HTTP_404_NOT_FOUND, detail=f"Graph #{graph_id} not found"
+        )
 
-    result = await graph_db.get_execution(execution_id=graph_exec_id, user_id=user_id)
-    if not result:
-        raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")
+    result = await execution_db.get_graph_execution(
+        user_id=user_id,
+        execution_id=graph_exec_id,
+        include_node_executions=graph.user_id == user_id,
+    )
+    if not result or result.graph_id != graph_id:
+        raise HTTPException(
+            status_code=404, detail=f"Graph execution #{graph_exec_id} not found."
+        )
 
     return result
 
@@ -642,7 +683,9 @@ async def delete_graph_execution(
     graph_exec_id: str,
     user_id: Annotated[str, Depends(get_user_id)],
 ) -> None:
-    await execution_db.delete_execution(graph_exec_id=graph_exec_id, user_id=user_id)
+    await execution_db.delete_graph_execution(
+        graph_exec_id=graph_exec_id, user_id=user_id
+    )
 
 
 ########################################################
@@ -665,7 +708,7 @@ class ScheduleCreationRequest(pydantic.BaseModel):
 async def create_schedule(
     user_id: Annotated[str, Depends(get_user_id)],
     schedule: ScheduleCreationRequest,
-) -> scheduler.JobInfo:
+) -> scheduler.ExecutionJobInfo:
     graph = await graph_db.get_graph(
         schedule.graph_id, schedule.graph_version, user_id=user_id
     )
@@ -707,7 +750,7 @@ def delete_schedule(
 def get_execution_schedules(
     user_id: Annotated[str, Depends(get_user_id)],
     graph_id: str | None = None,
-) -> list[scheduler.JobInfo]:
+) -> list[scheduler.ExecutionJobInfo]:
     return execution_scheduler_client().get_execution_schedules(
         user_id=user_id,
         graph_id=graph_id,

autogpt_platform/backend/backend/server/v2/admin/store_admin_routes.py

@@ -0,0 +1,100 @@
+import logging
+import typing
+
+import autogpt_libs.auth.depends
+import fastapi
+import fastapi.responses
+import prisma.enums
+
+import backend.server.v2.store.db
+import backend.server.v2.store.exceptions
+import backend.server.v2.store.model
+
+logger = logging.getLogger(__name__)
+
+router = fastapi.APIRouter(prefix="/admin", tags=["store", "admin"])
+
+
+@router.get(
+    "/listings",
+    response_model=backend.server.v2.store.model.StoreListingsWithVersionsResponse,
+    dependencies=[fastapi.Depends(autogpt_libs.auth.depends.requires_admin_user)],
+)
+async def get_admin_listings_with_versions(
+    status: typing.Optional[prisma.enums.SubmissionStatus] = None,
+    search: typing.Optional[str] = None,
+    page: int = 1,
+    page_size: int = 20,
+):
+    """
+    Get store listings with their version history for admins.
+
+    This provides a consolidated view of listings with their versions,
+    allowing for an expandable UI in the admin dashboard.
+
+    Args:
+        status: Filter by submission status (PENDING, APPROVED, REJECTED)
+        search: Search by name, description, or user email
+        page: Page number for pagination
+        page_size: Number of items per page
+
+    Returns:
+        StoreListingsWithVersionsResponse with listings and their versions
+    """
+    try:
+        listings = await backend.server.v2.store.db.get_admin_listings_with_versions(
+            status=status,
+            search_query=search,
+            page=page,
+            page_size=page_size,
+        )
+        return listings
+    except Exception as e:
+        logger.exception("Error getting admin listings with versions: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={
+                "detail": "An error occurred while retrieving listings with versions"
+            },
+        )
+
+
+@router.post(
+    "/submissions/{store_listing_version_id}/review",
+    response_model=backend.server.v2.store.model.StoreSubmission,
+    dependencies=[fastapi.Depends(autogpt_libs.auth.depends.requires_admin_user)],
+)
+async def review_submission(
+    store_listing_version_id: str,
+    request: backend.server.v2.store.model.ReviewSubmissionRequest,
+    user: typing.Annotated[
+        autogpt_libs.auth.models.User,
+        fastapi.Depends(autogpt_libs.auth.depends.requires_admin_user),
+    ],
+):
+    """
+    Review a store listing submission.
+
+    Args:
+        store_listing_version_id: ID of the submission to review
+        request: Review details including approval status and comments
+        user: Authenticated admin user performing the review
+
+    Returns:
+        StoreSubmission with updated review information
+    """
+    try:
+        submission = await backend.server.v2.store.db.review_store_submission(
+            store_listing_version_id=store_listing_version_id,
+            is_approved=request.is_approved,
+            external_comments=request.comments,
+            internal_comments=request.internal_comments or "",
+            reviewer_id=user.user_id,
+        )
+        return submission
+    except Exception as e:
+        logger.exception("Error reviewing submission: %s", e)
+        return fastapi.responses.JSONResponse(
+            status_code=500,
+            content={"detail": "An error occurred while reviewing the submission"},
+        )