Merge branch 'swiftyos/example-block' of github.com:Significant-Gravitas/AutoGPT into swiftyos/example-block

Co-authored-by: Reinier van der Leer <pwuts@agpt.co>

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

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

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

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

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

@@ -80,35 +80,18 @@ jobs:
 
       - name: Install Poetry (Unix)
         run: |
-          # 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 -
+          curl -sSL https://install.python-poetry.org | 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 --ignore-matching-lines="^# " poetry.lock; then
+          if ! git diff --quiet poetry.lock; then
             echo "Error: poetry.lock not up to date."
             echo
             git diff poetry.lock
@@ -135,7 +118,6 @@ jobs:
         run: poetry run prisma migrate dev --name updates
         env:
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
-          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
 
       - id: lint
         name: Run Linter
@@ -152,13 +134,12 @@ jobs:
         env:
           LOG_LEVEL: ${{ runner.debug && 'DEBUG' || 'INFO' }}
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
-          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
           SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
           SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
           SUPABASE_JWT_SECRET: ${{ steps.supabase.outputs.JWT_SECRET }}
-          REDIS_HOST: "localhost"
-          REDIS_PORT: "6379"
-          REDIS_PASSWORD: "testpassword"
+          REDIS_HOST: 'localhost'
+          REDIS_PORT: '6379'
+          REDIS_PASSWORD: 'testpassword'
 
     env:
       CI: true
@@ -171,8 +152,8 @@ jobs:
       # If you want to replace this, you can do so by making our entire system generate
       # new credentials for each local user and update the environment variables in
       # the backend service, docker composes, and examples
-      RABBITMQ_DEFAULT_USER: "rabbitmq_user_default"
-      RABBITMQ_DEFAULT_PASS: "k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7"
+      RABBITMQ_DEFAULT_USER: 'rabbitmq_user_default'
+      RABBITMQ_DEFAULT_PASS: 'k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7'
 
       # - name: Upload coverage reports to Codecov
       #   uses: codecov/codecov-action@v4

.github/workflows/repo-close-stale-issues.yml

@@ -16,7 +16,7 @@ jobs:
           # operations-per-run: 5000
           stale-issue-message: >
             This issue has automatically been marked as _stale_ because it has not had
-            any activity in the last 170 days. You can _unstale_ it by commenting or
+            any activity in the last 50 days. You can _unstale_ it by commenting or
             removing the label. Otherwise, this issue will be closed in 10 days.
           stale-pr-message: >
             This pull request has automatically been marked as _stale_ because it has
@@ -25,7 +25,7 @@ jobs:
           close-issue-message: >
             This issue was closed automatically because it has been stale for 10 days
             with no activity.
-          days-before-stale: 170
+          days-before-stale: 100
           days-before-close: 10
           # Do not touch meta issues:
           exempt-issue-labels: meta,fridge,project management

.pre-commit-config.yaml

@@ -140,7 +140,7 @@ repos:
         language: system
 
   - repo: https://github.com/psf/black
-    rev: 24.10.0
+    rev: 23.12.1
     # 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

@@ -2,6 +2,9 @@
 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

@@ -15,11 +15,7 @@
 > 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.
 
-### 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/)
-
+https://github.com/user-attachments/assets/d04273a5-b36a-4a37-818e-f631ce72d603
 
 This tutorial assumes you have Docker, VSCode, git and npm installed.
 

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -1,9 +1,11 @@
+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

@@ -1,11 +1,14 @@
 import os
 
+from dotenv import load_dotenv
+
+load_dotenv()
+
 
 class Settings:
-    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"
+    JWT_SECRET_KEY: str = os.getenv("SUPABASE_JWT_SECRET", "")
+    ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
+    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
+from .formatters import AGPTFormatter, StructuredLoggingFormatter
 
 LOG_DIR = Path(__file__).parent.parent.parent.parent / "logs"
 LOG_FILE = "activity.log"
@@ -81,26 +81,9 @@ 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
@@ -114,7 +97,28 @@ 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:
@@ -152,6 +156,7 @@ 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

@@ -1,6 +1,7 @@
 import logging
 
 from colorama import Fore, Style
+from google.cloud.logging_v2.handlers import CloudLoggingFilter, StructuredLogHandler
 
 from .utils import remove_color_codes
 
@@ -79,3 +80,16 @@ 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

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

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -1,59 +1,20 @@
-import inspect
 import threading
-from typing import Awaitable, Callable, ParamSpec, TypeVar, cast, overload
+from typing import Callable, ParamSpec, TypeVar
 
 P = ParamSpec("P")
 R = TypeVar("R")
 
 
-@overload
-def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
-
-
-@overload
-def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...
-
-
-def thread_cached(
-    func: Callable[P, R] | Callable[P, Awaitable[R]],
-) -> Callable[P, R] | Callable[P, Awaitable[R]]:
+def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
     thread_local = threading.local()
 
-    def _clear():
-        if hasattr(thread_local, "cache"):
-            del thread_local.cache
+    def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+        cache = getattr(thread_local, "cache", None)
+        if cache is None:
+            cache = thread_local.cache = {}
+        key = (args, tuple(sorted(kwargs.items())))
+        if key not in cache:
+            cache[key] = func(*args, **kwargs)
+        return cache[key]
 
-    if inspect.iscoroutinefunction(func):
-
-        async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
-            cache = getattr(thread_local, "cache", None)
-            if cache is None:
-                cache = thread_local.cache = {}
-            key = (args, tuple(sorted(kwargs.items())))
-            if key not in cache:
-                cache[key] = await cast(Callable[P, Awaitable[R]], func)(
-                    *args, **kwargs
-                )
-            return cache[key]
-
-        setattr(async_wrapper, "clear_cache", _clear)
-        return async_wrapper
-
-    else:
-
-        def sync_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
-            cache = getattr(thread_local, "cache", None)
-            if cache is None:
-                cache = thread_local.cache = {}
-            key = (args, tuple(sorted(kwargs.items())))
-            if key not in cache:
-                cache[key] = func(*args, **kwargs)
-            return cache[key]
-
-        setattr(sync_wrapper, "clear_cache", _clear)
-        return sync_wrapper
-
-
-def clear_thread_cache(func: Callable) -> None:
-    if clear := getattr(func, "clear_cache", None):
-        clear()
+    return wrapper

autogpt_platform/autogpt_libs/autogpt_libs/utils/synchronize.py

@@ -31,7 +31,7 @@ class RedisKeyedMutex:
         try:
             yield
         finally:
-            if lock.locked() and lock.owned():
+            if lock.locked():
                 lock.release()
 
     def acquire(self, key: Any) -> "RedisLock":

autogpt_platform/autogpt_libs/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -14,104 +14,113 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.15"
+version = "3.10.5"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "aiohttp-3.11.15-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:078b1ce274f967951b42a65d5b7ec115b7886343a5271f2eed330458ea87bb48"},
-    {file = "aiohttp-3.11.15-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:48d790d05c76b6cb93a1259b44d3f0019b61d507f8ebf0d49da3ef5ac858b05d"},
-    {file = "aiohttp-3.11.15-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e13767424425bb607931a0b9e703b95d2693650011ef8f0166b4cd80066b66b9"},
-    {file = "aiohttp-3.11.15-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ac49c32901489343b4dab064ab520f6b879a03fb4f9667c84620b66f07bed69"},
-    {file = "aiohttp-3.11.15-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:576b56a34d26ea8a8f0e1a30b8a069ba4ab121fb8eb796d1b89fedda7c74c553"},
-    {file = "aiohttp-3.11.15-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:71ed71d9431e770550aab27a77ef9d30d33ce6f558ab7818be7205ae6aca635d"},
-    {file = "aiohttp-3.11.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c982b2cbd7b8f4b31e9faf2de09e22b060a6cd0a693f20892dda41a8fb0f46ef"},
-    {file = "aiohttp-3.11.15-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f00e7540a60460fbeaffff4a7dcf72fe8b710f8280a542a4d798273787c64e72"},
-    {file = "aiohttp-3.11.15-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:9b97fae7f75a0b666ce4281627856d1b1a85477f26c2e8b266292e770c17df36"},
-    {file = "aiohttp-3.11.15-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:a449c48f5b02c0c14f5310881558ca861bff8e00b1f79be4cf6a53f638464c30"},
-    {file = "aiohttp-3.11.15-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:8c6e39f0bb2418f839841f92b3cd64077ff5166d724c984ab450ca08d5e51d92"},
-    {file = "aiohttp-3.11.15-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:e792131352418dde7b0c598e217e89ecf6a26889f46f35f910e5544ffdebf3ae"},
-    {file = "aiohttp-3.11.15-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:c28cbae1ce76dc48d0fcccb045ac21d00dcc1b306bb745910cf35585ce89404e"},
-    {file = "aiohttp-3.11.15-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d9c8417a24063f35b526f8bf14f6f4bdea6f3f49850457337b6ea928901adbbc"},
-    {file = "aiohttp-3.11.15-cp310-cp310-win32.whl", hash = "sha256:a50b86eb9cf74fa5b6f1386e08e1520dcbe83d7dfd4c8bf6f2ca72b03d42e79f"},
-    {file = "aiohttp-3.11.15-cp310-cp310-win_amd64.whl", hash = "sha256:a0361cafb50b185356a5f346c169aea1d14783df99e6da714d626b104586e0b5"},
-    {file = "aiohttp-3.11.15-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:5bd37d615cd26d09321bd0168305f8508778712cf38aeffeed550274fb48a2ee"},
-    {file = "aiohttp-3.11.15-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a3d706afcc808f6add4208dfa13f911fd93c2a3dab6be484fee4fd0602a0867e"},
-    {file = "aiohttp-3.11.15-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:43625253e3dc018d34867b70909149f15f29eac0382802afe027f2fbf17bcb9c"},
-    {file = "aiohttp-3.11.15-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:437eee9e057a7907b11e4af2b18df56b6c795b28e0a3ac250691936cf6bf40eb"},
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-    {file = "aiohttp-3.11.15-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ae3a5d9f2fbe736fec7d24be25c57aa78c2d78d96540439ea68a8abbed9906fc"},
-    {file = "aiohttp-3.11.15-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:269d145c593a65f78fb9a64dece90341561ddb2e91a96d42681132b2f706c42a"},
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-    {file = "aiohttp-3.11.15-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:7cf4b2b5a0f7a738ecd759eaeaef800fc7c57683b7be9d8a43fcb86ca62701b4"},
-    {file = "aiohttp-3.11.15-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:f1e0369f0dc8c895e718ce37147f56d46142d37596be183ab7a34192c5e6e4c5"},
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-    {file = "aiohttp-3.11.15-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:5a61df20fa77792e83307e266f76790f7cb67980dd476948948de212ee7ec64c"},
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 ]
 
 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-propcache = ">=0.2.0"
-yarl = ">=1.17.0,<2.0"
+yarl = ">=1.0,<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\""]
@@ -177,7 +186,7 @@ files = [
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 ]
-markers = {main = "python_version == \"3.10\"", dev = "python_full_version < \"3.11.3\""}
+markers = {main = "python_version < \"3.11\"", dev = "python_full_version < \"3.11.3\""}
 
 [[package]]
 name = "attrs"
@@ -375,7 +384,7 @@ description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "python_version == \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
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@@ -1062,114 +1071,6 @@ 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.dependencies]
@@ -1372,14 +1273,14 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydantic-settings"
-version = "2.8.1"
+version = "2.7.1"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1434,14 +1335,14 @@ dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments
 
 [[package]]
 name = "pytest-asyncio"
-version = "0.26.0"
+version = "0.25.3"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -1501,21 +1402,21 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "realtime"
-version = "2.4.2"
+version = "2.0.2"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
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 [package.dependencies]
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+aiohttp = ">=3.10.2,<4.0.0"
 python-dateutil = ">=2.8.1,<3.0.0"
 typing-extensions = ">=4.12.2,<5.0.0"
-websockets = ">=11,<15"
+websockets = ">=11,<13"
 
 [[package]]
 name = "redis"
@@ -1575,30 +1476,30 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.11.2"
+version = "0.9.10"
 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]]
@@ -1660,21 +1561,21 @@ test = ["pylint", "pytest", "pytest-black", "pytest-cov", "pytest-pylint"]
 
 [[package]]
 name = "supabase"
-version = "2.15.0"
+version = "2.13.0"
 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,<1.1"
-realtime = ">=2.4.0,<2.5.0"
+postgrest = ">=0.19,<0.20"
+realtime = ">=2.0.0,<3.0.0"
 storage3 = ">=0.10,<0.12"
 supafunc = ">=0.9,<0.10"
 
@@ -1701,7 +1602,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version == \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
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@@ -1719,21 +1620,6 @@ 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"]
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-]
-
-[package.dependencies]
-typing-extensions = ">=4.12.0"
-
 [[package]]
 name = "urllib3"
 version = "2.2.2"
@@ -1916,100 +1802,109 @@ files = [
 
 [[package]]
 name = "yarl"
-version = "1.18.3"
+version = "1.11.1"
 description = "Yet another URL library"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["main"]
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+    {file = "yarl-1.11.1-py3-none-any.whl", hash = "sha256:72bf26f66456baa0584eff63e44545c9f0eaed9b73cb6601b647c91f14c11f38"},
+    {file = "yarl-1.11.1.tar.gz", hash = "sha256:1bb2d9e212fb7449b8fb73bc461b51eaa17cc8430b4a87d87be7b25052d92f53"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
-propcache = ">=0.2.0"
 
 [[package]]
 name = "zipp"
@@ -2034,4 +1929,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "c8e23c0609cae0717447f575849b658bee9203b784ec7270b62629cddbbbd9ca"
+content-hash = "931772287f71c539575d601e6398423bf68e09ca87ae1a144057c7f5707cf978"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -10,17 +10,18 @@ packages = [{ include = "autogpt_libs" }]
 colorama = "^0.4.6"
 expiringdict = "^1.2.2"
 google-cloud-logging = "^3.11.4"
-pydantic = "^2.11.1"
-pydantic-settings = "^2.8.1"
+pydantic = "^2.10.6"
+pydantic-settings = "^2.7.1"
 pyjwt = "^2.10.1"
-pytest-asyncio = "^0.26.0"
+pytest-asyncio = "^0.25.3"
 pytest-mock = "^3.14.0"
 python = ">=3.10,<4.0"
-supabase = "^2.15.0"
+python-dotenv = "^1.0.1"
+supabase = "^2.13.0"
 
 [tool.poetry.group.dev.dependencies]
 redis = "^5.2.1"
-ruff = "^0.11.0"
+ruff = "^0.9.10"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.example

@@ -8,7 +8,6 @@ DB_CONNECT_TIMEOUT=60
 DB_POOL_TIMEOUT=300
 DB_SCHEMA=platform
 DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
-DIRECT_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
 PRISMA_SCHEMA="postgres/schema.prisma"
 
 # EXECUTOR
@@ -174,6 +173,9 @@ EXA_API_KEY=
 # E2B
 E2B_API_KEY=
 
+# Example API Key
+EXAMPLE_API_KEY=
+
 # Mem0
 MEM0_API_KEY=
 
@@ -189,8 +191,6 @@ SMARTLEAD_API_KEY=
 # ZeroBounce
 ZEROBOUNCE_API_KEY=
 
-## ===== OPTIONAL API KEYS END ===== ##
-
 # Logging Configuration
 LOG_LEVEL=INFO
 ENABLE_CLOUD_LOGGING=false

autogpt_platform/backend/Dockerfile

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

autogpt_platform/backend/backend/blocks/__init__.py

@@ -2,103 +2,88 @@ import importlib
 import os
 import re
 from pathlib import Path
-from typing import TYPE_CHECKING, TypeVar
+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]] = {}
 
-if TYPE_CHECKING:
-    from backend.data.block import Block
 
 T = TypeVar("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]]:
+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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/basic.py

@@ -4,19 +4,22 @@ from typing import Any, List
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
 from backend.data.model import SchemaField
 from backend.util import json
-from backend.util.file import store_media_file
+from backend.util.file import MediaFile, store_media_file
 from backend.util.mock import MockObject
-from backend.util.type import MediaFileType, convert
+from backend.util.text import TextFormatter
+from backend.util.type import convert
+
+formatter = TextFormatter()
 
 
 class FileStoreBlock(Block):
     class Input(BlockSchema):
-        file_in: MediaFileType = SchemaField(
+        file_in: MediaFile = 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: MediaFileType = SchemaField(
+        file_out: MediaFile = SchemaField(
             description="The relative path to the stored file in the temporary directory."
         )
 
@@ -88,6 +91,29 @@ 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")
@@ -148,10 +174,192 @@ class FindInDictionaryBlock(Block):
             yield "missing", input_data.input
 
 
+class AgentInputBlock(Block):
+    """
+    This block is used to provide input to the graph.
+
+    It takes in a value, name, description, default values list and bool to limit selection to default values.
+
+    It Outputs the value passed as input.
+    """
+
+    class Input(BlockSchema):
+        name: str = SchemaField(description="The name of the input.")
+        value: Any = SchemaField(
+            description="The value to be passed as input.",
+            default=None,
+        )
+        title: str | None = SchemaField(
+            description="The title of the input.", default=None, advanced=True
+        )
+        description: str | None = SchemaField(
+            description="The description of the input.",
+            default=None,
+            advanced=True,
+        )
+        placeholder_values: List[Any] = SchemaField(
+            description="The placeholder values to be passed as input.",
+            default=[],
+            advanced=True,
+        )
+        limit_to_placeholder_values: bool = SchemaField(
+            description="Whether to limit the selection to placeholder values.",
+            default=False,
+            advanced=True,
+        )
+        advanced: bool = SchemaField(
+            description="Whether to show the input in the advanced section, if the field is not required.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the input should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
+
+    class Output(BlockSchema):
+        result: Any = SchemaField(description="The value passed as input.")
+
+    def __init__(self):
+        super().__init__(
+            id="c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
+            description="This block is used to provide input to the graph.",
+            input_schema=AgentInputBlock.Input,
+            output_schema=AgentInputBlock.Output,
+            test_input=[
+                {
+                    "value": "Hello, World!",
+                    "name": "input_1",
+                    "description": "This is a test input.",
+                    "placeholder_values": [],
+                    "limit_to_placeholder_values": False,
+                },
+                {
+                    "value": "Hello, World!",
+                    "name": "input_2",
+                    "description": "This is a test input.",
+                    "placeholder_values": ["Hello, World!"],
+                    "limit_to_placeholder_values": True,
+                },
+            ],
+            test_output=[
+                ("result", "Hello, World!"),
+                ("result", "Hello, World!"),
+            ],
+            categories={BlockCategory.INPUT, BlockCategory.BASIC},
+            block_type=BlockType.INPUT,
+            static_output=True,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        yield "result", input_data.value
+
+
+class AgentOutputBlock(Block):
+    """
+    Records the output of the graph for users to see.
+
+    Behavior:
+        If `format` is provided and the `value` is of a type that can be formatted,
+        the block attempts to format the recorded_value using the `format`.
+        If formatting fails or no `format` is provided, the raw `value` is output.
+    """
+
+    class Input(BlockSchema):
+        value: Any = SchemaField(
+            description="The value to be recorded as output.",
+            default=None,
+            advanced=False,
+        )
+        name: str = SchemaField(description="The name of the output.")
+        title: str | None = SchemaField(
+            description="The title of the output.",
+            default=None,
+            advanced=True,
+        )
+        description: str | None = SchemaField(
+            description="The description of the output.",
+            default=None,
+            advanced=True,
+        )
+        format: str = SchemaField(
+            description="The format string to be used to format the recorded_value. Use Jinja2 syntax.",
+            default="",
+            advanced=True,
+        )
+        advanced: bool = SchemaField(
+            description="Whether to treat the output as advanced.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the output should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
+
+    class Output(BlockSchema):
+        output: Any = SchemaField(description="The value recorded as output.")
+        name: Any = SchemaField(description="The name of the value recorded as output.")
+
+    def __init__(self):
+        super().__init__(
+            id="363ae599-353e-4804-937e-b2ee3cef3da4",
+            description="Stores the output of the graph for users to see.",
+            input_schema=AgentOutputBlock.Input,
+            output_schema=AgentOutputBlock.Output,
+            test_input=[
+                {
+                    "value": "Hello, World!",
+                    "name": "output_1",
+                    "description": "This is a test output.",
+                    "format": "{{ output_1 }}!!",
+                },
+                {
+                    "value": "42",
+                    "name": "output_2",
+                    "description": "This is another test output.",
+                    "format": "{{ output_2 }}",
+                },
+                {
+                    "value": MockObject(value="!!", key="key"),
+                    "name": "output_3",
+                    "description": "This is a test output with a mock object.",
+                    "format": "{{ output_3 }}",
+                },
+            ],
+            test_output=[
+                ("output", "Hello, World!!!"),
+                ("output", "42"),
+                ("output", MockObject(value="!!", key="key")),
+            ],
+            categories={BlockCategory.OUTPUT, BlockCategory.BASIC},
+            block_type=BlockType.OUTPUT,
+            static_output=True,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        """
+        Attempts to format the recorded_value using the fmt_string if provided.
+        If formatting fails or no fmt_string is given, returns the original recorded_value.
+        """
+        if input_data.format:
+            try:
+                yield "output", formatter.format_string(
+                    input_data.format, {input_data.name: input_data.value}
+                )
+            except Exception as e:
+                yield "output", f"Error: {e}, {input_data.value}"
+        else:
+            yield "output", input_data.value
+            yield "name", input_data.name
+
+
 class AddToDictionaryBlock(Block):
     class Input(BlockSchema):
         dictionary: dict[Any, Any] = SchemaField(
-            default_factory=dict,
+            default={},
             description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
         )
         key: str = SchemaField(
@@ -167,7 +375,7 @@ class AddToDictionaryBlock(Block):
             advanced=False,
         )
         entries: dict[Any, Any] = SchemaField(
-            default_factory=dict,
+            default={},
             description="The entries to add to the dictionary. This is the batch version of the `key` and `value` fields.",
             advanced=True,
         )
@@ -229,7 +437,7 @@ class AddToDictionaryBlock(Block):
 class AddToListBlock(Block):
     class Input(BlockSchema):
         list: List[Any] = SchemaField(
-            default_factory=list,
+            default=[],
             advanced=False,
             description="The list to add the entry to. If not provided, a new list will be created.",
         )
@@ -239,7 +447,7 @@ class AddToListBlock(Block):
             default=None,
         )
         entries: List[Any] = SchemaField(
-            default_factory=lambda: list(),
+            default=[],
             description="The entries to add to the list. This is the batch version of the `entry` field.",
             advanced=True,
         )

autogpt_platform/backend/backend/blocks/code_executor.py

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

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

@@ -8,7 +8,6 @@ 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
 
 
@@ -43,7 +42,7 @@ class CompassAITriggerBlock(Block):
             input_schema=CompassAITriggerBlock.Input,
             output_schema=CompassAITriggerBlock.Output,
             webhook_config=BlockManualWebhookConfig(
-                provider=ProviderName.COMPASS,
+                provider="compass",
                 webhook_type=CompassWebhookType.TRANSCRIPTION,
             ),
             test_input=[

autogpt_platform/backend/backend/blocks/csv.py

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

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/example/_api.py

@@ -0,0 +1,137 @@
+"""
+API module for Example API integration.
+
+This module provides a example of how to create a client for an API.
+"""
+
+# We also have a Json Wrapper library available in backend.util.json
+from json import JSONDecodeError
+from typing import Any, Optional
+
+from pydantic import BaseModel
+
+from backend.data.model import APIKeyCredentials
+
+# This is a wrapper around the requests library that is used to make API requests.
+from backend.util.request import Requests
+
+
+class ExampleAPIException(Exception):
+    def __init__(self, message: str, status_code: int):
+        super().__init__(message)
+        self.status_code = status_code
+
+
+class CreateResourceResponse(BaseModel):
+    message: str
+    is_funny: bool
+
+
+class GetResourceResponse(BaseModel):
+    message: str
+    is_funny: bool
+
+
+class ExampleClient:
+    """Client for the Example API"""
+
+    API_BASE_URL = "https://api.example.com/v1"
+
+    def __init__(
+        self,
+        credentials: Optional[APIKeyCredentials] = None,
+        custom_requests: Optional[Requests] = None,
+    ):
+        if custom_requests:
+            self._requests = custom_requests
+        else:
+            headers: dict[str, str] = {
+                "Content-Type": "application/json",
+            }
+            if credentials:
+                headers["Authorization"] = credentials.auth_header()
+
+            self._requests = Requests(
+                extra_headers=headers,
+                raise_for_status=False,
+            )
+
+    @staticmethod
+    def _handle_response(response) -> Any:
+        """
+        Handles API response and checks for errors.
+
+        Args:
+            response: The response object from the request.
+
+        Returns:
+            The parsed JSON response data.
+
+        Raises:
+            ExampleAPIException: If the API request fails.
+        """
+        if not response.ok:
+            try:
+                error_data = response.json()
+                error_message = error_data.get("error", {}).get("message", "")
+            except JSONDecodeError:
+                error_message = response.text
+
+            raise ExampleAPIException(
+                f"Example API request failed ({response.status_code}): {error_message}",
+                response.status_code,
+            )
+
+        response_data = response.json()
+        if "errors" in response_data:
+            # This is an example error and needs to be
+            # replaced with how the real API returns errors
+            error_messages = [
+                error.get("message", "") for error in response_data["errors"]
+            ]
+            raise ExampleAPIException(
+                f"Example API returned errors: {', '.join(error_messages)}",
+                response.status_code,
+            )
+
+        return response_data
+
+    def get_resource(self, resource_id: str) -> GetResourceResponse:
+        """
+        Fetches a resource from the Example API.
+
+        Args:
+            resource_id: The ID of the resource to fetch.
+
+        Returns:
+            The resource data as a GetResourceResponse object.
+
+        Raises:
+            ExampleAPIException: If the API request fails.
+        """
+        try:
+            response = self._requests.get(
+                f"{self.API_BASE_URL}/resources/{resource_id}"
+            )
+            return GetResourceResponse(**self._handle_response(response))
+        except Exception as e:
+            raise ExampleAPIException(f"Failed to get resource: {str(e)}", 500)
+
+    def create_resource(self, data: dict) -> CreateResourceResponse:
+        """
+        Creates a new resource via the Example API.
+
+        Args:
+            data: The resource data to create.
+
+        Returns:
+            The created resource data as a CreateResourceResponse object.
+
+        Raises:
+            ExampleAPIException: If the API request fails.
+        """
+        try:
+            response = self._requests.post(f"{self.API_BASE_URL}/resources", json=data)
+            return CreateResourceResponse(**self._handle_response(response))
+        except Exception as e:
+            raise ExampleAPIException(f"Failed to create resource: {str(e)}", 500)

autogpt_platform/backend/backend/blocks/example/_auth.py

@@ -0,0 +1,37 @@
+"""
+Authentication module for Example API integration.
+
+This module provides credential types and test credentials for the Example API integration.
+It defines the structure for API key credentials used to authenticate with the Example API
+and provides mock credentials for testing purposes.
+"""
+
+from typing import Literal
+
+from pydantic import SecretStr
+
+from backend.data.model import APIKeyCredentials, CredentialsMetaInput
+from backend.integrations.providers import ProviderName
+
+# Define the type of credentials input expected for Example API
+ExampleCredentialsInput = CredentialsMetaInput[
+    Literal[ProviderName.EXAMPLE_PROVIDER], Literal["api_key"]
+]
+
+
+# Mock credentials for testing Example API integration
+TEST_CREDENTIALS = APIKeyCredentials(
+    id="9191c4f0-498f-4235-a79c-59c0e37454d4",
+    provider="example-provider",
+    api_key=SecretStr("mock-example-api-key"),
+    title="Mock Example API key",
+    expires_at=None,
+)
+
+# Dictionary representation of test credentials for input fields
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.title,
+}

autogpt_platform/backend/backend/blocks/example/example.py

@@ -0,0 +1,154 @@
+import logging
+
+from pydantic import BaseModel
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import APIKeyCredentials, CredentialsField, SchemaField
+
+from ._api import ExampleClient
+from ._auth import TEST_CREDENTIALS, TEST_CREDENTIALS_INPUT, ExampleCredentialsInput
+
+logger = logging.getLogger(__name__)
+
+
+class GreetingMessage(BaseModel):
+    message: str
+    is_funny: bool
+
+
+class ExampleBlock(Block):
+
+    class Input(BlockSchema):
+        name: str = SchemaField(
+            description="The name of the example block", placeholder="Enter a name"
+        )
+        greetings: list[str] = SchemaField(
+            description="The greetings to display", default=["Hello", "Hi", "Hey"]
+        )
+        is_funny: bool = SchemaField(
+            description="Whether the block is funny",
+            placeholder="True",
+            default=True,
+            # Advanced fields are moved to the "Advanced" dropdown in the UI
+            advanced=True,
+        )
+        greeting_context: str = SchemaField(
+            description="The context of the greeting",
+            placeholder="Enter a context",
+            default="The user is looking for an inspirational greeting",
+            # Hidden fields are not shown in the UI at all
+            hidden=True,
+        )
+        # Only if the block needs credentials
+        credentials: ExampleCredentialsInput = CredentialsField(
+            description="The credentials for the example block"
+        )
+
+    class Output(BlockSchema):
+        response: GreetingMessage = SchemaField(
+            description="The response object generated by the example block."
+        )
+        all_responses: list[GreetingMessage] = SchemaField(
+            description="All the responses from the example block."
+        )
+        greeting_count: int = SchemaField(
+            description="The number of greetings in the input."
+        )
+        error: str = SchemaField(description="The error from the example block")
+
+    def __init__(self):
+        super().__init__(
+            # The unique identifier for the block, this value will be persisted in the DB.
+            # It should be unique and constant across the application run.
+            # Use the UUID format for the ID.
+            id="380694d5-3b2e-4130-bced-b43752b70de9",
+            # The description of the block, explaining what the block does.
+            description="The example block",
+            # The set of categories that the block belongs to.
+            # Each category is an instance of BlockCategory Enum.
+            categories={BlockCategory.BASIC},
+            # The schema, defined as a Pydantic model, for the input data.
+            input_schema=ExampleBlock.Input,
+            # The schema, defined as a Pydantic model, for the output data.
+            output_schema=ExampleBlock.Output,
+            # The list or single sample input data for the block, for testing.
+            # This is an instance of the Input schema with sample values.
+            test_input={
+                "name": "Craig",
+                "greetings": ["Hello", "Hi", "Hey"],
+                "is_funny": True,
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            # The list or single expected output if the test_input is run.
+            # Each output is a tuple of (output_name, output_data).
+            test_output=[
+                ("response", GreetingMessage(message="Hello, world!", is_funny=True)),
+                (
+                    "response",
+                    GreetingMessage(message="Hello, world!", is_funny=True),
+                ),  # We mock the function
+                (
+                    "response",
+                    GreetingMessage(message="Hello, world!", is_funny=True),
+                ),  # We mock the function
+                (
+                    "all_responses",
+                    [
+                        GreetingMessage(message="Hello, world!", is_funny=True),
+                        GreetingMessage(message="Hello, world!", is_funny=True),
+                        GreetingMessage(message="Hello, world!", is_funny=True),
+                    ],
+                ),
+                ("greeting_count", 3),
+            ],
+            # Function names on the block implementation to mock on test run.
+            # Each mock is a dictionary with function names as keys and mock implementations as values.
+            test_mock={
+                "my_function_that_can_be_mocked": lambda *args, **kwargs: GreetingMessage(
+                    message="Hello, world!", is_funny=True
+                )
+            },
+            # The credentials required for testing the block.
+            # This is an instance of APIKeyCredentials with sample values.
+            test_credentials=TEST_CREDENTIALS,
+        )
+
+    def my_function_that_can_be_mocked(
+        self, name: str, credentials: APIKeyCredentials
+    ) -> GreetingMessage:
+        logger.info("my_function_that_can_be_mocked called with input: %s", name)
+
+        # Use the ExampleClient from _api.py to make an API call
+        client = ExampleClient(credentials=credentials)
+
+        # Create a sample resource using the client
+        resource_data = {"name": name, "type": "greeting"}
+        # If your API response object matches the return type of the function,
+        # there is no need to convert the object. In this case we have a different
+        # object type for the response and the return type of the function.
+        return GreetingMessage(**client.create_resource(resource_data).model_dump())
+
+    def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        """
+        The run function implements the block's core logic. It processes the input_data
+        and yields the block's output.
+
+        In addition to credentials, the following parameters can be specified:
+            graph_id: The ID of the graph containing this block.
+            node_id: The ID of this block's node in the graph.
+            graph_exec_id: The ID of the current graph execution.
+            node_exec_id: The ID of the current node execution.
+            user_id: The ID of the user executing the block.
+        """
+        rtn_all_responses: list[GreetingMessage] = []
+        # Here we deomonstrate best practice for blocks that need to yield multiple items.
+        # We yield each item from the list to allow for operations on each element.
+        # We also yield the complete list for situations when the full list is needed.
+        for greeting in input_data.greetings:
+            message = self.my_function_that_can_be_mocked(greeting, credentials)
+            rtn_all_responses.append(message)
+            yield "response", message
+        yield "all_responses", rtn_all_responses
+        yield "greeting_count", len(input_data.greetings)

autogpt_platform/backend/backend/blocks/example/triggers.py

@@ -0,0 +1,65 @@
+import logging
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockManualWebhookConfig,
+    BlockOutput,
+    BlockSchema,
+)
+from backend.data.model import SchemaField
+from backend.integrations.webhooks.example import ExampleWebhookEventType
+
+logger = logging.getLogger(__name__)
+
+
+class ExampleTriggerBlock(Block):
+    """
+    A trigger block that is activated by an external webhook event.
+
+    Unlike standard blocks that are manually executed, trigger blocks are automatically
+    activated when a webhook event is received from the specified provider.
+    """
+
+    class Input(BlockSchema):
+        # The payload field is hidden because it's automatically populated by the webhook
+        # system rather than being manually entered by the user
+        payload: dict = SchemaField(hidden=True)
+
+    class Output(BlockSchema):
+        event_data: dict = SchemaField(
+            description="The contents of the example webhook event."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="7c5933ce-d60c-42dd-9c4e-db82496474a3",
+            description="This block will output the contents of an example webhook event.",
+            categories={BlockCategory.BASIC},
+            input_schema=ExampleTriggerBlock.Input,
+            output_schema=ExampleTriggerBlock.Output,
+            # The webhook_config is a key difference from standard blocks
+            # It defines which external service can trigger this block and what type of events it responds to
+            webhook_config=BlockManualWebhookConfig(
+                provider="example_provider",  # The external service that will send webhook events
+                webhook_type=ExampleWebhookEventType.EXAMPLE_EVENT,  # The specific event type this block responds to
+            ),
+            # Test input for trigger blocks should mimic the payload structure that would be received from the webhook
+            test_input=[
+                {
+                    "payload": {
+                        "event_type": "example",
+                        "data": "Sample webhook data",
+                    }
+                }
+            ],
+            test_output=[
+                ("event_data", {"event_type": "example", "data": "Sample webhook data"})
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        # For trigger blocks, the run method is called automatically when a webhook event is received
+        # The payload from the webhook is passed in as input_data.payload
+        logger.info("Example trigger block run with payload: %s", input_data.payload)
+        yield "event_data", input_data.payload

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.",
+            description="Generation progress logs.", optional=True
         )
 
     def __init__(self):

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

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

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

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

autogpt_platform/backend/backend/blocks/http.py

@@ -1,16 +1,11 @@
 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"
@@ -34,7 +29,7 @@ class SendWebRequestBlock(Block):
         )
         headers: dict[str, str] = SchemaField(
             description="The headers to include in the request",
-            default_factory=dict,
+            default={},
         )
         json_format: bool = SchemaField(
             title="JSON format",
@@ -48,9 +43,8 @@ class SendWebRequestBlock(Block):
 
     class Output(BlockSchema):
         response: object = SchemaField(description="The response from the server")
-        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")
+        client_error: object = SchemaField(description="The error on 4xx status codes")
+        server_error: object = SchemaField(description="The error on 5xx status codes")
 
     def __init__(self):
         super().__init__(
@@ -74,40 +68,20 @@ class SendWebRequestBlock(Block):
                     # we should send it as plain text instead
                     input_data.json_format = False
 
-        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
+        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:
             yield "response", result
-
-        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)
+        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}")

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/io.py

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

autogpt_platform/backend/backend/blocks/iteration.py

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

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

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

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

@@ -1,4 +1,4 @@
-from urllib.parse import quote
+from groq._utils._utils 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,
-            ge=0,
-            le=4,
+            minimum=0,
+            maximum=4,
         )
         project_name: str | None = SchemaField(
             description="Name of the project to create the issue on",

autogpt_platform/backend/backend/blocks/llm.py

@@ -4,24 +4,30 @@ from abc import ABC
 from enum import Enum, EnumMeta
 from json import JSONDecodeError
 from types import MappingProxyType
-from typing import Any, Iterable, List, Literal, NamedTuple, Optional
+from typing import TYPE_CHECKING, Any, Iterable, List, Literal, NamedTuple, Optional
+
+from pydantic import BaseModel, SecretStr
+
+from backend.data.model import NodeExecutionStats
+from backend.integrations.providers import ProviderName
+
+if TYPE_CHECKING:
+    from enum import _EnumMemberT
 
 import anthropic
 import ollama
 import openai
+from anthropic._types import NotGiven
 from anthropic.types import ToolParam
 from groq import Groq
-from pydantic import BaseModel, SecretStr
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
     CredentialsMetaInput,
-    NodeExecutionStats,
     SchemaField,
 )
-from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.settings import BehaveAs, Settings
 from backend.util.text import TextFormatter
@@ -71,10 +77,12 @@ class ModelMetadata(NamedTuple):
 
 class LlmModelMeta(EnumMeta):
     @property
-    def __members__(self) -> MappingProxyType:
+    def __members__(
+        self: type["_EnumMemberT"],
+    ) -> MappingProxyType[str, "_EnumMemberT"]:
         if Settings().config.behave_as == BehaveAs.LOCAL:
             members = super().__members__
-            return MappingProxyType(members)
+            return members
         else:
             removed_providers = ["ollama"]
             existing_members = super().__members__
@@ -89,17 +97,14 @@ class LlmModelMeta(EnumMeta):
 class LlmModel(str, Enum, metaclass=LlmModelMeta):
     # OpenAI models
     O3_MINI = "o3-mini"
-    O3 = "o3-2025-04-16"
     O1 = "o1"
     O1_PREVIEW = "o1-preview"
     O1_MINI = "o1-mini"
-    GPT41 = "gpt-4.1-2025-04-14"
     GPT4O_MINI = "gpt-4o-mini"
     GPT4O = "gpt-4o"
     GPT4_TURBO = "gpt-4-turbo"
     GPT3_5_TURBO = "gpt-3.5-turbo"
     # Anthropic models
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
     CLAUDE_3_5_SONNET = "claude-3-5-sonnet-latest"
     CLAUDE_3_5_HAIKU = "claude-3-5-haiku-latest"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
@@ -120,7 +125,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     OLLAMA_DOLPHIN = "dolphin-mistral:latest"
     # OpenRouter models
     GEMINI_FLASH_1_5 = "google/gemini-flash-1.5"
-    GEMINI_2_5_PRO = "google/gemini-2.5-pro-preview-03-25"
     GROK_BETA = "x-ai/grok-beta"
     MISTRAL_NEMO = "mistralai/mistral-nemo"
     COHERE_COMMAND_R_08_2024 = "cohere/command-r-08-2024"
@@ -138,8 +142,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     AMAZON_NOVA_PRO_V1 = "amazon/nova-pro-v1"
     MICROSOFT_WIZARDLM_2_8X22B = "microsoft/wizardlm-2-8x22b"
     GRYPHE_MYTHOMAX_L2_13B = "gryphe/mythomax-l2-13b"
-    META_LLAMA_4_SCOUT = "meta-llama/llama-4-scout"
-    META_LLAMA_4_MAVERICK = "meta-llama/llama-4-maverick"
 
     @property
     def metadata(self) -> ModelMetadata:
@@ -160,14 +162,12 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
 
 MODEL_METADATA = {
     # https://platform.openai.com/docs/models
-    LlmModel.O3: ModelMetadata("openai", 200000, 100000),
     LlmModel.O3_MINI: ModelMetadata("openai", 200000, 100000),  # o3-mini-2025-01-31
     LlmModel.O1: ModelMetadata("openai", 200000, 100000),  # o1-2024-12-17
     LlmModel.O1_PREVIEW: ModelMetadata(
         "openai", 128000, 32768
     ),  # o1-preview-2024-09-12
     LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
-    LlmModel.GPT41: ModelMetadata("openai", 1047576, 32768),
     LlmModel.GPT4O_MINI: ModelMetadata(
         "openai", 128000, 16384
     ),  # gpt-4o-mini-2024-07-18
@@ -177,9 +177,6 @@ MODEL_METADATA = {
     ),  # gpt-4-turbo-2024-04-09
     LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, 4096),  # gpt-3.5-turbo-0125
     # https://docs.anthropic.com/en/docs/about-claude/models
-    LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 8192
-    ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_5_SONNET: ModelMetadata(
         "anthropic", 200000, 8192
     ),  # claude-3-5-sonnet-20241022
@@ -205,7 +202,6 @@ MODEL_METADATA = {
     LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
     # https://openrouter.ai/models
     LlmModel.GEMINI_FLASH_1_5: ModelMetadata("open_router", 1000000, 8192),
-    LlmModel.GEMINI_2_5_PRO: ModelMetadata("open_router", 1050000, 8192),
     LlmModel.GROK_BETA: ModelMetadata("open_router", 131072, 131072),
     LlmModel.MISTRAL_NEMO: ModelMetadata("open_router", 128000, 4096),
     LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata("open_router", 128000, 4096),
@@ -227,8 +223,6 @@ MODEL_METADATA = {
     LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata("open_router", 300000, 5120),
     LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata("open_router", 65536, 4096),
     LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata("open_router", 4096, 4096),
-    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata("open_router", 131072, 131072),
-    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata("open_router", 1048576, 1000000),
 }
 
 for model in LlmModel:
@@ -258,7 +252,7 @@ class LLMResponse(BaseModel):
 
 def convert_openai_tool_fmt_to_anthropic(
     openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | anthropic.NotGiven:
+) -> Iterable[ToolParam] | NotGiven:
     """
     Convert OpenAI tool format to Anthropic tool format.
     """
@@ -288,13 +282,6 @@ def convert_openai_tool_fmt_to_anthropic(
     return anthropic_tools
 
 
-def estimate_token_count(prompt_messages: list[dict]) -> int:
-    char_count = sum(len(str(msg.get("content", ""))) for msg in prompt_messages)
-    message_overhead = len(prompt_messages) * 4
-    estimated_tokens = (char_count // 4) + message_overhead
-    return int(estimated_tokens * 1.2)
-
-
 def llm_call(
     credentials: APIKeyCredentials,
     llm_model: LlmModel,
@@ -303,7 +290,6 @@ def llm_call(
     max_tokens: int | None,
     tools: list[dict] | None = None,
     ollama_host: str = "localhost:11434",
-    parallel_tool_calls: bool | None = None,
 ) -> LLMResponse:
     """
     Make a call to a language model.
@@ -326,14 +312,7 @@ def llm_call(
             - completion_tokens: The number of tokens used in the completion.
     """
     provider = llm_model.metadata.provider
-
-    # Calculate available tokens based on context window and input length
-    estimated_input_tokens = estimate_token_count(prompt)
-    context_window = llm_model.context_window
-    model_max_output = llm_model.max_output_tokens or 4096
-    user_max = max_tokens or model_max_output
-    available_tokens = max(context_window - estimated_input_tokens, 0)
-    max_tokens = max(min(available_tokens, model_max_output, user_max), 0)
+    max_tokens = max_tokens or llm_model.max_output_tokens or 4096
 
     if provider == "openai":
         tools_param = tools if tools else openai.NOT_GIVEN
@@ -356,9 +335,6 @@ def llm_call(
             response_format=response_format,  # type: ignore
             max_completion_tokens=max_tokens,
             tools=tools_param,  # type: ignore
-            parallel_tool_calls=(
-                openai.NOT_GIVEN if parallel_tool_calls is None else parallel_tool_calls
-            ),
         )
 
         if response.choices[0].message.tool_calls:
@@ -448,7 +424,7 @@ def llm_call(
                 response=(
                     resp.content[0].name
                     if isinstance(resp.content[0], anthropic.types.ToolUseBlock)
-                    else getattr(resp.content[0], "text", "")
+                    else resp.content[0].text
                 ),
                 tool_calls=tool_calls,
                 prompt_tokens=resp.usage.input_tokens,
@@ -489,7 +465,6 @@ def llm_call(
             model=llm_model.value,
             prompt=f"{sys_messages}\n\n{usr_messages}",
             stream=False,
-            options={"num_ctx": max_tokens},
         )
         return LLMResponse(
             raw_response=response.get("response") or "",
@@ -515,9 +490,6 @@ def llm_call(
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
-            parallel_tool_calls=(
-                openai.NOT_GIVEN if parallel_tool_calls is None else parallel_tool_calls
-            ),
         )
 
         # If there's no response, raise an error
@@ -556,7 +528,7 @@ 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)
@@ -586,7 +558,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             description="The system prompt to provide additional context to the model.",
         )
         conversation_history: list[dict] = SchemaField(
-            default_factory=list,
+            default=[],
             description="The conversation history to provide context for the prompt.",
         )
         retry: int = SchemaField(
@@ -596,7 +568,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         )
         prompt_values: dict[str, str] = SchemaField(
             advanced=False,
-            default_factory=dict,
+            default={},
             description="Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.",
         )
         max_tokens: int | None = SchemaField(
@@ -615,7 +587,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         response: dict[str, Any] = SchemaField(
             description="The response object generated by the language model."
         )
-        prompt: list = SchemaField(description="The prompt sent to the language model.")
+        prompt: str = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -637,7 +609,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             test_credentials=TEST_CREDENTIALS,
             test_output=[
                 ("response", {"key1": "key1Value", "key2": "key2Value"}),
-                ("prompt", list),
+                ("prompt", str),
             ],
             test_mock={
                 "llm_call": lambda *args, **kwargs: LLMResponse(
@@ -670,7 +642,6 @@ 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,
@@ -788,16 +759,6 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 prompt.append({"role": "user", "content": retry_prompt})
             except Exception as e:
                 logger.exception(f"Error calling LLM: {e}")
-                if (
-                    "maximum context length" in str(e).lower()
-                    or "token limit" in str(e).lower()
-                ):
-                    if input_data.max_tokens is None:
-                        input_data.max_tokens = llm_model.max_output_tokens or 4096
-                    input_data.max_tokens = int(input_data.max_tokens * 0.85)
-                    logger.debug(
-                        f"Reducing max_tokens to {input_data.max_tokens} for next attempt"
-                    )
                 retry_prompt = f"Error calling LLM: {e}"
             finally:
                 self.merge_stats(
@@ -835,7 +796,7 @@ class AITextGeneratorBlock(AIBlockBase):
         )
         prompt_values: dict[str, str] = SchemaField(
             advanced=False,
-            default_factory=dict,
+            default={},
             description="Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.",
         )
         ollama_host: str = SchemaField(
@@ -853,7 +814,7 @@ class AITextGeneratorBlock(AIBlockBase):
         response: str = SchemaField(
             description="The response generated by the language model."
         )
-        prompt: list = SchemaField(description="The prompt sent to the language model.")
+        prompt: str = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -870,7 +831,7 @@ class AITextGeneratorBlock(AIBlockBase):
             test_credentials=TEST_CREDENTIALS,
             test_output=[
                 ("response", "Response text"),
-                ("prompt", list),
+                ("prompt", str),
             ],
             test_mock={"llm_call": lambda *args, **kwargs: "Response text"},
         )
@@ -889,10 +850,7 @@ class AITextGeneratorBlock(AIBlockBase):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         object_input_data = AIStructuredResponseGeneratorBlock.Input(
-            **{
-                attr: getattr(input_data, attr)
-                for attr in AITextGeneratorBlock.Input.model_fields
-            },
+            **{attr: getattr(input_data, attr) for attr in input_data.model_fields},
             expected_format={},
         )
         yield "response", self.llm_call(object_input_data, credentials)
@@ -949,7 +907,7 @@ class AITextSummarizerBlock(AIBlockBase):
 
     class Output(BlockSchema):
         summary: str = SchemaField(description="The final summary of the text.")
-        prompt: list = SchemaField(description="The prompt sent to the language model.")
+        prompt: str = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -966,7 +924,7 @@ class AITextSummarizerBlock(AIBlockBase):
             test_credentials=TEST_CREDENTIALS,
             test_output=[
                 ("summary", "Final summary of a long text"),
-                ("prompt", list),
+                ("prompt", str),
             ],
             test_mock={
                 "llm_call": lambda input_data, credentials: (
@@ -1075,14 +1033,8 @@ class AITextSummarizerBlock(AIBlockBase):
 
 class AIConversationBlock(AIBlockBase):
     class Input(BlockSchema):
-        prompt: str = SchemaField(
-            description="The prompt to send to the language model.",
-            placeholder="Enter your prompt here...",
-            default="",
-            advanced=False,
-        )
         messages: List[Any] = SchemaField(
-            description="List of messages in the conversation.",
+            description="List of messages in the conversation.", min_length=1
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
@@ -1105,7 +1057,7 @@ class AIConversationBlock(AIBlockBase):
         response: str = SchemaField(
             description="The model's response to the conversation."
         )
-        prompt: list = SchemaField(description="The prompt sent to the language model.")
+        prompt: str = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(description="Error message if the API call failed.")
 
     def __init__(self):
@@ -1134,7 +1086,7 @@ class AIConversationBlock(AIBlockBase):
                     "response",
                     "The 2020 World Series was played at Globe Life Field in Arlington, Texas.",
                 ),
-                ("prompt", list),
+                ("prompt", str),
             ],
             test_mock={
                 "llm_call": lambda *args, **kwargs: "The 2020 World Series was played at Globe Life Field in Arlington, Texas."
@@ -1156,7 +1108,7 @@ class AIConversationBlock(AIBlockBase):
     ) -> BlockOutput:
         response = self.llm_call(
             AIStructuredResponseGeneratorBlock.Input(
-                prompt=input_data.prompt,
+                prompt="",
                 credentials=input_data.credentials,
                 model=input_data.model,
                 conversation_history=input_data.messages,
@@ -1214,7 +1166,7 @@ class AIListGeneratorBlock(AIBlockBase):
         list_item: str = SchemaField(
             description="Each individual item in the list.",
         )
-        prompt: list = SchemaField(description="The prompt sent to the language model.")
+        prompt: str = SchemaField(description="The prompt sent to the language model.")
         error: str = SchemaField(
             description="Error message if the list generation failed."
         )
@@ -1246,7 +1198,7 @@ class AIListGeneratorBlock(AIBlockBase):
                     "generated_list",
                     ["Zylora Prime", "Kharon-9", "Vortexia", "Oceara", "Draknos"],
                 ),
-                ("prompt", list),
+                ("prompt", str),
                 ("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 MediaFileType, get_exec_file_path, store_media_file
+from backend.util.file import MediaFile, get_exec_file_path, store_media_file
 
 
 class MediaDurationBlock(Block):
 
     class Input(BlockSchema):
-        media_in: MediaFileType = SchemaField(
+        media_in: MediaFile = 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: MediaFileType = SchemaField(
+        video_in: MediaFile = 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 = MediaFileType(
+        output_filename = MediaFile(
             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: MediaFileType = SchemaField(
+        video_in: MediaFile = SchemaField(
             description="Video input (URL, data URI, or local path)."
         )
-        audio_in: MediaFileType = SchemaField(
+        audio_in: MediaFile = 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: MediaFileType = SchemaField(
+        video_out: MediaFile = 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 = MediaFileType(
+        output_filename = MediaFile(
             f"{node_exec_id}_audio_attached_{os.path.basename(local_video_path)}"
         )
         output_abspath = os.path.join(abs_temp_dir, output_filename)

autogpt_platform/backend/backend/blocks/mem0.py

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

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

@@ -6,14 +6,13 @@ 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: MediaFileType = SchemaField(
-            description="Image to analyze for deepfakes",
+        image_base64: str = SchemaField(
+            description="Image to analyze for deepfakes", image_upload=True
         )
         return_image: bool = SchemaField(
             description="Whether to return the processed image with markings",
@@ -23,12 +22,16 @@ class NvidiaDeepfakeDetectBlock(Block):
     class Output(BlockSchema):
         status: str = SchemaField(
             description="Detection status (SUCCESS, ERROR, CONTENT_FILTERED)",
+            default="",
         )
-        image: MediaFileType = SchemaField(
+        image: str = SchemaField(
             description="Processed image with detection markings (if return_image=True)",
+            default="",
+            image_output=True,
         )
         is_deepfake: float = SchemaField(
             description="Probability that the image is a deepfake (0-1)",
+            default=0.0,
         )
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/pinecone.py

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

autogpt_platform/backend/backend/blocks/screenshotone.py

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

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

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

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -14,6 +14,7 @@ from backend.data.block import (
     BlockOutput,
     BlockSchema,
     BlockType,
+    get_block,
 )
 from backend.data.model import SchemaField
 from backend.util import json
@@ -154,7 +155,7 @@ class SmartDecisionMakerBlock(Block):
             description="The system prompt to provide additional context to the model.",
         )
         conversation_history: list[dict] = SchemaField(
-            default_factory=list,
+            default=[],
             description="The conversation history to provide context for the prompt.",
         )
         last_tool_output: Any = SchemaField(
@@ -168,7 +169,7 @@ class SmartDecisionMakerBlock(Block):
         )
         prompt_values: dict[str, str] = SchemaField(
             advanced=False,
-            default_factory=dict,
+            default={},
             description="Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.",
         )
         max_tokens: int | None = SchemaField(
@@ -246,10 +247,6 @@ class SmartDecisionMakerBlock(Block):
             test_credentials=llm.TEST_CREDENTIALS,
         )
 
-    @staticmethod
-    def cleanup(s: str):
-        return re.sub(r"[^a-zA-Z0-9_-]", "_", s).lower()
-
     @staticmethod
     def _create_block_function_signature(
         sink_node: "Node", links: list["Link"]
@@ -267,10 +264,12 @@ class SmartDecisionMakerBlock(Block):
         Raises:
             ValueError: If the block specified by sink_node.block_id is not found.
         """
-        block = sink_node.block
+        block = get_block(sink_node.block_id)
+        if not block:
+            raise ValueError(f"Block not found: {sink_node.block_id}")
 
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(block.name),
+            "name": re.sub(r"[^a-zA-Z0-9_-]", "_", block.name).lower(),
             "description": block.description,
         }
 
@@ -285,7 +284,7 @@ class SmartDecisionMakerBlock(Block):
                 and sink_block_input_schema.model_fields[link.sink_name].description
                 else f"The {link.sink_name} of the tool"
             )
-            properties[SmartDecisionMakerBlock.cleanup(link.sink_name)] = {
+            properties[link.sink_name.lower()] = {
                 "type": "string",
                 "description": description,
             }
@@ -330,7 +329,7 @@ class SmartDecisionMakerBlock(Block):
             )
 
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(sink_graph_meta.name),
+            "name": re.sub(r"[^a-zA-Z0-9_-]", "_", sink_graph_meta.name).lower(),
             "description": sink_graph_meta.description,
         }
 
@@ -345,7 +344,7 @@ class SmartDecisionMakerBlock(Block):
                 in sink_block_input_schema["properties"][link.sink_name]
                 else f"The {link.sink_name} of the tool"
             )
-            properties[SmartDecisionMakerBlock.cleanup(link.sink_name)] = {
+            properties[link.sink_name.lower()] = {
                 "type": "string",
                 "description": description,
             }
@@ -495,7 +494,6 @@ class SmartDecisionMakerBlock(Block):
             max_tokens=input_data.max_tokens,
             tools=tool_functions,
             ollama_host=input_data.ollama_host,
-            parallel_tool_calls=False,
         )
 
         if not response.tool_calls:
@@ -507,7 +505,7 @@ class SmartDecisionMakerBlock(Block):
             tool_args = json.loads(tool_call.function.arguments)
 
             for arg_name, arg_value in tool_args.items():
-                yield f"tools_^_{tool_name}_~_{arg_name}", arg_value
+                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/smartlead/campaign.py

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

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

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

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

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

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

@@ -39,6 +39,7 @@ class TwitterGetListBlock(Block):
         list_id: str = SchemaField(
             description="The ID of the List to lookup",
             placeholder="Enter list ID",
+            required=True,
         )
 
     class Output(BlockSchema):
@@ -183,6 +184,7 @@ 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

@@ -45,11 +45,13 @@ 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):
@@ -118,11 +120,13 @@ 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):
@@ -191,6 +195,7 @@ 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(
@@ -371,6 +376,7 @@ 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

@@ -42,6 +42,7 @@ 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

@@ -28,6 +28,7 @@ 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

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

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

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

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

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

autogpt_platform/backend/backend/cli.py

@@ -8,6 +8,7 @@ import pathlib
 import click
 import psutil
 
+from backend import app
 from backend.util.process import AppProcess
 
 
@@ -41,13 +42,8 @@ def write_pid(pid: int):
 
 class MainApp(AppProcess):
     def run(self):
-        from backend import app
-
         app.main(silent=True)
 
-    def cleanup(self):
-        pass
-
 
 @click.group()
 def main():
@@ -224,8 +220,9 @@ def event():
 
 @test.command()
 @click.argument("server_address")
-@click.argument("graph_exec_id")
-def websocket(server_address: str, graph_exec_id: str):
+@click.argument("graph_id")
+@click.argument("graph_version")
+def websocket(server_address: str, graph_id: str, graph_version: int):
     """
     Tests the websocket connection.
     """
@@ -233,20 +230,16 @@ def websocket(server_address: str, graph_exec_id: str):
 
     import websockets.asyncio.client
 
-    from backend.server.ws_api import (
-        WSMessage,
-        WSMethod,
-        WSSubscribeGraphExecutionRequest,
-    )
+    from backend.server.ws_api import ExecutionSubscription, Methods, WsMessage
 
     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=WSMethod.SUBSCRIBE_GRAPH_EXEC,
-                    data=WSSubscribeGraphExecutionRequest(
-                        graph_exec_id=graph_exec_id,
+                msg = WsMessage(
+                    method=Methods.SUBSCRIBE,
+                    data=ExecutionSubscription(
+                        graph_id=graph_id, graph_version=graph_version
                     ).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=prisma.types.AnalyticsDetailsCreateInput(
-            userId=user_id,
-            type=type,
-            data=prisma.Json(data),
-            dataIndex=data_index,
-        )
+        data={
+            "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=prisma.types.AnalyticsMetricsCreateInput(
-            value=metric_value,
-            analyticMetric=metric_name,
-            userId=user_id,
-            dataString=data_string,
-        )
+        data={
+            "value": metric_value,
+            "analyticMetric": metric_name,
+            "userId": user_id,
+            "dataString": data_string,
+        },
     )
 
     return result

autogpt_platform/backend/backend/data/block.py

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

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -2,6 +2,7 @@ from typing import Type
 
 from backend.blocks.ai_music_generator import AIMusicGeneratorBlock
 from backend.blocks.ai_shortform_video_block import AIShortformVideoCreatorBlock
+from backend.blocks.example.example import ExampleBlock
 from backend.blocks.ideogram import IdeogramModelBlock
 from backend.blocks.jina.embeddings import JinaEmbeddingBlock
 from backend.blocks.jina.search import ExtractWebsiteContentBlock, SearchTheWebBlock
@@ -23,6 +24,7 @@ from backend.data.cost import BlockCost, BlockCostType
 from backend.integrations.credentials_store import (
     anthropic_credentials,
     did_credentials,
+    example_credentials,
     groq_credentials,
     ideogram_credentials,
     jina_credentials,
@@ -36,17 +38,14 @@ from backend.integrations.credentials_store import (
 # =============== Configure the cost for each LLM Model call =============== #
 
 MODEL_COST: dict[LlmModel, int] = {
-    LlmModel.O3: 7,
     LlmModel.O3_MINI: 2,  # $1.10 / $4.40
     LlmModel.O1: 16,  # $15 / $60
     LlmModel.O1_PREVIEW: 16,
     LlmModel.O1_MINI: 4,
-    LlmModel.GPT41: 2,
     LlmModel.GPT4O_MINI: 1,
     LlmModel.GPT4O: 3,
     LlmModel.GPT4_TURBO: 10,
     LlmModel.GPT3_5_TURBO: 1,
-    LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_5_SONNET: 4,
     LlmModel.CLAUDE_3_5_HAIKU: 1,  # $0.80 / $4.00
     LlmModel.CLAUDE_3_HAIKU: 1,
@@ -63,7 +62,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.DEEPSEEK_LLAMA_70B: 1,  # ? / ?
     LlmModel.OLLAMA_DOLPHIN: 1,
     LlmModel.GEMINI_FLASH_1_5: 1,
-    LlmModel.GEMINI_2_5_PRO: 4,
     LlmModel.GROK_BETA: 5,
     LlmModel.MISTRAL_NEMO: 1,
     LlmModel.COHERE_COMMAND_R_08_2024: 1,
@@ -79,8 +77,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.AMAZON_NOVA_PRO_V1: 1,
     LlmModel.MICROSOFT_WIZARDLM_2_8X22B: 1,
     LlmModel.GRYPHE_MYTHOMAX_L2_13B: 1,
-    LlmModel.META_LLAMA_4_SCOUT: 1,
-    LlmModel.META_LLAMA_4_MAVERICK: 1,
 }
 
 for model in LlmModel:
@@ -273,4 +269,16 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
         )
     ],
     SmartDecisionMakerBlock: LLM_COST,
+    ExampleBlock: [
+        BlockCost(
+            cost_amount=1,
+            cost_filter={
+                "credentials": {
+                    "id": example_credentials.id,
+                    "provider": example_credentials.provider,
+                    "type": example_credentials.type,
+                }
+            },
+        )
+    ],
 }

autogpt_platform/backend/backend/data/credit.py

@@ -11,19 +11,18 @@ from prisma.enums import (
     CreditRefundRequestStatus,
     CreditTransactionType,
     NotificationType,
-    OnboardingStep,
 )
 from prisma.errors import UniqueViolationError
 from prisma.models import CreditRefundRequest, CreditTransaction, User
-from prisma.types import (
-    CreditRefundRequestCreateInput,
-    CreditTransactionCreateInput,
-    CreditTransactionWhereInput,
-)
+from prisma.types import CreditTransactionCreateInput, CreditTransactionWhereInput
+from pydantic import BaseModel
+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
+from backend.data.cost import BlockCost, BlockCostType
+from backend.data.execution import NodeExecutionEntry
 from backend.data.model import (
     AutoTopUpConfig,
     RefundRequest,
@@ -32,10 +31,8 @@ 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.retry import func_retry
 from backend.util.service import get_service_client
 from backend.util.settings import Settings
 
@@ -94,20 +91,20 @@ class UserCreditBase(ABC):
     @abstractmethod
     async def spend_credits(
         self,
-        user_id: str,
-        cost: int,
-        metadata: UsageTransactionMetadata,
+        entry: NodeExecutionEntry,
+        data_size: float,
+        run_time: float,
     ) -> int:
         """
-        Spend the credits for the user based on the cost.
+        Spend the credits for the user based on the block usage.
 
         Args:
-            user_id (str): The user ID.
-            cost (int): The cost to spend.
-            metadata (UsageTransactionMetadata): The metadata of the transaction.
+            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.
 
         Returns:
-            int: The remaining balance.
+            int: amount of credit spent
         """
         pass
 
@@ -122,18 +119,6 @@ class UserCreditBase(ABC):
         """
         pass
 
-    @abstractmethod
-    async def onboarding_reward(self, user_id: str, credits: int, step: OnboardingStep):
-        """
-        Reward the user with credits for completing an onboarding step.
-        Won't reward if the user has already received credits for the step.
-
-        Args:
-            user_id (str): The user ID.
-            step (OnboardingStep): The onboarding step.
-        """
-        pass
-
     @abstractmethod
     async def top_up_intent(self, user_id: str, amount: int) -> str:
         """
@@ -226,7 +211,7 @@ class UserCreditBase(ABC):
                 "userId": user_id,
                 "createdAt": {"lte": top_time},
                 "isActive": True,
-                "NOT": [{"runningBalance": None}],
+                "runningBalance": {"not": None},  # type: ignore
             },
             order={"createdAt": "desc"},
         )
@@ -262,7 +247,11 @@ class UserCreditBase(ABC):
         )
         return transaction_balance, transaction_time
 
-    @func_retry
+    @retry(
+        stop=stop_after_attempt(5),
+        wait=wait_exponential(multiplier=1, min=1, max=10),
+        reraise=True,
+    )
     async def _enable_transaction(
         self,
         transaction_key: str,
@@ -344,21 +333,31 @@ class UserCreditBase(ABC):
                 amount = min(-user_balance, 0)
 
             # Create the transaction
-            transaction_data = CreditTransactionCreateInput(
-                userId=user_id,
-                amount=amount,
-                runningBalance=user_balance + amount,
-                type=transaction_type,
-                metadata=metadata,
-                isActive=is_active,
-                createdAt=self.time_now(),
-            )
+            transaction_data: CreditTransactionCreateInput = {
+                "userId": user_id,
+                "amount": amount,
+                "runningBalance": user_balance + amount,
+                "type": transaction_type,
+                "metadata": metadata,
+                "isActive": is_active,
+                "createdAt": self.time_now(),
+            }
             if transaction_key:
                 transaction_data["transactionKey"] = transaction_key
             tx = await CreditTransaction.prisma().create(data=transaction_data)
             return user_balance + amount, tx.transactionKey
 
 
+class UsageTransactionMetadata(BaseModel):
+    graph_exec_id: str | None = None
+    graph_id: str | None = None
+    node_id: str | None = None
+    node_exec_id: str | None = None
+    block_id: str | None = None
+    block: str | None = None
+    input: BlockInput | None = None
+
+
 class UserCredit(UserCreditBase):
     @thread_cached
     def notification_client(self) -> NotificationManager:
@@ -379,21 +378,89 @@ 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,
-        user_id: str,
-        cost: int,
-        metadata: UsageTransactionMetadata,
+        entry: NodeExecutionEntry,
+        data_size: float,
+        run_time: float,
     ) -> 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=user_id,
+            user_id=entry.user_id,
             amount=-cost,
             transaction_type=CreditTransactionType.USAGE,
-            metadata=Json(metadata.model_dump()),
+            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()
+            ),
         )
+        user_id = entry.user_id
 
         # Auto top-up if balance is below threshold.
         auto_top_up = await get_auto_top_up(user_id)
@@ -403,7 +470,7 @@ 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}-{metadata.graph_exec_id}",
+                    key=f"AUTO-TOP-UP-{user_id}-{entry.graph_exec_id}",
                     ceiling_balance=auto_top_up.threshold,
                 )
             except Exception as e:
@@ -412,29 +479,11 @@ class UserCredit(UserCreditBase):
                     f"Auto top-up failed for user {user_id}, balance: {balance}, amount: {auto_top_up.amount}, error: {e}"
                 )
 
-        return balance
+        return cost
 
     async def top_up_credits(self, user_id: str, amount: int):
         await self._top_up_credits(user_id, amount)
 
-    async def onboarding_reward(self, user_id: str, credits: int, step: OnboardingStep):
-        key = f"REWARD-{user_id}-{step.value}"
-        if not await CreditTransaction.prisma().find_first(
-            where={
-                "userId": user_id,
-                "transactionKey": key,
-            }
-        ):
-            await self._add_transaction(
-                user_id=user_id,
-                amount=credits,
-                transaction_type=CreditTransactionType.GRANT,
-                transaction_key=key,
-                metadata=Json(
-                    {"reason": f"Reward for completing {step.value} onboarding step."}
-                ),
-            )
-
     async def top_up_refund(
         self, user_id: str, transaction_key: str, metadata: dict[str, str]
     ) -> int:
@@ -453,15 +502,15 @@ class UserCredit(UserCreditBase):
 
         try:
             refund_request = await CreditRefundRequest.prisma().create(
-                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.",
-                )
+                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.",
+                }
             )
         except UniqueViolationError:
             raise ValueError(
@@ -922,9 +971,6 @@ class DisabledUserCredit(UserCreditBase):
     async def top_up_credits(self, *args, **kwargs):
         pass
 
-    async def onboarding_reward(self, *args, **kwargs):
-        pass
-
     async def top_up_intent(self, *args, **kwargs) -> str:
         return ""
 

autogpt_platform/backend/backend/data/db.py

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

autogpt_platform/backend/backend/data/graph.py

@@ -1,40 +1,36 @@
 import logging
 import uuid
 from collections import defaultdict
-from typing import Any, Literal, Optional, cast
+from datetime import datetime, timezone
+from typing import Any, Literal, Optional, Type
 
 import prisma
 from prisma import Json
-from prisma.enums import SubmissionStatus
-from prisma.models import AgentGraph, AgentNode, AgentNodeLink, StoreListingVersion
-from prisma.types import (
-    AgentGraphCreateInput,
-    AgentGraphWhereInput,
-    AgentNodeCreateInput,
-    AgentNodeLinkCreateInput,
+from prisma.models import (
+    AgentGraph,
+    AgentGraphExecution,
+    AgentNode,
+    AgentNodeLink,
+    StoreListingVersion,
 )
-from pydantic import create_model
-from pydantic.fields import computed_field
+from prisma.types import AgentGraphExecutionWhereInput, AgentGraphWhereInput
+from pydantic.fields import Field, computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
-from backend.blocks.io import AgentInputBlock, AgentOutputBlock
-from backend.blocks.llm import LlmModel
-from backend.data.db import prisma as db
-from backend.data.model import (
-    CredentialsField,
-    CredentialsFieldInfo,
-    CredentialsMetaInput,
-    is_credentials_field_name,
-)
+from backend.blocks.basic import AgentInputBlock, AgentOutputBlock
 from backend.util import type as type_utils
 
 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
@@ -65,23 +61,22 @@ class Node(BaseDbModel):
     input_links: list[Link] = []
     output_links: list[Link] = []
 
-    @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
+    webhook_id: Optional[str] = None
 
 
 class NodeModel(Node):
     graph_id: str
     graph_version: int
 
-    webhook_id: Optional[str] = None
     webhook: Optional[Webhook] = 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")
+        return block
+
     @staticmethod
     def from_db(node: AgentNode, for_export: bool = False) -> "NodeModel":
         obj = NodeModel(
@@ -101,7 +96,8 @@ class NodeModel(Node):
         return obj
 
     def is_triggered_by_event_type(self, event_type: str) -> bool:
-        block = self.block
+        if not (block := get_block(self.block_id)):
+            raise ValueError(f"Block #{self.block_id} not found for node #{self.id}")
         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:
@@ -165,6 +161,99 @@ class NodeModel(Node):
 Webhook.model_rebuild()
 
 
+class GraphExecutionMeta(BaseDbModel):
+    execution_id: str
+    started_at: datetime
+    ended_at: datetime
+    cost: Optional[int] = Field(..., description="Execution cost in credits")
+    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_utils.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,
+            cost=stats.get("cost", None),
+            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.get("value", None)
+                )
+
+        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 BaseGraph(BaseDbModel):
     version: int = 1
     is_active: bool = True
@@ -172,58 +261,51 @@ class BaseGraph(BaseDbModel):
     description: str
     nodes: list[Node] = []
     links: list[Link] = []
-    forked_from_id: str | None = None
-    forked_from_version: int | None = None
 
     @computed_field
     @property
     def input_schema(self) -> dict[str, Any]:
         return self._generate_schema(
-            *(
-                (block.input_schema, node.input_default)
+            AgentInputBlock.Input,
+            [
+                node.input_default
                 for node in self.nodes
-                if (block := node.block).block_type == BlockType.INPUT
-                and issubclass(block.input_schema, AgentInputBlock.Input)
-            )
+                if (b := get_block(node.block_id))
+                and b.block_type == BlockType.INPUT
+                and "name" in node.input_default
+            ],
         )
 
     @computed_field
     @property
     def output_schema(self) -> dict[str, Any]:
         return self._generate_schema(
-            *(
-                (block.input_schema, node.input_default)
+            AgentOutputBlock.Input,
+            [
+                node.input_default
                 for node in self.nodes
-                if (block := node.block).block_type == BlockType.OUTPUT
-                and issubclass(block.input_schema, AgentOutputBlock.Input)
-            )
+                if (b := get_block(node.block_id))
+                and b.block_type == BlockType.OUTPUT
+                and "name" in node.input_default
+            ],
         )
 
-    @computed_field
-    @property
-    def credentials_input_schema(self) -> dict[str, Any]:
-        return self._credentials_input_schema.jsonschema()
-
     @staticmethod
     def _generate_schema(
-        *props: tuple[type[AgentInputBlock.Input] | type[AgentOutputBlock.Input], dict],
+        type_class: Type[AgentInputBlock.Input] | Type[AgentOutputBlock.Input],
+        data: list[dict],
     ) -> dict[str, Any]:
-        schema_fields: list[AgentInputBlock.Input | AgentOutputBlock.Input] = []
-        for type_class, input_default in props:
+        props = []
+        for p in data:
             try:
-                schema_fields.append(type_class(**input_default))
+                props.append(type_class(**p))
             except Exception as e:
-                logger.warning(f"Invalid {type_class}: {input_default}, {e}")
+                logger.warning(f"Invalid {type_class}: {p}, {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,
@@ -231,93 +313,9 @@ class BaseGraph(BaseDbModel):
                     **({"description": p.description} if p.description else {}),
                     **({"default": p.value} if p.value is not None else {}),
                 }
-                for p in schema_fields
+                for p in props
             },
-            "required": [p.name for p in schema_fields if p.value is None],
-        }
-
-    @property
-    def _credentials_input_schema(self) -> type[BlockSchema]:
-        graph_credentials_inputs = self.aggregate_credentials_inputs()
-        logger.debug(
-            f"Combined credentials input fields for graph #{self.id} ({self.name}): "
-            f"{graph_credentials_inputs}"
-        )
-
-        # Warn if same-provider credentials inputs can't be combined (= bad UX)
-        graph_cred_fields = list(graph_credentials_inputs.values())
-        for i, (field, keys) in enumerate(graph_cred_fields):
-            for other_field, other_keys in list(graph_cred_fields)[i + 1 :]:
-                if field.provider != other_field.provider:
-                    continue
-
-                # If this happens, that means a block implementation probably needs
-                # to be updated.
-                logger.warning(
-                    "Multiple combined credentials fields "
-                    f"for provider {field.provider} "
-                    f"on graph #{self.id} ({self.name}); "
-                    f"fields: {field} <> {other_field};"
-                    f"keys: {keys} <> {other_keys}."
-                )
-
-        fields: dict[str, tuple[type[CredentialsMetaInput], CredentialsMetaInput]] = {
-            agg_field_key: (
-                CredentialsMetaInput[
-                    Literal[tuple(field_info.provider)],  # type: ignore
-                    Literal[tuple(field_info.supported_types)],  # type: ignore
-                ],
-                CredentialsField(
-                    required_scopes=set(field_info.required_scopes or []),
-                    discriminator=field_info.discriminator,
-                    discriminator_mapping=field_info.discriminator_mapping,
-                ),
-            )
-            for agg_field_key, (field_info, _) in graph_credentials_inputs.items()
-        }
-
-        return create_model(
-            self.name.replace(" ", "") + "CredentialsInputSchema",
-            __base__=BlockSchema,
-            **fields,  # type: ignore
-        )
-
-    def aggregate_credentials_inputs(
-        self,
-    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]]]]:
-        """
-        Returns:
-            dict[aggregated_field_key, tuple(
-                CredentialsFieldInfo: A spec for one aggregated credentials field
-                set[(node_id, field_name)]: Node credentials fields that are
-                    compatible with this aggregated field spec
-            )]
-        """
-        return {
-            "_".join(sorted(agg_field_info.provider))
-            + "_"
-            + "_".join(sorted(agg_field_info.supported_types))
-            + "_credentials": (agg_field_info, node_fields)
-            for agg_field_info, node_fields in CredentialsFieldInfo.combine(
-                *(
-                    (
-                        # Apply discrimination before aggregating credentials inputs
-                        (
-                            field_info.discriminate(
-                                node.input_default[field_info.discriminator]
-                            )
-                            if (
-                                field_info.discriminator
-                                and node.input_default.get(field_info.discriminator)
-                            )
-                            else field_info
-                        ),
-                        (node.id, field_name),
-                    )
-                    for node in self.nodes
-                    for field_name, field_info in node.block.input_schema.get_credentials_fields_info().items()
-                )
-            )
+            "required": [p.name for p in props if p.value is None],
         }
 
 
@@ -329,16 +327,13 @@ class GraphModel(Graph):
     user_id: str
     nodes: list[NodeModel] = []  # type: ignore
 
-    @computed_field
     @property
-    def has_webhook_trigger(self) -> bool:
-        return self.webhook_input_node is not None
-
-    @property
-    def starting_nodes(self) -> list[NodeModel]:
+    def starting_nodes(self) -> list[Node]:
         outbound_nodes = {link.sink_id for link in self.links}
         input_nodes = {
-            node.id for node in self.nodes if node.block.block_type == BlockType.INPUT
+            v.id
+            for v in self.nodes
+            if (b := get_block(v.block_id)) and b.block_type == BlockType.INPUT
         }
         return [
             node
@@ -346,18 +341,6 @@ class GraphModel(Graph):
             if node.id not in outbound_nodes or node.id in input_nodes
         ]
 
-    @property
-    def webhook_input_node(self) -> NodeModel | None:
-        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.
@@ -381,6 +364,7 @@ class GraphModel(Graph):
         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]
@@ -411,16 +395,15 @@ class GraphModel(Graph):
 
     @staticmethod
     def _validate_graph(graph: BaseGraph, for_run: bool = False):
-        def is_tool_pin(name: str) -> bool:
-            return name.startswith("tools_^_")
-
         def sanitize(name):
             sanitized_name = name.split("_#_")[0].split("_@_")[0].split("_$_")[0]
-            if is_tool_pin(sanitized_name):
-                return "tools"
+            if sanitized_name.startswith("tools_^_"):
+                return sanitized_name.split("_^_")[0]
             return sanitized_name
 
         # Validate smart decision maker nodes
+        smart_decision_maker_nodes = set()
+        agent_nodes = set()
         nodes_block = {
             node.id: block
             for node in graph.nodes
@@ -431,6 +414,13 @@ class GraphModel(Graph):
             if (block := nodes_block.get(node.id)) is None:
                 raise ValueError(f"Invalid block {node.block_id} for node #{node.id}")
 
+            # Smart decision maker nodes
+            if block.block_type == BlockType.AI:
+                smart_decision_maker_nodes.add(node.id)
+            # Agent nodes
+            elif block.block_type == BlockType.AGENT:
+                agent_nodes.add(node.id)
+
         input_links = defaultdict(list)
 
         for link in graph.links:
@@ -445,21 +435,16 @@ class GraphModel(Graph):
                 [sanitize(name) for name in node.input_default]
                 + [sanitize(link.sink_name) for link in input_links.get(node.id, [])]
             )
-            input_schema = block.input_schema
-            for name in (required_fields := input_schema.get_required_fields()):
+            for name in block.input_schema.get_required_fields():
                 if (
                     name not in provided_inputs
-                    # Webhook payload is passed in by ExecutionManager
                     and not (
                         name == "payload"
                         and block.block_type
                         in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
                     )
-                    # Checking availability of credentials is done by ExecutionManager
-                    and name not in input_schema.get_credentials_fields()
-                    # Validate only I/O nodes, or validate everything when executing
                     and (
-                        for_run
+                        for_run  # Skip input completion validation, unless when executing.
                         or block.block_type
                         in [
                             BlockType.INPUT,
@@ -472,18 +457,9 @@ class GraphModel(Graph):
                         f"Node {block.name} #{node.id} required input missing: `{name}`"
                     )
 
-                if (
-                    block.block_type == BlockType.INPUT
-                    and (input_key := node.input_default.get("name"))
-                    and is_credentials_field_name(input_key)
-                ):
-                    raise ValueError(
-                        f"Agent input node uses reserved name '{input_key}'; "
-                        "'credentials' and `*_credentials` are reserved input names"
-                    )
-
             # Get input schema properties and check dependencies
-            input_fields = input_schema.model_fields
+            input_schema = block.input_schema.model_fields
+            required_fields = block.input_schema.get_required_fields()
 
             def has_value(name):
                 return (
@@ -491,21 +467,14 @@ class GraphModel(Graph):
                     and name in node.input_default
                     and node.input_default[name] is not None
                     and str(node.input_default[name]).strip() != ""
-                ) or (name in input_fields and input_fields[name].default is not None)
+                ) or (name in input_schema and input_schema[name].default is not None)
 
             # Validate dependencies between fields
-            for field_name, field_info in input_fields.items():
+            for field_name, field_info in input_schema.items():
                 # Apply input dependency validation only on run & field with depends_on
                 json_schema_extra = field_info.json_schema_extra or {}
-                if not (
-                    for_run
-                    and isinstance(json_schema_extra, dict)
-                    and (
-                        dependencies := cast(
-                            list[str], json_schema_extra.get("depends_on", [])
-                        )
-                    )
-                ):
+                dependencies = json_schema_extra.get("depends_on", [])
+                if not for_run or not dependencies:
                     continue
 
                 # Check if dependent field has value in input_default
@@ -522,7 +491,9 @@ class GraphModel(Graph):
         node_map = {v.id: v for v in graph.nodes}
 
         def is_static_output_block(nid: str) -> bool:
-            return node_map[nid].block.static_output
+            bid = node_map[nid].block_id
+            b = get_block(bid)
+            return b.static_output if b else False
 
         # Links: links are connected and the connected pin data type are compatible.
         for link in graph.links:
@@ -558,7 +529,7 @@ class GraphModel(Graph):
                         if block.block_type not in [BlockType.AGENT]
                         else vals.get("input_schema", {}).get("properties", {}).keys()
                     )
-                if sanitized_name not in fields and not is_tool_pin(name):
+                if sanitized_name not in fields and not name.startswith("tools_^_"):
                     fields_msg = f"Allowed fields: {fields}"
                     raise ValueError(f"{prefix}, `{name}` invalid, {fields_msg}")
 
@@ -575,16 +546,16 @@ class GraphModel(Graph):
             id=graph.id,
             user_id=graph.userId if not for_export else "",
             version=graph.version,
-            forked_from_id=graph.forkedFromId,
-            forked_from_version=graph.forkedFromVersion,
             is_active=graph.isActive,
             name=graph.name or "",
             description=graph.description or "",
-            nodes=[NodeModel.from_db(node, for_export) for node in graph.Nodes or []],
+            nodes=[
+                NodeModel.from_db(node, for_export) for node in graph.AgentNodes or []
+            ],
             links=list(
                 {
                     Link.from_db(link)
-                    for node in graph.Nodes or []
+                    for node in graph.AgentNodes or []
                     for link in (node.Input or []) + (node.Output or [])
                 }
             ),
@@ -659,6 +630,52 @@ async def get_graphs(
     return graph_models
 
 
+async def get_graph_executions(
+    graph_id: Optional[str] = None,
+    user_id: Optional[str] = None,
+) -> list[GraphExecutionMeta]:
+    where_filter: AgentGraphExecutionWhereInput = {
+        "isDeleted": False,
+    }
+    if user_id:
+        where_filter["userId"] = user_id
+    if graph_id:
+        where_filter["agentGraphId"] = graph_id
+
+    executions = await AgentGraphExecution.prisma().find_many(
+        where=where_filter,
+        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, "isDeleted": False, "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, "isDeleted": False, "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,
@@ -669,6 +686,7 @@ 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"},
     )
 
@@ -715,10 +733,10 @@ async def get_graph(
         and not (
             await StoreListingVersion.prisma().find_first(
                 where={
-                    "agentGraphId": graph_id,
-                    "agentGraphVersion": version or graph.version,
+                    "agentId": graph_id,
+                    "agentVersion": version or graph.version,
                     "isDeleted": False,
-                    "submissionStatus": SubmissionStatus.APPROVED,
+                    "StoreListing": {"is": {"isApproved": True}},
                 }
             )
         )
@@ -726,7 +744,7 @@ async def get_graph(
         return None
 
     if for_export:
-        sub_graphs = await get_sub_graphs(graph)
+        sub_graphs = await _get_sub_graphs(graph)
         return GraphModel.from_db(
             graph=graph,
             sub_graphs=sub_graphs,
@@ -736,7 +754,7 @@ async def get_graph(
     return GraphModel.from_db(graph, for_export)
 
 
-async def get_sub_graphs(graph: AgentGraph) -> list[AgentGraph]:
+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.
@@ -750,16 +768,12 @@ async def get_sub_graphs(graph: AgentGraph) -> list[AgentGraph]:
         sub_graph_ids = [
             (graph_id, graph_version)
             for graph in search_graphs
-            for node in graph.Nodes or []
+            for node in graph.AgentNodes or []
             if (
                 node.AgentBlock
                 and node.AgentBlock.id == agent_block_id
-                and (graph_id := cast(str, dict(node.constantInput).get("graph_id")))
-                and (
-                    graph_version := cast(
-                        int, dict(node.constantInput).get("graph_version")
-                    )
-                )
+                and (graph_id := dict(node.constantInput).get("graph_id"))
+                and (graph_version := dict(node.constantInput).get("graph_version"))
             )
         ]
         if not sub_graph_ids:
@@ -774,7 +788,7 @@ async def get_sub_graphs(graph: AgentGraph) -> list[AgentGraph]:
                         "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,
         )
@@ -788,7 +802,7 @@ async def get_sub_graphs(graph: AgentGraph) -> list[AgentGraph]:
 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}},
+        include={"AgentNodeSink": {"include": AGENT_NODE_INCLUDE}},  # type: ignore
     )
     return [
         (Link.from_db(link), NodeModel.from_db(link.AgentNodeSink))
@@ -854,56 +868,34 @@ async def create_graph(graph: Graph, user_id: str) -> GraphModel:
     raise ValueError(f"Created graph {graph.id} v{graph.version} is not in DB")
 
 
-async def fork_graph(graph_id: str, graph_version: int, user_id: str) -> GraphModel:
-    """
-    Forks a graph by copying it and all its nodes and links to a new graph.
-    """
-    async with transaction() as tx:
-        graph = await get_graph(graph_id, graph_version, user_id, True)
-        if not graph:
-            raise ValueError(f"Graph {graph_id} v{graph_version} not found")
-
-        # Set forked from ID and version as itself as it's about ot be copied
-        graph.forked_from_id = graph.id
-        graph.forked_from_version = graph.version
-        graph.name = f"{graph.name} (copy)"
-        graph.reassign_ids(user_id=user_id, reassign_graph_id=True)
-        graph.validate_graph(for_run=False)
-
-        await __create_graph(tx, graph, user_id)
-
-    return graph
-
-
 async def __create_graph(tx, graph: Graph, user_id: str):
     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,
-                forkedFromId=graph.forked_from_id,
-                forkedFromVersion=graph.forked_from_version,
-            )
+            {
+                "id": graph.id,
+                "version": graph.version,
+                "name": graph.name,
+                "description": graph.description,
+                "isActive": graph.is_active,
+                "userId": user_id,
+            }
             for graph in graphs
         ]
     )
 
     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),
-            )
+            {
+                "id": node.id,
+                "agentGraphId": graph.id,
+                "agentGraphVersion": graph.version,
+                "agentBlockId": node.block_id,
+                "constantInput": Json(node.input_default),
+                "metadata": Json(node.metadata),
+                "webhookId": node.webhook_id,
+            }
             for graph in graphs
             for node in graph.nodes
         ]
@@ -911,14 +903,14 @@ async def __create_graph(tx, graph: Graph, user_id: str):
 
     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,
-            )
+            {
+                "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
         ]
@@ -969,12 +961,12 @@ async def fix_llm_provider_credentials():
             SELECT    graph."userId"       user_id,
                   node.id              node_id,
                   node."constantInput" node_preset_input
-            FROM      platform."AgentNode"  node
-            LEFT JOIN platform."AgentGraph" graph
-            ON        node."agentGraphId" = graph.id
-            WHERE     node."constantInput"::jsonb->'credentials'->>'provider' = 'llm'
-            ORDER BY  graph."userId";
-            """
+        FROM      platform."AgentNode"  node
+        LEFT JOIN platform."AgentGraph" graph
+        ON        node."agentGraphId" = graph.id
+        WHERE     node."constantInput"::jsonb->'credentials'->>'provider' = 'llm'
+        ORDER BY  graph."userId";
+        """
         )
         logger.info(f"Fixing LLM credential inputs on {len(broken_nodes)} nodes")
     except Exception as e:
@@ -1029,47 +1021,3 @@ 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
-
-    # Convert enum values to a list of strings for the SQL query
-    enum_values = [v.value for v in LlmModel]
-    escaped_enum_values = repr(tuple(enum_values))  # hack but works
-
-    # Update each block
-    for id, path in llm_model_fields.items():
-        query = f"""
-            UPDATE platform."AgentNode"
-            SET "constantInput" = jsonb_set("constantInput", $1, to_jsonb($2), true)
-            WHERE "agentBlockId" = $3
-            AND "constantInput" ? ($4)::text
-            AND "constantInput"->>($4)::text NOT IN {escaped_enum_values}
-            """
-
-        await db.execute_raw(
-            query,  # type: ignore - is supposed to be LiteralString
-            [path],
-            migrate_to.value,
-            id,
-            path,
-        )

autogpt_platform/backend/backend/data/includes.py

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

autogpt_platform/backend/backend/data/integrations.py

@@ -3,14 +3,12 @@ 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
 
@@ -67,35 +65,28 @@ class Webhook(BaseDbModel):
 
 async def create_webhook(webhook: Webhook) -> Webhook:
     created_webhook = await IntegrationWebhook.prisma().create(
-        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,
-        )
+        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,
+        }
     )
     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.
-
-    Raises:
-        NotFoundError: if no record with the given ID exists
-    """
-    webhook = await IntegrationWebhook.prisma().find_unique(
+    """⚠️ No `user_id` check: DO NOT USE without check in user-facing endpoints."""
+    webhook = await IntegrationWebhook.prisma().find_unique_or_raise(
         where={"id": webhook_id},
         include=INTEGRATION_WEBHOOK_INCLUDE,
     )
-    if not webhook:
-        raise NotFoundError(f"Webhook #{webhook_id} not found")
     return Webhook.from_db(webhook)
 
 

autogpt_platform/backend/backend/data/model.py

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

autogpt_platform/backend/backend/data/notifications.py

@@ -6,14 +6,10 @@ 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 (
-    NotificationEventCreateInput,
-    UserNotificationBatchCreateInput,
-    UserNotificationBatchWhereInput,
-)
+from prisma.types import UserNotificationBatchWhereInput
 
 # from backend.notifications.models import NotificationEvent
-from pydantic import BaseModel, ConfigDict, EmailStr, Field, field_validator
+from pydantic import BaseModel, EmailStr, Field, field_validator
 
 from backend.server.v2.store.exceptions import DatabaseError
 
@@ -39,7 +35,8 @@ class QueueType(Enum):
 
 
 class BaseNotificationData(BaseModel):
-    model_config = ConfigDict(extra="allow")
+    class Config:
+        extra = "allow"
 
 
 class AgentRunData(BaseNotificationData):
@@ -375,7 +372,7 @@ class UserNotificationBatchDTO(BaseModel):
             type=model.type,
             notifications=[
                 UserNotificationEventDTO.from_db(notification)
-                for notification in model.Notifications or []
+                for notification in model.notifications or []
             ],
             created_at=model.createdAt,
             updated_at=model.updatedAt,
@@ -401,8 +398,6 @@ async def create_or_add_to_user_notification_batch(
         logger.info(
             f"Creating or adding to notification batch for {user_id} with type {notification_type} and data {notification_data}"
         )
-        if not notification_data.data:
-            raise ValueError("Notification data must be provided")
 
         # Serialize the data
         json_data: Json = Json(notification_data.data.model_dump())
@@ -415,44 +410,44 @@ 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=NotificationEventCreateInput(
-                        type=notification_type,
-                        data=json_data,
-                    )
+                    data={
+                        "type": notification_type,
+                        "data": json_data,
+                    }
                 )
 
                 # Create new batch
                 resp = await tx.usernotificationbatch.create(
-                    data=UserNotificationBatchCreateInput(
-                        userId=user_id,
-                        type=notification_type,
-                        Notifications={"connect": [{"id": notification_event.id}]},
-                    ),
-                    include={"Notifications": True},
+                    data={
+                        "userId": user_id,
+                        "type": notification_type,
+                        "notifications": {"connect": [{"id": notification_event.id}]},
+                    },
+                    include={"notifications": True},
                 )
                 return UserNotificationBatchDTO.from_db(resp)
         else:
             async with transaction() as tx:
                 notification_event = await tx.notificationevent.create(
-                    data=NotificationEventCreateInput(
-                        type=notification_type,
-                        data=json_data,
-                        UserNotificationBatch={"connect": {"id": existing_batch.id}},
-                    )
+                    data={
+                        "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(
@@ -472,13 +467,13 @@ async def get_user_notification_oldest_message_in_batch(
     try:
         batch = await UserNotificationBatch.prisma().find_first(
             where={"userId": user_id, "type": notification_type},
-            include={"Notifications": True},
+            include={"notifications": True},
         )
         if not batch:
             return None
-        if not batch.Notifications:
+        if not batch.notifications:
             return None
-        sorted_notifications = sorted(batch.Notifications, key=lambda x: x.createdAt)
+        sorted_notifications = sorted(batch.notifications, key=lambda x: x.createdAt)
 
         return (
             UserNotificationEventDTO.from_db(sorted_notifications[0])
@@ -523,7 +518,7 @@ async def get_user_notification_batch(
     try:
         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:
@@ -539,11 +534,11 @@ async def get_all_batches_by_type(
         batches = await UserNotificationBatch.prisma().find_many(
             where={
                 "type": notification_type,
-                "Notifications": {
+                "notifications": {
                     "some": {}  # Only return batches with at least one notification
                 },
             },
-            include={"Notifications": True},
+            include={"notifications": True},
         )
         return [UserNotificationBatchDTO.from_db(batch) for batch in batches]
     except Exception as e:

autogpt_platform/backend/backend/data/onboarding.py

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

autogpt_platform/backend/backend/data/queue.py

@@ -1,6 +1,8 @@
 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
@@ -12,6 +14,13 @@ 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)
 
 
@@ -23,12 +32,8 @@ 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 = self.Message(payload=item).model_dump_json()
+        message = json.dumps(item.model_dump(), cls=DateTimeEncoder)
         channel_name = f"{self.event_bus_name}/{channel_key}"
         logger.debug(f"[{channel_name}] Publishing an event to Redis {message}")
         return message, channel_name
@@ -38,8 +43,9 @@ class BaseRedisEventBus(Generic[M], ABC):
         if msg["type"] != message_type:
             return None
         try:
-            logger.debug(f"[{channel_key}] Consuming an event from Redis {msg['data']}")
-            return self.Message.model_validate_json(msg["data"]).payload
+            data = json.loads(msg["data"])
+            logger.debug(f"Consuming an event from Redis {data}")
+            return self.Model(**data)
         except Exception as e:
             logger.error(f"Failed to parse event result from Redis {msg} {e}")
 
@@ -51,16 +57,9 @@ class BaseRedisEventBus(Generic[M], ABC):
         return pubsub, full_channel_name
 
 
-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):
+    Model: type[M]
+
     @property
     def connection(self) -> redis.Redis:
         return redis.get_redis()
@@ -86,6 +85,8 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
 
 
 class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
+    Model: type[M]
+
     @property
     async def connection(self) -> redis.AsyncRedis:
         return await redis.get_redis_async()

autogpt_platform/backend/backend/data/rabbitmq.py

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

autogpt_platform/backend/backend/data/user.py

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

autogpt_platform/backend/backend/exec.py

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

autogpt_platform/backend/backend/executor/database.py

@@ -1,18 +1,16 @@
-import logging
-
-from backend.data import db
-from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
+from backend.data.credit import get_user_credit_model
 from backend.data.execution import (
+    ExecutionResult,
+    NodeExecutionEntry,
+    RedisExecutionEventBus,
     create_graph_execution,
-    get_graph_execution,
-    get_incomplete_node_executions,
-    get_latest_node_execution,
-    get_node_execution_results,
+    get_execution_results,
+    get_incomplete_executions,
+    get_latest_execution,
+    update_execution_status,
     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,
 )
@@ -39,48 +37,38 @@ from backend.data.user import (
     update_user_integrations,
     update_user_metadata,
 )
-from backend.util.service import AppService, exposed_run_and_wait
+from backend.util.service import AppService, expose, exposed_run_and_wait
 from backend.util.settings import Config
 
 config = Config()
 _user_credit_model = get_user_credit_model()
-logger = logging.getLogger(__name__)
 
 
-async def _spend_credits(
-    user_id: str, cost: int, metadata: UsageTransactionMetadata
-) -> int:
-    return await _user_credit_model.spend_credits(user_id, cost, metadata)
+async def _spend_credits(entry: NodeExecutionEntry) -> int:
+    return await _user_credit_model.spend_credits(entry, 0, 0)
 
 
 class DatabaseManager(AppService):
-
-    def run_service(self) -> None:
-        logger.info(f"[{self.service_name}] ⏳ Connecting to Database...")
-        self.run_and_wait(db.connect())
-        super().run_service()
-
-    def cleanup(self):
-        super().cleanup()
-        logger.info(f"[{self.service_name}] ⏳ Disconnecting Database...")
-        self.run_and_wait(db.disconnect())
+    def __init__(self):
+        super().__init__()
+        self.use_db = True
+        self.use_redis = True
+        self.event_queue = 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)
+
     # Executions
-    get_graph_execution = exposed_run_and_wait(get_graph_execution)
     create_graph_execution = exposed_run_and_wait(create_graph_execution)
-    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
-    )
+    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)
     update_graph_execution_start_time = exposed_run_and_wait(
         update_graph_execution_start_time
     )

autogpt_platform/backend/backend/executor/scheduler.py

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

autogpt_platform/backend/backend/executor/utils.py

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

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -161,14 +161,6 @@ smartlead_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
-google_maps_credentials = APIKeyCredentials(
-    id="9aa1bde0-4947-4a70-a20c-84daa3850d52",
-    provider="google_maps",
-    api_key=SecretStr(settings.secrets.google_maps_api_key),
-    title="Use Credits for Google Maps",
-    expires_at=None,
-)
-
 zerobounce_credentials = APIKeyCredentials(
     id="63a6e279-2dc2-448e-bf57-85776f7176dc",
     provider="zerobounce",
@@ -177,7 +169,16 @@ zerobounce_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
+example_credentials = APIKeyCredentials(
+    id="a2b7f68f-aa6a-4995-99ec-b45b40d33498",
+    provider="example-provider",
+    api_key=SecretStr(settings.secrets.example_api_key),
+    title="Use Credits for Example",
+    expires_at=None,
+)
+
 DEFAULT_CREDENTIALS = [
+    example_credentials,
     ollama_credentials,
     revid_credentials,
     ideogram_credentials,
@@ -198,7 +199,6 @@ DEFAULT_CREDENTIALS = [
     apollo_credentials,
     smartlead_credentials,
     zerobounce_credentials,
-    google_maps_credentials,
 ]
 
 
@@ -234,6 +234,8 @@ class IntegrationCredentialsStore:
         all_credentials.append(ollama_credentials)
 
         # These will only be added if the API key is set
+        if settings.secrets.example_api_key:
+            all_credentials.append(example_credentials)
         if settings.secrets.revid_api_key:
             all_credentials.append(revid_credentials)
         if settings.secrets.ideogram_api_key:
@@ -272,8 +274,6 @@ class IntegrationCredentialsStore:
             all_credentials.append(smartlead_credentials)
         if settings.secrets.zerobounce_api_key:
             all_credentials.append(zerobounce_credentials)
-        if settings.secrets.google_maps_api_key:
-            all_credentials.append(google_maps_credentials)
         return all_credentials
 
     def get_creds_by_id(self, user_id: str, credentials_id: str) -> Credentials | None:

autogpt_platform/backend/backend/integrations/creds_manager.py

@@ -10,7 +10,6 @@ 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
 
@@ -93,7 +92,7 @@ class IntegrationCredentialsManager:
 
                     fresh_credentials = oauth_handler.refresh_tokens(credentials)
                     self.store.update_creds(user_id, fresh_credentials)
-                    if _lock and _lock.locked() and _lock.owned():
+                    if _lock and _lock.locked():
                         _lock.release()
 
                     credentials = fresh_credentials
@@ -145,7 +144,7 @@ class IntegrationCredentialsManager:
         try:
             yield
         finally:
-            if lock.locked() and lock.owned():
+            if lock.locked():
                 lock.release()
 
     def release_all_locks(self):
@@ -154,13 +153,12 @@ class IntegrationCredentialsManager:
         self.store.locks.release_all_locks()
 
 
-def _get_provider_oauth_handler(provider_name_str: str) -> "BaseOAuthHandler":
-    provider_name = ProviderName(provider_name_str)
+def _get_provider_oauth_handler(provider_name: str) -> "BaseOAuthHandler":
     if provider_name not in HANDLERS_BY_NAME:
         raise KeyError(f"Unknown provider '{provider_name}'")
 
-    client_id = getattr(settings.secrets, f"{provider_name.value}_client_id")
-    client_secret = getattr(settings.secrets, f"{provider_name.value}_client_secret")
+    client_id = getattr(settings.secrets, f"{provider_name}_client_id")
+    client_secret = getattr(settings.secrets, f"{provider_name}_client_secret")
     if not (client_id and client_secret):
         raise MissingConfigError(
             f"Integration with provider '{provider_name}' is not configured",

autogpt_platform/backend/backend/integrations/providers.py

@@ -10,8 +10,8 @@ class ProviderName(str, Enum):
     D_ID = "d_id"
     E2B = "e2b"
     EXA = "exa"
+    EXAMPLE_PROVIDER = "example-provider"
     FAL = "fal"
-    GENERIC_WEBHOOK = "generic_webhook"
     GITHUB = "github"
     GOOGLE = "google"
     GOOGLE_MAPS = "google_maps"

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

@@ -1,45 +1,24 @@
 from typing import TYPE_CHECKING
 
+from .compass import CompassWebhookManager
+from .example import ExampleWebhookManager
+from .github import GithubWebhooksManager
+from .slant3d import Slant3DWebhooksManager
+
 if TYPE_CHECKING:
     from ..providers import ProviderName
     from ._base import BaseWebhooksManager
 
-_WEBHOOK_MANAGERS: dict["ProviderName", type["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,
+        ExampleWebhookManager,
+    ]
+}
+# --8<-- [end: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"]
+__all__ = ["WEBHOOK_MANAGERS_BY_NAME"]

autogpt_platform/backend/backend/integrations/webhooks/example.py

@@ -0,0 +1,147 @@
+import logging
+
+import requests
+from fastapi import Request
+from strenum import StrEnum
+
+from backend.data import integrations
+from backend.data.model import APIKeyCredentials, Credentials
+from backend.integrations.providers import ProviderName
+
+from ._manual_base import ManualWebhookManagerBase
+
+logger = logging.getLogger(__name__)
+
+
+class ExampleWebhookEventType(StrEnum):
+    EXAMPLE_EVENT = "example_event"
+    ANOTHER_EXAMPLE_EVENT = "another_example_event"
+
+
+# ExampleWebhookManager is a class that manages webhooks for a hypothetical provider.
+# It extends ManualWebhookManagerBase, which provides base functionality for manual webhook management.
+class ExampleWebhookManager(ManualWebhookManagerBase):
+    # Define the provider name for this webhook manager.
+    PROVIDER_NAME = ProviderName.EXAMPLE_PROVIDER
+    # Define the types of webhooks this manager can handle.
+    WebhookEventType = ExampleWebhookEventType
+
+    BASE_URL = "https://api.example.com"
+
+    @classmethod
+    async def validate_payload(
+        cls, webhook: integrations.Webhook, request: Request
+    ) -> tuple[dict, str]:
+        """
+        Validate the incoming webhook payload.
+
+        Args:
+            webhook (integrations.Webhook): The webhook object.
+            request (Request): The incoming request object.
+
+        Returns:
+            tuple: A tuple containing the payload as a dictionary and the event type as a string.
+        """
+        # Extract the JSON payload from the request.
+        payload = await request.json()
+        # Set the event type based on the webhook type in the payload.
+        event_type = payload.get("webhook_type", ExampleWebhookEventType.EXAMPLE_EVENT)
+
+        # For the payload its better to return a pydantic model
+        # rather than a weakly typed dict here
+        return payload, event_type
+
+    async def _register_webhook(
+        self,
+        credentials: Credentials,
+        webhook_type: str,
+        resource: str,
+        events: list[str],
+        ingress_url: str,
+        secret: str,
+    ) -> tuple[str, dict]:
+        """
+        Register a new webhook with the provider.
+
+        Args:
+            credentials (Credentials): The credentials required for authentication.
+            webhook_type (str): The type of webhook to register.
+            resource (str): The resource associated with the webhook.
+            events (list[str]): The list of events to subscribe to.
+            ingress_url (str): The URL where the webhook will send data.
+            secret (str): A secret for securing the webhook.
+
+        Returns:
+            tuple: A tuple containing the provider's webhook ID, if any, and the webhook configuration as a dictionary.
+        """
+        # Ensure the credentials are of the correct type.
+        if not isinstance(credentials, APIKeyCredentials):
+            raise ValueError("API key is required to register a webhook")
+
+        # Prepare the headers for the request, including the API key.
+        headers = {
+            "api-key": credentials.api_key.get_secret_value(),
+            "Content-Type": "application/json",
+        }
+
+        # Prepare the payload for the request. Note that the events list is not used.
+        # This is just a fake example
+        payload = {"endPoint": ingress_url}
+
+        # Send a POST request to register the webhook.
+        response = requests.post(
+            f"{self.BASE_URL}/example/webhookSubscribe", headers=headers, json=payload
+        )
+
+        # Check if the response indicates a failure.
+        if not response.ok:
+            error = response.json().get("error", "Unknown error")
+            raise RuntimeError(f"Failed to register webhook: {error}")
+
+        # Prepare the webhook configuration to return.
+        webhook_config = {
+            "endpoint": ingress_url,
+            "provider": self.PROVIDER_NAME,
+            "events": ["example_event"],
+            "type": webhook_type,
+        }
+
+        return "", webhook_config
+
+    async def _deregister_webhook(
+        self, webhook: integrations.Webhook, credentials: Credentials
+    ) -> None:
+        """
+        Deregister a webhook with the provider.
+
+        Args:
+            webhook (integrations.Webhook): The webhook object to deregister.
+            credentials (Credentials): The credentials associated with the webhook.
+
+        Raises:
+            ValueError: If the webhook doesn't belong to the credentials or if deregistration fails.
+        """
+        if webhook.credentials_id != credentials.id:
+            raise ValueError(
+                f"Webhook #{webhook.id} does not belong to credentials {credentials.id}"
+            )
+
+        if not isinstance(credentials, APIKeyCredentials):
+            raise ValueError("API key is required to deregister a webhook")
+
+        headers = {
+            "api-key": credentials.api_key.get_secret_value(),
+            "Content-Type": "application/json",
+        }
+
+        # Construct the delete URL based on the webhook information
+        delete_url = f"{self.BASE_URL}/example/webhooks/{webhook.provider_webhook_id}"
+
+        response = requests.delete(delete_url, headers=headers)
+
+        if response.status_code not in [204, 404]:
+            # 204 means successful deletion, 404 means the webhook was already deleted
+            error = response.json().get("error", "Unknown error")
+            raise ValueError(f"Failed to delete webhook: {error}")
+
+        # If we reach here, the webhook was successfully deleted or didn't exist

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

@@ -1,29 +0,0 @@
-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
+from backend.data.block import BlockSchema, BlockWebhookConfig, get_block
 from backend.data.graph import set_node_webhook
-from backend.integrations.webhooks import get_webhook_manager, supports_webhooks
+from backend.integrations.webhooks import WEBHOOK_MANAGERS_BY_NAME
 
 if TYPE_CHECKING:
     from backend.data.graph import GraphModel, NodeModel
@@ -29,7 +29,12 @@ async def on_graph_activate(
     # Compare nodes in new_graph_version with previous_graph_version
     updated_nodes = []
     for new_node in graph.nodes:
-        block_input_schema = cast(BlockSchema, new_node.block.input_schema)
+        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)
 
         node_credentials = None
         if (
@@ -70,7 +75,12 @@ async def on_graph_deactivate(
     """
     updated_nodes = []
     for node in graph.nodes:
-        block_input_schema = cast(BlockSchema, node.block.input_schema)
+        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)
 
         node_credentials = None
         if (
@@ -103,13 +113,17 @@ async def on_node_activate(
 ) -> "NodeModel":
     """Hook to be called when the node is activated/created"""
 
-    block = node.block
+    block = get_block(node.block_id)
+    if not block:
+        raise ValueError(
+            f"Node #{node.id} is instance of unknown block #{node.block_id}"
+        )
 
     if not block.webhook_config:
         return node
 
     provider = block.webhook_config.provider
-    if not supports_webhooks(provider):
+    if provider not in WEBHOOK_MANAGERS_BY_NAME:
         raise ValueError(
             f"Block #{block.id} has webhook_config for provider {provider} "
             "which does not support webhooks"
@@ -119,7 +133,7 @@ async def on_node_activate(
         f"Activating webhook node #{node.id} with config {block.webhook_config}"
     )
 
-    webhooks_manager = get_webhook_manager(provider)
+    webhooks_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
 
     if auto_setup_webhook := isinstance(block.webhook_config, BlockWebhookConfig):
         try:
@@ -210,19 +224,23 @@ async def on_node_deactivate(
     """Hook to be called when node is deactivated/deleted"""
 
     logger.debug(f"Deactivating node #{node.id}")
-    block = node.block
+    block = get_block(node.block_id)
+    if not block:
+        raise ValueError(
+            f"Node #{node.id} is instance of unknown block #{node.block_id}"
+        )
 
     if not block.webhook_config:
         return node
 
     provider = block.webhook_config.provider
-    if not supports_webhooks(provider):
+    if provider not in WEBHOOK_MANAGERS_BY_NAME:
         raise ValueError(
             f"Block #{block.id} has webhook_config for provider {provider} "
             "which does not support webhooks"
         )
 
-    webhooks_manager = get_webhook_manager(provider)
+    webhooks_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
 
     if node.webhook_id:
         logger.debug(f"Node #{node.id} has webhook_id {node.webhook_id}")

autogpt_platform/backend/backend/notifications/notifications.py

@@ -9,7 +9,6 @@ from autogpt_libs.utils.cache import thread_cached
 from prisma.enums import NotificationType
 from pydantic import BaseModel
 
-from backend.data import rabbitmq
 from backend.data.notifications import (
     BaseSummaryData,
     BaseSummaryParams,
@@ -129,20 +128,6 @@ class NotificationManager(AppService):
         self.running = True
         self.email_sender = EmailSender()
 
-    @property
-    def rabbit(self) -> rabbitmq.AsyncRabbitMQ:
-        """Access the RabbitMQ service. Will raise if not configured."""
-        if not self.rabbitmq_service:
-            raise RuntimeError("RabbitMQ not configured for this service")
-        return self.rabbitmq_service
-
-    @property
-    def rabbit_config(self) -> rabbitmq.RabbitMQConfig:
-        """Access the RabbitMQ config. Will raise if not configured."""
-        if not self.rabbitmq_config:
-            raise RuntimeError("RabbitMQ not configured for this service")
-        return self.rabbitmq_config
-
     @classmethod
     def get_port(cls) -> int:
         return settings.config.notification_service_port
@@ -260,26 +245,20 @@ class NotificationManager(AppService):
                             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
+
+                        events = [
+                            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,
+                                }
+                            )
+                            for db_event in batch_data.notifications
+                        ]
                         logger.info(f"{events=}")
 
                         self.email_sender.send_templated(
@@ -689,8 +668,6 @@ 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(
@@ -698,19 +675,15 @@ class NotificationManager(AppService):
                 )
                 self.run_and_wait(message.reject(requeue=False))
             else:
-                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=}"
+                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}"
                 )
 
     def run_service(self):
-        logger.info(f"[{self.service_name}] ⏳ Configuring RabbitMQ...")
-        self.rabbitmq_service = rabbitmq.AsyncRabbitMQ(self.rabbitmq_config)
-        self.run_and_wait(self.rabbitmq_service.connect())
-
         logger.info(f"[{self.service_name}] Started notification service")
 
         # Set up scheduler for batch processing of all notification types
-        # this can be changed later to spawn different cleanups on different schedules
+        # this can be changed later to spawn differnt cleanups on different schedules
         try:
             get_scheduler().add_batched_notification_schedule(
                 notification_types=list(NotificationType),
@@ -772,5 +745,3 @@ class NotificationManager(AppService):
         """Cleanup service resources"""
         self.running = False
         super().cleanup()
-        logger.info(f"[{self.service_name}] ⏳ Disconnecting RabbitMQ...")
-        self.run_and_wait(self.rabbitmq_service.disconnect())

autogpt_platform/backend/backend/server/conn_manager.py

@@ -2,17 +2,8 @@ from typing import Dict, Set
 
 from fastapi import WebSocket
 
-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,
-}
+from backend.data import execution
+from backend.server.model import Methods, WsMessage
 
 
 class ConnectionManager:
@@ -20,96 +11,37 @@ class ConnectionManager:
         self.active_connections: Set[WebSocket] = set()
         self.subscriptions: Dict[str, Set[WebSocket]] = {}
 
-    async def connect_socket(self, websocket: WebSocket):
+    async def connect(self, websocket: WebSocket):
         await websocket.accept()
         self.active_connections.add(websocket)
 
-    def disconnect_socket(self, websocket: WebSocket):
+    def disconnect(self, websocket: WebSocket):
         self.active_connections.remove(websocket)
         for subscribers in self.subscriptions.values():
             subscribers.discard(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 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_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 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 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(),
+    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(),
             ).model_dump_json()
-            for connection in self.subscriptions[channel]:
+            for connection in self.subscriptions[key]:
                 await connection.send_text(message)
-                n_sent += 1
-
-        return n_sent
-
-    async def _subscribe(self, channel_key: str, websocket: WebSocket) -> str:
-        if channel_key not in self.subscriptions:
-            self.subscriptions[channel_key] = set()
-        self.subscriptions[channel_key].add(websocket)
-        return channel_key
-
-    async def _unsubscribe(self, channel_key: str, websocket: WebSocket) -> str | None:
-        if channel_key in self.subscriptions:
-            self.subscriptions[channel_key].discard(websocket)
-            if not self.subscriptions[channel_key]:
-                del self.subscriptions[channel_key]
-            return channel_key
-        return None
-
-
-def _graph_exec_channel_key(user_id: str, *, graph_exec_id: str) -> str:
-    return f"{user_id}|graph_exec#{graph_exec_id}"
-
-
-def _graph_execs_channel_key(user_id: str, *, graph_id: str) -> str:
-    return f"{user_id}|graph#{graph_id}|executions"

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

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

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

@@ -1,10 +1,8 @@
-import asyncio
 import logging
-from typing import TYPE_CHECKING, Annotated, Awaitable, Literal
+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 (
@@ -15,12 +13,13 @@ from backend.data.integrations import (
     wait_for_webhook_event,
 )
 from backend.data.model import Credentials, CredentialsType, OAuth2Credentials
-from backend.executor.utils import add_graph_execution_async
+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 get_webhook_manager
-from backend.util.exceptions import NeedConfirmation, NotFoundError
+from backend.integrations.webhooks import WEBHOOK_MANAGERS_BY_NAME
+from backend.util.exceptions import NeedConfirmation
+from backend.util.service import get_service_client
 from backend.util.settings import Settings
 
 if TYPE_CHECKING:
@@ -282,14 +281,8 @@ 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 = 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
+    webhook_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
+    webhook = await get_webhook(webhook_id)
     logger.debug(f"Webhook #{webhook_id}: {webhook}")
     payload, event_type = await webhook_manager.validate_payload(webhook, request)
     logger.debug(
@@ -309,22 +302,19 @@ async def webhook_ingress_generic(
     if not webhook.attached_nodes:
         return
 
-    executions: list[Awaitable] = []
+    executor = get_service_client(ExecutionManager)
     for node in webhook.attached_nodes:
         logger.debug(f"Webhook-attached node: {node}")
         if not node.is_triggered_by_event_type(event_type):
             logger.debug(f"Node #{node.id} doesn't trigger on event {event_type}")
             continue
         logger.debug(f"Executing graph #{node.graph_id} node #{node.id}")
-        executions.append(
-            add_graph_execution_async(
-                user_id=webhook.user_id,
-                graph_id=node.graph_id,
-                graph_version=node.graph_version,
-                inputs={f"webhook_{webhook_id}_payload": payload},
-            )
+        executor.add_execution(
+            graph_id=node.graph_id,
+            graph_version=node.graph_version,
+            data={f"webhook_{webhook_id}_payload": payload},
+            user_id=webhook.user_id,
         )
-    asyncio.gather(*executions)
 
 
 @router.post("/webhooks/{webhook_id}/ping")
@@ -333,7 +323,7 @@ async def webhook_ping(
     user_id: Annotated[str, Depends(get_user_id)],  # require auth
 ):
     webhook = await get_webhook(webhook_id)
-    webhook_manager = get_webhook_manager(webhook.provider)
+    webhook_manager = WEBHOOK_MANAGERS_BY_NAME[webhook.provider]()
 
     credentials = (
         creds_manager.get(user_id, webhook.credentials_id)
@@ -368,6 +358,14 @@ 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"
@@ -378,7 +376,7 @@ async def remove_all_webhooks_for_credentials(
             await set_node_webhook(node.id, None)
 
         # Prune the webhook
-        webhook_manager = get_webhook_manager(ProviderName(credentials.provider))
+        webhook_manager = WEBHOOK_MANAGERS_BY_NAME[credentials.provider]()
         success = await webhook_manager.prune_webhook_if_dangling(
             webhook.id, credentials
         )

autogpt_platform/backend/backend/server/model.py

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

@@ -11,24 +11,22 @@ 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.routers.postmark.postmark
+import backend.server.integrations.router
 import backend.server.routers.v1
-import backend.server.v2.admin.store_admin_routes
 import backend.server.v2.library.db
 import backend.server.v2.library.model
 import backend.server.v2.library.routes
 import backend.server.v2.otto.routes
+import backend.server.v2.postmark.postmark
 import backend.server.v2.store.model
 import backend.server.v2.store.routes
 import backend.util.service
 import backend.util.settings
-from backend.blocks.llm import LlmModel
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
 from backend.server.external.api import external_app
@@ -57,7 +55,6 @@ async def lifespan_context(app: fastapi.FastAPI):
     await backend.data.block.initialize_blocks()
     await backend.data.user.migrate_and_encrypt_user_integrations()
     await backend.data.graph.fix_llm_provider_credentials()
-    await backend.data.graph.migrate_llm_models(LlmModel.GPT4O)
     with launch_darkly_context():
         yield
     await backend.data.db.disconnect()
@@ -102,11 +99,6 @@ 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"
 )
@@ -115,8 +107,8 @@ app.include_router(
 )
 
 app.include_router(
-    backend.server.routers.postmark.postmark.router,
-    tags=["v1", "email"],
+    backend.server.v2.postmark.postmark.router,
+    tags=["v2", "email"],
     prefix="/api/email",
 )
 
@@ -141,13 +133,8 @@ class AgentServer(backend.util.service.AppProcess):
             server_app,
             host=backend.util.settings.Config().agent_api_host,
             port=backend.util.settings.Config().agent_api_port,
-            log_config=generate_uvicorn_config(),
         )
 
-    def cleanup(self):
-        super().cleanup()
-        logger.info(f"[{self.service_name}] ⏳ Shutting down Agent Server...")
-
     @staticmethod
     async def test_execute_graph(
         graph_id: str,
@@ -155,12 +142,11 @@ class AgentServer(backend.util.service.AppProcess):
         graph_version: Optional[int] = None,
         node_input: Optional[dict[str, Any]] = None,
     ):
-        return await backend.server.routers.v1.execute_graph(
+        return backend.server.routers.v1.execute_graph(
             user_id=user_id,
             graph_id=graph_id,
             graph_version=graph_version,
-            inputs=node_input or {},
-            credentials_inputs={},
+            node_input=node_input or {},
         )
 
     @staticmethod
@@ -183,15 +169,21 @@ class AgentServer(backend.util.service.AppProcess):
 
     @staticmethod
     async def test_get_graph_run_status(graph_exec_id: str, user_id: str):
-        from backend.data.execution import get_graph_execution_meta
-
-        execution = await get_graph_execution_meta(
+        execution = await backend.data.graph.get_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(
@@ -258,16 +250,12 @@ 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.admin.store_admin_routes.review_submission(
-            request.store_listing_version_id, request, user
-        )
+        return await backend.server.v2.store.routes.review_submission(request, user)
 
     @staticmethod
     def test_create_credentials(
@@ -275,9 +263,7 @@ class AgentServer(backend.util.service.AppProcess):
         provider: ProviderName,
         credentials: Credentials,
     ) -> Credentials:
-        from backend.server.integrations.router import create_credentials
-
-        return create_credentials(
+        return backend.server.integrations.router.create_credentials(
             user_id=user_id, provider=provider, credentials=credentials
         )