fix(blocks): Avoid appending None last_tool_output on SmartDecisionMakerBlock

.github/dependabot.yml

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

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

@@ -34,7 +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

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

@@ -82,7 +82,7 @@ jobs:
 
       - name: Copy default supabase .env
         run: |
-          cp ../.env.example ../.env
+          cp ../supabase/docker/.env.example ../.env
 
       - name: Copy backend .env
         run: |

.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

.gitmodules

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

.pre-commit-config.yaml

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

.vscode/launch.json

@@ -32,9 +32,9 @@
       "type": "debugpy",
       "request": "launch",
       "module": "backend.app",
-      "env": {
-        "OBJC_DISABLE_INITIALIZE_FORK_SAFETY": "YES"
-      },
+      // "env": {
+      //   "ENV": "dev"
+      // },
       "envFile": "${workspaceFolder}/backend/.env",
       "justMyCode": false,
       "cwd": "${workspaceFolder}/autogpt_platform/backend"

AGENTS.md

@@ -1,50 +0,0 @@
-# AutoGPT Platform Contribution Guide
-
-This guide provides context for Codex when updating the **autogpt_platform** folder.
-
-## Directory overview
-- `autogpt_platform/backend` – FastAPI based backend service.
-- `autogpt_platform/autogpt_libs` – Shared Python libraries.
-- `autogpt_platform/frontend` – Next.js + Typescript frontend.
-- `autogpt_platform/docker-compose.yml` – development stack.
-
-See `docs/content/platform/getting-started.md` for setup instructions.
-
-## Code style
-- Format Python code with `poetry run format`.
-- Format frontend code using `yarn format`.
-
-## Testing
-- Backend: `poetry run test` (runs pytest with a docker based postgres + prisma).
-- Frontend: `yarn test` or `yarn test-ui` for Playwright tests. See `docs/content/platform/contributing/tests.md` for tips.
-
-Always run the relevant linters and tests before committing.
-Use conventional commit messages for all commits (e.g. `feat(backend): add API`).
-  Types:
-    - feat
-    - fix
-    - refactor
-    - ci
-    - dx (developer experience)
-  Scopes:
-    - platform
-      - platform/library
-      - platform/marketplace
-      - backend
-        - backend/executor
-      - frontend
-        - frontend/library
-        - frontend/marketplace
-      - blocks
-
-## Pull requests
-- Use the template in `.github/PULL_REQUEST_TEMPLATE.md`.
-- Rely on the pre-commit checks for linting and formatting
-- Fill out the **Changes** section and the checklist.
-- Use conventional commit titles with a scope (e.g. `feat(frontend): add feature`).
-- Keep out-of-scope changes under 20% of the PR.
-- Ensure PR descriptions are complete.
-- For changes touching `data/*.py`, validate user ID checks or explain why not needed.
-- If adding protected frontend routes, update `frontend/lib/supabase/middleware.ts`.
-- Use the linear ticket branch structure if given codex/open-1668-resume-dropped-runs
-

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.
 

SECURITY.md

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

autogpt_platform/.env.example

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

autogpt_platform/README.md

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

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -1,9 +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/auth/middleware.py

@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
 async def auth_middleware(request: Request):
     if not settings.ENABLE_AUTH:
         # If authentication is disabled, allow the request to proceed
-        logger.warning("Auth disabled")
+        logger.warn("Auth disabled")
         return {}
 
     security = HTTPBearer()

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

@@ -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"]
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 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
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 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\""]
@@ -562,19 +571,19 @@ protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
 
 [[package]]
 name = "google-cloud-audit-log"
-version = "0.3.2"
+version = "0.3.0"
 description = "Google Cloud Audit Protos"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
 files = [
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+    {file = "google_cloud_audit_log-0.3.0.tar.gz", hash = "sha256:901428b257020d8c1d1133e0fa004164a555e5a395c7ca3cdbb8486513df3a65"},
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 [package.dependencies]
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+googleapis-common-protos = ">=1.56.2,<2.0dev"
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 [[package]]
 name = "google-cloud-core"
@@ -597,30 +606,30 @@ grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"]
 
 [[package]]
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+version = "3.11.4"
 description = "Stackdriver Logging API client library"
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 [[package]]
 name = "googleapis-common-protos"
@@ -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\""}
 
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 [[package]]
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 [[package]]
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 typing-extensions = ">=4.12.2,<5.0.0"
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 [[package]]
 name = "redis"
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 [[package]]
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 [[package]]
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-version = "2.15.1"
+version = "2.13.0"
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-realtime = ">=2.4.0,<2.5.0"
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+realtime = ">=2.0.0,<3.0.0"
 storage3 = ">=0.10,<0.12"
 supafunc = ">=0.9,<0.10"
 
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-
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-typing-extensions = ">=4.12.0"
-
 [[package]]
 name = "urllib3"
 version = "2.2.2"
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+    {file = "yarl-1.11.1-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:61a5f2c14d0a1adfdd82258f756b23a550c13ba4c86c84106be4c111a3a4e413"},
+    {file = "yarl-1.11.1-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:f3a6d90cab0bdf07df8f176eae3a07127daafcf7457b997b2bf46776da2c7eb7"},
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+    {file = "yarl-1.11.1-cp38-cp38-win32.whl", hash = "sha256:238a21849dd7554cb4d25a14ffbfa0ef380bb7ba201f45b144a14454a72ffa5a"},
+    {file = "yarl-1.11.1-cp38-cp38-win_amd64.whl", hash = "sha256:67459cf8cf31da0e2cbdb4b040507e535d25cfbb1604ca76396a3a66b8ba37a6"},
+    {file = "yarl-1.11.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:884eab2ce97cbaf89f264372eae58388862c33c4f551c15680dd80f53c89a269"},
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+    {file = "yarl-1.11.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:48dfd117ab93f0129084577a07287376cc69c08138694396f305636e229caa1a"},
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+    {file = "yarl-1.11.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:04293941646647b3bfb1719d1d11ff1028e9c30199509a844da3c0f5919dc520"},
+    {file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:250e888fa62d73e721f3041e3a9abf427788a1934b426b45e1b92f62c1f68366"},
+    {file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:e8f63904df26d1a66aabc141bfd258bf738b9bc7bc6bdef22713b4f5ef789a4c"},
+    {file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:aac44097d838dda26526cffb63bdd8737a2dbdf5f2c68efb72ad83aec6673c7e"},
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+    {file = "yarl-1.11.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:6907daa4b9d7a688063ed098c472f96e8181733c525e03e866fb5db480a424df"},
+    {file = "yarl-1.11.1-cp39-cp39-win32.whl", hash = "sha256:14438dfc5015661f75f85bc5adad0743678eefee266ff0c9a8e32969d5d69f74"},
+    {file = "yarl-1.11.1-cp39-cp39-win_amd64.whl", hash = "sha256:94d0caaa912bfcdc702a4204cd5e2bb01eb917fc4f5ea2315aa23962549561b0"},
+    {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"
@@ -2037,4 +1929,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "78ebf65cdef769cfbe92fe204f01e32d219cca9ee5a6ca9e657aa0630be63802"
+content-hash = "f5cd0d1dafeb2b5c97d0ef27bef8a2235d4a1f54e3c60583d05ef582ac49c0e6"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -7,20 +7,21 @@ readme = "README.md"
 packages = [{ include = "autogpt_libs" }]
 
 [tool.poetry.dependencies]
-python = ">=3.10,<4.0"
 colorama = "^0.4.6"
 expiringdict = "^1.2.2"
-google-cloud-logging = "^3.12.1"
-pydantic = "^2.11.4"
-pydantic-settings = "^2.9.1"
+google-cloud-logging = "^3.11.4"
+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"
-supabase = "^2.15.1"
+python = ">=3.10,<4.0"
+python-dotenv = "^1.0.1"
+supabase = "^2.13.0"
 
 [tool.poetry.group.dev.dependencies]
 redis = "^5.2.1"
-ruff = "^0.11.10"
+ruff = "^0.9.6"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.example

@@ -2,24 +2,13 @@ DB_USER=postgres
 DB_PASS=your-super-secret-and-long-postgres-password
 DB_NAME=postgres
 DB_PORT=5432
-DB_HOST=localhost
-DB_CONNECTION_LIMIT=12
-DB_CONNECT_TIMEOUT=60
-DB_POOL_TIMEOUT=300
-DB_SCHEMA=platform
-DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
-DIRECT_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
+DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@localhost:${DB_PORT}/${DB_NAME}?connect_timeout=60&schema=platform"
 PRISMA_SCHEMA="postgres/schema.prisma"
 
-# EXECUTOR
-NUM_GRAPH_WORKERS=10
-NUM_NODE_WORKERS=3
-
 BACKEND_CORS_ALLOW_ORIGINS=["http://localhost:3000"]
 
 # generate using `from cryptography.fernet import Fernet;Fernet.generate_key().decode()`
 ENCRYPTION_KEY='dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw='
-UNSUBSCRIBE_SECRET_KEY = 'HlP8ivStJjmbf6NKi78m_3FnOogut0t5ckzjsIqeaio='
 
 REDIS_HOST=localhost
 REDIS_PORT=6379
@@ -66,13 +55,6 @@ MEDIA_GCS_BUCKET_NAME=
 ## and tunnel it to your locally running backend.
 PLATFORM_BASE_URL=http://localhost:3000
 
-## Cloudflare Turnstile (CAPTCHA) Configuration
-## Get these from the Cloudflare Turnstile dashboard: https://dash.cloudflare.com/?to=/:account/turnstile
-## This is the backend secret key
-TURNSTILE_SECRET_KEY=
-## This is the verify URL
-TURNSTILE_VERIFY_URL=https://challenges.cloudflare.com/turnstile/v0/siteverify
-
 ## == INTEGRATION CREDENTIALS == ##
 # Each set of server side credentials is required for the corresponding 3rd party
 # integration to work.
@@ -129,7 +111,6 @@ OPENAI_API_KEY=
 ANTHROPIC_API_KEY=
 GROQ_API_KEY=
 OPEN_ROUTER_API_KEY=
-LLAMA_API_KEY=
 
 # Reddit
 # Go to https://www.reddit.com/prefs/apps and create a new app
@@ -197,8 +178,6 @@ 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/README.advanced.md

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

autogpt_platform/backend/README.md

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

autogpt_platform/backend/backend/app.py

@@ -32,7 +32,7 @@ def main(**kwargs):
     Run all the processes required for the AutoGPT-server (REST and WebSocket APIs).
     """
 
-    from backend.executor import DatabaseManager, ExecutionManager, Scheduler
+    from backend.executor import DatabaseManager, ExecutionManager, ExecutionScheduler
     from backend.notifications import NotificationManager
     from backend.server.rest_api import AgentServer
     from backend.server.ws_api import WebsocketServer
@@ -40,7 +40,7 @@ def main(**kwargs):
     run_processes(
         DatabaseManager(),
         ExecutionManager(),
-        Scheduler(),
+        ExecutionScheduler(),
         NotificationManager(),
         WebsocketServer(),
         AgentServer(),

autogpt_platform/backend/backend/blocks/__init__.py

@@ -1,99 +1,89 @@
-import functools
 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")
 
 
-@functools.cache
-def load_all_blocks() -> dict[str, type["Block"]]:
-    from backend.data.block import Block
-
-    # Dynamically load all modules under backend.blocks
-    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__)
-
-    # Load all Block instances from the available modules
-    available_blocks: dict[str, type["Block"]] = {}
-    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

@@ -1,5 +1,7 @@
 import logging
-from typing import Any, Optional
+from typing import Any
+
+from autogpt_libs.utils.cache import thread_cached
 
 from backend.data.block import (
     Block,
@@ -11,33 +13,44 @@ from backend.data.block import (
     get_block,
 )
 from backend.data.execution import ExecutionStatus
-from backend.data.model import CredentialsMetaInput, SchemaField
+from backend.data.model import SchemaField
 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")
         graph_id: str = SchemaField(description="Graph ID")
         graph_version: int = SchemaField(description="Graph Version")
 
-        inputs: BlockInput = SchemaField(description="Input data for the graph")
+        data: BlockInput = SchemaField(description="Input data for the graph")
         input_schema: dict = SchemaField(description="Input schema for the graph")
         output_schema: dict = SchemaField(description="Output schema for the graph")
 
-        node_credentials_input_map: Optional[
-            dict[str, dict[str, CredentialsMetaInput]]
-        ] = SchemaField(default=None, hidden=True)
-
         @classmethod
         def get_input_schema(cls, data: BlockInput) -> dict[str, Any]:
             return data.get("input_schema", {})
 
         @classmethod
         def get_input_defaults(cls, data: BlockInput) -> BlockInput:
-            return data.get("inputs", {})
+            return data.get("data", {})
 
         @classmethod
         def get_missing_input(cls, data: BlockInput) -> set[str]:
@@ -62,27 +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.inputs,
-            node_credentials_input_map=input_data.node_credentials_input_map,
+            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,
@@ -93,10 +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
@@ -111,7 +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/ai_image_generator_block.py

@@ -1,8 +1,8 @@
 from enum import Enum
 from typing import Literal
 
+import replicate
 from pydantic import SecretStr
-from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
 from backend.data.block import Block, BlockCategory, BlockSchema
@@ -170,7 +170,7 @@ class AIImageGeneratorBlock(Block):
     ):
         try:
             # Initialize Replicate client
-            client = ReplicateClient(api_token=credentials.api_key.get_secret_value())
+            client = replicate.Client(api_token=credentials.api_key.get_secret_value())
 
             # Run the model with input parameters
             output = client.run(model_name, input=input_params, wait=False)

autogpt_platform/backend/backend/blocks/ai_music_generator.py

@@ -3,8 +3,8 @@ import time
 from enum import Enum
 from typing import Literal
 
+import replicate
 from pydantic import SecretStr
-from replicate.client import Client as ReplicateClient
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
@@ -196,7 +196,7 @@ class AIMusicGeneratorBlock(Block):
         normalization_strategy: NormalizationStrategy,
     ):
         # Initialize Replicate client with the API key
-        client = ReplicateClient(api_token=api_key.get_secret_value())
+        client = replicate.Client(api_token=api_key.get_secret_value())
 
         # Run the model with parameters
         output = client.run(

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

@@ -3,20 +3,22 @@ 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."
         )
 
@@ -90,10 +92,9 @@ class StoreValueBlock(Block):
 
 class PrintToConsoleBlock(Block):
     class Input(BlockSchema):
-        text: Any = SchemaField(description="The data to print to the console.")
+        text: str = SchemaField(description="The text to print to the console.")
 
     class Output(BlockSchema):
-        output: Any = SchemaField(description="The data printed to the console.")
         status: str = SchemaField(description="The status of the print operation.")
 
     def __init__(self):
@@ -104,14 +105,11 @@ class PrintToConsoleBlock(Block):
             input_schema=PrintToConsoleBlock.Input,
             output_schema=PrintToConsoleBlock.Output,
             test_input={"text": "Hello, World!"},
-            test_output=[
-                ("output", "Hello, World!"),
-                ("status", "printed"),
-            ],
+            test_output=("status", "printed"),
         )
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        yield "output", input_data.text
+        print(">>>>> Print: ", input_data.text)
         yield "status", "printed"
 
 
@@ -155,9 +153,6 @@ class FindInDictionaryBlock(Block):
         obj = input_data.input
         key = input_data.key
 
-        if isinstance(obj, str):
-            obj = json.loads(obj)
-
         if isinstance(obj, dict) and key in obj:
             yield "output", obj[key]
         elif isinstance(obj, list) and isinstance(key, int) and 0 <= key < len(obj):
@@ -175,10 +170,192 @@ 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(
@@ -194,7 +371,7 @@ 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,
         )
@@ -256,7 +433,7 @@ 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.",
         )
@@ -266,7 +443,7 @@ 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

@@ -49,9 +49,8 @@ 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")
 
     def __init__(self):
         super().__init__(

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/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/_api.py

@@ -1,30 +1,19 @@
-from typing import overload
 from urllib.parse import urlparse
 
 from backend.blocks.github._auth import (
     GithubCredentials,
     GithubFineGrainedAPICredentials,
 )
-from backend.util.request import URL, Requests
+from backend.util.request import Requests
 
 
-@overload
-def _convert_to_api_url(url: str) -> str: ...
-
-
-@overload
-def _convert_to_api_url(url: URL) -> URL: ...
-
-
-def _convert_to_api_url(url: str | URL) -> str | URL:
+def _convert_to_api_url(url: str) -> str:
     """
     Converts a standard GitHub URL to the corresponding GitHub API URL.
     Handles repository URLs, issue URLs, pull request URLs, and more.
     """
-    if url_as_str := isinstance(url, str):
-        url = urlparse(url)
-
-    path_parts = url.path.strip("/").split("/")
+    parsed_url = urlparse(url)
+    path_parts = parsed_url.path.strip("/").split("/")
 
     if len(path_parts) >= 2:
         owner, repo = path_parts[0], path_parts[1]
@@ -39,7 +28,7 @@ def _convert_to_api_url(url: str | URL) -> str | URL:
     else:
         raise ValueError("Invalid GitHub URL format.")
 
-    return api_url if url_as_str else urlparse(api_url)
+    return api_url
 
 
 def _get_headers(credentials: GithubCredentials) -> dict[str, str]:

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/google/calendar.py

@@ -1,598 +0,0 @@
-import enum
-import uuid
-from datetime import datetime, timedelta, timezone
-from typing import Literal
-
-from google.oauth2.credentials import Credentials
-from googleapiclient.discovery import build
-from pydantic import BaseModel
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-from backend.util.settings import AppEnvironment, Settings
-
-from ._auth import (
-    GOOGLE_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    GoogleCredentials,
-    GoogleCredentialsField,
-    GoogleCredentialsInput,
-)
-
-
-class CalendarEvent(BaseModel):
-    """Structured representation of a Google Calendar event."""
-
-    id: str
-    title: str
-    start_time: str
-    end_time: str
-    is_all_day: bool
-    location: str | None
-    description: str | None
-    organizer: str | None
-    attendees: list[str]
-    has_video_call: bool
-    video_link: str | None
-    calendar_link: str
-    is_recurring: bool
-
-
-class GoogleCalendarReadEventsBlock(Block):
-    class Input(BlockSchema):
-        credentials: GoogleCredentialsInput = GoogleCredentialsField(
-            ["https://www.googleapis.com/auth/calendar.readonly"]
-        )
-        calendar_id: str = SchemaField(
-            description="Calendar ID (use 'primary' for your main calendar)",
-            default="primary",
-        )
-        max_events: int = SchemaField(
-            description="Maximum number of events to retrieve", default=10
-        )
-        start_time: datetime = SchemaField(
-            description="Retrieve events starting from this time",
-            default_factory=lambda: datetime.now(tz=timezone.utc),
-        )
-        time_range_days: int = SchemaField(
-            description="Number of days to look ahead for events", default=30
-        )
-        search_term: str | None = SchemaField(
-            description="Optional search term to filter events by", default=None
-        )
-
-        page_token: str | None = SchemaField(
-            description="Page token from previous request to get the next batch of events. You can use this if you have lots of events you want to process in a loop",
-            default=None,
-        )
-        include_declined_events: bool = SchemaField(
-            description="Include events you've declined", default=False
-        )
-
-    class Output(BlockSchema):
-        events: list[CalendarEvent] = SchemaField(
-            description="List of calendar events in the requested time range",
-            default_factory=list,
-        )
-        event: CalendarEvent = SchemaField(
-            description="One of the calendar events in the requested time range"
-        )
-        next_page_token: str | None = SchemaField(
-            description="Token for retrieving the next page of events if more exist",
-            default=None,
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed",
-        )
-
-    def __init__(self):
-        settings = Settings()
-
-        # Create realistic test data for events
-        test_now = datetime.now(tz=timezone.utc)
-        test_tomorrow = test_now + timedelta(days=1)
-
-        test_event_dict = {
-            "id": "event1id",
-            "title": "Team Meeting",
-            "start_time": test_tomorrow.strftime("%Y-%m-%d %H:%M"),
-            "end_time": (test_tomorrow + timedelta(hours=1)).strftime("%Y-%m-%d %H:%M"),
-            "is_all_day": False,
-            "location": "Conference Room A",
-            "description": "Weekly team sync",
-            "organizer": "manager@example.com",
-            "attendees": ["colleague1@example.com", "colleague2@example.com"],
-            "has_video_call": True,
-            "video_link": "https://meet.google.com/abc-defg-hij",
-            "calendar_link": "https://calendar.google.com/calendar/event?eid=event1id",
-            "is_recurring": True,
-        }
-
-        super().__init__(
-            id="80bc3ed1-e9a4-449e-8163-a8fc86f74f6a",
-            description="Retrieves upcoming events from a Google Calendar with filtering options",
-            categories={BlockCategory.PRODUCTIVITY, BlockCategory.DATA},
-            input_schema=GoogleCalendarReadEventsBlock.Input,
-            output_schema=GoogleCalendarReadEventsBlock.Output,
-            disabled=not GOOGLE_OAUTH_IS_CONFIGURED
-            or settings.config.app_env == AppEnvironment.PRODUCTION,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "calendar_id": "primary",
-                "max_events": 5,
-                "start_time": test_now.isoformat(),
-                "time_range_days": 7,
-                "search_term": None,
-                "include_declined_events": False,
-                "page_token": None,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("event", test_event_dict),
-                ("events", [test_event_dict]),
-            ],
-            test_mock={
-                "_read_calendar": lambda *args, **kwargs: {
-                    "items": [
-                        {
-                            "id": "event1id",
-                            "summary": "Team Meeting",
-                            "start": {
-                                "dateTime": test_tomorrow.isoformat(),
-                                "timeZone": "UTC",
-                            },
-                            "end": {
-                                "dateTime": (
-                                    test_tomorrow + timedelta(hours=1)
-                                ).isoformat(),
-                                "timeZone": "UTC",
-                            },
-                            "location": "Conference Room A",
-                            "description": "Weekly team sync",
-                            "organizer": {"email": "manager@example.com"},
-                            "attendees": [
-                                {"email": "colleague1@example.com"},
-                                {"email": "colleague2@example.com"},
-                            ],
-                            "conferenceData": {
-                                "conferenceUrl": "https://meet.google.com/abc-defg-hij"
-                            },
-                            "htmlLink": "https://calendar.google.com/calendar/event?eid=event1id",
-                            "recurrence": ["RRULE:FREQ=WEEKLY;COUNT=10"],
-                        }
-                    ],
-                    "nextPageToken": None,
-                },
-                "_format_events": lambda *args, **kwargs: [test_event_dict],
-            },
-        )
-
-    def run(
-        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
-    ) -> BlockOutput:
-        try:
-            service = self._build_service(credentials, **kwargs)
-
-            # Calculate end time based on start time and time range
-            end_time = input_data.start_time + timedelta(
-                days=input_data.time_range_days
-            )
-
-            # Call Google Calendar API
-            result = self._read_calendar(
-                service=service,
-                calendarId=input_data.calendar_id,
-                time_min=input_data.start_time.isoformat(),
-                time_max=end_time.isoformat(),
-                max_results=input_data.max_events,
-                single_events=True,
-                search_term=input_data.search_term,
-                show_deleted=False,
-                show_hidden=input_data.include_declined_events,
-                page_token=input_data.page_token,
-            )
-
-            # Format events into a user-friendly structure
-            formatted_events = self._format_events(result.get("items", []))
-
-            # Include next page token if available
-            if next_page_token := result.get("nextPageToken"):
-                yield "next_page_token", next_page_token
-
-            for event in formatted_events:
-                yield "event", event
-
-            yield "events", formatted_events
-
-        except Exception as e:
-            yield "error", str(e)
-
-    @staticmethod
-    def _build_service(credentials: GoogleCredentials, **kwargs):
-        creds = Credentials(
-            token=(
-                credentials.access_token.get_secret_value()
-                if credentials.access_token
-                else None
-            ),
-            refresh_token=(
-                credentials.refresh_token.get_secret_value()
-                if credentials.refresh_token
-                else None
-            ),
-            token_uri="https://oauth2.googleapis.com/token",
-            client_id=Settings().secrets.google_client_id,
-            client_secret=Settings().secrets.google_client_secret,
-            scopes=credentials.scopes,
-        )
-        return build("calendar", "v3", credentials=creds)
-
-    def _read_calendar(
-        self,
-        service,
-        calendarId: str,
-        time_min: str,
-        time_max: str,
-        max_results: int,
-        single_events: bool,
-        search_term: str | None = None,
-        show_deleted: bool = False,
-        show_hidden: bool = False,
-        page_token: str | None = None,
-    ) -> dict:
-        """Read calendar events with optional filtering."""
-        calendar = service.events()
-
-        # Build query parameters
-        params = {
-            "calendarId": calendarId,
-            "timeMin": time_min,
-            "timeMax": time_max,
-            "maxResults": max_results,
-            "singleEvents": single_events,
-            "orderBy": "startTime",
-            "showDeleted": show_deleted,
-            "showHiddenInvitations": show_hidden,
-            **({"pageToken": page_token} if page_token else {}),
-        }
-
-        # Add search term if provided
-        if search_term:
-            params["q"] = search_term
-
-        result = calendar.list(**params).execute()
-        return result
-
-    def _format_events(self, events: list[dict]) -> list[CalendarEvent]:
-        """Format Google Calendar API events into user-friendly structure."""
-        formatted_events = []
-
-        for event in events:
-            # Determine if all-day event
-            is_all_day = "date" in event.get("start", {})
-
-            # Format start and end times
-            if is_all_day:
-                start_time = event.get("start", {}).get("date", "")
-                end_time = event.get("end", {}).get("date", "")
-            else:
-                # Convert ISO format to more readable format
-                start_datetime = datetime.fromisoformat(
-                    event.get("start", {}).get("dateTime", "").replace("Z", "+00:00")
-                )
-                end_datetime = datetime.fromisoformat(
-                    event.get("end", {}).get("dateTime", "").replace("Z", "+00:00")
-                )
-                start_time = start_datetime.strftime("%Y-%m-%d %H:%M")
-                end_time = end_datetime.strftime("%Y-%m-%d %H:%M")
-
-            # Extract attendees
-            attendees = []
-            for attendee in event.get("attendees", []):
-                if email := attendee.get("email"):
-                    attendees.append(email)
-
-            # Check for video call link
-            has_video_call = False
-            video_link = None
-            if conf_data := event.get("conferenceData"):
-                if conf_url := conf_data.get("conferenceUrl"):
-                    has_video_call = True
-                    video_link = conf_url
-                elif entry_points := conf_data.get("entryPoints", []):
-                    for entry in entry_points:
-                        if entry.get("entryPointType") == "video":
-                            has_video_call = True
-                            video_link = entry.get("uri")
-                            break
-
-            # Create formatted event
-            formatted_event = CalendarEvent(
-                id=event.get("id", ""),
-                title=event.get("summary", "Untitled Event"),
-                start_time=start_time,
-                end_time=end_time,
-                is_all_day=is_all_day,
-                location=event.get("location"),
-                description=event.get("description"),
-                organizer=event.get("organizer", {}).get("email"),
-                attendees=attendees,
-                has_video_call=has_video_call,
-                video_link=video_link,
-                calendar_link=event.get("htmlLink", ""),
-                is_recurring=bool(event.get("recurrence")),
-            )
-
-            formatted_events.append(formatted_event)
-
-        return formatted_events
-
-
-class ReminderPreset(enum.Enum):
-    """Common reminder times before an event."""
-
-    TEN_MINUTES = 10
-    THIRTY_MINUTES = 30
-    ONE_HOUR = 60
-    ONE_DAY = 1440  # 24 hours in minutes
-
-
-class RecurrenceFrequency(enum.Enum):
-    """Frequency options for recurring events."""
-
-    DAILY = "DAILY"
-    WEEKLY = "WEEKLY"
-    MONTHLY = "MONTHLY"
-    YEARLY = "YEARLY"
-
-
-class ExactTiming(BaseModel):
-    """Model for specifying start and end times."""
-
-    discriminator: Literal["exact_timing"]
-    start_datetime: datetime
-    end_datetime: datetime
-
-
-class DurationTiming(BaseModel):
-    """Model for specifying start time and duration."""
-
-    discriminator: Literal["duration_timing"]
-    start_datetime: datetime
-    duration_minutes: int
-
-
-class OneTimeEvent(BaseModel):
-    """Model for a one-time event."""
-
-    discriminator: Literal["one_time"]
-
-
-class RecurringEvent(BaseModel):
-    """Model for a recurring event."""
-
-    discriminator: Literal["recurring"]
-    frequency: RecurrenceFrequency
-    count: int
-
-
-class GoogleCalendarCreateEventBlock(Block):
-    class Input(BlockSchema):
-        credentials: GoogleCredentialsInput = GoogleCredentialsField(
-            ["https://www.googleapis.com/auth/calendar"]
-        )
-        # Event Details
-        event_title: str = SchemaField(description="Title of the event")
-        location: str | None = SchemaField(
-            description="Location of the event", default=None
-        )
-        description: str | None = SchemaField(
-            description="Description of the event", default=None
-        )
-
-        # Timing
-        timing: ExactTiming | DurationTiming = SchemaField(
-            discriminator="discriminator",
-            advanced=False,
-            description="Specify when the event starts and ends",
-            default_factory=lambda: DurationTiming(
-                discriminator="duration_timing",
-                start_datetime=datetime.now().replace(microsecond=0, second=0, minute=0)
-                + timedelta(hours=1),
-                duration_minutes=60,
-            ),
-        )
-
-        # Calendar selection
-        calendar_id: str = SchemaField(
-            description="Calendar ID (use 'primary' for your main calendar)",
-            default="primary",
-        )
-
-        # Guests
-        guest_emails: list[str] = SchemaField(
-            description="Email addresses of guests to invite", default_factory=list
-        )
-        send_notifications: bool = SchemaField(
-            description="Send email notifications to guests", default=True
-        )
-
-        # Extras
-        add_google_meet: bool = SchemaField(
-            description="Include a Google Meet video conference link", default=False
-        )
-        recurrence: OneTimeEvent | RecurringEvent = SchemaField(
-            discriminator="discriminator",
-            description="Whether the event repeats",
-            default_factory=lambda: OneTimeEvent(discriminator="one_time"),
-        )
-        reminder_minutes: list[ReminderPreset] = SchemaField(
-            description="When to send reminders before the event",
-            default_factory=lambda: [ReminderPreset.TEN_MINUTES],
-        )
-
-    class Output(BlockSchema):
-        event_id: str = SchemaField(description="ID of the created event")
-        event_link: str = SchemaField(
-            description="Link to view the event in Google Calendar"
-        )
-        error: str = SchemaField(description="Error message if event creation failed")
-
-    def __init__(self):
-        settings = Settings()
-
-        super().__init__(
-            id="ed2ec950-fbff-4204-94c0-023fb1d625e0",
-            description="This block creates a new event in Google Calendar with customizable parameters.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=GoogleCalendarCreateEventBlock.Input,
-            output_schema=GoogleCalendarCreateEventBlock.Output,
-            disabled=not GOOGLE_OAUTH_IS_CONFIGURED
-            or settings.config.app_env == AppEnvironment.PRODUCTION,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "event_title": "Team Meeting",
-                "location": "Conference Room A",
-                "description": "Weekly team sync-up",
-                "calendar_id": "primary",
-                "guest_emails": ["colleague1@example.com", "colleague2@example.com"],
-                "add_google_meet": True,
-                "send_notifications": True,
-                "reminder_minutes": [
-                    ReminderPreset.TEN_MINUTES.value,
-                    ReminderPreset.ONE_HOUR.value,
-                ],
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("event_id", "abc123event_id"),
-                ("event_link", "https://calendar.google.com/calendar/event?eid=abc123"),
-            ],
-            test_mock={
-                "_create_event": lambda *args, **kwargs: {
-                    "id": "abc123event_id",
-                    "htmlLink": "https://calendar.google.com/calendar/event?eid=abc123",
-                }
-            },
-        )
-
-    def run(
-        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
-    ) -> BlockOutput:
-        try:
-            service = self._build_service(credentials, **kwargs)
-
-            # Get start and end times based on the timing option
-            if input_data.timing.discriminator == "exact_timing":
-                start_datetime = input_data.timing.start_datetime
-                end_datetime = input_data.timing.end_datetime
-            else:  # duration_timing
-                start_datetime = input_data.timing.start_datetime
-                end_datetime = start_datetime + timedelta(
-                    minutes=input_data.timing.duration_minutes
-                )
-
-            # Format datetimes for Google Calendar API
-            start_time_str = start_datetime.isoformat()
-            end_time_str = end_datetime.isoformat()
-
-            # Build the event body
-            event_body = {
-                "summary": input_data.event_title,
-                "start": {"dateTime": start_time_str},
-                "end": {"dateTime": end_time_str},
-            }
-
-            # Add optional fields
-            if input_data.location:
-                event_body["location"] = input_data.location
-
-            if input_data.description:
-                event_body["description"] = input_data.description
-
-            # Add guests
-            if input_data.guest_emails:
-                event_body["attendees"] = [
-                    {"email": email} for email in input_data.guest_emails
-                ]
-
-            # Add reminders
-            if input_data.reminder_minutes:
-                event_body["reminders"] = {
-                    "useDefault": False,
-                    "overrides": [
-                        {"method": "popup", "minutes": reminder.value}
-                        for reminder in input_data.reminder_minutes
-                    ],
-                }
-
-            # Add Google Meet
-            if input_data.add_google_meet:
-                event_body["conferenceData"] = {
-                    "createRequest": {
-                        "requestId": f"meet-{uuid.uuid4()}",
-                        "conferenceSolutionKey": {"type": "hangoutsMeet"},
-                    }
-                }
-
-            # Add recurrence
-            if input_data.recurrence.discriminator == "recurring":
-                rule = f"RRULE:FREQ={input_data.recurrence.frequency.value}"
-                rule += f";COUNT={input_data.recurrence.count}"
-                event_body["recurrence"] = [rule]
-
-            # Create the event
-            result = self._create_event(
-                service=service,
-                calendar_id=input_data.calendar_id,
-                event_body=event_body,
-                send_notifications=input_data.send_notifications,
-                conference_data_version=1 if input_data.add_google_meet else 0,
-            )
-
-            yield "event_id", result.get("id", "")
-            yield "event_link", result.get("htmlLink", "")
-        except Exception as e:
-            yield "error", str(e)
-
-    @staticmethod
-    def _build_service(credentials: GoogleCredentials, **kwargs):
-        creds = Credentials(
-            token=(
-                credentials.access_token.get_secret_value()
-                if credentials.access_token
-                else None
-            ),
-            refresh_token=(
-                credentials.refresh_token.get_secret_value()
-                if credentials.refresh_token
-                else None
-            ),
-            token_uri="https://oauth2.googleapis.com/token",
-            client_id=Settings().secrets.google_client_id,
-            client_secret=Settings().secrets.google_client_secret,
-            scopes=credentials.scopes,
-        )
-        return build("calendar", "v3", credentials=creds)
-
-    def _create_event(
-        self,
-        service,
-        calendar_id: str,
-        event_body: dict,
-        send_notifications: bool = False,
-        conference_data_version: int = 0,
-    ) -> dict:
-        """Create a new event in Google Calendar."""
-        calendar = service.events()
-
-        # Make the API call
-        result = calendar.insert(
-            calendarId=calendar_id,
-            body=event_body,
-            sendNotifications=send_notifications,
-            conferenceDataVersion=conference_data_version,
-        ).execute()
-
-        return result

autogpt_platform/backend/backend/blocks/google/gmail.py

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

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

@@ -3,7 +3,6 @@ from googleapiclient.discovery import build
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
-from backend.util.settings import AppEnvironment, Settings
 
 from ._auth import (
     GOOGLE_OAUTH_IS_CONFIGURED,
@@ -36,15 +35,13 @@ class GoogleSheetsReadBlock(Block):
         )
 
     def __init__(self):
-        settings = Settings()
         super().__init__(
             id="5724e902-3635-47e9-a108-aaa0263a4988",
             description="This block reads data from a Google Sheets spreadsheet.",
             categories={BlockCategory.DATA},
             input_schema=GoogleSheetsReadBlock.Input,
             output_schema=GoogleSheetsReadBlock.Output,
-            disabled=not GOOGLE_OAUTH_IS_CONFIGURED
-            or settings.config.app_env == AppEnvironment.PRODUCTION,
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
             test_input={
                 "spreadsheet_id": "1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms",
                 "range": "Sheet1!A1:B2",
@@ -89,8 +86,8 @@ class GoogleSheetsReadBlock(Block):
                 else None
             ),
             token_uri="https://oauth2.googleapis.com/token",
-            client_id=Settings().secrets.google_client_id,
-            client_secret=Settings().secrets.google_client_secret,
+            client_id=kwargs.get("client_id"),
+            client_secret=kwargs.get("client_secret"),
             scopes=credentials.scopes,
         )
         return build("sheets", "v4", credentials=creds)

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__(
@@ -74,48 +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,
-            )
-
-            if input_data.json_format:
-                if response.status_code == 204 or not response.content.strip():
-                    result = None
-                else:
-                    result = response.json()
-            else:
-                result = 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

@@ -4,30 +4,35 @@ 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.logging import TruncatedLogger
 from backend.util.settings import BehaveAs, Settings
 from backend.util.text import TextFormatter
 
-logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]")
+logger = logging.getLogger(__name__)
 fmt = TextFormatter()
 
 LLMProviderName = Literal[
@@ -36,7 +41,6 @@ LLMProviderName = Literal[
     ProviderName.OLLAMA,
     ProviderName.OPENAI,
     ProviderName.OPEN_ROUTER,
-    ProviderName.LLAMA_API,
 ]
 AICredentials = CredentialsMetaInput[LLMProviderName, Literal["api_key"]]
 
@@ -73,10 +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__
@@ -91,19 +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_4_OPUS = "claude-opus-4-20250514"
-    CLAUDE_4_SONNET = "claude-sonnet-4-20250514"
-    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"
@@ -124,7 +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"
@@ -142,13 +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"
-    # Llama API models
-    LLAMA_API_LLAMA_4_SCOUT = "Llama-4-Scout-17B-16E-Instruct-FP8"
-    LLAMA_API_LLAMA4_MAVERICK = "Llama-4-Maverick-17B-128E-Instruct-FP8"
-    LLAMA_API_LLAMA3_3_8B = "Llama-3.3-8B-Instruct"
-    LLAMA_API_LLAMA3_3_70B = "Llama-3.3-70B-Instruct"
 
     @property
     def metadata(self) -> ModelMetadata:
@@ -169,14 +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
@@ -186,15 +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_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 8192
-    ),  # claude-4-opus-20250514
-    LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 8192
-    ),  # claude-4-sonnet-20250514
-    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
@@ -220,7 +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),
@@ -242,13 +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),
-    # Llama API models
-    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata("llama_api", 128000, 4028),
-    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata("llama_api", 128000, 4028),
-    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata("llama_api", 128000, 4028),
-    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata("llama_api", 128000, 4028),
 }
 
 for model in LlmModel:
@@ -278,7 +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.
     """
@@ -308,13 +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,
@@ -323,7 +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.
@@ -346,14 +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
@@ -376,9 +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:
@@ -459,7 +415,7 @@ def llm_call(
 
             if not tool_calls and resp.stop_reason == "tool_use":
                 logger.warning(
-                    f"Tool use stop reason but no tool calls found in content. {resp}"
+                    "Tool use stop reason but no tool calls found in content. %s", resp
                 )
 
             return LLMResponse(
@@ -468,7 +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,
@@ -509,7 +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 "",
@@ -558,56 +513,6 @@ def llm_call(
         else:
             tool_calls = None
 
-        return LLMResponse(
-            raw_response=response.choices[0].message,
-            prompt=prompt,
-            response=response.choices[0].message.content or "",
-            tool_calls=tool_calls,
-            prompt_tokens=response.usage.prompt_tokens if response.usage else 0,
-            completion_tokens=response.usage.completion_tokens if response.usage else 0,
-        )
-    elif provider == "llama_api":
-        tools_param = tools if tools else openai.NOT_GIVEN
-        client = openai.OpenAI(
-            base_url="https://api.llama.com/compat/v1/",
-            api_key=credentials.api_key.get_secret_value(),
-        )
-
-        response = client.chat.completions.create(
-            extra_headers={
-                "HTTP-Referer": "https://agpt.co",
-                "X-Title": "AutoGPT",
-            },
-            model=llm_model.value,
-            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
-        if not response.choices:
-            if response:
-                raise ValueError(f"Llama API error: {response}")
-            else:
-                raise ValueError("No response from Llama API.")
-
-        if response.choices[0].message.tool_calls:
-            tool_calls = [
-                ToolContentBlock(
-                    id=tool.id,
-                    type=tool.type,
-                    function=ToolCall(
-                        name=tool.function.name, arguments=tool.function.arguments
-                    ),
-                )
-                for tool in response.choices[0].message.tool_calls
-            ]
-        else:
-            tool_calls = None
-
         return LLMResponse(
             raw_response=response.choices[0].message,
             prompt=prompt,
@@ -623,7 +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)
@@ -653,7 +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(
@@ -663,7 +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(
@@ -682,7 +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):
@@ -704,7 +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(
@@ -737,7 +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,
@@ -855,16 +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(
@@ -902,7 +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(
@@ -920,7 +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):
@@ -937,7 +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"},
         )
@@ -956,10 +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)
@@ -1016,7 +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):
@@ -1033,7 +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: (
@@ -1142,14 +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",
@@ -1172,7 +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):
@@ -1201,7 +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."
@@ -1223,7 +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,
@@ -1281,7 +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."
         )
@@ -1313,7 +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(
@@ -124,10 +124,8 @@ class AddMemoryBlock(Block, Mem0Base):
 
             if isinstance(input_data.content, Conversation):
                 messages = input_data.content.messages
-            elif isinstance(input_data.content, Content):
-                messages = [{"role": "user", "content": input_data.content.content}]
             else:
-                messages = [{"role": "user", "content": str(input_data.content)}]
+                messages = [{"role": "user", "content": input_data.content}]
 
             params = {
                 "user_id": user_id,
@@ -154,7 +152,7 @@ class AddMemoryBlock(Block, Mem0Base):
                 yield "action", "NO_CHANGE"
 
         except Exception as e:
-            yield "error", str(e)
+            yield "error", str(object=e)
 
 
 class SearchMemoryBlock(Block, Mem0Base):
@@ -175,7 +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/replicate_flux_advanced.py

@@ -2,8 +2,8 @@ import os
 from enum import Enum
 from typing import Literal
 
+import replicate
 from pydantic import SecretStr
-from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
@@ -198,7 +198,7 @@ class ReplicateFluxAdvancedModelBlock(Block):
         safety_tolerance,
     ):
         # Initialize Replicate client with the API key
-        client = ReplicateClient(api_token=api_key.get_secret_value())
+        client = replicate.Client(api_token=api_key.get_secret_value())
 
         # Run the model with additional parameters
         output: FileOutput | list[FileOutput] = client.run(  # type: ignore This is because they changed the return type, and didn't update the type hint! It should be overloaded depending on the value of `use_file_output` to `FileOutput | list[FileOutput]` but it's `Any | Iterator[Any]`

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
@@ -26,10 +27,10 @@ logger = logging.getLogger(__name__)
 
 @thread_cached
 def get_database_manager_client():
-    from backend.executor import DatabaseManagerClient
+    from backend.executor import DatabaseManager
     from backend.util.service import get_service_client
 
-    return get_service_client(DatabaseManagerClient)
+    return get_service_client(DatabaseManager)
 
 
 def _get_tool_requests(entry: dict[str, Any]) -> list[str]:
@@ -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(
@@ -185,7 +186,7 @@ class SmartDecisionMakerBlock(Block):
         @classmethod
         def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
             # conversation_history & last_tool_output validation is handled differently
-            missing_links = super().get_missing_links(
+            return super().get_missing_links(
                 data,
                 [
                     link
@@ -195,19 +196,6 @@ class SmartDecisionMakerBlock(Block):
                 ],
             )
 
-            # Avoid executing the block if the last_tool_output is connected to a static
-            # link, like StoreValueBlock or AgentInputBlock.
-            if any(link.sink_name == "conversation_history" for link in links) and any(
-                link.sink_name == "last_tool_output" and link.is_static
-                for link in links
-            ):
-                raise ValueError(
-                    "Last Tool Output can't be connected to a static (dashed line) "
-                    "link like the output of `StoreValue` or `AgentInput` block"
-                )
-
-            return missing_links
-
         @classmethod
         def get_missing_input(cls, data: BlockInput) -> set[str]:
             if missing_input := super().get_missing_input(data):
@@ -246,10 +234,6 @@ 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 +251,12 @@ 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 +271,7 @@ 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 +316,7 @@ 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 +331,7 @@ 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 +481,6 @@ 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 +492,7 @@ 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/time_blocks.py

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

autogpt_platform/backend/backend/blocks/todoist/comments.py

@@ -7,7 +7,6 @@ from typing_extensions import Optional
 from backend.blocks.todoist._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TODOIST_OAUTH_IS_CONFIGURED,
     TodoistCredentials,
     TodoistCredentialsField,
     TodoistCredentialsInput,
@@ -62,7 +61,6 @@ class TodoistCreateCommentBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistCreateCommentBlock.Input,
             output_schema=TodoistCreateCommentBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "content": "Test comment",
@@ -166,7 +164,6 @@ class TodoistGetCommentsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetCommentsBlock.Input,
             output_schema=TodoistGetCommentsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "id_type": {"discriminator": "task", "task_id": "2995104339"},
@@ -271,7 +268,6 @@ class TodoistGetCommentBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetCommentBlock.Input,
             output_schema=TodoistGetCommentBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "comment_id": "2992679862",
@@ -350,7 +346,6 @@ class TodoistUpdateCommentBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistUpdateCommentBlock.Input,
             output_schema=TodoistUpdateCommentBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "comment_id": "2992679862",
@@ -409,7 +404,6 @@ class TodoistDeleteCommentBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistDeleteCommentBlock.Input,
             output_schema=TodoistDeleteCommentBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "comment_id": "2992679862",

autogpt_platform/backend/backend/blocks/todoist/labels.py

@@ -4,7 +4,6 @@ from typing_extensions import Optional
 from backend.blocks.todoist._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TODOIST_OAUTH_IS_CONFIGURED,
     TodoistCredentials,
     TodoistCredentialsField,
     TodoistCredentialsInput,
@@ -43,7 +42,6 @@ class TodoistCreateLabelBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistCreateLabelBlock.Input,
             output_schema=TodoistCreateLabelBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "name": "Test Label",
@@ -132,7 +130,6 @@ class TodoistListLabelsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistListLabelsBlock.Input,
             output_schema=TodoistListLabelsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT},
             test_credentials=TEST_CREDENTIALS,
             test_output=[
@@ -214,7 +211,6 @@ class TodoistGetLabelBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetLabelBlock.Input,
             output_schema=TodoistGetLabelBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "label_id": "2156154810",
@@ -297,7 +293,6 @@ class TodoistUpdateLabelBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistUpdateLabelBlock.Input,
             output_schema=TodoistUpdateLabelBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "label_id": "2156154810",
@@ -369,7 +364,6 @@ class TodoistDeleteLabelBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistDeleteLabelBlock.Input,
             output_schema=TodoistDeleteLabelBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "label_id": "2156154810",
@@ -421,7 +415,6 @@ class TodoistGetSharedLabelsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetSharedLabelsBlock.Input,
             output_schema=TodoistGetSharedLabelsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("labels", ["Label1", "Label2", "Label3"])],
@@ -478,7 +471,6 @@ class TodoistRenameSharedLabelsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistRenameSharedLabelsBlock.Input,
             output_schema=TodoistRenameSharedLabelsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "name": "OldLabel",
@@ -534,7 +526,6 @@ class TodoistRemoveSharedLabelsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistRemoveSharedLabelsBlock.Input,
             output_schema=TodoistRemoveSharedLabelsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "name": "LabelToRemove"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("success", True)],

autogpt_platform/backend/backend/blocks/todoist/projects.py

@@ -4,7 +4,6 @@ from typing_extensions import Optional
 from backend.blocks.todoist._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TODOIST_OAUTH_IS_CONFIGURED,
     TodoistCredentials,
     TodoistCredentialsField,
     TodoistCredentialsInput,
@@ -36,7 +35,6 @@ class TodoistListProjectsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistListProjectsBlock.Input,
             output_schema=TodoistListProjectsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
@@ -152,7 +150,6 @@ class TodoistCreateProjectBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistCreateProjectBlock.Input,
             output_schema=TodoistCreateProjectBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "name": "Test Project"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("success", True)],
@@ -233,7 +230,6 @@ class TodoistGetProjectBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetProjectBlock.Input,
             output_schema=TodoistGetProjectBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "project_id": "2203306141",
@@ -336,7 +332,6 @@ class TodoistUpdateProjectBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistUpdateProjectBlock.Input,
             output_schema=TodoistUpdateProjectBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "project_id": "2203306141",
@@ -418,7 +413,6 @@ class TodoistDeleteProjectBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistDeleteProjectBlock.Input,
             output_schema=TodoistDeleteProjectBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "project_id": "2203306141",
@@ -487,7 +481,6 @@ class TodoistListCollaboratorsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistListCollaboratorsBlock.Input,
             output_schema=TodoistListCollaboratorsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "project_id": "2203306141",

autogpt_platform/backend/backend/blocks/todoist/sections.py

@@ -4,7 +4,6 @@ from typing_extensions import Optional
 from backend.blocks.todoist._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TODOIST_OAUTH_IS_CONFIGURED,
     TodoistCredentials,
     TodoistCredentialsField,
     TodoistCredentialsInput,
@@ -37,7 +36,6 @@ class TodoistListSectionsBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistListSectionsBlock.Input,
             output_schema=TodoistListSectionsBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "project_id": "2203306141",
@@ -209,7 +207,6 @@ class TodoistGetSectionBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetSectionBlock.Input,
             output_schema=TodoistGetSectionBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "section_id": "7025"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[
@@ -278,7 +275,6 @@ class TodoistDeleteSectionBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistDeleteSectionBlock.Input,
             output_schema=TodoistDeleteSectionBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "section_id": "7025"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("success", True)],

autogpt_platform/backend/backend/blocks/todoist/tasks.py

@@ -7,7 +7,6 @@ from typing_extensions import Optional
 from backend.blocks.todoist._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TODOIST_OAUTH_IS_CONFIGURED,
     TodoistCredentials,
     TodoistCredentialsField,
     TodoistCredentialsInput,
@@ -87,7 +86,6 @@ class TodoistCreateTaskBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistCreateTaskBlock.Input,
             output_schema=TodoistCreateTaskBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "content": "Buy groceries",
@@ -219,7 +217,6 @@ class TodoistGetTasksBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetTasksBlock.Input,
             output_schema=TodoistGetTasksBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "project_id": "2203306141",
@@ -312,7 +309,6 @@ class TodoistGetTaskBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistGetTaskBlock.Input,
             output_schema=TodoistGetTaskBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "task_id": "2995104339"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[
@@ -432,7 +428,6 @@ class TodoistUpdateTaskBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistUpdateTaskBlock.Input,
             output_schema=TodoistUpdateTaskBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "task_id": "2995104339",
@@ -472,24 +467,32 @@ class TodoistUpdateTaskBlock(Block):
             )
 
             task_updates = {}
-            update_fields = {
-                "content": input_data.content,
-                "description": input_data.description,
-                "project_id": input_data.project_id,
-                "section_id": input_data.section_id,
-                "parent_id": input_data.parent_id,
-                "order": input_data.order,
-                "labels": input_data.labels,
-                "priority": input_data.priority,
-                "due_date": due_date,
-                "deadline_date": deadline_date,
-                "assignee_id": input_data.assignee_id,
-                "duration": input_data.duration,
-                "duration_unit": input_data.duration_unit,
-            }
-
-            # Filter out None values
-            task_updates = {k: v for k, v in update_fields.items() if v is not None}
+            if input_data.content is not None:
+                task_updates["content"] = input_data.content
+            if input_data.description is not None:
+                task_updates["description"] = input_data.description
+            if input_data.project_id is not None:
+                task_updates["project_id"] = input_data.project_id
+            if input_data.section_id is not None:
+                task_updates["section_id"] = input_data.section_id
+            if input_data.parent_id is not None:
+                task_updates["parent_id"] = input_data.parent_id
+            if input_data.order is not None:
+                task_updates["order"] = input_data.order
+            if input_data.labels is not None:
+                task_updates["labels"] = input_data.labels
+            if input_data.priority is not None:
+                task_updates["priority"] = input_data.priority
+            if due_date is not None:
+                task_updates["due_date"] = due_date
+            if deadline_date is not None:
+                task_updates["deadline_date"] = deadline_date
+            if input_data.assignee_id is not None:
+                task_updates["assignee_id"] = input_data.assignee_id
+            if input_data.duration is not None:
+                task_updates["duration"] = input_data.duration
+            if input_data.duration_unit is not None:
+                task_updates["duration_unit"] = input_data.duration_unit
 
             self.update_task(
                 credentials,
@@ -523,7 +526,6 @@ class TodoistCloseTaskBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistCloseTaskBlock.Input,
             output_schema=TodoistCloseTaskBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "task_id": "2995104339"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("success", True)],
@@ -574,7 +576,6 @@ class TodoistReopenTaskBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistReopenTaskBlock.Input,
             output_schema=TodoistReopenTaskBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "task_id": "2995104339"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[
@@ -627,7 +628,6 @@ class TodoistDeleteTaskBlock(Block):
             categories={BlockCategory.PRODUCTIVITY},
             input_schema=TodoistDeleteTaskBlock.Input,
             output_schema=TodoistDeleteTaskBlock.Output,
-            disabled=not TODOIST_OAUTH_IS_CONFIGURED,
             test_input={"credentials": TEST_CREDENTIALS_INPUT, "task_id": "2995104339"},
             test_credentials=TEST_CREDENTIALS,
             test_output=[

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

@@ -1,4 +1,4 @@
-# Todo : Add new Type support, and disable block if it's Oauth is not configured
+# Todo : Add new Type support
 
 # from typing import cast
 # import tweepy

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

@@ -1,4 +1,4 @@
-# Todo : Add new Type support, and disable block if it's Oauth is not configured
+# Todo : Add new Type support
 
 # from typing import cast
 
@@ -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_follows.py

@@ -4,7 +4,6 @@ import tweepy
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -45,7 +44,6 @@ class TwitterUnfollowListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterUnfollowListBlock.Input,
             output_schema=TwitterUnfollowListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={"list_id": "123456789", "credentials": TEST_CREDENTIALS_INPUT},
             test_credentials=TEST_CREDENTIALS,
             test_output=[
@@ -108,7 +106,6 @@ class TwitterFollowListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterFollowListBlock.Input,
             output_schema=TwitterFollowListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={"list_id": "123456789", "credentials": TEST_CREDENTIALS_INPUT},
             test_credentials=TEST_CREDENTIALS,
             test_output=[

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -40,6 +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):
@@ -64,7 +64,6 @@ class TwitterGetListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetListBlock.Input,
             output_schema=TwitterGetListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "list_id": "84839422",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -185,6 +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(
@@ -222,7 +222,6 @@ class TwitterGetOwnedListsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetOwnedListsBlock.Input,
             output_schema=TwitterGetOwnedListsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "user_id": "2244994945",
                 "max_results": 10,

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -46,11 +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):
@@ -66,7 +67,6 @@ class TwitterRemoveListMemberBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterRemoveListMemberBlock.Input,
             output_schema=TwitterRemoveListMemberBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "list_id": "123456789",
                 "user_id": "987654321",
@@ -120,11 +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):
@@ -140,7 +142,6 @@ class TwitterAddListMemberBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterAddListMemberBlock.Input,
             output_schema=TwitterAddListMemberBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "list_id": "123456789",
                 "user_id": "987654321",
@@ -194,6 +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(
@@ -232,7 +234,6 @@ class TwitterGetListMembersBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetListMembersBlock.Input,
             output_schema=TwitterGetListMembersBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "list_id": "123456789",
                 "max_results": 2,
@@ -375,6 +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(
@@ -409,7 +411,6 @@ class TwitterGetListMembershipsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetListMembershipsBlock.Input,
             output_schema=TwitterGetListMembershipsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "user_id": "123456789",
                 "max_results": 1,

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -43,6 +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(
@@ -82,7 +82,6 @@ class TwitterGetListTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetListTweetsBlock.Input,
             output_schema=TwitterGetListTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "list_id": "84839422",
                 "max_results": 1,

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -29,6 +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):
@@ -42,7 +42,6 @@ class TwitterDeleteListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterDeleteListBlock.Input,
             output_schema=TwitterDeleteListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={"list_id": "1234567890", "credentials": TEST_CREDENTIALS_INPUT},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("success", True)],
@@ -120,7 +119,6 @@ class TwitterUpdateListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterUpdateListBlock.Input,
             output_schema=TwitterUpdateListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "list_id": "1234567890",
                 "name": "Updated List Name",
@@ -217,7 +215,6 @@ class TwitterCreateListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterCreateListBlock.Input,
             output_schema=TwitterCreateListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "name": "New List Name",
                 "description": "New List Description",

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -40,6 +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):
@@ -53,7 +53,6 @@ class TwitterUnpinListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterUnpinListBlock.Input,
             output_schema=TwitterUnpinListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={"list_id": "123456789", "credentials": TEST_CREDENTIALS_INPUT},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("success", True)],
@@ -104,6 +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):
@@ -117,7 +117,6 @@ class TwitterPinListBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterPinListBlock.Input,
             output_schema=TwitterPinListBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={"list_id": "123456789", "credentials": TEST_CREDENTIALS_INPUT},
             test_credentials=TEST_CREDENTIALS,
             test_output=[("success", True)],
@@ -187,7 +186,6 @@ class TwitterGetPinnedListsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetPinnedListsBlock.Input,
             output_schema=TwitterGetPinnedListsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "expansions": None,
                 "list_fields": None,

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -79,7 +78,6 @@ class TwitterSearchSpacesBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterSearchSpacesBlock.Input,
             output_schema=TwitterSearchSpacesBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "query": "tech",
                 "max_results": 1,

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

@@ -7,7 +7,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -45,7 +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,
     )
 
@@ -55,7 +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,
     )
 
@@ -95,7 +94,6 @@ class TwitterGetSpacesBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetSpacesBlock.Input,
             output_schema=TwitterGetSpacesBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "identifier": {
                     "discriminator": "space_list",
@@ -229,6 +227,7 @@ class TwitterGetSpaceByIdBlock(Block):
         space_id: str = SchemaField(
             description="Space ID to lookup",
             placeholder="Enter Space ID",
+            required=True,
         )
 
     class Output(BlockSchema):
@@ -251,7 +250,6 @@ class TwitterGetSpaceByIdBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetSpaceByIdBlock.Input,
             output_schema=TwitterGetSpaceByIdBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "space_id": "1DXxyRYNejbKM",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -391,6 +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):
@@ -412,7 +411,6 @@ class TwitterGetSpaceBuyersBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetSpaceBuyersBlock.Input,
             output_schema=TwitterGetSpaceBuyersBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "space_id": "1DXxyRYNejbKM",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -519,6 +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):
@@ -541,7 +540,6 @@ class TwitterGetSpaceTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetSpaceTweetsBlock.Input,
             output_schema=TwitterGetSpaceTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "space_id": "1DXxyRYNejbKM",
                 "credentials": TEST_CREDENTIALS_INPUT,

autogpt_platform/backend/backend/blocks/twitter/tweets/bookmark.py

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -56,7 +55,6 @@ class TwitterBookmarkTweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterBookmarkTweetBlock.Input,
             output_schema=TwitterBookmarkTweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -149,7 +147,6 @@ class TwitterGetBookmarkedTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetBookmarkedTweetsBlock.Input,
             output_schema=TwitterGetBookmarkedTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "max_results": 2,
                 "pagination_token": None,
@@ -333,7 +330,6 @@ class TwitterRemoveBookmarkTweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterRemoveBookmarkTweetBlock.Input,
             output_schema=TwitterRemoveBookmarkTweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,

autogpt_platform/backend/backend/blocks/twitter/tweets/hide.py

@@ -3,7 +3,6 @@ import tweepy
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -39,7 +38,6 @@ class TwitterHideReplyBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterHideReplyBlock.Input,
             output_schema=TwitterHideReplyBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -111,7 +109,6 @@ class TwitterUnhideReplyBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterUnhideReplyBlock.Input,
             output_schema=TwitterUnhideReplyBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,

autogpt_platform/backend/backend/blocks/twitter/tweets/like.py

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -61,7 +60,6 @@ class TwitterLikeTweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterLikeTweetBlock.Input,
             output_schema=TwitterLikeTweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -161,7 +159,6 @@ class TwitterGetLikingUsersBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetLikingUsersBlock.Input,
             output_schema=TwitterGetLikingUsersBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "max_results": 1,
@@ -340,7 +337,6 @@ class TwitterGetLikedTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetLikedTweetsBlock.Input,
             output_schema=TwitterGetLikedTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "user_id": "1234567890",
                 "max_results": 2,
@@ -535,7 +531,6 @@ class TwitterUnlikeTweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterUnlikeTweetBlock.Input,
             output_schema=TwitterUnlikeTweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,

autogpt_platform/backend/backend/blocks/twitter/tweets/manage.py

@@ -8,7 +8,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -132,7 +131,6 @@ class TwitterPostTweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterPostTweetBlock.Input,
             output_schema=TwitterPostTweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_text": "This is a test tweet.",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -265,7 +263,6 @@ class TwitterDeleteTweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterDeleteTweetBlock.Input,
             output_schema=TwitterDeleteTweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -360,7 +357,6 @@ class TwitterSearchRecentTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterSearchRecentTweetsBlock.Input,
             output_schema=TwitterSearchRecentTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "query": "from:twitterapi #twitterapi",
                 "credentials": TEST_CREDENTIALS_INPUT,

autogpt_platform/backend/backend/blocks/twitter/tweets/quote.py

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -87,7 +86,6 @@ class TwitterGetQuoteTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetQuoteTweetsBlock.Input,
             output_schema=TwitterGetQuoteTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "max_results": 2,

autogpt_platform/backend/backend/blocks/twitter/tweets/retweet.py

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -53,7 +52,6 @@ class TwitterRetweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterRetweetBlock.Input,
             output_schema=TwitterRetweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -130,7 +128,6 @@ class TwitterRemoveRetweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterRemoveRetweetBlock.Input,
             output_schema=TwitterRemoveRetweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -234,7 +231,6 @@ class TwitterGetRetweetersBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetRetweetersBlock.Input,
             output_schema=TwitterGetRetweetersBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1234567890",
                 "credentials": TEST_CREDENTIALS_INPUT,

autogpt_platform/backend/backend/blocks/twitter/tweets/timeline.py

@@ -7,7 +7,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -92,7 +91,6 @@ class TwitterGetUserMentionsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetUserMentionsBlock.Input,
             output_schema=TwitterGetUserMentionsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "user_id": "12345",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -334,7 +332,6 @@ class TwitterGetHomeTimelineBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetHomeTimelineBlock.Input,
             output_schema=TwitterGetHomeTimelineBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "max_results": 2,
@@ -571,7 +568,6 @@ class TwitterGetUserTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetUserTweetsBlock.Input,
             output_schema=TwitterGetUserTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "user_id": "12345",
                 "credentials": TEST_CREDENTIALS_INPUT,

autogpt_platform/backend/backend/blocks/twitter/tweets/tweet_lookup.py

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -68,7 +67,6 @@ class TwitterGetTweetBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetTweetBlock.Input,
             output_schema=TwitterGetTweetBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_id": "1460323737035677698",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -231,7 +229,6 @@ class TwitterGetTweetsBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetTweetsBlock.Input,
             output_schema=TwitterGetTweetsBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "tweet_ids": ["1460323737035677698"],
                 "credentials": TEST_CREDENTIALS_INPUT,

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -65,7 +64,6 @@ class TwitterGetBlockedUsersBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetBlockedUsersBlock.Input,
             output_schema=TwitterGetBlockedUsersBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "max_results": 10,
                 "pagination_token": "",

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -56,7 +55,6 @@ class TwitterUnfollowUserBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterUnfollowUserBlock.Input,
             output_schema=TwitterUnfollowUserBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "target_user_id": "12345",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -128,7 +126,6 @@ class TwitterFollowUserBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterFollowUserBlock.Input,
             output_schema=TwitterFollowUserBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "target_user_id": "12345",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -216,7 +213,6 @@ class TwitterGetFollowersBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetFollowersBlock.Input,
             output_schema=TwitterGetFollowersBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "target_user_id": "12345",
                 "max_results": 1,
@@ -390,7 +386,6 @@ class TwitterGetFollowingBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetFollowingBlock.Input,
             output_schema=TwitterGetFollowingBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "target_user_id": "12345",
                 "max_results": 1,

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

@@ -6,7 +6,6 @@ from tweepy.client import Response
 from backend.blocks.twitter._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
-    TWITTER_OAUTH_IS_CONFIGURED,
     TwitterCredentials,
     TwitterCredentialsField,
     TwitterCredentialsInput,
@@ -56,7 +55,6 @@ class TwitterUnmuteUserBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterUnmuteUserBlock.Input,
             output_schema=TwitterUnmuteUserBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "target_user_id": "12345",
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -141,7 +139,6 @@ class TwitterGetMutedUsersBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterGetMutedUsersBlock.Input,
             output_schema=TwitterGetMutedUsersBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "max_results": 2,
                 "pagination_token": "",
@@ -292,7 +289,6 @@ class TwitterMuteUserBlock(Block):
             categories={BlockCategory.SOCIAL},
             input_schema=TwitterMuteUserBlock.Input,
             output_schema=TwitterMuteUserBlock.Output,
-            disabled=not TWITTER_OAUTH_IS_CONFIGURED,
             test_input={
                 "target_user_id": "12345",
                 "credentials": TEST_CREDENTIALS_INPUT,