Update client.ts

.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/classic-autogpt-ci.yml

@@ -115,7 +115,6 @@ jobs:
           poetry run pytest -vv \
             --cov=autogpt --cov-branch --cov-report term-missing --cov-report xml \
             --numprocesses=logical --durations=10 \
-            --junitxml=junit.xml -o junit_family=legacy \
             tests/unit tests/integration
         env:
           CI: true
@@ -125,14 +124,8 @@ jobs:
           AWS_ACCESS_KEY_ID: minioadmin
           AWS_SECRET_ACCESS_KEY: minioadmin
 
-      - name: Upload test results to Codecov
-        if: ${{ !cancelled() }}  # Run even if tests fail
-        uses: codecov/test-results-action@v1
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-
       - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v5
+        uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           flags: autogpt-agent,${{ runner.os }}

.github/workflows/classic-benchmark-ci.yml

@@ -87,20 +87,13 @@ jobs:
           poetry run pytest -vv \
             --cov=agbenchmark --cov-branch --cov-report term-missing --cov-report xml \
             --durations=10 \
-            --junitxml=junit.xml -o junit_family=legacy \
             tests
         env:
           CI: true
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
 
-      - name: Upload test results to Codecov
-        if: ${{ !cancelled() }}  # Run even if tests fail
-        uses: codecov/test-results-action@v1
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-
       - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v5
+        uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           flags: agbenchmark,${{ runner.os }}

.github/workflows/classic-forge-ci.yml

@@ -139,7 +139,6 @@ jobs:
           poetry run pytest -vv \
             --cov=forge --cov-branch --cov-report term-missing --cov-report xml \
             --durations=10 \
-            --junitxml=junit.xml -o junit_family=legacy \
             forge
         env:
           CI: true
@@ -149,14 +148,8 @@ jobs:
           AWS_ACCESS_KEY_ID: minioadmin
           AWS_SECRET_ACCESS_KEY: minioadmin
 
-      - name: Upload test results to Codecov
-        if: ${{ !cancelled() }}  # Run even if tests fail
-        uses: codecov/test-results-action@v1
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-
       - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v5
+        uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           flags: forge,${{ runner.os }}

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

@@ -42,14 +42,6 @@ jobs:
           REDIS_PASSWORD: testpassword
         ports:
           - 6379:6379
-      rabbitmq:
-        image: rabbitmq:3.12-management
-        ports:
-          - 5672:5672
-          - 15672:15672
-        env:
-          RABBITMQ_DEFAULT_USER: ${{ env.RABBITMQ_DEFAULT_USER }}
-          RABBITMQ_DEFAULT_PASS: ${{ env.RABBITMQ_DEFAULT_PASS }}
 
     steps:
       - name: Checkout repository
@@ -66,7 +58,7 @@ jobs:
       - name: Setup Supabase
         uses: supabase/setup-cli@v1
         with:
-          version: 1.178.1
+          version: latest
 
       - id: get_date
         name: Get date
@@ -147,13 +139,6 @@ jobs:
       RUN_ENV: local
       PORT: 8080
       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      # We know these are here, don't report this as a security vulnerability
-      # This is used as the default credential for the entire system's RabbitMQ instance
-      # 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'
 
       # - name: Upload coverage reports to Codecov
       #   uses: codecov/codecov-action@v4

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

@@ -77,12 +77,12 @@ jobs:
       - name: Free Disk Space (Ubuntu)
         uses: jlumbroso/free-disk-space@main
         with:
-          large-packages: false # slow
-          docker-images: false # limited benefit
+          large-packages: false  # slow
+          docker-images: false  # limited benefit
 
       - name: Copy default supabase .env
         run: |
-          cp ../.env.example ../.env
+          cp ../supabase/docker/.env.example ../.env
 
       - name: Copy backend .env
         run: |
@@ -104,12 +104,11 @@ jobs:
         run: yarn playwright install --with-deps ${{ matrix.browser }}
 
       - name: Run tests
-        timeout-minutes: 20
         run: |
           yarn test --project=${{ matrix.browser }}
 
-      - name: Print Final Docker Compose logs
-        if: always()
+      - name: Print Docker Compose logs in debug mode
+        if: runner.debug
         run: |
           docker compose -f ../docker-compose.yml logs
 

.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:
@@ -170,16 +170,6 @@ repos:
         files: ^classic/benchmark/(agbenchmark|tests)/((?!reports).)*[/.]
         args: [--config=classic/benchmark/.flake8]
 
-  - repo: local
-    hooks:
-      - id: prettier
-        name: Format (Prettier) - AutoGPT Platform - Frontend
-        alias: format-platform-frontend
-        entry: bash -c 'cd autogpt_platform/frontend && npx prettier --write $(echo "$@" | sed "s|autogpt_platform/frontend/||g")' --
-        files: ^autogpt_platform/frontend/
-        types: [file]
-        language: system
-
   - repo: local
     # To have watertight type checking, we check *all* the files in an affected
     # project. To trigger on poetry.lock we also reset the file `types` filter.
@@ -231,16 +221,6 @@ repos:
         language: system
         pass_filenames: false
 
-  - repo: local
-    hooks:
-      - id: tsc
-        name: Typecheck - AutoGPT Platform - Frontend
-        entry: bash -c 'cd autogpt_platform/frontend && npm run type-check'
-        files: ^autogpt_platform/frontend/
-        types: [file]
-        language: system
-        pass_filenames: false
-
   - repo: local
     hooks:
       - id: pytest

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

LICENSE

@@ -1,8 +1,5 @@
-All portions of this repository are under one of two licenses.
-
-The all files outside of the autogpt_platform folder are under the MIT License below. 
-
-The autogpt_platform folder is under the Polyform Shield License below.
+All portions of this repository are under one of two licenses. The majority of the AutoGPT repository is under the MIT License below. The autogpt_platform folder is under the
+Polyform Shield License. 
 
 
 MIT License
@@ -30,169 +27,3 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
-
-
-# PolyForm Shield License 1.0.0
-
-<https://polyformproject.org/licenses/shield/1.0.0>
-
-## Acceptance
-
-In order to get any license under these terms, you must agree
-to them as both strict obligations and conditions to all
-your licenses.
-
-## Copyright License
-
-The licensor grants you a copyright license for the
-software to do everything you might do with the software
-that would otherwise infringe the licensor's copyright
-in it for any permitted purpose.  However, you may
-only distribute the software according to [Distribution
-License](#distribution-license) and make changes or new works
-based on the software according to [Changes and New Works
-License](#changes-and-new-works-license).
-
-## Distribution License
-
-The licensor grants you an additional copyright license
-to distribute copies of the software.  Your license
-to distribute covers distributing the software with
-changes and new works permitted by [Changes and New Works
-License](#changes-and-new-works-license).
-
-## Notices
-
-You must ensure that anyone who gets a copy of any part of
-the software from you also gets a copy of these terms or the
-URL for them above, as well as copies of any plain-text lines
-beginning with `Required Notice:` that the licensor provided
-with the software.  For example:
-
-> Required Notice: Copyright Yoyodyne, Inc. (http://example.com)
-
-## Changes and New Works License
-
-The licensor grants you an additional copyright license to
-make changes and new works based on the software for any
-permitted purpose.
-
-## Patent License
-
-The licensor grants you a patent license for the software that
-covers patent claims the licensor can license, or becomes able
-to license, that you would infringe by using the software.
-
-## Noncompete
-
-Any purpose is a permitted purpose, except for providing any
-product that competes with the software or any product the
-licensor or any of its affiliates provides using the software.
-
-## Competition
-
-Goods and services compete even when they provide functionality
-through different kinds of interfaces or for different technical
-platforms.  Applications can compete with services, libraries
-with plugins, frameworks with development tools, and so on,
-even if they're written in different programming languages
-or for different computer architectures.  Goods and services
-compete even when provided free of charge.  If you market a
-product as a practical substitute for the software or another
-product, it definitely competes.
-
-## New Products
-
-If you are using the software to provide a product that does
-not compete, but the licensor or any of its affiliates brings
-your product into competition by providing a new version of
-the software or another product using the software, you may
-continue using versions of the software available under these
-terms beforehand to provide your competing product, but not
-any later versions.
-
-## Discontinued Products
-
-You may begin using the software to compete with a product
-or service that the licensor or any of its affiliates has
-stopped providing, unless the licensor includes a plain-text
-line beginning with `Licensor Line of Business:` with the
-software that mentions that line of business.  For example:
-
-> Licensor Line of Business: YoyodyneCMS Content Management
-System (http://example.com/cms)
-
-## Sales of Business
-
-If the licensor or any of its affiliates sells a line of
-business developing the software or using the software
-to provide a product, the buyer can also enforce
-[Noncompete](#noncompete) for that product.
-
-## Fair Use
-
-You may have "fair use" rights for the software under the
-law. These terms do not limit them.
-
-## No Other Rights
-
-These terms do not allow you to sublicense or transfer any of
-your licenses to anyone else, or prevent the licensor from
-granting licenses to anyone else.  These terms do not imply
-any other licenses.
-
-## Patent Defense
-
-If you make any written claim that the software infringes or
-contributes to infringement of any patent, your patent license
-for the software granted under these terms ends immediately. If
-your company makes such a claim, your patent license ends
-immediately for work on behalf of your company.
-
-## Violations
-
-The first time you are notified in writing that you have
-violated any of these terms, or done anything with the software
-not covered by your licenses, your licenses can nonetheless
-continue if you come into full compliance with these terms,
-and take practical steps to correct past violations, within
-32 days of receiving notice.  Otherwise, all your licenses
-end immediately.
-
-## No Liability
-
-***As far as the law allows, the software comes as is, without
-any warranty or condition, and the licensor will not be liable
-to you for any damages arising out of these terms or the use
-or nature of the software, under any kind of legal claim.***
-
-## Definitions
-
-The **licensor** is the individual or entity offering these
-terms, and the **software** is the software the licensor makes
-available under these terms.
-
-A **product** can be a good or service, or a combination
-of them.
-
-**You** refers to the individual or entity agreeing to these
-terms.
-
-**Your company** is any legal entity, sole proprietorship,
-or other kind of organization that you work for, plus all
-its affiliates.
-
-**Affiliates** means the other organizations than an
-organization has control over, is under the control of, or is
-under common control with.
-
-**Control** means ownership of substantially all the assets of
-an entity, or the power to direct its management and policies
-by vote, contract, or otherwise.  Control can be direct or
-indirect.
-
-**Your licenses** are all the licenses granted to you for the
-software under these terms.
-
-**Use** means anything you do with the software requiring one
-of your licenses.

README.md

@@ -2,6 +2,7 @@
 
 [![Discord Follow](https://dcbadge.vercel.app/api/server/autogpt?style=flat)](https://discord.gg/autogpt)  
 [![Twitter Follow](https://img.shields.io/twitter/follow/Auto_GPT?style=social)](https://twitter.com/Auto_GPT)  
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 **AutoGPT** is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows. 
 
@@ -79,7 +80,7 @@ Be part of the revolution! **AutoGPT** is here to stay, at the forefront of AI i
 
 **Licensing:**
 
-MIT License: All files outside of autogpt_platform folder are under the MIT License.
+MIT License: The majority of the AutoGPT repository is under the MIT License.
 
 Polyform Shield License: This license applies to the autogpt_platform folder. 
 

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/Contributor License Agreement (CLA).md

@@ -2,7 +2,7 @@
 
 **Contributor License Agreement (“Agreement”)**
 
-Thank you for your interest in the AutoGPT project at [https://github.com/Significant-Gravitas/AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) stewarded by Determinist Ltd (“**Determinist**”), with offices at 3rd Floor 1 Ashley Road, Altrincham, Cheshire, WA14 2DT, United Kingdom. The form of license below is a document that clarifies the terms under which You, the person listed below, may contribute software code described below (the “**Contribution**”) to the project.  We appreciate your participation in our project, and your help in improving our products, so we want you to understand what will be done with the Contributions.  This license is for your protection as well as the protection of Determinist and its licensees; it does not change your rights to use your own Contributions for any other purpose.        
+Thank you for your interest in the AutoGPT open source project at [https://github.com/Significant-Gravitas/AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) stewarded by Determinist Ltd (“**Determinist**”), with offices at 3rd Floor 1 Ashley Road, Altrincham, Cheshire, WA14 2DT, United Kingdom. The form of license below is a document that clarifies the terms under which You, the person listed below, may contribute software code described below (the “**Contribution**”) to the project.  We appreciate your participation in our project, and your help in improving our products, so we want you to understand what will be done with the Contributions.  This license is for your protection as well as the protection of Determinist and its licensees; it does not change your rights to use your own Contributions for any other purpose.        
 
 By submitting a Pull Request which modifies the content of the “autogpt\_platform” folder at [https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt\_platform](https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt_platform), You hereby agree:  
 

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

@@ -1,13 +1,14 @@
+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 .middleware import auth_middleware
 from .models import User
 
 __all__ = [
+    "Settings",
     "parse_jwt_token",
     "requires_user",
     "requires_admin_user",
-    "APIKeyValidator",
     "auth_middleware",
     "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

@@ -1,10 +1,7 @@
-import inspect
 import logging
-from typing import Any, Callable, Optional
 
-from fastapi import HTTPException, Request, Security
-from fastapi.security import APIKeyHeader, HTTPBearer
-from starlette.status import HTTP_401_UNAUTHORIZED
+from fastapi import HTTPException, Request
+from fastapi.security import HTTPBearer
 
 from .config import settings
 from .jwt_utils import parse_jwt_token
@@ -32,104 +29,3 @@ async def auth_middleware(request: Request):
     except ValueError as e:
         raise HTTPException(status_code=401, detail=str(e))
     return payload
-
-
-class APIKeyValidator:
-    """
-    Configurable API key validator that supports custom validation functions
-    for FastAPI applications.
-
-    This class provides a flexible way to implement API key authentication with optional
-    custom validation logic. It can be used for simple token matching
-    or more complex validation scenarios like database lookups.
-
-    Examples:
-        Simple token validation:
-        ```python
-        validator = APIKeyValidator(
-            header_name="X-API-Key",
-            expected_token="your-secret-token"
-        )
-
-        @app.get("/protected", dependencies=[Depends(validator.get_dependency())])
-        def protected_endpoint():
-            return {"message": "Access granted"}
-        ```
-
-        Custom validation with database lookup:
-        ```python
-        async def validate_with_db(api_key: str):
-            api_key_obj = await db.get_api_key(api_key)
-            return api_key_obj if api_key_obj and api_key_obj.is_active else None
-
-        validator = APIKeyValidator(
-            header_name="X-API-Key",
-            validate_fn=validate_with_db
-        )
-        ```
-
-    Args:
-        header_name (str): The name of the header containing the API key
-        expected_token (Optional[str]): The expected API key value for simple token matching
-        validate_fn (Optional[Callable]): Custom validation function that takes an API key
-            string and returns a boolean or object. Can be async.
-        error_status (int): HTTP status code to use for validation errors
-        error_message (str): Error message to return when validation fails
-    """
-
-    def __init__(
-        self,
-        header_name: str,
-        expected_token: Optional[str] = None,
-        validate_fn: Optional[Callable[[str], bool]] = None,
-        error_status: int = HTTP_401_UNAUTHORIZED,
-        error_message: str = "Invalid API key",
-    ):
-        # Create the APIKeyHeader as a class property
-        self.security_scheme = APIKeyHeader(name=header_name)
-        self.expected_token = expected_token
-        self.custom_validate_fn = validate_fn
-        self.error_status = error_status
-        self.error_message = error_message
-
-    async def default_validator(self, api_key: str) -> bool:
-        return api_key == self.expected_token
-
-    async def __call__(
-        self, request: Request, api_key: str = Security(APIKeyHeader)
-    ) -> Any:
-        if api_key is None:
-            raise HTTPException(status_code=self.error_status, detail="Missing API key")
-
-        # Use custom validation if provided, otherwise use default equality check
-        validator = self.custom_validate_fn or self.default_validator
-        result = (
-            await validator(api_key)
-            if inspect.iscoroutinefunction(validator)
-            else validator(api_key)
-        )
-
-        if not result:
-            raise HTTPException(
-                status_code=self.error_status, detail=self.error_message
-            )
-
-        # Store validation result in request state if it's not just a boolean
-        if result is not True:
-            request.state.api_key = result
-
-        return result
-
-    def get_dependency(self):
-        """
-        Returns a callable dependency that FastAPI will recognize as a security scheme
-        """
-
-        async def validate_api_key(
-            request: Request, api_key: str = Security(self.security_scheme)
-        ) -> Any:
-            return await self(request, api_key)
-
-        # This helps FastAPI recognize it as a security dependency
-        validate_api_key.__name__ = f"validate_{self.security_scheme.model.name}"
-        return validate_api_key

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client.py

@@ -13,6 +13,7 @@ from typing_extensions import ParamSpec
 from .config import SETTINGS
 
 logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.DEBUG)
 
 P = ParamSpec("P")
 T = TypeVar("T")

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -99,6 +99,7 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
         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)
@@ -117,6 +118,7 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
             stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
 
         log_handlers += [stdout, stderr]
+        print("Console logging enabled")
 
     # File logging setup
     if config.enable_file_logging:
@@ -154,6 +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/pyproject.toml

@@ -10,17 +10,18 @@ packages = [{ include = "autogpt_libs" }]
 colorama = "^0.4.6"
 expiringdict = "^1.2.2"
 google-cloud-logging = "^3.11.4"
-pydantic = "^2.11.1"
-pydantic-settings = "^2.8.1"
+pydantic = "^2.10.6"
+pydantic-settings = "^2.7.1"
 pyjwt = "^2.10.1"
-pytest-asyncio = "^0.26.0"
+pytest-asyncio = "^0.25.3"
 pytest-mock = "^3.14.0"
 python = ">=3.10,<4.0"
-supabase = "^2.15.0"
+python-dotenv = "^1.0.1"
+supabase = "^2.13.0"
 
 [tool.poetry.group.dev.dependencies]
 redis = "^5.2.1"
-ruff = "^0.11.0"
+ruff = "^0.9.3"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.example

@@ -2,23 +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=${DB_CONNECT_TIMEOUT}"
+DATABASE_URL="postgresql://${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
@@ -35,11 +25,6 @@ BEHAVE_AS=local
 PYRO_HOST=localhost
 SENTRY_DSN=
 
-# Email For Postmark so we can send emails
-POSTMARK_SERVER_API_TOKEN=
-POSTMARK_SENDER_EMAIL=invalid@invalid.com
-POSTMARK_WEBHOOK_TOKEN=
-
 ## User auth with Supabase is required for any of the 3rd party integrations with auth to work.
 ENABLE_AUTH=true
 SUPABASE_URL=http://localhost:8000
@@ -52,9 +37,6 @@ RABBITMQ_PORT=5672
 RABBITMQ_DEFAULT_USER=rabbitmq_user_default
 RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7
 
-## GCS bucket is required for marketplace and library functionality
-MEDIA_GCS_BUCKET_NAME=
-
 ## For local development, you may need to set FRONTEND_BASE_URL for the OAuth flow
 ## for integrations to work. Defaults to the value of PLATFORM_BASE_URL if not set.
 # FRONTEND_BASE_URL=http://localhost:3000
@@ -179,17 +161,6 @@ MEM0_API_KEY=
 # Nvidia
 NVIDIA_API_KEY=
 
-# Apollo
-APOLLO_API_KEY=
-
-# SmartLead
-SMARTLEAD_API_KEY=
-
-# ZeroBounce
-ZEROBOUNCE_API_KEY=
-
-## ===== OPTIONAL API KEYS END ===== ##
-
 # Logging Configuration
 LOG_LEVEL=INFO
 ENABLE_CLOUD_LOGGING=false

autogpt_platform/backend/README.advanced.md

@@ -1 +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

@@ -1,30 +1,22 @@
-import logging
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
     from backend.util.process import AppProcess
 
-logger = logging.getLogger(__name__)
-
 
 def run_processes(*processes: "AppProcess", **kwargs):
     """
     Execute all processes in the app. The last process is run in the foreground.
-    Includes enhanced error handling and process lifecycle management.
     """
     try:
-        # Run all processes except the last one in the background.
         for process in processes[:-1]:
             process.start(background=True, **kwargs)
 
-        # Run the last process in the foreground.
+        # Run the last process in the foreground
         processes[-1].start(background=False, **kwargs)
     finally:
         for process in processes:
-            try:
-                process.stop()
-            except Exception as e:
-                logger.exception(f"[{process.service_name}] unable to stop: {e}")
+            process.stop()
 
 
 def main(**kwargs):
@@ -32,16 +24,14 @@ 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.notifications import NotificationManager
+    from backend.executor import DatabaseManager, ExecutionManager, ExecutionScheduler
     from backend.server.rest_api import AgentServer
     from backend.server.ws_api import WebsocketServer
 
     run_processes(
         DatabaseManager(),
         ExecutionManager(),
-        Scheduler(),
-        NotificationManager(),
+        ExecutionScheduler(),
         WebsocketServer(),
         AgentServer(),
         **kwargs,

autogpt_platform/backend/backend/blocks/__init__.py

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

autogpt_platform/backend/backend/blocks/agent.py

@@ -1,5 +1,4 @@
 import logging
-from typing import Any
 
 from autogpt_libs.utils.cache import thread_cached
 
@@ -14,7 +13,6 @@ from backend.data.block import (
 )
 from backend.data.execution import ExecutionStatus
 from backend.data.model import SchemaField
-from backend.util import json
 
 logger = logging.getLogger(__name__)
 
@@ -44,23 +42,6 @@ class AgentExecutorBlock(Block):
         input_schema: dict = SchemaField(description="Input schema for the graph")
         output_schema: dict = SchemaField(description="Output schema for the graph")
 
-        @classmethod
-        def get_input_schema(cls, data: BlockInput) -> dict[str, Any]:
-            return data.get("input_schema", {})
-
-        @classmethod
-        def get_input_defaults(cls, data: BlockInput) -> BlockInput:
-            return data.get("data", {})
-
-        @classmethod
-        def get_missing_input(cls, data: BlockInput) -> set[str]:
-            required_fields = cls.get_input_schema(data).get("required", [])
-            return set(required_fields) - set(data)
-
-        @classmethod
-        def get_mismatch_error(cls, data: BlockInput) -> str | None:
-            return json.validate_with_jsonschema(cls.get_input_schema(data), data)
-
     class Output(BlockSchema):
         pass
 
@@ -75,8 +56,6 @@ class AgentExecutorBlock(Block):
         )
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        from backend.data.execution import ExecutionEventType
-
         executor_manager = get_executor_manager_client()
         event_bus = get_event_bus()
 
@@ -90,11 +69,13 @@ class AgentExecutorBlock(Block):
         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.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,
@@ -105,10 +86,6 @@ class AgentExecutorBlock(Block):
                 else:
                     continue
 
-            logger.info(
-                f"Execution {log_id} produced input {event.input_data} output {event.output_data}"
-            )
-
             if not event.block_id:
                 logger.warning(f"{log_id} received event without block_id {event}")
                 continue

autogpt_platform/backend/backend/blocks/apollo/_api.py

@@ -1,108 +0,0 @@
-import logging
-from typing import List
-
-from backend.blocks.apollo._auth import ApolloCredentials
-from backend.blocks.apollo.models import (
-    Contact,
-    Organization,
-    SearchOrganizationsRequest,
-    SearchOrganizationsResponse,
-    SearchPeopleRequest,
-    SearchPeopleResponse,
-)
-from backend.util.request import Requests
-
-logger = logging.getLogger(name=__name__)
-
-
-class ApolloClient:
-    """Client for the Apollo API"""
-
-    API_URL = "https://api.apollo.io/api/v1"
-
-    def __init__(self, credentials: ApolloCredentials):
-        self.credentials = credentials
-        self.requests = Requests()
-
-    def _get_headers(self) -> dict[str, str]:
-        return {"x-api-key": self.credentials.api_key.get_secret_value()}
-
-    def search_people(self, query: SearchPeopleRequest) -> List[Contact]:
-        """Search for people in Apollo"""
-        response = self.requests.get(
-            f"{self.API_URL}/mixed_people/search",
-            headers=self._get_headers(),
-            params=query.model_dump(exclude={"credentials", "max_results"}),
-        )
-        parsed_response = SearchPeopleResponse(**response.json())
-        if parsed_response.pagination.total_entries == 0:
-            return []
-
-        people = parsed_response.people
-
-        # handle pagination
-        if (
-            query.max_results is not None
-            and query.max_results < parsed_response.pagination.total_entries
-            and len(people) < query.max_results
-        ):
-            while (
-                len(people) < query.max_results
-                and query.page < parsed_response.pagination.total_pages
-                and len(parsed_response.people) > 0
-            ):
-                query.page += 1
-                response = self.requests.get(
-                    f"{self.API_URL}/mixed_people/search",
-                    headers=self._get_headers(),
-                    params=query.model_dump(exclude={"credentials", "max_results"}),
-                )
-                parsed_response = SearchPeopleResponse(**response.json())
-                people.extend(parsed_response.people[: query.max_results - len(people)])
-
-        logger.info(f"Found {len(people)} people")
-        return people[: query.max_results] if query.max_results else people
-
-    def search_organizations(
-        self, query: SearchOrganizationsRequest
-    ) -> List[Organization]:
-        """Search for organizations in Apollo"""
-        response = self.requests.get(
-            f"{self.API_URL}/mixed_companies/search",
-            headers=self._get_headers(),
-            params=query.model_dump(exclude={"credentials", "max_results"}),
-        )
-        parsed_response = SearchOrganizationsResponse(**response.json())
-        if parsed_response.pagination.total_entries == 0:
-            return []
-
-        organizations = parsed_response.organizations
-
-        # handle pagination
-        if (
-            query.max_results is not None
-            and query.max_results < parsed_response.pagination.total_entries
-            and len(organizations) < query.max_results
-        ):
-            while (
-                len(organizations) < query.max_results
-                and query.page < parsed_response.pagination.total_pages
-                and len(parsed_response.organizations) > 0
-            ):
-                query.page += 1
-                response = self.requests.get(
-                    f"{self.API_URL}/mixed_companies/search",
-                    headers=self._get_headers(),
-                    params=query.model_dump(exclude={"credentials", "max_results"}),
-                )
-                parsed_response = SearchOrganizationsResponse(**response.json())
-                organizations.extend(
-                    parsed_response.organizations[
-                        : query.max_results - len(organizations)
-                    ]
-                )
-
-        logger.info(f"Found {len(organizations)} organizations")
-        return (
-            organizations[: query.max_results] if query.max_results else organizations
-        )

autogpt_platform/backend/backend/blocks/apollo/_auth.py

@@ -1,35 +0,0 @@
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
-from backend.integrations.providers import ProviderName
-
-ApolloCredentials = APIKeyCredentials
-ApolloCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.APOLLO],
-    Literal["api_key"],
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="apollo",
-    api_key=SecretStr("mock-apollo-api-key"),
-    title="Mock Apollo API key",
-    expires_at=None,
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-def ApolloCredentialsField() -> ApolloCredentialsInput:
-    """
-    Creates a Apollo credentials input on a block.
-    """
-    return CredentialsField(
-        description="The Apollo integration can be used with an API Key.",
-    )

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

@@ -1,543 +0,0 @@
-from enum import Enum
-from typing import Any, Optional
-
-from pydantic import BaseModel
-
-from backend.data.model import SchemaField
-
-
-class PrimaryPhone(BaseModel):
-    """A primary phone in Apollo"""
-
-    number: str
-    source: str
-    sanitized_number: str
-
-
-class SenorityLevels(str, Enum):
-    """Seniority levels in Apollo"""
-
-    OWNER = "owner"
-    FOUNDER = "founder"
-    C_SUITE = "c_suite"
-    PARTNER = "partner"
-    VP = "vp"
-    HEAD = "head"
-    DIRECTOR = "director"
-    MANAGER = "manager"
-    SENIOR = "senior"
-    ENTRY = "entry"
-    INTERN = "intern"
-
-
-class ContactEmailStatuses(str, Enum):
-    """Contact email statuses in Apollo"""
-
-    VERIFIED = "verified"
-    UNVERIFIED = "unverified"
-    LIKELY_TO_ENGAGE = "likely_to_engage"
-    UNAVAILABLE = "unavailable"
-
-
-class RuleConfigStatus(BaseModel):
-    """A rule config status in Apollo"""
-
-    _id: str
-    created_at: str
-    rule_action_config_id: str
-    rule_config_id: str
-    status_cd: str
-    updated_at: str
-    id: str
-    key: str
-
-
-class ContactCampaignStatus(BaseModel):
-    """A contact campaign status in Apollo"""
-
-    id: str
-    emailer_campaign_id: str
-    send_email_from_user_id: str
-    inactive_reason: str
-    status: str
-    added_at: str
-    added_by_user_id: str
-    finished_at: str
-    paused_at: str
-    auto_unpause_at: str
-    send_email_from_email_address: str
-    send_email_from_email_account_id: str
-    manually_set_unpause: str
-    failure_reason: str
-    current_step_id: str
-    in_response_to_emailer_message_id: str
-    cc_emails: str
-    bcc_emails: str
-    to_emails: str
-
-
-class Account(BaseModel):
-    """An account in Apollo"""
-
-    id: str
-    name: str
-    website_url: str
-    blog_url: str
-    angellist_url: str
-    linkedin_url: str
-    twitter_url: str
-    facebook_url: str
-    primary_phone: PrimaryPhone
-    languages: list[str]
-    alexa_ranking: int
-    phone: str
-    linkedin_uid: str
-    founded_year: int
-    publicly_traded_symbol: str
-    publicly_traded_exchange: str
-    logo_url: str
-    chrunchbase_url: str
-    primary_domain: str
-    domain: str
-    team_id: str
-    organization_id: str
-    account_stage_id: str
-    source: str
-    original_source: str
-    creator_id: str
-    owner_id: str
-    created_at: str
-    phone_status: str
-    hubspot_id: str
-    salesforce_id: str
-    crm_owner_id: str
-    parent_account_id: str
-    sanitized_phone: str
-    # no listed type on the API docs
-    account_playbook_statues: list[Any]
-    account_rule_config_statuses: list[RuleConfigStatus]
-    existence_level: str
-    label_ids: list[str]
-    typed_custom_fields: Any
-    custom_field_errors: Any
-    modality: str
-    source_display_name: str
-    salesforce_record_id: str
-    crm_record_url: str
-
-
-class ContactEmail(BaseModel):
-    """A contact email in Apollo"""
-
-    email: str = ""
-    email_md5: str = ""
-    email_sha256: str = ""
-    email_status: str = ""
-    email_source: str = ""
-    extrapolated_email_confidence: str = ""
-    position: int = 0
-    email_from_customer: str = ""
-    free_domain: bool = True
-
-
-class EmploymentHistory(BaseModel):
-    """An employment history in Apollo"""
-
-    class Config:
-        extra = "allow"
-        arbitrary_types_allowed = True
-        from_attributes = True
-        populate_by_name = True
-
-    _id: Optional[str] = None
-    created_at: Optional[str] = None
-    current: Optional[bool] = None
-    degree: Optional[str] = None
-    description: Optional[str] = None
-    emails: Optional[str] = None
-    end_date: Optional[str] = None
-    grade_level: Optional[str] = None
-    kind: Optional[str] = None
-    major: Optional[str] = None
-    organization_id: Optional[str] = None
-    organization_name: Optional[str] = None
-    raw_address: Optional[str] = None
-    start_date: Optional[str] = None
-    title: Optional[str] = None
-    updated_at: Optional[str] = None
-    id: Optional[str] = None
-    key: Optional[str] = None
-
-
-class Breadcrumb(BaseModel):
-    """A breadcrumb in Apollo"""
-
-    label: Optional[str] = "N/A"
-    signal_field_name: Optional[str] = "N/A"
-    value: str | list | None = "N/A"
-    display_name: Optional[str] = "N/A"
-
-
-class TypedCustomField(BaseModel):
-    """A typed custom field in Apollo"""
-
-    id: Optional[str] = "N/A"
-    value: Optional[str] = "N/A"
-
-
-class Pagination(BaseModel):
-    """Pagination in Apollo"""
-
-    class Config:
-        extra = "allow"  # Allow extra fields
-        arbitrary_types_allowed = True  # Allow any type
-        from_attributes = True  # Allow from_orm
-        populate_by_name = True  # Allow field aliases to work both ways
-
-    page: int = 0
-    per_page: int = 0
-    total_entries: int = 0
-    total_pages: int = 0
-
-
-class DialerFlags(BaseModel):
-    """A dialer flags in Apollo"""
-
-    country_name: str
-    country_enabled: bool
-    high_risk_calling_enabled: bool
-    potential_high_risk_number: bool
-
-
-class PhoneNumber(BaseModel):
-    """A phone number in Apollo"""
-
-    raw_number: str = ""
-    sanitized_number: str = ""
-    type: str = ""
-    position: int = 0
-    status: str = ""
-    dnc_status: str = ""
-    dnc_other_info: str = ""
-    dailer_flags: DialerFlags = DialerFlags(
-        country_name="",
-        country_enabled=True,
-        high_risk_calling_enabled=True,
-        potential_high_risk_number=True,
-    )
-
-
-class Organization(BaseModel):
-    """An organization in Apollo"""
-
-    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"
-    website_url: Optional[str] = "N/A"
-    blog_url: Optional[str] = "N/A"
-    angellist_url: Optional[str] = "N/A"
-    linkedin_url: Optional[str] = "N/A"
-    twitter_url: Optional[str] = "N/A"
-    facebook_url: Optional[str] = "N/A"
-    primary_phone: Optional[PrimaryPhone] = PrimaryPhone(
-        number="N/A", source="N/A", sanitized_number="N/A"
-    )
-    languages: list[str] = []
-    alexa_ranking: Optional[int] = 0
-    phone: Optional[str] = "N/A"
-    linkedin_uid: Optional[str] = "N/A"
-    founded_year: Optional[int] = 0
-    publicly_traded_symbol: Optional[str] = "N/A"
-    publicly_traded_exchange: Optional[str] = "N/A"
-    logo_url: Optional[str] = "N/A"
-    chrunchbase_url: Optional[str] = "N/A"
-    primary_domain: Optional[str] = "N/A"
-    sanitized_phone: Optional[str] = "N/A"
-    owned_by_organization_id: Optional[str] = "N/A"
-    intent_strength: Optional[str] = "N/A"
-    show_intent: bool = True
-    has_intent_signal_account: Optional[bool] = True
-    intent_signal_account: Optional[str] = "N/A"
-
-
-class Contact(BaseModel):
-    """A contact in Apollo"""
-
-    class Config:
-        extra = "allow"
-        arbitrary_types_allowed = True
-        from_attributes = True
-        populate_by_name = True
-
-    contact_roles: list[Any] = []
-    id: Optional[str] = None
-    first_name: Optional[str] = None
-    last_name: Optional[str] = None
-    name: Optional[str] = None
-    linkedin_url: Optional[str] = None
-    title: Optional[str] = None
-    contact_stage_id: Optional[str] = None
-    owner_id: Optional[str] = None
-    creator_id: Optional[str] = None
-    person_id: Optional[str] = None
-    email_needs_tickling: bool = True
-    organization_name: Optional[str] = None
-    source: Optional[str] = None
-    original_source: Optional[str] = None
-    organization_id: Optional[str] = None
-    headline: Optional[str] = None
-    photo_url: Optional[str] = None
-    present_raw_address: Optional[str] = None
-    linkededin_uid: Optional[str] = None
-    extrapolated_email_confidence: Optional[float] = None
-    salesforce_id: Optional[str] = None
-    salesforce_lead_id: Optional[str] = None
-    salesforce_contact_id: Optional[str] = None
-    saleforce_account_id: Optional[str] = None
-    crm_owner_id: Optional[str] = None
-    created_at: Optional[str] = None
-    emailer_campaign_ids: list[str] = []
-    direct_dial_status: Optional[str] = None
-    direct_dial_enrichment_failed_at: Optional[str] = None
-    email_status: Optional[str] = None
-    email_source: Optional[str] = None
-    account_id: Optional[str] = None
-    last_activity_date: Optional[str] = None
-    hubspot_vid: Optional[str] = None
-    hubspot_company_id: Optional[str] = None
-    crm_id: Optional[str] = None
-    sanitized_phone: Optional[str] = None
-    merged_crm_ids: Optional[str] = None
-    updated_at: Optional[str] = None
-    queued_for_crm_push: bool = True
-    suggested_from_rule_engine_config_id: Optional[str] = None
-    email_unsubscribed: Optional[str] = None
-    label_ids: list[Any] = []
-    has_pending_email_arcgate_request: bool = True
-    has_email_arcgate_request: bool = True
-    existence_level: Optional[str] = None
-    email: Optional[str] = None
-    email_from_customer: Optional[str] = None
-    typed_custom_fields: list[TypedCustomField] = []
-    custom_field_errors: Any = None
-    salesforce_record_id: Optional[str] = None
-    crm_record_url: Optional[str] = None
-    email_status_unavailable_reason: Optional[str] = None
-    email_true_status: Optional[str] = None
-    updated_email_true_status: bool = True
-    contact_rule_config_statuses: list[RuleConfigStatus] = []
-    source_display_name: Optional[str] = None
-    twitter_url: Optional[str] = None
-    contact_campaign_statuses: list[ContactCampaignStatus] = []
-    state: Optional[str] = None
-    city: Optional[str] = None
-    country: Optional[str] = None
-    account: Optional[Account] = None
-    contact_emails: list[ContactEmail] = []
-    organization: Optional[Organization] = None
-    employment_history: list[EmploymentHistory] = []
-    time_zone: Optional[str] = None
-    intent_strength: Optional[str] = None
-    show_intent: bool = True
-    phone_numbers: list[PhoneNumber] = []
-    account_phone_note: Optional[str] = None
-    free_domain: bool = True
-    is_likely_to_engage: bool = True
-    email_domain_catchall: bool = True
-    contact_job_change_event: Optional[str] = None
-
-
-class SearchOrganizationsRequest(BaseModel):
-    """Request for Apollo's search organizations API"""
-
-    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=[0, 1000000],
-    )
-
-    organization_locations: list[str] = SchemaField(
-        description="""The location of the company headquarters. You can search across cities, US states, and countries.
-
-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, any Boston-based companies will not appearch in your search results, even if they match other parameters.
-
-To exclude companies based on location, use the organization_not_locations parameter.
-""",
-        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=[],
-    )
-    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."""
-    )
-    q_organization_name: str = SchemaField(
-        description="""Filter search results to include a specific company name.
-
-If the value you enter for this parameter does not match with a company's name, the company will not appear in search results, even if it matches other parameters. Partial matches are accepted. For example, if you filter by the value marketing, a company called NY Marketing Unlimited would still be eligible as a search result, but NY Market Analysis would not be eligible."""
-    )
-    organization_ids: list[str] = SchemaField(
-        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=[],
-    )
-    max_results: int = SchemaField(
-        description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
-        default=100,
-        ge=1,
-        le=50000,
-        advanced=True,
-    )
-
-    page: int = SchemaField(
-        description="""The page number of the Apollo data that you want to retrieve.
-
-Use this parameter in combination with the per_page parameter to make search results for navigable and improve the performance of the endpoint.""",
-        default=1,
-    )
-    per_page: int = SchemaField(
-        description="""The number of search results that should be returned for each page. Limited the number of results per page improves the endpoint's performance.
-
-Use the page parameter to search the different pages of data.""",
-        default=100,
-    )
-
-
-class SearchOrganizationsResponse(BaseModel):
-    """Response from Apollo's search organizations API"""
-
-    breadcrumbs: list[Breadcrumb] = []
-    partial_results_only: bool = True
-    has_join: bool = True
-    disable_eu_prospecting: bool = True
-    partial_results_limit: int = 0
-    pagination: Pagination = Pagination(
-        page=0, per_page=0, total_entries=0, total_pages=0
-    )
-    # no listed type on the API docs
-    accounts: list[Any] = []
-    organizations: list[Organization] = []
-    models_ids: list[str] = []
-    num_fetch_result: Optional[str] = "N/A"
-    derived_params: Optional[str] = "N/A"
-
-
-class SearchPeopleRequest(BaseModel):
-    """Request for Apollo's search people API"""
-
-    person_titles: list[str] = SchemaField(
-        description="""Job titles held by the people you want to find. For a person to be included in search results, they only need to match 1 of the job titles you add. Adding more job titles expands your search results.
-
-Results also include job titles with the same terms, even if they are not exact matches. For example, searching for marketing manager might return people with the job title content marketing manager.
-
-Use this parameter in combination with the person_seniorities[] parameter to find people based on specific job functions and seniority levels.
-""",
-        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=[],
-    )
-    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.
-
-For a person to be included in search results, they only need to match 1 of the seniorities you add. Adding more seniorities expands your search results.
-
-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=[],
-    )
-    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.
-
-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=[],
-    )
-    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.
-
-You can add multiple domains to search across companies.
-
-  Examples: apollo.io and microsoft.com""",
-        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=[],
-    )
-    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=[],
-    )
-    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=[],
-    )
-    q_keywords: str = SchemaField(
-        description="""A string of words over which we want to filter the results""",
-        default="",
-    )
-    page: int = SchemaField(
-        description="""The page number of the Apollo data that you want to retrieve.
-
-Use this parameter in combination with the per_page parameter to make search results for navigable and improve the performance of the endpoint.""",
-        default=1,
-    )
-    per_page: int = SchemaField(
-        description="""The number of search results that should be returned for each page. Limited the number of results per page improves the endpoint's performance.
-
-Use the page parameter to search the different pages of data.""",
-        default=100,
-    )
-    max_results: int = SchemaField(
-        description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
-        default=100,
-        ge=1,
-        le=50000,
-        advanced=True,
-    )
-
-
-class SearchPeopleResponse(BaseModel):
-    """Response from Apollo's search people API"""
-
-    class Config:
-        extra = "allow"  # Allow extra fields
-        arbitrary_types_allowed = True  # Allow any type
-        from_attributes = True  # Allow from_orm
-        populate_by_name = True  # Allow field aliases to work both ways
-
-    breadcrumbs: list[Breadcrumb] = []
-    partial_results_only: bool = True
-    has_join: bool = True
-    disable_eu_prospecting: bool = True
-    partial_results_limit: int = 0
-    pagination: Pagination = Pagination(
-        page=0, per_page=0, total_entries=0, total_pages=0
-    )
-    contacts: list[Contact] = []
-    people: list[Contact] = []
-    model_ids: list[str] = []
-    num_fetch_result: Optional[str] = "N/A"
-    derived_params: Optional[str] = "N/A"

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

@@ -1,219 +0,0 @@
-from backend.blocks.apollo._api import ApolloClient
-from backend.blocks.apollo._auth import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    ApolloCredentials,
-    ApolloCredentialsInput,
-)
-from backend.blocks.apollo.models import (
-    Organization,
-    PrimaryPhone,
-    SearchOrganizationsRequest,
-)
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-
-
-class SearchOrganizationsBlock(Block):
-    """Search for organizations in Apollo"""
-
-    class Input(BlockSchema):
-        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=[0, 1000000],
-        )
-
-        organization_locations: list[str] = SchemaField(
-            description="""The location of the company headquarters. You can search across cities, US states, and countries.
-
-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, any Boston-based companies will not appearch in your search results, even if they match other parameters.
-
-To exclude companies based on location, use the organization_not_locations parameter.
-""",
-            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=[],
-        )
-        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=[],
-        )
-        q_organization_name: str = SchemaField(
-            description="""Filter search results to include a specific company name.
-
-If the value you enter for this parameter does not match with a company's name, the company will not appear in search results, even if it matches other parameters. Partial matches are accepted. For example, if you filter by the value marketing, a company called NY Marketing Unlimited would still be eligible as a search result, but NY Market Analysis would not be eligible.""",
-            default="",
-            advanced=False,
-        )
-        organization_ids: list[str] = SchemaField(
-            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=[],
-        )
-        max_results: int = SchemaField(
-            description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
-            default=100,
-            ge=1,
-            le=50000,
-            advanced=True,
-        )
-        credentials: ApolloCredentialsInput = SchemaField(
-            description="Apollo credentials",
-        )
-
-    class Output(BlockSchema):
-        organizations: list[Organization] = SchemaField(
-            description="List of organizations found",
-            default=[],
-        )
-        organization: Organization = SchemaField(
-            description="Each found organization, one at a time",
-        )
-        error: str = SchemaField(
-            description="Error message if the search failed",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3d71270d-599e-4148-9b95-71b35d2f44f0",
-            description="Search for organizations in Apollo",
-            categories={BlockCategory.SEARCH},
-            input_schema=SearchOrganizationsBlock.Input,
-            output_schema=SearchOrganizationsBlock.Output,
-            test_credentials=TEST_CREDENTIALS,
-            test_input={"query": "Google", "credentials": TEST_CREDENTIALS_INPUT},
-            test_output=[
-                (
-                    "organization",
-                    Organization(
-                        id="1",
-                        name="Google",
-                        website_url="https://google.com",
-                        blog_url="https://google.com/blog",
-                        angellist_url="https://angel.co/google",
-                        linkedin_url="https://linkedin.com/company/google",
-                        twitter_url="https://twitter.com/google",
-                        facebook_url="https://facebook.com/google",
-                        primary_phone=PrimaryPhone(
-                            source="google",
-                            number="1234567890",
-                            sanitized_number="1234567890",
-                        ),
-                        languages=["en"],
-                        alexa_ranking=1000,
-                        phone="1234567890",
-                        linkedin_uid="1234567890",
-                        founded_year=2000,
-                        publicly_traded_symbol="GOOGL",
-                        publicly_traded_exchange="NASDAQ",
-                        logo_url="https://google.com/logo.png",
-                        chrunchbase_url="https://chrunchbase.com/google",
-                        primary_domain="google.com",
-                        sanitized_phone="1234567890",
-                        owned_by_organization_id="1",
-                        intent_strength="strong",
-                        show_intent=True,
-                        has_intent_signal_account=True,
-                        intent_signal_account="1",
-                    ),
-                ),
-                (
-                    "organizations",
-                    [
-                        Organization(
-                            id="1",
-                            name="Google",
-                            website_url="https://google.com",
-                            blog_url="https://google.com/blog",
-                            angellist_url="https://angel.co/google",
-                            linkedin_url="https://linkedin.com/company/google",
-                            twitter_url="https://twitter.com/google",
-                            facebook_url="https://facebook.com/google",
-                            primary_phone=PrimaryPhone(
-                                source="google",
-                                number="1234567890",
-                                sanitized_number="1234567890",
-                            ),
-                            languages=["en"],
-                            alexa_ranking=1000,
-                            phone="1234567890",
-                            linkedin_uid="1234567890",
-                            founded_year=2000,
-                            publicly_traded_symbol="GOOGL",
-                            publicly_traded_exchange="NASDAQ",
-                            logo_url="https://google.com/logo.png",
-                            chrunchbase_url="https://chrunchbase.com/google",
-                            primary_domain="google.com",
-                            sanitized_phone="1234567890",
-                            owned_by_organization_id="1",
-                            intent_strength="strong",
-                            show_intent=True,
-                            has_intent_signal_account=True,
-                            intent_signal_account="1",
-                        ),
-                    ],
-                ),
-            ],
-            test_mock={
-                "search_organizations": lambda *args, **kwargs: [
-                    Organization(
-                        id="1",
-                        name="Google",
-                        website_url="https://google.com",
-                        blog_url="https://google.com/blog",
-                        angellist_url="https://angel.co/google",
-                        linkedin_url="https://linkedin.com/company/google",
-                        twitter_url="https://twitter.com/google",
-                        facebook_url="https://facebook.com/google",
-                        primary_phone=PrimaryPhone(
-                            source="google",
-                            number="1234567890",
-                            sanitized_number="1234567890",
-                        ),
-                        languages=["en"],
-                        alexa_ranking=1000,
-                        phone="1234567890",
-                        linkedin_uid="1234567890",
-                        founded_year=2000,
-                        publicly_traded_symbol="GOOGL",
-                        publicly_traded_exchange="NASDAQ",
-                        logo_url="https://google.com/logo.png",
-                        chrunchbase_url="https://chrunchbase.com/google",
-                        primary_domain="google.com",
-                        sanitized_phone="1234567890",
-                        owned_by_organization_id="1",
-                        intent_strength="strong",
-                        show_intent=True,
-                        has_intent_signal_account=True,
-                        intent_signal_account="1",
-                    )
-                ]
-            },
-        )
-
-    @staticmethod
-    def search_organizations(
-        query: SearchOrganizationsRequest, credentials: ApolloCredentials
-    ) -> list[Organization]:
-        client = ApolloClient(credentials)
-        return client.search_organizations(query)
-
-    def run(
-        self, input_data: Input, *, credentials: ApolloCredentials, **kwargs
-    ) -> BlockOutput:
-        query = SearchOrganizationsRequest(
-            **input_data.model_dump(exclude={"credentials"})
-        )
-        organizations = self.search_organizations(query, credentials)
-        for organization in organizations:
-            yield "organization", organization
-        yield "organizations", organizations

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

@@ -1,394 +0,0 @@
-from backend.blocks.apollo._api import ApolloClient
-from backend.blocks.apollo._auth import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    ApolloCredentials,
-    ApolloCredentialsInput,
-)
-from backend.blocks.apollo.models import (
-    Contact,
-    ContactEmailStatuses,
-    SearchPeopleRequest,
-    SenorityLevels,
-)
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-
-
-class SearchPeopleBlock(Block):
-    """Search for people in Apollo"""
-
-    class Input(BlockSchema):
-        person_titles: list[str] = SchemaField(
-            description="""Job titles held by the people you want to find. For a person to be included in search results, they only need to match 1 of the job titles you add. Adding more job titles expands your search results.
-
-        Results also include job titles with the same terms, even if they are not exact matches. For example, searching for marketing manager might return people with the job title content marketing manager.
-
-        Use this parameter in combination with the person_seniorities[] parameter to find people based on specific job functions and seniority levels.
-        """,
-            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=[],
-            advanced=False,
-        )
-        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.
-
-        For a person to be included in search results, they only need to match 1 of the seniorities you add. Adding more seniorities expands your search results.
-
-        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=[],
-            advanced=False,
-        )
-        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.
-
-        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=[],
-            advanced=False,
-        )
-        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.
-
-        You can add multiple domains to search across companies.
-
-          Examples: apollo.io and microsoft.com""",
-            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=[],
-            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=[],
-            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=[],
-            advanced=False,
-        )
-        q_keywords: str = SchemaField(
-            description="""A string of words over which we want to filter the results""",
-            default="",
-            advanced=False,
-        )
-        max_results: int = SchemaField(
-            description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
-            default=100,
-            ge=1,
-            le=50000,
-            advanced=True,
-        )
-
-        credentials: ApolloCredentialsInput = SchemaField(
-            description="Apollo credentials",
-        )
-
-    class Output(BlockSchema):
-        people: list[Contact] = SchemaField(
-            description="List of people found",
-            default=[],
-        )
-        person: Contact = SchemaField(
-            description="Each found person, one at a time",
-        )
-        error: str = SchemaField(
-            description="Error message if the search failed",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="c2adb3aa-5aae-488d-8a6e-4eb8c23e2ed6",
-            description="Search for people in Apollo",
-            categories={BlockCategory.SEARCH},
-            input_schema=SearchPeopleBlock.Input,
-            output_schema=SearchPeopleBlock.Output,
-            test_credentials=TEST_CREDENTIALS,
-            test_input={"credentials": TEST_CREDENTIALS_INPUT},
-            test_output=[
-                (
-                    "person",
-                    Contact(
-                        contact_roles=[],
-                        id="1",
-                        name="John Doe",
-                        first_name="John",
-                        last_name="Doe",
-                        linkedin_url="https://www.linkedin.com/in/johndoe",
-                        title="Software Engineer",
-                        organization_name="Google",
-                        organization_id="123456",
-                        contact_stage_id="1",
-                        owner_id="1",
-                        creator_id="1",
-                        person_id="1",
-                        email_needs_tickling=True,
-                        source="apollo",
-                        original_source="apollo",
-                        headline="Software Engineer",
-                        photo_url="https://www.linkedin.com/in/johndoe",
-                        present_raw_address="123 Main St, Anytown, USA",
-                        linkededin_uid="123456",
-                        extrapolated_email_confidence=0.8,
-                        salesforce_id="123456",
-                        salesforce_lead_id="123456",
-                        salesforce_contact_id="123456",
-                        saleforce_account_id="123456",
-                        crm_owner_id="123456",
-                        created_at="2021-01-01",
-                        emailer_campaign_ids=[],
-                        direct_dial_status="active",
-                        direct_dial_enrichment_failed_at="2021-01-01",
-                        email_status="active",
-                        email_source="apollo",
-                        account_id="123456",
-                        last_activity_date="2021-01-01",
-                        hubspot_vid="123456",
-                        hubspot_company_id="123456",
-                        crm_id="123456",
-                        sanitized_phone="123456",
-                        merged_crm_ids="123456",
-                        updated_at="2021-01-01",
-                        queued_for_crm_push=True,
-                        suggested_from_rule_engine_config_id="123456",
-                        email_unsubscribed=None,
-                        label_ids=[],
-                        has_pending_email_arcgate_request=True,
-                        has_email_arcgate_request=True,
-                        existence_level=None,
-                        email=None,
-                        email_from_customer=None,
-                        typed_custom_fields=[],
-                        custom_field_errors=None,
-                        salesforce_record_id=None,
-                        crm_record_url=None,
-                        email_status_unavailable_reason=None,
-                        email_true_status=None,
-                        updated_email_true_status=True,
-                        contact_rule_config_statuses=[],
-                        source_display_name=None,
-                        twitter_url=None,
-                        contact_campaign_statuses=[],
-                        state=None,
-                        city=None,
-                        country=None,
-                        account=None,
-                        contact_emails=[],
-                        organization=None,
-                        employment_history=[],
-                        time_zone=None,
-                        intent_strength=None,
-                        show_intent=True,
-                        phone_numbers=[],
-                        account_phone_note=None,
-                        free_domain=True,
-                        is_likely_to_engage=True,
-                        email_domain_catchall=True,
-                        contact_job_change_event=None,
-                    ),
-                ),
-                (
-                    "people",
-                    [
-                        Contact(
-                            contact_roles=[],
-                            id="1",
-                            name="John Doe",
-                            first_name="John",
-                            last_name="Doe",
-                            linkedin_url="https://www.linkedin.com/in/johndoe",
-                            title="Software Engineer",
-                            organization_name="Google",
-                            organization_id="123456",
-                            contact_stage_id="1",
-                            owner_id="1",
-                            creator_id="1",
-                            person_id="1",
-                            email_needs_tickling=True,
-                            source="apollo",
-                            original_source="apollo",
-                            headline="Software Engineer",
-                            photo_url="https://www.linkedin.com/in/johndoe",
-                            present_raw_address="123 Main St, Anytown, USA",
-                            linkededin_uid="123456",
-                            extrapolated_email_confidence=0.8,
-                            salesforce_id="123456",
-                            salesforce_lead_id="123456",
-                            salesforce_contact_id="123456",
-                            saleforce_account_id="123456",
-                            crm_owner_id="123456",
-                            created_at="2021-01-01",
-                            emailer_campaign_ids=[],
-                            direct_dial_status="active",
-                            direct_dial_enrichment_failed_at="2021-01-01",
-                            email_status="active",
-                            email_source="apollo",
-                            account_id="123456",
-                            last_activity_date="2021-01-01",
-                            hubspot_vid="123456",
-                            hubspot_company_id="123456",
-                            crm_id="123456",
-                            sanitized_phone="123456",
-                            merged_crm_ids="123456",
-                            updated_at="2021-01-01",
-                            queued_for_crm_push=True,
-                            suggested_from_rule_engine_config_id="123456",
-                            email_unsubscribed=None,
-                            label_ids=[],
-                            has_pending_email_arcgate_request=True,
-                            has_email_arcgate_request=True,
-                            existence_level=None,
-                            email=None,
-                            email_from_customer=None,
-                            typed_custom_fields=[],
-                            custom_field_errors=None,
-                            salesforce_record_id=None,
-                            crm_record_url=None,
-                            email_status_unavailable_reason=None,
-                            email_true_status=None,
-                            updated_email_true_status=True,
-                            contact_rule_config_statuses=[],
-                            source_display_name=None,
-                            twitter_url=None,
-                            contact_campaign_statuses=[],
-                            state=None,
-                            city=None,
-                            country=None,
-                            account=None,
-                            contact_emails=[],
-                            organization=None,
-                            employment_history=[],
-                            time_zone=None,
-                            intent_strength=None,
-                            show_intent=True,
-                            phone_numbers=[],
-                            account_phone_note=None,
-                            free_domain=True,
-                            is_likely_to_engage=True,
-                            email_domain_catchall=True,
-                            contact_job_change_event=None,
-                        ),
-                    ],
-                ),
-            ],
-            test_mock={
-                "search_people": lambda query, credentials: [
-                    Contact(
-                        id="1",
-                        name="John Doe",
-                        first_name="John",
-                        last_name="Doe",
-                        linkedin_url="https://www.linkedin.com/in/johndoe",
-                        title="Software Engineer",
-                        organization_name="Google",
-                        organization_id="123456",
-                        contact_stage_id="1",
-                        owner_id="1",
-                        creator_id="1",
-                        person_id="1",
-                        email_needs_tickling=True,
-                        source="apollo",
-                        original_source="apollo",
-                        headline="Software Engineer",
-                        photo_url="https://www.linkedin.com/in/johndoe",
-                        present_raw_address="123 Main St, Anytown, USA",
-                        linkededin_uid="123456",
-                        extrapolated_email_confidence=0.8,
-                        salesforce_id="123456",
-                        salesforce_lead_id="123456",
-                        salesforce_contact_id="123456",
-                        saleforce_account_id="123456",
-                        crm_owner_id="123456",
-                        created_at="2021-01-01",
-                        emailer_campaign_ids=[],
-                        direct_dial_status="active",
-                        direct_dial_enrichment_failed_at="2021-01-01",
-                        email_status="active",
-                        email_source="apollo",
-                        account_id="123456",
-                        last_activity_date="2021-01-01",
-                        hubspot_vid="123456",
-                        hubspot_company_id="123456",
-                        crm_id="123456",
-                        sanitized_phone="123456",
-                        merged_crm_ids="123456",
-                        updated_at="2021-01-01",
-                        queued_for_crm_push=True,
-                        suggested_from_rule_engine_config_id="123456",
-                        email_unsubscribed=None,
-                        label_ids=[],
-                        has_pending_email_arcgate_request=True,
-                        has_email_arcgate_request=True,
-                        existence_level=None,
-                        email=None,
-                        email_from_customer=None,
-                        typed_custom_fields=[],
-                        custom_field_errors=None,
-                        salesforce_record_id=None,
-                        crm_record_url=None,
-                        email_status_unavailable_reason=None,
-                        email_true_status=None,
-                        updated_email_true_status=True,
-                        contact_rule_config_statuses=[],
-                        source_display_name=None,
-                        twitter_url=None,
-                        contact_campaign_statuses=[],
-                        state=None,
-                        city=None,
-                        country=None,
-                        account=None,
-                        contact_emails=[],
-                        organization=None,
-                        employment_history=[],
-                        time_zone=None,
-                        intent_strength=None,
-                        show_intent=True,
-                        phone_numbers=[],
-                        account_phone_note=None,
-                        free_domain=True,
-                        is_likely_to_engage=True,
-                        email_domain_catchall=True,
-                        contact_job_change_event=None,
-                    ),
-                ]
-            },
-        )
-
-    @staticmethod
-    def search_people(
-        query: SearchPeopleRequest, credentials: ApolloCredentials
-    ) -> list[Contact]:
-        client = ApolloClient(credentials)
-        return client.search_people(query)
-
-    def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: ApolloCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-
-        query = SearchPeopleRequest(**input_data.model_dump(exclude={"credentials"}))
-        people = self.search_people(query, credentials)
-        for person in people:
-            yield "person", person
-        yield "people", people

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."
         )
 
@@ -88,6 +90,29 @@ class StoreValueBlock(Block):
         yield "output", input_data.data or input_data.input
 
 
+class PrintToConsoleBlock(Block):
+    class Input(BlockSchema):
+        text: str = SchemaField(description="The text to print to the console.")
+
+    class Output(BlockSchema):
+        status: str = SchemaField(description="The status of the print operation.")
+
+    def __init__(self):
+        super().__init__(
+            id="f3b1c1b2-4c4f-4f0d-8d2f-4c4f0d8d2f4c",
+            description="Print the given text to the console, this is used for a debugging purpose.",
+            categories={BlockCategory.BASIC},
+            input_schema=PrintToConsoleBlock.Input,
+            output_schema=PrintToConsoleBlock.Output,
+            test_input={"text": "Hello, World!"},
+            test_output=("status", "printed"),
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        print(">>>>> Print: ", input_data.text)
+        yield "status", "printed"
+
+
 class FindInDictionaryBlock(Block):
     class Input(BlockSchema):
         input: Any = SchemaField(description="Dictionary to lookup from")
@@ -128,9 +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):
@@ -148,6 +170,188 @@ class FindInDictionaryBlock(Block):
             yield "missing", input_data.input
 
 
+class AgentInputBlock(Block):
+    """
+    This block is used to provide input to the graph.
+
+    It takes in a value, name, description, default values list and bool to limit selection to default values.
+
+    It Outputs the value passed as input.
+    """
+
+    class Input(BlockSchema):
+        name: str = SchemaField(description="The name of the input.")
+        value: Any = SchemaField(
+            description="The value to be passed as input.",
+            default=None,
+        )
+        title: str | None = SchemaField(
+            description="The title of the input.", default=None, advanced=True
+        )
+        description: str | None = SchemaField(
+            description="The description of the input.",
+            default=None,
+            advanced=True,
+        )
+        placeholder_values: List[Any] = SchemaField(
+            description="The placeholder values to be passed as input.",
+            default=[],
+            advanced=True,
+        )
+        limit_to_placeholder_values: bool = SchemaField(
+            description="Whether to limit the selection to placeholder values.",
+            default=False,
+            advanced=True,
+        )
+        advanced: bool = SchemaField(
+            description="Whether to show the input in the advanced section, if the field is not required.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the input should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
+
+    class Output(BlockSchema):
+        result: Any = SchemaField(description="The value passed as input.")
+
+    def __init__(self):
+        super().__init__(
+            id="c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
+            description="This block is used to provide input to the graph.",
+            input_schema=AgentInputBlock.Input,
+            output_schema=AgentInputBlock.Output,
+            test_input=[
+                {
+                    "value": "Hello, World!",
+                    "name": "input_1",
+                    "description": "This is a test input.",
+                    "placeholder_values": [],
+                    "limit_to_placeholder_values": False,
+                },
+                {
+                    "value": "Hello, World!",
+                    "name": "input_2",
+                    "description": "This is a test input.",
+                    "placeholder_values": ["Hello, World!"],
+                    "limit_to_placeholder_values": True,
+                },
+            ],
+            test_output=[
+                ("result", "Hello, World!"),
+                ("result", "Hello, World!"),
+            ],
+            categories={BlockCategory.INPUT, BlockCategory.BASIC},
+            block_type=BlockType.INPUT,
+            static_output=True,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        yield "result", input_data.value
+
+
+class AgentOutputBlock(Block):
+    """
+    Records the output of the graph for users to see.
+
+    Behavior:
+        If `format` is provided and the `value` is of a type that can be formatted,
+        the block attempts to format the recorded_value using the `format`.
+        If formatting fails or no `format` is provided, the raw `value` is output.
+    """
+
+    class Input(BlockSchema):
+        value: Any = SchemaField(
+            description="The value to be recorded as output.",
+            default=None,
+            advanced=False,
+        )
+        name: str = SchemaField(description="The name of the output.")
+        title: str | None = SchemaField(
+            description="The title of the output.",
+            default=None,
+            advanced=True,
+        )
+        description: str | None = SchemaField(
+            description="The description of the output.",
+            default=None,
+            advanced=True,
+        )
+        format: str = SchemaField(
+            description="The format string to be used to format the recorded_value. Use Jinja2 syntax.",
+            default="",
+            advanced=True,
+        )
+        advanced: bool = SchemaField(
+            description="Whether to treat the output as advanced.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the output should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
+
+    class Output(BlockSchema):
+        output: Any = SchemaField(description="The value recorded as output.")
+        name: Any = SchemaField(description="The name of the value recorded as output.")
+
+    def __init__(self):
+        super().__init__(
+            id="363ae599-353e-4804-937e-b2ee3cef3da4",
+            description="Stores the output of the graph for users to see.",
+            input_schema=AgentOutputBlock.Input,
+            output_schema=AgentOutputBlock.Output,
+            test_input=[
+                {
+                    "value": "Hello, World!",
+                    "name": "output_1",
+                    "description": "This is a test output.",
+                    "format": "{{ output_1 }}!!",
+                },
+                {
+                    "value": "42",
+                    "name": "output_2",
+                    "description": "This is another test output.",
+                    "format": "{{ output_2 }}",
+                },
+                {
+                    "value": MockObject(value="!!", key="key"),
+                    "name": "output_3",
+                    "description": "This is a test output with a mock object.",
+                    "format": "{{ output_3 }}",
+                },
+            ],
+            test_output=[
+                ("output", "Hello, World!!!"),
+                ("output", "42"),
+                ("output", MockObject(value="!!", key="key")),
+            ],
+            categories={BlockCategory.OUTPUT, BlockCategory.BASIC},
+            block_type=BlockType.OUTPUT,
+            static_output=True,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        """
+        Attempts to format the recorded_value using the fmt_string if provided.
+        If formatting fails or no fmt_string is given, returns the original recorded_value.
+        """
+        if input_data.format:
+            try:
+                yield "output", formatter.format_string(
+                    input_data.format, {input_data.name: input_data.value}
+                )
+            except Exception as e:
+                yield "output", f"Error: {e}, {input_data.value}"
+        else:
+            yield "output", input_data.value
+            yield "name", input_data.name
+
+
 class AddToDictionaryBlock(Block):
     class Input(BlockSchema):
         dictionary: dict[Any, Any] = SchemaField(

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

@@ -8,7 +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/exa/contents.py

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

autogpt_platform/backend/backend/blocks/github/_api.py

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

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

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

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

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

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

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

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,
@@ -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/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 Settings
 
 from ._auth import (
     GOOGLE_OAUTH_IS_CONFIGURED,
@@ -87,8 +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"
@@ -48,9 +43,8 @@ class SendWebRequestBlock(Block):
 
     class Output(BlockSchema):
         response: object = SchemaField(description="The response from the server")
-        client_error: object = SchemaField(description="Errors on 4xx status codes")
-        server_error: object = SchemaField(description="Errors on 5xx status codes")
-        error: str = SchemaField(description="Errors for all other exceptions")
+        client_error: object = SchemaField(description="The error on 4xx status codes")
+        server_error: object = SchemaField(description="The error on 5xx status codes")
 
     def __init__(self):
         super().__init__(
@@ -74,40 +68,20 @@ class SendWebRequestBlock(Block):
                     # we should send it as plain text instead
                     input_data.json_format = False
 
-        try:
-            response = requests.request(
-                input_data.method.value,
-                input_data.url,
-                headers=input_data.headers,
-                json=body if input_data.json_format else None,
-                data=body if not input_data.json_format else None,
-            )
-            result = response.json() if input_data.json_format else response.text
+        response = requests.request(
+            input_data.method.value,
+            input_data.url,
+            headers=input_data.headers,
+            json=body if input_data.json_format else None,
+            data=body if not input_data.json_format else None,
+        )
+        result = response.json() if input_data.json_format else response.text
+
+        if response.status_code // 100 == 2:
             yield "response", result
-
-        except HTTPError as e:
-            # Handle error responses
-            try:
-                result = e.response.json() if input_data.json_format else str(e)
-            except json.JSONDecodeError:
-                result = str(e)
-
-            if 400 <= e.response.status_code < 500:
-                yield "client_error", result
-            elif 500 <= e.response.status_code < 600:
-                yield "server_error", result
-            else:
-                error_msg = (
-                    "Unexpected status code "
-                    f"{e.response.status_code} '{e.response.reason}'"
-                )
-                logger.warning(error_msg)
-                yield "error", error_msg
-
-        except RequestException as e:
-            # Handle other request-related exceptions
-            yield "error", str(e)
-
-        except Exception as e:
-            # Catch any other unexpected exceptions
-            yield "error", str(e)
+        elif response.status_code // 100 == 4:
+            yield "client_error", result
+        elif response.status_code // 100 == 5:
+            yield "server_error", result
+        else:
+            raise ValueError(f"Unexpected status code: {response.status_code}")

autogpt_platform/backend/backend/blocks/ideogram.py

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

autogpt_platform/backend/backend/blocks/io.py

@@ -1,555 +0,0 @@
-from datetime import date, time
-from typing import Any, Optional
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
-from backend.data.model import SchemaField
-from backend.util.file import store_media_file
-from backend.util.mock import MockObject
-from backend.util.settings import Config
-from backend.util.text import TextFormatter
-from backend.util.type import LongTextType, MediaFileType, ShortTextType
-
-formatter = TextFormatter()
-config = Config()
-
-
-class AgentInputBlock(Block):
-    """
-    This block is used to provide input to the graph.
-
-    It takes in a value, name, description, default values list and bool to limit selection to default values.
-
-    It Outputs the value passed as input.
-    """
-
-    class Input(BlockSchema):
-        name: str = SchemaField(description="The name of the input.")
-        value: Any = SchemaField(
-            description="The value to be passed as input.",
-            default=None,
-        )
-        title: str | None = SchemaField(
-            description="The title of the input.", default=None, advanced=True
-        )
-        description: str | None = SchemaField(
-            description="The description of the input.",
-            default=None,
-            advanced=True,
-        )
-        placeholder_values: list = SchemaField(
-            description="The placeholder values to be passed as input.",
-            default=[],
-            advanced=True,
-            hidden=True,
-        )
-        advanced: bool = SchemaField(
-            description="Whether to show the input in the advanced section, if the field is not required.",
-            default=False,
-            advanced=True,
-        )
-        secret: bool = SchemaField(
-            description="Whether the input should be treated as a secret.",
-            default=False,
-            advanced=True,
-        )
-
-        def generate_schema(self):
-            schema = self.get_field_schema("value")
-            if possible_values := self.placeholder_values:
-                schema["enum"] = possible_values
-            return schema
-
-    class Output(BlockSchema):
-        result: Any = SchemaField(description="The value passed as input.")
-
-    def __init__(self, **kwargs):
-        super().__init__(
-            **{
-                "id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-                "description": "Base block for user inputs.",
-                "input_schema": AgentInputBlock.Input,
-                "output_schema": AgentInputBlock.Output,
-                "test_input": [
-                    {
-                        "value": "Hello, World!",
-                        "name": "input_1",
-                        "description": "Example test input.",
-                        "placeholder_values": [],
-                    },
-                    {
-                        "value": "Hello, World!",
-                        "name": "input_2",
-                        "description": "Example test input with placeholders.",
-                        "placeholder_values": ["Hello, World!"],
-                    },
-                ],
-                "test_output": [
-                    ("result", "Hello, World!"),
-                    ("result", "Hello, World!"),
-                ],
-                "categories": {BlockCategory.INPUT, BlockCategory.BASIC},
-                "block_type": BlockType.INPUT,
-                "static_output": True,
-                **kwargs,
-            }
-        )
-
-    def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
-        if input_data.value is not None:
-            yield "result", input_data.value
-
-
-class AgentOutputBlock(Block):
-    """
-    Records the output of the graph for users to see.
-
-    Behavior:
-        If `format` is provided and the `value` is of a type that can be formatted,
-        the block attempts to format the recorded_value using the `format`.
-        If formatting fails or no `format` is provided, the raw `value` is output.
-    """
-
-    class Input(BlockSchema):
-        value: Any = SchemaField(
-            description="The value to be recorded as output.",
-            default=None,
-            advanced=False,
-        )
-        name: str = SchemaField(description="The name of the output.")
-        title: str | None = SchemaField(
-            description="The title of the output.",
-            default=None,
-            advanced=True,
-        )
-        description: str | None = SchemaField(
-            description="The description of the output.",
-            default=None,
-            advanced=True,
-        )
-        format: str = SchemaField(
-            description="The format string to be used to format the recorded_value. Use Jinja2 syntax.",
-            default="",
-            advanced=True,
-        )
-        advanced: bool = SchemaField(
-            description="Whether to treat the output as advanced.",
-            default=False,
-            advanced=True,
-        )
-        secret: bool = SchemaField(
-            description="Whether the output should be treated as a secret.",
-            default=False,
-            advanced=True,
-        )
-
-        def generate_schema(self):
-            return self.get_field_schema("value")
-
-    class Output(BlockSchema):
-        output: Any = SchemaField(description="The value recorded as output.")
-        name: Any = SchemaField(description="The name of the value recorded as output.")
-
-    def __init__(self):
-        super().__init__(
-            id="363ae599-353e-4804-937e-b2ee3cef3da4",
-            description="Stores the output of the graph for users to see.",
-            input_schema=AgentOutputBlock.Input,
-            output_schema=AgentOutputBlock.Output,
-            test_input=[
-                {
-                    "value": "Hello, World!",
-                    "name": "output_1",
-                    "description": "This is a test output.",
-                    "format": "{{ output_1 }}!!",
-                },
-                {
-                    "value": "42",
-                    "name": "output_2",
-                    "description": "This is another test output.",
-                    "format": "{{ output_2 }}",
-                },
-                {
-                    "value": MockObject(value="!!", key="key"),
-                    "name": "output_3",
-                    "description": "This is a test output with a mock object.",
-                    "format": "{{ output_3 }}",
-                },
-            ],
-            test_output=[
-                ("output", "Hello, World!!!"),
-                ("output", "42"),
-                ("output", MockObject(value="!!", key="key")),
-            ],
-            categories={BlockCategory.OUTPUT, BlockCategory.BASIC},
-            block_type=BlockType.OUTPUT,
-            static_output=True,
-        )
-
-    def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
-        """
-        Attempts to format the recorded_value using the fmt_string if provided.
-        If formatting fails or no fmt_string is given, returns the original recorded_value.
-        """
-        if input_data.format:
-            try:
-                yield "output", formatter.format_string(
-                    input_data.format, {input_data.name: input_data.value}
-                )
-            except Exception as e:
-                yield "output", f"Error: {e}, {input_data.value}"
-        else:
-            yield "output", input_data.value
-            yield "name", input_data.name
-
-
-class AgentShortTextInputBlock(AgentInputBlock):
-    class Input(AgentInputBlock.Input):
-        value: Optional[ShortTextType] = SchemaField(
-            description="Short text input.",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: str = SchemaField(description="Short text result.")
-
-    def __init__(self):
-        super().__init__(
-            id="7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
-            description="Block for short text input (single-line).",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentShortTextInputBlock.Input,
-            output_schema=AgentShortTextInputBlock.Output,
-            test_input=[
-                {
-                    "value": "Hello",
-                    "name": "short_text_1",
-                    "description": "Short text example 1",
-                    "placeholder_values": [],
-                },
-                {
-                    "value": "Quick test",
-                    "name": "short_text_2",
-                    "description": "Short text example 2",
-                    "placeholder_values": ["Quick test", "Another option"],
-                },
-            ],
-            test_output=[
-                ("result", "Hello"),
-                ("result", "Quick test"),
-            ],
-        )
-
-
-class AgentLongTextInputBlock(AgentInputBlock):
-    class Input(AgentInputBlock.Input):
-        value: Optional[LongTextType] = SchemaField(
-            description="Long text input (potentially multi-line).",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: str = SchemaField(description="Long text result.")
-
-    def __init__(self):
-        super().__init__(
-            id="90a56ffb-7024-4b2b-ab50-e26c5e5ab8ba",
-            description="Block for long text input (multi-line).",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentLongTextInputBlock.Input,
-            output_schema=AgentLongTextInputBlock.Output,
-            test_input=[
-                {
-                    "value": "Lorem ipsum dolor sit amet...",
-                    "name": "long_text_1",
-                    "description": "Long text example 1",
-                    "placeholder_values": [],
-                },
-                {
-                    "value": "Another multiline text input.",
-                    "name": "long_text_2",
-                    "description": "Long text example 2",
-                    "placeholder_values": ["Another multiline text input."],
-                },
-            ],
-            test_output=[
-                ("result", "Lorem ipsum dolor sit amet..."),
-                ("result", "Another multiline text input."),
-            ],
-        )
-
-
-class AgentNumberInputBlock(AgentInputBlock):
-    class Input(AgentInputBlock.Input):
-        value: Optional[int] = SchemaField(
-            description="Number input.",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: int = SchemaField(description="Number result.")
-
-    def __init__(self):
-        super().__init__(
-            id="96dae2bb-97a2-41c2-bd2f-13a3b5a8ea98",
-            description="Block for number input.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentNumberInputBlock.Input,
-            output_schema=AgentNumberInputBlock.Output,
-            test_input=[
-                {
-                    "value": 42,
-                    "name": "number_input_1",
-                    "description": "Number example 1",
-                    "placeholder_values": [],
-                },
-                {
-                    "value": 314,
-                    "name": "number_input_2",
-                    "description": "Number example 2",
-                    "placeholder_values": [314, 2718],
-                },
-            ],
-            test_output=[
-                ("result", 42),
-                ("result", 314),
-            ],
-        )
-
-
-class AgentDateInputBlock(AgentInputBlock):
-    class Input(AgentInputBlock.Input):
-        value: Optional[date] = SchemaField(
-            description="Date input (YYYY-MM-DD).",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: date = SchemaField(description="Date result.")
-
-    def __init__(self):
-        super().__init__(
-            id="7e198b09-4994-47db-8b4d-952d98241817",
-            description="Block for date input.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentDateInputBlock.Input,
-            output_schema=AgentDateInputBlock.Output,
-            test_input=[
-                {
-                    # If your system can parse JSON date strings to date objects
-                    "value": str(date(2025, 3, 19)),
-                    "name": "date_input_1",
-                    "description": "Example date input 1",
-                },
-                {
-                    "value": str(date(2023, 12, 31)),
-                    "name": "date_input_2",
-                    "description": "Example date input 2",
-                },
-            ],
-            test_output=[
-                ("result", date(2025, 3, 19)),
-                ("result", date(2023, 12, 31)),
-            ],
-        )
-
-
-class AgentTimeInputBlock(AgentInputBlock):
-    class Input(AgentInputBlock.Input):
-        value: Optional[time] = SchemaField(
-            description="Time input (HH:MM:SS).",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: time = SchemaField(description="Time result.")
-
-    def __init__(self):
-        super().__init__(
-            id="2a1c757e-86cf-4c7e-aacf-060dc382e434",
-            description="Block for time input.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentTimeInputBlock.Input,
-            output_schema=AgentTimeInputBlock.Output,
-            test_input=[
-                {
-                    "value": str(time(9, 30, 0)),
-                    "name": "time_input_1",
-                    "description": "Time example 1",
-                },
-                {
-                    "value": str(time(23, 59, 59)),
-                    "name": "time_input_2",
-                    "description": "Time example 2",
-                },
-            ],
-            test_output=[
-                ("result", time(9, 30, 0)),
-                ("result", time(23, 59, 59)),
-            ],
-        )
-
-
-class AgentFileInputBlock(AgentInputBlock):
-    """
-    A simplified file-upload block. In real usage, you might have a custom
-    file type or handle binary data. Here, we'll store a string path as the example.
-    """
-
-    class Input(AgentInputBlock.Input):
-        value: Optional[MediaFileType] = SchemaField(
-            description="Path or reference to an uploaded file.",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: str = SchemaField(description="File reference/path result.")
-
-    def __init__(self):
-        super().__init__(
-            id="95ead23f-8283-4654-aef3-10c053b74a31",
-            description="Block for file upload input (string path for example).",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentFileInputBlock.Input,
-            output_schema=AgentFileInputBlock.Output,
-            test_input=[
-                {
-                    "value": "data:image/png;base64,MQ==",
-                    "name": "file_upload_1",
-                    "description": "Example file upload 1",
-                },
-            ],
-            test_output=[
-                ("result", str),
-            ],
-        )
-
-    def run(
-        self,
-        input_data: Input,
-        *,
-        graph_exec_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        if not input_data.value:
-            return
-
-        file_path = store_media_file(
-            graph_exec_id=graph_exec_id,
-            file=input_data.value,
-            return_content=False,
-        )
-        yield "result", file_path
-
-
-class AgentDropdownInputBlock(AgentInputBlock):
-    """
-    A specialized text input block that relies on placeholder_values to present a dropdown.
-    """
-
-    class Input(AgentInputBlock.Input):
-        value: Optional[str] = SchemaField(
-            description="Text selected from a dropdown.",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-        placeholder_values: list = SchemaField(
-            description="Possible values for the dropdown.",
-            default=[],
-            advanced=False,
-            title="Dropdown Options",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: str = SchemaField(description="Selected dropdown value.")
-
-    def __init__(self):
-        super().__init__(
-            id="655d6fdf-a334-421c-b733-520549c07cd1",
-            description="Block for dropdown text selection.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentDropdownInputBlock.Input,
-            output_schema=AgentDropdownInputBlock.Output,
-            test_input=[
-                {
-                    "value": "Option A",
-                    "name": "dropdown_1",
-                    "placeholder_values": ["Option A", "Option B", "Option C"],
-                    "description": "Dropdown example 1",
-                },
-                {
-                    "value": "Option C",
-                    "name": "dropdown_2",
-                    "placeholder_values": ["Option A", "Option B", "Option C"],
-                    "description": "Dropdown example 2",
-                },
-            ],
-            test_output=[
-                ("result", "Option A"),
-                ("result", "Option C"),
-            ],
-        )
-
-
-class AgentToggleInputBlock(AgentInputBlock):
-    class Input(AgentInputBlock.Input):
-        value: bool = SchemaField(
-            description="Boolean toggle input.",
-            default=False,
-            advanced=False,
-            title="Default Value",
-        )
-
-    class Output(AgentInputBlock.Output):
-        result: bool = SchemaField(description="Boolean toggle result.")
-
-    def __init__(self):
-        super().__init__(
-            id="cbf36ab5-df4a-43b6-8a7f-f7ed8652116e",
-            description="Block for boolean toggle input.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentToggleInputBlock.Input,
-            output_schema=AgentToggleInputBlock.Output,
-            test_input=[
-                {
-                    "value": True,
-                    "name": "toggle_1",
-                    "description": "Toggle example 1",
-                },
-                {
-                    "value": False,
-                    "name": "toggle_2",
-                    "description": "Toggle example 2",
-                },
-            ],
-            test_output=[
-                ("result", True),
-                ("result", False),
-            ],
-        )
-
-
-IO_BLOCK_IDs = [
-    AgentInputBlock().id,
-    AgentOutputBlock().id,
-    AgentShortTextInputBlock().id,
-    AgentLongTextInputBlock().id,
-    AgentNumberInputBlock().id,
-    AgentDateInputBlock().id,
-    AgentTimeInputBlock().id,
-    AgentFileInputBlock().id,
-    AgentDropdownInputBlock().id,
-    AgentToggleInputBlock().id,
-]

autogpt_platform/backend/backend/blocks/llm.py

@@ -4,11 +4,10 @@ from abc import ABC
 from enum import Enum, EnumMeta
 from json import JSONDecodeError
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Iterable, List, Literal, NamedTuple, Optional
+from typing import TYPE_CHECKING, Any, List, Literal, NamedTuple
 
-from pydantic import BaseModel, SecretStr
+from pydantic import SecretStr
 
-from backend.data.model import NodeExecutionStats
 from backend.integrations.providers import ProviderName
 
 if TYPE_CHECKING:
@@ -17,8 +16,6 @@ if TYPE_CHECKING:
 import anthropic
 import ollama
 import openai
-from anthropic._types import NotGiven
-from anthropic.types import ToolParam
 from groq import Groq
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
@@ -230,299 +227,15 @@ for model in LlmModel:
         raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
 
 
-class ToolCall(BaseModel):
-    name: str
-    arguments: str
+class MessageRole(str, Enum):
+    SYSTEM = "system"
+    USER = "user"
+    ASSISTANT = "assistant"
 
 
-class ToolContentBlock(BaseModel):
-    id: str
-    type: str
-    function: ToolCall
-
-
-class LLMResponse(BaseModel):
-    raw_response: Any
-    prompt: List[Any]
-    response: str
-    tool_calls: Optional[List[ToolContentBlock]] | None
-    prompt_tokens: int
-    completion_tokens: int
-
-
-def convert_openai_tool_fmt_to_anthropic(
-    openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | NotGiven:
-    """
-    Convert OpenAI tool format to Anthropic tool format.
-    """
-    if not openai_tools or len(openai_tools) == 0:
-        return anthropic.NOT_GIVEN
-
-    anthropic_tools = []
-    for tool in openai_tools:
-        if "function" in tool:
-            # Handle case where tool is already in OpenAI format with "type" and "function"
-            function_data = tool["function"]
-        else:
-            # Handle case where tool is just the function definition
-            function_data = tool
-
-        anthropic_tool: anthropic.types.ToolParam = {
-            "name": function_data["name"],
-            "description": function_data.get("description", ""),
-            "input_schema": {
-                "type": "object",
-                "properties": function_data.get("parameters", {}).get("properties", {}),
-                "required": function_data.get("parameters", {}).get("required", []),
-            },
-        }
-        anthropic_tools.append(anthropic_tool)
-
-    return anthropic_tools
-
-
-def llm_call(
-    credentials: APIKeyCredentials,
-    llm_model: LlmModel,
-    prompt: list[dict],
-    json_format: bool,
-    max_tokens: int | None,
-    tools: list[dict] | None = None,
-    ollama_host: str = "localhost:11434",
-) -> LLMResponse:
-    """
-    Make a call to a language model.
-
-    Args:
-        credentials: The API key credentials to use.
-        llm_model: The LLM model to use.
-        prompt: The prompt to send to the LLM.
-        json_format: Whether the response should be in JSON format.
-        max_tokens: The maximum number of tokens to generate in the chat completion.
-        tools: The tools to use in the chat completion.
-        ollama_host: The host for ollama to use.
-
-    Returns:
-        LLMResponse object containing:
-            - prompt: The prompt sent to the LLM.
-            - response: The text response from the LLM.
-            - tool_calls: Any tool calls the model made, if applicable.
-            - prompt_tokens: The number of tokens used in the prompt.
-            - completion_tokens: The number of tokens used in the completion.
-    """
-    provider = llm_model.metadata.provider
-    max_tokens = max_tokens or llm_model.max_output_tokens or 4096
-
-    if provider == "openai":
-        tools_param = tools if tools else openai.NOT_GIVEN
-        oai_client = openai.OpenAI(api_key=credentials.api_key.get_secret_value())
-        response_format = None
-
-        if llm_model in [LlmModel.O1_MINI, LlmModel.O1_PREVIEW]:
-            sys_messages = [p["content"] for p in prompt if p["role"] == "system"]
-            usr_messages = [p["content"] for p in prompt if p["role"] != "system"]
-            prompt = [
-                {"role": "user", "content": "\n".join(sys_messages)},
-                {"role": "user", "content": "\n".join(usr_messages)},
-            ]
-        elif json_format:
-            response_format = {"type": "json_object"}
-
-        response = oai_client.chat.completions.create(
-            model=llm_model.value,
-            messages=prompt,  # type: ignore
-            response_format=response_format,  # type: ignore
-            max_completion_tokens=max_tokens,
-            tools=tools_param,  # type: ignore
-        )
-
-        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,
-            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 == "anthropic":
-
-        an_tools = convert_openai_tool_fmt_to_anthropic(tools)
-
-        system_messages = [p["content"] for p in prompt if p["role"] == "system"]
-        sysprompt = " ".join(system_messages)
-
-        messages = []
-        last_role = None
-        for p in prompt:
-            if p["role"] in ["user", "assistant"]:
-                if (
-                    p["role"] == last_role
-                    and isinstance(messages[-1]["content"], str)
-                    and isinstance(p["content"], str)
-                ):
-                    # If the role is the same as the last one, combine the content
-                    messages[-1]["content"] += p["content"]
-                else:
-                    messages.append({"role": p["role"], "content": p["content"]})
-                    last_role = p["role"]
-
-        client = anthropic.Anthropic(api_key=credentials.api_key.get_secret_value())
-        try:
-            resp = client.messages.create(
-                model=llm_model.value,
-                system=sysprompt,
-                messages=messages,
-                max_tokens=max_tokens,
-                tools=an_tools,
-            )
-
-            if not resp.content:
-                raise ValueError("No content returned from Anthropic.")
-
-            tool_calls = None
-            for content_block in resp.content:
-                # Antropic is different to openai, need to iterate through
-                # the content blocks to find the tool calls
-                if content_block.type == "tool_use":
-                    if tool_calls is None:
-                        tool_calls = []
-                    tool_calls.append(
-                        ToolContentBlock(
-                            id=content_block.id,
-                            type=content_block.type,
-                            function=ToolCall(
-                                name=content_block.name,
-                                arguments=json.dumps(content_block.input),
-                            ),
-                        )
-                    )
-
-            if not tool_calls and resp.stop_reason == "tool_use":
-                logger.warning(
-                    "Tool use stop reason but no tool calls found in content. %s", resp
-                )
-
-            return LLMResponse(
-                raw_response=resp,
-                prompt=prompt,
-                response=(
-                    resp.content[0].name
-                    if isinstance(resp.content[0], anthropic.types.ToolUseBlock)
-                    else resp.content[0].text
-                ),
-                tool_calls=tool_calls,
-                prompt_tokens=resp.usage.input_tokens,
-                completion_tokens=resp.usage.output_tokens,
-            )
-        except anthropic.APIError as e:
-            error_message = f"Anthropic API error: {str(e)}"
-            logger.error(error_message)
-            raise ValueError(error_message)
-    elif provider == "groq":
-        if tools:
-            raise ValueError("Groq does not support tools.")
-
-        client = Groq(api_key=credentials.api_key.get_secret_value())
-        response_format = {"type": "json_object"} if json_format else None
-        response = client.chat.completions.create(
-            model=llm_model.value,
-            messages=prompt,  # type: ignore
-            response_format=response_format,  # type: ignore
-            max_tokens=max_tokens,
-        )
-        return LLMResponse(
-            raw_response=response.choices[0].message,
-            prompt=prompt,
-            response=response.choices[0].message.content or "",
-            tool_calls=None,
-            prompt_tokens=response.usage.prompt_tokens if response.usage else 0,
-            completion_tokens=response.usage.completion_tokens if response.usage else 0,
-        )
-    elif provider == "ollama":
-        if tools:
-            raise ValueError("Ollama does not support tools.")
-
-        client = ollama.Client(host=ollama_host)
-        sys_messages = [p["content"] for p in prompt if p["role"] == "system"]
-        usr_messages = [p["content"] for p in prompt if p["role"] != "system"]
-        response = client.generate(
-            model=llm_model.value,
-            prompt=f"{sys_messages}\n\n{usr_messages}",
-            stream=False,
-        )
-        return LLMResponse(
-            raw_response=response.get("response") or "",
-            prompt=prompt,
-            response=response.get("response") or "",
-            tool_calls=None,
-            prompt_tokens=response.get("prompt_eval_count") or 0,
-            completion_tokens=response.get("eval_count") or 0,
-        )
-    elif provider == "open_router":
-        tools_param = tools if tools else openai.NOT_GIVEN
-        client = openai.OpenAI(
-            base_url="https://openrouter.ai/api/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
-        )
-
-        # If there's no response, raise an error
-        if not response.choices:
-            if response:
-                raise ValueError(f"OpenRouter error: {response}")
-            else:
-                raise ValueError("No response from OpenRouter.")
-
-        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,
-            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,
-        )
-    else:
-        raise ValueError(f"Unsupported LLM provider: {provider}")
+class Message(BlockSchema):
+    role: MessageRole
+    content: str
 
 
 class AIBlockBase(Block, ABC):
@@ -547,7 +260,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=LlmModel.GPT4O,
+            default=LlmModel.GPT4_TURBO,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -557,7 +270,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             default="",
             description="The system prompt to provide additional context to the model.",
         )
-        conversation_history: list[dict] = SchemaField(
+        conversation_history: list[Message] = SchemaField(
             default=[],
             description="The conversation history to provide context for the prompt.",
         )
@@ -598,7 +311,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             input_schema=AIStructuredResponseGeneratorBlock.Input,
             output_schema=AIStructuredResponseGeneratorBlock.Output,
             test_input={
-                "model": LlmModel.GPT4O,
+                "model": LlmModel.GPT4_TURBO,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "expected_format": {
                     "key1": "value1",
@@ -612,21 +325,19 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 ("prompt", str),
             ],
             test_mock={
-                "llm_call": lambda *args, **kwargs: LLMResponse(
-                    raw_response="",
-                    prompt=[""],
-                    response=json.dumps(
+                "llm_call": lambda *args, **kwargs: (
+                    json.dumps(
                         {
                             "key1": "key1Value",
                             "key2": "key2Value",
                         }
                     ),
-                    tool_calls=None,
-                    prompt_tokens=0,
-                    completion_tokens=0,
+                    0,
+                    0,
                 )
             },
         )
+        self.prompt = ""
 
     def llm_call(
         self,
@@ -635,28 +346,160 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         prompt: list[dict],
         json_format: bool,
         max_tokens: int | None,
-        tools: list[dict] | None = None,
         ollama_host: str = "localhost:11434",
-    ) -> LLMResponse:
+    ) -> tuple[str, int, int]:
         """
-        Test mocks work only on class functions, this wraps the llm_call function
-        so that it can be mocked withing the block testing framework.
+        Args:
+            credentials: The API key credentials to use.
+            llm_model: The LLM model to use.
+            prompt: The prompt to send to the LLM.
+            json_format: Whether the response should be in JSON format.
+            max_tokens: The maximum number of tokens to generate in the chat completion.
+            ollama_host: The host for ollama to use
+
+        Returns:
+            The response from the LLM.
+            The number of tokens used in the prompt.
+            The number of tokens used in the completion.
         """
-        return llm_call(
-            credentials=credentials,
-            llm_model=llm_model,
-            prompt=prompt,
-            json_format=json_format,
-            max_tokens=max_tokens,
-            tools=tools,
-            ollama_host=ollama_host,
-        )
+        provider = llm_model.metadata.provider
+        max_tokens = max_tokens or llm_model.max_output_tokens or 4096
+
+        if provider == "openai":
+            oai_client = openai.OpenAI(api_key=credentials.api_key.get_secret_value())
+            response_format = None
+
+            if llm_model in [LlmModel.O1_MINI, LlmModel.O1_PREVIEW]:
+                sys_messages = [p["content"] for p in prompt if p["role"] == "system"]
+                usr_messages = [p["content"] for p in prompt if p["role"] != "system"]
+                prompt = [
+                    {"role": "user", "content": "\n".join(sys_messages)},
+                    {"role": "user", "content": "\n".join(usr_messages)},
+                ]
+            elif json_format:
+                response_format = {"type": "json_object"}
+
+            response = oai_client.chat.completions.create(
+                model=llm_model.value,
+                messages=prompt,  # type: ignore
+                response_format=response_format,  # type: ignore
+                max_completion_tokens=max_tokens,
+            )
+            self.prompt = json.dumps(prompt)
+
+            return (
+                response.choices[0].message.content or "",
+                response.usage.prompt_tokens if response.usage else 0,
+                response.usage.completion_tokens if response.usage else 0,
+            )
+        elif provider == "anthropic":
+            system_messages = [p["content"] for p in prompt if p["role"] == "system"]
+            sysprompt = " ".join(system_messages)
+
+            messages = []
+            last_role = None
+            for p in prompt:
+                if p["role"] in ["user", "assistant"]:
+                    if p["role"] != last_role:
+                        messages.append({"role": p["role"], "content": p["content"]})
+                        last_role = p["role"]
+                    else:
+                        # If the role is the same as the last one, combine the content
+                        messages[-1]["content"] += "\n" + p["content"]
+
+            client = anthropic.Anthropic(api_key=credentials.api_key.get_secret_value())
+            try:
+                resp = client.messages.create(
+                    model=llm_model.value,
+                    system=sysprompt,
+                    messages=messages,
+                    max_tokens=max_tokens,
+                )
+                self.prompt = json.dumps(prompt)
+
+                if not resp.content:
+                    raise ValueError("No content returned from Anthropic.")
+
+                return (
+                    (
+                        resp.content[0].name
+                        if isinstance(resp.content[0], anthropic.types.ToolUseBlock)
+                        else resp.content[0].text
+                    ),
+                    resp.usage.input_tokens,
+                    resp.usage.output_tokens,
+                )
+            except anthropic.APIError as e:
+                error_message = f"Anthropic API error: {str(e)}"
+                logger.error(error_message)
+                raise ValueError(error_message)
+        elif provider == "groq":
+            client = Groq(api_key=credentials.api_key.get_secret_value())
+            response_format = {"type": "json_object"} if json_format else None
+            response = client.chat.completions.create(
+                model=llm_model.value,
+                messages=prompt,  # type: ignore
+                response_format=response_format,  # type: ignore
+                max_tokens=max_tokens,
+            )
+            self.prompt = json.dumps(prompt)
+            return (
+                response.choices[0].message.content or "",
+                response.usage.prompt_tokens if response.usage else 0,
+                response.usage.completion_tokens if response.usage else 0,
+            )
+        elif provider == "ollama":
+            client = ollama.Client(host=ollama_host)
+            sys_messages = [p["content"] for p in prompt if p["role"] == "system"]
+            usr_messages = [p["content"] for p in prompt if p["role"] != "system"]
+            response = client.generate(
+                model=llm_model.value,
+                prompt=f"{sys_messages}\n\n{usr_messages}",
+                stream=False,
+            )
+            self.prompt = json.dumps(prompt)
+            return (
+                response.get("response") or "",
+                response.get("prompt_eval_count") or 0,
+                response.get("eval_count") or 0,
+            )
+        elif provider == "open_router":
+            client = openai.OpenAI(
+                base_url="https://openrouter.ai/api/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,
+            )
+            self.prompt = json.dumps(prompt)
+
+            # If there's no response, raise an error
+            if not response.choices:
+                if response:
+                    raise ValueError(f"OpenRouter error: {response}")
+                else:
+                    raise ValueError("No response from OpenRouter.")
+
+            return (
+                response.choices[0].message.content or "",
+                response.usage.prompt_tokens if response.usage else 0,
+                response.usage.completion_tokens if response.usage else 0,
+            )
+        else:
+            raise ValueError(f"Unsupported LLM provider: {provider}")
 
     def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         logger.debug(f"Calling LLM with input data: {input_data}")
-        prompt = [json.to_dict(p) for p in input_data.conversation_history]
+        prompt = [p.model_dump() for p in input_data.conversation_history]
 
         def trim_prompt(s: str) -> str:
             lines = s.strip().split("\n")
@@ -706,7 +549,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
 
         for retry_count in range(input_data.retry):
             try:
-                llm_response = self.llm_call(
+                response_text, input_token, output_token = self.llm_call(
                     credentials=credentials,
                     llm_model=llm_model,
                     prompt=prompt,
@@ -714,12 +557,11 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                     ollama_host=input_data.ollama_host,
                     max_tokens=input_data.max_tokens,
                 )
-                response_text = llm_response.response
                 self.merge_stats(
-                    NodeExecutionStats(
-                        input_token_count=llm_response.prompt_tokens,
-                        output_token_count=llm_response.completion_tokens,
-                    )
+                    {
+                        "input_token_count": input_token,
+                        "output_token_count": output_token,
+                    }
                 )
                 logger.info(f"LLM attempt-{retry_count} response: {response_text}")
 
@@ -762,10 +604,10 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 retry_prompt = f"Error calling LLM: {e}"
             finally:
                 self.merge_stats(
-                    NodeExecutionStats(
-                        llm_call_count=retry_count + 1,
-                        llm_retry_count=retry_count,
-                    )
+                    {
+                        "llm_call_count": retry_count + 1,
+                        "llm_retry_count": retry_count,
+                    }
                 )
 
         raise RuntimeError(retry_prompt)
@@ -779,7 +621,7 @@ class AITextGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=LlmModel.GPT4O,
+            default=LlmModel.GPT4_TURBO,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -872,7 +714,7 @@ class AITextSummarizerBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=LlmModel.GPT4O,
+            default=LlmModel.GPT4_TURBO,
             description="The language model to use for summarizing the text.",
         )
         focus: str = SchemaField(
@@ -1033,12 +875,12 @@ class AITextSummarizerBlock(AIBlockBase):
 
 class AIConversationBlock(AIBlockBase):
     class Input(BlockSchema):
-        messages: List[Any] = SchemaField(
+        messages: List[Message] = SchemaField(
             description="List of messages in the conversation.", min_length=1
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=LlmModel.GPT4O,
+            default=LlmModel.GPT4_TURBO,
             description="The language model to use for the conversation.",
         )
         credentials: AICredentials = AICredentialsField()
@@ -1077,7 +919,7 @@ class AIConversationBlock(AIBlockBase):
                     },
                     {"role": "user", "content": "Where was it played?"},
                 ],
-                "model": LlmModel.GPT4O,
+                "model": LlmModel.GPT4_TURBO,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
@@ -1139,7 +981,7 @@ class AIListGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=LlmModel.GPT4O,
+            default=LlmModel.GPT4_TURBO,
             description="The language model to use for generating the list.",
             advanced=True,
         )
@@ -1188,7 +1030,7 @@ class AIListGeneratorBlock(AIBlockBase):
                     "drawing explorers to uncover its mysteries. Each planet showcases the limitless possibilities of "
                     "fictional worlds."
                 ),
-                "model": LlmModel.GPT4O,
+                "model": LlmModel.GPT4_TURBO,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "max_retries": 3,
             },

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/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/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
 
@@ -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

@@ -1,511 +0,0 @@
-import logging
-import re
-from collections import Counter
-from typing import TYPE_CHECKING, Any
-
-from autogpt_libs.utils.cache import thread_cached
-
-import backend.blocks.llm as llm
-from backend.blocks.agent import AgentExecutorBlock
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockInput,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    get_block,
-)
-from backend.data.model import SchemaField
-from backend.util import json
-
-if TYPE_CHECKING:
-    from backend.data.graph import Link, Node
-
-logger = logging.getLogger(__name__)
-
-
-@thread_cached
-def get_database_manager_client():
-    from backend.executor import DatabaseManager
-    from backend.util.service import get_service_client
-
-    return get_service_client(DatabaseManager)
-
-
-def _get_tool_requests(entry: dict[str, Any]) -> list[str]:
-    """
-    Return a list of tool_call_ids if the entry is a tool request.
-    Supports both OpenAI and Anthropics formats.
-    """
-    tool_call_ids = []
-    if entry.get("role") != "assistant":
-        return tool_call_ids
-
-    # OpenAI: check for tool_calls in the entry.
-    calls = entry.get("tool_calls")
-    if isinstance(calls, list):
-        for call in calls:
-            if tool_id := call.get("id"):
-                tool_call_ids.append(tool_id)
-
-    # Anthropics: check content items for tool_use type.
-    content = entry.get("content")
-    if isinstance(content, list):
-        for item in content:
-            if item.get("type") != "tool_use":
-                continue
-            if tool_id := item.get("id"):
-                tool_call_ids.append(tool_id)
-
-    return tool_call_ids
-
-
-def _get_tool_responses(entry: dict[str, Any]) -> list[str]:
-    """
-    Return a list of tool_call_ids if the entry is a tool response.
-    Supports both OpenAI and Anthropics formats.
-    """
-    tool_call_ids: list[str] = []
-
-    # OpenAI: a tool response message with role "tool" and key "tool_call_id".
-    if entry.get("role") == "tool":
-        if tool_call_id := entry.get("tool_call_id"):
-            tool_call_ids.append(str(tool_call_id))
-
-    # Anthropics: check content items for tool_result type.
-    if entry.get("role") == "user":
-        content = entry.get("content")
-        if isinstance(content, list):
-            for item in content:
-                if item.get("type") != "tool_result":
-                    continue
-                if tool_call_id := item.get("tool_use_id"):
-                    tool_call_ids.append(tool_call_id)
-
-    return tool_call_ids
-
-
-def _create_tool_response(call_id: str, output: dict[str, Any]) -> dict[str, Any]:
-    """
-    Create a tool response message for either OpenAI or Anthropics,
-    based on the tool_id format.
-    """
-    content = output if isinstance(output, str) else json.dumps(output)
-
-    # Anthropics format: tool IDs typically start with "toolu_"
-    if call_id.startswith("toolu_"):
-        return {
-            "role": "user",
-            "type": "message",
-            "content": [
-                {"tool_use_id": call_id, "type": "tool_result", "content": content}
-            ],
-        }
-
-    # OpenAI format: tool IDs typically start with "call_".
-    # Or default fallback (if the tool_id doesn't match any known prefix)
-    return {"role": "tool", "tool_call_id": call_id, "content": content}
-
-
-def get_pending_tool_calls(conversation_history: list[Any]) -> dict[str, int]:
-    """
-    All the tool calls entry in the conversation history requires a response.
-    This function returns the pending tool calls that has not generated an output yet.
-
-    Return: dict[str, int] - A dictionary of pending tool call IDs with their count.
-    """
-    pending_calls = Counter()
-    for history in conversation_history:
-        for call_id in _get_tool_requests(history):
-            pending_calls[call_id] += 1
-
-        for call_id in _get_tool_responses(history):
-            pending_calls[call_id] -= 1
-
-    return {call_id: count for call_id, count in pending_calls.items() if count > 0}
-
-
-class SmartDecisionMakerBlock(Block):
-    """
-    A block that uses a language model to make smart decisions based on a given prompt.
-    """
-
-    class Input(BlockSchema):
-        prompt: str = SchemaField(
-            description="The prompt to send to the language model.",
-            placeholder="Enter your prompt here...",
-        )
-        model: llm.LlmModel = SchemaField(
-            title="LLM Model",
-            default=llm.LlmModel.GPT4O,
-            description="The language model to use for answering the prompt.",
-            advanced=False,
-        )
-        credentials: llm.AICredentials = llm.AICredentialsField()
-        sys_prompt: str = SchemaField(
-            title="System Prompt",
-            default="Thinking carefully step by step decide which function to call. "
-            "Always choose a function call from the list of function signatures, "
-            "and always provide the complete argument provided with the type "
-            "matching the required jsonschema signature, no missing argument is allowed. "
-            "If you have already completed the task objective, you can end the task "
-            "by providing the end result of your work as a finish message. "
-            "Only provide EXACTLY one function call, multiple tool calls is strictly prohibited.",
-            description="The system prompt to provide additional context to the model.",
-        )
-        conversation_history: list[dict] = SchemaField(
-            default=[],
-            description="The conversation history to provide context for the prompt.",
-        )
-        last_tool_output: Any = SchemaField(
-            default=None,
-            description="The output of the last tool that was called.",
-        )
-        retry: int = SchemaField(
-            title="Retry Count",
-            default=3,
-            description="Number of times to retry the LLM call if the response does not match the expected format.",
-        )
-        prompt_values: dict[str, str] = SchemaField(
-            advanced=False,
-            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(
-            advanced=True,
-            default=None,
-            description="The maximum number of tokens to generate in the chat completion.",
-        )
-        ollama_host: str = SchemaField(
-            advanced=True,
-            default="localhost:11434",
-            description="Ollama host for local  models",
-        )
-
-        @classmethod
-        def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
-            # conversation_history & last_tool_output validation is handled differently
-            missing_links = super().get_missing_links(
-                data,
-                [
-                    link
-                    for link in links
-                    if link.sink_name
-                    not in ["conversation_history", "last_tool_output"]
-                ],
-            )
-
-            # Avoid executing the block if the last_tool_output is connected to a static
-            # link, like StoreValueBlock or AgentInputBlock.
-            if any(link.sink_name == "conversation_history" for link in links) and any(
-                link.sink_name == "last_tool_output" and link.is_static
-                for link in links
-            ):
-                raise ValueError(
-                    "Last Tool Output can't be connected to a static (dashed line) "
-                    "link like the output of `StoreValue` or `AgentInput` block"
-                )
-
-            return missing_links
-
-        @classmethod
-        def get_missing_input(cls, data: BlockInput) -> set[str]:
-            if missing_input := super().get_missing_input(data):
-                return missing_input
-
-            conversation_history = data.get("conversation_history", [])
-            pending_tool_calls = get_pending_tool_calls(conversation_history)
-            last_tool_output = data.get("last_tool_output")
-            if not last_tool_output and pending_tool_calls:
-                return {"last_tool_output"}
-            return set()
-
-    class Output(BlockSchema):
-        error: str = SchemaField(description="Error message if the API call failed.")
-        tools: Any = SchemaField(description="The tools that are available to use.")
-        finished: str = SchemaField(
-            description="The finished message to display to the user."
-        )
-        conversations: list[Any] = SchemaField(
-            description="The conversation history to provide context for the prompt."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3b191d9f-356f-482d-8238-ba04b6d18381",
-            description="Uses AI to intelligently decide what tool to use.",
-            categories={BlockCategory.AI},
-            block_type=BlockType.AI,
-            input_schema=SmartDecisionMakerBlock.Input,
-            output_schema=SmartDecisionMakerBlock.Output,
-            test_input={
-                "prompt": "Hello, World!",
-                "credentials": llm.TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[],
-            test_credentials=llm.TEST_CREDENTIALS,
-        )
-
-    @staticmethod
-    def _create_block_function_signature(
-        sink_node: "Node", links: list["Link"]
-    ) -> dict[str, Any]:
-        """
-        Creates a function signature for a block node.
-
-        Args:
-            sink_node: The node for which to create a function signature.
-            links: The list of links connected to the sink node.
-
-        Returns:
-            A dictionary representing the function signature in the format expected by LLM tools.
-
-        Raises:
-            ValueError: If the block specified by sink_node.block_id is not found.
-        """
-        block = get_block(sink_node.block_id)
-        if not block:
-            raise ValueError(f"Block not found: {sink_node.block_id}")
-
-        tool_function: dict[str, Any] = {
-            "name": re.sub(r"[^a-zA-Z0-9_-]", "_", block.name).lower(),
-            "description": block.description,
-        }
-
-        properties = {}
-        required = []
-
-        for link in links:
-            sink_block_input_schema = block.input_schema
-            description = (
-                sink_block_input_schema.model_fields[link.sink_name].description
-                if link.sink_name in sink_block_input_schema.model_fields
-                and sink_block_input_schema.model_fields[link.sink_name].description
-                else f"The {link.sink_name} of the tool"
-            )
-            properties[link.sink_name.lower()] = {
-                "type": "string",
-                "description": description,
-            }
-
-        tool_function["parameters"] = {
-            "type": "object",
-            "properties": properties,
-            "required": required,
-            "additionalProperties": False,
-            "strict": True,
-        }
-
-        return {"type": "function", "function": tool_function}
-
-    @staticmethod
-    def _create_agent_function_signature(
-        sink_node: "Node", links: list["Link"]
-    ) -> dict[str, Any]:
-        """
-        Creates a function signature for an agent node.
-
-        Args:
-            sink_node: The agent node for which to create a function signature.
-            links: The list of links connected to the sink node.
-
-        Returns:
-            A dictionary representing the function signature in the format expected by LLM tools.
-
-        Raises:
-            ValueError: If the graph metadata for the specified graph_id and graph_version is not found.
-        """
-        graph_id = sink_node.input_default.get("graph_id")
-        graph_version = sink_node.input_default.get("graph_version")
-        if not graph_id or not graph_version:
-            raise ValueError("Graph ID or Graph Version not found in sink node.")
-
-        db_client = get_database_manager_client()
-        sink_graph_meta = db_client.get_graph_metadata(graph_id, graph_version)
-        if not sink_graph_meta:
-            raise ValueError(
-                f"Sink graph metadata not found: {graph_id} {graph_version}"
-            )
-
-        tool_function: dict[str, Any] = {
-            "name": re.sub(r"[^a-zA-Z0-9_-]", "_", sink_graph_meta.name).lower(),
-            "description": sink_graph_meta.description,
-        }
-
-        properties = {}
-        required = []
-
-        for link in links:
-            sink_block_input_schema = sink_node.input_default["input_schema"]
-            description = (
-                sink_block_input_schema["properties"][link.sink_name]["description"]
-                if "description"
-                in sink_block_input_schema["properties"][link.sink_name]
-                else f"The {link.sink_name} of the tool"
-            )
-            properties[link.sink_name.lower()] = {
-                "type": "string",
-                "description": description,
-            }
-
-        tool_function["parameters"] = {
-            "type": "object",
-            "properties": properties,
-            "required": required,
-            "additionalProperties": False,
-            "strict": True,
-        }
-
-        return {"type": "function", "function": tool_function}
-
-    @staticmethod
-    def _create_function_signature(node_id: str) -> list[dict[str, Any]]:
-        """
-        Creates function signatures for tools linked to a specified node within a graph.
-
-        This method filters the graph links to identify those that are tools and are
-        connected to the given node_id. It then constructs function signatures for each
-        tool based on the metadata and input schema of the linked nodes.
-
-        Args:
-            node_id: The node_id for which to create function signatures.
-
-        Returns:
-            list[dict[str, Any]]: A list of dictionaries, each representing a function signature
-                                  for a tool, including its name, description, and parameters.
-
-        Raises:
-            ValueError: If no tool links are found for the specified node_id, or if a sink node
-                        or its metadata cannot be found.
-        """
-        db_client = get_database_manager_client()
-        tools = [
-            (link, node)
-            for link, node in db_client.get_connected_output_nodes(node_id)
-            if link.source_name.startswith("tools_^_") and link.source_id == node_id
-        ]
-        if not tools:
-            raise ValueError("There is no next node to execute.")
-
-        return_tool_functions = []
-
-        grouped_tool_links: dict[str, tuple["Node", list["Link"]]] = {}
-        for link, node in tools:
-            if link.sink_id not in grouped_tool_links:
-                grouped_tool_links[link.sink_id] = (node, [link])
-            else:
-                grouped_tool_links[link.sink_id][1].append(link)
-
-        for sink_node, links in grouped_tool_links.values():
-            if not sink_node:
-                raise ValueError(f"Sink node not found: {links[0].sink_id}")
-
-            if sink_node.block_id == AgentExecutorBlock().id:
-                return_tool_functions.append(
-                    SmartDecisionMakerBlock._create_agent_function_signature(
-                        sink_node, links
-                    )
-                )
-            else:
-                return_tool_functions.append(
-                    SmartDecisionMakerBlock._create_block_function_signature(
-                        sink_node, links
-                    )
-                )
-
-        return return_tool_functions
-
-    def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: llm.APIKeyCredentials,
-        graph_id: str,
-        node_id: str,
-        graph_exec_id: str,
-        node_exec_id: str,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        tool_functions = self._create_function_signature(node_id)
-
-        input_data.conversation_history = input_data.conversation_history or []
-        prompt = [json.to_dict(p) for p in input_data.conversation_history if p]
-
-        pending_tool_calls = get_pending_tool_calls(input_data.conversation_history)
-        if pending_tool_calls and not input_data.last_tool_output:
-            raise ValueError(f"Tool call requires an output for {pending_tool_calls}")
-
-        # Prefill all missing tool calls with the last tool output/
-        # TODO: we need a better way to handle this.
-        tool_output = [
-            _create_tool_response(pending_call_id, input_data.last_tool_output)
-            for pending_call_id, count in pending_tool_calls.items()
-            for _ in range(count)
-        ]
-
-        # If the SDM block only calls 1 tool at a time, this should not happen.
-        if len(tool_output) > 1:
-            logger.warning(
-                f"[SmartDecisionMakerBlock-node_exec_id={node_exec_id}] "
-                f"Multiple pending tool calls are prefilled using a single output. "
-                f"Execution may not be accurate."
-            )
-
-        # Fallback on adding tool output in the conversation history as user prompt.
-        if len(tool_output) == 0 and input_data.last_tool_output:
-            logger.warning(
-                f"[SmartDecisionMakerBlock-node_exec_id={node_exec_id}] "
-                f"No pending tool calls found. This may indicate an issue with the "
-                f"conversation history, or an LLM calling two tools at the same time."
-            )
-            tool_output.append(
-                {
-                    "role": "user",
-                    "content": f"Last tool output: {json.dumps(input_data.last_tool_output)}",
-                }
-            )
-
-        prompt.extend(tool_output)
-
-        values = input_data.prompt_values
-        if values:
-            input_data.prompt = llm.fmt.format_string(input_data.prompt, values)
-            input_data.sys_prompt = llm.fmt.format_string(input_data.sys_prompt, values)
-
-        prefix = "[Main Objective Prompt]: "
-
-        if input_data.sys_prompt and not any(
-            p["role"] == "system" and p["content"].startswith(prefix) for p in prompt
-        ):
-            prompt.append({"role": "system", "content": prefix + input_data.sys_prompt})
-
-        if input_data.prompt and not any(
-            p["role"] == "user" and p["content"].startswith(prefix) for p in prompt
-        ):
-            prompt.append({"role": "user", "content": prefix + input_data.prompt})
-
-        response = llm.llm_call(
-            credentials=credentials,
-            llm_model=input_data.model,
-            prompt=prompt,
-            json_format=False,
-            max_tokens=input_data.max_tokens,
-            tools=tool_functions,
-            ollama_host=input_data.ollama_host,
-        )
-
-        if not response.tool_calls:
-            yield "finished", response.response
-            return
-
-        for tool_call in response.tool_calls:
-            tool_name = tool_call.function.name
-            tool_args = json.loads(tool_call.function.arguments)
-
-            for arg_name, arg_value in tool_args.items():
-                yield f"tools_^_{tool_name}_{arg_name}".lower(), arg_value
-
-        response.prompt.append(response.raw_response)
-        yield "conversations", response.prompt

autogpt_platform/backend/backend/blocks/smartlead/_api.py

@@ -1,97 +0,0 @@
-from backend.blocks.smartlead.models import (
-    AddLeadsRequest,
-    AddLeadsToCampaignResponse,
-    CreateCampaignRequest,
-    CreateCampaignResponse,
-    SaveSequencesRequest,
-    SaveSequencesResponse,
-)
-from backend.util.request import Requests
-
-
-class SmartLeadClient:
-    """Client for the SmartLead API"""
-
-    # This api is stupid and requires your api key in the url. DO NOT RAISE ERRORS FOR BAD REQUESTS.
-    # FILTER OUT THE API KEY FROM THE ERROR MESSAGE.
-
-    API_URL = "https://server.smartlead.ai/api/v1"
-
-    def __init__(self, api_key: str):
-        self.api_key = api_key
-        self.requests = Requests()
-
-    def _add_auth_to_url(self, url: str) -> str:
-        return f"{url}?api_key={self.api_key}"
-
-    def _handle_error(self, e: Exception) -> str:
-        return e.__str__().replace(self.api_key, "API KEY")
-
-    def create_campaign(self, request: CreateCampaignRequest) -> CreateCampaignResponse:
-        try:
-            response = self.requests.post(
-                self._add_auth_to_url(f"{self.API_URL}/campaigns/create"),
-                json=request.model_dump(),
-            )
-            response_data = response.json()
-            return CreateCampaignResponse(**response_data)
-        except ValueError as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        except Exception as e:
-            raise ValueError(f"Failed to create campaign: {self._handle_error(e)}")
-
-    def add_leads_to_campaign(
-        self, request: AddLeadsRequest
-    ) -> AddLeadsToCampaignResponse:
-        try:
-            response = self.requests.post(
-                self._add_auth_to_url(
-                    f"{self.API_URL}/campaigns/{request.campaign_id}/leads"
-                ),
-                json=request.model_dump(exclude={"campaign_id"}),
-            )
-            response_data = response.json()
-            response_parsed = AddLeadsToCampaignResponse(**response_data)
-            if not response_parsed.ok:
-                raise ValueError(
-                    f"Failed to add leads to campaign: {response_parsed.error}"
-                )
-            return response_parsed
-        except ValueError as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        except Exception as e:
-            raise ValueError(
-                f"Failed to add leads to campaign: {self._handle_error(e)}"
-            )
-
-    def save_campaign_sequences(
-        self, campaign_id: int, request: SaveSequencesRequest
-    ) -> SaveSequencesResponse:
-        """
-        Save sequences within a campaign.
-
-        Args:
-            campaign_id: ID of the campaign to save sequences for
-            request: SaveSequencesRequest containing the sequences configuration
-
-        Returns:
-            SaveSequencesResponse with the result of the operation
-
-        Note:
-            For variant_distribution_type:
-            - MANUAL_EQUAL: Equally distributes variants across leads
-            - AI_EQUAL: Requires winning_metric_property and lead_distribution_percentage
-            - MANUAL_PERCENTAGE: Requires variant_distribution_percentage in seq_variants
-        """
-        try:
-            response = self.requests.post(
-                self._add_auth_to_url(
-                    f"{self.API_URL}/campaigns/{campaign_id}/sequences"
-                ),
-                json=request.model_dump(exclude_none=True),
-            )
-            return SaveSequencesResponse(**response.json())
-        except Exception as e:
-            raise ValueError(
-                f"Failed to save campaign sequences: {e.__str__().replace(self.api_key, 'API KEY')}"
-            )

autogpt_platform/backend/backend/blocks/smartlead/_auth.py

@@ -1,35 +0,0 @@
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
-from backend.integrations.providers import ProviderName
-
-SmartLeadCredentials = APIKeyCredentials
-SmartLeadCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.SMARTLEAD],
-    Literal["api_key"],
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="smartlead",
-    api_key=SecretStr("mock-smartlead-api-key"),
-    title="Mock SmartLead API key",
-    expires_at=None,
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-def SmartLeadCredentialsField() -> SmartLeadCredentialsInput:
-    """
-    Creates a SmartLead credentials input on a block.
-    """
-    return CredentialsField(
-        description="The SmartLead integration can be used with an API Key.",
-    )

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

@@ -1,326 +0,0 @@
-from backend.blocks.smartlead._api import SmartLeadClient
-from backend.blocks.smartlead._auth import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    SmartLeadCredentials,
-    SmartLeadCredentialsInput,
-)
-from backend.blocks.smartlead.models import (
-    AddLeadsRequest,
-    AddLeadsToCampaignResponse,
-    CreateCampaignRequest,
-    CreateCampaignResponse,
-    LeadInput,
-    LeadUploadSettings,
-    SaveSequencesRequest,
-    SaveSequencesResponse,
-    Sequence,
-)
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-
-
-class CreateCampaignBlock(Block):
-    """Create a campaign in SmartLead"""
-
-    class Input(BlockSchema):
-        name: str = SchemaField(
-            description="The name of the campaign",
-        )
-        credentials: SmartLeadCredentialsInput = SchemaField(
-            description="SmartLead credentials",
-        )
-
-    class Output(BlockSchema):
-        id: int = SchemaField(
-            description="The ID of the created campaign",
-        )
-        name: str = SchemaField(
-            description="The name of the created campaign",
-        )
-        created_at: str = SchemaField(
-            description="The date and time the campaign was created",
-        )
-        error: str = SchemaField(
-            description="Error message if the search failed",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8865699f-9188-43c4-89b0-79c84cfaa03e",
-            description="Create a campaign in SmartLead",
-            categories={BlockCategory.CRM},
-            input_schema=CreateCampaignBlock.Input,
-            output_schema=CreateCampaignBlock.Output,
-            test_credentials=TEST_CREDENTIALS,
-            test_input={"name": "Test Campaign", "credentials": TEST_CREDENTIALS_INPUT},
-            test_output=[
-                (
-                    "id",
-                    1,
-                ),
-                (
-                    "name",
-                    "Test Campaign",
-                ),
-                (
-                    "created_at",
-                    "2024-01-01T00:00:00Z",
-                ),
-            ],
-            test_mock={
-                "create_campaign": lambda name, credentials: CreateCampaignResponse(
-                    ok=True,
-                    id=1,
-                    name=name,
-                    created_at="2024-01-01T00:00:00Z",
-                )
-            },
-        )
-
-    @staticmethod
-    def create_campaign(
-        name: str, credentials: SmartLeadCredentials
-    ) -> CreateCampaignResponse:
-        client = SmartLeadClient(credentials.api_key.get_secret_value())
-        return client.create_campaign(CreateCampaignRequest(name=name))
-
-    def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: SmartLeadCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        response = self.create_campaign(input_data.name, credentials)
-
-        yield "id", response.id
-        yield "name", response.name
-        yield "created_at", response.created_at
-        if not response.ok:
-            yield "error", "Failed to create campaign"
-
-
-class AddLeadToCampaignBlock(Block):
-    """Add a lead to a campaign in SmartLead"""
-
-    class Input(BlockSchema):
-        campaign_id: int = SchemaField(
-            description="The ID of the campaign to add the lead to",
-        )
-        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=[],
-            advanced=False,
-        )
-        settings: LeadUploadSettings = SchemaField(
-            description="Settings for lead upload",
-            default=LeadUploadSettings(),
-        )
-        credentials: SmartLeadCredentialsInput = SchemaField(
-            description="SmartLead credentials",
-        )
-
-    class Output(BlockSchema):
-        campaign_id: int = SchemaField(
-            description="The ID of the campaign the lead was added to (passed through)",
-        )
-        upload_count: int = SchemaField(
-            description="The number of leads added to the campaign",
-        )
-        already_added_to_campaign: int = SchemaField(
-            description="The number of leads that were already added to the campaign",
-        )
-        duplicate_count: int = SchemaField(
-            description="The number of emails that were duplicates",
-        )
-        invalid_email_count: int = SchemaField(
-            description="The number of emails that were invalidly formatted",
-        )
-        is_lead_limit_exhausted: bool = SchemaField(
-            description="Whether the lead limit was exhausted",
-        )
-        lead_import_stopped_count: int = SchemaField(
-            description="The number of leads that were not added to the campaign because the lead import was stopped",
-        )
-        error: str = SchemaField(
-            description="Error message if the lead was not added to the campaign",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="fb8106a4-1a8f-42f9-a502-f6d07e6fe0ec",
-            description="Add a lead to a campaign in SmartLead",
-            categories={BlockCategory.CRM},
-            input_schema=AddLeadToCampaignBlock.Input,
-            output_schema=AddLeadToCampaignBlock.Output,
-            test_credentials=TEST_CREDENTIALS,
-            test_input={
-                "campaign_id": 1,
-                "lead_list": [],
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "campaign_id",
-                    1,
-                ),
-                (
-                    "upload_count",
-                    1,
-                ),
-            ],
-            test_mock={
-                "add_leads_to_campaign": lambda campaign_id, lead_list, credentials: AddLeadsToCampaignResponse(
-                    ok=True,
-                    upload_count=1,
-                    already_added_to_campaign=0,
-                    duplicate_count=0,
-                    invalid_email_count=0,
-                    is_lead_limit_exhausted=False,
-                    lead_import_stopped_count=0,
-                    error="",
-                    total_leads=1,
-                    block_count=0,
-                    invalid_emails=[],
-                    unsubscribed_leads=[],
-                    bounce_count=0,
-                )
-            },
-        )
-
-    @staticmethod
-    def add_leads_to_campaign(
-        campaign_id: int, lead_list: list[LeadInput], credentials: SmartLeadCredentials
-    ) -> AddLeadsToCampaignResponse:
-        client = SmartLeadClient(credentials.api_key.get_secret_value())
-        return client.add_leads_to_campaign(
-            AddLeadsRequest(
-                campaign_id=campaign_id,
-                lead_list=lead_list,
-                settings=LeadUploadSettings(
-                    ignore_global_block_list=False,
-                    ignore_unsubscribe_list=False,
-                    ignore_community_bounce_list=False,
-                    ignore_duplicate_leads_in_other_campaign=False,
-                ),
-            ),
-        )
-
-    def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: SmartLeadCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        response = self.add_leads_to_campaign(
-            input_data.campaign_id, input_data.lead_list, credentials
-        )
-
-        yield "campaign_id", input_data.campaign_id
-        yield "upload_count", response.upload_count
-        if response.already_added_to_campaign:
-            yield "already_added_to_campaign", response.already_added_to_campaign
-        if response.duplicate_count:
-            yield "duplicate_count", response.duplicate_count
-        if response.invalid_email_count:
-            yield "invalid_email_count", response.invalid_email_count
-        if response.is_lead_limit_exhausted:
-            yield "is_lead_limit_exhausted", response.is_lead_limit_exhausted
-        if response.lead_import_stopped_count:
-            yield "lead_import_stopped_count", response.lead_import_stopped_count
-        if response.error:
-            yield "error", response.error
-        if not response.ok:
-            yield "error", "Failed to add leads to campaign"
-
-
-class SaveCampaignSequencesBlock(Block):
-    """Save sequences within a campaign"""
-
-    class Input(BlockSchema):
-        campaign_id: int = SchemaField(
-            description="The ID of the campaign to save sequences for",
-        )
-        sequences: list[Sequence] = SchemaField(
-            description="The sequences to save",
-            default=[],
-            advanced=False,
-        )
-        credentials: SmartLeadCredentialsInput = SchemaField(
-            description="SmartLead credentials",
-        )
-
-    class Output(BlockSchema):
-        data: dict | str | None = SchemaField(
-            description="Data from the API",
-            default=None,
-        )
-        message: str = SchemaField(
-            description="Message from the API",
-            default="",
-        )
-        error: str = SchemaField(
-            description="Error message if the sequences were not saved",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="e7d9f41c-dc10-4f39-98ba-a432abd128c0",
-            description="Save sequences within a campaign",
-            categories={BlockCategory.CRM},
-            input_schema=SaveCampaignSequencesBlock.Input,
-            output_schema=SaveCampaignSequencesBlock.Output,
-            test_credentials=TEST_CREDENTIALS,
-            test_input={
-                "campaign_id": 1,
-                "sequences": [],
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "message",
-                    "Sequences saved successfully",
-                ),
-            ],
-            test_mock={
-                "save_campaign_sequences": lambda campaign_id, sequences, credentials: SaveSequencesResponse(
-                    ok=True,
-                    message="Sequences saved successfully",
-                )
-            },
-        )
-
-    @staticmethod
-    def save_campaign_sequences(
-        campaign_id: int, sequences: list[Sequence], credentials: SmartLeadCredentials
-    ) -> SaveSequencesResponse:
-        client = SmartLeadClient(credentials.api_key.get_secret_value())
-        return client.save_campaign_sequences(
-            campaign_id=campaign_id, request=SaveSequencesRequest(sequences=sequences)
-        )
-
-    def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: SmartLeadCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        response = self.save_campaign_sequences(
-            input_data.campaign_id, input_data.sequences, credentials
-        )
-
-        if response.data:
-            yield "data", response.data
-        if response.message:
-            yield "message", response.message
-        if response.error:
-            yield "error", response.error
-        if not response.ok:
-            yield "error", "Failed to save sequences"

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

@@ -1,147 +0,0 @@
-from enum import Enum
-
-from pydantic import BaseModel
-
-from backend.data.model import SchemaField
-
-
-class CreateCampaignResponse(BaseModel):
-    ok: bool
-    id: int
-    name: str
-    created_at: str
-
-
-class CreateCampaignRequest(BaseModel):
-    name: str
-    client_id: str | None = None
-
-
-class AddLeadsToCampaignResponse(BaseModel):
-    ok: bool
-    upload_count: int
-    total_leads: int
-    block_count: int
-    duplicate_count: int
-    invalid_email_count: int
-    invalid_emails: list[str]
-    already_added_to_campaign: int
-    unsubscribed_leads: list[str]
-    is_lead_limit_exhausted: bool
-    lead_import_stopped_count: int
-    bounce_count: int
-    error: str | None = None
-
-
-class LeadCustomFields(BaseModel):
-    """Custom fields for a lead (max 20 fields)"""
-
-    fields: dict[str, str] = SchemaField(
-        description="Custom fields for a lead (max 20 fields)",
-        max_length=20,
-        default={},
-    )
-
-
-class LeadInput(BaseModel):
-    """Single lead input data"""
-
-    first_name: str
-    last_name: str
-    email: str
-    phone_number: str | None = None  # Changed from int to str for phone numbers
-    company_name: str | None = None
-    website: str | None = None
-    location: str | None = None
-    custom_fields: LeadCustomFields | None = None
-    linkedin_profile: str | None = None
-    company_url: str | None = None
-
-
-class LeadUploadSettings(BaseModel):
-    """Settings for lead upload"""
-
-    ignore_global_block_list: bool = SchemaField(
-        description="Ignore the global block list",
-        default=False,
-    )
-    ignore_unsubscribe_list: bool = SchemaField(
-        description="Ignore the unsubscribe list",
-        default=False,
-    )
-    ignore_community_bounce_list: bool = SchemaField(
-        description="Ignore the community bounce list",
-        default=False,
-    )
-    ignore_duplicate_leads_in_other_campaign: bool = SchemaField(
-        description="Ignore duplicate leads in other campaigns",
-        default=False,
-    )
-
-
-class AddLeadsRequest(BaseModel):
-    """Request body for adding leads to a campaign"""
-
-    lead_list: list[LeadInput] = SchemaField(
-        description="List of leads to add to the campaign",
-        max_length=100,
-        default=[],
-    )
-    settings: LeadUploadSettings
-    campaign_id: int
-
-
-class VariantDistributionType(str, Enum):
-    MANUAL_EQUAL = "MANUAL_EQUAL"
-    MANUAL_PERCENTAGE = "MANUAL_PERCENTAGE"
-    AI_EQUAL = "AI_EQUAL"
-
-
-class WinningMetricProperty(str, Enum):
-    OPEN_RATE = "OPEN_RATE"
-    CLICK_RATE = "CLICK_RATE"
-    REPLY_RATE = "REPLY_RATE"
-    POSITIVE_REPLY_RATE = "POSITIVE_REPLY_RATE"
-
-
-class SequenceDelayDetails(BaseModel):
-    delay_in_days: int
-
-
-class SequenceVariant(BaseModel):
-    subject: str
-    email_body: str
-    variant_label: str
-    id: int | None = None  # Optional for creation, required for updates
-    variant_distribution_percentage: int | None = None
-
-
-class Sequence(BaseModel):
-    seq_number: int = SchemaField(
-        description="The sequence number",
-        default=1,
-    )
-    seq_delay_details: SequenceDelayDetails
-    id: int | None = None
-    variant_distribution_type: VariantDistributionType | None = None
-    lead_distribution_percentage: int | None = SchemaField(
-        None, ge=20, le=100
-    )  # >= 20% for fair calculation
-    winning_metric_property: WinningMetricProperty | None = None
-    seq_variants: list[SequenceVariant] | None = None
-    subject: str = ""  # blank makes the follow up in the same thread
-    email_body: str | None = None
-
-
-class SaveSequencesRequest(BaseModel):
-    sequences: list[Sequence]
-
-
-class SaveSequencesResponse(BaseModel):
-    ok: bool
-    message: str = SchemaField(
-        description="Message from the API",
-        default="",
-    )
-    data: dict | str | None = None
-    error: str | None = None

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/zerobounce/_api.py

@@ -1,10 +0,0 @@
-from zerobouncesdk import ZBValidateResponse, ZeroBounce
-
-
-class ZeroBounceClient:
-    def __init__(self, api_key: str):
-        self.api_key = api_key
-        self.client = ZeroBounce(api_key)
-
-    def validate_email(self, email: str, ip_address: str) -> ZBValidateResponse:
-        return self.client.validate(email, ip_address)

autogpt_platform/backend/backend/blocks/zerobounce/_auth.py

@@ -1,35 +0,0 @@
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
-from backend.integrations.providers import ProviderName
-
-ZeroBounceCredentials = APIKeyCredentials
-ZeroBounceCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.ZEROBOUNCE],
-    Literal["api_key"],
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="zerobounce",
-    api_key=SecretStr("mock-zerobounce-api-key"),
-    title="Mock ZeroBounce API key",
-    expires_at=None,
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-def ZeroBounceCredentialsField() -> ZeroBounceCredentialsInput:
-    """
-    Creates a ZeroBounce credentials input on a block.
-    """
-    return CredentialsField(
-        description="The ZeroBounce integration can be used with an API Key.",
-    )

autogpt_platform/backend/backend/blocks/zerobounce/validate_emails.py

@@ -1,175 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel
-from zerobouncesdk.zb_validate_response import (
-    ZBValidateResponse,
-    ZBValidateStatus,
-    ZBValidateSubStatus,
-)
-
-from backend.blocks.zerobounce._api import ZeroBounceClient
-from backend.blocks.zerobounce._auth import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    ZeroBounceCredentials,
-    ZeroBounceCredentialsInput,
-)
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-
-
-class Response(BaseModel):
-    address: str = SchemaField(
-        description="The email address you are validating.", default="N/A"
-    )
-    status: ZBValidateStatus = SchemaField(
-        description="The status of the email address.", default=ZBValidateStatus.unknown
-    )
-    sub_status: ZBValidateSubStatus = SchemaField(
-        description="The sub-status of the email address.",
-        default=ZBValidateSubStatus.none,
-    )
-    account: Optional[str] = SchemaField(
-        description="The portion of the email address before the '@' symbol.",
-        default="N/A",
-    )
-    domain: Optional[str] = SchemaField(
-        description="The portion of the email address after the '@' symbol."
-    )
-    did_you_mean: Optional[str] = SchemaField(
-        description="Suggestive Fix for an email typo",
-        default=None,
-    )
-    domain_age_days: Optional[str] = SchemaField(
-        description="Age of the email domain in days or [null].",
-        default=None,
-    )
-    free_email: Optional[bool] = SchemaField(
-        description="Whether the email address is a free email provider.", default=False
-    )
-    mx_found: Optional[bool] = SchemaField(
-        description="Whether the MX record was found.", default=False
-    )
-    mx_record: Optional[str] = SchemaField(
-        description="The MX record of the email address.", default=None
-    )
-    smtp_provider: Optional[str] = SchemaField(
-        description="The SMTP provider of the email address.", default=None
-    )
-    firstname: Optional[str] = SchemaField(
-        description="The first name of the email address.", default=None
-    )
-    lastname: Optional[str] = SchemaField(
-        description="The last name of the email address.", default=None
-    )
-    gender: Optional[str] = SchemaField(
-        description="The gender of the email address.", default=None
-    )
-    city: Optional[str] = SchemaField(
-        description="The city of the email address.", default=None
-    )
-    region: Optional[str] = SchemaField(
-        description="The region of the email address.", default=None
-    )
-    zipcode: Optional[str] = SchemaField(
-        description="The zipcode of the email address.", default=None
-    )
-    country: Optional[str] = SchemaField(
-        description="The country of the email address.", default=None
-    )
-
-
-class ValidateEmailsBlock(Block):
-    """Search for people in Apollo"""
-
-    class Input(BlockSchema):
-        email: str = SchemaField(
-            description="Email to validate",
-        )
-        ip_address: str = SchemaField(
-            description="IP address to validate",
-            default="",
-        )
-        credentials: ZeroBounceCredentialsInput = SchemaField(
-            description="ZeroBounce credentials",
-        )
-
-    class Output(BlockSchema):
-        response: Response = SchemaField(
-            description="Response from ZeroBounce",
-        )
-        error: str = SchemaField(
-            description="Error message if the search failed",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="e3950439-fa0b-40e8-b19f-e0dca0bf5853",
-            description="Validate emails",
-            categories={BlockCategory.SEARCH},
-            input_schema=ValidateEmailsBlock.Input,
-            output_schema=ValidateEmailsBlock.Output,
-            test_credentials=TEST_CREDENTIALS,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "email": "test@test.com",
-            },
-            test_output=[
-                (
-                    "response",
-                    Response(
-                        address="test@test.com",
-                        status=ZBValidateStatus.valid,
-                        sub_status=ZBValidateSubStatus.allowed,
-                        account="test",
-                        domain="test.com",
-                        did_you_mean=None,
-                        domain_age_days=None,
-                        free_email=False,
-                        mx_found=False,
-                        mx_record=None,
-                        smtp_provider=None,
-                    ),
-                )
-            ],
-            test_mock={
-                "validate_email": lambda email, ip_address, credentials: ZBValidateResponse(
-                    data={
-                        "address": email,
-                        "status": ZBValidateStatus.valid,
-                        "sub_status": ZBValidateSubStatus.allowed,
-                        "account": "test",
-                        "domain": "test.com",
-                        "did_you_mean": None,
-                        "domain_age_days": None,
-                        "free_email": False,
-                        "mx_found": False,
-                        "mx_record": None,
-                        "smtp_provider": None,
-                    }
-                )
-            },
-        )
-
-    @staticmethod
-    def validate_email(
-        email: str, ip_address: str, credentials: ZeroBounceCredentials
-    ) -> ZBValidateResponse:
-        client = ZeroBounceClient(credentials.api_key.get_secret_value())
-        return client.validate_email(email, ip_address)
-
-    def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: ZeroBounceCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        response: ZBValidateResponse = self.validate_email(
-            input_data.email, input_data.ip_address, credentials
-        )
-
-        response_model = Response(**response.__dict__)
-
-        yield "response", response_model

autogpt_platform/backend/backend/cli.py

@@ -220,8 +220,9 @@ def event():
 
 @test.command()
 @click.argument("server_address")
-@click.argument("graph_exec_id")
-def websocket(server_address: str, graph_exec_id: str):
+@click.argument("graph_id")
+@click.argument("graph_version")
+def websocket(server_address: str, graph_id: str, graph_version: int):
     """
     Tests the websocket connection.
     """
@@ -229,20 +230,16 @@ def websocket(server_address: str, graph_exec_id: str):
 
     import websockets.asyncio.client
 
-    from backend.server.ws_api import (
-        WSMessage,
-        WSMethod,
-        WSSubscribeGraphExecutionRequest,
-    )
+    from backend.server.ws_api import ExecutionSubscription, Methods, WsMessage
 
     async def send_message(server_address: str):
         uri = f"ws://{server_address}"
         async with websockets.asyncio.client.connect(uri) as websocket:
             try:
-                msg = WSMessage(
-                    method=WSMethod.SUBSCRIBE_GRAPH_EXEC,
-                    data=WSSubscribeGraphExecutionRequest(
-                        graph_exec_id=graph_exec_id,
+                msg = WsMessage(
+                    method=Methods.SUBSCRIBE,
+                    data=ExecutionSubscription(
+                        graph_id=graph_id, graph_version=graph_version
                     ).model_dump(),
                 ).model_dump_json()
                 await websocket.send(msg)

autogpt_platform/backend/backend/data/block.py

@@ -2,7 +2,6 @@ import inspect
 from abc import ABC, abstractmethod
 from enum import Enum
 from typing import (
-    TYPE_CHECKING,
     Any,
     ClassVar,
     Generator,
@@ -19,8 +18,6 @@ import jsonschema
 from prisma.models import AgentBlock
 from pydantic import BaseModel
 
-from backend.data.model import NodeExecutionStats
-from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.settings import Config
 
@@ -31,9 +28,6 @@ from .model import (
     is_credentials_field_name,
 )
 
-if TYPE_CHECKING:
-    from .graph import Link
-
 app_config = Config()
 
 BlockData = tuple[str, Any]  # Input & Output data should be a tuple of (name, data).
@@ -50,7 +44,6 @@ class BlockType(Enum):
     WEBHOOK = "Webhook"
     WEBHOOK_MANUAL = "Webhook (manual)"
     AGENT = "Agent"
-    AI = "AI"
 
 
 class BlockCategory(Enum):
@@ -116,30 +109,21 @@ class BlockSchema(BaseModel):
     def validate_data(cls, data: BlockInput) -> str | None:
         return json.validate_with_jsonschema(schema=cls.jsonschema(), data=data)
 
-    @classmethod
-    def get_mismatch_error(cls, data: BlockInput) -> str | None:
-        return cls.validate_data(data)
-
-    @classmethod
-    def get_field_schema(cls, field_name: str) -> dict[str, Any]:
-        model_schema = cls.jsonschema().get("properties", {})
-        if not model_schema:
-            raise ValueError(f"Invalid model schema {cls}")
-
-        property_schema = model_schema.get(field_name)
-        if not property_schema:
-            raise ValueError(f"Invalid property name {field_name}")
-
-        return property_schema
-
     @classmethod
     def validate_field(cls, field_name: str, data: BlockInput) -> str | None:
         """
         Validate the data against a specific property (one of the input/output name).
         Returns the validation error message if the data does not match the schema.
         """
+        model_schema = cls.jsonschema().get("properties", {})
+        if not model_schema:
+            return f"Invalid model schema {cls}"
+
+        property_schema = model_schema.get(field_name)
+        if not property_schema:
+            return f"Invalid property name {field_name}"
+
         try:
-            property_schema = cls.get_field_schema(field_name)
             jsonschema.validate(json.to_dict(data), property_schema)
             return None
         except jsonschema.ValidationError as e:
@@ -202,19 +186,6 @@ class BlockSchema(BaseModel):
             )
         }
 
-    @classmethod
-    def get_input_defaults(cls, data: BlockInput) -> BlockInput:
-        return data  # Return as is, by default.
-
-    @classmethod
-    def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
-        input_fields_from_nodes = {link.sink_name for link in links}
-        return input_fields_from_nodes - set(data)
-
-    @classmethod
-    def get_missing_input(cls, data: BlockInput) -> set[str]:
-        return cls.get_required_fields() - set(data)
-
 
 BlockSchemaInputType = TypeVar("BlockSchemaInputType", bound=BlockSchema)
 BlockSchemaOutputType = TypeVar("BlockSchemaOutputType", bound=BlockSchema)
@@ -231,7 +202,7 @@ class BlockManualWebhookConfig(BaseModel):
     the user has to manually set up the webhook at the provider.
     """
 
-    provider: ProviderName
+    provider: str
     """The service provider that the webhook connects to"""
 
     webhook_type: str
@@ -323,7 +294,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.static_output = static_output
         self.block_type = block_type
         self.webhook_config = webhook_config
-        self.execution_stats: NodeExecutionStats = NodeExecutionStats()
+        self.execution_stats = {}
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -380,14 +351,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         Run the block with the given input data.
         Args:
             input_data: The input data with the structure of input_schema.
-
-        Kwargs: Currently 14/02/2025 these include
-            graph_id: The ID of the graph.
-            node_id: The ID of the node.
-            graph_exec_id: The ID of the graph execution.
-            node_exec_id: The ID of the node execution.
-            user_id: The ID of the user.
-
         Returns:
             A Generator that yields (output_name, output_data).
             output_name: One of the output name defined in Block's output_schema.
@@ -401,29 +364,18 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                 return data
         raise ValueError(f"{self.name} did not produce any output for {output}")
 
-    def merge_stats(self, stats: NodeExecutionStats) -> NodeExecutionStats:
-        stats_dict = stats.model_dump()
-        current_stats = self.execution_stats.model_dump()
-
-        for key, value in stats_dict.items():
-            if key not in current_stats:
-                # Field doesn't exist yet, just set it, but this will probably
-                # not happen, just in case though so we throw for invalid when
-                # converting back in
-                current_stats[key] = value
-            elif isinstance(value, dict) and isinstance(current_stats[key], dict):
-                current_stats[key].update(value)
-            elif isinstance(value, (int, float)) and isinstance(
-                current_stats[key], (int, float)
-            ):
-                current_stats[key] += value
-            elif isinstance(value, list) and isinstance(current_stats[key], list):
-                current_stats[key].extend(value)
+    def merge_stats(self, stats: dict[str, Any]) -> dict[str, Any]:
+        for key, value in stats.items():
+            if isinstance(value, dict):
+                self.execution_stats.setdefault(key, {}).update(value)
+            elif isinstance(value, (int, float)):
+                self.execution_stats.setdefault(key, 0)
+                self.execution_stats[key] += value
+            elif isinstance(value, list):
+                self.execution_stats.setdefault(key, [])
+                self.execution_stats[key].extend(value)
             else:
-                current_stats[key] = value
-
-        self.execution_stats = NodeExecutionStats(**current_stats)
-
+                self.execution_stats[key] = value
         return self.execution_stats
 
     @property
@@ -446,6 +398,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         }
 
     def execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
+        # Merge the input data with the extra execution arguments, preferring the args for security
         if error := self.input_schema.validate_data(input_data):
             raise ValueError(
                 f"Unable to execute block with invalid input data: {error}"
@@ -467,9 +420,9 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
 
 
 def get_blocks() -> dict[str, Type[Block]]:
-    from backend.blocks import load_all_blocks
+    from backend.blocks import AVAILABLE_BLOCKS  # noqa: E402
 
-    return load_all_blocks()
+    return AVAILABLE_BLOCKS
 
 
 async def initialize_blocks() -> None:

autogpt_platform/backend/backend/data/block_cost_config.py

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

autogpt_platform/backend/backend/data/credit.py

@@ -1,43 +1,28 @@
-import asyncio
 import logging
 from abc import ABC, abstractmethod
 from collections import defaultdict
 from datetime import datetime, timezone
 
 import stripe
-from autogpt_libs.utils.cache import thread_cached
 from prisma import Json
-from prisma.enums import (
-    CreditRefundRequestStatus,
-    CreditTransactionType,
-    NotificationType,
-)
+from prisma.enums import CreditTransactionType
 from prisma.errors import UniqueViolationError
-from prisma.models import CreditRefundRequest, CreditTransaction, User
+from prisma.models import CreditTransaction, User
 from prisma.types import CreditTransactionCreateInput, CreditTransactionWhereInput
-from tenacity import retry, stop_after_attempt, wait_exponential
+from pydantic import BaseModel
 
 from backend.data import db
+from backend.data.block import Block, BlockInput, get_block
 from backend.data.block_cost_config import BLOCK_COSTS
-from backend.data.cost import BlockCost
-from backend.data.model import (
-    AutoTopUpConfig,
-    RefundRequest,
-    TransactionHistory,
-    UserTransaction,
-)
-from backend.data.notifications import NotificationEventDTO, RefundRequestData
+from backend.data.cost import BlockCost, BlockCostType
+from backend.data.execution import NodeExecutionEntry
+from backend.data.model import AutoTopUpConfig, TransactionHistory, UserTransaction
 from backend.data.user import get_user_by_id
-from backend.executor.utils import UsageTransactionMetadata
-from backend.notifications import NotificationManager
-from backend.util.exceptions import InsufficientBalanceError
-from backend.util.service import get_service_client
 from backend.util.settings import Settings
 
 settings = Settings()
 stripe.api_key = settings.secrets.stripe_api_key
 logger = logging.getLogger(__name__)
-base_url = settings.config.frontend_base_url or settings.config.platform_base_url
 
 
 class UserCreditBase(ABC):
@@ -55,61 +40,46 @@ class UserCreditBase(ABC):
     async def get_transaction_history(
         self,
         user_id: str,
+        transaction_time: datetime,
         transaction_count_limit: int,
-        transaction_time_ceiling: datetime | None = None,
-        transaction_type: str | None = None,
     ) -> TransactionHistory:
         """
         Get the credit transactions for the user.
 
         Args:
             user_id (str): The user ID.
+            transaction_time (datetime): The upper bound of the transaction time.
             transaction_count_limit (int): The transaction count limit.
-            transaction_time_ceiling (datetime): The upper bound of the transaction time.
-            transaction_type (str): The transaction type filter.
 
         Returns:
             TransactionHistory: The credit transactions for the user.
         """
         pass
 
-    @abstractmethod
-    async def get_refund_requests(self, user_id: str) -> list[RefundRequest]:
-        """
-        Get the refund requests for the user.
-
-        Args:
-            user_id (str): The user ID.
-
-        Returns:
-            list[RefundRequest]: The refund requests for the user.
-        """
-        pass
-
     @abstractmethod
     async def spend_credits(
         self,
-        user_id: str,
-        cost: int,
-        metadata: UsageTransactionMetadata,
+        entry: NodeExecutionEntry,
+        data_size: float,
+        run_time: float,
     ) -> int:
         """
-        Spend the credits for the user based on the cost.
+        Spend the credits for the user based on the block usage.
 
         Args:
-            user_id (str): The user ID.
-            cost (int): The cost to spend.
-            metadata (UsageTransactionMetadata): The metadata of the transaction.
+            entry (NodeExecutionEntry): The node execution identifiers & data.
+            data_size (float): The size of the data being processed.
+            run_time (float): The time taken to run the block.
 
         Returns:
-            int: The remaining balance.
+            int: amount of credit spent
         """
         pass
 
     @abstractmethod
     async def top_up_credits(self, user_id: str, amount: int):
         """
-        Top up the credits for the user.
+        Top up the credits for the user immediately.
 
         Args:
             user_id (str): The user ID.
@@ -131,46 +101,6 @@ class UserCreditBase(ABC):
         """
         pass
 
-    @abstractmethod
-    async def top_up_refund(
-        self, user_id: str, transaction_key: str, metadata: dict[str, str]
-    ) -> int:
-        """
-        Refund the top-up transaction for the user.
-
-        Args:
-            user_id (str): The user ID.
-            transaction_key (str): The top-up transaction key to refund.
-            metadata (dict[str, str]): The metadata of the refund.
-
-        Returns:
-            int: The amount refunded.
-        """
-        pass
-
-    @abstractmethod
-    async def deduct_credits(
-        self,
-        request: stripe.Refund | stripe.Dispute,
-    ):
-        """
-        Deduct the credits for the user based on the dispute or refund of the top-up.
-
-        Args:
-            request (stripe.Refund | stripe.Dispute): The refund or dispute request.
-        """
-        pass
-
-    @abstractmethod
-    async def handle_dispute(self, dispute: stripe.Dispute):
-        """
-        Handle the dispute for the user based on the dispute request.
-
-        Args:
-            dispute (stripe.Dispute): The dispute request.
-        """
-        pass
-
     @abstractmethod
     async def fulfill_checkout(
         self, *, session_id: str | None = None, user_id: str | None = None
@@ -184,14 +114,6 @@ class UserCreditBase(ABC):
         """
         pass
 
-    @staticmethod
-    async def create_billing_portal_session(user_id: str) -> str:
-        session = stripe.billing_portal.Session.create(
-            customer=await get_stripe_customer_id(user_id),
-            return_url=base_url + "/profile/credits",
-        )
-        return session.url
-
     @staticmethod
     def time_now() -> datetime:
         return datetime.now(timezone.utc)
@@ -245,18 +167,10 @@ class UserCreditBase(ABC):
         )
         return transaction_balance, transaction_time
 
-    @retry(
-        stop=stop_after_attempt(5),
-        wait=wait_exponential(multiplier=1, min=1, max=10),
-        reraise=True,
-    )
     async def _enable_transaction(
-        self,
-        transaction_key: str,
-        user_id: str,
-        metadata: Json,
-        new_transaction_key: str | None = None,
+        self, transaction_key: str, user_id: str, metadata: Json
     ):
+
         transaction = await CreditTransaction.prisma().find_first_or_raise(
             where={"transactionKey": transaction_key, "userId": user_id}
         )
@@ -274,7 +188,6 @@ class UserCreditBase(ABC):
                     }
                 },
                 data={
-                    "transactionKey": new_transaction_key or transaction_key,
                     "isActive": True,
                     "runningBalance": user_balance + transaction.amount,
                     "createdAt": self.time_now(),
@@ -290,7 +203,6 @@ class UserCreditBase(ABC):
         is_active: bool = True,
         transaction_key: str | None = None,
         ceiling_balance: int | None = None,
-        fail_insufficient_credits: bool = True,
         metadata: Json = Json({}),
     ) -> tuple[int, str]:
         """
@@ -304,7 +216,6 @@ class UserCreditBase(ABC):
             is_active (bool): Whether the transaction is active or needs to be manually activated through _enable_transaction.
             transaction_key (str | None): The transaction key. Avoids adding transaction if the key already exists.
             ceiling_balance (int | None): The ceiling balance. Avoids adding more credits if the balance is already above the ceiling.
-            fail_insufficient_credits (bool): Whether to fail if the user has insufficient credits.
             metadata (Json): The metadata of the transaction.
 
         Returns:
@@ -314,21 +225,15 @@ class UserCreditBase(ABC):
             # Get latest balance snapshot
             user_balance, _ = await self._get_credits(user_id)
 
-            if ceiling_balance and amount > 0 and user_balance >= ceiling_balance:
+            if ceiling_balance and user_balance >= ceiling_balance:
                 raise ValueError(
-                    f"You already have enough balance of ${user_balance/100}, top-up is not required when you already have at least ${ceiling_balance/100}"
+                    f"You already have enough balance for user {user_id}, balance: {user_balance}, ceiling: {ceiling_balance}"
                 )
 
-            if amount < 0 and user_balance + amount < 0:
-                if fail_insufficient_credits:
-                    raise InsufficientBalanceError(
-                        message=f"Insufficient balance of ${user_balance/100}, where this will cost ${abs(amount)/100}",
-                        user_id=user_id,
-                        balance=user_balance,
-                        amount=amount,
-                    )
-
-                amount = min(-user_balance, 0)
+            if amount < 0 and user_balance < abs(amount):
+                raise ValueError(
+                    f"Insufficient balance of ${user_balance/100} to run the block that costs ${abs(amount)/100}"
+                )
 
             # Create the transaction
             transaction_data: CreditTransactionCreateInput = {
@@ -346,41 +251,101 @@ class UserCreditBase(ABC):
             return user_balance + amount, tx.transactionKey
 
 
-class UserCredit(UserCreditBase):
-    @thread_cached
-    def notification_client(self) -> NotificationManager:
-        return get_service_client(NotificationManager)
+class UsageTransactionMetadata(BaseModel):
+    graph_exec_id: str | None = None
+    graph_id: str | None = None
+    node_id: str | None = None
+    node_exec_id: str | None = None
+    block_id: str | None = None
+    block: str | None = None
+    input: BlockInput | None = None
 
-    async def _send_refund_notification(
+
+class UserCredit(UserCreditBase):
+
+    def _block_usage_cost(
         self,
-        notification_request: RefundRequestData,
-        notification_type: NotificationType,
-    ):
-        await asyncio.to_thread(
-            lambda: self.notification_client().queue_notification(
-                NotificationEventDTO(
-                    user_id=notification_request.user_id,
-                    type=notification_type,
-                    data=notification_request.model_dump(),
+        block: Block,
+        input_data: BlockInput,
+        data_size: float,
+        run_time: float,
+    ) -> tuple[int, BlockInput]:
+        block_costs = BLOCK_COSTS.get(type(block))
+        if not block_costs:
+            return 0, {}
+
+        for block_cost in block_costs:
+            if not self._is_cost_filter_match(block_cost.cost_filter, input_data):
+                continue
+
+            if block_cost.cost_type == BlockCostType.RUN:
+                return block_cost.cost_amount, block_cost.cost_filter
+
+            if block_cost.cost_type == BlockCostType.SECOND:
+                return (
+                    int(run_time * block_cost.cost_amount),
+                    block_cost.cost_filter,
                 )
-            )
+
+            if block_cost.cost_type == BlockCostType.BYTE:
+                return (
+                    int(data_size * block_cost.cost_amount),
+                    block_cost.cost_filter,
+                )
+
+        return 0, {}
+
+    def _is_cost_filter_match(
+        self, cost_filter: BlockInput, input_data: BlockInput
+    ) -> bool:
+        """
+        Filter rules:
+          - If cost_filter is an object, then check if cost_filter is the subset of input_data
+          - Otherwise, check if cost_filter is equal to input_data.
+          - Undefined, null, and empty string are considered as equal.
+        """
+        if not isinstance(cost_filter, dict) or not isinstance(input_data, dict):
+            return cost_filter == input_data
+
+        return all(
+            (not input_data.get(k) and not v)
+            or (input_data.get(k) and self._is_cost_filter_match(v, input_data[k]))
+            for k, v in cost_filter.items()
         )
 
     async def spend_credits(
         self,
-        user_id: str,
-        cost: int,
-        metadata: UsageTransactionMetadata,
+        entry: NodeExecutionEntry,
+        data_size: float,
+        run_time: float,
     ) -> int:
+        block = get_block(entry.block_id)
+        if not block:
+            raise ValueError(f"Block not found: {entry.block_id}")
+
+        cost, matching_filter = self._block_usage_cost(
+            block=block, input_data=entry.data, data_size=data_size, run_time=run_time
+        )
         if cost == 0:
             return 0
 
         balance, _ = await self._add_transaction(
-            user_id=user_id,
+            user_id=entry.user_id,
             amount=-cost,
             transaction_type=CreditTransactionType.USAGE,
-            metadata=Json(metadata.model_dump()),
+            metadata=Json(
+                UsageTransactionMetadata(
+                    graph_exec_id=entry.graph_exec_id,
+                    graph_id=entry.graph_id,
+                    node_id=entry.node_id,
+                    node_exec_id=entry.node_exec_id,
+                    block_id=entry.block_id,
+                    block=block.name,
+                    input=matching_filter,
+                ).model_dump()
+            ),
         )
+        user_id = entry.user_id
 
         # Auto top-up if balance is below threshold.
         auto_top_up = await get_auto_top_up(user_id)
@@ -390,7 +355,7 @@ class UserCredit(UserCreditBase):
                     user_id=user_id,
                     amount=auto_top_up.amount,
                     # Avoid multiple auto top-ups within the same graph execution.
-                    key=f"AUTO-TOP-UP-{user_id}-{metadata.graph_exec_id}",
+                    key=f"AUTO-TOP-UP-{user_id}-{entry.graph_exec_id}",
                     ceiling_balance=auto_top_up.threshold,
                 )
             except Exception as e:
@@ -399,187 +364,11 @@ class UserCredit(UserCreditBase):
                     f"Auto top-up failed for user {user_id}, balance: {balance}, amount: {auto_top_up.amount}, error: {e}"
                 )
 
-        return balance
+        return cost
 
     async def top_up_credits(self, user_id: str, amount: int):
         await self._top_up_credits(user_id, amount)
 
-    async def top_up_refund(
-        self, user_id: str, transaction_key: str, metadata: dict[str, str]
-    ) -> int:
-        transaction = await CreditTransaction.prisma().find_first_or_raise(
-            where={
-                "transactionKey": transaction_key,
-                "userId": user_id,
-                "isActive": True,
-                "type": CreditTransactionType.TOP_UP,
-            }
-        )
-        balance = await self.get_credits(user_id)
-        amount = transaction.amount
-        refund_key_format = settings.config.refund_request_time_key_format
-        refund_key = f"{transaction.createdAt.strftime(refund_key_format)}-{user_id}"
-
-        try:
-            refund_request = await CreditRefundRequest.prisma().create(
-                data={
-                    "id": refund_key,
-                    "transactionKey": transaction_key,
-                    "userId": user_id,
-                    "amount": amount,
-                    "reason": metadata.get("reason", ""),
-                    "status": CreditRefundRequestStatus.PENDING,
-                    "result": "The refund request is under review.",
-                }
-            )
-        except UniqueViolationError:
-            raise ValueError(
-                "Unable to request a refund for this transaction, the request of the top-up transaction within the same week has already been made."
-            )
-
-        if amount - balance > settings.config.refund_credit_tolerance_threshold:
-            user_data = await get_user_by_id(user_id)
-            await self._send_refund_notification(
-                RefundRequestData(
-                    user_id=user_id,
-                    user_name=user_data.name or "AutoGPT Platform User",
-                    user_email=user_data.email,
-                    transaction_id=transaction_key,
-                    refund_request_id=refund_request.id,
-                    reason=refund_request.reason,
-                    amount=amount,
-                    balance=balance,
-                ),
-                NotificationType.REFUND_REQUEST,
-            )
-            return 0  # Register the refund request for manual approval.
-
-        # Auto refund the top-up.
-        refund = stripe.Refund.create(payment_intent=transaction_key, metadata=metadata)
-        return refund.amount
-
-    async def deduct_credits(self, request: stripe.Refund | stripe.Dispute):
-        if isinstance(request, stripe.Refund) and request.status != "succeeded":
-            logger.warning(
-                f"Skip processing refund #{request.id} with status {request.status}"
-            )
-            return
-
-        if isinstance(request, stripe.Dispute) and request.status != "lost":
-            logger.warning(
-                f"Skip processing dispute #{request.id} with status {request.status}"
-            )
-            return
-
-        transaction = await CreditTransaction.prisma().find_first_or_raise(
-            where={
-                "transactionKey": str(request.payment_intent),
-                "isActive": True,
-                "type": CreditTransactionType.TOP_UP,
-            }
-        )
-        if request.amount <= 0 or request.amount > transaction.amount:
-            raise AssertionError(
-                f"Invalid amount to deduct ${request.amount/100} from ${transaction.amount/100} top-up"
-            )
-
-        balance, _ = await self._add_transaction(
-            user_id=transaction.userId,
-            amount=-request.amount,
-            transaction_type=CreditTransactionType.REFUND,
-            transaction_key=request.id,
-            metadata=Json(request),
-            fail_insufficient_credits=False,
-        )
-
-        # Update the result of the refund request if it exists.
-        await CreditRefundRequest.prisma().update_many(
-            where={
-                "userId": transaction.userId,
-                "transactionKey": transaction.transactionKey,
-            },
-            data={
-                "amount": request.amount,
-                "status": CreditRefundRequestStatus.APPROVED,
-                "result": "The refund request has been approved, the amount will be credited back to your account.",
-            },
-        )
-
-        user_data = await get_user_by_id(transaction.userId)
-        await self._send_refund_notification(
-            RefundRequestData(
-                user_id=user_data.id,
-                user_name=user_data.name or "AutoGPT Platform User",
-                user_email=user_data.email,
-                transaction_id=transaction.transactionKey,
-                refund_request_id=request.id,
-                reason=str(request.reason or "-"),
-                amount=transaction.amount,
-                balance=balance,
-            ),
-            NotificationType.REFUND_PROCESSED,
-        )
-
-    async def handle_dispute(self, dispute: stripe.Dispute):
-        transaction = await CreditTransaction.prisma().find_first_or_raise(
-            where={
-                "transactionKey": str(dispute.payment_intent),
-                "isActive": True,
-                "type": CreditTransactionType.TOP_UP,
-            }
-        )
-        user_id = transaction.userId
-        amount = dispute.amount
-        balance = await self.get_credits(user_id)
-
-        # If the user has enough balance, just let them win the dispute.
-        if balance - amount >= settings.config.refund_credit_tolerance_threshold:
-            logger.warning(f"Accepting dispute from {user_id} for ${amount/100}")
-            dispute.close()
-            return
-
-        logger.warning(
-            f"Adding extra info for dispute from {user_id} for ${amount/100}"
-        )
-        # Retrieve recent transaction history to support our evidence.
-        # This provides a concise timeline that shows service usage and proper credit application.
-        transaction_history = await self.get_transaction_history(
-            user_id, transaction_count_limit=None
-        )
-        user = await get_user_by_id(user_id)
-
-        # Build a comprehensive explanation message that includes:
-        # - Confirmation that the top-up transaction was processed and credits were applied.
-        # - A summary of recent transaction history.
-        # - An explanation that the funds were used to render the agreed service.
-        evidence_text = (
-            f"The top-up transaction of ${transaction.amount / 100:.2f} was processed successfully, and the corresponding credits "
-            "were applied to the user’s account. Our records confirm that the funds were utilized for the intended services. "
-            "Below is a summary of recent transaction activity:\n"
-        )
-        for tx in transaction_history.transactions:
-            if tx.transaction_key == transaction.transactionKey:
-                additional_comment = (
-                    " [This top-up transaction is the subject of the dispute]."
-                )
-            else:
-                additional_comment = ""
-
-            evidence_text += (
-                f"- {tx.description}: Amount ${tx.amount / 100:.2f} on {tx.transaction_time.isoformat()}, "
-                f"resulting balance ${tx.balance / 100:.2f} {additional_comment}\n"
-            )
-        evidence_text += (
-            "\nThis evidence demonstrates that the transaction was authorized and that the charged amount was used to render the service as agreed."
-            "\nAdditionally, we provide an automated refund functionality, so the user could have used it if they were not satisfied with the service. "
-        )
-        evidence: stripe.Dispute.ModifyParamsEvidence = {
-            "product_description": "AutoGPT Platform Credits",
-            "customer_email_address": user.email,
-            "uncategorized_text": evidence_text[:20000],
-        }
-        stripe.Dispute.modify(dispute.id, evidence=evidence)
-
     async def _top_up_credits(
         self,
         user_id: str,
@@ -597,15 +386,10 @@ class UserCredit(UserCreditBase):
         ):
             raise ValueError(f"Transaction key {key} already exists for user {user_id}")
 
-        if amount == 0:
-            transaction_type = CreditTransactionType.CARD_CHECK
-        else:
-            transaction_type = CreditTransactionType.TOP_UP
-
         _, transaction_key = await self._add_transaction(
             user_id=user_id,
             amount=amount,
-            transaction_type=transaction_type,
+            transaction_type=CreditTransactionType.TOP_UP,
             is_active=False,
             transaction_key=key,
             ceiling_balance=ceiling_balance,
@@ -617,10 +401,8 @@ class UserCredit(UserCreditBase):
         if not payment_methods:
             raise ValueError("No payment method found, please add it on the platform.")
 
-        successful_transaction = None
-        new_transaction_key = None
         for payment_method in payment_methods:
-            if transaction_type == CreditTransactionType.CARD_CHECK:
+            if amount == 0:
                 setup_intent = stripe.SetupIntent.create(
                     customer=customer_id,
                     usage="off_session",
@@ -632,9 +414,8 @@ class UserCredit(UserCreditBase):
                     },
                 )
                 if setup_intent.status == "succeeded":
-                    successful_transaction = Json({"setup_intent": setup_intent})
-                    new_transaction_key = setup_intent.id
-                    break
+                    return
+
             else:
                 payment_intent = stripe.PaymentIntent.create(
                     amount=amount,
@@ -650,20 +431,15 @@ class UserCredit(UserCreditBase):
                     },
                 )
                 if payment_intent.status == "succeeded":
-                    successful_transaction = Json({"payment_intent": payment_intent})
-                    new_transaction_key = payment_intent.id
-                    break
+                    await self._enable_transaction(
+                        transaction_key=transaction_key,
+                        user_id=user_id,
+                        metadata=Json({"payment_intent": payment_intent}),
+                    )
+                    return
 
-        if not successful_transaction:
-            raise ValueError(
-                f"Out of {len(payment_methods)} payment methods tried, none is supported"
-            )
-
-        await self._enable_transaction(
-            transaction_key=transaction_key,
-            new_transaction_key=new_transaction_key,
-            user_id=user_id,
-            metadata=successful_transaction,
+        raise ValueError(
+            f"Out of {len(payment_methods)} payment methods tried, none is supported"
         )
 
     async def top_up_intent(self, user_id: str, amount: int) -> str:
@@ -694,8 +470,10 @@ class UserCredit(UserCreditBase):
             ui_mode="hosted",
             payment_intent_data={"setup_future_usage": "off_session"},
             saved_payment_method_options={"payment_method_save": "enabled"},
-            success_url=base_url + "/profile/credits?topup=success",
-            cancel_url=base_url + "/profile/credits?topup=cancel",
+            success_url=settings.config.frontend_base_url
+            + "/marketplace/credits?topup=success",
+            cancel_url=settings.config.frontend_base_url
+            + "/marketplace/credits?topup=cancel",
             allow_promotion_codes=True,
         )
 
@@ -705,7 +483,7 @@ class UserCredit(UserCreditBase):
             transaction_type=CreditTransactionType.TOP_UP,
             transaction_key=checkout_session.id,
             is_active=False,
-            metadata=Json(checkout_session),
+            metadata=Json({"checkout_session": checkout_session}),
         )
 
         return checkout_session.url or ""
@@ -721,7 +499,6 @@ class UserCredit(UserCreditBase):
         find_filter: CreditTransactionWhereInput = {
             "type": CreditTransactionType.TOP_UP,
             "isActive": False,
-            "amount": {"gt": 0},
         }
         if session_id:
             find_filter["transactionKey"] = session_id
@@ -738,25 +515,18 @@ class UserCredit(UserCreditBase):
         if not credit_transaction:
             return
 
-        # If the transaction is not a checkout session, then skip the fulfillment
-        if not credit_transaction.transactionKey.startswith("cs_"):
-            return
-
         # Retrieve the Checkout Session from the API
         checkout_session = stripe.checkout.Session.retrieve(
-            credit_transaction.transactionKey,
-            expand=["payment_intent"],
+            credit_transaction.transactionKey
         )
 
         # Check the Checkout Session's payment_status property
         # to determine if fulfillment should be performed
         if checkout_session.payment_status in ["paid", "no_payment_required"]:
-            assert isinstance(checkout_session.payment_intent, stripe.PaymentIntent)
             await self._enable_transaction(
                 transaction_key=credit_transaction.transactionKey,
-                new_transaction_key=checkout_session.payment_intent.id,
                 user_id=credit_transaction.userId,
-                metadata=Json(checkout_session),
+                metadata=Json({"checkout_session": checkout_session}),
             )
 
     async def get_credits(self, user_id: str) -> int:
@@ -766,23 +536,15 @@ class UserCredit(UserCreditBase):
     async def get_transaction_history(
         self,
         user_id: str,
-        transaction_count_limit: int | None = 100,
-        transaction_time_ceiling: datetime | None = None,
-        transaction_type: str | None = None,
+        transaction_time: datetime,
+        transaction_count_limit: int,
     ) -> TransactionHistory:
-        transactions_filter: CreditTransactionWhereInput = {
-            "userId": user_id,
-            "isActive": True,
-        }
-        if transaction_time_ceiling:
-            transaction_time_ceiling = transaction_time_ceiling.replace(
-                tzinfo=timezone.utc
-            )
-            transactions_filter["createdAt"] = {"lt": transaction_time_ceiling}
-        if transaction_type:
-            transactions_filter["type"] = CreditTransactionType[transaction_type]
         transactions = await CreditTransaction.prisma().find_many(
-            where=transactions_filter,
+            where={
+                "userId": user_id,
+                "createdAt": {"lt": transaction_time},
+                "isActive": True,
+            },
             order={"createdAt": "desc"},
             take=transaction_count_limit,
         )
@@ -797,7 +559,7 @@ class UserCredit(UserCreditBase):
                 if t.metadata
                 else UsageTransactionMetadata()
             )
-            tx_time = t.createdAt.replace(tzinfo=timezone.utc)
+            tx_time = t.createdAt.replace(tzinfo=None)
 
             if t.type == CreditTransactionType.USAGE and metadata.graph_exec_id:
                 gt = grouped_transactions[metadata.graph_exec_id]
@@ -811,7 +573,6 @@ class UserCredit(UserCreditBase):
             else:
                 gt = grouped_transactions[t.transactionKey]
                 gt.description = f"{t.type} Transaction"
-                gt.transaction_key = t.transactionKey
 
             gt.amount += t.amount
             gt.transaction_type = t.type
@@ -827,25 +588,6 @@ class UserCredit(UserCreditBase):
             ),
         )
 
-    async def get_refund_requests(self, user_id: str) -> list[RefundRequest]:
-        return [
-            RefundRequest(
-                id=r.id,
-                user_id=r.userId,
-                transaction_key=r.transactionKey,
-                amount=r.amount,
-                reason=r.reason,
-                result=r.result,
-                status=r.status,
-                created_at=r.createdAt,
-                updated_at=r.updatedAt,
-            )
-            for r in await CreditRefundRequest.prisma().find_many(
-                where={"userId": user_id},
-                order={"createdAt": "desc"},
-            )
-        ]
-
 
 class BetaUserCredit(UserCredit):
     """
@@ -866,7 +608,7 @@ class BetaUserCredit(UserCredit):
             balance, _ = await self._add_transaction(
                 user_id=user_id,
                 amount=max(self.num_user_credits_refill - balance, 0),
-                transaction_type=CreditTransactionType.GRANT,
+                transaction_type=CreditTransactionType.TOP_UP,
                 transaction_key=f"MONTHLY-CREDIT-TOP-UP-{cur_time}",
             )
             return balance
@@ -882,9 +624,6 @@ class DisabledUserCredit(UserCreditBase):
     async def get_transaction_history(self, *args, **kwargs) -> TransactionHistory:
         return TransactionHistory(transactions=[], next_transaction_time=None)
 
-    async def get_refund_requests(self, *args, **kwargs) -> list[RefundRequest]:
-        return []
-
     async def spend_credits(self, *args, **kwargs) -> int:
         return 0
 
@@ -894,15 +633,6 @@ class DisabledUserCredit(UserCreditBase):
     async def top_up_intent(self, *args, **kwargs) -> str:
         return ""
 
-    async def top_up_refund(self, *args, **kwargs) -> int:
-        return 0
-
-    async def deduct_credits(self, *args, **kwargs):
-        pass
-
-    async def handle_dispute(self, *args, **kwargs):
-        pass
-
     async def fulfill_checkout(self, *args, **kwargs):
         pass
 
@@ -927,11 +657,7 @@ async def get_stripe_customer_id(user_id: str) -> str:
     if user.stripeCustomerId:
         return user.stripeCustomerId
 
-    customer = stripe.Customer.create(
-        name=user.name or "",
-        email=user.email,
-        metadata={"user_id": user_id},
-    )
+    customer = stripe.Customer.create(name=user.name or "", email=user.email)
     await User.prisma().update(
         where={"id": user_id}, data={"stripeCustomerId": customer.id}
     )

autogpt_platform/backend/backend/data/db.py

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

autogpt_platform/backend/backend/data/execution.py

@@ -1,199 +1,70 @@
-import logging
 from collections import defaultdict
 from datetime import datetime, timezone
-from enum import Enum
 from multiprocessing import Manager
-from typing import (
-    Annotated,
-    Any,
-    AsyncGenerator,
-    Generator,
-    Generic,
-    Literal,
-    Optional,
-    TypeVar,
-    overload,
-)
+from typing import Any, AsyncGenerator, Generator, Generic, Optional, Type, TypeVar
 
 from prisma import Json
 from prisma.enums import AgentExecutionStatus
+from prisma.errors import PrismaError
 from prisma.models import (
     AgentGraphExecution,
     AgentNodeExecution,
     AgentNodeExecutionInputOutput,
 )
-from prisma.types import (
-    AgentGraphExecutionWhereInput,
-    AgentNodeExecutionUpdateInput,
-    AgentNodeExecutionWhereInput,
-)
 from pydantic import BaseModel
-from pydantic.fields import Field
 
+from backend.data.block import BlockData, BlockInput, CompletedBlockOutput
+from backend.data.includes import EXECUTION_RESULT_INCLUDE, GRAPH_EXECUTION_INCLUDE
+from backend.data.queue import AsyncRedisEventBus, RedisEventBus
 from backend.server.v2.store.exceptions import DatabaseError
-from backend.util import mock
-from backend.util import type as type_utils
+from backend.util import mock, type
 from backend.util.settings import Config
 
-from .block import BlockData, BlockInput, BlockType, CompletedBlockOutput, get_block
-from .db import BaseDbModel
-from .includes import (
-    EXECUTION_RESULT_INCLUDE,
-    GRAPH_EXECUTION_INCLUDE,
-    GRAPH_EXECUTION_INCLUDE_WITH_NODES,
-)
-from .model import GraphExecutionStats, NodeExecutionStats
-from .queue import AsyncRedisEventBus, RedisEventBus
 
-T = TypeVar("T")
-
-logger = logging.getLogger(__name__)
-config = Config()
+class GraphExecutionEntry(BaseModel):
+    user_id: str
+    graph_exec_id: str
+    graph_id: str
+    graph_version: int
+    start_node_execs: list["NodeExecutionEntry"]
 
 
-# -------------------------- Models -------------------------- #
+class NodeExecutionEntry(BaseModel):
+    user_id: str
+    graph_exec_id: str
+    graph_id: str
+    node_exec_id: str
+    node_id: str
+    block_id: str
+    data: BlockInput
 
 
 ExecutionStatus = AgentExecutionStatus
 
-
-class GraphExecutionMeta(BaseDbModel):
-    user_id: str
-    started_at: datetime
-    ended_at: datetime
-    cost: Optional[int] = Field(..., description="Execution cost in credits")
-    duration: float = Field(..., description="Seconds from start to end of run")
-    total_run_time: float = Field(..., description="Seconds of node runtime")
-    status: ExecutionStatus
-    graph_id: str
-    graph_version: int
-    preset_id: Optional[str] = None
-
-    @staticmethod
-    def from_db(_graph_exec: AgentGraphExecution):
-        now = datetime.now(timezone.utc)
-        start_time = _graph_exec.startedAt or _graph_exec.createdAt
-        end_time = _graph_exec.updatedAt or now
-        duration = (end_time - start_time).total_seconds()
-        total_run_time = duration
-
-        try:
-            stats = GraphExecutionStats.model_validate(_graph_exec.stats)
-        except ValueError as e:
-            if _graph_exec.stats is not None:
-                logger.warning(
-                    "Failed to parse invalid graph execution stats "
-                    f"{_graph_exec.stats}: {e}"
-                )
-            stats = None
-
-        duration = stats.walltime if stats else duration
-        total_run_time = stats.nodes_walltime if stats else total_run_time
-
-        return GraphExecutionMeta(
-            id=_graph_exec.id,
-            user_id=_graph_exec.userId,
-            started_at=start_time,
-            ended_at=end_time,
-            cost=stats.cost if stats else None,
-            duration=duration,
-            total_run_time=total_run_time,
-            status=ExecutionStatus(_graph_exec.executionStatus),
-            graph_id=_graph_exec.agentGraphId,
-            graph_version=_graph_exec.agentGraphVersion,
-            preset_id=_graph_exec.agentPresetId,
-        )
+T = TypeVar("T")
 
 
-class GraphExecution(GraphExecutionMeta):
-    inputs: BlockInput
-    outputs: CompletedBlockOutput
+class ExecutionQueue(Generic[T]):
+    """
+    Queue for managing the execution of agents.
+    This will be shared between different processes
+    """
 
-    @staticmethod
-    def from_db(_graph_exec: AgentGraphExecution):
-        if _graph_exec.AgentNodeExecutions is None:
-            raise ValueError("Node executions must be included in query")
+    def __init__(self):
+        self.queue = Manager().Queue()
 
-        graph_exec = GraphExecutionMeta.from_db(_graph_exec)
+    def add(self, execution: T) -> T:
+        self.queue.put(execution)
+        return execution
 
-        node_executions = sorted(
-            [
-                NodeExecutionResult.from_db(ne, _graph_exec.userId)
-                for ne in _graph_exec.AgentNodeExecutions
-            ],
-            key=lambda ne: (ne.queue_time is None, ne.queue_time or ne.add_time),
-        )
+    def get(self) -> T:
+        return self.queue.get()
 
-        inputs = {
-            **{
-                # inputs from Agent Input Blocks
-                exec.input_data["name"]: exec.input_data.get("value")
-                for exec in node_executions
-                if (
-                    (block := get_block(exec.block_id))
-                    and block.block_type == BlockType.INPUT
-                )
-            },
-            **{
-                # input from webhook-triggered block
-                "payload": exec.input_data["payload"]
-                for exec in node_executions
-                if (
-                    (block := get_block(exec.block_id))
-                    and block.block_type
-                    in [BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL]
-                )
-            },
-        }
-
-        outputs: CompletedBlockOutput = defaultdict(list)
-        for exec in node_executions:
-            if (
-                block := get_block(exec.block_id)
-            ) and block.block_type == BlockType.OUTPUT:
-                outputs[exec.input_data["name"]].append(
-                    exec.input_data.get("value", None)
-                )
-
-        return GraphExecution(
-            **{
-                field_name: getattr(graph_exec, field_name)
-                for field_name in graph_exec.model_fields
-            },
-            inputs=inputs,
-            outputs=outputs,
-        )
+    def empty(self) -> bool:
+        return self.queue.empty()
 
 
-class GraphExecutionWithNodes(GraphExecution):
-    node_executions: list["NodeExecutionResult"]
-
-    @staticmethod
-    def from_db(_graph_exec: AgentGraphExecution):
-        if _graph_exec.AgentNodeExecutions is None:
-            raise ValueError("Node executions must be included in query")
-
-        graph_exec_with_io = GraphExecution.from_db(_graph_exec)
-
-        node_executions = sorted(
-            [
-                NodeExecutionResult.from_db(ne, _graph_exec.userId)
-                for ne in _graph_exec.AgentNodeExecutions
-            ],
-            key=lambda ne: (ne.queue_time is None, ne.queue_time or ne.add_time),
-        )
-
-        return GraphExecutionWithNodes(
-            **{
-                field_name: getattr(graph_exec_with_io, field_name)
-                for field_name in graph_exec_with_io.model_fields
-            },
-            node_executions=node_executions,
-        )
-
-
-class NodeExecutionResult(BaseModel):
-    user_id: str
+class ExecutionResult(BaseModel):
     graph_id: str
     graph_version: int
     graph_exec_id: str
@@ -209,30 +80,43 @@ class NodeExecutionResult(BaseModel):
     end_time: datetime | None
 
     @staticmethod
-    def from_db(execution: AgentNodeExecution, user_id: Optional[str] = None):
+    def from_graph(graph: AgentGraphExecution):
+        return ExecutionResult(
+            graph_id=graph.agentGraphId,
+            graph_version=graph.agentGraphVersion,
+            graph_exec_id=graph.id,
+            node_exec_id="",
+            node_id="",
+            block_id="",
+            status=graph.executionStatus,
+            # TODO: Populate input_data & output_data from AgentNodeExecutions
+            #       Input & Output comes AgentInputBlock & AgentOutputBlock.
+            input_data={},
+            output_data={},
+            add_time=graph.createdAt,
+            queue_time=graph.createdAt,
+            start_time=graph.startedAt,
+            end_time=graph.updatedAt,
+        )
+
+    @staticmethod
+    def from_db(execution: AgentNodeExecution):
         if execution.executionData:
             # Execution that has been queued for execution will persist its data.
-            input_data = type_utils.convert(execution.executionData, dict[str, Any])
+            input_data = type.convert(execution.executionData, dict[str, Any])
         else:
             # For incomplete execution, executionData will not be yet available.
             input_data: BlockInput = defaultdict()
             for data in execution.Input or []:
-                input_data[data.name] = type_utils.convert(data.data, type[Any])
+                input_data[data.name] = type.convert(data.data, Type[Any])
 
         output_data: CompletedBlockOutput = defaultdict(list)
         for data in execution.Output or []:
-            output_data[data.name].append(type_utils.convert(data.data, type[Any]))
+            output_data[data.name].append(type.convert(data.data, Type[Any]))
 
         graph_execution: AgentGraphExecution | None = execution.AgentGraphExecution
-        if graph_execution:
-            user_id = graph_execution.userId
-        elif not user_id:
-            raise ValueError(
-                "AgentGraphExecution must be included or user_id passed in"
-            )
 
-        return NodeExecutionResult(
-            user_id=user_id,
+        return ExecutionResult(
             graph_id=graph_execution.agentGraphId if graph_execution else "",
             graph_version=graph_execution.agentGraphVersion if graph_execution else 0,
             graph_exec_id=execution.agentGraphExecutionId,
@@ -252,88 +136,13 @@ class NodeExecutionResult(BaseModel):
 # --------------------- Model functions --------------------- #
 
 
-async def get_graph_executions(
-    graph_id: Optional[str] = None,
-    user_id: Optional[str] = None,
-) -> list[GraphExecutionMeta]:
-    where_filter: AgentGraphExecutionWhereInput = {
-        "isDeleted": False,
-    }
-    if user_id:
-        where_filter["userId"] = user_id
-    if graph_id:
-        where_filter["agentGraphId"] = graph_id
-
-    executions = await AgentGraphExecution.prisma().find_many(
-        where=where_filter,
-        order={"createdAt": "desc"},
-    )
-    return [GraphExecutionMeta.from_db(execution) for execution in executions]
-
-
-async def get_graph_execution_meta(
-    user_id: str, execution_id: str
-) -> GraphExecutionMeta | None:
-    execution = await AgentGraphExecution.prisma().find_first(
-        where={"id": execution_id, "isDeleted": False, "userId": user_id}
-    )
-    return GraphExecutionMeta.from_db(execution) if execution else None
-
-
-@overload
-async def get_graph_execution(
-    user_id: str,
-    execution_id: str,
-    include_node_executions: Literal[True],
-) -> GraphExecutionWithNodes | None: ...
-
-
-@overload
-async def get_graph_execution(
-    user_id: str,
-    execution_id: str,
-    include_node_executions: Literal[False] = False,
-) -> GraphExecution | None: ...
-
-
-@overload
-async def get_graph_execution(
-    user_id: str,
-    execution_id: str,
-    include_node_executions: bool = False,
-) -> GraphExecution | GraphExecutionWithNodes | None: ...
-
-
-async def get_graph_execution(
-    user_id: str,
-    execution_id: str,
-    include_node_executions: bool = False,
-) -> GraphExecution | GraphExecutionWithNodes | None:
-    execution = await AgentGraphExecution.prisma().find_first(
-        where={"id": execution_id, "isDeleted": False, "userId": user_id},
-        include=(
-            GRAPH_EXECUTION_INCLUDE_WITH_NODES
-            if include_node_executions
-            else GRAPH_EXECUTION_INCLUDE
-        ),
-    )
-    if not execution:
-        return None
-
-    return (
-        GraphExecutionWithNodes.from_db(execution)
-        if include_node_executions
-        else GraphExecution.from_db(execution)
-    )
-
-
 async def create_graph_execution(
     graph_id: str,
     graph_version: int,
     nodes_input: list[tuple[str, BlockInput]],
     user_id: str,
     preset_id: str | None = None,
-) -> GraphExecutionWithNodes:
+) -> tuple[str, list[ExecutionResult]]:
     """
     Create a new AgentGraphExecution record.
     Returns:
@@ -348,8 +157,7 @@ async def create_graph_execution(
                 "create": [  # type: ignore
                     {
                         "agentNodeId": node_id,
-                        "executionStatus": ExecutionStatus.QUEUED,
-                        "queuedTime": datetime.now(tz=timezone.utc),
+                        "executionStatus": ExecutionStatus.INCOMPLETE,
                         "Input": {
                             "create": [
                                 {"name": name, "data": Json(data)}
@@ -363,10 +171,13 @@ async def create_graph_execution(
             "userId": user_id,
             "agentPresetId": preset_id,
         },
-        include=GRAPH_EXECUTION_INCLUDE_WITH_NODES,
+        include=GRAPH_EXECUTION_INCLUDE,
     )
 
-    return GraphExecutionWithNodes.from_db(result)
+    return result.id, [
+        ExecutionResult.from_db(execution)
+        for execution in result.AgentNodeExecutions or []
+    ]
 
 
 async def upsert_execution_input(
@@ -388,20 +199,17 @@ async def upsert_execution_input(
         node_exec_id: [Optional] The id of the AgentNodeExecution that has no `input_name` as input. If not provided, it will find the eligible incomplete AgentNodeExecution or create a new one.
 
     Returns:
-        str: The id of the created or existing AgentNodeExecution.
-        dict[str, Any]: Node input data; key is the input name, value is the input data.
+        * The id of the created or existing AgentNodeExecution.
+        * Dict of node input data, key is the input name, value is the input data.
     """
-    existing_exec_query_filter: AgentNodeExecutionWhereInput = {
-        "agentNodeId": node_id,
-        "agentGraphExecutionId": graph_exec_id,
-        "executionStatus": ExecutionStatus.INCOMPLETE,
-        "Input": {"every": {"name": {"not": input_name}}},
-    }
-    if node_exec_id:
-        existing_exec_query_filter["id"] = node_exec_id
-
     existing_execution = await AgentNodeExecution.prisma().find_first(
-        where=existing_exec_query_filter,
+        where={  # type: ignore
+            **({"id": node_exec_id} if node_exec_id else {}),
+            "agentNodeId": node_id,
+            "agentGraphExecutionId": graph_exec_id,
+            "executionStatus": ExecutionStatus.INCOMPLETE,
+            "Input": {"every": {"name": {"not": input_name}}},
+        },
         order={"addedTime": "asc"},
         include={"Input": True},
     )
@@ -417,7 +225,7 @@ async def upsert_execution_input(
         )
         return existing_execution.id, {
             **{
-                input_data.name: type_utils.convert(input_data.data, type[Any])
+                input_data.name: type.convert(input_data.data, Type[Any])
                 for input_data in existing_execution.Input or []
             },
             input_name: input_data,
@@ -457,289 +265,141 @@ async def upsert_execution_output(
     )
 
 
-async def update_graph_execution_start_time(graph_exec_id: str) -> GraphExecution:
-    res = await AgentGraphExecution.prisma().update(
+async def update_graph_execution_start_time(graph_exec_id: str):
+    await AgentGraphExecution.prisma().update(
         where={"id": graph_exec_id},
         data={
             "executionStatus": ExecutionStatus.RUNNING,
             "startedAt": datetime.now(tz=timezone.utc),
         },
-        include=GRAPH_EXECUTION_INCLUDE,
     )
-    if not res:
-        raise ValueError(f"Graph execution #{graph_exec_id} not found")
-
-    return GraphExecution.from_db(res)
 
 
 async def update_graph_execution_stats(
     graph_exec_id: str,
     status: ExecutionStatus,
-    stats: GraphExecutionStats | None = None,
-) -> GraphExecution | None:
-    data = stats.model_dump() if stats else {}
-    if isinstance(data.get("error"), Exception):
-        data["error"] = str(data["error"])
+    stats: dict[str, Any],
+) -> ExecutionResult:
     res = await AgentGraphExecution.prisma().update(
-        where={
-            "id": graph_exec_id,
-            "OR": [
-                {"executionStatus": ExecutionStatus.RUNNING},
-                {"executionStatus": ExecutionStatus.QUEUED},
-            ],
-        },
+        where={"id": graph_exec_id},
         data={
             "executionStatus": status,
-            "stats": Json(data),
+            "stats": Json(stats),
         },
-        include=GRAPH_EXECUTION_INCLUDE,
     )
+    if not res:
+        raise ValueError(f"Execution {graph_exec_id} not found.")
 
-    return GraphExecution.from_db(res) if res else None
+    return ExecutionResult.from_graph(res)
 
 
-async def update_node_execution_stats(node_exec_id: str, stats: NodeExecutionStats):
-    data = stats.model_dump()
-    if isinstance(data["error"], Exception):
-        data["error"] = str(data["error"])
+async def update_node_execution_stats(node_exec_id: str, stats: dict[str, Any]):
     await AgentNodeExecution.prisma().update(
         where={"id": node_exec_id},
-        data={"stats": Json(data)},
+        data={"stats": Json(stats)},
     )
 
 
-async def update_node_execution_status_batch(
-    node_exec_ids: list[str],
-    status: ExecutionStatus,
-    stats: dict[str, Any] | None = None,
-):
-    await AgentNodeExecution.prisma().update_many(
-        where={"id": {"in": node_exec_ids}},
-        data=_get_update_status_data(status, None, stats),
-    )
-
-
-async def update_node_execution_status(
+async def update_execution_status(
     node_exec_id: str,
     status: ExecutionStatus,
     execution_data: BlockInput | None = None,
     stats: dict[str, Any] | None = None,
-) -> NodeExecutionResult:
+) -> ExecutionResult:
     if status == ExecutionStatus.QUEUED and execution_data is None:
         raise ValueError("Execution data must be provided when queuing an execution.")
 
+    now = datetime.now(tz=timezone.utc)
+    data = {
+        **({"executionStatus": status}),
+        **({"queuedTime": now} if status == ExecutionStatus.QUEUED else {}),
+        **({"startedTime": now} if status == ExecutionStatus.RUNNING else {}),
+        **({"endedTime": now} if status == ExecutionStatus.FAILED else {}),
+        **({"endedTime": now} if status == ExecutionStatus.COMPLETED else {}),
+        **({"executionData": Json(execution_data)} if execution_data else {}),
+        **({"stats": Json(stats)} if stats else {}),
+    }
+
     res = await AgentNodeExecution.prisma().update(
         where={"id": node_exec_id},
-        data=_get_update_status_data(status, execution_data, stats),
+        data=data,  # type: ignore
         include=EXECUTION_RESULT_INCLUDE,
     )
     if not res:
         raise ValueError(f"Execution {node_exec_id} not found.")
 
-    return NodeExecutionResult.from_db(res)
+    return ExecutionResult.from_db(res)
 
 
-def _get_update_status_data(
-    status: ExecutionStatus,
-    execution_data: BlockInput | None = None,
-    stats: dict[str, Any] | None = None,
-) -> AgentNodeExecutionUpdateInput:
-    now = datetime.now(tz=timezone.utc)
-    update_data: AgentNodeExecutionUpdateInput = {"executionStatus": status}
+async def get_execution(
+    execution_id: str, user_id: str
+) -> Optional[AgentNodeExecution]:
+    """
+    Get an execution by ID. Returns None if not found.
 
-    if status == ExecutionStatus.QUEUED:
-        update_data["queuedTime"] = now
-    elif status == ExecutionStatus.RUNNING:
-        update_data["startedTime"] = now
-    elif status in (ExecutionStatus.FAILED, ExecutionStatus.COMPLETED):
-        update_data["endedTime"] = now
+    Args:
+        execution_id: The ID of the execution to retrieve
 
-    if execution_data:
-        update_data["executionData"] = Json(execution_data)
-    if stats:
-        update_data["stats"] = Json(stats)
-
-    return update_data
-
-
-async def delete_graph_execution(
-    graph_exec_id: str, user_id: str, soft_delete: bool = True
-) -> None:
-    if soft_delete:
-        deleted_count = await AgentGraphExecution.prisma().update_many(
-            where={"id": graph_exec_id, "userId": user_id}, data={"isDeleted": True}
-        )
-    else:
-        deleted_count = await AgentGraphExecution.prisma().delete_many(
-            where={"id": graph_exec_id, "userId": user_id}
-        )
-    if deleted_count < 1:
-        raise DatabaseError(
-            f"Could not delete graph execution #{graph_exec_id}: not found"
+    Returns:
+        The execution if found, None otherwise
+    """
+    try:
+        execution = await AgentNodeExecution.prisma().find_unique(
+            where={
+                "id": execution_id,
+                "userId": user_id,
+            }
         )
+        return execution
+    except PrismaError:
+        return None
 
 
-async def get_node_execution_results(
-    graph_exec_id: str,
-    block_ids: list[str] | None = None,
-    statuses: list[ExecutionStatus] | None = None,
-    limit: int | None = None,
-) -> list[NodeExecutionResult]:
-    where_clause: AgentNodeExecutionWhereInput = {
-        "agentGraphExecutionId": graph_exec_id,
-    }
-    if block_ids:
-        where_clause["AgentNode"] = {"is": {"agentBlockId": {"in": block_ids}}}
-    if statuses:
-        where_clause["OR"] = [{"executionStatus": status} for status in statuses]
-
+async def get_execution_results(graph_exec_id: str) -> list[ExecutionResult]:
     executions = await AgentNodeExecution.prisma().find_many(
-        where=where_clause,
+        where={"agentGraphExecutionId": graph_exec_id},
         include=EXECUTION_RESULT_INCLUDE,
-        take=limit,
+        order=[
+            {"queuedTime": "asc"},
+            {"addedTime": "asc"},  # Fallback: Incomplete execs has no queuedTime.
+        ],
     )
-    res = [NodeExecutionResult.from_db(execution) for execution in executions]
+    res = [ExecutionResult.from_db(execution) for execution in executions]
     return res
 
 
-async def get_graph_executions_in_timerange(
+async def get_executions_in_timerange(
     user_id: str, start_time: str, end_time: str
-) -> list[GraphExecution]:
+) -> list[ExecutionResult]:
     try:
         executions = await AgentGraphExecution.prisma().find_many(
             where={
-                "startedAt": {
-                    "gte": datetime.fromisoformat(start_time),
-                    "lte": datetime.fromisoformat(end_time),
-                },
-                "userId": user_id,
-                "isDeleted": False,
+                "AND": [
+                    {
+                        "startedAt": {
+                            "gte": datetime.fromisoformat(start_time),
+                            "lte": datetime.fromisoformat(end_time),
+                        }
+                    },
+                    {"userId": user_id},
+                ]
             },
             include=GRAPH_EXECUTION_INCLUDE,
         )
-        return [GraphExecution.from_db(execution) for execution in executions]
+        return [ExecutionResult.from_graph(execution) for execution in executions]
     except Exception as e:
         raise DatabaseError(
             f"Failed to get executions in timerange {start_time} to {end_time} for user {user_id}: {e}"
         ) from e
 
 
-async def get_latest_node_execution(
-    node_id: str, graph_eid: str
-) -> NodeExecutionResult | None:
-    execution = await AgentNodeExecution.prisma().find_first(
-        where={
-            "agentNodeId": node_id,
-            "agentGraphExecutionId": graph_eid,
-            "executionStatus": {"not": ExecutionStatus.INCOMPLETE},  # type: ignore
-        },
-        order=[
-            {"queuedTime": "desc"},
-            {"addedTime": "desc"},
-        ],
-        include=EXECUTION_RESULT_INCLUDE,
-    )
-    if not execution:
-        return None
-    return NodeExecutionResult.from_db(execution)
-
-
-async def get_incomplete_node_executions(
-    node_id: str, graph_eid: str
-) -> list[NodeExecutionResult]:
-    executions = await AgentNodeExecution.prisma().find_many(
-        where={
-            "agentNodeId": node_id,
-            "agentGraphExecutionId": graph_eid,
-            "executionStatus": ExecutionStatus.INCOMPLETE,
-        },
-        include=EXECUTION_RESULT_INCLUDE,
-    )
-    return [NodeExecutionResult.from_db(execution) for execution in executions]
-
-
-# ----------------- Execution Infrastructure ----------------- #
-
-
-class GraphExecutionEntry(BaseModel):
-    user_id: str
-    graph_exec_id: str
-    graph_id: str
-    graph_version: int
-    start_node_execs: list["NodeExecutionEntry"]
-
-
-class NodeExecutionEntry(BaseModel):
-    user_id: str
-    graph_exec_id: str
-    graph_id: str
-    node_exec_id: str
-    node_id: str
-    block_id: str
-    data: BlockInput
-
-
-class ExecutionQueue(Generic[T]):
-    """
-    Queue for managing the execution of agents.
-    This will be shared between different processes
-    """
-
-    def __init__(self):
-        self.queue = Manager().Queue()
-
-    def add(self, execution: T) -> T:
-        self.queue.put(execution)
-        return execution
-
-    def get(self) -> T:
-        return self.queue.get()
-
-    def empty(self) -> bool:
-        return self.queue.empty()
-
-
-# ------------------- Execution Utilities -------------------- #
-
-
 LIST_SPLIT = "_$_"
 DICT_SPLIT = "_#_"
 OBJC_SPLIT = "_@_"
 
 
 def parse_execution_output(output: BlockData, name: str) -> Any | None:
-    """
-    Extracts partial output data by name from a given BlockData.
-
-    The function supports extracting data from lists, dictionaries, and objects
-    using specific naming conventions:
-    - For lists: <output_name>_$_<index>
-    - For dictionaries: <output_name>_#_<key>
-    - For objects: <output_name>_@_<attribute>
-
-    Args:
-        output (BlockData): A tuple containing the output name and data.
-        name (str): The name used to extract specific data from the output.
-
-    Returns:
-        Any | None: The extracted data if found, otherwise None.
-
-    Examples:
-        >>> output = ("result", [10, 20, 30])
-        >>> parse_execution_output(output, "result_$_1")
-        20
-
-        >>> output = ("config", {"key1": "value1", "key2": "value2"})
-        >>> parse_execution_output(output, "config_#_key1")
-        'value1'
-
-        >>> class Sample:
-        ...     attr1 = "value1"
-        ...     attr2 = "value2"
-        >>> output = ("object", Sample())
-        >>> parse_execution_output(output, "object_@_attr1")
-        'value1'
-    """
+    # Allow extracting partial output data by name.
     output_name, output_data = output
 
     if name == output_name:
@@ -768,37 +428,11 @@ def parse_execution_output(output: BlockData, name: str) -> Any | None:
 
 def merge_execution_input(data: BlockInput) -> BlockInput:
     """
-    Merges dynamic input pins into a single list, dictionary, or object based on naming patterns.
-
-    This function processes input keys that follow specific patterns to merge them into a unified structure:
-    - `<input_name>_$_<index>` for list inputs.
-    - `<input_name>_#_<index>` for dictionary inputs.
-    - `<input_name>_@_<index>` for object inputs.
-
-    Args:
-        data (BlockInput): A dictionary containing input keys and their corresponding values.
-
-    Returns:
-        BlockInput: A dictionary with merged inputs.
-
-    Raises:
-        ValueError: If a list index is not an integer.
-
-    Examples:
-        >>> data = {
-        ...     "list_$_0": "a",
-        ...     "list_$_1": "b",
-        ...     "dict_#_key1": "value1",
-        ...     "dict_#_key2": "value2",
-        ...     "object_@_attr1": "value1",
-        ...     "object_@_attr2": "value2"
-        ... }
-        >>> merge_execution_input(data)
-        {
-            "list": ["a", "b"],
-            "dict": {"key1": "value1", "key2": "value2"},
-            "object": <MockObject attr1="value1" attr2="value2">
-        }
+    Merge all dynamic input pins which described by the following pattern:
+    - <input_name>_$_<index> for list input.
+    - <input_name>_#_<index> for dict input.
+    - <input_name>_@_<index> for object input.
+    This function will construct pins with the same name into a single list/dict/object.
     """
 
     # Merge all input with <input_name>_$_<index> into a single list.
@@ -837,84 +471,69 @@ def merge_execution_input(data: BlockInput) -> BlockInput:
     return data
 
 
+async def get_latest_execution(node_id: str, graph_eid: str) -> ExecutionResult | None:
+    execution = await AgentNodeExecution.prisma().find_first(
+        where={
+            "agentNodeId": node_id,
+            "agentGraphExecutionId": graph_eid,
+            "executionStatus": {"not": ExecutionStatus.INCOMPLETE},  # type: ignore
+        },
+        order={"queuedTime": "desc"},
+        include=EXECUTION_RESULT_INCLUDE,
+    )
+    if not execution:
+        return None
+    return ExecutionResult.from_db(execution)
+
+
+async def get_incomplete_executions(
+    node_id: str, graph_eid: str
+) -> list[ExecutionResult]:
+    executions = await AgentNodeExecution.prisma().find_many(
+        where={
+            "agentNodeId": node_id,
+            "agentGraphExecutionId": graph_eid,
+            "executionStatus": ExecutionStatus.INCOMPLETE,
+        },
+        include=EXECUTION_RESULT_INCLUDE,
+    )
+    return [ExecutionResult.from_db(execution) for execution in executions]
+
+
 # --------------------- Event Bus --------------------- #
 
-
-class ExecutionEventType(str, Enum):
-    GRAPH_EXEC_UPDATE = "graph_execution_update"
-    NODE_EXEC_UPDATE = "node_execution_update"
+config = Config()
 
 
-class GraphExecutionEvent(GraphExecution):
-    event_type: Literal[ExecutionEventType.GRAPH_EXEC_UPDATE] = (
-        ExecutionEventType.GRAPH_EXEC_UPDATE
-    )
-
-
-class NodeExecutionEvent(NodeExecutionResult):
-    event_type: Literal[ExecutionEventType.NODE_EXEC_UPDATE] = (
-        ExecutionEventType.NODE_EXEC_UPDATE
-    )
-
-
-ExecutionEvent = Annotated[
-    GraphExecutionEvent | NodeExecutionEvent, Field(discriminator="event_type")
-]
-
-
-class RedisExecutionEventBus(RedisEventBus[ExecutionEvent]):
-    Model = ExecutionEvent  # type: ignore
+class RedisExecutionEventBus(RedisEventBus[ExecutionResult]):
+    Model = ExecutionResult
 
     @property
     def event_bus_name(self) -> str:
         return config.execution_event_bus_name
 
-    def publish(self, res: GraphExecution | NodeExecutionResult):
-        if isinstance(res, GraphExecution):
-            self.publish_graph_exec_update(res)
-        else:
-            self.publish_node_exec_update(res)
-
-    def publish_node_exec_update(self, res: NodeExecutionResult):
-        event = NodeExecutionEvent.model_validate(res.model_dump())
-        self.publish_event(event, f"{res.user_id}/{res.graph_id}/{res.graph_exec_id}")
-
-    def publish_graph_exec_update(self, res: GraphExecution):
-        event = GraphExecutionEvent.model_validate(res.model_dump())
-        self.publish_event(event, f"{res.user_id}/{res.graph_id}/{res.id}")
+    def publish(self, res: ExecutionResult):
+        self.publish_event(res, f"{res.graph_id}/{res.graph_exec_id}")
 
     def listen(
-        self, user_id: str, graph_id: str = "*", graph_exec_id: str = "*"
-    ) -> Generator[ExecutionEvent, None, None]:
-        for event in self.listen_events(f"{user_id}/{graph_id}/{graph_exec_id}"):
-            yield event
+        self, graph_id: str = "*", graph_exec_id: str = "*"
+    ) -> Generator[ExecutionResult, None, None]:
+        for execution_result in self.listen_events(f"{graph_id}/{graph_exec_id}"):
+            yield execution_result
 
 
-class AsyncRedisExecutionEventBus(AsyncRedisEventBus[ExecutionEvent]):
-    Model = ExecutionEvent  # type: ignore
+class AsyncRedisExecutionEventBus(AsyncRedisEventBus[ExecutionResult]):
+    Model = ExecutionResult
 
     @property
     def event_bus_name(self) -> str:
         return config.execution_event_bus_name
 
-    async def publish(self, res: GraphExecutionMeta | NodeExecutionResult):
-        if isinstance(res, GraphExecutionMeta):
-            await self.publish_graph_exec_update(res)
-        else:
-            await self.publish_node_exec_update(res)
-
-    async def publish_node_exec_update(self, res: NodeExecutionResult):
-        event = NodeExecutionEvent.model_validate(res.model_dump())
-        await self.publish_event(
-            event, f"{res.user_id}/{res.graph_id}/{res.graph_exec_id}"
-        )
-
-    async def publish_graph_exec_update(self, res: GraphExecutionMeta):
-        event = GraphExecutionEvent.model_validate(res.model_dump())
-        await self.publish_event(event, f"{res.user_id}/{res.graph_id}/{res.id}")
+    async def publish(self, res: ExecutionResult):
+        await self.publish_event(res, f"{res.graph_id}/{res.graph_exec_id}")
 
     async def listen(
-        self, user_id: str, graph_id: str = "*", graph_exec_id: str = "*"
-    ) -> AsyncGenerator[ExecutionEvent, None]:
-        async for event in self.listen_events(f"{user_id}/{graph_id}/{graph_exec_id}"):
-            yield event
+        self, graph_id: str = "*", graph_exec_id: str = "*"
+    ) -> AsyncGenerator[ExecutionResult, None]:
+        async for execution_result in self.listen_events(f"{graph_id}/{graph_exec_id}"):
+            yield execution_result

autogpt_platform/backend/backend/data/graph.py

@@ -1,23 +1,29 @@
+import asyncio
 import logging
 import uuid
 from collections import defaultdict
+from datetime import datetime, timezone
 from typing import Any, Literal, Optional, Type
 
 import prisma
 from prisma import Json
-from prisma.enums import SubmissionStatus
-from prisma.models import AgentGraph, AgentNode, AgentNodeLink, StoreListingVersion
+from prisma.models import (
+    AgentGraph,
+    AgentGraphExecution,
+    AgentNode,
+    AgentNodeLink,
+    StoreListingVersion,
+)
 from prisma.types import AgentGraphWhereInput
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
-from backend.blocks.io import AgentInputBlock, AgentOutputBlock
-from backend.blocks.llm import LlmModel
-from backend.data.db import prisma as db
-from backend.util import type as type_utils
+from backend.blocks.basic import AgentInputBlock, AgentOutputBlock
+from backend.util import type
 
-from .block import Block, BlockInput, BlockSchema, BlockType, get_block, get_blocks
+from .block import BlockInput, BlockType, get_block, get_blocks
 from .db import BaseDbModel, transaction
+from .execution import ExecutionStatus
 from .includes import AGENT_GRAPH_INCLUDE, AGENT_NODE_INCLUDE
 from .integrations import Webhook
 
@@ -62,20 +68,15 @@ class NodeModel(Node):
 
     webhook: Optional[Webhook] = None
 
-    @property
-    def block(self) -> Block[BlockSchema, BlockSchema]:
-        block = get_block(self.block_id)
-        if not block:
-            raise ValueError(f"Block #{self.block_id} does not exist")
-        return block
-
     @staticmethod
-    def from_db(node: AgentNode, for_export: bool = False) -> "NodeModel":
+    def from_db(node: AgentNode):
+        if not node.AgentBlock:
+            raise ValueError(f"Invalid node {node.id}, invalid AgentBlock.")
         obj = NodeModel(
             id=node.id,
-            block_id=node.agentBlockId,
-            input_default=type_utils.convert(node.constantInput, dict[str, Any]),
-            metadata=type_utils.convert(node.metadata, dict[str, Any]),
+            block_id=node.AgentBlock.id,
+            input_default=type.convert(node.constantInput, dict[str, Any]),
+            metadata=type.convert(node.metadata, dict[str, Any]),
             graph_id=node.agentGraphId,
             graph_version=node.agentGraphVersion,
             webhook_id=node.webhookId,
@@ -83,8 +84,6 @@ class NodeModel(Node):
         )
         obj.input_links = [Link.from_db(link) for link in node.Input or []]
         obj.output_links = [Link.from_db(link) for link in node.Output or []]
-        if for_export:
-            return obj.stripped_for_export()
         return obj
 
     def is_triggered_by_event_type(self, event_type: str) -> bool:
@@ -103,59 +102,54 @@ class NodeModel(Node):
             if event_filter[k] is True
         ]
 
-    def stripped_for_export(self) -> "NodeModel":
-        """
-        Returns a copy of the node model, stripped of any non-transferable properties
-        """
-        stripped_node = self.model_copy(deep=True)
-        # Remove credentials from node input
-        if stripped_node.input_default:
-            stripped_node.input_default = NodeModel._filter_secrets_from_node_input(
-                stripped_node.input_default, self.block.input_schema.jsonschema()
-            )
-
-        if (
-            stripped_node.block.block_type == BlockType.INPUT
-            and "value" in stripped_node.input_default
-        ):
-            stripped_node.input_default["value"] = ""
-
-        # Remove webhook info
-        stripped_node.webhook_id = None
-        stripped_node.webhook = None
-
-        return stripped_node
-
-    @staticmethod
-    def _filter_secrets_from_node_input(
-        input_data: dict[str, Any], schema: dict[str, Any] | None
-    ) -> dict[str, Any]:
-        sensitive_keys = ["credentials", "api_key", "password", "token", "secret"]
-        field_schemas = schema.get("properties", {}) if schema else {}
-        result = {}
-        for key, value in input_data.items():
-            field_schema: dict | None = field_schemas.get(key)
-            if (field_schema and field_schema.get("secret", False)) or any(
-                sensitive_key in key.lower() for sensitive_key in sensitive_keys
-            ):
-                # This is a secret value -> filter this key-value pair out
-                continue
-            elif isinstance(value, dict):
-                result[key] = NodeModel._filter_secrets_from_node_input(
-                    value, field_schema
-                )
-            else:
-                result[key] = value
-        return result
-
 
 # Fix 2-way reference Node <-> Webhook
 Webhook.model_rebuild()
 
 
-class BaseGraph(BaseDbModel):
+class GraphExecution(BaseDbModel):
+    execution_id: str
+    started_at: datetime
+    ended_at: datetime
+    duration: float
+    total_run_time: float
+    status: ExecutionStatus
+    graph_id: str
+    graph_version: int
+
+    @staticmethod
+    def from_db(execution: AgentGraphExecution):
+        now = datetime.now(timezone.utc)
+        start_time = execution.startedAt or execution.createdAt
+        end_time = execution.updatedAt or now
+        duration = (end_time - start_time).total_seconds()
+        total_run_time = duration
+
+        try:
+            stats = type.convert(execution.stats or {}, dict[str, Any])
+        except ValueError:
+            stats = {}
+
+        duration = stats.get("walltime", duration)
+        total_run_time = stats.get("nodes_walltime", total_run_time)
+
+        return GraphExecution(
+            id=execution.id,
+            execution_id=execution.id,
+            started_at=start_time,
+            ended_at=end_time,
+            duration=duration,
+            total_run_time=total_run_time,
+            status=ExecutionStatus(execution.executionStatus),
+            graph_id=execution.agentGraphId,
+            graph_version=execution.agentGraphVersion,
+        )
+
+
+class Graph(BaseDbModel):
     version: int = 1
     is_active: bool = True
+    is_template: bool = False
     name: str
     description: str
     nodes: list[Node] = []
@@ -165,48 +159,46 @@ class BaseGraph(BaseDbModel):
     @property
     def input_schema(self) -> dict[str, Any]:
         return self._generate_schema(
-            *(
-                (b.input_schema, node.input_default)
+            AgentInputBlock.Input,
+            [
+                node.input_default
                 for node in self.nodes
                 if (b := get_block(node.block_id))
                 and b.block_type == BlockType.INPUT
-                and issubclass(b.input_schema, AgentInputBlock.Input)
-            )
+                and "name" in node.input_default
+            ],
         )
 
     @computed_field
     @property
     def output_schema(self) -> dict[str, Any]:
         return self._generate_schema(
-            *(
-                (b.input_schema, node.input_default)
+            AgentOutputBlock.Input,
+            [
+                node.input_default
                 for node in self.nodes
                 if (b := get_block(node.block_id))
                 and b.block_type == BlockType.OUTPUT
-                and issubclass(b.input_schema, AgentOutputBlock.Input)
-            )
+                and "name" in node.input_default
+            ],
         )
 
     @staticmethod
     def _generate_schema(
-        *props: tuple[Type[AgentInputBlock.Input] | Type[AgentOutputBlock.Input], dict],
+        type_class: Type[AgentInputBlock.Input] | Type[AgentOutputBlock.Input],
+        data: list[dict],
     ) -> dict[str, Any]:
-        schema = []
-        for type_class, input_default in props:
+        props = []
+        for p in data:
             try:
-                schema.append(type_class(**input_default))
+                props.append(type_class(**p))
             except Exception as e:
-                logger.warning(f"Invalid {type_class}: {input_default}, {e}")
+                logger.warning(f"Invalid {type_class}: {p}, {e}")
 
         return {
             "type": "object",
             "properties": {
                 p.name: {
-                    **{
-                        k: v
-                        for k, v in p.generate_schema().items()
-                        if k not in ["description", "default"]
-                    },
                     "secret": p.secret,
                     # Default value has to be set for advanced fields.
                     "advanced": p.advanced and p.value is not None,
@@ -214,16 +206,12 @@ class BaseGraph(BaseDbModel):
                     **({"description": p.description} if p.description else {}),
                     **({"default": p.value} if p.value is not None else {}),
                 }
-                for p in schema
+                for p in props
             },
-            "required": [p.name for p in schema if p.value is None],
+            "required": [p.name for p in props if p.value is None],
         }
 
 
-class Graph(BaseGraph):
-    sub_graphs: list[BaseGraph] = []  # Flattened sub-graphs, only used in export
-
-
 class GraphModel(Graph):
     user_id: str
     nodes: list[NodeModel] = []  # type: ignore
@@ -247,88 +235,42 @@ class GraphModel(Graph):
         Reassigns all IDs in the graph to new UUIDs.
         This method can be used before storing a new graph to the database.
         """
-        if reassign_graph_id:
-            graph_id_map = {
-                self.id: str(uuid.uuid4()),
-                **{sub_graph.id: str(uuid.uuid4()) for sub_graph in self.sub_graphs},
-            }
-        else:
-            graph_id_map = {}
 
-        self._reassign_ids(self, user_id, graph_id_map)
-        for sub_graph in self.sub_graphs:
-            self._reassign_ids(sub_graph, user_id, graph_id_map)
-
-    @staticmethod
-    def _reassign_ids(
-        graph: BaseGraph,
-        user_id: str,
-        graph_id_map: dict[str, str],
-    ):
         # Reassign Graph ID
-        if graph.id in graph_id_map:
-            graph.id = graph_id_map[graph.id]
+        id_map = {node.id: str(uuid.uuid4()) for node in self.nodes}
+        if reassign_graph_id:
+            self.id = str(uuid.uuid4())
 
         # Reassign Node IDs
-        id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
-        for node in graph.nodes:
+        for node in self.nodes:
             node.id = id_map[node.id]
 
         # Reassign Link IDs
-        for link in graph.links:
+        for link in self.links:
             link.source_id = id_map[link.source_id]
             link.sink_id = id_map[link.sink_id]
 
         # Reassign User IDs for agent blocks
-        for node in graph.nodes:
+        for node in self.nodes:
             if node.block_id != AgentExecutorBlock().id:
                 continue
             node.input_default["user_id"] = user_id
             node.input_default.setdefault("data", {})
-            if (graph_id := node.input_default.get("graph_id")) in graph_id_map:
-                node.input_default["graph_id"] = graph_id_map[graph_id]
+
+        self.validate_graph()
 
     def validate_graph(self, for_run: bool = False):
-        self._validate_graph(self, for_run)
-        for sub_graph in self.sub_graphs:
-            self._validate_graph(sub_graph, for_run)
-
-    @staticmethod
-    def _validate_graph(graph: BaseGraph, for_run: bool = False):
         def sanitize(name):
-            sanitized_name = name.split("_#_")[0].split("_@_")[0].split("_$_")[0]
-            if sanitized_name.startswith("tools_^_"):
-                return sanitized_name.split("_^_")[0]
-            return sanitized_name
-
-        # Validate smart decision maker nodes
-        smart_decision_maker_nodes = set()
-        agent_nodes = set()
-        nodes_block = {
-            node.id: block
-            for node in graph.nodes
-            if (block := get_block(node.block_id)) is not None
-        }
-
-        for node in graph.nodes:
-            if (block := nodes_block.get(node.id)) is None:
-                raise ValueError(f"Invalid block {node.block_id} for node #{node.id}")
-
-            # Smart decision maker nodes
-            if block.block_type == BlockType.AI:
-                smart_decision_maker_nodes.add(node.id)
-            # Agent nodes
-            elif block.block_type == BlockType.AGENT:
-                agent_nodes.add(node.id)
+            return name.split("_#_")[0].split("_@_")[0].split("_$_")[0]
 
         input_links = defaultdict(list)
-
-        for link in graph.links:
+        for link in self.links:
             input_links[link.sink_id].append(link)
 
         # Nodes: required fields are filled or connected and dependencies are satisfied
-        for node in graph.nodes:
-            if (block := nodes_block.get(node.id)) is None:
+        for node in self.nodes:
+            block = get_block(node.block_id)
+            if block is None:
                 raise ValueError(f"Invalid block {node.block_id} for node #{node.id}")
 
             provided_inputs = set(
@@ -345,12 +287,9 @@ class GraphModel(Graph):
                     )
                     and (
                         for_run  # Skip input completion validation, unless when executing.
-                        or block.block_type
-                        in [
-                            BlockType.INPUT,
-                            BlockType.OUTPUT,
-                            BlockType.AGENT,
-                        ]
+                        or block.block_type == BlockType.INPUT
+                        or block.block_type == BlockType.OUTPUT
+                        or block.block_type == BlockType.AGENT
                     )
                 ):
                     raise ValueError(
@@ -388,7 +327,7 @@ class GraphModel(Graph):
                         f"Node {block.name} #{node.id}: Field `{field_name}` requires [{', '.join(missing_deps)}] to be set"
                     )
 
-        node_map = {v.id: v for v in graph.nodes}
+        node_map = {v.id: v for v in self.nodes}
 
         def is_static_output_block(nid: str) -> bool:
             bid = node_map[nid].block_id
@@ -396,23 +335,23 @@ class GraphModel(Graph):
             return b.static_output if b else False
 
         # Links: links are connected and the connected pin data type are compatible.
-        for link in graph.links:
+        for link in self.links:
             source = (link.source_id, link.source_name)
             sink = (link.sink_id, link.sink_name)
-            prefix = f"Link {source} <-> {sink}"
+            suffix = f"Link {source} <-> {sink}"
 
             for i, (node_id, name) in enumerate([source, sink]):
                 node = node_map.get(node_id)
                 if not node:
                     raise ValueError(
-                        f"{prefix}, {node_id} is invalid node id, available nodes: {node_map.keys()}"
+                        f"{suffix}, {node_id} is invalid node id, available nodes: {node_map.keys()}"
                     )
 
                 block = get_block(node.block_id)
                 if not block:
                     blocks = {v().id: v().name for v in get_blocks().values()}
                     raise ValueError(
-                        f"{prefix}, {node.block_id} is invalid block id, available blocks: {blocks}"
+                        f"{suffix}, {node.block_id} is invalid block id, available blocks: {blocks}"
                     )
 
                 sanitized_name = sanitize(name)
@@ -420,37 +359,35 @@ class GraphModel(Graph):
                 if i == 0:
                     fields = (
                         block.output_schema.get_fields()
-                        if block.block_type not in [BlockType.AGENT]
+                        if block.block_type != BlockType.AGENT
                         else vals.get("output_schema", {}).get("properties", {}).keys()
                     )
                 else:
                     fields = (
                         block.input_schema.get_fields()
-                        if block.block_type not in [BlockType.AGENT]
+                        if block.block_type != BlockType.AGENT
                         else vals.get("input_schema", {}).get("properties", {}).keys()
                     )
-                if sanitized_name not in fields and not name.startswith("tools_^_"):
+                if sanitized_name not in fields:
                     fields_msg = f"Allowed fields: {fields}"
-                    raise ValueError(f"{prefix}, `{name}` invalid, {fields_msg}")
+                    raise ValueError(f"{suffix}, `{name}` invalid, {fields_msg}")
 
             if is_static_output_block(link.source_id):
                 link.is_static = True  # Each value block output should be static.
 
     @staticmethod
-    def from_db(
-        graph: AgentGraph,
-        for_export: bool = False,
-        sub_graphs: list[AgentGraph] | None = None,
-    ):
+    def from_db(graph: AgentGraph, for_export: bool = False):
         return GraphModel(
             id=graph.id,
-            user_id=graph.userId if not for_export else "",
+            user_id=graph.userId,
             version=graph.version,
             is_active=graph.isActive,
+            is_template=graph.isTemplate,
             name=graph.name or "",
             description=graph.description or "",
             nodes=[
-                NodeModel.from_db(node, for_export) for node in graph.AgentNodes or []
+                NodeModel.from_db(GraphModel._process_node(node, for_export))
+                for node in graph.AgentNodes or []
             ],
             links=list(
                 {
@@ -459,12 +396,59 @@ class GraphModel(Graph):
                     for link in (node.Input or []) + (node.Output or [])
                 }
             ),
-            sub_graphs=[
-                GraphModel.from_db(sub_graph, for_export)
-                for sub_graph in sub_graphs or []
-            ],
         )
 
+    @staticmethod
+    def _process_node(node: AgentNode, for_export: bool) -> AgentNode:
+        if for_export:
+            # Remove credentials from node input
+            if node.constantInput:
+                constant_input = type.convert(node.constantInput, dict[str, Any])
+                constant_input = GraphModel._hide_node_input_credentials(constant_input)
+                node.constantInput = Json(constant_input)
+
+            # Remove webhook info
+            node.webhookId = None
+            node.Webhook = None
+
+        return node
+
+    @staticmethod
+    def _hide_node_input_credentials(input_data: dict[str, Any]) -> dict[str, Any]:
+        sensitive_keys = ["credentials", "api_key", "password", "token", "secret"]
+        result = {}
+        for key, value in input_data.items():
+            if isinstance(value, dict):
+                result[key] = GraphModel._hide_node_input_credentials(value)
+            elif isinstance(value, str) and any(
+                sensitive_key in key.lower() for sensitive_key in sensitive_keys
+            ):
+                # Skip this key-value pair in the result
+                continue
+            else:
+                result[key] = value
+        return result
+
+    def clean_graph(self):
+        blocks = [block() for block in get_blocks().values()]
+
+        input_blocks = [
+            node
+            for node in self.nodes
+            if next(
+                (
+                    b
+                    for b in blocks
+                    if b.id == node.block_id and b.block_type == BlockType.INPUT
+                ),
+                None,
+            )
+        ]
+
+        for node in self.nodes:
+            if any(input_block.id == node.id for input_block in input_blocks):
+                node.input_default["value"] = ""
+
 
 # --------------------- CRUD functions --------------------- #
 
@@ -494,14 +478,14 @@ async def set_node_webhook(node_id: str, webhook_id: str | None) -> NodeModel:
 
 async def get_graphs(
     user_id: str,
-    filter_by: Literal["active"] | None = "active",
+    filter_by: Literal["active", "template"] | None = "active",
 ) -> list[GraphModel]:
     """
     Retrieves graph metadata objects.
     Default behaviour is to get all currently active graphs.
 
     Args:
-        filter_by: An optional filter to either select graphs.
+        filter_by: An optional filter to either select templates or active graphs.
         user_id: The ID of the user that owns the graph.
 
     Returns:
@@ -511,6 +495,8 @@ async def get_graphs(
 
     if filter_by == "active":
         where_clause["isActive"] = True
+    elif filter_by == "template":
+        where_clause["isTemplate"] = True
 
     graphs = await AgentGraph.prisma().find_many(
         where=where_clause,
@@ -530,40 +516,32 @@ async def get_graphs(
     return graph_models
 
 
-async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph | None:
-    where_clause: AgentGraphWhereInput = {
-        "id": graph_id,
-    }
-
-    if version is not None:
-        where_clause["version"] = version
-
-    graph = await AgentGraph.prisma().find_first(
-        where=where_clause,
-        order={"version": "desc"},
+async def get_executions(user_id: str) -> list[GraphExecution]:
+    executions = await AgentGraphExecution.prisma().find_many(
+        where={"userId": user_id},
+        order={"createdAt": "desc"},
     )
+    return [GraphExecution.from_db(execution) for execution in executions]
 
-    if not graph:
-        return None
 
-    return Graph(
-        id=graph.id,
-        name=graph.name or "",
-        description=graph.description or "",
-        version=graph.version,
-        is_active=graph.isActive,
+async def get_execution(user_id: str, execution_id: str) -> GraphExecution | None:
+    execution = await AgentGraphExecution.prisma().find_first(
+        where={"id": execution_id, "userId": user_id}
     )
+    return GraphExecution.from_db(execution) if execution else None
 
 
 async def get_graph(
     graph_id: str,
     version: int | None = None,
+    template: bool = False,  # note: currently not in use; TODO: remove from DB entirely
     user_id: str | None = None,
     for_export: bool = False,
 ) -> GraphModel | None:
     """
     Retrieves a graph from the DB.
-    Defaults to the version with `is_active` if `version` is not passed.
+    Defaults to the version with `is_active` if `version` is not passed,
+    or the latest version with `is_template` if `template=True`.
 
     Returns `None` if the record is not found.
     """
@@ -573,6 +551,8 @@ async def get_graph(
 
     if version is not None:
         where_clause["version"] = version
+    elif not template:
+        where_clause["isActive"] = True
 
     graph = await AgentGraph.prisma().find_first(
         where=where_clause,
@@ -589,81 +569,16 @@ async def get_graph(
                     "agentId": graph_id,
                     "agentVersion": version or graph.version,
                     "isDeleted": False,
-                    "submissionStatus": SubmissionStatus.APPROVED,
+                    "StoreListing": {"is": {"isApproved": True}},
                 }
             )
         )
     ):
         return None
 
-    if for_export:
-        sub_graphs = await get_sub_graphs(graph)
-        return GraphModel.from_db(
-            graph=graph,
-            sub_graphs=sub_graphs,
-            for_export=for_export,
-        )
-
     return GraphModel.from_db(graph, for_export)
 
 
-async def get_sub_graphs(graph: AgentGraph) -> list[AgentGraph]:
-    """
-    Iteratively fetches all sub-graphs of a given graph, and flattens them into a list.
-    This call involves a DB fetch in batch, breadth-first, per-level of graph depth.
-    On each DB fetch we will only fetch the sub-graphs that are not already in the list.
-    """
-    sub_graphs = {graph.id: graph}
-    search_graphs = [graph]
-    agent_block_id = AgentExecutorBlock().id
-
-    while search_graphs:
-        sub_graph_ids = [
-            (graph_id, graph_version)
-            for graph in search_graphs
-            for node in graph.AgentNodes or []
-            if (
-                node.AgentBlock
-                and node.AgentBlock.id == agent_block_id
-                and (graph_id := dict(node.constantInput).get("graph_id"))
-                and (graph_version := dict(node.constantInput).get("graph_version"))
-            )
-        ]
-        if not sub_graph_ids:
-            break
-
-        graphs = await AgentGraph.prisma().find_many(
-            where={
-                "OR": [
-                    {
-                        "id": graph_id,
-                        "version": graph_version,
-                        "userId": graph.userId,  # Ensure the sub-graph is owned by the same user
-                    }
-                    for graph_id, graph_version in sub_graph_ids
-                ]  # type: ignore
-            },
-            include=AGENT_GRAPH_INCLUDE,
-        )
-
-        search_graphs = [graph for graph in graphs if graph.id not in sub_graphs]
-        sub_graphs.update({graph.id: graph for graph in search_graphs})
-
-    return [g for g in sub_graphs.values() if g.id != graph.id]
-
-
-async def get_connected_output_nodes(node_id: str) -> list[tuple[Link, Node]]:
-    links = await AgentNodeLink.prisma().find_many(
-        where={"agentNodeSourceId": node_id},
-        include={"AgentNodeSink": {"include": AGENT_NODE_INCLUDE}},  # type: ignore
-    )
-    return [
-        (Link.from_db(link), NodeModel.from_db(link.AgentNodeSink))
-        for link in links
-        if link.AgentNodeSink
-    ]
-
-
 async def set_graph_active_version(graph_id: str, version: int, user_id: str) -> None:
     # Activate the requested version if it exists and is owned by the user.
     updated_count = await AgentGraph.prisma().update_many(
@@ -715,56 +630,50 @@ async def create_graph(graph: Graph, user_id: str) -> GraphModel:
     async with transaction() as tx:
         await __create_graph(tx, graph, user_id)
 
-    if created_graph := await get_graph(graph.id, graph.version, user_id=user_id):
+    if created_graph := await get_graph(
+        graph.id, graph.version, graph.is_template, user_id=user_id
+    ):
         return created_graph
 
     raise ValueError(f"Created graph {graph.id} v{graph.version} is not in DB")
 
 
 async def __create_graph(tx, graph: Graph, user_id: str):
-    graphs = [graph] + graph.sub_graphs
-
-    await AgentGraph.prisma(tx).create_many(
-        data=[
-            {
-                "id": graph.id,
-                "version": graph.version,
-                "name": graph.name,
-                "description": graph.description,
-                "isActive": graph.is_active,
-                "userId": user_id,
-            }
-            for graph in graphs
-        ]
+    await AgentGraph.prisma(tx).create(
+        data={
+            "id": graph.id,
+            "version": graph.version,
+            "name": graph.name,
+            "description": graph.description,
+            "isTemplate": graph.is_template,
+            "isActive": graph.is_active,
+            "userId": user_id,
+            "AgentNodes": {
+                "create": [
+                    {
+                        "id": node.id,
+                        "agentBlockId": node.block_id,
+                        "constantInput": Json(node.input_default),
+                        "metadata": Json(node.metadata),
+                    }
+                    for node in graph.nodes
+                ]
+            },
+        }
     )
 
-    await AgentNode.prisma(tx).create_many(
-        data=[
-            {
-                "id": node.id,
-                "agentGraphId": graph.id,
-                "agentGraphVersion": graph.version,
-                "agentBlockId": node.block_id,
-                "constantInput": Json(node.input_default),
-                "metadata": Json(node.metadata),
-                "webhookId": node.webhook_id,
-            }
-            for graph in graphs
-            for node in graph.nodes
-        ]
-    )
-
-    await AgentNodeLink.prisma(tx).create_many(
-        data=[
-            {
-                "id": str(uuid.uuid4()),
-                "sourceName": link.source_name,
-                "sinkName": link.sink_name,
-                "agentNodeSourceId": link.source_id,
-                "agentNodeSinkId": link.sink_id,
-                "isStatic": link.is_static,
-            }
-            for graph in graphs
+    await asyncio.gather(
+        *[
+            AgentNodeLink.prisma(tx).create(
+                {
+                    "id": str(uuid.uuid4()),
+                    "sourceName": link.source_name,
+                    "sinkName": link.sink_name,
+                    "agentNodeSourceId": link.source_id,
+                    "agentNodeSinkId": link.sink_id,
+                    "isStatic": link.is_static,
+                }
+            )
             for link in graph.links
         ]
     )
@@ -807,11 +716,9 @@ async def fix_llm_provider_credentials():
 
     store = IntegrationCredentialsStore()
 
-    broken_nodes = []
-    try:
-        broken_nodes = await prisma.get_client().query_raw(
-            """
-            SELECT    graph."userId"       user_id,
+    broken_nodes = await prisma.get_client().query_raw(
+        """
+        SELECT    graph."userId"       user_id,
                   node.id              node_id,
                   node."constantInput" node_preset_input
         FROM      platform."AgentNode"  node
@@ -820,10 +727,8 @@ async def fix_llm_provider_credentials():
         WHERE     node."constantInput"::jsonb->'credentials'->>'provider' = 'llm'
         ORDER BY  graph."userId";
         """
-        )
-        logger.info(f"Fixing LLM credential inputs on {len(broken_nodes)} nodes")
-    except Exception as e:
-        logger.error(f"Error fixing LLM credential inputs: {e}")
+    )
+    logger.info(f"Fixing LLM credential inputs on {len(broken_nodes)} nodes")
 
     user_id: str = ""
     user_integrations = None
@@ -874,40 +779,3 @@ async def fix_llm_provider_credentials():
             where={"id": node_id},
             data={"constantInput": Json(node_preset_input)},
         )
-
-
-async def migrate_llm_models(migrate_to: LlmModel):
-    """
-    Update all LLM models in all AI blocks that don't exist in the enum.
-    Note: Only updates top level LlmModel SchemaFields of blocks (won't update nested fields).
-    """
-    logger.info("Migrating LLM models")
-    # Scan all blocks and search for LlmModel fields
-    llm_model_fields: dict[str, str] = {}  # {block_id: field_name}
-
-    # Search for all LlmModel fields
-    for block_type in get_blocks().values():
-        block = block_type()
-        from pydantic.fields import FieldInfo
-
-        fields: dict[str, FieldInfo] = block.input_schema.model_fields
-
-        # Collect top-level LlmModel fields
-        for field_name, field in fields.items():
-            if field.annotation == LlmModel:
-                llm_model_fields[block.id] = field_name
-
-    # Update each block
-    for id, path in llm_model_fields.items():
-        # Convert enum values to a list of strings for the SQL query
-        enum_values = [v.value for v in LlmModel.__members__.values()]
-
-        query = f"""
-            UPDATE "AgentNode"
-            SET "constantInput" = jsonb_set("constantInput", '{{{path}}}', '"{migrate_to.value}"', true)
-            WHERE "agentBlockId" = '{id}'
-            AND "constantInput" ? '{path}'
-            AND "constantInput"->>'{path}' NOT IN ({','.join(f"'{value}'" for value in enum_values)})
-            """
-
-        await db.execute_raw(query)

autogpt_platform/backend/backend/data/includes.py

@@ -1,7 +1,5 @@
 import prisma
 
-from backend.blocks.io import IO_BLOCK_IDs
-
 AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
     "Input": True,
     "Output": True,
@@ -20,49 +18,17 @@ EXECUTION_RESULT_INCLUDE: prisma.types.AgentNodeExecutionInclude = {
     "AgentGraphExecution": True,
 }
 
-MAX_NODE_EXECUTIONS_FETCH = 1000
-
-GRAPH_EXECUTION_INCLUDE_WITH_NODES: prisma.types.AgentGraphExecutionInclude = {
+GRAPH_EXECUTION_INCLUDE: prisma.types.AgentGraphExecutionInclude = {
     "AgentNodeExecutions": {
         "include": {
             "Input": True,
             "Output": True,
             "AgentNode": True,
             "AgentGraphExecution": True,
-        },
-        "order_by": [
-            {"queuedTime": "desc"},
-            # Fallback: Incomplete execs has no queuedTime.
-            {"addedTime": "desc"},
-        ],
-        "take": MAX_NODE_EXECUTIONS_FETCH,  # Avoid loading excessive node executions.
+        }
     }
 }
 
-GRAPH_EXECUTION_INCLUDE: prisma.types.AgentGraphExecutionInclude = {
-    "AgentNodeExecutions": {
-        **GRAPH_EXECUTION_INCLUDE_WITH_NODES["AgentNodeExecutions"],  # type: ignore
-        "where": {
-            "AgentNode": {
-                "AgentBlock": {"id": {"in": IO_BLOCK_IDs}},  # type: ignore
-            },
-        },
-    }
-}
-
-
 INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
     "AgentNodes": {"include": AGENT_NODE_INCLUDE}  # type: ignore
 }
-
-
-def library_agent_include(user_id: str) -> prisma.types.LibraryAgentInclude:
-    return {
-        "Agent": {
-            "include": {
-                **AGENT_GRAPH_INCLUDE,
-                "AgentGraphExecution": {"where": {"userId": user_id}},
-            }
-        },
-        "Creator": True,
-    }

autogpt_platform/backend/backend/data/integrations.py

@@ -9,7 +9,6 @@ from backend.data.includes import INTEGRATION_WEBHOOK_INCLUDE
 from backend.data.queue import AsyncRedisEventBus
 from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks.utils import webhook_ingress_url
-from backend.util.exceptions import NotFoundError
 
 from .db import BaseDbModel
 
@@ -83,18 +82,11 @@ async def create_webhook(webhook: Webhook) -> Webhook:
 
 
 async def get_webhook(webhook_id: str) -> Webhook:
-    """
-    ⚠️ No `user_id` check: DO NOT USE without check in user-facing endpoints.
-
-    Raises:
-        NotFoundError: if no record with the given ID exists
-    """
-    webhook = await IntegrationWebhook.prisma().find_unique(
+    """⚠️ No `user_id` check: DO NOT USE without check in user-facing endpoints."""
+    webhook = await IntegrationWebhook.prisma().find_unique_or_raise(
         where={"id": webhook_id},
         include=INTEGRATION_WEBHOOK_INCLUDE,
     )
-    if not webhook:
-        raise NotFoundError(f"Webhook #{webhook_id} not found")
     return Webhook.from_db(webhook)
 
 

autogpt_platform/backend/backend/data/model.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import base64
 import logging
-from datetime import datetime, timezone
+from datetime import datetime
 from typing import (
     TYPE_CHECKING,
     Annotated,
@@ -141,8 +141,9 @@ def SchemaField(
     secret: bool = False,
     exclude: bool = False,
     hidden: Optional[bool] = None,
-    depends_on: Optional[list[str]] = None,
-    json_schema_extra: Optional[dict[str, Any]] = None,
+    depends_on: list[str] | None = None,
+    image_upload: Optional[bool] = None,
+    image_output: Optional[bool] = None,
     **kwargs,
 ) -> T:
     if default is PydanticUndefined and default_factory is None:
@@ -150,7 +151,7 @@ def SchemaField(
     elif advanced is None:
         advanced = True
 
-    json_schema_extra = {
+    json_extra = {
         k: v
         for k, v in {
             "placeholder": placeholder,
@@ -158,7 +159,8 @@ def SchemaField(
             "advanced": advanced,
             "hidden": hidden,
             "depends_on": depends_on,
-            **(json_schema_extra or {}),
+            "image_upload": image_upload,
+            "image_output": image_output,
         }.items()
         if v is not None
     }
@@ -170,7 +172,7 @@ def SchemaField(
         title=title,
         description=description,
         exclude=exclude,
-        json_schema_extra=json_schema_extra,
+        json_schema_extra=json_extra,
         **kwargs,
     )  # type: ignore
 
@@ -373,8 +375,7 @@ class AutoTopUpConfig(BaseModel):
 
 
 class UserTransaction(BaseModel):
-    transaction_key: str = ""
-    transaction_time: datetime = datetime.min.replace(tzinfo=timezone.utc)
+    transaction_time: datetime = datetime.min
     transaction_type: CreditTransactionType = CreditTransactionType.USAGE
     amount: int = 0
     balance: int = 0
@@ -382,62 +383,9 @@ class UserTransaction(BaseModel):
     usage_graph_id: str | None = None
     usage_execution_id: str | None = None
     usage_node_count: int = 0
-    usage_start_time: datetime = datetime.max.replace(tzinfo=timezone.utc)
+    usage_start_time: datetime = datetime.max
 
 
 class TransactionHistory(BaseModel):
     transactions: list[UserTransaction]
     next_transaction_time: datetime | None
-
-
-class RefundRequest(BaseModel):
-    id: str
-    user_id: str
-    transaction_key: str
-    amount: int
-    reason: str
-    result: str | None = None
-    status: str
-    created_at: datetime
-    updated_at: datetime
-
-
-class NodeExecutionStats(BaseModel):
-    """Execution statistics for a node execution."""
-
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "allow"
-
-    error: Optional[Exception | str] = None
-    walltime: float = 0
-    cputime: float = 0
-    input_size: int = 0
-    output_size: int = 0
-    llm_call_count: int = 0
-    llm_retry_count: int = 0
-    input_token_count: int = 0
-    output_token_count: int = 0
-
-
-class GraphExecutionStats(BaseModel):
-    """Execution statistics for a graph execution."""
-
-    class Config:
-        arbitrary_types_allowed = True
-        extra = "allow"
-
-    error: Optional[Exception | str] = None
-    walltime: float = Field(
-        default=0, description="Time between start and end of run (seconds)"
-    )
-    cputime: float = 0
-    nodes_walltime: float = Field(
-        default=0, description="Total node execution time (seconds)"
-    )
-    nodes_cputime: float = 0
-    node_count: int = Field(default=0, description="Total number of node executions")
-    node_error_count: int = Field(
-        default=0, description="Total number of errors generated"
-    )
-    cost: int = Field(default=0, description="Total execution cost (cents)")

autogpt_platform/backend/backend/data/notifications.py

@@ -1,7 +1,7 @@
 import logging
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timedelta
 from enum import Enum
-from typing import Annotated, Any, Generic, Optional, TypeVar, Union
+from typing import Annotated, Generic, Optional, TypeVar, Union
 
 from prisma import Json
 from prisma.enums import NotificationType
@@ -18,34 +18,27 @@ from .db import transaction
 logger = logging.getLogger(__name__)
 
 
-NotificationDataType_co = TypeVar(
-    "NotificationDataType_co", bound="BaseNotificationData", covariant=True
-)
-SummaryParamsType_co = TypeVar(
-    "SummaryParamsType_co", bound="BaseSummaryParams", covariant=True
-)
+T_co = TypeVar("T_co", bound="BaseNotificationData", covariant=True)
 
 
-class QueueType(Enum):
+class BatchingStrategy(Enum):
     IMMEDIATE = "immediate"  # Send right away (errors, critical notifications)
-    BATCH = "batch"  # Batch for up to an hour (usage reports)
-    SUMMARY = "summary"  # Daily digest (summary notifications)
+    HOURLY = "hourly"  # Batch for up to an hour (usage reports)
+    DAILY = "daily"  # Daily digest (summary notifications)
     BACKOFF = "backoff"  # Backoff strategy (exponential backoff)
-    ADMIN = "admin"  # Admin notifications (errors, critical notifications)
 
 
 class BaseNotificationData(BaseModel):
-    class Config:
-        extra = "allow"
+    pass
 
 
 class AgentRunData(BaseNotificationData):
     agent_name: str
     credits_used: float
+    # remaining_balance: float
     execution_time: float
-    node_count: int = Field(..., description="Number of nodes executed")
     graph_id: str
-    outputs: list[dict[str, Any]] = Field(..., description="Outputs of the agent")
+    node_count: int = Field(..., description="Number of nodes executed")
 
 
 class ZeroBalanceData(BaseNotificationData):
@@ -53,21 +46,12 @@ class ZeroBalanceData(BaseNotificationData):
     last_transaction_time: datetime
     top_up_link: str
 
-    @field_validator("last_transaction_time")
-    @classmethod
-    def validate_timezone(cls, value: datetime):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
 
 class LowBalanceData(BaseNotificationData):
-    agent_name: str = Field(..., description="Name of the agent")
-    current_balance: float = Field(
-        ..., description="Current balance in credits (100 = $1)"
-    )
-    billing_page_link: str = Field(..., description="Link to billing page")
-    shortfall: float = Field(..., description="Amount of credits needed to continue")
+    current_balance: float
+    threshold_amount: float
+    top_up_link: str
+    recent_usage: float = Field(..., description="Usage in the last 24 hours")
 
 
 class BlockExecutionFailedData(BaseNotificationData):
@@ -88,13 +72,6 @@ class ContinuousAgentErrorData(BaseNotificationData):
     error_time: datetime
     attempts: int = Field(..., description="Number of retry attempts made")
 
-    @field_validator("start_time", "error_time")
-    @classmethod
-    def validate_timezone(cls, value: datetime):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
 
 class BaseSummaryData(BaseNotificationData):
     total_credits_used: float
@@ -107,66 +84,20 @@ class BaseSummaryData(BaseNotificationData):
     cost_breakdown: dict[str, float]
 
 
-class BaseSummaryParams(BaseModel):
-    pass
-
-
-class DailySummaryParams(BaseSummaryParams):
-    date: datetime
-
-    @field_validator("date")
-    def validate_timezone(cls, value):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
-
-class WeeklySummaryParams(BaseSummaryParams):
-    start_date: datetime
-    end_date: datetime
-
-    @field_validator("start_date", "end_date")
-    def validate_timezone(cls, value):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
-
 class DailySummaryData(BaseSummaryData):
     date: datetime
 
-    @field_validator("date")
-    def validate_timezone(cls, value):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
 
 class WeeklySummaryData(BaseSummaryData):
     start_date: datetime
     end_date: datetime
-
-    @field_validator("start_date", "end_date")
-    def validate_timezone(cls, value):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
-
-class MonthlySummaryData(BaseNotificationData):
-    month: int
+    week_number: int
     year: int
 
 
-class RefundRequestData(BaseNotificationData):
-    user_id: str
-    user_name: str
-    user_email: str
-    transaction_id: str
-    refund_request_id: str
-    reason: str
-    amount: float
-    balance: int
+class MonthlySummaryData(BaseSummaryData):
+    month: int
+    year: int
 
 
 NotificationData = Annotated[
@@ -177,10 +108,6 @@ NotificationData = Annotated[
         BlockExecutionFailedData,
         ContinuousAgentErrorData,
         MonthlySummaryData,
-        WeeklySummaryData,
-        DailySummaryData,
-        RefundRequestData,
-        BaseSummaryData,
     ],
     Field(discriminator="type"),
 ]
@@ -190,25 +117,17 @@ class NotificationEventDTO(BaseModel):
     user_id: str
     type: NotificationType
     data: dict
-    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
-    retry_count: int = 0
+    created_at: datetime = Field(default_factory=datetime.now)
 
 
-class SummaryParamsEventDTO(BaseModel):
+class NotificationEventModel(BaseModel, Generic[T_co]):
     user_id: str
     type: NotificationType
-    data: dict
-    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
-
-
-class NotificationEventModel(BaseModel, Generic[NotificationDataType_co]):
-    user_id: str
-    type: NotificationType
-    data: NotificationDataType_co
-    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
+    data: T_co
+    created_at: datetime = Field(default_factory=datetime.now)
 
     @property
-    def strategy(self) -> QueueType:
+    def strategy(self) -> BatchingStrategy:
         return NotificationTypeOverride(self.type).strategy
 
     @field_validator("type", mode="before")
@@ -222,14 +141,7 @@ class NotificationEventModel(BaseModel, Generic[NotificationDataType_co]):
         return NotificationTypeOverride(self.type).template
 
 
-class SummaryParamsEventModel(BaseModel, Generic[SummaryParamsType_co]):
-    user_id: str
-    type: NotificationType
-    data: SummaryParamsType_co
-    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
-
-
-def get_notif_data_type(
+def get_data_type(
     notification_type: NotificationType,
 ) -> type[BaseNotificationData]:
     return {
@@ -241,25 +153,14 @@ def get_notif_data_type(
         NotificationType.DAILY_SUMMARY: DailySummaryData,
         NotificationType.WEEKLY_SUMMARY: WeeklySummaryData,
         NotificationType.MONTHLY_SUMMARY: MonthlySummaryData,
-        NotificationType.REFUND_REQUEST: RefundRequestData,
-        NotificationType.REFUND_PROCESSED: RefundRequestData,
-    }[notification_type]
-
-
-def get_summary_params_type(
-    notification_type: NotificationType,
-) -> type[BaseSummaryParams]:
-    return {
-        NotificationType.DAILY_SUMMARY: DailySummaryParams,
-        NotificationType.WEEKLY_SUMMARY: WeeklySummaryParams,
     }[notification_type]
 
 
 class NotificationBatch(BaseModel):
     user_id: str
     events: list[NotificationEvent]
-    strategy: QueueType
-    last_update: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
+    strategy: BatchingStrategy
+    last_update: datetime = datetime.now()
 
 
 class NotificationResult(BaseModel):
@@ -272,22 +173,21 @@ class NotificationTypeOverride:
         self.notification_type = notification_type
 
     @property
-    def strategy(self) -> QueueType:
+    def strategy(self) -> BatchingStrategy:
         BATCHING_RULES = {
             # These are batched by the notification service
-            NotificationType.AGENT_RUN: QueueType.BATCH,
+            NotificationType.AGENT_RUN: BatchingStrategy.IMMEDIATE,
             # These are batched by the notification service, but with a backoff strategy
-            NotificationType.ZERO_BALANCE: QueueType.BACKOFF,
-            NotificationType.LOW_BALANCE: QueueType.IMMEDIATE,
-            NotificationType.BLOCK_EXECUTION_FAILED: QueueType.BACKOFF,
-            NotificationType.CONTINUOUS_AGENT_ERROR: QueueType.BACKOFF,
-            NotificationType.DAILY_SUMMARY: QueueType.SUMMARY,
-            NotificationType.WEEKLY_SUMMARY: QueueType.SUMMARY,
-            NotificationType.MONTHLY_SUMMARY: QueueType.SUMMARY,
-            NotificationType.REFUND_REQUEST: QueueType.ADMIN,
-            NotificationType.REFUND_PROCESSED: QueueType.ADMIN,
+            NotificationType.ZERO_BALANCE: BatchingStrategy.BACKOFF,
+            NotificationType.LOW_BALANCE: BatchingStrategy.BACKOFF,
+            NotificationType.BLOCK_EXECUTION_FAILED: BatchingStrategy.BACKOFF,
+            NotificationType.CONTINUOUS_AGENT_ERROR: BatchingStrategy.BACKOFF,
+            # These aren't batched by the notification service, so we send them right away
+            NotificationType.DAILY_SUMMARY: BatchingStrategy.IMMEDIATE,
+            NotificationType.WEEKLY_SUMMARY: BatchingStrategy.IMMEDIATE,
+            NotificationType.MONTHLY_SUMMARY: BatchingStrategy.IMMEDIATE,
         }
-        return BATCHING_RULES.get(self.notification_type, QueueType.IMMEDIATE)
+        return BATCHING_RULES.get(self.notification_type, BatchingStrategy.HOURLY)
 
     @property
     def template(self) -> str:
@@ -301,33 +201,8 @@ class NotificationTypeOverride:
             NotificationType.DAILY_SUMMARY: "daily_summary.html",
             NotificationType.WEEKLY_SUMMARY: "weekly_summary.html",
             NotificationType.MONTHLY_SUMMARY: "monthly_summary.html",
-            NotificationType.REFUND_REQUEST: "refund_request.html",
-            NotificationType.REFUND_PROCESSED: "refund_processed.html",
         }[self.notification_type]
 
-    @property
-    def subject(self) -> str:
-        return {
-            NotificationType.AGENT_RUN: "Agent Run Report",
-            NotificationType.ZERO_BALANCE: "You're out of credits!",
-            NotificationType.LOW_BALANCE: "Low Balance Warning!",
-            NotificationType.BLOCK_EXECUTION_FAILED: "Uh oh! Block Execution Failed",
-            NotificationType.CONTINUOUS_AGENT_ERROR: "Shoot! Continuous Agent Error",
-            NotificationType.DAILY_SUMMARY: "Here's your daily summary!",
-            NotificationType.WEEKLY_SUMMARY: "Look at all the cool stuff you did last week!",
-            NotificationType.MONTHLY_SUMMARY: "We did a lot this month!",
-            NotificationType.REFUND_REQUEST: "[ACTION REQUIRED] You got a ${{data.amount / 100}} refund request from {{data.user_name}}",
-            NotificationType.REFUND_PROCESSED: "Refund for ${{data.amount / 100}} to {{data.user_name}} has been processed",
-        }[self.notification_type]
-
-
-class NotificationPreferenceDTO(BaseModel):
-    email: EmailStr = Field(..., description="User's email address")
-    preferences: dict[NotificationType, bool] = Field(
-        ..., description="Which notifications the user wants"
-    )
-    daily_limit: int = Field(..., description="Max emails per day")
-
 
 class NotificationPreference(BaseModel):
     user_id: str
@@ -337,51 +212,12 @@ class NotificationPreference(BaseModel):
     )
     daily_limit: int = 10  # Max emails per day
     emails_sent_today: int = 0
-    last_reset_date: datetime = Field(
-        default_factory=lambda: datetime.now(timezone.utc)
-    )
-
-
-class UserNotificationEventDTO(BaseModel):
-    type: NotificationType
-    data: dict
-    created_at: datetime
-    updated_at: datetime
-
-    @staticmethod
-    def from_db(model: NotificationEvent) -> "UserNotificationEventDTO":
-        return UserNotificationEventDTO(
-            type=model.type,
-            data=dict(model.data),
-            created_at=model.createdAt,
-            updated_at=model.updatedAt,
-        )
-
-
-class UserNotificationBatchDTO(BaseModel):
-    user_id: str
-    type: NotificationType
-    notifications: list[UserNotificationEventDTO]
-    created_at: datetime
-    updated_at: datetime
-
-    @staticmethod
-    def from_db(model: UserNotificationBatch) -> "UserNotificationBatchDTO":
-        return UserNotificationBatchDTO(
-            user_id=model.userId,
-            type=model.type,
-            notifications=[
-                UserNotificationEventDTO.from_db(notification)
-                for notification in model.Notifications or []
-            ],
-            created_at=model.createdAt,
-            updated_at=model.updatedAt,
-        )
+    last_reset_date: datetime = Field(default_factory=datetime.now)
 
 
 def get_batch_delay(notification_type: NotificationType) -> timedelta:
     return {
-        NotificationType.AGENT_RUN: timedelta(minutes=60),
+        NotificationType.AGENT_RUN: timedelta(seconds=1),
         NotificationType.ZERO_BALANCE: timedelta(minutes=60),
         NotificationType.LOW_BALANCE: timedelta(minutes=60),
         NotificationType.BLOCK_EXECUTION_FAILED: timedelta(minutes=60),
@@ -392,15 +228,19 @@ def get_batch_delay(notification_type: NotificationType) -> timedelta:
 async def create_or_add_to_user_notification_batch(
     user_id: str,
     notification_type: NotificationType,
-    notification_data: NotificationEventModel,
-) -> UserNotificationBatchDTO:
+    data: str,  # type: 'NotificationEventModel'
+) -> dict:
     try:
         logger.info(
-            f"Creating or adding to notification batch for {user_id} with type {notification_type} and data {notification_data}"
+            f"Creating or adding to notification batch for {user_id} with type {notification_type} and data {data}"
         )
 
+        notification_data = NotificationEventModel[
+            get_data_type(notification_type)
+        ].model_validate_json(data)
+
         # Serialize the data
-        json_data: Json = Json(notification_data.data.model_dump())
+        json_data: Json = Json(notification_data.data.model_dump_json())
 
         # First try to find existing batch
         existing_batch = await UserNotificationBatch.prisma().find_unique(
@@ -410,7 +250,7 @@ async def create_or_add_to_user_notification_batch(
                     "type": notification_type,
                 }
             },
-            include={"Notifications": True},
+            include={"notifications": True},
         )
 
         if not existing_batch:
@@ -427,11 +267,11 @@ async def create_or_add_to_user_notification_batch(
                     data={
                         "userId": user_id,
                         "type": notification_type,
-                        "Notifications": {"connect": [{"id": notification_event.id}]},
+                        "notifications": {"connect": [{"id": notification_event.id}]},
                     },
-                    include={"Notifications": True},
+                    include={"notifications": True},
                 )
-                return UserNotificationBatchDTO.from_db(resp)
+                return resp.model_dump()
         else:
             async with transaction() as tx:
                 notification_event = await tx.notificationevent.create(
@@ -445,41 +285,35 @@ async def create_or_add_to_user_notification_batch(
                 resp = await tx.usernotificationbatch.update(
                     where={"id": existing_batch.id},
                     data={
-                        "Notifications": {"connect": [{"id": notification_event.id}]}
+                        "notifications": {"connect": [{"id": notification_event.id}]}
                     },
-                    include={"Notifications": True},
+                    include={"notifications": True},
                 )
             if not resp:
                 raise DatabaseError(
                     f"Failed to add notification event {notification_event.id} to existing batch {existing_batch.id}"
                 )
-            return UserNotificationBatchDTO.from_db(resp)
+            return resp.model_dump()
     except Exception as e:
         raise DatabaseError(
             f"Failed to create or add to notification batch for user {user_id} and type {notification_type}: {e}"
         ) from e
 
 
-async def get_user_notification_oldest_message_in_batch(
+async def get_user_notification_last_message_in_batch(
     user_id: str,
     notification_type: NotificationType,
-) -> UserNotificationEventDTO | None:
+) -> NotificationEvent | None:
     try:
         batch = await UserNotificationBatch.prisma().find_first(
             where={"userId": user_id, "type": notification_type},
-            include={"Notifications": True},
+            order={"createdAt": "desc"},
         )
         if not batch:
             return None
-        if not batch.Notifications:
+        if not batch.notifications:
             return None
-        sorted_notifications = sorted(batch.Notifications, key=lambda x: x.createdAt)
-
-        return (
-            UserNotificationEventDTO.from_db(sorted_notifications[0])
-            if sorted_notifications
-            else None
-        )
+        return batch.notifications[-1]
     except Exception as e:
         raise DatabaseError(
             f"Failed to get user notification last message in batch for user {user_id} and type {notification_type}: {e}"
@@ -514,34 +348,13 @@ async def empty_user_notification_batch(
 async def get_user_notification_batch(
     user_id: str,
     notification_type: NotificationType,
-) -> UserNotificationBatchDTO | None:
+) -> UserNotificationBatch | None:
     try:
-        batch = await UserNotificationBatch.prisma().find_first(
+        return await UserNotificationBatch.prisma().find_first(
             where={"userId": user_id, "type": notification_type},
-            include={"Notifications": True},
+            include={"notifications": True},
         )
-        return UserNotificationBatchDTO.from_db(batch) if batch else None
     except Exception as e:
         raise DatabaseError(
             f"Failed to get user notification batch for user {user_id} and type {notification_type}: {e}"
         ) from e
-
-
-async def get_all_batches_by_type(
-    notification_type: NotificationType,
-) -> list[UserNotificationBatchDTO]:
-    try:
-        batches = await UserNotificationBatch.prisma().find_many(
-            where={
-                "type": notification_type,
-                "Notifications": {
-                    "some": {}  # Only return batches with at least one notification
-                },
-            },
-            include={"Notifications": True},
-        )
-        return [UserNotificationBatchDTO.from_db(batch) for batch in batches]
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to get all batches by type {notification_type}: {e}"
-        ) from e

autogpt_platform/backend/backend/data/onboarding.py

@@ -1,276 +0,0 @@
-import re
-from typing import Any, Optional
-
-import prisma
-import pydantic
-from prisma import Json
-from prisma.enums import OnboardingStep
-from prisma.models import UserOnboarding
-from prisma.types import UserOnboardingUpdateInput
-
-from backend.data.block import get_blocks
-from backend.data.graph import GraphModel
-from backend.data.model import CredentialsMetaInput
-from backend.server.v2.store.model import StoreAgentDetails
-
-# Mapping from user reason id to categories to search for when choosing agent to show
-REASON_MAPPING: dict[str, list[str]] = {
-    "content_marketing": ["writing", "marketing", "creative"],
-    "business_workflow_automation": ["business", "productivity"],
-    "data_research": ["data", "research"],
-    "ai_innovation": ["development", "research"],
-    "personal_productivity": ["personal", "productivity"],
-}
-POINTS_AGENT_COUNT = 50  # Number of agents to calculate points for
-MIN_AGENT_COUNT = 2  # Minimum number of marketplace agents to enable onboarding
-
-
-class UserOnboardingUpdate(pydantic.BaseModel):
-    completedSteps: Optional[list[OnboardingStep]] = None
-    usageReason: Optional[str] = None
-    integrations: Optional[list[str]] = None
-    otherIntegrations: Optional[str] = None
-    selectedStoreListingVersionId: Optional[str] = None
-    agentInput: Optional[dict[str, Any]] = None
-
-
-async def get_user_onboarding(user_id: str):
-    return await UserOnboarding.prisma().upsert(
-        where={"userId": user_id},
-        data={
-            "create": {"userId": user_id},  # type: ignore
-            "update": {},
-        },
-    )
-
-
-async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
-    update: UserOnboardingUpdateInput = {}
-    if data.completedSteps is not None:
-        update["completedSteps"] = list(set(data.completedSteps))
-    if data.usageReason is not None:
-        update["usageReason"] = data.usageReason
-    if data.integrations is not None:
-        update["integrations"] = data.integrations
-    if data.otherIntegrations is not None:
-        update["otherIntegrations"] = data.otherIntegrations
-    if data.selectedStoreListingVersionId is not None:
-        update["selectedStoreListingVersionId"] = data.selectedStoreListingVersionId
-    if data.agentInput is not None:
-        update["agentInput"] = Json(data.agentInput)
-
-    return await UserOnboarding.prisma().upsert(
-        where={"userId": user_id},
-        data={
-            "create": {"userId": user_id, **update},  # type: ignore
-            "update": update,
-        },
-    )
-
-
-def clean_and_split(text: str) -> list[str]:
-    """
-    Removes all special characters from a string, truncates it to 100 characters,
-    and splits it by whitespace and commas.
-
-    Args:
-        text (str): The input string.
-
-    Returns:
-        list[str]: A list of cleaned words.
-    """
-    # Remove all special characters (keep only alphanumeric and whitespace)
-    cleaned_text = re.sub(r"[^a-zA-Z0-9\s,]", "", text.strip()[:100])
-
-    # Split by whitespace and commas
-    words = re.split(r"[\s,]+", cleaned_text)
-
-    # Remove empty strings from the list
-    words = [word.lower() for word in words if word]
-
-    return words
-
-
-def calculate_points(
-    agent, categories: list[str], custom: list[str], integrations: list[str]
-) -> int:
-    """
-    Calculates the total points for an agent based on the specified criteria.
-
-    Args:
-        agent: The agent object.
-        categories (list[str]): List of categories to match.
-        words (list[str]): List of words to match in the description.
-
-    Returns:
-        int: Total points for the agent.
-    """
-    points = 0
-
-    # 1. Category Matches
-    matched_categories = sum(
-        1 for category in categories if category in agent.categories
-    )
-    points += matched_categories * 100
-
-    # 2. Description Word Matches
-    description_words = agent.description.split()  # Split description into words
-    matched_words = sum(1 for word in custom if word in description_words)
-    points += matched_words * 100
-
-    matched_words = sum(1 for word in integrations if word in description_words)
-    points += matched_words * 50
-
-    # 3. Featured Bonus
-    if agent.featured:
-        points += 50
-
-    # 4. Rating Bonus
-    points += agent.rating * 10
-
-    # 5. Runs Bonus
-    runs_points = min(agent.runs / 1000 * 100, 100)  # Cap at 100 points
-    points += runs_points
-
-    return int(points)
-
-
-def get_credentials_blocks() -> dict[str, str]:
-    # Returns a dictionary of block id to credentials field name
-    creds: dict[str, str] = {}
-    blocks = get_blocks()
-    for id, block in blocks.items():
-        for field_name, field_info in block().input_schema.model_fields.items():
-            if field_info.annotation == CredentialsMetaInput:
-                creds[id] = field_name
-    return creds
-
-
-CREDENTIALS_FIELDS: dict[str, str] = get_credentials_blocks()
-
-
-async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
-    user_onboarding = await get_user_onboarding(user_id)
-    categories = REASON_MAPPING.get(user_onboarding.usageReason or "", [])
-
-    where_clause: dict[str, Any] = {}
-
-    custom = clean_and_split((user_onboarding.usageReason or "").lower())
-
-    if categories:
-        where_clause["OR"] = [
-            {"categories": {"has": category}} for category in categories
-        ]
-    else:
-        where_clause["OR"] = [
-            {"description": {"contains": word, "mode": "insensitive"}}
-            for word in custom
-        ]
-
-    where_clause["OR"] += [
-        {"description": {"contains": word, "mode": "insensitive"}}
-        for word in user_onboarding.integrations
-    ]
-
-    storeAgents = await prisma.models.StoreAgent.prisma().find_many(
-        where=prisma.types.StoreAgentWhereInput(**where_clause),
-        order=[
-            {"featured": "desc"},
-            {"runs": "desc"},
-            {"rating": "desc"},
-        ],
-        take=100,
-    )
-
-    agentListings = await prisma.models.StoreListingVersion.prisma().find_many(
-        where={
-            "id": {"in": [agent.storeListingVersionId for agent in storeAgents]},
-        },
-        include={"Agent": True},
-    )
-
-    for listing in agentListings:
-        agent = listing.Agent
-        if agent is None:
-            continue
-        graph = GraphModel.from_db(agent)
-        # Remove agents with empty input schema
-        if not graph.input_schema:
-            storeAgents = [
-                a for a in storeAgents if a.storeListingVersionId != listing.id
-            ]
-            continue
-
-        # Remove agents with empty credentials
-        # Get nodes from this agent that have credentials
-        nodes = await prisma.models.AgentNode.prisma().find_many(
-            where={
-                "agentGraphId": agent.id,
-                "agentBlockId": {"in": list(CREDENTIALS_FIELDS.keys())},
-            },
-        )
-        for node in nodes:
-            block_id = node.agentBlockId
-            field_name = CREDENTIALS_FIELDS[block_id]
-            # If there are no credentials or they are empty, remove the agent
-            # FIXME ignores default values
-            if (
-                field_name not in node.constantInput
-                or node.constantInput[field_name] is None
-            ):
-                storeAgents = [
-                    a for a in storeAgents if a.storeListingVersionId != listing.id
-                ]
-                break
-
-    # If there are less than 2 agents, add more agents to the list
-    if len(storeAgents) < 2:
-        storeAgents += await prisma.models.StoreAgent.prisma().find_many(
-            where={
-                "listing_id": {"not_in": [agent.listing_id for agent in storeAgents]},
-            },
-            order=[
-                {"featured": "desc"},
-                {"runs": "desc"},
-                {"rating": "desc"},
-            ],
-            take=2 - len(storeAgents),
-        )
-
-    # Calculate points for the first X agents and choose the top 2
-    agent_points = []
-    for agent in storeAgents[:POINTS_AGENT_COUNT]:
-        points = calculate_points(
-            agent, categories, custom, user_onboarding.integrations
-        )
-        agent_points.append((agent, points))
-
-    agent_points.sort(key=lambda x: x[1], reverse=True)
-    recommended_agents = [agent for agent, _ in agent_points[:2]]
-
-    return [
-        StoreAgentDetails(
-            store_listing_version_id=agent.storeListingVersionId,
-            slug=agent.slug,
-            agent_name=agent.agent_name,
-            agent_video=agent.agent_video or "",
-            agent_image=agent.agent_image,
-            creator=agent.creator_username,
-            creator_avatar=agent.creator_avatar,
-            sub_heading=agent.sub_heading,
-            description=agent.description,
-            categories=agent.categories,
-            runs=agent.runs,
-            rating=agent.rating,
-            versions=agent.versions,
-            last_updated=agent.updated_at,
-        )
-        for agent in recommended_agents
-    ]
-
-
-async def onboarding_enabled() -> bool:
-    count = await prisma.models.StoreAgent.prisma().count(take=MIN_AGENT_COUNT + 1)
-
-    # Onboading is enabled if there are at least 2 agents in the store
-    return count >= MIN_AGENT_COUNT

autogpt_platform/backend/backend/data/queue.py

@@ -1,6 +1,8 @@
 import asyncio
+import json
 import logging
 from abc import ABC, abstractmethod
+from datetime import datetime
 from typing import Any, AsyncGenerator, Generator, Generic, Optional, TypeVar
 
 from pydantic import BaseModel
@@ -12,6 +14,13 @@ from backend.data import redis
 logger = logging.getLogger(__name__)
 
 
+class DateTimeEncoder(json.JSONEncoder):
+    def default(self, o):
+        if isinstance(o, datetime):
+            return o.isoformat()
+        return super().default(o)
+
+
 M = TypeVar("M", bound=BaseModel)
 
 
@@ -23,14 +32,10 @@ class BaseRedisEventBus(Generic[M], ABC):
     def event_bus_name(self) -> str:
         pass
 
-    @property
-    def Message(self) -> type["_EventPayloadWrapper[M]"]:
-        return _EventPayloadWrapper[self.Model]
-
     def _serialize_message(self, item: M, channel_key: str) -> tuple[str, str]:
-        message = self.Message(payload=item).model_dump_json()
+        message = json.dumps(item.model_dump(), cls=DateTimeEncoder)
         channel_name = f"{self.event_bus_name}/{channel_key}"
-        logger.debug(f"[{channel_name}] Publishing an event to Redis {message}")
+        logger.info(f"[{channel_name}] Publishing an event to Redis {message}")
         return message, channel_name
 
     def _deserialize_message(self, msg: Any, channel_key: str) -> M | None:
@@ -38,8 +43,9 @@ class BaseRedisEventBus(Generic[M], ABC):
         if msg["type"] != message_type:
             return None
         try:
-            logger.debug(f"[{channel_key}] Consuming an event from Redis {msg['data']}")
-            return self.Message.model_validate_json(msg["data"]).payload
+            data = json.loads(msg["data"])
+            logger.info(f"Consuming an event from Redis {data}")
+            return self.Model(**data)
         except Exception as e:
             logger.error(f"Failed to parse event result from Redis {msg} {e}")
 
@@ -51,16 +57,9 @@ class BaseRedisEventBus(Generic[M], ABC):
         return pubsub, full_channel_name
 
 
-class _EventPayloadWrapper(BaseModel, Generic[M]):
-    """
-    Wrapper model to allow `RedisEventBus.Model` to be a discriminated union
-    of multiple event types.
-    """
-
-    payload: M
-
-
 class RedisEventBus(BaseRedisEventBus[M], ABC):
+    Model: type[M]
+
     @property
     def connection(self) -> redis.Redis:
         return redis.get_redis()
@@ -86,6 +85,8 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
 
 
 class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
+    Model: type[M]
+
     @property
     async def connection(self) -> redis.AsyncRedis:
         return await redis.get_redis_async()

autogpt_platform/backend/backend/data/user.py

@@ -1,24 +1,18 @@
-import base64
-import hashlib
-import hmac
 import logging
 from datetime import datetime, timedelta
 from typing import Optional, cast
-from urllib.parse import quote_plus
 
 from autogpt_libs.auth.models import DEFAULT_USER_ID
 from fastapi import HTTPException
 from prisma import Json
 from prisma.enums import NotificationType
 from prisma.models import User
-from prisma.types import UserUpdateInput
 
 from backend.data.db import prisma
 from backend.data.model import UserIntegrations, UserMetadata, UserMetadataRaw
-from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
+from backend.data.notifications import NotificationPreference
 from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.encryption import JSONCryptor
-from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
 
@@ -55,31 +49,6 @@ async def get_user_by_id(user_id: str) -> User:
     return User.model_validate(user)
 
 
-async def get_user_email_by_id(user_id: str) -> Optional[str]:
-    try:
-        user = await prisma.user.find_unique(where={"id": user_id})
-        return user.email if user else None
-    except Exception as e:
-        raise DatabaseError(f"Failed to get user email for user {user_id}: {e}") from e
-
-
-async def get_user_by_email(email: str) -> Optional[User]:
-    try:
-        user = await prisma.user.find_unique(where={"email": email})
-        return User.model_validate(user) if user else None
-    except Exception as e:
-        raise DatabaseError(f"Failed to get user by email {email}: {e}") from e
-
-
-async def update_user_email(user_id: str, email: str):
-    try:
-        await prisma.user.update(where={"id": user_id}, data={"email": email})
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to update user email for user {user_id}: {e}"
-        ) from e
-
-
 async def create_default_user() -> Optional[User]:
     user = await prisma.user.find_unique(where={"id": DEFAULT_USER_ID})
     if not user:
@@ -209,16 +178,16 @@ async def get_user_notification_preference(user_id: str) -> NotificationPreferen
 
         # enable notifications by default if user has no notification preference (shouldn't ever happen though)
         preferences: dict[NotificationType, bool] = {
-            NotificationType.AGENT_RUN: user.notifyOnAgentRun or False,
-            NotificationType.ZERO_BALANCE: user.notifyOnZeroBalance or False,
-            NotificationType.LOW_BALANCE: user.notifyOnLowBalance or False,
+            NotificationType.AGENT_RUN: user.notifyOnAgentRun or True,
+            NotificationType.ZERO_BALANCE: user.notifyOnZeroBalance or True,
+            NotificationType.LOW_BALANCE: user.notifyOnLowBalance or True,
             NotificationType.BLOCK_EXECUTION_FAILED: user.notifyOnBlockExecutionFailed
-            or False,
+            or True,
             NotificationType.CONTINUOUS_AGENT_ERROR: user.notifyOnContinuousAgentError
-            or False,
-            NotificationType.DAILY_SUMMARY: user.notifyOnDailySummary or False,
-            NotificationType.WEEKLY_SUMMARY: user.notifyOnWeeklySummary or False,
-            NotificationType.MONTHLY_SUMMARY: user.notifyOnMonthlySummary or False,
+            or True,
+            NotificationType.DAILY_SUMMARY: user.notifyOnDailySummary or True,
+            NotificationType.WEEKLY_SUMMARY: user.notifyOnWeeklySummary or True,
+            NotificationType.MONTHLY_SUMMARY: user.notifyOnMonthlySummary or True,
         }
         daily_limit = user.maxEmailsPerDay or 3
         notification_preference = NotificationPreference(
@@ -236,162 +205,3 @@ async def get_user_notification_preference(user_id: str) -> NotificationPreferen
         raise DatabaseError(
             f"Failed to upsert user notification preference for user {user_id}: {e}"
         ) from e
-
-
-async def update_user_notification_preference(
-    user_id: str, data: NotificationPreferenceDTO
-) -> NotificationPreference:
-    try:
-        update_data: UserUpdateInput = {}
-        if data.email:
-            update_data["email"] = data.email
-        if NotificationType.AGENT_RUN in data.preferences:
-            update_data["notifyOnAgentRun"] = data.preferences[
-                NotificationType.AGENT_RUN
-            ]
-        if NotificationType.ZERO_BALANCE in data.preferences:
-            update_data["notifyOnZeroBalance"] = data.preferences[
-                NotificationType.ZERO_BALANCE
-            ]
-        if NotificationType.LOW_BALANCE in data.preferences:
-            update_data["notifyOnLowBalance"] = data.preferences[
-                NotificationType.LOW_BALANCE
-            ]
-        if NotificationType.BLOCK_EXECUTION_FAILED in data.preferences:
-            update_data["notifyOnBlockExecutionFailed"] = data.preferences[
-                NotificationType.BLOCK_EXECUTION_FAILED
-            ]
-        if NotificationType.CONTINUOUS_AGENT_ERROR in data.preferences:
-            update_data["notifyOnContinuousAgentError"] = data.preferences[
-                NotificationType.CONTINUOUS_AGENT_ERROR
-            ]
-        if NotificationType.DAILY_SUMMARY in data.preferences:
-            update_data["notifyOnDailySummary"] = data.preferences[
-                NotificationType.DAILY_SUMMARY
-            ]
-        if NotificationType.WEEKLY_SUMMARY in data.preferences:
-            update_data["notifyOnWeeklySummary"] = data.preferences[
-                NotificationType.WEEKLY_SUMMARY
-            ]
-        if NotificationType.MONTHLY_SUMMARY in data.preferences:
-            update_data["notifyOnMonthlySummary"] = data.preferences[
-                NotificationType.MONTHLY_SUMMARY
-            ]
-        if data.daily_limit:
-            update_data["maxEmailsPerDay"] = data.daily_limit
-
-        user = await User.prisma().update(
-            where={"id": user_id},
-            data=update_data,
-        )
-        if not user:
-            raise ValueError(f"User not found with ID: {user_id}")
-        preferences: dict[NotificationType, bool] = {
-            NotificationType.AGENT_RUN: user.notifyOnAgentRun or True,
-            NotificationType.ZERO_BALANCE: user.notifyOnZeroBalance or True,
-            NotificationType.LOW_BALANCE: user.notifyOnLowBalance or True,
-            NotificationType.BLOCK_EXECUTION_FAILED: user.notifyOnBlockExecutionFailed
-            or True,
-            NotificationType.CONTINUOUS_AGENT_ERROR: user.notifyOnContinuousAgentError
-            or True,
-            NotificationType.DAILY_SUMMARY: user.notifyOnDailySummary or True,
-            NotificationType.WEEKLY_SUMMARY: user.notifyOnWeeklySummary or True,
-            NotificationType.MONTHLY_SUMMARY: user.notifyOnMonthlySummary or True,
-        }
-        notification_preference = NotificationPreference(
-            user_id=user.id,
-            email=user.email,
-            preferences=preferences,
-            daily_limit=user.maxEmailsPerDay or 3,
-            # TODO with other changes later, for now we just will email them
-            emails_sent_today=0,
-            last_reset_date=datetime.now(),
-        )
-        return NotificationPreference.model_validate(notification_preference)
-
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to update user notification preference for user {user_id}: {e}"
-        ) from e
-
-
-async def set_user_email_verification(user_id: str, verified: bool) -> None:
-    """Set the email verification status for a user."""
-    try:
-        await User.prisma().update(
-            where={"id": user_id},
-            data={"emailVerified": verified},
-        )
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to set email verification status for user {user_id}: {e}"
-        ) from e
-
-
-async def get_user_email_verification(user_id: str) -> bool:
-    """Get the email verification status for a user."""
-    try:
-        user = await User.prisma().find_unique_or_raise(
-            where={"id": user_id},
-        )
-        return user.emailVerified
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to get email verification status for user {user_id}: {e}"
-        ) from e
-
-
-def generate_unsubscribe_link(user_id: str) -> str:
-    """Generate a link to unsubscribe from all notifications"""
-    # Create an HMAC using a secret key
-    secret_key = Settings().secrets.unsubscribe_secret_key
-    signature = hmac.new(
-        secret_key.encode("utf-8"), user_id.encode("utf-8"), hashlib.sha256
-    ).digest()
-
-    # Create a token that combines the user_id and signature
-    token = base64.urlsafe_b64encode(
-        f"{user_id}:{signature.hex()}".encode("utf-8")
-    ).decode("utf-8")
-    logger.info(f"Generating unsubscribe link for user {user_id}")
-
-    base_url = Settings().config.platform_base_url
-    return f"{base_url}/api/email/unsubscribe?token={quote_plus(token)}"
-
-
-async def unsubscribe_user_by_token(token: str) -> None:
-    """Unsubscribe a user from all notifications using the token"""
-    try:
-        # Decode the token
-        decoded = base64.urlsafe_b64decode(token).decode("utf-8")
-        user_id, received_signature_hex = decoded.split(":", 1)
-
-        # Verify the signature
-        secret_key = Settings().secrets.unsubscribe_secret_key
-        expected_signature = hmac.new(
-            secret_key.encode("utf-8"), user_id.encode("utf-8"), hashlib.sha256
-        ).digest()
-
-        if not hmac.compare_digest(expected_signature.hex(), received_signature_hex):
-            raise ValueError("Invalid token signature")
-
-        user = await get_user_by_id(user_id)
-        await update_user_notification_preference(
-            user.id,
-            NotificationPreferenceDTO(
-                email=user.email,
-                daily_limit=0,
-                preferences={
-                    NotificationType.AGENT_RUN: False,
-                    NotificationType.ZERO_BALANCE: False,
-                    NotificationType.LOW_BALANCE: False,
-                    NotificationType.BLOCK_EXECUTION_FAILED: False,
-                    NotificationType.CONTINUOUS_AGENT_ERROR: False,
-                    NotificationType.DAILY_SUMMARY: False,
-                    NotificationType.WEEKLY_SUMMARY: False,
-                    NotificationType.MONTHLY_SUMMARY: False,
-                },
-            ),
-        )
-    except Exception as e:
-        raise DatabaseError(f"Failed to unsubscribe user by token {token}: {e}") from e

autogpt_platform/backend/backend/exec.py

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

autogpt_platform/backend/backend/executor/__init__.py

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

autogpt_platform/backend/backend/executor/database.py

@@ -1,55 +1,45 @@
-from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
+from functools import wraps
+from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
+
+from backend.data.credit import get_user_credit_model
 from backend.data.execution import (
-    GraphExecution,
-    NodeExecutionResult,
+    ExecutionResult,
+    NodeExecutionEntry,
     RedisExecutionEventBus,
     create_graph_execution,
-    get_graph_execution,
-    get_incomplete_node_executions,
-    get_latest_node_execution,
-    get_node_execution_results,
-    update_graph_execution_start_time,
+    get_execution_results,
+    get_executions_in_timerange,
+    get_incomplete_executions,
+    get_latest_execution,
+    update_execution_status,
     update_graph_execution_stats,
     update_node_execution_stats,
-    update_node_execution_status,
-    update_node_execution_status_batch,
     upsert_execution_input,
     upsert_execution_output,
 )
-from backend.data.graph import (
-    get_connected_output_nodes,
-    get_graph,
-    get_graph_metadata,
-    get_node,
-)
+from backend.data.graph import get_graph, get_node
 from backend.data.notifications import (
     create_or_add_to_user_notification_batch,
     empty_user_notification_batch,
-    get_all_batches_by_type,
     get_user_notification_batch,
-    get_user_notification_oldest_message_in_batch,
+    get_user_notification_last_message_in_batch,
 )
 from backend.data.user import (
     get_active_user_ids_in_timerange,
-    get_user_email_by_id,
-    get_user_email_verification,
+    get_active_users_ids,
+    get_user_by_id,
     get_user_integrations,
     get_user_metadata,
     get_user_notification_preference,
     update_user_integrations,
     update_user_metadata,
 )
-from backend.util.service import AppService, expose, exposed_run_and_wait
+from backend.util.service import AppService, expose, register_pydantic_serializers
 from backend.util.settings import Config
 
+P = ParamSpec("P")
+R = TypeVar("R")
 config = Config()
-_user_credit_model = get_user_credit_model()
-
-
-async def _spend_credits(
-    user_id: str, cost: int, metadata: UsageTransactionMetadata
-) -> int:
-    return await _user_credit_model.spend_credits(user_id, cost, metadata)
 
 
 class DatabaseManager(AppService):
@@ -57,70 +47,75 @@ class DatabaseManager(AppService):
         super().__init__()
         self.use_db = True
         self.use_redis = True
-        self.execution_event_bus = RedisExecutionEventBus()
+        self.event_queue = RedisExecutionEventBus()
 
     @classmethod
     def get_port(cls) -> int:
         return config.database_api_port
 
     @expose
-    def send_execution_update(
-        self, execution_result: GraphExecution | NodeExecutionResult
-    ):
-        self.execution_event_bus.publish(execution_result)
+    def send_execution_update(self, execution_result: ExecutionResult):
+        self.event_queue.publish(execution_result)
+
+    @staticmethod
+    def exposed_run_and_wait(
+        f: Callable[P, Coroutine[None, None, R]]
+    ) -> Callable[Concatenate[object, P], R]:
+        @expose
+        @wraps(f)
+        def wrapper(self, *args: P.args, **kwargs: P.kwargs) -> R:
+            coroutine = f(*args, **kwargs)
+            res = self.run_and_wait(coroutine)
+            return res
+
+        # Register serializers for annotations on bare function
+        register_pydantic_serializers(f)
+
+        return wrapper
 
     # Executions
-    get_graph_execution = exposed_run_and_wait(get_graph_execution)
     create_graph_execution = exposed_run_and_wait(create_graph_execution)
-    get_node_execution_results = exposed_run_and_wait(get_node_execution_results)
-    get_incomplete_node_executions = exposed_run_and_wait(
-        get_incomplete_node_executions
-    )
-    get_latest_node_execution = exposed_run_and_wait(get_latest_node_execution)
-    update_node_execution_status = exposed_run_and_wait(update_node_execution_status)
-    update_node_execution_status_batch = exposed_run_and_wait(
-        update_node_execution_status_batch
-    )
-    update_graph_execution_start_time = exposed_run_and_wait(
-        update_graph_execution_start_time
-    )
+    get_execution_results = exposed_run_and_wait(get_execution_results)
+    get_incomplete_executions = exposed_run_and_wait(get_incomplete_executions)
+    get_latest_execution = exposed_run_and_wait(get_latest_execution)
+    update_execution_status = exposed_run_and_wait(update_execution_status)
     update_graph_execution_stats = exposed_run_and_wait(update_graph_execution_stats)
     update_node_execution_stats = exposed_run_and_wait(update_node_execution_stats)
     upsert_execution_input = exposed_run_and_wait(upsert_execution_input)
     upsert_execution_output = exposed_run_and_wait(upsert_execution_output)
+    get_executions_in_timerange = exposed_run_and_wait(get_executions_in_timerange)
 
     # Graphs
     get_node = exposed_run_and_wait(get_node)
     get_graph = exposed_run_and_wait(get_graph)
-    get_connected_output_nodes = exposed_run_and_wait(get_connected_output_nodes)
-    get_graph_metadata = exposed_run_and_wait(get_graph_metadata)
 
     # Credits
-    spend_credits = exposed_run_and_wait(_spend_credits)
+    user_credit_model = get_user_credit_model()
+    spend_credits = cast(
+        Callable[[Any, NodeExecutionEntry, float, float], int],
+        exposed_run_and_wait(user_credit_model.spend_credits),
+    )
 
-    # User + User Metadata + User Integrations
+    # User + User Metadata + User Integrations + User Notification Preferences
     get_user_metadata = exposed_run_and_wait(get_user_metadata)
     update_user_metadata = exposed_run_and_wait(update_user_metadata)
     get_user_integrations = exposed_run_and_wait(get_user_integrations)
     update_user_integrations = exposed_run_and_wait(update_user_integrations)
-
-    # User Comms - async
     get_active_user_ids_in_timerange = exposed_run_and_wait(
         get_active_user_ids_in_timerange
     )
-    get_user_email_by_id = exposed_run_and_wait(get_user_email_by_id)
-    get_user_email_verification = exposed_run_and_wait(get_user_email_verification)
+    get_user_by_id = exposed_run_and_wait(get_user_by_id)
     get_user_notification_preference = exposed_run_and_wait(
         get_user_notification_preference
     )
+    get_active_users_ids = exposed_run_and_wait(get_active_users_ids)
 
-    # Notifications - async
+    # Notifications
     create_or_add_to_user_notification_batch = exposed_run_and_wait(
         create_or_add_to_user_notification_batch
     )
-    empty_user_notification_batch = exposed_run_and_wait(empty_user_notification_batch)
-    get_all_batches_by_type = exposed_run_and_wait(get_all_batches_by_type)
-    get_user_notification_batch = exposed_run_and_wait(get_user_notification_batch)
-    get_user_notification_oldest_message_in_batch = exposed_run_and_wait(
-        get_user_notification_oldest_message_in_batch
+    get_user_notification_last_message_in_batch = exposed_run_and_wait(
+        get_user_notification_last_message_in_batch
     )
+    empty_user_notification_batch = exposed_run_and_wait(empty_user_notification_batch)
+    get_user_notification_batch = exposed_run_and_wait(get_user_notification_batch)

autogpt_platform/backend/backend/executor/manager.py

@@ -12,19 +12,8 @@ from typing import TYPE_CHECKING, Any, Generator, Optional, TypeVar, cast
 
 from redis.lock import Lock as RedisLock
 
-from backend.blocks.io import AgentOutputBlock
-from backend.data.model import GraphExecutionStats, NodeExecutionStats
-from backend.data.notifications import (
-    AgentRunData,
-    LowBalanceData,
-    NotificationEventDTO,
-    NotificationType,
-)
-from backend.util.exceptions import InsufficientBalanceError
-
 if TYPE_CHECKING:
     from backend.executor import DatabaseManager
-    from backend.notifications.notifications import NotificationManager
 
 from autogpt_libs.utils.cache import thread_cached
 
@@ -40,19 +29,14 @@ from backend.data.block import (
 )
 from backend.data.execution import (
     ExecutionQueue,
+    ExecutionResult,
     ExecutionStatus,
     GraphExecutionEntry,
     NodeExecutionEntry,
-    NodeExecutionResult,
     merge_execution_input,
     parse_execution_output,
 )
 from backend.data.graph import GraphModel, Link, Node
-from backend.executor.utils import (
-    UsageTransactionMetadata,
-    block_usage_cost,
-    execution_usage_cost,
-)
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util import json
 from backend.util.decorator import error_logged, time_measured
@@ -114,10 +98,7 @@ class LogMetadata:
         logger.exception(msg, extra={"json_fields": {**self.metadata, **extra}})
 
     def _wrap(self, msg: str, **extra):
-        extra_msg = str(extra or "")
-        if len(extra_msg) > 1000:
-            extra_msg = extra_msg[:1000] + "..."
-        return f"{self.prefix} {msg} {extra_msg}"
+        return f"{self.prefix} {msg} {extra}"
 
 
 T = TypeVar("T")
@@ -128,7 +109,7 @@ def execute_node(
     db_client: "DatabaseManager",
     creds_manager: IntegrationCredentialsManager,
     data: NodeExecutionEntry,
-    execution_stats: NodeExecutionStats | None = None,
+    execution_stats: dict[str, Any] | None = None,
 ) -> ExecutionStream:
     """
     Execute a node in the graph. This will trigger a block execution on a node,
@@ -149,9 +130,8 @@ def execute_node(
     node_exec_id = data.node_exec_id
     node_id = data.node_id
 
-    def update_execution_status(status: ExecutionStatus) -> NodeExecutionResult:
-        """Sets status and fetches+broadcasts the latest state of the node execution"""
-        exec_update = db_client.update_node_execution_status(node_exec_id, status)
+    def update_execution(status: ExecutionStatus) -> ExecutionResult:
+        exec_update = db_client.update_execution_status(node_exec_id, status)
         db_client.send_execution_update(exec_update)
         return exec_update
 
@@ -162,17 +142,6 @@ def execute_node(
         logger.error(f"Block {node.block_id} not found.")
         return
 
-    def push_output(output_name: str, output_data: Any) -> None:
-        _push_node_execution_output(
-            db_client=db_client,
-            user_id=user_id,
-            graph_exec_id=graph_exec_id,
-            node_exec_id=node_exec_id,
-            block_id=node_block.id,
-            output_name=output_name,
-            output_data=output_data,
-        )
-
     log_metadata = LogMetadata(
         user_id=user_id,
         graph_eid=graph_exec_id,
@@ -186,8 +155,8 @@ def execute_node(
     input_data, error = validate_exec(node, data.data, resolve_input=False)
     if input_data is None:
         log_metadata.error(f"Skip execution, input validation error: {error}")
-        push_output("error", error)
-        update_execution_status(ExecutionStatus.FAILED)
+        db_client.upsert_execution_output(node_exec_id, "error", error)
+        update_execution(ExecutionStatus.FAILED)
         return
 
     # Re-shape the input data for agent block.
@@ -200,7 +169,7 @@ def execute_node(
     input_data_str = json.dumps(input_data)
     input_size = len(input_data_str)
     log_metadata.info("Executed node with input", input=input_data_str)
-    update_execution_status(ExecutionStatus.RUNNING)
+    update_execution(ExecutionStatus.RUNNING)
 
     # Inject extra execution arguments for the blocks via kwargs
     extra_exec_kwargs: dict = {
@@ -224,15 +193,18 @@ def execute_node(
 
     output_size = 0
     try:
-        outputs: dict[str, Any] = {}
+        # Charge the user for the execution before running the block.
+        # TODO: We assume the block is executed within 0 seconds.
+        #       This is fine because for now, there is no block that is charged by time.
+        db_client.spend_credits(data, input_size + output_size, 0)
+
         for output_name, output_data in node_block.execute(
             input_data, **extra_exec_kwargs
         ):
-            output_data = json.convert_pydantic_to_json(output_data)
             output_size += len(json.dumps(output_data))
             log_metadata.info("Node produced output", **{output_name: output_data})
-            push_output(output_name, output_data)
-            outputs[output_name] = output_data
+            db_client.upsert_execution_output(node_exec_id, output_name, output_data)
+
             for execution in _enqueue_next_nodes(
                 db_client=db_client,
                 node=node,
@@ -244,12 +216,12 @@ def execute_node(
             ):
                 yield execution
 
-        update_execution_status(ExecutionStatus.COMPLETED)
+        update_execution(ExecutionStatus.COMPLETED)
 
     except Exception as e:
         error_msg = str(e)
-        push_output("error", error_msg)
-        update_execution_status(ExecutionStatus.FAILED)
+        db_client.upsert_execution_output(node_exec_id, "error", error_msg)
+        update_execution(ExecutionStatus.FAILED)
 
         for execution in _enqueue_next_nodes(
             db_client=db_client,
@@ -265,7 +237,7 @@ def execute_node(
         raise e
     finally:
         # Ensure credentials are released even if execution fails
-        if creds_lock and creds_lock.locked():
+        if creds_lock:
             try:
                 creds_lock.release()
             except Exception as e:
@@ -273,40 +245,9 @@ def execute_node(
 
         # Update execution stats
         if execution_stats is not None:
-            execution_stats = execution_stats.model_copy(
-                update=node_block.execution_stats.model_dump()
-            )
-            execution_stats.input_size = input_size
-            execution_stats.output_size = output_size
-
-
-def _push_node_execution_output(
-    db_client: "DatabaseManager",
-    user_id: str,
-    graph_exec_id: str,
-    node_exec_id: str,
-    block_id: str,
-    output_name: str,
-    output_data: Any,
-):
-    from backend.blocks.io import IO_BLOCK_IDs
-
-    db_client.upsert_execution_output(
-        node_exec_id=node_exec_id,
-        output_name=output_name,
-        output_data=output_data,
-    )
-
-    # Automatically push execution updates for all agent I/O
-    if block_id in IO_BLOCK_IDs:
-        graph_exec = db_client.get_graph_execution(
-            user_id=user_id, execution_id=graph_exec_id
-        )
-        if not graph_exec:
-            raise ValueError(
-                f"Graph execution #{graph_exec_id} for user #{user_id} not found"
-            )
-        db_client.send_execution_update(graph_exec)
+            execution_stats.update(node_block.execution_stats)
+            execution_stats["input_size"] = input_size
+            execution_stats["output_size"] = output_size
 
 
 def _enqueue_next_nodes(
@@ -321,7 +262,7 @@ def _enqueue_next_nodes(
     def add_enqueued_execution(
         node_exec_id: str, node_id: str, block_id: str, data: BlockInput
     ) -> NodeExecutionEntry:
-        exec_update = db_client.update_node_execution_status(
+        exec_update = db_client.update_execution_status(
             node_exec_id, ExecutionStatus.QUEUED, data
         )
         db_client.send_execution_update(exec_update)
@@ -366,7 +307,7 @@ def _enqueue_next_nodes(
                 if link.is_static and link.sink_name not in next_node_input
             }
             if static_link_names and (
-                latest_execution := db_client.get_latest_node_execution(
+                latest_execution := db_client.get_latest_execution(
                     next_node_id, graph_exec_id
                 )
             ):
@@ -399,7 +340,7 @@ def _enqueue_next_nodes(
 
             # If link is static, there could be some incomplete executions waiting for it.
             # Load and complete the input missing input data, and try to re-enqueue them.
-            for iexec in db_client.get_incomplete_node_executions(
+            for iexec in db_client.get_incomplete_executions(
                 next_node_id, graph_exec_id
             ):
                 idata = iexec.input_data
@@ -459,30 +400,46 @@ def validate_exec(
     node_block: Block | None = get_block(node.block_id)
     if not node_block:
         return None, f"Block for {node.block_id} not found."
-    schema = node_block.input_schema
 
-    # Convert non-matching data types to the expected input schema.
-    for name, data_type in schema.__annotations__.items():
-        if (value := data.get(name)) and (type(value) is not data_type):
-            data[name] = convert(value, data_type)
+    if isinstance(node_block, AgentExecutorBlock):
+        # Validate the execution metadata for the agent executor block.
+        try:
+            exec_data = AgentExecutorBlock.Input(**node.input_default)
+        except Exception as e:
+            return None, f"Input data doesn't match {node_block.name}: {str(e)}"
+
+        # Validation input
+        input_schema = exec_data.input_schema
+        required_fields = set(input_schema["required"])
+        input_default = exec_data.data
+    else:
+        # Convert non-matching data types to the expected input schema.
+        for name, data_type in node_block.input_schema.__annotations__.items():
+            if (value := data.get(name)) and (type(value) is not data_type):
+                data[name] = convert(value, data_type)
+
+        # Validation input
+        input_schema = node_block.input_schema.jsonschema()
+        required_fields = node_block.input_schema.get_required_fields()
+        input_default = node.input_default
 
     # Input data (without default values) should contain all required fields.
     error_prefix = f"Input data missing or mismatch for `{node_block.name}`:"
-    if missing_links := schema.get_missing_links(data, node.input_links):
-        return None, f"{error_prefix} unpopulated links {missing_links}"
+    input_fields_from_nodes = {link.sink_name for link in node.input_links}
+    if not input_fields_from_nodes.issubset(data):
+        return None, f"{error_prefix} {input_fields_from_nodes - set(data)}"
 
     # Merge input data with default values and resolve dynamic dict/list/object pins.
-    input_default = schema.get_input_defaults(node.input_default)
     data = {**input_default, **data}
     if resolve_input:
         data = merge_execution_input(data)
 
     # Input data post-merge should contain all required fields from the schema.
-    if missing_input := schema.get_missing_input(data):
-        return None, f"{error_prefix} missing input {missing_input}"
+    if not required_fields.issubset(data):
+        return None, f"{error_prefix} {required_fields - set(data)}"
 
     # Last validation: Validate the input values against the schema.
-    if error := schema.get_mismatch_error(data):
+    if error := json.validate_with_jsonschema(schema=input_schema, data=data):
         error_message = f"{error_prefix} {error}"
         logger.error(error_message)
         return None, error_message
@@ -563,7 +520,7 @@ class Executor:
         cls,
         q: ExecutionQueue[NodeExecutionEntry],
         node_exec: NodeExecutionEntry,
-    ) -> NodeExecutionStats:
+    ) -> dict[str, Any]:
         log_metadata = LogMetadata(
             user_id=node_exec.user_id,
             graph_eid=node_exec.graph_exec_id,
@@ -573,15 +530,13 @@ class Executor:
             block_name="-",
         )
 
-        execution_stats = NodeExecutionStats()
+        execution_stats = {}
         timing_info, _ = cls._on_node_execution(
             q, node_exec, log_metadata, execution_stats
         )
-        execution_stats.walltime = timing_info.wall_time
-        execution_stats.cputime = timing_info.cpu_time
+        execution_stats["walltime"] = timing_info.wall_time
+        execution_stats["cputime"] = timing_info.cpu_time
 
-        if isinstance(execution_stats.error, Exception):
-            execution_stats.error = str(execution_stats.error)
         cls.db_client.update_node_execution_stats(
             node_exec.node_exec_id, execution_stats
         )
@@ -594,15 +549,12 @@ class Executor:
         q: ExecutionQueue[NodeExecutionEntry],
         node_exec: NodeExecutionEntry,
         log_metadata: LogMetadata,
-        stats: NodeExecutionStats | None = None,
+        stats: dict[str, Any] | None = None,
     ):
         try:
             log_metadata.info(f"Start node execution {node_exec.node_exec_id}")
             for execution in execute_node(
-                db_client=cls.db_client,
-                creds_manager=cls.creds_manager,
-                data=node_exec,
-                execution_stats=stats,
+                cls.db_client, cls.creds_manager, node_exec, stats
             ):
                 q.add(execution)
             log_metadata.info(f"Finished node execution {node_exec.node_exec_id}")
@@ -617,9 +569,6 @@ class Executor:
                     f"Failed node execution {node_exec.node_exec_id}: {e}"
                 )
 
-            if stats is not None:
-                stats.error = e
-
     @classmethod
     def on_graph_executor_start(cls):
         configure_logging()
@@ -628,7 +577,6 @@ class Executor:
         cls.db_client = get_db_client()
         cls.pool_size = settings.config.num_node_workers
         cls.pid = os.getpid()
-        cls.notification_service = get_notification_service()
         cls._init_node_executor_pool()
         logger.info(
             f"Graph executor {cls.pid} started with {cls.pool_size} node workers"
@@ -666,72 +614,18 @@ class Executor:
             node_eid="*",
             block_name="-",
         )
-        exec_meta = cls.db_client.update_graph_execution_start_time(
-            graph_exec.graph_exec_id
-        )
-        cls.db_client.send_execution_update(exec_meta)
         timing_info, (exec_stats, status, error) = cls._on_graph_execution(
             graph_exec, cancel, log_metadata
         )
-        exec_stats.walltime = timing_info.wall_time
-        exec_stats.cputime = timing_info.cpu_time
-        exec_stats.error = str(error)
-
-        if graph_exec_result := cls.db_client.update_graph_execution_stats(
+        exec_stats["walltime"] = timing_info.wall_time
+        exec_stats["cputime"] = timing_info.cpu_time
+        exec_stats["error"] = str(error) if error else None
+        result = cls.db_client.update_graph_execution_stats(
             graph_exec_id=graph_exec.graph_exec_id,
             status=status,
             stats=exec_stats,
-        ):
-            cls.db_client.send_execution_update(graph_exec_result)
-
-        cls._handle_agent_run_notif(graph_exec, exec_stats)
-
-    @classmethod
-    def _charge_usage(
-        cls,
-        node_exec: NodeExecutionEntry,
-        execution_count: int,
-        execution_stats: GraphExecutionStats,
-    ) -> int:
-        block = get_block(node_exec.block_id)
-        if not block:
-            logger.error(f"Block {node_exec.block_id} not found.")
-            return execution_count
-
-        cost, matching_filter = block_usage_cost(block=block, input_data=node_exec.data)
-        if cost > 0:
-            cls.db_client.spend_credits(
-                user_id=node_exec.user_id,
-                cost=cost,
-                metadata=UsageTransactionMetadata(
-                    graph_exec_id=node_exec.graph_exec_id,
-                    graph_id=node_exec.graph_id,
-                    node_exec_id=node_exec.node_exec_id,
-                    node_id=node_exec.node_id,
-                    block_id=node_exec.block_id,
-                    block=block.name,
-                    input=matching_filter,
-                ),
-            )
-            execution_stats.cost += cost
-
-        cost, execution_count = execution_usage_cost(execution_count)
-        if cost > 0:
-            cls.db_client.spend_credits(
-                user_id=node_exec.user_id,
-                cost=cost,
-                metadata=UsageTransactionMetadata(
-                    graph_exec_id=node_exec.graph_exec_id,
-                    graph_id=node_exec.graph_id,
-                    input={
-                        "execution_count": execution_count,
-                        "charge": "Execution Cost",
-                    },
-                ),
-            )
-            execution_stats.cost += cost
-
-        return execution_count
+        )
+        cls.db_client.send_execution_update(result)
 
     @classmethod
     @time_measured
@@ -740,7 +634,7 @@ class Executor:
         graph_exec: GraphExecutionEntry,
         cancel: threading.Event,
         log_metadata: LogMetadata,
-    ) -> tuple[GraphExecutionStats, ExecutionStatus, Exception | None]:
+    ) -> tuple[dict[str, Any], ExecutionStatus, Exception | None]:
         """
         Returns:
             dict: The execution statistics of the graph execution.
@@ -748,7 +642,11 @@ class Executor:
             Exception | None: The error that occurred during the execution, if any.
         """
         log_metadata.info(f"Start graph execution {graph_exec.graph_exec_id}")
-        exec_stats = GraphExecutionStats()
+        exec_stats = {
+            "nodes_walltime": 0,
+            "nodes_cputime": 0,
+            "node_count": 0,
+        }
         error = None
         finished = False
 
@@ -767,24 +665,24 @@ class Executor:
         try:
             queue = ExecutionQueue[NodeExecutionEntry]()
             for node_exec in graph_exec.start_node_execs:
+                exec_update = cls.db_client.update_execution_status(
+                    node_exec.node_exec_id, ExecutionStatus.QUEUED, node_exec.data
+                )
+                cls.db_client.send_execution_update(exec_update)
                 queue.add(node_exec)
 
-            exec_cost_counter = 0
             running_executions: dict[str, AsyncResult] = {}
 
             def make_exec_callback(exec_data: NodeExecutionEntry):
+                node_id = exec_data.node_id
+
                 def callback(result: object):
-                    running_executions.pop(exec_data.node_id)
-
-                    if not isinstance(result, NodeExecutionStats):
-                        return
-
+                    running_executions.pop(node_id)
                     nonlocal exec_stats
-                    exec_stats.node_count += 1
-                    exec_stats.nodes_cputime += result.cputime
-                    exec_stats.nodes_walltime += result.walltime
-                    if (err := result.error) and isinstance(err, Exception):
-                        exec_stats.node_error_count += 1
+                    if isinstance(result, dict):
+                        exec_stats["node_count"] += 1
+                        exec_stats["nodes_cputime"] += result.get("cputime", 0)
+                        exec_stats["nodes_walltime"] += result.get("walltime", 0)
 
                 return callback
 
@@ -807,38 +705,6 @@ class Executor:
                     f"Dispatching node execution {exec_data.node_exec_id} "
                     f"for node {exec_data.node_id}",
                 )
-
-                try:
-                    exec_cost_counter = cls._charge_usage(
-                        node_exec=exec_data,
-                        execution_count=exec_cost_counter + 1,
-                        execution_stats=exec_stats,
-                    )
-                except InsufficientBalanceError as error:
-                    node_exec_id = exec_data.node_exec_id
-                    _push_node_execution_output(
-                        db_client=cls.db_client,
-                        user_id=graph_exec.user_id,
-                        graph_exec_id=graph_exec.graph_exec_id,
-                        node_exec_id=node_exec_id,
-                        block_id=exec_data.block_id,
-                        output_name="error",
-                        output_data=str(error),
-                    )
-
-                    exec_update = cls.db_client.update_node_execution_status(
-                        node_exec_id, ExecutionStatus.FAILED
-                    )
-                    cls.db_client.send_execution_update(exec_update)
-
-                    cls._handle_low_balance_notif(
-                        graph_exec.user_id,
-                        graph_exec.graph_id,
-                        exec_stats,
-                        error,
-                    )
-                    raise
-
                 running_executions[exec_data.node_id] = cls.executor.apply_async(
                     cls.on_node_execution,
                     (queue, exec_data),
@@ -861,87 +727,22 @@ class Executor:
                         execution.wait(3)
 
             log_metadata.info(f"Finished graph execution {graph_exec.graph_exec_id}")
-
         except Exception as e:
+            log_metadata.exception(
+                f"Failed graph execution {graph_exec.graph_exec_id}: {e}"
+            )
             error = e
         finally:
-            if error:
-                log_metadata.error(
-                    f"Failed graph execution {graph_exec.graph_exec_id}: {error}"
-                )
-                execution_status = ExecutionStatus.FAILED
-            else:
-                execution_status = ExecutionStatus.COMPLETED
-
             if not cancel.is_set():
                 finished = True
                 cancel.set()
             cancel_thread.join()
             clean_exec_files(graph_exec.graph_exec_id)
 
-            return exec_stats, execution_status, error
-
-    @classmethod
-    def _handle_agent_run_notif(
-        cls,
-        graph_exec: GraphExecutionEntry,
-        exec_stats: GraphExecutionStats,
-    ):
-        metadata = cls.db_client.get_graph_metadata(
-            graph_exec.graph_id, graph_exec.graph_version
-        )
-        outputs = cls.db_client.get_node_execution_results(
-            graph_exec.graph_exec_id,
-            block_ids=[AgentOutputBlock().id],
-        )
-
-        named_outputs = [
-            {
-                key: value[0] if key == "name" else value
-                for key, value in output.output_data.items()
-            }
-            for output in outputs
-        ]
-
-        event = NotificationEventDTO(
-            user_id=graph_exec.user_id,
-            type=NotificationType.AGENT_RUN,
-            data=AgentRunData(
-                outputs=named_outputs,
-                agent_name=metadata.name if metadata else "Unknown Agent",
-                credits_used=exec_stats.cost,
-                execution_time=exec_stats.walltime,
-                graph_id=graph_exec.graph_id,
-                node_count=exec_stats.node_count,
-            ).model_dump(),
-        )
-
-        cls.notification_service.queue_notification(event)
-
-    @classmethod
-    def _handle_low_balance_notif(
-        cls,
-        user_id: str,
-        graph_id: str,
-        exec_stats: GraphExecutionStats,
-        e: InsufficientBalanceError,
-    ):
-        shortfall = e.balance - e.amount
-        metadata = cls.db_client.get_graph_metadata(graph_id)
-        base_url = (
-            settings.config.frontend_base_url or settings.config.platform_base_url
-        )
-        cls.notification_service.queue_notification(
-            NotificationEventDTO(
-                user_id=user_id,
-                type=NotificationType.LOW_BALANCE,
-                data=LowBalanceData(
-                    current_balance=exec_stats.cost,
-                    billing_page_link=f"{base_url}/profile/credits",
-                    shortfall=shortfall,
-                    agent_name=metadata.name if metadata else "Unknown Agent",
-                ).model_dump(),
-            )
+        return (
+            exec_stats,
+            ExecutionStatus.FAILED if error else ExecutionStatus.COMPLETED,
+            error,
         )
 
 
@@ -1046,26 +847,17 @@ class ExecutionManager(AppService):
             else:
                 nodes_input.append((node.id, input_data))
 
-        if not nodes_input:
-            raise ValueError(
-                "No starting nodes found for the graph, make sure an AgentInput or blocks with no inbound links are present as starting nodes."
-            )
-
-        graph_exec = self.db_client.create_graph_execution(
+        graph_exec_id, node_execs = self.db_client.create_graph_execution(
             graph_id=graph_id,
             graph_version=graph.version,
             nodes_input=nodes_input,
             user_id=user_id,
             preset_id=preset_id,
         )
-        self.db_client.send_execution_update(graph_exec)
 
-        graph_exec_entry = GraphExecutionEntry(
-            user_id=user_id,
-            graph_id=graph_id,
-            graph_version=graph_version or 0,
-            graph_exec_id=graph_exec.id,
-            start_node_execs=[
+        starting_node_execs = []
+        for node_exec in node_execs:
+            starting_node_execs.append(
                 NodeExecutionEntry(
                     user_id=user_id,
                     graph_exec_id=node_exec.graph_exec_id,
@@ -1075,12 +867,18 @@ class ExecutionManager(AppService):
                     block_id=node_exec.block_id,
                     data=node_exec.input_data,
                 )
-                for node_exec in graph_exec.node_executions
-            ],
-        )
-        self.queue.add(graph_exec_entry)
+            )
 
-        return graph_exec_entry
+        graph_exec = GraphExecutionEntry(
+            user_id=user_id,
+            graph_id=graph_id,
+            graph_version=graph_version or 0,
+            graph_exec_id=graph_exec_id,
+            start_node_execs=starting_node_execs,
+        )
+        self.queue.add(graph_exec)
+
+        return graph_exec
 
     @expose
     def cancel_execution(self, graph_exec_id: str) -> None:
@@ -1092,36 +890,29 @@ class ExecutionManager(AppService):
         3. Update execution statuses in DB and set `error` outputs to `"TERMINATED"`.
         """
         if graph_exec_id not in self.active_graph_runs:
-            logger.warning(
+            raise Exception(
                 f"Graph execution #{graph_exec_id} not active/running: "
                 "possibly already completed/cancelled."
             )
-        else:
-            future, cancel_event = self.active_graph_runs[graph_exec_id]
-            if not cancel_event.is_set():
-                cancel_event.set()
-                future.result()
 
-        # Update the status of the graph & node executions
-        self.db_client.update_graph_execution_stats(
-            graph_exec_id,
-            ExecutionStatus.TERMINATED,
-        )
-        node_execs = self.db_client.get_node_execution_results(
-            graph_exec_id=graph_exec_id,
-            statuses=[
-                ExecutionStatus.QUEUED,
-                ExecutionStatus.RUNNING,
-                ExecutionStatus.INCOMPLETE,
-            ],
-        )
-        self.db_client.update_node_execution_status_batch(
-            [node_exec.node_exec_id for node_exec in node_execs],
-            ExecutionStatus.TERMINATED,
-        )
+        future, cancel_event = self.active_graph_runs[graph_exec_id]
+        if cancel_event.is_set():
+            return
+
+        cancel_event.set()
+        future.result()
+
+        # Update the status of the unfinished node executions
+        node_execs = self.db_client.get_execution_results(graph_exec_id)
         for node_exec in node_execs:
-            node_exec.status = ExecutionStatus.TERMINATED
-            self.db_client.send_execution_update(node_exec)
+            if node_exec.status not in (
+                ExecutionStatus.COMPLETED,
+                ExecutionStatus.FAILED,
+            ):
+                exec_update = self.db_client.update_execution_status(
+                    node_exec.node_exec_id, ExecutionStatus.TERMINATED
+                )
+                self.db_client.send_execution_update(exec_update)
 
     def _validate_node_input_credentials(self, graph: GraphModel, user_id: str):
         """Checks all credentials for all nodes of the graph"""
@@ -1177,13 +968,6 @@ def get_db_client() -> "DatabaseManager":
     return get_service_client(DatabaseManager)
 
 
-@thread_cached
-def get_notification_service() -> "NotificationManager":
-    from backend.notifications import NotificationManager
-
-    return get_service_client(NotificationManager)
-
-
 @contextmanager
 def synchronized(key: str, timeout: int = 60):
     lock: RedisLock = redis.get_redis().lock(f"lock:{key}", timeout=timeout)

autogpt_platform/backend/backend/executor/scheduler.py

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

autogpt_platform/backend/backend/executor/utils.py

@@ -1,97 +0,0 @@
-from pydantic import BaseModel
-
-from backend.data.block import Block, BlockInput
-from backend.data.block_cost_config import BLOCK_COSTS
-from backend.data.cost import BlockCostType
-from backend.util.settings import Config
-
-config = Config()
-
-
-class UsageTransactionMetadata(BaseModel):
-    graph_exec_id: str | None = None
-    graph_id: str | None = None
-    node_id: str | None = None
-    node_exec_id: str | None = None
-    block_id: str | None = None
-    block: str | None = None
-    input: BlockInput | None = None
-
-
-def execution_usage_cost(execution_count: int) -> tuple[int, int]:
-    """
-    Calculate the cost of executing a graph based on the number of executions.
-
-    Args:
-        execution_count: Number of executions
-
-    Returns:
-        Tuple of cost amount and remaining execution count
-    """
-    return (
-        execution_count
-        // config.execution_cost_count_threshold
-        * config.execution_cost_per_threshold,
-        execution_count % config.execution_cost_count_threshold,
-    )
-
-
-def block_usage_cost(
-    block: Block,
-    input_data: BlockInput,
-    data_size: float = 0,
-    run_time: float = 0,
-) -> tuple[int, BlockInput]:
-    """
-    Calculate the cost of using a block based on the input data and the block type.
-
-    Args:
-        block: Block object
-        input_data: Input data for the block
-        data_size: Size of the input data in bytes
-        run_time: Execution time of the block in seconds
-
-    Returns:
-        Tuple of cost amount and cost filter
-    """
-    block_costs = BLOCK_COSTS.get(type(block))
-    if not block_costs:
-        return 0, {}
-
-    for block_cost in block_costs:
-        if not _is_cost_filter_match(block_cost.cost_filter, input_data):
-            continue
-
-        if block_cost.cost_type == BlockCostType.RUN:
-            return block_cost.cost_amount, block_cost.cost_filter
-
-        if block_cost.cost_type == BlockCostType.SECOND:
-            return (
-                int(run_time * block_cost.cost_amount),
-                block_cost.cost_filter,
-            )
-
-        if block_cost.cost_type == BlockCostType.BYTE:
-            return (
-                int(data_size * block_cost.cost_amount),
-                block_cost.cost_filter,
-            )
-
-    return 0, {}
-
-
-def _is_cost_filter_match(cost_filter: BlockInput, input_data: BlockInput) -> bool:
-    """
-    Filter rules:
-      - If cost_filter is an object, then check if cost_filter is the subset of input_data
-      - Otherwise, check if cost_filter is equal to input_data.
-      - Undefined, null, and empty string are considered as equal.
-    """
-    if not isinstance(cost_filter, dict) or not isinstance(input_data, dict):
-        return cost_filter == input_data
-
-    return all(
-        (not input_data.get(k) and not v)
-        or (input_data.get(k) and _is_cost_filter_match(v, input_data[k]))
-        for k, v in cost_filter.items()
-    )

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -145,29 +145,6 @@ mem0_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
-apollo_credentials = APIKeyCredentials(
-    id="544c62b5-1d0f-4156-8fb4-9525f11656eb",
-    provider="apollo",
-    api_key=SecretStr(settings.secrets.apollo_api_key),
-    title="Use Credits for Apollo",
-    expires_at=None,
-)
-
-smartlead_credentials = APIKeyCredentials(
-    id="3bcdbda3-84a3-46af-8fdb-bfd2472298b8",
-    provider="smartlead",
-    api_key=SecretStr(settings.secrets.smartlead_api_key),
-    title="Use Credits for SmartLead",
-    expires_at=None,
-)
-
-zerobounce_credentials = APIKeyCredentials(
-    id="63a6e279-2dc2-448e-bf57-85776f7176dc",
-    provider="zerobounce",
-    api_key=SecretStr(settings.secrets.zerobounce_api_key),
-    title="Use Credits for ZeroBounce",
-    expires_at=None,
-)
 
 DEFAULT_CREDENTIALS = [
     ollama_credentials,
@@ -187,9 +164,6 @@ DEFAULT_CREDENTIALS = [
     mem0_credentials,
     nvidia_credentials,
     screenshotone_credentials,
-    apollo_credentials,
-    smartlead_credentials,
-    zerobounce_credentials,
 ]
 
 
@@ -257,12 +231,6 @@ class IntegrationCredentialsStore:
             all_credentials.append(screenshotone_credentials)
         if settings.secrets.mem0_api_key:
             all_credentials.append(mem0_credentials)
-        if settings.secrets.apollo_api_key:
-            all_credentials.append(apollo_credentials)
-        if settings.secrets.smartlead_api_key:
-            all_credentials.append(smartlead_credentials)
-        if settings.secrets.zerobounce_api_key:
-            all_credentials.append(zerobounce_credentials)
         return all_credentials
 
     def get_creds_by_id(self, user_id: str, credentials_id: str) -> Credentials | None:

autogpt_platform/backend/backend/integrations/providers.py

@@ -4,7 +4,6 @@ from enum import Enum
 # --8<-- [start:ProviderName]
 class ProviderName(str, Enum):
     ANTHROPIC = "anthropic"
-    APOLLO = "apollo"
     COMPASS = "compass"
     DISCORD = "discord"
     D_ID = "d_id"
@@ -33,10 +32,8 @@ class ProviderName(str, Enum):
     REVID = "revid"
     SCREENSHOTONE = "screenshotone"
     SLANT3D = "slant3d"
-    SMARTLEAD = "smartlead"
     SMTP = "smtp"
     TWITTER = "twitter"
     TODOIST = "todoist"
     UNREAL_SPEECH = "unreal_speech"
-    ZEROBOUNCE = "zerobounce"
     # --8<-- [end:ProviderName]

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

@@ -1,43 +1,22 @@
 from typing import TYPE_CHECKING
 
+from .compass import CompassWebhookManager
+from .github import GithubWebhooksManager
+from .slant3d import Slant3DWebhooksManager
+
 if TYPE_CHECKING:
     from ..providers import ProviderName
     from ._base import BaseWebhooksManager
 
-_WEBHOOK_MANAGERS: dict["ProviderName", type["BaseWebhooksManager"]] = {}
+# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
+WEBHOOK_MANAGERS_BY_NAME: dict["ProviderName", type["BaseWebhooksManager"]] = {
+    handler.PROVIDER_NAME: handler
+    for handler in [
+        CompassWebhookManager,
+        GithubWebhooksManager,
+        Slant3DWebhooksManager,
+    ]
+}
+# --8<-- [end:WEBHOOK_MANAGERS_BY_NAME]
 
-
-# --8<-- [start:load_webhook_managers]
-def load_webhook_managers() -> dict["ProviderName", type["BaseWebhooksManager"]]:
-    if _WEBHOOK_MANAGERS:
-        return _WEBHOOK_MANAGERS
-
-    from .compass import CompassWebhookManager
-    from .github import GithubWebhooksManager
-    from .slant3d import Slant3DWebhooksManager
-
-    _WEBHOOK_MANAGERS.update(
-        {
-            handler.PROVIDER_NAME: handler
-            for handler in [
-                CompassWebhookManager,
-                GithubWebhooksManager,
-                Slant3DWebhooksManager,
-            ]
-        }
-    )
-    return _WEBHOOK_MANAGERS
-
-
-# --8<-- [end:load_webhook_managers]
-
-
-def get_webhook_manager(provider_name: "ProviderName") -> "BaseWebhooksManager":
-    return load_webhook_managers()[provider_name]()
-
-
-def supports_webhooks(provider_name: "ProviderName") -> bool:
-    return provider_name in load_webhook_managers()
-
-
-__all__ = ["get_webhook_manager", "supports_webhooks"]
+__all__ = ["WEBHOOK_MANAGERS_BY_NAME"]

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

@@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Callable, Optional, cast
 
 from backend.data.block import BlockSchema, BlockWebhookConfig, get_block
 from backend.data.graph import set_node_webhook
-from backend.integrations.webhooks import get_webhook_manager, supports_webhooks
+from backend.integrations.webhooks import WEBHOOK_MANAGERS_BY_NAME
 
 if TYPE_CHECKING:
     from backend.data.graph import GraphModel, NodeModel
@@ -123,7 +123,7 @@ async def on_node_activate(
         return node
 
     provider = block.webhook_config.provider
-    if not supports_webhooks(provider):
+    if provider not in WEBHOOK_MANAGERS_BY_NAME:
         raise ValueError(
             f"Block #{block.id} has webhook_config for provider {provider} "
             "which does not support webhooks"
@@ -133,7 +133,7 @@ async def on_node_activate(
         f"Activating webhook node #{node.id} with config {block.webhook_config}"
     )
 
-    webhooks_manager = get_webhook_manager(provider)
+    webhooks_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
 
     if auto_setup_webhook := isinstance(block.webhook_config, BlockWebhookConfig):
         try:
@@ -234,13 +234,13 @@ async def on_node_deactivate(
         return node
 
     provider = block.webhook_config.provider
-    if not supports_webhooks(provider):
+    if provider not in WEBHOOK_MANAGERS_BY_NAME:
         raise ValueError(
             f"Block #{block.id} has webhook_config for provider {provider} "
             "which does not support webhooks"
         )
 
-    webhooks_manager = get_webhook_manager(provider)
+    webhooks_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
 
     if node.webhook_id:
         logger.debug(f"Node #{node.id} has webhook_id {node.webhook_id}")

autogpt_platform/backend/backend/notifications/__init__.py

@@ -1,5 +0,0 @@
-from .notifications import NotificationManager
-
-__all__ = [
-    "NotificationManager",
-]

autogpt_platform/backend/backend/notifications/email.py

@@ -1,136 +0,0 @@
-import logging
-import pathlib
-
-from postmarker.core import PostmarkClient
-from postmarker.models.emails import EmailManager
-from prisma.enums import NotificationType
-from pydantic import BaseModel
-
-from backend.data.notifications import (
-    NotificationDataType_co,
-    NotificationEventModel,
-    NotificationTypeOverride,
-)
-from backend.util.settings import Settings
-from backend.util.text import TextFormatter
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-
-
-# The following is a workaround to get the type checker to recognize the EmailManager type
-# This is a temporary solution and should be removed once the Postmark library is updated
-# to support type annotations.
-class TypedPostmarkClient(PostmarkClient):
-    emails: EmailManager
-
-
-class Template(BaseModel):
-    subject_template: str
-    body_template: str
-    base_template: str
-
-
-class EmailSender:
-    def __init__(self):
-        if settings.secrets.postmark_server_api_token:
-            self.postmark = TypedPostmarkClient(
-                server_token=settings.secrets.postmark_server_api_token
-            )
-        else:
-            logger.warning(
-                "Postmark server API token not found, email sending disabled"
-            )
-            self.postmark = None
-        self.formatter = TextFormatter()
-
-    def send_templated(
-        self,
-        notification: NotificationType,
-        user_email: str,
-        data: (
-            NotificationEventModel[NotificationDataType_co]
-            | list[NotificationEventModel[NotificationDataType_co]]
-        ),
-        user_unsub_link: str | None = None,
-    ):
-        """Send an email to a user using a template pulled from the notification type"""
-        if not self.postmark:
-            logger.warning("Postmark client not initialized, email not sent")
-            return
-        template = self._get_template(notification)
-
-        base_url = (
-            settings.config.frontend_base_url or settings.config.platform_base_url
-        )
-
-        # Handle the case when data is a list
-        template_data = data
-        if isinstance(data, list):
-            # Create a dictionary with a 'notifications' key containing the list
-            template_data = {"notifications": data}
-
-        try:
-            subject, full_message = self.formatter.format_email(
-                base_template=template.base_template,
-                subject_template=template.subject_template,
-                content_template=template.body_template,
-                data=template_data,
-                unsubscribe_link=f"{base_url}/profile/settings",
-            )
-        except Exception as e:
-            logger.error(f"Error formatting full message: {e}")
-            raise e
-
-        self._send_email(
-            user_email=user_email,
-            user_unsubscribe_link=user_unsub_link,
-            subject=subject,
-            body=full_message,
-        )
-
-    def _get_template(self, notification: NotificationType):
-        # convert the notification type to a notification type override
-        notification_type_override = NotificationTypeOverride(notification)
-        # find the template in templates/name.html (the .template returns with the .html)
-        template_path = f"templates/{notification_type_override.template}.jinja2"
-        logger.debug(
-            f"Template full path: {pathlib.Path(__file__).parent / template_path}"
-        )
-        base_template_path = "templates/base.html.jinja2"
-        with open(pathlib.Path(__file__).parent / base_template_path, "r") as file:
-            base_template = file.read()
-        with open(pathlib.Path(__file__).parent / template_path, "r") as file:
-            template = file.read()
-        return Template(
-            subject_template=notification_type_override.subject,
-            body_template=template,
-            base_template=base_template,
-        )
-
-    def _send_email(
-        self,
-        user_email: str,
-        subject: str,
-        body: str,
-        user_unsubscribe_link: str | None = None,
-    ):
-        if not self.postmark:
-            logger.warning("Email tried to send without postmark configured")
-            return
-        logger.debug(f"Sending email to {user_email} with subject {subject}")
-        self.postmark.emails.send(
-            From=settings.config.postmark_sender_email,
-            To=user_email,
-            Subject=subject,
-            HtmlBody=body,
-            # Headers default to None internally so this is fine
-            Headers=(
-                {
-                    "List-Unsubscribe-Post": "List-Unsubscribe=One-Click",
-                    "List-Unsubscribe": f"<{user_unsubscribe_link}>",
-                }
-                if user_unsubscribe_link
-                else None
-            ),
-        )

autogpt_platform/backend/backend/notifications/notifications.py

@@ -1,747 +0,0 @@
-import logging
-import time
-from datetime import datetime, timedelta, timezone
-from typing import Callable
-
-import aio_pika
-from aio_pika.exceptions import QueueEmpty
-from autogpt_libs.utils.cache import thread_cached
-from prisma.enums import NotificationType
-from pydantic import BaseModel
-
-from backend.data.notifications import (
-    BaseSummaryData,
-    BaseSummaryParams,
-    DailySummaryData,
-    DailySummaryParams,
-    NotificationEventDTO,
-    NotificationEventModel,
-    NotificationResult,
-    NotificationTypeOverride,
-    QueueType,
-    SummaryParamsEventDTO,
-    SummaryParamsEventModel,
-    WeeklySummaryData,
-    WeeklySummaryParams,
-    get_batch_delay,
-    get_notif_data_type,
-    get_summary_params_type,
-)
-from backend.data.rabbitmq import Exchange, ExchangeType, Queue, RabbitMQConfig
-from backend.data.user import generate_unsubscribe_link
-from backend.notifications.email import EmailSender
-from backend.util.service import AppService, expose, get_service_client
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-
-
-class NotificationEvent(BaseModel):
-    event: NotificationEventDTO
-    model: NotificationEventModel
-
-
-def create_notification_config() -> RabbitMQConfig:
-    """Create RabbitMQ configuration for notifications"""
-    notification_exchange = Exchange(name="notifications", type=ExchangeType.TOPIC)
-
-    dead_letter_exchange = Exchange(name="dead_letter", type=ExchangeType.TOPIC)
-
-    queues = [
-        # Main notification queues
-        Queue(
-            name="immediate_notifications",
-            exchange=notification_exchange,
-            routing_key="notification.immediate.#",
-            arguments={
-                "x-dead-letter-exchange": dead_letter_exchange.name,
-                "x-dead-letter-routing-key": "failed.immediate",
-            },
-        ),
-        Queue(
-            name="admin_notifications",
-            exchange=notification_exchange,
-            routing_key="notification.admin.#",
-            arguments={
-                "x-dead-letter-exchange": dead_letter_exchange.name,
-                "x-dead-letter-routing-key": "failed.admin",
-            },
-        ),
-        # Summary notification queues
-        Queue(
-            name="summary_notifications",
-            exchange=notification_exchange,
-            routing_key="notification.summary.#",
-            arguments={
-                "x-dead-letter-exchange": dead_letter_exchange.name,
-                "x-dead-letter-routing-key": "failed.summary",
-            },
-        ),
-        # Batch Queue
-        Queue(
-            name="batch_notifications",
-            exchange=notification_exchange,
-            routing_key="notification.batch.#",
-            arguments={
-                "x-dead-letter-exchange": dead_letter_exchange.name,
-                "x-dead-letter-routing-key": "failed.batch",
-            },
-        ),
-        # Failed notifications queue
-        Queue(
-            name="failed_notifications",
-            exchange=dead_letter_exchange,
-            routing_key="failed.#",
-        ),
-    ]
-
-    return RabbitMQConfig(
-        exchanges=[
-            notification_exchange,
-            dead_letter_exchange,
-        ],
-        queues=queues,
-    )
-
-
-@thread_cached
-def get_scheduler():
-    from backend.executor import Scheduler
-
-    return get_service_client(Scheduler)
-
-
-@thread_cached
-def get_db():
-    from backend.executor.database import DatabaseManager
-
-    return get_service_client(DatabaseManager)
-
-
-class NotificationManager(AppService):
-    """Service for handling notifications with batching support"""
-
-    def __init__(self):
-        super().__init__()
-        self.rabbitmq_config = create_notification_config()
-        self.running = True
-        self.email_sender = EmailSender()
-
-    @classmethod
-    def get_port(cls) -> int:
-        return settings.config.notification_service_port
-
-    def get_routing_key(self, event_type: NotificationType) -> str:
-        strategy = NotificationTypeOverride(event_type).strategy
-        """Get the appropriate routing key for an event"""
-        if strategy == QueueType.IMMEDIATE:
-            return f"notification.immediate.{event_type.value}"
-        elif strategy == QueueType.BACKOFF:
-            return f"notification.backoff.{event_type.value}"
-        elif strategy == QueueType.ADMIN:
-            return f"notification.admin.{event_type.value}"
-        elif strategy == QueueType.BATCH:
-            return f"notification.batch.{event_type.value}"
-        elif strategy == QueueType.SUMMARY:
-            return f"notification.summary.{event_type.value}"
-        return f"notification.{event_type.value}"
-
-    @expose
-    def queue_weekly_summary(self):
-        """Process weekly summary for specified notification types"""
-        try:
-            logger.info("Processing weekly summary queuing operation")
-            processed_count = 0
-            current_time = datetime.now(tz=timezone.utc)
-            start_time = current_time - timedelta(days=7)
-            users = get_db().get_active_user_ids_in_timerange(
-                end_time=current_time.isoformat(),
-                start_time=start_time.isoformat(),
-            )
-            for user in users:
-
-                self._queue_scheduled_notification(
-                    SummaryParamsEventDTO(
-                        user_id=user,
-                        type=NotificationType.WEEKLY_SUMMARY,
-                        data=WeeklySummaryParams(
-                            start_date=start_time,
-                            end_date=current_time,
-                        ).model_dump(),
-                    ),
-                )
-                processed_count += 1
-
-            logger.info(f"Processed {processed_count} weekly summaries into queue")
-
-        except Exception as e:
-            logger.exception(f"Error processing weekly summary: {e}")
-
-    @expose
-    def process_existing_batches(self, notification_types: list[NotificationType]):
-        """Process existing batches for specified notification types"""
-        try:
-            processed_count = 0
-            current_time = datetime.now(tz=timezone.utc)
-
-            for notification_type in notification_types:
-                # Get all batches for this notification type
-                batches = get_db().get_all_batches_by_type(notification_type)
-
-                for batch in batches:
-                    # Check if batch has aged out
-                    oldest_message = (
-                        get_db().get_user_notification_oldest_message_in_batch(
-                            batch.user_id, notification_type
-                        )
-                    )
-
-                    if not oldest_message:
-                        # this should never happen
-                        logger.error(
-                            f"Batch for user {batch.user_id} and type {notification_type} has no oldest message whichshould never happen!!!!!!!!!!!!!!!!"
-                        )
-                        continue
-
-                    max_delay = get_batch_delay(notification_type)
-
-                    # If batch has aged out, process it
-                    if oldest_message.created_at + max_delay < current_time:
-                        recipient_email = get_db().get_user_email_by_id(batch.user_id)
-
-                        if not recipient_email:
-                            logger.error(
-                                f"User email not found for user {batch.user_id}"
-                            )
-                            continue
-
-                        should_send = self._should_email_user_based_on_preference(
-                            batch.user_id, notification_type
-                        )
-
-                        if not should_send:
-                            logger.debug(
-                                f"User {batch.user_id} does not want to receive {notification_type} notifications"
-                            )
-                            # Clear the batch
-                            get_db().empty_user_notification_batch(
-                                batch.user_id, notification_type
-                            )
-                            continue
-
-                        batch_data = get_db().get_user_notification_batch(
-                            batch.user_id, notification_type
-                        )
-
-                        if not batch_data or not batch_data.notifications:
-                            logger.error(
-                                f"Batch data not found for user {batch.user_id}"
-                            )
-                            # Clear the batch
-                            get_db().empty_user_notification_batch(
-                                batch.user_id, notification_type
-                            )
-                            continue
-
-                        unsub_link = generate_unsubscribe_link(batch.user_id)
-
-                        events = [
-                            NotificationEventModel[
-                                get_notif_data_type(db_event.type)
-                            ].model_validate(
-                                {
-                                    "user_id": batch.user_id,
-                                    "type": db_event.type,
-                                    "data": db_event.data,
-                                    "created_at": db_event.created_at,
-                                }
-                            )
-                            for db_event in batch_data.notifications
-                        ]
-                        logger.info(f"{events=}")
-
-                        self.email_sender.send_templated(
-                            notification=notification_type,
-                            user_email=recipient_email,
-                            data=events,
-                            user_unsub_link=unsub_link,
-                        )
-
-                        # Clear the batch
-                        get_db().empty_user_notification_batch(
-                            batch.user_id, notification_type
-                        )
-
-                        processed_count += 1
-
-            logger.info(f"Processed {processed_count} aged batches")
-            return {
-                "success": True,
-                "processed_count": processed_count,
-                "notification_types": [nt.value for nt in notification_types],
-                "timestamp": current_time.isoformat(),
-            }
-
-        except Exception as e:
-            logger.exception(f"Error processing batches: {e}")
-            return {
-                "success": False,
-                "error": str(e),
-                "notification_types": [nt.value for nt in notification_types],
-                "timestamp": datetime.now(tz=timezone.utc).isoformat(),
-            }
-
-    @expose
-    def queue_notification(self, event: NotificationEventDTO) -> NotificationResult:
-        """Queue a notification - exposed method for other services to call"""
-        try:
-            logger.info(f"Received Request to queue {event=}")
-            # Workaround for not being able to serialize generics over the expose bus
-            parsed_event = NotificationEventModel[
-                get_notif_data_type(event.type)
-            ].model_validate(event.model_dump())
-            routing_key = self.get_routing_key(parsed_event.type)
-            message = parsed_event.model_dump_json()
-
-            logger.info(f"Received Request to queue {message=}")
-
-            exchange = "notifications"
-
-            # Publish to RabbitMQ
-            self.run_and_wait(
-                self.rabbit.publish_message(
-                    routing_key=routing_key,
-                    message=message,
-                    exchange=next(
-                        ex for ex in self.rabbit_config.exchanges if ex.name == exchange
-                    ),
-                )
-            )
-
-            return NotificationResult(
-                success=True,
-                message=f"Notification queued with routing key: {routing_key}",
-            )
-
-        except Exception as e:
-            logger.exception(f"Error queueing notification: {e}")
-            return NotificationResult(success=False, message=str(e))
-
-    def _queue_scheduled_notification(self, event: SummaryParamsEventDTO):
-        """Queue a scheduled notification - exposed method for other services to call"""
-        try:
-            logger.info(f"Received Request to queue scheduled notification {event=}")
-
-            parsed_event = SummaryParamsEventModel[
-                get_summary_params_type(event.type)
-            ].model_validate(event.model_dump())
-
-            routing_key = self.get_routing_key(event.type)
-            message = parsed_event.model_dump_json()
-
-            logger.info(f"Received Request to queue {message=}")
-
-            exchange = "notifications"
-
-            # Publish to RabbitMQ
-            self.run_and_wait(
-                self.rabbit.publish_message(
-                    routing_key=routing_key,
-                    message=message,
-                    exchange=next(
-                        ex for ex in self.rabbit_config.exchanges if ex.name == exchange
-                    ),
-                )
-            )
-
-        except Exception as e:
-            logger.exception(f"Error queueing notification: {e}")
-
-    def _should_email_user_based_on_preference(
-        self, user_id: str, event_type: NotificationType
-    ) -> bool:
-        """Check if a user wants to receive a notification based on their preferences and email verification status"""
-        validated_email = get_db().get_user_email_verification(user_id)
-        preference = (
-            get_db()
-            .get_user_notification_preference(user_id)
-            .preferences.get(event_type, True)
-        )
-        # only if both are true, should we email this person
-        return validated_email and preference
-
-    def _gather_summary_data(
-        self, user_id: str, event_type: NotificationType, params: BaseSummaryParams
-    ) -> BaseSummaryData:
-        """Gathers the data to build a summary notification"""
-
-        logger.info(
-            f"Gathering summary data for {user_id} and {event_type} wiht {params=}"
-        )
-
-        # total_credits_used = self.run_and_wait(
-        #     get_total_credits_used(user_id, start_time, end_time)
-        # )
-
-        # total_executions = self.run_and_wait(
-        #     get_total_executions(user_id, start_time, end_time)
-        # )
-
-        # most_used_agent = self.run_and_wait(
-        #     get_most_used_agent(user_id, start_time, end_time)
-        # )
-
-        # execution_times = self.run_and_wait(
-        #     get_execution_time(user_id, start_time, end_time)
-        # )
-
-        # runs = self.run_and_wait(
-        #     get_runs(user_id, start_time, end_time)
-        # )
-        total_credits_used = 3.0
-        total_executions = 2
-        most_used_agent = {"name": "Some"}
-        execution_times = [1, 2, 3]
-        runs = [{"status": "COMPLETED"}, {"status": "FAILED"}]
-
-        successful_runs = len([run for run in runs if run["status"] == "COMPLETED"])
-        failed_runs = len([run for run in runs if run["status"] != "COMPLETED"])
-        average_execution_time = (
-            sum(execution_times) / len(execution_times) if execution_times else 0
-        )
-        # cost_breakdown = self.run_and_wait(
-        #     get_cost_breakdown(user_id, start_time, end_time)
-        # )
-
-        cost_breakdown = {
-            "agent1": 1.0,
-            "agent2": 2.0,
-        }
-
-        if event_type == NotificationType.DAILY_SUMMARY and isinstance(
-            params, DailySummaryParams
-        ):
-            return DailySummaryData(
-                total_credits_used=total_credits_used,
-                total_executions=total_executions,
-                most_used_agent=most_used_agent["name"],
-                total_execution_time=sum(execution_times),
-                successful_runs=successful_runs,
-                failed_runs=failed_runs,
-                average_execution_time=average_execution_time,
-                cost_breakdown=cost_breakdown,
-                date=params.date,
-            )
-        elif event_type == NotificationType.WEEKLY_SUMMARY and isinstance(
-            params, WeeklySummaryParams
-        ):
-            return WeeklySummaryData(
-                total_credits_used=total_credits_used,
-                total_executions=total_executions,
-                most_used_agent=most_used_agent["name"],
-                total_execution_time=sum(execution_times),
-                successful_runs=successful_runs,
-                failed_runs=failed_runs,
-                average_execution_time=average_execution_time,
-                cost_breakdown=cost_breakdown,
-                start_date=params.start_date,
-                end_date=params.end_date,
-            )
-        else:
-            raise ValueError("Invalid event type or params")
-
-    def _should_batch(
-        self, user_id: str, event_type: NotificationType, event: NotificationEventModel
-    ) -> bool:
-
-        get_db().create_or_add_to_user_notification_batch(user_id, event_type, event)
-
-        oldest_message = get_db().get_user_notification_oldest_message_in_batch(
-            user_id, event_type
-        )
-        if not oldest_message:
-            logger.error(
-                f"Batch for user {user_id} and type {event_type} has no oldest message whichshould never happen!!!!!!!!!!!!!!!!"
-            )
-            return False
-        oldest_age = oldest_message.created_at
-
-        max_delay = get_batch_delay(event_type)
-
-        if oldest_age + max_delay < datetime.now(tz=timezone.utc):
-            logger.info(f"Batch for user {user_id} and type {event_type} is old enough")
-            return True
-        logger.info(
-            f"Batch for user {user_id} and type {event_type} is not old enough: {oldest_age + max_delay} < {datetime.now(tz=timezone.utc)} max_delay={max_delay}"
-        )
-        return False
-
-    def _parse_message(self, message: str) -> NotificationEvent | None:
-        try:
-            event = NotificationEventDTO.model_validate_json(message)
-            model = NotificationEventModel[
-                get_notif_data_type(event.type)
-            ].model_validate_json(message)
-            return NotificationEvent(event=event, model=model)
-        except Exception as e:
-            logger.error(f"Error parsing message due to non matching schema {e}")
-            return None
-
-    def _process_admin_message(self, message: str) -> bool:
-        """Process a single notification, sending to an admin, returning whether to put into the failed queue"""
-        try:
-            parsed = self._parse_message(message)
-            if not parsed:
-                return False
-            event = parsed.event
-            model = parsed.model
-            logger.debug(f"Processing notification for admin: {model}")
-            recipient_email = settings.config.refund_notification_email
-            self.email_sender.send_templated(event.type, recipient_email, model)
-            return True
-        except Exception as e:
-            logger.exception(f"Error processing notification for admin queue: {e}")
-            return False
-
-    def _process_immediate(self, message: str) -> bool:
-        """Process a single notification immediately, returning whether to put into the failed queue"""
-        try:
-            parsed = self._parse_message(message)
-            if not parsed:
-                return False
-            event = parsed.event
-            model = parsed.model
-            logger.debug(f"Processing immediate notification: {model}")
-
-            recipient_email = get_db().get_user_email_by_id(event.user_id)
-            if not recipient_email:
-                logger.error(f"User email not found for user {event.user_id}")
-                return False
-
-            should_send = self._should_email_user_based_on_preference(
-                event.user_id, event.type
-            )
-            if not should_send:
-                logger.debug(
-                    f"User {event.user_id} does not want to receive {event.type} notifications"
-                )
-                return True
-
-            unsub_link = generate_unsubscribe_link(event.user_id)
-
-            self.email_sender.send_templated(
-                notification=event.type,
-                user_email=recipient_email,
-                data=model,
-                user_unsub_link=unsub_link,
-            )
-            return True
-        except Exception as e:
-            logger.exception(f"Error processing notification for immediate queue: {e}")
-            return False
-
-    def _process_batch(self, message: str) -> bool:
-        """Process a single notification with a batching strategy, returning whether to put into the failed queue"""
-        try:
-            parsed = self._parse_message(message)
-            if not parsed:
-                return False
-            event = parsed.event
-            model = parsed.model
-            logger.info(f"Processing batch notification: {model}")
-
-            recipient_email = get_db().get_user_email_by_id(event.user_id)
-            if not recipient_email:
-                logger.error(f"User email not found for user {event.user_id}")
-                return False
-
-            should_send = self._should_email_user_based_on_preference(
-                event.user_id, event.type
-            )
-            if not should_send:
-                logger.info(
-                    f"User {event.user_id} does not want to receive {event.type} notifications"
-                )
-                return True
-
-            should_send = self._should_batch(event.user_id, event.type, model)
-
-            if not should_send:
-                logger.info("Batch not old enough to send")
-                return False
-            batch = get_db().get_user_notification_batch(event.user_id, event.type)
-            if not batch or not batch.notifications:
-                logger.error(f"Batch not found for user {event.user_id}")
-                return False
-            unsub_link = generate_unsubscribe_link(event.user_id)
-
-            batch_messages = [
-                NotificationEventModel[
-                    get_notif_data_type(db_event.type)
-                ].model_validate(
-                    {
-                        "user_id": event.user_id,
-                        "type": db_event.type,
-                        "data": db_event.data,
-                        "created_at": db_event.created_at,
-                    }
-                )
-                for db_event in batch.notifications
-            ]
-
-            self.email_sender.send_templated(
-                notification=event.type,
-                user_email=recipient_email,
-                data=batch_messages,
-                user_unsub_link=unsub_link,
-            )
-            # only empty the batch if we sent the email successfully
-            get_db().empty_user_notification_batch(event.user_id, event.type)
-            return True
-        except Exception as e:
-            logger.exception(f"Error processing notification for batch queue: {e}")
-            return False
-
-    def _process_summary(self, message: str) -> bool:
-        """Process a single notification with a summary strategy, returning whether to put into the failed queue"""
-        try:
-            logger.info(f"Processing summary notification: {message}")
-            event = SummaryParamsEventDTO.model_validate_json(message)
-            model = SummaryParamsEventModel[
-                get_summary_params_type(event.type)
-            ].model_validate_json(message)
-
-            logger.info(f"Processing summary notification: {model}")
-
-            recipient_email = get_db().get_user_email_by_id(event.user_id)
-            if not recipient_email:
-                logger.error(f"User email not found for user {event.user_id}")
-                return False
-            should_send = self._should_email_user_based_on_preference(
-                event.user_id, event.type
-            )
-            if not should_send:
-                logger.info(
-                    f"User {event.user_id} does not want to receive {event.type} notifications"
-                )
-                return True
-
-            summary_data = self._gather_summary_data(
-                event.user_id, event.type, model.data
-            )
-
-            unsub_link = generate_unsubscribe_link(event.user_id)
-
-            data = NotificationEventModel(
-                user_id=event.user_id,
-                type=event.type,
-                data=summary_data,
-            )
-
-            self.email_sender.send_templated(
-                notification=event.type,
-                user_email=recipient_email,
-                data=data,
-                user_unsub_link=unsub_link,
-            )
-            return True
-        except Exception as e:
-            logger.exception(f"Error processing notification for summary queue: {e}")
-            return False
-
-    def _run_queue(
-        self,
-        queue: aio_pika.abc.AbstractQueue,
-        process_func: Callable[[str], bool],
-        error_queue_name: str,
-    ):
-        message: aio_pika.abc.AbstractMessage | None = None
-        try:
-            # This parameter "no_ack" is named like shit, think of it as "auto_ack"
-            message = self.run_and_wait(queue.get(timeout=1.0, no_ack=False))
-            result = process_func(message.body.decode())
-            if result:
-                self.run_and_wait(message.ack())
-            else:
-                self.run_and_wait(message.reject(requeue=False))
-
-        except QueueEmpty:
-            logger.debug(f"Queue {error_queue_name} empty")
-        except Exception as e:
-            if message:
-                logger.error(
-                    f"Error in notification service loop, message rejected {e}"
-                )
-                self.run_and_wait(message.reject(requeue=False))
-            else:
-                logger.error(
-                    f"Error in notification service loop, message unable to be rejected, and will have to be manually removed to free space in the queue: {e}"
-                )
-
-    def run_service(self):
-        logger.info(f"[{self.service_name}] Started notification service")
-
-        # Set up scheduler for batch processing of all notification types
-        # this can be changed later to spawn differnt cleanups on different schedules
-        try:
-            get_scheduler().add_batched_notification_schedule(
-                notification_types=list(NotificationType),
-                data={},
-                cron="0 * * * *",
-            )
-            # get_scheduler().add_weekly_notification_schedule(
-            #     # weekly on Friday at 12pm
-            #     cron="0 12 * * 5",
-            # )
-            logger.info("Scheduled notification cleanup")
-        except Exception as e:
-            logger.error(f"Error scheduling notification cleanup: {e}")
-
-        # Set up queue consumers
-        channel = self.run_and_wait(self.rabbit.get_channel())
-
-        immediate_queue = self.run_and_wait(
-            channel.get_queue("immediate_notifications")
-        )
-        batch_queue = self.run_and_wait(channel.get_queue("batch_notifications"))
-
-        admin_queue = self.run_and_wait(channel.get_queue("admin_notifications"))
-
-        summary_queue = self.run_and_wait(channel.get_queue("summary_notifications"))
-
-        while self.running:
-            try:
-                self._run_queue(
-                    queue=immediate_queue,
-                    process_func=self._process_immediate,
-                    error_queue_name="immediate_notifications",
-                )
-                self._run_queue(
-                    queue=admin_queue,
-                    process_func=self._process_admin_message,
-                    error_queue_name="admin_notifications",
-                )
-                self._run_queue(
-                    queue=batch_queue,
-                    process_func=self._process_batch,
-                    error_queue_name="batch_notifications",
-                )
-
-                self._run_queue(
-                    queue=summary_queue,
-                    process_func=self._process_summary,
-                    error_queue_name="summary_notifications",
-                )
-
-                time.sleep(0.1)
-
-            except QueueEmpty as e:
-                logger.debug(f"Queue empty: {e}")
-            except Exception as e:
-                logger.error(f"Error in notification service loop: {e}")
-
-    def cleanup(self):
-        """Cleanup service resources"""
-        self.running = False
-        super().cleanup()

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

@@ -1,142 +0,0 @@
-{# Agent Run #}
-{# Template variables:
-notification.data: the stuff below but a list of them
-data.agent_name: the name of the agent
-data.credits_used: the number of credits used by the agent
-data.node_count: the number of nodes the agent ran on
-data.execution_time: the time it took to run the agent
-data.graph_id: the id of the graph the agent ran on
-data.outputs: the list of outputs of the agent
-#}
-{% if notifications is defined %}
-  {# BATCH MODE #}
-  <div style="font-family: 'Poppins', sans-serif; color: #070629;">
-    <h2 style="color: #5D23BB; margin-bottom: 15px;">Agent Run Summary</h2>
-    <p style="font-size: 16px; line-height: 165%; margin-top: 0; margin-bottom: 15px;">
-      <strong>{{ notifications|length }}</strong> agent runs have completed!
-    </p>
-    
-    {# Calculate summary stats #}
-    {% set total_time = 0 %}
-    {% set total_nodes = 0 %}
-    {% set total_credits = 0 %}
-    {% set agent_names = [] %}
-    
-    {% for notification in notifications %}
-      {% set total_time = total_time + notification.data.execution_time %}
-      {% set total_nodes = total_nodes + notification.data.node_count %}
-      {% set total_credits = total_credits + notification.data.credits_used %}
-      {% if notification.data.agent_name not in agent_names %}
-        {% set agent_names = agent_names + [notification.data.agent_name] %}
-      {% endif %}
-    {% endfor %}
-    
-    <div style="background-color: #f8f7ff; border-radius: 8px; padding: 15px; margin-bottom: 25px;">
-      <h3 style="margin-top: 0; margin-bottom: 10px; color: #5D23BB;">Summary</h3>
-      <p style="margin: 5px 0;"><strong>Agents:</strong> {{ agent_names|join(", ") }}</p>
-      <p style="margin: 5px 0;"><strong>Total Time:</strong> {{ total_time | int }} seconds</p>
-      <p style="margin: 5px 0;"><strong>Total Nodes:</strong> {{ total_nodes }}</p>
-      <p style="margin: 5px 0;"><strong>Total Cost:</strong> ${{ "{:.2f}".format((total_credits|float)/100) }}</p>
-    </div>
-    
-    <h3 style="margin-top: 25px; margin-bottom: 15px; color: #5D23BB;">Individual Runs</h3>
-    
-    {% for notification in notifications %}
-      <div style="margin-bottom: 30px; border-left: 3px solid #5D23BB; padding-left: 15px;">
-        <p style="font-size: 16px; font-weight: 600; margin-top: 0; margin-bottom: 10px;">
-          Agent: <strong>{{ notification.data.agent_name }}</strong>
-        </p>
-        
-        <div style="margin-left: 10px;">
-          <p style="margin: 5px 0;"><strong>Time:</strong> {{ notification.data.execution_time | int }} seconds</p>
-          <p style="margin: 5px 0;"><strong>Nodes:</strong> {{ notification.data.node_count }}</p>
-          <p style="margin: 5px 0;"><strong>Cost:</strong> ${{ "{:.2f}".format((notification.data.credits_used|float)/100) }}</p>
-        </div>
-        
-        {% if notification.data.outputs and notification.data.outputs|length > 0 %}
-        <div style="margin-left: 10px; margin-top: 15px;">
-          <p style="font-weight: 600; margin-bottom: 10px;">Results:</p>
-          
-          {% for output in notification.data.outputs %}
-          <div style="margin-left: 10px; margin-bottom: 12px;">
-            <p style="color: #5D23BB; font-weight: 500; margin-top: 0; margin-bottom: 5px;">
-              {{ output.name }}
-            </p>
-            
-            {% for key, value in output.items() %}
-              {% if key != 'name' %}
-              <div style="margin-left: 10px; background-color: #f5f5ff; padding: 8px 12px; border-radius: 4px; 
-                          font-family: 'Roboto Mono', monospace; white-space: pre-wrap; word-break: break-word; 
-                          overflow-wrap: break-word; max-width: 100%; overflow-x: auto; margin-top: 3px; 
-                          margin-bottom: 8px; line-height: 1.4;">
-              {% if value is iterable and value is not string %}
-                {% if value|length == 1 %}
-                  {{ value[0] }}
-                {% else %}
-                  [{% for item in value %}{{ item }}{% if not loop.last %}, {% endif %}{% endfor %}]
-                {% endif %}
-              {% else %}
-                {{ value }}
-              {% endif %}
-              </div>
-              {% endif %}
-            {% endfor %}
-          </div>
-          {% endfor %}
-        </div>
-        {% endif %}
-      </div>
-    {% endfor %}
-  </div>
-
-{% else %}
-  {# SINGLE NOTIFICATION MODE - Original template #}
-  <p style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 165%; 
-            margin-top: 0; margin-bottom: 10px;">
-    Your agent, <strong>{{ data.agent_name }}</strong>, has completed its run!
-  </p>
-  
-  <p style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 165%; 
-            margin-top: 0; margin-bottom: 20px; padding-left: 20px;">
-    <p style="margin-bottom: 10px;"><strong>Time Taken:</strong> {{ data.execution_time | int }} seconds</p>
-    <p style="margin-bottom: 10px;"><strong>Nodes Used:</strong> {{ data.node_count }}</p>
-    <p style="margin-bottom: 10px;"><strong>Cost:</strong> ${{ "{:.2f}".format((data.credits_used|float)/100) }}</p>
-  </p>
-  
-  {% if data.outputs and data.outputs|length > 0 %}
-  <div style="margin-left: 15px; margin-bottom: 20px;">
-    <p style="font-family: 'Poppins', sans-serif; color: #070629; font-weight: 600; 
-              font-size: 16px; margin-bottom: 10px;">
-      Results:
-    </p>
-  
-    {% for output in data.outputs %}
-    <div style="margin-left: 15px; margin-bottom: 15px;">
-      <p style="font-family: 'Poppins', sans-serif; color: #5D23BB; font-weight: 500; 
-                font-size: 16px; margin-top: 0; margin-bottom: 8px;">
-        {{ output.name }}
-      </p>
-      
-      {% for key, value in output.items() %}
-        {% if key != 'name' %}
-        <div style="margin-left: 15px; background-color: #f5f5ff; padding: 8px 12px; border-radius: 4px; 
-                    font-family: 'Roboto Mono', monospace; white-space: pre-wrap; word-break: break-word; 
-                    overflow-wrap: break-word; max-width: 100%; overflow-x: auto; margin-top: 5px; 
-                    margin-bottom: 10px; line-height: 1.4;">
-        {% if value is iterable and value is not string %}
-          {% if value|length == 1 %}
-            {{ value[0] }}
-          {% else %}
-            [{% for item in value %}{{ item }}{% if not loop.last %}, {% endif %}{% endfor %}]
-          {% endif %}
-        {% else %}
-          {{ value }}
-        {% endif %}
-        </div>
-        {% endif %}
-      {% endfor %}
-    </div>
-    {% endfor %}
-  </div>
-  {% endif %}
-{% endif %}

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

@@ -1,343 +0,0 @@
-{# Base Template #}
-{# Template variables:
-    data.message: the message to display in the email
-    data.title: the title of the email
-    data.unsubscribe_link: the link to unsubscribe from the email
-#}
-<!doctype html>
-<html lang="ltr" xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office">
-
-<head>
-  <meta charset="utf-8">
-  <meta http-equiv="X-UA-Compatible" content="IE=edge">
-  <meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=yes">
-  <meta name="format-detection" content="telephone=no, date=no, address=no, email=no, url=no">
-  <meta name="x-apple-disable-message-reformatting">
-  <!--[if !mso]>
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <![endif]-->
-  <!--[if mso]>
-    <style>
-        * { font-family: sans-serif !important; }
-    </style>
-    <noscript>
-        <xml>
-            <o:OfficeDocumentSettings>
-                <o:PixelsPerInch>96</o:PixelsPerInch>
-            </o:OfficeDocumentSettings>
-        </xml>
-    </noscript>
-    <![endif]-->
-  <style type="text/css">
-    /* RESET STYLES */
-    html,
-    body {
-      margin: 0 !important;
-      padding: 0 !important;
-      width: 100% !important;
-      height: 100% !important;
-    }
-
-    body {
-      -webkit-font-smoothing: antialiased;
-      -moz-osx-font-smoothing: grayscale;
-      text-rendering: optimizeLegibility;
-    }
-
-    .document {
-      margin: 0 !important;
-      padding: 0 !important;
-      width: 100% !important;
-    }
-
-    img {
-      border: 0;
-      outline: none;
-      text-decoration: none;
-      -ms-interpolation-mode: bicubic;
-    }
-
-    table {
-      border-collapse: collapse;
-    }
-
-    table,
-    td {
-      mso-table-lspace: 0pt;
-      mso-table-rspace: 0pt;
-    }
-
-    body,
-    table,
-    td,
-    a {
-      -webkit-text-size-adjust: 100%;
-      -ms-text-size-adjust: 100%;
-    }
-
-    h1,
-    h2,
-    h3,
-    h4,
-    h5,
-    p {
-      margin: 0;
-      word-break: break-word;
-    }
-
-    /* iOS BLUE LINKS */
-    a[x-apple-data-detectors] {
-      color: inherit !important;
-      text-decoration: none !important;
-      font-size: inherit !important;
-      font-family: inherit !important;
-      font-weight: inherit !important;
-      line-height: inherit !important;
-    }
-
-    /* ANDROID CENTER FIX */
-    div[style*="margin: 16px 0;"] {
-      margin: 0 !important;
-    }
-
-    /* MEDIA QUERIES */
-    @media all and (max-width:639px) {
-      .wrapper {
-        width: 100% !important;
-      }
-
-      .container {
-        width: 100% !important;
-        min-width: 100% !important;
-        padding: 0 !important;
-      }
-
-      .row {
-        padding-left: 20px !important;
-        padding-right: 20px !important;
-      }
-
-      .col-mobile {
-        width: 20px !important;
-      }
-
-      .col {
-        display: block !important;
-        width: 100% !important;
-      }
-
-      .mobile-center {
-        text-align: center !important;
-        float: none !important;
-      }
-
-      .mobile-mx-auto {
-        margin: 0 auto !important;
-        float: none !important;
-      }
-
-      .mobile-left {
-        text-align: center !important;
-        float: left !important;
-      }
-
-      .mobile-hide {
-        display: none !important;
-      }
-
-      .img {
-        width: 100% !important;
-        height: auto !important;
-      }
-
-      .ml-btn {
-        width: 100% !important;
-        max-width: 100% !important;
-      }
-
-      .ml-btn-container {
-        width: 100% !important;
-        max-width: 100% !important;
-      }
-    }
-  </style>
-  <style type="text/css">
-    @import url("https://assets.mlcdn.com/fonts-v2.css?version=1729862");
-  </style>
-  <style type="text/css">
-    @media screen {
-      body {
-        font-family: 'Poppins', sans-serif;
-      }
-    }
-  </style>
-  <title>{{data.title}}</title>
-</head>
-
-<body style="margin: 0 !important; padding: 0 !important; background-color:#070629;">
-  <div class="document" role="article" aria-roledescription="email" aria-label lang dir="ltr"
-    style="background-color:#070629; line-height: 100%; font-size:medium; font-size:max(16px, 1rem);">
-    <!-- Main Content -->
-    <table width="100%" align="center" cellspacing="0" cellpadding="0" border="0">
-      <tr>
-        <td class="background" bgcolor="#070629" align="center" valign="top" style="padding: 0 8px;">
-          <!-- Email Content -->
-          <table class="container" align="center" width="640" cellpadding="0" cellspacing="0" border="0"
-            style="max-width: 640px;">
-            <tr>
-              <td align="center">
-                <!-- Logo Section -->
-                <table class="container ml-4 ml-default-border" width="640" bgcolor="#E2ECFD" align="center" border="0"
-                  cellspacing="0" cellpadding="0" style="width: 640px; min-width: 640px;">
-                  <tr>
-                    <td class="ml-default-border container" height="40" style="line-height: 40px; min-width: 640px;">
-                    </td>
-                  </tr>
-                  <tr>
-                    <td>
-                      <table align="center" width="100%" border="0" cellspacing="0" cellpadding="0">
-                        <tr>
-                          <td class="row" align="center" style="padding: 0 50px;">
-                            <img
-                              src="https://storage.mlcdn.com/account_image/597379/8QJ8kOjXakVvfe1kJLY2wWCObU1mp5EiDLfBlbQa.png"
-                              border="0" alt="" width="120" class="logo"
-                              style="max-width: 120px; display: inline-block;">
-                          </td>
-                        </tr>
-                      </table>
-                    </td>
-                  </tr>
-                </table>
-
-                <!-- Main Content Section -->
-                <table class="container ml-6 ml-default-border" width="640" bgcolor="#E2ECFD" align="center" border="0"
-                  cellspacing="0" cellpadding="0" style="color: #070629; width: 640px; min-width: 640px;">
-                  <tr>
-                    <td class="row" style="padding: 0 50px;">
-                      {{data.message|safe}}
-                    </td>
-                  </tr>
-                </table>
-
-                <!-- Signature Section -->
-                <table class="container ml-8 ml-default-border" width="640" bgcolor="#E2ECFD" align="center" border="0"
-                  cellspacing="0" cellpadding="0" style="color: #070629; width: 640px; min-width: 640px;">
-                  <tr>
-                    <td class="row mobile-center" align="left" style="padding: 0 50px;">
-                      <table class="ml-8 wrapper" border="0" cellspacing="0" cellpadding="0"
-                        style="color: #070629; text-align: left;">
-                        <tr>
-                          <td class="col center mobile-center" align>
-                            <p
-                              style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 165%; margin-top: 0; margin-bottom: 0;">
-                              Thank you for being a part of the AutoGPT community! Join the conversation on our Discord <a href="https://discord.gg/autogpt" style="color: #4285F4; text-decoration: underline;">here</a> and share your thoughts with us anytime.
-                            </p>
-                          </td>
-                        </tr>
-                      </table>
-                    </td>
-                  </tr>
-                </table>
-
-                <!-- Footer Section -->
-                <table class="container ml-10 ml-default-border" width="640" bgcolor="#ffffff" align="center" border="0"
-                  cellspacing="0" cellpadding="0" style="width: 640px; min-width: 640px;">
-                  <tr>
-                    <td class="row" style="padding: 0 50px;">
-                      <table align="center" width="100%" border="0" cellspacing="0" cellpadding="0">
-                        <tr>
-                          <td>
-                            <!-- Footer Content -->
-                            <table align="center" width="100%" border="0" cellspacing="0" cellpadding="0">
-                              <tr>
-                                <td class="col" align="left" valign="middle" width="120">
-                                  <img
-                                    src="https://storage.mlcdn.com/account_image/597379/8QJ8kOjXakVvfe1kJLY2wWCObU1mp5EiDLfBlbQa.png"
-                                    border="0" alt="" width="120" class="logo"
-                                    style="max-width: 120px; display: inline-block;">
-                                </td>
-                                <td class="col" width="40" height="30" style="line-height: 30px;"></td>
-                                <td class="col mobile-left" align="right" valign="middle" width="250">
-                                  <table role="presentation" cellpadding="0" cellspacing="0" border="0">
-                                    <tr>
-                                      <td align="center" valign="middle" width="18" style="padding: 0 5px 0 0;">
-                                        <a href="https://x.com/auto_gpt" target="blank" style="text-decoration: none;">
-                                          <img
-                                            src="https://assets.mlcdn.com/ml/images/icons/default/rounded_corners/black/x.png"
-                                            width="18" alt="x">
-                                        </a>
-                                      </td>
-                                      <td align="center" valign="middle" width="18" style="padding: 0 5px;">
-                                        <a href="https://discord.gg/autogpt" target="blank"
-                                          style="text-decoration: none;">
-                                          <img
-                                            src="https://assets.mlcdn.com/ml/images/icons/default/rounded_corners/black/discord.png"
-                                            width="18" alt="discord">
-                                        </a>
-                                      </td>
-                                      <td align="center" valign="middle" width="18" style="padding: 0 0 0 5px;">
-                                        <a href="https://agpt.co/" target="blank" style="text-decoration: none;">
-                                          <img
-                                            src="https://assets.mlcdn.com/ml/images/icons/default/rounded_corners/black/website.png"
-                                            width="18" alt="website">
-                                        </a>
-                                      </td>
-                                    </tr>
-                                  </table>
-                                </td>
-                              </tr>
-                            </table>
-                          </td>
-                        </tr>
-                        <tr>
-                          <td align="center" style="text-align: left!important;">
-                            <h5
-                              style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 15px; line-height: 125%; font-weight: bold; font-style: normal; text-decoration: none; margin-bottom: 6px;">
-                              AutoGPT
-                            </h5>
-                          </td>
-                        </tr>
-                        <tr>
-                          <td align="center" style="text-align: left!important;">
-                            <p
-                              style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 14px; line-height: 150%; display: inline-block; margin-bottom: 0;">
-                              3rd Floor 1 Ashley Road, Cheshire, United Kingdom, WA14 2DT, Altrincham<br>United Kingdom
-                            </p>
-                          </td>
-                        </tr>
-                        <tr>
-                          <td height="8" style="line-height: 8px;"></td>
-                        </tr>
-                        <tr>
-                          <td align="left" style="text-align: left!important;">
-                            <p
-                              style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 14px; line-height: 150%; display: inline-block; margin-bottom: 0;">
-                              You received this email because you signed up on our website.</p>
-                          </td>
-                        </tr>
-                        <tr>
-                          <td height="1" style="line-height: 12px;"></td>
-                        </tr>
-                        <tr>
-                          <td align="left">
-                            <p
-                              style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 14px; line-height: 150%; display: inline-block; margin-bottom: 0;">
-                              <a href="{{data.unsubscribe_link}}"
-                                style="color: #4285F4; font-weight: normal; font-style: normal; text-decoration: underline;">Unsubscribe</a>
-                            </p>
-                          </td>
-                        </tr>
-                      </table>
-                    </td>
-                  </tr>
-                </table>
-              </td>
-            </tr>
-          </table>
-        </td>
-      </tr>
-    </table>
-  </div>
-</body>
-
-</html>

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

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

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

@@ -1,51 +0,0 @@
-{# Refund Processed Notification Email Template #}
-{#
-  Template variables:
-  data.user_id: the ID of the user
-  data.user_name: the user's name
-  data.user_email: the user's email address
-  data.transaction_id: the transaction ID for the refund request
-  data.refund_request_id: the refund request ID
-  data.reason: the reason for the refund request
-  data.amount: the refund amount in cents (divide by 100 for dollars)
-  data.balance: the user's latest balance in cents (after the refund deduction)
-
-  Subject: Refund for ${{ data.amount / 100 }} to {{ data.user_name }} has been processed
-#}
-
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta charset="UTF-8">
-    <title>Refund Processed Notification</title>
-  </head>
-  <body style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 1.65; margin: 0; padding: 20px;">
-    <p style="margin-bottom: 10px;">Hello Administrator,</p>
-
-    <p style="margin-bottom: 10px;">
-      This is to notify you that the refund for <strong>${{ data.amount / 100 }}</strong> to <strong>{{ data.user_name }}</strong> has been processed successfully.
-    </p>
-
-    <h2 style="margin-bottom: 10px;">Refund Details</h2>
-    <ul style="margin-bottom: 10px;">
-      <li><strong>User ID:</strong> {{ data.user_id }}</li>
-      <li><strong>User Name:</strong> {{ data.user_name }}</li>
-      <li><strong>User Email:</strong> {{ data.user_email }}</li>
-      <li><strong>Transaction ID:</strong> {{ data.transaction_id }}</li>
-      <li><strong>Refund Request ID:</strong> {{ data.refund_request_id }}</li>
-      <li><strong>Refund Amount:</strong> ${{ data.amount / 100 }}</li>
-      <li><strong>Reason for Refund:</strong> {{ data.reason }}</li>
-      <li><strong>Latest User Balance:</strong> ${{ data.balance / 100 }}</li>
-    </ul>
-
-    <p style="margin-bottom: 10px;">
-      The user's balance has been updated accordingly after the deduction.
-    </p>
-
-    <p style="margin-bottom: 10px;">
-      Please contact the support team if you have any questions or need further assistance regarding this refund.
-    </p>
-
-    <p style="margin-bottom: 0;">Best regards,<br>Your Notification System</p>
-  </body>
-</html>

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

@@ -1,72 +0,0 @@
-{# Refund Request Email Template #}
-{#
-  Template variables:
-  data.user_id: the ID of the user
-  data.user_name: the user's name
-  data.user_email: the user's email address
-  data.transaction_id: the transaction ID for the refund request
-  data.refund_request_id: the refund request ID
-  data.reason: the reason for the refund request
-  data.amount: the refund amount in cents (divide by 100 for dollars)
-  data.balance: the user's balance in cents (divide by 100 for dollars)
-
-  Subject: [ACTION REQUIRED] You got a ${{ data.amount / 100 }} refund request from {{ data.user_name }}
-#}
-
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta charset="UTF-8">
-    <title>Refund Request Approval Needed</title>
-  </head>
-  <body style="font-family: 'Poppins', sans-serif; color: #070629; font-size: 16px; line-height: 1.65; margin: 0; padding: 20px;">
-    <p style="margin-bottom: 10px;">Hello Administrator,</p>
-    <p style="margin-bottom: 10px;">
-      A refund request has been submitted by a user and requires your approval.
-    </p>
-
-    <h2 style="margin-bottom: 10px;">Refund Request Details</h2>
-    <ul style="margin-bottom: 10px;">
-      <li><strong>User ID:</strong> {{ data.user_id }}</li>
-      <li><strong>User Name:</strong> {{ data.user_name }}</li>
-      <li><strong>User Email:</strong> {{ data.user_email }}</li>
-      <li><strong>Transaction ID:</strong> {{ data.transaction_id }}</li>
-      <li><strong>Refund Request ID:</strong> {{ data.refund_request_id }}</li>
-      <li><strong>Refund Amount:</strong> ${{ data.amount / 100 }}</li>
-      <li><strong>User Balance:</strong> ${{ data.balance / 100 }}</li>
-      <li><strong>Reason for Refund:</strong> {{ data.reason }}</li>
-    </ul>
-
-    <p style="margin-bottom: 10px;">
-      To approve this refund, please click on the following Stripe link:
-      https://dashboard.stripe.com/test/payments/{{data.transaction_id}}
-      <br/>
-      And then click on the "Refund" button.
-    </p>
-
-    <p style="margin-bottom: 10px;">
-      To reject this refund, please follow these steps:
-    </p>
-    <ol style="margin-bottom: 10px;">
-      <li>
-        Visit the Supabase Dashboard:
-        https://supabase.com/dashboard/project/bgwpwdsxblryihinutbx/editor
-      </li>
-      <li>
-        Navigate to the <strong>RefundRequest</strong> table.
-      </li>
-      <li>
-        Filter the <code>transactionKey</code> column with the Transaction ID: <strong>{{ data.transaction_id }}</strong>.
-      </li>
-      <li>
-        Update the <code>status</code> field to <strong>REJECTED</strong> and enter the rejection reason in the <code>result</code> column.
-      </li>
-    </ol>
-
-    <p style="margin-bottom: 10px;">
-      Please take the necessary action at your earliest convenience.
-    </p>
-    <p style="margin-bottom: 10px;">Thank you for your prompt attention.</p>
-    <p style="margin-bottom: 0;">Best regards,<br>Your Notification System</p>
-  </body>
-</html>

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

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

autogpt_platform/backend/backend/rest.py

@@ -1,6 +1,5 @@
 from backend.app import run_processes
-from backend.executor import DatabaseManager, Scheduler
-from backend.notifications.notifications import NotificationManager
+from backend.executor import DatabaseManager, ExecutionScheduler
 from backend.server.rest_api import AgentServer
 
 
@@ -9,9 +8,8 @@ def main():
     Run all the processes required for the AutoGPT-server REST API.
     """
     run_processes(
-        NotificationManager(),
         DatabaseManager(),
-        Scheduler(),
+        ExecutionScheduler(),
         AgentServer(),
     )
 

autogpt_platform/backend/backend/server/conn_manager.py

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

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

@@ -12,7 +12,7 @@ from backend.data import execution as execution_db
 from backend.data import graph as graph_db
 from backend.data.api_key import APIKey
 from backend.data.block import BlockInput, CompletedBlockOutput
-from backend.data.execution import NodeExecutionResult
+from backend.data.execution import ExecutionResult
 from backend.executor import ExecutionManager
 from backend.server.external.middleware import require_permission
 from backend.util.service import get_service_client
@@ -53,7 +53,7 @@ class GraphExecutionResult(TypedDict):
     output: Optional[List[Dict[str, str]]]
 
 
-def get_outputs_with_names(results: list[NodeExecutionResult]) -> list[dict[str, str]]:
+def get_outputs_with_names(results: List[ExecutionResult]) -> List[Dict[str, str]]:
     outputs = []
     for result in results:
         if "output" in result.output_data:
@@ -71,7 +71,7 @@ def get_outputs_with_names(results: list[NodeExecutionResult]) -> list[dict[str,
 )
 def get_graph_blocks() -> Sequence[dict[Any, Any]]:
     blocks = [block() for block in backend.data.block.get_blocks().values()]
-    return [b.to_dict() for b in blocks if not b.disabled]
+    return [b.to_dict() for b in blocks]
 
 
 @v1_router.post(
@@ -130,7 +130,7 @@ async def get_graph_execution_results(
     if not graph:
         raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")
 
-    results = await execution_db.get_node_execution_results(graph_exec_id)
+    results = await execution_db.get_execution_results(graph_exec_id)
     last_result = results[-1] if results else None
     execution_status = (
         last_result.status if last_result else AgentExecutionStatus.INCOMPLETE

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

@@ -3,7 +3,6 @@ from typing import TYPE_CHECKING, Annotated, Literal
 
 from fastapi import APIRouter, Body, Depends, HTTPException, Path, Query, Request
 from pydantic import BaseModel, Field
-from starlette.status import HTTP_404_NOT_FOUND
 
 from backend.data.graph import set_node_webhook
 from backend.data.integrations import (
@@ -18,8 +17,8 @@ from backend.executor.manager import ExecutionManager
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.oauth import HANDLERS_BY_NAME
 from backend.integrations.providers import ProviderName
-from backend.integrations.webhooks import get_webhook_manager
-from backend.util.exceptions import NeedConfirmation, NotFoundError
+from backend.integrations.webhooks import WEBHOOK_MANAGERS_BY_NAME
+from backend.util.exceptions import NeedConfirmation
 from backend.util.service import get_service_client
 from backend.util.settings import Settings
 
@@ -282,14 +281,8 @@ async def webhook_ingress_generic(
     webhook_id: Annotated[str, Path(title="Our ID for the webhook")],
 ):
     logger.debug(f"Received {provider.value} webhook ingress for ID {webhook_id}")
-    webhook_manager = get_webhook_manager(provider)
-    try:
-        webhook = await get_webhook(webhook_id)
-    except NotFoundError as e:
-        logger.warning(f"Webhook payload received for unknown webhook: {e}")
-        raise HTTPException(
-            status_code=HTTP_404_NOT_FOUND, detail=f"Webhook #{webhook_id} not found"
-        ) from e
+    webhook_manager = WEBHOOK_MANAGERS_BY_NAME[provider]()
+    webhook = await get_webhook(webhook_id)
     logger.debug(f"Webhook #{webhook_id}: {webhook}")
     payload, event_type = await webhook_manager.validate_payload(webhook, request)
     logger.debug(
@@ -330,7 +323,7 @@ async def webhook_ping(
     user_id: Annotated[str, Depends(get_user_id)],  # require auth
 ):
     webhook = await get_webhook(webhook_id)
-    webhook_manager = get_webhook_manager(webhook.provider)
+    webhook_manager = WEBHOOK_MANAGERS_BY_NAME[webhook.provider]()
 
     credentials = (
         creds_manager.get(user_id, webhook.credentials_id)
@@ -365,6 +358,14 @@ async def remove_all_webhooks_for_credentials(
         NeedConfirmation: If any of the webhooks are still in use and `force` is `False`
     """
     webhooks = await get_all_webhooks_by_creds(credentials.id)
+    if credentials.provider not in WEBHOOK_MANAGERS_BY_NAME:
+        if webhooks:
+            logger.error(
+                f"Credentials #{credentials.id} for provider {credentials.provider} "
+                f"are attached to {len(webhooks)} webhooks, "
+                f"but there is no available WebhooksHandler for {credentials.provider}"
+            )
+        return
     if any(w.attached_nodes for w in webhooks) and not force:
         raise NeedConfirmation(
             "Some webhooks linked to these credentials are still in use by an agent"
@@ -375,7 +376,7 @@ async def remove_all_webhooks_for_credentials(
             await set_node_webhook(node.id, None)
 
         # Prune the webhook
-        webhook_manager = get_webhook_manager(ProviderName(credentials.provider))
+        webhook_manager = WEBHOOK_MANAGERS_BY_NAME[credentials.provider]()
         success = await webhook_manager.prune_webhook_if_dangling(
             webhook.id, credentials
         )

autogpt_platform/backend/backend/server/model.py

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

autogpt_platform/backend/backend/server/rest_api.py

@@ -16,20 +16,14 @@ import backend.data.block
 import backend.data.db
 import backend.data.graph
 import backend.data.user
-import backend.server.integrations.router
 import backend.server.routers.v1
-import backend.server.v2.admin.store_admin_routes
 import backend.server.v2.library.db
 import backend.server.v2.library.model
 import backend.server.v2.library.routes
-import backend.server.v2.otto.routes
-import backend.server.v2.postmark.postmark
 import backend.server.v2.store.model
 import backend.server.v2.store.routes
 import backend.util.service
 import backend.util.settings
-from backend.data.model import Credentials
-from backend.integrations.providers import ProviderName
 from backend.server.external.api import external_app
 
 settings = backend.util.settings.Settings()
@@ -56,8 +50,6 @@ async def lifespan_context(app: fastapi.FastAPI):
     await backend.data.block.initialize_blocks()
     await backend.data.user.migrate_and_encrypt_user_integrations()
     await backend.data.graph.fix_llm_provider_credentials()
-    # FIXME ERROR: operator does not exist: text ? unknown
-    # await backend.data.graph.migrate_llm_models(LlmModel.GPT4O)
     with launch_darkly_context():
         yield
     await backend.data.db.disconnect()
@@ -72,7 +64,8 @@ docs_url = (
 app = fastapi.FastAPI(
     title="AutoGPT Agent Server",
     description=(
-        "This server is used to execute agents that are created by the AutoGPT system."
+        "This server is used to execute agents that are created by the "
+        "AutoGPT system."
     ),
     summary="AutoGPT Agent Server",
     version="0.1",
@@ -102,23 +95,9 @@ app.include_router(backend.server.routers.v1.v1_router, tags=["v1"], prefix="/ap
 app.include_router(
     backend.server.v2.store.routes.router, tags=["v2"], prefix="/api/store"
 )
-app.include_router(
-    backend.server.v2.admin.store_admin_routes.router,
-    tags=["v2", "admin"],
-    prefix="/api/store",
-)
 app.include_router(
     backend.server.v2.library.routes.router, tags=["v2"], prefix="/api/library"
 )
-app.include_router(
-    backend.server.v2.otto.routes.router, tags=["v2"], prefix="/api/otto"
-)
-
-app.include_router(
-    backend.server.v2.postmark.postmark.router,
-    tags=["v2", "email"],
-    prefix="/api/email",
-)
 
 app.mount("/external-api", external_app)
 
@@ -162,10 +141,9 @@ class AgentServer(backend.util.service.AppProcess):
         graph_id: str,
         graph_version: int,
         user_id: str,
-        for_export: bool = False,
     ):
         return await backend.server.routers.v1.get_graph(
-            graph_id, user_id, graph_version, for_export
+            graph_id, user_id, graph_version
         )
 
     @staticmethod
@@ -177,15 +155,21 @@ class AgentServer(backend.util.service.AppProcess):
 
     @staticmethod
     async def test_get_graph_run_status(graph_exec_id: str, user_id: str):
-        from backend.data.execution import get_graph_execution_meta
-
-        execution = await get_graph_execution_meta(
+        execution = await backend.data.graph.get_execution(
             user_id=user_id, execution_id=graph_exec_id
         )
         if not execution:
             raise ValueError(f"Execution {graph_exec_id} not found")
         return execution.status
 
+    @staticmethod
+    async def test_get_graph_run_node_execution_results(
+        graph_id: str, graph_exec_id: str, user_id: str
+    ):
+        return await backend.server.routers.v1.get_graph_run_node_execution_results(
+            graph_id, graph_exec_id, user_id
+        )
+
     @staticmethod
     async def test_delete_graph(graph_id: str, user_id: str):
         await backend.server.v2.library.db.delete_library_agent_by_graph_id(
@@ -252,26 +236,12 @@ class AgentServer(backend.util.service.AppProcess):
     ):
         return await backend.server.v2.store.routes.create_submission(request, user_id)
 
-    ### ADMIN ###
-
     @staticmethod
     async def test_review_store_listing(
         request: backend.server.v2.store.model.ReviewSubmissionRequest,
         user: autogpt_libs.auth.models.User,
     ):
-        return await backend.server.v2.admin.store_admin_routes.review_submission(
-            request.store_listing_version_id, request, user
-        )
-
-    @staticmethod
-    def test_create_credentials(
-        user_id: str,
-        provider: ProviderName,
-        credentials: Credentials,
-    ) -> Credentials:
-        return backend.server.integrations.router.create_credentials(
-            user_id=user_id, provider=provider, credentials=credentials
-        )
+        return await backend.server.v2.store.routes.review_submission(request, user)
 
     def set_test_dependency_overrides(self, overrides: dict):
         app.dependency_overrides.update(overrides)