add rag blocks

add on deletes

.dockerignore

@@ -1,28 +1,40 @@
 # Ignore everything by default, selectively add things to context
-*
+classic/run
 
 # AutoGPT
-!autogpt/autogpt/
-!autogpt/pyproject.toml
-!autogpt/poetry.lock
-!autogpt/README.md
-!autogpt/tests/
+!classic/original_autogpt/autogpt/
+!classic/original_autogpt/pyproject.toml
+!classic/original_autogpt/poetry.lock
+!classic/original_autogpt/README.md
+!classic/original_autogpt/tests/
 
 # Benchmark
-!benchmark/agbenchmark/
-!benchmark/pyproject.toml
-!benchmark/poetry.lock
-!benchmark/README.md
+!classic/benchmark/agbenchmark/
+!classic/benchmark/pyproject.toml
+!classic/benchmark/poetry.lock
+!classic/benchmark/README.md
 
 # Forge
-!forge/forge/
-!forge/pyproject.toml
-!forge/poetry.lock
-!forge/README.md
+!classic/forge/
+!classic/forge/pyproject.toml
+!classic/forge/poetry.lock
+!classic/forge/README.md
 
 # Frontend
-!frontend/build/web/
+!classic/frontend/build/web/
+
+# Platform
+!autogpt_platform/
 
 # Explicitly re-ignore some folders
 .*
 **/__pycache__
+
+autogpt_platform/frontend/.next/
+autogpt_platform/frontend/node_modules
+autogpt_platform/frontend/.env.example
+autogpt_platform/frontend/.env.local
+autogpt_platform/backend/.env
+autogpt_platform/backend/.venv/
+
+autogpt_platform/market/.env

.gitattributes

@@ -1,8 +1,10 @@
-frontend/build/** linguist-generated
+classic/frontend/build/** linguist-generated
 
 **/poetry.lock linguist-generated
 
 docs/_javascript/** linguist-vendored
 
 # Exclude VCR cassettes from stats
-forge/tests/vcr_cassettes/**/**.y*ml linguist-generated
+classic/forge/tests/vcr_cassettes/**/**.y*ml linguist-generated
+
+* text=auto

.github/CODEOWNERS

@@ -1,5 +1,7 @@
-.github/workflows/  @Significant-Gravitas/devops
-autogpt/   @Significant-Gravitas/maintainers
-forge/     @Significant-Gravitas/forge-maintainers
-benchmark/          @Significant-Gravitas/benchmark-maintainers
-frontend/           @Significant-Gravitas/frontend-maintainers
+* @Significant-Gravitas/maintainers
+.github/workflows/ @Significant-Gravitas/devops
+classic/forge/ @Significant-Gravitas/forge-maintainers
+classic/benchmark/ @Significant-Gravitas/benchmark-maintainers
+classic/frontend/ @Significant-Gravitas/frontend-maintainers
+autogpt_platform/infra @Significant-Gravitas/devops
+.github/CODEOWNERS @Significant-Gravitas/admins

.github/PULL_REQUEST_TEMPLATE.md

@@ -6,26 +6,18 @@
 
 <!-- Concisely describe all of the changes made in this pull request: -->
 
-### PR Quality Scorecard ✨
+
+### Testing 🔍
+> [!NOTE] 
+Only for the new autogpt platform, currently in autogpt_platform/
 
 <!--
-Check out our contribution guide:
-https://github.com/Significant-Gravitas/AutoGPT/wiki/Contributing
-
-1. Avoid duplicate work, issues, PRs etc.
-2. Also consider contributing something other than code; see the [contribution guide]
-   for options.
-3. Clearly explain your changes.
-4. Avoid making unnecessary changes, especially if they're purely based on personal
-   preferences. Doing so is the maintainers' job. ;-)
+Please make sure your changes have been tested and are in good working condition. 
+Here is a list of our critical paths, if you need some inspiration on what and how to test:
 -->
 
-- [x] Have you used the PR description template? &ensp; `+2 pts`
-- [ ] Is your pull request atomic, focusing on a single change? &ensp; `+5 pts`
-- [ ] Have you linked the GitHub issue(s) that this PR addresses? &ensp; `+5 pts`
-- [ ] Have you documented your changes clearly and comprehensively? &ensp; `+5 pts`
-- [ ] Have you changed or added a feature? &ensp; `-4 pts`
-  - [ ] Have you added/updated corresponding documentation? &ensp; `+4 pts`
-  - [ ] Have you added/updated corresponding integration tests? &ensp; `+5 pts`
-- [ ] Have you changed the behavior of AutoGPT? &ensp; `-5 pts`
-  - [ ] Have you also run `agbenchmark` to verify that these changes do not regress performance? &ensp; `+10 pts`
+- Create from scratch and execute an agent with at least 3 blocks
+- Import an agent from file upload, and confirm it executes correctly
+- Upload agent to marketplace
+- Import an agent from marketplace and confirm it executes correctly
+- Edit an agent from monitor, and confirm it executes correctly

.github/labeler.yml

@@ -1,27 +1,32 @@
-AutoGPT Agent:
+Classic AutoGPT Agent:
 - changed-files:
-  - any-glob-to-any-file: autogpt/**
+  - any-glob-to-any-file: classic/original_autogpt/**
+
+Classic Benchmark:
+- changed-files:
+  - any-glob-to-any-file: classic/benchmark/**
+
+Classic Frontend:
+- changed-files:
+  - any-glob-to-any-file: classic/frontend/**
 
 Forge:
 - changed-files:
-  - any-glob-to-any-file: forge/**
-
-Benchmark:
-- changed-files:
-  - any-glob-to-any-file: benchmark/**
-
-Frontend:
-- changed-files:
-  - any-glob-to-any-file: frontend/**
+  - any-glob-to-any-file: classic/forge/**
 
 documentation:
 - changed-files:
   - any-glob-to-any-file: docs/**
 
-Builder:
+platform/frontend:
 - changed-files:
-  - any-glob-to-any-file: rnd/autogpt_builder/**
+  - any-glob-to-any-file: autogpt_platform/frontend/**
 
-Server:
+platform/backend:
 - changed-files:
-  - any-glob-to-any-file: rnd/autogpt_server/**
+  - any-glob-to-any-file: autogpt_platform/backend/**
+  - all-globs-to-all-files: '!autogpt_platform/backend/backend/blocks/**'
+
+platform/blocks:
+- changed-files:
+  - any-glob-to-any-file: autogpt_platform/backend/backend/blocks/**

.github/workflows/autogpt-builder-ci.yml

@@ -1,36 +0,0 @@
-name: AutoGPT Builder CI
-
-on:
-  push:
-    branches: [ master ]
-    paths:
-      - '.github/workflows/autogpt-builder-ci.yml'
-      - 'rnd/autogpt_builder/**'
-  pull_request:
-    paths:
-      - '.github/workflows/autogpt-builder-ci.yml'
-      - 'rnd/autogpt_builder/**'
-
-defaults:
-  run:
-    shell: bash
-    working-directory: rnd/autogpt_builder
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '21'
-
-    - name: Install dependencies
-      run: |
-        npm install
-
-    - name: Run lint
-      run: |
-        npm run lint

.github/workflows/autogpt-server-ci.yml

@@ -1,148 +0,0 @@
-name: AutoGPT Server CI
-
-on:
-  push:
-    branches: [master, development, ci-test*]
-    paths:
-      - ".github/workflows/autogpt-server-ci.yml"
-      - "rnd/autogpt_server/**"
-  pull_request:
-    branches: [master, development, release-*]
-    paths:
-      - ".github/workflows/autogpt-server-ci.yml"
-      - "rnd/autogpt_server/**"
-
-concurrency:
-  group: ${{ format('autogpt-server-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
-  cancel-in-progress: ${{ startsWith(github.event_name, 'pull_request') }}
-
-defaults:
-  run:
-    shell: bash
-    working-directory: rnd/autogpt_server
-
-jobs:
-  test:
-    permissions:
-      contents: read
-    timeout-minutes: 30
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.10"]
-        platform-os: [ubuntu, macos, macos-arm64, windows]
-    runs-on: ${{ matrix.platform-os != 'macos-arm64' && format('{0}-latest', matrix.platform-os) || 'macos-14' }}
-
-    steps:
-      - name: Setup PostgreSQL
-        uses: ikalnytskyi/action-setup-postgres@v6
-        with:
-          username: ${{ secrets.DB_USER }}
-          password: ${{ secrets.DB_PASS }}
-          database: postgres
-          port: 5432
-        id: postgres
-
-      # Quite slow on macOS (2~4 minutes to set up Docker)
-      # - name: Set up Docker (macOS)
-      #   if: runner.os == 'macOS'
-      #   uses: crazy-max/ghaction-setup-docker@v3
-
-      - name: Start MinIO service (Linux)
-        if: runner.os == 'Linux'
-        working-directory: "."
-        run: |
-          docker pull minio/minio:edge-cicd
-          docker run -d -p 9000:9000 minio/minio:edge-cicd
-
-      - name: Start MinIO service (macOS)
-        if: runner.os == 'macOS'
-        working-directory: ${{ runner.temp }}
-        run: |
-          brew install minio/stable/minio
-          mkdir data
-          minio server ./data &
-
-      # No MinIO on Windows:
-      # - Windows doesn't support running Linux Docker containers
-      # - It doesn't seem possible to start background processes on Windows. They are
-      #   killed after the step returns.
-      #   See: https://github.com/actions/runner/issues/598#issuecomment-2011890429
-
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          submodules: true
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - id: get_date
-        name: Get date
-        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
-
-      - name: Set up Python dependency cache
-        # On Windows, unpacking cached dependencies takes longer than just installing them
-        if: runner.os != 'Windows'
-        uses: actions/cache@v4
-        with:
-          path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('rnd/autogpt_server/poetry.lock') }}
-
-      - name: Install Poetry (Unix)
-        if: runner.os != 'Windows'
-        run: |
-          curl -sSL https://install.python-poetry.org | python3 -
-
-          if [ "${{ runner.os }}" = "macOS" ]; then
-            PATH="$HOME/.local/bin:$PATH"
-            echo "$HOME/.local/bin" >> $GITHUB_PATH
-          fi
-
-      - name: Install Poetry (Windows)
-        if: runner.os == 'Windows'
-        shell: pwsh
-        run: |
-          (Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -
-
-          $env:PATH += ";$env:APPDATA\Python\Scripts"
-          echo "$env:APPDATA\Python\Scripts" >> $env:GITHUB_PATH
-
-      - name: Install Python dependencies
-        run: poetry install
-
-      - name: Generate Prisma Client
-        run: poetry run prisma generate --schema postgres/schema.prisma
-
-      - name: Run Database Migrations
-        run: poetry run prisma migrate dev --schema postgres/schema.prisma --name updates
-        env:
-          CONNECTION_STR: ${{ steps.postgres.outputs.connection-uri }}
-
-      - name: Run Linter
-        run: poetry run lint
-
-      - name: Run pytest with coverage
-        run: |
-          poetry run pytest -vv \
-            test
-    env:
-      CI: true
-      PLAIN_OUTPUT: True
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      DB_USER: ${{ secrets.DB_USER }}
-      DB_PASS: ${{ secrets.DB_PASS }}
-      DB_NAME: postgres
-      DB_PORT: 5432
-      RUN_ENV: local
-      PORT: 8080
-      DATABASE_URL: postgresql://${{ secrets.DB_USER }}:${{ secrets.DB_PASS }}@localhost:5432/${{ secrets.DB_NAME }}
-
-      # - name: Upload coverage reports to Codecov
-      #   uses: codecov/codecov-action@v4
-      #   with:
-      #     token: ${{ secrets.CODECOV_TOKEN }}
-      #     flags: autogpt-server,${{ runner.os }}

.github/workflows/autogpts-benchmark.yml

@@ -1,97 +0,0 @@
-name: AutoGPTs Nightly Benchmark
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: '0 2 * * *'
-
-jobs:
-  benchmark:
-    permissions:
-      contents: write
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        agent-name: [ autogpt ]
-      fail-fast: false
-    timeout-minutes: 120
-    env:
-      min-python-version: '3.10'
-      REPORTS_BRANCH: data/benchmark-reports
-      REPORTS_FOLDER: ${{ format('benchmark/reports/{0}', matrix.agent-name) }}
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          submodules: true
-
-      - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ env.min-python-version }}
-
-      - name: Install Poetry
-        run: curl -sSL https://install.python-poetry.org | python -
-
-      - name: Prepare reports folder
-        run: mkdir -p ${{ env.REPORTS_FOLDER }}
-
-      - run: poetry -C benchmark install
-
-      - name: Benchmark ${{ matrix.agent-name }}
-        run: |
-          ./run agent start ${{ matrix.agent-name }}
-          cd ${{ matrix.agent-name }}
-
-          set +e  # Do not quit on non-zero exit codes
-          poetry run agbenchmark run -N 3 \
-            --test=ReadFile \
-            --test=BasicRetrieval --test=RevenueRetrieval2 \
-            --test=CombineCsv --test=LabelCsv --test=AnswerQuestionCombineCsv \
-            --test=UrlShortener --test=TicTacToe --test=Battleship \
-            --test=WebArenaTask_0 --test=WebArenaTask_21 --test=WebArenaTask_124 \
-            --test=WebArenaTask_134 --test=WebArenaTask_163
-
-            # Convert exit code 1 (some challenges failed) to exit code 0
-            if [ $? -eq 0 ] || [ $? -eq 1 ]; then
-              exit 0
-            else
-              exit $?
-            fi
-        env:
-          AGENT_NAME: ${{ matrix.agent-name }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          REQUESTS_CA_BUNDLE: /etc/ssl/certs/ca-certificates.crt
-          REPORTS_FOLDER: ${{ format('../../{0}', env.REPORTS_FOLDER) }}  # account for changed workdir
-
-          TELEMETRY_ENVIRONMENT: autogpt-benchmark-ci
-          TELEMETRY_OPT_IN: ${{ github.ref_name == 'master' }}
-
-      - name: Push reports to data branch
-        run: |
-          # BODGE: Remove success_rate.json and regression_tests.json to avoid conflicts on checkout
-          rm ${{ env.REPORTS_FOLDER }}/*.json
-
-          # Find folder with newest (untracked) report in it
-          report_subfolder=$(find ${{ env.REPORTS_FOLDER }} -type f -name 'report.json' \
-            | xargs -I {} dirname {} \
-            | xargs -I {} git ls-files --others --exclude-standard {} \
-            | xargs -I {} dirname {} \
-            | sort -u)
-          json_report_file="$report_subfolder/report.json"
-
-          # Convert JSON report to Markdown
-          markdown_report_file="$report_subfolder/report.md"
-          poetry -C benchmark run benchmark/reports/format.py "$json_report_file" > "$markdown_report_file"
-          cat "$markdown_report_file" >> $GITHUB_STEP_SUMMARY
-
-          git config --global user.name 'GitHub Actions'
-          git config --global user.email 'github-actions@agpt.co'
-          git fetch origin ${{ env.REPORTS_BRANCH }}:${{ env.REPORTS_BRANCH }} \
-            && git checkout ${{ env.REPORTS_BRANCH }} \
-            || git checkout --orphan ${{ env.REPORTS_BRANCH }}
-          git reset --hard
-          git add ${{ env.REPORTS_FOLDER }}
-          git commit -m "Benchmark report for ${{ matrix.agent-name }} @ $(date +'%Y-%m-%d')" \
-            && git push origin ${{ env.REPORTS_BRANCH }}

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

@@ -1,25 +1,25 @@
-name: AutoGPT CI
+name: Classic - AutoGPT CI
 
 on:
   push:
     branches: [ master, development, ci-test* ]
     paths:
-      - '.github/workflows/autogpt-ci.yml'
-      - 'autogpt/**'
+      - '.github/workflows/classic-autogpt-ci.yml'
+      - 'classic/original_autogpt/**'
   pull_request:
     branches: [ master, development, release-* ]
     paths:
-      - '.github/workflows/autogpt-ci.yml'
-      - 'autogpt/**'
+      - '.github/workflows/classic-autogpt-ci.yml'
+      - 'classic/original_autogpt/**'
 
 concurrency:
-  group: ${{ format('autogpt-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
+  group: ${{ format('classic-autogpt-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
   cancel-in-progress: ${{ startsWith(github.event_name, 'pull_request') }}
 
 defaults:
   run:
     shell: bash
-    working-directory: autogpt
+    working-directory: classic/original_autogpt
 
 jobs:
   test:
@@ -86,7 +86,7 @@ jobs:
         uses: actions/cache@v4
         with:
           path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt/poetry.lock') }}
+          key: poetry-${{ runner.os }}-${{ hashFiles('classic/original_autogpt/poetry.lock') }}
 
       - name: Install Poetry (Unix)
         if: runner.os != 'Windows'
@@ -135,4 +135,4 @@ jobs:
         uses: actions/upload-artifact@v4
         with:
           name: test-logs
-          path: autogpt/logs/
+          path: classic/original_autogpt/logs/

.github/workflows/classic-autogpt-docker-cache-clean.yml

@@ -1,4 +1,4 @@
-name: Purge Auto-GPT Docker CI cache
+name: Classic - Purge Auto-GPT Docker CI cache
 
 on:
   schedule:
@@ -25,7 +25,8 @@ jobs:
       name: Build image
       uses: docker/build-push-action@v5
       with:
-        file: Dockerfile.autogpt
+        context: classic/
+        file: classic/Dockerfile.autogpt
         build-args: BUILD_TYPE=${{ matrix.build-type }}
         load: true    # save to docker images
         # use GHA cache as read-only

.github/workflows/classic-autogpt-docker-ci.yml

@@ -1,24 +1,26 @@
-name: AutoGPT Docker CI
+name: Classic - AutoGPT Docker CI
 
 on:
   push:
     branches: [ master, development ]
     paths:
-      - '.github/workflows/autogpt-docker-ci.yml'
-      - 'autogpt/**'
+      - '.github/workflows/classic-autogpt-docker-ci.yml'
+      - 'classic/original_autogpt/**'
+      - 'classic/forge/**'
   pull_request:
     branches: [ master, development, release-* ]
     paths:
-      - '.github/workflows/autogpt-docker-ci.yml'
-      - 'autogpt/**'
+      - '.github/workflows/classic-autogpt-docker-ci.yml'
+      - 'classic/original_autogpt/**'
+      - 'classic/forge/**'
 
 concurrency:
-  group: ${{ format('autogpt-docker-ci-{0}', github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha) }}
+  group: ${{ format('classic-autogpt-docker-ci-{0}', github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha) }}
   cancel-in-progress: ${{ github.event_name == 'pull_request' }}
 
 defaults:
   run:
-    working-directory: autogpt
+    working-directory: classic/original_autogpt
 
 env:
   IMAGE_NAME: auto-gpt
@@ -47,7 +49,8 @@ jobs:
       name: Build image
       uses: docker/build-push-action@v5
       with:
-        file: Dockerfile.autogpt
+        context: classic/
+        file: classic/Dockerfile.autogpt
         build-args: BUILD_TYPE=${{ matrix.build-type }}
         tags: ${{ env.IMAGE_NAME }}
         labels: GIT_REVISION=${{ github.sha }}
@@ -116,7 +119,8 @@ jobs:
         name: Build image
         uses: docker/build-push-action@v5
         with:
-          file: Dockerfile.autogpt
+          context: classic/
+          file: classic/Dockerfile.autogpt
           build-args: BUILD_TYPE=dev  # include pytest
           tags: >
             ${{ env.IMAGE_NAME }},

.github/workflows/classic-autogpt-docker-release.yml

@@ -1,4 +1,4 @@
-name: AutoGPT Docker Release
+name: Classic - AutoGPT Docker Release
 
 on:
   release:
@@ -44,6 +44,7 @@ jobs:
       name: Build image
       uses: docker/build-push-action@v5
       with:
+        context: classic/
         file: Dockerfile.autogpt
         build-args: BUILD_TYPE=release
         load: true    # save to docker images

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

@@ -1,4 +1,4 @@
-name: Agent smoke tests
+name: Classic - Agent smoke tests
 
 on:
   workflow_dispatch:
@@ -7,32 +7,37 @@ on:
   push:
     branches: [ master, development, ci-test* ]
     paths:
-      - '.github/workflows/autogpts-ci.yml'
-      - 'autogpt/**'
-      - 'forge/**'
-      - 'benchmark/**'
-      - 'run'
-      - 'cli.py'
-      - 'setup.py'
+      - '.github/workflows/classic-autogpts-ci.yml'
+      - 'classic/original_autogpt/**'
+      - 'classic/forge/**'
+      - 'classic/benchmark/**'
+      - 'classic/run'
+      - 'classic/cli.py'
+      - 'classic/setup.py'
       - '!**/*.md'
   pull_request:
     branches: [ master, development, release-* ]
     paths:
-      - '.github/workflows/autogpts-ci.yml'
-      - 'autogpt/**'
-      - 'forge/**'
-      - 'benchmark/**'
-      - 'run'
-      - 'cli.py'
-      - 'setup.py'
+      - '.github/workflows/classic-autogpts-ci.yml'
+      - 'classic/original_autogpt/**'
+      - 'classic/forge/**'
+      - 'classic/benchmark/**'
+      - 'classic/run'
+      - 'classic/cli.py'
+      - 'classic/setup.py'
       - '!**/*.md'
 
+defaults:
+  run:
+    shell: bash
+    working-directory: classic
+
 jobs:
   serve-agent-protocol:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        agent-name: [ autogpt ]
+        agent-name: [ original_autogpt ]
       fail-fast: false
     timeout-minutes: 20
     env:
@@ -50,7 +55,7 @@ jobs:
           python-version: ${{ env.min-python-version }}
 
       - name: Install Poetry
-        working-directory: ./${{ matrix.agent-name }}/
+        working-directory: ./classic/${{ matrix.agent-name }}/
         run: |
           curl -sSL https://install.python-poetry.org | python -
 

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

@@ -1,18 +1,18 @@
-name: AGBenchmark CI
+name: Classic - AGBenchmark CI
 
 on:
   push:
     branches: [ master, development, ci-test* ]
     paths:
-      - 'benchmark/**'
-      - .github/workflows/benchmark-ci.yml
-      - '!benchmark/reports/**'
+      - 'classic/benchmark/**'
+      - '!classic/benchmark/reports/**'
+      - .github/workflows/classic-benchmark-ci.yml
   pull_request:
     branches: [ master, development, release-* ]
     paths:
-      - 'benchmark/**'
-      - '!benchmark/reports/**'
-      - .github/workflows/benchmark-ci.yml
+      - 'classic/benchmark/**'
+      - '!classic/benchmark/reports/**'
+      - .github/workflows/classic-benchmark-ci.yml
 
 concurrency:
   group: ${{ format('benchmark-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
@@ -39,7 +39,7 @@ jobs:
     defaults:
       run:
         shell: bash
-        working-directory: benchmark
+        working-directory: classic/benchmark
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -58,7 +58,7 @@ jobs:
         uses: actions/cache@v4
         with:
           path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('benchmark/poetry.lock') }}
+          key: poetry-${{ runner.os }}-${{ hashFiles('classic/benchmark/poetry.lock') }}
 
       - name: Install Poetry (Unix)
         if: runner.os != 'Windows'
@@ -122,7 +122,7 @@ jobs:
           curl -sSL https://install.python-poetry.org | python -
 
       - name: Run regression tests
-        working-directory: .
+        working-directory: classic
         run: |
           ./run agent start ${{ matrix.agent-name }}
           cd ${{ matrix.agent-name }}
@@ -155,7 +155,7 @@ jobs:
 
           poetry run agbenchmark --mock
 
-          CHANGED=$(git diff --name-only | grep -E '(agbenchmark/challenges)|(../frontend/assets)') || echo "No diffs"
+          CHANGED=$(git diff --name-only | grep -E '(agbenchmark/challenges)|(../classic/frontend/assets)') || echo "No diffs"
           if [ ! -z "$CHANGED" ]; then
             echo "There are unstaged changes please run agbenchmark and commit those changes since they are needed."
             echo "$CHANGED"

.github/workflows/classic-benchmark_publish_package.yml

@@ -1,4 +1,4 @@
-name: Publish to PyPI
+name: Classic - Publish to PyPI
 
 on:
   workflow_dispatch:
@@ -21,21 +21,21 @@ jobs:
         python-version: 3.8
 
     - name: Install Poetry
-      working-directory: ./benchmark/
+      working-directory: ./classic/benchmark/
       run: |
         curl -sSL https://install.python-poetry.org | python3 -
         echo "$HOME/.poetry/bin" >> $GITHUB_PATH
 
     - name: Build project for distribution
-      working-directory: ./benchmark/
+      working-directory: ./classic/benchmark/
       run: poetry build
 
     - name: Install dependencies
-      working-directory: ./benchmark/
+      working-directory: ./classic/benchmark/
       run: poetry install
 
     - name: Check Version
-      working-directory: ./benchmark/
+      working-directory: ./classic/benchmark/
       id: check-version
       run: |
         echo version=$(poetry version --short) >> $GITHUB_OUTPUT
@@ -43,7 +43,7 @@ jobs:
     - name: Create Release
       uses: ncipollo/release-action@v1
       with:
-        artifacts: "benchmark/dist/*"
+        artifacts: "classic/benchmark/dist/*"
         token: ${{ secrets.GITHUB_TOKEN }}
         draft: false
         generateReleaseNotes: false
@@ -51,5 +51,5 @@ jobs:
         commit: master
 
     - name: Build and publish
-      working-directory: ./benchmark/
+      working-directory: ./classic/benchmark/
       run: poetry publish -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}

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

@@ -1,18 +1,18 @@
-name: Forge CI
+name: Classic - Forge CI
 
 on:
   push:
     branches: [ master, development, ci-test* ]
     paths:
-      - '.github/workflows/forge-ci.yml'
-      - 'forge/**'
-      - '!forge/tests/vcr_cassettes'
+      - '.github/workflows/classic-forge-ci.yml'
+      - 'classic/forge/**'
+      - '!classic/forge/tests/vcr_cassettes'
   pull_request:
     branches: [ master, development, release-* ]
     paths:
-      - '.github/workflows/forge-ci.yml'
-      - 'forge/**'
-      - '!forge/tests/vcr_cassettes'
+      - '.github/workflows/classic-forge-ci.yml'
+      - 'classic/forge/**'
+      - '!classic/forge/tests/vcr_cassettes'
 
 concurrency:
   group: ${{ format('forge-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
@@ -21,7 +21,7 @@ concurrency:
 defaults:
   run:
     shell: bash
-    working-directory: forge
+    working-directory: classic/forge
 
 jobs:
   test:
@@ -110,7 +110,7 @@ jobs:
         uses: actions/cache@v4
         with:
           path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('forge/poetry.lock') }}
+          key: poetry-${{ runner.os }}-${{ hashFiles('classic/forge/poetry.lock') }}
 
       - name: Install Poetry (Unix)
         if: runner.os != 'Windows'
@@ -233,4 +233,4 @@ jobs:
         uses: actions/upload-artifact@v4
         with:
           name: test-logs
-          path: forge/logs/
+          path: classic/forge/logs/

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

@@ -1,4 +1,4 @@
-name: Frontend CI/CD
+name: Classic - Frontend CI/CD
 
 on:
   push:
@@ -7,12 +7,12 @@ on:
       - development
       - 'ci-test*' # This will match any branch that starts with "ci-test"
     paths:
-      - 'frontend/**'
-      - '.github/workflows/frontend-ci.yml'
+      - 'classic/frontend/**'
+      - '.github/workflows/classic-frontend-ci.yml'
   pull_request:
     paths:
-      - 'frontend/**'
-      - '.github/workflows/frontend-ci.yml'
+      - 'classic/frontend/**'
+      - '.github/workflows/classic-frontend-ci.yml'
 
 jobs:
   build:
@@ -21,7 +21,7 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     env:
-      BUILD_BRANCH: ${{ format('frontend-build/{0}', github.ref_name) }}
+      BUILD_BRANCH: ${{ format('classic-frontend-build/{0}', github.ref_name) }}
 
     steps:
     - name: Checkout Repo
@@ -34,7 +34,7 @@ jobs:
 
     - name: Build Flutter to Web
       run: |
-        cd frontend
+        cd classic/frontend
         flutter build web --base-href /app/
 
     # - name: Commit and Push to ${{ env.BUILD_BRANCH }}
@@ -42,7 +42,7 @@ jobs:
     #   run: |
     #     git config --local user.email "action@github.com"
     #     git config --local user.name "GitHub Action"
-    #     git add frontend/build/web
+    #     git add classic/frontend/build/web
     #     git checkout -B ${{ env.BUILD_BRANCH }}
     #     git commit -m "Update frontend build to ${GITHUB_SHA:0:7}" -a
     #     git push -f origin ${{ env.BUILD_BRANCH }}
@@ -51,7 +51,7 @@ jobs:
       if: github.event_name == 'push'
       uses: peter-evans/create-pull-request@v6
       with:
-        add-paths: frontend/build/web
+        add-paths: classic/frontend/build/web
         base: ${{ github.ref_name }}
         branch: ${{ env.BUILD_BRANCH }}
         delete-branch: true

.github/workflows/classic-python-checks.yml

@@ -1,27 +1,27 @@
-name: Python checks
+name: Classic - Python checks
 
 on:
   push:
     branches: [ master, development, ci-test* ]
     paths:
-      - '.github/workflows/lint-ci.yml'
-      - 'autogpt/**'
-      - 'forge/**'
-      - 'benchmark/**'
+      - '.github/workflows/classic-python-checks-ci.yml'
+      - 'classic/original_autogpt/**'
+      - 'classic/forge/**'
+      - 'classic/benchmark/**'
       - '**.py'
-      - '!forge/tests/vcr_cassettes'
+      - '!classic/forge/tests/vcr_cassettes'
   pull_request:
     branches: [ master, development, release-* ]
     paths:
-      - '.github/workflows/lint-ci.yml'
-      - 'autogpt/**'
-      - 'forge/**'
-      - 'benchmark/**'
+      - '.github/workflows/classic-python-checks-ci.yml'
+      - 'classic/original_autogpt/**'
+      - 'classic/forge/**'
+      - 'classic/benchmark/**'
       - '**.py'
-      - '!forge/tests/vcr_cassettes'
+      - '!classic/forge/tests/vcr_cassettes'
 
 concurrency:
-  group: ${{ format('lint-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
+  group: ${{ format('classic-python-checks-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
   cancel-in-progress: ${{ startsWith(github.event_name, 'pull_request') }}
 
 defaults:
@@ -40,18 +40,18 @@ jobs:
         uses: dorny/paths-filter@v3
         with:
           filters: |
-            autogpt:
-              - autogpt/autogpt/**
-              - autogpt/tests/**
-              - autogpt/poetry.lock
+            original_autogpt:
+              - classic/original_autogpt/autogpt/**
+              - classic/original_autogpt/tests/**
+              - classic/original_autogpt/poetry.lock
             forge:
-              - forge/forge/**
-              - forge/tests/**
-              - forge/poetry.lock
+              - classic/forge/forge/**
+              - classic/forge/tests/**
+              - classic/forge/poetry.lock
             benchmark:
-              - benchmark/agbenchmark/**
-              - benchmark/tests/**
-              - benchmark/poetry.lock
+              - classic/benchmark/agbenchmark/**
+              - classic/benchmark/tests/**
+              - classic/benchmark/poetry.lock
     outputs:
       changed-parts: ${{ steps.changes-in.outputs.changes }}
 
@@ -89,23 +89,23 @@ jobs:
       # Install dependencies
 
       - name: Install Python dependencies
-        run: poetry -C ${{ matrix.sub-package }} install
+        run: poetry -C classic/${{ matrix.sub-package }} install
 
       # Lint
 
       - name: Lint (isort)
         run: poetry run isort --check .
-        working-directory: ${{ matrix.sub-package }}
+        working-directory: classic/${{ matrix.sub-package }}
 
       - name: Lint (Black)
         if: success() || failure()
         run: poetry run black --check .
-        working-directory: ${{ matrix.sub-package }}
+        working-directory: classic/${{ matrix.sub-package }}
 
       - name: Lint (Flake8)
         if: success() || failure()
         run: poetry run flake8 .
-        working-directory: ${{ matrix.sub-package }}
+        working-directory: classic/${{ matrix.sub-package }}
 
   types:
     needs: get-changed-parts
@@ -141,11 +141,11 @@ jobs:
       # Install dependencies
 
       - name: Install Python dependencies
-        run: poetry -C ${{ matrix.sub-package }} install
+        run: poetry -C classic/${{ matrix.sub-package }} install
 
       # Typecheck
 
       - name: Typecheck
         if: success() || failure()
         run: poetry run pyright
-        working-directory: ${{ matrix.sub-package }}
+        working-directory: classic/${{ matrix.sub-package }}

.github/workflows/codeql.yml

@@ -0,0 +1,97 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL"
+
+on:
+  push:
+    branches: [ "master", "release-*" ]
+  pull_request:
+    branches: [ "master", "release-*" ]
+  schedule:
+    - cron: '15 4 * * 0'
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        - language: typescript
+          build-mode: none
+        - language: python
+          build-mode: none
+        # CodeQL supports the following values keywords for 'language': 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+        config: |
+          paths-ignore:
+            - classic/frontend/build/**
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

.github/workflows/hackathon.yml

@@ -1,133 +0,0 @@
-name: Hackathon
-
-on:
-  workflow_dispatch:
-    inputs:
-      agents:
-        description: "Agents to run (comma-separated)"
-        required: false
-        default: "autogpt" # Default agents if none are specified
-
-jobs:
-  matrix-setup:
-    runs-on: ubuntu-latest
-    # Service containers to run with `matrix-setup`
-    services:
-      # Label used to access the service container
-      postgres:
-        # Docker Hub image
-        image: postgres
-        # Provide the password for postgres
-        env:
-          POSTGRES_PASSWORD: postgres
-        # Set health checks to wait until postgres has started
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-        ports:
-          # Maps tcp port 5432 on service container to the host
-          - 5432:5432
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-      env-name: ${{ steps.set-matrix.outputs.env-name }}
-    steps:
-      - id: set-matrix
-        run: |
-          if [ "${{ github.event_name }}" == "schedule" ]; then
-            echo "::set-output name=env-name::production"
-            echo "::set-output name=matrix::[ 'irrelevant']"
-          elif [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
-            IFS=',' read -ra matrix_array <<< "${{ github.event.inputs.agents }}"
-            matrix_string="[ \"$(echo "${matrix_array[@]}" | sed 's/ /", "/g')\" ]"
-            echo "::set-output name=env-name::production"
-            echo "::set-output name=matrix::$matrix_string"
-          else
-            echo "::set-output name=env-name::testing"
-            echo "::set-output name=matrix::[ 'irrelevant' ]"
-          fi
-
-  tests:
-    environment:
-      name: "${{ needs.matrix-setup.outputs.env-name }}"
-    needs: matrix-setup
-    env:
-      min-python-version: "3.10"
-    name: "${{ matrix.agent-name }}"
-    runs-on: ubuntu-latest
-    services:
-      # Label used to access the service container
-      postgres:
-        # Docker Hub image
-        image: postgres
-        # Provide the password for postgres
-        env:
-          POSTGRES_PASSWORD: postgres
-        # Set health checks to wait until postgres has started
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-        ports:
-          # Maps tcp port 5432 on service container to the host
-          - 5432:5432
-    timeout-minutes: 50
-    strategy:
-      fail-fast: false
-      matrix:
-        agent-name: ${{fromJson(needs.matrix-setup.outputs.matrix)}}
-    steps:
-      - name: Print Environment Name
-        run: |
-          echo "Matrix Setup Environment Name: ${{ needs.matrix-setup.outputs.env-name }}"
-
-      - name: Check Docker Container
-        id: check
-        run: docker ps
-
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          submodules: true
-
-      - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ env.min-python-version }}
-
-      - id: get_date
-        name: Get date
-        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
-
-      - name: Install Poetry
-        run: |
-          curl -sSL https://install.python-poetry.org | python -
-
-      - name: Install Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: v18.15
-
-      - name: Run benchmark
-        run: |
-          link=$(jq -r '.["github_repo_url"]' arena/$AGENT_NAME.json)
-          branch=$(jq -r '.["branch_to_benchmark"]' arena/$AGENT_NAME.json)
-          git clone "$link" -b "$branch" "$AGENT_NAME"
-          cd $AGENT_NAME
-          cp ./$AGENT_NAME/.env.example ./$AGENT_NAME/.env || echo "file not found"
-          ./run agent start $AGENT_NAME
-          cd ../benchmark
-          poetry install
-          poetry run agbenchmark --no-dep
-        env:
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          SERP_API_KEY: ${{ secrets.SERP_API_KEY }}
-          SERPAPI_API_KEY: ${{ secrets.SERP_API_KEY }}
-          WEAVIATE_API_KEY: ${{ secrets.WEAVIATE_API_KEY }}
-          WEAVIATE_URL: ${{ secrets.WEAVIATE_URL }}
-          GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
-          GOOGLE_CUSTOM_SEARCH_ENGINE_ID: ${{ secrets.GOOGLE_CUSTOM_SEARCH_ENGINE_ID }}
-          AGENT_NAME: ${{ matrix.agent-name }}

.github/workflows/platform-autogpt-infra-ci.yml

@@ -0,0 +1,56 @@
+name: AutoGPT Platform - Infra
+
+on:
+  push:
+    branches: [ master ]
+    paths:
+      - '.github/workflows/platform-autogpt-infra-ci.yml'
+      - 'autogpt_platform/infra/**'
+  pull_request:
+    paths:
+      - '.github/workflows/platform-autogpt-infra-ci.yml'
+      - 'autogpt_platform/infra/**'
+
+defaults:
+  run:
+    shell: bash
+    working-directory: autogpt_platform/infra
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: TFLint
+        uses: pauloconnor/tflint-action@v0.0.2
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+            tflint_path: terraform/
+            tflint_recurse: true
+            tflint_changed_only: false
+
+      - name: Set up Helm
+        uses: azure/setup-helm@v4.2.0
+        with:
+          version: v3.14.4
+
+      - name: Set up chart-testing
+        uses: helm/chart-testing-action@v2.6.0
+
+      - name: Run chart-testing (list-changed)
+        id: list-changed
+        run: |
+          changed=$(ct list-changed --target-branch ${{ github.event.repository.default_branch }})
+          if [[ -n "$changed" ]]; then
+            echo "changed=true" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Run chart-testing (lint)
+        if: steps.list-changed.outputs.changed == 'true'
+        run: ct lint --target-branch ${{ github.event.repository.default_branch }}

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

@@ -0,0 +1,133 @@
+name: AutoGPT Platform - Backend CI
+
+on:
+  push:
+    branches: [master, development, ci-test*]
+    paths:
+      - ".github/workflows/platform-backend-ci.yml"
+      - "autogpt_platform/backend/**"
+  pull_request:
+    branches: [master, development, release-*]
+    paths:
+      - ".github/workflows/platform-backend-ci.yml"
+      - "autogpt_platform/backend/**"
+
+concurrency:
+  group: ${{ format('backend-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
+  cancel-in-progress: ${{ startsWith(github.event_name, 'pull_request') }}
+
+defaults:
+  run:
+    shell: bash
+    working-directory: autogpt_platform/backend
+
+jobs:
+  test:
+    permissions:
+      contents: read
+    timeout-minutes: 30
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10"]
+    runs-on: ubuntu-latest
+
+    services:
+      redis:
+        image: bitnami/redis:6.2
+        env:
+          REDIS_PASSWORD: testpassword
+        ports:
+          - 6379:6379
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          submodules: true
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Setup Supabase
+        uses: supabase/setup-cli@v1
+        with:
+          version: latest
+
+      - id: get_date
+        name: Get date
+        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
+
+      - name: Set up Python dependency cache
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pypoetry
+          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
+
+      - name: Install Poetry (Unix)
+        run: |
+          curl -sSL https://install.python-poetry.org | python3 -
+
+          if [ "${{ runner.os }}" = "macOS" ]; then
+            PATH="$HOME/.local/bin:$PATH"
+            echo "$HOME/.local/bin" >> $GITHUB_PATH
+          fi
+
+      - name: Install Python dependencies
+        run: poetry install
+
+      - name: Generate Prisma Client
+        run: poetry run prisma generate
+
+      - id: supabase
+        name: Start Supabase
+        working-directory: .
+        run: |
+          supabase init
+          supabase start --exclude postgres-meta,realtime,storage-api,imgproxy,inbucket,studio,edge-runtime,logflare,vector,supavisor
+          supabase status -o env | sed 's/="/=/; s/"$//' >> $GITHUB_OUTPUT
+        # outputs:
+        # DB_URL, API_URL, GRAPHQL_URL, ANON_KEY, SERVICE_ROLE_KEY, JWT_SECRET
+
+      - name: Run Database Migrations
+        run: poetry run prisma migrate dev --name updates
+        env:
+          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
+
+      - id: lint
+        name: Run Linter
+        run: poetry run lint
+
+      - name: Run pytest with coverage
+        run: |
+          if [[ "${{ runner.debug }}" == "1" ]]; then
+            poetry run pytest -s -vv -o log_cli=true -o log_cli_level=DEBUG test
+          else
+            poetry run pytest -s -vv test
+          fi
+        if: success() || (failure() && steps.lint.outcome == 'failure')
+        env:
+          LOG_LEVEL: ${{ runner.debug && 'DEBUG' || 'INFO' }}
+          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
+          SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
+          SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
+          SUPABASE_JWT_SECRET: ${{ steps.supabase.outputs.JWT_SECRET }}
+          REDIS_HOST: 'localhost'
+          REDIS_PORT: '6379'
+          REDIS_PASSWORD: 'testpassword'
+
+    env:
+      CI: true
+      PLAIN_OUTPUT: True
+      RUN_ENV: local
+      PORT: 8080
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+
+      # - name: Upload coverage reports to Codecov
+      #   uses: codecov/codecov-action@v4
+      #   with:
+      #     token: ${{ secrets.CODECOV_TOKEN }}
+      #     flags: backend,${{ runner.os }}

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

@@ -0,0 +1,83 @@
+name: AutoGPT Platform - Frontend CI
+
+on:
+  push:
+    branches: [master]
+    paths:
+      - ".github/workflows/platform-frontend-ci.yml"
+      - "autogpt_platform/frontend/**"
+  pull_request:
+    paths:
+      - ".github/workflows/platform-frontend-ci.yml"
+      - "autogpt_platform/frontend/**"
+
+defaults:
+  run:
+    shell: bash
+    working-directory: autogpt_platform/frontend
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "21"
+
+      - name: Install dependencies
+        run: |
+          npm install
+
+      - name: Check formatting with Prettier
+        run: |
+          npx prettier --check .
+
+      - name: Run lint
+        run: |
+          npm run lint
+
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          submodules: recursive
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "21"
+
+      - name: Copy default supabase .env
+        run: |
+          cp ../supabase/docker/.env.example ../.env
+
+      - name: Run docker compose
+        run: |
+          docker compose -f ../docker-compose.yml up -d
+
+      - name: Install dependencies
+        run: |
+          npm install
+
+      - name: Setup Builder .env
+        run: |
+          cp .env.example .env
+
+      - name: Install Playwright Browsers
+        run: npx playwright install --with-deps
+
+      - name: Run tests
+        run: |
+          npm run test
+
+      - uses: actions/upload-artifact@v4
+        if: ${{ !cancelled() }}
+        with:
+          name: playwright-report
+          path: playwright-report/
+          retention-days: 30

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

@@ -1,4 +1,4 @@
-name: 'Close stale issues'
+name: Repo - Close stale issues
 on:
   schedule:
     - cron: '30 1 * * *'

.github/workflows/repo-pr-label.yml

@@ -1,12 +1,12 @@
-name: "Pull Request auto-label"
+name: Repo - Pull Request auto-label
 
 on:
   # So that PRs touching the same files as the push are updated
   push:
     branches: [ master, development, release-* ]
     paths-ignore:
-      - 'forge/tests/vcr_cassettes'
-      - 'benchmark/reports/**'
+      - 'classic/forge/tests/vcr_cassettes'
+      - 'classic/benchmark/reports/**'
   # So that the `dirtyLabel` is removed if conflicts are resolve
   # We recommend `pull_request_target` so that github secrets are available.
   # In `pull_request` we wouldn't be able to change labels of fork PRs

.github/workflows/repo-stats.yml

@@ -1,4 +1,4 @@
-name: github-repo-stats
+name: Repo - Github Stats
 
 on:
   schedule:

.github/workflows/repo-workflow-checker.yml

@@ -0,0 +1,31 @@
+name: Repo - PR Status Checker
+on:
+  pull_request:
+    types: [opened, synchronize, reopened]
+
+jobs:
+  status-check:
+    name: Check PR Status
+    runs-on: ubuntu-latest
+    steps:
+      # - name: Wait some time for all actions to start
+      #   run: sleep 30
+      - uses: actions/checkout@v4
+        # with:
+          # fetch-depth: 0
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install requests
+      - name: Check PR Status
+        run: |
+          echo "Current directory before running Python script:"
+          pwd
+          echo "Attempting to run Python script:"
+          python .github/workflows/scripts/check_actions_status.py
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/scripts/check_actions_status.py

@@ -1,55 +1,111 @@
+import json
 import os
 import requests
 import sys
+import time
+from typing import Dict, List, Tuple
 
-# GitHub API endpoint
-api_url = os.environ["GITHUB_API_URL"]
-repo = os.environ["GITHUB_REPOSITORY"]
-sha = os.environ["GITHUB_SHA"]
+CHECK_INTERVAL = 30
 
-# GitHub token for authentication
-github_token = os.environ["GITHUB_TOKEN"]
+def get_environment_variables() -> Tuple[str, str, str, str, str]:
+    """Retrieve and return necessary environment variables."""
+    try:
+        with open(os.environ["GITHUB_EVENT_PATH"]) as f:
+            event = json.load(f)
 
-# API endpoint for check runs for the specific SHA
-endpoint = f"{api_url}/repos/{repo}/commits/{sha}/check-runs"
+        sha = event["pull_request"]["head"]["sha"]
 
-# Set up headers for authentication
-headers = {
-    "Authorization": f"token {github_token}",
-    "Accept": "application/vnd.github.v3+json"
-}
+        return (
+            os.environ["GITHUB_API_URL"],
+            os.environ["GITHUB_REPOSITORY"],
+            sha,
+            os.environ["GITHUB_TOKEN"],
+            os.environ["GITHUB_RUN_ID"],
+        )
+    except KeyError as e:
+        print(f"Error: Missing required environment variable or event data: {e}")
+        sys.exit(1)
 
-# Make the API request
-response = requests.get(endpoint, headers=headers)
 
-if response.status_code != 200:
-    print(f"Error: Unable to fetch check runs data. Status code: {response.status_code}")
-    sys.exit(1)
+def make_api_request(url: str, headers: Dict[str, str]) -> Dict:
+    """Make an API request and return the JSON response."""
+    try:
+        print("Making API request to:", url)
+        response = requests.get(url, headers=headers, timeout=10)
+        response.raise_for_status()
+        return response.json()
+    except requests.RequestException as e:
+        print(f"Error: API request failed. {e}")
+        sys.exit(1)
 
-check_runs = response.json()["check_runs"]
 
-# Flag to track if all other check runs have passed
-all_others_passed = True
+def process_check_runs(check_runs: List[Dict]) -> Tuple[bool, bool]:
+    """Process check runs and return their status."""
+    runs_in_progress = False
+    all_others_passed = True
 
-# Current run id
-current_run_id = os.environ["GITHUB_RUN_ID"]
+    for run in check_runs:
+        if str(run["name"]) != "Check PR Status":
+            status = run["status"]
+            conclusion = run["conclusion"]
 
-for run in check_runs:
-    if str(run["id"]) != current_run_id:
-        status = run["status"]
-        conclusion = run["conclusion"]
-        
-        if status == "completed":
-            if conclusion not in ["success", "skipped", "neutral"]:
+            if status == "completed":
+                if conclusion not in ["success", "skipped", "neutral"]:
+                    all_others_passed = False
+                    print(
+                        f"Check run {run['name']} (ID: {run['id']}) has conclusion: {conclusion}"
+                    )
+            else:
+                runs_in_progress = True
+                print(f"Check run {run['name']} (ID: {run['id']}) is still {status}.")
                 all_others_passed = False
-                print(f"Check run {run['name']} (ID: {run['id']}) has conclusion: {conclusion}")
         else:
-            print(f"Check run {run['name']} (ID: {run['id']}) is still {status}.")
-            all_others_passed = False
+            print(
+                f"Skipping check run {run['name']} (ID: {run['id']}) as it is the current run."
+            )
 
-if all_others_passed:
-    print("All other completed check runs have passed. This check passes.")
-    sys.exit(0)
-else:
-    print("Some check runs have failed or have not completed. This check fails.")
-    sys.exit(1)
+    return runs_in_progress, all_others_passed
+
+
+def main():
+    api_url, repo, sha, github_token, current_run_id = get_environment_variables()
+
+    endpoint = f"{api_url}/repos/{repo}/commits/{sha}/check-runs"
+    headers = {
+        "Accept": "application/vnd.github.v3+json",
+    }
+    if github_token:
+        headers["Authorization"] = f"token {github_token}"
+
+    print(f"Current run ID: {current_run_id}")
+
+    while True:
+        data = make_api_request(endpoint, headers)
+
+        check_runs = data["check_runs"]
+
+        print("Processing check runs...")
+
+        print(check_runs)
+
+        runs_in_progress, all_others_passed = process_check_runs(check_runs)
+
+        if not runs_in_progress:
+            break
+
+        print(
+            "Some check runs are still in progress. "
+            f"Waiting {CHECK_INTERVAL} seconds before checking again..."
+        )
+        time.sleep(CHECK_INTERVAL)
+
+    if all_others_passed:
+        print("All other completed check runs have passed. This check passes.")
+        sys.exit(0)
+    else:
+        print("Some check runs have failed or have not completed. This check fails.")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

.github/workflows/workflow-checker.yml

@@ -1,51 +0,0 @@
-name: PR Status Checker
-on:
-  workflow_run:
-    workflows: ["*"]
-    types:
-      - completed
-
-jobs:
-  status-check:
-    name: Check Actions Status
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version: "3.10"
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install requests
-    - name: Debug Information
-      run: |
-        echo "Event name: ${{ github.event_name }}"
-        echo "Workflow: ${{ github.workflow }}"
-        echo "Action: ${{ github.action }}"
-        echo "Actor: ${{ github.actor }}"
-        echo "Repository: ${{ github.repository }}"
-        echo "Ref: ${{ github.ref }}"
-        echo "Head ref: ${{ github.head_ref }}"
-        echo "Base ref: ${{ github.base_ref }}"
-        echo "Event payload:"
-        cat $GITHUB_EVENT_PATH
-    - name: Debug File Structure
-      run: |
-        echo "Current directory:"
-        pwd
-        echo "Directory contents:"
-        ls -R
-        echo "GitHub workspace:"
-        echo $GITHUB_WORKSPACE
-        echo "GitHub workspace contents:"
-        ls -R $GITHUB_WORKSPACE
-    - name: Check Actions Status
-      run: |
-        echo "Current directory before running Python script:"
-        pwd
-        echo "Attempting to run Python script:"
-        python .github/scripts/check_actions_status.py
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -1,7 +1,7 @@
 ## Original ignores
 .github_access_token
-autogpt/keys.py
-autogpt/*.json
+classic/original_autogpt/keys.py
+classic/original_autogpt/*.json
 auto_gpt_workspace/*
 *.mpeg
 .env
@@ -157,7 +157,7 @@ openai/
 CURRENT_BULLETIN.md
 
 # AgBenchmark
-agbenchmark/reports/
+classic/benchmark/agbenchmark/reports/
 
 # Nodejs
 package-lock.json
@@ -170,4 +170,4 @@ pri*
 ig*
 .github_access_token
 LICENSE.rtf
-rnd/autogpt_server/settings.py
+autogpt_platform/backend/settings.py

.gitmodules

@@ -1,3 +1,6 @@
-[submodule "forge/tests/vcr_cassettes"]
-	path = forge/tests/vcr_cassettes
+[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

@@ -16,22 +16,22 @@ repos:
     hooks:
       - id: isort-autogpt
         name: Lint (isort) - AutoGPT
-        entry: poetry -C autogpt run isort
-        files: ^autogpt/
+        entry: poetry -C classic/original_autogpt run isort
+        files: ^classic/original_autogpt/
         types: [file, python]
         language: system
 
       - id: isort-forge
         name: Lint (isort) - Forge
-        entry: poetry -C forge run isort
-        files: ^forge/
+        entry: poetry -C classic/forge run isort
+        files: ^classic/forge/
         types: [file, python]
         language: system
 
       - id: isort-benchmark
         name: Lint (isort) - Benchmark
-        entry: poetry -C benchmark run isort
-        files: ^benchmark/
+        entry: poetry -C classic/benchmark run isort
+        files: ^classic/benchmark/
         types: [file, python]
         language: system
 
@@ -52,20 +52,20 @@ repos:
       - id: flake8
         name: Lint (Flake8) - AutoGPT
         alias: flake8-autogpt
-        files: ^autogpt/(autogpt|scripts|tests)/
-        args: [--config=autogpt/.flake8]
+        files: ^classic/original_autogpt/(autogpt|scripts|tests)/
+        args: [--config=classic/original_autogpt/.flake8]
 
       - id: flake8
         name: Lint (Flake8) - Forge
         alias: flake8-forge
-        files: ^forge/(forge|tests)/
-        args: [--config=forge/.flake8]
+        files: ^classic/forge/(forge|tests)/
+        args: [--config=classic/forge/.flake8]
 
       - id: flake8
         name: Lint (Flake8) - Benchmark
         alias: flake8-benchmark
-        files: ^benchmark/(agbenchmark|tests)/((?!reports).)*[/.]
-        args: [--config=benchmark/.flake8]
+        files: ^classic/benchmark/(agbenchmark|tests)/((?!reports).)*[/.]
+        args: [--config=classic/benchmark/.flake8]
 
   - repo: local
     # To have watertight type checking, we check *all* the files in an affected
@@ -74,10 +74,10 @@ repos:
       - id: pyright
         name: Typecheck - AutoGPT
         alias: pyright-autogpt
-        entry: poetry -C autogpt run pyright
+        entry: poetry -C classic/original_autogpt run pyright
         args: [-p, autogpt, autogpt]
         # include forge source (since it's a path dependency) but exclude *_test.py files:
-        files: ^(autogpt/((autogpt|scripts|tests)/|poetry\.lock$)|forge/(forge/.*(?<!_test)\.py|poetry\.lock)$)
+        files: ^(classic/original_autogpt/((autogpt|scripts|tests)/|poetry\.lock$)|classic/forge/(classic/forge/.*(?<!_test)\.py|poetry\.lock)$)
         types: [file]
         language: system
         pass_filenames: false
@@ -85,9 +85,9 @@ repos:
       - id: pyright
         name: Typecheck - Forge
         alias: pyright-forge
-        entry: poetry -C forge run pyright
+        entry: poetry -C classic/forge run pyright
         args: [-p, forge, forge]
-        files: ^forge/(forge/|poetry\.lock$)
+        files: ^classic/forge/(classic/forge/|poetry\.lock$)
         types: [file]
         language: system
         pass_filenames: false
@@ -95,9 +95,9 @@ repos:
       - id: pyright
         name: Typecheck - Benchmark
         alias: pyright-benchmark
-        entry: poetry -C benchmark run pyright
+        entry: poetry -C classic/benchmark run pyright
         args: [-p, benchmark, benchmark]
-        files: ^benchmark/(agbenchmark/|tests/|poetry\.lock$)
+        files: ^classic/benchmark/(agbenchmark/|tests/|poetry\.lock$)
         types: [file]
         language: system
         pass_filenames: false
@@ -106,22 +106,22 @@ repos:
     hooks:
       - id: pytest-autogpt
         name: Run tests - AutoGPT (excl. slow tests)
-        entry: bash -c 'cd autogpt && poetry run pytest --cov=autogpt -m "not slow" tests/unit tests/integration'
+        entry: bash -c 'cd classic/original_autogpt && poetry run pytest --cov=autogpt -m "not slow" tests/unit tests/integration'
         # include forge source (since it's a path dependency) but exclude *_test.py files:
-        files: ^(autogpt/((autogpt|tests)/|poetry\.lock$)|forge/(forge/.*(?<!_test)\.py|poetry\.lock)$)
+        files: ^(classic/original_autogpt/((autogpt|tests)/|poetry\.lock$)|classic/forge/(classic/forge/.*(?<!_test)\.py|poetry\.lock)$)
         language: system
         pass_filenames: false
 
       - id: pytest-forge
         name: Run tests - Forge (excl. slow tests)
-        entry: bash -c 'cd forge && poetry run pytest --cov=forge -m "not slow"'
-        files: ^forge/(forge/|tests/|poetry\.lock$)
+        entry: bash -c 'cd classic/forge && poetry run pytest --cov=forge -m "not slow"'
+        files: ^classic/forge/(classic/forge/|tests/|poetry\.lock$)
         language: system
         pass_filenames: false
 
       - id: pytest-benchmark
         name: Run tests - Benchmark
-        entry: bash -c 'cd benchmark && poetry run pytest --cov=benchmark'
-        files: ^benchmark/(agbenchmark/|tests/|poetry\.lock$)
+        entry: bash -c 'cd classic/benchmark && poetry run pytest --cov=benchmark'
+        files: ^classic/benchmark/(agbenchmark/|tests/|poetry\.lock$)
         language: system
         pass_filenames: false

.vscode/all-projects.code-workspace

@@ -1,39 +1,54 @@
 {
   "folders": [
     {
-      "name": "autogpt",
-      "path": "../autogpt"
+      "name": "frontend",
+      "path": "../autogpt_platform/frontend"
     },
     {
-      "name": "benchmark",
-      "path": "../benchmark"
+      "name": "backend",
+      "path": "../autogpt_platform/backend"
+    },
+    {
+      "name": "market",
+      "path": "../autogpt_platform/market"
+    },
+    {
+      "name": "lib",
+      "path": "../autogpt_platform/autogpt_libs"
+    },
+    {
+      "name": "infra",
+      "path": "../autogpt_platform/infra"
     },
     {
       "name": "docs",
       "path": "../docs"
     },
+
     {
-      "name": "forge",
-      "path": "../forge"
+      "name": "classic - autogpt",
+      "path": "../classic/original_autogpt"
     },
     {
-      "name": "frontend",
-      "path": "../frontend"
+      "name": "classic - benchmark",
+      "path": "../classic/benchmark"
     },
     {
-      "name": "autogpt_server",
-      "path": "../rnd/autogpt_server"
+      "name": "classic - forge",
+      "path": "../classic/forge"
     },
     {
-      "name": "autogpt_builder",
-      "path": "../rnd/autogpt_builder"
+      "name": "classic - frontend",
+      "path": "../classic/frontend"
     },
     {
       "name": "[root]",
       "path": ".."
     }
   ],
-  "settings": {},
+  "settings": {
+    "python.analysis.typeCheckingMode": "basic"
+  },
   "extensions": {
     "recommendations": [
       "charliermarsh.ruff",

CONTRIBUTING.md

@@ -10,6 +10,9 @@ Also check out our [🚀 Roadmap][roadmap] for information about our priorities
 [roadmap]: https://github.com/Significant-Gravitas/AutoGPT/discussions/6971
 [kanban board]: https://github.com/orgs/Significant-Gravitas/projects/1
 
+## Contributing to the AutoGPT Platform Folder
+All contributions to [the autogpt_platform folder](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform) will be under our [Contribution License Agreement](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/Contributor%20License%20Agreement%20(CLA).md). By making a pull request contributing to this folder, you agree to the terms of our CLA for your contribution.
+
 ## In short
 1. Avoid duplicate work, issues, PRs etc.
 2. We encourage you to collaborate with fellow community members on some of our bigger

README.md

@@ -1,41 +1,68 @@
-# AutoGPT: Build & Use AI Agents
+# AutoGPT: Build, Deploy, and Run AI Agents
 
 [![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 tool that lets you create and run intelligent agents. These agents can perform various tasks automatically, making your life easier.
+**AutoGPT** is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows. 
 
-## How to Get Started
+## Hosting Options 
+   - Download to self-host
+   - [Join the Waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta  
 
-https://github.com/user-attachments/assets/8508f4dc-b362-4cab-900f-644964a96cdf
+## How to Setup for Self-Hosting
+> [!NOTE]
+> Setting up and hosting the AutoGPT Platform yourself is a technical process. 
+> If you'd rather something that just works, we recommend [joining the waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta.
 
-### 🧱 AutoGPT Builder 
+https://github.com/user-attachments/assets/d04273a5-b36a-4a37-818e-f631ce72d603
 
-The AutoGPT Builder is the frontend. It allows you to design agents using an easy flowchart style. You build your agent by connecting blocks, where each block performs a single action. It's simple and intuitive!
+This tutorial assumes you have Docker, VSCode, git and npm installed.
+
+### 🧱 AutoGPT Frontend
+
+The AutoGPT frontend is where users interact with our powerful AI automation platform. It offers multiple ways to engage with and leverage our AI agents. This is the interface where you'll bring your AI automation ideas to life:
+
+   **Agent Builder:** For those who want to customize, our intuitive, low-code interface allows you to design and configure your own AI agents. 
+   
+   **Workflow Management:** Build, modify, and optimize your automation workflows with ease. You build your agent by connecting blocks, where each block     performs a single action.
+   
+   **Deployment Controls:** Manage the lifecycle of your agents, from testing to production.
+   
+   **Ready-to-Use Agents:** Don't want to build? Simply select from our library of pre-configured agents and put them to work immediately.
+   
+   **Agent Interaction:** Whether you've built your own or are using pre-configured agents, easily run and interact with them through our user-friendly      interface.
+
+   **Monitoring and Analytics:** Keep track of your agents' performance and gain insights to continually improve your automation processes.
 
 [Read this guide](https://docs.agpt.co/server/new_blocks/) to learn how to build your own custom blocks.
 
 ### 💽 AutoGPT Server
 
-The AutoGPT Server is the backend. This is where your agents run. Once deployed, agents can be triggered by external sources and can operate continuously.
+The AutoGPT Server is the powerhouse of our platform This is where your agents run. Once deployed, agents can be triggered by external sources and can operate continuously. It contains all the essential components that make AutoGPT run smoothly.
+
+   **Source Code:** The core logic that drives our agents and automation processes.
+   
+   **Infrastructure:** Robust systems that ensure reliable and scalable performance.
+   
+   **Marketplace:** A comprehensive marketplace where you can find and deploy a wide range of pre-built agents.
 
 ### 🐙 Example Agents
 
 Here are two examples of what you can do with AutoGPT:
 
-1. **Reddit Marketing Agent**
-   - This agent reads comments on Reddit.
-   - It looks for people asking about your product.
-   - It then automatically responds to them.
+1. **Generate Viral Videos from Trending Topics**
+   - This agent reads topics on Reddit.
+   - It identifies trending topics.
+   - It then automatically creates a short-form video based on the content. 
 
-2. **YouTube Content Repurposing Agent**
+2. **Identify Top Quotes from Videos for Social Media**
    - This agent subscribes to your YouTube channel.
    - When you post a new video, it transcribes it.
-   - It uses AI to write a search engine optimized blog post.
-   - Then, it publishes this blog post to your Medium account.
+   - It uses AI to identify the most impactful quotes to generate a summary.
+   - Then, it writes a post to automatically publish to your social media. 
 
-These examples show just a glimpse of what you can achieve with AutoGPT!
+These examples show just a glimpse of what you can achieve with AutoGPT! You can create customized workflows to build agents for any use case.
 
 ---
 Our mission is to provide the tools, so that you can focus on what matters:
@@ -55,15 +82,16 @@ Be part of the revolution! **AutoGPT** is here to stay, at the forefront of AI i
 ## 🤖 AutoGPT Classic
 > Below is information about the classic version of AutoGPT.
 
-**🛠️ [Build your own Agent - Quickstart](FORGE-QUICKSTART.md)**
+**🛠️ [Build your own Agent - Quickstart](classic/FORGE-QUICKSTART.md)**
+
 ### 🏗️ Forge
 
-**Forge your own agent!** – Forge is a ready-to-go template for your agent application. All the boilerplate code is already handled, letting you channel all your creativity into the things that set *your* agent apart. All tutorials are located [here](https://medium.com/@aiedge/autogpt-forge-e3de53cc58ec). Components from the [`forge.sdk`](/forge/forge/sdk) can also be used individually to speed up development and reduce boilerplate in your agent project.
+**Forge your own agent!** – Forge is a ready-to-go toolkit to build your own agent application. It handles most of the boilerplate code, letting you channel all your creativity into the things that set *your* agent apart. All tutorials are located [here](https://medium.com/@aiedge/autogpt-forge-e3de53cc58ec). Components from [`forge`](/classic/forge/) can also be used individually to speed up development and reduce boilerplate in your agent project.
 
-🚀 [**Getting Started with Forge**](https://github.com/Significant-Gravitas/AutoGPT/blob/master/forge/tutorials/001_getting_started.md) –
+🚀 [**Getting Started with Forge**](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/forge/tutorials/001_getting_started.md) –
 This guide will walk you through the process of creating your own agent and using the benchmark and user interface.
 
-📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/forge) about Forge
+📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge) about Forge
 
 ### 🎯 Benchmark
 
@@ -83,7 +111,7 @@ This guide will walk you through the process of creating your own agent and usin
 
 The frontend works out-of-the-box with all agents in the repo. Just use the [CLI] to run your agent of choice!
 
-📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/frontend) about the Frontend
+📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend) about the Frontend
 
 ### ⌨️ CLI
 

autogpt/autogpt/agents/prompt_strategies/code_flow.py

@@ -1,355 +0,0 @@
-import inspect
-import re
-from logging import Logger
-from typing import Callable, Iterable, Sequence, get_args, get_origin
-
-from forge.command import Command
-from forge.components.code_flow_executor import CodeFlowExecutionComponent
-from forge.config.ai_directives import AIDirectives
-from forge.config.ai_profile import AIProfile
-from forge.json.parsing import extract_dict_from_json
-from forge.llm.prompting import ChatPrompt, LanguageModelClassification, PromptStrategy
-from forge.llm.prompting.utils import indent
-from forge.llm.providers.schema import (
-    AssistantChatMessage,
-    AssistantFunctionCall,
-    ChatMessage,
-)
-from forge.models.config import SystemConfiguration
-from forge.models.json_schema import JSONSchema
-from forge.utils.exceptions import InvalidAgentResponseError
-from forge.utils.function.code_validation import CodeValidator
-from forge.utils.function.model import FunctionDef
-from pydantic import BaseModel, Field
-
-from autogpt.agents.prompt_strategies.one_shot import (
-    AssistantThoughts,
-    OneShotAgentActionProposal,
-    OneShotAgentPromptConfiguration,
-)
-
-_RESPONSE_INTERFACE_NAME = "AssistantResponse"
-
-
-class CodeFlowAgentActionProposal(BaseModel):
-    thoughts: AssistantThoughts
-    immediate_plan: str = Field(
-        ...,
-        description="We will be running an iterative process to execute the plan, "
-        "Write the partial / immediate plan to execute your plan as detailed and "
-        "efficiently as possible without the help of the reasoning/intelligence. "
-        "The plan should describe the output of the immediate plan, so that the next "
-        "iteration can be executed by taking the output into account. "
-        "Try to do as much as possible without making any assumption or uninformed "
-        "guesses. Avoid large output at all costs!!!\n"
-        "Format: Objective[Objective of this iteration, explain what's the use of this "
-        "iteration for the next one] Plan[Plan that does not require any reasoning or "
-        "intelligence] Output[Output of the plan / should be small, avoid whole file "
-        "output]",
-    )
-    python_code: str = Field(
-        ...,
-        description=(
-            "Write the fully-functional Python code of the immediate plan. "
-            "The output will be an `async def main() -> str` function of the immediate "
-            "plan that return the string output, the output will be passed into the "
-            "LLM context window so avoid returning the whole content!. "
-            "Use ONLY the listed available functions and built-in Python features. "
-            "Leverage the given magic functions to implement function calls for which "
-            "the arguments can't be determined yet. "
-            "Example:`async def main() -> str:\n"
-            "    return await provided_function('arg1', 'arg2').split('\\n')[0]`"
-        ),
-    )
-
-
-FINAL_INSTRUCTION: str = (
-    "You have to give the answer in the from of JSON schema specified previously. "
-    "For the `python_code` field, you have to write Python code to execute your plan "
-    "as efficiently as possible. Your code will be executed directly without any "
-    "editing, if it doesn't work you will be held responsible. "
-    "Use ONLY the listed available functions and built-in Python features. "
-    "Do not make uninformed assumptions "
-    "(e.g. about the content or format of an unknown file). Leverage the given magic "
-    "functions to implement function calls for which the arguments can't be determined "
-    "yet. Reduce the amount of unnecessary data passed into these magic functions "
-    "where possible, because magic costs money and magically processing large amounts "
-    "of data is expensive. If you think are done with the task, you can simply call "
-    "finish(reason='your reason') to end the task, "
-    "a function that has one `finish` command, don't mix finish with other functions! "
-    "If you still need to do other functions, "
-    "let the next cycle execute the `finish` function. "
-    "Avoid hard-coding input values as input, and avoid returning large outputs. "
-    "The code that you have been executing in the past cycles can also be buggy, "
-    "so if you see undesired output, you can always try to re-plan, and re-code. "
-)
-
-
-class CodeFlowAgentPromptStrategy(PromptStrategy):
-    default_configuration: OneShotAgentPromptConfiguration = (
-        OneShotAgentPromptConfiguration()
-    )
-
-    def __init__(
-        self,
-        configuration: SystemConfiguration,
-        logger: Logger,
-    ):
-        self.config = configuration
-        self.response_schema = JSONSchema.from_dict(
-            CodeFlowAgentActionProposal.model_json_schema()
-        )
-        self.logger = logger
-        self.commands: Sequence[Command] = []  # Sequence -> disallow list modification
-
-    @property
-    def llm_classification(self) -> LanguageModelClassification:
-        return LanguageModelClassification.SMART_MODEL  # FIXME: dynamic switching
-
-    def build_prompt(
-        self,
-        *,
-        messages: list[ChatMessage],
-        task: str,
-        ai_profile: AIProfile,
-        ai_directives: AIDirectives,
-        commands: Sequence[Command],
-        **extras,
-    ) -> ChatPrompt:
-        """Constructs and returns a prompt with the following structure:
-        1. System prompt
-        3. `cycle_instruction`
-        """
-        system_prompt, response_prefill = self.build_system_prompt(
-            ai_profile=ai_profile,
-            ai_directives=ai_directives,
-            commands=commands,
-        )
-
-        self.commands = commands
-        final_instruction_msg = ChatMessage.system(FINAL_INSTRUCTION)
-
-        return ChatPrompt(
-            messages=[
-                ChatMessage.system(system_prompt),
-                ChatMessage.user(f'"""{task}"""'),
-                *messages,
-                *(
-                    [final_instruction_msg]
-                    if not any(m.role == "assistant" for m in messages)
-                    else []
-                ),
-            ],
-            prefill_response=response_prefill,
-        )
-
-    def build_system_prompt(
-        self,
-        ai_profile: AIProfile,
-        ai_directives: AIDirectives,
-        commands: Iterable[Command],
-    ) -> tuple[str, str]:
-        """
-        Builds the system prompt.
-
-        Returns:
-            str: The system prompt body
-            str: The desired start for the LLM's response; used to steer the output
-        """
-        response_fmt_instruction, response_prefill = self.response_format_instruction()
-        system_prompt_parts = (
-            self._generate_intro_prompt(ai_profile)
-            + [
-                "## Your Task\n"
-                "The user will specify a task for you to execute, in triple quotes,"
-                " in the next message. Your job is to complete the task, "
-                "and terminate when your task is done."
-            ]
-            + ["## Available Functions\n" + self._generate_function_headers(commands)]
-            + ["## RESPONSE FORMAT\n" + response_fmt_instruction]
-        )
-
-        # Join non-empty parts together into paragraph format
-        return (
-            "\n\n".join(filter(None, system_prompt_parts)).strip("\n"),
-            response_prefill,
-        )
-
-    def response_format_instruction(self) -> tuple[str, str]:
-        response_schema = self.response_schema.model_copy(deep=True)
-        assert response_schema.properties
-
-        # Unindent for performance
-        response_format = re.sub(
-            r"\n\s+",
-            "\n",
-            response_schema.to_typescript_object_interface(_RESPONSE_INTERFACE_NAME),
-        )
-        response_prefill = f'{{\n    "{list(response_schema.properties.keys())[0]}":'
-
-        return (
-            (
-                f"YOU MUST ALWAYS RESPOND WITH A JSON OBJECT OF THE FOLLOWING TYPE:\n"
-                f"{response_format}"
-            ),
-            response_prefill,
-        )
-
-    def _generate_intro_prompt(self, ai_profile: AIProfile) -> list[str]:
-        """Generates the introduction part of the prompt.
-
-        Returns:
-            list[str]: A list of strings forming the introduction part of the prompt.
-        """
-        return [
-            f"You are {ai_profile.ai_name}, {ai_profile.ai_role.rstrip('.')}.",
-            # "Your decisions must always be made independently without seeking "
-            # "user assistance. Play to your strengths as an LLM and pursue "
-            # "simple strategies with no legal complications.",
-        ]
-
-    def _generate_function_headers(self, commands: Iterable[Command]) -> str:
-        function_stubs: list[str] = []
-        annotation_types_in_context: set[type] = set()
-        for f in commands:
-            # Add source code of non-builtin types from function signatures
-            new_annotation_types = extract_annotation_types(f.method).difference(
-                annotation_types_in_context
-            )
-            new_annotation_types_src = [
-                f"# {a.__module__}.{a.__qualname__}\n{inspect.getsource(a)}"
-                for a in new_annotation_types
-            ]
-            annotation_types_in_context.update(new_annotation_types)
-
-            param_descriptions = "\n".join(
-                f"{param.name}: {param.spec.description}"
-                for param in f.parameters
-                if param.spec.description
-            )
-            full_function_stub = (
-                ("\n".join(new_annotation_types_src) + "\n" + f.header).strip()
-                + "\n"
-                + indent(
-                    (
-                        '"""\n'
-                        f"{f.description}\n\n"
-                        f"Params:\n{indent(param_descriptions)}\n"
-                        '"""\n'
-                        "pass"
-                    ),
-                )
-            )
-            function_stubs.append(full_function_stub)
-
-        return "\n\n\n".join(function_stubs)
-
-    async def parse_response_content(
-        self,
-        response: AssistantChatMessage,
-    ) -> OneShotAgentActionProposal:
-        if not response.content:
-            raise InvalidAgentResponseError("Assistant response has no text content")
-
-        self.logger.debug(
-            "LLM response content:"
-            + (
-                f"\n{response.content}"
-                if "\n" in response.content
-                else f" '{response.content}'"
-            )
-        )
-        assistant_reply_dict = extract_dict_from_json(response.content)
-
-        parsed_response = CodeFlowAgentActionProposal.model_validate(
-            assistant_reply_dict
-        )
-        if not parsed_response.python_code:
-            raise ValueError("python_code is empty")
-
-        available_functions = {
-            c.name: FunctionDef(
-                name=c.name,
-                arg_types=[(p.name, p.spec.python_type) for p in c.parameters],
-                arg_descs={p.name: p.spec.description for p in c.parameters},
-                arg_defaults={
-                    p.name: p.spec.default or "None"
-                    for p in c.parameters
-                    if p.spec.default or not p.spec.required
-                },
-                return_type=c.return_type,
-                return_desc="Output of the function",
-                function_desc=c.description,
-                is_async=c.is_async,
-            )
-            for c in self.commands
-        }
-        available_functions.update(
-            {
-                "main": FunctionDef(
-                    name="main",
-                    arg_types=[],
-                    arg_descs={},
-                    return_type="str",
-                    return_desc="Output of the function",
-                    function_desc="The main function to execute the plan",
-                    is_async=True,
-                )
-            }
-        )
-        code_validation = await CodeValidator(
-            function_name="main",
-            available_functions=available_functions,
-        ).validate_code(parsed_response.python_code)
-
-        clean_response = response.model_copy()
-        clean_response.content = parsed_response.model_dump_json(indent=4)
-
-        # TODO: prevent combining finish with other functions
-        if _finish_call := re.search(
-            r"finish\((reason=)?(.*?)\)", code_validation.functionCode
-        ):
-            finish_reason = _finish_call.group(2)[1:-1]  # remove quotes
-            result = OneShotAgentActionProposal(
-                thoughts=parsed_response.thoughts,
-                use_tool=AssistantFunctionCall(
-                    name="finish",
-                    arguments={"reason": finish_reason},
-                ),
-                raw_message=clean_response,
-            )
-        else:
-            result = OneShotAgentActionProposal(
-                thoughts=parsed_response.thoughts,
-                use_tool=AssistantFunctionCall(
-                    name=CodeFlowExecutionComponent.execute_code_flow.name,
-                    arguments={
-                        "python_code": code_validation.functionCode,
-                        "plan_text": parsed_response.immediate_plan,
-                    },
-                ),
-                raw_message=clean_response,
-            )
-        return result
-
-
-def extract_annotation_types(func: Callable) -> set[type]:
-    annotation_types = set()
-    for annotation in inspect.get_annotations(func).values():
-        annotation_types.update(_get_nested_types(annotation))
-    return annotation_types
-
-
-def _get_nested_types(annotation: type) -> Iterable[type]:
-    if _args := get_args(annotation):
-        for a in _args:
-            yield from _get_nested_types(a)
-    if not _is_builtin_type(_a := get_origin(annotation) or annotation):
-        yield _a
-
-
-def _is_builtin_type(_type: type):
-    """Check if a given type is a built-in type."""
-    import sys
-
-    return _type.__module__ in sys.stdlib_module_names

autogpt/tests/unit/test_code_flow_strategy.py

@@ -1,126 +0,0 @@
-import logging
-from typing import Optional
-
-import pytest
-from forge.agent.protocols import CommandProvider
-from forge.command import Command, command
-from forge.components.code_flow_executor import CodeFlowExecutionComponent
-from forge.config.ai_directives import AIDirectives
-from forge.config.ai_profile import AIProfile
-from forge.llm.providers import AssistantChatMessage
-from forge.llm.providers.schema import JSONSchema
-
-from autogpt.agents.prompt_strategies.code_flow import CodeFlowAgentPromptStrategy
-
-logger = logging.getLogger(__name__)
-config = CodeFlowAgentPromptStrategy.default_configuration.copy(deep=True)
-prompt_strategy = CodeFlowAgentPromptStrategy(config, logger)
-
-
-class MockWebSearchProvider(CommandProvider):
-    def get_commands(self):
-        yield self.mock_web_search
-
-    @command(
-        description="Searches the web",
-        parameters={
-            "query": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The search query",
-                required=True,
-            ),
-            "num_results": JSONSchema(
-                type=JSONSchema.Type.INTEGER,
-                description="The number of results to return",
-                minimum=1,
-                maximum=10,
-                required=False,
-            ),
-        },
-    )
-    def mock_web_search(self, query: str, num_results: Optional[int] = None) -> str:
-        return "results"
-
-
-@pytest.mark.asyncio
-async def test_code_flow_build_prompt():
-    commands = list(MockWebSearchProvider().get_commands())
-
-    ai_profile = AIProfile()
-    ai_profile.ai_name = "DummyGPT"
-    ai_profile.ai_goals = ["A model for testing purposes"]
-    ai_profile.ai_role = "Help Testing"
-
-    ai_directives = AIDirectives()
-    ai_directives.resources = ["resource_1"]
-    ai_directives.constraints = ["constraint_1"]
-    ai_directives.best_practices = ["best_practice_1"]
-
-    prompt = str(
-        prompt_strategy.build_prompt(
-            task="Figure out from file.csv how much was spent on utilities",
-            messages=[],
-            ai_profile=ai_profile,
-            ai_directives=ai_directives,
-            commands=commands,
-        )
-    )
-    assert "DummyGPT" in prompt
-    assert (
-        "def mock_web_search(query: str, num_results: Optional[int] = None)" in prompt
-    )
-
-
-@pytest.mark.asyncio
-async def test_code_flow_parse_response():
-    response_content = """
-{
-"thoughts": {
-  "past_action_summary": "This is the past action summary.",
-  "observations": "This is the observation.",
-  "text": "Some text on the AI's thoughts.",
-  "reasoning": "This is the reasoning.",
-  "self_criticism": "This is the self-criticism.",
-  "plan": [
-    "Plan 1",
-    "Plan 2",
-    "Plan 3"
-  ],
-  "speak": "This is what the AI would say."
-},
-"immediate_plan": "Objective[objective1] Plan[plan1] Output[out1]",
-"python_code": "async def main() -> str:\n    return 'You passed the test.'",
-}
-    """
-    response = await CodeFlowAgentPromptStrategy(config, logger).parse_response_content(
-        AssistantChatMessage(content=response_content)
-    )
-    assert "This is the observation." == response.thoughts.observations
-    assert "This is the reasoning." == response.thoughts.reasoning
-
-    assert CodeFlowExecutionComponent.execute_code_flow.name == response.use_tool.name
-    assert "async def main() -> str" in response.use_tool.arguments["python_code"]
-    assert (
-        "Objective[objective1] Plan[plan1] Output[out1]"
-        in response.use_tool.arguments["plan_text"]
-    )
-
-
-@pytest.mark.asyncio
-async def test_code_flow_execution():
-    executor = CodeFlowExecutionComponent(
-        lambda: [
-            Command(
-                names=["test_func"],
-                description="",
-                parameters=[],
-                method=lambda: "You've passed the test!",
-            )
-        ]
-    )
-
-    result = await executor.execute_code_flow(
-        python_code="async def main() -> str:\n    return test_func()",
-        plan_text="This is the plan text.",
-    )
-    assert "You've passed the test!" in result

autogpt/tests/unit/test_function_code_validation.py

@@ -1,75 +0,0 @@
-import pytest
-from forge.utils.function.code_validation import CodeValidator, FunctionDef
-
-
-@pytest.mark.asyncio
-async def test_code_validation():
-    validator = CodeValidator(
-        available_functions={
-            "read_webpage": FunctionDef(
-                name="read_webpage",
-                arg_types=[("url", "str"), ("query", "str")],
-                arg_descs={
-                    "url": "URL to read",
-                    "query": "Query to search",
-                    "return_type": "Type of return value",
-                },
-                return_type="str",
-                return_desc="Information matching the query",
-                function_desc="Read a webpage and return the info matching the query",
-                is_async=True,
-            ),
-            "web_search": FunctionDef(
-                name="web_search",
-                arg_types=[("query", "str")],
-                arg_descs={"query": "Query to search"},
-                return_type="list[(str,str)]",
-                return_desc="List of tuples with title and URL",
-                function_desc="Search the web and return the search results",
-                is_async=True,
-            ),
-            "main": FunctionDef(
-                name="main",
-                arg_types=[],
-                arg_descs={},
-                return_type="str",
-                return_desc="Answer in the text format",
-                function_desc="Get the num of contributors to the autogpt github repo",
-                is_async=False,
-            ),
-        },
-        available_objects={},
-    )
-    response = await validator.validate_code(
-        raw_code="""
-def crawl_info(url: str, query: str) -> str | None:
-    info = await read_webpage(url, query)
-    if info:
-        return info
-
-    urls = await read_webpage(url, "autogpt github contributor page")
-    for url in urls.split('\\n'):
-        info = await crawl_info(url, query)
-        if info:
-            return info
-
-    return None
-
-def hehe():
-    return 'hehe'
-
-def main() -> str:
-    query = "Find the number of contributors to the autogpt github repository"
-    for title, url in ("autogpt github contributor page"):
-        info = await crawl_info(url, query)
-        if info:
-            return info
-    x = await hehe()
-    return "No info found"
-""",
-        packages=[],
-    )
-    assert response.functionCode is not None
-    assert "async def crawl_info" in response.functionCode  # async is added
-    assert "async def main" in response.functionCode
-    assert "x = hehe()" in response.functionCode  # await is removed

autogpt_platform/Contributor License Agreement (CLA).md

@@ -0,0 +1,21 @@
+**Determinist Ltd** 
+
+**Contributor License Agreement (“Agreement”)**
+
+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:  
+
+1\.	**You grant us the ability to use the Contributions in any way**.  You hereby grant to Determinist  a non-exclusive, irrevocable, worldwide, royalty-free, sublicenseable, transferable license under all of Your relevant intellectual property rights (including copyright, patent, and any other rights), to use, copy, prepare derivative works of, distribute and publicly perform and display the Contributions on any licensing terms, including without limitation: (a) open source licenses like the GNU General Public License (GPL), the GNU Lesser General Public License (LGPL), the Common Public License, or the Berkeley Science Division license (BSD); and (b) binary, proprietary, or commercial licenses.
+
+2\. 	**Grant of Patent License**. You hereby grant to Determinist a worldwide, non-exclusive, royalty-free, irrevocable, license, under any rights you may have, now or in the future, in any patents or patent applications, to make, have made, use, offer to sell, sell, and import products containing the Contribution or portions of the Contribution.  This license extends to patent claims that are infringed by the Contribution alone or by combination of the Contribution with other inventions.  
+
+4\. 	**Limitations on Licenses**.  The licenses granted in this Agreement will continue for the duration of the applicable patent or intellectual property right under which such license is granted.   The licenses granted in this Agreement will include the right to grant and authorize sublicenses, so long as the sublicenses are within the scope of the licenses granted in this Agreement.  Except for the licenses granted herein, You reserve all right, title, and interest in and to the Contribution.    
+
+5\. 	**You are able to grant us these rights**.  You represent that You are legally entitled to grant the above license.  If Your employer has rights to intellectual property that You create, You represent that You are authorized to make the Contributions on behalf of that employer, or that Your employer has waived such rights for the Contributions.  
+
+3\.	**The Contributions are your original work**.  You represent that the Contributions are Your original works of authorship, and to Your knowledge, no other person claims, or has the right to claim, any right in any invention or patent related to the Contributions.  You also represent that You are not legally obligated, whether by entering into an agreement or otherwise, in any way that conflicts with the terms of this license.  For example, if you have signed an agreement requiring you to assign the intellectual property rights in the Contributions to an employer or customer, that would conflict with the terms of this license.  
+    
+6\.  **We determine the code that is in our products**.  You understand that the decision to include the Contribution in any product or source repository is entirely that of Determinist, and this agreement does not guarantee that the Contributions will be included in any product. 
+
+7\. **No Implied Warranties.**  Determinist acknowledges that, except as explicitly described in this Agreement, the Contribution is provided on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT   LIMITATION, ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.    

autogpt_platform/LICENCE.txt

@@ -0,0 +1,164 @@
+# 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.

autogpt_platform/README.md

@@ -0,0 +1,154 @@
+# AutoGPT Platform
+
+Welcome to the AutoGPT Platform - a powerful system for creating and running AI agents to solve business problems. This platform enables you to harness the power of artificial intelligence to automate tasks, analyze data, and generate insights for your organization.
+
+## Getting Started
+
+### Prerequisites
+
+- Docker
+- Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
+- Node.js & NPM (for running the frontend application)
+
+### Running the System
+
+To run the AutoGPT Platform, follow these steps:
+
+1. Clone this repository to your local machine and navigate to the `autogpt_platform` directory within the repository:
+   ```
+   git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git>
+   cd AutoGPT/autogpt_platform
+   ```
+
+2. Run the following command:
+   ```
+   git submodule update --init --recursive
+   ```
+   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.
+
+5. Navigate to `frontend` within the `autogpt_platform` directory:
+   ```
+   cd frontend
+   ```
+   You will need to run your frontend application separately on your local machine.
+
+6. Run the following command: 
+   ```
+   cp .env.example .env
+   ```
+   This command will copy the `.env.example` file to `.env` in the `frontend` directory. You can modify the `.env` within this folder to add your own environment variables for the frontend application.
+
+7. Run the following command:
+   ```
+   npm install
+   npm run dev
+   ```
+   This command will install the necessary dependencies and start the frontend application in development mode.
+   If you are using Yarn, you can run the following commands instead:
+   ```
+   yarn install && yarn dev
+   ```
+
+8. Open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
+
+### Docker Compose Commands
+
+Here are some useful Docker Compose commands for managing your AutoGPT Platform:
+
+- `docker compose up -d`: Start the services in detached mode.
+- `docker compose stop`: Stop the running services without removing them.
+- `docker compose rm`: Remove stopped service containers.
+- `docker compose build`: Build or rebuild services.
+- `docker compose down`: Stop and remove containers, networks, and volumes.
+- `docker compose watch`: Watch for changes in your services and automatically update them.
+
+
+### Sample Scenarios
+
+Here are some common scenarios where you might use multiple Docker Compose commands:
+
+1. Updating and restarting a specific service:
+   ```
+   docker compose build api_srv
+   docker compose up -d --no-deps api_srv
+   ```
+   This rebuilds the `api_srv` service and restarts it without affecting other services.
+
+2. Viewing logs for troubleshooting:
+   ```
+   docker compose logs -f api_srv ws_srv
+   ```
+   This shows and follows the logs for both `api_srv` and `ws_srv` services.
+
+3. Scaling a service for increased load:
+   ```
+   docker compose up -d --scale executor=3
+   ```
+   This scales the `executor` service to 3 instances to handle increased load.
+
+4. Stopping the entire system for maintenance:
+   ```
+   docker compose stop
+   docker compose rm -f
+   docker compose pull
+   docker compose up -d
+   ```
+   This stops all services, removes containers, pulls the latest images, and restarts the system.
+
+5. Developing with live updates:
+   ```
+   docker compose watch
+   ```
+   This watches for changes in your code and automatically updates the relevant services.
+
+6. Checking the status of services:
+   ```
+   docker compose ps
+   ```
+   This shows the current status of all services defined in your docker-compose.yml file.
+
+These scenarios demonstrate how to use Docker Compose commands in combination to manage your AutoGPT Platform effectively.
+
+
+### Persisting Data
+
+To persist data for PostgreSQL and Redis, you can modify the `docker-compose.yml` file to add volumes. Here's how:
+
+1. Open the `docker-compose.yml` file in a text editor.
+2. Add volume configurations for PostgreSQL and Redis services:
+
+   ```yaml
+   services:
+     postgres:
+       # ... other configurations ...
+       volumes:
+         - postgres_data:/var/lib/postgresql/data
+
+     redis:
+       # ... other configurations ...
+       volumes:
+         - redis_data:/data
+
+   volumes:
+     postgres_data:
+     redis_data:
+   ```
+
+3. Save the file and run `docker compose up -d` to apply the changes.
+
+This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
+
+
+

autogpt_platform/autogpt_libs/README.md

@@ -0,0 +1,3 @@
+# AutoGPT Libs
+
+This is a new project to store shared functionality across different services in NextGen AutoGPT (e.g. authentication)

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -0,0 +1,14 @@
+from .config import Settings
+from .depends import requires_admin_user, requires_user
+from .jwt_utils import parse_jwt_token
+from .middleware import auth_middleware
+from .models import User
+
+__all__ = [
+    "Settings",
+    "parse_jwt_token",
+    "requires_user",
+    "requires_admin_user",
+    "auth_middleware",
+    "User",
+]

autogpt_platform/autogpt_libs/autogpt_libs/auth/config.py

@@ -0,0 +1,18 @@
+import os
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+class Settings:
+    JWT_SECRET_KEY: str = os.getenv("SUPABASE_JWT_SECRET", "")
+    ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
+    JWT_ALGORITHM: str = "HS256"
+
+    @property
+    def is_configured(self) -> bool:
+        return bool(self.JWT_SECRET_KEY)
+
+
+settings = Settings()

autogpt_platform/autogpt_libs/autogpt_libs/auth/depends.py

@@ -0,0 +1,32 @@
+import fastapi
+
+from .middleware import auth_middleware
+from .models import User
+
+
+def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
+    return verify_user(payload, admin_only=False)
+
+
+def requires_admin_user(
+    payload: dict = fastapi.Depends(auth_middleware),
+) -> User:
+    return verify_user(payload, admin_only=True)
+
+
+def verify_user(payload: dict | None, admin_only: bool) -> User:
+    if not payload:
+        # This handles the case when authentication is disabled
+        payload = {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"}
+
+    user_id = payload.get("sub")
+
+    if not user_id:
+        raise fastapi.HTTPException(
+            status_code=401, detail="User ID not found in token"
+        )
+
+    if admin_only and payload["role"] != "admin":
+        raise fastapi.HTTPException(status_code=403, detail="Admin access required")
+
+    return User.from_payload(payload)

autogpt_platform/autogpt_libs/autogpt_libs/auth/depends_tests.py

@@ -0,0 +1,68 @@
+import pytest
+
+from .depends import requires_admin_user, requires_user, verify_user
+
+
+def test_verify_user_no_payload():
+    user = verify_user(None, admin_only=False)
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "admin"
+
+
+def test_verify_user_no_user_id():
+    with pytest.raises(Exception):
+        verify_user({"role": "admin"}, admin_only=False)
+
+
+def test_verify_user_not_admin():
+    with pytest.raises(Exception):
+        verify_user(
+            {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
+            admin_only=True,
+        )
+
+
+def test_verify_user_with_admin_role():
+    user = verify_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"},
+        admin_only=True,
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "admin"
+
+
+def test_verify_user_with_user_role():
+    user = verify_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
+        admin_only=False,
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "user"
+
+
+def test_requires_user():
+    user = requires_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "user"
+
+
+def test_requires_user_no_user_id():
+    with pytest.raises(Exception):
+        requires_user({"role": "user"})
+
+
+def test_requires_admin_user():
+    user = requires_admin_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"}
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "admin"
+
+
+def test_requires_admin_user_not_admin():
+    with pytest.raises(Exception):
+        requires_admin_user(
+            {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
+        )

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py

@@ -0,0 +1,27 @@
+from typing import Any, Dict
+
+import jwt
+
+from .config import settings
+
+
+def parse_jwt_token(token: str) -> Dict[str, Any]:
+    """
+    Parse and validate a JWT token.
+
+    :param token: The token to parse
+    :return: The decoded payload
+    :raises ValueError: If the token is invalid or expired
+    """
+    try:
+        payload = jwt.decode(
+            token,
+            settings.JWT_SECRET_KEY,
+            algorithms=[settings.JWT_ALGORITHM],
+            audience="authenticated",
+        )
+        return payload
+    except jwt.ExpiredSignatureError:
+        raise ValueError("Token has expired")
+    except jwt.InvalidTokenError as e:
+        raise ValueError(f"Invalid token: {str(e)}")

autogpt_platform/autogpt_libs/autogpt_libs/auth/middleware.py

@@ -0,0 +1,31 @@
+import logging
+
+from fastapi import HTTPException, Request
+from fastapi.security import HTTPBearer
+
+from .config import settings
+from .jwt_utils import parse_jwt_token
+
+security = HTTPBearer()
+logger = logging.getLogger(__name__)
+
+
+async def auth_middleware(request: Request):
+    if not settings.ENABLE_AUTH:
+        # If authentication is disabled, allow the request to proceed
+        logger.warn("Auth disabled")
+        return {}
+
+    security = HTTPBearer()
+    credentials = await security(request)
+
+    if not credentials:
+        raise HTTPException(status_code=401, detail="Authorization header is missing")
+
+    try:
+        payload = parse_jwt_token(credentials.credentials)
+        request.state.user = payload
+        logger.debug("Token decoded successfully")
+    except ValueError as e:
+        raise HTTPException(status_code=401, detail=str(e))
+    return payload

autogpt_platform/autogpt_libs/autogpt_libs/auth/models.py

@@ -0,0 +1,19 @@
+from dataclasses import dataclass
+
+
+# Using dataclass here to avoid adding dependency on pydantic
+@dataclass(frozen=True)
+class User:
+    user_id: str
+    email: str
+    phone_number: str
+    role: str
+
+    @classmethod
+    def from_payload(cls, payload):
+        return cls(
+            user_id=payload["sub"],
+            email=payload.get("email", ""),
+            phone_number=payload.get("phone", ""),
+            role=payload["role"],
+        )

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -0,0 +1,166 @@
+"""Logging module for Auto-GPT."""
+
+import logging
+import sys
+from pathlib import Path
+
+from pydantic import Field, field_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from .filters import BelowLevelFilter
+from .formatters import AGPTFormatter, StructuredLoggingFormatter
+
+LOG_DIR = Path(__file__).parent.parent.parent.parent / "logs"
+LOG_FILE = "activity.log"
+DEBUG_LOG_FILE = "debug.log"
+ERROR_LOG_FILE = "error.log"
+
+SIMPLE_LOG_FORMAT = "%(asctime)s %(levelname)s  %(title)s%(message)s"
+
+DEBUG_LOG_FORMAT = (
+    "%(asctime)s %(levelname)s %(filename)s:%(lineno)d" "  %(title)s%(message)s"
+)
+
+
+class LoggingConfig(BaseSettings):
+
+    level: str = Field(
+        default="INFO",
+        description="Logging level",
+        validation_alias="LOG_LEVEL",
+    )
+
+    enable_cloud_logging: bool = Field(
+        default=False,
+        description="Enable logging to Google Cloud Logging",
+    )
+
+    enable_file_logging: bool = Field(
+        default=False,
+        description="Enable logging to file",
+    )
+    # File output
+    log_dir: Path = Field(
+        default=LOG_DIR,
+        description="Log directory",
+    )
+
+    model_config = SettingsConfigDict(
+        env_prefix="",
+        env_file=".env",
+        env_file_encoding="utf-8",
+        extra="ignore",
+    )
+
+    @field_validator("level", mode="before")
+    @classmethod
+    def parse_log_level(cls, v):
+        if isinstance(v, str):
+            v = v.upper()
+            if v not in ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]:
+                raise ValueError(f"Invalid log level: {v}")
+            return v
+        return v
+
+
+def configure_logging(force_cloud_logging: bool = False) -> None:
+    """Configure the native logging module based on the LoggingConfig settings.
+
+    This function sets up logging handlers and formatters according to the
+    configuration specified in the LoggingConfig object. It supports various
+    logging outputs including console, file, cloud, and JSON logging.
+
+    The function uses the LoggingConfig object to determine which logging
+    features to enable and how to configure them. This includes setting
+    log levels, log formats, and output destinations.
+
+    No arguments are required as the function creates its own LoggingConfig
+    instance internally.
+
+    Note: This function is typically called at the start of the application
+    to set up the logging infrastructure.
+    """
+
+    config = LoggingConfig()
+
+    log_handlers: list[logging.Handler] = []
+
+    # Cloud logging setup
+    if config.enable_cloud_logging or force_cloud_logging:
+        import google.cloud.logging
+        from google.cloud.logging.handlers import CloudLoggingHandler
+        from google.cloud.logging_v2.handlers.transports.sync import SyncTransport
+
+        client = google.cloud.logging.Client()
+        cloud_handler = CloudLoggingHandler(
+            client,
+            name="autogpt_logs",
+            transport=SyncTransport,
+        )
+        cloud_handler.setLevel(config.level)
+        cloud_handler.setFormatter(StructuredLoggingFormatter())
+        log_handlers.append(cloud_handler)
+        print("Cloud logging enabled")
+    else:
+        # Console output handlers
+        stdout = logging.StreamHandler(stream=sys.stdout)
+        stdout.setLevel(config.level)
+        stdout.addFilter(BelowLevelFilter(logging.WARNING))
+        if config.level == logging.DEBUG:
+            stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+        else:
+            stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+
+        stderr = logging.StreamHandler()
+        stderr.setLevel(logging.WARNING)
+        if config.level == logging.DEBUG:
+            stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+        else:
+            stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+
+        log_handlers += [stdout, stderr]
+        print("Console logging enabled")
+
+    # File logging setup
+    if config.enable_file_logging:
+        # create log directory if it doesn't exist
+        if not config.log_dir.exists():
+            config.log_dir.mkdir(parents=True, exist_ok=True)
+
+        print(f"Log directory: {config.log_dir}")
+
+        # Activity log handler (INFO and above)
+        activity_log_handler = logging.FileHandler(
+            config.log_dir / LOG_FILE, "a", "utf-8"
+        )
+        activity_log_handler.setLevel(config.level)
+        activity_log_handler.setFormatter(
+            AGPTFormatter(SIMPLE_LOG_FORMAT, no_color=True)
+        )
+        log_handlers.append(activity_log_handler)
+
+        if config.level == logging.DEBUG:
+            # Debug log handler (all levels)
+            debug_log_handler = logging.FileHandler(
+                config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
+            )
+            debug_log_handler.setLevel(logging.DEBUG)
+            debug_log_handler.setFormatter(
+                AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True)
+            )
+            log_handlers.append(debug_log_handler)
+
+        # Error log handler (ERROR and above)
+        error_log_handler = logging.FileHandler(
+            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
+        )
+        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(
+        format=DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT,
+        level=config.level,
+        handlers=log_handlers,
+    )

autogpt_platform/autogpt_libs/autogpt_libs/logging/formatters.py

@@ -0,0 +1,95 @@
+import logging
+
+from colorama import Fore, Style
+from google.cloud.logging_v2.handlers import CloudLoggingFilter, StructuredLogHandler
+
+from .utils import remove_color_codes
+
+
+class FancyConsoleFormatter(logging.Formatter):
+    """
+    A custom logging formatter designed for console output.
+
+    This formatter enhances the standard logging output with color coding. The color
+    coding is based on the level of the log message, making it easier to distinguish
+    between different types of messages in the console output.
+
+    The color for each level is defined in the LEVEL_COLOR_MAP class attribute.
+    """
+
+    # level -> (level & text color, title color)
+    LEVEL_COLOR_MAP = {
+        logging.DEBUG: Fore.LIGHTBLACK_EX,
+        logging.INFO: Fore.BLUE,
+        logging.WARNING: Fore.YELLOW,
+        logging.ERROR: Fore.RED,
+        logging.CRITICAL: Fore.RED + Style.BRIGHT,
+    }
+
+    def format(self, record: logging.LogRecord) -> str:
+        # Make sure `msg` is a string
+        if not hasattr(record, "msg"):
+            record.msg = ""
+        elif type(record.msg) is not str:
+            record.msg = str(record.msg)
+
+        # Determine default color based on error level
+        level_color = ""
+        if record.levelno in self.LEVEL_COLOR_MAP:
+            level_color = self.LEVEL_COLOR_MAP[record.levelno]
+            record.levelname = f"{level_color}{record.levelname}{Style.RESET_ALL}"
+
+        # Determine color for message
+        color = getattr(record, "color", level_color)
+        color_is_specified = hasattr(record, "color")
+
+        # Don't color INFO messages unless the color is explicitly specified.
+        if color and (record.levelno != logging.INFO or color_is_specified):
+            record.msg = f"{color}{record.msg}{Style.RESET_ALL}"
+
+        return super().format(record)
+
+
+class AGPTFormatter(FancyConsoleFormatter):
+    def __init__(self, *args, no_color: bool = False, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.no_color = no_color
+
+    def format(self, record: logging.LogRecord) -> str:
+        # Make sure `msg` is a string
+        if not hasattr(record, "msg"):
+            record.msg = ""
+        elif type(record.msg) is not str:
+            record.msg = str(record.msg)
+
+        # Strip color from the message to prevent color spoofing
+        if record.msg and not getattr(record, "preserve_color", False):
+            record.msg = remove_color_codes(record.msg)
+
+        # Determine color for title
+        title = getattr(record, "title", "")
+        title_color = getattr(record, "title_color", "") or self.LEVEL_COLOR_MAP.get(
+            record.levelno, ""
+        )
+        if title and title_color:
+            title = f"{title_color + Style.BRIGHT}{title}{Style.RESET_ALL}"
+        # Make sure record.title is set, and padded with a space if not empty
+        record.title = f"{title} " if title else ""
+
+        if self.no_color:
+            return remove_color_codes(super().format(record))
+        else:
+            return super().format(record)
+
+
+class StructuredLoggingFormatter(StructuredLogHandler, logging.Formatter):
+    def __init__(self):
+        # Set up CloudLoggingFilter to add diagnostic info to the log records
+        self.cloud_logging_filter = CloudLoggingFilter()
+
+        # Init StructuredLogHandler
+        super().__init__()
+
+    def format(self, record: logging.LogRecord) -> str:
+        self.cloud_logging_filter.filter(record)
+        return super().format(record)

autogpt_platform/autogpt_libs/autogpt_libs/logging/handlers.py

@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+import json
+import logging
+
+
+class JsonFileHandler(logging.FileHandler):
+    def format(self, record: logging.LogRecord) -> str:
+        record.json_data = json.loads(record.getMessage())
+        return json.dumps(getattr(record, "json_data"), ensure_ascii=False, indent=4)
+
+    def emit(self, record: logging.LogRecord) -> None:
+        with open(self.baseFilename, "w", encoding="utf-8") as f:
+            f.write(self.format(record))

autogpt_platform/autogpt_libs/autogpt_libs/logging/utils.py

@@ -0,0 +1,27 @@
+import logging
+import re
+from typing import Any
+
+from colorama import Fore
+
+
+def remove_color_codes(s: str) -> str:
+    return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
+
+
+def fmt_kwargs(kwargs: dict) -> str:
+    return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
+
+
+def print_attribute(
+    title: str, value: Any, title_color: str = Fore.GREEN, value_color: str = ""
+) -> None:
+    logger = logging.getLogger()
+    logger.info(
+        str(value),
+        extra={
+            "title": f"{title.rstrip(':')}:",
+            "title_color": title_color,
+            "color": value_color,
+        },
+    )

autogpt_platform/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/__init__.py

@@ -0,0 +1,8 @@
+from .store import SupabaseIntegrationCredentialsStore
+from .types import APIKeyCredentials, OAuth2Credentials
+
+__all__ = [
+    "SupabaseIntegrationCredentialsStore",
+    "APIKeyCredentials",
+    "OAuth2Credentials",
+]

autogpt_platform/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/store.py

@@ -0,0 +1,180 @@
+import secrets
+from datetime import datetime, timedelta, timezone
+from typing import cast
+
+from supabase import Client
+
+from .types import (
+    Credentials,
+    OAuth2Credentials,
+    OAuthState,
+    UserMetadata,
+    UserMetadataRaw,
+)
+
+
+class SupabaseIntegrationCredentialsStore:
+    def __init__(self, supabase: Client):
+        self.supabase = supabase
+
+    def add_creds(self, user_id: str, credentials: Credentials) -> None:
+        if self.get_creds_by_id(user_id, credentials.id):
+            raise ValueError(
+                f"Can not re-create existing credentials with ID {credentials.id} "
+                f"for user with ID {user_id}"
+            )
+        self._set_user_integration_creds(
+            user_id, [*self.get_all_creds(user_id), credentials]
+        )
+
+    def get_all_creds(self, user_id: str) -> list[Credentials]:
+        user_metadata = self._get_user_metadata(user_id)
+        return UserMetadata.model_validate(user_metadata).integration_credentials
+
+    def get_creds_by_id(self, user_id: str, credentials_id: str) -> Credentials | None:
+        credentials = self.get_all_creds(user_id)
+        return next((c for c in credentials if c.id == credentials_id), None)
+
+    def get_creds_by_provider(self, user_id: str, provider: str) -> list[Credentials]:
+        credentials = self.get_all_creds(user_id)
+        return [c for c in credentials if c.provider == provider]
+
+    def get_authorized_providers(self, user_id: str) -> list[str]:
+        credentials = self.get_all_creds(user_id)
+        return list(set(c.provider for c in credentials))
+
+    def update_creds(self, user_id: str, updated: Credentials) -> None:
+        current = self.get_creds_by_id(user_id, updated.id)
+        if not current:
+            raise ValueError(
+                f"Credentials with ID {updated.id} "
+                f"for user with ID {user_id} not found"
+            )
+        if type(current) is not type(updated):
+            raise TypeError(
+                f"Can not update credentials with ID {updated.id} "
+                f"from type {type(current)} "
+                f"to type {type(updated)}"
+            )
+
+        # Ensure no scopes are removed when updating credentials
+        if (
+            isinstance(updated, OAuth2Credentials)
+            and isinstance(current, OAuth2Credentials)
+            and not set(updated.scopes).issuperset(current.scopes)
+        ):
+            raise ValueError(
+                f"Can not update credentials with ID {updated.id} "
+                f"and scopes {current.scopes} "
+                f"to more restrictive set of scopes {updated.scopes}"
+            )
+
+        # Update the credentials
+        updated_credentials_list = [
+            updated if c.id == updated.id else c for c in self.get_all_creds(user_id)
+        ]
+        self._set_user_integration_creds(user_id, updated_credentials_list)
+
+    def delete_creds_by_id(self, user_id: str, credentials_id: str) -> None:
+        filtered_credentials = [
+            c for c in self.get_all_creds(user_id) if c.id != credentials_id
+        ]
+        self._set_user_integration_creds(user_id, filtered_credentials)
+
+    async def store_state_token(
+        self, user_id: str, provider: str, scopes: list[str]
+    ) -> str:
+        token = secrets.token_urlsafe(32)
+        expires_at = datetime.now(timezone.utc) + timedelta(minutes=10)
+
+        state = OAuthState(
+            token=token,
+            provider=provider,
+            expires_at=int(expires_at.timestamp()),
+            scopes=scopes,
+        )
+
+        user_metadata = self._get_user_metadata(user_id)
+        oauth_states = user_metadata.get("integration_oauth_states", [])
+        oauth_states.append(state.model_dump())
+        user_metadata["integration_oauth_states"] = oauth_states
+
+        self.supabase.auth.admin.update_user_by_id(
+            user_id, {"user_metadata": user_metadata}
+        )
+
+        return token
+
+    async def get_any_valid_scopes_from_state_token(
+        self, user_id: str, token: str, provider: str
+    ) -> list[str]:
+        """
+        Get the valid scopes from the OAuth state token. This will return any valid scopes
+        from any OAuth state token for the given provider. If no valid scopes are found,
+        an empty list is returned. DO NOT RELY ON THIS TOKEN TO AUTHENTICATE A USER, AS IT
+        IS TO CHECK IF THE USER HAS GIVEN PERMISSIONS TO THE APPLICATION BEFORE EXCHANGING
+        THE CODE FOR TOKENS.
+        """
+        user_metadata = self._get_user_metadata(user_id)
+        oauth_states = user_metadata.get("integration_oauth_states", [])
+
+        now = datetime.now(timezone.utc)
+        valid_state = next(
+            (
+                state
+                for state in oauth_states
+                if state["token"] == token
+                and state["provider"] == provider
+                and state["expires_at"] > now.timestamp()
+            ),
+            None,
+        )
+
+        if valid_state:
+            return valid_state.get("scopes", [])
+
+        return []
+
+    async def verify_state_token(self, user_id: str, token: str, provider: str) -> bool:
+        user_metadata = self._get_user_metadata(user_id)
+        oauth_states = user_metadata.get("integration_oauth_states", [])
+
+        now = datetime.now(timezone.utc)
+        valid_state = next(
+            (
+                state
+                for state in oauth_states
+                if state["token"] == token
+                and state["provider"] == provider
+                and state["expires_at"] > now.timestamp()
+            ),
+            None,
+        )
+
+        if valid_state:
+            # Remove the used state
+            oauth_states.remove(valid_state)
+            user_metadata["integration_oauth_states"] = oauth_states
+            self.supabase.auth.admin.update_user_by_id(
+                user_id, {"user_metadata": user_metadata}
+            )
+            return True
+
+        return False
+
+    def _set_user_integration_creds(
+        self, user_id: str, credentials: list[Credentials]
+    ) -> None:
+        raw_metadata = self._get_user_metadata(user_id)
+        raw_metadata.update(
+            {"integration_credentials": [c.model_dump() for c in credentials]}
+        )
+        self.supabase.auth.admin.update_user_by_id(
+            user_id, {"user_metadata": raw_metadata}
+        )
+
+    def _get_user_metadata(self, user_id: str) -> UserMetadataRaw:
+        response = self.supabase.auth.admin.get_user_by_id(user_id)
+        if not response.user:
+            raise ValueError(f"User with ID {user_id} not found")
+        return cast(UserMetadataRaw, response.user.user_metadata)

autogpt_platform/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/types.py

@@ -0,0 +1,69 @@
+from typing import Annotated, Any, Literal, Optional, TypedDict
+from uuid import uuid4
+
+from pydantic import BaseModel, Field, SecretStr, field_serializer
+
+
+class _BaseCredentials(BaseModel):
+    id: str = Field(default_factory=lambda: str(uuid4()))
+    provider: str
+    title: Optional[str]
+
+    @field_serializer("*")
+    def dump_secret_strings(value: Any, _info):
+        if isinstance(value, SecretStr):
+            return value.get_secret_value()
+        return value
+
+
+class OAuth2Credentials(_BaseCredentials):
+    type: Literal["oauth2"] = "oauth2"
+    username: Optional[str]
+    """Username of the third-party service user that these credentials belong to"""
+    access_token: SecretStr
+    access_token_expires_at: Optional[int]
+    """Unix timestamp (seconds) indicating when the access token expires (if at all)"""
+    refresh_token: Optional[SecretStr]
+    refresh_token_expires_at: Optional[int]
+    """Unix timestamp (seconds) indicating when the refresh token expires (if at all)"""
+    scopes: list[str]
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+    def bearer(self) -> str:
+        return f"Bearer {self.access_token.get_secret_value()}"
+
+
+class APIKeyCredentials(_BaseCredentials):
+    type: Literal["api_key"] = "api_key"
+    api_key: SecretStr
+    expires_at: Optional[int]
+    """Unix timestamp (seconds) indicating when the API key expires (if at all)"""
+
+    def bearer(self) -> str:
+        return f"Bearer {self.api_key.get_secret_value()}"
+
+
+Credentials = Annotated[
+    OAuth2Credentials | APIKeyCredentials,
+    Field(discriminator="type"),
+]
+
+
+CredentialsType = Literal["api_key", "oauth2"]
+
+
+class OAuthState(BaseModel):
+    token: str
+    provider: str
+    expires_at: int
+    """Unix timestamp (seconds) indicating when this OAuth state expires"""
+
+
+class UserMetadata(BaseModel):
+    integration_credentials: list[Credentials] = Field(default_factory=list)
+    integration_oauth_states: list[OAuthState] = Field(default_factory=list)
+
+
+class UserMetadataRaw(TypedDict, total=False):
+    integration_credentials: list[dict]
+    integration_oauth_states: list[dict]

autogpt_platform/autogpt_libs/pyproject.toml

@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "autogpt-libs"
+version = "0.2.0"
+description = "Shared libraries across NextGen AutoGPT"
+authors = ["Aarushi <aarushik93@gmail.com>"]
+readme = "README.md"
+packages = [{ include = "autogpt_libs" }]
+
+[tool.poetry.dependencies]
+colorama = "^0.4.6"
+google-cloud-logging = "^3.8.0"
+pydantic = "^2.8.2"
+pydantic-settings = "^2.5.2"
+pyjwt = "^2.8.0"
+python = ">=3.10,<4.0"
+python-dotenv = "^1.0.1"
+supabase = "^2.7.2"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

autogpt_platform/backend/.env.example

@@ -0,0 +1,100 @@
+DB_USER=postgres
+DB_PASS=your-super-secret-and-long-postgres-password
+DB_NAME=postgres
+DB_PORT=5432
+DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@localhost:${DB_PORT}/${DB_NAME}?connect_timeout=60&schema=platform"
+PRISMA_SCHEMA="postgres/schema.prisma"
+
+BACKEND_CORS_ALLOW_ORIGINS=["http://localhost:3000"]
+
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_PASSWORD=password
+
+ENABLE_CREDIT=false
+APP_ENV="local"
+PYRO_HOST=localhost
+SENTRY_DSN=
+
+## User auth with Supabase is required for any of the 3rd party integrations with auth to work.
+ENABLE_AUTH=false
+SUPABASE_URL=http://localhost:8000
+SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
+SUPABASE_JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
+
+# For local development, you may need to set FRONTEND_BASE_URL for the OAuth flow for integrations to work.
+# FRONTEND_BASE_URL=http://localhost:3000
+
+## == INTEGRATION CREDENTIALS == ##
+# Each set of server side credentials is required for the corresponding 3rd party
+# integration to work.
+
+# For the OAuth callback URL, use <your_frontend_url>/auth/integrations/oauth_callback,
+# e.g. http://localhost:3000/auth/integrations/oauth_callback
+
+# GitHub OAuth App server credentials - https://github.com/settings/developers
+GITHUB_CLIENT_ID=
+GITHUB_CLIENT_SECRET=
+
+# Google OAuth App server credentials - https://console.cloud.google.com/apis/credentials, and enable gmail api and set scopes
+# https://console.cloud.google.com/apis/credentials/consent ?project=<your_project_id>
+
+# You'll need to add/enable the following scopes (minimum):
+# https://console.developers.google.com/apis/api/gmail.googleapis.com/overview ?project=<your_project_id>
+# https://console.cloud.google.com/apis/library/sheets.googleapis.com/ ?project=<your_project_id>
+GOOGLE_CLIENT_ID=
+GOOGLE_CLIENT_SECRET=
+
+## ===== OPTIONAL API KEYS ===== ##
+
+# LLM
+OPENAI_API_KEY=
+ANTHROPIC_API_KEY=
+GROQ_API_KEY=
+
+# Reddit
+REDDIT_CLIENT_ID=
+REDDIT_CLIENT_SECRET=
+REDDIT_USERNAME=
+REDDIT_PASSWORD=
+
+# Discord
+DISCORD_BOT_TOKEN=
+
+# SMTP/Email
+SMTP_SERVER=
+SMTP_PORT=
+SMTP_USERNAME=
+SMTP_PASSWORD=
+
+# D-ID
+DID_API_KEY=
+
+# Open Weather Map
+OPENWEATHERMAP_API_KEY=
+
+# SMTP
+SMTP_SERVER=
+SMTP_PORT=
+SMTP_USERNAME=
+SMTP_PASSWORD=
+
+# Medium
+MEDIUM_API_KEY=
+MEDIUM_AUTHOR_ID=
+
+# Google Maps
+GOOGLE_MAPS_API_KEY=
+
+# Replicate
+REPLICATE_API_KEY=
+
+# Ideogram
+IDEOGRAM_API_KEY=
+
+# Logging Configuration
+LOG_LEVEL=INFO
+ENABLE_CLOUD_LOGGING=false
+ENABLE_FILE_LOGGING=false
+# Use to manually set the log directory
+# LOG_DIR=./logs

autogpt_platform/backend/.gitignore

@@ -0,0 +1,8 @@
+database.db
+database.db-journal
+dev.db
+dev.db-journal
+build/
+config.json
+secrets/*
+!secrets/.gitkeep

autogpt_platform/backend/Dockerfile

@@ -0,0 +1,78 @@
+FROM python:3.11-slim-buster AS builder
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+
+WORKDIR /app
+
+# Install build dependencies
+RUN apt-get update \
+    && apt-get install -y build-essential curl ffmpeg wget libcurl4-gnutls-dev libexpat1-dev gettext libz-dev libssl-dev postgresql-client git \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV POETRY_VERSION=1.8.3 \
+    POETRY_HOME="/opt/poetry" \
+    POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_CREATE=false \
+    PATH="$POETRY_HOME/bin:$PATH"
+
+# Upgrade pip and setuptools to fix security vulnerabilities
+RUN pip3 install --upgrade pip setuptools
+
+RUN pip3 install poetry
+
+# Copy and install dependencies
+COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
+COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml /app/autogpt_platform/backend/
+WORKDIR /app/autogpt_platform/backend
+RUN poetry config virtualenvs.create false \
+    && poetry install --no-interaction --no-ansi
+
+# Generate Prisma client
+COPY autogpt_platform/backend/schema.prisma ./
+RUN poetry config virtualenvs.create false \
+    && poetry run prisma generate
+
+FROM python:3.11-slim-buster AS server_dependencies
+
+WORKDIR /app
+
+ENV POETRY_VERSION=1.8.3 \
+    POETRY_HOME="/opt/poetry" \
+    POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_CREATE=false \
+    PATH="$POETRY_HOME/bin:$PATH"
+
+
+# Upgrade pip and setuptools to fix security vulnerabilities
+RUN pip3 install --upgrade pip setuptools
+
+# Copy only necessary files from builder
+COPY --from=builder /app /app
+COPY --from=builder /usr/local/lib/python3.11 /usr/local/lib/python3.11
+COPY --from=builder /usr/local/bin /usr/local/bin
+# Copy Prisma binaries
+COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
+
+
+ENV PATH="/app/.venv/bin:$PATH"
+
+RUN mkdir -p /app/autogpt_platform/autogpt_libs
+RUN mkdir -p /app/autogpt_platform/backend
+
+COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
+
+COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml /app/autogpt_platform/backend/
+
+WORKDIR /app/autogpt_platform/backend
+
+FROM server_dependencies AS server
+
+COPY autogpt_platform/backend /app/autogpt_platform/backend
+
+ENV DATABASE_URL=""
+ENV PORT=8000
+
+CMD ["poetry", "run", "rest"]

autogpt_platform/backend/README.advanced.md

@@ -28,7 +28,13 @@ We use the Poetry to manage the dependencies. To set up the project, follow thes
    poetry install
    ```
    
-4. Generate the Prisma client
+4. Copy .env.example to .env
+
+   ```sh
+   cp .env.example .env
+   ```
+
+5. Generate the Prisma client
 
    ```sh
    poetry run prisma generate --schema postgres/schema.prisma
@@ -42,19 +48,21 @@ We use the Poetry to manage the dependencies. To set up the project, follow thes
    > ```
    >
    > Then run the generation again. The path *should* look something like this:  
-   > `<some path>/pypoetry/virtualenvs/autogpt-server-TQIRSwR6-py3.12/bin/prisma`
+   > `<some path>/pypoetry/virtualenvs/backend-TQIRSwR6-py3.12/bin/prisma`
 
-5. Run the postgres database from the /rnd folder
+6. Run the postgres database from the /rnd folder
 
    ```sh
+   cd autogpt_platform/
    docker compose up -d
-  ```
+   ```
 
-6. Run the migrations
+7. Run the migrations (from the backend folder)
 
    ```sh
+   cd ../backend
    prisma migrate dev --schema postgres/schema.prisma
-  ```
+   ```
 
 ## Running The Server
 
@@ -64,4 +72,4 @@ Run the following command:
 
 ```sh
 poetry run app
-```
+```

autogpt_platform/backend/README.md

@@ -3,6 +3,10 @@
 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:
@@ -28,8 +32,14 @@ We use the Poetry to manage the dependencies. To set up the project, follow thes
    ```sh
    poetry install
    ```
+
+4. Copy .env.example to .env
+
+   ```sh
+   cp .env.example .env
+   ```
    
-4. Generate the Prisma client
+5. Generate the Prisma client
 
    ```sh
    poetry run prisma generate
@@ -43,30 +53,63 @@ We use the Poetry to manage the dependencies. To set up the project, follow thes
    > ```
    >
    > Then run the generation again. The path *should* look something like this:  
-   > `<some path>/pypoetry/virtualenvs/autogpt-server-TQIRSwR6-py3.12/bin/prisma`
+   > `<some path>/pypoetry/virtualenvs/backend-TQIRSwR6-py3.12/bin/prisma`
 
-5. Migrate the database. Be careful because this deletes current data in the database.
+6. Migrate the database. Be careful because this deletes current data in the database.
 
    ```sh
-   poetry run prisma migrate dev
-  ```
+   docker compose up db redis -d
+   poetry run prisma migrate dev 
+   ```
 
 ## Running The Server
 
-### Starting the server directly
+### Starting the server without Docker
 
-Run the following command:
+Run the following command to build the dockerfiles:
 
 ```sh
 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 pytest
+poetry run test
 ```
 
 ## Development
@@ -140,10 +183,17 @@ A communication layer (`service.py`) is created to decouple the communication li
 
 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` (`autogpt_server.blocks`) module.
+* 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.

autogpt_platform/backend/backend/app.py

@@ -0,0 +1,40 @@
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from backend.util.process import AppProcess
+
+
+def run_processes(*processes: "AppProcess", **kwargs):
+    """
+    Execute all processes in the app. The last process is run in the foreground.
+    """
+    try:
+        for process in processes[:-1]:
+            process.start(background=True, **kwargs)
+
+        # Run the last process in the foreground
+        processes[-1].start(background=False, **kwargs)
+    finally:
+        for process in processes:
+            process.stop()
+
+
+def main(**kwargs):
+    """
+    Run all the processes required for the AutoGPT-server (REST and WebSocket APIs).
+    """
+
+    from backend.executor import ExecutionManager, ExecutionScheduler
+    from backend.server import AgentServer, WebsocketServer
+
+    run_processes(
+        ExecutionManager(),
+        ExecutionScheduler(),
+        WebsocketServer(),
+        AgentServer(),
+        **kwargs,
+    )
+
+
+if __name__ == "__main__":
+    main()

autogpt_platform/backend/backend/blocks/__init__.py

@@ -0,0 +1,74 @@
+import importlib
+import os
+import re
+from pathlib import Path
+
+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-z_.]+$", module):
+        raise ValueError(
+            f"Block module {module} error: module name must be lowercase, "
+            "separated by underscores, and contain only alphabet characters"
+        )
+
+    importlib.import_module(f".{module}", package=__name__)
+    AVAILABLE_MODULES.append(module)
+
+# Load all Block instances from the available modules
+AVAILABLE_BLOCKS = {}
+
+
+def all_subclasses(clz):
+    subclasses = clz.__subclasses__()
+    for subclass in subclasses:
+        subclasses += all_subclasses(subclass)
+    return subclasses
+
+
+for cls in all_subclasses(Block):
+    name = cls.__name__
+
+    if cls.__name__.endswith("Base"):
+        continue
+
+    if not cls.__name__.endswith("Block"):
+        raise ValueError(
+            f"Block class {cls.__name__} does not end with 'Block', If you are creating an abstract class, please name the class with 'Base' at the end"
+        )
+
+    block = cls()
+
+    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")
+
+    # Prevent duplicate field name in input_schema and output_schema
+    duplicate_field_names = set(block.input_schema.model_fields.keys()) & set(
+        block.output_schema.model_fields.keys()
+    )
+    if duplicate_field_names:
+        raise ValueError(
+            f"{block.name} has duplicate field names in input_schema and output_schema: {duplicate_field_names}"
+        )
+
+    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
+
+__all__ = ["AVAILABLE_MODULES", "AVAILABLE_BLOCKS"]

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -0,0 +1,307 @@
+import logging
+import time
+from enum import Enum
+from typing import Optional
+
+import requests
+from pydantic import Field
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class AudioTrack(str, Enum):
+    OBSERVER = ("Observer",)
+    FUTURISTIC_BEAT = ("Futuristic Beat",)
+    SCIENCE_DOCUMENTARY = ("Science Documentary",)
+    HOTLINE = ("Hotline",)
+    BLADERUNNER_2049 = ("Bladerunner 2049",)
+    A_FUTURE = ("A Future",)
+    ELYSIAN_EMBERS = ("Elysian Embers",)
+    INSPIRING_CINEMATIC = ("Inspiring Cinematic",)
+    BLADERUNNER_REMIX = ("Bladerunner Remix",)
+    IZZAMUZZIC = ("Izzamuzzic",)
+    NAS = ("Nas",)
+    PARIS_ELSE = ("Paris - Else",)
+    SNOWFALL = ("Snowfall",)
+    BURLESQUE = ("Burlesque",)
+    CORNY_CANDY = ("Corny Candy",)
+    HIGHWAY_NOCTURNE = ("Highway Nocturne",)
+    I_DONT_THINK_SO = ("I Don't Think So",)
+    LOSING_YOUR_MARBLES = ("Losing Your Marbles",)
+    REFRESHER = ("Refresher",)
+    TOURIST = ("Tourist",)
+    TWIN_TYCHES = ("Twin Tyches",)
+
+    @property
+    def audio_url(self):
+        audio_urls = {
+            AudioTrack.OBSERVER: "https://cdn.tfrv.xyz/audio/observer.mp3",
+            AudioTrack.FUTURISTIC_BEAT: "https://cdn.tfrv.xyz/audio/_futuristic-beat.mp3",
+            AudioTrack.SCIENCE_DOCUMENTARY: "https://cdn.tfrv.xyz/audio/_science-documentary.mp3",
+            AudioTrack.HOTLINE: "https://cdn.tfrv.xyz/audio/_hotline.mp3",
+            AudioTrack.BLADERUNNER_2049: "https://cdn.tfrv.xyz/audio/_bladerunner-2049.mp3",
+            AudioTrack.A_FUTURE: "https://cdn.tfrv.xyz/audio/a-future.mp3",
+            AudioTrack.ELYSIAN_EMBERS: "https://cdn.tfrv.xyz/audio/elysian-embers.mp3",
+            AudioTrack.INSPIRING_CINEMATIC: "https://cdn.tfrv.xyz/audio/inspiring-cinematic-ambient.mp3",
+            AudioTrack.BLADERUNNER_REMIX: "https://cdn.tfrv.xyz/audio/bladerunner-remix.mp3",
+            AudioTrack.IZZAMUZZIC: "https://cdn.tfrv.xyz/audio/_izzamuzzic.mp3",
+            AudioTrack.NAS: "https://cdn.tfrv.xyz/audio/_nas.mp3",
+            AudioTrack.PARIS_ELSE: "https://cdn.tfrv.xyz/audio/_paris-else.mp3",
+            AudioTrack.SNOWFALL: "https://cdn.tfrv.xyz/audio/_snowfall.mp3",
+            AudioTrack.BURLESQUE: "https://cdn.tfrv.xyz/audio/burlesque.mp3",
+            AudioTrack.CORNY_CANDY: "https://cdn.tfrv.xyz/audio/corny-candy.mp3",
+            AudioTrack.HIGHWAY_NOCTURNE: "https://cdn.tfrv.xyz/audio/highway-nocturne.mp3",
+            AudioTrack.I_DONT_THINK_SO: "https://cdn.tfrv.xyz/audio/i-dont-think-so.mp3",
+            AudioTrack.LOSING_YOUR_MARBLES: "https://cdn.tfrv.xyz/audio/losing-your-marbles.mp3",
+            AudioTrack.REFRESHER: "https://cdn.tfrv.xyz/audio/refresher.mp3",
+            AudioTrack.TOURIST: "https://cdn.tfrv.xyz/audio/tourist.mp3",
+            AudioTrack.TWIN_TYCHES: "https://cdn.tfrv.xyz/audio/twin-tynches.mp3",
+        }
+        return audio_urls[self]
+
+
+class GenerationPreset(str, Enum):
+    LEONARDO = ("Default",)
+    ANIME = ("Anime",)
+    REALISM = ("Realist",)
+    ILLUSTRATION = ("Illustration",)
+    SKETCH_COLOR = ("Sketch Color",)
+    SKETCH_BW = ("Sketch B&W",)
+    PIXAR = ("Pixar",)
+    INK = ("Japanese Ink",)
+    RENDER_3D = ("3D Render",)
+    LEGO = ("Lego",)
+    SCIFI = ("Sci-Fi",)
+    RECRO_CARTOON = ("Retro Cartoon",)
+    PIXEL_ART = ("Pixel Art",)
+    CREATIVE = ("Creative",)
+    PHOTOGRAPHY = ("Photography",)
+    RAYTRACED = ("Raytraced",)
+    ENVIRONMENT = ("Environment",)
+    FANTASY = ("Fantasy",)
+    ANIME_SR = ("Anime Realism",)
+    MOVIE = ("Movie",)
+    STYLIZED_ILLUSTRATION = ("Stylized Illustration",)
+    MANGA = ("Manga",)
+
+
+class Voice(str, Enum):
+    LILY = "Lily"
+    DANIEL = "Daniel"
+    BRIAN = "Brian"
+    JESSICA = "Jessica"
+    CHARLOTTE = "Charlotte"
+    CALLUM = "Callum"
+
+    @property
+    def voice_id(self):
+        voice_id_map = {
+            Voice.LILY: "pFZP5JQG7iQjIQuC4Bku",
+            Voice.DANIEL: "onwK4e9ZLuTAKqWW03F9",
+            Voice.BRIAN: "nPczCjzI2devNBz1zQrb",
+            Voice.JESSICA: "cgSgspJ2msm6clMCkdW9",
+            Voice.CHARLOTTE: "XB0fDUnXU5powFXDhCwa",
+            Voice.CALLUM: "N2lVS1w4EtoT3dr4eOWO",
+        }
+        return voice_id_map[self]
+
+    def __str__(self):
+        return self.value
+
+
+class VisualMediaType(str, Enum):
+    STOCK_VIDEOS = ("stockVideo",)
+    MOVING_AI_IMAGES = ("movingImage",)
+    AI_VIDEO = ("aiVideo",)
+
+
+logger = logging.getLogger(__name__)
+
+
+class AIShortformVideoCreatorBlock(Block):
+    class Input(BlockSchema):
+        api_key: BlockSecret = SecretField(
+            key="revid_api_key",
+            description="Your revid.ai API key",
+            placeholder="Enter your revid.ai API key",
+        )
+        script: str = SchemaField(
+            description="""1. Use short and punctuated sentences\n\n2. Use linebreaks to create a new clip\n\n3. Text outside of brackets is spoken by the AI, and [text between brackets] will be used to guide the visual generation. For example, [close-up of a cat] will show a close-up of a cat.""",
+            placeholder="[close-up of a cat] Meow!",
+        )
+        ratio: str = Field(description="Aspect ratio of the video", default="9 / 16")
+        resolution: str = Field(description="Resolution of the video", default="720p")
+        frame_rate: int = Field(description="Frame rate of the video", default=60)
+        generation_preset: GenerationPreset = SchemaField(
+            description="Generation preset for visual style - only effects AI generated visuals",
+            default=GenerationPreset.LEONARDO,
+            placeholder=GenerationPreset.LEONARDO,
+        )
+        background_music: AudioTrack = SchemaField(
+            description="Background music track",
+            default=AudioTrack.HIGHWAY_NOCTURNE,
+            placeholder=AudioTrack.HIGHWAY_NOCTURNE,
+        )
+        voice: Voice = SchemaField(
+            description="AI voice to use for narration",
+            default=Voice.LILY,
+            placeholder=Voice.LILY,
+        )
+        video_style: VisualMediaType = SchemaField(
+            description="Type of visual media to use for the video",
+            default=VisualMediaType.STOCK_VIDEOS,
+            placeholder=VisualMediaType.STOCK_VIDEOS,
+        )
+
+    class Output(BlockSchema):
+        video_url: str = Field(description="The URL of the created video")
+        error: Optional[str] = Field(description="Error message if the request failed")
+
+    def __init__(self):
+        super().__init__(
+            id="361697fb-0c4f-4feb-aed3-8320c88c771b",
+            description="Creates a shortform video using revid.ai",
+            categories={BlockCategory.SOCIAL, BlockCategory.AI},
+            input_schema=AIShortformVideoCreatorBlock.Input,
+            output_schema=AIShortformVideoCreatorBlock.Output,
+            test_input={
+                "api_key": "test_api_key",
+                "script": "[close-up of a cat] Meow!",
+                "ratio": "9 / 16",
+                "resolution": "720p",
+                "frame_rate": 60,
+                "generation_preset": GenerationPreset.LEONARDO,
+                "background_music": AudioTrack.HIGHWAY_NOCTURNE,
+                "voice": Voice.LILY,
+                "video_style": VisualMediaType.STOCK_VIDEOS,
+            },
+            test_output=(
+                "video_url",
+                "https://example.com/video.mp4",
+            ),
+            test_mock={
+                "create_webhook": lambda: (
+                    "test_uuid",
+                    "https://webhook.site/test_uuid",
+                ),
+                "create_video": lambda api_key, payload: {"pid": "test_pid"},
+                "wait_for_video": lambda api_key, pid, webhook_token, max_wait_time=1000: "https://example.com/video.mp4",
+            },
+        )
+
+    def create_webhook(self):
+        url = "https://webhook.site/token"
+        headers = {"Accept": "application/json", "Content-Type": "application/json"}
+        response = requests.post(url, headers=headers)
+        response.raise_for_status()
+        webhook_data = response.json()
+        return webhook_data["uuid"], f"https://webhook.site/{webhook_data['uuid']}"
+
+    def create_video(self, api_key: str, payload: dict) -> dict:
+        url = "https://www.revid.ai/api/public/v2/render"
+        headers = {"key": api_key}
+        response = requests.post(url, json=payload, headers=headers)
+        logger.debug(
+            f"API Response Status Code: {response.status_code}, Content: {response.text}"
+        )
+        response.raise_for_status()
+        return response.json()
+
+    def check_video_status(self, api_key: str, pid: str) -> dict:
+        url = f"https://www.revid.ai/api/public/v2/status?pid={pid}"
+        headers = {"key": api_key}
+        response = requests.get(url, headers=headers)
+        response.raise_for_status()
+        return response.json()
+
+    def wait_for_video(
+        self, api_key: str, pid: str, webhook_token: str, max_wait_time: int = 1000
+    ) -> str:
+        start_time = time.time()
+        while time.time() - start_time < max_wait_time:
+            status = self.check_video_status(api_key, pid)
+            logger.debug(f"Video status: {status}")
+
+            if status.get("status") == "ready" and "videoUrl" in status:
+                return status["videoUrl"]
+            elif status.get("status") == "error":
+                error_message = status.get("error", "Unknown error occurred")
+                logger.error(f"Video creation failed: {error_message}")
+                raise ValueError(f"Video creation failed: {error_message}")
+            elif status.get("status") in ["FAILED", "CANCELED"]:
+                logger.error(f"Video creation failed: {status.get('message')}")
+                raise ValueError(f"Video creation failed: {status.get('message')}")
+
+            time.sleep(10)
+
+        logger.error("Video creation timed out")
+        raise TimeoutError("Video creation timed out")
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            # Create a new Webhook.site URL
+            webhook_token, webhook_url = self.create_webhook()
+            logger.debug(f"Webhook URL: {webhook_url}")
+
+            audio_url = input_data.background_music.audio_url
+
+            payload = {
+                "frameRate": input_data.frame_rate,
+                "resolution": input_data.resolution,
+                "frameDurationMultiplier": 18,
+                "webhook": webhook_url,
+                "creationParams": {
+                    "mediaType": input_data.video_style,
+                    "captionPresetName": "Wrap 1",
+                    "selectedVoice": input_data.voice.voice_id,
+                    "hasEnhancedGeneration": True,
+                    "generationPreset": input_data.generation_preset.name,
+                    "selectedAudio": input_data.background_music,
+                    "origin": "/create",
+                    "inputText": input_data.script,
+                    "flowType": "text-to-video",
+                    "slug": "create-tiktok-video",
+                    "hasToGenerateVoice": True,
+                    "hasToTranscript": False,
+                    "hasToSearchMedia": True,
+                    "hasAvatar": False,
+                    "hasWebsiteRecorder": False,
+                    "hasTextSmallAtBottom": False,
+                    "ratio": input_data.ratio,
+                    "sourceType": "contentScraping",
+                    "selectedStoryStyle": {"value": "custom", "label": "Custom"},
+                    "hasToGenerateVideos": input_data.video_style
+                    != VisualMediaType.STOCK_VIDEOS,
+                    "audioUrl": audio_url,
+                },
+            }
+
+            logger.debug("Creating video...")
+            response = self.create_video(input_data.api_key.get_secret_value(), payload)
+            pid = response.get("pid")
+
+            if not pid:
+                logger.error(
+                    f"Failed to create video: No project ID returned. API Response: {response}"
+                )
+                yield "error", "Failed to create video: No project ID returned"
+            else:
+                logger.debug(
+                    f"Video created with project ID: {pid}. Waiting for completion..."
+                )
+                video_url = self.wait_for_video(
+                    input_data.api_key.get_secret_value(), pid, webhook_token
+                )
+                logger.debug(f"Video ready: {video_url}")
+                yield "video_url", video_url
+
+        except requests.RequestException as e:
+            logger.exception("Error creating video")
+            yield "error", f"Error creating video: {str(e)}"
+        except ValueError as e:
+            logger.exception("Error in video creation process")
+            yield "error", str(e)
+        except TimeoutError as e:
+            logger.exception("Video creation timed out")
+            yield "error", str(e)

autogpt_platform/backend/backend/blocks/basic.py

@@ -0,0 +1,445 @@
+import re
+from typing import Any, List
+
+from jinja2 import BaseLoader, Environment
+from pydantic import Field
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
+from backend.data.model import SchemaField
+from backend.util.mock import MockObject
+
+jinja = Environment(loader=BaseLoader())
+
+
+class StoreValueBlock(Block):
+    """
+    This block allows you to provide a constant value as a block, in a stateless manner.
+    The common use-case is simply pass the `input` data, it will `output` the same data.
+    The block output will be static, the output can be consumed multiple times.
+    """
+
+    class Input(BlockSchema):
+        input: Any = Field(
+            description="Trigger the block to produce the output. "
+            "The value is only used when `data` is None."
+        )
+        data: Any = Field(
+            description="The constant data to be retained in the block. "
+            "This value is passed as `output`.",
+            default=None,
+        )
+
+    class Output(BlockSchema):
+        output: Any
+
+    def __init__(self):
+        super().__init__(
+            id="1ff065e9-88e8-4358-9d82-8dc91f622ba9",
+            description="This block forwards an input value as output, allowing reuse without change.",
+            categories={BlockCategory.BASIC},
+            input_schema=StoreValueBlock.Input,
+            output_schema=StoreValueBlock.Output,
+            test_input=[
+                {"input": "Hello, World!"},
+                {"input": "Hello, World!", "data": "Existing Data"},
+            ],
+            test_output=[
+                ("output", "Hello, World!"),  # No data provided, so trigger is returned
+                ("output", "Existing Data"),  # Data is provided, so data is returned.
+            ],
+            static_output=True,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        yield "output", input_data.data or input_data.input
+
+
+class PrintToConsoleBlock(Block):
+    class Input(BlockSchema):
+        text: str
+
+    class Output(BlockSchema):
+        status: str
+
+    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 = Field(description="Dictionary to lookup from")
+        key: str | int = Field(description="Key to lookup in the dictionary")
+
+    class Output(BlockSchema):
+        output: Any = Field(description="Value found for the given key")
+        missing: Any = Field(description="Value of the input that missing the key")
+
+    def __init__(self):
+        super().__init__(
+            id="0e50422c-6dee-4145-83d6-3a5a392f65de",
+            description="Lookup the given key in the input dictionary/object/list and return the value.",
+            input_schema=FindInDictionaryBlock.Input,
+            output_schema=FindInDictionaryBlock.Output,
+            test_input=[
+                {"input": {"apple": 1, "banana": 2, "cherry": 3}, "key": "banana"},
+                {"input": {"x": 10, "y": 20, "z": 30}, "key": "w"},
+                {"input": [1, 2, 3], "key": 1},
+                {"input": [1, 2, 3], "key": 3},
+                {"input": MockObject(value="!!", key="key"), "key": "key"},
+                {"input": [{"k1": "v1"}, {"k2": "v2"}, {"k1": "v3"}], "key": "k1"},
+            ],
+            test_output=[
+                ("output", 2),
+                ("missing", {"x": 10, "y": 20, "z": 30}),
+                ("output", 2),
+                ("missing", [1, 2, 3]),
+                ("output", "key"),
+                ("output", ["v1", "v3"]),
+            ],
+            categories={BlockCategory.BASIC},
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        obj = input_data.input
+        key = input_data.key
+
+        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):
+            yield "output", obj[key]
+        elif isinstance(obj, list) and isinstance(key, str):
+            if len(obj) == 0:
+                yield "output", []
+            elif isinstance(obj[0], dict) and key in obj[0]:
+                yield "output", [item[key] for item in obj if key in item]
+            else:
+                yield "output", [getattr(val, key) for val in obj if hasattr(val, key)]
+        elif isinstance(obj, object) and isinstance(key, str) and hasattr(obj, key):
+            yield "output", getattr(obj, key)
+        else:
+            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):
+        value: Any = SchemaField(description="The value to be passed as input.")
+        name: str = SchemaField(description="The name of the input.")
+        description: str = SchemaField(
+            description="The description of the input.",
+            default="",
+            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,
+        )
+
+    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,
+        )
+
+    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.
+
+    Attributes:
+        recorded_value: The value to be recorded as output.
+        name: The name of the output.
+        description: The description of the output.
+        fmt_string: The format string to be used to format the recorded_value.
+
+    Outputs:
+        output: The formatted recorded_value if fmt_string is provided and the recorded_value
+                can be formatted, otherwise the raw recorded_value.
+
+    Behavior:
+        If fmt_string is provided and the recorded_value is of a type that can be formatted,
+        the block attempts to format the recorded_value using the fmt_string.
+        If formatting fails or no fmt_string is provided, the raw recorded_value is output.
+    """
+
+    class Input(BlockSchema):
+        value: Any = SchemaField(description="The value to be recorded as output.")
+        name: str = SchemaField(description="The name of the output.")
+        description: str = SchemaField(
+            description="The description of the output.",
+            default="",
+            advanced=True,
+        )
+        format: str = SchemaField(
+            description="The format string to be used to format the recorded_value.",
+            default="",
+            advanced=True,
+        )
+
+    class Output(BlockSchema):
+        output: Any = SchemaField(description="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,
+        )
+
+    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:
+                fmt = re.sub(r"(?<!{){[ a-zA-Z0-9_]+}", r"{\g<0>}", input_data.format)
+                template = jinja.from_string(fmt)
+                yield "output", template.render({input_data.name: input_data.value})
+            except Exception as e:
+                yield "output", f"Error: {e}, {input_data.value}"
+        else:
+            yield "output", input_data.value
+
+
+class AddToDictionaryBlock(Block):
+    class Input(BlockSchema):
+        dictionary: dict | None = SchemaField(
+            default=None,
+            description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
+            placeholder='{"key1": "value1", "key2": "value2"}',
+        )
+        key: str = SchemaField(
+            description="The key for the new entry.", placeholder="new_key"
+        )
+        value: Any = SchemaField(
+            description="The value for the new entry.", placeholder="new_value"
+        )
+
+    class Output(BlockSchema):
+        updated_dictionary: dict = SchemaField(
+            description="The dictionary with the new entry added."
+        )
+        error: str = SchemaField(description="Error message if the operation failed.")
+
+    def __init__(self):
+        super().__init__(
+            id="31d1064e-7446-4693-a7d4-65e5ca1180d1",
+            description="Adds a new key-value pair to a dictionary. If no dictionary is provided, a new one is created.",
+            categories={BlockCategory.BASIC},
+            input_schema=AddToDictionaryBlock.Input,
+            output_schema=AddToDictionaryBlock.Output,
+            test_input=[
+                {
+                    "dictionary": {"existing_key": "existing_value"},
+                    "key": "new_key",
+                    "value": "new_value",
+                },
+                {"key": "first_key", "value": "first_value"},
+            ],
+            test_output=[
+                (
+                    "updated_dictionary",
+                    {"existing_key": "existing_value", "new_key": "new_value"},
+                ),
+                ("updated_dictionary", {"first_key": "first_value"}),
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            # If no dictionary is provided, create a new one
+            if input_data.dictionary is None:
+                updated_dict = {}
+            else:
+                # Create a copy of the input dictionary to avoid modifying the original
+                updated_dict = input_data.dictionary.copy()
+
+            # Add the new key-value pair
+            updated_dict[input_data.key] = input_data.value
+
+            yield "updated_dictionary", updated_dict
+        except Exception as e:
+            yield "error", f"Failed to add entry to dictionary: {str(e)}"
+
+
+class AddToListBlock(Block):
+    class Input(BlockSchema):
+        list: List[Any] | None = SchemaField(
+            default=None,
+            description="The list to add the entry to. If not provided, a new list will be created.",
+            placeholder='[1, "string", {"key": "value"}]',
+        )
+        entry: Any = SchemaField(
+            description="The entry to add to the list. Can be of any type (string, int, dict, etc.).",
+            placeholder='{"new_key": "new_value"}',
+        )
+        position: int | None = SchemaField(
+            default=None,
+            description="The position to insert the new entry. If not provided, the entry will be appended to the end of the list.",
+            placeholder="0",
+        )
+
+    class Output(BlockSchema):
+        updated_list: List[Any] = SchemaField(
+            description="The list with the new entry added."
+        )
+        error: str = SchemaField(description="Error message if the operation failed.")
+
+    def __init__(self):
+        super().__init__(
+            id="aeb08fc1-2fc1-4141-bc8e-f758f183a822",
+            description="Adds a new entry to a list. The entry can be of any type. If no list is provided, a new one is created.",
+            categories={BlockCategory.BASIC},
+            input_schema=AddToListBlock.Input,
+            output_schema=AddToListBlock.Output,
+            test_input=[
+                {
+                    "list": [1, "string", {"existing_key": "existing_value"}],
+                    "entry": {"new_key": "new_value"},
+                    "position": 1,
+                },
+                {"entry": "first_entry"},
+                {"list": ["a", "b", "c"], "entry": "d"},
+            ],
+            test_output=[
+                (
+                    "updated_list",
+                    [
+                        1,
+                        {"new_key": "new_value"},
+                        "string",
+                        {"existing_key": "existing_value"},
+                    ],
+                ),
+                ("updated_list", ["first_entry"]),
+                ("updated_list", ["a", "b", "c", "d"]),
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            # If no list is provided, create a new one
+            if input_data.list is None:
+                updated_list = []
+            else:
+                # Create a copy of the input list to avoid modifying the original
+                updated_list = input_data.list.copy()
+
+            # Add the new entry
+            if input_data.position is None:
+                updated_list.append(input_data.entry)
+            else:
+                updated_list.insert(input_data.position, input_data.entry)
+
+            yield "updated_list", updated_list
+        except Exception as e:
+            yield "error", f"Failed to add entry to list: {str(e)}"
+
+
+class NoteBlock(Block):
+    class Input(BlockSchema):
+        text: str = SchemaField(description="The text to display in the sticky note.")
+
+    class Output(BlockSchema):
+        output: str = SchemaField(description="The text to display in the sticky note.")
+
+    def __init__(self):
+        super().__init__(
+            id="cc10ff7b-7753-4ff2-9af6-9399b1a7eddc",
+            description="This block is used to display a sticky note with the given text.",
+            categories={BlockCategory.BASIC},
+            input_schema=NoteBlock.Input,
+            output_schema=NoteBlock.Output,
+            test_input={"text": "Hello, World!"},
+            test_output=[
+                ("output", "Hello, World!"),
+            ],
+            block_type=BlockType.NOTE,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        yield "output", input_data.text

autogpt_platform/backend/backend/blocks/block.py

@@ -2,8 +2,7 @@ import os
 import re
 from typing import Type
 
-from autogpt_server.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from autogpt_server.util.test import execute_block_test
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 
 
 class BlockInstallationBlock(Block):
@@ -29,9 +28,10 @@ class BlockInstallationBlock(Block):
             categories={BlockCategory.BASIC},
             input_schema=BlockInstallationBlock.Input,
             output_schema=BlockInstallationBlock.Output,
+            disabled=True,
         )
 
-    def run(self, input_data: Input) -> BlockOutput:
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
         code = input_data.code
 
         if search := re.search(r"class (\w+)\(Block\):", code):
@@ -48,7 +48,7 @@ class BlockInstallationBlock(Block):
 
         block_dir = os.path.dirname(__file__)
         file_path = f"{block_dir}/{file_name}.py"
-        module_name = f"autogpt_server.blocks.{file_name}"
+        module_name = f"backend.blocks.{file_name}"
         with open(file_path, "w") as f:
             f.write(code)
 
@@ -56,6 +56,9 @@ class BlockInstallationBlock(Block):
             module = __import__(module_name, fromlist=[class_name])
             block_class: Type[Block] = getattr(module, class_name)
             block = block_class()
+
+            from backend.util.test import execute_block_test
+
             execute_block_test(block)
             yield "success", "Block installed successfully."
         except Exception as e:

autogpt_platform/backend/backend/blocks/branching.py

@@ -0,0 +1,102 @@
+from enum import Enum
+from typing import Any
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+
+class ComparisonOperator(Enum):
+    EQUAL = "=="
+    NOT_EQUAL = "!="
+    GREATER_THAN = ">"
+    LESS_THAN = "<"
+    GREATER_THAN_OR_EQUAL = ">="
+    LESS_THAN_OR_EQUAL = "<="
+
+
+class ConditionBlock(Block):
+    class Input(BlockSchema):
+        value1: Any = SchemaField(
+            description="Enter the first value for comparison",
+            placeholder="For example: 10 or 'hello' or True",
+        )
+        operator: ComparisonOperator = SchemaField(
+            description="Choose the comparison operator",
+            placeholder="Select an operator",
+        )
+        value2: Any = SchemaField(
+            description="Enter the second value for comparison",
+            placeholder="For example: 20 or 'world' or False",
+        )
+        yes_value: Any = SchemaField(
+            description="(Optional) Value to output if the condition is true. If not provided, value1 will be used.",
+            placeholder="Leave empty to use value1, or enter a specific value",
+            default=None,
+        )
+        no_value: Any = SchemaField(
+            description="(Optional) Value to output if the condition is false. If not provided, value1 will be used.",
+            placeholder="Leave empty to use value1, or enter a specific value",
+            default=None,
+        )
+
+    class Output(BlockSchema):
+        result: bool = SchemaField(
+            description="The result of the condition evaluation (True or False)"
+        )
+        yes_output: Any = SchemaField(
+            description="The output value if the condition is true"
+        )
+        no_output: Any = SchemaField(
+            description="The output value if the condition is false"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="715696a0-e1da-45c8-b209-c2fa9c3b0be6",
+            input_schema=ConditionBlock.Input,
+            output_schema=ConditionBlock.Output,
+            description="Handles conditional logic based on comparison operators",
+            categories={BlockCategory.LOGIC},
+            test_input={
+                "value1": 10,
+                "operator": ComparisonOperator.GREATER_THAN.value,
+                "value2": 5,
+                "yes_value": "Greater",
+                "no_value": "Not greater",
+            },
+            test_output=[
+                ("result", True),
+                ("yes_output", "Greater"),
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        value1 = input_data.value1
+        operator = input_data.operator
+        value2 = input_data.value2
+        yes_value = input_data.yes_value if input_data.yes_value is not None else value1
+        no_value = input_data.no_value if input_data.no_value is not None else value1
+
+        comparison_funcs = {
+            ComparisonOperator.EQUAL: lambda a, b: a == b,
+            ComparisonOperator.NOT_EQUAL: lambda a, b: a != b,
+            ComparisonOperator.GREATER_THAN: lambda a, b: a > b,
+            ComparisonOperator.LESS_THAN: lambda a, b: a < b,
+            ComparisonOperator.GREATER_THAN_OR_EQUAL: lambda a, b: a >= b,
+            ComparisonOperator.LESS_THAN_OR_EQUAL: lambda a, b: a <= b,
+        }
+
+        try:
+            result = comparison_funcs[operator](value1, value2)
+
+            yield "result", result
+
+            if result:
+                yield "yes_output", yes_value
+            else:
+                yield "no_output", no_value
+
+        except Exception:
+            yield "result", None
+            yield "yes_output", None
+            yield "no_output", None

autogpt_platform/backend/backend/blocks/csv.py

@@ -1,4 +1,5 @@
-from autogpt_server.data.block import Block, BlockOutput, BlockSchema
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import ContributorDetails
 
 
 class ReadCsvBlock(Block):
@@ -13,23 +14,34 @@ class ReadCsvBlock(Block):
         skip_columns: list[str] = []
 
     class Output(BlockSchema):
-        data: dict[str, str]
+        row: dict[str, str]
+        all_data: list[dict[str, str]]
 
     def __init__(self):
         super().__init__(
             id="acf7625e-d2cb-4941-bfeb-2819fc6fc015",
             input_schema=ReadCsvBlock.Input,
             output_schema=ReadCsvBlock.Output,
+            description="Reads a CSV file and outputs the data as a list of dictionaries and individual rows via rows.",
+            contributors=[ContributorDetails(name="Nicholas Tindle")],
+            categories={BlockCategory.TEXT, BlockCategory.DATA},
             test_input={
                 "contents": "a, b, c\n1,2,3\n4,5,6",
             },
             test_output=[
-                ("data", {"a": "1", "b": "2", "c": "3"}),
-                ("data", {"a": "4", "b": "5", "c": "6"}),
+                ("row", {"a": "1", "b": "2", "c": "3"}),
+                ("row", {"a": "4", "b": "5", "c": "6"}),
+                (
+                    "all_data",
+                    [
+                        {"a": "1", "b": "2", "c": "3"},
+                        {"a": "4", "b": "5", "c": "6"},
+                    ],
+                ),
             ],
         )
 
-    def run(self, input_data: Input) -> BlockOutput:
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
         import csv
         from io import StringIO
 
@@ -50,8 +62,7 @@ class ReadCsvBlock(Block):
         for _ in range(input_data.skip_rows):
             next(reader)
 
-        # join the data with the header
-        for row in reader:
+        def process_row(row):
             data = {}
             for i, value in enumerate(row):
                 if i not in input_data.skip_columns:
@@ -59,4 +70,12 @@ class ReadCsvBlock(Block):
                         data[header[i]] = value.strip() if input_data.strip else value
                     else:
                         data[str(i)] = value.strip() if input_data.strip else value
-            yield "data", data
+            return data
+
+        all_data = []
+        for row in reader:
+            processed_row = process_row(row)
+            all_data.append(processed_row)
+            yield "row", processed_row
+
+        yield "all_data", all_data

autogpt_platform/backend/backend/blocks/decoder_block.py

@@ -0,0 +1,42 @@
+import codecs
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+
+class TextDecoderBlock(Block):
+    class Input(BlockSchema):
+        text: str = SchemaField(
+            description="A string containing escaped characters to be decoded",
+            placeholder='Your entire text block with \\n and \\" escaped characters',
+        )
+
+    class Output(BlockSchema):
+        decoded_text: str = SchemaField(
+            description="The decoded text with escape sequences processed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="2570e8fe-8447-43ed-84c7-70d657923231",
+            description="Decodes a string containing escape sequences into actual text",
+            categories={BlockCategory.TEXT},
+            input_schema=TextDecoderBlock.Input,
+            output_schema=TextDecoderBlock.Output,
+            test_input={"text": """Hello\nWorld!\nThis is a \"quoted\" string."""},
+            test_output=[
+                (
+                    "decoded_text",
+                    """Hello
+World!
+This is a "quoted" string.""",
+                )
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            decoded_text = codecs.decode(input_data.text, "unicode_escape")
+            yield "decoded_text", decoded_text
+        except Exception as e:
+            yield "error", f"Error decoding text: {str(e)}"

autogpt_platform/backend/backend/blocks/discord.py

@@ -0,0 +1,219 @@
+import asyncio
+
+import aiohttp
+import discord
+from pydantic import Field
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SecretField
+
+
+class ReadDiscordMessagesBlock(Block):
+    class Input(BlockSchema):
+        discord_bot_token: BlockSecret = SecretField(
+            key="discord_bot_token", description="Discord bot token"
+        )
+        continuous_read: bool = Field(
+            description="Whether to continuously read messages", default=True
+        )
+
+    class Output(BlockSchema):
+        message_content: str = Field(description="The content of the message received")
+        channel_name: str = Field(
+            description="The name of the channel the message was received from"
+        )
+        username: str = Field(
+            description="The username of the user who sent the message"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="df06086a-d5ac-4abb-9996-2ad0acb2eff7",
+            input_schema=ReadDiscordMessagesBlock.Input,  # Assign input schema
+            output_schema=ReadDiscordMessagesBlock.Output,  # Assign output schema
+            description="Reads messages from a Discord channel using a bot token.",
+            categories={BlockCategory.SOCIAL},
+            test_input={"discord_bot_token": "test_token", "continuous_read": False},
+            test_output=[
+                (
+                    "message_content",
+                    "Hello!\n\nFile from user: example.txt\nContent: This is the content of the file.",
+                ),
+                ("channel_name", "general"),
+                ("username", "test_user"),
+            ],
+            test_mock={
+                "run_bot": lambda token: asyncio.Future()  # Create a Future object for mocking
+            },
+        )
+
+    async def run_bot(self, token: str):
+        intents = discord.Intents.default()
+        intents.message_content = True
+
+        client = discord.Client(intents=intents)
+
+        self.output_data = None
+        self.channel_name = None
+        self.username = None
+
+        @client.event
+        async def on_ready():
+            print(f"Logged in as {client.user}")
+
+        @client.event
+        async def on_message(message):
+            if message.author == client.user:
+                return
+
+            self.output_data = message.content
+            self.channel_name = message.channel.name
+            self.username = message.author.name
+
+            if message.attachments:
+                attachment = message.attachments[0]  # Process the first attachment
+                if attachment.filename.endswith((".txt", ".py")):
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(attachment.url) as response:
+                            file_content = await response.text()
+                            self.output_data += f"\n\nFile from user: {attachment.filename}\nContent: {file_content}"
+
+            await client.close()
+
+        await client.start(token)
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        while True:
+            for output_name, output_value in self.__run(input_data):
+                yield output_name, output_value
+            if not input_data.continuous_read:
+                break
+
+    def __run(self, input_data: Input) -> BlockOutput:
+        try:
+            loop = asyncio.get_event_loop()
+            future = self.run_bot(input_data.discord_bot_token.get_secret_value())
+
+            # If it's a Future (mock), set the result
+            if isinstance(future, asyncio.Future):
+                future.set_result(
+                    {
+                        "output_data": "Hello!\n\nFile from user: example.txt\nContent: This is the content of the file.",
+                        "channel_name": "general",
+                        "username": "test_user",
+                    }
+                )
+
+            result = loop.run_until_complete(future)
+
+            # For testing purposes, use the mocked result
+            if isinstance(result, dict):
+                self.output_data = result.get("output_data")
+                self.channel_name = result.get("channel_name")
+                self.username = result.get("username")
+
+            if (
+                self.output_data is None
+                or self.channel_name is None
+                or self.username is None
+            ):
+                raise ValueError("No message, channel name, or username received.")
+
+            yield "message_content", self.output_data
+            yield "channel_name", self.channel_name
+            yield "username", self.username
+
+        except discord.errors.LoginFailure as login_err:
+            raise ValueError(f"Login error occurred: {login_err}")
+        except Exception as e:
+            raise ValueError(f"An error occurred: {e}")
+
+
+class SendDiscordMessageBlock(Block):
+    class Input(BlockSchema):
+        discord_bot_token: BlockSecret = SecretField(
+            key="discord_bot_token", description="Discord bot token"
+        )
+        message_content: str = Field(description="The content of the message received")
+        channel_name: str = Field(
+            description="The name of the channel the message was received from"
+        )
+
+    class Output(BlockSchema):
+        status: str = Field(
+            description="The status of the operation (e.g., 'Message sent', 'Error')"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d0822ab5-9f8a-44a3-8971-531dd0178b6b",
+            input_schema=SendDiscordMessageBlock.Input,  # Assign input schema
+            output_schema=SendDiscordMessageBlock.Output,  # Assign output schema
+            description="Sends a message to a Discord channel using a bot token.",
+            categories={BlockCategory.SOCIAL},
+            test_input={
+                "discord_bot_token": "YOUR_DISCORD_BOT_TOKEN",
+                "channel_name": "general",
+                "message_content": "Hello, Discord!",
+            },
+            test_output=[("status", "Message sent")],
+            test_mock={
+                "send_message": lambda token, channel_name, message_content: asyncio.Future()
+            },
+        )
+
+    async def send_message(self, token: str, channel_name: str, message_content: str):
+        intents = discord.Intents.default()
+        intents.guilds = True  # Required for fetching guild/channel information
+        client = discord.Client(intents=intents)
+
+        @client.event
+        async def on_ready():
+            print(f"Logged in as {client.user}")
+            for guild in client.guilds:
+                for channel in guild.text_channels:
+                    if channel.name == channel_name:
+                        # Split message into chunks if it exceeds 2000 characters
+                        for chunk in self.chunk_message(message_content):
+                            await channel.send(chunk)
+                        self.output_data = "Message sent"
+                        await client.close()
+                        return
+
+            self.output_data = "Channel not found"
+            await client.close()
+
+        await client.start(token)
+
+    def chunk_message(self, message: str, limit: int = 2000) -> list:
+        """Splits a message into chunks not exceeding the Discord limit."""
+        return [message[i : i + limit] for i in range(0, len(message), limit)]
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            loop = asyncio.get_event_loop()
+            future = self.send_message(
+                input_data.discord_bot_token.get_secret_value(),
+                input_data.channel_name,
+                input_data.message_content,
+            )
+
+            # If it's a Future (mock), set the result
+            if isinstance(future, asyncio.Future):
+                future.set_result("Message sent")
+
+            result = loop.run_until_complete(future)
+
+            # For testing purposes, use the mocked result
+            if isinstance(result, str):
+                self.output_data = result
+
+            if self.output_data is None:
+                raise ValueError("No status message received.")
+
+            yield "status", self.output_data
+
+        except discord.errors.LoginFailure as login_err:
+            raise ValueError(f"Login error occurred: {login_err}")
+        except Exception as e:
+            raise ValueError(f"An error occurred: {e}")

autogpt_platform/backend/backend/blocks/email_block.py

@@ -0,0 +1,101 @@
+import smtplib
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class EmailCredentials(BaseModel):
+    smtp_server: str = Field(
+        default="smtp.gmail.com", description="SMTP server address"
+    )
+    smtp_port: int = Field(default=25, description="SMTP port number")
+    smtp_username: BlockSecret = SecretField(key="smtp_username")
+    smtp_password: BlockSecret = SecretField(key="smtp_password")
+
+    model_config = ConfigDict(title="Email Credentials")
+
+
+class SendEmailBlock(Block):
+    class Input(BlockSchema):
+        to_email: str = SchemaField(
+            description="Recipient email address", placeholder="recipient@example.com"
+        )
+        subject: str = SchemaField(
+            description="Subject of the email", placeholder="Enter the email subject"
+        )
+        body: str = SchemaField(
+            description="Body of the email", placeholder="Enter the email body"
+        )
+        creds: EmailCredentials = Field(
+            description="SMTP credentials",
+            default=EmailCredentials(),
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(description="Status of the email sending operation")
+        error: str = SchemaField(
+            description="Error message if the email sending failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="4335878a-394e-4e67-adf2-919877ff49ae",
+            description="This block sends an email using the provided SMTP credentials.",
+            categories={BlockCategory.OUTPUT},
+            input_schema=SendEmailBlock.Input,
+            output_schema=SendEmailBlock.Output,
+            test_input={
+                "to_email": "recipient@example.com",
+                "subject": "Test Email",
+                "body": "This is a test email.",
+                "creds": {
+                    "smtp_server": "smtp.gmail.com",
+                    "smtp_port": 25,
+                    "smtp_username": "your-email@gmail.com",
+                    "smtp_password": "your-gmail-password",
+                },
+            },
+            test_output=[("status", "Email sent successfully")],
+            test_mock={"send_email": lambda *args, **kwargs: "Email sent successfully"},
+        )
+
+    @staticmethod
+    def send_email(
+        creds: EmailCredentials, to_email: str, subject: str, body: str
+    ) -> str:
+        try:
+            smtp_server = creds.smtp_server
+            smtp_port = creds.smtp_port
+            smtp_username = creds.smtp_username.get_secret_value()
+            smtp_password = creds.smtp_password.get_secret_value()
+
+            msg = MIMEMultipart()
+            msg["From"] = smtp_username
+            msg["To"] = to_email
+            msg["Subject"] = subject
+            msg.attach(MIMEText(body, "plain"))
+
+            with smtplib.SMTP(smtp_server, smtp_port) as server:
+                server.starttls()
+                server.login(smtp_username, smtp_password)
+                server.sendmail(smtp_username, to_email, msg.as_string())
+
+            return "Email sent successfully"
+        except Exception as e:
+            return f"Failed to send email: {str(e)}"
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        status = self.send_email(
+            input_data.creds,
+            input_data.to_email,
+            input_data.subject,
+            input_data.body,
+        )
+        if "successfully" in status:
+            yield "status", status
+        else:
+            yield "error", status

autogpt_platform/backend/backend/blocks/github/_auth.py

@@ -0,0 +1,54 @@
+from typing import Literal
+
+from autogpt_libs.supabase_integration_credentials_store.types import (
+    APIKeyCredentials,
+    OAuth2Credentials,
+)
+from pydantic import SecretStr
+
+from backend.data.model import CredentialsField, CredentialsMetaInput
+from backend.util.settings import Secrets
+
+secrets = Secrets()
+GITHUB_OAUTH_IS_CONFIGURED = bool(
+    secrets.github_client_id and secrets.github_client_secret
+)
+
+GithubCredentials = APIKeyCredentials | OAuth2Credentials
+GithubCredentialsInput = CredentialsMetaInput[
+    Literal["github"],
+    Literal["api_key", "oauth2"] if GITHUB_OAUTH_IS_CONFIGURED else Literal["api_key"],
+]
+
+
+def GithubCredentialsField(scope: str) -> GithubCredentialsInput:
+    """
+    Creates a GitHub credentials input on a block.
+
+    Params:
+        scope: The authorization scope needed for the block to work. ([list of available scopes](https://docs.github.com/en/apps/oauth-apps/building-oauth-apps/scopes-for-oauth-apps#available-scopes))
+    """  # noqa
+    return CredentialsField(
+        provider="github",
+        supported_credential_types=(
+            {"api_key", "oauth2"} if GITHUB_OAUTH_IS_CONFIGURED else {"api_key"}
+        ),
+        required_scopes={scope},
+        description="The GitHub integration can be used with OAuth, "
+        "or any API key with sufficient permissions for the blocks it is used on.",
+    )
+
+
+TEST_CREDENTIALS = APIKeyCredentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="github",
+    api_key=SecretStr("mock-github-api-key"),
+    title="Mock GitHub API key",
+    expires_at=None,
+)
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.type,
+}

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

@@ -0,0 +1,683 @@
+import requests
+from typing_extensions import TypedDict
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+from ._auth import (
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    GithubCredentials,
+    GithubCredentialsField,
+    GithubCredentialsInput,
+)
+
+
+class GithubCommentBlock(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",
+        )
+        comment: str = SchemaField(
+            description="Comment to post on the issue or pull request",
+            placeholder="Enter your comment",
+        )
+
+    class Output(BlockSchema):
+        id: int = SchemaField(description="ID of the created comment")
+        url: str = SchemaField(description="URL to the comment on GitHub")
+        error: str = SchemaField(
+            description="Error message if the comment posting failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="a8db4d8d-db1c-4a25-a1b0-416a8c33602b",
+            description="This block posts a comment on a specified GitHub issue or pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubCommentBlock.Input,
+            output_schema=GithubCommentBlock.Output,
+            test_input={
+                "issue_url": "https://github.com/owner/repo/issues/1",
+                "comment": "This is a test comment.",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("id", 1337),
+                ("url", "https://github.com/owner/repo/issues/1#issuecomment-1337"),
+            ],
+            test_mock={
+                "post_comment": lambda *args, **kwargs: (
+                    1337,
+                    "https://github.com/owner/repo/issues/1#issuecomment-1337",
+                )
+            },
+        )
+
+    @staticmethod
+    def post_comment(
+        credentials: GithubCredentials, issue_url: str, body_text: str
+    ) -> tuple[int, str]:
+        if "/pull/" in issue_url:
+            api_url = (
+                issue_url.replace("github.com", "api.github.com/repos").replace(
+                    "/pull/", "/issues/"
+                )
+                + "/comments"
+            )
+        else:
+            api_url = (
+                issue_url.replace("github.com", "api.github.com/repos") + "/comments"
+            )
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"body": body_text}
+
+        response = requests.post(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        comment = response.json()
+        return comment["id"], comment["html_url"]
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            id, url = self.post_comment(
+                credentials,
+                input_data.issue_url,
+                input_data.comment,
+            )
+            yield "id", id
+            yield "url", url
+        except Exception as e:
+            yield "error", f"Failed to post comment: {str(e)}"
+
+
+class GithubMakeIssueBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+        title: str = SchemaField(
+            description="Title of the issue", placeholder="Enter the issue title"
+        )
+        body: str = SchemaField(
+            description="Body of the issue", placeholder="Enter the issue body"
+        )
+
+    class Output(BlockSchema):
+        number: int = SchemaField(description="Number of the created issue")
+        url: str = SchemaField(description="URL of the created issue")
+        error: str = SchemaField(
+            description="Error message if the issue creation failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="691dad47-f494-44c3-a1e8-05b7990f2dab",
+            description="This block creates a new issue on a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubMakeIssueBlock.Input,
+            output_schema=GithubMakeIssueBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "title": "Test Issue",
+                "body": "This is a test issue.",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("number", 1),
+                ("url", "https://github.com/owner/repo/issues/1"),
+            ],
+            test_mock={
+                "create_issue": lambda *args, **kwargs: (
+                    1,
+                    "https://github.com/owner/repo/issues/1",
+                )
+            },
+        )
+
+    @staticmethod
+    def create_issue(
+        credentials: GithubCredentials, repo_url: str, title: str, body: str
+    ) -> tuple[int, str]:
+        api_url = repo_url.replace("github.com", "api.github.com/repos") + "/issues"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"title": title, "body": body}
+
+        response = requests.post(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        issue = response.json()
+        return issue["number"], issue["html_url"]
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            number, url = self.create_issue(
+                credentials,
+                input_data.repo_url,
+                input_data.title,
+                input_data.body,
+            )
+            yield "number", number
+            yield "url", url
+        except Exception as e:
+            yield "error", f"Failed to create issue: {str(e)}"
+
+
+class GithubReadIssueBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        issue_url: str = SchemaField(
+            description="URL of the GitHub issue",
+            placeholder="https://github.com/owner/repo/issues/1",
+        )
+
+    class Output(BlockSchema):
+        title: str = SchemaField(description="Title of the issue")
+        body: str = SchemaField(description="Body of the issue")
+        user: str = SchemaField(description="User who created the issue")
+        error: str = SchemaField(
+            description="Error message if reading the issue failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="6443c75d-032a-4772-9c08-230c707c8acc",
+            description="This block reads the body, title, and user of a specified GitHub issue.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubReadIssueBlock.Input,
+            output_schema=GithubReadIssueBlock.Output,
+            test_input={
+                "issue_url": "https://github.com/owner/repo/issues/1",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("title", "Title of the issue"),
+                ("body", "This is the body of the issue."),
+                ("user", "username"),
+            ],
+            test_mock={
+                "read_issue": lambda *args, **kwargs: (
+                    "Title of the issue",
+                    "This is the body of the issue.",
+                    "username",
+                )
+            },
+        )
+
+    @staticmethod
+    def read_issue(
+        credentials: GithubCredentials, issue_url: str
+    ) -> tuple[str, str, str]:
+        api_url = issue_url.replace("github.com", "api.github.com/repos")
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        title = data.get("title", "No title found")
+        body = data.get("body", "No body content found")
+        user = data.get("user", {}).get("login", "No user found")
+
+        return title, body, user
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            title, body, user = self.read_issue(
+                credentials,
+                input_data.issue_url,
+            )
+            yield "title", title
+            yield "body", body
+            yield "user", user
+        except Exception as e:
+            yield "error", f"Failed to read issue: {str(e)}"
+
+
+class GithubListIssuesBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+
+    class Output(BlockSchema):
+        class IssueItem(TypedDict):
+            title: str
+            url: str
+
+        issue: IssueItem = SchemaField(
+            title="Issue", description="Issues with their title and URL"
+        )
+        error: str = SchemaField(description="Error message if listing issues failed")
+
+    def __init__(self):
+        super().__init__(
+            id="c215bfd7-0e57-4573-8f8c-f7d4963dcd74",
+            description="This block lists all issues for a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListIssuesBlock.Input,
+            output_schema=GithubListIssuesBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "issue",
+                    {
+                        "title": "Issue 1",
+                        "url": "https://github.com/owner/repo/issues/1",
+                    },
+                )
+            ],
+            test_mock={
+                "list_issues": lambda *args, **kwargs: [
+                    {
+                        "title": "Issue 1",
+                        "url": "https://github.com/owner/repo/issues/1",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_issues(
+        credentials: GithubCredentials, repo_url: str
+    ) -> list[Output.IssueItem]:
+        api_url = repo_url.replace("github.com", "api.github.com/repos") + "/issues"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        issues: list[GithubListIssuesBlock.Output.IssueItem] = [
+            {"title": issue["title"], "url": issue["html_url"]} for issue in data
+        ]
+
+        return issues
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            issues = self.list_issues(
+                credentials,
+                input_data.repo_url,
+            )
+            yield from (("issue", issue) for issue in issues)
+        except Exception as e:
+            yield "error", f"Failed to list issues: {str(e)}"
+
+
+class GithubAddLabelBlock(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",
+        )
+        label: str = SchemaField(
+            description="Label to add to the issue or pull request",
+            placeholder="Enter the label",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(description="Status of the label addition operation")
+        error: str = SchemaField(
+            description="Error message if the label addition failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="98bd6b77-9506-43d5-b669-6b9733c4b1f1",
+            description="This block adds a label to a specified GitHub issue or pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubAddLabelBlock.Input,
+            output_schema=GithubAddLabelBlock.Output,
+            test_input={
+                "issue_url": "https://github.com/owner/repo/issues/1",
+                "label": "bug",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Label added successfully")],
+            test_mock={"add_label": lambda *args, **kwargs: "Label added successfully"},
+        )
+
+    @staticmethod
+    def add_label(credentials: GithubCredentials, issue_url: str, label: str) -> str:
+        # Convert the provided GitHub URL to the API URL
+        if "/pull/" in issue_url:
+            api_url = (
+                issue_url.replace("github.com", "api.github.com/repos").replace(
+                    "/pull/", "/issues/"
+                )
+                + "/labels"
+            )
+        else:
+            api_url = (
+                issue_url.replace("github.com", "api.github.com/repos") + "/labels"
+            )
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"labels": [label]}
+
+        response = requests.post(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        return "Label added successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.add_label(
+                credentials,
+                input_data.issue_url,
+                input_data.label,
+            )
+            yield "status", status
+        except Exception as e:
+            yield "error", f"Failed to add label: {str(e)}"
+
+
+class GithubRemoveLabelBlock(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",
+        )
+        label: str = SchemaField(
+            description="Label to remove from the issue or pull request",
+            placeholder="Enter the label",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(description="Status of the label removal operation")
+        error: str = SchemaField(
+            description="Error message if the label removal failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="78f050c5-3e3a-48c0-9e5b-ef1ceca5589c",
+            description="This block removes a label from a specified GitHub issue or pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubRemoveLabelBlock.Input,
+            output_schema=GithubRemoveLabelBlock.Output,
+            test_input={
+                "issue_url": "https://github.com/owner/repo/issues/1",
+                "label": "bug",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Label removed successfully")],
+            test_mock={
+                "remove_label": lambda *args, **kwargs: "Label removed successfully"
+            },
+        )
+
+    @staticmethod
+    def remove_label(credentials: GithubCredentials, issue_url: str, label: str) -> str:
+        # Convert the provided GitHub URL to the API URL
+        if "/pull/" in issue_url:
+            api_url = (
+                issue_url.replace("github.com", "api.github.com/repos").replace(
+                    "/pull/", "/issues/"
+                )
+                + f"/labels/{label}"
+            )
+        else:
+            api_url = (
+                issue_url.replace("github.com", "api.github.com/repos")
+                + f"/labels/{label}"
+            )
+
+        # Log the constructed API URL for debugging
+        print(f"Constructed API URL: {api_url}")
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.delete(api_url, headers=headers)
+        response.raise_for_status()
+
+        return "Label removed successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.remove_label(
+                credentials,
+                input_data.issue_url,
+                input_data.label,
+            )
+            yield "status", status
+        except Exception as e:
+            yield "error", f"Failed to remove label: {str(e)}"
+
+
+class GithubAssignIssueBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        issue_url: str = SchemaField(
+            description="URL of the GitHub issue",
+            placeholder="https://github.com/owner/repo/issues/1",
+        )
+        assignee: str = SchemaField(
+            description="Username to assign to the issue",
+            placeholder="Enter the username",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(
+            description="Status of the issue assignment operation"
+        )
+        error: str = SchemaField(
+            description="Error message if the issue assignment failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="90507c72-b0ff-413a-886a-23bbbd66f542",
+            description="This block assigns a user to a specified GitHub issue.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubAssignIssueBlock.Input,
+            output_schema=GithubAssignIssueBlock.Output,
+            test_input={
+                "issue_url": "https://github.com/owner/repo/issues/1",
+                "assignee": "username1",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Issue assigned successfully")],
+            test_mock={
+                "assign_issue": lambda *args, **kwargs: "Issue assigned successfully"
+            },
+        )
+
+    @staticmethod
+    def assign_issue(
+        credentials: GithubCredentials,
+        issue_url: str,
+        assignee: str,
+    ) -> str:
+        # Extracting repo path and issue number from the issue URL
+        repo_path, issue_number = issue_url.replace("https://github.com/", "").split(
+            "/issues/"
+        )
+        api_url = (
+            f"https://api.github.com/repos/{repo_path}/issues/{issue_number}/assignees"
+        )
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"assignees": [assignee]}
+
+        response = requests.post(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        return "Issue assigned successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.assign_issue(
+                credentials,
+                input_data.issue_url,
+                input_data.assignee,
+            )
+            yield "status", status
+        except Exception as e:
+            yield "error", f"Failed to assign issue: {str(e)}"
+
+
+class GithubUnassignIssueBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        issue_url: str = SchemaField(
+            description="URL of the GitHub issue",
+            placeholder="https://github.com/owner/repo/issues/1",
+        )
+        assignee: str = SchemaField(
+            description="Username to unassign from the issue",
+            placeholder="Enter the username",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(
+            description="Status of the issue unassignment operation"
+        )
+        error: str = SchemaField(
+            description="Error message if the issue unassignment failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d154002a-38f4-46c2-962d-2488f2b05ece",
+            description="This block unassigns a user from a specified GitHub issue.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubUnassignIssueBlock.Input,
+            output_schema=GithubUnassignIssueBlock.Output,
+            test_input={
+                "issue_url": "https://github.com/owner/repo/issues/1",
+                "assignee": "username1",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Issue unassigned successfully")],
+            test_mock={
+                "unassign_issue": lambda *args, **kwargs: "Issue unassigned successfully"
+            },
+        )
+
+    @staticmethod
+    def unassign_issue(
+        credentials: GithubCredentials,
+        issue_url: str,
+        assignee: str,
+    ) -> str:
+        # Extracting repo path and issue number from the issue URL
+        repo_path, issue_number = issue_url.replace("https://github.com/", "").split(
+            "/issues/"
+        )
+        api_url = (
+            f"https://api.github.com/repos/{repo_path}/issues/{issue_number}/assignees"
+        )
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"assignees": [assignee]}
+
+        response = requests.delete(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        return "Issue unassigned successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.unassign_issue(
+                credentials,
+                input_data.issue_url,
+                input_data.assignee,
+            )
+            yield "status", status
+        except Exception as e:
+            yield "error", f"Failed to unassign issue: {str(e)}"

autogpt_platform/backend/backend/blocks/github/pull_requests.py

@@ -0,0 +1,596 @@
+import requests
+from typing_extensions import TypedDict
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+from ._auth import (
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    GithubCredentials,
+    GithubCredentialsField,
+    GithubCredentialsInput,
+)
+
+
+class GithubListPullRequestsBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+
+    class Output(BlockSchema):
+        class PRItem(TypedDict):
+            title: str
+            url: str
+
+        pull_request: PRItem = SchemaField(
+            title="Pull Request", description="PRs with their title and URL"
+        )
+        error: str = SchemaField(description="Error message if listing issues failed")
+
+    def __init__(self):
+        super().__init__(
+            id="ffef3c4c-6cd0-48dd-817d-459f975219f4",
+            description="This block lists all pull requests for a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListPullRequestsBlock.Input,
+            output_schema=GithubListPullRequestsBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "pull_request",
+                    {
+                        "title": "Pull request 1",
+                        "url": "https://github.com/owner/repo/pull/1",
+                    },
+                )
+            ],
+            test_mock={
+                "list_prs": lambda *args, **kwargs: [
+                    {
+                        "title": "Pull request 1",
+                        "url": "https://github.com/owner/repo/pull/1",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_prs(credentials: GithubCredentials, repo_url: str) -> list[Output.PRItem]:
+        api_url = repo_url.replace("github.com", "api.github.com/repos") + "/pulls"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        pull_requests: list[GithubListPullRequestsBlock.Output.PRItem] = [
+            {"title": pr["title"], "url": pr["html_url"]} for pr in data
+        ]
+
+        return pull_requests
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            pull_requests = self.list_prs(
+                credentials,
+                input_data.repo_url,
+            )
+            yield from (("pull_request", pr) for pr in pull_requests)
+        except Exception as e:
+            yield "error", f"Failed to list pull requests: {str(e)}"
+
+
+class GithubMakePullRequestBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+        title: str = SchemaField(
+            description="Title of the pull request",
+            placeholder="Enter the pull request title",
+        )
+        body: str = SchemaField(
+            description="Body of the pull request",
+            placeholder="Enter the pull request body",
+        )
+        head: str = SchemaField(
+            description="The name of the branch where your changes are implemented. For cross-repository pull requests in the same network, namespace head with a user like this: username:branch.",
+            placeholder="Enter the head branch",
+        )
+        base: str = SchemaField(
+            description="The name of the branch you want the changes pulled into.",
+            placeholder="Enter the base branch",
+        )
+
+    class Output(BlockSchema):
+        number: int = SchemaField(description="Number of the created pull request")
+        url: str = SchemaField(description="URL of the created pull request")
+        error: str = SchemaField(
+            description="Error message if the pull request creation failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="dfb987f8-f197-4b2e-bf19-111812afd692",
+            description="This block creates a new pull request on a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubMakePullRequestBlock.Input,
+            output_schema=GithubMakePullRequestBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "title": "Test Pull Request",
+                "body": "This is a test pull request.",
+                "head": "feature-branch",
+                "base": "main",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("number", 1),
+                ("url", "https://github.com/owner/repo/pull/1"),
+            ],
+            test_mock={
+                "create_pr": lambda *args, **kwargs: (
+                    1,
+                    "https://github.com/owner/repo/pull/1",
+                )
+            },
+        )
+
+    @staticmethod
+    def create_pr(
+        credentials: GithubCredentials,
+        repo_url: str,
+        title: str,
+        body: str,
+        head: str,
+        base: str,
+    ) -> tuple[int, str]:
+        repo_path = repo_url.replace("https://github.com/", "")
+        api_url = f"https://api.github.com/repos/{repo_path}/pulls"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"title": title, "body": body, "head": head, "base": base}
+
+        response = requests.post(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        pr_data = response.json()
+        return pr_data["number"], pr_data["html_url"]
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            number, url = self.create_pr(
+                credentials,
+                input_data.repo_url,
+                input_data.title,
+                input_data.body,
+                input_data.head,
+                input_data.base,
+            )
+            yield "number", number
+            yield "url", url
+        except requests.exceptions.HTTPError as http_err:
+            if http_err.response.status_code == 422:
+                error_details = http_err.response.json()
+                error_message = error_details.get("message", "Unknown error")
+            else:
+                error_message = str(http_err)
+            yield "error", f"Failed to create pull request: {error_message}"
+        except Exception as e:
+            yield "error", f"Failed to create pull request: {str(e)}"
+
+
+class GithubReadPullRequestBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        pr_url: str = SchemaField(
+            description="URL of the GitHub pull request",
+            placeholder="https://github.com/owner/repo/pull/1",
+        )
+        include_pr_changes: bool = SchemaField(
+            description="Whether to include the changes made in the pull request",
+            default=False,
+        )
+
+    class Output(BlockSchema):
+        title: str = SchemaField(description="Title of the pull request")
+        body: str = SchemaField(description="Body of the pull request")
+        author: str = SchemaField(description="User who created the pull request")
+        changes: str = SchemaField(description="Changes made in the pull request")
+        error: str = SchemaField(
+            description="Error message if reading the pull request failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="bf94b2a4-1a30-4600-a783-a8a44ee31301",
+            description="This block reads the body, title, user, and changes of a specified GitHub pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubReadPullRequestBlock.Input,
+            output_schema=GithubReadPullRequestBlock.Output,
+            test_input={
+                "pr_url": "https://github.com/owner/repo/pull/1",
+                "include_pr_changes": True,
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("title", "Title of the pull request"),
+                ("body", "This is the body of the pull request."),
+                ("author", "username"),
+                ("changes", "List of changes made in the pull request."),
+            ],
+            test_mock={
+                "read_pr": lambda *args, **kwargs: (
+                    "Title of the pull request",
+                    "This is the body of the pull request.",
+                    "username",
+                ),
+                "read_pr_changes": lambda *args, **kwargs: "List of changes made in the pull request.",
+            },
+        )
+
+    @staticmethod
+    def read_pr(credentials: GithubCredentials, pr_url: str) -> tuple[str, str, str]:
+        api_url = pr_url.replace("github.com", "api.github.com/repos").replace(
+            "/pull/", "/issues/"
+        )
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        title = data.get("title", "No title found")
+        body = data.get("body", "No body content found")
+        author = data.get("user", {}).get("login", "No user found")
+
+        return title, body, author
+
+    @staticmethod
+    def read_pr_changes(credentials: GithubCredentials, pr_url: str) -> str:
+        api_url = (
+            pr_url.replace("github.com", "api.github.com/repos").replace(
+                "/pull/", "/pulls/"
+            )
+            + "/files"
+        )
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        files = response.json()
+        changes = []
+        for file in files:
+            filename = file.get("filename")
+            patch = file.get("patch")
+            if filename and patch:
+                changes.append(f"File: {filename}\n{patch}")
+
+        return "\n\n".join(changes)
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            title, body, author = self.read_pr(
+                credentials,
+                input_data.pr_url,
+            )
+            yield "title", title
+            yield "body", body
+            yield "author", author
+
+            if input_data.include_pr_changes:
+                changes = self.read_pr_changes(
+                    credentials,
+                    input_data.pr_url,
+                )
+                yield "changes", changes
+        except Exception as e:
+            yield "error", f"Failed to read pull request: {str(e)}"
+
+
+class GithubAssignPRReviewerBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        pr_url: str = SchemaField(
+            description="URL of the GitHub pull request",
+            placeholder="https://github.com/owner/repo/pull/1",
+        )
+        reviewer: str = SchemaField(
+            description="Username of the reviewer to assign",
+            placeholder="Enter the reviewer's username",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(
+            description="Status of the reviewer assignment operation"
+        )
+        error: str = SchemaField(
+            description="Error message if the reviewer assignment failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="c0d22c5e-e688-43e3-ba43-d5faba7927fd",
+            description="This block assigns a reviewer to a specified GitHub pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubAssignPRReviewerBlock.Input,
+            output_schema=GithubAssignPRReviewerBlock.Output,
+            test_input={
+                "pr_url": "https://github.com/owner/repo/pull/1",
+                "reviewer": "reviewer_username",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Reviewer assigned successfully")],
+            test_mock={
+                "assign_reviewer": lambda *args, **kwargs: "Reviewer assigned successfully"
+            },
+        )
+
+    @staticmethod
+    def assign_reviewer(
+        credentials: GithubCredentials, pr_url: str, reviewer: str
+    ) -> str:
+        # Convert the PR URL to the appropriate API endpoint
+        api_url = (
+            pr_url.replace("github.com", "api.github.com/repos").replace(
+                "/pull/", "/pulls/"
+            )
+            + "/requested_reviewers"
+        )
+
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"reviewers": [reviewer]}
+
+        response = requests.post(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        return "Reviewer assigned successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.assign_reviewer(
+                credentials,
+                input_data.pr_url,
+                input_data.reviewer,
+            )
+            yield "status", status
+        except requests.exceptions.HTTPError as http_err:
+            if http_err.response.status_code == 422:
+                error_msg = (
+                    "Failed to assign reviewer: "
+                    f"The reviewer '{input_data.reviewer}' may not have permission "
+                    "or the pull request is not in a valid state. "
+                    f"Detailed error: {http_err.response.text}"
+                )
+            else:
+                error_msg = f"HTTP error: {http_err} - {http_err.response.text}"
+            yield "error", error_msg
+        except Exception as e:
+            yield "error", f"Failed to assign reviewer: {str(e)}"
+
+
+class GithubUnassignPRReviewerBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        pr_url: str = SchemaField(
+            description="URL of the GitHub pull request",
+            placeholder="https://github.com/owner/repo/pull/1",
+        )
+        reviewer: str = SchemaField(
+            description="Username of the reviewer to unassign",
+            placeholder="Enter the reviewer's username",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(
+            description="Status of the reviewer unassignment operation"
+        )
+        error: str = SchemaField(
+            description="Error message if the reviewer unassignment failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="9637945d-c602-4875-899a-9c22f8fd30de",
+            description="This block unassigns a reviewer from a specified GitHub pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubUnassignPRReviewerBlock.Input,
+            output_schema=GithubUnassignPRReviewerBlock.Output,
+            test_input={
+                "pr_url": "https://github.com/owner/repo/pull/1",
+                "reviewer": "reviewer_username",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Reviewer unassigned successfully")],
+            test_mock={
+                "unassign_reviewer": lambda *args, **kwargs: "Reviewer unassigned successfully"
+            },
+        )
+
+    @staticmethod
+    def unassign_reviewer(
+        credentials: GithubCredentials, pr_url: str, reviewer: str
+    ) -> str:
+        api_url = (
+            pr_url.replace("github.com", "api.github.com/repos").replace(
+                "/pull/", "/pulls/"
+            )
+            + "/requested_reviewers"
+        )
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+        data = {"reviewers": [reviewer]}
+
+        response = requests.delete(api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        return "Reviewer unassigned successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.unassign_reviewer(
+                credentials,
+                input_data.pr_url,
+                input_data.reviewer,
+            )
+            yield "status", status
+        except Exception as e:
+            yield "error", f"Failed to unassign reviewer: {str(e)}"
+
+
+class GithubListPRReviewersBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        pr_url: str = SchemaField(
+            description="URL of the GitHub pull request",
+            placeholder="https://github.com/owner/repo/pull/1",
+        )
+
+    class Output(BlockSchema):
+        class ReviewerItem(TypedDict):
+            username: str
+            url: str
+
+        reviewer: ReviewerItem = SchemaField(
+            title="Reviewer",
+            description="Reviewers with their username and profile URL",
+        )
+        error: str = SchemaField(
+            description="Error message if listing reviewers failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="2646956e-96d5-4754-a3df-034017e7ed96",
+            description="This block lists all reviewers for a specified GitHub pull request.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListPRReviewersBlock.Input,
+            output_schema=GithubListPRReviewersBlock.Output,
+            test_input={
+                "pr_url": "https://github.com/owner/repo/pull/1",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "reviewer",
+                    {
+                        "username": "reviewer1",
+                        "url": "https://github.com/reviewer1",
+                    },
+                )
+            ],
+            test_mock={
+                "list_reviewers": lambda *args, **kwargs: [
+                    {
+                        "username": "reviewer1",
+                        "url": "https://github.com/reviewer1",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_reviewers(
+        credentials: GithubCredentials, pr_url: str
+    ) -> list[Output.ReviewerItem]:
+        api_url = (
+            pr_url.replace("github.com", "api.github.com/repos").replace(
+                "/pull/", "/pulls/"
+            )
+            + "/requested_reviewers"
+        )
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        reviewers: list[GithubListPRReviewersBlock.Output.ReviewerItem] = [
+            {"username": reviewer["login"], "url": reviewer["html_url"]}
+            for reviewer in data.get("users", [])
+        ]
+
+        return reviewers
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            reviewers = self.list_reviewers(
+                credentials,
+                input_data.pr_url,
+            )
+            yield from (("reviewer", reviewer) for reviewer in reviewers)
+        except Exception as e:
+            yield "error", f"Failed to list reviewers: {str(e)}"

autogpt_platform/backend/backend/blocks/github/repo.py

@@ -0,0 +1,786 @@
+import base64
+
+import requests
+from typing_extensions import TypedDict
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+from ._auth import (
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    GithubCredentials,
+    GithubCredentialsField,
+    GithubCredentialsInput,
+)
+
+
+class GithubListTagsBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+
+    class Output(BlockSchema):
+        class TagItem(TypedDict):
+            name: str
+            url: str
+
+        tag: TagItem = SchemaField(
+            title="Tag", description="Tags with their name and file tree browser URL"
+        )
+        error: str = SchemaField(description="Error message if listing tags failed")
+
+    def __init__(self):
+        super().__init__(
+            id="358924e7-9a11-4d1a-a0f2-13c67fe59e2e",
+            description="This block lists all tags for a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListTagsBlock.Input,
+            output_schema=GithubListTagsBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "tag",
+                    {
+                        "name": "v1.0.0",
+                        "url": "https://github.com/owner/repo/tree/v1.0.0",
+                    },
+                )
+            ],
+            test_mock={
+                "list_tags": lambda *args, **kwargs: [
+                    {
+                        "name": "v1.0.0",
+                        "url": "https://github.com/owner/repo/tree/v1.0.0",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_tags(
+        credentials: GithubCredentials, repo_url: str
+    ) -> list[Output.TagItem]:
+        repo_path = repo_url.replace("https://github.com/", "")
+        api_url = f"https://api.github.com/repos/{repo_path}/tags"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        tags: list[GithubListTagsBlock.Output.TagItem] = [
+            {
+                "name": tag["name"],
+                "url": f"https://github.com/{repo_path}/tree/{tag['name']}",
+            }
+            for tag in data
+        ]
+
+        return tags
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            tags = self.list_tags(
+                credentials,
+                input_data.repo_url,
+            )
+            yield from (("tag", tag) for tag in tags)
+        except Exception as e:
+            yield "error", f"Failed to list tags: {str(e)}"
+
+
+class GithubListBranchesBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+
+    class Output(BlockSchema):
+        class BranchItem(TypedDict):
+            name: str
+            url: str
+
+        branch: BranchItem = SchemaField(
+            title="Branch",
+            description="Branches with their name and file tree browser URL",
+        )
+        error: str = SchemaField(description="Error message if listing branches failed")
+
+    def __init__(self):
+        super().__init__(
+            id="74243e49-2bec-4916-8bf4-db43d44aead5",
+            description="This block lists all branches for a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListBranchesBlock.Input,
+            output_schema=GithubListBranchesBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "branch",
+                    {
+                        "name": "main",
+                        "url": "https://github.com/owner/repo/tree/main",
+                    },
+                )
+            ],
+            test_mock={
+                "list_branches": lambda *args, **kwargs: [
+                    {
+                        "name": "main",
+                        "url": "https://github.com/owner/repo/tree/main",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_branches(
+        credentials: GithubCredentials, repo_url: str
+    ) -> list[Output.BranchItem]:
+        api_url = repo_url.replace("github.com", "api.github.com/repos") + "/branches"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        branches: list[GithubListBranchesBlock.Output.BranchItem] = [
+            {"name": branch["name"], "url": branch["commit"]["url"]} for branch in data
+        ]
+
+        return branches
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            branches = self.list_branches(
+                credentials,
+                input_data.repo_url,
+            )
+            yield from (("branch", branch) for branch in branches)
+        except Exception as e:
+            yield "error", f"Failed to list branches: {str(e)}"
+
+
+class GithubListDiscussionsBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+        num_discussions: int = SchemaField(
+            description="Number of discussions to fetch", default=5
+        )
+
+    class Output(BlockSchema):
+        class DiscussionItem(TypedDict):
+            title: str
+            url: str
+
+        discussion: DiscussionItem = SchemaField(
+            title="Discussion", description="Discussions with their title and URL"
+        )
+        error: str = SchemaField(
+            description="Error message if listing discussions failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3ef1a419-3d76-4e07-b761-de9dad4d51d7",
+            description="This block lists recent discussions for a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListDiscussionsBlock.Input,
+            output_schema=GithubListDiscussionsBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "num_discussions": 3,
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "discussion",
+                    {
+                        "title": "Discussion 1",
+                        "url": "https://github.com/owner/repo/discussions/1",
+                    },
+                )
+            ],
+            test_mock={
+                "list_discussions": lambda *args, **kwargs: [
+                    {
+                        "title": "Discussion 1",
+                        "url": "https://github.com/owner/repo/discussions/1",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_discussions(
+        credentials: GithubCredentials, repo_url: str, num_discussions: int
+    ) -> list[Output.DiscussionItem]:
+        repo_path = repo_url.replace("https://github.com/", "")
+        owner, repo = repo_path.split("/")
+        query = """
+        query($owner: String!, $repo: String!, $num: Int!) {
+            repository(owner: $owner, name: $repo) {
+                discussions(first: $num) {
+                    nodes {
+                        title
+                        url
+                    }
+                }
+            }
+        }
+        """
+        variables = {"owner": owner, "repo": repo, "num": num_discussions}
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.post(
+            "https://api.github.com/graphql",
+            json={"query": query, "variables": variables},
+            headers=headers,
+        )
+        response.raise_for_status()
+
+        data = response.json()
+        discussions: list[GithubListDiscussionsBlock.Output.DiscussionItem] = [
+            {"title": discussion["title"], "url": discussion["url"]}
+            for discussion in data["data"]["repository"]["discussions"]["nodes"]
+        ]
+
+        return discussions
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            discussions = self.list_discussions(
+                credentials, input_data.repo_url, input_data.num_discussions
+            )
+            yield from (("discussion", discussion) for discussion in discussions)
+        except Exception as e:
+            yield "error", f"Failed to list discussions: {str(e)}"
+
+
+class GithubListReleasesBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+
+    class Output(BlockSchema):
+        class ReleaseItem(TypedDict):
+            name: str
+            url: str
+
+        release: ReleaseItem = SchemaField(
+            title="Release",
+            description="Releases with their name and file tree browser URL",
+        )
+        error: str = SchemaField(description="Error message if listing releases failed")
+
+    def __init__(self):
+        super().__init__(
+            id="3460367a-6ba7-4645-8ce6-47b05d040b92",
+            description="This block lists all releases for a specified GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubListReleasesBlock.Input,
+            output_schema=GithubListReleasesBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "release",
+                    {
+                        "name": "v1.0.0",
+                        "url": "https://github.com/owner/repo/releases/tag/v1.0.0",
+                    },
+                )
+            ],
+            test_mock={
+                "list_releases": lambda *args, **kwargs: [
+                    {
+                        "name": "v1.0.0",
+                        "url": "https://github.com/owner/repo/releases/tag/v1.0.0",
+                    }
+                ]
+            },
+        )
+
+    @staticmethod
+    def list_releases(
+        credentials: GithubCredentials, repo_url: str
+    ) -> list[Output.ReleaseItem]:
+        repo_path = repo_url.replace("https://github.com/", "")
+        api_url = f"https://api.github.com/repos/{repo_path}/releases"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        data = response.json()
+        releases: list[GithubListReleasesBlock.Output.ReleaseItem] = [
+            {"name": release["name"], "url": release["html_url"]} for release in data
+        ]
+
+        return releases
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            releases = self.list_releases(
+                credentials,
+                input_data.repo_url,
+            )
+            yield from (("release", release) for release in releases)
+        except Exception as e:
+            yield "error", f"Failed to list releases: {str(e)}"
+
+
+class GithubReadFileBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+        file_path: str = SchemaField(
+            description="Path to the file in the repository",
+            placeholder="path/to/file",
+        )
+        branch: str = SchemaField(
+            description="Branch to read from",
+            placeholder="branch_name",
+            default="master",
+        )
+
+    class Output(BlockSchema):
+        text_content: str = SchemaField(
+            description="Content of the file (decoded as UTF-8 text)"
+        )
+        raw_content: str = SchemaField(
+            description="Raw base64-encoded content of the file"
+        )
+        size: int = SchemaField(description="The size of the file (in bytes)")
+        error: str = SchemaField(description="Error message if the file reading failed")
+
+    def __init__(self):
+        super().__init__(
+            id="87ce6c27-5752-4bbc-8e26-6da40a3dcfd3",
+            description="This block reads the content of a specified file from a GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubReadFileBlock.Input,
+            output_schema=GithubReadFileBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "file_path": "path/to/file",
+                "branch": "master",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("raw_content", "RmlsZSBjb250ZW50"),
+                ("text_content", "File content"),
+                ("size", 13),
+            ],
+            test_mock={"read_file": lambda *args, **kwargs: ("RmlsZSBjb250ZW50", 13)},
+        )
+
+    @staticmethod
+    def read_file(
+        credentials: GithubCredentials, repo_url: str, file_path: str, branch: str
+    ) -> tuple[str, int]:
+        repo_path = repo_url.replace("https://github.com/", "")
+        api_url = f"https://api.github.com/repos/{repo_path}/contents/{file_path}?ref={branch}"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        content = response.json()
+
+        if isinstance(content, list):
+            # Multiple entries of different types exist at this path
+            if not (file := next((f for f in content if f["type"] == "file"), None)):
+                raise TypeError("Not a file")
+            content = file
+
+        if content["type"] != "file":
+            raise TypeError("Not a file")
+
+        return content["content"], content["size"]
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            raw_content, size = self.read_file(
+                credentials,
+                input_data.repo_url,
+                input_data.file_path.lstrip("/"),
+                input_data.branch,
+            )
+            yield "raw_content", raw_content
+            yield "text_content", base64.b64decode(raw_content).decode("utf-8")
+            yield "size", size
+        except Exception as e:
+            yield "error", f"Failed to read file: {str(e)}"
+
+
+class GithubReadFolderBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+        folder_path: str = SchemaField(
+            description="Path to the folder in the repository",
+            placeholder="path/to/folder",
+        )
+        branch: str = SchemaField(
+            description="Branch name to read from (defaults to master)",
+            placeholder="branch_name",
+            default="master",
+        )
+
+    class Output(BlockSchema):
+        class DirEntry(TypedDict):
+            name: str
+            path: str
+
+        class FileEntry(TypedDict):
+            name: str
+            path: str
+            size: int
+
+        file: FileEntry = SchemaField(description="Files in the folder")
+        dir: DirEntry = SchemaField(description="Directories in the folder")
+        error: str = SchemaField(
+            description="Error message if reading the folder failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="1355f863-2db3-4d75-9fba-f91e8a8ca400",
+            description="This block reads the content of a specified folder from a GitHub repository.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubReadFolderBlock.Input,
+            output_schema=GithubReadFolderBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "folder_path": "path/to/folder",
+                "branch": "master",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "file",
+                    {
+                        "name": "file1.txt",
+                        "path": "path/to/folder/file1.txt",
+                        "size": 1337,
+                    },
+                ),
+                ("dir", {"name": "dir2", "path": "path/to/folder/dir2"}),
+            ],
+            test_mock={
+                "read_folder": lambda *args, **kwargs: (
+                    [
+                        {
+                            "name": "file1.txt",
+                            "path": "path/to/folder/file1.txt",
+                            "size": 1337,
+                        }
+                    ],
+                    [{"name": "dir2", "path": "path/to/folder/dir2"}],
+                )
+            },
+        )
+
+    @staticmethod
+    def read_folder(
+        credentials: GithubCredentials, repo_url: str, folder_path: str, branch: str
+    ) -> tuple[list[Output.FileEntry], list[Output.DirEntry]]:
+        repo_path = repo_url.replace("https://github.com/", "")
+        api_url = f"https://api.github.com/repos/{repo_path}/contents/{folder_path}?ref={branch}"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+
+        content = response.json()
+
+        if isinstance(content, list):
+            # Multiple entries of different types exist at this path
+            if not (dir := next((d for d in content if d["type"] == "dir"), None)):
+                raise TypeError("Not a folder")
+            content = dir
+
+        if content["type"] != "dir":
+            raise TypeError("Not a folder")
+
+        return (
+            [
+                GithubReadFolderBlock.Output.FileEntry(
+                    name=entry["name"],
+                    path=entry["path"],
+                    size=entry["size"],
+                )
+                for entry in content["entries"]
+                if entry["type"] == "file"
+            ],
+            [
+                GithubReadFolderBlock.Output.DirEntry(
+                    name=entry["name"],
+                    path=entry["path"],
+                )
+                for entry in content["entries"]
+                if entry["type"] == "dir"
+            ],
+        )
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            files, dirs = self.read_folder(
+                credentials,
+                input_data.repo_url,
+                input_data.folder_path.lstrip("/"),
+                input_data.branch,
+            )
+            yield from (("file", file) for file in files)
+            yield from (("dir", dir) for dir in dirs)
+        except Exception as e:
+            yield "error", f"Failed to read folder: {str(e)}"
+
+
+class GithubMakeBranchBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+        new_branch: str = SchemaField(
+            description="Name of the new branch",
+            placeholder="new_branch_name",
+        )
+        source_branch: str = SchemaField(
+            description="Name of the source branch",
+            placeholder="source_branch_name",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(description="Status of the branch creation operation")
+        error: str = SchemaField(
+            description="Error message if the branch creation failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="944cc076-95e7-4d1b-b6b6-b15d8ee5448d",
+            description="This block creates a new branch from a specified source branch.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubMakeBranchBlock.Input,
+            output_schema=GithubMakeBranchBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "new_branch": "new_branch_name",
+                "source_branch": "source_branch_name",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Branch created successfully")],
+            test_mock={
+                "create_branch": lambda *args, **kwargs: "Branch created successfully"
+            },
+        )
+
+    @staticmethod
+    def create_branch(
+        credentials: GithubCredentials,
+        repo_url: str,
+        new_branch: str,
+        source_branch: str,
+    ) -> str:
+        repo_path = repo_url.replace("https://github.com/", "")
+        ref_api_url = (
+            f"https://api.github.com/repos/{repo_path}/git/refs/heads/{source_branch}"
+        )
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.get(ref_api_url, headers=headers)
+        response.raise_for_status()
+
+        sha = response.json()["object"]["sha"]
+
+        create_branch_api_url = f"https://api.github.com/repos/{repo_path}/git/refs"
+        data = {"ref": f"refs/heads/{new_branch}", "sha": sha}
+
+        response = requests.post(create_branch_api_url, headers=headers, json=data)
+        response.raise_for_status()
+
+        return "Branch created successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.create_branch(
+                credentials,
+                input_data.repo_url,
+                input_data.new_branch,
+                input_data.source_branch,
+            )
+            yield "status", status
+        except Exception as e:
+            yield "error", f"Failed to create branch: {str(e)}"
+
+
+class GithubDeleteBranchBlock(Block):
+    class Input(BlockSchema):
+        credentials: GithubCredentialsInput = GithubCredentialsField("repo")
+        repo_url: str = SchemaField(
+            description="URL of the GitHub repository",
+            placeholder="https://github.com/owner/repo",
+        )
+        branch: str = SchemaField(
+            description="Name of the branch to delete",
+            placeholder="branch_name",
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(description="Status of the branch deletion operation")
+        error: str = SchemaField(
+            description="Error message if the branch deletion failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="0d4130f7-e0ab-4d55-adc3-0a40225e80f4",
+            description="This block deletes a specified branch.",
+            categories={BlockCategory.DEVELOPER_TOOLS},
+            input_schema=GithubDeleteBranchBlock.Input,
+            output_schema=GithubDeleteBranchBlock.Output,
+            test_input={
+                "repo_url": "https://github.com/owner/repo",
+                "branch": "branch_name",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("status", "Branch deleted successfully")],
+            test_mock={
+                "delete_branch": lambda *args, **kwargs: "Branch deleted successfully"
+            },
+        )
+
+    @staticmethod
+    def delete_branch(
+        credentials: GithubCredentials, repo_url: str, branch: str
+    ) -> str:
+        repo_path = repo_url.replace("https://github.com/", "")
+        api_url = f"https://api.github.com/repos/{repo_path}/git/refs/heads/{branch}"
+        headers = {
+            "Authorization": credentials.bearer(),
+            "Accept": "application/vnd.github.v3+json",
+        }
+
+        response = requests.delete(api_url, headers=headers)
+        response.raise_for_status()
+
+        return "Branch deleted successfully"
+
+    def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: GithubCredentials,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            status = self.delete_branch(
+                credentials,
+                input_data.repo_url,
+                input_data.branch,
+            )
+            yield "status", status
+        except Exception as e:
+            yield "error", f"Failed to delete branch: {str(e)}"

autogpt_platform/backend/backend/blocks/google/_auth.py

@@ -0,0 +1,53 @@
+from typing import Literal
+
+from autogpt_libs.supabase_integration_credentials_store.types import OAuth2Credentials
+from pydantic import SecretStr
+
+from backend.data.model import CredentialsField, CredentialsMetaInput
+from backend.util.settings import Secrets
+
+secrets = Secrets()
+GOOGLE_OAUTH_IS_CONFIGURED = bool(
+    secrets.google_client_id and secrets.google_client_secret
+)
+
+GoogleCredentials = OAuth2Credentials
+GoogleCredentialsInput = CredentialsMetaInput[Literal["google"], Literal["oauth2"]]
+
+
+def GoogleCredentialsField(scopes: list[str]) -> GoogleCredentialsInput:
+    """
+    Creates a Google credentials input on a block.
+
+    Params:
+        scopes: The authorization scopes needed for the block to work.
+    """
+    return CredentialsField(
+        provider="google",
+        supported_credential_types={"oauth2"},
+        required_scopes=set(scopes),
+        description="The Google integration requires OAuth2 authentication.",
+    )
+
+
+TEST_CREDENTIALS = OAuth2Credentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="google",
+    access_token=SecretStr("mock-google-access-token"),
+    refresh_token=SecretStr("mock-google-refresh-token"),
+    access_token_expires_at=1234567890,
+    scopes=[
+        "https://www.googleapis.com/auth/gmail.readonly",
+        "https://www.googleapis.com/auth/gmail.send",
+    ],
+    title="Mock Google OAuth2 Credentials",
+    username="mock-google-username",
+    refresh_token_expires_at=1234567890,
+)
+
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.title,
+}

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

@@ -0,0 +1,522 @@
+import base64
+from email.utils import parseaddr
+from typing import List
+
+from google.oauth2.credentials import Credentials
+from googleapiclient.discovery import build
+from pydantic import BaseModel
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+from ._auth import (
+    GOOGLE_OAUTH_IS_CONFIGURED,
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    GoogleCredentials,
+    GoogleCredentialsField,
+    GoogleCredentialsInput,
+)
+
+
+class Attachment(BaseModel):
+    filename: str
+    content_type: str
+    size: int
+    attachment_id: str
+
+
+class Email(BaseModel):
+    id: str
+    subject: str
+    snippet: str
+    from_: str
+    to: str
+    date: str
+    body: str = ""  # Default to an empty string
+    sizeEstimate: int
+    attachments: List[Attachment]
+
+
+class GmailReadBlock(Block):
+    class Input(BlockSchema):
+        credentials: GoogleCredentialsInput = GoogleCredentialsField(
+            ["https://www.googleapis.com/auth/gmail.readonly"]
+        )
+        query: str = SchemaField(
+            description="Search query for reading emails",
+            default="is:unread",
+        )
+        max_results: int = SchemaField(
+            description="Maximum number of emails to retrieve",
+            default=10,
+        )
+
+    class Output(BlockSchema):
+        email: Email = SchemaField(
+            description="Email data",
+        )
+        emails: list[Email] = SchemaField(
+            description="List of email data",
+        )
+        error: str = SchemaField(
+            description="Error message if any",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="25310c70-b89b-43ba-b25c-4dfa7e2a481c",
+            description="This block reads emails from Gmail.",
+            categories={BlockCategory.COMMUNICATION},
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
+            input_schema=GmailReadBlock.Input,
+            output_schema=GmailReadBlock.Output,
+            test_input={
+                "query": "is:unread",
+                "max_results": 5,
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "result",
+                    [
+                        {
+                            "id": "1",
+                            "subject": "Test Email",
+                            "snippet": "This is a test email",
+                        }
+                    ],
+                ),
+            ],
+            test_mock={
+                "_read_emails": lambda *args, **kwargs: [
+                    {
+                        "id": "1",
+                        "subject": "Test Email",
+                        "snippet": "This is a test email",
+                    }
+                ],
+                "_send_email": lambda *args, **kwargs: {"id": "1", "status": "sent"},
+            },
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            service = self._build_service(credentials, **kwargs)
+            messages = self._read_emails(
+                service, input_data.query, input_data.max_results
+            )
+            for email in messages:
+                yield "email", email
+            yield "emails", messages
+        except Exception as e:
+            yield "error", str(e)
+
+    @staticmethod
+    def _build_service(credentials: GoogleCredentials, **kwargs):
+        creds = Credentials(
+            token=(
+                credentials.access_token.get_secret_value()
+                if credentials.access_token
+                else None
+            ),
+            refresh_token=(
+                credentials.refresh_token.get_secret_value()
+                if credentials.refresh_token
+                else None
+            ),
+            token_uri="https://oauth2.googleapis.com/token",
+            client_id=kwargs.get("client_id"),
+            client_secret=kwargs.get("client_secret"),
+            scopes=credentials.scopes,
+        )
+        return build("gmail", "v1", credentials=creds)
+
+    def _read_emails(
+        self, service, query: str | None, max_results: int | None
+    ) -> list[Email]:
+        results = (
+            service.users()
+            .messages()
+            .list(userId="me", q=query or "", maxResults=max_results or 10)
+            .execute()
+        )
+        messages = results.get("messages", [])
+
+        email_data = []
+        for message in messages:
+            msg = (
+                service.users()
+                .messages()
+                .get(userId="me", id=message["id"], format="full")
+                .execute()
+            )
+
+            headers = {
+                header["name"].lower(): header["value"]
+                for header in msg["payload"]["headers"]
+            }
+
+            attachments = self._get_attachments(service, msg)
+
+            email = Email(
+                id=msg["id"],
+                subject=headers.get("subject", "No Subject"),
+                snippet=msg["snippet"],
+                from_=parseaddr(headers.get("from", ""))[1],
+                to=parseaddr(headers.get("to", ""))[1],
+                date=headers.get("date", ""),
+                body=self._get_email_body(msg),
+                sizeEstimate=msg["sizeEstimate"],
+                attachments=attachments,
+            )
+            email_data.append(email)
+
+        return email_data
+
+    def _get_email_body(self, msg):
+        if "parts" in msg["payload"]:
+            for part in msg["payload"]["parts"]:
+                if part["mimeType"] == "text/plain":
+                    return base64.urlsafe_b64decode(part["body"]["data"]).decode(
+                        "utf-8"
+                    )
+        elif msg["payload"]["mimeType"] == "text/plain":
+            return base64.urlsafe_b64decode(msg["payload"]["body"]["data"]).decode(
+                "utf-8"
+            )
+
+        return "This email does not contain a text body."
+
+    def _get_attachments(self, service, message):
+        attachments = []
+        if "parts" in message["payload"]:
+            for part in message["payload"]["parts"]:
+                if part["filename"]:
+                    attachment = Attachment(
+                        filename=part["filename"],
+                        content_type=part["mimeType"],
+                        size=int(part["body"].get("size", 0)),
+                        attachment_id=part["body"]["attachmentId"],
+                    )
+                    attachments.append(attachment)
+        return attachments
+
+    # Add a new method to download attachment content
+    def download_attachment(self, service, message_id: str, attachment_id: str):
+        attachment = (
+            service.users()
+            .messages()
+            .attachments()
+            .get(userId="me", messageId=message_id, id=attachment_id)
+            .execute()
+        )
+        file_data = base64.urlsafe_b64decode(attachment["data"].encode("UTF-8"))
+        return file_data
+
+
+class GmailSendBlock(Block):
+    class Input(BlockSchema):
+        credentials: GoogleCredentialsInput = GoogleCredentialsField(
+            ["https://www.googleapis.com/auth/gmail.send"]
+        )
+        to: str = SchemaField(
+            description="Recipient email address",
+        )
+        subject: str = SchemaField(
+            description="Email subject",
+        )
+        body: str = SchemaField(
+            description="Email body",
+        )
+
+    class Output(BlockSchema):
+        result: dict = SchemaField(
+            description="Send confirmation",
+        )
+        error: str = SchemaField(
+            description="Error message if any",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="6c27abc2-e51d-499e-a85f-5a0041ba94f0",
+            description="This block sends an email using Gmail.",
+            categories={BlockCategory.COMMUNICATION},
+            input_schema=GmailSendBlock.Input,
+            output_schema=GmailSendBlock.Output,
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
+            test_input={
+                "to": "recipient@example.com",
+                "subject": "Test Email",
+                "body": "This is a test email sent from GmailSendBlock.",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("result", {"id": "1", "status": "sent"}),
+            ],
+            test_mock={
+                "_send_email": lambda *args, **kwargs: {"id": "1", "status": "sent"},
+            },
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            service = GmailReadBlock._build_service(credentials, **kwargs)
+            send_result = self._send_email(
+                service, input_data.to, input_data.subject, input_data.body
+            )
+            yield "result", send_result
+        except Exception as e:
+            yield "error", str(e)
+
+    def _send_email(self, service, to: str, subject: str, body: str) -> dict:
+        if not to or not subject or not body:
+            raise ValueError("To, subject, and body are required for sending an email")
+        message = self._create_message(to, subject, body)
+        sent_message = (
+            service.users().messages().send(userId="me", body=message).execute()
+        )
+        return {"id": sent_message["id"], "status": "sent"}
+
+    def _create_message(self, to: str, subject: str, body: str) -> dict:
+        import base64
+        from email.mime.text import MIMEText
+
+        message = MIMEText(body)
+        message["to"] = to
+        message["subject"] = subject
+        raw_message = base64.urlsafe_b64encode(message.as_bytes()).decode("utf-8")
+        return {"raw": raw_message}
+
+
+class GmailListLabelsBlock(Block):
+    class Input(BlockSchema):
+        credentials: GoogleCredentialsInput = GoogleCredentialsField(
+            ["https://www.googleapis.com/auth/gmail.labels"]
+        )
+
+    class Output(BlockSchema):
+        result: list[dict] = SchemaField(
+            description="List of labels",
+        )
+        error: str = SchemaField(
+            description="Error message if any",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3e1c2c1c-c689-4520-b956-1f3bf4e02bb7",
+            description="This block lists all labels in Gmail.",
+            categories={BlockCategory.COMMUNICATION},
+            input_schema=GmailListLabelsBlock.Input,
+            output_schema=GmailListLabelsBlock.Output,
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
+            test_input={
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "result",
+                    [
+                        {"id": "Label_1", "name": "Important"},
+                        {"id": "Label_2", "name": "Work"},
+                    ],
+                ),
+            ],
+            test_mock={
+                "_list_labels": lambda *args, **kwargs: [
+                    {"id": "Label_1", "name": "Important"},
+                    {"id": "Label_2", "name": "Work"},
+                ],
+            },
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            service = GmailReadBlock._build_service(credentials, **kwargs)
+            labels = self._list_labels(service)
+            yield "result", labels
+        except Exception as e:
+            yield "error", str(e)
+
+    def _list_labels(self, service) -> list[dict]:
+        results = service.users().labels().list(userId="me").execute()
+        labels = results.get("labels", [])
+        return [{"id": label["id"], "name": label["name"]} for label in labels]
+
+
+class GmailAddLabelBlock(Block):
+    class Input(BlockSchema):
+        credentials: GoogleCredentialsInput = GoogleCredentialsField(
+            ["https://www.googleapis.com/auth/gmail.modify"]
+        )
+        message_id: str = SchemaField(
+            description="Message ID to add label to",
+        )
+        label_name: str = SchemaField(
+            description="Label name to add",
+        )
+
+    class Output(BlockSchema):
+        result: dict = SchemaField(
+            description="Label addition result",
+        )
+        error: str = SchemaField(
+            description="Error message if any",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="f884b2fb-04f4-4265-9658-14f433926ac9",
+            description="This block adds a label to a Gmail message.",
+            categories={BlockCategory.COMMUNICATION},
+            input_schema=GmailAddLabelBlock.Input,
+            output_schema=GmailAddLabelBlock.Output,
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
+            test_input={
+                "message_id": "12345",
+                "label_name": "Important",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "result",
+                    {"status": "Label added successfully", "label_id": "Label_1"},
+                ),
+            ],
+            test_mock={
+                "_add_label": lambda *args, **kwargs: {
+                    "status": "Label added successfully",
+                    "label_id": "Label_1",
+                },
+            },
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            service = GmailReadBlock._build_service(credentials, **kwargs)
+            result = self._add_label(
+                service, input_data.message_id, input_data.label_name
+            )
+            yield "result", result
+        except Exception as e:
+            yield "error", str(e)
+
+    def _add_label(self, service, message_id: str, label_name: str) -> dict:
+        label_id = self._get_or_create_label(service, label_name)
+        service.users().messages().modify(
+            userId="me", id=message_id, body={"addLabelIds": [label_id]}
+        ).execute()
+        return {"status": "Label added successfully", "label_id": label_id}
+
+    def _get_or_create_label(self, service, label_name: str) -> str:
+        label_id = self._get_label_id(service, label_name)
+        if not label_id:
+            label = (
+                service.users()
+                .labels()
+                .create(userId="me", body={"name": label_name})
+                .execute()
+            )
+            label_id = label["id"]
+        return label_id
+
+    def _get_label_id(self, service, label_name: str) -> str | None:
+        results = service.users().labels().list(userId="me").execute()
+        labels = results.get("labels", [])
+        for label in labels:
+            if label["name"] == label_name:
+                return label["id"]
+        return None
+
+
+class GmailRemoveLabelBlock(Block):
+    class Input(BlockSchema):
+        credentials: GoogleCredentialsInput = GoogleCredentialsField(
+            ["https://www.googleapis.com/auth/gmail.modify"]
+        )
+        message_id: str = SchemaField(
+            description="Message ID to remove label from",
+        )
+        label_name: str = SchemaField(
+            description="Label name to remove",
+        )
+
+    class Output(BlockSchema):
+        result: dict = SchemaField(
+            description="Label removal result",
+        )
+        error: str = SchemaField(
+            description="Error message if any",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="0afc0526-aba1-4b2b-888e-a22b7c3f359d",
+            description="This block removes a label from a Gmail message.",
+            categories={BlockCategory.COMMUNICATION},
+            input_schema=GmailRemoveLabelBlock.Input,
+            output_schema=GmailRemoveLabelBlock.Output,
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
+            test_input={
+                "message_id": "12345",
+                "label_name": "Important",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "result",
+                    {"status": "Label removed successfully", "label_id": "Label_1"},
+                ),
+            ],
+            test_mock={
+                "_remove_label": lambda *args, **kwargs: {
+                    "status": "Label removed successfully",
+                    "label_id": "Label_1",
+                },
+            },
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            service = GmailReadBlock._build_service(credentials, **kwargs)
+            result = self._remove_label(
+                service, input_data.message_id, input_data.label_name
+            )
+            yield "result", result
+        except Exception as e:
+            yield "error", str(e)
+
+    def _remove_label(self, service, message_id: str, label_name: str) -> dict:
+        label_id = self._get_label_id(service, label_name)
+        if label_id:
+            service.users().messages().modify(
+                userId="me", id=message_id, body={"removeLabelIds": [label_id]}
+            ).execute()
+            return {"status": "Label removed successfully", "label_id": label_id}
+        else:
+            return {"status": "Label not found", "label_name": label_name}
+
+    def _get_label_id(self, service, label_name: str) -> str | None:
+        results = service.users().labels().list(userId="me").execute()
+        labels = results.get("labels", [])
+        for label in labels:
+            if label["name"] == label_name:
+                return label["id"]
+        return None

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

@@ -0,0 +1,192 @@
+from google.oauth2.credentials import Credentials
+from googleapiclient.discovery import build
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+from ._auth import (
+    GOOGLE_OAUTH_IS_CONFIGURED,
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    GoogleCredentials,
+    GoogleCredentialsField,
+    GoogleCredentialsInput,
+)
+
+
+class GoogleSheetsReadBlock(Block):
+    class Input(BlockSchema):
+        credentials: GoogleCredentialsInput = GoogleCredentialsField(
+            ["https://www.googleapis.com/auth/spreadsheets.readonly"]
+        )
+        spreadsheet_id: str = SchemaField(
+            description="The ID of the spreadsheet to read from",
+        )
+        range: str = SchemaField(
+            description="The A1 notation of the range to read",
+        )
+
+    class Output(BlockSchema):
+        result: list[list[str]] = SchemaField(
+            description="The data read from the spreadsheet",
+        )
+        error: str = SchemaField(
+            description="Error message if any",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="5724e902-3635-47e9-a108-aaa0263a4988",
+            description="This block reads data from a Google Sheets spreadsheet.",
+            categories={BlockCategory.DATA},
+            input_schema=GoogleSheetsReadBlock.Input,
+            output_schema=GoogleSheetsReadBlock.Output,
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
+            test_input={
+                "spreadsheet_id": "1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms",
+                "range": "Sheet1!A1:B2",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "result",
+                    [
+                        ["Name", "Score"],
+                        ["Alice", "85"],
+                    ],
+                ),
+            ],
+            test_mock={
+                "_read_sheet": lambda *args, **kwargs: [
+                    ["Name", "Score"],
+                    ["Alice", "85"],
+                ],
+            },
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            service = self._build_service(credentials, **kwargs)
+            data = self._read_sheet(
+                service, input_data.spreadsheet_id, input_data.range
+            )
+            yield "result", data
+        except Exception as e:
+            yield "error", str(e)
+
+    @staticmethod
+    def _build_service(credentials: GoogleCredentials, **kwargs):
+        creds = Credentials(
+            token=(
+                credentials.access_token.get_secret_value()
+                if credentials.access_token
+                else None
+            ),
+            refresh_token=(
+                credentials.refresh_token.get_secret_value()
+                if credentials.refresh_token
+                else None
+            ),
+            token_uri="https://oauth2.googleapis.com/token",
+            client_id=kwargs.get("client_id"),
+            client_secret=kwargs.get("client_secret"),
+            scopes=credentials.scopes,
+        )
+        return build("sheets", "v4", credentials=creds)
+
+    def _read_sheet(self, service, spreadsheet_id: str, range: str) -> list[list[str]]:
+        sheet = service.spreadsheets()
+        result = sheet.values().get(spreadsheetId=spreadsheet_id, range=range).execute()
+        return result.get("values", [])
+
+
+class GoogleSheetsWriteBlock(Block):
+    class Input(BlockSchema):
+        credentials: GoogleCredentialsInput = GoogleCredentialsField(
+            ["https://www.googleapis.com/auth/spreadsheets"]
+        )
+        spreadsheet_id: str = SchemaField(
+            description="The ID of the spreadsheet to write to",
+        )
+        range: str = SchemaField(
+            description="The A1 notation of the range to write",
+        )
+        values: list[list[str]] = SchemaField(
+            description="The data to write to the spreadsheet",
+        )
+
+    class Output(BlockSchema):
+        result: dict = SchemaField(
+            description="The result of the write operation",
+        )
+        error: str = SchemaField(
+            description="Error message if any",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d9291e87-301d-47a8-91fe-907fb55460e5",
+            description="This block writes data to a Google Sheets spreadsheet.",
+            categories={BlockCategory.DATA},
+            input_schema=GoogleSheetsWriteBlock.Input,
+            output_schema=GoogleSheetsWriteBlock.Output,
+            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
+            test_input={
+                "spreadsheet_id": "1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms",
+                "range": "Sheet1!A1:B2",
+                "values": [
+                    ["Name", "Score"],
+                    ["Bob", "90"],
+                ],
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "result",
+                    {"updatedCells": 4, "updatedColumns": 2, "updatedRows": 2},
+                ),
+            ],
+            test_mock={
+                "_write_sheet": lambda *args, **kwargs: {
+                    "updatedCells": 4,
+                    "updatedColumns": 2,
+                    "updatedRows": 2,
+                },
+            },
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: GoogleCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            service = GoogleSheetsReadBlock._build_service(credentials, **kwargs)
+            result = self._write_sheet(
+                service,
+                input_data.spreadsheet_id,
+                input_data.range,
+                input_data.values,
+            )
+            yield "result", result
+        except Exception as e:
+            yield "error", str(e)
+
+    def _write_sheet(
+        self, service, spreadsheet_id: str, range: str, values: list[list[str]]
+    ) -> dict:
+        body = {"values": values}
+        result = (
+            service.spreadsheets()
+            .values()
+            .update(
+                spreadsheetId=spreadsheet_id,
+                range=range,
+                valueInputOption="USER_ENTERED",
+                body=body,
+            )
+            .execute()
+        )
+        return result

autogpt_platform/backend/backend/blocks/google_maps.py

@@ -0,0 +1,127 @@
+import googlemaps
+from pydantic import BaseModel
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class Place(BaseModel):
+    name: str
+    address: str
+    phone: str
+    rating: float
+    reviews: int
+    website: str
+
+
+class GoogleMapsSearchBlock(Block):
+    class Input(BlockSchema):
+        api_key: BlockSecret = SecretField(
+            key="google_maps_api_key",
+            description="Google Maps API Key",
+        )
+        query: str = SchemaField(
+            description="Search query for local businesses",
+            placeholder="e.g., 'restaurants in New York'",
+        )
+        radius: int = SchemaField(
+            description="Search radius in meters (max 50000)",
+            default=5000,
+            ge=1,
+            le=50000,
+        )
+        max_results: int = SchemaField(
+            description="Maximum number of results to return (max 60)",
+            default=20,
+            ge=1,
+            le=60,
+        )
+
+    class Output(BlockSchema):
+        place: Place = SchemaField(description="Place found")
+        error: str = SchemaField(description="Error message if the search failed")
+
+    def __init__(self):
+        super().__init__(
+            id="f47ac10b-58cc-4372-a567-0e02b2c3d479",
+            description="This block searches for local businesses using Google Maps API.",
+            categories={BlockCategory.SEARCH},
+            input_schema=GoogleMapsSearchBlock.Input,
+            output_schema=GoogleMapsSearchBlock.Output,
+            test_input={
+                "api_key": "your_test_api_key",
+                "query": "restaurants in new york",
+                "radius": 5000,
+                "max_results": 5,
+            },
+            test_output=[
+                (
+                    "place",
+                    {
+                        "name": "Test Restaurant",
+                        "address": "123 Test St, New York, NY 10001",
+                        "phone": "+1 (555) 123-4567",
+                        "rating": 4.5,
+                        "reviews": 100,
+                        "website": "https://testrestaurant.com",
+                    },
+                ),
+            ],
+            test_mock={
+                "search_places": lambda *args, **kwargs: [
+                    {
+                        "name": "Test Restaurant",
+                        "address": "123 Test St, New York, NY 10001",
+                        "phone": "+1 (555) 123-4567",
+                        "rating": 4.5,
+                        "reviews": 100,
+                        "website": "https://testrestaurant.com",
+                    }
+                ]
+            },
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            places = self.search_places(
+                input_data.api_key.get_secret_value(),
+                input_data.query,
+                input_data.radius,
+                input_data.max_results,
+            )
+            for place in places:
+                yield "place", place
+        except Exception as e:
+            yield "error", str(e)
+
+    def search_places(self, api_key, query, radius, max_results):
+        client = googlemaps.Client(key=api_key)
+        return self._search_places(client, query, radius, max_results)
+
+    def _search_places(self, client, query, radius, max_results):
+        results = []
+        next_page_token = None
+        while len(results) < max_results:
+            response = client.places(
+                query=query,
+                radius=radius,
+                page_token=next_page_token,
+            )
+            for place in response["results"]:
+                if len(results) >= max_results:
+                    break
+                place_details = client.place(place["place_id"])["result"]
+                results.append(
+                    Place(
+                        name=place_details.get("name", ""),
+                        address=place_details.get("formatted_address", ""),
+                        phone=place_details.get("formatted_phone_number", ""),
+                        rating=place_details.get("rating", 0),
+                        reviews=place_details.get("user_ratings_total", 0),
+                        website=place_details.get("website", ""),
+                    )
+                )
+            next_page_token = response.get("next_page_token")
+            if not next_page_token:
+                break
+        return results

autogpt_platform/backend/backend/blocks/http.py

@@ -1,8 +1,9 @@
+import json
 from enum import Enum
 
 import requests
 
-from autogpt_server.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 
 
 class HttpMethod(Enum):
@@ -15,7 +16,7 @@ class HttpMethod(Enum):
     HEAD = "HEAD"
 
 
-class HttpRequestBlock(Block):
+class SendWebRequestBlock(Block):
     class Input(BlockSchema):
         url: str
         method: HttpMethod = HttpMethod.POST
@@ -31,12 +32,15 @@ class HttpRequestBlock(Block):
         super().__init__(
             id="6595ae1f-b924-42cb-9a41-551a0611c4b4",
             description="This block makes an HTTP request to the given URL.",
-            categories={BlockCategory.BASIC},
-            input_schema=HttpRequestBlock.Input,
-            output_schema=HttpRequestBlock.Output,
+            categories={BlockCategory.OUTPUT},
+            input_schema=SendWebRequestBlock.Input,
+            output_schema=SendWebRequestBlock.Output,
         )
 
-    def run(self, input_data: Input) -> BlockOutput:
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        if isinstance(input_data.body, str):
+            input_data.body = json.loads(input_data.body)
+
         response = requests.request(
             input_data.method.value,
             input_data.url,

autogpt_platform/backend/backend/blocks/ideogram.py

@@ -0,0 +1,264 @@
+from enum import Enum
+from typing import Any, Dict, Optional
+
+import requests
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class IdeogramModelName(str, Enum):
+    V2 = "V_2"
+    V1 = "V_1"
+    V1_TURBO = "V_1_TURBO"
+    V2_TURBO = "V_2_TURBO"
+
+
+class MagicPromptOption(str, Enum):
+    AUTO = "AUTO"
+    ON = "ON"
+    OFF = "OFF"
+
+
+class StyleType(str, Enum):
+    AUTO = "AUTO"
+    GENERAL = "GENERAL"
+    REALISTIC = "REALISTIC"
+    DESIGN = "DESIGN"
+    RENDER_3D = "RENDER_3D"
+    ANIME = "ANIME"
+
+
+class ColorPalettePreset(str, Enum):
+    NONE = "NONE"
+    EMBER = "EMBER"
+    FRESH = "FRESH"
+    JUNGLE = "JUNGLE"
+    MAGIC = "MAGIC"
+    MELON = "MELON"
+    MOSAIC = "MOSAIC"
+    PASTEL = "PASTEL"
+    ULTRAMARINE = "ULTRAMARINE"
+
+
+class AspectRatio(str, Enum):
+    ASPECT_10_16 = "ASPECT_10_16"
+    ASPECT_16_10 = "ASPECT_16_10"
+    ASPECT_9_16 = "ASPECT_9_16"
+    ASPECT_16_9 = "ASPECT_16_9"
+    ASPECT_3_2 = "ASPECT_3_2"
+    ASPECT_2_3 = "ASPECT_2_3"
+    ASPECT_4_3 = "ASPECT_4_3"
+    ASPECT_3_4 = "ASPECT_3_4"
+    ASPECT_1_1 = "ASPECT_1_1"
+    ASPECT_1_3 = "ASPECT_1_3"
+    ASPECT_3_1 = "ASPECT_3_1"
+
+
+class UpscaleOption(str, Enum):
+    AI_UPSCALE = "AI Upscale"
+    NO_UPSCALE = "No Upscale"
+
+
+class IdeogramModelBlock(Block):
+    class Input(BlockSchema):
+        api_key: BlockSecret = SecretField(
+            key="ideogram_api_key",
+            description="Ideogram API Key",
+        )
+        prompt: str = SchemaField(
+            description="Text prompt for image generation",
+            placeholder="e.g., 'A futuristic cityscape at sunset'",
+            title="Prompt",
+        )
+        ideogram_model_name: IdeogramModelName = SchemaField(
+            description="The name of the Image Generation Model, e.g., V_2",
+            default=IdeogramModelName.V2,
+            title="Image Generation Model",
+            enum=IdeogramModelName,
+            advanced=False,
+        )
+        aspect_ratio: AspectRatio = SchemaField(
+            description="Aspect ratio for the generated image",
+            default=AspectRatio.ASPECT_1_1,
+            title="Aspect Ratio",
+            enum=AspectRatio,
+            advanced=False,
+        )
+        upscale: UpscaleOption = SchemaField(
+            description="Upscale the generated image",
+            default=UpscaleOption.NO_UPSCALE,
+            title="Upscale Image",
+            enum=UpscaleOption,
+            advanced=False,
+        )
+        magic_prompt_option: MagicPromptOption = SchemaField(
+            description="Whether to use MagicPrompt for enhancing the request",
+            default=MagicPromptOption.AUTO,
+            title="Magic Prompt Option",
+            enum=MagicPromptOption,
+            advanced=True,
+        )
+        seed: Optional[int] = SchemaField(
+            description="Random seed. Set for reproducible generation",
+            default=None,
+            title="Seed",
+            advanced=True,
+        )
+        style_type: StyleType = SchemaField(
+            description="Style type to apply, applicable for V_2 and above",
+            default=StyleType.AUTO,
+            title="Style Type",
+            enum=StyleType,
+            advanced=True,
+        )
+        negative_prompt: Optional[str] = SchemaField(
+            description="Description of what to exclude from the image",
+            default=None,
+            title="Negative Prompt",
+            advanced=True,
+        )
+        color_palette_name: ColorPalettePreset = SchemaField(
+            description="Color palette preset name, choose 'None' to skip",
+            default=ColorPalettePreset.NONE,
+            title="Color Palette Preset",
+            enum=ColorPalettePreset,
+            advanced=True,
+        )
+
+    class Output(BlockSchema):
+        result: str = SchemaField(description="Generated image URL")
+        error: Optional[str] = SchemaField(
+            description="Error message if the model run failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="6ab085e2-20b3-4055-bc3e-08036e01eca6",
+            description="This block runs Ideogram models with both simple and advanced settings.",
+            categories={BlockCategory.AI},
+            input_schema=IdeogramModelBlock.Input,
+            output_schema=IdeogramModelBlock.Output,
+            test_input={
+                "api_key": "test_api_key",
+                "ideogram_model_name": IdeogramModelName.V2,
+                "prompt": "A futuristic cityscape at sunset",
+                "aspect_ratio": AspectRatio.ASPECT_1_1,
+                "upscale": UpscaleOption.NO_UPSCALE,
+                "magic_prompt_option": MagicPromptOption.AUTO,
+                "seed": None,
+                "style_type": StyleType.AUTO,
+                "negative_prompt": None,
+                "color_palette_name": ColorPalettePreset.NONE,
+            },
+            test_output=[
+                (
+                    "result",
+                    "https://ideogram.ai/api/images/test-generated-image-url.png",
+                ),
+            ],
+            test_mock={
+                "run_model": lambda api_key, model_name, prompt, seed, aspect_ratio, magic_prompt_option, style_type, negative_prompt, color_palette_name: "https://ideogram.ai/api/images/test-generated-image-url.png",
+                "upscale_image": lambda api_key, image_url: "https://ideogram.ai/api/images/test-upscaled-image-url.png",
+            },
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        seed = input_data.seed
+
+        try:
+            # Step 1: Generate the image
+            result = self.run_model(
+                api_key=input_data.api_key.get_secret_value(),
+                model_name=input_data.ideogram_model_name.value,
+                prompt=input_data.prompt,
+                seed=seed,
+                aspect_ratio=input_data.aspect_ratio.value,
+                magic_prompt_option=input_data.magic_prompt_option.value,
+                style_type=input_data.style_type.value,
+                negative_prompt=input_data.negative_prompt,
+                color_palette_name=input_data.color_palette_name.value,
+            )
+
+            # Step 2: Upscale the image if requested
+            if input_data.upscale == UpscaleOption.AI_UPSCALE:
+                result = self.upscale_image(
+                    api_key=input_data.api_key.get_secret_value(),
+                    image_url=result,
+                )
+
+            yield "result", result
+        except Exception as e:
+            yield "error", str(e)
+
+    def run_model(
+        self,
+        api_key: str,
+        model_name: str,
+        prompt: str,
+        seed: Optional[int],
+        aspect_ratio: str,
+        magic_prompt_option: str,
+        style_type: str,
+        negative_prompt: Optional[str],
+        color_palette_name: str,
+    ):
+        url = "https://api.ideogram.ai/generate"
+        headers = {"Api-Key": api_key, "Content-Type": "application/json"}
+
+        data: Dict[str, Any] = {
+            "image_request": {
+                "prompt": prompt,
+                "model": model_name,
+                "aspect_ratio": aspect_ratio,
+                "magic_prompt_option": magic_prompt_option,
+                "style_type": style_type,
+            }
+        }
+
+        if seed is not None:
+            data["image_request"]["seed"] = seed
+
+        if negative_prompt:
+            data["image_request"]["negative_prompt"] = negative_prompt
+
+        if color_palette_name != "NONE":
+            data["image_request"]["color_palette"] = {"name": color_palette_name}
+
+        try:
+            response = requests.post(url, json=data, headers=headers)
+            response.raise_for_status()
+            return response.json()["data"][0]["url"]
+        except requests.exceptions.RequestException as e:
+            raise Exception(f"Failed to fetch image: {str(e)}")
+
+    def upscale_image(self, api_key: str, image_url: str):
+        url = "https://api.ideogram.ai/upscale"
+        headers = {
+            "Api-Key": api_key,
+        }
+
+        try:
+            # Step 1: Download the image from the provided URL
+            image_response = requests.get(image_url)
+            image_response.raise_for_status()
+
+            # Step 2: Send the downloaded image to the upscale API
+            files = {
+                "image_file": ("image.png", image_response.content, "image/png"),
+            }
+
+            response = requests.post(
+                url,
+                headers=headers,
+                data={
+                    "image_request": "{}",  # Empty JSON object
+                },
+                files=files,
+            )
+
+            response.raise_for_status()
+            return response.json()["data"][0]["url"]
+
+        except requests.exceptions.RequestException as e:
+            raise Exception(f"Failed to upscale image: {str(e)}")

autogpt_platform/backend/backend/blocks/iteration.py

@@ -0,0 +1,52 @@
+from typing import Any
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+
+class StepThroughItemsBlock(Block):
+    class Input(BlockSchema):
+        items: list | dict = SchemaField(
+            description="The list or dictionary of items to iterate over",
+            placeholder="[1, 2, 3, 4, 5] or {'key1': 'value1', 'key2': 'value2'}",
+        )
+
+    class Output(BlockSchema):
+        item: Any = SchemaField(description="The current item in the iteration")
+        key: Any = SchemaField(
+            description="The key or index of the current item in the iteration",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="f66a3543-28d3-4ab5-8945-9b336371e2ce",
+            input_schema=StepThroughItemsBlock.Input,
+            output_schema=StepThroughItemsBlock.Output,
+            categories={BlockCategory.LOGIC},
+            description="Iterates over a list or dictionary and outputs each item.",
+            test_input={"items": [1, 2, 3, {"key1": "value1", "key2": "value2"}]},
+            test_output=[
+                ("item", 1),
+                ("key", 0),
+                ("item", 2),
+                ("key", 1),
+                ("item", 3),
+                ("key", 2),
+                ("item", {"key1": "value1", "key2": "value2"}),
+                ("key", 3),
+            ],
+            test_mock={},
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        items = input_data.items
+        if isinstance(items, dict):
+            # If items is a dictionary, iterate over its values
+            for item in items.values():
+                yield "item", item
+                yield "key", item
+        else:
+            # If items is a list, iterate over the list
+            for index, item in enumerate(items):
+                yield "item", item
+                yield "key", index

autogpt_platform/backend/backend/blocks/jina_chunking.py

@@ -0,0 +1,63 @@
+import requests
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class JinaChunkingBlock(Block):
+    class Input(BlockSchema):
+        texts: list = SchemaField(description="List of texts to chunk")
+        api_key: BlockSecret = SecretField(
+            key="jina_api_key", description="Jina API Key"
+        )
+        max_chunk_length: int = SchemaField(
+            description="Maximum length of each chunk", default=1000
+        )
+        return_tokens: bool = SchemaField(
+            description="Whether to return token information", default=False
+        )
+
+    class Output(BlockSchema):
+        chunks: list = SchemaField(description="List of chunked texts")
+        tokens: list = SchemaField(
+            description="List of token information for each chunk", optional=True
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="806fb15e-830f-4796-8692-557d300ff43c",
+            description="Chunks texts using Jina AI's segmentation service",
+            categories={BlockCategory.AI, BlockCategory.TEXT},
+            input_schema=JinaChunkingBlock.Input,
+            output_schema=JinaChunkingBlock.Output,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        url = "https://segment.jina.ai/"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {input_data.api_key.get_secret_value()}",
+        }
+
+        all_chunks = []
+        all_tokens = []
+
+        for text in input_data.texts:
+            data = {
+                "content": text,
+                "return_tokens": str(input_data.return_tokens).lower(),
+                "return_chunks": "true",
+                "max_chunk_length": str(input_data.max_chunk_length),
+            }
+
+            response = requests.post(url, headers=headers, json=data)
+            response.raise_for_status()
+            result = response.json()
+
+            all_chunks.extend(result.get("chunks", []))
+            if input_data.return_tokens:
+                all_tokens.extend(result.get("tokens", []))
+
+        yield "chunks", all_chunks
+        if input_data.return_tokens:
+            yield "tokens", all_tokens

autogpt_platform/backend/backend/blocks/jina_embeddings.py

@@ -0,0 +1,39 @@
+import requests
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class JinaEmbeddingBlock(Block):
+    class Input(BlockSchema):
+        texts: list = SchemaField(description="List of texts to embed")
+        api_key: BlockSecret = SecretField(
+            key="jina_api_key", description="Jina API Key"
+        )
+        model: str = SchemaField(
+            description="Jina embedding model to use",
+            default="jina-embeddings-v2-base-en",
+        )
+
+    class Output(BlockSchema):
+        embeddings: list = SchemaField(description="List of embeddings")
+
+    def __init__(self):
+        super().__init__(
+            id="7c56b3ab-62e7-43a2-a2dc-4ec4245660b6",
+            description="Generates embeddings using Jina AI",
+            categories={BlockCategory.AI},
+            input_schema=JinaEmbeddingBlock.Input,
+            output_schema=JinaEmbeddingBlock.Output,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        url = "https://api.jina.ai/v1/embeddings"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {input_data.api_key.get_secret_value()}",
+        }
+        data = {"input": input_data.texts, "model": input_data.model}
+        response = requests.post(url, headers=headers, json=data)
+        embeddings = [e["embedding"] for e in response.json()["data"]]
+        yield "embeddings", embeddings

autogpt_platform/backend/backend/blocks/llm.py

@@ -0,0 +1,855 @@
+import ast
+import logging
+from enum import Enum
+from json import JSONDecodeError
+from typing import Any, List, NamedTuple
+
+import anthropic
+import ollama
+import openai
+from groq import Groq
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+from backend.util import json
+
+logger = logging.getLogger(__name__)
+
+LlmApiKeys = {
+    "openai": BlockSecret("openai_api_key"),
+    "anthropic": BlockSecret("anthropic_api_key"),
+    "groq": BlockSecret("groq_api_key"),
+    "ollama": BlockSecret(value=""),
+}
+
+
+class ModelMetadata(NamedTuple):
+    provider: str
+    context_window: int
+    cost_factor: int
+
+
+class LlmModel(str, Enum):
+    # OpenAI models
+    O1_PREVIEW = "o1-preview"
+    O1_MINI = "o1-mini"
+    GPT4O_MINI = "gpt-4o-mini"
+    GPT4O = "gpt-4o"
+    GPT4_TURBO = "gpt-4-turbo"
+    GPT3_5_TURBO = "gpt-3.5-turbo"
+    # Anthropic models
+    CLAUDE_3_5_SONNET = "claude-3-5-sonnet-20240620"
+    CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
+    # Groq models
+    LLAMA3_8B = "llama3-8b-8192"
+    LLAMA3_70B = "llama3-70b-8192"
+    MIXTRAL_8X7B = "mixtral-8x7b-32768"
+    GEMMA_7B = "gemma-7b-it"
+    GEMMA2_9B = "gemma2-9b-it"
+    # New Groq models (Preview)
+    LLAMA3_1_405B = "llama-3.1-405b-reasoning"
+    LLAMA3_1_70B = "llama-3.1-70b-versatile"
+    LLAMA3_1_8B = "llama-3.1-8b-instant"
+    # Ollama models
+    OLLAMA_LLAMA3_8B = "llama3"
+    OLLAMA_LLAMA3_405B = "llama3.1:405b"
+
+    @property
+    def metadata(self) -> ModelMetadata:
+        return MODEL_METADATA[self]
+
+
+MODEL_METADATA = {
+    LlmModel.O1_PREVIEW: ModelMetadata("openai", 32000, cost_factor=60),
+    LlmModel.O1_MINI: ModelMetadata("openai", 62000, cost_factor=30),
+    LlmModel.GPT4O_MINI: ModelMetadata("openai", 128000, cost_factor=10),
+    LlmModel.GPT4O: ModelMetadata("openai", 128000, cost_factor=12),
+    LlmModel.GPT4_TURBO: ModelMetadata("openai", 128000, cost_factor=11),
+    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, cost_factor=8),
+    LlmModel.CLAUDE_3_5_SONNET: ModelMetadata("anthropic", 200000, cost_factor=14),
+    LlmModel.CLAUDE_3_HAIKU: ModelMetadata("anthropic", 200000, cost_factor=13),
+    LlmModel.LLAMA3_8B: ModelMetadata("groq", 8192, cost_factor=6),
+    LlmModel.LLAMA3_70B: ModelMetadata("groq", 8192, cost_factor=9),
+    LlmModel.MIXTRAL_8X7B: ModelMetadata("groq", 32768, cost_factor=7),
+    LlmModel.GEMMA_7B: ModelMetadata("groq", 8192, cost_factor=6),
+    LlmModel.GEMMA2_9B: ModelMetadata("groq", 8192, cost_factor=7),
+    LlmModel.LLAMA3_1_405B: ModelMetadata("groq", 8192, cost_factor=10),
+    # Limited to 16k during preview
+    LlmModel.LLAMA3_1_70B: ModelMetadata("groq", 131072, cost_factor=15),
+    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 131072, cost_factor=13),
+    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192, cost_factor=7),
+    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192, cost_factor=11),
+}
+
+for model in LlmModel:
+    if model not in MODEL_METADATA:
+        raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
+
+
+class AIStructuredResponseGeneratorBlock(Block):
+    class Input(BlockSchema):
+        prompt: str
+        expected_format: dict[str, str] = SchemaField(
+            description="Expected format of the response. If provided, the response will be validated against this format. "
+            "The keys should be the expected fields in the response, and the values should be the description of the field.",
+        )
+        model: LlmModel = SchemaField(
+            title="LLM Model",
+            default=LlmModel.GPT4_TURBO,
+            description="The language model to use for answering the prompt.",
+            advanced=False,
+        )
+        api_key: BlockSecret = SecretField(value="")
+        sys_prompt: str = ""
+        retry: int = 3
+        prompt_values: dict[str, str] = SchemaField(
+            advanced=False, default={}, description="Values used to fill in the prompt."
+        )
+
+    class Output(BlockSchema):
+        response: dict[str, Any]
+        error: str
+
+    def __init__(self):
+        super().__init__(
+            id="ed55ac19-356e-4243-a6cb-bc599e9b716f",
+            description="Call a Large Language Model (LLM) to generate formatted object based on the given prompt.",
+            categories={BlockCategory.AI},
+            input_schema=AIStructuredResponseGeneratorBlock.Input,
+            output_schema=AIStructuredResponseGeneratorBlock.Output,
+            test_input={
+                "model": LlmModel.GPT4_TURBO,
+                "api_key": "fake-api",
+                "expected_format": {
+                    "key1": "value1",
+                    "key2": "value2",
+                },
+                "prompt": "User prompt",
+            },
+            test_output=("response", {"key1": "key1Value", "key2": "key2Value"}),
+            test_mock={
+                "llm_call": lambda *args, **kwargs: json.dumps(
+                    {
+                        "key1": "key1Value",
+                        "key2": "key2Value",
+                    }
+                )
+            },
+        )
+
+    @staticmethod
+    def llm_call(
+        api_key: str, model: LlmModel, prompt: list[dict], json_format: bool
+    ) -> str:
+        provider = model.metadata.provider
+
+        if provider == "openai":
+            openai.api_key = api_key
+            response_format = None
+
+            if 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 = openai.chat.completions.create(
+                model=model.value,
+                messages=prompt,  # type: ignore
+                response_format=response_format,  # type: ignore
+            )
+            return response.choices[0].message.content or ""
+        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=api_key)
+            try:
+                response = client.messages.create(
+                    model=model.value,
+                    max_tokens=4096,
+                    system=sysprompt,
+                    messages=messages,
+                )
+                return response.content[0].text if response.content else ""
+            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=api_key)
+            response_format = {"type": "json_object"} if json_format else None
+            response = client.chat.completions.create(
+                model=model.value,
+                messages=prompt,  # type: ignore
+                response_format=response_format,  # type: ignore
+            )
+            return response.choices[0].message.content or ""
+        elif provider == "ollama":
+            response = ollama.generate(
+                model=model.value,
+                prompt=prompt[0]["content"],
+            )
+            return response["response"]
+        else:
+            raise ValueError(f"Unsupported LLM provider: {provider}")
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        logger.debug(f"Calling LLM with input data: {input_data}")
+        prompt = []
+
+        def trim_prompt(s: str) -> str:
+            lines = s.strip().split("\n")
+            return "\n".join([line.strip().lstrip("|") for line in lines])
+
+        values = input_data.prompt_values
+        if values:
+            input_data.prompt = input_data.prompt.format(**values)
+            input_data.sys_prompt = input_data.sys_prompt.format(**values)
+
+        if input_data.sys_prompt:
+            prompt.append({"role": "system", "content": input_data.sys_prompt})
+
+        if input_data.expected_format:
+            expected_format = [
+                f'"{k}": "{v}"' for k, v in input_data.expected_format.items()
+            ]
+            format_prompt = ",\n  ".join(expected_format)
+            sys_prompt = trim_prompt(
+                f"""
+                  |Reply strictly only in the following JSON format:
+                  |{{
+                  |  {format_prompt}
+                  |}}
+                """
+            )
+            prompt.append({"role": "system", "content": sys_prompt})
+
+        prompt.append({"role": "user", "content": input_data.prompt})
+
+        def parse_response(resp: str) -> tuple[dict[str, Any], str | None]:
+            try:
+                parsed = json.loads(resp)
+                if not isinstance(parsed, dict):
+                    return {}, f"Expected a dictionary, but got {type(parsed)}"
+                miss_keys = set(input_data.expected_format.keys()) - set(parsed.keys())
+                if miss_keys:
+                    return parsed, f"Missing keys: {miss_keys}"
+                return parsed, None
+            except JSONDecodeError as e:
+                return {}, f"JSON decode error: {e}"
+
+        logger.info(f"LLM request: {prompt}")
+        retry_prompt = ""
+        model = input_data.model
+        api_key = (
+            input_data.api_key.get_secret_value()
+            or LlmApiKeys[model.metadata.provider].get_secret_value()
+        )
+
+        for retry_count in range(input_data.retry):
+            try:
+                response_text = self.llm_call(
+                    api_key=api_key,
+                    model=model,
+                    prompt=prompt,
+                    json_format=bool(input_data.expected_format),
+                )
+                logger.info(f"LLM attempt-{retry_count} response: {response_text}")
+
+                if input_data.expected_format:
+                    parsed_dict, parsed_error = parse_response(response_text)
+                    if not parsed_error:
+                        yield "response", {
+                            k: (
+                                json.loads(v)
+                                if isinstance(v, str)
+                                and v.startswith("[")
+                                and v.endswith("]")
+                                else (", ".join(v) if isinstance(v, list) else v)
+                            )
+                            for k, v in parsed_dict.items()
+                        }
+                        return
+                else:
+                    yield "response", {"response": response_text}
+                    return
+
+                retry_prompt = trim_prompt(
+                    f"""
+                  |This is your previous error response:
+                  |--
+                  |{response_text}
+                  |--
+                  |
+                  |And this is the error:
+                  |--
+                  |{parsed_error}
+                  |--
+                """
+                )
+                prompt.append({"role": "user", "content": retry_prompt})
+            except Exception as e:
+                logger.error(f"Error calling LLM: {e}")
+                retry_prompt = f"Error calling LLM: {e}"
+
+        yield "error", retry_prompt
+
+
+class AITextGeneratorBlock(Block):
+    class Input(BlockSchema):
+        prompt: str
+        model: LlmModel = SchemaField(
+            title="LLM Model",
+            default=LlmModel.GPT4_TURBO,
+            description="The language model to use for answering the prompt.",
+            advanced=False,
+        )
+        api_key: BlockSecret = SecretField(value="")
+        sys_prompt: str = ""
+        retry: int = 3
+        prompt_values: dict[str, str] = SchemaField(
+            advanced=False, default={}, description="Values used to fill in the prompt."
+        )
+
+    class Output(BlockSchema):
+        response: str
+        error: str
+
+    def __init__(self):
+        super().__init__(
+            id="1f292d4a-41a4-4977-9684-7c8d560b9f91",
+            description="Call a Large Language Model (LLM) to generate a string based on the given prompt.",
+            categories={BlockCategory.AI},
+            input_schema=AITextGeneratorBlock.Input,
+            output_schema=AITextGeneratorBlock.Output,
+            test_input={"prompt": "User prompt"},
+            test_output=("response", "Response text"),
+            test_mock={"llm_call": lambda *args, **kwargs: "Response text"},
+        )
+
+    @staticmethod
+    def llm_call(input_data: AIStructuredResponseGeneratorBlock.Input) -> str:
+        object_block = AIStructuredResponseGeneratorBlock()
+        for output_name, output_data in object_block.run(input_data):
+            if output_name == "response":
+                return output_data["response"]
+            else:
+                raise RuntimeError(output_data)
+        raise ValueError("Failed to get a response from the LLM.")
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            object_input_data = AIStructuredResponseGeneratorBlock.Input(
+                **{attr: getattr(input_data, attr) for attr in input_data.model_fields},
+                expected_format={},
+            )
+            yield "response", self.llm_call(object_input_data)
+        except Exception as e:
+            yield "error", str(e)
+
+
+class SummaryStyle(Enum):
+    CONCISE = "concise"
+    DETAILED = "detailed"
+    BULLET_POINTS = "bullet points"
+    NUMBERED_LIST = "numbered list"
+
+
+class AITextSummarizerBlock(Block):
+    class Input(BlockSchema):
+        text: str
+        model: LlmModel = SchemaField(
+            title="LLM Model",
+            default=LlmModel.GPT4_TURBO,
+            description="The language model to use for summarizing the text.",
+        )
+        focus: str = "general information"
+        style: SummaryStyle = SummaryStyle.CONCISE
+        api_key: BlockSecret = SecretField(value="")
+        # TODO: Make this dynamic
+        max_tokens: int = 4000  # Adjust based on the model's context window
+        chunk_overlap: int = 100  # Overlap between chunks to maintain context
+
+    class Output(BlockSchema):
+        summary: str
+        error: str
+
+    def __init__(self):
+        super().__init__(
+            id="a0a69be1-4528-491c-a85a-a4ab6873e3f0",
+            description="Utilize a Large Language Model (LLM) to summarize a long text.",
+            categories={BlockCategory.AI, BlockCategory.TEXT},
+            input_schema=AITextSummarizerBlock.Input,
+            output_schema=AITextSummarizerBlock.Output,
+            test_input={"text": "Lorem ipsum..." * 100},
+            test_output=("summary", "Final summary of a long text"),
+            test_mock={
+                "llm_call": lambda input_data: (
+                    {"final_summary": "Final summary of a long text"}
+                    if "final_summary" in input_data.expected_format
+                    else {"summary": "Summary of a chunk of text"}
+                )
+            },
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            for output in self._run(input_data):
+                yield output
+        except Exception as e:
+            yield "error", str(e)
+
+    def _run(self, input_data: Input) -> BlockOutput:
+        chunks = self._split_text(
+            input_data.text, input_data.max_tokens, input_data.chunk_overlap
+        )
+        summaries = []
+
+        for chunk in chunks:
+            chunk_summary = self._summarize_chunk(chunk, input_data)
+            summaries.append(chunk_summary)
+
+        final_summary = self._combine_summaries(summaries, input_data)
+        yield "summary", final_summary
+
+    @staticmethod
+    def _split_text(text: str, max_tokens: int, overlap: int) -> list[str]:
+        words = text.split()
+        chunks = []
+        chunk_size = max_tokens - overlap
+
+        for i in range(0, len(words), chunk_size):
+            chunk = " ".join(words[i : i + max_tokens])
+            chunks.append(chunk)
+
+        return chunks
+
+    @staticmethod
+    def llm_call(
+        input_data: AIStructuredResponseGeneratorBlock.Input,
+    ) -> dict[str, str]:
+        llm_block = AIStructuredResponseGeneratorBlock()
+        for output_name, output_data in llm_block.run(input_data):
+            if output_name == "response":
+                return output_data
+        raise ValueError("Failed to get a response from the LLM.")
+
+    def _summarize_chunk(self, chunk: str, input_data: Input) -> str:
+        prompt = f"Summarize the following text in a {input_data.style} form. Focus your summary on the topic of `{input_data.focus}` if present, otherwise just provide a general summary:\n\n```{chunk}```"
+
+        llm_response = self.llm_call(
+            AIStructuredResponseGeneratorBlock.Input(
+                prompt=prompt,
+                api_key=input_data.api_key,
+                model=input_data.model,
+                expected_format={"summary": "The summary of the given text."},
+            )
+        )
+
+        return llm_response["summary"]
+
+    def _combine_summaries(self, summaries: list[str], input_data: Input) -> str:
+        combined_text = "\n\n".join(summaries)
+
+        if len(combined_text.split()) <= input_data.max_tokens:
+            prompt = f"Provide a final summary of the following section summaries in a {input_data.style} form, focus your summary on the topic of `{input_data.focus}` if present:\n\n ```{combined_text}```\n\n Just respond with the final_summary in the format specified."
+
+            llm_response = self.llm_call(
+                AIStructuredResponseGeneratorBlock.Input(
+                    prompt=prompt,
+                    api_key=input_data.api_key,
+                    model=input_data.model,
+                    expected_format={
+                        "final_summary": "The final summary of all provided summaries."
+                    },
+                )
+            )
+
+            return llm_response["final_summary"]
+        else:
+            # If combined summaries are still too long, recursively summarize
+            return self._run(
+                AITextSummarizerBlock.Input(
+                    text=combined_text,
+                    api_key=input_data.api_key,
+                    model=input_data.model,
+                    max_tokens=input_data.max_tokens,
+                    chunk_overlap=input_data.chunk_overlap,
+                )
+            ).send(None)[
+                1
+            ]  # Get the first yielded value
+
+
+class MessageRole(str, Enum):
+    SYSTEM = "system"
+    USER = "user"
+    ASSISTANT = "assistant"
+
+
+class Message(BlockSchema):
+    role: MessageRole
+    content: str
+
+
+class AIConversationBlock(Block):
+    class Input(BlockSchema):
+        messages: List[Message] = SchemaField(
+            description="List of messages in the conversation.", min_length=1
+        )
+        model: LlmModel = SchemaField(
+            title="LLM Model",
+            default=LlmModel.GPT4_TURBO,
+            description="The language model to use for the conversation.",
+        )
+        api_key: BlockSecret = SecretField(
+            value="", description="API key for the chosen language model provider."
+        )
+        max_tokens: int | None = SchemaField(
+            default=None,
+            description="The maximum number of tokens to generate in the chat completion.",
+            ge=1,
+        )
+
+    class Output(BlockSchema):
+        response: str = SchemaField(
+            description="The model's response to the conversation."
+        )
+        error: str = SchemaField(description="Error message if the API call failed.")
+
+    def __init__(self):
+        super().__init__(
+            id="32a87eab-381e-4dd4-bdb8-4c47151be35a",
+            description="Advanced LLM call that takes a list of messages and sends them to the language model.",
+            categories={BlockCategory.AI},
+            input_schema=AIConversationBlock.Input,
+            output_schema=AIConversationBlock.Output,
+            test_input={
+                "messages": [
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {"role": "user", "content": "Who won the world series in 2020?"},
+                    {
+                        "role": "assistant",
+                        "content": "The Los Angeles Dodgers won the World Series in 2020.",
+                    },
+                    {"role": "user", "content": "Where was it played?"},
+                ],
+                "model": LlmModel.GPT4_TURBO,
+                "api_key": "test_api_key",
+            },
+            test_output=(
+                "response",
+                "The 2020 World Series was played at Globe Life Field in Arlington, Texas.",
+            ),
+            test_mock={
+                "llm_call": lambda *args, **kwargs: "The 2020 World Series was played at Globe Life Field in Arlington, Texas."
+            },
+        )
+
+    @staticmethod
+    def llm_call(
+        api_key: str,
+        model: LlmModel,
+        messages: List[dict[str, str]],
+        max_tokens: int | None = None,
+    ) -> str:
+        provider = model.metadata.provider
+
+        if provider == "openai":
+            openai.api_key = api_key
+            response = openai.chat.completions.create(
+                model=model.value,
+                messages=messages,  # type: ignore
+                max_tokens=max_tokens,
+            )
+            return response.choices[0].message.content or ""
+        elif provider == "anthropic":
+            client = anthropic.Anthropic(api_key=api_key)
+            response = client.messages.create(
+                model=model.value,
+                max_tokens=max_tokens or 4096,
+                messages=messages,  # type: ignore
+            )
+            return response.content[0].text if response.content else ""
+        elif provider == "groq":
+            client = Groq(api_key=api_key)
+            response = client.chat.completions.create(
+                model=model.value,
+                messages=messages,  # type: ignore
+                max_tokens=max_tokens,
+            )
+            return response.choices[0].message.content or ""
+        elif provider == "ollama":
+            response = ollama.chat(
+                model=model.value,
+                messages=messages,  # type: ignore
+                stream=False,  # type: ignore
+            )
+            return response["message"]["content"]
+        else:
+            raise ValueError(f"Unsupported LLM provider: {provider}")
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            api_key = (
+                input_data.api_key.get_secret_value()
+                or LlmApiKeys[input_data.model.metadata.provider].get_secret_value()
+            )
+
+            messages = [message.model_dump() for message in input_data.messages]
+
+            response = self.llm_call(
+                api_key=api_key,
+                model=input_data.model,
+                messages=messages,
+                max_tokens=input_data.max_tokens,
+            )
+
+            yield "response", response
+        except Exception as e:
+            yield "error", f"Error calling LLM: {str(e)}"
+
+
+class AIListGeneratorBlock(Block):
+    class Input(BlockSchema):
+        focus: str | None = SchemaField(
+            description="The focus of the list to generate.",
+            placeholder="The top 5 most interesting news stories in the data.",
+            default=None,
+            advanced=False,
+        )
+        source_data: str | None = SchemaField(
+            description="The data to generate the list from.",
+            placeholder="News Today: Humans land on Mars: Today humans landed on mars. -- AI wins Nobel Prize: AI wins Nobel Prize for solving world hunger. -- New AI Model: A new AI model has been released.",
+            default=None,
+            advanced=False,
+        )
+        model: LlmModel = SchemaField(
+            title="LLM Model",
+            default=LlmModel.GPT4_TURBO,
+            description="The language model to use for generating the list.",
+            advanced=True,
+        )
+        api_key: BlockSecret = SecretField(value="")
+        max_retries: int = SchemaField(
+            default=3,
+            description="Maximum number of retries for generating a valid list.",
+            ge=1,
+            le=5,
+        )
+
+    class Output(BlockSchema):
+        generated_list: List[str] = SchemaField(description="The generated list.")
+        list_item: str = SchemaField(
+            description="Each individual item in the list.",
+        )
+        error: str = SchemaField(
+            description="Error message if the list generation failed."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="9c0b0450-d199-458b-a731-072189dd6593",
+            description="Generate a Python list based on the given prompt using a Large Language Model (LLM).",
+            categories={BlockCategory.AI, BlockCategory.TEXT},
+            input_schema=AIListGeneratorBlock.Input,
+            output_schema=AIListGeneratorBlock.Output,
+            test_input={
+                "focus": "planets",
+                "source_data": (
+                    "Zylora Prime is a glowing jungle world with bioluminescent plants, "
+                    "while Kharon-9 is a harsh desert planet with underground cities. "
+                    "Vortexia's constant storms power floating cities, and Oceara is a water-covered world home to "
+                    "intelligent marine life. On icy Draknos, ancient ruins lie buried beneath its frozen landscape, "
+                    "drawing explorers to uncover its mysteries. Each planet showcases the limitless possibilities of "
+                    "fictional worlds."
+                ),
+                "model": LlmModel.GPT4_TURBO,
+                "api_key": "test_api_key",
+                "max_retries": 3,
+            },
+            test_output=[
+                (
+                    "generated_list",
+                    ["Zylora Prime", "Kharon-9", "Vortexia", "Oceara", "Draknos"],
+                ),
+                ("list_item", "Zylora Prime"),
+                ("list_item", "Kharon-9"),
+                ("list_item", "Vortexia"),
+                ("list_item", "Oceara"),
+                ("list_item", "Draknos"),
+            ],
+            test_mock={
+                "llm_call": lambda input_data: {
+                    "response": "['Zylora Prime', 'Kharon-9', 'Vortexia', 'Oceara', 'Draknos']"
+                },
+            },
+        )
+
+    @staticmethod
+    def llm_call(
+        input_data: AIStructuredResponseGeneratorBlock.Input,
+    ) -> dict[str, str]:
+        llm_block = AIStructuredResponseGeneratorBlock()
+        for output_name, output_data in llm_block.run(input_data):
+            if output_name == "response":
+                logger.debug(f"Received response from LLM: {output_data}")
+                return output_data
+        raise ValueError("Failed to get a response from the LLM.")
+
+    @staticmethod
+    def string_to_list(string):
+        """
+        Converts a string representation of a list into an actual Python list object.
+        """
+        logger.debug(f"Converting string to list. Input string: {string}")
+        try:
+            # Use ast.literal_eval to safely evaluate the string
+            python_list = ast.literal_eval(string)
+            if isinstance(python_list, list):
+                logger.debug(f"Successfully converted string to list: {python_list}")
+                return python_list
+            else:
+                logger.error(f"The provided string '{string}' is not a valid list")
+                raise ValueError(f"The provided string '{string}' is not a valid list.")
+        except (SyntaxError, ValueError) as e:
+            logger.error(f"Failed to convert string to list: {e}")
+            raise ValueError("Invalid list format. Could not convert to list.")
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        logger.debug(f"Starting AIListGeneratorBlock.run with input data: {input_data}")
+
+        # Check for API key
+        api_key_check = (
+            input_data.api_key.get_secret_value()
+            or LlmApiKeys[input_data.model.metadata.provider].get_secret_value()
+        )
+        if not api_key_check:
+            logger.error("No LLM API key provided.")
+            yield "error", "No LLM API key provided."
+            return
+
+        # Prepare the system prompt
+        sys_prompt = """You are a Python list generator. Your task is to generate a Python list based on the user's prompt. 
+            |Respond ONLY with a valid python list. 
+            |The list can contain strings, numbers, or nested lists as appropriate. 
+            |Do not include any explanations or additional text.
+
+            |Valid Example string formats:
+
+            |Example 1:
+            |```
+            |['1', '2', '3', '4']
+            |```
+
+            |Example 2:
+            |```
+            |[['1', '2'], ['3', '4'], ['5', '6']]
+            |```
+
+            |Example 3:
+            |```
+            |['1', ['2', '3'], ['4', ['5', '6']]]
+            |```
+
+            |Example 4:
+            |```
+            |['a', 'b', 'c']
+            |```
+
+            |Example 5:
+            |```
+            |['1', '2.5', 'string', 'True', ['False', 'None']]
+            |```
+
+            |Do not include any explanations or additional text, just respond with the list in the format specified above.
+            """
+        # If a focus is provided, add it to the prompt
+        if input_data.focus:
+            prompt = f"Generate a list with the following focus:\n<focus>\n\n{input_data.focus}</focus>"
+        else:
+            # If there's source data
+            if input_data.source_data:
+                prompt = "Extract the main focus of the source data to a list.\ni.e if the source data is a news website, the focus would be the news stories rather than the social links in the footer."
+            else:
+                # No focus or source data provided, generat a random list
+                prompt = "Generate a random list."
+
+        # If the source data is provided, add it to the prompt
+        if input_data.source_data:
+            prompt += f"\n\nUse the following source data to generate the list from:\n\n<source_data>\n\n{input_data.source_data}</source_data>\n\nDo not invent fictional data that is not present in the source data."
+        # Else, tell the LLM to synthesize the data
+        else:
+            prompt += "\n\nInvent the data to generate the list from."
+
+        for attempt in range(input_data.max_retries):
+            try:
+                logger.debug("Calling LLM")
+                llm_response = self.llm_call(
+                    AIStructuredResponseGeneratorBlock.Input(
+                        sys_prompt=sys_prompt,
+                        prompt=prompt,
+                        api_key=input_data.api_key,
+                        model=input_data.model,
+                        expected_format={},  # Do not use structured response
+                    )
+                )
+
+                logger.debug(f"LLM response: {llm_response}")
+
+                # Extract Response string
+                response_string = llm_response["response"]
+                logger.debug(f"Response string: {response_string}")
+
+                # Convert the string to a Python list
+                logger.debug("Converting string to Python list")
+                parsed_list = self.string_to_list(response_string)
+                logger.debug(f"Parsed list: {parsed_list}")
+
+                # If we reach here, we have a valid Python list
+                logger.debug("Successfully generated a valid Python list")
+                yield "generated_list", parsed_list
+
+                # Yield each item in the list
+                for item in parsed_list:
+                    yield "list_item", item
+                return
+
+            except Exception as e:
+                logger.error(f"Error in attempt {attempt + 1}: {str(e)}")
+                if attempt == input_data.max_retries - 1:
+                    logger.error(
+                        f"Failed to generate a valid Python list after {input_data.max_retries} attempts"
+                    )
+                    yield "error", f"Failed to generate a valid Python list after {input_data.max_retries} attempts. Last error: {str(e)}"
+                else:
+                    # Add a retry prompt
+                    logger.debug("Preparing retry prompt")
+                    prompt = f"""
+                    The previous attempt failed due to `{e}`
+                    Generate a valid Python list based on the original prompt.
+                    Remember to respond ONLY with a valid Python list as per the format specified earlier.
+                    Original prompt: 
+                    ```{prompt}```
+                    
+                    Respond only with the list in the format specified with no commentary or apologies.
+                    """
+                    logger.debug(f"Retry prompt: {prompt}")
+
+        logger.debug("AIListGeneratorBlock.run completed")

autogpt_platform/backend/backend/blocks/maths.py

@@ -0,0 +1,124 @@
+import operator
+from enum import Enum
+from typing import Any
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+
+class Operation(Enum):
+    ADD = "Add"
+    SUBTRACT = "Subtract"
+    MULTIPLY = "Multiply"
+    DIVIDE = "Divide"
+    POWER = "Power"
+
+
+class CalculatorBlock(Block):
+    class Input(BlockSchema):
+        operation: Operation = SchemaField(
+            description="Choose the math operation you want to perform",
+            placeholder="Select an operation",
+        )
+        a: float = SchemaField(
+            description="Enter the first number (A)", placeholder="For example: 10"
+        )
+        b: float = SchemaField(
+            description="Enter the second number (B)", placeholder="For example: 5"
+        )
+        round_result: bool = SchemaField(
+            description="Do you want to round the result to a whole number?",
+            default=False,
+        )
+
+    class Output(BlockSchema):
+        result: float = SchemaField(description="The result of your calculation")
+
+    def __init__(self):
+        super().__init__(
+            id="b1ab9b19-67a6-406d-abf5-2dba76d00c79",
+            input_schema=CalculatorBlock.Input,
+            output_schema=CalculatorBlock.Output,
+            description="Performs a mathematical operation on two numbers.",
+            categories={BlockCategory.LOGIC},
+            test_input={
+                "operation": Operation.ADD.value,
+                "a": 10.0,
+                "b": 5.0,
+                "round_result": False,
+            },
+            test_output=[
+                ("result", 15.0),
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        operation = input_data.operation
+        a = input_data.a
+        b = input_data.b
+
+        operations = {
+            Operation.ADD: operator.add,
+            Operation.SUBTRACT: operator.sub,
+            Operation.MULTIPLY: operator.mul,
+            Operation.DIVIDE: operator.truediv,
+            Operation.POWER: operator.pow,
+        }
+
+        op_func = operations[operation]
+
+        try:
+            if operation == Operation.DIVIDE and b == 0:
+                raise ZeroDivisionError("Cannot divide by zero")
+
+            result = op_func(a, b)
+
+            if input_data.round_result:
+                result = round(result)
+
+            yield "result", result
+
+        except ZeroDivisionError:
+            yield "result", float("inf")  # Return infinity for division by zero
+        except Exception:
+            yield "result", float("nan")  # Return NaN for other errors
+
+
+class CountItemsBlock(Block):
+    class Input(BlockSchema):
+        collection: Any = SchemaField(
+            description="Enter the collection you want to count. This can be a list, dictionary, string, or any other iterable.",
+            placeholder="For example: [1, 2, 3] or {'a': 1, 'b': 2} or 'hello'",
+        )
+
+    class Output(BlockSchema):
+        count: int = SchemaField(description="The number of items in the collection")
+
+    def __init__(self):
+        super().__init__(
+            id="3c9c2f42-b0c3-435f-ba35-05f7a25c772a",
+            input_schema=CountItemsBlock.Input,
+            output_schema=CountItemsBlock.Output,
+            description="Counts the number of items in a collection.",
+            categories={BlockCategory.LOGIC},
+            test_input={"collection": [1, 2, 3, 4, 5]},
+            test_output=[
+                ("count", 5),
+            ],
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        collection = input_data.collection
+
+        try:
+            if isinstance(collection, (str, list, tuple, set, dict)):
+                count = len(collection)
+            elif hasattr(collection, "__iter__"):
+                count = sum(1 for _ in collection)
+            else:
+                raise ValueError("Input is not a countable collection")
+
+            yield "count", count
+
+        except Exception:
+            yield "count", -1  # Return -1 to indicate an error

autogpt_platform/backend/backend/blocks/medium.py

@@ -2,11 +2,11 @@ from typing import List
 
 import requests
 
-from autogpt_server.data.block import Block, BlockOutput, BlockSchema
-from autogpt_server.data.model import BlockSecret, SchemaField, SecretField
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
 
 
-class CreateMediumPostBlock(Block):
+class PublishToMediumBlock(Block):
     class Input(BlockSchema):
         author_id: BlockSecret = SecretField(
             key="medium_author_id",
@@ -57,7 +57,6 @@ class CreateMediumPostBlock(Block):
     class Output(BlockSchema):
         post_id: str = SchemaField(description="The ID of the created Medium post")
         post_url: str = SchemaField(description="The URL of the created Medium post")
-        author_id: str = SchemaField(description="The Medium user ID of the author")
         published_at: int = SchemaField(
             description="The timestamp when the post was published"
         )
@@ -68,8 +67,10 @@ class CreateMediumPostBlock(Block):
     def __init__(self):
         super().__init__(
             id="3f7b2dcb-4a78-4e3f-b0f1-88132e1b89df",
-            input_schema=CreateMediumPostBlock.Input,
-            output_schema=CreateMediumPostBlock.Output,
+            input_schema=PublishToMediumBlock.Input,
+            output_schema=PublishToMediumBlock.Output,
+            description="Publishes a post to Medium.",
+            categories={BlockCategory.SOCIAL},
             test_input={
                 "author_id": "1234567890abcdef",
                 "title": "Test Post",
@@ -84,7 +85,6 @@ class CreateMediumPostBlock(Block):
             test_output=[
                 ("post_id", "e6f36a"),
                 ("post_url", "https://medium.com/@username/test-post-e6f36a"),
-                ("author_id", "1234567890abcdef"),
                 ("published_at", 1626282600),
             ],
             test_mock={
@@ -137,7 +137,7 @@ class CreateMediumPostBlock(Block):
 
         return response.json()
 
-    def run(self, input_data: Input) -> BlockOutput:
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
         try:
             response = self.create_post(
                 input_data.api_key.get_secret_value(),
@@ -155,7 +155,6 @@ class CreateMediumPostBlock(Block):
             if "data" in response:
                 yield "post_id", response["data"]["id"]
                 yield "post_url", response["data"]["url"]
-                yield "author_id", response["data"]["authorId"]
                 yield "published_at", response["data"]["publishedAt"]
             else:
                 error_message = response.get("errors", [{}])[0].get(

autogpt_platform/backend/backend/blocks/pinecone.py

@@ -0,0 +1,60 @@
+from pinecone import Pinecone, ServerlessSpec
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class PineconeInitBlock(Block):
+    class Input(BlockSchema):
+        api_key: BlockSecret = SecretField(
+            key="pinecone_api_key", description="Pinecone API Key"
+        )
+        index_name: str = SchemaField(description="Name of the Pinecone index")
+        dimension: int = SchemaField(
+            description="Dimension of the vectors", default=768
+        )
+        metric: str = SchemaField(
+            description="Distance metric for the index", default="cosine"
+        )
+        cloud: str = SchemaField(
+            description="Cloud provider for serverless", default="aws"
+        )
+        region: str = SchemaField(
+            description="Region for serverless", default="us-east-1"
+        )
+
+    class Output(BlockSchema):
+        index: str = SchemaField(description="Name of the initialized Pinecone index")
+        message: str = SchemaField(description="Status message")
+
+    def __init__(self):
+        super().__init__(
+            id="48d8fdab-8f03-41f3-8407-8107ba11ec9b",
+            description="Initializes a Pinecone index",
+            categories={BlockCategory.LOGIC},
+            input_schema=PineconeInitBlock.Input,
+            output_schema=PineconeInitBlock.Output,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        pc = Pinecone(api_key=input_data.api_key.get_secret_value())
+
+        try:
+            if input_data.index_name not in pc.list_indexes():
+                pc.create_index(
+                    name=input_data.index_name,
+                    dimension=input_data.dimension,
+                    metric=input_data.metric,
+                    spec=ServerlessSpec(
+                        cloud=input_data.cloud, region=input_data.region
+                    ),
+                )
+                message = f"Created new index: {input_data.index_name}"
+            else:
+                message = f"Using existing index: {input_data.index_name}"
+
+            # Instead of yielding the index object, we yield the index name
+            yield "index", input_data.index_name
+            yield "message", message
+        except Exception as e:
+            yield "message", f"Error initializing Pinecone index: {str(e)}"

autogpt_platform/backend/backend/blocks/pinecone_query.py

@@ -0,0 +1,43 @@
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+
+class PineconeQueryBlock(Block):
+    class Input(BlockSchema):
+        index: object = SchemaField(description="Initialized Pinecone index")
+        query_vector: list = SchemaField(description="Query vector")
+        namespace: str = SchemaField(
+            description="Namespace to query in Pinecone", default=""
+        )
+        top_k: int = SchemaField(
+            description="Number of top results to return", default=3
+        )
+        include_values: bool = SchemaField(
+            description="Whether to include vector values in the response",
+            default=False,
+        )
+        include_metadata: bool = SchemaField(
+            description="Whether to include metadata in the response", default=True
+        )
+
+    class Output(BlockSchema):
+        results: dict = SchemaField(description="Query results from Pinecone")
+
+    def __init__(self):
+        super().__init__(
+            id="9ad93d0f-91b4-4c9c-8eb1-82e26b4a01c5",
+            description="Queries a Pinecone index",
+            categories={BlockCategory.LOGIC},
+            input_schema=PineconeQueryBlock.Input,
+            output_schema=PineconeQueryBlock.Output,
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        results = input_data.index.query(
+            namespace=input_data.namespace,
+            vector=input_data.query_vector,
+            top_k=input_data.top_k,
+            include_values=input_data.include_values,
+            include_metadata=input_data.include_metadata,
+        )
+        yield "results", results

autogpt_platform/backend/backend/blocks/rag_prompting.py

@@ -0,0 +1,303 @@
+from enum import Enum
+
+import openai
+import requests
+from pinecone import Pinecone
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+class RAGTechnique(str, Enum):
+    BASIC = "basic"
+    COT = "chain_of_thought"
+    HYDE = "hypothetical_document"
+    MULTI_QUERY = "multi_query"
+
+
+class RAGPromptingBlock(Block):
+    class Input(BlockSchema):
+        index_name: str = SchemaField(description="Name of the Pinecone index")
+        pinecone_api_key: BlockSecret = SecretField(
+            key="pinecone_api_key", description="Pinecone API Key"
+        )
+        jina_api_key: BlockSecret = SecretField(
+            key="jina_api_key", description="Jina API Key"
+        )
+        openai_api_key: BlockSecret = SecretField(
+            key="openai_api_key", description="OpenAI API Key"
+        )
+        query: str = SchemaField(description="Natural language query")
+        namespace: str = SchemaField(
+            description="Namespace to query in Pinecone", default=""
+        )
+        top_k: int = SchemaField(
+            description="Number of top results to retrieve", default=3
+        )
+        rag_technique: RAGTechnique = SchemaField(
+            description="RAG technique to use", default=RAGTechnique.BASIC
+        )
+
+    class Output(BlockSchema):
+        response: str = SchemaField(
+            description="Natural language response based on retrieved information"
+        )
+        technique_used: str = SchemaField(
+            description="RAG technique used for this query"
+        )
+        error: str = SchemaField(description="Error message if query fails", default="")
+
+    def __init__(self):
+        super().__init__(
+            id="e9aeec7e-6333-44e7-a80e-4846f2a0b60b",
+            description="Advanced Pinecone query block with multiple RAG techniques",
+            categories={BlockCategory.AI, BlockCategory.LOGIC},
+            input_schema=RAGPromptingBlock.Input,
+            output_schema=RAGPromptingBlock.Output,
+        )
+
+    def get_embedding(self, text: str, api_key: str) -> list:
+        url = "https://api.jina.ai/v1/embeddings"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+        }
+        data = {"input": [text], "model": "jina-embeddings-v2-base-en"}
+        response = requests.post(url, headers=headers, json=data)
+        response.raise_for_status()
+        return response.json()["data"][0]["embedding"]
+
+    def query_pinecone(
+        self, index_name: str, api_key: str, vector: list, namespace: str, top_k: int
+    ) -> list:
+        pc = Pinecone(api_key=api_key)
+        index = pc.Index(index_name)
+        results = index.query(
+            vector=vector, top_k=top_k, include_metadata=True, namespace=namespace
+        )
+        return results.matches
+
+    def generate_hypothetical_document(self, query: str, api_key: str) -> str:
+        openai.api_key = api_key
+        response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an AI that generates hypothetical documents based on queries.",
+                },
+                {
+                    "role": "user",
+                    "content": f"Write a passage containing information about the following query: {query}",
+                },
+            ],
+            max_tokens=300,
+            n=1,
+            stop=None,
+            temperature=0.7,
+        )
+        return response.choices[0].message.content.strip()
+
+    def generate_sub_queries(
+        self, query: str, api_key: str, num_queries: int = 3
+    ) -> list:
+        openai.api_key = api_key
+        response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an AI that generates similar sub-queries based on an original query.",
+                },
+                {
+                    "role": "user",
+                    "content": f"Generate {num_queries} similar sub-queries for the following query: {query}",
+                },
+            ],
+            max_tokens=200,
+            n=1,
+            stop=None,
+            temperature=0.7,
+        )
+        sub_queries = response.choices[0].message.content.strip().split("\n")
+        return [
+            sq.split(". ", 1)[-1] for sq in sub_queries
+        ]  # Remove numbering if present
+
+    def basic_technique(self, query: str, api_keys: dict) -> str:
+        query_embedding = self.get_embedding(query, api_keys["jina"])
+        results = self.query_pinecone(
+            self.input_data.index_name,
+            api_keys["pinecone"],
+            query_embedding,
+            self.input_data.namespace,
+            self.input_data.top_k,
+        )
+        context = "\n".join([result["metadata"]["text"] for result in results])
+        prompt = f"Based on the following information, please answer the question: '{query}'\n\nContext:\n{context}\n\nAnswer:"
+
+        openai.api_key = api_keys["openai"]
+        response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a helpful assistant that answers questions based on the provided context.",
+                },
+                {"role": "user", "content": prompt},
+            ],
+            max_tokens=4096,
+            n=1,
+            stop=None,
+            temperature=0.7,
+        )
+        return response.choices[0].message.content.strip()
+
+    def chain_of_thought_technique(self, query: str, api_keys: dict) -> str:
+        # Retrieve relevant information
+        query_embedding = self.get_embedding(query, api_keys["jina"])
+        results = self.query_pinecone(
+            self.input_data.index_name,
+            api_keys["pinecone"],
+            query_embedding,
+            self.input_data.namespace,
+            self.input_data.top_k,
+        )
+        context = "\n".join([result["metadata"]["text"] for result in results])
+
+        # Construct the CoT prompt
+        cot_prompt = f"""To answer the question: '{query}', let's approach this step-by-step using the following information:
+
+Context:
+{context}
+
+Please follow these steps:
+1. Identify the key elements of the question.
+2. Analyze the relevant information from the context.
+3. Form a logical chain of reasoning.
+4. Arrive at a conclusion.
+
+Provide your thought process for each step, then give the final answer.
+
+Step-by-step reasoning:"""
+
+        openai.api_key = api_keys["openai"]
+        response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a helpful assistant that uses chain-of-thought reasoning to answer questions based on provided context.",
+                },
+                {"role": "user", "content": cot_prompt},
+            ],
+            max_tokens=4096,
+            n=1,
+            stop=None,
+            temperature=0.7,
+        )
+        return response.choices[0].message.content.strip()
+
+    def hyde_technique(self, query: str, api_keys: dict) -> str:
+        hyde_doc = self.generate_hypothetical_document(query, api_keys["openai"])
+        hyde_embedding = self.get_embedding(hyde_doc, api_keys["jina"])
+        results = self.query_pinecone(
+            self.input_data.index_name,
+            api_keys["pinecone"],
+            hyde_embedding,
+            self.input_data.namespace,
+            self.input_data.top_k,
+        )
+        context = "\n".join([result["metadata"]["text"] for result in results])
+        prompt = f"Based on the following information, please answer the question: '{query}'\n\nContext:\n{context}\n\nAnswer:"
+
+        openai.api_key = api_keys["openai"]
+        response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a helpful assistant that answers questions based on the provided context.",
+                },
+                {"role": "user", "content": prompt},
+            ],
+            max_tokens=4096,
+            n=1,
+            stop=None,
+            temperature=0.7,
+        )
+        return response.choices[0].message.content.strip()
+
+    def multi_query_technique(self, query: str, api_keys: dict) -> str:
+        # Generate sub-queries
+        sub_queries = self.generate_sub_queries(query, api_keys["openai"])
+
+        # Retrieve information for each sub-query and the original query
+        all_contexts = []
+        for q in [query] + sub_queries:
+            embedding = self.get_embedding(q, api_keys["jina"])
+            results = self.query_pinecone(
+                self.input_data.index_name,
+                api_keys["pinecone"],
+                embedding,
+                self.input_data.namespace,
+                self.input_data.top_k,
+            )
+            context = "\n".join([result["metadata"]["text"] for result in results])
+            all_contexts.append(f"Query: {q}\nContext: {context}\n")
+
+        # Combine all contexts
+        combined_context = "\n".join(all_contexts)
+
+        # Generate final answer using all retrieved information
+        prompt = f"""Based on the following information from multiple related queries, please provide a comprehensive answer to the original question: '{query}'
+
+Context from multiple queries:
+{combined_context}
+
+Comprehensive Answer:"""
+
+        openai.api_key = api_keys["openai"]
+        response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a helpful assistant that provides comprehensive answers based on information from multiple related queries.",
+                },
+                {"role": "user", "content": prompt},
+            ],
+            max_tokens=4096,
+            n=1,
+            stop=None,
+            temperature=0.7,
+        )
+        return response.choices[0].message.content.strip()
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        self.input_data = input_data
+        api_keys = {
+            "openai": input_data.openai_api_key.get_secret_value(),
+            "pinecone": input_data.pinecone_api_key.get_secret_value(),
+            "jina": input_data.jina_api_key.get_secret_value(),
+        }
+
+        try:
+            if input_data.rag_technique == RAGTechnique.BASIC:
+                response = self.basic_technique(input_data.query, api_keys)
+            elif input_data.rag_technique == RAGTechnique.HYDE:
+                response = self.hyde_technique(input_data.query, api_keys)
+            elif input_data.rag_technique == RAGTechnique.MULTI_QUERY:
+                response = self.multi_query_technique(input_data.query, api_keys)
+            elif input_data.rag_technique == RAGTechnique.COT:
+                response = self.chain_of_thought_technique(input_data.query, api_keys)
+            else:
+                raise ValueError(f"Unknown RAG technique: {input_data.rag_technique}")
+
+            yield "response", response
+            yield "technique_used", input_data.rag_technique.value
+        except Exception as e:
+            error_message = f"Error during query process: {str(e)}"
+            yield "error", error_message
+            yield "response", "I'm sorry, but I encountered an error while processing your query."
+            yield "technique_used", "none"

autogpt_platform/backend/backend/blocks/reddit.py

@@ -4,9 +4,9 @@ from typing import Iterator
 import praw
 from pydantic import BaseModel, ConfigDict, Field
 
-from autogpt_server.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from autogpt_server.data.model import BlockSecret, SecretField
-from autogpt_server.util.mock import MockObject
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SecretField
+from backend.util.mock import MockObject
 
 
 class RedditCredentials(BaseModel):
@@ -46,7 +46,7 @@ def get_praw(creds: RedditCredentials) -> praw.Reddit:
     return client
 
 
-class RedditGetPostsBlock(Block):
+class GetRedditPostsBlock(Block):
     class Input(BlockSchema):
         subreddit: str = Field(description="Subreddit name")
         creds: RedditCredentials = Field(
@@ -73,8 +73,8 @@ class RedditGetPostsBlock(Block):
             id="c6731acb-4285-4ee1-bc9b-03d0766c370f",
             description="This block fetches Reddit posts from a defined subreddit name.",
             categories={BlockCategory.SOCIAL},
-            input_schema=RedditGetPostsBlock.Input,
-            output_schema=RedditGetPostsBlock.Output,
+            input_schema=GetRedditPostsBlock.Input,
+            output_schema=GetRedditPostsBlock.Output,
             test_input={
                 "creds": {
                     "client_id": "client_id",
@@ -116,7 +116,7 @@ class RedditGetPostsBlock(Block):
         subreddit = client.subreddit(input_data.subreddit)
         return subreddit.new(limit=input_data.post_limit)
 
-    def run(self, input_data: Input) -> BlockOutput:
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
         current_time = datetime.now(tz=timezone.utc)
         for post in self.get_posts(input_data):
             if input_data.last_minutes:
@@ -138,7 +138,7 @@ class RedditGetPostsBlock(Block):
             )
 
 
-class RedditPostCommentBlock(Block):
+class PostRedditCommentBlock(Block):
     class Input(BlockSchema):
         creds: RedditCredentials = Field(
             description="Reddit credentials", default=RedditCredentials()
@@ -153,8 +153,8 @@ class RedditPostCommentBlock(Block):
             id="4a92261b-701e-4ffb-8970-675fd28e261f",
             description="This block posts a Reddit comment on a specified Reddit post.",
             categories={BlockCategory.SOCIAL},
-            input_schema=RedditPostCommentBlock.Input,
-            output_schema=RedditPostCommentBlock.Output,
+            input_schema=PostRedditCommentBlock.Input,
+            output_schema=PostRedditCommentBlock.Output,
             test_input={"data": {"post_id": "id", "comment": "comment"}},
             test_output=[("comment_id", "dummy_comment_id")],
             test_mock={"reply_post": lambda creds, comment: "dummy_comment_id"},
@@ -167,5 +167,5 @@ class RedditPostCommentBlock(Block):
         comment = submission.reply(comment.comment)
         return comment.id  # type: ignore
 
-    def run(self, input_data: Input) -> BlockOutput:
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
         yield "comment_id", self.reply_post(input_data.creds, input_data.data)

autogpt_platform/backend/backend/blocks/replicate_flux_advanced.py

@@ -0,0 +1,204 @@
+import os
+from enum import Enum
+
+import replicate
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import BlockSecret, SchemaField, SecretField
+
+
+# Model name enum
+class ReplicateFluxModelName(str, Enum):
+    FLUX_SCHNELL = ("Flux Schnell",)
+    FLUX_PRO = ("Flux Pro",)
+    FLUX_PRO1_1 = ("Flux Pro 1.1",)
+
+    @property
+    def api_name(self):
+        api_names = {
+            ReplicateFluxModelName.FLUX_SCHNELL: "black-forest-labs/flux-schnell",
+            ReplicateFluxModelName.FLUX_PRO: "black-forest-labs/flux-pro",
+            ReplicateFluxModelName.FLUX_PRO1_1: "black-forest-labs/flux-1.1-pro",
+        }
+        return api_names[self]
+
+
+# Image type Enum
+class ImageType(str, Enum):
+    WEBP = "webp"
+    JPG = "jpg"
+    PNG = "png"
+
+
+class ReplicateFluxAdvancedModelBlock(Block):
+    class Input(BlockSchema):
+        api_key: BlockSecret = SecretField(
+            key="replicate_api_key",
+            description="Replicate API Key",
+        )
+        prompt: str = SchemaField(
+            description="Text prompt for image generation",
+            placeholder="e.g., 'A futuristic cityscape at sunset'",
+            title="Prompt",
+        )
+        replicate_model_name: ReplicateFluxModelName = SchemaField(
+            description="The name of the Image Generation Model, i.e Flux Schnell",
+            default=ReplicateFluxModelName.FLUX_SCHNELL,
+            title="Image Generation Model",
+            advanced=False,
+        )
+        seed: int | None = SchemaField(
+            description="Random seed. Set for reproducible generation",
+            default=None,
+            title="Seed",
+        )
+        steps: int = SchemaField(
+            description="Number of diffusion steps",
+            default=25,
+            title="Steps",
+        )
+        guidance: float = SchemaField(
+            description=(
+                "Controls the balance between adherence to the text prompt and image quality/diversity. "
+                "Higher values make the output more closely match the prompt but may reduce overall image quality."
+            ),
+            default=3,
+            title="Guidance",
+        )
+        interval: float = SchemaField(
+            description=(
+                "Interval is a setting that increases the variance in possible outputs. "
+                "Setting this value low will ensure strong prompt following with more consistent outputs."
+            ),
+            default=2,
+            title="Interval",
+        )
+        aspect_ratio: str = SchemaField(
+            description="Aspect ratio for the generated image",
+            default="1:1",
+            title="Aspect Ratio",
+            placeholder="Choose from: 1:1, 16:9, 2:3, 3:2, 4:5, 5:4, 9:16",
+        )
+        output_format: ImageType = SchemaField(
+            description="File format of the output image",
+            default=ImageType.WEBP,
+            title="Output Format",
+        )
+        output_quality: int = SchemaField(
+            description=(
+                "Quality when saving the output images, from 0 to 100. "
+                "Not relevant for .png outputs"
+            ),
+            default=80,
+            title="Output Quality",
+        )
+        safety_tolerance: int = SchemaField(
+            description="Safety tolerance, 1 is most strict and 5 is most permissive",
+            default=2,
+            title="Safety Tolerance",
+        )
+
+    class Output(BlockSchema):
+        result: str = SchemaField(description="Generated output")
+        error: str = SchemaField(description="Error message if the model run failed")
+
+    def __init__(self):
+        super().__init__(
+            id="90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
+            description="This block runs Flux models on Replicate with advanced settings.",
+            categories={BlockCategory.AI},
+            input_schema=ReplicateFluxAdvancedModelBlock.Input,
+            output_schema=ReplicateFluxAdvancedModelBlock.Output,
+            test_input={
+                "api_key": "test_api_key",
+                "replicate_model_name": ReplicateFluxModelName.FLUX_SCHNELL,
+                "prompt": "A beautiful landscape painting of a serene lake at sunrise",
+                "seed": None,
+                "steps": 25,
+                "guidance": 3.0,
+                "interval": 2.0,
+                "aspect_ratio": "1:1",
+                "output_format": ImageType.PNG,
+                "output_quality": 80,
+                "safety_tolerance": 2,
+            },
+            test_output=[
+                (
+                    "result",
+                    "https://replicate.com/output/generated-image-url.jpg",
+                ),
+            ],
+            test_mock={
+                "run_model": lambda api_key, model_name, prompt, seed, steps, guidance, interval, aspect_ratio, output_format, output_quality, safety_tolerance: "https://replicate.com/output/generated-image-url.jpg",
+            },
+        )
+
+    def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        # If the seed is not provided, generate a random seed
+        seed = input_data.seed
+        if seed is None:
+            seed = int.from_bytes(os.urandom(4), "big")
+
+        try:
+            # Run the model using the provided inputs
+            result = self.run_model(
+                api_key=input_data.api_key.get_secret_value(),
+                model_name=input_data.replicate_model_name.api_name,
+                prompt=input_data.prompt,
+                seed=seed,
+                steps=input_data.steps,
+                guidance=input_data.guidance,
+                interval=input_data.interval,
+                aspect_ratio=input_data.aspect_ratio,
+                output_format=input_data.output_format,
+                output_quality=input_data.output_quality,
+                safety_tolerance=input_data.safety_tolerance,
+            )
+            yield "result", result
+        except Exception as e:
+            yield "error", str(e)
+
+    def run_model(
+        self,
+        api_key,
+        model_name,
+        prompt,
+        seed,
+        steps,
+        guidance,
+        interval,
+        aspect_ratio,
+        output_format,
+        output_quality,
+        safety_tolerance,
+    ):
+        # Initialize Replicate client with the API key
+        client = replicate.Client(api_token=api_key)
+
+        # Run the model with additional parameters
+        output = client.run(
+            f"{model_name}",
+            input={
+                "prompt": prompt,
+                "seed": seed,
+                "steps": steps,
+                "guidance": guidance,
+                "interval": interval,
+                "aspect_ratio": aspect_ratio,
+                "output_format": output_format,
+                "output_quality": output_quality,
+                "safety_tolerance": safety_tolerance,
+            },
+        )
+
+        # Check if output is a list or a string and extract accordingly; otherwise, assign a default message
+        if isinstance(output, list) and len(output) > 0:
+            result_url = output[0]  # If output is a list, get the first element
+        elif isinstance(output, str):
+            result_url = output  # If output is a string, use it directly
+        else:
+            result_url = (
+                "No output received"  # Fallback message if output is not as expected
+            )
+
+        return result_url