fix: determine clarification answered state from conversation

Instead of localStorage, check if there's a user message after the
clarification request in the conversation. This works across browsers
since the conversation is stored in the backend.

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

@@ -6,15 +6,11 @@ on:
     paths:
       - '.github/workflows/classic-autogpt-ci.yml'
       - 'classic/original_autogpt/**'
-      - 'classic/direct_benchmark/**'
-      - 'classic/forge/**'
   pull_request:
     branches: [ master, dev, release-* ]
     paths:
       - '.github/workflows/classic-autogpt-ci.yml'
       - 'classic/original_autogpt/**'
-      - 'classic/direct_benchmark/**'
-      - 'classic/forge/**'
 
 concurrency:
   group: ${{ format('classic-autogpt-ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
@@ -23,22 +19,47 @@ concurrency:
 defaults:
   run:
     shell: bash
-    working-directory: classic
+    working-directory: classic/original_autogpt
 
 jobs:
   test:
     permissions:
       contents: read
     timeout-minutes: 30
-    runs-on: ubuntu-latest
+    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: Start MinIO service
+      # 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:
@@ -50,23 +71,41 @@ jobs:
           git config --global user.name "Auto-GPT-Bot"
           git config --global user.email "github-bot@agpt.co"
 
-      - name: Set up Python 3.12
+      - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          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: ~/.cache/pypoetry
+          path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('classic/poetry.lock') }}
+          key: poetry-${{ runner.os }}-${{ hashFiles('classic/original_autogpt/poetry.lock') }}
 
-      - name: Install Poetry
+      - name: Install Poetry (Unix)
-        run: curl -sSL https://install.python-poetry.org | python3 -
+        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
@@ -77,12 +116,12 @@ jobs:
             --cov=autogpt --cov-branch --cov-report term-missing --cov-report xml \
             --numprocesses=logical --durations=10 \
             --junitxml=junit.xml -o junit_family=legacy \
-            original_autogpt/tests/unit original_autogpt/tests/integration
+            tests/unit tests/integration
         env:
           CI: true
           PLAIN_OUTPUT: True
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          S3_ENDPOINT_URL: http://127.0.0.1:9000
+          S3_ENDPOINT_URL: ${{ runner.os != 'Windows' && 'http://127.0.0.1:9000' || '' }}
           AWS_ACCESS_KEY_ID: minioadmin
           AWS_SECRET_ACCESS_KEY: minioadmin
 
@@ -96,11 +135,11 @@ jobs:
         uses: codecov/codecov-action@v5
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-          flags: autogpt-agent
+          flags: autogpt-agent,${{ runner.os }}
 
       - name: Upload logs to artifact
         if: always()
         uses: actions/upload-artifact@v4
         with:
           name: test-logs
-          path: classic/logs/
+          path: classic/original_autogpt/logs/

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

@@ -11,6 +11,9 @@ on:
       - 'classic/original_autogpt/**'
       - 'classic/forge/**'
       - 'classic/benchmark/**'
+      - 'classic/run'
+      - 'classic/cli.py'
+      - 'classic/setup.py'
       - '!**/*.md'
   pull_request:
     branches: [ master, dev, release-* ]
@@ -19,6 +22,9 @@ on:
       - 'classic/original_autogpt/**'
       - 'classic/forge/**'
       - 'classic/benchmark/**'
+      - 'classic/run'
+      - 'classic/cli.py'
+      - 'classic/setup.py'
       - '!**/*.md'
 
 defaults:
@@ -29,9 +35,13 @@ defaults:
 jobs:
   serve-agent-protocol:
     runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        agent-name: [ original_autogpt ]
+      fail-fast: false
     timeout-minutes: 20
     env:
-      min-python-version: '3.12'
+      min-python-version: '3.10'
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -45,22 +55,22 @@ jobs:
           python-version: ${{ env.min-python-version }}
 
       - name: Install Poetry
+        working-directory: ./classic/${{ matrix.agent-name }}/
         run: |
           curl -sSL https://install.python-poetry.org | python -
 
-      - name: Install dependencies
+      - name: Run regression tests
-        run: poetry install
-
-      - name: Run smoke tests with direct-benchmark
         run: |
-          poetry run direct-benchmark run \
+          ./run agent start ${{ matrix.agent-name }}
-            --strategies one_shot \
+          cd ${{ matrix.agent-name }}
-            --models claude \
+          poetry run agbenchmark --mock --test=BasicRetrieval --test=Battleship --test=WebArenaTask_0
-            --tests ReadFile,WriteFile \
+          poetry run agbenchmark --test=WriteFile
-            --json
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          AGENT_NAME: ${{ matrix.agent-name }}
           REQUESTS_CA_BUNDLE: /etc/ssl/certs/ca-certificates.crt
-          NONINTERACTIVE_MODE: "true"
+          HELICONE_CACHE_ENABLED: false
-          CI: true
+          HELICONE_PROPERTY_AGENT: ${{ matrix.agent-name }}
+          REPORTS_FOLDER: ${{ format('../../reports/{0}', matrix.agent-name) }}
+          TELEMETRY_ENVIRONMENT: autogpt-ci
+          TELEMETRY_OPT_IN: ${{ github.ref_name == 'master' }}

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

@@ -1,21 +1,17 @@
-name: Classic - Direct Benchmark CI
+name: Classic - AGBenchmark CI
 
 on:
   push:
     branches: [ master, dev, ci-test* ]
     paths:
-      - 'classic/direct_benchmark/**'
+      - 'classic/benchmark/**'
-      - 'classic/benchmark/agbenchmark/challenges/**'
+      - '!classic/benchmark/reports/**'
-      - 'classic/original_autogpt/**'
-      - 'classic/forge/**'
       - .github/workflows/classic-benchmark-ci.yml
   pull_request:
     branches: [ master, dev, release-* ]
     paths:
-      - 'classic/direct_benchmark/**'
+      - 'classic/benchmark/**'
-      - 'classic/benchmark/agbenchmark/challenges/**'
+      - '!classic/benchmark/reports/**'
-      - 'classic/original_autogpt/**'
-      - 'classic/forge/**'
       - .github/workflows/classic-benchmark-ci.yml
 
 concurrency:
@@ -27,16 +23,23 @@ defaults:
     shell: bash
 
 env:
-  min-python-version: '3.12'
+  min-python-version: '3.10'
 
 jobs:
-  benchmark-tests:
+  test:
-    runs-on: ubuntu-latest
+    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' }}
     defaults:
       run:
         shell: bash
-        working-directory: classic
+        working-directory: classic/benchmark
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -44,88 +47,71 @@ jobs:
           fetch-depth: 0
           submodules: true
 
-      - name: Set up Python ${{ env.min-python-version }}
+      - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
-          python-version: ${{ env.min-python-version }}
+          python-version: ${{ matrix.python-version }}
 
       - 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: ~/.cache/pypoetry
+          path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('classic/poetry.lock') }}
+          key: poetry-${{ runner.os }}-${{ hashFiles('classic/benchmark/poetry.lock') }}
 
-      - name: Install Poetry
+      - name: Install Poetry (Unix)
+        if: runner.os != 'Windows'
         run: |
           curl -sSL https://install.python-poetry.org | python3 -
 
-      - name: Install dependencies
+          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: Run basic benchmark tests
+      - name: Run pytest with coverage
         run: |
-          echo "Testing ReadFile challenge with one_shot strategy..."
+          poetry run pytest -vv \
-          poetry run direct-benchmark run \
+            --cov=agbenchmark --cov-branch --cov-report term-missing --cov-report xml \
-            --fresh \
+            --durations=10 \
-            --strategies one_shot \
+            --junitxml=junit.xml -o junit_family=legacy \
-            --models claude \
+            tests
-            --tests ReadFile \
-            --json
-
-          echo "Testing WriteFile challenge..."
-          poetry run direct-benchmark run \
-            --fresh \
-            --strategies one_shot \
-            --models claude \
-            --tests WriteFile \
-            --json
         env:
           CI: true
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          NONINTERACTIVE_MODE: "true"
 
-      - name: Test category filtering
+      - name: Upload test results to Codecov
-        run: |
+        if: ${{ !cancelled() }}  # Run even if tests fail
-          echo "Testing coding category..."
+        uses: codecov/test-results-action@v1
-          poetry run direct-benchmark run \
+        with:
-            --fresh \
+          token: ${{ secrets.CODECOV_TOKEN }}
-            --strategies one_shot \
-            --models claude \
-            --categories coding \
-            --tests ReadFile,WriteFile \
-            --json
-        env:
-          CI: true
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          NONINTERACTIVE_MODE: "true"
 
-      - name: Test multiple strategies
+      - name: Upload coverage reports to Codecov
-        run: |
+        uses: codecov/codecov-action@v5
-          echo "Testing multiple strategies..."
+        with:
-          poetry run direct-benchmark run \
+          token: ${{ secrets.CODECOV_TOKEN }}
-            --fresh \
+          flags: agbenchmark,${{ runner.os }}
-            --strategies one_shot,plan_execute \
-            --models claude \
-            --tests ReadFile \
-            --parallel 2 \
-            --json
-        env:
-          CI: true
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          NONINTERACTIVE_MODE: "true"
 
-  # Run regression tests on maintain challenges
+  self-test-with-agent:
-  regression-tests:
     runs-on: ubuntu-latest
-    timeout-minutes: 45
+    strategy:
-    if: github.ref == 'refs/heads/master' || github.ref == 'refs/heads/dev'
+      matrix:
-    defaults:
+        agent-name: [forge]
-      run:
+      fail-fast: false
-        shell: bash
+    timeout-minutes: 20
-        working-directory: classic
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -140,23 +126,51 @@ jobs:
 
       - name: Install Poetry
         run: |
-          curl -sSL https://install.python-poetry.org | python3 -
+          curl -sSL https://install.python-poetry.org | python -
-
-      - name: Install dependencies
-        run: poetry install
 
       - name: Run regression tests
+        working-directory: classic
         run: |
-          echo "Running regression tests (previously beaten challenges)..."
+          ./run agent start ${{ matrix.agent-name }}
-          poetry run direct-benchmark run \
+          cd ${{ matrix.agent-name }}
-            --fresh \
+
-            --strategies one_shot \
+          set +e # Ignore non-zero exit codes and continue execution
-            --models claude \
+          echo "Running the following command: poetry run agbenchmark --maintain --mock"
-            --maintain \
+          poetry run agbenchmark --maintain --mock
-            --parallel 4 \
+          EXIT_CODE=$?
-            --json
+          set -e  # Stop ignoring non-zero exit codes
+          # Check if the exit code was 5, and if so, exit with 0 instead
+          if [ $EXIT_CODE -eq 5 ]; then
+            echo "regression_tests.json is empty."
+          fi
+
+          echo "Running the following command: poetry run agbenchmark --mock"
+          poetry run agbenchmark --mock
+
+          echo "Running the following command: poetry run agbenchmark --mock --category=data"
+          poetry run agbenchmark --mock --category=data
+
+          echo "Running the following command: poetry run agbenchmark --mock --category=coding"
+          poetry run agbenchmark --mock --category=coding
+
+          # echo "Running the following command: poetry run agbenchmark --test=WriteFile"
+          # poetry run agbenchmark --test=WriteFile
+          cd ../benchmark
+          poetry install
+          echo "Adding the BUILD_SKILL_TREE environment variable. This will attempt to add new elements in the skill tree. If new elements are added, the CI fails because they should have been pushed"
+          export BUILD_SKILL_TREE=true
+
+          # poetry run agbenchmark --mock
+
+          # CHANGED=$(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"
+          #   exit 1
+          # else
+          #   echo "No unstaged changes."
+          # fi
         env:
-          CI: true
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          NONINTERACTIVE_MODE: "true"
+          TELEMETRY_ENVIRONMENT: autogpt-benchmark-ci
+          TELEMETRY_OPT_IN: ${{ github.ref_name == 'master' }}

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

@@ -6,11 +6,13 @@ on:
     paths:
       - '.github/workflows/classic-forge-ci.yml'
       - 'classic/forge/**'
+      - '!classic/forge/tests/vcr_cassettes'
   pull_request:
     branches: [ master, dev, release-* ]
     paths:
       - '.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) }}
@@ -19,38 +21,115 @@ concurrency:
 defaults:
   run:
     shell: bash
-    working-directory: classic
+    working-directory: classic/forge
 
 jobs:
   test:
     permissions:
       contents: read
     timeout-minutes: 30
-    runs-on: ubuntu-latest
+    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: Start MinIO service
+      # 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 3.12
+      - name: Checkout cassettes
+        if: ${{ startsWith(github.event_name, 'pull_request') }}
+        env:
+          PR_BASE: ${{ github.event.pull_request.base.ref }}
+          PR_BRANCH: ${{ github.event.pull_request.head.ref }}
+          PR_AUTHOR: ${{ github.event.pull_request.user.login }}
+        run: |
+          cassette_branch="${PR_AUTHOR}-${PR_BRANCH}"
+          cassette_base_branch="${PR_BASE}"
+          cd tests/vcr_cassettes
+
+          if ! git ls-remote --exit-code --heads origin $cassette_base_branch ; then
+            cassette_base_branch="master"
+          fi
+
+          if git ls-remote --exit-code --heads origin $cassette_branch ; then
+            git fetch origin $cassette_branch
+            git fetch origin $cassette_base_branch
+
+            git checkout $cassette_branch
+
+            # Pick non-conflicting cassette updates from the base branch
+            git merge --no-commit --strategy-option=ours origin/$cassette_base_branch
+            echo "Using cassettes from mirror branch '$cassette_branch'," \
+              "synced to upstream branch '$cassette_base_branch'."
+          else
+            git checkout -b $cassette_branch
+            echo "Branch '$cassette_branch' does not exist in cassette submodule." \
+              "Using cassettes from '$cassette_base_branch'."
+          fi
+
+      - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: ${{ matrix.python-version }}
 
       - 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: ~/.cache/pypoetry
+          path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('classic/poetry.lock') }}
+          key: poetry-${{ runner.os }}-${{ hashFiles('classic/forge/poetry.lock') }}
 
-      - name: Install Poetry
+      - name: Install Poetry (Unix)
-        run: curl -sSL https://install.python-poetry.org | python3 -
+        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
@@ -61,15 +140,12 @@ jobs:
             --cov=forge --cov-branch --cov-report term-missing --cov-report xml \
             --durations=10 \
             --junitxml=junit.xml -o junit_family=legacy \
-            forge/forge forge/tests
+            forge
         env:
           CI: true
           PLAIN_OUTPUT: True
-          # API keys - tests that need these will skip if not available
-          # Secrets are not available to fork PRs (GitHub security feature)
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          S3_ENDPOINT_URL: ${{ runner.os != 'Windows' && 'http://127.0.0.1:9000' || '' }}
-          S3_ENDPOINT_URL: http://127.0.0.1:9000
           AWS_ACCESS_KEY_ID: minioadmin
           AWS_SECRET_ACCESS_KEY: minioadmin
 
@@ -83,11 +159,85 @@ jobs:
         uses: codecov/codecov-action@v5
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-          flags: forge
+          flags: forge,${{ runner.os }}
+
+      - id: setup_git_auth
+        name: Set up git token authentication
+        # Cassettes may be pushed even when tests fail
+        if: success() || failure()
+        run: |
+          config_key="http.${{ github.server_url }}/.extraheader"
+          if [ "${{ runner.os }}" = 'macOS' ]; then
+            base64_pat=$(echo -n "pat:${{ secrets.PAT_REVIEW }}" | base64)
+          else
+            base64_pat=$(echo -n "pat:${{ secrets.PAT_REVIEW }}" | base64 -w0)
+          fi
+
+          git config "$config_key" \
+            "Authorization: Basic $base64_pat"
+
+          cd tests/vcr_cassettes
+          git config "$config_key" \
+            "Authorization: Basic $base64_pat"
+
+          echo "config_key=$config_key" >> $GITHUB_OUTPUT
+
+      - id: push_cassettes
+        name: Push updated cassettes
+        # For pull requests, push updated cassettes even when tests fail
+        if: github.event_name == 'push' || (! github.event.pull_request.head.repo.fork && (success() || failure()))
+        env:
+          PR_BRANCH: ${{ github.event.pull_request.head.ref }}
+          PR_AUTHOR: ${{ github.event.pull_request.user.login }}
+        run: |
+          if [ "${{ startsWith(github.event_name, 'pull_request') }}" = "true" ]; then
+            is_pull_request=true
+            cassette_branch="${PR_AUTHOR}-${PR_BRANCH}"
+          else
+            cassette_branch="${{ github.ref_name }}"
+          fi
+
+          cd tests/vcr_cassettes
+          # Commit & push changes to cassettes if any
+          if ! git diff --quiet; then
+            git add .
+            git commit -m "Auto-update cassettes"
+            git push origin HEAD:$cassette_branch
+            if [ ! $is_pull_request ]; then
+              cd ../..
+              git add tests/vcr_cassettes
+              git commit -m "Update cassette submodule"
+              git push origin HEAD:$cassette_branch
+            fi
+            echo "updated=true" >> $GITHUB_OUTPUT
+          else
+            echo "updated=false" >> $GITHUB_OUTPUT
+            echo "No cassette changes to commit"
+          fi
+
+      - name: Post Set up git token auth
+        if: steps.setup_git_auth.outcome == 'success'
+        run: |
+          git config --unset-all '${{ steps.setup_git_auth.outputs.config_key }}'
+          git submodule foreach git config --unset-all '${{ steps.setup_git_auth.outputs.config_key }}'
+
+      - name: Apply "behaviour change" label and comment on PR
+        if: ${{ startsWith(github.event_name, 'pull_request') }}
+        run: |
+          PR_NUMBER="${{ github.event.pull_request.number }}"
+          TOKEN="${{ secrets.PAT_REVIEW }}"
+          REPO="${{ github.repository }}"
+
+          if [[ "${{ steps.push_cassettes.outputs.updated }}" == "true" ]]; then
+            echo "Adding label and comment..."
+            echo $TOKEN | gh auth login --with-token
+            gh issue edit $PR_NUMBER --add-label "behaviour change"
+            gh issue comment $PR_NUMBER --body "You changed AutoGPT's behaviour on ${{ runner.os }}. The cassettes have been updated and will be merged to the submodule when this Pull Request gets merged."
+          fi
 
       - name: Upload logs to artifact
         if: always()
         uses: actions/upload-artifact@v4
         with:
           name: test-logs
-          path: classic/logs/
+          path: classic/forge/logs/

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

@@ -0,0 +1,60 @@
+name: Classic - Frontend CI/CD
+
+on:
+  push:
+    branches:
+      - master
+      - dev
+      - 'ci-test*' # This will match any branch that starts with "ci-test"
+    paths:
+      - 'classic/frontend/**'
+      - '.github/workflows/classic-frontend-ci.yml'
+  pull_request:
+    paths:
+      - 'classic/frontend/**'
+      - '.github/workflows/classic-frontend-ci.yml'
+
+jobs:
+  build:
+    permissions:
+      contents: write
+      pull-requests: write
+    runs-on: ubuntu-latest
+    env:
+      BUILD_BRANCH: ${{ format('classic-frontend-build/{0}', github.ref_name) }}
+
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v4
+
+      - name: Setup Flutter
+        uses: subosito/flutter-action@v2
+        with:
+          flutter-version: '3.13.2'
+
+      - name: Build Flutter to Web
+        run: |
+          cd classic/frontend
+          flutter build web --base-href /app/
+
+      # - name: Commit and Push to ${{ env.BUILD_BRANCH }}
+      #   if: github.event_name == 'push'
+      #   run: |
+      #     git config --local user.email "action@github.com"
+      #     git config --local user.name "GitHub Action"
+      #     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 }}
+
+      - name: Create PR ${{ env.BUILD_BRANCH }} -> ${{ github.ref_name }}
+        if: github.event_name == 'push'
+        uses: peter-evans/create-pull-request@v7
+        with:
+          add-paths: classic/frontend/build/web
+          base: ${{ github.ref_name }}
+          branch: ${{ env.BUILD_BRANCH }}
+          delete-branch: true
+          title: "Update frontend build in `${{ github.ref_name }}`"
+          body: "This PR updates the frontend build based on commit ${{ github.sha }}."
+          commit-message: "Update frontend build based on commit ${{ github.sha }}"

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

@@ -7,9 +7,7 @@ on:
       - '.github/workflows/classic-python-checks-ci.yml'
       - 'classic/original_autogpt/**'
       - 'classic/forge/**'
-      - 'classic/direct_benchmark/**'
+      - 'classic/benchmark/**'
-      - 'classic/pyproject.toml'
-      - 'classic/poetry.lock'
       - '**.py'
       - '!classic/forge/tests/vcr_cassettes'
   pull_request:
@@ -18,9 +16,7 @@ on:
       - '.github/workflows/classic-python-checks-ci.yml'
       - 'classic/original_autogpt/**'
       - 'classic/forge/**'
-      - 'classic/direct_benchmark/**'
+      - 'classic/benchmark/**'
-      - 'classic/pyproject.toml'
-      - 'classic/poetry.lock'
       - '**.py'
       - '!classic/forge/tests/vcr_cassettes'
 
@@ -31,13 +27,44 @@ concurrency:
 defaults:
   run:
     shell: bash
-    working-directory: classic
 
 jobs:
+  get-changed-parts:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - id: changes-in
+        name: Determine affected subprojects
+        uses: dorny/paths-filter@v3
+        with:
+          filters: |
+            original_autogpt:
+              - classic/original_autogpt/autogpt/**
+              - classic/original_autogpt/tests/**
+              - classic/original_autogpt/poetry.lock
+            forge:
+              - classic/forge/forge/**
+              - classic/forge/tests/**
+              - classic/forge/poetry.lock
+            benchmark:
+              - classic/benchmark/agbenchmark/**
+              - classic/benchmark/tests/**
+              - classic/benchmark/poetry.lock
+    outputs:
+      changed-parts: ${{ steps.changes-in.outputs.changes }}
+
   lint:
+    needs: get-changed-parts
     runs-on: ubuntu-latest
     env:
-      min-python-version: "3.12"
+      min-python-version: "3.10"
+
+    strategy:
+      matrix:
+        sub-package: ${{ fromJson(needs.get-changed-parts.outputs.changed-parts) }}
+      fail-fast: false
 
     steps:
       - name: Checkout repository
@@ -54,31 +81,42 @@ jobs:
         uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
-          key: ${{ runner.os }}-poetry-${{ hashFiles('classic/poetry.lock') }}
+          key: ${{ runner.os }}-poetry-${{ hashFiles(format('{0}/poetry.lock', matrix.sub-package)) }}
 
       - name: Install Poetry
         run: curl -sSL https://install.python-poetry.org | python3 -
 
+      # Install dependencies
+
       - name: Install Python dependencies
-        run: poetry install
+        run: poetry -C classic/${{ matrix.sub-package }} install
 
       # Lint
 
       - name: Lint (isort)
         run: poetry run isort --check .
+        working-directory: classic/${{ matrix.sub-package }}
 
       - name: Lint (Black)
         if: success() || failure()
         run: poetry run black --check .
+        working-directory: classic/${{ matrix.sub-package }}
 
       - name: Lint (Flake8)
         if: success() || failure()
         run: poetry run flake8 .
+        working-directory: classic/${{ matrix.sub-package }}
 
   types:
+    needs: get-changed-parts
     runs-on: ubuntu-latest
     env:
-      min-python-version: "3.12"
+      min-python-version: "3.10"
+
+    strategy:
+      matrix:
+        sub-package: ${{ fromJson(needs.get-changed-parts.outputs.changed-parts) }}
+      fail-fast: false
 
     steps:
       - name: Checkout repository
@@ -95,16 +133,19 @@ jobs:
         uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry
-          key: ${{ runner.os }}-poetry-${{ hashFiles('classic/poetry.lock') }}
+          key: ${{ runner.os }}-poetry-${{ hashFiles(format('{0}/poetry.lock', matrix.sub-package)) }}
 
       - name: Install Poetry
         run: curl -sSL https://install.python-poetry.org | python3 -
 
+      # Install dependencies
+
       - name: Install Python dependencies
-        run: poetry install
+        run: poetry -C classic/${{ matrix.sub-package }} install
 
       # Typecheck
 
       - name: Typecheck
         if: success() || failure()
         run: poetry run pyright
+        working-directory: classic/${{ matrix.sub-package }}

.gitignore

@@ -3,7 +3,6 @@
 classic/original_autogpt/keys.py
 classic/original_autogpt/*.json
 auto_gpt_workspace/*
-.autogpt/
 *.mpeg
 .env
 # Root .env files
@@ -160,10 +159,6 @@ CURRENT_BULLETIN.md
 
 # AgBenchmark
 classic/benchmark/agbenchmark/reports/
-classic/reports/
-classic/direct_benchmark/reports/
-classic/.benchmark_workspaces/
-classic/direct_benchmark/.benchmark_workspaces/
 
 # Nodejs
 package-lock.json
@@ -182,10 +177,6 @@ autogpt_platform/backend/settings.py
 
 *.ign.*
 .test-contents
-**/.claude/settings.local.json
 .claude/settings.local.json
 CLAUDE.local.md
 /autogpt_platform/backend/logs
-
-# Test database
-test.db

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "classic/forge/tests/vcr_cassettes"]
+	path = classic/forge/tests/vcr_cassettes
+	url = https://github.com/Significant-Gravitas/Auto-GPT-test-cassettes

.pre-commit-config.yaml

@@ -43,10 +43,29 @@ repos:
         pass_filenames: false
 
       - id: poetry-install
-        name: Check & Install dependencies - Classic
+        name: Check & Install dependencies - Classic - AutoGPT
-        alias: poetry-install-classic
+        alias: poetry-install-classic-autogpt
-        entry: poetry -C classic install
+        entry: poetry -C classic/original_autogpt install
-        files: ^classic/poetry\.lock$
+        # include forge source (since it's a path dependency)
+        files: ^classic/(original_autogpt|forge)/poetry\.lock$
+        types: [file]
+        language: system
+        pass_filenames: false
+
+      - id: poetry-install
+        name: Check & Install dependencies - Classic - Forge
+        alias: poetry-install-classic-forge
+        entry: poetry -C classic/forge install
+        files: ^classic/forge/poetry\.lock$
+        types: [file]
+        language: system
+        pass_filenames: false
+
+      - id: poetry-install
+        name: Check & Install dependencies - Classic - Benchmark
+        alias: poetry-install-classic-benchmark
+        entry: poetry -C classic/benchmark install
+        files: ^classic/benchmark/poetry\.lock$
         types: [file]
         language: system
         pass_filenames: false
@@ -97,10 +116,26 @@ repos:
         language: system
 
       - id: isort
-        name: Lint (isort) - Classic
+        name: Lint (isort) - Classic - AutoGPT
-        alias: isort-classic
+        alias: isort-classic-autogpt
-        entry: bash -c 'cd classic && poetry run isort $(echo "$@" | sed "s|classic/||g")' --
+        entry: poetry -P classic/original_autogpt run isort -p autogpt
-        files: ^classic/(original_autogpt|forge|direct_benchmark)/
+        files: ^classic/original_autogpt/
+        types: [file, python]
+        language: system
+
+      - id: isort
+        name: Lint (isort) - Classic - Forge
+        alias: isort-classic-forge
+        entry: poetry -P classic/forge run isort -p forge
+        files: ^classic/forge/
+        types: [file, python]
+        language: system
+
+      - id: isort
+        name: Lint (isort) - Classic - Benchmark
+        alias: isort-classic-benchmark
+        entry: poetry -P classic/benchmark run isort -p agbenchmark
+        files: ^classic/benchmark/
         types: [file, python]
         language: system
 
@@ -114,13 +149,26 @@ repos:
 
   - repo: https://github.com/PyCQA/flake8
     rev: 7.0.0
-    # Use consolidated flake8 config at classic/.flake8
+    # To have flake8 load the config of the individual subprojects, we have to call
+    # them separately.
     hooks:
       - id: flake8
-        name: Lint (Flake8) - Classic
+        name: Lint (Flake8) - Classic - AutoGPT
-        alias: flake8-classic
+        alias: flake8-classic-autogpt
-        files: ^classic/(original_autogpt|forge|direct_benchmark)/
+        files: ^classic/original_autogpt/(autogpt|scripts|tests)/
-        args: [--config=classic/.flake8]
+        args: [--config=classic/original_autogpt/.flake8]
+
+      - id: flake8
+        name: Lint (Flake8) - Classic - Forge
+        alias: flake8-classic-forge
+        files: ^classic/forge/(forge|tests)/
+        args: [--config=classic/forge/.flake8]
+
+      - id: flake8
+        name: Lint (Flake8) - Classic - Benchmark
+        alias: flake8-classic-benchmark
+        files: ^classic/benchmark/(agbenchmark|tests)/((?!reports).)*[/.]
+        args: [--config=classic/benchmark/.flake8]
 
   - repo: local
     hooks:
@@ -156,10 +204,29 @@ repos:
         pass_filenames: false
 
       - id: pyright
-        name: Typecheck - Classic
+        name: Typecheck - Classic - AutoGPT
-        alias: pyright-classic
+        alias: pyright-classic-autogpt
-        entry: poetry -C classic run pyright
+        entry: poetry -C classic/original_autogpt run pyright
-        files: ^classic/(original_autogpt|forge|direct_benchmark)/.*\.py$|^classic/poetry\.lock$
+        # include forge source (since it's a path dependency) but exclude *_test.py files:
+        files: ^(classic/original_autogpt/((autogpt|scripts|tests)/|poetry\.lock$)|classic/forge/(forge/.*(?<!_test)\.py|poetry\.lock)$)
+        types: [file]
+        language: system
+        pass_filenames: false
+
+      - id: pyright
+        name: Typecheck - Classic - Forge
+        alias: pyright-classic-forge
+        entry: poetry -C classic/forge run pyright
+        files: ^classic/forge/(forge/|poetry\.lock$)
+        types: [file]
+        language: system
+        pass_filenames: false
+
+      - id: pyright
+        name: Typecheck - Classic - Benchmark
+        alias: pyright-classic-benchmark
+        entry: poetry -C classic/benchmark run pyright
+        files: ^classic/benchmark/(agbenchmark/|tests/|poetry\.lock$)
         types: [file]
         language: system
         pass_filenames: false

autogpt_platform/backend/backend/api/features/chat/service.py

@@ -1834,6 +1834,11 @@ async def _execute_long_running_tool(
             tool_call_id=tool_call_id,
             result=error_response.model_dump_json(),
         )
+        # Generate LLM continuation so user sees explanation even for errors
+        try:
+            await _generate_llm_continuation(session_id=session_id, user_id=user_id)
+        except Exception as llm_err:
+            logger.warning(f"Failed to generate LLM continuation for error: {llm_err}")
     finally:
         await _mark_operation_completed(tool_call_id)
 

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py

@@ -2,30 +2,54 @@
 
 from .core import (
     AgentGeneratorNotConfiguredError,
+    AgentJsonValidationError,
+    AgentSummary,
+    DecompositionResult,
+    DecompositionStep,
+    LibraryAgentSummary,
+    MarketplaceAgentSummary,
     decompose_goal,
+    enrich_library_agents_from_steps,
+    extract_search_terms_from_steps,
+    extract_uuids_from_text,
     generate_agent,
     generate_agent_patch,
     get_agent_as_json,
+    get_all_relevant_agents_for_generation,
+    get_library_agent_by_graph_id,
+    get_library_agent_by_id,
+    get_library_agents_for_generation,
     json_to_graph,
     save_agent_to_library,
+    search_marketplace_agents_for_generation,
 )
 from .errors import get_user_message_for_error
 from .service import health_check as check_external_service_health
 from .service import is_external_service_configured
 
 __all__ = [
-    # Core functions
+    "AgentGeneratorNotConfiguredError",
+    "AgentJsonValidationError",
+    "AgentSummary",
+    "DecompositionResult",
+    "DecompositionStep",
+    "LibraryAgentSummary",
+    "MarketplaceAgentSummary",
+    "check_external_service_health",
     "decompose_goal",
+    "enrich_library_agents_from_steps",
+    "extract_search_terms_from_steps",
+    "extract_uuids_from_text",
     "generate_agent",
     "generate_agent_patch",
-    "save_agent_to_library",
     "get_agent_as_json",
-    "json_to_graph",
+    "get_all_relevant_agents_for_generation",
-    # Exceptions
+    "get_library_agent_by_graph_id",
-    "AgentGeneratorNotConfiguredError",
+    "get_library_agent_by_id",
-    # Service
+    "get_library_agents_for_generation",
-    "is_external_service_configured",
-    "check_external_service_health",
-    # Error handling
     "get_user_message_for_error",
+    "is_external_service_configured",
+    "json_to_graph",
+    "save_agent_to_library",
+    "search_marketplace_agents_for_generation",
 ]

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -1,11 +1,21 @@
 """Core agent generation functions."""
 
 import logging
+import re
 import uuid
-from typing import Any
+from typing import Any, NotRequired, TypedDict
 
 from backend.api.features.library import db as library_db
-from backend.data.graph import Graph, Link, Node, create_graph
+from backend.api.features.store import db as store_db
+from backend.data.graph import (
+    Graph,
+    Link,
+    Node,
+    create_graph,
+    get_graph,
+    get_graph_all_versions,
+)
+from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .service import (
     decompose_goal_external,
@@ -16,6 +26,75 @@ from .service import (
 
 logger = logging.getLogger(__name__)
 
+# Block ID for AgentExecutorBlock - used to identify sub-agent nodes
+AGENT_EXECUTOR_BLOCK_ID = "e189baac-8c20-45a1-94a7-55177ea42565"
+
+
+class ExecutionSummary(TypedDict):
+    """Summary of a single execution for quality assessment."""
+
+    status: str
+    correctness_score: NotRequired[float]
+    activity_summary: NotRequired[str]
+
+
+class LibraryAgentSummary(TypedDict):
+    """Summary of a library agent for sub-agent composition.
+
+    Includes recent executions to help the LLM decide whether to use this agent.
+    Each execution shows status, correctness_score (0-1), and activity_summary.
+    """
+
+    graph_id: str
+    graph_version: int
+    name: str
+    description: str
+    input_schema: dict[str, Any]
+    output_schema: dict[str, Any]
+    recent_executions: NotRequired[list[ExecutionSummary]]
+
+
+class MarketplaceAgentSummary(TypedDict):
+    """Summary of a marketplace agent for sub-agent composition."""
+
+    name: str
+    description: str
+    sub_heading: str
+    creator: str
+    is_marketplace_agent: bool
+
+
+class DecompositionStep(TypedDict, total=False):
+    """A single step in decomposed instructions."""
+
+    description: str
+    action: str
+    block_name: str
+    tool: str
+    name: str
+
+
+class DecompositionResult(TypedDict, total=False):
+    """Result from decompose_goal - can be instructions, questions, or error."""
+
+    type: str
+    steps: list[DecompositionStep]
+    questions: list[dict[str, Any]]
+    error: str
+    error_type: str
+
+
+AgentSummary = LibraryAgentSummary | MarketplaceAgentSummary | dict[str, Any]
+
+
+def _to_dict_list(
+    agents: list[AgentSummary] | list[dict[str, Any]] | None,
+) -> list[dict[str, Any]] | None:
+    """Convert typed agent summaries to plain dicts for external service calls."""
+    if agents is None:
+        return None
+    return [dict(a) for a in agents]
+
 
 class AgentGeneratorNotConfiguredError(Exception):
     """Raised when the external Agent Generator service is not configured."""
@@ -36,15 +115,421 @@ def _check_service_configured() -> None:
         )
 
 
-async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
+_UUID_PATTERN = re.compile(
+    r"[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}",
+    re.IGNORECASE,
+)
+
+
+def extract_uuids_from_text(text: str) -> list[str]:
+    """Extract all UUID v4 strings from text.
+
+    Args:
+        text: Text that may contain UUIDs (e.g., user's goal description)
+
+    Returns:
+        List of unique UUIDs found in the text (lowercase)
+    """
+    matches = _UUID_PATTERN.findall(text)
+    return list({m.lower() for m in matches})
+
+
+async def get_library_agent_by_id(
+    user_id: str, agent_id: str
+) -> LibraryAgentSummary | None:
+    """Fetch a specific library agent by its ID (library agent ID or graph_id).
+
+    This function tries multiple lookup strategies:
+    1. First tries to find by graph_id (AgentGraph primary key)
+    2. If not found, tries to find by library agent ID (LibraryAgent primary key)
+
+    This handles both cases:
+    - User provides graph_id (e.g., from AgentExecutorBlock)
+    - User provides library agent ID (e.g., from library URL)
+
+    Args:
+        user_id: The user ID
+        agent_id: The ID to look up (can be graph_id or library agent ID)
+
+    Returns:
+        LibraryAgentSummary if found, None otherwise
+    """
+    try:
+        agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id)
+        if agent:
+            logger.debug(f"Found library agent by graph_id: {agent.name}")
+            return LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.debug(f"Could not fetch library agent by graph_id {agent_id}: {e}")
+
+    try:
+        agent = await library_db.get_library_agent(agent_id, user_id)
+        if agent:
+            logger.debug(f"Found library agent by library_id: {agent.name}")
+            return LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+    except NotFoundError:
+        logger.debug(f"Library agent not found by library_id: {agent_id}")
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(
+            f"Could not fetch library agent by library_id {agent_id}: {e}",
+            exc_info=True,
+        )
+
+    return None
+
+
+get_library_agent_by_graph_id = get_library_agent_by_id
+
+
+async def get_library_agents_for_generation(
+    user_id: str,
+    search_query: str | None = None,
+    exclude_graph_id: str | None = None,
+    max_results: int = 15,
+) -> list[LibraryAgentSummary]:
+    """Fetch user's library agents formatted for Agent Generator.
+
+    Uses search-based fetching to return relevant agents instead of all agents.
+    This is more scalable for users with large libraries.
+
+    Includes recent_executions list to help the LLM assess agent quality:
+    - Each execution has status, correctness_score (0-1), and activity_summary
+    - This gives the LLM concrete examples of recent performance
+
+    Args:
+        user_id: The user ID
+        search_query: Optional search term to find relevant agents (user's goal/description)
+        exclude_graph_id: Optional graph ID to exclude (prevents circular references)
+        max_results: Maximum number of agents to return (default 15)
+
+    Returns:
+        List of LibraryAgentSummary with schemas and recent executions for sub-agent composition
+    """
+    try:
+        response = await library_db.list_library_agents(
+            user_id=user_id,
+            search_term=search_query,
+            page=1,
+            page_size=max_results,
+            include_executions=True,
+        )
+
+        results: list[LibraryAgentSummary] = []
+        for agent in response.agents:
+            if exclude_graph_id is not None and agent.graph_id == exclude_graph_id:
+                continue
+
+            summary = LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+            if agent.recent_executions:
+                exec_summaries: list[ExecutionSummary] = []
+                for ex in agent.recent_executions:
+                    exec_sum = ExecutionSummary(status=ex.status)
+                    if ex.correctness_score is not None:
+                        exec_sum["correctness_score"] = ex.correctness_score
+                    if ex.activity_summary:
+                        exec_sum["activity_summary"] = ex.activity_summary
+                    exec_summaries.append(exec_sum)
+                summary["recent_executions"] = exec_summaries
+            results.append(summary)
+        return results
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(f"Failed to fetch library agents: {e}")
+        return []
+
+
+async def search_marketplace_agents_for_generation(
+    search_query: str,
+    max_results: int = 10,
+) -> list[MarketplaceAgentSummary]:
+    """Search marketplace agents formatted for Agent Generator.
+
+    Note: This returns basic agent info. Full input/output schemas would require
+    additional graph fetches and is a potential future enhancement.
+
+    Args:
+        search_query: Search term to find relevant public agents
+        max_results: Maximum number of agents to return (default 10)
+
+    Returns:
+        List of MarketplaceAgentSummary (without detailed schemas for now)
+    """
+    try:
+        response = await store_db.get_store_agents(
+            search_query=search_query,
+            page=1,
+            page_size=max_results,
+        )
+
+        results: list[MarketplaceAgentSummary] = []
+        for agent in response.agents:
+            results.append(
+                MarketplaceAgentSummary(
+                    name=agent.agent_name,
+                    description=agent.description,
+                    sub_heading=agent.sub_heading,
+                    creator=agent.creator,
+                    is_marketplace_agent=True,
+                )
+            )
+        return results
+    except Exception as e:
+        logger.warning(f"Failed to search marketplace agents: {e}")
+        return []
+
+
+async def get_all_relevant_agents_for_generation(
+    user_id: str,
+    search_query: str | None = None,
+    exclude_graph_id: str | None = None,
+    include_library: bool = True,
+    include_marketplace: bool = True,
+    max_library_results: int = 15,
+    max_marketplace_results: int = 10,
+) -> list[AgentSummary]:
+    """Fetch relevant agents from library and/or marketplace.
+
+    Searches both user's library and marketplace by default.
+    Explicitly mentioned UUIDs in the search query are always looked up.
+
+    Args:
+        user_id: The user ID
+        search_query: Search term to find relevant agents (user's goal/description)
+        exclude_graph_id: Optional graph ID to exclude (prevents circular references)
+        include_library: Whether to search user's library (default True)
+        include_marketplace: Whether to also search marketplace (default True)
+        max_library_results: Max library agents to return (default 15)
+        max_marketplace_results: Max marketplace agents to return (default 10)
+
+    Returns:
+        List of AgentSummary, library agents first (with full schemas),
+        then marketplace agents (basic info only)
+    """
+    agents: list[AgentSummary] = []
+    seen_graph_ids: set[str] = set()
+
+    if search_query:
+        mentioned_uuids = extract_uuids_from_text(search_query)
+        for graph_id in mentioned_uuids:
+            if graph_id == exclude_graph_id:
+                continue
+            try:
+                agent = await get_library_agent_by_graph_id(user_id, graph_id)
+            except Exception as e:
+                logger.warning(
+                    f"Failed to fetch explicitly mentioned agent {graph_id}: {e}",
+                    exc_info=True,
+                )
+                continue
+            agent_graph_id = agent.get("graph_id") if agent else None
+            if agent and agent_graph_id and agent_graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(agent_graph_id)
+                logger.debug(
+                    f"Found explicitly mentioned agent: {agent.get('name') or 'Unknown'}"
+                )
+
+    if include_library:
+        library_agents = await get_library_agents_for_generation(
+            user_id=user_id,
+            search_query=search_query,
+            exclude_graph_id=exclude_graph_id,
+            max_results=max_library_results,
+        )
+        for agent in library_agents:
+            graph_id = agent.get("graph_id")
+            if graph_id and graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(graph_id)
+
+    if include_marketplace and search_query:
+        marketplace_agents = await search_marketplace_agents_for_generation(
+            search_query=search_query,
+            max_results=max_marketplace_results,
+        )
+        library_names: set[str] = set()
+        for a in agents:
+            name = a.get("name")
+            if name and isinstance(name, str):
+                library_names.add(name.lower())
+        for agent in marketplace_agents:
+            agent_name = agent.get("name")
+            if agent_name and isinstance(agent_name, str):
+                if agent_name.lower() not in library_names:
+                    agents.append(agent)
+
+    return agents
+
+
+def extract_search_terms_from_steps(
+    decomposition_result: DecompositionResult | dict[str, Any],
+) -> list[str]:
+    """Extract search terms from decomposed instruction steps.
+
+    Analyzes the decomposition result to extract relevant keywords
+    for additional library agent searches.
+
+    Args:
+        decomposition_result: Result from decompose_goal containing steps
+
+    Returns:
+        List of unique search terms extracted from steps
+    """
+    search_terms: list[str] = []
+
+    if decomposition_result.get("type") != "instructions":
+        return search_terms
+
+    steps = decomposition_result.get("steps", [])
+    if not steps:
+        return search_terms
+
+    step_keys: list[str] = ["description", "action", "block_name", "tool", "name"]
+
+    for step in steps:
+        for key in step_keys:
+            value = step.get(key)  # type: ignore[union-attr]
+            if isinstance(value, str) and len(value) > 3:
+                search_terms.append(value)
+
+    seen: set[str] = set()
+    unique_terms: list[str] = []
+    for term in search_terms:
+        term_lower = term.lower()
+        if term_lower not in seen:
+            seen.add(term_lower)
+            unique_terms.append(term)
+
+    return unique_terms
+
+
+async def enrich_library_agents_from_steps(
+    user_id: str,
+    decomposition_result: DecompositionResult | dict[str, Any],
+    existing_agents: list[AgentSummary] | list[dict[str, Any]],
+    exclude_graph_id: str | None = None,
+    include_marketplace: bool = True,
+    max_additional_results: int = 10,
+) -> list[AgentSummary] | list[dict[str, Any]]:
+    """Enrich library agents list with additional searches based on decomposed steps.
+
+    This implements two-phase search: after decomposition, we search for additional
+    relevant agents based on the specific steps identified.
+
+    Args:
+        user_id: The user ID
+        decomposition_result: Result from decompose_goal containing steps
+        existing_agents: Already fetched library agents from initial search
+        exclude_graph_id: Optional graph ID to exclude
+        include_marketplace: Whether to also search marketplace
+        max_additional_results: Max additional agents per search term (default 10)
+
+    Returns:
+        Combined list of library agents (existing + newly discovered)
+    """
+    search_terms = extract_search_terms_from_steps(decomposition_result)
+
+    if not search_terms:
+        return existing_agents
+
+    existing_ids: set[str] = set()
+    existing_names: set[str] = set()
+
+    for agent in existing_agents:
+        agent_name = agent.get("name")
+        if agent_name and isinstance(agent_name, str):
+            existing_names.add(agent_name.lower())
+        graph_id = agent.get("graph_id")  # type: ignore[call-overload]
+        if graph_id and isinstance(graph_id, str):
+            existing_ids.add(graph_id)
+
+    all_agents: list[AgentSummary] | list[dict[str, Any]] = list(existing_agents)
+
+    for term in search_terms[:3]:
+        try:
+            additional_agents = await get_all_relevant_agents_for_generation(
+                user_id=user_id,
+                search_query=term,
+                exclude_graph_id=exclude_graph_id,
+                include_marketplace=include_marketplace,
+                max_library_results=max_additional_results,
+                max_marketplace_results=5,
+            )
+
+            for agent in additional_agents:
+                agent_name = agent.get("name")
+                if not agent_name or not isinstance(agent_name, str):
+                    continue
+                agent_name_lower = agent_name.lower()
+
+                if agent_name_lower in existing_names:
+                    continue
+
+                graph_id = agent.get("graph_id")  # type: ignore[call-overload]
+                if graph_id and graph_id in existing_ids:
+                    continue
+
+                all_agents.append(agent)
+                existing_names.add(agent_name_lower)
+                if graph_id and isinstance(graph_id, str):
+                    existing_ids.add(graph_id)
+
+        except DatabaseError:
+            logger.error(f"Database error searching for agents with term '{term}'")
+            raise
+        except Exception as e:
+            logger.warning(
+                f"Failed to search for additional agents with term '{term}': {e}"
+            )
+
+    logger.debug(
+        f"Enriched library agents: {len(existing_agents)} initial + "
+        f"{len(all_agents) - len(existing_agents)} additional = {len(all_agents)} total"
+    )
+
+    return all_agents
+
+
+async def decompose_goal(
+    description: str,
+    context: str = "",
+    library_agents: list[AgentSummary] | None = None,
+) -> DecompositionResult | None:
     """Break down a goal into steps or return clarifying questions.
 
     Args:
         description: Natural language goal description
         context: Additional context (e.g., answers to previous questions)
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
-        Dict with either:
+        DecompositionResult with either:
         - {"type": "clarifying_questions", "questions": [...]}
         - {"type": "instructions", "steps": [...]}
         Or None on error
@@ -54,14 +539,21 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any]
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for decompose_goal")
-    return await decompose_goal_external(description, context)
+    result = await decompose_goal_external(
+        description, context, _to_dict_list(library_agents)
+    )
+    return result  # type: ignore[return-value]
 
 
-async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
+async def generate_agent(
+    instructions: DecompositionResult | dict[str, Any],
+    library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None,
+) -> dict[str, Any] | None:
     """Generate agent JSON from instructions.
 
     Args:
         instructions: Structured instructions from decompose_goal
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Agent JSON dict, error dict {"type": "error", ...}, or None on error
@@ -71,12 +563,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent")
-    result = await generate_agent_external(instructions)
+    result = await generate_agent_external(
+        dict(instructions), _to_dict_list(library_agents)
+    )
     if result:
-        # Check if it's an error response - pass through as-is
         if isinstance(result, dict) and result.get("type") == "error":
             return result
-        # Ensure required fields for successful agent generation
         if "id" not in result:
             result["id"] = str(uuid.uuid4())
         if "version" not in result:
@@ -86,6 +578,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
     return result
 
 
+class AgentJsonValidationError(Exception):
+    """Raised when agent JSON is invalid or missing required fields."""
+
+    pass
+
+
 def json_to_graph(agent_json: dict[str, Any]) -> Graph:
     """Convert agent JSON dict to Graph model.
 
@@ -94,25 +592,55 @@ def json_to_graph(agent_json: dict[str, Any]) -> Graph:
 
     Returns:
         Graph ready for saving
+
+    Raises:
+        AgentJsonValidationError: If required fields are missing from nodes or links
     """
     nodes = []
-    for n in agent_json.get("nodes", []):
+    for idx, n in enumerate(agent_json.get("nodes", [])):
+        block_id = n.get("block_id")
+        if not block_id:
+            node_id = n.get("id", f"index_{idx}")
+            raise AgentJsonValidationError(
+                f"Node '{node_id}' is missing required field 'block_id'"
+            )
         node = Node(
             id=n.get("id", str(uuid.uuid4())),
-            block_id=n["block_id"],
+            block_id=block_id,
             input_default=n.get("input_default", {}),
             metadata=n.get("metadata", {}),
         )
         nodes.append(node)
 
     links = []
-    for link_data in agent_json.get("links", []):
+    for idx, link_data in enumerate(agent_json.get("links", [])):
+        source_id = link_data.get("source_id")
+        sink_id = link_data.get("sink_id")
+        source_name = link_data.get("source_name")
+        sink_name = link_data.get("sink_name")
+
+        missing_fields = []
+        if not source_id:
+            missing_fields.append("source_id")
+        if not sink_id:
+            missing_fields.append("sink_id")
+        if not source_name:
+            missing_fields.append("source_name")
+        if not sink_name:
+            missing_fields.append("sink_name")
+
+        if missing_fields:
+            link_id = link_data.get("id", f"index_{idx}")
+            raise AgentJsonValidationError(
+                f"Link '{link_id}' is missing required fields: {', '.join(missing_fields)}"
+            )
+
         link = Link(
             id=link_data.get("id", str(uuid.uuid4())),
-            source_id=link_data["source_id"],
+            source_id=source_id,
-            sink_id=link_data["sink_id"],
+            sink_id=sink_id,
-            source_name=link_data["source_name"],
+            source_name=source_name,
-            sink_name=link_data["sink_name"],
+            sink_name=sink_name,
             is_static=link_data.get("is_static", False),
         )
         links.append(link)
@@ -133,22 +661,40 @@ def _reassign_node_ids(graph: Graph) -> None:
 
     This is needed when creating a new version to avoid unique constraint violations.
     """
-    # Create mapping from old node IDs to new UUIDs
     id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
 
-    # Reassign node IDs
     for node in graph.nodes:
         node.id = id_map[node.id]
 
-    # Update link references to use new node IDs
     for link in graph.links:
-        link.id = str(uuid.uuid4())  # Also give links new IDs
+        link.id = str(uuid.uuid4())
         if link.source_id in id_map:
             link.source_id = id_map[link.source_id]
         if link.sink_id in id_map:
             link.sink_id = id_map[link.sink_id]
 
 
+def _populate_agent_executor_user_ids(agent_json: dict[str, Any], user_id: str) -> None:
+    """Populate user_id in AgentExecutorBlock nodes.
+
+    The external agent generator creates AgentExecutorBlock nodes with empty user_id.
+    This function fills in the actual user_id so sub-agents run with correct permissions.
+
+    Args:
+        agent_json: Agent JSON dict (modified in place)
+        user_id: User ID to set
+    """
+    for node in agent_json.get("nodes", []):
+        if node.get("block_id") == AGENT_EXECUTOR_BLOCK_ID:
+            input_default = node.get("input_default", {})
+            if not input_default.get("user_id"):
+                input_default["user_id"] = user_id
+                node["input_default"] = input_default
+                logger.debug(
+                    f"Set user_id for AgentExecutorBlock node {node.get('id')}"
+                )
+
+
 async def save_agent_to_library(
     agent_json: dict[str, Any], user_id: str, is_update: bool = False
 ) -> tuple[Graph, Any]:
@@ -162,33 +708,27 @@ async def save_agent_to_library(
     Returns:
         Tuple of (created Graph, LibraryAgent)
     """
-    from backend.data.graph import get_graph_all_versions
+    # Populate user_id in AgentExecutorBlock nodes before conversion
+    _populate_agent_executor_user_ids(agent_json, user_id)
 
     graph = json_to_graph(agent_json)
 
     if is_update:
-        # For updates, keep the same graph ID but increment version
-        # and reassign node/link IDs to avoid conflicts
         if graph.id:
             existing_versions = await get_graph_all_versions(graph.id, user_id)
             if existing_versions:
                 latest_version = max(v.version for v in existing_versions)
                 graph.version = latest_version + 1
-                # Reassign node IDs (but keep graph ID the same)
                 _reassign_node_ids(graph)
                 logger.info(f"Updating agent {graph.id} to version {graph.version}")
     else:
-        # For new agents, always generate a fresh UUID to avoid collisions
         graph.id = str(uuid.uuid4())
         graph.version = 1
-        # Reassign all node IDs as well
         _reassign_node_ids(graph)
         logger.info(f"Creating new agent with ID {graph.id}")
 
-    # Save to database
     created_graph = await create_graph(graph, user_id)
 
-    # Add to user's library (or update existing library agent)
     library_agents = await library_db.create_library_agent(
         graph=created_graph,
         user_id=user_id,
@@ -200,25 +740,31 @@ async def save_agent_to_library(
 
 
 async def get_agent_as_json(
-    graph_id: str, user_id: str | None
+    agent_id: str, user_id: str | None
 ) -> dict[str, Any] | None:
     """Fetch an agent and convert to JSON format for editing.
 
     Args:
-        graph_id: Graph ID or library agent ID
+        agent_id: Graph ID or library agent ID
         user_id: User ID
 
     Returns:
         Agent as JSON dict or None if not found
     """
-    from backend.data.graph import get_graph
+    graph = await get_graph(agent_id, version=None, user_id=user_id)
+
+    if not graph and user_id:
+        try:
+            library_agent = await library_db.get_library_agent(agent_id, user_id)
+            graph = await get_graph(
+                library_agent.graph_id, version=None, user_id=user_id
+            )
+        except NotFoundError:
+            pass
 
-    # Try to get the graph (version=None gets the active version)
-    graph = await get_graph(graph_id, version=None, user_id=user_id)
     if not graph:
         return None
 
-    # Convert to JSON format
     nodes = []
     for node in graph.nodes:
         nodes.append(
@@ -256,7 +802,9 @@ async def get_agent_as_json(
 
 
 async def generate_agent_patch(
-    update_request: str, current_agent: dict[str, Any]
+    update_request: str,
+    current_agent: dict[str, Any],
+    library_agents: list[AgentSummary] | None = None,
 ) -> dict[str, Any] | None:
     """Update an existing agent using natural language.
 
@@ -268,6 +816,7 @@ async def generate_agent_patch(
     Args:
         update_request: Natural language description of changes
         current_agent: Current agent JSON
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
@@ -278,4 +827,6 @@ async def generate_agent_patch(
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent_patch")
-    return await generate_agent_patch_external(update_request, current_agent)
+    return await generate_agent_patch_external(
+        update_request, current_agent, _to_dict_list(library_agents)
+    )

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/errors.py

@@ -1,11 +1,43 @@
 """Error handling utilities for agent generator."""
 
+import re
+
+
+def _sanitize_error_details(details: str) -> str:
+    """Sanitize error details to remove sensitive information.
+
+    Strips common patterns that could expose internal system info:
+    - File paths (Unix and Windows)
+    - Database connection strings
+    - URLs with credentials
+    - Stack trace internals
+
+    Args:
+        details: Raw error details string
+
+    Returns:
+        Sanitized error details safe for user display
+    """
+    sanitized = re.sub(
+        r"/[a-zA-Z0-9_./\-]+\.(py|js|ts|json|yaml|yml)", "[path]", details
+    )
+    sanitized = re.sub(r"[A-Z]:\\[a-zA-Z0-9_\\.\\-]+", "[path]", sanitized)
+    sanitized = re.sub(
+        r"(postgres|mysql|mongodb|redis)://[^\s]+", "[database_url]", sanitized
+    )
+    sanitized = re.sub(r"https?://[^:]+:[^@]+@[^\s]+", "[url]", sanitized)
+    sanitized = re.sub(r", line \d+", "", sanitized)
+    sanitized = re.sub(r'File "[^"]+",?', "", sanitized)
+
+    return sanitized.strip()
+
 
 def get_user_message_for_error(
     error_type: str,
     operation: str = "process the request",
     llm_parse_message: str | None = None,
     validation_message: str | None = None,
+    error_details: str | None = None,
 ) -> str:
     """Get a user-friendly error message based on error type.
 
@@ -19,25 +51,45 @@ def get_user_message_for_error(
             message (e.g., "analyze the goal", "generate the agent")
         llm_parse_message: Custom message for llm_parse_error type
         validation_message: Custom message for validation_error type
+        error_details: Optional additional details about the error
 
     Returns:
         User-friendly error message suitable for display to the user
     """
+    base_message = ""
+
     if error_type == "llm_parse_error":
-        return (
+        base_message = (
             llm_parse_message
             or "The AI had trouble processing this request. Please try again."
         )
     elif error_type == "validation_error":
-        return (
+        base_message = (
             validation_message
-            or "The request failed validation. Please try rephrasing."
+            or "The generated agent failed validation. "
+            "This usually happens when the agent structure doesn't match "
+            "what the platform expects. Please try simplifying your goal "
+            "or breaking it into smaller parts."
         )
     elif error_type == "patch_error":
-        return "Failed to apply the changes. Please try a different approach."
+        base_message = (
+            "Failed to apply the changes. The modification couldn't be "
+            "validated. Please try a different approach or simplify the change."
+        )
     elif error_type in ("timeout", "llm_timeout"):
-        return "The request took too long. Please try again."
+        base_message = (
+            "The request took too long to process. This can happen with "
+            "complex agents. Please try again or simplify your goal."
+        )
     elif error_type in ("rate_limit", "llm_rate_limit"):
-        return "The service is currently busy. Please try again in a moment."
+        base_message = "The service is currently busy. Please try again in a moment."
     else:
-        return f"Failed to {operation}. Please try again."
+        base_message = f"Failed to {operation}. Please try again."
+
+    if error_details:
+        details = _sanitize_error_details(error_details)
+        if len(details) > 200:
+            details = details[:200] + "..."
+        base_message += f"\n\nTechnical details: {details}"
+
+    return base_message

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py

@@ -117,13 +117,16 @@ def _get_client() -> httpx.AsyncClient:
 
 
 async def decompose_goal_external(
-    description: str, context: str = ""
+    description: str,
+    context: str = "",
+    library_agents: list[dict[str, Any]] | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to decompose a goal.
 
     Args:
         description: Natural language goal description
         context: Additional context (e.g., answers to previous questions)
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Dict with either:
@@ -141,6 +144,8 @@ async def decompose_goal_external(
     if context:
         # The external service uses user_instruction for additional context
         payload["user_instruction"] = context
+    if library_agents:
+        payload["library_agents"] = library_agents
 
     try:
         response = await client.post("/api/decompose-description", json=payload)
@@ -207,21 +212,25 @@ async def decompose_goal_external(
 
 async def generate_agent_external(
     instructions: dict[str, Any],
+    library_agents: list[dict[str, Any]] | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to generate an agent from instructions.
 
     Args:
         instructions: Structured instructions from decompose_goal
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Agent JSON dict on success, or error dict {"type": "error", ...} on error
     """
     client = _get_client()
 
+    payload: dict[str, Any] = {"instructions": instructions}
+    if library_agents:
+        payload["library_agents"] = library_agents
+
     try:
-        response = await client.post(
+        response = await client.post("/api/generate-agent", json=payload)
-            "/api/generate-agent", json={"instructions": instructions}
-        )
         response.raise_for_status()
         data = response.json()
 
@@ -229,8 +238,7 @@ async def generate_agent_external(
             error_msg = data.get("error", "Unknown error from Agent Generator")
             error_type = data.get("error_type", "unknown")
             logger.error(
-                f"Agent Generator generation failed: {error_msg} "
+                f"Agent Generator generation failed: {error_msg} (type: {error_type})"
-                f"(type: {error_type})"
             )
             return _create_error_response(error_msg, error_type)
 
@@ -251,27 +259,31 @@ async def generate_agent_external(
 
 
 async def generate_agent_patch_external(
-    update_request: str, current_agent: dict[str, Any]
+    update_request: str,
+    current_agent: dict[str, Any],
+    library_agents: list[dict[str, Any]] | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to generate a patch for an existing agent.
 
     Args:
         update_request: Natural language description of changes
         current_agent: Current agent JSON
+        library_agents: User's library agents available for sub-agent composition
 
     Returns:
         Updated agent JSON, clarifying questions dict, or error dict on error
     """
     client = _get_client()
 
+    payload: dict[str, Any] = {
+        "update_request": update_request,
+        "current_agent_json": current_agent,
+    }
+    if library_agents:
+        payload["library_agents"] = library_agents
+
     try:
-        response = await client.post(
+        response = await client.post("/api/update-agent", json=payload)
-            "/api/update-agent",
-            json={
-                "update_request": update_request,
-                "current_agent_json": current_agent,
-            },
-        )
         response.raise_for_status()
         data = response.json()
 

autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py

@@ -1,6 +1,7 @@
 """Shared agent search functionality for find_agent and find_library_agent tools."""
 
 import logging
+import re
 from typing import Literal
 
 from backend.api.features.library import db as library_db
@@ -19,6 +20,85 @@ logger = logging.getLogger(__name__)
 
 SearchSource = Literal["marketplace", "library"]
 
+_UUID_PATTERN = re.compile(
+    r"^[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$",
+    re.IGNORECASE,
+)
+
+
+def _is_uuid(text: str) -> bool:
+    """Check if text is a valid UUID v4."""
+    return bool(_UUID_PATTERN.match(text.strip()))
+
+
+async def _get_library_agent_by_id(user_id: str, agent_id: str) -> AgentInfo | None:
+    """Fetch a library agent by ID (library agent ID or graph_id).
+
+    Tries multiple lookup strategies:
+    1. First by graph_id (AgentGraph primary key)
+    2. Then by library agent ID (LibraryAgent primary key)
+
+    Args:
+        user_id: The user ID
+        agent_id: The ID to look up (can be graph_id or library agent ID)
+
+    Returns:
+        AgentInfo if found, None otherwise
+    """
+    try:
+        agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id)
+        if agent:
+            logger.debug(f"Found library agent by graph_id: {agent.name}")
+            return AgentInfo(
+                id=agent.id,
+                name=agent.name,
+                description=agent.description or "",
+                source="library",
+                in_library=True,
+                creator=agent.creator_name,
+                status=agent.status.value,
+                can_access_graph=agent.can_access_graph,
+                has_external_trigger=agent.has_external_trigger,
+                new_output=agent.new_output,
+                graph_id=agent.graph_id,
+            )
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(
+            f"Could not fetch library agent by graph_id {agent_id}: {e}",
+            exc_info=True,
+        )
+
+    try:
+        agent = await library_db.get_library_agent(agent_id, user_id)
+        if agent:
+            logger.debug(f"Found library agent by library_id: {agent.name}")
+            return AgentInfo(
+                id=agent.id,
+                name=agent.name,
+                description=agent.description or "",
+                source="library",
+                in_library=True,
+                creator=agent.creator_name,
+                status=agent.status.value,
+                can_access_graph=agent.can_access_graph,
+                has_external_trigger=agent.has_external_trigger,
+                new_output=agent.new_output,
+                graph_id=agent.graph_id,
+            )
+    except NotFoundError:
+        logger.debug(f"Library agent not found by library_id: {agent_id}")
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(
+            f"Could not fetch library agent by library_id {agent_id}: {e}",
+            exc_info=True,
+        )
+
+    return None
+
 
 async def search_agents(
     query: str,
@@ -69,29 +149,37 @@ async def search_agents(
                         is_featured=False,
                     )
                 )
-        else:  # library
+        else:
-            logger.info(f"Searching user library for: {query}")
+            if _is_uuid(query):
-            results = await library_db.list_library_agents(
+                logger.info(f"Query looks like UUID, trying direct lookup: {query}")
-                user_id=user_id,  # type: ignore[arg-type]
+                agent = await _get_library_agent_by_id(user_id, query)  # type: ignore[arg-type]
-                search_term=query,
+                if agent:
-                page_size=10,
+                    agents.append(agent)
-            )
+                    logger.info(f"Found agent by direct ID lookup: {agent.name}")
-            for agent in results.agents:
+
-                agents.append(
+            if not agents:
-                    AgentInfo(
+                logger.info(f"Searching user library for: {query}")
-                        id=agent.id,
+                results = await library_db.list_library_agents(
-                        name=agent.name,
+                    user_id=user_id,  # type: ignore[arg-type]
-                        description=agent.description or "",
+                    search_term=query,
-                        source="library",
+                    page_size=10,
-                        in_library=True,
-                        creator=agent.creator_name,
-                        status=agent.status.value,
-                        can_access_graph=agent.can_access_graph,
-                        has_external_trigger=agent.has_external_trigger,
-                        new_output=agent.new_output,
-                        graph_id=agent.graph_id,
-                    )
                 )
+                for agent in results.agents:
+                    agents.append(
+                        AgentInfo(
+                            id=agent.id,
+                            name=agent.name,
+                            description=agent.description or "",
+                            source="library",
+                            in_library=True,
+                            creator=agent.creator_name,
+                            status=agent.status.value,
+                            can_access_graph=agent.can_access_graph,
+                            has_external_trigger=agent.has_external_trigger,
+                            new_output=agent.new_output,
+                            graph_id=agent.graph_id,
+                        )
+                    )
         logger.info(f"Found {len(agents)} agents in {source}")
     except NotFoundError:
         pass

autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py

@@ -8,7 +8,9 @@ from backend.api.features.chat.model import ChatSession
 from .agent_generator import (
     AgentGeneratorNotConfiguredError,
     decompose_goal,
+    enrich_library_agents_from_steps,
     generate_agent,
+    get_all_relevant_agents_for_generation,
     get_user_message_for_error,
     save_agent_to_library,
 )
@@ -103,9 +105,24 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 1: Decompose goal into steps
+        library_agents = None
+        if user_id:
+            try:
+                library_agents = await get_all_relevant_agents_for_generation(
+                    user_id=user_id,
+                    search_query=description,
+                    include_marketplace=True,
+                )
+                logger.debug(
+                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
+                )
+            except Exception as e:
+                logger.warning(f"Failed to fetch library agents: {e}")
+
         try:
-            decomposition_result = await decompose_goal(description, context)
+            decomposition_result = await decompose_goal(
+                description, context, library_agents
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -124,7 +141,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if the result is an error from the external service
         if decomposition_result.get("type") == "error":
             error_msg = decomposition_result.get("error", "Unknown error")
             error_type = decomposition_result.get("error_type", "unknown")
@@ -144,7 +160,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if LLM returned clarifying questions
         if decomposition_result.get("type") == "clarifying_questions":
             questions = decomposition_result.get("questions", [])
             return ClarificationNeededResponse(
@@ -163,7 +178,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check for unachievable/vague goals
         if decomposition_result.get("type") == "unachievable_goal":
             suggested = decomposition_result.get("suggested_goal", "")
             reason = decomposition_result.get("reason", "")
@@ -190,9 +204,22 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 2: Generate agent JSON (external service handles fixing and validation)
+        if user_id and library_agents is not None:
+            try:
+                library_agents = await enrich_library_agents_from_steps(
+                    user_id=user_id,
+                    decomposition_result=decomposition_result,
+                    existing_agents=library_agents,
+                    include_marketplace=True,
+                )
+                logger.debug(
+                    f"After enrichment: {len(library_agents)} total agents for sub-agent composition"
+                )
+            except Exception as e:
+                logger.warning(f"Failed to enrich library agents from steps: {e}")
+
         try:
-            agent_json = await generate_agent(decomposition_result)
+            agent_json = await generate_agent(decomposition_result, library_agents)
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -211,7 +238,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if the result is an error from the external service
         if isinstance(agent_json, dict) and agent_json.get("type") == "error":
             error_msg = agent_json.get("error", "Unknown error")
             error_type = agent_json.get("error_type", "unknown")
@@ -219,7 +245,12 @@ class CreateAgentTool(BaseTool):
                 error_type,
                 operation="generate the agent",
                 llm_parse_message="The AI had trouble generating the agent. Please try again or simplify your goal.",
-                validation_message="The generated agent failed validation. Please try rephrasing your goal.",
+                validation_message=(
+                    "I wasn't able to create a valid agent for this request. "
+                    "The generated workflow had some structural issues. "
+                    "Please try simplifying your goal or breaking it into smaller steps."
+                ),
+                error_details=error_msg,
             )
             return ErrorResponse(
                 message=user_message,
@@ -237,7 +268,6 @@ class CreateAgentTool(BaseTool):
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
-        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
@@ -252,7 +282,6 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Save to library
         if not user_id:
             return ErrorResponse(
                 message="You must be logged in to save agents.",
@@ -270,7 +299,7 @@ class CreateAgentTool(BaseTool):
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{library_agent.id}",
+                library_agent_link=f"/library/agents/{library_agent.id}",
                 agent_page_link=f"/build?flowID={created_graph.id}",
                 session_id=session_id,
             )

autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py

@@ -9,6 +9,7 @@ from .agent_generator import (
     AgentGeneratorNotConfiguredError,
     generate_agent_patch,
     get_agent_as_json,
+    get_all_relevant_agents_for_generation,
     get_user_message_for_error,
     save_agent_to_library,
 )
@@ -117,7 +118,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 1: Fetch current agent
         current_agent = await get_agent_as_json(agent_id, user_id)
 
         if current_agent is None:
@@ -127,14 +127,30 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Build the update request with context
+        library_agents = None
+        if user_id:
+            try:
+                graph_id = current_agent.get("id")
+                library_agents = await get_all_relevant_agents_for_generation(
+                    user_id=user_id,
+                    search_query=changes,
+                    exclude_graph_id=graph_id,
+                    include_marketplace=True,
+                )
+                logger.debug(
+                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
+                )
+            except Exception as e:
+                logger.warning(f"Failed to fetch library agents: {e}")
+
         update_request = changes
         if context:
             update_request = f"{changes}\n\nAdditional context:\n{context}"
 
-        # Step 2: Generate updated agent (external service handles fixing and validation)
         try:
-            result = await generate_agent_patch(update_request, current_agent)
+            result = await generate_agent_patch(
+                update_request, current_agent, library_agents
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -153,7 +169,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if the result is an error from the external service
         if isinstance(result, dict) and result.get("type") == "error":
             error_msg = result.get("error", "Unknown error")
             error_type = result.get("error_type", "unknown")
@@ -162,6 +177,7 @@ class EditAgentTool(BaseTool):
                 operation="generate the changes",
                 llm_parse_message="The AI had trouble generating the changes. Please try again or simplify your request.",
                 validation_message="The generated changes failed validation. Please try rephrasing your request.",
+                error_details=error_msg,
             )
             return ErrorResponse(
                 message=user_message,
@@ -175,7 +191,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if LLM returned clarifying questions
         if result.get("type") == "clarifying_questions":
             questions = result.get("questions", [])
             return ClarificationNeededResponse(
@@ -194,7 +209,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Result is the updated agent JSON
         updated_agent = result
 
         agent_name = updated_agent.get("name", "Updated Agent")
@@ -202,7 +216,6 @@ class EditAgentTool(BaseTool):
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
-        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
@@ -218,7 +231,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Save to library (creates a new version)
         if not user_id:
             return ErrorResponse(
                 message="You must be logged in to save agents.",
@@ -236,7 +248,7 @@ class EditAgentTool(BaseTool):
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{library_agent.id}",
+                library_agent_link=f"/library/agents/{library_agent.id}",
                 agent_page_link=f"/build?flowID={created_graph.id}",
                 session_id=session_id,
             )

autogpt_platform/backend/backend/api/features/chat/tools/utils.py

@@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -266,13 +266,14 @@ async def match_user_credentials_to_graph(
         credential_requirements,
         _node_fields,
     ) in aggregated_creds.items():
-        # Find first matching credential by provider and type
+        # Find first matching credential by provider, type, and scopes
         matching_cred = next(
             (
                 cred
                 for cred in available_creds
                 if cred.provider in credential_requirements.provider
                 and cred.type in credential_requirements.supported_types
+                and _credential_has_required_scopes(cred, credential_requirements)
             ),
             None,
         )
@@ -296,10 +297,17 @@ async def match_user_credentials_to_graph(
                     f"{credential_field_name} (validation failed: {e})"
                 )
         else:
+            # Build a helpful error message including scope requirements
+            error_parts = [
+                f"provider in {list(credential_requirements.provider)}",
+                f"type in {list(credential_requirements.supported_types)}",
+            ]
+            if credential_requirements.required_scopes:
+                error_parts.append(
+                    f"scopes including {list(credential_requirements.required_scopes)}"
+                )
             missing_creds.append(
-                f"{credential_field_name} "
+                f"{credential_field_name} (requires {', '.join(error_parts)})"
-                f"(requires provider in {list(credential_requirements.provider)}, "
-                f"type in {list(credential_requirements.supported_types)})"
             )
 
     logger.info(
@@ -309,6 +317,28 @@ async def match_user_credentials_to_graph(
     return graph_credentials_inputs, missing_creds
 
 
+def _credential_has_required_scopes(
+    credential: Credentials,
+    requirements: CredentialsFieldInfo,
+) -> bool:
+    """
+    Check if a credential has all the scopes required by the block.
+
+    For OAuth2 credentials, verifies that the credential's scopes are a superset
+    of the required scopes. For other credential types, returns True (no scope check).
+    """
+    # Only OAuth2 credentials have scopes to check
+    if credential.type != "oauth2":
+        return True
+
+    # If no scopes are required, any credential matches
+    if not requirements.required_scopes:
+        return True
+
+    # Check that credential scopes are a superset of required scopes
+    return set(credential.scopes).issuperset(requirements.required_scopes)
+
+
 async def check_user_has_required_credentials(
     user_id: str,
     required_credentials: list[CredentialsMetaInput],

autogpt_platform/backend/backend/api/features/library/db.py

@@ -39,6 +39,7 @@ async def list_library_agents(
     sort_by: library_model.LibraryAgentSort = library_model.LibraryAgentSort.UPDATED_AT,
     page: int = 1,
     page_size: int = 50,
+    include_executions: bool = False,
 ) -> library_model.LibraryAgentResponse:
     """
     Retrieves a paginated list of LibraryAgent records for a given user.
@@ -49,6 +50,9 @@ async def list_library_agents(
         sort_by: Sorting field (createdAt, updatedAt, isFavorite, isCreatedByUser).
         page: Current page (1-indexed).
         page_size: Number of items per page.
+        include_executions: Whether to include execution data for status calculation.
+            Defaults to False for performance (UI fetches status separately).
+            Set to True when accurate status/metrics are needed (e.g., agent generator).
 
     Returns:
         A LibraryAgentResponse containing the list of agents and pagination details.
@@ -76,24 +80,32 @@ async def list_library_agents(
         "isArchived": False,
     }
 
-    # Build search filter if applicable
     if search_term:
-        where_clause["OR"] = [
+        words = [w.strip() for w in search_term.split() if len(w.strip()) >= 3]
-            {
+        if words:
-                "AgentGraph": {
+            or_conditions: list[prisma.types.LibraryAgentWhereInput] = []
-                    "is": {"name": {"contains": search_term, "mode": "insensitive"}}
+            for word in words:
-                }
+                or_conditions.append(
-            },
+                    {
-            {
+                        "AgentGraph": {
-                "AgentGraph": {
+                            "is": {"name": {"contains": word, "mode": "insensitive"}}
-                    "is": {
+                        }
-                        "description": {"contains": search_term, "mode": "insensitive"}
                     }
-                }
+                )
-            },
+                or_conditions.append(
-        ]
+                    {
+                        "AgentGraph": {
+                            "is": {
+                                "description": {
+                                    "contains": word,
+                                    "mode": "insensitive",
+                                }
+                            }
+                        }
+                    }
+                )
+            where_clause["OR"] = or_conditions
 
-    # Determine sorting
     order_by: prisma.types.LibraryAgentOrderByInput | None = None
 
     if sort_by == library_model.LibraryAgentSort.CREATED_AT:
@@ -105,7 +117,7 @@ async def list_library_agents(
         library_agents = await prisma.models.LibraryAgent.prisma().find_many(
             where=where_clause,
             include=library_agent_include(
-                user_id, include_nodes=False, include_executions=False
+                user_id, include_nodes=False, include_executions=include_executions
             ),
             order=order_by,
             skip=(page - 1) * page_size,

autogpt_platform/backend/backend/api/features/library/model.py

@@ -9,6 +9,7 @@ import pydantic
 from backend.data.block import BlockInput
 from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
 from backend.data.model import CredentialsMetaInput, is_credentials_field_name
+from backend.util.json import loads as json_loads
 from backend.util.models import Pagination
 
 if TYPE_CHECKING:
@@ -16,10 +17,10 @@ if TYPE_CHECKING:
 
 
 class LibraryAgentStatus(str, Enum):
-    COMPLETED = "COMPLETED"  # All runs completed
+    COMPLETED = "COMPLETED"
-    HEALTHY = "HEALTHY"  # Agent is running (not all runs have completed)
+    HEALTHY = "HEALTHY"
-    WAITING = "WAITING"  # Agent is queued or waiting to start
+    WAITING = "WAITING"
-    ERROR = "ERROR"  # Agent is in an error state
+    ERROR = "ERROR"
 
 
 class MarketplaceListingCreator(pydantic.BaseModel):
@@ -39,6 +40,30 @@ class MarketplaceListing(pydantic.BaseModel):
     creator: MarketplaceListingCreator
 
 
+class RecentExecution(pydantic.BaseModel):
+    """Summary of a recent execution for quality assessment.
+
+    Used by the LLM to understand the agent's recent performance with specific examples
+    rather than just aggregate statistics.
+    """
+
+    status: str
+    correctness_score: float | None = None
+    activity_summary: str | None = None
+
+
+def _parse_settings(settings: dict | str | None) -> GraphSettings:
+    """Parse settings from database, handling both dict and string formats."""
+    if settings is None:
+        return GraphSettings()
+    try:
+        if isinstance(settings, str):
+            settings = json_loads(settings)
+        return GraphSettings.model_validate(settings)
+    except Exception:
+        return GraphSettings()
+
+
 class LibraryAgent(pydantic.BaseModel):
     """
     Represents an agent in the library, including metadata for display and
@@ -48,7 +73,7 @@ class LibraryAgent(pydantic.BaseModel):
     id: str
     graph_id: str
     graph_version: int
-    owner_user_id: str  # ID of user who owns/created this agent graph
+    owner_user_id: str
 
     image_url: str | None
 
@@ -64,7 +89,7 @@ class LibraryAgent(pydantic.BaseModel):
     description: str
     instructions: str | None = None
 
-    input_schema: dict[str, Any]  # Should be BlockIOObjectSubSchema in frontend
+    input_schema: dict[str, Any]
     output_schema: dict[str, Any]
     credentials_input_schema: dict[str, Any] | None = pydantic.Field(
         description="Input schema for credentials required by the agent",
@@ -81,25 +106,19 @@ class LibraryAgent(pydantic.BaseModel):
     )
     trigger_setup_info: Optional[GraphTriggerInfo] = None
 
-    # Indicates whether there's a new output (based on recent runs)
     new_output: bool
-
+    execution_count: int = 0
-    # Whether the user can access the underlying graph
+    success_rate: float | None = None
+    avg_correctness_score: float | None = None
+    recent_executions: list[RecentExecution] = pydantic.Field(
+        default_factory=list,
+        description="List of recent executions with status, score, and summary",
+    )
     can_access_graph: bool
-
-    # Indicates if this agent is the latest version
     is_latest_version: bool
-
-    # Whether the agent is marked as favorite by the user
     is_favorite: bool
-
-    # Recommended schedule cron (from marketplace agents)
     recommended_schedule_cron: str | None = None
-
-    # User-specific settings for this library agent
     settings: GraphSettings = pydantic.Field(default_factory=GraphSettings)
-
-    # Marketplace listing information if the agent has been published
     marketplace_listing: Optional["MarketplaceListing"] = None
 
     @staticmethod
@@ -123,7 +142,6 @@ class LibraryAgent(pydantic.BaseModel):
         agent_updated_at = agent.AgentGraph.updatedAt
         lib_agent_updated_at = agent.updatedAt
 
-        # Compute updated_at as the latest between library agent and graph
         updated_at = (
             max(agent_updated_at, lib_agent_updated_at)
             if agent_updated_at
@@ -136,7 +154,6 @@ class LibraryAgent(pydantic.BaseModel):
             creator_name = agent.Creator.name or "Unknown"
             creator_image_url = agent.Creator.avatarUrl or ""
 
-        # Logic to calculate status and new_output
         week_ago = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(
             days=7
         )
@@ -145,13 +162,55 @@ class LibraryAgent(pydantic.BaseModel):
         status = status_result.status
         new_output = status_result.new_output
 
-        # Check if user can access the graph
+        execution_count = len(executions)
-        can_access_graph = agent.AgentGraph.userId == agent.userId
+        success_rate: float | None = None
+        avg_correctness_score: float | None = None
+        if execution_count > 0:
+            success_count = sum(
+                1
+                for e in executions
+                if e.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED
+            )
+            success_rate = (success_count / execution_count) * 100
 
-        # Hard-coded to True until a method to check is implemented
+            correctness_scores = []
+            for e in executions:
+                if e.stats and isinstance(e.stats, dict):
+                    score = e.stats.get("correctness_score")
+                    if score is not None and isinstance(score, (int, float)):
+                        correctness_scores.append(float(score))
+            if correctness_scores:
+                avg_correctness_score = sum(correctness_scores) / len(
+                    correctness_scores
+                )
+
+        recent_executions: list[RecentExecution] = []
+        for e in executions:
+            exec_score: float | None = None
+            exec_summary: str | None = None
+            if e.stats and isinstance(e.stats, dict):
+                score = e.stats.get("correctness_score")
+                if score is not None and isinstance(score, (int, float)):
+                    exec_score = float(score)
+                summary = e.stats.get("activity_status")
+                if summary is not None and isinstance(summary, str):
+                    exec_summary = summary
+            exec_status = (
+                e.executionStatus.value
+                if hasattr(e.executionStatus, "value")
+                else str(e.executionStatus)
+            )
+            recent_executions.append(
+                RecentExecution(
+                    status=exec_status,
+                    correctness_score=exec_score,
+                    activity_summary=exec_summary,
+                )
+            )
+
+        can_access_graph = agent.AgentGraph.userId == agent.userId
         is_latest_version = True
 
-        # Build marketplace_listing if available
         marketplace_listing_data = None
         if store_listing and store_listing.ActiveVersion and profile:
             creator_data = MarketplaceListingCreator(
@@ -190,11 +249,15 @@ class LibraryAgent(pydantic.BaseModel):
             has_sensitive_action=graph.has_sensitive_action,
             trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
+            execution_count=execution_count,
+            success_rate=success_rate,
+            avg_correctness_score=avg_correctness_score,
+            recent_executions=recent_executions,
             can_access_graph=can_access_graph,
             is_latest_version=is_latest_version,
             is_favorite=agent.isFavorite,
             recommended_schedule_cron=agent.AgentGraph.recommendedScheduleCron,
-            settings=GraphSettings.model_validate(agent.settings),
+            settings=_parse_settings(agent.settings),
             marketplace_listing=marketplace_listing_data,
         )
 
@@ -220,18 +283,15 @@ def _calculate_agent_status(
     if not executions:
         return AgentStatusResult(status=LibraryAgentStatus.COMPLETED, new_output=False)
 
-    # Track how many times each execution status appears
     status_counts = {status: 0 for status in prisma.enums.AgentExecutionStatus}
     new_output = False
 
     for execution in executions:
-        # Check if there's a completed run more recent than `recent_threshold`
         if execution.createdAt >= recent_threshold:
             if execution.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED:
                 new_output = True
         status_counts[execution.executionStatus] += 1
 
-    # Determine the final status based on counts
     if status_counts[prisma.enums.AgentExecutionStatus.FAILED] > 0:
         return AgentStatusResult(status=LibraryAgentStatus.ERROR, new_output=new_output)
     elif status_counts[prisma.enums.AgentExecutionStatus.QUEUED] > 0:

autogpt_platform/backend/backend/blocks/llm.py

@@ -115,7 +115,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
     CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
     CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
     # AI/ML API models
     AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
@@ -280,9 +279,6 @@ MODEL_METADATA = {
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
     ),  # claude-haiku-4-5-20251001
-    LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
-    ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
         "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
     ),  # claude-3-haiku-20240307

autogpt_platform/backend/backend/blocks/stagehand/blocks.py

@@ -83,7 +83,7 @@ class StagehandRecommendedLlmModel(str, Enum):
     GPT41_MINI = "gpt-4.1-mini-2025-04-14"
 
     # Anthropic
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
+    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
 
     @property
     def provider_name(self) -> str:
@@ -137,7 +137,7 @@ class StagehandObserveBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -230,7 +230,7 @@ class StagehandActBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -330,7 +330,7 @@ class StagehandExtractBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -81,7 +81,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_5_HAIKU: 4,
     LlmModel.CLAUDE_4_5_OPUS: 14,
     LlmModel.CLAUDE_4_5_SONNET: 9,
-    LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_HAIKU: 1,
     LlmModel.AIML_API_QWEN2_5_72B: 1,
     LlmModel.AIML_API_LLAMA3_1_70B: 1,

autogpt_platform/backend/backend/data/model.py

@@ -666,10 +666,16 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
         if not (self.discriminator and self.discriminator_mapping):
             return self
 
+        try:
+            provider = self.discriminator_mapping[discriminator_value]
+        except KeyError:
+            raise ValueError(
+                f"Model '{discriminator_value}' is not supported. "
+                "It may have been deprecated. Please update your agent configuration."
+            )
+
         return CredentialsFieldInfo(
-            credentials_provider=frozenset(
+            credentials_provider=frozenset([provider]),
-                [self.discriminator_mapping[discriminator_value]]
-            ),
             credentials_types=self.supported_types,
             credentials_scopes=self.required_scopes,
             discriminator=self.discriminator,

autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql

@@ -0,0 +1,22 @@
+-- Migrate Claude 3.7 Sonnet to Claude 4.5 Sonnet
+-- This updates all AgentNode blocks that use the deprecated Claude 3.7 Sonnet model
+-- Anthropic is retiring claude-3-7-sonnet-20250219 on February 19, 2026
+
+-- Update AgentNode constant inputs
+UPDATE "AgentNode"
+SET    "constantInput" = JSONB_SET(
+         "constantInput"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "constantInput"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';
+
+-- Update AgentPreset input overrides (stored in AgentNodeExecutionInputOutput)
+UPDATE "AgentNodeExecutionInputOutput"
+SET    "data" = JSONB_SET(
+         "data"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "agentPresetId" IS NOT NULL
+  AND  "data"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';

autogpt_platform/backend/snapshots/lib_agts_search

@@ -31,6 +31,10 @@
       "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
+      "execution_count": 0,
+      "success_rate": null,
+      "avg_correctness_score": null,
+      "recent_executions": [],
       "can_access_graph": true,
       "is_latest_version": true,
       "is_favorite": false,
@@ -72,6 +76,10 @@
       "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
+      "execution_count": 0,
+      "success_rate": null,
+      "avg_correctness_score": null,
+      "recent_executions": [],
       "can_access_graph": false,
       "is_latest_version": true,
       "is_favorite": false,

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -57,7 +57,8 @@ class TestDecomposeGoal:
 
             result = await core.decompose_goal("Build a chatbot")
 
-            mock_external.assert_called_once_with("Build a chatbot", "")
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Build a chatbot", "", None)
             assert result == expected_result
 
     @pytest.mark.asyncio
@@ -74,7 +75,8 @@ class TestDecomposeGoal:
 
             await core.decompose_goal("Build a chatbot", "Use Python")
 
-            mock_external.assert_called_once_with("Build a chatbot", "Use Python")
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Build a chatbot", "Use Python", None)
 
     @pytest.mark.asyncio
     async def test_returns_none_on_service_failure(self):
@@ -109,7 +111,8 @@ class TestGenerateAgent:
             instructions = {"type": "instructions", "steps": ["Step 1"]}
             result = await core.generate_agent(instructions)
 
-            mock_external.assert_called_once_with(instructions)
+            # library_agents defaults to None
+            mock_external.assert_called_once_with(instructions, None)
             # Result should have id, version, is_active added if not present
             assert result is not None
             assert result["name"] == "Test Agent"
@@ -174,7 +177,8 @@ class TestGenerateAgentPatch:
             current_agent = {"nodes": [], "links": []}
             result = await core.generate_agent_patch("Add a node", current_agent)
 
-            mock_external.assert_called_once_with("Add a node", current_agent)
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Add a node", current_agent, None)
             assert result == expected_result
 
     @pytest.mark.asyncio

autogpt_platform/backend/test/agent_generator/test_library_agents.py

@@ -0,0 +1,841 @@
+"""
+Tests for library agent fetching functionality in agent generator.
+
+This test suite verifies the search-based library agent fetching,
+including the combination of library and marketplace agents.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import core
+
+
+class TestGetLibraryAgentsForGeneration:
+    """Test get_library_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_fetches_agents_with_search_term(self):
+        """Test that search_term is passed to the library db."""
+        # Create a mock agent with proper attribute values
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "agent-123"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Email Agent"
+        mock_agent.description = "Sends emails"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+        mock_agent.recent_executions = []
+
+        mock_response = MagicMock()
+        mock_response.agents = [mock_agent]
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ) as mock_list:
+            result = await core.get_library_agents_for_generation(
+                user_id="user-123",
+                search_query="send email",
+            )
+
+            mock_list.assert_called_once_with(
+                user_id="user-123",
+                search_term="send email",
+                page=1,
+                page_size=15,
+                include_executions=True,
+            )
+
+        # Verify result format
+        assert len(result) == 1
+        assert result[0]["graph_id"] == "agent-123"
+        assert result[0]["name"] == "Email Agent"
+
+    @pytest.mark.asyncio
+    async def test_excludes_specified_graph_id(self):
+        """Test that agents with excluded graph_id are filtered out."""
+        mock_response = MagicMock()
+        mock_response.agents = [
+            MagicMock(
+                graph_id="agent-123",
+                graph_version=1,
+                name="Agent 1",
+                description="First agent",
+                input_schema={},
+                output_schema={},
+                recent_executions=[],
+            ),
+            MagicMock(
+                graph_id="agent-456",
+                graph_version=1,
+                name="Agent 2",
+                description="Second agent",
+                input_schema={},
+                output_schema={},
+                recent_executions=[],
+            ),
+        ]
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ):
+            result = await core.get_library_agents_for_generation(
+                user_id="user-123",
+                exclude_graph_id="agent-123",
+            )
+
+        # Verify the excluded agent is not in results
+        assert len(result) == 1
+        assert result[0]["graph_id"] == "agent-456"
+
+    @pytest.mark.asyncio
+    async def test_respects_max_results(self):
+        """Test that max_results parameter limits the page_size."""
+        mock_response = MagicMock()
+        mock_response.agents = []
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ) as mock_list:
+            await core.get_library_agents_for_generation(
+                user_id="user-123",
+                max_results=5,
+            )
+
+            mock_list.assert_called_once_with(
+                user_id="user-123",
+                search_term=None,
+                page=1,
+                page_size=5,
+                include_executions=True,
+            )
+
+
+class TestSearchMarketplaceAgentsForGeneration:
+    """Test search_marketplace_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_searches_marketplace_with_query(self):
+        """Test that marketplace is searched with the query."""
+        mock_response = MagicMock()
+        mock_response.agents = [
+            MagicMock(
+                agent_name="Public Agent",
+                description="A public agent",
+                sub_heading="Does something useful",
+                creator="creator-1",
+            )
+        ]
+
+        # The store_db is dynamically imported, so patch the import path
+        with patch(
+            "backend.api.features.store.db.get_store_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ) as mock_search:
+            result = await core.search_marketplace_agents_for_generation(
+                search_query="automation",
+                max_results=10,
+            )
+
+            mock_search.assert_called_once_with(
+                search_query="automation",
+                page=1,
+                page_size=10,
+            )
+
+        assert len(result) == 1
+        assert result[0]["name"] == "Public Agent"
+        assert result[0]["is_marketplace_agent"] is True
+
+    @pytest.mark.asyncio
+    async def test_handles_marketplace_error_gracefully(self):
+        """Test that marketplace errors don't crash the function."""
+        with patch(
+            "backend.api.features.store.db.get_store_agents",
+            new_callable=AsyncMock,
+            side_effect=Exception("Marketplace unavailable"),
+        ):
+            result = await core.search_marketplace_agents_for_generation(
+                search_query="test"
+            )
+
+        # Should return empty list, not raise exception
+        assert result == []
+
+
+class TestGetAllRelevantAgentsForGeneration:
+    """Test get_all_relevant_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_combines_library_and_marketplace_agents(self):
+        """Test that agents from both sources are combined."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        marketplace_agents = [
+            {
+                "name": "Market Agent",
+                "description": "From marketplace",
+                "sub_heading": "Sub heading",
+                "creator": "creator-1",
+                "is_marketplace_agent": True,
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+                return_value=marketplace_agents,
+            ):
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test query",
+                    include_marketplace=True,
+                )
+
+        # Library agents should come first
+        assert len(result) == 2
+        assert result[0]["name"] == "Library Agent"
+        assert result[1]["name"] == "Market Agent"
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_name(self):
+        """Test that marketplace agents with same name as library are excluded."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Shared Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        marketplace_agents = [
+            {
+                "name": "Shared Agent",  # Same name, should be deduplicated
+                "description": "From marketplace",
+                "sub_heading": "Sub heading",
+                "creator": "creator-1",
+                "is_marketplace_agent": True,
+            },
+            {
+                "name": "Unique Agent",
+                "description": "Only in marketplace",
+                "sub_heading": "Sub heading",
+                "creator": "creator-2",
+                "is_marketplace_agent": True,
+            },
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+                return_value=marketplace_agents,
+            ):
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test",
+                    include_marketplace=True,
+                )
+
+        # Shared Agent from marketplace should be excluded
+        assert len(result) == 2
+        names = [a["name"] for a in result]
+        assert "Shared Agent" in names
+        assert "Unique Agent" in names
+
+    @pytest.mark.asyncio
+    async def test_skips_marketplace_when_disabled(self):
+        """Test that marketplace is not searched when include_marketplace=False."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+            ) as mock_marketplace:
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test",
+                    include_marketplace=False,
+                )
+
+        # Marketplace should not be called
+        mock_marketplace.assert_not_called()
+        assert len(result) == 1
+
+    @pytest.mark.asyncio
+    async def test_skips_marketplace_when_no_search_query(self):
+        """Test that marketplace is not searched without a search query."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+            ) as mock_marketplace:
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query=None,  # No search query
+                    include_marketplace=True,
+                )
+
+        # Marketplace should not be called without search query
+        mock_marketplace.assert_not_called()
+        assert len(result) == 1
+
+
+class TestExtractSearchTermsFromSteps:
+    """Test extract_search_terms_from_steps function."""
+
+    def test_extracts_terms_from_instructions_type(self):
+        """Test extraction from valid instructions decomposition result."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {
+                    "description": "Send an email notification",
+                    "block_name": "GmailSendBlock",
+                },
+                {"description": "Fetch weather data", "action": "Get weather API"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert "Send an email notification" in result
+        assert "GmailSendBlock" in result
+        assert "Fetch weather data" in result
+        assert "Get weather API" in result
+
+    def test_returns_empty_for_non_instructions_type(self):
+        """Test that non-instructions types return empty list."""
+        decomposition_result = {
+            "type": "clarifying_questions",
+            "questions": [{"question": "What email?"}],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert result == []
+
+    def test_deduplicates_terms_case_insensitively(self):
+        """Test that duplicate terms are removed (case-insensitive)."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "Send Email", "name": "send email"},
+                {"description": "Other task"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        # Should only have one "send email" variant
+        email_terms = [t for t in result if "email" in t.lower()]
+        assert len(email_terms) == 1
+
+    def test_filters_short_terms(self):
+        """Test that terms with 3 or fewer characters are filtered out."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "ab", "action": "xyz"},  # Both too short
+                {"description": "Valid term here"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert "ab" not in result
+        assert "xyz" not in result
+        assert "Valid term here" in result
+
+    def test_handles_empty_steps(self):
+        """Test handling of empty steps list."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert result == []
+
+
+class TestEnrichLibraryAgentsFromSteps:
+    """Test enrich_library_agents_from_steps function."""
+
+    @pytest.mark.asyncio
+    async def test_enriches_with_additional_agents(self):
+        """Test that additional agents are found based on steps."""
+        existing_agents = [
+            {
+                "graph_id": "existing-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        additional_agents = [
+            {
+                "graph_id": "new-456",
+                "graph_version": 1,
+                "name": "Email Agent",
+                "description": "For sending emails",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "Send email notification"},
+            ],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should have both existing and new agents
+        assert len(result) == 2
+        names = [a["name"] for a in result]
+        assert "Existing Agent" in names
+        assert "Email Agent" in names
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_graph_id(self):
+        """Test that agents with same graph_id are not duplicated."""
+        existing_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        # Additional search returns same agent
+        additional_agents = [
+            {
+                "graph_id": "agent-123",  # Same ID
+                "graph_version": 1,
+                "name": "Existing Agent Copy",
+                "description": "Same agent different name",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [{"description": "Some action"}],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should not duplicate
+        assert len(result) == 1
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_name(self):
+        """Test that agents with same name are not duplicated."""
+        existing_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Email Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        # Additional search returns agent with same name but different ID
+        additional_agents = [
+            {
+                "graph_id": "agent-456",  # Different ID
+                "graph_version": 1,
+                "name": "Email Agent",  # Same name
+                "description": "Different agent same name",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [{"description": "Send email"}],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should not duplicate by name
+        assert len(result) == 1
+        assert result[0].get("graph_id") == "agent-123"  # Original kept
+
+    @pytest.mark.asyncio
+    async def test_returns_existing_when_no_steps(self):
+        """Test that existing agents are returned when no search terms extracted."""
+        existing_agents = [
+            {
+                "graph_id": "existing-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "clarifying_questions",  # Not instructions type
+            "questions": [],
+        }
+
+        result = await core.enrich_library_agents_from_steps(
+            user_id="user-123",
+            decomposition_result=decomposition_result,
+            existing_agents=existing_agents,
+        )
+
+        # Should return existing unchanged
+        assert result == existing_agents
+
+    @pytest.mark.asyncio
+    async def test_limits_search_terms_to_three(self):
+        """Test that only first 3 search terms are used."""
+        existing_agents = []
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "First action"},
+                {"description": "Second action"},
+                {"description": "Third action"},
+                {"description": "Fourth action"},
+                {"description": "Fifth action"},
+            ],
+        }
+
+        call_count = 0
+
+        async def mock_get_agents(*args, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            return []
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            side_effect=mock_get_agents,
+        ):
+            await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should only make 3 calls (limited to first 3 terms)
+        assert call_count == 3
+
+
+class TestExtractUuidsFromText:
+    """Test extract_uuids_from_text function."""
+
+    def test_extracts_single_uuid(self):
+        """Test extraction of a single UUID from text."""
+        text = "Use my agent 46631191-e8a8-486f-ad90-84f89738321d for this task"
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 1
+        assert "46631191-e8a8-486f-ad90-84f89738321d" in result
+
+    def test_extracts_multiple_uuids(self):
+        """Test extraction of multiple UUIDs from text."""
+        text = (
+            "Combine agents 11111111-1111-4111-8111-111111111111 "
+            "and 22222222-2222-4222-9222-222222222222"
+        )
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 2
+        assert "11111111-1111-4111-8111-111111111111" in result
+        assert "22222222-2222-4222-9222-222222222222" in result
+
+    def test_deduplicates_uuids(self):
+        """Test that duplicate UUIDs are deduplicated."""
+        text = (
+            "Use 46631191-e8a8-486f-ad90-84f89738321d twice: "
+            "46631191-e8a8-486f-ad90-84f89738321d"
+        )
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 1
+
+    def test_normalizes_to_lowercase(self):
+        """Test that UUIDs are normalized to lowercase."""
+        text = "Use 46631191-E8A8-486F-AD90-84F89738321D"
+        result = core.extract_uuids_from_text(text)
+        assert result[0] == "46631191-e8a8-486f-ad90-84f89738321d"
+
+    def test_returns_empty_for_no_uuids(self):
+        """Test that empty list is returned when no UUIDs found."""
+        text = "Create an email agent that sends notifications"
+        result = core.extract_uuids_from_text(text)
+        assert result == []
+
+    def test_ignores_invalid_uuids(self):
+        """Test that invalid UUID-like strings are ignored."""
+        text = "Not a valid UUID: 12345678-1234-1234-1234-123456789abc"
+        result = core.extract_uuids_from_text(text)
+        # UUID v4 requires specific patterns (4 in third group, 8/9/a/b in fourth)
+        assert len(result) == 0
+
+
+class TestGetLibraryAgentById:
+    """Test get_library_agent_by_id function (and its alias get_library_agent_by_graph_id)."""
+
+    @pytest.mark.asyncio
+    async def test_returns_agent_when_found_by_graph_id(self):
+        """Test that agent is returned when found by graph_id."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "agent-123"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Test Agent"
+        mock_agent.description = "Test description"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+
+        with patch.object(
+            core.library_db,
+            "get_library_agent_by_graph_id",
+            new_callable=AsyncMock,
+            return_value=mock_agent,
+        ):
+            result = await core.get_library_agent_by_id("user-123", "agent-123")
+
+        assert result is not None
+        assert result["graph_id"] == "agent-123"
+        assert result["name"] == "Test Agent"
+
+    @pytest.mark.asyncio
+    async def test_falls_back_to_library_agent_id(self):
+        """Test that lookup falls back to library agent ID when graph_id not found."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "graph-456"  # Different from the lookup ID
+        mock_agent.graph_version = 1
+        mock_agent.name = "Library Agent"
+        mock_agent.description = "Found by library ID"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=None,  # Not found by graph_id
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                return_value=mock_agent,  # Found by library ID
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "library-id-123")
+
+        assert result is not None
+        assert result["graph_id"] == "graph-456"
+        assert result["name"] == "Library Agent"
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_not_found_by_either_method(self):
+        """Test that None is returned when agent not found by either method."""
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=None,
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                side_effect=core.NotFoundError("Not found"),
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "nonexistent")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_returns_none_on_exception(self):
+        """Test that None is returned when exception occurs in both lookups."""
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                side_effect=Exception("Database error"),
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                side_effect=Exception("Database error"),
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "agent-123")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_alias_works(self):
+        """Test that get_library_agent_by_graph_id is an alias for get_library_agent_by_id."""
+        assert core.get_library_agent_by_graph_id is core.get_library_agent_by_id
+
+
+class TestGetAllRelevantAgentsWithUuids:
+    """Test UUID extraction in get_all_relevant_agents_for_generation."""
+
+    @pytest.mark.asyncio
+    async def test_fetches_explicitly_mentioned_agents(self):
+        """Test that agents mentioned by UUID are fetched directly."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "46631191-e8a8-486f-ad90-84f89738321d"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Mentioned Agent"
+        mock_agent.description = "Explicitly mentioned"
+        mock_agent.input_schema = {}
+        mock_agent.output_schema = {}
+
+        mock_response = MagicMock()
+        mock_response.agents = []
+
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=mock_agent,
+            ),
+            patch.object(
+                core.library_db,
+                "list_library_agents",
+                new_callable=AsyncMock,
+                return_value=mock_response,
+            ),
+        ):
+            result = await core.get_all_relevant_agents_for_generation(
+                user_id="user-123",
+                search_query="Use agent 46631191-e8a8-486f-ad90-84f89738321d",
+                include_marketplace=False,
+            )
+
+        assert len(result) == 1
+        assert result[0].get("graph_id") == "46631191-e8a8-486f-ad90-84f89738321d"
+        assert result[0].get("name") == "Mentioned Agent"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -433,5 +433,139 @@ class TestGetBlocksExternal:
         assert result is None
 
 
+class TestLibraryAgentsPassthrough:
+    """Test that library_agents are passed correctly in all requests."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_passes_library_agents(self):
+        """Test that library_agents are included in decompose goal payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Email Sender",
+                "description": "Sends emails",
+                "input_schema": {"properties": {"to": {"type": "string"}}},
+                "output_schema": {"properties": {"sent": {"type": "boolean"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.decompose_goal_external(
+                "Send an email",
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_passes_library_agents(self):
+        """Test that library_agents are included in generate agent payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-456",
+                "graph_version": 2,
+                "name": "Data Fetcher",
+                "description": "Fetches data from API",
+                "input_schema": {"properties": {"url": {"type": "string"}}},
+                "output_schema": {"properties": {"data": {"type": "object"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": {"name": "Test Agent", "nodes": []},
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.generate_agent_external(
+                {"steps": ["Step 1"]},
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_patch_passes_library_agents(self):
+        """Test that library_agents are included in patch generation payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-789",
+                "graph_version": 1,
+                "name": "Slack Notifier",
+                "description": "Sends Slack messages",
+                "input_schema": {"properties": {"message": {"type": "string"}}},
+                "output_schema": {"properties": {"success": {"type": "boolean"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": {"name": "Updated Agent", "nodes": []},
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.generate_agent_patch_external(
+                "Add error handling",
+                {"name": "Original Agent", "nodes": []},
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_without_library_agents(self):
+        """Test that decompose goal works without library_agents."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.decompose_goal_external("Build a workflow")
+
+        # Verify library_agents was NOT passed when not provided
+        call_args = mock_client.post.call_args
+        assert "library_agents" not in call_args[1]["json"]
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])

autogpt_platform/backend/test/e2e_test_data.py

@@ -43,19 +43,24 @@ faker = Faker()
 # Constants for data generation limits (reduced for E2E tests)
 NUM_USERS = 15
 NUM_AGENT_BLOCKS = 30
-MIN_GRAPHS_PER_USER = 15
+MIN_GRAPHS_PER_USER = 25
-MAX_GRAPHS_PER_USER = 15
+MAX_GRAPHS_PER_USER = 25
 MIN_NODES_PER_GRAPH = 3
 MAX_NODES_PER_GRAPH = 6
 MIN_PRESETS_PER_USER = 2
 MAX_PRESETS_PER_USER = 3
-MIN_AGENTS_PER_USER = 15
+MIN_AGENTS_PER_USER = 25
-MAX_AGENTS_PER_USER = 15
+MAX_AGENTS_PER_USER = 25
 MIN_EXECUTIONS_PER_GRAPH = 2
 MAX_EXECUTIONS_PER_GRAPH = 8
 MIN_REVIEWS_PER_VERSION = 2
 MAX_REVIEWS_PER_VERSION = 5
 
+# Guaranteed minimums for marketplace tests (deterministic)
+GUARANTEED_FEATURED_AGENTS = 8
+GUARANTEED_FEATURED_CREATORS = 5
+GUARANTEED_TOP_AGENTS = 10
+
 
 def get_image():
     """Generate a consistent image URL using picsum.photos service."""
@@ -385,7 +390,7 @@ class TestDataCreator:
 
         library_agents = []
         for user in self.users:
-            num_agents = 10  # Create exactly 10 agents per user
+            num_agents = random.randint(MIN_AGENTS_PER_USER, MAX_AGENTS_PER_USER)
 
             # Get available graphs for this user
             user_graphs = [
@@ -507,14 +512,17 @@ class TestDataCreator:
             existing_profiles, min(num_creators, len(existing_profiles))
         )
 
-        # Mark about 50% of creators as featured (more for testing)
+        # Guarantee at least GUARANTEED_FEATURED_CREATORS featured creators
-        num_featured = max(2, int(num_creators * 0.5))
+        num_featured = max(GUARANTEED_FEATURED_CREATORS, int(num_creators * 0.5))
         num_featured = min(
             num_featured, len(selected_profiles)
         )  # Don't exceed available profiles
         featured_profile_ids = set(
             random.sample([p.id for p in selected_profiles], num_featured)
         )
+        print(
+            f"🎯 Creating {num_featured} featured creators (min: {GUARANTEED_FEATURED_CREATORS})"
+        )
 
         for profile in selected_profiles:
             try:
@@ -545,21 +553,25 @@ class TestDataCreator:
         return profiles
 
     async def create_test_store_submissions(self) -> List[Dict[str, Any]]:
-        """Create test store submissions using the API function."""
+        """Create test store submissions using the API function.
+
+        DETERMINISTIC: Guarantees minimum featured agents for E2E tests.
+        """
         print("Creating test store submissions...")
 
         submissions = []
         approved_submissions = []
+        featured_count = 0
+        submission_counter = 0
 
-        # Create a special test submission for test123@gmail.com
+        # Create a special test submission for test123@gmail.com (ALWAYS approved + featured)
         test_user = next(
             (user for user in self.users if user["email"] == "test123@gmail.com"), None
         )
-        if test_user:
+        if test_user and self.agent_graphs:
-            # Special test data for consistent testing
             test_submission_data = {
                 "user_id": test_user["id"],
-                "agent_id": self.agent_graphs[0]["id"],  # Use first available graph
+                "agent_id": self.agent_graphs[0]["id"],
                 "agent_version": 1,
                 "slug": "test-agent-submission",
                 "name": "Test Agent Submission",
@@ -580,37 +592,24 @@ class TestDataCreator:
                 submissions.append(test_submission.model_dump())
                 print("✅ Created special test store submission for test123@gmail.com")
 
-                # Randomly approve, reject, or leave pending the test submission
+                # ALWAYS approve and feature the test submission
                 if test_submission.store_listing_version_id:
-                    random_value = random.random()
+                    approved_submission = await review_store_submission(
-                    if random_value < 0.4:  # 40% chance to approve
+                        store_listing_version_id=test_submission.store_listing_version_id,
-                        approved_submission = await review_store_submission(
+                        is_approved=True,
-                            store_listing_version_id=test_submission.store_listing_version_id,
+                        external_comments="Test submission approved",
-                            is_approved=True,
+                        internal_comments="Auto-approved test submission",
-                            external_comments="Test submission approved",
+                        reviewer_id=test_user["id"],
-                            internal_comments="Auto-approved test submission",
+                    )
-                            reviewer_id=test_user["id"],
+                    approved_submissions.append(approved_submission.model_dump())
-                        )
+                    print("✅ Approved test store submission")
-                        approved_submissions.append(approved_submission.model_dump())
-                        print("✅ Approved test store submission")
 
-                        # Mark approved submission as featured
+                    await prisma.storelistingversion.update(
-                        await prisma.storelistingversion.update(
+                        where={"id": test_submission.store_listing_version_id},
-                            where={"id": test_submission.store_listing_version_id},
+                        data={"isFeatured": True},
-                            data={"isFeatured": True},
+                    )
-                        )
+                    featured_count += 1
-                        print("🌟 Marked test agent as FEATURED")
+                    print("🌟 Marked test agent as FEATURED")
-                    elif random_value < 0.7:  # 30% chance to reject (40% to 70%)
-                        await review_store_submission(
-                            store_listing_version_id=test_submission.store_listing_version_id,
-                            is_approved=False,
-                            external_comments="Test submission rejected - needs improvements",
-                            internal_comments="Auto-rejected test submission for E2E testing",
-                            reviewer_id=test_user["id"],
-                        )
-                        print("❌ Rejected test store submission")
-                    else:  # 30% chance to leave pending (70% to 100%)
-                        print("⏳ Left test submission pending for review")
 
             except Exception as e:
                 print(f"Error creating test store submission: {e}")
@@ -620,7 +619,6 @@ class TestDataCreator:
 
         # Create regular submissions for all users
         for user in self.users:
-            # Get available graphs for this specific user
             user_graphs = [
                 g for g in self.agent_graphs if g.get("userId") == user["id"]
             ]
@@ -631,18 +629,17 @@ class TestDataCreator:
                 )
                 continue
 
-            # Create exactly 4 store submissions per user
             for submission_index in range(4):
                 graph = random.choice(user_graphs)
+                submission_counter += 1
 
                 try:
                     print(
-                        f"Creating store submission for user {user['id']} with graph {graph['id']} (owner: {graph.get('userId')})"
+                        f"Creating store submission for user {user['id']} with graph {graph['id']}"
                     )
 
-                    # Use the API function to create store submission with correct parameters
                     submission = await create_store_submission(
-                        user_id=user["id"],  # Must match graph's userId
+                        user_id=user["id"],
                         agent_id=graph["id"],
                         agent_version=graph.get("version", 1),
                         slug=faker.slug(),
@@ -651,22 +648,24 @@ class TestDataCreator:
                         video_url=get_video_url() if random.random() < 0.3 else None,
                         image_urls=[get_image() for _ in range(3)],
                         description=faker.text(),
-                        categories=[
+                        categories=[get_category()],
-                            get_category()
-                        ],  # Single category from predefined list
                         changes_summary="Initial E2E test submission",
                     )
                     submissions.append(submission.model_dump())
                     print(f"✅ Created store submission: {submission.name}")
 
-                    # Randomly approve, reject, or leave pending the submission
                     if submission.store_listing_version_id:
-                        random_value = random.random()
+                        # DETERMINISTIC: First N submissions are always approved
-                        if random_value < 0.4:  # 40% chance to approve
+                        # First GUARANTEED_FEATURED_AGENTS of those are always featured
-                            try:
+                        should_approve = (
-                                # Pick a random user as the reviewer (admin)
+                            submission_counter <= GUARANTEED_TOP_AGENTS
-                                reviewer_id = random.choice(self.users)["id"]
+                            or random.random() < 0.4
+                        )
+                        should_feature = featured_count < GUARANTEED_FEATURED_AGENTS
 
+                        if should_approve:
+                            try:
+                                reviewer_id = random.choice(self.users)["id"]
                                 approved_submission = await review_store_submission(
                                     store_listing_version_id=submission.store_listing_version_id,
                                     is_approved=True,
@@ -681,16 +680,7 @@ class TestDataCreator:
                                     f"✅ Approved store submission: {submission.name}"
                                 )
 
-                                # Mark some agents as featured during creation (30% chance)
+                                if should_feature:
-                                # More likely for creators and first submissions
-                                is_creator = user["id"] in [
-                                    p.get("userId") for p in self.profiles
-                                ]
-                                feature_chance = (
-                                    0.5 if is_creator else 0.2
-                                )  # 50% for creators, 20% for others
-
-                                if random.random() < feature_chance:
                                     try:
                                         await prisma.storelistingversion.update(
                                             where={
@@ -698,8 +688,25 @@ class TestDataCreator:
                                             },
                                             data={"isFeatured": True},
                                         )
+                                        featured_count += 1
                                         print(
-                                            f"🌟 Marked agent as FEATURED: {submission.name}"
+                                            f"🌟 Marked agent as FEATURED ({featured_count}/{GUARANTEED_FEATURED_AGENTS}): {submission.name}"
+                                        )
+                                    except Exception as e:
+                                        print(
+                                            f"Warning: Could not mark submission as featured: {e}"
+                                        )
+                                elif random.random() < 0.2:
+                                    try:
+                                        await prisma.storelistingversion.update(
+                                            where={
+                                                "id": submission.store_listing_version_id
+                                            },
+                                            data={"isFeatured": True},
+                                        )
+                                        featured_count += 1
+                                        print(
+                                            f"🌟 Marked agent as FEATURED (bonus): {submission.name}"
                                         )
                                     except Exception as e:
                                         print(
@@ -710,11 +717,9 @@ class TestDataCreator:
                                 print(
                                     f"Warning: Could not approve submission {submission.name}: {e}"
                                 )
-                        elif random_value < 0.7:  # 30% chance to reject (40% to 70%)
+                        elif random.random() < 0.5:
                             try:
-                                # Pick a random user as the reviewer (admin)
                                 reviewer_id = random.choice(self.users)["id"]
-
                                 await review_store_submission(
                                     store_listing_version_id=submission.store_listing_version_id,
                                     is_approved=False,
@@ -729,7 +734,7 @@ class TestDataCreator:
                                 print(
                                     f"Warning: Could not reject submission {submission.name}: {e}"
                                 )
-                        else:  # 30% chance to leave pending (70% to 100%)
+                        else:
                             print(
                                 f"⏳ Left submission pending for review: {submission.name}"
                             )
@@ -743,9 +748,13 @@ class TestDataCreator:
                     traceback.print_exc()
                     continue
 
+        print("\n📊 Store Submissions Summary:")
+        print(f"   Created: {len(submissions)}")
+        print(f"   Approved: {len(approved_submissions)}")
         print(
-            f"Created {len(submissions)} store submissions, approved {len(approved_submissions)}"
+            f"   Featured: {featured_count} (guaranteed min: {GUARANTEED_FEATURED_AGENTS})"
         )
+
         self.store_submissions = submissions
         return submissions
 
@@ -825,12 +834,15 @@ class TestDataCreator:
         print(f"✅ Agent blocks available: {len(self.agent_blocks)}")
         print(f"✅ Agent graphs created: {len(self.agent_graphs)}")
         print(f"✅ Library agents created: {len(self.library_agents)}")
-        print(f"✅ Creator profiles updated: {len(self.profiles)} (some featured)")
+        print(f"✅ Creator profiles updated: {len(self.profiles)}")
-        print(
+        print(f"✅ Store submissions created: {len(self.store_submissions)}")
-            f"✅ Store submissions created: {len(self.store_submissions)} (some marked as featured during creation)"
-        )
         print(f"✅ API keys created: {len(self.api_keys)}")
         print(f"✅ Presets created: {len(self.presets)}")
+        print("\n🎯 Deterministic Guarantees:")
+        print(f"   • Featured agents: >= {GUARANTEED_FEATURED_AGENTS}")
+        print(f"   • Featured creators: >= {GUARANTEED_FEATURED_CREATORS}")
+        print(f"   • Top agents (approved): >= {GUARANTEED_TOP_AGENTS}")
+        print(f"   • Library agents per user: >= {MIN_AGENTS_PER_USER}")
         print("\n🚀 Your E2E test database is ready to use!")
 
 

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode.tsx

@@ -528,6 +528,10 @@ export const CustomNode = React.memo(
           const getInputPropKey = (key: string) =>
             nodeType == BlockUIType.AGENT ? `inputs.${key}` : key;
 
+          // For AGENT blocks, handle IDs need the inputs_#_ prefix to match link sink_names
+          const getHandleKey = (key: string) =>
+            nodeType == BlockUIType.AGENT ? `inputs_#_${key}` : key;
+
           return keys.map(([propKey, propSchema]) => {
             const isRequired = data.inputSchema.required?.includes(propKey);
             const isAdvanced = propSchema.advanced;
@@ -544,7 +548,8 @@ export const CustomNode = React.memo(
               !propKey.endsWith("_credentials") &&
               // For OUTPUT blocks, only show the 'value' (hides 'name') input connection handle
               !(nodeType == BlockUIType.OUTPUT && propKey == "name");
-            const isConnected = isInputHandleConnected(propKey);
+            const handleKey = getHandleKey(propKey);
+            const isConnected = isInputHandleConnected(handleKey);
 
             return (
               !isHidden &&
@@ -562,12 +567,12 @@ export const CustomNode = React.memo(
                     propSchema.discriminator
                   ) ? (
                     <NodeHandle
-                      keyName={propKey}
+                      keyName={handleKey}
                       isConnected={isConnected}
                       isRequired={isRequired}
                       schema={propSchema}
                       side="left"
-                      isBroken={isInputHandleBroken(propKey)}
+                      isBroken={isInputHandleBroken(handleKey)}
                     />
                   ) : (
                     propKey !== "credentials" &&
@@ -857,7 +862,7 @@ export const CustomNode = React.memo(
     })();
 
     const hasAdvancedFields =
-      data.inputSchema &&
+      data.inputSchema?.properties &&
       Object.entries(data.inputSchema.properties).some(([key, value]) => {
         return (
           value.advanced === true && !data.inputSchema.required?.includes(key)

autogpt_platform/frontend/src/app/api/openapi.json

@@ -7981,6 +7981,25 @@
             ]
           },
           "new_output": { "type": "boolean", "title": "New Output" },
+          "execution_count": {
+            "type": "integer",
+            "title": "Execution Count",
+            "default": 0
+          },
+          "success_rate": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Success Rate"
+          },
+          "avg_correctness_score": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Avg Correctness Score"
+          },
+          "recent_executions": {
+            "items": { "$ref": "#/components/schemas/RecentExecution" },
+            "type": "array",
+            "title": "Recent Executions",
+            "description": "List of recent executions with status, score, and summary"
+          },
           "can_access_graph": {
             "type": "boolean",
             "title": "Can Access Graph"
@@ -9374,6 +9393,23 @@
         "required": ["providers", "pagination"],
         "title": "ProviderResponse"
       },
+      "RecentExecution": {
+        "properties": {
+          "status": { "type": "string", "title": "Status" },
+          "correctness_score": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Correctness Score"
+          },
+          "activity_summary": {
+            "anyOf": [{ "type": "string" }, { "type": "null" }],
+            "title": "Activity Summary"
+          }
+        },
+        "type": "object",
+        "required": ["status"],
+        "title": "RecentExecution",
+        "description": "Summary of a recent execution for quality assessment.\n\nUsed by the LLM to understand the agent's recent performance with specific examples\nrather than just aggregate statistics."
+      },
       "RefundRequest": {
         "properties": {
           "id": { "type": "string", "title": "Id" },

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx

@@ -57,6 +57,7 @@ export function ChatInput({
     isStreaming,
     value,
     baseHandleKeyDown,
+    inputId,
   });
 
   return (

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts

@@ -15,6 +15,7 @@ interface Args {
   isStreaming?: boolean;
   value: string;
   baseHandleKeyDown: (event: KeyboardEvent<HTMLTextAreaElement>) => void;
+  inputId?: string;
 }
 
 export function useVoiceRecording({
@@ -23,6 +24,7 @@ export function useVoiceRecording({
   isStreaming = false,
   value,
   baseHandleKeyDown,
+  inputId,
 }: Args) {
   const [isRecording, setIsRecording] = useState(false);
   const [isTranscribing, setIsTranscribing] = useState(false);
@@ -103,7 +105,7 @@ export function useVoiceRecording({
         setIsTranscribing(false);
       }
     },
-    [handleTranscription],
+    [handleTranscription, inputId],
   );
 
   const stopRecording = useCallback(() => {
@@ -201,6 +203,15 @@ export function useVoiceRecording({
     }
   }, [error, toast]);
 
+  useEffect(() => {
+    if (!isTranscribing && inputId) {
+      const inputElement = document.getElementById(inputId);
+      if (inputElement) {
+        inputElement.focus();
+      }
+    }
+  }, [isTranscribing, inputId]);
+
   const handleKeyDown = useCallback(
     (event: KeyboardEvent<HTMLTextAreaElement>) => {
       if (event.key === " " && !value.trim() && !isTranscribing) {

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx

@@ -156,11 +156,20 @@ export function ChatMessage({
   }
 
   if (isClarificationNeeded && message.type === "clarification_needed") {
+    // Check if user already replied after this clarification (answered)
+    const hasUserReplyAfter =
+      index >= 0 &&
+      messages
+        .slice(index + 1)
+        .some((m) => m.type === "message" && m.role === "user");
+
     return (
       <ClarificationQuestionsWidget
         questions={message.questions}
         message={message.message}
+        sessionId={message.sessionId}
         onSubmitAnswers={handleClarificationAnswers}
+        isAnswered={hasUserReplyAfter}
         className={className}
       />
     );

autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx

@@ -6,7 +6,7 @@ import { Input } from "@/components/atoms/Input/Input";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
 import { CheckCircleIcon, QuestionIcon } from "@phosphor-icons/react";
-import { useState } from "react";
+import { useState, useEffect, useRef } from "react";
 
 export interface ClarifyingQuestion {
   question: string;
@@ -17,39 +17,97 @@ export interface ClarifyingQuestion {
 interface Props {
   questions: ClarifyingQuestion[];
   message: string;
+  sessionId?: string;
   onSubmitAnswers: (answers: Record<string, string>) => void;
   onCancel?: () => void;
+  isAnswered?: boolean;
   className?: string;
 }
 
+function getStorageKey(sessionId?: string): string | null {
+  if (!sessionId) return null;
+  return `clarification_answers_${sessionId}`;
+}
+
 export function ClarificationQuestionsWidget({
   questions,
   message,
+  sessionId,
   onSubmitAnswers,
   onCancel,
+  isAnswered = false,
   className,
 }: Props) {
   const [answers, setAnswers] = useState<Record<string, string>>({});
   const [isSubmitted, setIsSubmitted] = useState(false);
+  const lastSessionIdRef = useRef<string | undefined>(undefined);
+
+  useEffect(() => {
+    const storageKey = getStorageKey(sessionId);
+    if (!storageKey) {
+      setAnswers({});
+      setIsSubmitted(false);
+      lastSessionIdRef.current = sessionId;
+      return;
+    }
+
+    try {
+      const saved = localStorage.getItem(storageKey);
+      if (saved) {
+        const parsed = JSON.parse(saved) as Record<string, string>;
+        setAnswers(parsed);
+      } else {
+        setAnswers({});
+      }
+      setIsSubmitted(false);
+    } catch {
+      setAnswers({});
+      setIsSubmitted(false);
+    }
+    lastSessionIdRef.current = sessionId;
+  }, [sessionId]);
+
+  useEffect(() => {
+    if (lastSessionIdRef.current !== sessionId) {
+      return;
+    }
+    const storageKey = getStorageKey(sessionId);
+    if (!storageKey) return;
+
+    const hasAnswers = Object.values(answers).some((v) => v.trim());
+    try {
+      if (hasAnswers) {
+        localStorage.setItem(storageKey, JSON.stringify(answers));
+      } else {
+        localStorage.removeItem(storageKey);
+      }
+    } catch {}
+  }, [answers, sessionId]);
 
   function handleAnswerChange(keyword: string, value: string) {
     setAnswers((prev) => ({ ...prev, [keyword]: value }));
   }
 
   function handleSubmit() {
-    // Check if all questions are answered
     const allAnswered = questions.every((q) => answers[q.keyword]?.trim());
     if (!allAnswered) {
       return;
     }
     setIsSubmitted(true);
     onSubmitAnswers(answers);
+
+    const storageKey = getStorageKey(sessionId);
+    try {
+      if (storageKey) {
+        localStorage.removeItem(storageKey);
+      }
+    } catch {}
   }
 
   const allAnswered = questions.every((q) => answers[q.keyword]?.trim());
 
-  // Show submitted state after answers are submitted
+  // Show submitted state if answered from conversation or just submitted
-  if (isSubmitted) {
+  if (isAnswered || isSubmitted) {
     return (
       <div
         className={cn(

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/helpers.ts

@@ -30,9 +30,9 @@ export function getErrorMessage(result: unknown): string {
   }
   if (typeof result === "object" && result !== null) {
     const response = result as Record<string, unknown>;
-    if (response.error) return stripInternalReasoning(String(response.error));
     if (response.message)
       return stripInternalReasoning(String(response.message));
+    if (response.error) return stripInternalReasoning(String(response.error));
   }
   return "An error occurred";
 }
@@ -363,8 +363,8 @@ export function formatToolResponse(result: unknown, toolName: string): string {
 
     case "error":
       const errorMsg =
-        (response.error as string) || response.message || "An error occurred";
+        (response.message as string) || response.error || "An error occurred";
-      return `Error: ${errorMsg}`;
+      return stripInternalReasoning(String(errorMsg));
 
     case "no_results":
       const suggestions = (response.suggestions as string[]) || [];

autogpt_platform/frontend/src/tests/library.spec.ts

@@ -59,12 +59,13 @@ test.describe("Library", () => {
   });
 
   test("pagination works correctly", async ({ page }, testInfo) => {
-    test.setTimeout(testInfo.timeout * 3); // Increase timeout for pagination operations
+    test.setTimeout(testInfo.timeout * 3);
     await page.goto("/library");
 
+    const PAGE_SIZE = 20;
     const paginationResult = await libraryPage.testPagination();
 
-    if (paginationResult.initialCount >= 10) {
+    if (paginationResult.initialCount >= PAGE_SIZE) {
       expect(paginationResult.finalCount).toBeGreaterThanOrEqual(
         paginationResult.initialCount,
       );
@@ -133,7 +134,10 @@ test.describe("Library", () => {
     test.expect(clearedSearchValue).toBe("");
   });
 
-  test("pagination while searching works correctly", async ({ page }) => {
+  test("pagination while searching works correctly", async ({
+    page,
+  }, testInfo) => {
+    test.setTimeout(testInfo.timeout * 3);
     await page.goto("/library");
 
     const allAgents = await libraryPage.getAgents();
@@ -152,9 +156,10 @@ test.describe("Library", () => {
     );
     expect(matchingResults.length).toEqual(initialSearchResults.length);
 
+    const PAGE_SIZE = 20;
     const searchPaginationResult = await libraryPage.testPagination();
 
-    if (searchPaginationResult.initialCount >= 10) {
+    if (searchPaginationResult.initialCount >= PAGE_SIZE) {
       expect(searchPaginationResult.finalCount).toBeGreaterThanOrEqual(
         searchPaginationResult.initialCount,
       );

autogpt_platform/frontend/src/tests/marketplace-creator.spec.ts

@@ -69,9 +69,12 @@ test.describe("Marketplace Creator Page – Basic Functionality", () => {
       await marketplacePage.getFirstCreatorProfile(page);
     await firstCreatorProfile.click();
     await page.waitForURL("**/marketplace/creator/**");
+    await page.waitForLoadState("networkidle").catch(() => {});
+
     const firstAgent = page
       .locator('[data-testid="store-card"]:visible')
       .first();
+    await firstAgent.waitFor({ state: "visible", timeout: 30000 });
 
     await firstAgent.click();
     await page.waitForURL("**/marketplace/agent/**");

autogpt_platform/frontend/src/tests/marketplace.spec.ts

@@ -77,7 +77,6 @@ test.describe("Marketplace – Basic Functionality", () => {
 
     const firstFeaturedAgent =
       await marketplacePage.getFirstFeaturedAgent(page);
-    await firstFeaturedAgent.waitFor({ state: "visible" });
     await firstFeaturedAgent.click();
     await page.waitForURL("**/marketplace/agent/**");
     await matchesUrl(page, /\/marketplace\/agent\/.+/);
@@ -116,7 +115,15 @@ test.describe("Marketplace – Basic Functionality", () => {
     const searchTerm = page.getByText("DummyInput").first();
     await isVisible(searchTerm);
 
-    await page.waitForTimeout(10000);
+    await page.waitForLoadState("networkidle").catch(() => {});
+
+    await page
+      .waitForFunction(
+        () =>
+          document.querySelectorAll('[data-testid="store-card"]').length > 0,
+        { timeout: 15000 },
+      )
+      .catch(() => console.log("No search results appeared within timeout"));
 
     const results = await marketplacePage.getSearchResultsCount(page);
     expect(results).toBeGreaterThan(0);

autogpt_platform/frontend/src/tests/pages/library.page.ts

@@ -300,21 +300,27 @@ export class LibraryPage extends BasePage {
   async scrollToLoadMore(): Promise<void> {
     console.log(`scrolling to load more agents`);
 
-    // Get initial agent count
+    const initialCount = await this.getAgentCountByListLength();
-    const initialCount = await this.getAgentCount();
+    console.log(`Initial agent count (DOM cards): ${initialCount}`);
-    console.log(`Initial agent count: ${initialCount}`);
 
-    // Scroll down to trigger pagination
     await this.scrollToBottom();
 
-    // Wait for potential new agents to load
+    await this.page
-    await this.page.waitForTimeout(2000);
+      .waitForLoadState("networkidle", { timeout: 10000 })
+      .catch(() => console.log("Network idle timeout, continuing..."));
 
-    // Check if more agents loaded
+    await this.page
-    const newCount = await this.getAgentCount();
+      .waitForFunction(
-    console.log(`New agent count after scroll: ${newCount}`);
+        (prevCount) =>
+          document.querySelectorAll('[data-testid="library-agent-card"]')
+            .length > prevCount,
+        initialCount,
+        { timeout: 5000 },
+      )
+      .catch(() => {});
 
-    return;
+    const newCount = await this.getAgentCountByListLength();
+    console.log(`New agent count after scroll (DOM cards): ${newCount}`);
   }
 
   async testPagination(): Promise<{

autogpt_platform/frontend/src/tests/pages/marketplace.page.ts

@@ -9,6 +9,7 @@ export class MarketplacePage extends BasePage {
 
   async goto(page: Page) {
     await page.goto("/marketplace");
+    await page.waitForLoadState("networkidle").catch(() => {});
   }
 
   async getMarketplaceTitle(page: Page) {
@@ -109,16 +110,24 @@ export class MarketplacePage extends BasePage {
 
   async getFirstFeaturedAgent(page: Page) {
     const { getId } = getSelectors(page);
-    return getId("featured-store-card").first();
+    const card = getId("featured-store-card").first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getFirstTopAgent() {
-    return this.page.locator('[data-testid="store-card"]:visible').first();
+    const card = this.page
+      .locator('[data-testid="store-card"]:visible')
+      .first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getFirstCreatorProfile(page: Page) {
     const { getId } = getSelectors(page);
-    return getId("creator-card").first();
+    const card = getId("creator-card").first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getSearchResultsCount(page: Page) {

classic/.flake8

@@ -1,15 +1,12 @@
 [flake8]
 max-line-length = 88
-extend-ignore = E203
 exclude =
     .tox,
     __pycache__,
     *.pyc,
-    .env,
+    .env
-    venv*,
+    venv*/*,
-    .venv,
+    .venv/*,
-    reports,
+    reports/*,
-    dist,
+    dist/*,
-    data,
+    data/*,
-    .benchmark_workspaces,
-    .autogpt,

classic/CLAUDE.md

@@ -1,291 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Project Overview
-
-AutoGPT Classic is an experimental, **unsupported** project demonstrating autonomous GPT-4 operation. Dependencies will not be updated, and the codebase contains known vulnerabilities. This is preserved for educational/historical purposes.
-
-## Repository Structure
-
-```
-classic/
-├── pyproject.toml          # Single consolidated Poetry project
-├── poetry.lock             # Single lock file
-├── forge/
-│   └── forge/              # Core agent framework package
-├── original_autogpt/
-│   └── autogpt/            # AutoGPT agent package
-├── direct_benchmark/
-│   └── direct_benchmark/   # Benchmark harness package
-└── benchmark/              # Challenge definitions (data, not code)
-```
-
-All packages are managed by a single `pyproject.toml` at the classic/ root.
-
-## Common Commands
-
-### Setup & Install
-```bash
-# Install everything from classic/ directory
-cd classic
-poetry install
-```
-
-### Running Agents
-```bash
-# Run forge agent
-poetry run python -m forge
-
-# Run original autogpt server
-poetry run serve --debug
-
-# Run autogpt CLI
-poetry run autogpt
-```
-
-Agents run on `http://localhost:8000` by default.
-
-### Benchmarking
-```bash
-# Run benchmarks
-poetry run direct-benchmark run
-
-# Run specific strategies and models
-poetry run direct-benchmark run \
-    --strategies one_shot,rewoo \
-    --models claude \
-    --parallel 4
-
-# Run a single test
-poetry run direct-benchmark run --tests ReadFile
-
-# List available commands
-poetry run direct-benchmark --help
-```
-
-### Testing
-```bash
-poetry run pytest                              # All tests
-poetry run pytest forge/tests/                 # Forge tests only
-poetry run pytest original_autogpt/tests/      # AutoGPT tests only
-poetry run pytest -k test_name                 # Single test by name
-poetry run pytest path/to/test.py              # Specific test file
-poetry run pytest --cov                        # With coverage
-```
-
-### Linting & Formatting
-
-Run from the classic/ directory:
-
-```bash
-# Format everything (recommended to run together)
-poetry run black . && poetry run isort .
-
-# Check formatting (CI-style, no changes)
-poetry run black --check . && poetry run isort --check-only .
-
-# Lint
-poetry run flake8        # Style linting
-
-# Type check
-poetry run pyright       # Type checking (some errors are expected in infrastructure code)
-```
-
-Note: Always run linters over the entire directory, not specific files, for best results.
-
-## Architecture
-
-### Forge (Core Framework)
-The `forge` package is the foundation that other components depend on:
-- `forge/agent/` - Agent implementation and protocols
-- `forge/llm/` - Multi-provider LLM integrations (OpenAI, Anthropic, Groq, LiteLLM)
-- `forge/components/` - Reusable agent components
-- `forge/file_storage/` - File system abstraction
-- `forge/config/` - Configuration management
-
-### Original AutoGPT
-- `original_autogpt/autogpt/app/` - CLI application entry points
-- `original_autogpt/autogpt/agents/` - Agent implementations
-- `original_autogpt/autogpt/agent_factory/` - Agent creation logic
-
-### Direct Benchmark
-Benchmark harness for testing agent performance:
-- `direct_benchmark/direct_benchmark/` - CLI and harness code
-- `benchmark/agbenchmark/challenges/` - Test cases organized by category (code, retrieval, data, etc.)
-- Reports generated in `direct_benchmark/reports/`
-
-### Package Structure
-All three packages are included in a single Poetry project. Imports are fully qualified:
-- `from forge.agent.base import BaseAgent`
-- `from autogpt.agents.agent import Agent`
-- `from direct_benchmark.harness import BenchmarkHarness`
-
-## Code Style
-
-- Python 3.12 target
-- Line length: 88 characters (Black default)
-- Black for formatting, isort for imports (profile="black")
-- Type hints with Pyright checking
-
-## Testing Patterns
-
-- Async support via pytest-asyncio
-- Fixtures defined in `conftest.py` files provide: `tmp_project_root`, `storage`, `config`, `llm_provider`, `agent`
-- Tests requiring API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY) will skip if not set
-
-## Environment Setup
-
-Copy `.env.example` to `.env` in the relevant directory and add your API keys:
-```bash
-cp .env.example .env
-# Edit .env with your OPENAI_API_KEY, etc.
-```
-
-## Workspaces
-
-Agents operate within a **workspace** - a directory containing all agent data and files. The workspace root defaults to the current working directory.
-
-### Workspace Structure
-
-```
-{workspace}/
-├── .autogpt/
-│   ├── autogpt.yaml              # Workspace-level permissions
-│   ├── ap_server.db              # Agent Protocol database (server mode)
-│   └── agents/
-│       └── AutoGPT-{agent_id}/
-│           ├── state.json        # Agent profile, directives, action history
-│           ├── permissions.yaml  # Agent-specific permission overrides
-│           └── workspace/        # Agent's sandboxed working directory
-```
-
-### Key Concepts
-
-- **Multiple agents** can coexist in the same workspace (each gets its own subdirectory)
-- **File access** is sandboxed to the agent's `workspace/` directory by default
-- **State persistence** - agent state saves to `state.json` and survives across sessions
-- **Storage backends** - supports local filesystem, S3, and GCS (via `FILE_STORAGE_BACKEND` env var)
-
-### Specifying a Workspace
-
-```bash
-# Default: uses current directory
-cd /path/to/my/project && poetry run autogpt
-
-# Or specify explicitly via CLI (if supported)
-poetry run autogpt --workspace /path/to/workspace
-```
-
-## Settings Location
-
-Configuration uses a **layered system** with three levels (in order of precedence):
-
-### 1. Environment Variables (Global)
-
-Loaded from `.env` file in the working directory:
-
-```bash
-# Required
-OPENAI_API_KEY=sk-...
-
-# Optional LLM settings
-SMART_LLM=gpt-4o                    # Model for complex reasoning
-FAST_LLM=gpt-4o-mini                # Model for simple tasks
-EMBEDDING_MODEL=text-embedding-3-small
-
-# Optional search providers (for web search component)
-TAVILY_API_KEY=tvly-...
-SERPER_API_KEY=...
-GOOGLE_API_KEY=...
-GOOGLE_CUSTOM_SEARCH_ENGINE_ID=...
-
-# Optional infrastructure
-LOG_LEVEL=DEBUG                     # DEBUG, INFO, WARNING, ERROR
-DATABASE_STRING=sqlite:///agent.db  # Agent Protocol database
-PORT=8000                           # Server port
-FILE_STORAGE_BACKEND=local          # local, s3, or gcs
-```
-
-### 2. Workspace Settings (`{workspace}/.autogpt/autogpt.yaml`)
-
-Workspace-wide permissions that apply to **all agents** in this workspace:
-
-```yaml
-allow:
-  - read_file({workspace}/**)
-  - write_to_file({workspace}/**)
-  - list_folder({workspace}/**)
-  - web_search(*)
-
-deny:
-  - read_file(**.env)
-  - read_file(**.env.*)
-  - read_file(**.key)
-  - read_file(**.pem)
-  - execute_shell(rm -rf:*)
-  - execute_shell(sudo:*)
-```
-
-Auto-generated with sensible defaults if missing.
-
-### 3. Agent Settings (`{workspace}/.autogpt/agents/{id}/permissions.yaml`)
-
-Agent-specific permission overrides:
-
-```yaml
-allow:
-  - execute_python(*)
-  - web_search(*)
-
-deny:
-  - execute_shell(*)
-```
-
-## Permissions
-
-The permission system uses **pattern matching** with a **first-match-wins** evaluation order.
-
-### Permission Check Order
-
-1. Agent deny list → **Block**
-2. Workspace deny list → **Block**
-3. Agent allow list → **Allow**
-4. Workspace allow list → **Allow**
-5. Session denied list → **Block** (commands denied during this session)
-6. **Prompt user** → Interactive approval (if in interactive mode)
-
-### Pattern Syntax
-
-Format: `command_name(glob_pattern)`
-
-| Pattern | Description |
-|---------|-------------|
-| `read_file({workspace}/**)` | Read any file in workspace (recursive) |
-| `write_to_file({workspace}/*.txt)` | Write only .txt files in workspace root |
-| `execute_shell(python:**)` | Execute Python commands only |
-| `execute_shell(git:*)` | Execute any git command |
-| `web_search(*)` | Allow all web searches |
-
-Special tokens:
-- `{workspace}` - Replaced with actual workspace path
-- `**` - Matches any path including `/`
-- `*` - Matches any characters except `/`
-
-### Interactive Approval Scopes
-
-When prompted for permission, users can choose:
-
-| Scope | Effect |
-|-------|--------|
-| **Once** | Allow this one time only (not saved) |
-| **Agent** | Always allow for this agent (saves to agent `permissions.yaml`) |
-| **Workspace** | Always allow for all agents (saves to `autogpt.yaml`) |
-| **Deny** | Deny this command (saves to appropriate deny list) |
-
-### Default Security
-
-Out of the box, the following are **denied by default**:
-- Reading sensitive files (`.env`, `.key`, `.pem`)
-- Destructive shell commands (`rm -rf`, `sudo`)
-- Operations outside the workspace directory

classic/CLI-USAGE.md

@@ -0,0 +1,182 @@
+## CLI Documentation
+
+This document describes how to interact with the project's CLI (Command Line Interface). It includes the types of outputs you can expect from each command. Note that the `agents stop` command will terminate any process running on port 8000.
+
+### 1. Entry Point for the CLI
+
+Running the `./run` command without any parameters will display the help message, which provides a list of available commands and options. Additionally, you can append `--help` to any command to view help information specific to that command.
+
+```sh
+./run
+```
+
+**Output**:
+
+```
+Usage: cli.py [OPTIONS] COMMAND [ARGS]...
+
+Options:
+  --help  Show this message and exit.
+
+Commands:
+  agent     Commands to create, start and stop agents
+  benchmark  Commands to start the benchmark and list tests and categories
+  setup      Installs dependencies needed for your system.
+```
+
+If you need assistance with any command, simply add the `--help` parameter to the end of your command, like so:
+
+```sh
+./run COMMAND --help
+```
+
+This will display a detailed help message regarding that specific command, including a list of any additional options and arguments it accepts.
+
+### 2. Setup Command
+
+```sh
+./run setup
+```
+
+**Output**:
+
+```
+Setup initiated
+Installation has been completed.
+```
+
+This command initializes the setup of the project.
+
+### 3. Agents Commands
+
+**a. List All Agents**
+
+```sh
+./run agent list
+```
+
+**Output**:
+
+```
+Available agents: 🤖
+        🐙 forge
+        🐙 autogpt
+```
+
+Lists all the available agents.
+
+**b. Create a New Agent**
+
+```sh
+./run agent create my_agent
+```
+
+**Output**:
+
+```
+🎉 New agent 'my_agent' created and switched to the new directory in agents folder.
+```
+
+Creates a new agent named 'my_agent'.
+
+**c. Start an Agent**
+
+```sh
+./run agent start my_agent
+```
+
+**Output**:
+
+```
+... (ASCII Art representing the agent startup)
+[Date and Time] [forge.sdk.db] [DEBUG] 🐛  Initializing AgentDB with database_string: sqlite:///agent.db
+[Date and Time] [forge.sdk.agent] [INFO] 📝  Agent server starting on http://0.0.0.0:8000
+```
+
+Starts the 'my_agent' and displays startup ASCII art and logs.
+
+**d. Stop an Agent**
+
+```sh
+./run agent stop
+```
+
+**Output**:
+
+```
+Agent stopped
+```
+
+Stops the running agent.
+
+### 4. Benchmark Commands
+
+**a. List Benchmark Categories**
+
+```sh
+./run benchmark categories list
+```
+
+**Output**:
+
+```
+Available categories: 📚
+        📖 code
+        📖 safety
+        📖 memory
+        ... (and so on)
+```
+
+Lists all available benchmark categories.
+
+**b. List Benchmark Tests**
+
+```sh
+./run benchmark tests list
+```
+
+**Output**:
+
+```
+Available tests: 📚
+        📖 interface
+                🔬 Search - TestSearch
+                🔬 Write File - TestWriteFile
+        ... (and so on)
+```
+
+Lists all available benchmark tests.
+
+**c. Show Details of a Benchmark Test**
+
+```sh
+./run benchmark tests details TestWriteFile
+```
+
+**Output**:
+
+```
+TestWriteFile
+-------------
+
+        Category:  interface
+        Task:  Write the word 'Washington' to a .txt file
+        ... (and other details)
+```
+
+Displays the details of the 'TestWriteFile' benchmark test.
+
+**d. Start Benchmark for the Agent**
+
+```sh
+./run benchmark start my_agent
+```
+
+**Output**:
+
+```
+(more details about the testing process shown whilst the test are running)
+============= 13 failed, 1 passed in 0.97s ============...
+```
+
+Displays the results of the benchmark tests on 'my_agent'.

classic/Dockerfile.autogpt

@@ -2,7 +2,7 @@
 ARG BUILD_TYPE=dev
 
 # Use an official Python base image from the Docker Hub
-FROM python:3.12-slim AS autogpt-base
+FROM python:3.10-slim AS autogpt-base
 
 # Install browsers
 RUN apt-get update && apt-get install -y \
@@ -34,6 +34,9 @@ COPY original_autogpt/pyproject.toml original_autogpt/poetry.lock ./
 # Include forge so it can be used as a path dependency
 COPY forge/ ../forge
 
+# Include frontend
+COPY frontend/ ../frontend
+
 # Set the entrypoint
 ENTRYPOINT ["poetry", "run", "autogpt"]
 CMD []

classic/FORGE-QUICKSTART.md

@@ -0,0 +1,173 @@
+# Quickstart Guide
+
+> For the complete getting started [tutorial series](https://aiedge.medium.com/autogpt-forge-e3de53cc58ec) <- click here
+
+Welcome to the Quickstart Guide! This guide will walk you through setting up, building, and running your own AutoGPT agent. Whether you're a seasoned AI developer or just starting out, this guide will provide you with the steps to jumpstart your journey in AI development with AutoGPT.
+
+## System Requirements
+
+This project supports Linux (Debian-based), Mac, and Windows Subsystem for Linux (WSL). If you use a Windows system, you must install WSL. You can find the installation instructions for WSL [here](https://learn.microsoft.com/en-us/windows/wsl/).
+
+
+## Getting Setup
+1. **Fork the Repository**
+   To fork the repository, follow these steps:
+   - Navigate to the main page of the repository.
+
+   ![Repository](../docs/content/imgs/quickstart/001_repo.png)
+   - In the top-right corner of the page, click Fork.
+
+   ![Create Fork UI](../docs/content/imgs/quickstart/002_fork.png)
+   - On the next page, select your GitHub account to create the fork.
+   - Wait for the forking process to complete. You now have a copy of the repository in your GitHub account.
+
+2. **Clone the Repository**
+   To clone the repository, you need to have Git installed on your system. If you don't have Git installed, download it from [here](https://git-scm.com/downloads). Once you have Git installed, follow these steps:
+   - Open your terminal.
+   - Navigate to the directory where you want to clone the repository.
+   - Run the git clone command for the fork you just created
+
+   ![Clone the Repository](../docs/content/imgs/quickstart/003_clone.png)
+
+   - Then open your project in your ide
+
+   ![Open the Project in your IDE](../docs/content/imgs/quickstart/004_ide.png)
+
+4. **Setup the Project**
+    Next, we need to set up the required dependencies. We have a tool to help you perform all the tasks on the repo.
+    It can be accessed by running the `run` command by typing `./run` in the terminal.
+
+    The first command you need to use is `./run setup.` This will guide you through setting up your system.
+    Initially, you will get instructions for installing Flutter and Chrome and setting up your GitHub access token like the following image:
+    
+    ![Setup the Project](../docs/content/imgs/quickstart/005_setup.png)
+
+### For Windows Users
+
+If you're a Windows user and experience issues after installing WSL, follow the steps below to resolve them. 
+
+#### Update WSL 
+Run the following command in Powershell or Command Prompt:
+1. Enable the optional WSL and Virtual Machine Platform components.
+2. Download and install the latest Linux kernel.
+3. Set WSL 2 as the default.
+4. Download and install the Ubuntu Linux distribution (a reboot may be required).
+
+```shell
+wsl --install
+```
+
+For more detailed information and additional steps, refer to [Microsoft's WSL Setup Environment Documentation](https://learn.microsoft.com/en-us/windows/wsl/setup/environment).
+
+#### Resolve FileNotFoundError or "No such file or directory" Errors
+When you run `./run setup`, if you encounter errors like `No such file or directory` or `FileNotFoundError`, it might be because Windows-style line endings (CRLF - Carriage Return Line Feed) are not compatible with Unix/Linux style line endings (LF - Line Feed).
+
+To resolve this, you can use the `dos2unix` utility to convert the line endings in your script from CRLF to LF. Here’s how to install and run `dos2unix` on the script:
+
+```shell
+sudo apt update
+sudo apt install dos2unix
+dos2unix ./run
+```
+
+After executing the above commands, running `./run setup` should work successfully. 
+
+#### Store Project Files within the WSL File System
+If you continue to experience issues, consider storing your project files within the WSL file system instead of the Windows file system. This method avoids path translations and permissions issues and provides a more consistent development environment.
+
+You can keep running the command to get feedback on where you are up to with your setup. 
+When setup has been completed, the command will return an output like this:
+
+![Setup Complete](../docs/content/imgs/quickstart/006_setup_complete.png)
+
+## Creating Your Agent
+
+After completing the setup, the next step is to create your agent template.
+Execute the command `./run agent create YOUR_AGENT_NAME`, where `YOUR_AGENT_NAME` should be replaced with your chosen name.
+
+Tips for naming your agent:
+* Give it its own unique name, or name it after yourself
+* Include an important aspect of your agent in the name, such as its purpose
+
+Examples: `SwiftyosAssistant`, `PwutsPRAgent`, `MySuperAgent`
+
+![Create an Agent](../docs/content/imgs/quickstart/007_create_agent.png)
+
+## Running your Agent
+
+Your agent can be started using the command: `./run agent start YOUR_AGENT_NAME`
+
+This starts the agent on the URL: `http://localhost:8000/`
+
+![Start the Agent](../docs/content/imgs/quickstart/009_start_agent.png)
+
+The front end can be accessed from `http://localhost:8000/`; first, you must log in using either a Google account or your GitHub account.
+
+![Login](../docs/content/imgs/quickstart/010_login.png)
+
+Upon logging in, you will get a page that looks something like this: your task history down the left-hand side of the page, and the 'chat' window to send tasks to your agent.
+
+![Login](../docs/content/imgs/quickstart/011_home.png)
+
+When you have finished with your agent or just need to restart it, use Ctl-C to end the session. Then, you can re-run the start command.
+
+If you are having issues and want to ensure the agent has been stopped, there is a `./run agent stop` command, which will kill the process using port 8000, which should be the agent. 
+
+## Benchmarking your Agent
+
+The benchmarking system can also be accessed using the CLI too:
+
+```bash
+agpt % ./run benchmark
+Usage: cli.py benchmark [OPTIONS] COMMAND [ARGS]...
+
+  Commands to start the benchmark and list tests and categories
+
+Options:
+  --help  Show this message and exit.
+
+Commands:
+  categories  Benchmark categories group command
+  start       Starts the benchmark command
+  tests       Benchmark tests group command
+agpt % ./run benchmark categories     
+Usage: cli.py benchmark categories [OPTIONS] COMMAND [ARGS]...
+
+  Benchmark categories group command
+
+Options:
+  --help  Show this message and exit.
+
+Commands:
+  list  List benchmark categories command
+agpt % ./run benchmark tests      
+Usage: cli.py benchmark tests [OPTIONS] COMMAND [ARGS]...
+
+  Benchmark tests group command
+
+Options:
+  --help  Show this message and exit.
+
+Commands:
+  details  Benchmark test details command
+  list     List benchmark tests command
+```
+
+The benchmark has been split into different categories of skills you can test your agent on. You can see what categories are available with
+```bash
+./run benchmark categories list
+# And what tests are available with
+./run benchmark tests list
+```
+
+![Login](../docs/content/imgs/quickstart/012_tests.png)
+
+
+Finally, you can run the benchmark with
+
+```bash
+./run benchmark start YOUR_AGENT_NAME
+
+```
+
+>

classic/README.md

@@ -4,7 +4,7 @@ AutoGPT Classic was an experimental project to demonstrate autonomous GPT-4 oper
 
 ## Project Status
 
-**This project is unsupported, and dependencies will not be updated.** It was an experiment that has concluded its initial research phase. If you want to use AutoGPT, you should use the [AutoGPT Platform](/autogpt_platform).
+⚠️ **This project is unsupported, and dependencies will not be updated. It was an experiment that has concluded its initial research phase. If you want to use AutoGPT, you should use the [AutoGPT Platform](/autogpt_platform)**
 
 For those interested in autonomous AI agents, we recommend exploring more actively maintained alternatives or referring to this codebase for educational purposes only.
 
@@ -16,171 +16,37 @@ AutoGPT Classic was one of the first implementations of autonomous AI agents - A
 - Learn from the results and adjust its approach
 - Chain multiple actions together to achieve an objective
 
+## Key Features
+
+- 🔄 Autonomous task chaining
+- 🛠 Tool and API integration capabilities
+- 💾 Memory management for context retention
+- 🔍 Web browsing and information gathering
+- 📝 File operations and content creation
+- 🔄 Self-prompting and task breakdown
+
 ## Structure
 
-```
+The project is organized into several key components:
-classic/
+- `/benchmark` - Performance testing tools
-├── pyproject.toml          # Single consolidated Poetry project
+- `/forge` - Core autonomous agent framework
-├── poetry.lock             # Single lock file
+- `/frontend` - User interface components
-├── forge/                  # Core autonomous agent framework
+- `/original_autogpt` - Original implementation
-├── original_autogpt/       # Original implementation
-├── direct_benchmark/       # Benchmark harness
-└── benchmark/              # Challenge definitions (data)
-```
 
 ## Getting Started
 
-### Prerequisites
+While this project is no longer actively maintained, you can still explore the codebase:
-
-- Python 3.12+
-- [Poetry](https://python-poetry.org/docs/#installation)
-
-### Installation
 
+1. Clone the repository:
 ```bash
-# Clone the repository
 git clone https://github.com/Significant-Gravitas/AutoGPT.git
 cd classic
-
-# Install everything
-poetry install
 ```
 
-### Configuration
+2. Review the documentation:
-
+- For reference, see the [documentation](https://docs.agpt.co). You can browse at the same point in time as this commit so the docs don't change.
-Configuration uses a layered system:
+- Check `CLI-USAGE.md` for command-line interface details
-
+- Refer to `TROUBLESHOOTING.md` for common issues
-1. **Environment variables** (`.env` file)
-2. **Workspace settings** (`.autogpt/autogpt.yaml`)
-3. **Agent settings** (`.autogpt/agents/{id}/permissions.yaml`)
-
-Copy the example environment file and add your API keys:
-
-```bash
-cp .env.example .env
-```
-
-Key environment variables:
-```bash
-# Required
-OPENAI_API_KEY=sk-...
-
-# Optional LLM settings
-SMART_LLM=gpt-4o                    # Model for complex reasoning
-FAST_LLM=gpt-4o-mini                # Model for simple tasks
-
-# Optional search providers
-TAVILY_API_KEY=tvly-...
-SERPER_API_KEY=...
-
-# Optional infrastructure
-LOG_LEVEL=DEBUG
-PORT=8000
-FILE_STORAGE_BACKEND=local          # local, s3, or gcs
-```
-
-### Running
-
-All commands run from the `classic/` directory:
-
-```bash
-# Run forge agent
-poetry run python -m forge
-
-# Run original autogpt server
-poetry run serve --debug
-
-# Run autogpt CLI
-poetry run autogpt
-```
-
-Agents run on `http://localhost:8000` by default.
-
-### Benchmarking
-
-```bash
-poetry run direct-benchmark run
-```
-
-### Testing
-
-```bash
-poetry run pytest                        # All tests
-poetry run pytest forge/tests/           # Forge tests only
-poetry run pytest original_autogpt/tests/ # AutoGPT tests only
-```
-
-## Workspaces
-
-Agents operate within a **workspace** directory that contains all agent data and files:
-
-```
-{workspace}/
-├── .autogpt/
-│   ├── autogpt.yaml              # Workspace-level permissions
-│   ├── ap_server.db              # Agent Protocol database (server mode)
-│   └── agents/
-│       └── AutoGPT-{agent_id}/
-│           ├── state.json        # Agent profile, directives, history
-│           ├── permissions.yaml  # Agent-specific permissions
-│           └── workspace/        # Agent's sandboxed working directory
-```
-
-- The workspace defaults to the current working directory
-- Multiple agents can coexist in the same workspace
-- Agent file access is sandboxed to their `workspace/` subdirectory
-- State persists across sessions via `state.json`
-
-## Permissions
-
-AutoGPT uses a **layered permission system** with pattern matching:
-
-### Permission Files
-
-| File | Scope | Location |
-|------|-------|----------|
-| `autogpt.yaml` | All agents in workspace | `.autogpt/autogpt.yaml` |
-| `permissions.yaml` | Single agent | `.autogpt/agents/{id}/permissions.yaml` |
-
-### Permission Format
-
-```yaml
-allow:
-  - read_file({workspace}/**)     # Read any file in workspace
-  - write_to_file({workspace}/**) # Write any file in workspace
-  - web_search(*)                 # All web searches
-
-deny:
-  - read_file(**.env)             # Block .env files
-  - execute_shell(sudo:*)         # Block sudo commands
-```
-
-### Check Order (First Match Wins)
-
-1. Agent deny → Block
-2. Workspace deny → Block
-3. Agent allow → Allow
-4. Workspace allow → Allow
-5. Prompt user → Interactive approval
-
-### Interactive Approval
-
-When prompted, users can approve commands with different scopes:
-- **Once** - Allow this one time only
-- **Agent** - Always allow for this agent
-- **Workspace** - Always allow for all agents
-- **Deny** - Block this command
-
-### Default Security
-
-Denied by default:
-- Sensitive files (`.env`, `.key`, `.pem`)
-- Destructive commands (`rm -rf`, `sudo`)
-- Operations outside the workspace
-
-## Security Notice
-
-This codebase has **known vulnerabilities** and issues with its dependencies. It will not be updated to new dependencies. Use for educational purposes only.
 
 ## License
 
@@ -189,3 +55,27 @@ This project segment is licensed under the MIT License - see the [LICENSE](LICEN
 ## Documentation
 
 Please refer to the [documentation](https://docs.agpt.co) for more detailed information about the project's architecture and concepts.
+You can browse at the same point in time as this commit so the docs don't change.
+
+## Historical Impact
+
+AutoGPT Classic played a significant role in advancing the field of autonomous AI agents:
+- Demonstrated practical implementation of AI autonomy
+- Inspired numerous derivative projects and research
+- Contributed to the development of AI agent architectures
+- Helped identify key challenges in AI autonomy
+
+## Security Notice
+
+If you're studying this codebase, please understand this has KNOWN vulnerabilities and issues with its dependencies. It will not be updated to new dependencies.
+
+## Community & Support
+
+While active development has concluded:
+- The codebase remains available for study and reference
+- Historical discussions can be found in project issues
+- Related research and developments continue in the broader AI agent community
+
+## Acknowledgments
+
+Thanks to all contributors who participated in this experimental project and helped advance the field of autonomous AI agents.

classic/benchmark/.env.example

@@ -0,0 +1,4 @@
+AGENT_NAME=mini-agi
+REPORTS_FOLDER="reports/mini-agi"
+OPENAI_API_KEY="sk-" # for LLM eval
+BUILD_SKILL_TREE=false # set to true to build the skill tree.

classic/benchmark/.flake8

@@ -0,0 +1,12 @@
+[flake8]
+max-line-length = 88
+# Ignore rules that conflict with Black code style
+extend-ignore = E203, W503
+exclude =
+    __pycache__/,
+    *.pyc,
+    .pytest_cache/,
+    venv*/,
+    .venv/,
+    reports/,
+    agbenchmark/reports/,

classic/benchmark/.gitignore

@@ -0,0 +1,174 @@
+agbenchmark_config/workspace/
+backend/backend_stdout.txt
+reports/df*.pkl
+reports/raw*
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+.DS_Store
+```
+secrets.json
+agbenchmark_config/challenges_already_beaten.json
+agbenchmark_config/challenges/pri_*
+agbenchmark_config/updates.json
+agbenchmark_config/reports/*
+agbenchmark_config/reports/success_rate.json
+agbenchmark_config/reports/regression_tests.json

classic/benchmark/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 AutoGPT
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

classic/benchmark/README.md

@@ -0,0 +1,25 @@
+# Auto-GPT Benchmarks
+
+Built for the purpose of benchmarking the performance of agents regardless of how they work.
+
+Objectively know how well your agent is performing in categories like code, retrieval, memory, and safety.
+
+Save time and money while doing it through smart dependencies. The best part? It's all automated.
+
+## Scores:
+
+<img width="733" alt="Screenshot 2023-07-25 at 10 35 01 AM" src="https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks/assets/9652976/98963e0b-18b9-4b17-9a6a-4d3e4418af70">
+
+## Ranking overall:
+
+- 1- [Beebot](https://github.com/AutoPackAI/beebot)
+- 2- [mini-agi](https://github.com/muellerberndt/mini-agi)
+- 3- [Auto-GPT](https://github.com/Significant-Gravitas/AutoGPT)
+
+## Detailed results:
+
+<img width="733" alt="Screenshot 2023-07-25 at 10 42 15 AM" src="https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks/assets/9652976/39be464c-c842-4437-b28a-07d878542a83">
+
+[Click here to see the results and the raw data!](https://docs.google.com/spreadsheets/d/1WXm16P2AHNbKpkOI0LYBpcsGG0O7D8HYTG5Uj0PaJjA/edit#gid=203558751)!
+
+More agents coming soon !

classic/benchmark/agbenchmark/README.md

@@ -0,0 +1,69 @@
+## As a user
+
+1. `pip install auto-gpt-benchmarks`
+2. Add boilerplate code to run and kill agent
+3. `agbenchmark`
+   - `--category challenge_category` to run tests in a specific category
+   - `--mock` to only run mock tests if they exists for each test
+   - `--noreg` to skip any tests that have passed in the past. When you run without this flag and a previous challenge that passed fails, it will now not be regression tests
+4. We call boilerplate code for your agent
+5. Show pass rate of tests, logs, and any other metrics
+
+## Contributing
+
+##### Diagrams: https://whimsical.com/agbenchmark-5n4hXBq1ZGzBwRsK4TVY7x
+
+### To run the existing mocks
+
+1. clone the repo `auto-gpt-benchmarks`
+2. `pip install poetry`
+3. `poetry shell`
+4. `poetry install`
+5. `cp .env_example .env`
+6. `git submodule update --init --remote --recursive`
+7. `uvicorn server:app --reload`
+8. `agbenchmark --mock`
+   Keep config the same and watch the logs :)
+
+### To run with mini-agi
+
+1. Navigate to `auto-gpt-benchmarks/agent/mini-agi`
+2. `pip install -r requirements.txt`
+3. `cp .env_example .env`, set `PROMPT_USER=false` and add your `OPENAI_API_KEY=`. Sset `MODEL="gpt-3.5-turbo"` if you don't have access to `gpt-4` yet. Also make sure you have Python 3.10^ installed
+4. set `AGENT_NAME=mini-agi` in `.env` file and where you want your `REPORTS_FOLDER` to be
+5. Make sure to follow the commands above, and remove mock flag `agbenchmark`
+
+- To add requirements `poetry add requirement`.
+
+Feel free to create prs to merge with `main` at will (but also feel free to ask for review) - if you can't send msg in R&D chat for access.
+
+If you push at any point and break things - it'll happen to everyone - fix it asap. Step 1 is to revert `master` to last working commit
+
+Let people know what beautiful code you write does, document everything well
+
+Share your progress :)
+
+#### Dataset
+
+Manually created, existing challenges within Auto-Gpt, https://osu-nlp-group.github.io/Mind2Web/
+
+## How do I add new agents to agbenchmark ?
+
+Example with smol developer.
+
+1- Create a github branch with your agent following the same pattern as this example:
+
+https://github.com/smol-ai/developer/pull/114/files
+
+2- Create the submodule and the github workflow by following the same pattern as this example:
+
+https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks/pull/48/files
+
+## How do I run agent in different environments?
+
+**To just use as the benchmark for your agent**. `pip install` the package and run `agbenchmark`
+
+**For internal Auto-GPT ci runs**, specify the `AGENT_NAME` you want you use and set the `HOME_ENV`.
+Ex. `AGENT_NAME=mini-agi`
+
+**To develop agent alongside benchmark**, you can specify the `AGENT_NAME` you want you use and add as a submodule to the repo

classic/benchmark/agbenchmark/__main__.py

@@ -0,0 +1,352 @@
+import logging
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Optional
+
+import click
+from click_default_group import DefaultGroup
+from dotenv import load_dotenv
+
+from agbenchmark.config import AgentBenchmarkConfig
+from agbenchmark.utils.logging import configure_logging
+
+load_dotenv()
+
+# try:
+#     if os.getenv("HELICONE_API_KEY"):
+#         import helicone  # noqa
+
+#         helicone_enabled = True
+#     else:
+#         helicone_enabled = False
+# except ImportError:
+#     helicone_enabled = False
+
+
+class InvalidInvocationError(ValueError):
+    pass
+
+
+logger = logging.getLogger(__name__)
+
+BENCHMARK_START_TIME_DT = datetime.now(timezone.utc)
+BENCHMARK_START_TIME = BENCHMARK_START_TIME_DT.strftime("%Y-%m-%dT%H:%M:%S+00:00")
+
+
+# if helicone_enabled:
+#     from helicone.lock import HeliconeLockManager
+
+#     HeliconeLockManager.write_custom_property(
+#         "benchmark_start_time", BENCHMARK_START_TIME
+#     )
+
+
+@click.group(cls=DefaultGroup, default_if_no_args=True)
+@click.option("--debug", is_flag=True, help="Enable debug output")
+def cli(
+    debug: bool,
+) -> Any:
+    configure_logging(logging.DEBUG if debug else logging.INFO)
+
+
+@cli.command(hidden=True)
+def start():
+    raise DeprecationWarning(
+        "`agbenchmark start` is deprecated. Use `agbenchmark run` instead."
+    )
+
+
+@cli.command(default=True)
+@click.option(
+    "-N", "--attempts", default=1, help="Number of times to run each challenge."
+)
+@click.option(
+    "-c",
+    "--category",
+    multiple=True,
+    help="(+) Select a category to run.",
+)
+@click.option(
+    "-s",
+    "--skip-category",
+    multiple=True,
+    help="(+) Exclude a category from running.",
+)
+@click.option("--test", multiple=True, help="(+) Select a test to run.")
+@click.option("--maintain", is_flag=True, help="Run only regression tests.")
+@click.option("--improve", is_flag=True, help="Run only non-regression tests.")
+@click.option(
+    "--explore",
+    is_flag=True,
+    help="Run only challenges that have never been beaten.",
+)
+@click.option(
+    "--no-dep",
+    is_flag=True,
+    help="Run all (selected) challenges, regardless of dependency success/failure.",
+)
+@click.option("--cutoff", type=int, help="Override the challenge time limit (seconds).")
+@click.option("--nc", is_flag=True, help="Disable the challenge time limit.")
+@click.option("--mock", is_flag=True, help="Run with mock")
+@click.option("--keep-answers", is_flag=True, help="Keep answers")
+@click.option(
+    "--backend",
+    is_flag=True,
+    help="Write log output to a file instead of the terminal.",
+)
+# @click.argument(
+#     "agent_path",
+#     type=click.Path(exists=True, file_okay=False, path_type=Path),
+#     required=False,
+# )
+def run(
+    maintain: bool,
+    improve: bool,
+    explore: bool,
+    mock: bool,
+    no_dep: bool,
+    nc: bool,
+    keep_answers: bool,
+    test: tuple[str],
+    category: tuple[str],
+    skip_category: tuple[str],
+    attempts: int,
+    cutoff: Optional[int] = None,
+    backend: Optional[bool] = False,
+    # agent_path: Optional[Path] = None,
+) -> None:
+    """
+    Run the benchmark on the agent in the current directory.
+
+    Options marked with (+) can be specified multiple times, to select multiple items.
+    """
+    from agbenchmark.main import run_benchmark, validate_args
+
+    agbenchmark_config = AgentBenchmarkConfig.load()
+    logger.debug(f"agbenchmark_config: {agbenchmark_config.agbenchmark_config_dir}")
+    try:
+        validate_args(
+            maintain=maintain,
+            improve=improve,
+            explore=explore,
+            tests=test,
+            categories=category,
+            skip_categories=skip_category,
+            no_cutoff=nc,
+            cutoff=cutoff,
+        )
+    except InvalidInvocationError as e:
+        logger.error("Error: " + "\n".join(e.args))
+        sys.exit(1)
+
+    original_stdout = sys.stdout  # Save the original standard output
+    exit_code = None
+
+    if backend:
+        with open("backend/backend_stdout.txt", "w") as f:
+            sys.stdout = f
+            exit_code = run_benchmark(
+                config=agbenchmark_config,
+                maintain=maintain,
+                improve=improve,
+                explore=explore,
+                mock=mock,
+                no_dep=no_dep,
+                no_cutoff=nc,
+                keep_answers=keep_answers,
+                tests=test,
+                categories=category,
+                skip_categories=skip_category,
+                attempts_per_challenge=attempts,
+                cutoff=cutoff,
+            )
+
+        sys.stdout = original_stdout
+
+    else:
+        exit_code = run_benchmark(
+            config=agbenchmark_config,
+            maintain=maintain,
+            improve=improve,
+            explore=explore,
+            mock=mock,
+            no_dep=no_dep,
+            no_cutoff=nc,
+            keep_answers=keep_answers,
+            tests=test,
+            categories=category,
+            skip_categories=skip_category,
+            attempts_per_challenge=attempts,
+            cutoff=cutoff,
+        )
+
+        sys.exit(exit_code)
+
+
+@cli.command()
+@click.option("--port", type=int, help="Port to run the API on.")
+def serve(port: Optional[int] = None):
+    """Serve the benchmark frontend and API on port 8080."""
+    import uvicorn
+
+    from agbenchmark.app import setup_fastapi_app
+
+    config = AgentBenchmarkConfig.load()
+    app = setup_fastapi_app(config)
+
+    # Run the FastAPI application using uvicorn
+    port = port or int(os.getenv("PORT", 8080))
+    uvicorn.run(app, host="0.0.0.0", port=port)
+
+
+@cli.command()
+def config():
+    """Displays info regarding the present AGBenchmark config."""
+    from .utils.utils import pretty_print_model
+
+    try:
+        config = AgentBenchmarkConfig.load()
+    except FileNotFoundError as e:
+        click.echo(e, err=True)
+        return 1
+
+    pretty_print_model(config, include_header=False)
+
+
+@cli.group()
+def challenge():
+    logging.getLogger().setLevel(logging.WARNING)
+
+
+@challenge.command("list")
+@click.option(
+    "--all", "include_unavailable", is_flag=True, help="Include unavailable challenges."
+)
+@click.option(
+    "--names", "only_names", is_flag=True, help="List only the challenge names."
+)
+@click.option("--json", "output_json", is_flag=True)
+def list_challenges(include_unavailable: bool, only_names: bool, output_json: bool):
+    """Lists [available|all] challenges."""
+    import json
+
+    from tabulate import tabulate
+
+    from .challenges.builtin import load_builtin_challenges
+    from .challenges.webarena import load_webarena_challenges
+    from .utils.data_types import Category, DifficultyLevel
+    from .utils.utils import sorted_by_enum_index
+
+    DIFFICULTY_COLORS = {
+        difficulty: color
+        for difficulty, color in zip(
+            DifficultyLevel,
+            ["black", "blue", "cyan", "green", "yellow", "red", "magenta", "white"],
+        )
+    }
+    CATEGORY_COLORS = {
+        category: f"bright_{color}"
+        for category, color in zip(
+            Category,
+            ["blue", "cyan", "green", "yellow", "magenta", "red", "white", "black"],
+        )
+    }
+
+    # Load challenges
+    challenges = filter(
+        lambda c: c.info.available or include_unavailable,
+        [
+            *load_builtin_challenges(),
+            *load_webarena_challenges(skip_unavailable=False),
+        ],
+    )
+    challenges = sorted_by_enum_index(
+        challenges, DifficultyLevel, key=lambda c: c.info.difficulty
+    )
+
+    if only_names:
+        if output_json:
+            click.echo(json.dumps([c.info.name for c in challenges]))
+            return
+
+        for c in challenges:
+            click.echo(
+                click.style(c.info.name, fg=None if c.info.available else "black")
+            )
+        return
+
+    if output_json:
+        click.echo(
+            json.dumps([json.loads(c.info.model_dump_json()) for c in challenges])
+        )
+        return
+
+    headers = tuple(
+        click.style(h, bold=True) for h in ("Name", "Difficulty", "Categories")
+    )
+    table = [
+        tuple(
+            v if challenge.info.available else click.style(v, fg="black")
+            for v in (
+                challenge.info.name,
+                (
+                    click.style(
+                        challenge.info.difficulty.value,
+                        fg=DIFFICULTY_COLORS[challenge.info.difficulty],
+                    )
+                    if challenge.info.difficulty
+                    else click.style("-", fg="black")
+                ),
+                " ".join(
+                    click.style(cat.value, fg=CATEGORY_COLORS[cat])
+                    for cat in sorted_by_enum_index(challenge.info.category, Category)
+                ),
+            )
+        )
+        for challenge in challenges
+    ]
+    click.echo(tabulate(table, headers=headers))
+
+
+@challenge.command()
+@click.option("--json", is_flag=True)
+@click.argument("name")
+def info(name: str, json: bool):
+    from itertools import chain
+
+    from .challenges.builtin import load_builtin_challenges
+    from .challenges.webarena import load_webarena_challenges
+    from .utils.utils import pretty_print_model
+
+    for challenge in chain(
+        load_builtin_challenges(),
+        load_webarena_challenges(skip_unavailable=False),
+    ):
+        if challenge.info.name != name:
+            continue
+
+        if json:
+            click.echo(challenge.info.model_dump_json())
+            break
+
+        pretty_print_model(challenge.info)
+        break
+    else:
+        click.echo(click.style(f"Unknown challenge '{name}'", fg="red"), err=True)
+
+
+@cli.command()
+def version():
+    """Print version info for the AGBenchmark application."""
+    import toml
+
+    package_root = Path(__file__).resolve().parent.parent
+    pyproject = toml.load(package_root / "pyproject.toml")
+    version = pyproject["tool"]["poetry"]["version"]
+    click.echo(f"AGBenchmark version {version}")
+
+
+if __name__ == "__main__":
+    cli()

classic/benchmark/agbenchmark/agent_api_interface.py

@@ -0,0 +1,111 @@
+import logging
+import time
+from pathlib import Path
+from typing import AsyncIterator, Optional
+
+from agent_protocol_client import (
+    AgentApi,
+    ApiClient,
+    Configuration,
+    Step,
+    TaskRequestBody,
+)
+
+from agbenchmark.agent_interface import get_list_of_file_paths
+from agbenchmark.config import AgentBenchmarkConfig
+
+logger = logging.getLogger(__name__)
+
+
+async def run_api_agent(
+    task: str,
+    config: AgentBenchmarkConfig,
+    timeout: int,
+    artifacts_location: Optional[Path] = None,
+    *,
+    mock: bool = False,
+) -> AsyncIterator[Step]:
+    configuration = Configuration(host=config.host)
+    async with ApiClient(configuration) as api_client:
+        api_instance = AgentApi(api_client)
+        task_request_body = TaskRequestBody(input=task, additional_input=None)
+
+        start_time = time.time()
+        response = await api_instance.create_agent_task(
+            task_request_body=task_request_body
+        )
+        task_id = response.task_id
+
+        if artifacts_location:
+            logger.debug("Uploading task input artifacts to agent...")
+            await upload_artifacts(
+                api_instance, artifacts_location, task_id, "artifacts_in"
+            )
+
+        logger.debug("Running agent until finished or timeout...")
+        while True:
+            step = await api_instance.execute_agent_task_step(task_id=task_id)
+            yield step
+
+            if time.time() - start_time > timeout:
+                raise TimeoutError("Time limit exceeded")
+            if step and mock:
+                step.is_last = True
+            if not step or step.is_last:
+                break
+
+        if artifacts_location:
+            # In "mock" mode, we cheat by giving the correct artifacts to pass the test
+            if mock:
+                logger.debug("Uploading mock artifacts to agent...")
+                await upload_artifacts(
+                    api_instance, artifacts_location, task_id, "artifacts_out"
+                )
+
+            logger.debug("Downloading agent artifacts...")
+            await download_agent_artifacts_into_folder(
+                api_instance, task_id, config.temp_folder
+            )
+
+
+async def download_agent_artifacts_into_folder(
+    api_instance: AgentApi, task_id: str, folder: Path
+):
+    artifacts = await api_instance.list_agent_task_artifacts(task_id=task_id)
+
+    for artifact in artifacts.artifacts:
+        # current absolute path of the directory of the file
+        if artifact.relative_path:
+            path: str = (
+                artifact.relative_path
+                if not artifact.relative_path.startswith("/")
+                else artifact.relative_path[1:]
+            )
+            folder = (folder / path).parent
+
+        if not folder.exists():
+            folder.mkdir(parents=True)
+
+        file_path = folder / artifact.file_name
+        logger.debug(f"Downloading agent artifact {artifact.file_name} to {folder}")
+        with open(file_path, "wb") as f:
+            content = await api_instance.download_agent_task_artifact(
+                task_id=task_id, artifact_id=artifact.artifact_id
+            )
+
+            f.write(content)
+
+
+async def upload_artifacts(
+    api_instance: AgentApi, artifacts_location: Path, task_id: str, type: str
+) -> None:
+    for file_path in get_list_of_file_paths(artifacts_location, type):
+        relative_path: Optional[str] = "/".join(
+            str(file_path).split(f"{type}/", 1)[-1].split("/")[:-1]
+        )
+        if not relative_path:
+            relative_path = None
+
+        await api_instance.upload_agent_task_artifacts(
+            task_id=task_id, file=str(file_path), relative_path=relative_path
+        )

classic/benchmark/agbenchmark/agent_interface.py

@@ -0,0 +1,27 @@
+import os
+import shutil
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+HELICONE_GRAPHQL_LOGS = os.getenv("HELICONE_GRAPHQL_LOGS", "").lower() == "true"
+
+
+def get_list_of_file_paths(
+    challenge_dir_path: str | Path, artifact_folder_name: str
+) -> list[Path]:
+    source_dir = Path(challenge_dir_path) / artifact_folder_name
+    if not source_dir.exists():
+        return []
+    return list(source_dir.iterdir())
+
+
+def copy_challenge_artifacts_into_workspace(
+    challenge_dir_path: str | Path, artifact_folder_name: str, workspace: str | Path
+) -> None:
+    file_paths = get_list_of_file_paths(challenge_dir_path, artifact_folder_name)
+    for file_path in file_paths:
+        if file_path.is_file():
+            shutil.copy(file_path, workspace)

classic/benchmark/agbenchmark/app.py

@@ -0,0 +1,339 @@
+import datetime
+import glob
+import json
+import logging
+import sys
+import time
+import uuid
+from collections import deque
+from multiprocessing import Process
+from pathlib import Path
+from typing import Optional
+
+import httpx
+import psutil
+from agent_protocol_client import AgentApi, ApiClient, ApiException, Configuration
+from agent_protocol_client.models import Task, TaskRequestBody
+from fastapi import APIRouter, FastAPI, HTTPException, Request, Response
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, ConfigDict, ValidationError
+
+from agbenchmark.challenges import ChallengeInfo
+from agbenchmark.config import AgentBenchmarkConfig
+from agbenchmark.reports.processing.report_types_v2 import (
+    BenchmarkRun,
+    Metrics,
+    RepositoryInfo,
+    RunDetails,
+    TaskInfo,
+)
+from agbenchmark.schema import TaskEvalRequestBody
+from agbenchmark.utils.utils import write_pretty_json
+
+sys.path.append(str(Path(__file__).parent.parent))
+
+logger = logging.getLogger(__name__)
+
+CHALLENGES: dict[str, ChallengeInfo] = {}
+challenges_path = Path(__file__).parent / "challenges"
+challenge_spec_files = deque(
+    glob.glob(
+        f"{challenges_path}/**/data.json",
+        recursive=True,
+    )
+)
+
+logger.debug("Loading challenges...")
+while challenge_spec_files:
+    challenge_spec_file = Path(challenge_spec_files.popleft())
+    challenge_relpath = challenge_spec_file.relative_to(challenges_path.parent)
+    if challenge_relpath.is_relative_to("challenges/deprecated"):
+        continue
+
+    logger.debug(f"Loading {challenge_relpath}...")
+    try:
+        challenge_info = ChallengeInfo.model_validate_json(
+            challenge_spec_file.read_text()
+        )
+    except ValidationError as e:
+        if logging.getLogger().level == logging.DEBUG:
+            logger.warning(f"Spec file {challenge_relpath} failed to load:\n{e}")
+        logger.debug(f"Invalid challenge spec: {challenge_spec_file.read_text()}")
+        continue
+
+    if not challenge_info.eval_id:
+        challenge_info.eval_id = str(uuid.uuid4())
+        # this will sort all the keys of the JSON systematically
+        # so that the order is always the same
+        write_pretty_json(challenge_info.model_dump(), challenge_spec_file)
+
+    CHALLENGES[challenge_info.eval_id] = challenge_info
+
+
+class BenchmarkTaskInfo(BaseModel):
+    task_id: str
+    start_time: datetime.datetime
+    challenge_info: ChallengeInfo
+
+
+task_informations: dict[str, BenchmarkTaskInfo] = {}
+
+
+def find_agbenchmark_without_uvicorn():
+    pids = []
+    for process in psutil.process_iter(
+        attrs=[
+            "pid",
+            "cmdline",
+            "name",
+            "username",
+            "status",
+            "cpu_percent",
+            "memory_info",
+            "create_time",
+            "cwd",
+            "connections",
+        ]
+    ):
+        try:
+            # Convert the process.info dictionary values to strings and concatenate them
+            full_info = " ".join([str(v) for k, v in process.as_dict().items()])
+
+            if "agbenchmark" in full_info and "uvicorn" not in full_info:
+                pids.append(process.pid)
+        except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
+            pass
+    return pids
+
+
+class CreateReportRequest(BaseModel):
+    test: str
+    test_run_id: str
+    # category: Optional[str] = []
+    mock: Optional[bool] = False
+
+    model_config = ConfigDict(extra="forbid")
+
+
+updates_list = []
+
+origins = [
+    "http://localhost:8000",
+    "http://localhost:8080",
+    "http://127.0.0.1:5000",
+    "http://localhost:5000",
+]
+
+
+def stream_output(pipe):
+    for line in pipe:
+        print(line, end="")
+
+
+def setup_fastapi_app(agbenchmark_config: AgentBenchmarkConfig) -> FastAPI:
+    from agbenchmark.agent_api_interface import upload_artifacts
+    from agbenchmark.challenges import get_challenge_from_source_uri
+    from agbenchmark.main import run_benchmark
+
+    configuration = Configuration(
+        host=agbenchmark_config.host or "http://localhost:8000"
+    )
+    app = FastAPI()
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=origins,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+    router = APIRouter()
+
+    @router.post("/reports")
+    def run_single_test(body: CreateReportRequest) -> dict:
+        pids = find_agbenchmark_without_uvicorn()
+        logger.info(f"pids already running with agbenchmark: {pids}")
+
+        logger.debug(f"Request to /reports: {body.model_dump()}")
+
+        # Start the benchmark in a separate thread
+        benchmark_process = Process(
+            target=lambda: run_benchmark(
+                config=agbenchmark_config,
+                tests=(body.test,),
+                mock=body.mock or False,
+            )
+        )
+        benchmark_process.start()
+
+        # Wait for the benchmark to finish, with a timeout of 200 seconds
+        timeout = 200
+        start_time = time.time()
+        while benchmark_process.is_alive():
+            if time.time() - start_time > timeout:
+                logger.warning(f"Benchmark run timed out after {timeout} seconds")
+                benchmark_process.terminate()
+                break
+            time.sleep(1)
+        else:
+            logger.debug(f"Benchmark finished running in {time.time() - start_time} s")
+
+        # List all folders in the current working directory
+        reports_folder = agbenchmark_config.reports_folder
+        folders = [folder for folder in reports_folder.iterdir() if folder.is_dir()]
+
+        # Sort the folders based on their names
+        sorted_folders = sorted(folders, key=lambda x: x.name)
+
+        # Get the last folder
+        latest_folder = sorted_folders[-1] if sorted_folders else None
+
+        # Read report.json from this folder
+        if latest_folder:
+            report_path = latest_folder / "report.json"
+            logger.debug(f"Getting latest report from {report_path}")
+            if report_path.exists():
+                with report_path.open() as file:
+                    data = json.load(file)
+                logger.debug(f"Report data: {data}")
+            else:
+                raise HTTPException(
+                    502,
+                    "Could not get result after running benchmark: "
+                    f"'report.json' does not exist in '{latest_folder}'",
+                )
+        else:
+            raise HTTPException(
+                504, "Could not get result after running benchmark: no reports found"
+            )
+
+        return data
+
+    @router.post("/agent/tasks", tags=["agent"])
+    async def create_agent_task(task_eval_request: TaskEvalRequestBody) -> Task:
+        """
+        Creates a new task using the provided TaskEvalRequestBody and returns a Task.
+
+        Args:
+            task_eval_request: `TaskRequestBody` including an eval_id.
+
+        Returns:
+            Task: A new task with task_id, input, additional_input,
+                and empty lists for artifacts and steps.
+
+        Example:
+            Request (TaskEvalRequestBody defined in schema.py):
+                {
+                    ...,
+                    "eval_id": "50da533e-3904-4401-8a07-c49adf88b5eb"
+                }
+
+            Response (Task defined in `agent_protocol_client.models`):
+                {
+                    "task_id": "50da533e-3904-4401-8a07-c49adf88b5eb",
+                    "input": "Write the word 'Washington' to a .txt file",
+                    "artifacts": []
+                }
+        """
+        try:
+            challenge_info = CHALLENGES[task_eval_request.eval_id]
+            async with ApiClient(configuration) as api_client:
+                api_instance = AgentApi(api_client)
+                task_input = challenge_info.task
+
+                task_request_body = TaskRequestBody(
+                    input=task_input, additional_input=None
+                )
+                task_response = await api_instance.create_agent_task(
+                    task_request_body=task_request_body
+                )
+                task_info = BenchmarkTaskInfo(
+                    task_id=task_response.task_id,
+                    start_time=datetime.datetime.now(datetime.timezone.utc),
+                    challenge_info=challenge_info,
+                )
+                task_informations[task_info.task_id] = task_info
+
+                if input_artifacts_dir := challenge_info.task_artifacts_dir:
+                    await upload_artifacts(
+                        api_instance,
+                        input_artifacts_dir,
+                        task_response.task_id,
+                        "artifacts_in",
+                    )
+                return task_response
+        except ApiException as e:
+            logger.error(f"Error whilst trying to create a task:\n{e}")
+            logger.error(
+                "The above error was caused while processing request: "
+                f"{task_eval_request}"
+            )
+            raise HTTPException(500)
+
+    @router.post("/agent/tasks/{task_id}/steps")
+    async def proxy(request: Request, task_id: str):
+        timeout = httpx.Timeout(300.0, read=300.0)  # 5 minutes
+        async with httpx.AsyncClient(timeout=timeout) as client:
+            # Construct the new URL
+            new_url = f"{configuration.host}/ap/v1/agent/tasks/{task_id}/steps"
+
+            # Forward the request
+            response = await client.post(
+                new_url,
+                content=await request.body(),
+                headers=dict(request.headers),
+            )
+
+            # Return the response from the forwarded request
+            return Response(content=response.content, status_code=response.status_code)
+
+    @router.post("/agent/tasks/{task_id}/evaluations")
+    async def create_evaluation(task_id: str) -> BenchmarkRun:
+        task_info = task_informations[task_id]
+        challenge = get_challenge_from_source_uri(task_info.challenge_info.source_uri)
+        try:
+            async with ApiClient(configuration) as api_client:
+                api_instance = AgentApi(api_client)
+                eval_results = await challenge.evaluate_task_state(
+                    api_instance, task_id
+                )
+
+            eval_info = BenchmarkRun(
+                repository_info=RepositoryInfo(),
+                run_details=RunDetails(
+                    command=f"agbenchmark --test={challenge.info.name}",
+                    benchmark_start_time=(
+                        task_info.start_time.strftime("%Y-%m-%dT%H:%M:%S+00:00")
+                    ),
+                    test_name=challenge.info.name,
+                ),
+                task_info=TaskInfo(
+                    data_path=challenge.info.source_uri,
+                    is_regression=None,
+                    category=[c.value for c in challenge.info.category],
+                    task=challenge.info.task,
+                    answer=challenge.info.reference_answer or "",
+                    description=challenge.info.description or "",
+                ),
+                metrics=Metrics(
+                    success=all(e.passed for e in eval_results),
+                    success_percentage=(
+                        100 * sum(e.score for e in eval_results) / len(eval_results)
+                        if eval_results  # avoid division by 0
+                        else 0
+                    ),
+                    attempted=True,
+                ),
+                config={},
+            )
+
+            logger.debug(
+                f"Returning evaluation data:\n{eval_info.model_dump_json(indent=4)}"
+            )
+            return eval_info
+        except ApiException as e:
+            logger.error(f"Error {e} whilst trying to evaluate task: {task_id}")
+            raise HTTPException(500)
+
+    app.include_router(router, prefix="/ap/v1")
+
+    return app

classic/benchmark/agbenchmark/challenges/base.py

@@ -1,98 +1,19 @@
 import logging
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Any, AsyncIterator, Awaitable, ClassVar, Optional
+from typing import AsyncIterator, Awaitable, ClassVar, Optional
 
 import pytest
-from agbenchmark.config import AgentBenchmarkConfig
-from agbenchmark.utils.data_types import Category, DifficultyLevel, EvalResult
 from agent_protocol_client import AgentApi, Step
 from colorama import Fore, Style
 from pydantic import BaseModel, Field
 
+from agbenchmark.config import AgentBenchmarkConfig
+from agbenchmark.utils.data_types import Category, DifficultyLevel, EvalResult
+
 logger = logging.getLogger(__name__)
 
 
-def format_step_output(step: Step, step_num: int, challenge_name: str) -> str:
-    """Format a step for concise, informative console output.
-
-    Format: [Challenge] step N: tool_name(args) → result [$cost]
-    """
-    parts = [f"[{challenge_name}]", f"step {step_num}:"]
-
-    # Get additional_output data
-    ao: dict[str, Any] = step.additional_output or {}
-
-    # Get the tool being used in this step
-    use_tool = ao.get("use_tool", {})
-    tool_name = use_tool.get("name", "")
-    tool_args = use_tool.get("arguments", {})
-
-    if tool_name:
-        # Format tool call with abbreviated arguments
-        args_str = _format_tool_args(tool_name, tool_args)
-        parts.append(f"{Fore.CYAN}{tool_name}{Fore.RESET}({args_str})")
-    else:
-        parts.append(f"{Fore.YELLOW}(no tool){Fore.RESET}")
-
-    # Get result from last action (this step's tool will be executed next iteration)
-    last_action = ao.get("last_action", {})
-    if last_action:
-        result = last_action.get("result", {})
-        if isinstance(result, dict):
-            if result.get("error"):
-                parts.append(f"→ {Fore.RED}error{Fore.RESET}")
-            elif result.get("status") == "success":
-                parts.append(f"→ {Fore.GREEN}✓{Fore.RESET}")
-
-    # Add cost if available
-    cost = ao.get("task_cumulative_cost", 0)
-    if cost > 0:
-        parts.append(f"{Fore.BLUE}${cost:.3f}{Fore.RESET}")
-
-    return " ".join(parts)
-
-
-def _format_tool_args(tool_name: str, args: dict) -> str:
-    """Format tool arguments for display, keeping it concise."""
-    if not args:
-        return ""
-
-    # For common tools, show the most relevant argument
-    key_args = {
-        "read_file": ["filename"],
-        "write_file": ["filename"],
-        "open_file": ["filename", "file_path"],
-        "execute_python": ["filename"],
-        "execute_shell": ["command_line"],
-        "web_search": ["query"],
-        "read_webpage": ["url"],
-        "finish": ["reason"],
-        "ask_user": ["question"],
-        "todo_write": [],  # Skip args for todo_write (too verbose)
-    }
-
-    if tool_name in key_args:
-        keys = key_args[tool_name]
-        if not keys:
-            return "..."
-        values = [str(args.get(k, ""))[:40] for k in keys if k in args]
-        if values:
-            return ", ".join(
-                f'"{v}"' if " " not in v else f'"{v[:20]}..."' for v in values
-            )
-
-    # Default: show first arg value, abbreviated
-    if args:
-        first_key = next(iter(args))
-        first_val = str(args[first_key])[:30]
-        return f'{first_key}="{first_val}"' + (
-            "..." if len(str(args[first_key])) > 30 else ""
-        )
-
-    return ""
-
-
 class ChallengeInfo(BaseModel):
     eval_id: str = ""
     name: str
@@ -174,7 +95,7 @@ class BaseChallenge(ABC):
             cls.info.task, config, timeout, cls.info.task_artifacts_dir, mock=mock
         ):
             i += 1
-            print(format_step_output(step, i, cls.info.name))
+            print(f"[{cls.info.name}] - step {step.name} ({i}. request)")
             yield step
         logger.debug(f"Finished {cls.info.name} challenge run")
 
@@ -182,4 +103,5 @@ class BaseChallenge(ABC):
     @abstractmethod
     async def evaluate_task_state(
         cls, agent: AgentApi, task_id: str
-    ) -> list[EvalResult]: ...
+    ) -> list[EvalResult]:
+        ...

classic/benchmark/agbenchmark/challenges/builtin.py

@@ -10,16 +10,6 @@ from pathlib import Path
 from typing import Annotated, Any, ClassVar, Iterator, Literal, Optional
 
 import pytest
-from agbenchmark.agent_api_interface import download_agent_artifacts_into_folder
-from agbenchmark.agent_interface import copy_challenge_artifacts_into_workspace
-from agbenchmark.config import AgentBenchmarkConfig
-from agbenchmark.utils.data_types import Category, DifficultyLevel, EvalResult
-from agbenchmark.utils.prompts import (
-    END_PROMPT,
-    FEW_SHOT_EXAMPLES,
-    PROMPT_MAP,
-    SCORING_MAP,
-)
 from agent_protocol_client import AgentApi, ApiClient
 from agent_protocol_client import Configuration as ClientConfig
 from agent_protocol_client import Step
@@ -33,6 +23,17 @@ from pydantic import (
     field_validator,
 )
 
+from agbenchmark.agent_api_interface import download_agent_artifacts_into_folder
+from agbenchmark.agent_interface import copy_challenge_artifacts_into_workspace
+from agbenchmark.config import AgentBenchmarkConfig
+from agbenchmark.utils.data_types import Category, DifficultyLevel, EvalResult
+from agbenchmark.utils.prompts import (
+    END_PROMPT,
+    FEW_SHOT_EXAMPLES,
+    PROMPT_MAP,
+    SCORING_MAP,
+)
+
 from .base import BaseChallenge, ChallengeInfo
 
 logger = logging.getLogger(__name__)
@@ -68,9 +69,9 @@ class BuiltinChallengeSpec(BaseModel):
         class Eval(BaseModel):
             type: str
             scoring: Optional[Literal["percentage", "scale", "binary"]] = None
-            template: Optional[Literal["rubric", "reference", "question", "custom"]] = (
+            template: Optional[
-                None
+                Literal["rubric", "reference", "question", "custom"]
-            )
+            ] = None
             examples: Optional[str] = None
 
             @field_validator("scoring", "template")
@@ -227,11 +228,9 @@ class BuiltinChallenge(BaseChallenge):
         request.node.user_properties.append(
             (
                 "answers",
-                (
+                [r.result for r in eval_results]
-                    [r.result for r in eval_results]
+                if request.config.getoption("--keep-answers")
-                    if request.config.getoption("--keep-answers")
+                else None,
-                    else None
-                ),
             )
         )
         request.node.user_properties.append(("scores", [r.score for r in eval_results]))